U-Shaped Associations Between Metabolic Indices and Depression in Middle-aged and Older Adults: A Data-Driven Cross-Sectional Study from CHARLS

Main Article Content

Yajie Liang
Medical Department, Shenzhen University, Shenzhen, 518000, China

DOI: https://doi.org/10.70267/lshe.20260113

Keywords

digital economy, labor market structure, skill-biased technological progress

Abstract

Objective: While the relationship between metabolic dysregulation and depression is well-established, its specific nature in the elderly remains obscure, partially due to the limitations of traditional metrics like BMI in distinguishing visceral fat. This study aimed to systematically investigate the associations between novel composite metabolic indices and depressive symptoms in community-dwelling Chinese middle-aged and older adults, with a particular focus on non-linear threshold effects. Methods: This cross-sectional analysis utilized nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), comprising 10,195 participants aged ≥45 years. Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10). Four novel indices were calculated: China Visceral Adiposity Index (CVAI), Lipid Accumulation Product (LAP), Metabolic Score for Insulin Resistance (METS-IR), and Metabolic Score for Visceral Fat (METS-VF). Besides conventional statistics, we employed multivariable logistic regression to decode the independent associations, with a particular focus on identifying non-linear threshold effects. Results: After adjusting for sociodemographic, lifestyle, and clinical confounders, CVAI, LAP, METS-IR, and METS-VF allexhibited significant U-shaped nonlinear associations with depressive symptoms.  Contrary to linear expectations, moderate levels of these indices (typically corresponding to the third quartile) were associated with the lowest risk of depression, conferring a protective effect that diminished or disappeared at extremely high or low levels. Conclusion: In Chinese community-dwelling middle-aged and older adults, moderate visceral adiposity or insulin resistance may be associated with a reduced risk of depression, demonstrating a U-shaped threshold effect. These findings underscore the value of utilizing composite metabolic indices as computational biomarkers for precise mental health screening in aging populations.

