AUTHORIZATION TO LEND AND REPRODUCE THE THESIS
As the sole author of this thesis, I authorize Brown University to lend it to other institutions or individuals for the purpose of scholarly research.
Date ______03/22/2017 ______Jinjie Liu, Author
I future authorize Brown University to reproduce this thesis by photocopying or other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research.
Date ______03/22/2017 ______Jinjie Liu, Author Review of Sex Hormone-Binding Globlin, dietary factors and health
Relation of Dietary Carbohydrates Intake to Circulating Sex Hormone-Binding Globulin Levels in Postmenopausal Women
By Jinjie Liu B.S. Beijing Sport University
Thesis
Submitted in partial fulfillment of the requirements for the Degree of Master of Public Health in Brown University School of Public Health
PROVIDENCE, RHODE ISLAND
MAY, 2017 ii
This thesis by Jinjie Liu is accepted in its present form by the Brown University School of Public Health as satisfying the thesis requirements for the degree of Master of Public Health
Date:______Simin Liu, M.D., Ph.D., Advisor
Date:______Xi Luo, Ph.D., Reader
Date:______Patrick M. Vivier, M.D.,Ph.D. Director, Master of Public Health Program
Approved by the Graduate Council
Date:______
Andrew G. Campbell , Dean of the Graduate School iii
Vita
Jinjie Liu was born on July 5th, 1993 in Lanzhou, China to Chun Liu and Sudi Zhang.
During her study in Beijing Sport University in Beijing, China, she earned national scholarship, studied abroad in Indiana University Bloomington in Bloomington, Indiana for a year in school of Public Health. During that time, she was the team leader in Applied
Physical Education lab taking care of children with special needs and was inducted into
Honors Society. Growing passion in public health, she attended Brown University as a master of public health candidate after receiving her Bachelor of Science degree in
Beijing Sport University in 2015. While pursing her master degree in Brown with a focus in biostatistics, she did intensive training on quantitative research and analysis, which helped her gain a better understanding on the public health issues holding the knowledge and tools she got. Jinjie did her summer internship at Global Biostatistics
Department of Merck Serono in Beijing, assisting with clinical research on new drug development. Most recently, she has been helping with a research project in the Center for Alcohol and Addition Studies in Brown University as research assistant. In the meanwhile, she has been interning in SCIO Health Analytics in Hartford, Connecticut as a health care analyst, dealing with health insurance claim data and working with patients and health insurance companys. iv
Preface and Acknowledgments
I must first thank my thesis advisor Dr. Simin Liu, for being such a patient and supportive mentor who guided me through not only the work of my thesis, but also issues that I encountered with while pursuing my master degree at Brown. I was lucky enough to work with Mengna Huang, who gave me countless of useful information regarding my thesis as well as working together and finally got it published. She was an excellent example of being an outstanding student and a writer doing public health research. I also want to thank Xiaochen Lin, who also assisted me throughout the whole process, as a supervisor as well as a friend. Meanwhile, I want to thank my thesis reader Luo Xi, who has unbeatable computation skill and passion in big data research.
I am grateful for all the help from Joann Barao, Dr. Vivier and Diane Schlacter while studying at Brown, without whom I might never made it for lots of opportunities.
At the same time, I appreciate the opportunity Dr, Carey offered my as research assistant for the project that’s just within my interests. I also own a huge thank to my manager who offered me my current job, Emily Li. She is always in my concern and pushing me to grow and be better.
To my family, my incredible parents, my grandma, who passed away while I was abroad with no chance to fly back to see her, and to my grandpa. v
Table of Content
PART 1: LITERATURE REVIEW – SEX HORMONE-BINDING GLOBLIN, DIETARY FACTORS AND HEALTH ...... 1 SHBG AND SEXUAL HORMONES ...... 1 Sex hormones and health ...... 2 SHBG and health ...... 2 SHBG and women’s health ...... 4 SHBG AND LEADING FACTORS ...... 4 SHBG and lifestyle factors ...... 5 SHBG and weight related factors ...... 5 SHBG AND DIETARY FACTORS ...... 6 SHBG and restricted diet ...... 7 SHBG and fiber intake ...... 7 SHBG and fat intake ...... 8 SHBG and meat consumption ...... 8 SHBG and protein intake ...... 9 SHBG and sugar intake ...... 9 SHBG and carbohydrates intake ...... 10 PART 2: RESEARCH PAPER – RELATION OF DIETARY CARBOHYDRATES INTAKE TO CIRCULATING SEX HORMONE-BINDING GLOBULIN LEVELS IN POSTMENOPAUSAL WOMEN ...... 11 ABSTRACT ...... 11 INTRODUCTION ...... 13 METHODS ...... 14 Study Subjects ...... 14 Measurement of Serum SHBG Concentrations ...... 16 Dietary Measurements ...... 16 Statistical Analysis ...... 17 RESULTS ...... 19 DISCUSSION ...... 22 ACKNOWLEDGMENTS ...... 28 REFERENCES ...... 30 TABLES ...... 37 Table 1. Baseline characteristics by quartiles of untransformed plasma SHBG concentrations in a subpopulation of the postmenopausal women from the Women’s Health Initiative (n = 11,159) ...... 37 Table 2. Adjusted means of serum SHBG concentrations according to quartiles of dietary glycemic load, glycemic index, and intakes of fiber, sugar, and total carbohydrates ...... 39 Table 3. Adjusted means of serum SHBG concentrations according to quartiles of intake of carbohydrates abundant food items ...... 41 Table 4. Adjusted means of serum SHBG concentrations according to quartiles of dietary glycemic load, glycemic index, and intakes of fiber, sugar, and total carbohydrates in controls of the ancillary studies ...... 44 Table 5. Adjusted means of serum SHBG concentrations according to quartiles of intake of carbohydrates abundant food items in controls of the ancillary studies ...... 46 1
Part 1: Literature Review – Sex Hormone-Binding Globlin, dietary factors and health
The purpose of this review is to summary the previous studies on the serum
concentration of sex hormone-binding globulin (SHBG) and it’s relation with dietary
factors.
