Role of social determinants and lifestyle on women's metabolic risk during the perimenopausal transition: results from a cohort study.

OBJECTIVE: Social determinants have been understudied in relation to metabolic risk and menopause; this study aimed to identify metabolic risk factors during menopausal transition, changes in lifestyle, and other social determinants. METHODS: The Korean Genetic Epidemiologic Survey Community cohort data available for baseline, 2-year, and 4-year follow-up time points were analyzed. Healthy women ages 45 to 55 years, not taking hormonal therapy, were selected; 1,228 were analyzed. Menopausal transition was categorized as premenopausal, perimenopausal, and postmenopausal. Lifestyle patterns consisted of alcohol consumption, exercise, ever smoking, indirect smoking, and eating breakfast. Generalized estimating equations were used for analysis. RESULTS: During the period of study, roughly 30% had become postmenopausal and metabolic syndrome was found in 11.5% to 14.4%. Controlling for other variables, lower income levels showed more than 2 times greater risk for metabolic syndrome in postmenopausal women and those who continued to menstruate. Body mass index was a consistent factor of metabolic risk, which was more pronounced when analyzed by menopausal status, especially in obese menstruating women (odds ratio 30.72, P < 0.0001). Among women who experienced menopause during the observed time frame, less education and sedentary lifestyle were also significant factors in metabolic risk differences, showing 1.7 times and 1.59 times greater risk, respectively. Such differences in education, income, and sedentary lifestyle as significant risk factors in subgroups according to menstrual status change, may suggest vulnerable points in the transition. CONCLUSIONS: Implications include the need for stronger emphasis on weight control before midlife and experiencing menopause, promoting exercise across the menopausal transition, and supportive policy measures for economically disadvantaged women.

TRRUST: a reference database of human transcriptional regulatory interactions.

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data, and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.