089%4356/91 $3.00 + 0.00 Copyright Q 1991 Pergamon Press plc

J Clin Epidemiol Vol. 44, No. 7, pp. 671-684, 1991 Printed in Great Britain. All rights reserved

AN EXAMINATION OF RESEARCH DESIGN EFFECTS ON THE ASSOCIATION OF TESTOSTERONE AND MALE AGING: RESULTS OF A META-ANALYSIS ANNA GRAY,’ JESSEA. BERLIN,‘,* JOHN B. MCKINLAY’* and CHRISTOPHER LONGCOPE~ ‘New England Research Institute, 9 Galen Street, Watertown, MA 02172, ‘Harvard School of Public Health, Technology Assessment Group, 677 Huntington Avenue, Boston, MA 02115 and jDepartments of Obstetrics, and Gynecology and Medicine, Univeristy of Massachusetts Medical Center, Worcester, MA 01605, U.S.A. (Received in revised form 15 October 1990)

Abstract-The study of testosterone is likely to be prominent in future epidemiological work on endocrine function and the clinical treatment of age-related diseases. Thus, understanding the hormonal changes involved in the normal male aging process will be critical. Using techniques of meta-analysis, the authors examined 88 published studies of the age-testosterone relation in men. These studies reported conflicting results: age-testosterone correlations ranged from -0.68 to +0.68. In cross-study comparisons, certain research design characteristics (e.g. time of day of blood sampling) and various sample characteristics (e.g. volunteers vs patients as subjects) were related to both mean testosterone level and the slope of the age-testosterone relation. For example, for

subgroups of subjects that did not exclude ill men, the mean testosterone levels were low, and did not decline with age. Subgroups that included only healthy subjects, in contrast, had higher overall testosterone levels and showed a decline of testosterone with age. Implications of these results for design, analysis and reporting of future epidemiologic studies will be discussed. These results also illustrate the utility of meta-analysis for research with the aged. Testosterone Androgens

Aging

INTRODUCTION

It has been generally accepted by researchers that aging in men is accompanied by a decrease in testosterone (T) levels. This age-related decrease in testosterone was challenged by some early researchers, who found that plasma levels of T remained fairly constant through 80 years of age [l-3]. This has become a subject of scientific interest and debate in recent years [4-81. Several authors have suggested that the apparent inconsistency among study results may be due to differences in: (a) subject *Author for correspondence.

Endocrine function

Research design

Meta-analysis

characteristics, such as type of sample (patients, volunteers, geriatric institution inhabitants), general health status of subjects (inclusion of older subjects who suffer from chronic or acute illnesses), and subject use of medications that may alter levels of sex hormones (increasingly prevalent with advancing age) [4,7]; (b) study design characteristics, such as time of blood sampling (morning or afternoon) [9], number of blood samples, type (radioimmunoassay, double-isotope derivative, chromatography, etc.) and quality of hormone assessment. It is important to determine the exact pattern of T changes with age because of the central role T plays in sexual activity [lo] and dysfunctions, 671

612

ANNAGRAYet

maintenance of sexual characteristics in men [9], and diagnosis and management of several endocrine and metabolic diseases (e.g. prostatic carcinoma) [l 11. A careful assessment of the state of published scientific evidence is essential to developing both an understanding of the T-age relation and strategy for further research. The major goal of this paper, then, is to examine systematically the literature on the age-related change in T using quantitative review methods to determine the source(s) of discrepancies among study results. A secondary purpose is to illustrate the power and utility of a quantitative review in a basic science, such as endocrinology. The use of quantitative methods to summarize and analyze past research studies has been termed “meta-analysis” [12]. A formal metaanalytic review can be distingusihed from the traditional narrative review by the “application of statistics to the treatment of quantitative representations of study outcomes” [13]. Most meta-analytic reviews to date have been in education, industrial-organization, and socialpersonality psychology, but exploring divergent results of studies that have examined T levels and aging in men will illustrate the flexibility and utility of meta-analysis in a variety of different fields. Minimal requirements for such a review are that: (a) studies address the same hypothesis, and (b) the study outcomes and characteristics (e.g. sample selection and composition) can be stated in quantitative form (e.g. correlation coefficients, effect sizes) [ 141. Specifically, the intention of this review is to summarize the available research to determine the effects of group-level characteristics of the study sample, such as medication use and illness; and the influence of study design, such as time of blood sample and type and quality of hormone assessment on the observed relation between T concentration and advancing age. With endocrine function, especially circulating T, increasingly implicated in a broad range of health outcomes, e.g. impotence, idiopathic hemochromatosis [ 151,chronic renal failure [ 161, epilepsy [17], obesity [18], high density lipoprotein cholesterol levels [19], and prostatic hyperplasia [20], it is likely that T will figure prominently in future epidemiologic studies. The findings of this paper will bear directly on the expected effects of environmental and study characteristics on T levels and consequently, will yield suggestions for future epidemiologic work on endocrine function, and the clinical treatment of age-related diseases.

