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Epidemiology and Population Health

A U-shaped relationship between body mass index and the risk of elevated liver stiffness in older people: evidence from the 5‐year retrospective cohort study

Abstract

Background

The relationship between body mass index (BMI) and liver stiffness in older people remains unclear. This study aimed to examine the association between BMI and the risk of elevated liver stiffness in older people.

Methods

2736 participants from the West China Health and Aging Cohort Study (WCHAC) were included in the present study. Liver stiffness was assessed using transient elastography (TE). The association of the 5-year average BMI level with elevated liver stiffness risk was estimated using multinomial logistic regression. The group-based trajectory model (GBTM) was applied to identify BMI trajectories. Additionally, restricted cubic spline analysis was conducted to explore the dose-response association between BMI and the risk of elevated liver stiffness.

Results

Participants in the second BMI quartile (21.93–23.58) had the lowest prevalence of elevated liver stiffness, and then the risk increased with higher BMI quartiles (BMI Q4 vs. Q2, OR = 2.05, 95% CI: 1.37–3.11 and Q5 vs. Q2, OR = 2.82, 95% CI: 1.78–4.39). There were five BMI trajectories over the five-year period: low-normal-weight stable (7.42%), moderate-normal-weight stable (29.10%), low-level-overweight stable (36.22%), high-level-overweight stable (20.32%) and stable obesity (6.94%). Participants in the moderate-normal-weight stable group had the lowest prevalence of elevated liver stiffness. Compared with this group, the adjusted ORs (95% CI) elevated liver stiffness prevalence were 2.02 (1.39–2.97) for the high-level-overweight stable, and 2.83 (1.72–4.64) for the group of people with stable obesity. Dose-response analysis revealed a U-shaped relationship between BMI and elevated liver stiffness risk, suggesting an optimal BMI range of 21.8–24.3 kg/m² for older people to minimize elevated liver stiffness risk.

Conclusions

Our study elucidated the U-shaped relationship between BMI and the risk of elevated liver stiffness as measured by TE, and the optimal BMI range from 21.8 to 24.3 kg/m2 for the lowest risk of elevated liver stiffness in older people.

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Fig. 1: The trajectory of BMI during 2017–2022.
Fig. 2: Box plot of BMI changes during 2017–2022.
Fig. 3: Dose-response association between 5-year average BMI level and the risk of elevated liver stiffness.

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Data availability

The dataset that supports this article is from the WCHAC project. Data sharing requires approval, and further details on how to request the datasets can be provided upon inquiry. The full analysis code is available from the corresponding author.

Code availability

The dataset that supports this article is from the WCHAC project. Data sharing requires approval, and further details on how to request the datasets can be provided upon inquiry. The full analysis code is available from the corresponding author.

References

  1. Younossi ZM, Wong G, Anstee QM, Henry L. The global burden of liver disease. Clin Gastroenterol Hepatol. 2023;21:1978–91. https://doi.org/10.1016/j.cgh.2023.04.015.

    Article  PubMed  Google Scholar 

  2. Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79:516–37. https://doi.org/10.1016/j.jhep.2023.03.017.

    Article  PubMed  Google Scholar 

  3. Maeso-Díaz R, Gracia-Sancho J. Aging and chronic liver disease. Semin Liver Dis. 2020;40:373–84. https://doi.org/10.1055/s-0040-1715446.

    Article  CAS  PubMed  Google Scholar 

  4. United Nations, Department of Economic and Social Affairs. World Social Report 2023: Leaving No One Behind in an Ageing World. New York: United Nations; 2023. Available from: https://desapublications.un.org/publications/world-socialreport-2023-leaving-no-one-behind-ageing-world.

  5. Stebbing J, Farouk L, Panos G, Anderson M, Jiao LR, Mandalia S, et al. A meta-analysis of transient elastography for the detection of hepatic fibrosis. J Clin Gastroenterol. 2010;44:214–9. https://doi.org/10.1097/MCG.0b013e3181b4af1f.

