Evidence map›Paper›PMID 39825208›Full record

ArticleScience China. Life sciences2025

Dietary carbohydrate intake and risk of type 2 diabetes: a 16-year prospective cohort study.

Lu Gan, Yi Yang, Bin Zhao, Kai Yu, Kehua Guo, Fang Fang, Zhiguang Zhou, Demetrius Albanes, Jiaqi Huang

Abstract read
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In one paragraph

Article in Science China. Life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lu Gan *National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University; CSU-Sinocare Research Center for Nutrition and Metabolic Health, Xiangya School of Public Health, Central South University, Furong Laboratory, Changsha, 410011, China.
Yi Yang *National Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University; CSU-Sinocare Research Center for Nutrition and Metabolic Health, Xiangya School of Public Health, Central South University, Furong Laboratory, Changsha, 410011, China.
Bin ZhaoNational Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University; CSU-Sinocare Research Center for Nutrition and Metabolic Health, Xiangya School of Public Health, Central South University, Furong Laboratory, Changsha, 410011, China.
Kai YuDivision of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD, 20892, USA.
Kehua GuoSchool of Computer Science and Engineering, Central South University, Changsha, 410083, China.
Fang FangInstitute of Environmental Medicine, Karolinska Institute, Stockholm, 17177, Sweden.
Zhiguang ZhouNational Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, 410011, China. zhouzg@hotmail.com.
Demetrius AlbanesDivision of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, MD, 20892, USA. daa@nih.gov.
Jiaqi HuangNational Clinical Research Center for Metabolic Diseases, Metabolic Syndrome Research Center, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University; CSU-Sinocare Research Center for Nutrition and Metabolic Health, Xiangya School of Public Health, Central South University, Furong Laboratory, Changsha, 410011, China. jiaqi.huang@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite considerable research underscoring the importance of carbohydrate intake in relation to the risk of type 2 diabetes (T2D), a comprehensive assessment of this relationship is currently lacking. We aimed to examine the associations of various types and food sources of dietary carbohydrate intake with the risk of T2D, to evaluate potential effect modification by other factors, including genetic susceptibility, and to explore the potential mediators for such associations. The present study included 161,872 participants of the UK Biobank who were free of prevalent cancer, cardiovascular disease, or diabetes, and had at least one validated 24-h dietary recall assessment. Multivariable-adjusted age-stratified Cox proportional hazard regression models were applied to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for the associations of various types and food sources of dietary carbohydrate intake with risk of T2D. During a median follow-up of 13.6 years, 4,176 incident cases of T2D were identified. In the multivariable-adjusted models, a greater intake of fiber, carbohydrates from whole grains, and carbohydrates from non-starchy vegetables was significantly associated with a lower risk of T2D (highest vs. lowest quantile, HR [95% CI]=0.70 [0.62-0.79], 0.74 [0.67-0.82], and 0.83 [0.75-0.92], respectively, all P for trend <0.005). In contrast, a higher intake of starch and carbohydrate from starchy vegetables was associated with an increased risk of T2D (highest vs. lowest quantile, HR [95% CI]=1.31 [1.16-1.48] and 1.19 [1.09-1.31], respectively, both P for trend <0.005). Replacing one serving of refined grains or starchy vegetables with an equal amount of whole grains or non-starchy vegetables was associated with 4% to 10% lower risk of T2D (all P values <0.001). The observed associations were generally similar across population subgroups, including individuals with different genetic susceptibility to T2D. Mediation analyses of the inverse association between T2D risk and isocaloric substitution of carbohydrates from refined grains with carbohydrate from whole grains demonstrated that 39.6%, 43.4%, 44.0%, 27.8%, and 34.9% were mediated through body mass index, waist-to-hip ratio, glycosylated hemoglobin, high-density lipoprotein cholesterol, and C-reactive protein, respectively. In addition, the inverse association between the isocaloric substitution of carbohydrates from starchy vegetables with carbohydrates from non-starchy vegetables and T2D was partially mediated through high-density lipoprotein cholesterol (15.9%). These findings underscore the importance of dietary modifications of carbohydrates, particularly considering types and food sources of carbohydrate intake, in the primary prevention of T2D.

Indexed as

Diabetes Mellitus, Type 2Dietary CarbohydratesAdultAgedDietDietary FiberFemaleHumansMaleMiddle AgedProportional Hazards ModelsProspective StudiesRisk FactorsUnited KingdomDietary CarbohydratesDietary Fiberdietary carbohydrateprospective cohort studytype 2 diabetesUK Biobank

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.