The cincinnati myocardial infarction and hormone family study: Family resemblance for dehydroepiandrosterone sulfate in control and myocardial infarction families

The Cincinnati Myocardial Infarction and Hormone Family Study: Family Resemblance for Dehydroepiandrosterone Sulfate in Control and Myocardial Infarction Families Treva

Rice, Dennis

L. Sprecher,

lngrid

B. Borecki,

Laura E. Mitchell,

Peter M. Laskarzewski,

and D.C. Rao

Dehydroepiandrosterone sulfate (DHEAS) was examined in random (control) and nonrandom (case) families participating in the Cincinnati Myocardial Infarction and Hormone (CIMIH) family study. The case families were ascertained through white men who survived a myocardial infarction (MI) before the age-of 56, whereas control families were recruited through advertisements and through an adolescent boy maturation study. Both familial correlations and genetic effects of DHEAS were investigated. First, maximum likelihood estimates of the sex-specific familial correlations (corrected for nonrandom ascertainment) suggested that there was significant heterogeneity between the two sampling types. This heterogeneity was isolated to the male sibling correlation, which was higher in the case than control families. Post hoc analyses suggested that the sibling group heterogeneity may be in part a function of age, since the control sample offspring were on average much younger than those in case families. No sex dWerences other than those for the siblings were noted in the familial correlations. Second, heritability was investigated in control families using a simple path model (TAU) that allowed for sex differences. The only significant model parameter was the sex-specific familiality (combined polygenic and familial environmental effects), which was larger in females (74%) than in males (29%). In general, these analyses suggested that (1) DHEAS may play only a limited role in the increased risk for premature Ml, and (2) the degree of heritable (familial) variation may be dependent on sex. Copyright 0 1993 by W.B. Saunders Company

I

T IS WELL KNOWN THAT plasma lipid, lipoprotein, and apolipoprotein levels are associated with cardiovascular disease.‘-4 It is also well known that these measurements are related to endogenous hormone levels5-l” However, the familial nature of endogenous hormone concentrations and their association with the familial resemblance of lipoproteins remains unknown. The Cincinnati Myocardial Infarction and Hormone (CIMIH) family study is designed to assess familial relationships among lipids, lipoproteins, and apolipoproteins and their interrelationships with endogenous sex hormones. Our primary objective here is in understanding the familial nature of the individual measures, specifically the adrenal androgen hormone, dehydroepiandrosterone sulfate (DHEAS). Most of the variability of DHEAS levels is a function of age,” with concentrations being low before puberty, increasing at puberty, and then decreasing again with age.” DHEAS is inversely related to coronary artery diseasti.6.13J4 and inversely related to low-density lipoprotein This adrenal androgen is and triglyceride levels. 56.15-17 thought to play a role as a metabolic precursor (or byproduct) of more potent sex steroids (specifically testosterone and estrogen), in bone metabolism, and in the regulation of normal cellular growth.1’,‘s.‘8-2’ DHEAS appears to be familial, with the genetic heritability estimated to be

From the Division of Biostatistics and the Departments of Genetics and Psychiatry, Washington CJniversitySchool of Medicine, St Louis. MO; and the Lipid Research Division, Department of internal Medicine, Universiryof Cincinnati, Cincinnati, OH. Submitted August 3, 1992; accepted November 23. 1992. Supported by grants from the National Institutes of Health (GM 28719 and HD 18281) and the National Institutes of Mental Health (MH31302). Address reprint requests to Treva Rice, PhD, Washington Unrversi~ School of Medicine, Division of Biostatistics, Box 8067, 660 S Euclid Ave. St Louis, MO 63110. Copyright 0 1993 by W.B. Saunders Compaq 00260495193/4210-0009$03.OOiO 1284

greater than 60%. Rotter et aI,” using family data, reported that a genetic factor accounted for 65% of the variation in DHEAS, with about 12% attributable to polygenes alone and more than 50% due to a dominance component: however, there was no evidence for a major gene. The only other report of heritability for DHEAS involved a twin study of adult males, which suggested a heritability of nearly 60%.23 In addition to examining the familiality of DHEAS in the current study, we also explore the relationship between DHEAS and risk for cardiovascular disease. The CIMIH family study includes two samples of families, random and high-risk, with selection of the latter being based on surviving a premature myocardial infarction (MI). If the familial patterns are different between the two samples. then inferences can be made about the role of endogenous hormones on risk for MI. Whereas heterogeneity between samples may indicate involvement of the hormones in cardiovascular disease, homogeneity allows pooling of the samples in order to attain greater statistical power for investigating the significance of sex-specific familial patterns. Here we present maximum likelihood estimates of familial correlations for DHEAS in random (control) and high-risk (case) samples. Ascertainment correction in cast families is achieved by conditioning the likelihood function on the proband’s hormone value. Heterogeneity between case and control samples is examined, as are the significance and sex-specific patterns among correlations. In addition, we present sex-specific pseudopolygenic heritabilities for the control sample using a simple nuclear family path model (TAUz4).

