Artificial Intelligence-based Body Composition Analysis Using Computed Tomography Images Predicts Both Prevalence and Incidence of Diabetes Mellitus

Overview

Journal J Diabetes Investig

Specialty Endocrinology

Date 2024 Nov 22

PMID 39576146

Authors

Yoo Hyung Kim

Ji Won Yoon

Bon Hyang Lee

Jeong Hee Yoon

Hun Jee Choe

Tae Jung Oh

Jeong Min Lee

Young Min Cho

Affiliations

Soon will be listed here.

Abstract

Aim/introduction: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.

Materials And Methods: This was a cross-sectional and 10-year retrospective longitudinal study. The cross-sectional analysis included health check-up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow-up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI-based image analysis software.

Results: Visceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut-offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7-2.4] in men; 2.9 [2.2-3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1-1.4] in men; 1.4 [1.2-1.7] in women). These trends were consistently observed across each specified cut-off value.

Conclusions: AI-based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes.

References

. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004; 363(9403):157-63. DOI: 10.1016/S0140-6736(03)15268-3. View

. Cardiovascular risk profile assessment in glucose-intolerant Asian individuals--an evaluation of the World Health Organization two-step strategy: the DECODA Study (Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Asia). Diabet Med. 2002; 19(7):549-57. DOI: 10.1046/j.1464-5491.2002.00735.x. View

Morimoto A, Tatsumi Y, Deura K, Mizuno S, Ohno Y, Miyamatsu N . Impact of impaired insulin secretion and insulin resistance on the incidence of type 2 diabetes mellitus in a Japanese population: the Saku study. Diabetologia. 2013; 56(8):1671-9. DOI: 10.1007/s00125-013-2932-y. View

Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T, Borch-Johnsen K . A Danish diabetes risk score for targeted screening: the Inter99 study. Diabetes Care. 2004; 27(3):727-33. DOI: 10.2337/diacare.27.3.727. View

Wu H, Qi Q, Yu Z, Sun Q, Wang J, Franco O . Independent and opposite associations of trunk and leg fat depots with adipokines, inflammatory markers, and metabolic syndrome in middle-aged and older Chinese men and women. J Clin Endocrinol Metab. 2010; 95(9):4389-98. DOI: 10.1210/jc.2010-0181. View

Tolonen A, Pakarinen T, Sassi A, Kytta J, Cancino W, Rinta-Kiikka I . Methodology, clinical applications, and future directions of body composition analysis using computed tomography (CT) images: A review. Eur J Radiol. 2021; 145:109943. DOI: 10.1016/j.ejrad.2021.109943. View

Alberti K, Eckel R, Grundy S, Zimmet P, Cleeman J, Donato K . Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation;.... Circulation. 2009; 120(16):1640-5. DOI: 10.1161/CIRCULATIONAHA.109.192644. View

Bazzocchi A, Diano D, Ponti F, Andreone A, Sassi C, Albisinni U . Health and ageing: a cross-sectional study of body composition. Clin Nutr. 2012; 32(4):569-78. DOI: 10.1016/j.clnu.2012.10.004. View

Tabak A, Jokela M, Akbaraly T, Brunner E, Kivimaki M, Witte D . Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet. 2009; 373(9682):2215-21. PMC: 2726723. DOI: 10.1016/S0140-6736(09)60619-X. View

10.

Van Pelt R, Jankowski C, Gozansky W, Schwartz R, Kohrt W . Lower-body adiposity and metabolic protection in postmenopausal women. J Clin Endocrinol Metab. 2005; 90(8):4573-8. PMC: 2819700. DOI: 10.1210/jc.2004-1764. View

11.

Arsenault B, Pelletier Beaumont E, Despres J, Larose E . Mapping body fat distribution: a key step towards the identification of the vulnerable patient?. Ann Med. 2011; 44(8):758-72. DOI: 10.3109/07853890.2011.605387. View

12.

Oh Y, Moon J, Kim H, Kong M . Visceral-to-subcutaneous fat ratio as a predictor of the multiple metabolic risk factors for subjects with normal waist circumference in Korea. Diabetes Metab Syndr Obes. 2017; 10:505-511. PMC: 5729827. DOI: 10.2147/DMSO.S150914. View

13.

Hong J, Hong H, Choi Y, Kim D, Kim J, Yoon J . CT analysis of thoracolumbar body composition for estimating whole-body composition. Insights Imaging. 2023; 14(1):69. PMC: 10126176. DOI: 10.1186/s13244-023-01402-z. View

14.

Britton K, Massaro J, Murabito J, Kreger B, Hoffmann U, Fox C . Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. J Am Coll Cardiol. 2013; 62(10):921-5. PMC: 4142485. DOI: 10.1016/j.jacc.2013.06.027. View

15.

Bang H, Edwards A, Bomback A, Ballantyne C, Brillon D, Callahan M . Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med. 2009; 151(11):775-83. PMC: 3633111. DOI: 10.7326/0003-4819-151-11-200912010-00005. View

16.

Lee Y, Bang H, Kim H, Kim H, Park S, Kim D . A simple screening score for diabetes for the Korean population: development, validation, and comparison with other scores. Diabetes Care. 2012; 35(8):1723-30. PMC: 3402268. DOI: 10.2337/dc11-2347. View

17.

Ohn J, Kwak S, Cho Y, Lim S, Jang H, Park K . 10-year trajectory of β-cell function and insulin sensitivity in the development of type 2 diabetes: a community-based prospective cohort study. Lancet Diabetes Endocrinol. 2015; 4(1):27-34. DOI: 10.1016/S2213-8587(15)00336-8. View

18.

Pickhardt P, Summers R, Garrett J, Krishnaraj A, Agarwal S, Dreyer K . Opportunistic Screening: Scientific Expert Panel. Radiology. 2023; 307(5):e222044. PMC: 10315516. DOI: 10.1148/radiol.222044. View

19.

Wang T, Zhao Z, Wang G, Li Q, Xu Y, Li M . Age-related disparities in diabetes risk attributable to modifiable risk factor profiles in Chinese adults: a nationwide, population-based, cohort study. Lancet Healthy Longev. 2022; 2(10):e618-e628. DOI: 10.1016/S2666-7568(21)00177-X. View

20.

Lee M, Fried S . Sex-dependent Depot Differences in Adipose Tissue Development and Function; Role of Sex Steroids. J Obes Metab Syndr. 2019; 26(3):172-180. PMC: 6484911. DOI: 10.7570/jomes.2017.26.3.172. View