Huang DN, Ma J, Pan Q, Xu GG, Guo LX. Association of obesity-related indices with chronic kidney disease risk in diabetes: A cross-sectional study. World J Diabetes 2026; 17(5): 117338 [DOI: 10.4239/wjd.v17.i5.117338]
Corresponding Author of This Article
Li-Xin Guo, MD, PhD, Professor, Department of Endocrinology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, The Key Laboratory of Geriatrics of NHC, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Dongdan Dahua Road, Dongcheng District, Beijing 100730, China. glxwork2016@163.com
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Endocrinology & Metabolism
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Observational Study
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May 15, 2026 (publication date) through May 14, 2026
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World Journal of Diabetes
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Huang DN, Ma J, Pan Q, Xu GG, Guo LX. Association of obesity-related indices with chronic kidney disease risk in diabetes: A cross-sectional study. World J Diabetes 2026; 17(5): 117338 [DOI: 10.4239/wjd.v17.i5.117338]
Dong-Ni Huang, Qi Pan, Li-Xin Guo, Department of Endocrinology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, The Key Laboratory of Geriatrics of NHC, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
Jing Ma, Guo-Gang Xu, Health Management Institute, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China
Co-corresponding authors: Guo-Gang Xu and Li-Xin Guo.
Author contributions: Huang DN and Ma J performed data analysis and wrote the manuscript. They contributed equally to the study and are the co-first authors of this manuscript; Huang DN, Xu GG, and Guo LX conceptualized and designed the research study; Ma J and Pan Q screened patients and acquired clinical data; Xu GG provided guidance on study design and data interpretation and led critical revision of the manuscript; Guo LX applied for and secured the funding for this research project; and conceptualized, designed, and supervised the entire project; Xu GG and Guo LX have both played essential and indispensable roles in the research design, data interpretation, and manuscript preparation; they contributed equally to this article and are the co-corresponding authors of this manuscript; all authors thoroughly reviewed and approved the final manuscript.
Supported by Beijing Municipal Health Commission, No. 2022-1-4051; and Beijing Municipal Science and Technology Commission, No. Z221100007422007.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Beijing Hospital (approval No. 2025BJYYEC-KY039-01).
Informed consent statement: The ethics committee waived the requirement for obtaining written informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: No additional data are available.
Corresponding author: Li-Xin Guo, MD, PhD, Professor, Department of Endocrinology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, The Key Laboratory of Geriatrics of NHC, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Dongdan Dahua Road, Dongcheng District, Beijing 100730, China. glxwork2016@163.com
Received: December 5, 2025 Revised: March 7, 2026 Accepted: March 30, 2026 Published online: May 15, 2026 Processing time: 157 Days and 19.7 Hours
Abstract
BACKGROUND
Chronic kidney disease (CKD) is a major microvascular complication of diabetes and a leading cause of morbidity and mortality. Obesity and visceral fat accumulation contribute to renal dysfunction, but traditional measures such as body mass index may incompletely reflect metabolism-related harm from adipose tissue. Although numerous adiposity indices have been developed, comparative evidence on their associations with CKD in adults with diabetes remains limited.
AIM
To compare 10 adiposity indices and evaluate their associations and discriminative abilities for CKD among adults with diabetes.
METHODS
This retrospective cross-sectional study included 3526 adults with diabetes who underwent health examinations at a tertiary hospital. A total of 10 adiposity indices were calculated. CKD was classified as estimated glomerular filtration rate below 60 mL/min/1.73 m2 and/or an albumin-to-creatinine ratio ≥ 30 mg/g. Multivariable logistic regression estimated odds ratios per standard-deviation increase, with additional adjustment. Restricted cubic splines assessed dose-response patterns, and discrimination was evaluated using the area under the receiver operating characteristic curve.
RESULTS
Among the 3526 participants, 456 (12.9%) had CKD. The CKD group had a higher proportion of men (86.0% vs 80.7%, P = 0.007) and a higher prevalence of hypertension (62.7% vs 46.4%, P < 0.001) compared with the non-CKD group. In both the primary and the comorbidity-adjusted sensitivity models, all examined indices were positively associated with CKD (all P < 0.001). Restricted cubic splines analyses showed no evidence of nonlinearity for body mass index, Chinese visceral adiposity index (CVAI), body roundness index, conicity index, and waist-to-height ratio, whereas the other indices showed evidence of nonlinearity (P < 0.05). CVAI had the highest area under the curve (0.637), whereas a body shape index had the lowest (0.526).
