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Thomas G. Travison, Andre B. Araujo, Amy B. O’Donnell, Varant Kupelian, John B. McKinlay, A Population-Level Decline in Serum Testosterone Levels in American Men, The Journal of Clinical Endocrinology & Metabolism, Volume 92, Issue 1, January 2007, Pages 196–202, https://doi.org/10.1210/jc.2006-1375
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Abstract
Context: Age-specific estimates of mean testosterone (T) concentrations appear to vary by year of observation and by birth cohort, and estimates of longitudinal declines in T typically outstrip cross-sectional decreases. These observations motivate a hypothesis of a population-level decrease in T over calendar time, independent of chronological aging.
Objective: The goal of this study was to establish the magnitude of population-level changes in serum T concentrations and the degree to which they are explained by secular changes in relative weight and other factors.
Design: We describe a prospective cohort study of health and endocrine functioning in randomly selected men of age 45–79 yr. We provide three data collection waves: baseline (T1: 1987–1999) and two follow-ups (T2: 1995–1997, T3: 2002–2004).
Setting: This was an observational study of randomly selected men residing in greater Boston, Massachusetts.
Participants: Data obtained from 1374, 906, and 489 men at T1, T2, and T3, respectively, totaling 2769 observations taken on 1532 men.
Main Outcome Measures: The main outcome measures were serum total T and calculated bioavailable T.
Results: We observe a substantial age-independent decline in T that does not appear to be attributable to observed changes in explanatory factors, including health and lifestyle characteristics such as smoking and obesity. The estimated population-level declines are greater in magnitude than the cross-sectional declines in T typically associated with age.
Conclusions: These results indicate that recent years have seen a substantial, and as yet unrecognized, age-independent population-level decrease in T in American men, potentially attributable to birth cohort differences or to health or environmental effects not captured in observed data.