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A Population-Level Decline in Serum Testosterone
Levels in American Men
Thomas G. Travison, Andre B. Araujo, Amy B. O’Donnell, Varant Kupelian, and John B. McKinlay
New England Research Institutes, Watertown, Massachusetts 02472
Context: Age-specific estimates of mean testosterone (T) concentra-
tions appear to vary by year of observation and by birth cohort, and
estimates of longitudinal declines in T typically outstrip cross-sec-
tional decreases. These observations motivate a hypothesis of a pop-
ulation-level decrease in T over calendar time, independent of chro-
nological 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 endo-
crine 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. (J Clin Endocrinol Metab 92: 196 –202, 2007)
C
ONSIDERABLE LOSS OF serum testosterone (T) is
thought to be a feature of male chronological aging
(1–9). Low-serum T has been associated with numerous age-
related adverse health conditions including abdominal obe-
sity, diabetes, and prediabetic states (such as insulin resis-
tance, impaired glucose tolerance, and metabolic syndrome),
dyslipidemia, low bone and muscle mass, impaired sexual
function, depressed mood, frailty, and decreased quality of
life (10–12). T decline across the life span therefore represents
an issue of great concern for public health, but large studies
of within-person decreases in T are rare.
A previous analysis of baseline (T1: 1987–1989) and initial
follow-up (T2: 1995–1997) data from the Massachusetts Male
Aging Study (MMAS) indicated that the mean longitudinal
(within-subject) decline in serum total T (TT) per year of
aging was more than twice the baseline cross-sectional de-
crease in mean TT per year of age (13). Qualitative compar-
isons of other existing studies likewise indicate that longi-
tudinal decline within subjects is generally of greater
magnitude than corresponding cross-sectional trends. We
have hypothesized (13) that this disparity may be attribut-
able to rapid intrasubject declines in health among subjects
enrolled in longitudinal studies. A competing hypothesis,
however, asserts that a population-level decline in T con-
centrations confounds cross-sectional and longitudinal esti-
mates of T decline with age. A population-level decrease in
serum T levels could accelerate the longitudinal declines in
T concentrations typically associated with subjects’ aging
and compress cross-sectional decreases associated with age.
Completion of the latest follow-up wave of MMAS data
collection (T3: 2002–2004) allows us to investigate formally
the possibility of an age-independent decline in serum T
levels with calendar time.
To our knowledge, there exist no extensive published
studies of changes in the age-matched distribution of T over
time, but a population-level decline in serum T concentra-
tions would be consistent with evidence of secular decreases
in male fertility and sperm count (14, 15). In this analysis, we
estimated differences in serum total testosterone and calcu-
lated bioavailable T (BT) concentrations obtained from in-
dividuals of like age observed at different times (e.g. com-
paring TT in men who were 65 yr old in 1988 to those in
comparable men who were 65 yr old in 2003). Our working
hypothesis was that age-independent differences would be
attributable to population-level changes in health and life-
style observable during the nearly 20 yr of study follow-up.
Subjects and Methods
The MMAS is a prospective cohort study of men’s health and endo-
crine function. Its design and prior results are described elsewhere (1,
5, 13, 16). Briefly, from a randomly chosen sample of 1709 men living in
and around Boston, blood samples and interview data were obtained
during in-home visits by trained staff, with data collection comprising
First Published Online October 24, 2006
Abbreviations: BT, Bioavailable testosterone; CI, confidence interval;
MMAS, Massachusetts Male Aging Study; T, testosterone; TT, total T.
JCEM is published monthly by The Endocrine Society (http://www.
endo-society.org), the foremost professional society serving the en-
docrine community.
0021-972X/07/$15.00/0 The Journal of Clinical Endocrinology & Metabolism 92(1):196–202
Printed in U.S.A. Copyright © 2007 by The Endocrine Society
doi: 10.1210/jc.2006-1375
196
a baseline (T1) and two follow-up (T2, T3) waves. All study activities,
including informed consent protocol, were approved by the Institutional
Review Board of the New England Research Institutes.
