Clustering of Cardiometabolic Abnormalities in A Hypertensive Cohort: A Retrospective Hospital-Based Cross-Sectional Study

Abstract

Background: Hypertension often clusters with other cardiometabolic risk factors, increasing overall cardiovascular risk, especially in Nigeria. Understanding this pattern is essential for improving comprehensive and effective management. Aims: To determine the prevalence of individual cardiometabolic risk factors and their clustering patterns, and to identify factors associated with high metabolic burden in a hypertensive cohort attending a tertiary centre in South-South Nigeria. Study Design: Retrospective hospital-based cross-sectional study. Place and Duration of Study: Medical Outpatient Clinic (MOPC), University of Benin Teaching Hospital (UBTH), Benin City, Edo State, Nigeria. Methodology: Medical records of 826 adult hypertensive patients attending a tertiary centre in South-South Nigeria were reviewed in this retrospective hospital-based cross-sectional study. Bivariate associations were assessed using Chi-square tests, and binary logistic regression identified independent predictors of high metabolic burden, defined as hypertension co-occurring with two or more additional cardiometabolic abnormalities from a defined set of three (dysglycaemia, hypertriglyceridaemia, or low HDL-C). Results: The mean age was 54.93 ± 16.18 years; 56.9% were female and 74.2% were married. Any dyslipidaemia was present in 553 participants (66.9%). Metabolic burden distribution: 443 (53.6%) had hypertension only, 309 (37.4%) had one additional abnormality, and 74 (9.0%) had high metabolic burden. The most common comorbid phenotype was HTN with low HDL-C (19.9%), followed by HTN with dysglycaemia (14.3%). Sex (P = .007), marital status (P = .001), age group (P < .001), blood glucose category (P < .001), and proteinuria severity (P < .001) were significantly associated with high metabolic burden. On logistic regression, male sex (AOR 0.387; 95% CI 0.220–0.681), higher eGFR (AOR 1.013; 95% CI 1.004–1.021), and proteinuria (AOR 2.894; 95% CI 1.757–4.765) were independent predictors; age was not a significant independent predictor after adjustment. Conclusion: Cardiometabolic clustering affects a notable proportion of hypertensive patients at a Nigerian tertiary centre. Male sex, eGFR, and proteinuria independently predict high metabolic burden, underscoring the need for integrated metabolic and renal screening within hypertension management programmes in sub-Saharan Africa.