Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.
body composition
diabetes
myosteatosis
quantitative MRI
skeletal muscle
Journal
Journal of cachexia, sarcopenia and muscle
ISSN: 2190-6009
Titre abrégé: J Cachexia Sarcopenia Muscle
Pays: Germany
ID NLM: 101552883
Informations de publication
Date de publication:
15 Jul 2024
15 Jul 2024
Historique:
revised:
16
05
2024
received:
19
04
2023
accepted:
03
06
2024
medline:
16
7
2024
pubmed:
16
7
2024
entrez:
15
7
2024
Statut:
aheadofprint
Résumé
There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap. In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus. Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.
Sections du résumé
BACKGROUND
BACKGROUND
There is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap.
METHODS
METHODS
In this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus.
RESULTS
RESULTS
Model performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m
CONCLUSIONS
CONCLUSIONS
Quantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER1301A/B/C
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER1511D
Organisme : Bundesministerium für Bildung und Forschung
ID : 01ER1801A/B/C/D
Organisme : Helmholtz Association
Organisme : Leibniz-Gemeinschaft
Informations de copyright
© 2024 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.
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