T concentrations are subject to systematic variation due to compo-
nents of study design (17–19). Accordingly, the MMAS took steps to
minimize design bias. To counteract the effects of episodic secretion of
hormones, two samples were obtained at each visit and pooled in equal
aliquots at the time of assay. To control the effects of diurnal variation
in hormone concentrations (20), samples were obtained within4hof
subjects’ waking. Blood was kept in an ice-cooled container for transport
and centrifuged within 6 h. Serum was stored in 5-ml scintillation vials
at ⫺20 C, shipped to the laboratory within 1 wk by same-day courier,
and stored at ⫺70 C until the time of assay. All hormone values were
obtained by a single technician at the Endocrine Laboratory, University
of Massachusetts Medical Center, under the direction of Christopher
Longcope, M.D. TT concentrations were obtained by RIA (Diagnostic
Products Corp., Los Angeles, CA). T1 assays were performed in 1994,
whereas T2 and T3 samples were assayed soon after in-home visits. TT
inter-assay coefficients of variation were 8.0, 9.0, and 8.3 at T1, T2, and
T3, respectively. TT concentrations obtained in the MMAS fall near the
center of the distribution of concentrations obtained in other major
epidemiologic studies (16), and quality-control testing indicated negli-
gible change in concentrations between T1 and T2 due either to sample
storage or assay drift (5).
SHBG was measured using RIA kits at T1 and T2, and at T3 by
chemiluminescent enzyme immunometric assay using the Diagnostics
Products Corp. (Los Angeles, CA) Immulite technology. SHBG inter-
assay coefficients of variation were 10.9, 7.9, and 3.0% at T1, T2, and T3,
respectively. BT was calculated using the mass action equations de-
scribed by So¨dergard et al. (21), with association constants taken from
Vermeulen et al. (22).
Covariate data
Demographic characteristics (age, education, income, marital status),
health conditions (cancers, diabetes, heart disease, hypertension, and
ulcer), self-assessed general health (a five-point ordinal scale), and smok-
ing and daily alcohol consumption (23) were obtained via self-report.
Self-reported diagnoses of prostate cancer were supplemented with
examination of available medical records. Height, weight, and waist and
hip circumferences were obtained using methods developed for large-
scale epidemiological field work (24). Body mass index and waist-to-hip
ratio were derived by calculation. A comprehensive inventory of all
prescription medications used by subjects was obtained. Daily caloric
intake was measured using the Willett 1-yr food frequency question-
naire (25). Physical activity and energy expenditure were derived from
subjects’ 7-d recall of duration and frequency of their activities (26).
Depressive symptoms were measured using the Center for Epidemio-
logic Studies–Depression scale (27).
Analysis sample
To enhance comparability of age distributions across study waves
and to allow for analyses of T concentrations by subjects’ birth cohorts,
data were restricted to observations obtained on men of age 45–79 yr
born between 1916 and 1945, inclusive. This yielded potential samples
of 1399, 975, and 579 observations at T1, T2, and T3, respectively. Of
these, we excluded all observations on the seven men who had T1 serum
total T less than 100 ng/dl (3.5 nmol/liter), and two outlying observa-
tions with total T more than 1200 ng/dl (41.6 nmol/liter). One hundred
twenty-six observations were excluded because they were taken on
subjects who, before the relevant study wave, had a diagnosis of prostate
cancer, for which treatment via hormone suppression therapy could not
be ruled out. An additional 44 observations were excluded because
subjects lacked complete health data. This yielded samples of 1374, 906,
and 489 observations at T1, T2, and T3, respectively, totaling 2769 ob-
servations taken on 1532 men.
Statistical analysis
Exploratory analyses were conducted to assess the functional form of
associations. We used mixed-effects linear regression (28) with random
subject-level intercepts and slopes to estimate trends and test hypoth-
eses. Hormone concentrations were log (base e) transformed to remove
any effects of the mild skew in the data. For a covariate with associated
regression estimate

*, we approximated the corresponding percent
change in mean hormone concentrations using the quantity 100 ⫻ (e

*-1).
Results were considered statistically significant if null hypotheses could
be rejected at the 0.05 level. The significance of effects was evaluated
using Wald and likelihood ratio tests. Confounders were used in mul-
tivariate models if they had considerable theoretical importance or were
significantly associated with T concentrations in the presence of other
predictors. All confounders were allowed to vary with time and were
treated as internal time-dependent covariates (29).
