Obesity indices and type 2 diabetes risk: implications for dynamic assessment and metabolic mechanisms
Bo Wu1
, Yun Chen1, Haitao Xu2, Qi Zhao1, Yue Chen3, Xiaolian Dong2, Chaowei Fu1
Submitted Mar 12, 2026. Accepted for publication Apr 11, 2026. Published online Jul 24, 2026.
doi: 10.21037/hbsn-2026-0182
We are grateful for their interest in our study and for their thoughtful comments in their letter regarding our work on obesity indices and type 2 diabetes mellitus risk in a rural Chinese population. Their insights have provided valuable context for further discussion of our findings.
In their letter, the commentators raised several important points regarding clinical cut-off values for obesity indices, potential mechanisms underlying sex and age differences, the role of liver enzymes in the obesity-diabetes pathway, and the importance of dynamic risk assessment. We appreciate those and are pleased to note that many of them were already addressed in our study or have been further elaborated in our revised manuscript.
Specifically, in our work we developed and validated the Lipid-Obesity Metabolic Score (LOMS), a composite index based on body mass index (BMI), waist-to-height ratio (WHtR), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C), each indicator was coded according to established clinical cut-offs: abnormal levels were assigned 1 point, and normal levels were assigned 0 points. This score, ranging from 0 to 4 and categorized into low, moderate, and high groups, provides clinically interpretable risk estimates that align with the commentators’ call for actionable thresholds. We also calculated the Visceral Adiposity Index (VAI) and the Chinese Visceral Adiposity Index (CVAI) using sex-specific formulas, and confirmed their significant associations with incident type 2 diabetes mellitus (T2DM) (HR per SD increase: 1.03 and 1.02, respectively) as shown in Table 1 (1,2). Dose-response analyses further illustrated the relationships between these indices and T2DM risk (seen at Figure 1).
Table 1
| Case, n | PYs | ID/1,000PYs | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | aHR (95% CI) | aHR (95% CI) | P for trend | ||||||
| CVAI (per SD increase) | 1,554 | 113,168.57 | 13.73 | 1.02 (1.02, 1.03) | 1.02 (1.02, 1.03) | 1.02 (1.02, 1.03) | <0.001 | ||
| VAI (per SD increase) | 1,554 | 113,168.57 | 13.73 | 1.03 (1.02, 1.04) | 1.03 (1.02, 1.04) | 1.03 (1.02, 1.04) | <0.001 | ||
| LOMS | 1,554 | 113,168.57 | 13.73 | – | – | – | |||
| Normal (score ≤2) | 855 | 73,076.92308 | 11.70 | 1.00 | 1.00 | 1.00 | |||
| Middle | 472 | 29,730.53692 | 15.88 | 1.43 (1.32, 1.54) | 1.38 (1.26, 1.50) | 1.29 (1.16, 1.43) | |||
| High | 227 | 10,361.11 | 21.91 | 2.22 (2.03, 2.43) | 2.19 (2.00, 2.40) | 1.93 (1.76, 2.12) | |||
*, missing value exists. Model 1: without any adjustment for covariates; Model 2: adjusted for age (<60, ≥60 years), sex; Model 3: model 2 plus education, occupation, marriage, dietary preference, smoking status, alcohol use, regular physical exercise, baseline hypertension, baseline dyslipidemia, baseline impaired fasting glucose, family history of diabetes, baseline BMI (weight change) or baseline WC (WC change). aHR, adjusted hazard ratio; BMI, body mass index; CI, confidence interval; CVAI, Chinese Visceral Adiposity Index; HR, hazard ratio; ID, incident density; LOMS, Lipid-Obesity Metabolic Score; PYs, person-years; SD, standard deviation; T2DM, type 2 diabetes mellitus; VAI, Visceral Adiposity Index; WC, waist circumference.
We thank the commentators for their valuable suggestion regarding liver enzymes such as alanine aminotransferase (ALT). We fully agree that incorporating hepatic biomarkers could provide important insights into the “hepato-metabolic” axis linking obesity to diabetes. Although our current dataset did not include ALT measurements, future studies with such data would be well positioned to explore whether the associations we observed are independent of or mediated by liver fat and inflammation.
Finally, we fully concur with the emphasis on dynamic risk assessment. Our study demonstrated that long-term increases in both waist circumference (WC) and weight were independent risk factors for T2DM, underscoring the importance of monitoring adiposity trajectories rather than relying solely on single-point measurements. We have strengthened our discussion of the clinical implications of this finding and suggested directions for future intervention studies.
Acknowledgments
None.
Footnote
Provenance and Peer Review: This article was commissioned by the editorial office, HepatoBiliary Surgery and Nutrition. The article did not undergo external peer review.
Funding: This work was supported by
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-2026-0182/coif). C.F. reports grants from the National Nature Science Foundation of China (No. 82173600) and Shanghai Leading Academic Project of Public Health (No. GWVI-8). The other authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
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References
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