al.

METHODS

A comprehensive review of the scientific literature on T and aging was conducted using two search strategies to obtain original data: (a) a MEDLINE search using as the key words, testosterone paired with aging, aged, or elderly; and (b) consultation of references in review articles and books, as well as citations in studies reporting relations between T and aging. To be included in the meta-analysis, a study had to report either mean T levels according to an age categorization, a coefficient for the correlation between age and T, or both. In addition, a study had to provide original data (i.e. no review article was accepted unless it also provided new data) and had to measure serum or plasma T levels. Studies assessing urinary or salivary T were excluded because they were considered to be unreliable. When several papers reported results from the same subgroup of subjects, the most recent paper was used, unless an earlier paper gave data that were not available in the more recent study. The subjects had to be alive; thus autopsy studies were also excluded. Articles written in German or Japanese were translated by colleagues. The emphasis of this paper will be on a weighted regression analysis of mean T level on age and various aspects of study design. Many studies, however, have examined the correlation coefficient between age and T. There are at least two reasons why the correlations may be misleading in the specific context of the age-T relation. First, it is important to estimate the degree of increase or decrease in T with increasing age. This relation may not be a simple straight line, which is assumed when estimating the correlation coefficient. The correlation coefficient alone does not give any information about the degree of increase or decrease in T with age, whereas a regression slope does. Second, when calculating p-values associated with correlation coefficients, it is assumed that both variables follow a normal distribution. The results presented below suggest that, in fact, the distribution of T is skewed, with a small number of extremely large values, thus violating the normality assumption. Nevertheless, given the emphasis in the existing literature on estimating the ageT correlation, it seemed unreasonable to ignore these correlation studies by not summarizing their results. To be included as an analysis of mean T levels, a study was required to present means

Testosterone and Male Aging

rather than medians (to ensure consistent measures from study to study) and to present data on the variability of the T measures, i.e. either a standard deviation (SD) or a standard error of the mean (SEM). All values were converted to the SEM. Studies that provided the raw data with which to calculate these values were accepted as well. When means or correlations were not reported in an article, the authors were contacted by mail for the missing statistics. Letters were sent to 10 authors, but the necessary information was provided by 1 of those 10. In summary, in order to be included in this meta-analysis, a study had to report data on levels of plasma or serum T in living subjects, measures of variability for various age subgroups. A small number of studies reported means or correlations based on one of several measures of “Free T”. Variation was found across studies of more than an order of magnitude in the mean values reported for “Free T”, “Free T index”, “non-SHBG T”, etc. For this reason, a thorough analysis of the issues concerning “Free T” would be beyond the scope of this paper, which is to assess the relation between age and total T concentration, The quality of the laboratory methods described in each study was rated by one of the authors (CL) from methods sections that had been photocopied and separated from the full paper. Authors’ names, dates, and study results were removed. Thus, the rater was “blind” to the identity of the authors of the paper or the laboratory used. The scores ranged from 1 (totally unacceptable) to 5 (excellent assessment), with 3 (average assessment) as the midpoint. The criteria used for scoring were determined as follows: (1) the best assays had an extraction, chromatography and radioimmunoassay with a good quality antibody; and (2) sensitivity for the best assays had to be excellent with inter- and intra-assay coefficients of variation of less than 10%. For each study, several charactersitics were coded: the method by which subjects were selected (patients, volunteers, random sample of a population, etc.); whether people receiving medications (particularly those medications and treatments known to affect endocrine levels, e.g. androgens) were excluded; whether people with known illness (especially such illnesses as prostate cancer or diabetes that are known to affect endocrine levels) were excluded; the time of day when blood samples were collected; and