Results
A description of the analysis sample is given in Table 1.
Median baseline age was 58 yr, with interquartile range
52–64 yr. Seven hundred nineteen (52%) subjects reported at
least one chronic illness, 340 (25%) were current smokers, 296
(22%) were obese (body mass index ⱖ 30), and 300 (22%)
reported use of at least three prescription medications. Over
the course of study follow-up, we observed marked increases
in the proportion of subjects reporting at least one chronic
illness or who were overweight or obese, as well as in the
number of medications being used by subjects; there were
dramatic decreases in the proportion of subjects who were
current smokers or who were employed.
Table 2 presents descriptive statistics for age and T con-
centrations at all study waves. Median TT at baseline was 501
ng/dl (17.4 nmol/liter), with interquartile range 392–614
ng/dl (13.6 –21.3 nmol/liter); the corresponding values at T3
were 391 ng/dl (13.6 nmol/liter) and 310 –507 ng/dl (10.7–
17.6 nmol/liter). Among subjects on whom follow-up data
could be obtained, the median lag time between observations
at T1 and T2 was 8.8 yr, and between T2 and T3 was 6.4 yr.
Exploratory analyses
We used graphical displays to assess three interrelated
quantities: first, the cross-sectional association between T
concentrations and age at any study wave; second, the lon-
gitudinal decline of T over time associated with subjects’
aging; and third, the age-matched difference between, for
instance, mean T concentrations obtained from 65-yr-old
men in 1988 and concentrations obtained from 65-yr-old men
in 2003 (equivalently, we sought to compare T concentrations
obtained in 1988 from men born circa 1923 to concentrations
obtained in 2003 from men born circa 1938). A depiction of
mean TT concentrations is given in Fig. 1, which displays
nonparametric locally weighted estimates of TT by age sep-
arately for each study wave. The negative slopes of the wave-
specific fits correspond to the relatively modest cross-sec-
tional decline of mean TT with age. The age-matched
difference by time (denoted by the vertical distance between
the fitted curves in overlapping age ranges) is likewise ev-
ident. The data suggest that the cross-sectional decline of TT
within T1 is smaller than the age-matched difference be-
tween concentrations taken at T2 vs. T1, which are separated
by only approximately 9 yr in time; simple linear regression
estimates indicate cross-sectional TT decreases of 17 and 20
ng/dl (0.6 and 0.7 nmol/liter) per 10 yr of age at T1 and T2,
respectively, whereas the mean difference between subjects
age 65 at T1 vs. subjects age 65 at T2 is approximately 50
ng/dl (1.7 nmol/liter).
Travison et al. • Population-Level Declines in Male Serum T J Clin Endocrinol Metab, January 2007, 92(1):196–202 197
To explore more carefully trends associated with age and
time, it is useful to depict subjects by birth cohort. Figure 2
displays all (log-transformed) TT concentrations in the anal-
ysis sample vs. age and includes mixed-effects regression (28)
estimates of the average within-subject TT decline by 5-yr
birth cohort. A display fitting nonparamentric locally
weighted regression smooths (not shown) was similar. We
refer to 5-yr birth cohorts as cohort I (men born in the years
1916–1919), cohort II (1920–1924), and so on, to cohort VI
(1940–1945). Although the design of the MMAS precludes all
cohorts from being observed over all ages, the pattern of
decreasing TT concentrations across adjacent cohorts is com-
pelling. That the regression lines are approximately parallel
indicates that the age-matched decline over time (again in-
dicated by vertical distances between pairs of fitted lines) is
consistent across age groups.
Detailed exploratory analyses provide additional evidence
of an age-matched time trend. Table 3 provides an illustrative
example. Here we restrict our attention to cohorts II and IV
and their associated TT concentrations at T1 and T2. Calcu-
lation indicates that, among subjects in cohort IV (born 1930 –
1934), the proportionate decline in mean TT from T1 to T2
was 16.1% (the median age at T1 was 56 yr and at T2 was 64
yr). By contrast, a cross-sectional comparison at baseline
TABLE 2. Total and calculated bioavailable T concentrations, by study wave and corresponding age range
Study wave Observation years Age range (yr) n
TT (ng/dl)
a
Bioavailable T (ng/dl)
a
Median Interquartile range Median Interquartile range
T1 1987–89 45–71 1383 501 392–614 237 179 –294
T2 1995–97 50– 80 955 435 350–537 188 150 –234
T3 2002–04 57–80 568 391 310 –507 130 101–163
a
May be converted to nmol/liter via multiplication by 0.03467.