METHODS

AND RESULTS

Sample The CIMIH family study was designed to investigate the role of endogenous hormones and lipids in the etiology of Ml. The study design, sample selection, and laboratory methods are outlined Metabolism, Vol42, No 10 (October), 1993: pp 1284.1290

FAMILY RESEMBLANCE FOR DHEAS

1285

elsewhere.= Briefly, white men who had survived an MI before the age of 56 were recruited in 1988 and 1989 in the Cincinnati, OH, metropolitan area. MI diagnosis was made when the clinical history was consistent with an MI as determined by the referring cardiologist, and one of several criteria based on electrocardiogram q waves. TC-99 scans, nuclear scans, and angiograms were met. Recruitment of the MI population was through private cardiologist referrals, media advertising, and the data base of a previously conducted adolescent boy maturation study.2h.Z7 Approximately 25% of the MI probands were ascertained through the adolescent offspring of the latter study, while the remaining probands were directly ascertained through the MI subject himself. We arbitrarily identified the male subject with premature MI as the proband. Control families were recruited through the adolescent boy maturation study and through local blood centers, media advertising, and area food stores. These subjects were “random” controls, with the children’s father designated as the control “proband.” A further criterion for inclusion in the study was that the family structure consist of a proband, spouse, and at least one natural child all living in the same household; the proband’s parents and siblings also were invited to participate. In the final sample, there was a maximum of four generations in any family as follows: generation 1, parents of the proband; generation 2, spouse and siblings of the proband; generation 3, offspring of the proband; and generation 4, grandchildren of the proband. The analyses reported here are based exclusively on the generation 2 and 3 nuclear families (ie, MI probands and their spouses and children), since there were too few families in generations 1 and 2 or 3 and 4 to permit detailed analysis. However, for comparison purposes, some familial correlations are reported for the generation 1 and 2 nuclear families in which the proband is a sibling. Table 1 outlines the sample sizes, stratified by case versus control samples and by sex within generation; mean ages of individuals in each of the groups are also presented.

was used to define luteal phase in women and was indexed as a 0 or 1 variable, with the value 1 assigned if progesterone levels were greater than 3 ng/mL. Table 1 gives the means and standard deviations in raw (unadjusted) units stratified by sample, sex, and generation.

Data Adjustments DHEAS was adjusted for the effects of age. separately by sex, and for the effects of luteal phase in women on the mean and variance, using stepwise multiple regression. The regression models were developed using only the control families, with certain individuals being excluded, ie, medication users (antihypertensive agents, hormones, corticosteroids, diuretics, hypolipidemic agents, hypoglycemic agents, contraceptives), nonfasters ( < 10 hours), and females who were pregnant or breast-feeding. Luteal phase (in females) and up to a fifth-order polynomial in age was entertained for adjustment in the mean, and up to a fifth-order polynomial in age for adjustment in the variance (heteroscedasticity). Since DHEAS was not normally distributed, it was log-transformed prior to regression analysis. In males up to a fifth-order polynomial in age (accounting for 60.2% of the variance) and in females up to a fourth-order polynomial (accounting for 45.9% of the variance) was needed for the mean effect. Luteal phase did not enter the regression (at the 5% level), nor were there any age effects in the variance (ie, no heteroscedasticity). Using the derived regression models, an adjusted score was computed for every individual in the study including the MI sample, and constituted the phenotype that was analyzed. However, for the purposes of the current study, we excluded individuals who were nonfasting, diabetic, pregnant, breast-feeding, or using oral contraceptives, hormone supplements, or steroids. In addition, of the six pairs of twins, one cotwin was deleted at random (except for one pair that included an MI proband who was nonrandomly retained).

Measures Subjects were fasting for at least 12 hours before the clinic visit. Each family member completed questionnaires assessing physical activity, dietary consumption, and family and medical histories. Blood pressure and various anthropometric traits were assessed, and a graded exercise test was administered. Blood samples were drawn for lipid, lipoprotein, apoliproprotein. and endogenous hormone biochemical assays. Complete details concerning measurement, laboratory assays, and reliabilities are found in Sprecher et al.25 The endogenous hormone reported here is DHEAS, which was measured by double-antibody radioimmunoassay (RIA) without prior extraction; the assay sensitivity was 15 ng/mL. and intraassay and interassay coefficients of variation (CVs) were 5.2% and 10.5%. respectively. Progesterone level was also measured by doubleantibody RIA without prior extraction; the assay sensitivity was 0.1 ng/mL, with intraassay and interassay CVs of 5.8% and 9.0%, respectively. In the context of the present analysis, progesterone

