Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach

• Published in: Nutrition & diabetes Vol. 12; no. 1; pp. 27 - 11
• Main Authors: Bej, Saptarshi, Sarkar, Jit, Biswas, Saikat, Mitra, Pabitra, Chakrabarti, Partha, Wolkenhauer, Olaf
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27.05.2022; Nature Publishing Group
• Subjects: 692/308/174; 692/699/2743/137/773; Biology; Clinical Nutrition; Datasets; Demography; Diabetes; Diabetes Mellitus, Type 2 - epidemiology; Diet; Epidemiology; Health surveys; Humans; India - epidemiology; Internal Medicine; Lifestyles; Machine learning; Medicine; Medicine & Public Health; Metabolic Diseases; Nutrition; Obesity; Patients; Similarity measures; Sociodemographics; Unsupervised Machine Learning; India
• ISSN: 2044-4052; 2044-4052
• DOI: 10.1038/s41387-022-00206-2

Background Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the detection of T2DM clusters in epidemiological data, specifically analysing the National Family Health Survey-4 (NFHS-4) dataset from India containing a wide spectrum of features, including medical history, dietary and addiction habits, socio-economic and lifestyle patterns of 10,125 T2DM patients. Methods Epidemiological data provide challenges for analysis due to the diverse types of features in it. In this case, applying the state-of-the-art dimension reduction tool UMAP conventionally was found to be ineffective for the NFHS-4 dataset, which contains diverse feature types. We implemented a distributed clustering workflow combining different similarity measure settings of UMAP, for clustering continuous, ordinal and nominal features separately. We integrated the reduced dimensions from each feature-type-distributed clustering to obtain interpretable and unbiased clustering of the data. Results Our analysis reveals four significant clusters, with two of them comprising mainly of non-obese T2DM patients. These non-obese clusters have lower mean age and majorly comprises of rural residents. Surprisingly, one of the obese clusters had 90% of the T2DM patients practising a non-vegetarian diet though they did not show an increased intake of plant-based protein-rich foods. Conclusions From a methodological perspective, we show that for diverse data types, frequent in epidemiological datasets, feature-type-distributed clustering using UMAP is effective as opposed to the conventional use of the UMAP algorithm. The application of UMAP-based clustering workflow for this type of dataset is novel in itself. Our findings demonstrate the presence of heterogeneity among Indian T2DM patients with regard to socio-demography and dietary patterns. From our analysis, we conclude that the existence of significant non-obese T2DM sub-populations characterized by younger age groups and economic disadvantage raises the need for different screening criteria for T2DM among rural Indian residents.