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References

  • [1] de Winter, C. F., Bastiaanse, L. P., Hilgenkamp, T. I., Evenhuis, H. M. and Echteld, M. A. Overweight and obesity in older people with intellectual disability. Research in Developmental Disabilities. 2012, 33(2), pp. 398-405. https://doi.org/10.1016/j.ridd.2011.09.022.
  • [2] Liu, Q., He, H., Yang, J., Feng, X., Zhao, F. and Lyu, J. Changes in the global burden of depression from 1990 to 2017: Findings from the Global Burden of Disease study. Journal of Psychiatric Research. 2020, 126, pp. 134-140. https://doi.org/10.1016/j.jpsychires.2019.08.002.
  • [3] Ustun, T. B., Ayuso-Mateos, J. L., Chatterji, S., Mathers, C. and Murray, C. J. Global burden of depressive disorders in the year 2000. British Journal of Psychiatry. 2004, 184(5), pp. 386-92. https://doi.org/10.1192/bjp.184.5.386.
  • [4] Disease, G. B. D., Injury and Risk Factor, C. Burden of 375 diseases and injuries, risk-attributable burden of 88 risk factors, and healthy life expectancy in 204 countries and territories, including 660 subnational locations, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025, 406(10513), pp. 1873-1922. https://doi.org/10.1016/S0140-6736(25)01637-X.
  • [5] Zhou, X., Li, J., Gu, W., Wang, J., Zhu, Y., Zhang, G., Ding, Y. and Tang, Y. Prevalence and associated factors of anxiety and depression among patients with chronic respiratory diseases in eight general hospitals in Jiangsu Province of China: A cross-sectional study. Psychiatry Research. 2017, 251, pp. 48-53. https://doi.org/10.1016/j.psychres.2017.01.070.
  • [6] Petersen, M. C. and Shulman, G. I. Mechanisms of Insulin Action and Insulin Resistance. Physiological Reviews. 2018, 98(4), pp. 2133-2223. https://doi.org/10.1152/physrev.00063.2017.
  • [7] Zimmet, P., Alberti, K. G. and Shaw, J. Global and societal implications of the diabetes epidemic. Nature. 2001, 414(6865), pp. 782-7. https://doi.org/10.1038/414782a.
  • [8] Sharma, V. R., Matta, S. T., Haymond, M. W. and Chung, S. T. Measuring Insulin Resistance in Humans. Hormone Research in Paediatrics. 2020, 93(11-12), pp. 577-588. https://doi.org/10.1159/000515462.
  • [9] Zhou, X., Kang, C., Hu, Y. and Wang, X. Study on insulin resistance and ischemic cerebrovascular disease: A bibliometric analysis via CiteSpace. Front Public Health. 2023, 11, p. 1021378. https://doi.org/10.3389/fpubh.2023.1021378.
  • [10] Fernandes, B. S., Salagre, E., Enduru, N., Grande, I., Vieta, E. and Zhao, Z. Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation. Neuroscience and Biobehavioral Reviews. 2022, 139, p. 104758. https://doi.org/10.1016/j.neubiorev.2022.104758.
  • [11] Brouwer, A., van Raalte, D. H., Lamers, F., Rutters, F., Elders, P. J. M., Van Someren, E. J. W., Snoek, F. J., Beekman, A. T. F. and Bremmer, M. A. Insulin resistance as a marker for the immune-metabolic subtype of depression. Journal of Affective Disorders. 2021, 295, pp. 1371-1376. https://doi.org/10.1016/j.jad.2021.08.151.
  • [12] Xia, M. F., Chen, Y., Lin, H. D., Ma, H., Li, X. M., Aleteng, Q., Li, Q., Wang, D., Hu, Y., Pan, B. S., et al. A indicator of visceral adipose dysfunction to evaluate metabolic health in adult Chinese. Scientific Reports. 2016, 6(1), p. 38214. https://doi.org/10.1038/srep38214.
  • [13] Amato, M. C., Giordano, C., Galia, M., Criscimanna, A., Vitabile, S., Midiri, M., Galluzzo, A. and AlkaMeSy Study, G. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010, 33(4), pp. 920-2. https://doi.org/10.2337/dc09-1825.
  • [14] Yang, Y., Li, S., Ren, Q., Qiu, Y., Pan, M., Liu, G., Zheng, R., An, Z. and Li, S. The interaction between triglyceride-glucose index and visceral adiposity in cardiovascular disease risk: findings from a nationwide Chinese cohort. Cardiovascular Diabetology. 2024, 23(1), p. 427. https://doi.org/10.1186/s12933-024-02518-2.