TABLE 1. Descriptive statistics by MMAS study wave, mean (
SD), or count (%)
T1 (1987–1989)
(n ⫽ 1374)
T2 (1995–1997)
(n ⫽ 906)
T3 (2002–2004)
(n ⫽ 489)
Age (yr) 57.7 (7.2) 63.2 (7.8) 67.3 (6.5)
Chronic illness
Nonprostate cancers 89 (6%) 124 (14%) 85 (17%)
Diabetes 120 (9%) 80 (9%) 62 (13%)
Heart disease 196 (14%) 155 (17%) 114 (23%)
Hypertension 449 (33%) 340 (38%) 248 (51%)
Ulcer 146 (11%) 117 (13%) 64 (13%)
Any 719 (52%) 545 (60%) 349 (71%)
Depressive symptoms (CES-D ⱖ 16) 149 (11%) 96 (11%) 43 (9%)
Self-assessed general health
Excellent 417 (30%) 280 (31%) 127 (26%)
Very good 475 (35%) 336 (37%) 190 (39%)
Good 360 (26%) 219 (24%) 110 (27%)
Fair/poor 120 (9%) 71 (8%) 42 (9%)
Prescription medications
0 517 (38%) 196 (22%) 0 (0%)
1–2 557 (41%) 351 (39%) 170 (37%)
3–5 252 (18%) 270 (30%) 178 (38%)
6⫹ 48 (3%) 89 (10%) 116 (25%)
Education
⬍High school 173 (13%) 83 (9%) 34 (7%)
High school graduate 263 (19%) 137 (15%) 81 (17%)
⬎High school 938 (68%) 680 (76%) 374 (76%)
Marital status
Single/never married 108 (8%) 63 (7%) 40 (8%)
Married 1044 (76%) 701 (77%) 367 (75%)
Divorced/separated 171 (12%) 97 (11%) 55 (11%)
Widowed 51 (4%) 45 (5%) 27 (5%)
Household income
⬍$40,000/yr 546 (41%) 271 (31%) 122 (26%)
$40,000–$79,000/yr 530 (40%) 299 (34%) 153 (32%)
⬎$80,000/yr 250 (19%) 302 (35%) 199 (42%)
Currently employed 1032 (75%) 565 (62%) 257 (53%)
Weight and body shape
Body mass index (kg/m
2
)
27.4 (4.4) 27.6 (4.4) 28.3 (4.8)
Waist-to-hip ratio 0.95 (0.06) 0.96 (0.06) 0.97 (0.06)
Cigarette smoking 340 (25%) 118 (13%) 45 (9%)
Dietary intake
Total kcal/d 2069 (817) 2006 (720) 1911 (743)
Animal fat (g/day) 40.3 (22) 36.6 (19) 38.0 (20)
Sedentary activity levels 488 (36%) 285 (31%) 139 (28%)
198 J Clin Endocrinol Metab, January 2007, 92(1):196–202 Travison et al. • Population-Level Declines in Male Serum T
indicates that cohort II (median age 65 yr) T levels are only
5.5% lower than those of cohort IV (median age 56 yr). The
age-matched time difference (comparing observations on
men of similar age separated by time: cohort IV at T2 vs.
cohort II at T1) is approximately 11.2%, approximately the
difference between the cross-sectional and longitudinal
trends. Similar effects may be observed in other combina-
tions of birth cohorts and study waves.
