Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Distinct dietary patterns across type 2 diabetes subtypes: Insights from the SMART2D cohort

European Journal of Clinical Nutrition (2026)Cite this article

Subjects

Abstract

Background/objectives

We previously identified three validated clinical subtypes of type 2 diabetes (T2D) in a multi-ethnic Southeast Asian cohort, but their dietary patterns remained uncharacterised. This cross-sectional analysis explored whether dietary patterns differ across T2D subtypes and examined subtype-specific associations with diabetes-related comorbidities.

Subjects/methods

Dietary patterns were derived using factor analysis of 46 food groups from 1007 T2D adults (age:61 ± 11 years, 52.7% male) who completed a 125-item food frequency questionnaire. T2D subtypes including mild age-related diabetes with insulin insufficiency (MARD-II), mild obesity-related diabetes (MOD), and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) were classified using the nearest centroid approach. Each participant’s predominant dietary pattern was defined by their highest factor score. Associations between T2D subtypes and dietary pattern scores, and between predominant dietary patterns and comorbidities within each subtype, were assessed using multivariable regression analysis.

Results

Three patterns were identified: meat, fast food & eat-out; sugar-laden food & drinks; and plant-based & dairy. Among MARD-II, 40.0% had a predominant plant-based & dairy pattern, whereas both MOD and SIRD-RII had predominant sugar-laden food & drinks (~38%), followed by meat, fast food & eat-out (~31%) patterns. Compared with MARD-II, MOD and SIRD-RII were positively associated with meat, fast food & eat-out pattern and inversely with plant-based & dairy pattern (all P < 0.001). Predominant sugar-laden food & drinks and meat, fast food & eat-out patterns were differentially associated with comorbidities, particularly in MOD and SIRD-RII.

Conclusions

Our findings suggest distinct dietary intake/patterns and subtype-specific associations with comorbidities in multi-ethnic Southeast Asians with T2D.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Flow diagram of patient selection.
The alternative text for this image may have been generated using AI.
Fig. 2: Distribution of predominant dietary patterns by diabetes clinical subtypes.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. International Diabetes Federation, IDF Diabetes Atlas, 11th edn., Brussels, Belgium: 2025

  2. O’Hearn M, Lara-Castor L, Cudhea F, Miller V, Reedy J, Shi P, et al. Incident type 2 diabetes attributable to suboptimal diet in 184 countries. Nat Med. 2023;29:982–95. https://doi.org/10.1038/s41591-023-02278-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ahlqvist E, Storm P, Karajamaki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6:361–9. https://doi.org/10.1016/S2213-8587(18)30051-2.

    Article  PubMed  Google Scholar 

  4. Zaharia OP, Strassburger K, Strom A, Bonhof GJ, Karusheva Y, Antoniou S, et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 2019;7:684–94. https://doi.org/10.1016/S2213-8587(19)30187-1.

    Article  PubMed  Google Scholar 

  5. Wang J, Gao B, Wang J, Liu W, Yuan W, Chai Y, et al. Identifying subtypes of type 2 diabetes mellitus based on real-world electronic medical record data in China. Diabetes Res Clin Pract. 2024;217:111872. https://doi.org/10.1016/j.diabres.2024.111872.

    Article  PubMed  Google Scholar 

  6. Fedotkina O, Sulaieva O, Ozgumus T, Cherviakova L, Khalimon N, Svietleisha T, et al. Novel Reclassification of Adult Diabetes Is Useful to Distinguish Stages of beta-Cell Function Linked to the Risk of Vascular Complications: The DOLCE Study From Northern Ukraine. Front Genet. 2021;12:637945. https://doi.org/10.3389/fgene.2021.637945.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang J, Liu JJ, Gurung RL, Liu S, Lee J, Yiamunaa M, et al. Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes. Diabetologia. 2022;65:2146–56. https://doi.org/10.1007/s00125-022-05741-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Weber KS, Schlesinger S, Lang A, Strassburger K, Maalmi H, Zhu A, et al. Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes. Nutr Metab Cardiovasc Dis. 2024;34:911–24. https://doi.org/10.1016/j.numecd.2023.12.026.

    Article  PubMed  Google Scholar 

  9. Weber KS, Schlesinger S, Goletzke J, Strassburger K, Zaharia OP, Trenkamp S, et al. Associations of carbohydrate quality and cardiovascular risk factors vary among diabetes subtypes. Cardiovasc Diabetol. 2025;24:53. https://doi.org/10.1186/s12933-025-02580-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Moh MC, Sum CF, Tavintharan S, Ang K, Kwan PY, Lee SBM, et al. Gain in adiposity over 3 years is associated with progressive renal decline in multi-ethnic South-east Asians with type 2 diabetes. J Diabetes. 2019;11:316–25. https://doi.org/10.1111/1753-0407.12848.