Familial Correlation Model Nuclear families consist of fathers (F), mothers (M), sons (S), and daughters (D), leading to eight possible sex-specific correlations among family members (one spouse [FM], four parentoffspring [FS, FD, MS, MD]. and three sibling [SS, DD, SD]). A maximum likelihood method of analysis provides efficient estimates of the correlations and the means and variances that best describe the covariance structure by fitting the correlations directly to the observed family data. Means and variances for both offspring groups (males and females) are estimated separately by case versus control sample. Means and variances for the case fathers are fixed at the values of the control fathers. The mothers’ means and variances are fixed at their sample-specific values. Correction for ascertainment in the case families is by conditioning the loglikelihood of a family on the hormone value of the proband: in this study, all case probands are fathers. Heterogeneity between the case and control families is tested using the likelihood ratio

Table 1. Sample Sizes, Mean Ages (yr), and Mean DHEAS Concentrations

(ng/mL) Stratified by Sample, Generation, and Sex

ControlFamilies

Case Families

Mean Age

Mean DHEAS

(SD)

Group

N

(SD)

F

229

43.9 (6.7)

2.096.54 (1.144.12)

M

187

41.7 (5.6)

1,238.32 (716.23)

S

305

15.9 (8.6)

D

212

15.1 (9.1)

N

Mean Age

Mean DHEAS

(SD)

(SD)

119

50.9 (7.5)

1,266.45 (781.38)

85

47.1 (8.4)

1.153.81 (887.42)

1,718.70 (1.373.33)

124

21.4 (8.5)

2.13156

1.094.65 (1.077.25)

76

20.2 (8.5)

1,722.34 (1.451.99)

Abbreviations: F, father; M, mother; S, son; D, daughter.

(1.469.62)

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1286

criterion, where twice the difference between the values of the log-likelihood functions obtained under a model with k + w parameters estimated and a reduced model with only k of the parameters estimated is asymptotically distributed as a x2 with w degrees of freedom (dfl. In an overall test for heterogeneity between case and control families, all eight correlations are estimated for each of the two samples, for a total of 16 parameters. Because data for the two samples are independent, these correlations can be estimated independently for each group, and the log-likelihoods can be summed across them to obtain a total log-likelihood for the general model (heterogeneity model). A constrained analysis was performed on both samples simultaneously by equating each of the eight correlations across both samples (homogeneity model). The difference between the loglikelihoods obtained from the homogeneity model and those obtained from the heterogeneity model provides a x2 test of overall heterogeneity between samples with 8 df: Each specific correlation also may be tested for heterogeneity by allowing the parameter of interest to be estimated separately for each sample while the remaining seven parameters are equated across samples. The difference between this log-likelihood and that obtained from the pooled analysis (homogeneity model) provides a test for heterogeneity of the estimated parameter (1 df). In addition to testing for heterogeneity, specific hypotheses for the significance of each correlation as well as for sex-specific patterns in the familial correlations are tested. These tests are also conducted using the likelihood ratio criterion. Heterogeneity testing was performed first, so that homogeneous groups could be pooled to provide greater statistical power (due to larger sample sizes) for testing various sex-specific patterns: the computer program SEGPATHB was used. There are a series of multiple statistical tests. To minimize the possibility of falsely rejecting null hypotheses for one or more of the eight parameters, the overall significance level can be divided by the number of tests used to judge the significance of any test.‘Y However, this Bonferroni adjustment is conservative, since the multiple tests are not independent. It is not clear what an appropriate correction for multiple tests would be, or even if one is necessary.M Since any particular correction could be argued against, the exact test statistics and standard errors are presented so that the readers may draw their own inferences from the data. The unadjusted significance levels for heterogeneity tests between case and control samples are presented in the second column of Table 2. The overall test (first row) suggests that the case and control families are heterogeneous (P = ,041); this heterogeneTable 2. Unadjusted SignificanceLevels for Tests of Heterogeneity Between Case and Control Samples and for Tests of Sex-Specific Familial Patterns Pfor

Pfor Significance

All 8

,041

FM

318

FS

.783

FD

Test and Pfor Sex Differencesand FamilyPatterns

Only same-sex

i ,001

Only male pairs

< ,001

c ,001

Only female pairs

< ,001

.294

< .OOl

Sex-specific patterns:

MS

.596

< .OOl

FS = FD

.779

MD

.297

<.Ool

MS=MD

,083

FS = FD, MS = MD

,190

< ,001

FS=FD=MS=MD

,280

< ,001

SS = DD = SD-a

,049

c ,001

SS = DD = SD-b

,007

SS-a

< ,001

SS-b DD

,560

< .OOl

,002

.049

NOTE. The letter “a” denotes that the control son correlation is zero (SS) or that control sons are equated with the other siblings (SS = DD = SD), and “b” refers to the case sample sons.

