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  • 1.
    Beck, Dani
    et al.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Diakonhjemmet Hosp, Norway.
    de Lange, Ann-Marie G.
    Univ Oslo, Norway; CHU Vaudois, Switzerland; Univ Lausanne, Switzerland; Univ Oxford, England.
    Gurholt, Tiril P.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Voldsbekk, Irene
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Maximov, Ivan I.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Western Norway Univ Appl Sci, Norway.
    Subramaniapillai, Sivaniya
    Univ Oslo, Norway; CHU Vaudois, Switzerland; Univ Lausanne, Switzerland.
    Schindler, Louise
    Univ Oslo, Norway; CHU Vaudois, Switzerland.
    Hindley, Guy
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Leonardsen, Esten H.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Rahman, Zillur
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    van Der Meer, Dennis
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Korbmacher, Max
    Oslo Univ Hosp, Norway; Western Norway Univ Appl Sci, Norway.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Univ Oslo, Norway; AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Kalleberg, Karl T.
    Age Labs, Norway.
    Engvig, Andreas
    Oslo Univ Hosp, Norway.
    Sonderby, Ida
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Andreassen, Ole A.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Westlye, Lars T.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Dissecting unique and common variance across body and brain health indicators using age prediction2024In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 45, no 6, article id e26685Article in journal (Refereed)
    Abstract [en]

    Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals. A 'body age' model trained on health traits demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. Health traits may differentially influence age predictions beyond what is captured by the brain imaging data, revealing a degree of unique variance in brain and bodily ageing processes. image

  • 2.
    Pandey, Ambarish
    et al.
    Univ Texas Southwestern Med Ctr, TX USA; Univ Texas Southwestern Med Ctr, TX 75390 USA.
    Patel, Kershaw V.
    Houston Methodist DeBakey Heart & Vasc Ctr, TX USA.
    Segar, Matthew W.
    Texas Heart Inst, TX USA.
    Ayers, Colby
    Univ Texas Southwestern Med Ctr, TX USA.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Anker, Stefan D.
    Charite, Germany; Charite, Germany.
    Butler, Javed
    Baylor Univ, TX USA; Univ Mississippi, MS USA.
    Verma, Subodh
    Univ Toronto, Canada.
    Joshi, Parag H.
    Univ Texas Southwestern Med Ctr, TX USA.
    Neeland, Ian J.
    Univ Hosp Cleveland Med Ctr, OH USA; Case Western Reserve Univ, OH USA; Case Western Reserve Univ, OH 44118 USA.
    Effect of liraglutide on thigh muscle fat and muscle composition in adults with overweight or obesity: Results from a randomized clinical trial2024In: Journal of Cachexia, Sarcopenia and Muscle, ISSN 2190-5991, E-ISSN 2190-6009Article in journal (Refereed)
    Abstract [en]

    BackgroundExcess muscle fat is observed in obesity and associated with greater burden of cardiovascular risk factors and higher risk of mortality. Liraglutide reduces total body weight and visceral fat but its effect on muscle fat and adverse muscle composition is unknown.MethodsThis is a pre-specified secondary analysis of a randomized, double-blind, placebo-controlled trial that examined the effects of liraglutide plus a lifestyle intervention on visceral adipose tissue and ectopic fat among adults without diabetes with body mass index >= 30 kg/m2 or >= 27 kg/m2 and metabolic syndrome. Participants were randomly assigned to a once-daily subcutaneous injection of liraglutide (target dose 3.0 mg) or matching placebo for 40 weeks. Body fat distribution and muscle composition was assessed by magnetic resonance imaging at baseline and 40-week follow-up. Muscle composition was described by the combination of thigh muscle fat and muscle volume. Treatment difference (95% confidence intervals [CI]) was calculated by least-square means adjusted for baseline thigh muscle fat. The association between changes in thigh muscle fat and changes in body weight were assessed using Spearman correlation coefficients. The effect of liraglutide versus placebo on adverse muscle composition, denoted by high thigh muscle fat and low thigh muscle volume, was explored.ResultsAmong the 128 participants with follow-up imaging (92.2% women, 36.7% Black), median muscle fat at baseline was 7.8%. The mean percent change in thigh muscle fat over median follow-up of 36 weeks was -2.87% among participants randomized to liraglutide (n = 73) and 0.05% in the placebo group (absolute change: -0.23% vs. 0.01%). The estimated treatment difference adjusted for baseline thigh muscle fat was -0.24% (95% CI, -0.41 to -0.06, P-value 0.009). Longitudinal change in thigh muscle fat was significantly associated with change in body weight in the placebo group but not the liraglutide group. The proportion of participants with adverse muscle composition decreased from 11.0% to 8.2% over follow-up with liraglutide, but there was no change with placebo.ConclusionsIn a cohort of predominantly women with overweight or obesity in the absence of diabetes, once-daily subcutaneous liraglutide was associated with a reduction in thigh muscle fat and adverse muscle composition compared with placebo. The contribution of muscle fat improvement to the cardiometabolic benefits of liraglutide requires further study.

  • 3.
    Cariou, Bertrand
    et al.
    Nantes Univ, France.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Neeland, Ian J.
    Univ Hosp Cleveland Med Ctr, OH USA; Case Western Reserve Univ, OH USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Petersson, Mikael
    AMRA Med AB, Linkoping, Sweden.
    Lando, Laura Fernandez
    Eli Lilly & Co, IN 46225 USA.
    Bray, Ross
    Eli Lilly & Co, IN 46225 USA.
    Rodriguez, Angel
    Eli Lilly & Co, IN 46225 USA.
    Effect of tirzepatide on body fat distribution pattern in people with type 2 diabetes2024In: Diabetes, obesity and metabolism, ISSN 1462-8902, E-ISSN 1463-1326Article in journal (Refereed)
    Abstract [en]

    AimsTo describe the overall fat distribution patterns independent of body mass index (BMI) in participants with type 2 diabetes (T2D) in the SURPASS-3 MRI substudy by comparison with sex- and BMI-matched virtual control groups (VCGs) derived from the UK Biobank imaging study at baseline and Week 52. MethodsFor each study participant at baseline and Week 52 (N = 296), a VCG of >= 150 participants with the same sex and similar BMI was identified from the UK Biobank imaging study (N = 40 172). Average visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT) and liver fat (LF) levels and the observed standard deviations (SDs; standardized normal z-scores: z-VAT, z-aSAT and z-LF) were calculated based on the matched VCGs. Differences in z-scores between baseline and Week 52 were calculated to describe potential shifts in fat distribution pattern independent of weight change. ResultsBaseline fat distribution patterns were similar across pooled tirzepatide (5, 10 and 15 mg) and insulin degludec (IDeg) arms. Compared with matched VCGs, SURPASS-3 participants had higher baseline VAT (mean [SD] z-VAT +0.42 [1.23]; p < 0.001) and LF (z-LF +1.24 [0.92]; p < 0.001) but similar aSAT (z-aSAT -0.13 [1.11]; p = 0.083). Tirzepatide-treated participants had significant decreases in z-VAT (-0.18 [0.58]; p < 0.001) and z-LF (-0.54 [0.84]; p < 0.001) but increased z-aSAT (+0.11 [0.50]; p = 0.012). Participants treated with IDeg had a significant change in z-LF only (-0.46 [0.90]; p = 0.001), while no significant changes were observed for z-VAT (+0.13 [0.52]; p = 0.096) and z-aSAT (+0.09 [0.61]; p = 0.303). ConclusionIn this exploratory analysis, treatment with tirzepatide in people with T2D resulted in a significant reduction of z-VAT and z-LF, while z-aSAT was increased from an initially negative value, suggesting a possible treatment-related shift towards a more balanced fat distribution pattern with prominent VAT and LF loss.

  • 4.
    Gerdle, Björn
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Lund, Eva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ghafouri, Bijar
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Pain and the biochemistry of fibromyalgia: patterns of peripheral cytokines and chemokines contribute to the differentiation between fibromyalgia and controls and are associated with pain, fat infiltration and content2024In: FRONTIERS IN PAIN RESEARCH, ISSN 2673-561X, Vol. 5, article id 1288024Article in journal (Refereed)
    Abstract [en]

    Objectives This explorative study analyses interrelationships between peripheral compounds in saliva, plasma, and muscles together with body composition variables in healthy subjects and in fibromyalgia patients (FM). There is a need to better understand the extent cytokines and chemokines are associated with body composition and which cytokines and chemokines differentiate FM from healthy controls.Methods Here, 32 female FM patients and 30 age-matched female healthy controls underwent a clinical examination that included blood sample, saliva samples, and pain threshold tests. In addition, the subjects completed a health questionnaire. From these blood and saliva samples, a panel of 68 mainly cytokines and chemokines were determined. Microdialysis of trapezius and erector spinae muscles, phosphorus-31 magnetic resonance spectroscopy of erector spinae muscle, and whole-body magnetic resonance imaging for determination of body composition (BC)-i.e., muscle volume, fat content and infiltration-were also performed.Results After standardizing BC measurements to remove the confounding effect of Body Mass Index, fat infiltration and content are generally increased, and fat-free muscle volume is decreased in FM. Mainly saliva proteins differentiated FM from controls. When including all investigated compounds and BC variables, fat infiltration and content variables were most important, followed by muscle compounds and cytokines and chemokines from saliva and plasma. Various plasma proteins correlated positively with pain intensity in FM and negatively with pain thresholds in all subjects taken together. A mix of increased plasma cytokines and chemokines correlated with an index covering fat infiltration and content in different tissues. When muscle compounds were included in the analysis, several of these were identified as the most important regressors, although many plasma and saliva proteins remained significant.Discussion Peripheral factors were important for group differentiation between FM and controls. In saliva (but not plasma), cytokines and chemokines were significantly associated with group membership as saliva compounds were increased in FM. The importance of peripheral factors for group differentiation increased when muscle compounds and body composition variables were also included. Plasma proteins were important for pain intensity and sensitivity. Cytokines and chemokines mainly from plasma were also significantly and positively associated with a fat infiltration and content index.Conclusion Our findings of associations between cytokines and chemokines and fat infiltration and content in different tissues confirm that inflammation and immune factors are secreted from adipose tissue. FM is clearly characterized by complex interactions between peripheral tissues and the peripheral and central nervous systems, including nociceptive, immune, and neuroendocrine processes.

  • 5.
    Brandejsky, Vaclav
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Lund, Eva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Phosphorus-31: A table-top method for 3D B1-field amplitude and phase measurements2024In: Biochimica et Biophysica Acta - Biomembranes, ISSN 0005-2736, E-ISSN 1879-2642, Vol. 1866, no 4, article id 184307Article in journal (Refereed)
    Abstract [en]

    A novel method of high -spatial -resolution, 3D B1 -field distribution measurements is presented. The method is independent of the MR -scanner, and it allows for automated acquisitions of complete maps of all magnetic field vector components for both proton and heteronuclear MR coils of arbitrary geometrical shapes. The advantage of the method proposed here, compared with methods based on measurements with an MR -scanner, is that a complete image of both receive and transmit B1 -fields, including the phase of the B1 -field, can be acquired. The B1 field maps obtained in this manner can be used for absolute quantification of metabolites in MRS experiments, as well as for intensity compensations in imaging experiments, both of which are important concepts in biological and medical MR applications. Another use might be in coil development and testing. A comparison with B1 field magnitude maps obtained with an MR -scanner was included to validate the accuracy of the proposed method.

  • 6.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Badhusgatan 5, SE-58222 Linkoping, Sweden.
    Cariou, Bertrand
    Nantes Univ, France.
    Neeland, Ian J.
    Univ Hosp Cleveland Med Ctr, OH 44145 USA; Case Western Reserve Univ, OH 44145 USA.
    Petersson, Mikael
    AMRA Med AB, Badhusgatan 5, SE-58222 Linkoping, Sweden.
    Rodriguez, Angel
    Eli Lilly & Co, IN 46225 USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Badhusgatan 5, SE-58222 Linkoping, Sweden.
    Skewness in Body fat Distribution Pattern Links to Specific Cardiometabolic Disease Risk Profiles2024In: Journal of Clinical Endocrinology and Metabolism, ISSN 0021-972X, E-ISSN 1945-7197, Vol. 09, no 3, p. 783-791Article in journal (Refereed)
    Abstract [en]

    Objective: Fat distribution pattern could help determine cardiometabolic risk profile. This study aimed to evaluate the association of balance/imbalance between visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT), and liver fat (LF) with incident type 2 diabetes (T2D) and cardiovascular disease (CVD) in the UK Biobank prospective cohort study.Methods: Magnetic resonance images of 40 174 participants were analyzed for VAT, aSAT, and LF using AMRA (R) Researcher. To assess fat distribution patterns independent of body mass index (BMI), fat z-scores (z-VAT, z-aSAT, z-LF) were calculated. Participants without prevalent T2D/CVD (N = 35 138) were partitioned based on balance between (1) z-VAT and z-LF (z-scores = 0 as cut-points for high/low), (2) z-VAT and z-aSAT, and (3) z-LF and z-aSAT. Associations with T2D/CVD were investigated using Cox regression (crude and adjusted for sex, age, BMI, lifestyle, arterial hypertension, statin treatment).Results: T2D was significantly associated with z-LF (hazard ratio, [95% CI] 1.74 [1.52-1.98], P < .001) and z-VAT (1.70 [1.49-1.95], P < .001). Both remained significant after full adjustment. For z-scores balance, strongest associations with T2D were z-VAT > 0 and z-LF > 0 (4.61 [2.98-7.12]), z-VAT > 0 and z-aSAT < 0 (4.48 [2.85-7.06]), and z-LF > 0 and z-aSAT < 0 (2.69 [1.76-4.12]), all P < .001. CVD was most strongly associated with z-VAT (1.22 [1.16-1.28], P < .001) which remained significant after adjustment for sex, age, BMI, and lifestyle. For z-scores balance, strongest associations with CVD were z-VAT > 0 and z-LF < 0 (1.53 [1.34-1.76], P < .001) and z-VAT > 0 and z-aSAT < 0 (1.54 [1.34-1.76], P < .001). When adjusted for sex, age, and BMI, only z-VAT > 0 and z-LF < 0 remained significant.Conclusion: High VAT in relation to BMI (z-VAT > 0) was consistently linked to both T2D and CVD; z-LF > 0 was linked to T2D only. Skewed fat distribution patterns showed elevated risk for CVD (z-VAT > 0 and z-LF < 0 and z-VAT > 0 and z-aSAT < 0) and T2D (z-VAT > 0 and z-aSAT < 0).