    Article  CAS  PubMed  Google Scholar 

  11. Health Promotion Board Singapore. Report of the National nutrition survey 2010. Available: https://www.hpb.gov.sg/docs/default-source/pdf/nns-2010-report.pdf?sfvrsn=18e3f172_2 (accessed on 22 Sep 2024).

  12. Chia AR, de Seymour JV, Colega M, Chen LW, Chan YH, Aris IM, et al. A vegetable, fruit, and white rice dietary pattern during pregnancy is associated with a lower risk of preterm birth and larger birth size in a multiethnic Asian cohort: the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort study. Am J Clin Nutr. 2016;104:1416–23. https://doi.org/10.3945/ajcn.116.133892.

    Article  CAS  PubMed  Google Scholar 

  13. Willett W, Stampfer MJ. Total energy intake: implications for epidemiologic analyses. Am J Epidemiol. 1986;124:17–27. https://doi.org/10.1093/oxfordjournals.aje.a114366.

    Article  CAS  PubMed  Google Scholar 

  14. Burt VL, Cutler JA, Higgins M, Horan MJ, Labarthe D, Whelton P, et al. Trends in the prevalence, awareness, treatment, and control of hypertension in the adult US population. Data from the health examination surveys, 1960 to 1991. Hypertension. 1995;26:0–69. https://doi.org/10.1161/01.hyp.26.1.60.

    Article  CAS  Google Scholar 

  15. Ko SH, Han KD, Park YM, Yun JS, Kim K, Bae JH, et al. Diabetes Mellitus in the Elderly Adults in Korea: Based on Data from the Korea National Health and Nutrition Examination Survey 2019 to 2020. Diabetes Metab J. 2023;47:643–52. https://doi.org/10.4093/dmj.2023.0041.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42:503–8. https://doi.org/10.1016/j.dld.2009.08.002.

    Article  CAS  PubMed  Google Scholar 

  17. de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J. Temporal trends in the prevalence of diabetic kidney disease in the United States. JAMA. 2011;305:2532–9. https://doi.org/10.1001/jama.2011.861.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Schulze MB, Hoffmann K, Kroke A, Boeing H. An approach to construct simplified measures of dietary patterns from exploratory factor analysis. Br J Nutr. 2003;89:409–19. https://doi.org/10.1079/BJN2002778.

    Article  CAS  PubMed  Google Scholar 

  19. Hung Y, Wijnhoven HAH, Visser M, Verbeke W. Appetite and Protein Intake Strata of Older Adults in the European Union: Socio-Demographic and Health Characteristics, Diet-Related and Physical Activity Behaviours. Nutrients. 2019;11:777. https://doi.org/10.3390/nu11040777.

    Article  PubMed  PubMed Central  Google Scholar 

  20. McNaughton SA, Mishra GD, Stephen AM, Wadsworth ME. Dietary patterns throughout adult life are associated with body mass index, waist circumference, blood pressure, and red cell folate. J Nutr. 2007;137:99–105. https://doi.org/10.1093/jn/137.1.99.

    Article  CAS  PubMed  Google Scholar 

  21. Mishra GD, McNaughton SA, Bramwell GD, Wadsworth ME. Longitudinal changes in dietary patterns during adult life. Br J Nutr. 2006;96:735–44. https://doi.org/10.1079/BJN20061871.

    Article  CAS  PubMed  Google Scholar 

  22. Yilmaz A, Weech M, Bountziouka V, Jackson KG, Lovegrove JA. Association between empirically driven dietary patterns and cardiometabolic disease risk factors: a cross-sectional analysis in disease-free adults. Nutr Metab. 2025;22:73. https://doi.org/10.1186/s12986-025-00965-6.

    Article  CAS  Google Scholar 

  23. Baum JI, Kim IY, Wolfe RR. Protein Consumption and the Elderly: What Is the Optimal Level of Intake? Nutrients. 2016;8:359. https://doi.org/10.3390/nu8060359.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chernoff R. Protein and older adults. J Am Coll Nutr. 2004;23:627S–30S. https://doi.org/10.1080/07315724.2004.10719434.