CONCLUSION
In adults with diabetes, multiple adiposity indices are associated with CKD. CVAI shows the best discrimination and may aid CKD risk assessment.
Core Tip: In this retrospective cross-sectional study of 3526 adults with diabetes who underwent health examinations, 9 out of 10 adiposity indices were independently associated with chronic kidney disease (CKD) after multivariable adjustment. Restricted cubic spline analyses identified linear associations for several commonly used indices and nonlinearity for others. Among all indices, the Chinese visceral adiposity index showed the strongest discriminative ability for CKD, suggesting that indices reflecting visceral adiposity may be more informative than body mass index alone for assessing CKD risk in diabetes.
Citation: Huang DN, Ma J, Pan Q, Xu GG, Guo LX. Association of obesity-related indices with chronic kidney disease risk in diabetes: A cross-sectional study. World J Diabetes 2026; 17(5): 117338
Diabetes is a major chronic disease with a rapidly rising global prevalence, posing a substantial threat to human health[1]. As forecast by the International Diabetes Federation, the global count of individuals affected by diabetic conditions will reach 643 million by 2030 and 783 million by 2045[1]. This escalating burden is associated with excess mortality and disability, as well as considerable strain on healthcare systems[2,3]. As the dominant diabetic subtype, type 2 diabetes mellitus (T2DM) accounts for 90%-95% of diagnoses and is heavily influenced by aging, daily living choices, and the global overweight crisis[4,5].
Among individuals with diabetes, 30%-40% are expected to develop chronic kidney disease (CKD), with some progressing to end-stage renal disease[6]. Evidence suggests that diabetic nephropathy confers a nearly 2- to 5-fold higher mortality risk compared with other forms of CKD[7]. In China, CKD affects roughly 32.5% of individuals diagnosed with T2DM; however, awareness remains low (26.0%), and screening rates remain inadequate (55.3%)[8]. Moreover, comprehensive metabolic control—including glycemic and lipid management—remains inadequate in this population[8]. These findings underscore the need for early identification of patients with diabetes at high risk of CKD to enable timely and personalized intervention.
Obesity is a global public health challenge closely linked not only to T2DM and cardiovascular disease but also to CKD[9]. Diverse pathways drive this process, encompassing persistent mild inflammation, oxidative stress, dyslipidemia, and insulin resistance[10]. Many indices associated with obesity have been established, including body mass index (BMI), relative fat mass (RFM), Chinese visceral adiposity index (CVAI), waist-to-height ratio (WHtR), visceral adiposity index (VAI), body roundness index (BRI), a body shape index (ABSI), lipid accumulation product (LAP), waist-triglyceride index (WTI), and the conicity index (C-index)[11-15]. Prior studies have demonstrated that obesity is associated with an increased risk of stage 3 CKD[16]. Moreover, a meta-analysis of 41271 patients with T2DM showed that each 5-kg/m2 increase in BMI was associated with a 16% higher risk of diabetic kidney disease[17]. Therefore, for overweight individuals, abdominal obesity has been widely identified as a major predictor of CKD, with WHtR and BRI associated with CKD in diabetes[18,19] and ABSI linked to accelerated CKD progression[20].
Despite the availability of multiple obesity-related indices, their relative value for CKD risk stratification among patients with diabetes remains unclear. Given the considerable burden of obesity-related renal impairment, this research sought to explore connections among 10 frequently applied indices associated with obesity (BMI, RFM, CVAI, WHtR, VAI, BRI, ABSI, LAP, WTI, and C-index) and CKD in patients with diabetes. Using medical records from the Health Management Institute of the Second Medical Centre, PLA General Hospital, this study compared the associations and discriminatory performance of adiposity indices for CKD in adults with diabetes, with potential implications for risk assessment and early intervention.
MATERIALS AND METHODS
Study population
This retrospective cross-sectional study enrolled adults undergoing standard health checkups at PLA General Hospital from November 2009 to December 2016. Qualified subjects were at least 18 years old with a verified diabetic condition, defined as: (1) Self-reported physician-diagnosed diabetes; (2) Fasting plasma glucose (FPG) ≥ 126 mg/dL; (3) 2-hour blood glucose ≥ 200 mg/dL after a 75-g oral glucose tolerance test; or (4) Hemoglobin A1c (HbA1c) ≥ 6.5%[21]. Exclusion criteria were: (1) Missing data for key variables, including height, weight, waist circumference (WC), serum creatinine, albumin-to-creatinine ratio (ACR), blood pressure, lipid profile, or glucose measurements; (2) Current pregnancy, lactation, or planned pregnancy; (3) Presence of kidney disease unrelated to diabetes (e.g., IgA nephropathy, lupus nephritis, kidney stones, hydronephrosis) or terminal renal disease [estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m2 or receiving dialysis]; or (4) Severe systemic conditions, including malignancies, autoimmune disorders, advanced cardiopulmonary disease, or significant hepatic dysfunction.