  • 7.
    Nasr, Patrik
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Balkhed, Wile
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Jönsson, Cecilia
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlström, Nils
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Simonsson, Christian
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cai, Shan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cederborg, Anna
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Univ Gothenburg, Sweden; Sahlgrens Univ Hosp, Sweden.
    Henriksson, Martin
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences.
    Stjernman, Henrik
    Ryhov Hosp Jonkoping, Sweden.
    Rejler, Martin
    Reg Jonkoping Cty Council, Sweden; Jonkoping Univ, Sweden.
    Sjoegren, Daniel
    Reg Jonkoping Cty Council, Sweden.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Orebro Univ, Sweden; Orebro Univ, Sweden.
    Bartholomä, Wolf
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rydén, Ingvar
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Clinical Chemistry and Pharmacology. Linköping University, Faculty of Medicine and Health Sciences. Reg Kalmar Cty, Sweden.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kechagias, Stergios
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A rapid, non-invasive, clinical surveillance for CachExia, sarcopenia, portal hypertension, and hepatocellular carcinoma in end-stage liver disease: the ACCESS-ESLD study protocol2023In: BMC Gastroenterology, ISSN 1471-230X, E-ISSN 1471-230X, Vol. 23, no 1, article id 454Article in journal (Refereed)
    Abstract [en]

    BackgroundLiver cirrhosis, the advanced stage of many chronic liver diseases, is associated with escalated risks of liver-related complications like decompensation and hepatocellular carcinoma (HCC). Morbidity and mortality in cirrhosis patients are linked to portal hypertension, sarcopenia, and hepatocellular carcinoma. Although conventional cirrhosis management centered on treating complications, contemporary approaches prioritize preemptive measures. This study aims to formulate novel blood- and imaging-centric methodologies for monitoring liver cirrhosis patients.MethodsIn this prospective study, 150 liver cirrhosis patients will be enrolled from three Swedish liver clinics. Their conditions will be assessed through extensive blood-based markers and magnetic resonance imaging (MRI). The MRI protocol encompasses body composition profile with Muscle Assement Score, portal flow assessment, magnet resonance elastography, and a abbreviated MRI for HCC screening. Evaluation of lifestyle, muscular strength, physical performance, body composition, and quality of life will be conducted. Additionally, DNA, serum, and plasma biobanking will facilitate future investigations.DiscussionThe anticipated outcomes involve the identification and validation of non-invasive blood- and imaging-oriented biomarkers, enhancing the care paradigm for liver cirrhosis patients. Notably, the temporal evolution of these biomarkers will be crucial for understanding dynamic changes.Trial registrationClinicaltrials.gov, registration identifier NCT05502198. Registered on 16 August 2022. Link: https://classic.clinicaltrials.gov/ct2/show/NCT05502198.

  • 8.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Nasr, Patrik
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Sanyal, Arun J.
    VCU Sch Med, VA USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Adverse muscle composition is a significant risk factor for all-cause mortality in NAFLD2023In: JHEP REPORTS, ISSN 2589-5559, Vol. 5, no 3, article id 100663Article in journal (Refereed)
    Abstract [en]

    Background & Aims: Adverse muscle composition (MC) (i.e., low muscle volume and high muscle fat) has previously been linked to poor functional performance and comorbidities in non-alcoholic fatty liver disease (NAFLD). In this study we aimed to investigate associations of all-cause mortality with liver fat, NAFLD, and MC in the UK Biobank imaging study.Methods: Magnetic resonance images of 40,174 participants were analyzed for liver proton density fat fraction (PDFF), thigh fat-free muscle volume (FFMV) z-score, and muscle fat infiltration (MFI) using the AMRA (R) Researcher. Participants with NAFLD were sex-, age-, and BMI-matched to participants without NAFLD with low alcohol consumption. Adverse MC was identified using previously published cut-offs. All-cause mortality was investigated using Cox regression. Models within NAFLD were crude and subsequently adjusted for sex, age, BMI (M1), hand grip strength, physical activity, smoking, alcohol (M2), and previous cancer, coronary heart disease, type 2 diabetes (M3).Results: A total of 5,069 participants had NAFLD. During a mean (+/- SD) follow-up of 3.9 (+/- 1.4) years, 150 out of the 10,138 participants (53% men, age 64.4 [+/- 7.6] years, BMI 29.7 [+/- 4.4] kg/m2) died. In the matched dataset, neither NAFLD nor liver PDFF were associated with all-cause mortality, while all MC variables achieved significance. Within NAFLD, adverse MC, MFI and FFMV z-score were significantly associated with all-cause mortality and remained so in M1 and M2 (crude hazard ratios [HRs] 2.84, 95% CI 1.70-4.75, p <0.001; 1.15, 95% CI 1.07-1.24, p <0.001; 0.70, 95% CI 0.55-0.88, p <0.001). In M3, the rela-tionship was attenuated for adverse MC and FFMV z-score (adjusted HRs 1.72, 95% CI 1.00-2.98, p = 0.051; 0.77, 95% CI 0.58-1.02, p = 0.069) but remained significant for MFI (adjusted HR 1.13, 95% CI 1.01-1.26, p = 0.026).Conclusions: Neither NAFLD nor liver PDFF was predictive of all-cause mortality. Adverse MC was a strong predictor of all -cause mortality in individuals with NAFLD.Impact and implications: Individuals with fatty liver disease and poor muscle health more often suffer from poor functional performance and comorbidities. This study shows that they are also at a higher risk of dying. The study results indicate that measuring muscle health (the patients muscle volume and how much fat they have in their muscles) could help in the early detection of high-risk patients and enable targeted preventative care.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • 9.
    Karlsson, Markus
    et al.
    AMRA Med AB, Linkoping, Sweden.
    Indurain, Ainhoa
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Local Health Care Services in Central Östergötland, Department of Acute Internal Medicine and Geriatrics. Region Östergötland, Medicine Center, Department of Nephrology.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Tunon, Patrik
    AMRA Med AB, Linkoping, Sweden.
    Segelmark, Mårten
    Lund Univ, Sweden; Skane Univ Hosp, Sweden.
    Uhlin, Fredrik
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Medicine Center, Department of Nephrology. Tallinn Univ Technol, Estonia.
    Fernström, Anders
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Medicine Center, Department of Nephrology.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Assessing Tissue Hydration Dynamics Based on Water/Fat Separated MRI2023In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 58, no 2, p. 652-660Article in journal (Refereed)
    Abstract [en]

    Background:Optimal fluid status is an important issue in hemodialysis. Clinical evaluation of volume status and different diagnostic tools are used to determine hydration status in these patients. However, there is still no accurate method for this assessment. Purpose:To propose and evaluate relative lean water signal (LWSrel) as a water-fat MRI-based tissue hydration measurement. Study Type:Prospective. Population:A total of 16 healthy subjects (56 & PLUSMN; 6 years, 0 male) and 11 dialysis patients (60.3 +/- 12.3 years, 9 male; dialysis time per week 15 +/- 3.5 hours, dialysis duration 31.4 +/- 27.9 months). Field Strength/Sequence:A 3 T; 3D spoiled gradient echo. Assessment:LWSrel, a measurement of the water concentration of tissue, was estimated from fat-referenced MR images. Segmentations of total adipose tissue as well as thigh and calf muscles were used to measure LWSrel and tissue volumes. LWSrel was compared between healthy subjects and dialysis patients, the latter before and after dialysis. Bioimpedance-based body composition monitor over hydration (BCM OH) was also measured. Statistical Tests:T-tests were used to compare differences between the healthy subjects and dialysis patients, as well as changes between before and after dialysis. Pearson correlation was calculated between MRI and non-MRI biomarkers. A P value < 0.05 was considered statistically significant. Results:The LWSrel in adipose tissue was significantly higher in the dialysis cohort compared with the healthy cohort (246.8% +/- 60.0% vs. 100.0% +/- 10.8%) and decreased significantly after dialysis (246.8 +/- 60.0% vs. 233.8 +/- 63.4%). Thigh and calf muscle volumes also significantly decreased by 3.78% +/- 1.73% and 2.02% +/- 2.50% after dialysis. There was a significant correlation between changes in adipose tissue LWSrel and ultrafiltration volume (r = 87), as well as with BCM OH (r = 0.66). Data Conclusion:MRI-based LWSrel and tissue volume measurements are sensitive to tissue hydration changes occurring during dialysis.

  • 10.
    Khawaja, Tasveer
    et al.
    Univ Hosp Cleveland, OH USA; Case Western Reserve Univ, OH USA.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Al-Kindi, Sadeer G.
    Univ Hosp Cleveland, OH USA; Case Western Reserve Univ, OH USA.
    Rajagopalan, Sanjay
    Univ Hosp Cleveland, OH USA; Case Western Reserve Univ, OH USA.
    Khera, Amit
    Univ Texas Southwestern Med Ctr, TX USA.
    de Lemos, James A.
    Univ Texas Southwestern Med Ctr, TX USA.
    Joshi, Parag
    Univ Texas Southwestern Med Ctr, TX USA.
    Neeland, Ian J.
    Univ Hosp Cleveland, OH USA; Case Western Reserve Univ, OH USA; 11100 Euclid Ave, OH 44016 USA.
    Coronary artery calcium, hepatic steatosis, and atherosclerotic cardiovascular disease risk in patients with type 2 diabetes mellitus: Results from the Dallas heart study2023In: Progress in cardiovascular diseases, ISSN 0033-0620, E-ISSN 1873-1740, Vol. 78, p. 67-73Article, review/survey (Refereed)
    Abstract [en]

    Introduction: Cardiovascular disease (CVD) risk amongst those with type 2 diabetes (T2D) is heterogenous. The role of imaging-based cardiometabolic biomarkers (e.g., coronary artery calcium [CAC] score, and hepatic triglyceride content [HTC]) in CVD risk stratification in T2D is unclear. To better understand this, we sought to evaluate the individual and joint associations between CAC and hepatic steatosis (HS) with clinical atherosclerotic CVD (ASCVD) in Dallas Heart Study (DHS) participants with and without T2D. Methods: We examined participants in the DHS, a multi-ethnic cohort study, without self-reported ASCVD. CAC scoring was performed via computed tomography with the mean of two consecutive scores used. HTC was measured using magnetic resonance spectroscopy, and HS was defined as HTC >5.5% The primary outcome was incident ASCVD, defined as coronary heart disease (CHD; myocardial infarction, percutaneous coronary intervention, or coronary artery bypass graft surgery), ischemic stroke, transient ischemic attack, or CVD death. Cox regression analyses, and interaction testing was performed to evaluate the individual and joint associations between CAC and HS with ASCVD. The association between HS and coronary heart disease was validated in the UK Biobank (UKB).Results: A total of 1252 DHS participants were included with mean age 44.8 & PLUSMN; 9.3 years, mean body mass index 28.7 & PLUSMN; 5.9 kg/m2, 55% female, and 59% black with an overall prevalence of T2D of 9.7%. CAC scores were significantly higher (p < 0.01) and HS was significantly more prevalent in those with T2D (p < 0.01). Over a median of 12.3 years, 8.3% of participants experienced ASCVD events. The ASCVD event rate was significantly higher in participants with T2D (20.5% vs 7.0%, p < 0.01). Continuous CAC was associated with ASCVD events in the overall cohort regardless of T2D status with a significant interaction present between CAC and T2D status on ASCVD, Pinteraction = 0.02. HTC was not associated with ASCVD risk in participants without T2D but was inversely associated with risk in participants with T2D (HR 0.91, 95% CI 0.83-0.99 per 1% increase in HTC, p = 0.02), Pinteraction = 0.02. Amongst 37,266 UKB participants, 4.5% had T2D. CHD events occurred in 2.2% of participants, with 10.2% of events occurring amongst those with T2D. An inverse relationship between HTC and CHD was also found amongst those with T2D in UKB with a significant interaction between T2D status and HTC on CHD (HR per 1% increase in HTC 0.95, 95% CI 0.91-0.99, p = 0.01, Pinteraction = 0.02).Conclusions: In the DHS, we found that CAC was associated with ASCVD risk independent of T2D status. We did not observe an association between HTC and ASCVD in participants without T2D, but there was an inverse association between HTC and ASCVD in those with T2D that was replicated in the UKB cohort. Further investigation is warranted to understand the possible protective association of HS in participants with T2D.& COPY; 2023 Elsevier Inc. All rights reserved.