673

the assay method by which T levels were measured (radioimmunoassay, competitive protein binding, chromatography, etc.). For studies reporting mean T levels by age subgroup, all data were recorded separately for each subgroup, because the charactersitics of subgroups often varied within the same paper. The age range of subjects for each subgroup and/or the mean or median age for the subgroup was recorded as well as the actual means and a variability measure, i.e. standard deviation (SD) or SEM, or the correlation coefficient. Each study was read and coded independently by two of the authors (AG and JAB). For about 2% of the items coded, differences arose between readers resulting from minor reading errors. These differences were resolved in conference. STATISTICAL

METHODS

An important first step in the meta-analysis was deciding on a single summary measure of “age” for the analysis of mean T levels by age subgroup. For each subgroup, a “mid-age” was defined (in order of preference) as the mean age, if given, or the median age, if given, or the midpoint of the age interval (e.g. an age range of 40-50 would have a mid-age of 45). Several subgroups had only an upper or lower age limit reported, but not both (e.g. > 50 years, < 50 years). An upper age limit was estimated for these studies using an average of upper limits from an appropriate subset of the remaining subgroups. Descriptive measures and frequency distributions were computed for simple study-level attributes (quality of laboratory procedures, method of subject selection, etc.). As a measure of age, it was more appropriate to summarize the average age of subjects in the studies. This was accompanied by taking a weighted average of the subgroup ages, with weights equal to the sample size in the subgroup. Studies contribute to such an average in proportion to their sample sizes. With respect to obtaining average levels of T, it must be recalled that each observation represents a mean T level for a subgroup, and that the standard error of that mean was also recorded. Weighted averages were calculated using weights equal to the inverse of the variance of the mean (l/SEM’). While large subgroups generally contribute more to the average than small subgroups under this strategy, the contribution depends on more than just sample

674

ANNAGRAYet al.

size (e.g. large subgroups with a moderate number of extremely high values might have inflated standard errors). Means and standard deviations were also calculated separately for each stratum of various study-level characteristics (e.g. separately for healthy and ill subjects). To gain further appreciation of the statistical issues surrounding the relation between T and age, least squares regressions of the SEM of the T measure against sample size and the mean T value were performed. It is obvious that the chief determinant of the SEM should be sample size, i.e. large studies should tend to have a smaller SEM than small studies. For a given sample size, most statistical models, including t-tests and analyses of variance, assume that the actual level of T should be independent of the SEM. Two subgroups of 20 patients each, with very different mean T levels, should have similar values for the SEM. If the subgroup with the higher mean also has a higher SEM, it is probable that the logarithm of T would have been more suitable for statistical analyses [21]. Generally, such a pattern could arise because of several extremely high T values within a subgroup. These extreme values often have an inflationary effect on both the mean and the variance of the mean of a subgroup. This is problematic because these extreme values are more likely to appear in young than in old men, and a systematic violation of the assumptions for statistical analyses could arise. For the principal analysis of the mean T levels, weighted regressions of mean T level on mid-age and various other subgroup characteristics were performed. That is, the observed mean T value was assumed to be a function of age, type of subject (e.g. patient vs volunteer), presence or absence of illness in the subgroup, etc. The strength of the particular relations was tested in various regressions and the best-fitting model (i.e. the model that explained the most variance in T level) was chosen using multiple F-tests [22]. The weights in the weighted regressions were, again, equal to the inverse of the variance of the individual mean T levels. In all models, the adequacy of fitting a strictly linear relation between age and T was assessed by testing the statistical significance of a term for age squared. If this term were significant, a curvilinear relation would be suggested between age and T. The modeling procedure also involved testing terms for the interaction between age and

various subgroup characteristics. A simple term for time of day of sample (a.m. vs p.m.), for example, would only suggest that time of day is related to mean T level (e.g. a.m. samples might tend to be higher than p.m. samples). An interaction between age and time of day would suggest that the slope of the relation between age and T depends on the time of day of the sample (e.g. a.m. samples might produce a steeper slope than p.m. samples). The analysis of reported correlation coefficients was similar to the analysis of means. The primary goal was to average correlation coefficients across studies. This was accomplished by first transforming the reported coefficient to a Fisher’s Z statistic [23]. For studies with sample size equal to Ni, for example, a weighted average was taken with weights equal to (Ni - 3), the inverse of the variance of the transformed coefficient. A weighted regression of the Fisher’s Zs was performed with the same weights as for the averages.

