The association of race/ethnicity, age, and body mass index (BMI) with sex steroid hormone marker profiles among men

Jamie Ritchey, MPH, PhDThe University of South Carolina

Norman Arnold School of Public HealthDepartment of Epidemiology and Biostatistics

Presentation outline Background

Study Objectives

Methods

Results

Strengths and Limitations

Conclusions

Acknowledgements

Comments and Questions

Reference

This work was part of my student dissertation, was not funded, and I have no disclosures to report

BackgroundSingle sex hormone marker studies

Race/ethnicity Differing hormone levels have been implicated in disparities in chronic

diseases (1,2)• Levels of Testosterone (T), Estrodiol (E), and Sex Hormone Binding

Globulin (SHBG) are inconsistent across studies among race/ethnicity groups (3-26)

• Little is known about groups besides Whites and Blacks (3-26)• Few studies examined 3-α diol G (T metabolite)• Social construct with complex exposures (27)

Age T and E mostly decrease with increasing age (3-26) SHBG mostly increases with increasing age (3-26)

BMI Obese men typically have higher levels of E (29) Inverse correlation with BMI & T and SHBG levels has been observed

(29-35)

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To account for the metabolically linked relationship of sex steroid hormones statistically by determining hormone marker profiles using cluster analysis

To examine the hormone profiles, as the dependent variable (outcome) in multinomial logistic regression models and determine if there are differences by:

• Race/ethnicity groups• Age groups• Body Mass Index (BMI) groups

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Study Objectives

Methods National Health and Nutrition Examination Survey

(NHANES III) Data source

Cross-sectional survey (multiple questionnaires) (36,37)

Multistage stratified, clustered probability sample (36,37)

Includes US residents >2 months of age, civilian, non-institutionalized population (36,37)

Oversampled >65 years, Non-Hispanic Blacks, and Mexican Americans (36,37)

NHANES III phase I, the specialized hormone data collected 1988-1991 (36,37)

Data sources include Household interview surveys and Medical examinations, available at: http://www.cdc.gov/nchs/nhanes.htm

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MethodsNHANES III Study population utilized

93,653 NHANES III Screened Households

39,695 NHANES III Screened Individuals

14,781 Men Completed Mobile Center Exams

2,417 Men in Morning Phase I, 1988-1991

1,528 Analysis cohort: Men >17 with adequate hormone information and removal of data outliers

MethodsAnalysis variables

Main exposures Additional variables

Race/ethnicity Non-Hispanic Whites Non-Hispanic Blacks Mexican Americans Other

Age, years 17-29 30-49 50-69 70 and over

Body Mass Index, kg/m2

<18.5 18.5-24.9 25.0-29.9 >=30

Smoking

Alcohol consumption

Dietary fat (total, saturated, polysaturated, monosaturated)

Total calories

Liver enzyme levels

Cholesterol levels

Zinc

Lycopene

Laboratory day of the week

Fasting time in hours

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MethodsData analysis

K-means Cluster analysis in SAS 9.2 Finds clusters with roughly the same number of observations Robust to extreme values The number of clusters can be assigned (in this study 4-8) Can calculate statistics to compare clusters statistically

• Pseudo R-squared• Pseudo F-test• Cubic cluster criteria (CCC)

Multinomial logistic regression models SAS 9.2 Survey methods for complex design Hormone clusters used as outcome variables Main exposures: age, race/ethnicity, BMI Final reduced models included, smoking status, fasting (hrs), clinic day

of the week, liver enzyme levels, exercise amt per month, total calories total fat, monosaturated fat, polysaturated fat, saturated fat, lycopene, zinc, and fiber intake, smoking status

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ResultsTable 1. Cluster analysis statistics

Statistics Five cluster solution

Four cluster solution

R2 0.55 0.45

Pseudo F-stat 441.4 471.4

Cubic Cluster Criteria

-7.8, -8.5 0.9, 1.8

Cluster Analysis

groupTotal Pop’N

Mean T Mean E Mean SHBG

Mean 3-α

Diol G

Hormone Profile Names

Group 1, n=417 -0.25 0.32 -1.10 0.20 “Low SHBG”

Group 2, n=327 -0.02 -0.67 -0.08 0.78 “High 3-α Diol G”

Group 3, n=485 1.00 0.68 0.53 0.15 “High T, E, SHBG”

Group 4, n=299 -0.79 -0.71 0.25 -0.98 “Low T, E, 3-α Diol G

Total Pop’n, n=1,528

0.13 0.06 -0.15 0.16 All profiles

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Table 2. Hormone profiles by mean levels of single hormone markers†

Table 3. Weighted percentage of demographic characteristics among men, NHANES III, (n=1,528)

Demographic Weighted Percentage (95% CI)

Age, years

17-29 29.1% (24.0-34.1)

30-49 37.3% (37.3-47.2)

50-69 21.2% (17.6-24.7)

70 and over 7.5% (5.8-9.3)

Race/ethnicity

Non-Hispanic White 77.4% (71.0-83.7)

Non-Hispanic Black 9.8% (7.0-12.5)

Mexican American 5.3% (3.8-6.7)

Other 7.6% (3.4-11.9)

Body Mass Index, kg/m2

<18.5, underweight 1.4% (0.21-2.63)

