Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74)

Lirong Hu; Aiji Chen; Gang Tian

doi:10.4093/dmj.2025.0925

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Letter Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74): Lirong Hu¹, Aiji Chen¹, Gang Tian²; Diabetes & Metabolism Journal 2026;50(1):194-195.
DOI: https://doi.org/10.4093/dmj.2025.0925
Published online: December 15, 2025

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¹School of Stomatology, Southwest Medical University, Luzhou, China

²Department of Laboratory Medicine, The Fourth Affiliated Hospital of Southwest Medical University, Sichuan, China

Corresponding author: Gang Tian

Department of Laboratory Medicine, The Fourth Affiliated Hospital of Southwest Medical University, No. 168, Huantianfu New Area Expressway, Meishan Tianfu New Area, Meishan (Sichuan) 620564, China E-mail: tiangang@swmu.edu.cn

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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See the reply "Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74)" in Volume 50 on page 208.

See the article "Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies" on page 1064.

Kim et al. [1] synthesized 12 prospective cohorts and reported a positive association between ultra-processed food (UPF) intake and incident type 2 diabetes mellitus, providing valuable evidence on the potential link between UPF consumption and type 2 diabetes mellitus. This offers a basis for future dietary guidance, while several methodological issues and data inconsistencies deserve careful discussion to ensure the robustness and generalizability of the findings.

First, the diagram indicates ‘records identified, n=569,’ while listing 483 records from PubMed, 685 from Web of Science, and 1,080 from Embase (totaling 2,248). If 2,248 represents the pre-deduplication yield, removing 89 duplicates would result in 2,159 unique records, of which only 405 were screened—suggesting 1,754 citations were excluded without explanation. Conversely, if 569 is the actual number identified after merging, it does not align with the database-specific totals provided. This discrepancy in numbers obscures the screening denominator, hinders independent replication of the search, and raises concerns about potential pre-screening filters. It is recommended to publish corrected figures, a deduplicated bibliography, and detailed search strings in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure reproducibility [2].

Second, the perceived ‘nonlinearity’ observed in the relationship between UPF consumption and diabetes risk may stem from sparse data points at extreme intake levels. Specifically, Fig. 2D in original article shows that although all studies covered a range of >300 g/day, the data points were sparse (each study had only 1 to 2 extreme value intervals), which could affect the stability of the curve. The amalgamation of diverse exposure units (e.g., %, g/day, servings/day) across multiple cohorts further complicates interpretation. To enhance the reliability of the analysis, it is suggested to define spline knots a priori, confine the interpretation to the range of observed values, and employ a one-stage random-effects spline model that accounts for within-study covariance. Harmonizing exposure metrics (e.g., g/1,000 kcal or % energy) is also recommended to minimize the influence of unit-specific curvature. These methodological adjustments offer a more robust evaluation of nonlinearity and help mitigate the risk of misinterpreting threshold effects [3].

Third, it is likely that exposure misclassification occurred since the majority of cohorts relied on food-frequency questionnaires (FFQs) that were not tailored to capture processing levels. The translation of FFQ line-items into NOVA classes involves making assumptions and seeking expert opinions, which may differ, especially for breads, cereals, and mixed dishes [4]. To address residual confounding arising from dietary quantity and measurement inaccuracies, we also propose aligning exposure measurements on a standardized scale (e.g., grams per 1,000 kcal or as a percentage of energy) and implementing regression calibration methods across all cohorts [5]. Besides, the substantial heterogeneity (I²=73.3%) coupled with moderate study numbers suggests that Hartung-Knapp-Sidik-Jonkman adjustment would provide more conservative confidence intervals than the employed DerSimonian-Laird method [6]. The body mass index-mediated pathway, evidenced by attenuated but persistent associations after adjustment (relative risk, 1.48 to 1.29), aligns with an inpatient randomized controlled feeding trial demonstrating UPF-induced hyperphagia [7]. Heterogeneous outcome ascertainment across cohorts, ranging from self-report to biochemical verification, warrants stratified sensitivity analyses to assess potential misclassification bias [8].

In conclusion, this meta-analysis emphasizes a credible and policy-relevant connection between UPF exposure and the risk of diabetes, offering a significant indication for public health guidance. Improving exposure harmonization and validation, rectifying flow-diagram inconsistencies, and employing robust meta-analytic estimators will enhance the robustness and generalizability of the conclusions.

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CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported

REFERENCES

1. Kim Y, Cho Y, Kim JE, Lee DH, Oh H. Ultra-processed food intake and risk of type 2 diabetes mellitus: a dose-response meta-analysis of prospective studies. Diabetes Metab J 2025;49:1064-74.Article PubMed PMC PDF
2. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71.Article PubMed PMC
3. Crippa A, Discacciati A, Bottai M, Spiegelman D, Orsini N. One-stage dose-response meta-analysis for aggregated data. Stat Methods Med Res 2019;28:1579-96.Article PubMed PDF
4. Khandpur N, Rossato S, Drouin-Chartier JP, Du M, Steele EM, Sampson L, et al. Categorising ultra-processed foods in large-scale cohort studies: evidence from the Nurses’ Health Studies, the Health Professionals Follow-up Study, and the Growing Up Today Study. J Nutr Sci 2021;10:e77.Article PubMed PMC
5. Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst 2011;103:1086-92.Article PubMed PMC
6. IntHout J, Ioannidis JP, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method. BMC Med Res Methodol 2014;14:25.Article PubMed PMC PDF
7. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, et al. Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab 2019;30:67-77.e3.Article PubMed PMC
8. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 2004;57:1096-103.Article PubMed

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Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74)

Diabetes Metab J. 2026;50(1):194-195. Published online December 15, 2025

DOI: https://doi.org/10.4093/dmj.2025.0925

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Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74)

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Hu L, Chen A, Tian G. Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies (Diabetes Metab J 2025;49:1064-74). Diabetes Metab J. 2026;50(1):194-195.

DOI: https://doi.org/10.4093/dmj.2025.0925.

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