RESULTS

Eighty-eight articles and abstracts were evaluated by the authors. The 44 studies included in the meta-analysis of means are summarized in Table 1, and the 19 studies included in the meta-analysis of correlations are listed in Table 2. Nine studies were included in the reviews of both means and correlations. Overall 10 studies were excluded because no original data were reported [69,71,73,75,87, 92,94,95, 96, 981, 10 were excluded because either mean total T levels or correlations with age or both were not reported [68, 70, 72, 76, 79, 84, 91, 94, 98,991; 2 studies reported median T levels rather than means [86, 881; 3 studies reported no measure of variability of T [l, 77, 811; 1 study used salivary T instead of plasma T levels [I 11; 8 reported urinary T levels [68, 80, 82, 83, 85, 89, 90, 931; and 2 studies reported data from subjects who were not alive at the time of the blood sample [74, 971. Overall, 34 studies were excluded from analysis. These studies were excluded without consideration of the study’s results. The presentation of results is divided into two sections. The first section focuses on the results from the analysis of the studies reporting mean levels of T for various age subgroups. The second section deals with the results of the correlational studies on T and aging. All p-values cited are two-sided.

615

Testosterone and Male Aging

Table 1. Study characteristics of published investigations used in meta-analysis of research design effects on the relation between testosterone and aging (mean levels reported) Ftehrmce Barrett-Connor 8 Khaw (24) Bartsch (25) I

Medkatbn Exclusbn &Ye 8SltlDb 6l-i; tie 8ele&bn 599 3979 volunteers (82% N/A of a retirement communitv) ..

%tllDb

61 3655 volunteers 5a8opallents

32

N/A ‘.

llbess Exclusion pamal: Cl-ID

N/A

ptitlal;endocrine

Ttme of Blood Sample 07W1168

Testosterone Assay+ RIA

N/A ‘.

RtA

”

RIA

.

dlsorclem 6artsch et al. (26)

.

Bremner et at. (9) Deslypere 8 Mrmeu fen (27) Prkk (28) Frkk & Kbcl(29)

.

74 S-65 volunteers 5436-65’ 29 2382 volunteeni 33 2657 monKs 89 21 4

>l8 volunteers 1835patbnts 3.

N/A .

Yes

yes Yes

yes Yes

24 hourly samples o500,~, f266 1666,1969

RIA RIA

N/A

N/A

N/A

N/A I

N/A

N/A

CPB CPB

no;hadBPH

no;hadhtstok+ai dlagnosisof testkuler tls-

.

.

??

SUB

Gandy 8 Peterson (2)

.

14 @MO N/A 60

2040

Giusti et al. (36)

36

17-86 patiis

Hatberg et al. (31) Harman 8 Tsitouras (4) Haug et al. (32)

Horst et al. (33)

29

I . Horton et al. (20)

I

Hudson et al. (34) lshimaru et at. (35)

.

Kent 8 Acone (3) Lewtsetal. (36) Mahoudeau et al. (37)

.

.

N/A .

no; some had BPH or prostatk carcinoma

N/A I

Y

at lime of surgery

RIA

13 5669patbnts

partial; those Known to partial; endocrbs disorders excluded &CtT

6sS1ooo

RIA

89 2589 voluntesrs

partial; those Known to partial; except mild rdfectT prostatk hypemophy

1400,1415,1430. 1445

RIA

43

partial; those known to pertiat: endocrbe affect hypothatamk - dtseases; had CHD or piMary - gonadal axts ukers

9699looO

RlA

partial; hormonal treat- yes ment

o6aLlal6

RlA

z6

Wlenb volunteers

3067 volunteers

12 62-85 patients 7 4962 -

. no;used estrogen

16 2639 N/A 41 6990 patients

N/A ”

78 17-99 N/A N/A 16 2939 sameesHortonetal. (29) 7 61-86 patbnts N/A 68 2993 pattents. NIA volunteers 98 2986 N/A 56 2684patients

29 51-66 ”

NfA partial; hormonal drugs

,

I no;h&-JBPH no; had prostate cancer . 99961100 WA partial: major diseases; ”