Ultimately, 3526 subjects entered the final statistical evaluation. As this investigation adopted a retrospective design, formal written consent was unnecessary. The project received ethical approval from the Ethics Committee of Beijing Hospital (approval No. 2025BJYYEC-KY039-01) and was carried out in accordance with the Declaration of Helsinki.
Data collection
Baseline demographic and clinical data (age, sex, weight, height, WC, blood pressure, and medical history) were collected by trained physicians. Blood pressure was measured in the seated position after ≥ 5 min of rest using a calibrated automatic sphygmomanometer. Two consecutive measurements were taken 1 min apart, and the average was used for analysis.
Morning venous specimens were obtained following a minimum of 8-h overnight abstention from food and accredited medical testing facilities at PLA General Hospital. The following indicators were quantified: Complete blood panel, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), high-sensitivity C-reactive protein (hs-CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transferase (GGT), alkaline phosphatase (ALP), albumin (ALB), total bilirubin, FPG, fasting insulin, fasting C-peptide, HbA1c, glycated ALB, blood urea nitrogen, serum uric acid, serum creatinine, homocysteine, and ACR. Full blood evaluations were performed via automated hematology equipment. Serum lipids, hs-CRP, fasting insulin, fasting C-peptide, and glycated ALB were assessed by chemiluminescent immunoassay. Enzymatic methods were employed to determine ALT, AST, GGT, ALP, serum creatinine, urea, serum uric acid, and FPG. HbA1c was detected via high-performance liquid chromatography, while ACR was assessed using enzyme-linked immunosorbent assay. WC was recorded at the navel plane employing a soft measuring strip.
Definition of variables
Adiposity indices were computed using previously published equations[22]. BMI was calculated as weight divided by height squared, and sex-specific formulas were applied for indices that incorporate sex in their derivation (RFM, CVAI, VAI, and LAP). Details of index definitions and variable units are provided in Supplementary Methods. The main endpoint was CKD, classified by the 2022 Kidney Disease: Improving Global Outcomes (KDIGO) recommendations as an eGFR below 60 mL/min/1.73 m2 and/or urine ACR of at least 30 mg/g[23]. eGFR was estimated using the CKD-epidemiology collaboration equation[24]. Owing to the cross-sectional design, CKD was based on a single eGFR and ACR measurement, which does not meet the KDIGO requirement of ≥ 3 months persistence. However, this definition is widely applied in epidemiological studies and considered valid[23,25].
Other covariates were defined according to standard criteria. Smoking was classified as the consumption of a minimum of 10 cigarettes daily over no less than 12 months, and alcohol use as the consumption of alcoholic beverages on ≥ 12 occasions in the previous year. Hypertension was recognized via self-reported doctor confirmation. Abnormal lipid levels were categorized as TC ≥ 5.2 mmol/L, TG ≥ 1.7 mmol/L, LDL-C ≥ 3.4 mmol/L, HDL-C < 1.0 mmol/L, non-HDL-C ≥ 4.1 mmol/L, or a prior self-reported history of lipid disorders[26]. The triglyceride-glucose (TyG) index was computed using the formula: TyG = ln [TG (mg/dL) × FPG (mg/dL)/2].
Statistical analysis
Participants were categorized according to CKD status, and baseline characteristics were compared between groups. The associations between obesity-associated indices and CKD were assessed using multivariable logistic regression models. As an exploratory approach, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was performed using the λ.1se criterion. Multicollinearity among variables selected by LASSO was assessed using the variance inflation factor (VIF), with VIF > 5 considered indicative of potential collinearity. Discriminative ability of indices was evaluated with receiver operating characteristic (ROC) curves, and area under the curve (AUC) values were compared. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported. Restricted cubic spline (RCS) analysis was performed to explore potential nonlinear associations. As supplementary analyses, multivariable linear regression models were used to examine associations of lipid profiles (TC, TG, LDL-C, and HDL-C) with eGFR and log-transformed ACR [log (ACR + 1)]. In addition, lipid profiles and eGFR were summarized across albuminuria categories (A1-A3), with overall and trend tests.