ity is traced to the correlation between male siblings (ie, SS). If the Bonferroni correction is used, the male sibling correlation remains significantly different between cases and controls, while the overall test does not. The parsimonious heterogeneity model used as the general one to test all remaining hypotheses involves pooling all correlations across case and control families except for the SS correlation, which is allowed to vary between samples. Two levels of tests are conducted to examine the pattern of correlations among family members. In the first level, each parameter is evaluated for significance. The results of these tests are given in the third column of Table 2. Parameters that are designated “a” and “b” are relevant only for the heterogeneous correlation. The entry under “SS-a” is a test of the control sample parameter being fixed at zero, and that under “SS-b” is for the case family test. Each of the familial correlations is significantly different from zero (Table 2), although the control son correlation is of borderline significance (P = .049). Three additional hypotheses investigated whether only same-sex correlations were necessary (ie, only male-male and female-female pairs were estimated with all opposite-sex correlations being zero). whether only male-male pairs were significantly correlated, and whether only female-female pairs were significant. As shown in the last column of Table 2, none of these models fit the data; this suggests that in general the opposite sex correlations are significant. The second level of tests for sex differences involves examination of various sex-specific patterns. Sex effects in parent-offspring resemblance were tested by equating FS with FD and MS with MD. All four parent-offspring correlations could be equated: the only sex differences noted were among the siblings. All nonrejected sex-specific hypotheses were combined into a single overall test to derive the parsimonious model. Parameter estimates for the heterogeneity model (16 correlations), homogeneity model (eight correlations). parsimonious heterogeneity model. and most parsimonious sex-specific model are presented in Table 3. Under the most parsimonious model (last column of Table 3). spouse resemblance is significant (.1X7), and there are no sex differences in parent-offspring resemblance (FS = FD = MS = MD = 0.252). However, there are sex differences in the sibling correlations, with the SS correlation being lowest in control families (.151), but much higher in case families (.614). The correlations for case and control daughter and opposite-sex pairs are equal and intermediate (372) to the SS correlations.

TAU Model Compatibility of the familial resemblance with a simple hypothesis of pseudopolygenic inheritance was examined using a TAU model24 with sex-specific effects. In the TAU model, the heritability includes both genetic and environmental factors (Fig 1). The DHEAS phenotype (P) is assumed to result from the effects of an unmeasured transmissible factor (T) and an uncorrelated residual not shown in the diagram. Additional sibling resemblance is modeled in terms of correlated sibling residuals (R). Each of these factors is subscripted F for father, M for mother, S for son. and D for daughter. Effects of the transmissible factors on the phenotypes are denoted tmale and tfemale. with tiale and t&,,,,c being the “familiality” estimates (ie, both genetic and cultural heritabilities). The effects of transmissible factors of parents on those of offspring are denoted 7~s and 7~o (for father to son and daughter, respectively) and 7~s and 7~o (mother to son and daughter, respectively). When the four transmission parameters (7s) are fixed at I%,the ts include only polygenic and polygenic-like effects (pseudopolygenic). Correlations between sibling residual environments are denoted sss, soo, and sso. The residual paths, rmale and rfcrnalr, are simply derived as (1 - t$,,c)“Z and (1 - t&,,,,e)liZ, respectively.

FAMILY

RESEMBLANCE

FOR OHEAS

1287

Table 3. Familial Correlations (*SE) Correlation/ Group

Heterogeneity

Homogeneity

Parsimonious Heterogeneity

Most Parsimonious

Control

,147 + ,077

,190 -t ,060

,192 k ,060

,187 k ,061

Case

,274 + ,095 .247 + ,048

.242 r .049

,252 T ,033

,223 + ,062

,222 + ,062

,252

,190 k

,069 ,292f ,096

,235 + ,055

,220 + ,056

,252

Control

,303 f ,068

.357 k ,062

,355 + ,062

,252

Case

,265 + ,138

Control

,117 + ,075

,239 e ,069

,136 2 .073

,151 + .OJl

Case

,629 f ,089

,612 k ,093

,614 k ,092

,479 + ,079

,478 k ,079

,372 + ,051

,347 2 ,060

,346 + ,059

.372

FM

FS

t

Control

.211 + ,059

Case

,322 2 ,089

tfemale

malt

FD Control

,241 2 ,068

Case

,107 + ,149

MS Control Case MD

ss

DD Control

,454 + ,090

Case

,516 k ,177

SD Control

,320 f ,066

Case

,426 k ,131

X2 df P

16.077 8 ,041

5.046

5.663

7

4 ,654

,226

NOTE. If there is no correlation given for the case families, then the case and control families were pooled and the correlation is based on the pooled sample. If no standard error is given, then that group was pooled with a preceding one and the correlation is based on the pooled sample.

