Interactions between Water Minerals and Social Factors in the Neural Mechanisms of Mood Disorders
Poster No:
447
Submission Type:
Abstract Submission
Authors:
jinghan sun1, quan Zhang1, jiayuan Xu1, man Chong1, xiangrong chen1, shiji jiao1
Institutions:
1天津医科大学总医院, 天津, China
First Author:
Co-Author(s):
Introduction:
Previous studies have suggested potential links between water mineral content, socioeconomic disparities, and mood disorders, but longitudinal evidence remains limited. Leveraging data from 58,751 UK Biobank participants with a median follow-up of 14 years, this study investigates these associations prospectively.
We found that individuals exposed to lower mineral content in soft water and higher deprivation indices faced a significantly elevated risk of mood disorders, a finding that persisted after rigorous adjustment for confounders and propensity score matching. Mediation analyses revealed critical roles for brain regions such as the corpus callosum, frontal gyrus, and temporal gyrus, which are implicated in emotional regulation and cognitive processes.
Our results highlight the potential of modifiable environmental factors, particularly water mineral content, in shaping mental health outcomes.
We found that individuals exposed to lower mineral content in soft water and higher deprivation indices faced a significantly elevated risk of mood disorders, a finding that persisted after rigorous adjustment for confounders and propensity score matching. Mediation analyses revealed critical roles for brain regions such as the corpus callosum, frontal gyrus, and temporal gyrus, which are implicated in emotional regulation and cognitive processes.
Our results highlight the potential of modifiable environmental factors, particularly water mineral content, in shaping mental health outcomes.
Methods:
Participants
The UK Biobank included 502,402 participants aged 34–69 years from Scotland, England, and Wales (2006–2010). This analysis included 58,751 participants.
Domestic Hard Water
Water hardness, measured as calcium carbonate (CaCO3), was linked to participant postcodes and defined as CaCO3 >200 mg/L.
Outcome Definitions
Mood disorders were identified using ICD-10 codes F30–F39. Incident cases were new diagnoses during follow-up.
Confounders
Confounders included age, sex, assessment center, IMD, and genetic principal components.
Statistical Analysis
Propensity Score Matching (PSM)
PSM balanced cases (n = 18,824) and controls (n = 39,927). Logistic regression estimated propensity scores, and nearest-neighbor matching yielded 18,270 pairs.
Cox Regression Analysis
Cox models assessed associations between water hardness, IMD, and mood disorder risk:
Model 1: Adjusted for water hardness, IMD, age, sex, and ethnicity.
Model 2: Used matched data.
Model 3: Stratified by IMD.
Brain Imaging Analysis
Brain MRI data included gray matter volumes and white matter metrics.
Causal Mediation Analysis
Mediation analysis tested brain structures as mediators of water hardness effects. Moderation analysis assessed IMD's role using bootstrapping.
The UK Biobank included 502,402 participants aged 34–69 years from Scotland, England, and Wales (2006–2010). This analysis included 58,751 participants.
Domestic Hard Water
Water hardness, measured as calcium carbonate (CaCO3), was linked to participant postcodes and defined as CaCO3 >200 mg/L.
Outcome Definitions
Mood disorders were identified using ICD-10 codes F30–F39. Incident cases were new diagnoses during follow-up.
Confounders
Confounders included age, sex, assessment center, IMD, and genetic principal components.
Statistical Analysis
Propensity Score Matching (PSM)
PSM balanced cases (n = 18,824) and controls (n = 39,927). Logistic regression estimated propensity scores, and nearest-neighbor matching yielded 18,270 pairs.
Cox Regression Analysis
Cox models assessed associations between water hardness, IMD, and mood disorder risk:
Model 1: Adjusted for water hardness, IMD, age, sex, and ethnicity.
Model 2: Used matched data.
Model 3: Stratified by IMD.
Brain Imaging Analysis
Brain MRI data included gray matter volumes and white matter metrics.
Causal Mediation Analysis
Mediation analysis tested brain structures as mediators of water hardness effects. Moderation analysis assessed IMD's role using bootstrapping.