All statistical analyses were performed using R software (version 4.4.3). Variables with more than 20% missing data were excluded. For variables with missing values, imputation was performed under the assumption of missing at random. Multiple imputation by chained equations was adopted for continuous variables, and mode imputation was used for categorical variables with minimal missingness. The distribution of continuous variables was assessed using the Shapiro-Wilk test. Variables following a normal distribution were described as mean ± SD, and those with a skewed distribution were reported as median (interquartile range). Between-group comparisons were performed using independent-sample t tests or Wilcoxon rank-sum tests, as appropriate. Categorical variables were compared using the χ2 test.
RESULTS
Baseline characteristics of the study population
Baseline characteristics are summarized in Table 1. Among the 3526 adults with diabetes included in the study, 3070 had diabetes without CKD, and 456 had diabetes with CKD. Participants with CKD were more likely to be male (86.0% vs 80.7%, P = 0.007) and had a higher prevalence of hypertension (62.7% vs 46.4%, P < 0.001). They also exhibited higher blood pressure levels; faster pulse rate; and elevated white blood cell count, red blood cell count, and hemoglobin concentrations (all P < 0.05).
Table 1 Baseline demographic and clinical characteristics of the study population.
Metabolic abnormalities were more pronounced in the CKD group. Levels of triglycerides, uric acid, homocysteine, FPG, fasting insulin, fasting C-peptide, TyG index, HbA1c, and glycated ALB were all significantly higher (all P < 0.001), whereas HDL-C was lower (P < 0.05). Liver function markers (AST, GGT, ALP) and hs-CRP were elevated in patients with CKD (all P < 0.05), indicating greater systemic inflammation. In supplementary analyses, lipid profiles were further examined in relation to eGFR and log-transformed ACR [log (ACR + 1)], and TG and HDL-C showed significant trends across albuminuria categories A1-A3 (Supplementary Tables 1 and 2).
Most adiposity indices, including CVAI, WHtR, VAI, BRI, BMI, RFM, LAP, WTI, and C-index, were significantly higher in individuals with CKD (all P < 0.001). Only ABSI showed no statistically significant difference between the groups (P = 0.071).
Logistic regression analyses of obesity-related indices and CKD
Logistic regression modeling was applied to gauge links between nine obesity-associated indices (BMI, RFM, CVAI, WHtR, VAI, BRI, LAP, WTI, and C-index) and CKD risk (Table 2). In the unadjusted model, all indices showed positive associations with CKD, although the magnitudes varied. The ORs per SD increment ranged from 1.16 (95%CI: 1.06-1.28; P < 0.001) for RFM to 1.63 (95%CI: 1.48-1.80; P < 0.001) for CVAI. Significant associations were also observed for BMI (OR = 1.55; 95%CI: 1.41-1.70), WHtR (OR = 1.53; 95%CI: 1.39-1.68), VAI (OR = 1.22; 95%CI: 1.12-1.32), BRI (OR = 1.51; 95%CI: 1.37-1.66), LAP (OR = 1.35; 95%CI: 1.25-1.46), WTI (OR = 1.28; 95%CI: 1.18-1.39), and C-index (OR = 1.29; 95%CI: 1.16-1.42) (all P < 0.001).
Table 2 Multivariable logistic regression analyses of the associations between obesity-related indices and chronic kidney disease risk.
After adjustment for demographic and clinical covariates, including sex; age; and smoking, drinking, and marital status (Model 2), these associations remained significant. The adjusted ORs per SD increment were 1.56 (95%CI: 1.42-1.72; P < 0.001) for BMI, 1.21 (95%CI: 1.08-1.35; P < 0.001) for RFM, 1.63 (95%CI: 1.47-1.82; P < 0.001) for CVAI, 1.51 (95%CI: 1.37-1.67; P < 0.001) for WHtR, 1.24 (95%CI: 1.14-1.34; P < 0.001) for VAI, 1.49 (95%CI: 1.35-1.64; P < 0.001) for BRI, 1.38 (95%CI: 1.27-1.49; P < 0.001) for LAP, 1.31 (95%CI: 1.20-1.42; P < 0.001) for WTI, and 1.24 (95%CI: 1.12-1.38; P < 0.001) for C-index. Additional adjustment for hypertension and dyslipidemia (model 3) showed similar patterns (Table 2). For RFM, the primary models did not additionally adjust for sex because sex is embedded in the RFM calculation. An alternative specification including sex is provided in Supplementary Table 3.