    Article  PubMed  Google Scholar 

  6. Mueller S, Sandrin L. Liver stiffness: a novel parameter for the diagnosis of liver disease. Hepat Med. 2010;2:49–67. https://doi.org/10.2147/hmer.s7394.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Semmler G, Yang Z, Fritz L, Köck F, Hofer BS, Balcar L, et al. Dynamics in liver stiffness measurements predict outcomes in advanced chronic liver disease. Gastroenterology. 2023;165:1041–52. https://doi.org/10.1053/j.gastro.2023.06.030.

    Article  PubMed  Google Scholar 

  8. Singh S, Fujii LL, Murad MH, Wang Z, Asrani SK, Ehman RL, et al. Liver stiffness is associated with risk of decompensation, liver cancer, and death in patients with chronic liver diseases: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2013;11:1573–84.e1-2. https://doi.org/10.1016/j.cgh.2013.07.034.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang J, Li J, Zhou Q, Zhang D, Bi Q, Wu Y, et al. Liver stiffness measurement predicted liver-related events and all-cause mortality: a systematic review and nonlinear dose-response meta-analysis. Hepatol Commun. 2018;2:467–76. https://doi.org/10.1002/hep4.1154.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Angulo P, Kleiner DE, Dam-Larsen S, Adams LA, Bjornsson ES, Charatcharoenwitthaya P, et al. Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology. 2015;149:389–97.e10. https://doi.org/10.1053/j.gastro.2015.04.043.

    Article  PubMed  Google Scholar 

  11. Ciardullo S, Pizzi M, Pizzi P, Oltolini A, Muraca E, Perseghin G. Prevalence of elevated liver stiffness among potential candidates for bariatric surgery in the United States. Obes Surg. 2022;32:712–9. https://doi.org/10.1007/s11695-021-05885-x.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Atallah E, Grove JI, Crooks C, Burden-Teh E, Abhishek A, Moreea S, et al. Risk of liver fibrosis associated with long-term methotrexate therapy may be overestimated. J Hepatol. 2023;78:989–97. https://doi.org/10.1016/j.jhep.2022.12.034.

    Article  CAS  PubMed  Google Scholar 

  13. Das K, Sarkar R, Ahmed SM, Mridha AR, Mukherjee PS, Das K, et al. Normal” liver stiffness measure (LSM) values are higher in both lean and obese individuals: a population-based study from a developing country. Hepatology. 2012;55:584–93. https://doi.org/10.1002/hep.24694.

    Article  PubMed  Google Scholar 

  14. Wong GL, Chan HL, Choi PC, Chan AW, Lo AO, Chim AM, et al. Association between anthropometric parameters and measurements of liver stiffness by transient elastography. Clin Gastroenterol Hepatol. 2013;11:295–302.e1-3. https://doi.org/10.1016/j.cgh.2012.09.025.

    Article  PubMed  Google Scholar 

  15. Liu Y, Yuan S, Zuo J, Liu S, Tang X, Li X, et al. A J-shaped relationship between body mass index and the risk of elevated liver stiffness: a cross-sectional study. Eur J Med Res. 2023;28:557 https://doi.org/10.1186/s40001-023-01543-3.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27:2015–23. https://doi.org/10.1177/0962280216673085.

    Article  PubMed  Google Scholar 

  17. Dai H, Li F, Bragazzi NL, Wang J, Chen Z, Yuan H, et al. Distinct developmental trajectories of body mass index and diabetes risk: a 5-year longitudinal study of Chinese adults. J Diabetes Investig. 2020;11:466–74. https://doi.org/10.1111/jdi.13133.

