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Poor water quality may reverse protective effects of blue space on metabolic health

Nature Water (2026)Cite this article

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Abstract

Blue space has been considered a key environmental determinant of metabolic health worldwide. However, the existing relevant studies have focused on only water quantity. Here we investigate the associations of water quantity and quality with metabolic syndrome (MetS) and the underlying potential mechanisms. Our results show that, within 1-km buffer zones around the participants’ residence, a higher MetS risk was associated with lower water area percentage (odds ratio of 0.942 (0.895 to 0.992)) and higher frequency of appearance of black-odorous water (FABOW) (odds ratio of 1.220 (1.062 to 1.402)). At large spatial scales (3–5 km), FABOW reversed protective effects of water area percentage when water quality was poor. The association between higher FABOW and MetS risk was mediated partly by reduced moderate-to-vigorous physical activity at small scales (1–2 km) and by elevated particulate matter with aerodynamic diameter <2.5 µm concentration at large scales (3–5 km). This study underscores the importance of considering water quality in assessing the health effects of blue space, advancing the progress towards several sustainable development goals (for example, good health and wellbeing, clean water and sanitation and sustainable cities and communities).

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Fig. 1: FABOW-moderated conditional effects of water area percentage on MetS outcomes across 1–5-km buffers.
Fig. 2: Associations between blue space and MetS within 1-km buffer zones by subgroup.
Fig. 3: Mediation effects of MVPA and PM2.5 on the associations between FABOW and MetS across different buffer sizes.
Fig. 4: FABOW-moderated conditional ACME of water area percentage on MetS outcomes through PM2.5 within 3- and 5-km buffer zones.

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Data availability

The datasets from this study are held in coded form, and legal data sharing agreements prohibit the authors from making the dataset publicly available. Access to individual deidentified participant data (including data dictionaries) may be granted to those who send a reasonable request with specific data needs, analysis plans and dissemination plans to P.J. (email jiapengff@hotmail.com). The authors will give feedback within 30 days. However, individual identification information may not be available for public use.

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Acknowledgements

We thank the National Natural Science Foundation of China (grant no. 42271433, P.J.), National Key R&D Program of China (grant nos. 2023YFC3604702, S.Y.; 2023YFC3604704, P.J.), Fundamental Research Funds for the Central Universities (grant nos. 413000159, 2042024kf1024, P.J.), Natural Science Foundation of Sichuan Province (grant no. 2026NSFSC0592, S.Y.), Yunnan Provincial High-level Health and Technical Personnel Training Program (grant no. H-2024002, Y.S.) and the International Institute of Spatial Lifecourse Health (ISLE) for research support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Zhanpeng Wang, Yuanyuan Shi, Shujuan Yang.

Authors and Affiliations

  1. School of Resource and Environmental Sciences; Center for Metabolic and Panvascular Diseases, Renmin Hospital, Wuhan University, Wuhan, China

    Zhanpeng Wang, Kun Qin, Chen Li, Shuhan Yang & Chun Yin

  2. Center for Metabolic and Panvascular Diseases, Renmin Hospital of Wuhan University, Wuhan, China

    Yuanyuan Shi & Peng Jia

  3. Medical Remote Sensing Information Research Institute (MRSIRI), Renmin Hospital of Wuhan University, Wuhan, China

    Yuanyuan Shi, Kun Qin, Chen Li, Shuhan Yang, Chun Yin & Peng Jia

  4. International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China

    Yuanyuan Shi, Kun Qin, Chen Li, Shuhan Yang, Chun Yin & Peng Jia

  5. West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China

    Shujuan Yang

  6. Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China

    Xiaoqing Li

  7. Yunnan Center for Disease Control and Prevention, Kunming, China

    Ying Shao

  8. Hubei Center for Disease Control and Prevention, Wuhan, China

    Lan Zhang

  9. Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, China

    Bin Yu

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Contributions

P.J. conceived and supervised the study; Z.P.W. processed satellite data; Z.P.W., Y.Y.S. and S.J.Y. conducted statistical analysis; K.Q., C.L., S.H.Y., B.Y. and C.Y. assisted in data preprocessing and analysis; X.Q.L., Y.S. and L.Z. collected the data; Z.P.W., Y.Y.S., S.J.Y. and P.J. wrote original draft; all authors revised and edited the draft.

