Key result
Proteinuria linked to ~189% higher odds of high metabolic burden in hypertensive patients.
Why the study?
Hypertension often clusters with other cardiometabolic risk factors, increasing cardiovascular risk especially in Nigeria, making understanding these patterns essential for comprehensive management.
Cross-Sectional (n=826)
No
Cardiometabolic clustering affects a notable proportion of hypertensive patients in Nigeria, with proteinuria, higher eGFR, and male sex independently predicting high metabolic burden.
May warrant sex-specific metabolic evaluation in hypertensives; leaves open adoption pending larger prospective validation.
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.
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Ojeh-Oziegbe et al. (2026) conducted a cross-sectional in Hypertension (n=826). High metabolic burden affected 9.0% of hypertensive patients and was independently predicted by male sex (AOR 0.387), higher eGFR, and proteinuria (AOR 2.894; 95% CI 1.757-4.765).
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