    Article  PubMed  Google Scholar 

  18. Fan B, Yang Y, Dayimu A, Zhou G, Liu Y, Li S, et al. Body mass index trajectories during young adulthood and incident hypertension: a longitudinal cohort in Chinese population. J Am Heart Assoc. 2019;8:e011937 https://doi.org/10.1161/jaha.119.011937.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Murayama H, Shaw BA. Heterogeneity in trajectories of body mass index and their associations with mortality in old age: a literature review. J Obes Metab Syndr. 2017;26:181–7. https://doi.org/10.7570/jomes.2017.26.3.181.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Wang M, Yi Y, Roebothan B, Colbourne J, Maddalena V, Sun G, et al. Trajectories of body mass index among Canadian seniors and associated mortality risk. BMC Public Health. 2017;17:929 https://doi.org/10.1186/s12889-017-4917-0.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wu X, Liao J, Chen X, Xiao J, Dui X, Ma T, et al. The independent and combined associations of nocturnal sleep duration, sleep midpoint, and sleep onset latency with global cognitive function in older Chinese adults. Geroscience. 2025; https://doi.org/10.1007/s11357-024-01476-7.

  22. Friedrich-Rust M, Poynard T, Castera L. Critical comparison of elastography methods to assess chronic liver disease. Nat Rev Gastroenterol Hepatol. 2016;13:402–11. https://doi.org/10.1038/nrgastro.2016.86.

    Article  PubMed  Google Scholar 

  23. Boursier J, Zarski JP, de Ledinghen V, Rousselet MC, Sturm N, Lebail B, et al. Determination of reliability criteria for liver stiffness evaluation by transient elastography. Hepatology. 2013;57:1182–91. https://doi.org/10.1002/hep.25993.

    Article  PubMed  Google Scholar 

  24. Lim JK, Flamm SL, Singh S, Falck-Ytter YT. American gastroenterological association institute guideline on the role of elastography in the evaluation of liver fibrosis. Gastroenterology. 2017;152:1536–43. https://doi.org/10.1053/j.gastro.2017.03.017.

    Article  PubMed  Google Scholar 

  25. Jain V, Poddar U, Negi TS, Saraswat VA, Krishnani N, Yachha SK, et al. Utility and accuracy of transient elastography in determining liver fibrosis: a case-control study. Eur J Pediatr. 2020;179:671–7. https://doi.org/10.1007/s00431-019-03561-y.

    Article  CAS  PubMed  Google Scholar 

  26. Serra-Burriel M, Graupera I, Torán P, Thiele M, Roulot D, Wai-Sun Wong V, et al. Transient elastography for screening of liver fibrosis: cost-effectiveness analysis from six prospective cohorts in Europe and Asia. J Hepatol. 2019;71:1141–51. https://doi.org/10.1016/j.jhep.2019.08.019.

    Article  PubMed  Google Scholar 

  27. Kim BK, Kim SU, Choi GH, Han WK, Park MS, Kim EH, et al. Normal” liver stiffness values differ between men and women: a prospective study for healthy living liver and kidney donors in a native Korean population. J Gastroenterol Hepatol. 2012;27:781–8. https://doi.org/10.1111/j.1440-1746.2011.06962.x.

    Article  PubMed  Google Scholar 

  28. You SC, Kim KJ, Kim SU, Kim BK, Park JY, Kim DY, et al. Factors associated with significant liver fibrosis assessed using transient elastography in general population. World J Gastroenterol. 2015;21:1158–66. https://doi.org/10.3748/wjg.v21.i4.1158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wong VW, Vergniol J, Wong GL, Foucher J, Chan HL, Le Bail B, et al. Diagnosis of fibrosis and cirrhosis using liver stiffness measurement in nonalcoholic fatty liver disease. Hepatology. 2010;51:454–62. https://doi.org/10.1002/hep.23312.

    Article  CAS  PubMed  Google Scholar 

  30. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. https://doi.org/10.1146/annurev.clinpsy.121208.131413.

    Article  PubMed  Google Scholar 

  31. Zhang W, Chen Y, Chen N. Body mass index and trajectories of the cognition among Chinese middle and old-aged adults. BMC Geriatr. 2022;22:613 https://doi.org/10.1186/s12877-022-03301-2.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wang Y, Li W, Chen S, Zhang J, Liu X, Jiang J, et al. PM(2.5) constituents associated with childhood obesity and larger BMI growth trajectory: a 14-year longitudinal study. Environ Int. 2024;183:108417 https://doi.org/10.1016/j.envint.2024.108417.

