From associations to action: deepening the clinical and mechanistic implications of obesity indices in T2DM risk stratification

We read with great interest the cohort study by Wu et al., which prospectively investigated the differential associations of various obesity indices and their long-term changes with incident type 2 diabetes mellitus (T2DM) risk in a rural Chinese population, stratified by sex and age (1). The authors are to be commended for conducting this large-scale, long-term study in an under-represented setting, confirming the superior predictive value of abdominal obesity indices [waist circumference (WC), waist-to-height ratio (WHtR)] over body mass index (BMI) and revealing important modifications by sex and age. This work provides valuable epidemiological evidence for tailored prevention strategies. We wish to elaborate on several points to further bridge the gap between these robust associations and their potential clinical and pathophysiological implications.

First, the study rightly highlights the limitations of BMI and advocates for WC and WHtR. A pivotal next step for clinical translation is defining clear, population-specific cut-off points for these indices within the Chinese rural context. Although the study reported hazard ratios per standard deviation increase, establishing actionable thresholds would greatly enhance its practicality for frontline healthcare providers. For instance, the Chinese Guidelines for the Diagnosis and Treatment of Obesity (2024 Edition) specifies central obesity criteria: waist circumference ≥90 cm for men and ≥85 cm for women (2). Discussing how the observed risk gradients align with or challenge existing Chinese guidelines could offer immediate practical guidance.

Second, the finding that abdominal obesity indices are more strongly associated with T2DM risk in males and those <60 years, while general obesity (BMI) shows stronger associations in females and the elderly, is intriguing and warrants deeper mechanistic exploration. This may reflect differences in fat distribution and metabolic physiology. Men and younger individuals tend to accumulate visceral adipose tissue (VAT), which is highly pro-inflammatory and insulin resistant. In contrast, postmenopausal women and the elderly often have more subcutaneous and ectopic fat (e.g., intramuscular), where the metabolic risk per unit mass might be different, potentially making overall adiposity (captured by BMI) a relatively stronger marker (3). Delving into these potential mechanisms would enrich the discussion and connect the epidemiological findings to underlying biology.

Third, while the models adjusted for several confounders, the inclusion of liver enzymes such as alanine aminotransferase (ALT) could have been highly informative. ALT is a readily available marker closely linked to both non-alcoholic fatty liver disease (NAFLD/MASLD) and insulin resistance, serving as a potential mediator in the obesity-diabetes pathway (4,5). Exploring whether the association between obesity indices and T2DM risk is independent of or mediated by liver fat/inflammation (proxied by ALT) could have provided a more integrated view of the “hepato-metabolic” axis.

Finally, the study powerfully demonstrates that long-term increases in WC and weight are independent risk factors. This underscores the critical importance of monitoring trajectories of adiposity, not just single-point measurements. However, it is worth noting that WC tends to increase gradually with age (6). Future research could explore whether interventions aimed at stabilizing or reducing WC, even in individuals already classified as abdominally obese, confer differential risk reduction compared to interventions focused solely on weight.

In conclusion, Wu et al. have made a significant contribution to understanding obesity-related diabetes risk in rural China. By moving towards defining clinical cut-offs, exploring the pathophysiological basis of sex/age interactions, integrating hepatic biomarkers, and emphasizing dynamic risk assessment, subsequent research can transform these strong epidemiological signals into precise, mechanism-informed public health actions.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article did not undergo external peer review.

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2026-1-0120/coif). The author has no conflicts of interest to declare.

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References

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