18.5-24.9, normal 38.5% (33.9-43.0)

25.0-29.9, overweight 39.7% (35.9-43.6)

>=30, obese 20.4% (16.3-24.5)

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Table 4. Odds Ratios from multinomial logistic regressionDemographics High

3α diol GHigh T, E,

SHBGLow T, E, 3α diol G

Age, years

17-29 0.4† 0.4† 0.3†

30-49 (reference) 1.0 1.0 1.0

50-69 1.9 2.3† 11.5†

70 and over 2.2† 4.2† 24.3†

Race/ethnicity

Non-Hispanic White (reference) 1.0 1.0 1.0

Non-Hispanic Black 0.4† 1.0 0.7

Mexican American 1.5 1.4 3.1†

Other 0.8 0.4† 1.8

Body Mass Index, kg/m2

<18.5, underweight 2.1 1.9 1.0

18.5-24.9, normal (reference) 1.0 1.0 1.0

25.0-29.9, overweight 0.6 0.3† 0.4†

>=30, obese 0.2† 0.05† 0.1†

12†statistically significant, p<0.05

Hormone profile

Age Race/ethnicity BMI

Low SHBG 17-29Agrees

(6-10,25,33,38)

NH BlacksNovel

Obesity/Overweight Agrees (6-10,25,33,38)

High 3-α diol G

>70Agrees (7,9,14-16)

NH WhitesAgrees (7,9,14-16)

Normal Novel

High T, E, SHBG

>50, >70Disagrees (7,39,40)

No associationAgrees (3-26)†

NormalAgrees (7,39,40)

Low T, E, 3-α diol G

>50, >70Agrees (2,3,7,41,42)

Mexican AmericansNovel (7,8,12,14)

NormalAgrees (3-26)††

†Some studies reported higher T levels among Blacks in relation to prostate cancer, although most report no association††Most studies report low T levels with increasing obesity and higher E levels

Table 5. Hormone profile results compared to single hormone studies

Strengths and Limitations

Strengths Limitations

Main exposure 99-100% complete

US representative sample, oversamples minorities and over 65

Hormone measurements were standardized and included testing against control samples

Selected only morning samples

Controlled for fasting hrs.

Smoking, drinking, dietary self-reported

Single hormone measurements only

Profiles may still be an oversimplified model of metabolism

Does not include men in prisons

Hormone Data available is older 1988-1991

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Four distinct hormone marker profiles were statistically determined using cluster analysis, and need to be confirmed in other samples

Age Results were consistent with single hormone studies (6-10,14-16,25,33,38-42)

Older men were strongly associated with ‘low T, E, and 3-α diol G profile’

BMI Findings were consistent with single hormone studies (3-26,33,38-40)

Obesity was more strongly associated with ‘low SHBG’ profile

Race/ethnicity Results were novel, and not consistent with single hormone studies (3-26)

Mexican Americans were associated with ‘low T, E, and 3-α diol G profile’ Non-Hispanic Blacks were associated with ‘low SHBG profile’

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Conclusion

Acknowledgements Co-authorsWilfried Karmaus, MD, Dr.med.,MPH

University of South Carolina Department of Epidemiology and Biostatistics

Hongmei Zhang, PhDUniversity of South Carolina Department of Epidemiology and Biostatistics

Susan Steck, PhD, RD, MPHUniversity of South Carolina Department of Epidemiology and Biostatistics

Tara Sabo-Attwood, PhDUniversity of Florida, Department of Environmental and Global Health

NHANES III study participants

Mr. and Mrs. Norman J. Arnold

University of South Carolina, Department of Epidemiology and Biostatistics

Broward County Health Department

Questions and Comments

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References II

BackgroundSex Steroid Hormone Markers in Men

Testosterone (T) Derived from cholesterol (9) Development of reproductive tissues Muscle, bone, and hair growth

Androstanediol glucuronide (3-α diol G) Terminal metabolite of DHT (10, 15) Used as a marker of DHT conversion Many other metabolites of T metabolism

17-β Estradiol (E) Derived from cholesterol Reproductive and sexual function-- secondary to T Bone development and osteoporosis

Sex Hormone Binding Globulin (SHBG) T and E are bound to SHBG and albumin in the blood (7) Levels are decreased by high insulin and androgen Levels are increased by high growth hormone, estrogen and thyroxin

T, E, SHBG and 3-α diol G are metabolically linked20

Statistics Pseudo R^2

Is a goodness of fit measure. It tells us the proportion of variance accounted for by the clusters.

The values range from 0-100% with 100% explaining all of the variance.

Pseudo F

Another method for examining the number of clusters present in the data. Relatively large values indicate good numbers of clusters.

CCC

Positive values indicate true clusters.

The Cubic cluster criteria or CCC tests the null hypothesis that the data has been sampled from a uniform distribution, and the alternative is that the data has been sampled from a mixture of spherical multivariate normal distributions, with equal variances and sampling probabilities. Positive CCC values mean that the obtained R2 value is greater than would be expected if the sampling was from a uniform distribution (therefore, reject H0). The four cluster solution had a positive CCC value so we can reject the null, while the CCC negative values for the five cluster solution indicates we cannot reject the null.