Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population
Danquah, Ina; Mank, Isabel; Hampe, Christiane S; Meeks, Karlijn A C; Agyemang, Charles; Owusu-Dabo, Ellis; Smeeth, Liam; Klipstein-Grobusch, Kerstin; Bahendeka, Silver; Spranger, Joachim; Mockenhaupt, Frank P; Schulze, Matthias B; Rolandsson, Olov
(2023) Scientific Reports, volume 13, issue 1
(Article)
Abstract
Adult-onset diabetes mellitus (here: aDM) is not a uniform disease entity. In European populations, five diabetes subgroups have been identified by cluster analysis using simple clinical variables; these may elucidate diabetes aetiology and disease prognosis. We aimed at reproducing these subgroups among Ghanaians with aDM, and establishing their importance for
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diabetic complications in different health system contexts. We used data of 541 Ghanaians with aDM (age: 25-70 years; male sex: 44%) from the multi-center, cross-sectional Research on Obesity and Diabetes among African Migrants (RODAM) Study. Adult-onset DM was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, documented use of glucose-lowering medication or self-reported diabetes, and age of onset ≥ 18 years. We derived subgroups by cluster analysis using (i) a previously published set of variables: age at diabetes onset, HbA1c, body mass index, HOMA-beta, HOMA-IR, positivity of glutamic acid decarboxylase autoantibodies (GAD65Ab), and (ii) Ghana-specific variables: age at onset, waist circumference, FPG, and fasting insulin. For each subgroup, we calculated the clinical, treatment-related and morphometric characteristics, and the proportions of objectively measured and self-reported diabetic complications. We reproduced the five subgroups: cluster 1 (obesity-related, 73%) and cluster 5 (insulin-resistant, 5%) with no dominant diabetic complication patterns; cluster 2 (age-related, 10%) characterized by the highest proportions of coronary artery disease (CAD, 18%) and stroke (13%); cluster 3 (autoimmune-related, 5%) showing the highest proportions of kidney dysfunction (40%) and peripheral artery disease (PAD, 14%); and cluster 4 (insulin-deficient, 7%) characterized by the highest proportion of retinopathy (14%). The second approach yielded four subgroups: obesity- and age-related (68%) characterized by the highest proportion of CAD (9%); body fat-related and insulin-resistant (18%) showing the highest proportions of PAD (6%) and stroke (5%); malnutrition-related (8%) exhibiting the lowest mean waist circumference and the highest proportion of retinopathy (20%); and ketosis-prone (6%) with the highest proportion of kidney dysfunction (30%) and urinary ketones (6%). With the same set of clinical variables, the previously published aDM subgroups can largely be reproduced by cluster analysis in this Ghanaian population. This method may generate in-depth understanding of the aetiology and prognosis of aDM, particularly when choosing variables that are clinically relevant for the target population.
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Keywords: Adolescent, Adult, Aged, Cluster Analysis, Cross-Sectional Studies, Diabetes Complications/complications, Diabetes Mellitus, Type 2/complications, Ghana/epidemiology, Humans, Insulin, Male, Middle Aged, Obesity/complications, Retinal Diseases/complications, Stroke/complications, Journal Article, Research Support, Non-U.S. Gov't
ISSN: 2045-2322
Publisher: Nature Publishing Group
Note: Funding Information: Open Access funding enabled and organized by Projekt DEAL. This study was supported by the European Commission under the 7th Framework Program (Grant Number 278901), and by Region Västerbotten, Umeå University, Sweden to OR. KACM is supported by the Intramural Research Programme of the National Institutes of Health in the Centre for Research on Genomics and Global Health (CRGGH). CA is supported by the European Research Council Consolidation (grant number 772244). Ina Danquah is supported by the Robert Bosch Foundation (grant number 01000035-002). Publisher Copyright: © 2023, The Author(s).
(Peer reviewed)