  • [15] Pan, L., Xu, Q., Liu, J., Gao, Y., Li, J., Peng, H., Chen, L., Wang, M., Mai, G. and Yang, S. Dose-response relationship between Chinese visceral adiposity index and type 2 diabetes mellitus among middle-aged and elderly Chinese. Frontiers in Endocrinology. 2022, 13, p. 959860. https://doi.org/10.3389/fendo.2022.959860.
  • [16] Li, H., Zhang, Y., Luo, H. and Lin, R. The lipid accumulation product is a powerful tool to diagnose metabolic dysfunction-associated fatty liver disease in the United States adults. Frontiers in Endocrinology. 2022, 13, p. 977625. https://doi.org/10.3389/fendo.2022.977625.
  • [17] Zhou, T., Chen, S., Mao, J., Zhu, P., Yu, X. and Lin, R. Association between obstructive sleep apnea and visceral adiposity index and lipid accumulation product: NHANES 2015-2018. Lipids in Health and Disease. 2024, 23(1), p. 100. https://doi.org/10.1186/s12944-024-02081-5.
  • [18] Simental-Mendia, L. E., Rodriguez-Moran, M. and Guerrero-Romero, F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metabolic Syndrome and Related Disorders. 2008, 6(4), pp. 299-304. https://doi.org/10.1089/met.2008.0034.
  • [19] Bello-Chavolla, O. Y., Almeda-Valdes, P., Gomez-Velasco, D., Viveros-Ruiz, T., Cruz-Bautista, I., Romo-Romo, A., Sanchez-Lazaro, D., Meza-Oviedo, D., Vargas-Vazquez, A., Campos, O. A., et al. METS-IR, a novel score to evaluate insulin sensitivity, is predictive of visceral adiposity and incident type 2 diabetes. European Journal of Endocrinology of the European Federation of Endocrine Societies. 2018, 178(5), pp. 533-544. https://doi.org/10.1530/EJE-17-0883.
  • [20] Duan, M., Zhao, X., Li, S., Miao, G., Bai, L., Zhang, Q., Yang, W. and Zhao, X. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018. Cardiovascular Diabetology. 2024, 23(1), p. 243. https://doi.org/10.1186/s12933-024-02334-8.
  • [21] Su, X., Zhao, C. and Zhang, X. Association between METS-IR and heart failure: a cross-sectional study. Frontiers in Endocrinology. 2024, 15, p. 1416462. https://doi.org/10.3389/fendo.2024.1416462.
  • [22] Feng, L., Chen, T., Wang, X., Xiong, C., Chen, J., Wu, S., Ning, J. and Zou, H. Metabolism Score for Visceral Fat (METS-VF): A New Predictive Surrogate for CKD Risk. Diabetes, Metabolic Syndrome and Obesity. 2022, 15, pp. 2249-2258. https://doi.org/10.2147/DMSO.S370222.
  • [23] Tan, A., Yang, S., Pan, Y. and Lin, Q. Metabolism score for visceral fat (METS-VF): an innovative and powerful predictor of stroke. Archives of Medical Science. 2024, 20(5), pp. 1710-1714. https://doi.org/10.5114/aoms/193709.
  • [24] Bello-Chavolla, O. Y., Antonio-Villa, N. E., Vargas-Vazquez, A., Viveros-Ruiz, T. L., Almeda-Valdes, P., Gomez-Velasco, D., Mehta, R., Elias-Lopez, D., Cruz-Bautista, I., Roldan-Valadez, E., et al. Metabolic Score for Visceral Fat (METS-VF), a novel estimator of intra-abdominal fat content and cardio-metabolic health. Clinical Nutrition. 2020, 39(5), pp. 1613-1621. https://doi.org/10.1016/j.clnu.2019.07.012.
  • [25] Li, T., Ren, Y., Liu, M., Wang, Q., Sun, T., Cao, J. and Cui, H. Association between METS-VF and sarcopenia among middle-aged and older adults in China: The first longitudinal evidence from CHARLS. Experimental Gerontology. 2025, 206, p. 112778. https://doi.org/10.1016/j.exger.2025.112778.
  • [26] Xie, M., Zhang, Y., Wu, H., Wu, Z., Han, H., Xie, X., Zhang, R., Cheng, J. and Xu, J. Correlation of triglyceride-glucose index with the incidence and prognosis of hyperglycemic crises in critically ill patients with diabetes mellitus: a machine-learning-based multicenter retrospective cohort study. Frontiers in Nutrition. 2025, 12, p. 1649553. https://doi.org/10.3389/fnut.2025.1649553.
  • [27] Cheng, N., Chen, Y., Jin, L. and Chen, L. Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES. BMC Medical Informatics and Decision Making. 2025, 25(1), p. 223. https://doi.org/10.1186/s12911-025-03076-7.
