Improving Precision Diagnosis and Treatment of Diabetes in Low-Resource Settings in Africa Through Structured Clinical Phenotyping: A Narrative Review

Abstract

Precision diabetes care is an evolving evidence-based, personalised approach that uses an individual's clinical, metabolic, and genetic data to accurately classify them into distinct subgroups and tailor diabetes prevention, treatment, and monitoring strategies. The use of genomics data, advanced biomarker testing, and artificial intelligence-driven diagnostics has improved precision diabetes care in high-income countries. This has enabled accurate identification of diabetes subtypes and targeted treatment. Despite the rising burden of diabetes, high rates of undiagnosed cases, and emerging evidence of atypical manifestations of diabetes in Africa, such advancements in precision diabetes care remain unavailable in many low-resource settings, directly impacting treatment outcomes. To improve precision diabetes care in low-resource settings in Africa, we suggest using a structured clinical phenotyping approach based on routinely collected clinical characteristics. In this review, we explore how using a combination of readily available clinical characteristics, such as age at diagnosis, body mass index, clinical presentation, family history of diabetes, and coexisting medical conditions, can help healthcare professionals in Africa to diagnose and treat different diabetes subtypes accurately. By adopting a pragmatic, structured clinical phenotyping approach, African healthcare systems can deliver region-specific, cost-effective precision diabetes care without overreliance on advanced laboratory tests and digital technologies.