Abnormal adiposity indices are associated with an increased risk of diabetes in a non-obese Asian population.

OBJECTIVES: The aim of this study was to evaluate the association between adiposity indices and the risk of incident diabetes and to compare their predictive ability in non-obese healthy individuals. STUDY DESIGN: Population-based cohort study. METHODS: Data were taken from the NAGALA research study, which enrolled Japanese adults aged 18-79 years. Cox regression was used to evaluate the association between adiposity indices (including waist circumference [WC], waist-to-height ratio [WHtR], lipid accumulation product index [LAP], body roundness index [BRI], visceral adiposity index [VAI] and Chinese visceral adiposity index [CVAI]) and diabetes risk. The performance of the indices for predicting diabetes was explored using area under the receiver operating characteristic curve (AUC). A Chinese community-based population was used for validation. RESULTS: A total of 12,940 healthy Japanese individuals with normal body mass index and glycaemic levels were included and were followed up for a median of 6 years. Multivariable Cox models revealed a positive and significant association between all indices and incident diabetes, with the hazard ratios for the highest quartile of the indices ranging from 1.89 to 2.90 (all P-values < 0.01). A non-linear association between WC, BRI and VAI and a linear association between WHtR, LAP and CVAI and diabetes risk were observed. CVAI, VAI and LAP had comparable ability in predicting diabetes, with the highest AUC being 0.733 for CVAI. Data from 10,830 Chinese individuals confirmed these results. CONCLUSIONS: Adiposity indices are associated with incident diabetes in healthy non-obese individuals. Participants in the highest quartile of WC, WHtR, LAP, BRI, VAI and CVAI had an increased risk of developing diabetes.

Visceral fat and its dynamic change are associated with renal damage: Evidence from two cohorts.

BACKGROUND AND AIMS: To evaluate the association of Chinese visceral adiposity index (CVAI) and its dynamic trends with risk of renal damage, and to compare its prediction performance with that of other obesity indices. METHODS AND RESULTS: A community-based population with 23 905 participants from Shantou city was included in the cross-sectional analysis. A total of 9,778 individuals from two separated cohort were included in the longitudinal portion. Five patterns of CVAI change were predefined (low-stable, decreasing, moderate, increasing, and persistent-high). Logistic and Cox regressions were used to evaluate the association between CVAI and renal damage. We explored potential mechanisms using the mediating effect method, and the prediction performance was determined by receiver operating characteristic curve analysis. Results from both cross-sectional and longitudinal data revealed a positive and linear association between CVAI and risk of renal damage. Pooled analysis of the two cohorts showed that per unit increase in Z score of CVAI induced 18% increased risk of renal damage (P = .008). Longitudinal trends of CVAI were also associated with renal damage, and the moderate, increasing, and persistent-high patterns showing a higher risk. Blood pressure and glucose had a mediating effect on renal damage induced by CVAI. Among several obesity indices, CVAI was the optimal for predicting renal damage. CONCLUSION: A higher level of immediate CVAI and longitudinal increasing and persistent-high patterns of CVAI were independently associated with increased risk of renal damage. Monitoring immediate level and long-term trend of CVAI may contribute to the prevention of renal damage.

Predicting 1-, 3-, 5-, and 8-year all-cause mortality in a community-dwelling older adult cohort: relevance for predictive, preventive, and personalized medicine.

Background: Population aging is a global public health issue involving increased prevalence of age-related diseases, and concomitant burden on medical resources and the economy. Ninety-two diseases have been identified as age-related, accounting for 51.3% of the global adult disease burden. The economic cost per capita for older people over 60 years is 10 times that of the younger population. From the aspects of predictive, preventive, and personalized medicine (PPPM), developing a risk-prediction model can help identify individuals at high risk for all-cause mortality and provide an opportunity for targeted prevention through personalized intervention at an early stage. However, there is still a lack of predictive models to help community-dwelling older adults do well in healthcare. Objectives: This study aims to develop an accurate 1-, 3-, 5-, and 8-year all-cause mortality risk-prediction model by using clinical multidimensional variables, and investigate risk factors for 1-, 3-, 5-, and 8-year all-cause mortality in community-dwelling older adults to guide primary prevention. Methods: This is a two-center cohort study. Inclusion criteria: (1) community-dwelling adult, (2) resided in the districts of Chaonan or Haojiang for more than 6 months in the past 12 months, and (3) completed a health examination. Exclusion criteria: (1) age less than 60 years, (2) more than 30 incomplete variables, (3) no signed informed consent. The primary outcome of the study was all-cause mortality obtained from face-to-face interviews, telephone interviews, and the medical death database from 2012 to 2021. Finally, we enrolled 5085 community-dwelling adults, 60 years and older, who underwent routine health screening in the Chaonan and Haojiang districts, southern China, from 2012 to 2021. Of them, 3091 participants from Chaonan were recruited as the primary training and internal validation study cohort, while 1994 participants from Haojiang were recruited as the external validation cohort. A total of 95 clinical multidimensional variables, including demographics, lifestyle behaviors, symptoms, medical history, family history, physical examination, laboratory tests, and electrocardiogram (ECG) data were collected to identify candidate risk factors and characteristics. Risk factors were identified using least absolute shrinkage and selection operator (LASSO) models and multivariable Cox proportional hazards regression analysis. A nomogram predictive model for 1-, 3-, 5- and 8-year all-cause mortality was constructed. The accuracy and calibration of the nomogram prediction model were assessed using the concordance index (C-index), integrated Brier score (IBS), receiver operating characteristic (ROC), and calibration curves. The clinical validity of the model was assessed using decision curve analysis (DCA). Results: Nine independent risk factors for 1-, 3-, 5-, and 8-year all-cause mortality were identified, including increased age, male, alcohol status, higher daily liquor consumption, history of cancer, elevated fasting glucose, lower hemoglobin, higher heart rate, and the occurrence of heart block. The acquisition of risk factor criteria is low cost, easily obtained, convenient for clinical application, and provides new insights and targets for the development of personalized prevention and interventions for high-risk individuals. The areas under the curve (AUC) of the nomogram model were 0.767, 0.776, and 0.806, and the C-indexes were 0.765, 0.775, and 0.797, in the training, internal validation, and external validation sets, respectively. The IBS was less than 0.25, which indicates good calibration. Calibration and decision curves showed that the predicted probabilities were in good agreement with the actual probabilities and had good clinical predictive value for PPPM. Conclusion: The personalized risk prediction model can identify individuals at high risk of all-cause mortality, help offer primary care to prevent all-cause mortality, and provide personalized medical treatment for these high-risk individuals from the PPPM perspective. Strict control of daily liquor consumption, lowering fasting glucose, raising hemoglobin, controlling heart rate, and treatment of heart block could be beneficial for improving survival in elderly populations. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-023-00342-4.