Evidence map›Paper›PMID 42595741›Full record

ArticleDiabetes, obesity & metabolism2026

Age-Specific Associations of Moderate-to-Vigorous Physical Activity With Relative and Absolute Risk of Type 2 Diabetes in UK Biobank Participants.

Qiuyu Feng, Raymond Noordam, Denghui Hu, Gali Albalak, Ko Willems van Dijk, Diana van Heemst

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Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Qiuyu FengDepartment of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0003-0710-4905
Raymond NoordamDepartment of Clinical Epidemiology, Caecilia Institute for Population Health and Data Sciences, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0001-7801-809X
Denghui HuDepartment of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-1908-9637
Gali AlbalakDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0003-4133-5328
Ko Willems van DijkDepartment of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0002-2172-7394
Diana van HeemstDepartment of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands.ORCID https://orcid.org/0000-0001-8526-7748

Funding

China Scholarship Counsil 202306240049Dutch Research Council
6 · The paper itself

Abstract

aimsWe assessed the age-specific associations between moderate-to-vigorous physical activity (MVPA) and incident type 2 diabetes. MATERIALS AND

methodsWe included UK Biobank participants with valid accelerometry data without diabetes mellitus at baseline (N = 87 975; 62.7 years [IQR, 55.6-67.9]; 57% women). We examined whether age, sex and baseline metabolic disease status modified the associations of MVPA volume (lower versus higher) and, in exploratory analyses, MVPA timing (later-day versus earlier-day) with incident type 2 diabetes. Age-specific relative and absolute associations were assessed across 5-year age bins using hazard ratios (HRs) and incidence rate differences (IRDs), and additionally stratified by sex and baseline metabolic disease status.

resultsAssociations of MVPA volume with type 2 diabetes were most consistently modified by age, baseline prediabetes (all p-values for interaction < 0.05), with stronger associations observed among participants younger than the median population age (< 62.7 years, HR = 2.44 [95% CI: 2.05-2.90] versus HR = 1.65 [95% CI: 1.47-1.86]) and participants without prediabetes. Across 5-year age groups, HRs declined with age, whereas IRDs increased in the overall population. No clear effect modification was observed for the association between MVPA timing and type 2 diabetes risk.

conclusionsThe relative association between higher MVPA volume and lower type 2 diabetes risk attenuated with age and was weaker among individuals with prediabetes, whereas absolute risk reductions were larger among older and metabolically higher-risk individuals. These findings support considering both relative and absolute measures when targeting MVPA interventions for type 2 diabetes prevention.

Indexed as

Diabetes Mellitus, Type 2ExerciseAccelerometryAgedAge FactorsBiological Specimen BanksFemaleHumansIncidenceMaleMiddle AgedRisk FactorsUK BiobankUnited Kingdomcohort analysisexercisephysical activity epidemiologytriaxial accelerometertype 2 diabetes

Identifiers

PMID42595741
PMCPMC13646361

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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.