Results:
Key Findings:Higher water mineral was associated with a lower risk of mood disorders, particularly in socioeconomically deprived populations . This protective effect was validated after propensity score matching (PSM).Role of Brain Structures:Neuroimaging analyses revealed associations between water minerals and structural changes in specific brain regions:Left middle and inferior temporal gyri: Increased water minerals improved structural integrity, enhancing emotional regulation and reducing mood disorder risk.Corpus callosum (splenium): Higher water minerals negatively affected white matter microstructure, potentially impairing emotional regulation.Right rostral middle frontal gyrus: Improvements in this region contributed to better emotional regulation and reduced mood disorder riskIMD moderated the relationship between water minerals, brain structures, and mood disorders. Protective effects were stronger in high-IMD populations, reflecting the interaction of environmental and socioeconomic factors.Neuroimaging Validation:Imaging techniques, including fractional anisotropy (FA) and mean diffusivity (MD), confirmed the effects of water minerals on brain structure. Statistical corrections, such as FDR adjustments, ensured robust findings.Implications:These findings highlight the interaction between environmental and socioeconomic factors in mental health outcomes.
·an overview of analyses
·Associations between domestic hard water and brain imaging phenotypes
Conclusions:
This study shows that higher water mineral is linked to a reduced risk of mood disorders. Neuroimaging analyses identified brain structures, as mediators between water minerals and emotional regulation. The role of IMD highlights the interaction between environmental and social factors in mental health.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Higher Cognitive Functions:
Decision Making
Language:
Language Comprehension and Semantics
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 2
Keywords:
Affective Disorders
Anxiety
Cortex
Data analysis
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
Limbic Systems
Open Data
STRUCTURAL MRI
White Matter
1|2Indicates the priority used for review
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Antoniou, G., Lambourg, E., Steele, J. D., & Colvin, L. A. J. B. J. o. A. (2023). The effect of adverse childhood experiences on chronic pain and major depression in adulthood: a systematic review and meta-analysis. 130(6), 729-746.
Bateman, A., Singh, A., Kral, T., & Solomon, S. J. E. r. (1989). The immune-hypothalamic-pituitary-adrenal axis. 10(1), 92-112.
Beneyto, M., Kristiansen, L. V., Oni-Orisan, A., McCullumsmith, R. E., & Meador-Woodruff, J. H. J. N. (2007). Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and mood disorders. 32(9), 1888-1902.
Berk, M., Williams, L. J., Jacka, F. N., O’Neil, A., Pasco, J. A., Moylan, S., . . . Byrne, M. L. J. B. m. (2013). So depression is an inflammatory disease, but where does the inflammation come from? , 11(1), 200.
Botturi, A., Ciappolino, V., Delvecchio, G., Boscutti, A., Viscardi, B., & Brambilla, P. J. N. (2020). The role and the effect of magnesium in mental disorders: a systematic review. 12(6), 1661.
Bush, D. M., & Lipari, R. N. (2016). Substance use and substance use disorder by industry.
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., . . . O’Connell, J. J. N. (2018). The UK Biobank resource with deep phenotyping and genomic data. 562(7726), 203-209.
Cai, J., Wei, Z., Chen, M., He, L., Wang, H., Li, M., & Peng, Y. J. E. P. (2022). Socioeconomic status, individual behaviors and risk for mental disorders: a Mendelian randomization study. 65(1), e28.
Cathomas, F., Murrough, J. W., Nestler, E. J., Han, M.-H., & Russo, S. J. J. B. p. (2019). Neurobiology of resilience: interface between mind and body. 86(6), 410-420.
Charlton, J., Rudisill, C., Bhattarai, N., Gulliford, M. J. J. o. h. s. r., & policy. (2013). Impact of deprivation on occurrence, outcomes and health care costs of people with multiple morbidity. 18(4), 215-223.
Clune, J. W., & Cravotta III, C. A. (2019). Drinking water health standards comparison and chemical analysis of groundwater for 72 domestic wells in Bradford County, Pennsylvania, 2016 (2328-0328).