Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population
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 s...
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| Format: | Article |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37494-2 |
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| author | Ina Danquah Isabel Mank Christiane S. Hampe Karlijn A. C. Meeks Charles Agyemang Ellis Owusu-Dabo Liam Smeeth Kerstin Klipstein-Grobusch Silver Bahendeka Joachim Spranger Frank P. Mockenhaupt Matthias B. Schulze Olov Rolandsson |
| author_facet | Ina Danquah Isabel Mank Christiane S. Hampe Karlijn A. C. Meeks Charles Agyemang Ellis Owusu-Dabo Liam Smeeth Kerstin Klipstein-Grobusch Silver Bahendeka Joachim Spranger Frank P. Mockenhaupt Matthias B. Schulze Olov Rolandsson |
| author_sort | Ina Danquah |
| collection | DOAJ |
| description | 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 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. |
| first_indexed | 2024-03-13T00:44:04Z |
| format | Article |
| id | doaj.art-ec1f231656274fa9bf305f84baff4ec1 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T00:44:04Z |
| publishDate | 2023-07-01 |
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| spelling | doaj.art-ec1f231656274fa9bf305f84baff4ec12023-07-09T11:12:00ZengNature PortfolioScientific Reports2045-23222023-07-0113111110.1038/s41598-023-37494-2Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian populationIna Danquah0Isabel Mank1Christiane S. Hampe2Karlijn A. C. Meeks3Charles Agyemang4Ellis Owusu-Dabo5Liam Smeeth6Kerstin Klipstein-Grobusch7Silver Bahendeka8Joachim Spranger9Frank P. Mockenhaupt10Matthias B. Schulze11Olov Rolandsson12Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg UniversityHeidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg UniversityDepartment of Medicine, University of WashingtonCenter for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of HealthDepartment of Public Health, Amsterdam UMC, location AMC, University of AmsterdamKwame Nkrumah University of Science and Technology (KNUST)London School of Hygiene and Tropical Medicine (LSHTM)Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityMKPGMS-Uganda Martyrs UniversityDepartment of Endocrinology and Metabolism, Charité - Universitaetsmedizin Berlin, Corporate Member of Freie Universitaet Berlin, Humboldt-Universitaet zu Berlin, Berlin Institute of HealthInstitute of Tropical Medicine and International Health, Charité - Universitaetsmedizin Berlin, Corporate Member of Freie Universitaet Berlin, Humboldt-Universitaet zu Berlin, and Berlin Institute of HealthDepartment of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-RehbrueckeDepartment of Public Health and Clinical Medicine, Section of Family Medicine, Umeå UniversityAbstract 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 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.https://doi.org/10.1038/s41598-023-37494-2 |
| spellingShingle | Ina Danquah Isabel Mank Christiane S. Hampe Karlijn A. C. Meeks Charles Agyemang Ellis Owusu-Dabo Liam Smeeth Kerstin Klipstein-Grobusch Silver Bahendeka Joachim Spranger Frank P. Mockenhaupt Matthias B. Schulze Olov Rolandsson Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population Scientific Reports |
| title | Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population |
| title_full | Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population |
| title_fullStr | Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population |
| title_full_unstemmed | Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population |
| title_short | Subgroups of adult-onset diabetes: a data-driven cluster analysis in a Ghanaian population |
| title_sort | subgroups of adult onset diabetes a data driven cluster analysis in a ghanaian population |
| url | https://doi.org/10.1038/s41598-023-37494-2 |
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