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Correspondence to Peng Jia.

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Extended data

Extended Data Table 1 FABOW-moderated mediation effects of PM2.5 in the associations between water area % and MetS outcomes
Full size table

Extended Data Fig. 1 Exposure–response associations of blue space and MetS.

The associations between water area percentage and the probability of MetS are shown for 1-km (a), 2-km (c), 3-km (e), and 5-km buffer zones (g); the associations between FABOW and the probability of MetS are shown for 1-km (b), 2-km (d), 3-km (f), and 5-km buffer zones (h). Solid lines indicate the probability of MetS, and error bands (shaded areas) indicate the corresponding 95% CIs. All models adjusted for age, sex (reference: female), ethnicity (Han), educational level (college or above), occupation (employed), marital status (married), urbanicity (rural), smoking (never), and alcohol drinking (no). Water area % and FABOW are modelled using penalized smooth terms (k = 3). CI, confidence interval; edf, estimated degrees of freedom; FABOW, frequency of appearance of black-odorous water; MetS, metabolic syndrome.

Source Data

Extended Data Fig. 2 Exposure–response associations of blue space and MetS score.

The associations between water area percentage and MetS score are shown for 1-km (a), 2-km (c), 3-km (e), and 5-km buffer zones (g); the associations between FABOW and MetS score are shown for 1-km (b), 2-km (d), 3-km (f), and 5-km buffer zones (h). Solid lines indicate the MetS score, and error bands (shaded areas) indicate the corresponding 95% CIs. All models adjusted for age, sex (reference: female), ethnicity (Han), educational level (college or above), occupation (employed), marital status (married), urbanicity (rural), smoking (never), and alcohol drinking (no). Water area % and FABOW are modelled using penalized smooth terms (k = 3). CI, confidence interval; edf, estimated degrees of freedom; FABOW, frequency of appearance of black-odorous water; MetS, metabolic syndrome.

Source Data

Extended Data Fig. 3 Associations between blue space and MetS score within 1-km buffer zones by subgroup.

q-values for interaction are FDR-adjusted p-values, obtained from likelihood-ratio tests comparing models with and without the interaction term. Error bars denote β (center point) with 95% CIs. All p-values are two-sided, calculated by Wald tests. CI, confidence interval; FABOW, frequency of appearance of black-odorous water; FDR, false discovery rate; MetS, metabolic syndrome. *p < 0.05, **p < 0.01, ***p < 0.001.

Extended Data Fig. 4 Mediation effects of MVPA and PM2.5 on the associations between FABOW and MetS score across different buffer sizes.

The mediation effects of MVPA are shown for 1-km (a), 2-km (b), 3-km (c), and 5-km buffer zones (d); the mediation effects of PM2.5 are shown for 1-km (e), 2-km (f), 3-km (g), and 5-km buffer zones (h). Solid lines indicate statistically significant paths (p < 0.05), and dashed lines indicate non-significant paths.ACME, average causal mediation effect; ADE, average direct effect; CI, confidence interval; FABOW, frequency of appearance of black-odorous water; MetS, metabolic syndrome; MVPA, moderate-to-vigorous physical activity; PM, proportion mediated; PM2.5, particulate matter with aerodynamic diameter ≤2.5 µm; TE, total effect. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 5 Directed acyclic graph (DAG) depicting the determinants of metabolic syndrome (MetS) and their associations.

Red arrows denote the associations among the key variables in this study, while blue arrows denote the associations among the covariates of MetS.

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Wang, Z., Shi, Y., Yang, S. et al. Poor water quality may reverse protective effects of blue space on metabolic health. Nat Water (2026). https://doi.org/10.1038/s44221-026-00618-9

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