RIA RIA

had prostatk hyperPbsk N/A

DI

no; had BPH u8w partial; endocrine, 69691100 hepatk. renal diseases

RiA DI

Y p%t: BPH or prostatk %:z cancer; had urobgkai disorders paWI:prostatk cancer; ” hadBPH

RlA RlA

Yes

Yes

Marrama et al. (39)

110 1694 voluntWrri

Yes

partial; CHD, endocrine o8W-CQW disorders

Muta et al. (42)

31 23-92 patients 264 26195 patients

*

0745,8666,0615. 1945,2966,2045

RIA R!A

parttai; those Known to partial; kklney, hepatii, N/A sdfeCtT endocrbe diseases

RIA

N/A

NIA

before breakfast

RIA

18 2586 volunteers

yes

Yes

0830,9845 o900, 1636.1645,1798

RIA

88

partial; those affecting yes hypothaiamk - pk~itar-y- gonadal axlS

9898

RIA

18-95 volunteers

Nankln & Catkins (43)

20 2283 volunteers yes . 5 27-37 patients $iai: . . 15 4869 . *CPB: competttiie protein binding. RIA: radbimmunoassav. DI: double isotope,

. .

I .

N/A

46 21-98 volunteers

hbroz 8 VerKhratsky (41) Murono et sf. (5)

.

partial; anesthesiawas n&U had hernia lrkluded

htarrama et at. (38)

Ma221et al. (46)

DI

RlA were Impotent

.

. .

continued overlegf

ANNAGRAYet al.

616

Table l-continued lIe&ence Nawela et al. (44)

8ampis Age She Rang e 61 20-89

sample 8slsctbn volunteers

Medkatbn N/A

pattlai: endocrine, hqatk,renald~

pat&Its

N/A

no; had h#ertensbn, cwdbvascutar. cerebrovascular dlseapes

c#oo, 1200,1800, 2ooo,2400, OOJ

RtA

N/A N/A

N/A Yes no; had cardiac probterns,prostatk hypectrophy

N/A o&YS1@x

RtA RIA

Exclusbn

Illness Exclusbn

Ttme of BW 8tWllple morning

Tegtosterone RtA

Nkotau a at. (45)

25

Nieschtq et& (48) Nteschlag et al. (6) .

35 M80 11 24-37 228@88

N/A voluntwrs *

Rultagua e( al. (47)

25 84

2538 51-80

volunteers patients

Y’E”

I ;ia: endocrine, testkular condttbns; had prostatk CA

oooO,0815 ”

RtA

Pwsky et al. (48)

84

17-88

votunteers

N/A

N/A

CPB

. Plrkeet al. (49)

ogoo,0845,1005, 1025

6 1858

45 I!+81

pattents volunteers

Yes

:lat; hepatk, endocrlns dbeases ?? , butsome bed etsvated bbod sugar no;hadavarkocels

varted

RtA

??

85-87

??

.

I

35 W-93 InhabItantsof homes for aged 15 15-44 patients

. ,

25m-40 336w3

I

.

’ pattiat; homlonel drugs

. .

??

??

no;werelnfetllle no;hfd prostatk carcinoma

Rivarola 8 Migeon W) Rubetls et at. (51) .

13

18-40

30 w 30s”

N/A N/A

I_

.

*

.

.

’

.

”

.

.

. .

’

N/A

N/A

N/A

M

N/A I

Yes

N/A

RtA

loo@lloo

RtA

partial:prostatk hyper-

trophy: had athsfos-

a

.

ClWOSiS skolddors

et al.

loo

4@78

patients

Yes

42

ZS78

volunteers

Yes

(51) Snyder et al. (53)

partial: endocrtns disorders; had minor operalbns partial; those affecting hypothalamk - plluihty

3 samptes In am. by 15 mln

RtA

separated

yes

Yes

083Qo840

RtA

kysome using digl-

Yes ’

o800-1100

RtA

N/A 0800,osoO, 1400, 1500

CPB RtA

CWO, 1200,1800. 2ooo. 2400 ’

RtA

o8oScQoo

GC

Varied

RIA

- testicular axis 8parrowetat.

(7)

85

31-88

volunteersfrom Normative Aging SudY

steams et al. (54)

80 172

l&l9

80-98

volunt~ ' from gerta-

.

trk instltutbn Tanda (55) Tsltouras 8 Hagen (58) verrneuletl8 verdonck (57)

.

wnleuletl et at. (58) warner st al. (sa)

I .

