Advancing chronic kidney disease prevention and management
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Description
Background: The increasing burden of chronic kidney disease (CKD) and its main complications such as cardiovascular disease (CVD) on public health emphasizes the need for improved prevention and management approaches. However, these efforts are hindered by an incomplete understanding of the relationship with cardiovascular health (CVH), CKD risk factors, and the complexity of CKD subtypes. Objective: The overarching goal was to elucidate the interplays between CVH, joint risk factor control, and CKD subtype heterogeneity to enhance prevention and management strategies for CKD and its associated complications. Methods: The three projects were performed in a large prospective cohort-UK Biobank. In the first project, the overall CVH levels were evaluated according to the newly released Life’s essential 8 (LE8) metrics. An adjusted Cox proportional hazard model was used to investigate the association between CVH and CKD. The population attributable risk (PAR) was also calculated. In the second project, we included participants who were obese at baseline and matched normal weight control participants. The degree of joint risk factor control was assessed based on the major CKD risk factors, including blood pressure, low-density lipoprotein cholesterol, hemoglobin A1c, albuminuria, smoking, and physical activity. The Cox proportional hazards models were used to estimate associations between the degree of joint risk factor control and risk of CKD among obese individuals, and to investigate the risk of CKD among obese participants in comparison to matched control subjects according to the degree of risk factor control. Finally, in the third project, unsupervised k-means clustering based on glycated hemoglobin, body mass index, systolic blood pressure and estimated glomerular filtration rate was conducted to characterize the subtypes of CKD patients, and we replicated it in All of Us (AoU) cohort. Then in both cohorts, advanced statistical techniques were employed to evaluate the associations between distinct CKD subtypes and the risk of CVD. Results: In the first project, a higher LE8 score was significantly associated with a lower risk of CKD, with a linear dose-response relationship observed. Specifically, compared to low CVH, moderate and high CVH were associated with a 39% and 57% lower risk of CKD respectively. BMI had the highest PAR among the LE8 metrics. In the second project, in obese participants, a greater degree of joint risk factor control was associated with a significantly reduced risk of CKD compared to matched normal-weight controls. In the third project, the application of k-means clustering identified distinct CKD subtypes based on key variables like BMI, HbA1c, SBP, and eGFR. These subtypes showed varying risks for future CVD development. Conclusion: The findings suggest that an integrated approach, considering both cardiovascular health metrics and individualized risk factor management, can significantly enhance CKD prevention strategies and tailor treatment modalities. This research emphasizes the need for continued exploration of personalized health interventions to effectively combat the growing burden of CKD and its associated complications.