  • 11.
    Fredwall, Svein O.
    et al.
    Sunnaas Rehabil Hosp, Norway; Univ Oslo, Norway.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    de Vries, Olga
    Sunnaas Rehabil Hosp, Norway.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Eggesbo, Heidi Beate
    Univ Oslo, Norway.
    Weedon-Fekjaer, Harald
    Oslo Univ Hosp, Norway.
    Petersson, Mikael
    AMRA Med AB, Linkoping, Sweden.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. AMRA Med AB, Linkoping, Sweden.
    Manum, Grethe
    Sunnaas Rehabilitat Hosp, Norway.
    Savarirayan, Ravi
    Murdoch Childrens Res Inst, Australia; Univ Melbourne, Australia.
    Fat infiltration in the thigh muscles is associated with symptomatic spinal stenosis and reduced physical functioning in adults with achondroplasia2023In: Orphanet Journal of Rare Diseases, E-ISSN 1750-1172, Vol. 18, no 1, article id 35Article in journal (Refereed)
    Abstract [en]

    BackgroundSymptomatic spinal stenosis is a prevalent complication in adults with achondroplasia. Increased muscle fat infiltration (MFI) and reduced thigh muscle volumes have also been reported, but the pathophysiology is poorly understood. We explored whether the increased MFI and reduced thigh muscle volumes were associated with the presence of symptomatic spinal stenosis and physical functioning.MethodsMFI and thigh muscle volumes were assessed by MRI in 40 adults with achondroplasia, and compared to 80 average-statured controls, matched for BMI, gender, and age. In achondroplasia participants, the six-minute walk-test (6MWT), the 30-s sit-to-stand test (30sSTS), and a questionnaire (the IPAQ) assessed physical functioning.ResultsSymptomatic spinal stenosis was present in 25 of the participants (the stenosis group), while 15 did not have stenosis (the non-stenosis group). In the stenosis group, 84% (21/25) had undergone at least one spinal decompression surgery. The stenosis group had significantly higher MFI than the non-stenosis group, with an age-, gender and BMI-adjusted difference in total MFI of 3.3 percentage points (pp) (95% confidence interval [CI] 0.04 to 6.3 pp; p = 0.03). Compared to matched controls, the mean age-adjusted difference was 3.3 pp (95% CI 1.7 to 4.9 pp; p < 0.01). The non-stenosis group had MFI similar to controls (age-adjusted difference - 0.9 pp, 95% CI - 3.4 to 1.8 pp; p = 0.51). MFI was strongly correlated with the 6MWT (r = - 0.81, - 0.83, and - 0.86; all p-values < 0.01), and moderately correlated with the 30sSTS (r = - 0.56, - 0.57, and - 0.59; all p-values < 0.01). There were no significant differences in muscle volumes or physical activity level between the stenosis group and the non-stenosis group.ConclusionIncreased MFI in the thigh muscles was associated with the presence of symptomatic spinal stenosis, reduced functional walking capacity, and reduced lower limb muscle strength. The causality between spinal stenosis, accumulation of thigh MFI, and surgical outcomes need further study. We have demonstrated that MRI might serve as an objective muscle biomarker in future achondroplasia studies, in addition to functional outcome measures. The method could potentially aid in optimizing the timing of spinal decompression surgery and in planning of post-surgery rehabilitation.

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  • 12.
    Lund, Nils
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Elliott, James M
    Faculty of Medicine and Health, School of Health Sciences, Northern Sydney Local Health District, The Kolling Institute, University of Sydney, St Leonards, NSW, Australia; Feinberg School of Medicine, Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA.
    Peterson, Gunnel
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Zsigmond, Peter
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery. Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology.
    Karlsson, Anette
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Peolsson, Anneli
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Medicine Center, Occupational and Environmental Medicine Center.
    Fatty infiltrate and neck muscle volume in individuals with chronic whiplash associated disorders compared to healthy controls – a cross sectional case–control study2023In: BMC Musculoskeletal Disorders, E-ISSN 1471-2474, Vol. 24, no 1, article id 181Article in journal (Refereed)
    Abstract [en]

    Background: The underlying pathophysiological mechanisms of chronic Whiplash Associated Disorders (WAD) are not fully understood. More knowledge of morphology is needed to better understand the disorder, improve diagnostics and treatments. The aim was to investigate dorsal neck muscle volume (MV) and muscle fat infiltration (MFI) in relation to self-reported neck disability among 30 participants with chronic WAD grade II-III compared to 30 matched healthy controls.

    Methods: MV and MFI at spinal segments C4 through C7 in both sexes with mild- to moderate chronic WAD (n = 20), severe chronic WAD (n = 10), and age- and sex matched healthy controls (n = 30) was compared. Muscles: trapezius, splenius, semispinalis capitis and semispinalis cervicis were segmented by a blinded assessor and analyzed.

    Results: Higher MFI was found in right trapezius (p = 0.007, Cohen’s d = 0.9) among participants with severe chronic WAD compared to healthy controls. No other significant difference was found for MFI (p = 0.22–0.95) or MV (p = 0.20–0.76).

    Conclusions: There are quantifiable changes in muscle composition of right trapezius on the side of dominant pain and/or symptoms, among participants with severe chronic WAD. No other statistically significant differences were shown for MFI or MV. These findings add knowledge of the association between MFI, muscle size and self-reported neck disability in chronic WAD.

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  • 13.
    Iliodromiti, Stamatina
    et al.
    Queen Mary Univ London, England; Univ Glasgow, Scotland.
    McLaren, James
    Univ Glasgow, Scotland.
    Ghouri, Nazim
    Univ Glasgow, Scotland.
    Miller, Melissa R.
    Pfizer, MA USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Linge, Jennifer
    AMRA Med AB, Linkoping, Sweden.
    Ballantyne, Stuart
    Greater Glasgow & Clyde NHS, Scotland.
    Platt, Jonathan
    Greater Glasgow & Clyde NHS, Scotland.
    Foster, John
    Beatson West Scotland Canc Ctr, Scotland.
    Hanvey, Scott
    Derriford Hosp, England.
    Gujral, Unjali P.
    Emory Univ, GA 30322 USA.
    Kanaya, Alka
    Univ Calif San Francisco, CA 94143 USA.
    Sattar, Naveed
    Univ Glasgow, Scotland.
    Lumsden, Mary Ann
    Univ Glasgow, Scotland.
    Gill, Jason M. R.
    Univ Glasgow, Scotland.
    Liver, visceral and subcutaneous fat in men and women of South Asian and white European descent: a systematic review and meta-analysis of new and published data2023In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 66, no 1, p. 44-56Article, review/survey (Refereed)
    Abstract [en]

    Aims/hypothesis South Asians have a two- to fivefold higher risk of developing type 2 diabetes than those of white European descent. Greater central adiposity and storage of fat in deeper or ectopic depots are potential contributing mechanisms. We collated existing and new data on the amount of subcutaneous (SAT), visceral (VAT) and liver fat in adults of South Asian and white European descent to provide a robust assessment of potential ethnic differences in these factors. Methods We performed a systematic review of the Embase and PubMed databases from inception to August 2021. Unpublished imaging data were also included. The weighted standardised mean difference (SMD) for each adiposity measure was estimated using random-effects models. The quality of the studies was assessed using the ROBINS-E tool for risk of bias and overall certainty of the evidence was assessed using the GRADE approach. The study was pre-registered with the OSF Registries (https://osf. io/w5bf9). Results We summarised imaging data on SAT, VAT and liver fat from eight published and three previously unpublished datasets, including a total of 1156 South Asian and 2891 white European men, and 697 South Asian and 2271 white European women. Despite South Asian men having a mean BMI approximately 0.5-0.7 kg/m(2) lower than white European men (depending on the comparison), nine studies showed 0.34 SMD (95% CI 0.12, 0.55; I-2 =83%) more SAT and seven studies showed 0.56 SMD (95% CI 0.14, 0.98; I-2 =93%) more liver fat, but nine studies had similar VAT (-0.03 SMD; 95% CI -0.24, 0.19;1 2 =85%) compared with their white European counterparts. South Asian women had an approximately 0.9 kg/m(2) lower BMI but 0.31 SMD (95% CI 0.14, 0.48; I-2=53%) more liver fat than their white European counterparts in five studies. Subcutaneous fat levels (0.03 SMD; 95% CI -0.17, 0.23; I-2 =72%) and VAT levels (0.04 SMD; 95% CI -0.16, 0.24; I-2 =71%) did not differ significantly between ethnic groups in eight studies of women. Conclusions/interpretation South Asian men and women appear to store more ectopic fat in the liver compared with their white European counterparts with similar BMI levels. Given the emerging understanding of the importance of liver fat in diabetes pathogenesis, these findings help explain the greater diabetes risks in South Asians.

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  • 14.
    Beck, Dani
    et al.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Sunnaas Rehabil Hosp HT, Norway.
    De Lange, Ann-Marie G.
    Univ Oslo, Norway; Univ Oslo, Norway; CHU Vaudois, Switzerland; Univ Lausanne, Switzerland; Univ Oxford, England.
    Alnaes, Dag
    Univ Oslo, Norway; Univ Oslo, Norway; Bjorknes Coll, Norway.
    Maximov, Ivan I.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Western Norway Univ Appl Sci, Norway.
    Pedersen, Mads L.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Simon, Rozalyn
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Richard, Genevieve
    Univ Oslo, Norway; Univ Oslo, Norway.
    Ulrichsen, Kristine M.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Sunnaas Rehabil Hosp HT, Norway.
    Dorum, Erlend S.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Sunnaas Rehabil Hosp HT, Norway.
    Kolskår, Knut K.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Sunnaas Rehabil Hosp HT, Norway.
    Sanders, Anne-Marthe
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Sunnaas Rehabil Hosp HT, Norway.
    Winterton, Adriano
    Univ Oslo, Norway; Univ Oslo, Norway.
    Gurholt, Tiril P.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Kaufmann, Tobias
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Steen, Nils Eiel
    Univ Oslo, Norway; Univ Oslo, Norway.
    Nordvik, Jan Egil
    CatoSenteret Rehabil Ctr, Norway.
    Andreassen, Ole A.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Westlye, Lars T.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults2022In: NeuroImage: Clinical, E-ISSN 2213-1582, Vol. 33, article id 102949Article in journal (Refereed)
    Abstract [en]

    There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18-94 years (mean +/- standard deviation (SD) at baseline: 46.8 +/- 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19-86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) - the difference between actual age and the prediction of the brains biological age - at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

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  • 15.
    Niklasson, Erik
    et al.
    Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska Institutet, Stockholm, Sweden.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AMRA Medical AB, Linköping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. AMRA Medical AB, Linköping, Sweden.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. AMRA Medical AB, Linköping, Sweden.
    Andersson, Daniel P
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden.
    Wiik, Anna
    Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska Institutet, Stockholm, Sweden; Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden.
    Holmberg, Mats
    Department of Medicine, Karolinska Institutet, Karolinska University Hospital Huddinge, Stockholm, Sweden; ANOVA, Andrology, Sexual Medicine and Transgender Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Brismar, Torkel B
    Division of Radiology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Gustafsson, Thomas
    Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska Institutet, Stockholm, Sweden; Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden.
    Lundberg, Tommy R
    Department of Laboratory Medicine, Division of Clinical Physiology, Karolinska Institutet, Stockholm, Sweden; Unit of Clinical Physiology, Karolinska University Hospital, Stockholm, Sweden.
    Assessment of anterior thigh muscle size and fat infiltration using single-slice CT imaging versus automated MRI analysis in adults2022In: British Journal of Radiology, ISSN 0007-1285, E-ISSN 1748-880X, Vol. 95, no 1133, article id 20211094Article in journal (Refereed)
    Abstract [en]

    Objectives: We examined the longitudinal and cross- sectional relationship between automated MRI-analysis and single-slice axial CT imaging for determining muscle size and muscle fat infiltration (MFI) of the anterior thigh.

    Methods: Twenty-two patients completing sex-hormone treatment expected to result in muscle hypertrophy (n = 12) and atrophy (n = 10) underwent MRI scans using 2-point Dixon fat/water-separated sequences and CT scans using a system operating at 120 kV and a fixed flux of 100 mA. At baseline and 12 months after, auto- mated volumetric MRI analysis of the anterior thigh was performed bilaterally, and fat-free muscle volume and MFI were computed. In addition, cross-sectional area (CSA) and radiological attenuation (RA) (as a marker of fat infiltration) were calculated from single slice axial CT-images using threshold-assisted planimetry. Linear regression models were used to convert units.

