ArticleLipids in health and disease2024
Identification of risk factors and development of a predictive model for chronic kidney disease in patients with obesity: a four-year cohort study.
Article in Lipids in health and disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Pivotal gut microbiota and microbial metabolites involved in the regulation of simple obesity.Frontiers in microbiology · 2026Review
- Association of obesity and lipid indexes with rapid kidney function decline and the progression to chronic kidney disease: a study from a large longitudinal cohort among middle-aged and older adults in China.Frontiers in medicine · 2026Article
- Development and validation of nomogram and machine learning models to predict sarcopenia in patients with chronic kidney disease.Scientific reports · 2025Article
- Association of Remnant Cholesterol with Rapid Kidney Function Decline in Middle-Aged and Older Adults with Normal Kidney Function.Kidney diseases (Basel, Switzerland)Article
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Abstract
objectiveThe sneaky onset and dismal prognosis of chronic kidney disease (CKD) make it an important public health issue. Obesity-related kidney illness has garnered more attention in recent times. Establishing and validating a risk prediction model for chronic renal illness in overweight or obese adults was the goal of this investigation.
methodsData from the China Health and Retirement Longitudinal Study were used for analysis. The definition of CKD was reduced renal function (eGFR < 60 mL/min/1.73 m²), while overweight and obesity were characterized through a body mass index exceeding 24 kg/m². The dataset was divided into derivation and validation cohorts using a 7:3 ratio. With respect to the derivation cohort, we constructed a prediction model using LASSO analysis and multivariate logistic regression. The model's performance was evaluated using Hosmer-Lemeshow tests, calibration curves, decision curve analysis, and receiver operating characteristic (ROC) curves. The validation cohort's model was subjected to additional assessment.
resultsThe study was based on survey data from 2011 to 2015 and comprised 3246 individuals who were overweight or obese, with 2274 being part of the derivation cohort and 972 being part of the validation cohort. The research constructed a prediction model that included age, sex, fasting blood glucose, glycated hemoglobin, triglyceride, hypertension, and BMI. The validation cohort's area under the ROC curve was 0.812 (95% CI = 0.763, 0.859) while the derivation cohort's was 0.789 (95% CI = 0.754, 0.831). Hosmer-Lemeshow tests were utilized to evaluate the model's accuracy in the validation and derivation cohorts (P = 0.681 and 0.547, respectively). The calibration curve showed a high level of consistency between the actual observations and the projected outcomes. According to decision curve analysis, the model offered significant net advantages.
conclusionsThe forecasting model established in this research has predictive value for CKD in patients with overweight or obesity. These findings could help doctors conduct early detection and intervention in clinical practice and further improve patient prognosis.
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