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      Cluster analysis of Thai patients with newly diagnosed type 2 diabetes mellitus to predict disease progression and treatment outcomes : A prospective cohort study

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          Abstract

          Introduction

          Type 2 diabetes mellitus (T2D) is highly heterogeneous in disease progression and risk of complications. This study aimed to categorize Thai T2D into subgroups using variables that are commonly available based on routine clinical parameters to predict disease progression and treatment outcomes.

          Research design and methods

          This was a cohort study. Data-driven cluster analysis was performed using a Python program in patients with newly diagnosed T2D (n=721) of the Siriraj Diabetes Registry using five variables (age, body mass index (BMI), glycated hemoglobin (HbA 1c), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C)). Disease progression and risk of diabetic complications among clusters were compared using the Χ 2 and Kruskal-Wallis test. Cox regression and the Kaplan-Meier curve were used to compare the time to diabetic complications and the time to insulin initiation.

          Results

          The mean age was 53.4±11.3 years, 58.9% were women. The median follow-up time was 21.1 months (9.2–35.2). Four clusters were identified: cluster 1 (18.6%): high HbA 1c, low BMI (insulin-deficiency diabetes); cluster 2 (11.8%): high TG, low HDL-C, average age and BMI (metabolic syndrome group); cluster 3 (23.3%): high BMI, low HbA 1c, young age (obesity-related diabetes); cluster 4 (46.3%): older age and low HbA 1c at diagnosis (age-related diabetes). Patients in cluster 1 had the highest prevalence of insulin treatment. Patients in cluster 2 had the highest risk of diabetic kidney disease and diabetic retinopathy. Patients in cluster 4 had the lowest prevalence of diabetic retinopathy, nephropathy, and insulin use.

          Conclusions

          We were able to categorize Thai patients with newly diagnosed T2D into four clusters using five routine clinical parameters. This clustering method can help predict disease progression and risk of diabetic complications similar to previous studies using parameters including insulin resistance and insulin sensitivity markers.

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          Most cited references21

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          A new equation to estimate glomerular filtration rate.

          Andrew Levey, Lesley Stevens, Christopher H Schmid … (2009)
          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables

            Ulf Malmqvist, Leif Groop, Anders Rosengren … (2018)
            Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
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              The integrative biology of type 2 diabetes

              Michael Roden, Gerald I. Shulman (2019)
              Obesity and type 2 diabetes are the most frequent metabolic disorders, but their causes remain largely unclear. Insulin resistance, the common underlying abnormality, results from imbalance between energy intake and expenditure favouring nutrient-storage pathways, which evolved to maximize energy utilization and preserve adequate substrate supply to the brain. Initially, dysfunction of white adipose tissue and circulating metabolites modulate tissue communication and insulin signalling. However, when the energy imbalance is chronic, mechanisms such as inflammatory pathways accelerate these abnormalities. Here we summarize recent studies providing insights into insulin resistance and increased hepatic gluconeogenesis associated with obesity and type 2 diabetes, focusing on data from humans and relevant animal models.
              Article 0 comments Cited 325 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): BMJ Open Diabetes Res Care
                Journal ID (iso-abbrev): BMJ Open Diabetes Res Care
                Journal ID (hwp): bmjdrc
                Journal ID (publisher-id): bmjdrc
                Title: BMJ Open Diabetes Research & Care
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Electronic): 2052-4897
                Publication date Collection: 2022
                Publication date (Electronic): 29 December 2022
                Volume: 10
                Issue: 6
                Electronic Location Identifier: e003145
                Affiliations
                [1 ]departmentSiriraj Diabetes Center of Excellence, Faculty of Medicine Siriraj Hospital , Mahidol University , Bangkok, Thailand
                [2 ]departmentDepartment of Medicine, Faculty of Medicine Siriraj Hospital , Mahidol University , Bangkok, Thailand
                [3 ]departmentImmunology, Faculty of Medicine Siriraj Hospital , Mahidol University , Bangkok, Thailand
                [4 ]departmentDivision of Ambulatory Medicine, Department of Medicine, Faculty of Medicine Siriraj Hospital , Mahidol University , Bangkok, Thailand
                [5 ]departmentDivision of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine Siriraj Hospital , Mahidol University , Bangkok, Thailand
                Author notes
                [Correspondence to ] Dr Nuntakorn Thongtang; nuntakorn@ 123456hotmail.com
                Author information
                http://orcid.org/0000-0001-8496-6790
                http://orcid.org/0000-0002-7103-8466
                Article
                Publisher ID: bmjdrc-2022-003145
                DOI: 10.1136/bmjdrc-2022-003145
                PMC ID: 9806077
                PubMed ID: 36581330
                SO-VID: 842c76c7-f0f3-4279-99f6-93b9f71c4393
                Copyright © © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
                License:

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                Date received : 21 September 2022
                Date accepted : 15 December 2022
                Funding
                Funded by: Siriraj Research Fund, Faculty of Medicine Siriraj Hospital;
                Award ID: R016333047
                Categories
                Subject: Clinical care/Education/Nutrition
                Subject: 1506
                Subject: 1866
                Custom metadata
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                Keywords: type 2 diabetes,classification
                Data availability:
                Keywords: type 2 diabetes, classification

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