Evidence map›Paper›PMID 41340051›Full record

ArticleBMC public health2025

Differential impact of general and abdominal obesity on the labor-bone health association: a cross-sectional study in rural China.

Ziqi Li, Zihan Xu, Guohui Shen, Tiannuo Hong, Lu Chen, Yanting Gu, Juan Han, Yanzhi Guo

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Article in BMC public health, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Ziqi LiInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Zihan XuInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Guohui ShenInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Tiannuo HongInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Lu ChenInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Yanting GuInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China.
Juan HanInstitute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing, 100081, China. hanjuan@caas.cn.
Yanzhi GuoChinese Academy of Agricultural Sciences, Beijing, 100081, China. guoyanzhi@caas.cn.

Funding

Agricultural Science and Technology Innovation Program CAAS-ASTIP-2024-IFND
6 · The paper itself

Abstract

backgroundRural populations in low-resource settings often experience high physical labor demands and limited health awareness, increasing their vulnerability to skeletal disorders. While obesity is known to influence bone health, general and central adiposity may exert divergent effects. The interplay between labor exposure, obesity phenotypes, and bone health remains underexplored, particularly in structurally disadvantaged populations.

methodsWe analyzed data from 735 rural adults in Eastern China. Bone health was assessed using radial bone mineral density via dual-energy X-ray absorptiometry. Daily labor time was self-reported and categorized. Bayesian logistic regression estimated the association between labor time and bone health. Parallel mediation analysis assessed indirect effects through BMI and WHtR. Bayesian generalized additive models (GAMs) were used to examine nonlinear relationships, and subgroup analyses evaluated heterogeneity by gender alcohol use, and chronic disease status.

resultsLonger labor time was associated with better bone health (β = 0.42, 95% CI = [0.03, 0.81]). Mediation analysis revealed that BMI had a positive indirect effect (β = 0.27, 95%CI = [0.08, 0.50]), while WHtR exerted a negative effect (β = -0.09, 95%CI = [-0.21, -0.01]). GAMs identified nonlinear associations with diminishing benefits of increasing BMI and a U-shaped pattern for WHtR. Subgroup analysis indicated that the protective effect of BMI was more evident among women (β = 1.58, 95% CI = [1.07, 2.12]) and non-drinkers (β = 1.39, 95% CI = [0.99, 1.80]), while WHtR had stronger negative effects in females.

conclusionsLabor exposure affects bone health through distinct obesity-related pathways, with BMI and WHtR exhibiting opposing and population-specific effects. These findings underscore the need to consider adiposity type, labor conditions, and structural health disparities in designing targeted skeletal health interventions for rural communities.

Indexed as

Bone DensityEmploymentObesityObesity, AbdominalRural PopulationAbsorptiometry, PhotonAdultBayes TheoremBody Mass IndexChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedBayesian mediationBone healthGeneralized additive models (GAMs)Health inequalityObesity phenotypesRural labor

Identifiers

PMID41340051
PMCPMC12781507

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LicenceCC BY-NC-ND
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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.