Formal results: total T
An analysis of all data yields results in agreement with our
exploratory observations. To estimate cross-sectional and
longitudinal trends, we partition subjects’ ages into two piec-
es: age at baseline and subsequent aging, the latter defined
as calendar time since study entry. The per-year age-matched
time trend was estimated as the difference between the as-
sociated longitudinal and cross-sectional regression esti-
mates (30–32). Mean cross-sectional, longitudinal, and age-
matched trends derived from mixed-effects models of TT as
a function of age and aging are depicted on the left side of
Table 4. The estimated cross-sectional decline in TT is ⫺0.4%
per year of age, with a corresponding 95% confidence inter-
val (CI) of (⫺0.6%, ⫺0.2%). The longitudinal within-subject
decline is approximately ⫺1.6% per year (CI: ⫺1.8%, ⫺1.4%).
The age-matched time trend is ⫺1.2% per year (CI: ⫺1.4%,
⫺1.0%).
We hypothesized that the presence of the age-matched
time trend could be accounted for by observable secular
changes in health status or lifestyle characteristics. This hy-
pothesis relies upon an assertion that for men of, for example,
65 yr of age, health/lifestyle characteristics vary by obser-
vation time. For instance, the well-known and ongoing sec-
ular increase in obesity might explain the fact that the typical
blood sample taken from a 65-yr-old man in 2003 exhibited
lower TT concentrations than a sample taken from a different
65-yr-old subject in 1988 (the latter subject having been born
approximately 15 yr earlier than the former). In this analysis
we observed little evidence of age-independent trends with
respect to most covariate factors; notable exceptions to this
rule, however, were the aforementioned increases in relative
weight, as well as population-level changes in the prevalence
of smoking and the concurrent use of multiple medications
(polypharmacy). There were substantial age-specific in-
creases in obesity and polypharmacy over the course of study
follow-up, whereas the proportion of subjects who smoked
cigarettes declined dramatically in all age groups. These
trends are potentially important in accounting for an appar-
ent secular decline in TT levels, because weight gain, smok-
ing cessation, and the use of medications have been associ-
ated with decreases in serum T (33–37). However, although
controlling for these and other factors significantly associ-
ated with TT concentrations was sufficient to substantially
decrease the estimates of cross-sectional and longitudinal
decline in TT, the estimate of the age-matched time trend was
only slightly reduced (see Table 4). Results were essentially
unchanged when all covariate effects (see Subjects and Meth-
ods) were included in multivariate analyses.
Bioavailable T
As noted, the technology by which SHBG was measured
at T1 and T2 (RIA) differed from that employed at T3 (Im-
mulite). Because of this, observed variation in calculated BT
concentrations between T2 and T3 could be artificially in-
flated. We therefore restricted formal estimation of cross-
sectional, longitudinal, and age-matched time trends in BT to
values obtained at T1 and T2.
In the resulting models, as is consistent with other pub-
lished results, cross-sectional and longitudinal age trends in
BT were substantially sharper than those in TT. However, the
age-matched time trend was similar in magnitude to that in
TT and was likewise robust to control for all covariates.
When only the effects of age and aging were controlled, the
estimated age-matched time trend in BT values was approx-
imately ⫺1.4% per calendar year (95% CI: ⫺1.8%, ⫺1.1%),
whereas when the effects of all other covariates were ac-
FIG. 2. MMAS mean TT vs. age, by 5-yr birth cohort. Fitted lines are
obtained from cohort-specific mixed-effects regression of the log of TT
on centered age, with random effects for each subject. Data points in
the analytic sample are also depicted; each subject contributes up to
three observations. Models are fit using maximum likelihood.
FIG. 1. Crude mean TT concentrations, by MMAS study wave (T1,
T2, T3) with confidence bands (dotted lines). Estimates are obtained
from a generalized additive model with a lowess smoothing term.
Travison et al. • Population-Level Declines in Male Serum T J Clin Endocrinol Metab, January 2007, 92(1):196–202 199
counted for, the estimated age-matched trend was ⫺1.3% per
year (95% CI: ⫺1.7%, ⫺1.1%).
Sensitivity analyses. To test the robustness of all findings, we
performed a number of additional analyses. Analyses in-
cluding effects for town of residence, assay batch, month of
interview, and time of study visit yielded results nearly iden-
tical to those described above. Results did not change sub-
stantially when analyses of TT were restricted to data from
any two of the three study waves. Likewise, results were
similar when analyses of either TT or BT were restricted to
men above or below certain ages, to men with complete data
at all three waves, or to men in particular birth cohorts. In
addition, we examined the distribution of baseline TT and BT
concentrations among those subjects who had complete data
vs. those who did not and found that they were comparable.