Spouse resemblance is modeled as a correlation between mates’ phenotypes @). There are eight observed correlations in families and 10 parameters in the model. Since there are more parameters than observed correlations, some simplifying assumptions must be made for identification of the model. Either the residual sibling correlations (sss, snn, andssn) may be fixed at zero, or the parental transmission parameters (7s) may be fixed at %. In general, when the familial correlation pattern suggests significant additional sibling resemblance (ie, higher sibling than parent-offspring correlations as noted in these data), it is better to fix the 7s at l/2and estimate the residual sibling correlations as we have; therefore, we are fitting a pseudopolygenic model. Only control families were used for estimation of genetic effects using the TAU model. A complete analysis of both case and control samples would involve a formal heterogeneity procedure. Such a study is beyond the scope of the current investigation, and warrants further consideration within the context of other risk factors such as lipids and lipoproteins. A summary of the results for fitting the TAU model to the control family data is presented in Table 4. The only significant parameters are the familialities (&,,,I~ and trema& although the spouse correlation is of borderline significance (P = .062), as are sex differences in the familial@ (P = ,060). All nonrejected hypotheses were combined in an overall test (f,,le = tremale and p = sss = soo = ~so = 0). As indicated in Table 4, the combined model did not fit the data (P = .020). To achieve an acceptable fit, some of the borderline significant

Fig 1. TAU model of sex-specificfamilial resemblance.See text for explanation of symbols.

parameters were released back into the model. Several reduced models are presented in Table 5. When no sex differences were allowed (?,,,,I~ = tremale and sss = SDD = ~so), the familiality was 44%. When the residual sibling correlations were further constrained to be zero (rmafe = tremale and sss = SDD = sso = 0), the familial@ did not change appreciably (45%). When only sexspecific familiality estimates were allowed (p = sss = SDD = ~so = 0), the pseudopolygenic heritability was higher in females (.736) than in males (.285). The best model using both likelihood ratio tests and Akaike’s3’ information criterion ([AK], which is twice the number of estimated parameters minus twice the loglikelihood, and the “best” model is the one with the smallest AIC) is the latter one. as noted in the last column of Table 5. DISCUSSION

A complex biochemical system underlies the production of endogenous hormones, and there is a great deal of variability in plasma concentrations both between the sexes and within an individual. In males, testosterone is diurnal and pulsatile in nature, but estradiol levels are relatively stable; in females, estradiol levels are extremely variable during the course of the menstrual cycle, but testosterone levels are relatively stable. However, in comparison, DHEAS is relatively stable in all individuals, being produced primarily through the adrenal pathway throughout the life span. Age is a potent factor influencing the variability of nearly all

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1288

Table 4. Unadjusted Significance Levels for Tests of Familiality in Control Families Using a Pseudopolygenic TAU Model (Trs=TFD=TMs=tMD=1/2) Parameter Definition

Hypothesis

p=o

Correlation

-0

r male rfemale

-

0

=

Square root of familiality

in males

Square root of familiality

in females

,001 ,001

Correlation

between sons’ residuals

,664

SDD =

0

Correlation

between daughters’

,218

.s&

0

Correlation

between

=

t male

=

kxnale

sss= SDD= t m.le

=

t maie =

SSD

tfemale and sss= SOD = %D tIemale and P = sss= sDD = ssD = 0

General

No Sex Effects

No Sex Effects No Sibship

,349 018

Combined

,020

Most Parsimonious

,148 + ,077

,138 k ,078

,136 2 ,078

,278 2 ,096

,440 2 ,064

,452 + ,059

,285 * .082

440

,452

,736 2 ,111

,056 2.088

PI WI PI

,659 ? ,134

&ID GO

+ ,111

,171 + ,132

.056

,270 + ,277

,056

P AIC

12.000

WI

PI WI Ku

,018

,033

,208

16.064

14.473

9.878

NOTE. If no standard error is given, then that parameter was equated to a preceding

one. Parameters

in brackets

were fixed

,402

Overall sex effects

t2nd,

-.048

sibling residuals

Sex effect in residual sibling correlation

P r*tew&

residuals

,060

Table 5. TAU Model Parameter Estimates (*SE)