70 48

>15 21-85

47

0.40). As expected, SEM was also negatively correlated with sample size (r = -0.3192, p < 0.0001). The relation between T and SEM is essentially unaffected by the relation between SEM and sample size as indicated by almost no change when a multiple regression model is used (partial r = 0.2281, p < 0.005). The type of hormone assessment used does not appear to be predictive of the SEM (p > 0.40); therefore, competitive protein binding is no more variable in the observed studies than RIA, for a given level of T and sample size. These results support the suggestion from a visual inspection of graphs and raw data included in some of the studies, that the distribution of T levels is skewed. (b) Summary of study characteristics. From the 44 studies, 157 mean T levels were obtained for a variety of age subgroups, with a mean subgroup size of 24.7 and an average mid-age of 55.5 years. There were 12 subgroups (comprising 189 people) with mid-ages over 80 years. As shown in Table 3, under 25% of these subgroups were composed of subjects who were

Y=

every 20m for 24h

RlA RIA

.

RIA

completely free of any detected illnesses, and not taking any medication. Equal proportions of subgroups were selected from patient and volunteer samples, with each type representing about one-third of the subgroups. With regard to methodologic design, the majority of subgroups (about 73%) had blood samples collected in the morning only, and most had T assessed by RIA (about 77%). Less than onethird of the hormone assessment techniques were judged to be good or excellent. The large proportion of subgroups for which important sample and methodologic characteristics were not reported is particularly striking. For example, for about one-quarter of the subgroups it was not known whether subjects with concurrent illness were excluded, while for over 45% of the subgroups, current use of medication was not reported. Even the mode of selection of men who comprised the subgroup was not reported in 19% of the subgroups. There was substantial overlap between areas of poor reporting. For example, 50% of the studies giving no information on illness status also gave no information on sample selection.

ANNAGRAYet

678

al.

Table 3. Summary of characteristics of 157 subgroups included in meta-analysis of mean testosterone levels awacterlslks

Frequency (wcentage)

Mean mId-ai3e (yr)

Samplecomposltbn (1) Selectbn

All patients All volunteers Gerlatrlcinstituiloninhabitants Mixed Other (e.g., monk) No information

53 53 5 12 4 30

(34%) (34%) (3%) (W (3%) (IQ%)

58.8 53.5 79.2 55.2 61.7 46.7

(2) Healthstatus

All illnessexcllKled EndocrineIllnessexcluded No Illnessexclucled No information

3Q (25%) 63 (4w 17 (11%) 38 (24%)

53.8 5Q.5 56.1 49.6

(3) Medlcatbn usage

All medicatbn excluded Medicationaffectingendocrinefunctionexcluded No medkatbn excluded No Information

38 43 5 71

(24%) (27%) (3%) (45%)

2: 78.5 54.0

115 3 11 28

(73%) (2%) (7%) (16%)

fdethodobgb

design

(1) Time of bbod sampling

Morningonly Afternoon/evening0nV Mixedtimes No Information

(2) Hormoneassessmenttechnique Radblmmunoassay Competlive proteinbinding other (3) Hormoneessessmentquality

Totallyunacceptable Poor-fair Average/acceptable Good

Excellent Insufficientinformation

(c) Methodologic and sample effects on mean testosterone levels. These analyses were con-

cerned with the level of T observed across different study and subject characteristics. The overall weighted mean T level was 479ng/lOOml with an SEM of l.l5ng/lOOml. Figure 1 presents the mean T levels for categories of illness status, medication use, and sample selection. As might be expected patients had a lower mean T level than volunteers (473 vs 525 ng/lOO ml respectively). Inhabitants of geriatric institutions had the lowest mean T level (368 ng/lOO ml), but this result should be treated cautiously because of the small number of subgroups in this category. Medication status also appears to be related to T, with the lowest level occurring when subjects were not excluded because of medication usage (357 ng/lOO ml), and the highest level occurring when subjects receiving any medication were excluded (495 ng/ 100 ml). The results for health status are somewhat confusing because the subgroup in which only healthy men are included does not have the highest T level. Although when subjects with any diseases are excluded the level of T is slightly higher than when no subject was

12l (Two) 15 (10%) 2l (1%)

0 Kw 43 (27%) 42 45 6 21

(27%) (29%) (4%) (13%)