Exploratory and spline analyses
An exploratory analysis using LASSO-selected covariates is presented in Supplementary Table 4, with the selected variables shown in Figure 1. RCS analyses based on the primary adjusted model (model 2) are shown in Figures 2 and 3. No evidence of nonlinearity was observed for BMI, BRI, CVAI, WHtR, and the C-index (P for nonlinearity > 0.05), whereas evidence of nonlinearity was observed for LAP, RFM, VAI, and WTI (P for nonlinearity < 0.05).
Figure 2 Restricted cubic spline analyses of the associations between obesity-related indices and chronic kidney disease risk.
A: Linear association between body mass index and chronic kidney disease (CKD) risk; B: Linear association between Chinese visceral adiposity index and CKD risk; C: Linear association between waist-to-height ratio and CKD risk; D: Linear association between body roundness index and CKD risk. CI: Confidence interval; OR: Odds ratio; BMI: Body mass index; CVAI: Chinese visceral adiposity index; WHtR: Waist-to-height ratio; BRI: Body roundness index.
Figure 3 Restricted cubic spline analyses of the associations between obesity-related indices and chronic kidney disease risk.
A: Non-linear association between relative fat mass and chronic kidney disease (CKD) risk; B: Linear association between conicity index and CKD risk; C: Non-linear association between visceral adiposity index and CKD risk; D: Non-linear association between lipid accumulation product and CKD risk; E: Non-linear association between waist-triglyceride index and CKD risk. CI: Confidence interval; OR: Odds ratio; RFM: Relative fat mass; C-index: Conicity index; VAI: Visceral adiposity index; LAP: Lipid accumulation product; WTI: Waist-triglyceride index.
Discriminative ability of obesity-related indices for CKD
ROC analyses were conducted to compare the discriminatory performance of obesity-related indices (Table 3, Figure 4). AUC values ranged from 0.526 (95%CI: 0.498-0.554) for ABSI to 0.637 (95%CI: 0.610-0.663) for CVAI. CVAI showed the highest discriminative ability, followed by BMI (0.625), WHtR (0.620), and BRI (0.620).
Other indices demonstrated weaker performance: WTI (0.595), VAI (0.572), C-index (0.571), RFM (0.562), and ABSI (0.526).
DISCUSSION
This study systematically evaluated multiple obesity-related indices in relation to CKD among adults with diabetes. Across the primary model and the comorbidity-adjusted sensitivity model, all examined indices were positively associated with CKD. In ROC analyses, CVAI showed the highest discriminative ability, followed by BMI, WHtR, and BRI, although the differences in AUC among these top-performing indices were modest. In addition, RCS analyses suggested approximately linear dose-response patterns for BMI, BRI, CVAI, WHtR, and the C-index, whereas evidence of nonlinearity was observed for LAP, RFM, VAI, and WTI, indicating that the shape of the association may differ by index.
Among all indices, CVAI was particularly noteworthy. As a visceral fat index developed for the Chinese population[27], CVAI demonstrated the highest AUC in our analyses, suggesting that it may capture CKD-relevant adiposity-related risk features more effectively than several conventional indices. Prior studies have similarly shown that higher baseline and cumulative CVAI are associated with an increased risk of diabetic kidney disease, highlighting the potential value of monitoring CVAI in individuals with diabetes[28]. Moreover, CVAI has been validated across diverse disease domains, serving as an independent predictor of diabetes, stroke, cardiovascular disease, metabolic syndrome, and related complications[29-32]. Taken together, these findings support the clinical utility of CVAI as a practical marker for CKD risk assessment in diabetes. However, prospective cohort studies are still needed to clarify its predictive value for incident CKD and disease progression.
Beyond CVAI, several commonly used or emerging indices also showed clinical relevance. BMI, derived from weight and height, was consistently associated with CKD, aligning with previous meta-analyses[33]. WHtR, based on WC and height, provided comparable predictive value and has been negatively correlated with eGFR in earlier studies, supporting its potential role in predicting renal function decline[34]. BRI, a newer index reflecting body shape and visceral fat distribution, has gained increasing attention. Accumulating evidence has linked it to metabolic disorders and cardiovascular risk[35,36], and our findings further support its potential value for CKD risk assessment in diabetes. Notably, although RFM, VAI, LAP, WTI, and the C-index were also associated with CKD in regression models, their discriminative performance was weaker than that of CVAI, BMI, WHtR, and BRI, and several of these indices exhibited nonlinear relationships in spline analyses.
