Evidence map›Paper›PMID 42908643›Full record

SynthesisFrontiers in endocrinology2026

Comparative effects of different exercise modalities and doses on lipid metabolism in patients with type 2 diabetes mellitus: a systematic review with Bayesian dose-response network meta-analysis.

Jing Liu, Runjie Shi, Wenhui Fu, Ming Zhang

Abstract readSystematic ReviewNetwork Meta-AnalysisComparative Study
In one paragraph

Synthesis in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Jing LiuSchool of Wushu and Arts, Nanjing Sport Institute, Nanjing, China.
Runjie ShiNanyou Campus, Primary School Division, Xianlin School, High School Affiliated to Nanjing Normal University, Nanjing, China.
Wenhui FuSchool of Wushu and Arts, Nanjing Sport Institute, Nanjing, China.
Ming ZhangSchool of Wushu and Arts, Nanjing Sport Institute, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This review aimed to determine whether the effects of structured exercise on triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) differed according to exercise modality and weekly training volume in adults with type 2 diabetes mellitus (T2DM). Methods: Seven electronic databases were searched from inception to 18 December 2025. Randomized controlled trials were eligible when they enrolled adults with T2DM, evaluated a structured exercise intervention, and reported TG or LDL-C outcomes. Comparative effects across exercise categories were then estimated with a Bayesian random-effects network model. Weekly exercise volume was converted to MET-min/week and further examined using model-based dose-response network meta-analysis. Results: Seventy RCTs comprising 5,317 adults with T2DM met the eligibility criteria. The TG analysis was based on 68 trials and 5,216 participants, whereas the LDL-C analysis included 61 trials and 4,473 participants. In the Bayesian network model, all active exercise modalities were associated with greater reductions in TG and LDL-C than the non-exercise control condition. Among the included modalities, high-intensity interval training (HIIT) produced the most favorable estimate for TG reduction (MD = -0.48 mmol/L, 95% CrI: -0.67, -0.30; SUCRA = 92.53%), while combined aerobic and resistance exercise (CE) showed the highest ranking for LDL-C improvement (MD = -0.46 mmol/L, 95% CrI: -0.63, -0.29; SUCRA = 85.45%). Dose-response modeling indicated that the lipid-lowering effects did not increase in a strictly linear manner across weekly exercise volume. The 95% CrIs remained below zero at approximately 1100-1500 MET-min/week for TG and 950-1500 MET-min/week for LDL-C. Conclusions: The lipid response to exercise varied according to both training modality and weekly volume. HIIT generated the most favorable estimate for TG, whereas combined exercise showed the leading estimate for LDL-C. The identified dose intervals may provide quantitative reference points for exercise planning, but they should not be treated as fixed prescriptions because certainty of evidence, physical capacity, adherence, treatment background, and safety differ among patients. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261412956, identifier CRD420261412956.

Indexed as

Diabetes Mellitus, Type 2ExerciseExercise TherapyLipid MetabolismBayes TheoremCholesterol, LDLHumansRandomized Controlled Trials as TopicTriglyceridesCholesterol, LDLTriglyceridesBayesian network meta-analysisdose-response relationshipexerciselipid metabolismtype 2 diabetes mellitus

Identifiers

PMID42908643
PMCPMC13683165

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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.