Whole-body landscape in dimensional effect of socioeconomic factors on mental health
Poster No:
545
Submission Type:
Abstract Submission
Authors:
Guoshu Zhao1,2
Institutions:
1Nankai university, Tianjin, China, 2Department of Radiology and Tianjin Key Lab of Functional Imaging, Tianjin Institute of Radiology, State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital, Tianjin, China
First Author:
Guoshu Zhao
Nankai university|Department of Radiology and Tianjin Key Lab of Functional Imaging, Tianjin Institute of Radiology, State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital
Tianjin, China|Tianjin, China
Nankai university|Department of Radiology and Tianjin Key Lab of Functional Imaging, Tianjin Institute of Radiology, State Key Laboratory of Experimental Hematology, Tianjin Medical University General Hospital
Tianjin, China|Tianjin, China
Introduction:
Socioeconomic circumstances (SES) significantly impact health, morbidity, and longevity, which has been targeted by local and global health strategies[1-3]. In addition to individual SES, neighborhood SES factors also significantly impact mental health. For example, compared to individuals living in affluent and socially cohesive neighborhoods, those living in socioeconomically disadvantaged neighborhoods experience poorer physical health and a higher risk of mental disorders. The multiple indices of deprivation (IMD) in the United Kingdom aim to measure a more comprehensive concept of multiple deprivation, which consists of several distinct dimensions or domains in small areas of England, Scotland and Wales. However, the mechanisms underlying the effects of IMD on mental health are largely unknown. Furthermore, whether the IMD score of the residential environment affects physical health also needs to be explored. Potential mediation pathways, such as physical measures, blood biochemistry indicators, and brain imaging phenotypes, need to be identified to inform prevention and intervention strategies. In this paper, we identify endophenotypes of body systems associated with IMD scores and relate them to mental health problems. We aim to understand what combinations of endophenotypes of body systems are most relevant for IMD and mental health problems, and whether individual SES contributes to these combinations.
Methods:
Participants of UK Biobank underwent comprehensive assessments at 22 assessment centers throughout the UK that included demographics, genomics, environments, physical examinations, multi-organ imaging scans, blood and urine samples collections. We evaluated an individual's SES across two distinct dimensions, encompassing neighborhood SES (IMD score) and personal SES. We evaluated an individual's whole-body health across nine organic systems, encompassing anthropometry, nervous, cardiovascular, pulmonary, digestive, urinary, musculoskeletal, hematological, endocrine and metabolic system. For each organ system, as referred to previous study, we selected a set of classical and key phenotypes across physical, physiological, biochemistry and imaging measures to reflect the structure and function of the system in the baseline assessments of UKBB, resulting in 209 whole-body health assessments. Mental health was evaluated by assessing symptoms of depression and anxiety based on Patient Health Questionnaire (PHQ-4). The summed scores of corresponding items were considered as the final measure of depression and anxiety symptoms. First, we investigate the bidirectional causal association between neighborhood SES and mental health. Then, we investigated the associations between dimensional SES and mental health, we also investigated the associations between dimensional SES and whole-body health. Finally, for each association of dimensional SES with mental health and whole-body health assessment in both discovery and replication sample, we further investigated the complex pathways between neighborhood SES, personal SES, whole-body health endophenotypes and mental health trait using structural equation model.
Results:
The one-sample MR results show that the neighborhood SES was associated with both in depression and anxiety symptoms (Figure 1 a-d). The whole-body endophenotypes that were significantly associated with neighborhood SES or personal SES after meta-analyses were selected for SEM analyses, resulting in 52 indicators across eight organic systems (Figure 1 e, Figure 2 a-b). SEM model indicated the complex relationships among the neighborhood SES, individual SES, whole-body endophenotypes and mental health assessments (Figure 2 c-e).
Conclusions:
Regional deprivation can affect various systems of the human body and further affect mental health outcomes, which highlights the complex interplay between dimensional SES, physiological health markers, and psychological conditions.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 2
Genetics:
Genetic Association Studies
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Multivariate Approaches
Keywords:
Affective Disorders
Anxiety
Emotions
Nerves
Neurological
Psychiatric
Psychiatric Disorders
Other - Socioeconomic factor;whole-body health; dimensional effect
1|2Indicates the priority used for review
·Figure 1
·Figure 2
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2. Mackenbach, J. P. (2016). Changes in mortality inequalities over two decades: register based study of European countries. bmj, 353.
3. Stringhini, S. (2017). Socioeconomic status and the 25× 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1· 7 million men and women. The Lancet, 389(10075), 1229-1237.