51.8 55.5 56.9 51.4 56.7

excluded because of‘the presence of disease (486 vs 474 ng/lOO ml respectively), the highest level was observed in subgroups from which only those men with endocrine disorders were excluded (500ng/ 100 ml). With respect to methodologic characteristics (see Fig. 2), mean T appears to increase linearly with the quality of the analytic technique. The highest mean T was found in studies where not enough information was given to judge the quality of the hormone assessment (557 ng/ 100 ml). Not surprisingly the type of hormone assessment technique was also found to be related to mean T levels. The lowest levels are found in subgroups in which competitive protein binding was used (447 ng/lOO ml), intermediate levels for RIA (478 ng/lOO ml), and the highest levels occurring in subgroups in which other techniques were used (549 ng/lOO ml). An examination of the effect of time of blood sampling on T levels reveals a greater mean level when blood is sampled in the morning (480 ng/ 100 ml) than in the afternoon (414 ng/lOO ml), but when blood is sampled without consideration of time of day, T levels are the highest (500ng/lOOml). Again, these results are to be

679

Testosterone and Male Aging Selection patients 71

All

Geriatric

473

1525

(

All volunteers

368 p .~.~.~.~.~...~ ,.,.:.::.:.::I1:1.::1:::1::.:.,.::. ,...,.:::.,., ;:.:.,.;>:.: :;.:.:.:::;...:.:::::.:::::::.:.:::~:~:~::: :q 537

residents Mixed

.........................M

other w

“E 0” Y L? 5 s t

Sample All

illness

heolth

excluded

Endocrine

illness

No illness

status

71

486

ex

excluded 514

No information

: a z s

Medication

,z

All

I!

Endocrine

medication

excl.

Fig. 1. Mean testosterone

usage 71

~:,.~.~:::~~ I: ,,;j ~~.:?~.~: :,.,. ji.,::‘~::::.-~I

(d) Methodologic and sample efects on the relation between testosterone and aging. In the next analysis the slope of relation between T and

age was examined for each category of the methodologic and sample variables using general linear modeling. Overall, as expected there was a significant relation between T and advancing age (b = - 3.05, R* = 0.29, p < 0.0001). There was no evidence to support a curvilinear age-T relation over the age range studied; the coefficients for the age squared terms were not statistically significant. As shown in Fig. 3, the Time of blood Morning only 0

?

480

levels by sample characteristics. Results from 44 studies reporting mean testosterone levels by age group.

interpreted with caution due to a very small number of subgroups that did not have blood sampled in the morning.

Afternoon/

495 449

med. excl.

No medication excl. No information

3 E

506 500

No information

type of T assessment used clearly affects the ageassociated change in T (p < 0.05) with the strongest relation and steepest slope occurring when neither RIA nor competitive protein binding was used (R* = 0.69, b = 6.19, p = 0.001). A significant effect on the slope was found for time of blood sampling (p < 0.01) with subgroups where no relevant information was given showing steeper slopes and the few subgroups with afternoon blood sampling showing a non-significant positive relation with age (see Fig. 4). The type of T assessment also appeared to be related to the slope (p < 0.05) while method quality had a much weaker effect (p = 0.47).

sample

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Fig. 2. Mean testosterone levels by methodologic characteristics. Results from 44 studies reporting mean testosterone levels by age group. CE 44,7--E

ANNA GRAY et al.

680

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Fig. 3. Predicted relation between testosterone and aging by hormone assessment technique. Results from 44 studies reporting mean testosterone levels by age group. Values in parentheses indicate amount of variance in testosterone explained by age. The weighted general linear model relating mean testosterone (T) level to age and assessment technique indicates that the age-T slope varies significantly with type of assessment.

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Fig. 5. Predicted relation between testosterone and aging by health status. Results from 44 studies reporting mean testosterone levels by age group. Values in parentheses indicate the amount of variance in testosterone explained by age.

Multiple regression results

With respect to sample characteristics, medication usage had a small non-significant effect on the T-age relation. A significant effect on the age-related change in T for general health status was found (p < 0.005). The steepest slope resulted when no relevant information was given, and a near zero slope was found when no ill subjects were excluded (see Fig. 5). Finally, the selection of subjects significantly altered the slope between age and T (p < 0.02) where too, a steep slope was found when no information about selection was given. A non-significant relation was found when only patients were used (see Fig. 6).

