Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Construction of a Nomogram-Based Prediction Model for the Risk of Diabetic Kidney Disease in T2DM

Introduction

To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD.

Methods

The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.

Results

Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value.

Conclusion

The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.

Waist to height ratio as a simple tool for predicting mortality: a systematic review and meta-analysis

BACKGROUND

The association of central obesity with higher rates of mortality is not well studied. This study evaluates the association between waist-to-height ratio (WHtR), as a measure of central obesity, with mortality.

METHODS

Documents were retrieved from PubMed, Web of Science, Scopus, and Google Scholar databases until May 2022. Data were extracted from cohort studies reporting effect size (hazard ratio (HR)) regarding the association between WHtR as a continuous (per 1 SD increment) or categorical (highest/lowest) measure and all-cause and cause-specific mortality. Screening of included studies was performed independently by two authors. Moreover, the quality assessment of included studies was performed based on the Newcastle-Ottawa assessment scale. Finally, random effect meta-analysis was performed to pool the data, and the outcomes' certainty level was assess based on the GRADE criteria.

RESULTS

Of the 815 initial studies, 20 were included in the meta-analysis. Random effect meta-analysis showed that in the general population, the all-cause mortality HRs for categorical and continuous measurements of WHtR increased significantly by 23% (HR:1.23; 95% CI: 1.04-1.41) and 16% (HR:1.16; 95% CI: 1.07-1.25), respectively. Moreover, the hazard of cardiovascular (CVD) mortality increased significantly for categorical and continuous measurements of WHtR by 39% (HR:1.39; 95% CI: 1.18-1.59) and 19% (HR:1.19; 95% CI: 1.07-1.31). The quality assessment score of all included studies was high.

CONCLUSIONS

Higher levels of WHtR, indicating central obesity, were associated with an increased hazard of CVD and all-cause mortality. This measure can be used in the clinical setting as a simple tool for predicting mortality.