Parameter

opposite-sex

Sex effect in square root of familiality

endogenous hormones, with the lowest levels occurring prepubertally and later in life and the highest levels occurring during the reproductive years. In the current study, the DHEAS correlations were suggestive of a familial, possibly genetic, pattern. In general, a familial pattern is exhibited when the correlations are significant, as found in these data. Additionally, genetic factors can be inferred when parent-offspring and sibling correlations are greater than spouse correlations, since (on average) spouses share no genes in common, whereas siblings and parent-offspring pairs share one half of their genes in common. Although the parent-offspring correlation (.252 4 .033) tended to be larger than that between spouses (.187 ? .061), they were not significantly different based on a comparison of standard errors. Another indication of familial effects is when sibling correlations are larger than parent-offspring resemblance, since in addition to sharing one half of their genes in common, siblings also share other sources of covariance due to genetic dominance and/or correlated environments. Sibling resemblance, then, should be at least as large or larger than parent-offspring resemblance. With one exception, our results suggest that sibling resemblance (.372 * ,051 for female and cross-sex pairs and ,614 + .092 for male pairs in the MI families) is greater than that between parents and offspring, and also is greater than spouse resemblance. The exception is the SS correlation in control families (.151 ? .071), which is in fact smaller than either spouse or parent-offspring resemblance. Finally, when spouse correlations are significant, some

shown.

,062

0

.ss!j =

Sss

P

between spouses’ phenotypes

at the value

test

degree of cultural (common environmental) effect may be indicated. Thus both cultural and genetic sources of resemblance are suggested for DHEAS. The familiality hypothesis was further investigated using a simple path model (TAU), which allowed for sex differences in the estimates. Sibling resemblance in this model arises through two sources, that originating from parent to offspring transmission and that which is specific to siblings (extra sibship resemblance). The variance due to the latter component was not significant in these data. The familiality (t’) estimates in this model may be considered largely polygenic, since transmission from parents to otfspring was fixed at one half (pseudopolygenic). The results suggested that the pseudopolygenic heritabilities may vary as a function of sex, with a larger effect in females (74%) than in males (29%). When no sex differences were allowed, the pooled estimate was 45%, which is smaller than that reported by Rotter et al*? using a random sample of nuclear Families (65%) and that reported by Meikle et al’3 using a random sample of adult male twins (58%). In the former study, the genetic effect included both additive and dominance components, which may explain their larger estimate; furthermore, twin studies often give larger genetic estimates than family studies. The novel result arising here is the suggestion that the heritability for DHEAS may vary by sex, with a larger effect in females than in malts. Some support was found for a relationship between DHEAS and increased risk for MI. as indexed by the heterogeneity between case and control sons. and with greater resemblance in cases (.61) than in controls (.12). Although the same pattern was found for male siblings in the (older) generation 1 and 2 families in which the proband and his siblings constituted the sibship (.57 in case and .48 in control families), the magnitude of the difference in the correlations is certainly minimal. We also note in Table 1 that the case sons have higher mean levels than control sons (although the control son levels are not as low as those for the case fathers). If any mean level difference in sons was observed, we would have expected just the opposite pattern (ie, lower levels in case than in control). That is, it is known that DHEAS is inversely related to coronary artery disease,5~b~‘3J~and we did observe (in Table 1) the lowest DHEAS levels for the case fathers. Then, to

FAMILY

RESEMBLANCE

1289

FOR DHEAS

the degree that there are genetic factors influencing both DHEAS and coronary artery disease, the case sons also should have had lower levels, not the higher ones they exhibited. A possible confounding factor is that the control families (including the sons) are on average younger than the case families. This suggests that the lower mean value and smaller correlation in control sons may be due to the fact that they are on average younger, thus being less stable due to fluctuations in DHEAS levels because of pubertal development. In support, we previously observed that correlations between male siblings in the older generation were homogeneous across samples. In post hoc analyses (not reported), we investigated the age hypothesis by deleting all male offspring who were younger than age 16, thus ensuring that the majority of the sample was postpubertal. While the sibling correlations for these older sons remained heterogeneous between cases and controls (P = .02), the correlation in control sons did increase (to .23), as did the mean levels in both controls (2,788.06 ? 143.28) and cases (2,604.20 2 146.59). Although the difference in levels is now in the expected direction (ie, lower case than control mean), the difference is not significant based on a comparison of standard errors. Thus age probably explains the differences in mean levels across groups, and may explain part of the difference in the magnitude of the correlations as well if a Bonferroni correction is used. Other possible explanations for the group heterogeneity include environmental factors such as life-style changes in diet and exercise in the post-MI case families. Although post hoc examination of son groups as a function of time since father’s MI did not support this notion, additional data in terms of measures and sample sizes are needed to fully address the question. The underlying mechanism for the heritability of DHEAS remains unclear. The gene for 21-hydroxylase (related to congenital adrenal hyperplasia)32 has been mapped to the DNA region between HLA-DR and HLA-B, whereas