    Results: There was a strong correlation between MRI- derived fat-free muscle volume and CT-derived CSA (R = 0.91), and between MRI-derived MFI and CT-derived RA (R = −0.81). The 95% limits of agreement were ±0.32 L for muscle volume and ±1.3% units for %MFI. The longi- tudinal change in muscle size and MFI was comparable across imaging modalities.

    Conclusions: Both automated MRI and single-slice CT-imaging can be used to reliably quantify anterior thigh muscle size and MFI.

    Advances in knowledge: This is the first study examining the intermodal agreement between automated MRI anal- ysis and CT-image assessment of muscle size and MFI in the anterior thigh muscles. Our results support that both CT- and MRI-derived measures of muscle size and MFI can be used in clinical settings.

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  • 16.
    Schindler, Louise S.
    et al.
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway.
    Subramaniapillai, Sivaniya
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway.
    Barth, Claudia
    Univ Oslo, Norway; Univ Oslo, Norway; Diakonhjemmet Hosp, Norway.
    van der Meer, Dennis
    Univ Oslo, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Pedersen, Mads L.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Kaufmann, Tobias
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Maximov, Ivan I.
    Univ Oslo, Norway; Univ Oslo, Norway; Western Norway Univ Appl Sci, Norway.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Beck, Dani
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Gurholt, Tiril P.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Voldsbekk, Irene
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Suri, Sana
    Univ Oxford, England; Univ Oxford, England.
    Ebmeier, Klaus P.
    Univ Oxford, England.
    Draganski, Bogdan
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Max Planck Inst Human Cognit & Brain Sci, Germany.
    Andreassen, Ole A.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Westlye, Lars T.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    de Lange, Ann-Marie G.
    Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oslo, Norway; Univ Oxford, England.
    Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women2022In: NeuroImage: Clinical, E-ISSN 2213-1582, Vol. 36, article id 103239Article in journal (Refereed)
    Abstract [en]

    The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

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  • 17.
    Tejani, Sanaa
    et al.
    University of Texas Southwestern Medical School, Dallas, TX.
    McCoy, Cody
    Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
    Ayers, Colby R.
    Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
    Powell-Wiley, Tiffany M.
    Cardiovasular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD .
    Després, Jean-Pierre
    Department of Kinesiology, Faculty of Medicine, Université Laval and VITAM e Centre de rercherche en santé durable, CIUSSS Capitale-Nationale, Québec, QC, Canada.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical AB, Linköping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Petersson, Mikael
    AMRA Medical AB, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Neeland, Ian J.
    University Hospitals Harrington Heart and Vascular Institute and Case Western Reserve University School of Medicine, Cleveland, OH.
    Cardiometabolic Health Outcomes Associated With Discordant Visceral and Liver Fat Phenotypes: Insights From the Dallas Heart Study and UK Biobank2022In: Mayo Clinic proceedings, ISSN 0025-6196, E-ISSN 1942-5546, Vol. 97, no 2, p. 225-237Article in journal (Refereed)
    Abstract [en]

    Objective: To evaluate the cardiometabolic outcomes associated with discordant visceral adipose tissue (VAT) and liver fat (LF) phenotypes in 2 cohorts.

    Patients and Methods: Participants in the Dallas Heart Study underwent baseline imaging from January 1, 2000, through December 31, 2002, and were followed for incident cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM) through 2013. Associations between VAT-LF groups (low-low, high-low, low-high, and high-high) and outcomes were assessed using multivariable- adjusted regression and were replicated in the independent UK Biobank.

    Results: The Dallas Heart Study included 2064 participants (mean SD age, 449 years; 54% female; 47% black). High VATehigh LF and high VATelow LF were associated with prevalent atheroscle- rosis, whereas low VATehigh LF was not. Of 1731 participants without CVD/T2DM, 128 (7.4%) developed CVD and 95 (5.5%) T2DM over a median of 12 years. High VATehigh LF and high VATelow LF were associated with increased risk of CVD (hazard ratios [HRs], 2.0 [95% CI, 1.3 to 3.2] and 2.4 [95% CI, 1.4 to 4.1], respectively) and T2DM (odds ratios [ORs], 7.8 [95% CI, 3.8 to 15.8] and 3.3 [95% CI, 1.4 to 7.8], respectively), whereas low VATehigh LF was associated with T2DM (OR, 2.7 [95% CI, 1.1 to 6.7]). In the UK Biobank (N1⁄422,354; April 2014-May 2020), only high VATelow LF remained associated with CVD after multivariable adjustment for age and body mass index (HR, 1.5 [95% CI, 1.2 to 1.9]).

    Conclusion: Although VAT and LF are each associated with cardiometabolic risk, these observations demonstrate the importance of separating their cardiometabolic implications when there is presence or absence of either or both in an individual.

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  • 18.
    Edin, Carl
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken. Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Scheffel, Tobias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Karlsson, Markus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Medical AB, Sweden.
    Swahn, Eva
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Region Östergötland, Heart Center, Department of Cardiology in Linköping.
    Östgren, Carl Johan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Primary Care Center, Primary Health Care Center Ekholmen.
    Engvall, Jan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping.
    Ebbers, Tino
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Sweden.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Carlhäll, Carl-Johan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping.
    Ectopic fat is associated with cardiac remodeling - A comprehensive assessment of regional fat depots in type 2 diabetes using multi-parametric MRI.2022In: Frontiers in Cardiovascular Medicine, E-ISSN 2297-055X, Vol. 9, article id 813427Article in journal (Refereed)
    Abstract [en]

    Background: Different regional depots of fat have distinct metabolic properties and may relate differently to adverse cardiac remodeling. We sought to quantify regional depots of body fat and to investigate their relationship to cardiac structure and function in Type 2 Diabetes (T2D) and controls.

    Methods: From the SCAPIS cohort in Linköping, Sweden, we recruited 92 subjects (35% female, mean age 59.5 ± 4.6 years): 46 with T2D and 46 matched controls. In addition to the core SCAPIS data collection, participants underwent a comprehensive magnetic resonance imaging examination at 1.5 T for assessment of left ventricular (LV) structure and function (end-diastolic volume, mass, concentricity, ejection fraction), as well as regional body composition (liver proton density fat fraction, visceral adipose tissue, abdominal subcutaneous adipose tissue, thigh muscle fat infiltration, fat tissue-free thigh muscle volume and epicardial adipose tissue).

    Results: Compared to the control group, the T2D group had increased: visceral adipose tissue volume index (P < 0.001), liver fat percentage (P < 0.001), thigh muscle fat infiltration percentage (P = 0.02), LV concentricity (P < 0.001) and LV E/e'-ratio (P < 0.001). In a multiple linear regression analysis, a negative association between liver fat percentage and LV mass (St Beta -0.23, P < 0.05) as well as LV end-diastolic volume (St Beta -0.27, P < 0.05) was found. Epicardial adipose tissue volume and abdominal subcutaneous adipose tissue volume index were the only parameters of fat associated with LV diastolic dysfunction (E/e'-ratio) (St Beta 0.24, P < 0.05; St Beta 0.34, P < 0.01, respectively). In a multivariate logistic regression analysis, only visceral adipose tissue volume index was significantly associated with T2D, with an odds ratio for T2D of 3.01 (95% CI 1.28-7.05, P < 0.05) per L/m2 increase in visceral adipose tissue volume.

    Conclusions: Ectopic fat is predominantly associated with cardiac remodeling, independently of type 2 diabetes. Intriguingly, liver fat appears to be related to LV structure independently of VAT, while epicardial fat is linked to impaired LV diastolic function. Visceral fat is associated with T2D independently of liver fat and abdominal subcutaneous adipose tissue.

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  • 19.
    Gerdle, Björn
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Med AB, Linkoping, Sweden.
    Lund, Eva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Bengtsson, Ann
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ghafouri, Bijar
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Med AB, Linkoping, Sweden.
    Fibromyalgia: Associations Between Fat Infiltration, Physical Capacity, and Clinical Variables2022In: Journal of Pain Research, E-ISSN 1178-7090, Vol. 15, p. 2517-2535Article in journal (Refereed)
    Abstract [en]

    Background: Obesity is a risk factor for the development of fibromyalgia (FM) and generally most studies report increased Body Mass Index (BMI) in FM. Obesity in FM is associated with a worse clinical presentation. FM patients have low physical conditioning and obesity further exacerbates these aspects. Hitherto studies of FM have focused upon a surrogate for overall measure of fat content, ie, BMI. This study is motivated by that ectopic fat and adipose tissues are rarely investigated in FM including their relationships to physical capacity variables. Moreover, their relationships to clinical variables including are not known. Aims were to 1) compare body composition between FM and healthy controls and 2) investigate if significant associations exist between body composition and physical capacity aspects and important clinical variables.Methods: FM patients (n = 32) and healthy controls (CON; n = 30) underwent a clinical examination that included pressure pain thresholds and physical tests. They completed a health questionnaire and participated in whole-body magnetic resonance imaging (MRI) to determine body composition aspects.Results: Abdominal adipose tissues, muscle fat, and BMI were significantly higher in FM, whereas muscle volumes of quadriceps were smaller. Physical capacity variables correlated negatively with body composition variables in FM. Both body composition and physical capacity variables were significant regressors of group belonging; the physical capacity variables alone showed stronger relationships with group membership. A mix of body composition variables and physical capacity variables were significant regressors of pain intensity and impact in FM. Body composition variables were the strongest regressors of blood pressures, which were increased in FM.Conclusion: Obesity has a negative influence on FM symptomatology and increases the risk for other serious conditions. Hence, obesity, dietary habits, and physical activity should be considered when developing clinical management plans for patients with FM.

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  • 20.
    Ghafouri, Bijar
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Edman, Emelie
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Löf, Marie
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. Karolinska Inst, Sweden.
    Lund, Eva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Medical AB.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Medical AB.
    Gerdle, Björn
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Dong, Huan-Ji
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Fibromyalgia in women: association of inflammatory plasma proteins, muscle blood flow, and metabolism with body mass index and pain characteristics2022In: Pain Reports, E-ISSN 2471-2531, Vol. 7, no 6, article id e1042Article in journal (Refereed)
    Abstract [en]

    Introduction: Obesity is a common comorbidity in fibromyalgia (FM). Both FM and obesity have been connected to low-grade inflammation, although it is possible that previously reported inflammatory alterations in FM primarily may be linked to increased body mass index (BMI). Objective: This study aimed to investigate whether the inflammatory plasma protein profile, muscle blood flow, and metabolism and pain characteristics (clinical parameters and patient-reported outcome measurements) differed between female patients with FM with and without obesity. Methods: Patients with FM underwent clinical examinations, physical tests, and answered questionnaires. They were dichotomized according to BMI (&lt;30 kg/m(2) [n = 14]; &gt;= 30 kg/m(2) [n = 13]). Blood samples were collected and analyzed using a panel of 71 inflammatory plasma proteins. Results: There were significant (P &lt; 0.05) differences in blood pressure, pulse, max VO2, pain intensity, physical capacity, and Fibromyalgia Impact Questionnaire between the groups; the obese group had higher blood pressure, pulse, pain intensity, and Fibromyalgia Impact Questionnaire. There were 14 proteins that contributed to the group belonging. The 4 most important proteins for the group discrimination were MIP1 beta, MCP4, IL1RA, and IL6, which showed higher concentrations in obese patients with FM. Significantly decreased blood flow and increased concentration of pyruvate were detected in obese patients compared with nonobese patients. There was significant correlation between inflammatory proteins and sedentary behavior and health status in obese patients with FM. Conclusions: These findings suggest that metabolism and inflammation interact in female patients with FM with obesity and might cause chronic low-grade inflammation. Screening for obesity and monitoring of BMI changes should be considered in the treatment of patients with FM.

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  • 21.
    Peolsson, Anneli
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Medicine Center, Occupational and Environmental Medicine Center.
    Karlsson, Anette
    Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Peterson, Gunnel
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Uppsala Univ, Sweden.
    Borén, Hanna
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Zsigmond, Peter
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Neurosurgery.
    Elliott, James M.
    Univ Sydney, Australia; Kolling Inst, Australia.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Morphology and composition of the ventral neck muscles in individuals with chronic whiplash related disorders compared to matched healthy controls: a cross-sectional case-control study2022In: BMC Musculoskeletal Disorders, E-ISSN 1471-2474, Vol. 23, no 1, article id 867Article in journal (Refereed)
    Abstract [en]

    BackgroundObjective Studies of cross-sectional area (CSA) (morphology) and muscle fat infiltration (MFI) (composition) in ventral neck muscles is scarce in patients with chronic whiplash associated disorders (WAD), especially for men and those with severe WAD compared with matched healthy controls. The aim was to compare CSA and MFI of sternocleidomastoid (SCM), longus capitis (LCA) and longus colli (LCO) in patients with chronic right-sided dominant moderate (Neck Disability Index: NDI &lt; 40) or severe WAD (NDI &gt;= 40), compared to age- and sex-matched healthy controls. Methods Cross-sectional case-control study with blinded investigators. Thirty-one patients with chronic WAD (17 women and 14 men, mean age 40 years) (SD 12.6, range 20-62)) and 31 age- and sex-matched healthy controls underwent magnetic resonance imaging of ventral neck muscles segmental level C4. Results Unique to the severe group was a larger magnitude of MFI in right SCM (p = 0.02) compared with healthy controls. There was no significant difference between the groups with regards to the other muscles and measures. Conclusions Individuals with severe right-sided dominant WAD have a higher MFI in the right SCM compared to healthy controls. No other differences were found between the groups. The present study indicates that there are changes in the composition of muscles on the side of greatest pain.