    Article  CAS  PubMed  Google Scholar 

  33. van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Struct Equ Model. 2017;24:451–67. https://doi.org/10.1080/10705511.2016.1247646.

    Article  Google Scholar 

  34. Nagin DS. Group-based trajectory modeling: an overview. Ann Nutr Metab. 2014;65:205–10. https://doi.org/10.1159/000360229.

    Article  CAS  PubMed  Google Scholar 

  35. Álvarez-Bustos A, Carnicero-Carreño JA, Sanchez-Sanchez JL, Garcia-Garcia FJ, Alonso-Bouzón C, Rodríguez-Mañas L. Associations between frailty trajectories and frailty status and adverse outcomes in community-dwelling older adults. J Cachexia Sarcopenia Muscle. 2022;13:230–9. https://doi.org/10.1002/jcsm.12888.

    Article  PubMed  Google Scholar 

  36. Azzu V, Vacca M, Virtue S, Allison M, Vidal-Puig A. Adipose tissue-liver cross talk in the control of whole-body metabolism: implications in nonalcoholic fatty liver disease. Gastroenterology. 2020;158:1899–912. https://doi.org/10.1053/j.gastro.2019.12.054.

    Article  CAS  PubMed  Google Scholar 

  37. Unger RH. Lipid overload and overflow: metabolic trauma and the metabolic syndrome. Trends Endocrinol Metab. 2003;14:398–403. https://doi.org/10.1016/j.tem.2003.09.008.

    Article  CAS  PubMed  Google Scholar 

  38. Bijnen M, Josefs T, Cuijpers I, Maalsen CJ, van de Gaar J, Vroomen M, et al. Adipose tissue macrophages induce hepatic neutrophil recruitment and macrophage accumulation in mice. Gut. 2018;67:1317–27. https://doi.org/10.1136/gutjnl-2016-313654.

    Article  CAS  PubMed  Google Scholar 

  39. Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24:908–22. https://doi.org/10.1038/s41591-018-0104-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cuntz U, Voderholzer U. Liver damage is related to the degree of being underweight in anorexia nervosa and improves rapidly with weight gain. Nutrients. 2022;14:2378 https://doi.org/10.3390/nu14122378.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ichimiya T, Yamakawa T, Hirano T, Yokoyama Y, Hayashi Y, Hirayama D, et al. Autophagy and autophagy-related diseases: a review. Int J Mol Sci. 2020;21:8974 https://doi.org/10.3390/ijms21238974.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–96. https://doi.org/10.1016/s0140-6736(09)60318-4.

    Article  PubMed  Google Scholar 

  43. Winter JE, MacInnis RJ, Nowson CA. The influence of age the BMI and all-cause mortality association: a meta-analysis. J Nutr Health Aging. 2017;21:1254–8. https://doi.org/10.1007/s12603-016-0837-4.

    Article  CAS  PubMed  Google Scholar 

  44. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. J Hepatol. 2015;63:237–64. https://doi.org/10.1016/j.jhep.2015.04.006.

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Acknowledgements

All authors involved in this study extend their gratitude to the staff of the West China Health and Aging Cohort research team for their dedicated work. We would also like to express our sincere appreciation to Shenzhen Huibo Medical Equipment Co., Ltd. for providing the FibroScan equipment, which was instrumental in conducting liver stiffness measurements and crucial to the success of our study.

Funding

This study was supported by funds from National Key R&D Program of China (2020YFC2006505).

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Contributions

LLv, LLong, JLi, and JLiao contributed to the study design, data analysis, interpretation, and drafting of the manuscript. YZ, NY, JX, TM, XC, XD, XL, TZ, and HZ contributed to the acquisition of data and critical revision of the manuscript for important intellectual content. JLiao and JLi supervised the study. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Juan Liao or Jiayuan Li.

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Lv, L., Long, L., Zheng, Y. et al. A U-shaped relationship between body mass index and the risk of elevated liver stiffness in older people: evidence from the 5‐year retrospective cohort study. Int J Obes 49, 2110–2116 (2025). https://doi.org/10.1038/s41366-025-01849-8

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