Discussion
These findings indicate that the past 20 yr have seen sub-
stantial age-independent decreases in male serum T concen-
trations, decreases that do not appear to be the consequence
of the contemporaneous trends in health and lifestyle con-
sidered here. It remains unclear to what these apparent pop-
ulation-level decreases in T are attributable.
Because age, birth year, and observation time are perfectly
confounded (that is, knowledge of any two determines the
third), their influences are not separable through data anal-
ysis. Age-matched time differences cannot, therefore, be de-
finitively attributed to historical (prestudy) trends affecting
different birth cohorts in different ways or, rather, to con-
temporary secular changes in the underlying population (e.g.
to age-independent increases in obesity beyond those cap-
tured in the analyses described here). As noted previously,
there is little evidence that the association between T and age
(that is, the slope of a line depicting the relationship between
the two) depends on birth year, so that irrespective of birth
cohort, decreases in T with age are constant (see Fig. 2). This
evidence is consistent with, but does not prove, the notion
that the linear T/age association is consistent across different
generations and implies that the age-matched declines in T
levels associated with each year of calendar time apply
equally to men from 45 to 80 yr of age.
The presence of the age-matched trend itself, however,
suggests that neither cross-sectional nor longitudinal inves-
tigations may properly describe the true effect of aging per
se on T (30–32). Suppose, for instance, there were an un-
measured but persistent environmental exposure associated
with decreased T levels, affecting recent generations of men
at birth. In that case the cross-sectional decline in T with age
might be underestimated, because younger men could have
lower T levels than their historic counterparts and appear
more like their older contemporaries (born before the advent
of the exposure) than one would normally expect in the
absence of such a hypothetical exposure.
On the other hand, if the age-matched trend is not historic
but rather indicative of population-level changes occurring
during the time subjects were under study, the age-matched
trend denotes a secular trend in T concentrations over that
time. Under this scenario it is easy to see that longitudinal
estimates of change in T concentrations may in fact overstate
the true effect of aging, because the observed effect of a year
of aging would include not only the true age-related de-
creases in T but also whatever decreases the population-level
secular trend imposed on all men simultaneously. Such a
secular trend in T might be attributable to parallel popula-
tion-level changes in the distribution of health and lifestyle
factors, independent of age. We have observed, however,
TABLE 4. Longitudinal regression results
Unadjusted results Adjusted results
a
Mean decline (%/yr) 95% CI P
b
Mean decline (%/yr) 95% CI P
b
Cross-sectional trend (per year age) ⫺0.4 (⫺0.6, ⫺0.2 ) ⬍0.001 ⫺0.1 (⫺0.3, 0.1 ) 0.42
Longitudinal trend (per year aging) ⫺1.6 (⫺1.8, ⫺1.4 ) ⬍0.001 ⫺1.1 (⫺1.3, ⫺0.9 ) ⬍0.001
Age-matched time trend (per year time) ⫺1.2 (⫺1.5, ⫺1.0 ) ⬍0.001 ⫺1.0 (⫺1.3, ⫺0.8 ) ⬍0.001
Though apparent cross-sectional and longitudinal associations with age are reduced by statistical control for health and lifestyle, the
age-matched time trend remains large.
a
Adjusted for chronic illness, general health, medications, smoking, body mass index, employment, marital status.
b
Wald test of regression effect.
TABLE 3. Age-matched trends: illustrative example
Cohort Birth years
T1: 1987– 89 T2: 1995–97
Median age
(yr)
Mean (
SD)TT
(ng/dl
d
)
Median age
(yr)
Mean (
SD)TT
(ng/dl
d
)
II 1920–1924 65 500 (161)
IV 1930–1934 56 529 (183) 64 444 (145) Longitudinal difference: ⫺16.0%
b
Cross-sectional difference: ⫺5.5%
a
Age-matched time difference: ⫺11.2%
c
Crude cross-sectional, longitudinal, and age-matched trends in mean TT per year, age, or time, restricted to men born 1920–1924 (cohort
II) or 1930 –1934 (cohort IV). Men in cohort II have comparable age when observed at T1 (upper left) to that of men in cohort IV when observed
at T2 (lower right); the disparity between these measurements approximates the unadjusted age-matched time trend in TT. Median time
between observation at T1 and T2 is approximately 8.8 yr.