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  • 22.
    Widholm, Per
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ahlgren, Andre
    AMRA Med AB, Linkoping, Sweden.
    Karlsson, Markus
    AMRA Med AB, Linkoping, Sweden.
    Romu, Thobias
    AMRA Med AB, Linkoping, Sweden.
    Tawil, Rabi
    Univ Rochester, NY 14642 USA.
    Wagner, Kathryn R.
    Johns Hopkins Sch Med, MD USA.
    Statland, Jeffrey M.
    Univ Kansas, KS 66103 USA.
    Wang, Leo H.
    Univ Washington, WA USA.
    Shieh, Perry B.
    Univ Calif Los Angeles, CA USA.
    van Engelen, Baziel G. M.
    Radboud Univ Nijmegen, Netherlands.
    Cadavid, Diego
    Fulcrum Therapeut, MA 02139 USA.
    Ronco, Lucienne
    Fulcrum Therapeut, MA 02139 USA.
    Odueyungbo, Adefowope O.
    Fulcrum Therapeut, MA 02139 USA.
    Jiang, John G.
    Fulcrum Therapeut, MA 02139 USA.
    Mellion, Michelle L.
    Fulcrum Therapeut, MA 02139 USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Quantitative muscle analysis in facioscapulohumeral muscular dystrophy using whole-body fat-referenced MRI: Protocol development, multicenter feasibility, and repeatability2022In: Muscle and Nerve, ISSN 0148-639X, E-ISSN 1097-4598, Vol. 66, no 2, p. 183-192Article in journal (Refereed)
    Abstract [en]

    Introduction/Aims Functional performance tests are the gold standard to assess disease progression and treatment effects in neuromuscular disorders. These tests can be confounded by motivation, pain, fatigue, and learning effects, increasing variability and decreasing sensitivity to disease progression, limiting efficacy assessment in clinical trials with small sample sizes. We aimed to develop and validate a quantitative and objective method to measure skeletal muscle volume and fat content based on whole-body fat-referenced magnetic resonance imaging (MRI) for use in multisite clinical trials. Methods Subjects aged 18 to 65 years, genetically confirmed facioscapulohumeral muscular dystrophy 1 (FSHD1), clinical severity 2 to 4 (Riccis scale, range 0-5), were enrolled at six sites and imaged twice 4-12 weeks apart with T1-weighted two-point Dixon MRI covering the torso and upper and lower extremities. Thirty-six muscles were volumetrically segmented using semi-automatic multi-atlas-based segmentation. Muscle fat fraction (MFF), muscle fat infiltration (MFI), and lean muscle volume (LMV) were quantified for each muscle using fat-referenced quantification. Results Seventeen patients (mean age +/- SD, 49.4 years +/- 13.02; 12 men) were enrolled. Within-patient SD ranged from 1.00% to 3.51% for MFF and 0.40% to 1.48% for MFI in individual muscles. For LMV, coefficients of variation ranged from 2.7% to 11.7%. For the composite score average of all muscles, observed SDs were 0.70% and 0.32% for MFF and MFI, respectively; composite LMV coefficient of variation was 2.0%. Discussion We developed and validated a method for measuring skeletal muscle volume and fat content for use in multisite clinical trials of neuromuscular disorders.

  • 23.
    Mellion, Michelle L.
    et al.
    Fulcrum Therapeut, MA 02139 USA.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Karlsson, Markus
    AMRA Med AB, Linkoping, Sweden.
    Ahlgren, Andre
    AMRA Med AB, Linkoping, Sweden.
    Tawil, Rabi
    Univ Rochester, NY USA.
    Wagner, Kathryn R.
    Johns Hopkins Sch Med, MD USA.
    Statland, Jeffrey M.
    Univ Kansas, KS 66103 USA.
    Wang, Leo
    Univ Washington, WA 98195 USA.
    Shieh, Perry B.
    Ronald Reagan UCLA, CA USA.
    van Engelen, Baziel G. M.
    Radboud Univ Nijmegen, Netherlands.
    Kools, Joost
    Radboud Univ Nijmegen, Netherlands; Radboud Univ Nijmegen, Netherlands.
    Ronco, Lucienne
    Fulcrum Therapeut, MA 02139 USA.
    Odueyungbo, Adefowope
    Fulcrum Therapeut, MA 02139 USA.
    Jiang, John
    Fulcrum Therapeut, MA 02139 USA.
    Han, Jay J.
    Univ Calif Irvine, CA USA.
    Hatch, Maya
    Univ Calif Irvine, CA USA.
    Towles, Jeanette
    Synterex Inc, MA USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Cadavid, Diego
    Fulcrum Therapeut, MA 02139 USA.
    Quantitative Muscle Analysis in FSHD Using Whole-Body Fat-Referenced MRI Composite Scores for Longitudinal and Cross-sectional Analysis2022In: Neurology, ISSN 0028-3878, E-ISSN 1526-632X, Vol. 99, no 9, p. E877-E889Article in journal (Refereed)
    Abstract [en]

    Background and Objectives Facioscapulohumeral muscular dystrophy (FSHD) is a rare, debilitating disease characterized by progressive muscle weakness. MRI is a sensitive assessment of disease severity and progression. We developed a quantitative whole-body (WB) musculoskeletal MRI (WB-MSK-MRI) protocol analyzing muscles in their entirety. This study aimed to assess WB-MSK-MRI as a potential imaging biomarker providing reliable measurements of muscle health that capture disease heterogeneity and clinically meaningful composite assessments correlating with severity and more responsive to change in clinical trials. Methods Participants aged 18-65 years, with genetically confirmed FSHD1, clinical severity 2 to 4 (Ricci scale, range 0-5), and &gt;= 1 short tau inversion recovery-positive lower extremity muscle eligible for needle biopsy, enrolled at 6 sites and were imaged twice 4-12 weeks apart. Volumetric analysis of muscle fat infiltration (MFI), muscle fat fraction (MFF), and lean muscle volume (LMV) in 18 (36 total) muscles from bilateral shoulder, proximal arm, trunk, and legs was performed after automated atlas-based segmentation, followed by manual verification. A WB composite score, including muscles at highest risk for progression, and functional cross-sectional composites for correlation with relevant functional outcomes including timed up and go (TUG), FSHD-TUG, and reachable workspace (RWS), were developed. Results Seventeen participants enrolled in this study; 16 follow-up MRIs were performed at 52 days (range 36-85 days). Functional cross-sectional composites (MFF and MFI) showed moderate to strong correlations: TUG (rho = 0.71, rho = 0.83), FSHD-TUG (rho = 0.73, rho = 0.73), and RWS (left arm: rho = -0.71, rho = -0.53; right arm: rho = -0.61, rho = -0.65). WB composite variability: LMVtot, coefficient of variation (CV) 1.9% and 3.4%; MFFtot, within-subject SD (S-w) 0.5% and 1.5%; and MFItot (S-w), 0.3% and 0.4% for normal and intermediate muscles, respectively. CV and S-w were higher in intermediate (MFI &gt;= 0.10; MFF &lt;0.50) than in normal (MFI &lt;0.10, MFF &lt;0.50) muscles. Discussion We developed a WB-MSK-MRI protocol and composite measures that capture disease heterogeneity and assess muscle involvement as it correlates with FSHD-relevant clinical endpoints. Functional composites robustly correlate with functional assessments. Stability of the WB composite shows that it could be an assessment of change in therapeutic clinical trials. Classification of Evidence This study provides Class II evidence that quantitative WB-MSK-MRI findings associate with FSHD1 severity measured using established functional assessments.

  • 24.
    Persson, Lennart
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Respiratory Medicine.
    Sioutas, Apostolos
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Respiratory Medicine.
    Kentson, Magnus
    Linköping University, Department of Health, Medicine and Caring Sciences. Linköping University, Faculty of Medicine and Health Sciences. Ryhov Cty Hosp, Sweden.
    Jacobson, Petra
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Respiratory Medicine. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Skeletal Myosteatosis is Associated with Systemic Inflammation and a Loss of Muscle Bioenergetics in Stable COPD2022In: Journal of Inflammation Research, E-ISSN 1178-7031, Vol. 15, p. 4367-4384Article in journal (Refereed)
    Abstract [en]

    Background: Common features among patients with more advanced chronic obstructive pulmonary disease (COPD) are systemic inflammation and a loss of both muscle mass and normal muscle composition. In the present study, we investigated COPD subjects to better understand how thigh muscle fat infiltration (MFI) and energy metabolism relate to each other and to clinical features of COPD with emphasis on systemic inflammation. Methods: Thirty-two Caucasians with stable COPD were investigated using questionnaires, lung function tests, blood analysis and magnetic resonance imaging (MRI) for analysis of body-and thigh muscle composition. Bioenergetics in the resting thigh muscle, expressed as the PCr/Pi ratio, were analysed using (31)phosphorus magnetic resonance spectroscopy (P-31-MRS). Results: Based on the combination of the MFI adjusted for sex (MFIa) and the thigh fat-tissue free muscle volume, expressed as the deviation from the expected muscle volume of a matched virtual control group (FFMVvcg), all COPD subjects displayed abnormally composed thigh muscles. Clinical features of increased COPD severity, including a decrease of blood oxygenation (r = -0.44, p &lt; 0.05) and FEV1/FVC ratio, reflecting airway obstruction (r = -0.53, p &lt; 0.01) and an increase of COPD symptoms (r = 0.37, p &lt; 0.05) and breathing frequency at rest (r = 0.41, p &lt; 0.05), were all associated with a raise of the PCr/Pi ratio in the thigh muscle. Increased MFIa of the thigh muscle correlated positively with markers of systemic inflammation (white blood cell count, r = 0.41, p &lt; 0.05; fibrinogen, r = 0.44, p &lt; 0.05), and negatively with weekly physical activity (r = -0.40, p &lt; 0.05) and the PCr/Pi ratio in the resting thigh muscle (r = -0.41, p &lt; 0.05). Conclusion: The present study implies a link between systemic inflammation, excessive MFI and a loss of bioenergetics in subjects with stable COPD.

  • 25.
    van der Meer, Dennis
    et al.
    Univ Oslo, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Gurholt, Tiril P.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Sonderby, Ida E.
    Univ Oslo, Norway; Univ Oslo, Norway; Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Shadrin, Alexey A.
    Univ Oslo, Norway; Univ Oslo, Norway.
    Hindley, Guy
    Univ Oslo, Norway; Univ Oslo, Norway; Kings Coll London, England.
    Rahman, Zillur
    Univ Oslo, Norway; Univ Oslo, Norway.
    de Lange, Ann-Marie G.
    Univ Oslo, Norway; Univ Oslo, Norway; Lausanne Univ Hosp CHUV, Switzerland; Univ Lausanne, Switzerland; Univ Oxford, England.
    Frei, Oleksandr
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med, Linkoping, Sweden.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Simon, Rozalyn
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Beck, Dani
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Diakonhjemmet Hosp, Norway.
    Westlye, Lars T.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Halvorsen, Sigrun
    Oslo Univ Hosp Ulleval, Norway; Univ Oslo, Norway.
    Dale, Anders M.
    Univ Calif San Diego, CA 92037 USA.
    Karlsen, Tom H.
    Univ Oslo, Norway; Oslo Univ Hosp, Norway; Oslo Univ Hosp, Norway.
    Kaufmann, Tobias
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Andreassen, Ole A.
    Univ Oslo, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    The link between liver fat and cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition2022In: Communications Biology, E-ISSN 2399-3642, Vol. 5, no 1, article id 1271Article in journal (Refereed)
    Abstract [en]

    A GWAS study of European individuals uncovers genetic associations between whole-body MRI derived measures and cardiometabolic diseases and highlights the key role of liver fat in cardiometabolic health. Obesity and associated morbidities, metabolic associated fatty liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting genome-wide association studies (GWAS) of these MRI-derived measures. We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 33,588 White European UK Biobank participants (mean age of 64.5 years, 51.4% female). Through multivariate analysis, we discovered 100 loci with distributed effects across the body composition measures and 241 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h(2 )= .25 vs. .13, p = 1.8x10(-7)). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (r(g )= .49, p = 2.7x10(-22)). These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

  • 26.
    van der Meer, Dennis
    et al.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, The Netherlands.
    Gurholt, Tiril P.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    Sønderby, Ida E.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo.
    Shadrin, Alexey A.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    Hindley, Guy
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, United Kingdom.
    Rahman, Zillur
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
    de Lange, Ann-Marie G.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences, Dept. of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Psychiatry, University of Oxford, Oxford, UK.
    Frei, Oleksandr
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical, Linköping, Sweden.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical, Linköping, Sweden.
    Simon, Rozalyn
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westlye, Lars T.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo; Department of Psychology, University of Oslo, Oslo, Norway.
    Halvorsen, Sigrun
    Department of Cardiology, Oslo University Hospital Ullevål, and University of Oslo, Oslo, Norway.
    Dale, Anders M.
    Center for Multimodal Imaging and Genetics, University of California at San Diego, La Jolla, CA 92037, USA.
    Karlsen, Tom H.
    Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway; Research Institute for Internal Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital Rikshospitalet and University of Oslo, Oslo, Norway.
    Kaufmann, Tobias
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany.
    Andreassen, Ole A.
    Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo.
    The role of liver fat in cardiometabolic diseases is highlighted by genome-wide association study of MRI-derived measures of body composition2022Manuscript (preprint) (Other academic)
    Abstract [en]

    Background & Aims

    Obesity and associated morbidities, metabolic associated liver disease (MAFLD) included, constitute some of the largest public health threats worldwide. Body composition and related risk factors are known to be heritable and identification of their genetic determinants may aid in the development of better prevention and treatment strategies. Recently, large-scale whole-body MRI data has become available, providing more specific measures of body composition than anthropometrics such as body mass index. Here, we aimed to elucidate the genetic architecture of body composition, by conducting the first genome-wide association study (GWAS) of these MRI-derived measures.