A multiple regression model was fit, including main effect terms for all of the sample and design characteristics and all of the interactions with age. This was done to control for confounding among the predictors since, for example, studies that did not report information on one characteristic tended not to report information on others as well. In the full model, the significant predictors of T were general health status (p = O.Ol), time of blood sample (p = O.OOS), age (p = 0.002), and the interactions between age and the other variables (health status, p = 0.02 and time of day, p = 0.01). These 3 variables and the interactions all remained strong predictors in a model excluding the other non-significant variables.

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Testosterone and Male Aging

Analysis of the age-testosterone ies reporting correlations

relation in stud-

The problems in the interpretation of studies correlating age and T have been discussed above. The large number of previous correlational studies, however, made it impossible to ignore their results, in spite of the inherent weaknesses with such studies. Twenty-three correlations with an average sample size of 100 were obtained from the 19 studies. Overall, the average correlation between age and T, based on a weighted combination of Fisher Z-scores, was -0.18 (95% CI -0.21, -0.14). Thus, only about 3% of the variance in T is related to age under the assumption of a linear age-T relation. The effects of study design and sample composition were examined but the small number of studies with any particular characteristic made interpretation of the results difficult. Thus, these results are not reported here.

CONCLUSIONS

AND

IMPLICATIONS

The results of the meta-analysis may be summarized as follows: (1) T and sample size, but not hormone assessment technique, are independently related to the SEM in previous studies; (2) overall, there is a moderate relation between T and aging, but this relation is considerably weaker for studies reporting correlations; (3) general health status predicts both the level of T and the slope of the relation between T and aging; (4) sample selection significantly alters the age-T relation in a variable analysis, although the relation is not significant in a multiple regression model. To a small extent, sample selection predicts the level of T; (5) medication is a significant predictor of T level in the univariable setting but it does not affect the age-associated change in T; (6) hormone assessment technique predicts the level of T and affects the relation between T and advancing age, in a univariable model; (7) hormone assessment quality is a predictor of T level in a univariate model; (8) time of blood sampling significantly affects both the level of T and the slope of the relation between T and aging. (9) In general, studies with no information given for the characteristic of interest seem to have relatively high mean T levels and relatively steep slopes of the relation between T and age. (10) In a multiple regression model, the important predictors of both level of T and the

681

slope of the age-T relation are general health

status and time of blood sampling. The difference between results from the analysis of means and the analysis of correlations with respect to the strength of the age-T relation may be related to a statistical artifact. For studies of means, average T levels are taken from many subgroups and correlated with “average” ages from those subgroups. The variability of individual measurements around the subgroup means is lost (except as a weight) when computing the correlation. This would tend to strengthen correlations estimated in this manner. For correlation studies, the wide variability in T among individual men is an integral part of the correlation coefficient for that study. Such correlations, statistically, have to be relatively weak compared to those estimated from means across subgroups. This meta-analysis has demonstrated the importance of methodologic and sample factors as moderators of the observed association of T with male aging. Researchers must carefully consider time of blood sampling, type and quality of hormone assessment, subject selection, general health status, and medication usage, because these variables appear to influence T level and/or the age-T relation. Indeed, many of the inconsistencies observed among past research studies may be related to differences in design and sample composition across studies. Because these results suggest that sample and design characteristics are moderators of study results, future researchers who assess T need to report thoroughly information about all the study features examined in this review. In this way, the reader can assess the validity of the effect observed in the study. The importance of other subject characteristics such as smoking, obesity and design factors such as the number of blood samples drawn that were not assessed in the review, because very few studies reported this information, may also alter T levels and/or their relation to age. Apart from the substantive issues reported, this study has clearly illustrated the utility of meta-analysis in the field of endocrinology: (1) possible sources for the discrepancies in past results were found, (2) moderators of the agerelated decrease in T were identified, (3) the moderate overall relation across studies between T and advancing age was established, (4) a statistical problem with previous studies was discovered, and (5) suggestions for the reporting of future published results were made.

ANNAGRAYet

682

Acknowledgements-We thank Dr Donald Brambilla for assistance in the preparation of this manuscript, the visiting scholars at the Whitehead Institute for translation of German and Japanese articles, and Clare Plunkett for preparation of the manuscript. This research supported by NIH/NIA grant No. AG04673.

19.

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