APHA Nov 2011


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  • 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

    1. 1. 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
    2. 2. 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
    3. 3. 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
    4. 4. 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
    5. 5. 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
    6. 6. 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
    7. 7. 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
    8. 8. 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
    9. 9. Results Table 1. Cluster analysis statistics Statistics Five cluster Four cluster solution solution R2 0.55 0.45Pseudo F-stat 441.4 471.4Cubic Cluster -7.8, -8.5 0.9, 1.8 Criteria 9
    10. 10. 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
    11. 11. 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
    12. 12. 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
    13. 13. 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
    14. 14. 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
    15. 15. 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
    16. 16. 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
    17. 17. Questions and Comments
    18. 18. References I1. Hsing, A.W. and A.P. Chokkalingam, Prostate cancer epidemiology. Front Biosci, 2006. 11: p. 1388-413.2. Yeap, B.B., Are declining testosterone levels a major risk factor for ill-health in aging men? Int J Impot Res, 2009. 21(1): p. 24-36.3. Stanworth, R.D. and T.H. Jones, Testosterone for the aging male; current evidence and recommended practice. Clin Interv Aging, 2008. 3(1): p. 25-44.4. Orwoll, E., et al., Testosterone and estradiol among older men. J Clin Endocrinol Metab, 2006. 91(4): p. 1336-44.5. Vermeulen, A., et al., Estradiol in elderly men. Aging Male, 2002. 5(2): p. 98-102.6. Winters, S.J., et al., Testosterone, sex hormone-binding globulin, and body composition in young adult African American and Caucasian men. Metabolism, 2001. 50(10): p. 1242-7.7. Rohrmann, S., et al., Serum estrogen, but not testosterone, levels differ between black and white men in a nationally representative sample of Americans. J Clin Endocrinol Metab, 2007. 92(7): p. 2519-25.8. Ellis, L. and H. Nyborg, Racial/ethnic variations in male testosterone levels: a probable contributor to group differences in health. Steroids, 1992. 57(2): p. 72-5.9. Ross, R., et al., Serum testosterone levels in healthy young black and white men. J Natl Cancer Inst, 1986. 76(1): p. 45-8.10. Ettinger, B., et al., Racial differences in bone density between young adult black and white subjects persist after adjustment for anthropometric, lifestyle, and biochemical differences. J Clin Endocrinol Metab, 1997. 82(2): p. 429-3411. Ukkola, O., et al., Age, body mass index, race and other determinants of steroid hormone variability: the HERITAGE Family Study. Eur J Endocrinol, 2001. 145(1): p. 1-9.12. Litman, H.J., et al., Serum androgen levels in black, Hispanic, and white men. J Clin Endocrinol Metab, 2006. 91(11): p. 4326-34.13. Gapstur, S.M., et al., Serum androgen concentrations in young men: a longitudinal analysis of associations with age, obesity, and race. The CARDIA male hormone study. Cancer Epidemiol Biomarkers Prev, 2002. 11(10 Pt 1): p. 1041-7.14. Cheng, I., et al., Comparison of prostate-specific antigen and hormone levels among men in Singapore and the United States. Cancer Epidemiol Biomarkers Prev, 2005. 14(7): p. 1692-6.15. Platz, E.A., et al., Racial variation in prostate cancer incidence and in hormonal system markers among male health professionals. J Natl Cancer Inst, 2000. 92(24): p. 2009-17.16. Platz, E.A. and E. Giovannucci, The epidemiology of sex steroid hormones and their signaling and metabolic pathways in the etiology of prostate cancer. J Steroid Biochem Mol Biol, 2004. 92(4): p. 237-53.17. Ross, R.K., et al., 5-alpha-reductase activity and risk of prostate cancer among Japanese and US white and black males. Lancet, 1992. 339(8798): p. 887-9.18. Wu, A.H., et al., Lifestyle determinants of 5alpha-reductase metabolites in older African-American, white, and Asian-American men. Cancer Epidemiol Biomarkers Prev, 2001. 10(5): p. 533-8.19. Eaton, N.E., et al., Endogenous sex hormones and prostate cancer: a quantitative review of prospective studies. Br J Cancer, 1999. 80(7): p. 930-4.20. Wright, N.M., et al., Greater secretion of growth hormone in black than in white men: possible factor in greater bone mineral density--a clinical research center study. J Clin Endocrinol Metab, 1995. 80(8): p. 2291-7.21. Gann, P.H., et al., Prospective study of sex hormone levels and risk of prostate cancer.[comment]. Journal of the National Cancer Institute., 1996. 88(16): p. 1118-26.22. Wu, A.H., et al., Serum androgens and sex hormone-binding globulins in relation to lifestyle factors in older African-American, white, and Asian men in the United States and Canada. Cancer Epidemiol Biomarkers Prev, 1995. 4(7): p. 735-41.23. Atlantis, E., et al., Demographic, physical and lifestyle factors associated with androgen status: the Florey Adelaide Male Ageing Study (FAMAS). Clin Endocrinol (Oxf), 2009. 71(2): p. 261-72.24. Stanworth, R.D. and T.H. Jones, Testosterone in obesity, metabolic syndrome and type 2 diabetes. Front Horm Res, 2009. 37: p. 74-90.25. Derby, C.A., et al., Body mass index, waist circumference and waist to hip ratio and change in sex steroid hormones: the Massachusetts Male Ageing Study. Clin Endocrinol (Oxf), 2006. 65(1): p. 125-31.26. Osuna, J.A., et al., Relationship between BMI, total testosterone, sex hormone-binding-globulin, leptin, insulin and insulin resistance in obese men. Arch Androl, 2006. 52(5): p. 355-61.
