Evidence map›Paper›PMID 42566570›Full record

ArticleMedicine2026

Genetic evidence for behavioral preference factors on fatigue: A Mendelian randomization study of chronotype, morning alertness, and outdoor exposure.

Tao Luo, Fen Zhang, Hui Xue, Lanqing Wang

Abstract read
In one paragraph

Article in Medicine, 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

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Tao LuoDepartment of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Fen ZhangDepartment of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Hui XueZhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Lanqing WangZhongnan Hospital of Wuhan University, Wuhan, 430071, China.ORCID 0009-0004-8460-7404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fatigue is a multifactorial condition influenced by environmental, behavioral, genetic, and disease-related factors. While observational studies have identified key contributors like circadian disruption, sleep disturbances, and genetic predisposition, the causal relationships remain unclear. Mendelian randomization (MR) offers a robust approach to overcome limitations of traditional studies and establish causal links between modifiable behavioral preferences and fatigue. This study employed univariate and multivariate MR (MVMR) analyses using publicly available genome-wide association study (GWAS) summary statistics to investigate the potential causal relationships between these behavioral preference factors and the risk of fatigue. We obtained GWAS summary statistics for relevant variables from the Neale Lab and MRC Integrated Epidemiology Unit (MRC-IEU) databases. After systematic screening of multiple domains (internal microenvironment, indoor environment parameters, and natural environment characteristics), the inverse variance weighted (IVW) method served as the primary analysis, complemented by sensitivity analysis (heterogeneity test, pleiotropy analysis, leave-one-out analysis, and MR-PRESSO) to evaluate result robustness. Using GWAS data from 32 traits (1645,048 participants), we identified three significant behavioral preference-related determinants of fatigue: chronotype, ease of getting up in the morning, and time spent outdoors in summer. The MR results demonstrated: Protective effects against fatigue associated with greater ease of getting up in the morning (OR = 0.991, 95%CI 0.987-0.995; P < .001) and longer summer outdoor exposure (OR = 0.996, 95%CI 0.992-1.000; P = .030); Elevated fatigue risk,linked to evening chronotype (OR = 1.003, 95%CI 1.001-1.005; P = .013). MVMR analysis showed that after jointly incorporating variables, the impact of ease of getting up in the morning on fatigue remained significant (OR = 0.987, 95%CI: 0.980-0.995, P = .002). Sensitivity analyses confirmed the robustness of these findings: although significant heterogeneity was detected for ease of getting up in the morning (Cochran's Q test P < .05), no evidence of horizontal pleiotropy (MR-Egger intercept P > .05) or outlier SNPs (MR-PRESSO) was found, and results were consistent across multiple MR methods. These findings provide genetic evidence supporting causal relationships between modifiable behavioral preference factors and fatigue. Specifically, greater ease of getting up in the morning and longer summer outdoor exposure may reduce fatigue risk, while evening chronotype increases susceptibility.

Indexed as

Circadian RhythmFatigueChronotypeGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMendelian Randomization AnalysisPolymorphism, Single NucleotideSleepbehavioral preference factorschronotypefatigueMendelian randomizationoutdoor exposure

Identifiers

PMID42566570
PMCPMC13456699

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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.