Association between recovery/occurrence of metabolic syndrome and rapid estimated glomerular filtration rate decline in middle-aged and older populations: evidence from the China Health and Retirement Longitudinal Study

Association of changes in metabolic syndrome with new-onset and progression of chronic kidney disease

BACKGROUND

Metabolic syndrome (MetS) is an independent risk factor for new-onset and progression of chronic kidney disease (CKD). However, whether changes in MetS are associated with the new-onset CKD and its progression remains unknown.

METHODS

A total of 36,571 participants from the Kailuan Study were enrolled in this study, including 27,072 without CKD and 9499 with CKD at baseline. According to the changes of MetS, 4 groups were divided as follows: MetS-free group, MetS-recovered group, MetS-developed group, and MetS-persistent group. Cox regression models were used to explore the association of changes in MetS with new-onset and progression of CKD.

RESULTS

During a median follow-up of 8.38 years, 3313 cases of new-onset CKD were identified in participants without CKD. Compared with the MetS-free group, the hazard ratio (HR) and 95% confidence interval (95% CI) for new-onset CKD in the MetS-recovered, MetS-developed and MetS-persistent groups was 1.34 (1.18-1.53), 1.46 (1.30-1.63) and 1.85 (1.69-2.02), respectively. Among 9499 participants with CKD, during a median follow-up of 8.18 years, a total of 2305 experienced CKD progression. Compared with the MetS-free group, the HR (95% CI) for CKD progression in each group were 1.05 (0.91-1.22), 1.34 (1.17-1.55) and 1.65 (1.49-1.83), respectively. Furthermore, the association between changes in MetS and new-onset CKD was stronger in younger and middle-aged participants (≤60 years old) compared with older participants.

CONCLUSIONS

Developed MetS and persistent MetS were both risk factors for the new-onset and progression of CKD. Even with recovery from MetS, an association of MetS with kidney damage remained.

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

The dynamic progression of metabolic syndrome (MetS) includes developmental deterioration and reverse recovery; however, the key factors in this bidirectional progression have not been identified. Our study aimed to use the data obtained from the China Health and Retirement Longitudinal Study (CHARLS) and construct a Bayesian network to explore the causal relationship between influential factor and the development and recovery of MetS. Followed up at 4 years, forward progression of MetS occurred in 1543 and reverse recovery of MetS occurred in 1319 of 5581 subjects. Bayesian Networks showed that hyperuricemia and body mass index (BMI) levels directly influenced progression of MetS, and gender, exercise and age play an indirect role through hyperuricemia and BMI levels; high hemoglobin A1c (HbA1c) and BMI levels directly influenced recovery of MetS, and gender and exercise play an indirect role through BMI levels. Bayesian Network inference found that the rate of progression of MetS in subjects with hyperuricemia increases from 36 to 60%, the rate of progression of MetS in subjects with overweight or obese increases from 36 to 41% and the rate of reverse recovery rate of MetS in subjects with high HbA1c decreased from 33 to 20%. Therefore, attention to individuals at high risk of hyperuricemia, high HbA1c levels, and overweight/obesity should be enhanced, with early detection and following healthy behavioral interventions to prevent, control and delay the progression of MetS and its components.

Predicting diabetic kidney disease for type 2 diabetes mellitus by machine learning in the real world: a multicenter retrospective study

Objective

Diabetic kidney disease (DKD) has been reported as a main microvascular complication of diabetes mellitus. Although renal biopsy is capable of distinguishing DKD from Non Diabetic kidney disease(NDKD), no gold standard has been validated to assess the development of DKD.This study aimed to build an auxiliary diagnosis model for type 2 Diabetic kidney disease (T2DKD) based on machine learning algorithms.

Methods

Clinical data on 3624 individuals with type 2 diabetes (T2DM) was gathered from January 1, 2019 to December 31, 2019 using a multi-center retrospective database. The data fell into a training set and a validation set at random at a ratio of 8:2. To identify critical clinical variables, the absolute shrinkage and selection operator with the lowest number was employed. Fifteen machine learning models were built to support the diagnosis of T2DKD, and the optimal model was selected in accordance with the area under the receiver operating characteristic curve (AUC) and accuracy. The model was improved with the use of Bayesian Optimization methods. The Shapley Additive explanations (SHAP) approach was used to illustrate prediction findings.

Results

DKD was diagnosed in 1856 (51.2 percent) of the 3624 individuals within the final cohort. As revealed by the SHAP findings, the Categorical Boosting (CatBoost) model achieved the optimal performance 1in the prediction of the risk of T2DKD, with an AUC of 0.86 based on the top 38 characteristics. The SHAP findings suggested that a simplified CatBoost model with an AUC of 0.84 was built in accordance with the top 12 characteristics. The more basic model features consisted of systolic blood pressure (SBP), creatinine (CREA), length of stay (LOS), thrombin time (TT), Age, prothrombin time (PT), platelet large cell ratio (P-LCR), albumin (ALB), glucose (GLU), fibrinogen (FIB-C), red blood cell distribution width-standard deviation (RDW-SD), as well as hemoglobin A1C(HbA1C).

Conclusion

A machine learning-based model for the prediction of the risk of developing T2DKD was built, and its effectiveness was verified. The CatBoost model can contribute to the diagnosis of T2DKD. Clinicians could gain more insights into the outcomes if the ML model is made interpretable.