    Methods

    We ran both univariate and multivariate GWAS on fourteen MRI-derived measurements of adipose and muscle tissue distribution, derived from scans from 34,036 White European UK Biobank participants (mean age of 64.5 years, 51.5% female).

    Results

    Through multivariate analysis, we discovered 108 loci with distributed effects across the body composition measures and 256 significant genes primarily involved in immune system functioning. Liver fat stood out, with a highly discoverable and oligogenic architecture and the strongest genetic associations. Comparison with 21 common cardiometabolic traits revealed both shared and specific genetic influences, with higher mean heritability for the MRI measures (h2=.25 vs. .16, p=1.4×10−6). We found substantial genetic correlations between the body composition measures and a range of cardiometabolic diseases, with the strongest correlation between liver fat and type 2 diabetes (rg=.48, p=1.6×10−22).

    Conclusions

    These findings show that MRI-derived body composition measures complement conventional body anthropometrics and other biomarkers of cardiometabolic health, highlighting the central role of liver fat, and improving our knowledge of the genetic architecture of body composition and related diseases.

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  • 27.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical AB, Linköping, Sweden.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Adverse muscle composition is linked to poor functional performance and metabolic comorbidities in NAFLD2021In: JHEP reports : innovation in hepatology, ISSN 2589-5559, Vol. 3, no 1, article id 100197Article in journal (Refereed)
    Abstract [en]

    Background & Aims: Sarcopenia and frailty are recognised as important factors in later stages of liver disease. However, theirrole in non-alcoholic fatty liver disease (NAFLD) is not yet fully understood. In this study we investigate the associations ofMRI-measured adverse muscle composition (AMC: low muscle volume and high muscle fat) with poor function, sarcopenia,and metabolic comorbidity within NAFLD in the large UK Biobank imaging study.

    Methods: A total of 9,545 participants were included. Liver fat, fat-tissue free muscle volume, and muscle fat infiltration werequantified using a rapid MRI protocol and automated image analysis (AMRA® Researcher). For each participant, a personalisedmuscle volume z-score (sex- and body size-specific) was calculated and combined with muscle fat infiltration for AMC detection. The following outcomes were investigated: functional performance (hand grip strength, walking pace, stairclimbing, falls) and metabolic comorbidities (coronary heart disease, type 2 diabetes). Sarcopenia was detected by combiningMRI thresholds for low muscle quantity and low hand grip strength according to the European working group definition.

    Results: The prevalence of sarcopenia in NAFLD (1.6%) was significantly lower (p <0.05) compared with controls without fattyliver (3.4%), whereas the prevalence of poor function and metabolic comorbidity was similar or higher. Of the 1,204 participants with NAFLD, 169 (14%) had AMC and showed 1.7–2.4× higher prevalence of poor function (all p <0.05) as well as 2.1×and 3.3× higher prevalence of type 2 diabetes and coronary heart disease (p <0.001), respectively, compared with thosewithout AMC.

    Conclusions: AMC is a prevalent and highly vulnerable NAFLD phenotype displaying poor function and high prevalence ofmetabolic comorbidity. Sarcopenia guidelines can be strengthened by including cut-offs for muscle fat, enabling AMCdetection.

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  • 28.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Badhusgatan 4, S-58222 Linkoping, Sweden.
    Petersson, Mikael
    AMRA Med, Badhusgatan 4, S-58222 Linkoping, Sweden.
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. AMRA Med, Badhusgatan 4, S-58222 Linkoping, Sweden.
    Sanyal, Arun J.
    Virginia Commonwealth Univ, VA USA; Virginia Commonwealth Univ, VA USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med, Badhusgatan 4, S-58222 Linkoping, Sweden.
    Adverse muscle composition predicts all-cause mortality in the UK Biobank imaging study2021In: Journal of Cachexia, Sarcopenia and Muscle, ISSN 2190-5991, E-ISSN 2190-6009, Vol. 12, no 6, p. 1513-1526Article in journal (Refereed)
    Abstract [en]

    Background Adverse muscle composition (MC) as measured by magnetic resonance imaging has previously been linked to poor function, comorbidity, and increased hospitalization. The aim of this study was to investigate if adverse MC predicts all-cause mortality using data from UK Biobank. Methods There were 40 178 participants scanned using a 6 min magnetic resonance imaging protocol. Images were analysed for thigh fat-tissue free muscle volume and muscle fat infiltration (MFI) using AMRA (R) Researcher (AMRA Medical, Linkoping, Sweden). For each participant, a sex, weight, and height invariant muscle volume z-score was calculated. Participants were partitioned into four MC groups: (i) normal MC, (ii) only low muscle volume [&lt;25th percentile for muscle volume z-score (population wide)], (iii) only high MFI [&gt;75th percentile (population wide, sex-specific)], and (iv) adverse MC (low muscle volume z-score and high MFI). Association of MC groups with mortality was investigated using Cox proportional-hazard modelling with normal MC as referent (unadjusted and adjusted for low hand grip strength, sex, age, body mass index, previous diagnosis of disease (cancer, type 2 diabetes and coronary heart disease), lifestyle, and socioeconomic factors (smoking, alcohol consumption, physical activity, and Townsend deprivation index). Results Muscle composition measurements were complete for 39 804 participants [52% female, mean (SD) age 64.2 (7.6) years and body mass index 26.4 (4.4) kg/m(2)]. Three hundred twenty-eight deaths were recorded during a follow-up period of 2.9 (1.4) years after imaging. At imaging, adverse MC was detected in 10.5% of participants. The risk of death from any cause in adverse MC compared with normal MC was 3.71 (95% confidence interval 2.81-4.91, P &lt; 0.001). Only low muscle volume and only high MFI were independently associated with all-cause mortality [1.58 (1.13-2.21), P = 0.007, and 2.02 (1.51-2.71), P &lt; 0.001, respectively]. Adjustment of low hand grip strength [1.77 (1.28-2.44), P &lt; 0.001] did not attenuate the associations with any of the MC groups. In the fully adjusted model, adverse MC and only high MFI remained significant (P P = 0.020) while the association with only low muscle volume was attenuated to non-significance (P = 0.560). The predictive performance of adverse MC [1.96 (1.42-2.71), P &lt; 0.001] was comparable with that of previous cancer diagnosis [1.93 (1.47-2.53), P &lt; 0.001] and smoking [1.71 (1.02-2.84), P = 0.040]. Low hand grip strength was borderline non-significant [1.34 (0.96-1.88), P = 0.090]. Conclusions Adverse MC was a strong and independent predictor of all-cause mortality. Sarcopenia guidelines can be strengthened by including cut-offs for myosteatosis enabling detection of adverse MC.

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  • 29.
    Fredwall, Svein O.
    et al.
    Sunnaas Rehabil Hosp, Norway; Univ Oslo, Norway.
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Kjonigsen, Lisa
    Oslo Univ Hosp, Norway.
    Eggesbo, Heidi Beate
    Oslo Univ Hosp, Norway.
    Weedon-Fekjaer, Harald
    Oslo Univ Hosp, Norway.
    Lidal, Ingeborg Beate
    Sunnaas Rehabil Hosp, Norway.
    Manum, Grethe
    Sunnaas Rehabil Hosp, Norway.
    Savarirayan, Ravi
    Murdoch Childrens Res Inst, Australia; Univ Melbourne, Australia.
    Tonstad, Serena
    Oslo Univ Hosp, Norway.
    Cardiovascular risk factors and body composition in adults with achondroplasia2021In: Genetics in Medicine, ISSN 1098-3600, E-ISSN 1530-0366, Vol. 23, p. 732-739Article in journal (Refereed)
    Abstract [en]

    Purpose An increased cardiovascular mortality has been reported in achondroplasia. This population-based, case-control study investigated cardiovascular risk factors and body composition in Norwegian adults with achondroplasia. Methods We conducted anthropometric, clinical, and laboratory assessments in 49 participants with achondroplasia, of whom 40 completed magnetic resonance imaging (MRI) for body composition analysis. Controls consisted of 98 UK Biobank participants, matched for body mass index (BMI), sex, and age. Results Participants were well matched for BMI (33.3 versus 32.5 kg/m(2)) and sex, but achondroplasia participants were younger than controls (mean age 41.1 versus 54.3 years). Individuals with achondroplasia had lower age-adjusted mean blood pressure, total and low-density lipoprotein (LDL) cholesterol, and triglycerides compared with controls, but similar fasting glucose and HbA1c values. Age-adjusted mean visceral fat store was 1.9 versus 5.3 L (difference -2.7, 95% confidence interval [CI] -3.6 to -1.9; P &lt; 0.001), abdominal subcutaneous fat was 6.0 versus 11.2 L (-4.7, 95% CI -5.9 to -3.4; P &lt; 0.001), and liver fat was 2.2 versus 6.9% (-2.8, 95% CI -5.2 to -0.4; P = 0.02). Conclusion Despite a high BMI, the cardiovascular risks appeared similar or lower in achondroplasia compared with controls, indicating that other factors might contribute to the increased mortality observed in this condition.

  • 30.
    Nasr, Patrik
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Iredahl, Fredrik
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Primary Care Center, Primary Health Care Center Åby.
    Dahlström, Nils
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rådholm, Karin
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Primary Care Center, Primary Health Care Center Kärna.
    Henriksson, Pontus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ebbers, Tino
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Alfredsson, Joakim
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Region Östergötland, Heart Center, Department of Cardiology in Linköping. Linköping University, Faculty of Medicine and Health Sciences.
    Carlhäll, Carljohan
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart Center, Department of Clinical Physiology in Linköping.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kechagias, Stergios
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Evaluating the prevalence and severity of NAFLD in primary care: the EPSONIP study protocol2021In: BMC Gastroenterology, ISSN 1471-230X, E-ISSN 1471-230X, Vol. 21, no 1, article id 180Article in journal (Refereed)
    Abstract [en]

    BackgroundNon-alcoholic fatty liver disease (NAFLD) affects 20-30% of the general adult population. NAFLD patients with type 2 diabetes mellitus (T2DM) are at an increased risk of advanced fibrosis, which puts them at risk of cardiovascular complications, hepatocellular carcinoma, or liver failure. Liver biopsy is the gold standard for assessing hepatic fibrosis. However, its utility is inherently limited. Consequently, the prevalence and characteristics of T2DM patients with advanced fibrosis are unknown. Therefore, the purpose of the current study is to evaluate the prevalence and severity of NAFLD in patients with T2DM by recruiting participants from primary care, using the latest imaging modalities, to collect a cohort of well phenotyped patients.MethodsWe will prospectively recruit 400 patients with T2DM using biomarkers to assess their status. Specifically, we will evaluate liver fat content using magnetic resonance imaging (MRI); hepatic fibrosis using MR elastography and vibration-controlled transient elastography; muscle composition and body fat distribution using water-fat separated whole body MRI; and cardiac function, structure, and tissue characteristics, using cardiovascular MRI.DiscussionWe expect that the study will uncover potential mechanisms of advanced hepatic fibrosis in NAFLD and T2DM and equip the clinician with better diagnostic tools for the care of T2DM patients with NAFLD.Trial registration: Clinicaltrials.gov, identifier NCT03864510. Registered 6 March 2019, https://clinicaltrials.gov/ct2/show/NCT03864510.

