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Social networks and coronary artery disease: a comparison of the structure and function of social relations as predictors of disease.

Seeman, T E, Syme, S L·1987·Psychosomatic Medicine

Abstract

This study provides the first direct comparison of the relative importance of structural versus functional aspects of social network ties as they relate to susceptibility to coronary artery disease. Data from 119 men and 40 women undergoing coronary angiography provide an opportunity to compare these associations in relation to a direct and continuous measure of atherosclerosis while controlling for age, sex, income, hypertension, serum cholesterol, smoking, angina, diabetes, family history of heart disease, Type A behavior pattern, and hostility. Regression analyses indicate that network instrumental support and feelings of being loved are more important in predicting coronary atherosclerosis than is network size, independent of all covariables (relative extent of atherosclerosis, low/high support = 1.74 and 1.5, respectively). More "problem-oriented" emotional support did not show a similarly strong association (relative extent of atherosclerosis = 1.01). These findings suggest that certain functional aspects of social network ties are more strongly associated with host resistance to coronary atherosclerosis than are structural characteristics like network size.

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Metadata

Authors
Seeman, T. E., & Syme, S. L.
Venue
Psychosomatic Medicine
Published
1987-07
Volume
49
Issue
4
Pages
341-354
ISSN
0033-3174 (ISSN-L)
OpenAlex
W1986000948
Cited by
276
References
0
FWCI
6.60
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Social networks and coronary artery disease: a comparison of the structure and function of social relations as predictors of disease. https://app.cochat.ai/papers/10.1097/00006842-198707000-00003
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Seeman, T. E., & Syme, S. L. (1987). Social networks and coronary artery disease: a comparison of the structure and function of social relations as predictors of disease. Psychosomatic Medicine. https://doi.org/10.1097/00006842-198707000-00003

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.