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Metabolic Risk Predictors Associated With the Onset of Vascular Complications Among Patients With Type 2 Diabetes in Zambia: A Retrospective Statistical Modeling Study

Gunet Mwalungali Welani Chilengwe J. Mwaba E. Kazonga Richard Mutemwa M. Kampamba Larry L. Mweetwa A. Hamachila Christine Mutati Soddy Munsaka Jimmy Hangomma Rachel Milomba Mordecai Malambo Chanda Chisunka Nthele Mzaza Patience Buumba Chota Bwalya Pamela Mwansa

Sep 2026 · Health Science Reports · Vol 9 · 0 citations · 28 references

Medicine

Abstract

ABSTRACT Background and Aims Diabetes mellitus contributes significantly to the global disease burden, affecting approximately 537 million adults worldwide, with over 75% residing in low and middle‐income countries (LMICs). In Zambia, evidence on metabolic predictors of vascular complications among patients with Type 2 Diabetes Mellitus (T2DM) is limited. This study aimed to estimate the incidence of vascular complications and identify metabolic and clinical factors associated with their occurrence among newly diagnosed T2DM patients. Methods A retrospective cohort study was conducted among 315 adults aged ≥ 18 years, randomly selected from the University Teaching Hospital Diabetes Clinic registry in Zambia. Participants diagnosed between January 2013 and December 2016 were followed up until December 2023. Cox proportional hazards regression models estimated hazard ratios (HRs) at 95% confidence intervals, with best‐fitting model identified using Akaike Information Criterion. Statistical significance was set at p < 0.05. Results Of 315 participants, 52.1% were female, with a mean age of 49.6 ± 13.5 years. Increasing age (HR = 1.047, 95% CI: 1.02–1.07, p = 0.001), hypertension (HR = 2.196, 95% CI: 1.58–3.03, p < 0.001), elevated cholesterol (HR = 1.533, 95% CI: 1.00–2.33, p = 0.048), and poor glycaemic control (HR = 10.047, 95% CI: 2.04–49.31, p = 0.004) were predictors. Overweight and obesity increased risk (HR = 2.035, 95% CI: 1.24–3.33, p = 0.005; HR = 1.812, 95% CI: 1.13–2.90, p = 0.005). Age‐glycated hemoglobin interaction (HR = 1.006, 95% CI: 0.934–0.989, p = 0.007) indicated amplified poor glycaemic control effect with advancing age. Conclusion Advancing age, hypertension, dyslipidaemia, obesity, and poor glycaemic control were key predictors. Age‐HbA1c interaction underscores need for age‐sensitive management. Integrating metabolic risk prediction could enhance early detection, risk stratification, and prevention, reducing diabetes burden in Zambia and LMICs.

DOI PubMed Semantic Scholar

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