    19. 19. References II27. Kreiger, N. Refiguring “race”: epidemiology, racialized biology, and biological expressions of race relations. Int J Health Serv. 2000; 30(1): 211-6.28. Mohr, B.A., et al., The effect of changes in adiposity on testosterone levels in older men: longitudinal results from the Massachusetts Male Aging Study. Eur J Endocrinol, 2006. 155(3): p. 443-52.29. de Moor, P. and J.V. Joossens, An inverse relation between body weight and the activity of the steroid binding -globulin in human plasma. Steroidologia, 1970. 1(3): p. 129-36.30. Glass, A.R., et al., Low serum testosterone and sex-hormone-binding-globulin in massively obese men. J Clin Endocrinol Metab, 1977. 45(6): p. 1211-9.31. Amatruda, J.M., et al., Depressed plasma testosterone and fractional binding of testosterone in obese males. J Clin Endocrinol Metab, 1978. 47(2): p. 268-71.32. Giagulli, V.A., J.M. Kaufman, and A. Vermeulen, Pathogenesis of the decreased androgen levels in obese men. J Clin Endocrinol Metab, 1994. 79(4): p. 997-1000.33. Goncharov, N.P., et al., Testosterone and obesity in men under the age of 40 years. Andrologia, 2009. 41(2): p. 76-83.34. Barrett-Connor, E. and K.T. Khaw, Cigarette smoking and increased endogenous estrogen levels in men. Am J Epidemiol, 1987. 126(2): p. 187-92.35. Khaw, PT and Barrett-Connor E. Lower endogenous androgens predict central adiposity in men. Ann Epidemiol, 1992. 2 (5): 675-82.36. National Center for Health Statistics (NCHS). Analytic and Reporting Guidelines: The Third National Health and Nutrition Examination Survey, NHANES III (1988- 1994). Hyattsville: National Center for Health Statistics (NCHS), 2006.37. National Center for Health Statistics (NCHS). "Documentation, Codebook, and Frequencies Surplus Sera Laboratory Component: Racial/Ethnic Variation in sex steroid hormone concentrations across age in US men." ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHANES/NHANESIII/25a/sshormon.pdf (accessed July 9, 2010).38. Svartberg J, Midtby M, Bonaa KH, Sundsfjord J, Joakimsen RM, Jorde R. The associations of age, lifestyle factors and chronic disease with testosterone in men: the Tromso Study. Eur J Endocrinol 2003;149(2):145-152.39. Jones TH. Effects of testosterone on Type 2 diabetes and components of the metabolic syndrome. J Diabetes 2010;2(3):146-156.40. Saad F, Gooren LJ. The role of testosterone in the etiology and treatment of obesity, the metabolic syndrome, and diabetes mellitus type 2. J Obes 2011;2011.41. Diver MJ. Laboratory measurement of testosterone. Front Horm Res 2009;37:2142. Lapauw B, Taes Y, Goemaere S, Toye K, Zmierczak HG, Kaufman JM. Anthropometric and skeletal phenotype in men with idiopathic osteoporosis and their sons is consistent with deficient estrogen action during maturation. J Clin Endocrinol Metab 2009;94(11):4300-4308.
    20. 20. 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
    21. 21. 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.