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  • 31.
    Gurholt, Tiril P.
    et al.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Kaufmann, Tobias
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Univ Tubingen, Germany.
    Frei, Oleksandr
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Alnaes, Dag
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Haukvik, Unn K.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    van der Meer, Dennis
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Maastricht Univ, Netherlands.
    Moberget, Torgeir
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    OConnell, Kevin S.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Linge, Jennifer
    AMRA Med, Linkoping, Sweden.
    Simon, Rozalyn
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med, Linkoping, Sweden.
    Smeland, Olav B.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Sonderby, Ida E.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Oslo Univ Hosp, Norway.
    Winterton, Adriano
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Steen, Nils Eiel
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Westlye, Lars T.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway; Univ Oslo, Norway.
    Andreassen, Ole A.
    Oslo Univ Hosp, Norway; Univ Oslo, Norway.
    Population-based body-brain mapping links brain morphology with anthropometrics and body composition2021In: Translational Psychiatry, E-ISSN 2158-3188, Vol. 11, no 1, article id 295Article in journal (Refereed)
    Abstract [en]

    Understanding complex body-brain processes and the interplay between adipose tissue and brain health is important for understanding comorbidity between psychiatric and cardiometabolic disorders. We investigated associations between brain structure and anthropometric and body composition measures using brain magnetic resonance imaging (MRI; n=24,728) and body MRI (n=4973) of generally healthy participants in the UK Biobank. We derived regional and global measures of brain morphometry using FreeSurfer and tested their association with (i) anthropometric measures, and (ii) adipose and muscle tissue measured from body MRI. We identified several significant associations with small effect sizes. Anthropometric measures showed negative, nonlinear, associations with cerebellar/cortical gray matter, and brain stem structures, and positive associations with ventricular volumes. Subcortical structures exhibited mixed effect directionality, with strongest positive association for accumbens. Adipose tissue measures, including liver fat and muscle fat infiltration, were negatively associated with cortical/cerebellum structures, while total thigh muscle volume was positively associated with brain stem and accumbens. Regional investigations of cortical area, thickness, and volume indicated widespread and largely negative associations with anthropometric and adipose tissue measures, with an opposite pattern for thigh muscle volume. Self-reported diabetes, hypertension, or hypercholesterolemia were associated with brain structure. The findings provide new insight into physiological body-brain associations suggestive of shared mechanisms between cardiometabolic risk factors and brain health. Whereas the causality needs to be determined, the observed patterns of body-brain relationships provide a foundation for understanding the underlying mechanisms linking psychiatric disorders with obesity and cardiovascular disease, with potential for the development of new prevention strategies.

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  • 32.
    Ferguson, Lyn D
    et al.
    AMRA Medical, Linköping..
    Linge, Jennifer
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical, Linköping..
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical, Linköping..
    Woodward, Rosemary
    Glasgow Clinical Research Imaging Facility, Queen Elizabeth University Hospital..
    Hall Barrientos, Pauline
    Glasgow Clinical Research Imaging Facility, Queen Elizabeth University Hospital..
    Roditi, Giles
    Glasgow Clinical Research Imaging Facility, Queen Elizabeth University Hospital..
    Radjenovic, Aleksandra
    Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK..
    McInnes, Iain B
    Institute of Infection, Immunity, and Inflammation, University of Glasgow, Glasgow, UK..
    Siebert, Stefan
    Institute of Infection, Immunity, and Inflammation, University of Glasgow, Glasgow, UK..
    Sattar, Naveed
    Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK..
    Psoriatic arthritis is associated with adverse body composition predictive of greater coronary heart disease and type 2 diabetes propensity: a cross-sectional study2021In: Rheumatology, ISSN 1462-0324, E-ISSN 1462-0332, Vol. 60, no 4, p. 1858-1862Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: To compare body composition in PsA with metabolic disease free (MDF) controls and type 2 diabetes and assess body-composition predicted propensity for cardiometabolic disease.

    METHODS: Detailed MRI body composition profiles of 26 PsA participants from the IMAPA study were compared with 130 age, sex and BMI-matched MDF controls and 454 individuals with type 2 diabetes from UK Biobank. The body-composition predicted propensity for coronary heart disease (CHD) and type 2 diabetes was compared between PsA and matched MDF controls.

    RESULTS: PsA participants had a significantly greater visceral adipose tissue (VAT) volume [mean 5.89 l (s.d. 2.10 l)] compared with matched-MDF controls [mean 4.34 l (s.d. 1.83 l)] (P <0.001) and liver fat percentage [median 8.88% (interquartile range 4.42-13.18%)] compared with MDF controls [3.29% (1.98-7.25%)] (P <0.001). These differences remained significant after adjustment for age, sex and BMI. There were no statistically significant differences in VAT, liver fat or muscle fat infiltration (MFI) between PsA and type 2 diabetes. PsA participants had a lower thigh muscle volume than MDF controls and those with type 2 diabetes. Body composition-predicted propensity for CHD and type 2 diabetes was 1.27 and 1.83 times higher, respectively, for PsA compared with matched-MDF controls.

    CONCLUSION: Individuals with PsA have an adverse body composition phenotype with greater visceral and ectopic liver fat and lower thigh muscle volume than matched MDF controls. Body fat distribution in PsA is more in keeping with the pattern observed in type 2 diabetes and is associated with greater propensity to cardiometabolic disease. These data support the need for greater emphasis on weight loss in PsA management to lessen CHD and type 2 diabetes risk.

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  • 33.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Reply to: "Rationale of adding muscle volume to muscle fat infiltration in the definition of an adverse muscle composition is unclear"2021In: JHEP Reports, ISSN 2589-5559, Vol. 3, no 2, article id 100257Article in journal (Other academic)
    Abstract [en]

    n/a

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  • 34.
    Karlsson, Anette
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Peolsson, Anneli
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    AMRA Medical AB, Linköping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping, Sweden.
    Spetz, Anna-Clara
    Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center of Paediatrics and Gynaecology and Obstetrics, Department of Gynaecology and Obstetrics in Linköping. Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health.
    Thorell, Sofia
    Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    West, Janne
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. AMRA Medical AB, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. AMRA Medical AB, Linköping, Sweden.
    The effect on precision and T1 bias comparing two flip angles when estimating muscle fat infiltration using fat-referenced chemical shift-encoded imaging2021In: NMR in Biomedicine, ISSN 0952-3480, E-ISSN 1099-1492, Vol. 34, no 11, article id e4581Article in journal (Refereed)
    Abstract [en]

    Investigation of the effect on accuracy and precision of different parameter settings is important for quantitative Magnetic Resonance Imaging. The purpose of this study was to investigate T1-bias and precision for muscle fat infiltration (MFI) using fat-referenced chemical shift magnetic resonance imaging at 5° and 10° flip angle. This [MB1] experimental study was done on forty postmenopausal women using 3T MRI test and retest images using 4-point 3D spoiled gradient multi-echo acquisition including real and imaginary images for reconstruction acquired at Flip angles 5° and 10°. Post-processing included T2* correction and fat-referenced calibration of the fat signal. The mean MFI was calculated in six different automatically segmented muscle regions using both the fat-referenced fat signal and the fat fraction calculated from the fat and water image pair for each acquisition. The variance of the difference between mean MFI from test and retest was used as measure of precision. The SNR characteristics were analyzed by measuring difference of the full width half maximum of the fat signal distribution using Student’s t-test.There was no difference in the mean fat-referenced MFI at different flip angles with the fat-referenced technique, which was the case using the fat fraction. No significant difference in the precision was found in any of the muscles analyzed. However, the full width half maximum of the fat signal distribution was significantly lower at 10° flip angle compared to 5°. Fat-referenced MFI is insensitive to T1 bias in chemical shift magnetic resonance imaging enabling usage of a higher and more SNR effective flip angle. The lower full-width-at half-maximum in fat-referenced MFI at 10° indicates that high flip angle acquisition is advantageous although no significant differences in precision was observed comparing 5° and 10°.

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  • 35.
    Neeland, Ian J.
    et al.
    University of Texas Southwestern Medical Center, USA.
    Yokoo, Takeshi
    University of Texas Southwestern Medical Center, USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Linköping.
    Lavie, Carl J
    University of Queensland School of Medicine, USA.
    Twenty-First Century Advances in Multimodality Imaging of Obesity for Care of the Cardiovascular Patient2021In: JACC Cardiovascular Imaging, ISSN 1936-878X, E-ISSN 1876-7591, Vol. 14, no 2, p. 482-494Article, review/survey (Refereed)
    Abstract [en]

    Although obesity is typically defined by body mass index criteria, this does not differentiate true body fatness, as this includes both body fat and muscle. Therefore, other fat depots may better define cardiometabolic and cardiovascular disease (CVD) risk imposed by obesity. Data from translational, epidemiological, and clinical studies over the past 3 decades have clearly demonstrated that accumulation of adiposity in the abdominal viscera and within tissue depots lacking physiological adipose tissue storage capacity (termed "ectopic fat") is strongly associated with the development of a clinical syndrome characterized by atherogenic dyslipidemia, hyperinsulinemia/glucose intolerance/type 2 diabetes mellitus, hypertension, atherosclerosis, and abnormal cardiac remodeling and heart failure. This state-of-the-art paper discusses the impact of various body fat depots on cardiometabolic parameters and CVD risk. Specifically, it reviews novel and emerging imaging techniques to evaluate adiposity and the risk of cardiometabolic diseases and CVD.

  • 36.
    Mandić, Mirko
    et al.
    Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden.
    Rullman, Eric
    Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. AMRA Medical AB, Sweden.
    Lilja, Mats
    Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Medical AB, Sweden.
    Gustafsson, Thomas
    Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden.
    Lundberg, Tommy R.
    Institutet, Sweden; Karolinska University Hospital, Sweden.
    Automated assessment of regional muscle volume and hypertrophy using MRI2020In: Scientific Reports, E-ISSN 2045-2322, Vol. 10, no 1, article id 2239Article in journal (Refereed)
    Abstract [en]

    This study aimed to validate a fully automatic method to quantify knee-extensor muscle volume and exercise-induced hypertrophy. By using a magnetic resonance imaging-based fat-water separated two-point Dixon sequence, the agreement between automated and manual segmentation of a specific ~15-cm region (partial volume) of the quadriceps muscle was assessed. We then explored the sensitivity of the automated technique to detect changes in both complete and partial quadriceps volume in response to 8 weeks of resistance training in 26 healthy men and women. There was a very strong correlation (r = 0.98, P < 0.0001) between the manual and automated method for assessing partial quadriceps volume, yet the volume was 9.6% greater with automated compared with manual analysis (P < 0.0001, 95% limits of agreement -93.3 ± 137.8 cm3). Partial muscle volume showed a 6.0 ± 5.0% (manual) and 4.8 ± 8.3% (automated) increase with training (P < 0.0001). Similarly, the complete quadriceps increased 5.1 ± 5.5% with training (P < 0.0001). The intramuscular fat proportion decreased (P < 0.001) from 4.1% to 3.9% after training. In conclusion, the automated method showed excellent correlation with manual segmentation and could detect clinically relevant magnitudes of exercise-induced muscle hypertrophy. This method could have broad application to accurately measure muscle mass in sports or to monitor clinical conditions associated with muscle wasting and fat infiltration.

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  • 37.
    Forsgren, Mikael F
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nasr, Patrik
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Karlsson, Markus
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Dahlström, Nils
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ignatova, Simone
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Sinkus, Ralph
    King's College London, United Kingdom.
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical and Clinical Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ekstedt, Mattias
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Kechagias, Stergios
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Mag- tarmmedicinska kliniken.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Biomarkers of liver fibrosis: prospective comparison of multimodal magnetic resonance, serum algorithms and transient elastography.2020In: Scandinavian Journal of Gastroenterology, ISSN 0036-5521, E-ISSN 1502-7708, Vol. 55, no 7, p. 848-859Article in journal (Refereed)
    Abstract [en]

    BACKGROUND AND AIMS: Accurate biomarkers for quantifying liver fibrosis are important for clinical practice and trial end-points. We compared the diagnostic performance of magnetic resonance imaging (MRI), including gadoxetate-enhanced MRI and 31P-MR spectroscopy, with fibrosis stage and serum fibrosis algorithms in a clinical setting. Also, in a subset of patients, MR- and transient elastography (MRE and TE) was evaluated when available.

    METHODS: Patients were recruited prospectively if they were scheduled to undergo liver biopsy on a clinical indication due to elevated liver enzyme levels without decompensated cirrhosis. Within a month of the clinical work-up, an MR-examination and liver needle biopsy were performed on the same day. Based on late-phase gadoxetate-enhanced MRI, a mathematical model calculated hepatobiliary function (relating to OATP1 and MRP2). The hepatocyte gadoxetate uptake rate (KHep) and the normalised liver-to-spleen contrast ratio (LSC_N10) were also calculated. Nine serum fibrosis algorithms were investigated (GUCI, King's Score, APRI, FIB-4, Lok-Index, NIKEI, NASH-CRN regression score, Forns' score, and NAFLD-fibrosis score).

    RESULTS: The diagnostic performance (AUROC) for identification of significant fibrosis (F2-4) was 0.78, 0.80, 0.69, and 0.78 for MRE, TE, LSC_N10, and GUCI, respectively. For the identification of advanced fibrosis (F3-4), the AUROCs were 0.93, 0.84, 0.81, and 0.82 respectively.

    CONCLUSION: MRE and TE were superior for non-invasive identification of significant fibrosis. Serum fibrosis algorithms developed for specific liver diseases are applicable in this cohort of diverse liver diseases aetiologies. Gadoxetate-MRI was sufficiently sensitive to detect the low function losses associated with fibrosis. None was able to efficiently distinguish between stages within the low fibrosis stages.Lay summaryExcessive accumulation of scar tissue, fibrosis, in the liver is an important aspect in chronic liver disease. To replace the invasive needle biopsy, we have explored non-invasive methods to assess liver fibrosis. In our study we found that elastographic methods, which assess the mechanical properties of the liver, are superior in assessing fibrosis in a clinical setting. Of interest from a clinical trial point-of-view, none of the tested methods was sufficiently accurate to distinguish between adjacent moderate fibrosis stages.