The mechanisms linking adiposity-related indices to CKD are likely multifactorial. Excess visceral fat is closely related to insulin resistance, chronic low-grade inflammation, oxidative stress, and activation of the renin-angiotensin-aldosterone system, which may jointly contribute to renal injury[37-39]. Accumulation of ectopic fat around the kidney can further impair renal hemodynamics and promote glomerular hyperfiltration, while adipokines and inflammatory mediators secreted from visceral adipose tissue may accelerate endothelial dysfunction and fibrosis[40]. These pathways provide biological plausibility for why indices reflecting central/visceral adiposity, such as CVAI and WHtR, showed relatively better discrimination in this cohort. Future studies integrating anthropometric indices with biochemical and imaging markers may further improve risk stratification and enable earlier detection among individuals with diabetes.
Recent evidence suggests that combining insulin-resistance surrogates with anthropometric measures may provide a more comprehensive assessment of metabolic risk and has been associated with long-term cardio-renal-metabolic multimorbidity in longitudinal settings[41]. In parallel, the emerging cardiovascular-kidney-metabolic (CKM) syndrome framework emphasizes adipose tissue dysfunction as an upstream driver of systemic metabolic and vascular injury and motivates renewed interest in simple indices for risk stratification across disease stages; for example, weight-adjusted waist index has been reported to perform well for incident CVD prediction across CKM stages in large population data[42]. In this context, the indices that performed relatively well in our analyses (CVAI, BMI, WHtR, and BRI) may reflect CKM-relevant pathophysiology linked to kidney injury in diabetes. Future studies should directly compare these indices with TyG-obesity composite indices and weight-adjusted waist index, and evaluate whether they provide incremental value for CKD prediction and broader CKM outcomes.
Beyond adiposity indices, we also noted an unexpected finding: Participants with CKD exhibited higher RBC counts and Hb levels, in contrast to the conventional association between CKD and anemia. This paradox may reflect hyperglycemia-related hemoconcentration, early-stage diabetes-related nephropathy, or compensatory erythropoiesis due to subclinical hypoxia or dehydration[43,44]. Many CKD cases in this study were likely identified at early stages, when erythropoietic function may be preserved or enhanced. These results align with previous reports of slightly elevated Hb concentrations in early renal dysfunction and highlight the heterogeneity of hematologic responses across CKD stages[45]. Although not the primary focus of this study, this ancillary finding warrants further investigation.
In supplementary analyses, we further examined standard lipid parameters in relation to kidney function markers. TG was positively associated with log-transformed ACR, and HDL-C showed an inverse association with eGFR. TG increased and HDL-C decreased across albuminuria categories (A1-A3), with significant trends, suggesting that dyslipidemia may be more closely linked to the albuminuria dimension of CKD in this population. These findings should be interpreted cautiously, given the cross-sectional design and limited information on lipid-lowering therapy and other medications.
This study has several strengths. It focused on individuals with diabetes, a population at particularly high risk for CKD, and compared a broad set of obesity-related indices, providing practical insights into their relative utility for CKD risk assessment. However, several limitations must also be recognized. First, the single-center design may limit the generalizability of the findings. Second, the observational design prevents causal deduction and permits no evaluation of new-onset kidney disease or its advancement. Third, CKD was defined using single measurements of eGFR and ACR, which may lead to misclassification, because KDIGO guidelines require persistence for at least three months. Fourth, although we used a primary model and a comorbidity-adjusted sensitivity model, residual confounding remains possible owing to incomplete data on medication use, diet, physical activity, family history, and genetic factors. Finally, several indices include overlapping components, and extended adjustment sets may introduce variable overlap or overadjustment. Accordingly, the LASSO-based extended adjustment was treated as exploratory and presented in the supplementary material. In addition, abdominal ultrasonography was not routinely available for all participants, preventing imaging-based assessment of visceral adiposity.
CONCLUSION
In summary, multiple obesity-related indices were positively associated with CKD among adults with diabetes across the primary and comorbidity-adjusted models. CVAI showed the highest discriminative ability, with BMI, WHtR, and BRI also performing relatively well. These indices are simple to obtain and may aid CKD risk stratification in clinical and community settings. Future studies are warranted to validate these findings, clarify temporal relationships, and determine whether targeted interventions on adiposity can reduce CKD risk and progression in diabetes.
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