Introduction: Cardio-Kidney-Metabolic (CKM) syndrome represents a constellation of interconnected metabolic and renal abnormalities, including hypertension, diabetes, dyslipidemia, and chronic kidney disease (CKD), all of which contribute to increased cardiovascular disease (CVD) risk.While CKM syndrome has been proposed as a holistic framework for risk stratification, the relative impact of each individual component on major adverse cardiovascular events (MACEs) has not been well defined.Methods: We analyzed data from 181,691 participants in the UK Biobank without overlapping metabolic abnormalities, selecting those with only one of the four CKM components: hypertension, type 2 diabetes mellitus (T2DM), dyslipidemia, or CKD.Individuals with multiple CKM components were excluded to isolate the independent association of each factor.Incident MACE was defined as a composite of myocardial infarction, stroke, heart failure, or cardiovascular death.Cox proportional hazards models were used to estimate hazard ratios (HRs) for MACE, adjusting for age, sex, race, smoking status, alcohol consumption, household income, and physical activity (METs).Bonferroni-adjusted pairwise comparisons were performed to examine relative risk differences between CKM components.Results: During 2,016,204 person-years of follow-up, 26,263 MACE events occurred.The incidence rates per 1,000 person-years were 17.9, 9.4, 9.4, 8.2, and 6.3 for hypertension, diabetes, dyslipidemia, CKD, and the reference group with no components, respectively, with hypertension showing the highest incidence rate.In adjusted models, hypertension conferred the highest risk (HR 1.69, 95% CI 1.62-1.77),followed by CKD (HR 1.24), dyslipidemia (HR 1.21), and diabetes (HR 1.17).Bonferroni contrasts confirmed hypertension's significantly greater risk compared to the others (p < 0.001), while risk differences among the remaining components were not statistically significant.Conclusion: Hypertension emerged as the strongest individual driver of MACEs among CKM components.These findings support the prioritization of blood pressure control in CVD prevention strategies targeting CKM populations.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
Chau et al. (Wed,) studied this question.