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  • 38.
    Gerdle, Björn
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ghafouri, Bijar
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Lund, Eva
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics.
    Bengtsson, Ann
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Pain and Rehabilitation Center.
    Lundberg, Peter
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Veenstra, Helene
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Prevention, Rehabilitation and Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Med AB, SE-58222 Linkoping, Sweden.
    Forsgren, Mikael
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Medical radiation physics. AMRA Med AB, SE-58222 Linkoping, Sweden.
    Evidence of Mitochondrial Dysfunction in Fibromyalgia: Deviating Muscle Energy Metabolism Detected Using Microdialysis and Magnetic Resonance2020In: Journal of Clinical Medicine, E-ISSN 2077-0383, Vol. 9, no 11, article id 3527Article in journal (Refereed)
    Abstract [en]

    In fibromyalgia (FM) muscle metabolism, studies are sparse and conflicting associations have been found between muscle metabolism and pain aspects. This study compared alterations in metabolic substances and blood flow in erector spinae and trapezius of FM patients and healthy controls. FM patients (n = 33) and healthy controls (n = 31) underwent a clinical examination that included pressure pain thresholds and physical tests, completion of a health questionnaire, participation in microdialysis investigations of the etrapezius and erector spinae muscles, and also underwent phosphorus-31 magnetic resonance spectroscopy of the erector spinae muscle. At the baseline, FM had significantly higher levels of pyruvate in both muscles. Significantly lower concentrations of phosphocreatine (PCr) and nucleotide triphosphate (mainly adenosine triphosphate) in erector spinae were found in FM. Blood flow in erector spinae was significantly lower in FM. Significant associations between metabolic variables and pain aspects (pain intensity and pressure pain threshold PPT) were found in FM. Our results suggest that FM has mitochondrial dysfunction, although it is unclear whether inactivity, obesity, aging, and pain are causes of, the results of, or coincidental to the mitochondrial dysfunction. The significant regressions of pain intensity and PPT in FM agree with other studies reporting associations between peripheral biological factors and pain aspects.

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  • 39.
    Dahlqvist, Julia R.
    et al.
    Univ Copenhagen, Denmark.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Vissing, John
    Univ Copenhagen, Denmark.
    MRI in Neuromuscular Diseases: An Emerging Diagnostic Tool and Biomarker for Prognosis and Efficacy2020In: Annals of Neurology, ISSN 0364-5134, E-ISSN 1531-8249, Vol. 88, no 4, p. 669-681Article, review/survey (Refereed)
    Abstract [en]

    There is an unmet need to identify biomarkers sensitive to change in rare, slowly progressive neuromuscular diseases. Quantitative magnetic resonance imaging (MRI) of muscle may offer this opportunity, as it is noninvasive and can be carried out almost independent of patient cooperation and disease severity. Muscle fat content correlates with muscle function in neuromuscular diseases, and changes in fat content precede changes in function, which suggests that muscle MRI is a strong biomarker candidate to predict prognosis and treatment efficacy. In this paper, we review the evidence suggesting that muscle MRI may be an important biomarker for diagnosis and to monitor change in disease severity. ANN NEUROL 2020

  • 40.
    Wiik, Anna
    et al.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Lundberg, Tommy R.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Rullman, Eric
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Andersson, Daniel P.
    Karolinska Inst, Sweden.
    Holmberg, Mats
    Karolinska Univ Hosp, Sweden.
    Mandic, Mirko
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Brismar, Torkel B.
    Karolinska Inst, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. AMRA Med AB, Linkoping, Sweden.
    Chanpen, Setareh
    Karolinska Univ Hosp, Sweden.
    Flanagan, John N.
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Arver, Stefan
    Karolinska Univ Hosp, Sweden.
    Gustafsson, Thomas
    Karolinska Inst, Sweden; Karolinska Univ Hosp, Sweden.
    Muscle Strength, Size, and Composition Following 12 Months of Gender-affirming Treatment in Transgender Individuals2020In: Journal of Clinical Endocrinology and Metabolism, ISSN 0021-972X, E-ISSN 1945-7197, Vol. 105, no 3, p. E805-E813Article in journal (Refereed)
    Abstract [en]

    Context. As many sports are divided in male/female categories, governing bodies have formed regulations on the eligibility for transgender individuals to compete in these categories. Yet, the magnitude of change in muscle mass and strength with gender-affirming treatment remains insufficiently explored. Objective. This study explored the effects of gender-affirming treatment on muscle function, size, and composition during 12 months of therapy. Design, settings, participants. In this single-center observational cohort study, untrained transgender women (TW, n = 11) and transgender men (TM, n = 12), approved to start gender-affirming medical interventions, underwent assessments at baseline, 4 weeks after gonadal suppression of endogenous hormones but before hormone replacement, and 4 and 12 months after treatment initiation. Main outcome measures. Knee extensor and flexor strength were assessed at all examination time points, and muscle size and radiological density (using magnetic resonance imaging and computed tomography) at baseline and 12 months after treatment initiation. Results. Thigh muscle volume increased (15%) in TM, which was paralleled by increased quadriceps cross-sectional area (CSA) (15%) and radiological density (6%). In TW, the corresponding parameters decreased by -5% (muscle volume) and -4% (CSA), while density remained unaltered. The TM increased strength over the assessment period, while the TW generally maintained their strength levels. Conclusions. One year of gender-affirming treatment resulted in robust increases in muscle mass and strength in TM, but modest changes in TW. These findings add new knowledge on the magnitude of changes in muscle function, size, and composition with cross-hormone therapy, which could be relevant when evaluating the transgender eligibility rules for athletic competitions.

  • 41.
    Linge, Jennifer
    et al.
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences. AMRA Medical AB, Sweden.
    Heymsfield, Steven B.
    Pennington Biomedical Research Center, USA.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Medical AB, Sweden.
    On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank2020In: The journals of gerontology. Series A, Biological sciences and medical sciences, ISSN 1079-5006, E-ISSN 1758-535X, Vol. 75, no 7, p. 1309-1316Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Current consensus is to combine a functional measure with muscle quantity to assess/confirm sarcopenia. However, the proper body size adjustment for muscle quantity is debated and sarcopenia in obesity is not well described. Further, functional measures are not muscle-specific or sensitive to etiology, and can be confounded by, for example, fitness/pain. For effective detection/treatment/follow-up, muscle-specific biomarkers linked to function are needed.

    METHODS: Nine thousand six hundred and fifteen participants were included and current sarcopenia thresholds (EWGSOP2: DXA, hand grip strength) applied to investigate prevalence. Fat-tissue free muscle volume (FFMV) and muscle fat infiltration (MFI) were quantified through magnetic resonance imaging (MRI) and sex-and-body mass index (BMI)-matched virtual control groups (VCGs) were used to extract each participant's FFMV/height2 z-score (FFMVVCG). The value of combining FFMVVCG and MFI was investigated through hospital nights, hand grip strength, stair climbing, walking pace, and falls.

    RESULTS: Current thresholds showed decreased sarcopenia prevalence with increased BMI (underweight 8.5%/normal weight 4.3%/overweight 1.1%/obesity 0.1%). Contrary, the prevalence of low function increased with increasing BMI. Previously proposed body size adjustments (division by height2/weight/BMI) introduced body size correlations of larger/similar magnitude than before. VCG adjustment achieved normalization and strengthened associations with hospitalization/function. Hospital nights, low hand grip strength, slow walking pace, and no stair climbing were positively associated with MFI (p < .05) and negatively associated with FFMVVCG (p < .01). Only MFI was associated with falls (p < .01). FFMVVCG and MFI combined resulted in highest diagnostic performance detecting low function.

    CONCLUSIONS: VCG-adjusted FFMV enables proper sarcopenia assessment across BMI classes and strengthened the link to function. MFI and FFMV combined provides a more complete, muscle-specific description linked to function enabling objective sarcopenia detection.

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  • 42.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Ahlgren, Andre
    AMRA Med AB, Linkoping, Sweden.
    Romu, Thobias
    AMRA Med AB, Linkoping, Sweden.
    Widholm, Per
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Health, Medicine and Caring Sciences, Division of Diagnostics and Specialist Medicine. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    West, Janne
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). AMRA Med AB, Linkoping, Sweden.
    Reproducibility and repeatability of MRI-based body composition analysis2020In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 84, no 6, p. 3146-3156Article in journal (Refereed)
    Abstract [en]

    Purpose There is an absence of reproducibility studies on MRI-based body composition analysis in current literature. Therefore, the aim of this study was to investigate the between-scanner reproducibility and the repeatability of a method for MRI-based body composition analysis. Methods Eighteen healthy volunteers of varying body mass index and adiposity were each scanned twice on five different 1.5T and 3T scanners from three different vendors. Two-point Dixon neck-to knee images and two additional liver scans were acquired with similar protocols. Visceral adipose tissue (VAT) volume, abdominal subcutaneous adipose tissue (ASAT) volume, thigh muscle volume, and muscle fat infiltration (MFI) in the thigh muscle were measured. Liver proton density fat fraction (PDFF) was assessed using two different methods, the scanner vendors 6-point method and an in-house 2-point method. Within-scanner test-retest repeatability and between-scanner reproducibility were calculated using analysis of variance. Results Repeatability coefficients were 13 centiliters (cl) (VAT), 24 cl (ASAT), 17 cl (total thigh muscle volume), 0.53% (MFI), and 1.27-1.37% for liver PDFF. Reproducibility coefficients were 24 cl (VAT), 42 cl (ASAT), 31 cl (total thigh muscle volume), 1.44% (MFI), and 2.37-2.40% for liver PDFF. Conclusion For all measures except MFI, the within-scanner repeatability explained much of the overall reproducibility. The two methods for measuring liver fat had similar reproducibility. This study showed that the investigated method eliminates effects due to scanner differences. The results can be used for power calculations in clinical studies or to better understand the scanner-induced variability in clinical applications.

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  • 43.
    Karlsson, Markus
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Ekstedt, Mattias
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Forsgren, Mikael
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ignatova, Simone
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease2019In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 50, no 1, p. 325-333Article in journal (Refereed)
    Abstract [en]

    Background Liver iron content (LIC) in chronic liver disease (CLD) is currently determined by performing an invasive liver biopsy. MRI using R2* relaxometry is a noninvasive alternative for estimating LIC. Fat accumulation in the liver, or proton density fat fraction (PDFF), may be a possible confounder of R2* measurements. Previous studies of the effect of PDFF on R2* have not used quantitative LIC measurement. Purpose To assess the associations between R2*, LIC, PDFF, and liver histology in patients with suspected CLD. Study Type Prospective. Population Eighty-one patients with suspected CLD. Field Strength/Sequence 1.5 T. Multiecho turbo field echo to quantify R2*. PRESS MRS to quantify PDFF. Assessment Each patient underwent an MR examination, followed by two needle biopsies immediately following the MR examination. The first biopsy was used for conventional histological assessment of LIC, whereas the second biopsy was used to quantitatively measure LIC using mass spectrometry. R2* was correlated with both LIC and PDFF. A correction for the influence of fat on R2* was calculated. Statistical Tests Pearson correlation, linear regression, and area under the receiver operating curve. Results There was a positive linear correlation between R2* and PDFF (R = 0.69), after removing data from patients with elevated iron levels, as defined by LIC. R2*, corrected for PDFF, was the best method for identifying patients with elevated iron levels, with a correlation of R = 0.87 and a sensitivity and specificity of 87.5% and 98.6%, respectively. Data Conclusion PDFF increases R2*. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:325-333.

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  • 44.
    Forsgren, Mikael
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Karlsson, Markus
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist Leinhard, Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlström, Nils
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Norén, Bengt
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ignatova, Simone
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology.
    Ekstedt, Mattias
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Kechagias, Stergios
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Gastroentorology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Medical radiation physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Cedersund, Gunnar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 6, article id e1007157Article in journal (Refereed)
    Abstract [en]

    Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images.

    Author summary

    Being able to accurately and reliably estimate liver function is important when monitoring the progression of patients with liver disease, as well as when identifying drug-induced liver injury during drug development. A promising method for quantifying liver function is to use magnetic resonance imaging combined with gadoxetate. Gadoxetate is a liver-specific contrast agent, which is taken up by the hepatocytes and excreted into the bile. We have previously developed a mechanistic model for gadoxetate dynamics using averaged data from healthy volunteers. In this work, we extended our model with a non-linear mixed-effects modeling framework to give patient-specific estimates of the gadoxetate transport-rates. We validated the model by recruiting 100 patients with liver disease, covering a range of severity and etiologies. All patients underwent an MRI-examination and provided both blood and liver biopsies. Our validated model provides a new and deeper look into how the mechanisms of liver function varies across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate.

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    Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
  • 45.
    Ajmera, Veeral H.
    et al.
    Univ Calif San Diego Hlth, CA USA.
    Cachay, Edward
    Univ Calif San Diego, CA 92103 USA.
    Ramers, Christian