    Article  CAS  PubMed  Google Scholar 

  25. Shan R, Duan W, Liu L, Qi J, Gao J, Zhang Y, et al. Low-Carbohydrate, High-Protein, High-Fat Diets Rich in Livestock, Poultry and Their Products Predict Impending Risk of Type 2 Diabetes in Chinese Individuals that Exceed Their Calculated Caloric Requirement. Nutrients. 2018;10:77. https://doi.org/10.3390/nu10010077.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Asahara SI, Inoue H, Kido Y. Regulation of Pancreatic beta-Cell Mass by Gene-Environment Interaction. Diabetes Metab J. 2022;46:38–48. https://doi.org/10.4093/dmj.2021.0045.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Biondi G, Marrano N, Borrelli A, Rella M, Palma G, Calderoni I, et al. Adipose Tissue Secretion Pattern Influences beta-Cell Wellness in the Transition from Obesity to Type 2 Diabetes. Int J Mol Sci. 2022;23:5522. https://doi.org/10.3390/ijms23105522.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the staff from the Singapore Clinical Research Institute (SCRI) for their contribution to the study protocol and database design.

Funding

The SMART2D cohort is supported by the Singapore Ministry of Health’s National Medical Research Council CS-IRG (MOH-001704-00). SC Lim is supported by the Singapore Ministry of Health’s National Medical Research Council Clinician Scientist Award (MOH-001704-00 and MOH-001688-00).

Author information

Author notes

  1. These authors contributed equally: Mei Chung Moh, Tsz Kiu Kwan.

Authors and Affiliations

  1. Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Singapore

    Mei Chung Moh, Tsz Kiu Kwan, Chalani U. Ubeynarayana, Huili Zheng, Jian-Jun Liu, Keven Ang, Sylvia Liu, Tavintharan Subramaniam & Su Chi Lim

  2. Institute for Human Development and Potential (IHDP), A*STAR, Singapore, Singapore

    Jun Shi Lai, Marjorelee T. Colega & Mary Foong-Fong Chong

  3. National Healthcare Group Polyclinics, Singapore, Singapore

    Wern Ee Tang & Ziliang Lim

  4. Diabetes Centre, Admiralty Medical Centre, Khoo Teck Puat Hospital, Singapore, Singapore

    Tavintharan Subramaniam, Chee Fang Sum & Su Chi Lim

  5. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Mary Foong-Fong Chong & Su Chi Lim

  6. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

    Su Chi Lim

Authors
  1. Mei Chung Moh
    View author publications

    Search author on:PubMed Google Scholar

  2. Tsz Kiu Kwan
    View author publications

    Search author on:PubMed Google Scholar

  3. Jun Shi Lai
    View author publications

    Search author on:PubMed Google Scholar

  4. Marjorelee T. Colega
    View author publications

    Search author on:PubMed Google Scholar

  5. Chalani U. Ubeynarayana
    View author publications

    Search author on:PubMed Google Scholar

  6. Huili Zheng
    View author publications

    Search author on:PubMed Google Scholar

  7. Jian-Jun Liu
    View author publications

    Search author on:PubMed Google Scholar

  8. Keven Ang
    View author publications

    Search author on:PubMed Google Scholar

  9. Sylvia Liu
    View author publications

    Search author on:PubMed Google Scholar

  10. Wern Ee Tang
    View author publications

    Search author on:PubMed Google Scholar

  11. Ziliang Lim
    View author publications

    Search author on:PubMed Google Scholar

  12. Tavintharan Subramaniam
    View author publications

    Search author on:PubMed Google Scholar

  13. Chee Fang Sum
    View author publications

    Search author on:PubMed Google Scholar

  14. Mary Foong-Fong Chong
    View author publications

    Search author on:PubMed Google Scholar

  15. Su Chi Lim
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization and methodology: WET, ZL, TS, CFS, MFFC, and SCL. Project administration and data curation: KA, and TKK. Formal analysis and visualisation: MMC, TKK, KA, CUU, HZ, JJL, and SL. Supervision: SCL, LJS, MTC, and MFFC. Writing – original draft: MMC. Writing – review & editing: TKK, JJL, MFFC and SCL. Funding acquisition: SCL. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Su Chi Lim.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

This study was approved by the National Healthcare Group Domain Specific Review Board, and was conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments (ECOS Reference: 2024/3805). All participants provided written informed consent prior to their participation in the study.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moh, M.C., Kwan, T.K., Lai, J.S. et al. Distinct dietary patterns across type 2 diabetes subtypes: Insights from the SMART2D cohort. Eur J Clin Nutr (2026). https://doi.org/10.1038/s41430-026-01753-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41430-026-01753-y

Search

Advanced search

Quick links