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Maternal Education in Early Life and Risk of Metabolic Syndrome in Young Adult American Females and Males

Disentangling Life Course Processes Through Causal Models

Huang, Jonathan Yinhaoa; Gariépy, Genevièveb; Gavin, Amelia R.c; Rowhani-Rahbar, Alid; Siscovick, David S.e; Enquobahrie, Daniel A.d

doi: 10.1097/EDE.0000000000001068
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Background: Maternal education in a child’s early life may directly affect the child’s adult cardiometabolic health, but this is difficult to disentangle from biological, social, and behavioral life course processes that are associated with maternal education. These processes may also differ between males and females.

Methods: Using data from the National Longitudinal Study of Adolescent to Adult Health (1995–2009) (N = 4,026 females and 3,192 males), we estimated sex-stratified associations between maternal attainment of less than high school (<HS), high school diploma (HS), or college degree (CD) at the respondent’s birth and respondent’s risk of metabolic syndrome (MetS); we used marginal structural models (MSM) to account for the influence of major life course risk factors, such as childhood maltreatment, adolescent overweight, adult education, household income, smoking, and physical activity, in mediating associations between maternal education and offspring MetS risk.

Results: Each higher level of maternal education was associated with a 36% (Relative Risk = 0.64 [95% Confidence Interval (CI): 0.50-0.82]) reduced risk of MetS among females, but only 19% (RR = 0.81 [95% CI: 0.64-1.01]) reduction among males (P-value interaction < 0.05). Stronger inverse associations were also observed for waist circumference and glycated hemoglobin (HbA1c) among females compared with males (−5 cm vs. −2.4 cm and −1.5% vs. −1.0%, respectively).

Conclusion: High maternal education in early life was associated with a lower risk of MetS in young adulthood even after accounting for life course risk factors, particularly among females. Results were robust to altered model specifications.

From the aSingapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore

bDepartment of Social and Preventive Medicine, University of Montreal, Montreal, Quebec, Canada

cDepartment of Social Work, University of Washington, Seattle, WA

dDepartment of Epidemiology, University of Washington, Seattle, WA

eNew York Academy of Medicine, New York, NY

Submitted October 12, 2017; accepted July 21, 2019.

Portions of this work was supported by National Institutes of Health (NIH) Reproductive, Perinatal, and Pediatric Epidemiology (RPPE) Training Grant 5T32HD052462-08 and Canadian Institutes for Health Research (CIHR) Operating Grant #115214 funded to J.Y.H., though no funds were specifically set aside for this article. D.A.E. was funded by NIH Career Development Award K01HL103174. G.G. was funded by Canadian Institutes for Health Research (CIHR) postdoctoral fellowship.

The authors report no conflicts of interest.

Data Access and Computing Code: Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). Computing code can be obtained by emailing the corresponding author.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

Correspondence: Jonathan Huang, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research; 30 Medical Drive, Singapore, 117609. E-mail: jonathan_huang@sics.a-star.edu.sg

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