Research Article: The association between urinary genistein levels and mortality among adults in the United States

Date Published: January 25, 2019

Publisher: Public Library of Science

Author(s): Carolyn Marcelo, Melissa Warwick, Catherine Marcelo, Rehan Qayyum, Stephen L. Atkin.

http://doi.org/10.1371/journal.pone.0211368

Abstract

Current research on the relationship between phytoestrogens and mortality has been inconclusive. We explored the relationship between genistein, a phytoestrogen, and mortality in a large cohort representative of the United States population.

Data were analyzed from the National Health and Nutrition Examination Survey (NHANES) from 1999–2010. Normalized urinary genistein (nUG) was analyzed as a log-transformed continuous variable and in quartiles. Mortality data were obtained from the National Death Index and matched to the NHANES participants. Survival analyses were conducted using the Kaplan-Meier analysis. Cox proportional hazard models were constructed for all-cause and cause-specific mortality without and with adjustment for potential confounding variables.

Of 11,497 participants, 944 died during the 64,443 person-years follow-up. The all-cause mortality rate was significantly lower in the lowest quartile compared to the highest quartile (incidence rate ratio = 2.14, 95%CI = 1.76 to 2.60). Compared to the lowest quartile, the highest quartile had significantly higher adjusted all-cause (HR = 1.57, 95%CI = 1.23 to 2.00), cardiovascular (HR = 1.67, 95%CI = 1.04 to 2.68), and other-cause (HR = 1.85, 95%CI = 1.33 to 2.57) mortality.

We found that high urinary genistein levels were associated with increased risk of all-cause, cardiovascular, and other-cause mortality. This is contrary to popular opinion on the health benefits of genistein and needs further research.

Partial Text

Phytoestrogens are plant-based chemical compounds with structural similarities to female sex hormones such as 17-β-estradiol and diethylstilbestrol [1]. The wide range of naturally-occurring phytoestrogens can be sub-classified into several groups: isoflavones, prenylflavonoids, coumestans, and lignans. The most common phytoestrogens are isoflavones found in a variety of fruits and vegetables, particularly soy products, legumes, and flax [2]. One of the major isoflavones that is commonly consumed by humans is genistein, which is typically found in soy products [1].

We used the continuous National Health and Nutrition Examination Survey (NHANES), an ongoing cross-sectional, complex, multistage, stratified, clustered sampling design survey representative of the civilian, noninstitutionalized population of the United States. NHANES was approved by the Centers for Disease Control and Prevention’s Institutional Review Board, and all participants provided written informed consent. Detailed descriptions of survey methods and procedures are available on the NHANES website [21]. Participants from six survey cycles (1999–2010) who were older than 20 years were included in the study. Mortality data were obtained from the National Death Index (NDI), which is a centralized database of death records. The National Center for Health Statistics linked surveys to death records from the NDI by matching identifying information such as Social Security number. We categorized cause of deaths into all-cause deaths, cardiovascular deaths, deaths due to cancer, and deaths due to other causes (other-cause mortality).

In this study, we report an association between higher urinary genistein levels and increased risk of all-cause and other-cause mortality that was independent of potential confounding variables and robust to distributional assumptions of the nUG. We further found that nUG has no association with cancer mortality. The association between nUG and cardiovascular mortality was complex; the association was independent of potential confounding variables when nUG was examined in quartiles but not when examined as a log-transformed continuous variable. Our results were robust to statistical modeling assumptions and remained unchanged in sensitivity analyses using competing-risk models.

 

Source:

http://doi.org/10.1371/journal.pone.0211368

 

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