serum testosterone levels in women33 (which are largely derived from DHEAS) have been found to be associated with the HLA gene region on chromosome 6. These data may recommend the histocompatibility region as being relevant to the expression of various adrenal enzymes and thereby partially responsible for DHEAS heritability. Alternatively, response elements in the corticotropin gene may be transmitted between generations. Corticotropin infusion has been associated, after a significant time delay, with enhanced DHEAS plasma levels.34 The corticotropin DNA sequence resides on the pro-opiomelanocortin gene located on chromosome 2.35 However, heritability of plasma cortisol (a hormone more acutely responsive to corticotropin stimulation) and DHEAS has not demonstrated shared factors in the past.‘” It is possible that disparate metabolic characteristics for these two hormones may confound such a comparison. Third, it has been reported that DHEAS plasma levels are associated with obesity, which in turn has heritable components.jh Finally, steroid sulfatase, relevant to conversion of DHEAS to DHEA, has been isolated, cloned, and located on the X chromosome.37JR The role this enzyme may play in DHEAS plasma concentration may explain the differences between male and female heritability observed for DHEAS in the current report. Together, the correlation and genetic analyses of the CIMIH data support the notion that DHEAS may be familial (possibly genetic) in origin, with possible sex differences in the genetic variance. We also note that DHEAS concentrations may be indicated as a risk factor for MI, a finding recently supported in a case-control study of these same probands?” However, age and possibly unspecified environmental factors may have influenced these results. Further study of how these cofactors relate to the familial aggregation of DHEAS is suggested. Finally, investigation of the relationship between the genetic components of DHEAS and those of cardiovascular risk factors such as lipids and lipoproteins is indicated.

REFERENCES

1. Goldbourt ease mortality: Arteriosclerosis

U, Yaari S: Cholesterol and coronary heart disA 23-year follow-up study of 9902 men in Israel. 10512-519, 1990

2. Pekkanen J, Linn S, Heiss G, et al: Ten-year mortality from cardiovascular disease in relation to cholesterol level among men with and without preexisting cardiovascular disease. N Engl J Med 322:1700-1707,199O 3. Steinberg D, Parthasarathy S, Carew TE, et al: Beyond cholesterol: Modifications of low-density lipoprotein that increase its atherogenicity. N Engl J Med 320:915-924, 1989 4. Berenson GS, Srinivasan SR, Wattigney W, et al: Insight into a bad omen for white men: Coronary artery disease-The Bogalusa Heart study. Am J Cardiol64:32C-39C, 1989 5. Herrington DM, Gordon GB, Achuff SC, et al: Plasma dehydroepiandrosterone and dehydroepiandrosterone sulfate in patients undergoing diagnostic coronary angiography. J Am Coll Cardiol 16:862-870, 1990 6. Nestler JE, Barlascini CO, Clore JN, et al: Dehydroepiandrosterone reduces serum low density lipoprotein levels and body fat

but does not alter insulin sensitivity Endocrinol Metab 66:57-61. 1988

in normal men. J Clin

7. Freedman DS, O’Brien TR, Flanders WD, et al: Relation of serum testosterone levels to high density lipoprotein cholesterol and other characteristics in men. Arteriosclerosis 11:307-315, 1991 8. Mendoza SG, Zerpa A, Velazquez E, et al: Sex hormones, lipids, lipoprotein cholesterols, and apolipoproteins in normal and obese subjects: Atherogenic relationships. Int J Obes 10:427-441, 1986 9. Hill P, Wynder E, Garbaczewski L, et al: Plasma hormones and lipids in men at different risks for coronary heart disease. Am J Clin Nutr 33:1010-1018, 1980 10. Berenson G, Srinivasan SR, Cresanta JL, et al: Dynamic changes of serum lipoproteins in children during adolescence and sexual maturation. Am J Epidemiol 113:157-170, 1981 11. Drucker WD, David RR: Plasma dehydroepiandrosterone sulfate (DHAS) in normals and patients with hyperprolactinemia, in Genazzani A (ed): Adrenal Androgens. New York, NY. Raven, 1980, pp 89-93