a
T1: Cohort II vs. cohort IV; estimates mean cross-sectional decrease per 9 yr age.
b
Cohort IV: T2 vs. T1; estimates mean longitudinal decline per 9 yr aging.
c
Cohort IV, T2, vs. cohort II, T1; estimates mean age-matched decline per 9 yr time.
d
May be converted to nmol/liter via multiplication by 0.03467.
200 J Clin Endocrinol Metab, January 2007, 92(1):196–202 Travison et al. • Population-Level Declines in Male Serum T
that although baseline and evolving health states in the study
sample successfully account for a substantial proportion of
the cross-sectional and longitudinal associations between
age and T, they do not explain the age-matched decline in T
concentrations.
We therefore hypothesize that the observed age-matched
decline in serum testosterone is due to some undocumented
historical or contemporary influence, health-related or en-
vironmental, which manifests in observable age-matched
differences in T concentrations separated either by time of
observation or by birth cohort.
It is interesting to note that the estimated age-matched time
trends in TT and BT are of comparable magnitude. This may not
in itself be surprising, because the time trends are explicitly
intended to remove the effects of aging itself, leaving only
secular changes in other factors as contributors to changes in T
levels with time. We can currently offer, however, no additional
speculation as to whether one would expect a secular trend in
BT to differ markedly from that in TT.
Some limitations of this study should be acknowledged.
Though the consistency of the methods by which TT con-
centrations were obtained, as well as that of the age-matched
time trend across all pairs of study waves, indicates that
design artifacts are likely not the cause of these observations,
they cannot be completely discounted as contributors to the
age-matched time trends, because relatively subtle changes
in measurement may contribute substantially to differences
between observations separated by time. Likewise, though
the evidence suggests that subject loss to follow-up has not
influenced our result, we must acknowledge the possibility
of biases arising from subject dropout. However, under the
assumption of such a survival bias, those subjects who re-
mained in the study, being younger (and presumably more
healthy) than those lost to follow-up, would be likely to
exhibit higher mean T concentrations during follow-up than
would the complete sample had it been fully observed. In
such a scenario, it is likely that the estimates of longitudinal
and age-matched decline described here would be too low,
rather than too high.
An added concern is that the covariates considered in this
analysis cannot account for all known causes of T decline.
Indeed, it is exceedingly unlikely that population-level T
concentrations would decline with calendar time, indepen-
dently of age, of their own accord. Rather, if such declines
exist, they have one or several causes that themselves may be
evolving over time. We have observed that several candidate
causes observable at the level of the individual subject, most
notably the well-known secular declines in smoking rates
and increases in relative weight, do not appear to explain
completely the observed age-matched trends in T. It remains
possible, however, that more detailed and comprehensive
measurement of such factors could fully account for the
age-matched trends in T.
If the trends observed in the MMAS are real and continue,
the prevalence of low T in American men will exhibit in-
creases in excess of those to be expected given the projected
aging of the population (38). As such, it is important that
future research endeavors to confirm or disprove the exis-
tence of age-independent T declines and to discover their
causes, environmental or otherwise, so that they may be
addressed through prevention.
Acknowledgments
The authors thank Dr. Don Brambilla for helpful discussions and also
acknowledge the many contributions of Dr. Christopher Longcope, who
passed away in 2004. For nearly 20 yr, he was an indispensable colleague
on the Massachusetts Male Aging Study. His scientific expertise and
collegiality are missed.
Received June 27, 2006. Accepted September 25, 2006.
Address all correspondence and requests for reprints to: Thomas G.
Travison, Ph.D., New England Research Institutes, 9 Galen Street, Wa-
tertown, Massachusetts 02472. E-mail: ttravison@neriscience.com.
This work was supported by the National Institutes of Health (Na-
tional Institute of Diabetes and Digestive and Kidney Diseases:
DK44995, DK51345; National Institute on Aging: AG04673).
Disclosure Statement: The authors have nothing to disclose.
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