Trajectory Patterns of Metabolic Syndrome Severity Score and Risk of Chronic Kidney Diseases.

INTRODUCTION

Despite the reported connection between different combinations of the standard MetS criteria and chronic kidney diseases (CKDs), most data raise significant concerns about its predictive usefulness in clinical settings beyond its components. Metabolic syndrome severity, expressed by the continuous metabolic syndrome severity score (cMetS-S), is a more applicable health metric that may more accurately predict future health outcomes. However, no evidence is known about the association between the trajectory of cMetS-S and the development of CKD.

METHODS

In the population-based Tehran Lipid and Glucose Study, 4,462 participants aged 20-60 years free of CKD at baseline were included and followed at 3-year intervals. We examined the trajectories of cMetS-S over 9 years (1999-2009) using latent growth mixture modeling and subsequent risks of incident CKD 8 years later (2010-2018). The prospective association of identified trajectories with CKD was examined using the Cox proportional hazard model adjusting for age, sex, education, and family history of diabetes, physical activity, obesity (BMI ≥30 kg/m), antihypertensive, and lipid-lowering medication, and baseline fasting plasma glucose in a stepwise manner.

RESULTS

Three cMetS-S trajectory groups of low (28.3%), medium (50.0%), and high (21.7%) were identified during the exposure period. High cMetS-S trajectory pattern was associated with increased risk of CKD adjusting for age, sex, education, smoking, physical activity, baseline estimated glomerular filtration rate, and even after further adjustment for MetS components (1.32; 95% CI: 1.04-1.67). The associated risk remained significant even in normoglycemic, nonobese, and non-hypertensive individuals. Sex-specific subgroup analysis showed that MetS severity score is associated with CKD only in men.

CONCLUSION

The trend of cMetS-S over time is associated with the development of CKD, even in those without major risk factors, for example, obesity, diabetes mellitus, and hypertension. It could be clinically helpful in identifying individuals at elevated risk rather than stating it as a predictive or causative factor. It could be clinically beneficial in identifying and tracking individuals at elevated risk rather than stating it as a predictive or causative factor.