1290

12. Akamine Y, Kato K, Ibayashi H: Studies on changes in the concentration of serum adrenal androgens in pubertal twins. Acta Endocrinol93:356-364, 1980 13. Barrett-Connor E, Khaw K-T, Yen SSC: A prospective study of dehydroepiandrosterone sulfate, mortality. and cardiovascular disease. N Engl J Med 315:1519-1524.1986 14. Orenteich N, Brind JL. Rizer RL, et al: Age changes and sex differences in serum dehydroepiandrosterone sulfate concentrations throughout adulthood. J Clin Endocrinol Metab 59551-555, 1984 15. Parker CR, Simpson ER, Biheimer DW, et al: Inverse relation between low density lipoprotein cholesterol and dehydroepiandrosterone sulfate in human fetal plasma. Science 208512. 514.1980 16. Sonka J, Fassati M, Fassati P, et al: Serum lipids and dehydroepiandrosterone excretion in normal subjects. J Lipid Res 91769-772, I968 17. Adlercreutz H, Kerstell J, Schaumann K-O. et al: Plasma lipids and steroid hormones in patients with hypercholesterolemia or hyperlipidemia during dehydroepiandrosterone sulfate administration. Eur J Clin Invest 2:91-95, 1972 18. Brody S, Carlstrom K, Lagrelius A, et al: Adrenal steroids, bone mineral content and endometrial pathology in postmenopausal women. Acta Obstet Gynecol Stand 60:325-326, 1981 19. Marks P, Banks J: Inhibition of mammalian glucose-6phosphate dehydrogenase by steroids. Proc Natl Acad Sci USA 46~447-452, 1960 20. Schwartz A: Inhibition of spontaneous breast cancer formation in female C3H(A/a) mice by long term treatment with dehydroepiandrosterone. Cancer Res 39:1129-l 131, 1979 21. Coleman D, Schwizer R, Leiter E: Effect of genetic background on the therapeutic effects of DHA in diabetes-obesity mutants and in aged normal mice. Diabetes 33:26-32. 1984 22. Rotter JI, Wong FL, Lifrak ET, et al: A genetic component to the variation of dehydroepiandrosterone sulfate. Metabolism 34:731-736, 1985 23. Meikle AW, Stringham JD, Woodward MG, et al: Heritability of variation of plasma cortisol levels. Metabolism 37514-517. 1988 24. Rice J, Cloninger CR, Reich T: Multifactorial inheritance with cultural transmission and assortative mating. I. Description and basic properties of the unitary models. Am J Hum Genet 42~467-477, 1978 25. Sprecher DL. Huster G. Feigelson HS, et al: The Cincinnati

RICE ET AL

Myocardial Infarction and Hormone (CIMIH) family study: Proband demographics. lipids, apolipoproteins. and endogenous hormones. (submitted) 26. Morrison JA, Lucky AW, Mellies MJ, et al: Sex hormones and lipoprotein abnormalities in adolescent male offspring of premature coronary heart disease patients: Cincinnati Maturation Study. Circulation 74:202, 1986 (suppl2) 27. Lucky AW. Biro FM, Huster GA, et al: Acne vulgaris in early adolescent boys: Correlation with pubertal maturitation and age. Arch Dermatol 127:210-216. 1991 28. Province MA. Rao DC: SEGPATH: A computer program for creating general, combined segregation and path analysis models. 1st Meeting of the International Genetic Epidemiology Society, Minneapolis, MN, June, 1992 29. Miller RG Jr: Simultaneous Statistical Inference. New York. NY. Springer. 1981 30. Rothman KJ: No adjustments are needed for multiple comparisons. Epidemiology 1:43-46. 1990 31. Akaike H: A new look at the statistical model identification. IEEE Trans Automat Control AC 19:716-723. 1974 32. White PC, Werkmeister J, New MI. et al: Steroid :!Ihydroxylase deficiency and the major histocompatibility complex. Hum Immunol 15:404-415. 1986 33. Gerencer M, Tajic M, Kerhin-Brkljafif V, et al: An association between serum testosterone level and HLA phenotype. Immunol Lett 4:155-158. 1982 34. Nieschlag E. Loriaux DL. Ruder HJ. et al: The secretion of dehydroepiandrosterone and dehydroepiandrosterone sulphate in man. J Endocrinol57: 123-134. 1973 35. Orth DN. Kovacs WJ. Debold CR: The adrenal cortex. in Wilson JD, Foster DW (eds): Williams Textbook of Endocrinology (ed 8). Philadelphia, PA, Saunders. 1992. pp 489-619 36. Bouchard C, Despres J-P, Mauriege P, et al: The genes in the constellation of determinants of regional fat distribution. lnt J Obes 159-18, 1991 (suppl?) 37. Kerr CB, Wells RS: Enzymatic basis of typical X-linked ichthyosis and the Xg groups. Lancet 2:1369-1370, 1964 38. Shapiro LJ: Steroid sulfatase deficiency and X-linked ichthyosis, in Striver CR, Beaudet AL, Sly WS. et al (eds): The Metabolic Basis of Inherited Disease (ed 6). New York. NY, McGraw-Hill. 1989. pp 19451964 39. Mitchell LE. Sprecher DL. Borecki IB. et al: Further evidence for an association between dehydrogepiandrosterone sulfate and premature myocardial infarction in males. (submitted)