Age and BMI continuous, categorized for analysisCheck the definition for the race variable- NHANES analytic notes:When using this variable, code 4 (all other) includes otherHispanics, Asians, and Native Americans. The sample size is toosmall to be used analytically and the category is too difficult tolabel. Therefore, this category should be deleted in all tables.However, the "All race-ethnic groups" or "Total" category shouldinclude all persons included in the NHANES III.
We chose k-means cluster for a variety of practical reasons.K means method works by first selecting the number of clusters. We compared 4 to 8 clusters. Then a number of centroid points are determined based on the number of clusters set. These centroid points are chosen based on those that are furthest away from each other. Then each hormone observation is placed into the group with the nearest centroid value. Then the centroid is recalculated as the cluster centers, and then a new binding is done between the same set of data points and the nearest new centroid. This loop then changes the cluster centroid, but then stops moving the center and at this point the final solution is established
The r-squared values were slightly higher for the five cluster solution, otherwise the four cluster solution performed better. Note the pseudo-f statistic was higher for the four cluster solution and the CCC was positive and closer to 2 which indicates valid clusters. The negative values indicate that outliers may be present even though we removed outliers from the analysis.
For the population, most men were 30-49 years of age, Non-Hispanic White and overweight. We used the 30-49 and Non-Hispanic White groups as references in our mutlinomial logistic models. We wanted to explore the overweight body mass index group, so we used the normal weight group as the reference group for BMI. Keep in mind that the percentages and 95% Cis take into account the study design, so 1 person does not represent 1 person. For instance, 365/1528=23.9%, not 29.1%.
We used multinomial logistic regression to examine how age, race, and body mass index are associated with the four hormone profiles. We used the hormone profile as the dependent variable.By age. Note that men in the low SHBG profile 17-29 were less likely to be in all three groups compared to SHBG. Note that men in the low T, E, and 3 alpha diol G profile were more likely to be 50-69 and 70 and over compared to men 30-49 in other profiles.By race. Note that non-hispanic black men were more likely to be in the low SHBG profile compared to non-hispanic whites. So while a higher proportion of Blacks were seen in the high T, E, and SHBG profile, there was not a higher odds of being black in this profile compared to being white. Note that Mexican americans were more likely to be in the low T, E, and three alpha diol G profile compared to non-hispanic whites.By body mass index. Men in all three profiles were less likely than men in the low shbg profile to be obese. Men in the high T, E, and SHBG profile were less likely to be obese or overweight, and more likely to be normal weight compared to low shbg and other profiles.Compared to single hormone studies, our results by age and body mass largely agreed, while our findings by race are novel for Non-Hispanic Blacks and Mexican Americans.
APHA Nov 2011
The association of race/ethnicity, age, and body mass index (BMI) with sex steroid hormone marker profiles among men Jamie Ritchey, MPH, PhD The University of South Carolina Norman Arnold School of Public Health Department 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
Background Single 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) 3
Study Objectives Toaccount for the metabolically linked relationship of sex steroid hormones statistically by determining hormone marker profiles using cluster analysis Toexamine 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 4
MethodsNational 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 5
Methods NHANES 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, 1988-19911,528 Analysis cohort: Men >17 with adequate hormone information and removal of data outliers
Methods Analysis variables Main exposures Additional variables Race/ethnicity Smoking Non-Hispanic Whites Non-Hispanic Blacks Alcohol consumption Mexican Americans Other Dietary fat (total, saturated, polysaturated, monosaturated) Age, years Total calories 17-29 30-49 Liver enzyme levels 50-69 70 and over Cholesterol levels Zinc Body Mass Index, kg/m2 <18.5 Lycopene 18.5-24.9 25.0-29.9 Laboratory day of the week >=30 Fasting time in hours 7
Methods Data 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 8
Table 2. Hormone profiles by mean levels of single hormone markers† Cluster Mean T Mean E Mean Mean Hormone Profile Analysis SHBG 3-α Names group Diol G Total Pop’NGroup 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 GTotal Pop‟n, 0.13 0.06 -0.15 0.16 All profilesn=1,528 10
Table 3. Weighted percentage of demographic characteristicsamong 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) 11
Table 4. Odds Ratios from multinomial logistic regressionDemographics High High T, E, Low T, E, 3α diol G SHBG 3α diol GAge, years17-29 0.4† 0.4† 0.3†30-49 (reference) 1.0 1.0 1.050-69 1.9 2.3† 11.5†70 and over 2.2† 4.2† 24.3†Race/ethnicityNon-Hispanic White (reference) 1.0 1.0 1.0Non-Hispanic Black 0.4† 1.0 0.7Mexican American 1.5 1.4 3.1†Other 0.8 0.4† 1.8Body Mass Index, kg/m2<18.5, underweight 2.1 1.9 1.018.5-24.9, normal (reference) 1.0 1.0 1.025.0-29.9, overweight 0.6 0.3† 0.4†>=30, obese 0.2† 0.05† 0.1† †statistically significant, p<0.05 12
Table 5. Hormone profile results compared tosingle hormone studies Hormone Age Race/ethnicity BMI profileLow SHBG 17-29 NH Blacks Obesity/Overweight Agrees Novel Agrees (6-10,25,33,38) (6-10,25,33,38)High 3-α diol >70 NH Whites NormalG Agrees (7,9,14-16) Agrees (7,9,14-16) NovelHigh T, E, >50, >70 No association NormalSHBG Disagrees (7,39,40) Agrees (3-26)† Agrees (7,39,40)Low T, E, 3-α >50, >70 Mexican Americans Normaldiol G Agrees (2,3,7,41,42) Novel (7,8,12,14) Agrees (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
Strengths and Limitations Strengths Limitations Main exposure 99-100% Smoking, drinking, dietary complete self-reported USrepresentative sample, Single hormone oversamples minorities and over 65 measurements only Hormone measurements Profiles may still be an were standardized and oversimplified model of included testing against metabolism control samples Does not include men in Selected only morning samples prisons Hormone Data available is Controlled for fasting hrs. older 1988-1991 14
Conclusion 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‟ 15
Acknowledgements Co-authorsWilfried Karmaus, MD, Dr.med.,MPH NHANES III study participants University of South Carolina Department of Epidemiology and Mr. and Mrs. Norman J. Arnold BiostatisticsHongmei Zhang, PhD University of South Carolina, University of South Carolina Department of Epidemiology Department of Epidemiology and and Biostatistics BiostatisticsSusan Steck, PhD, RD, MPH Broward County Health University of South Carolina Department Department of Epidemiology and BiostatisticsTara Sabo-Attwood, PhD University of Florida, Department of Environmental and Global Health
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Background Sex 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 linked 20
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.