  • [28] Chen, H. and Mui, A. C. Factorial validity of the Center for Epidemiologic Studies Depression Scale short form in older population in China. International Psychogeriatrics. 2014, 26(1), pp. 49-57. https://doi.org/10.1017/S1041610213001701.
  • [29] Boey, K. W. Cross-validation of a short form of the CES-D in Chinese elderly. International Journal of Geriatric Psychiatry. 1999, 14(8), pp. 608-17. https://doi.org/10.1002/(sici)1099-1166(199908)14:8<608::aid-gps991>3.0.co;2-z.
  • [30] Andresen, E. M., Malmgren, J. A., Carter, W. B. and Patrick, D. L. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). American Journal of Preventive Medicine. 1994, 10(2), pp. 77-84.
  • [31] Zhang, P., Wang, L., Zhou, Q., Dong, X., Guo, Y., Wang, P., He, W., Wang, R., Wu, T., Yao, Z., et al. A network analysis of anxiety and depression symptoms in Chinese disabled elderly. Journal of Affective Disorders. 2023, 333, pp. 535-542. https://doi.org/10.1016/j.jad.2023.04.065.
  • [32] Sheng, G., Lu, S., Xie, Q., Peng, N., Kuang, M. and Zou, Y. The usefulness of obesity and lipid-related indices to predict the presence of Non-alcoholic fatty liver disease. Lipids in Health and Disease. 2021, 20(1), p. 134. https://doi.org/10.1186/s12944-021-01561-2.
  • [33] Huo, R. R., Liao, Q., Zhai, L., You, X. M. and Zuo, Y. L. Interacting and joint effects of triglyceride-glucose index (TyG) and body mass index on stroke risk and the mediating role of TyG in middle-aged and older Chinese adults: a nationwide prospective cohort study. Cardiovascular Diabetology. 2024, 23(1), p. 30. https://doi.org/10.1186/s12933-024-02122-4.
  • [34] Bai, S., Wang, J., Liu, J., Miao, Y., Zhang, A. and Zhang, Z. Analysis of depression incidence and influence factors among middle-aged and elderly diabetic patients in China: based on CHARLS data. BMC Psychiatry. 2024, 24(1), p. 146. https://doi.org/10.1186/s12888-023-05473-6.
  • [35] Crisp, A. H. and McGuiness, B. Jolly fat: relation between obesity and psychoneurosis in general population. British Medical Journal. 1976, 1(6000), pp. 7-9. https://doi.org/10.1136/bmj.1.6000.7.
  • [36] Kundakovic, M. and Rocks, D. Sex hormone fluctuation and increased female risk for depression and anxiety disorders: From clinical evidence to molecular mechanisms. Frontiers in Neuroendocrinology. 2022, 66, p. 101010. https://doi.org/10.1016/j.yfrne.2022.101010.
  • [37] Huang, Y., Zhao, D., Yang, Z., Wei, C. and Qiu, X. The relationship between VAI, LAP, and depression and the mediation role of sleep duration-evidence from NHANES 2005-2020. BMC Psychiatry. 2025, 25(1), p. 228. https://doi.org/10.1186/s12888-025-06631-8.
  • [38] Huang, Q., Wang, D., Chen, S., Tang, L. and Ma, C. Association of METS-IR index with depressive symptoms in US adults: A cross-sectional study. Journal of Affective Disorders. 2024, 355, pp. 355-362. https://doi.org/10.1016/j.jad.2024.03.129.
  • [39] Liu, H., Dong, H., Zhou, Y., Jin, M., Hao, H., Yuan, Y. and Jia, H. The association between Metabolic Score for Visceral Fat and depression in overweight or obese individuals: evidence from NHANES. Frontiers in Endocrinology. 2024, 15, p. 1482003. https://doi.org/10.3389/fendo.2024.1482003.

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Published
Mar 17, 2026
Conference Proceedings Volume
Vol. 12 (2026): Proceedings of the 2026 International Conference on Life Sciences and Health Engineering (IC-LSHE 2026)
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Articles
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How to Cite

Liang, Yajie. “U-Shaped Associations Between Metabolic Indices and Depression in Middle-Aged and Older Adults: A Data-Driven Cross-Sectional Study from CHARLS”. Exploring Science Academic Conference Series, vol. 12, Mar. 2026, pp. 104-17, https://doi.org/10.70267/lshe.20260113.