ArticleMenopause (New York, N.Y.)2018
Understanding the complex relationships underlying hot flashes: a Bayesian network approach.
Article in Menopause (New York, N.Y.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SLCO1B1 genetic variation and hormone therapy in menopausal women.Menopause (New York, N.Y.) · 2018Trial
- Nocturnal Hot Flashes, but Not Serum Hormone Concentrations, as a Predictor of Insomnia in Menopausal Women: Results from the Midlife Women's Health Study.Journal of women's health (2002) · 2023Article
- Urinary phthalate metabolite concentrations and hot flash outcomes: Longitudinal associations in the Midlife Women's Health Study.Environmental research · 2023Article
- Study on a Bayes evaluation of the working ability of petroleum workers in the Karamay region, Xinjiang, China.Frontiers in psychology · 2022Article
- Urinary phthalate metabolite concentrations and serum hormone levels in pre- and perimenopausal women from the Midlife Women's Health Study.Environment international · 2021Article
- Urinary phthalate metabolite concentrations and hot flashes in women from an urban convenience sample of midlife women.Environmental research · 2021Article
- Bayesian Network Modeling Applied to Feline Calicivirus Infection Among Cats in Switzerland.Frontiers in veterinary science · 2020Article
- Hormone variability and hot flash experience: Results from the midlife women's health study.Maturitas · 2019Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
objectiveThe mechanism underlying hot flashes is not well-understood, primarily because of complex relationships between and among hot flashes and their risk factors.
methodsWe explored those relationships using a Bayesian network approach based on a 2006 to 2015 cohort study of hot flashes among 776 female residents, 45 to 54 years old, in the Baltimore area. Bayesian networks were fit for each outcome (current hot flashes, hot flashes before the end of the study, hot flash severity, hot flash frequency, and age at first hot flashes) separately and together with a list of risk factors (estrogen, progesterone, testosterone, body mass index and obesity, race, income level, education level, smoking history, drinking history, and activity level). Each fitting was conducted separately on all women and only perimenopausal women, at enrollment and 4 years after enrollment.
resultsHormone levels, almost always interrelated, were the most common variable linked to hot flashes; hormone levels were sometimes related to body mass index, but were not directly related to any other risk factors. Smoking was also frequently associated with increased likelihood of severe symptoms, but not through an antiestrogenic pathway. The age at first hot flashes was related only to race. All other factors were either not related to outcomes or were mediated entirely by race, hormone levels, or smoking.
conclusionsThese models can serve as a guide for design of studies into the causal network underlying hot flashes.
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