SHBG and sexual hormones
Serum concentration of sex hormone-binding globulin (SHBG), as a major plasma transport protein made by human’s liver, binds to any of sex hormones and transports them through bloodstream to the body. The sexual hormones are referred to as bioactive or free when they are not bound to SHBG. Bioactive sexual hormones can be used freely on human body. 1 We can get total hormone level by adding up the amount of free and
SHBG-binding hormone. Thus SHBG is conventionally viewed as a hormone sequester that regulates the bioavailability of sex hormones. SHBG binds testosterone with high affinity compared with estrone, which has profound effects on the balance between bioavailable androgens and estrogens. 1,5Apart from the traditional free hormone
hypothesis, a growing body of evidence suggests that SHBG may regulate cellular
uptake and biologic action of sex steroids, as well as cell proliferation in
hormone-responsive tissues through a direct intracellular signaling cascade mediated by
membrane-associated protein. 1-5 2
Sex hormones and health
Strong evidence showed that the balance of sex hormones could crucially impact your health. In both men and women, low levels of free testosterone can affect muscle protein synthesis that leads to muscle growth and post-workout recovery. 2, 3,40,41 Lower total testosterone was found associated with HbA1c, which was positively associated with body mass index (BMI), waist circumference, obesity and the risk of having diabetes and cardiovascular diseases in both men and women. 2,40 SHBG, controlling the amount of testosterone and estrogen, plays a key role for diagnosing hormonal disorders.
SHBG and health
Although SHBG is viewed as a hormone sequester of sex hormones, growing evidence also suggest that SHBG are strongly associated with several health conditions including obesity, anorexia nervosa, thyroid hormone disorders, metabolic syndrome, type 2 diabetes, polycystic ovary syndrome, cardiovascular diseases and hormone-dependent cancers indirectly by modulating the biologic effects of testosterone or exert more direct effects through its own SHBG receptor.26
Obese humans of all age proved to have lower plasma SHBG levels while patients with anorexia nervosa have higher plasma SHBG levels. This made SHBG a reliable index of diagnostic factor for eating disorders.
Low SHBG is associated with elevated levels of triglycerides and low-density lipoprotein 3
(LDL), which linked with multiple cardiovascular illnesses in both sexes, including heart
disease, type 2 diabetes, and high blood pressure. 2,3 Earlier studies had demonstrated
that SHBG was directly associated with insulin sensitivity and glucose metabolism in
nondiabetic women and men. 24 Then Li et al. reported that low SHBG concentrations
were associated with an increased risk for developing metabolic syndrome
independently of insulin resistance. 25, 26 Lower levels of SHBG and higher level of total estradiol were associated with increased risk of type 2 diabetes, the association came
from 13 population-based prospective studies involving moreThis made SHBG not only a
biomarker for monitoring liver function, but also a reliable predictor of the development of
type 2 diabetes as well as cardiovascular diseases.
SHBG and certain sex hormones level were also found to have inverse impact on
gender-related health conditions between men and women. Lower free testosterone
levels due to excessively high level of SHBG are associated with erectile dysfunction as
well as other sign and symptoms of hypogonadism in men, especially when total
testosterone level is low.42 On the contrary, decreased levels of SHBG with relatively
high blood concentrations of estrogens and free testosterone can result in
androgenization in women.1, 6
Thus, optimal sex hormones and SHBG levels are the key in crucial in measuring and
maintaining both men and women’s health.4,43 4
SHBG and women’s health
Most studies on SHBG and women’s health targeted pre and postmenopausal adult
women. Low SHBG is typically seen in women who have polycystic ovary syndrome
(PCOS), which is a common endocrine disorder that results in excess androgen levels.
Most of those with PCOS are obese and more likely to develop type 2 diabetes.6 Overt
insulin resistance is prevalent in 6% to 35% of women with PCOS, depending upon the
population studied and how PCOS is diagnosed.5,7 Decreased SHBG level has been
proved to be associated with risk of cardiovascular diseases in young to middle-aged
women.6 However it was yet to determined whether SHBG was the indirect cause or had
its independent effects on pre-menopause women.6,50
For postmenopausal women, several studies have suggested the direct role SHBG plays
on type 2 diabetes and cardiovascular diseases.7,8,51 Regardless of direct or indirect role
SHBG is playing, consistent evidence conclude that lower level of SHBG impact adult
women’s health.
SHBG and leading factors
Given the role the serum concentration of sex hormone-binding globulin (SHBG) may play in the etiologies of type 2 diabetes, cardiovascular diseases, and cancers, investigating the determinants of plasma SHBG levels is of great importance. 5
SHBG and lifestyle factors
Apart from hormonal factors that have impact on SHBG, age, use of exogenous estrogen, physical activity, and regular coffee intake were found to be positively associated with
SHBG concentrations on postmenopausal women.9,53 Several studies have shown that coffee and caffeine consumption reduces the risk of developing type 2 diabetes. Goto, et al. showed that coffee consumption was positively associated with having higher concentrations of SHBG in post-menopausal women drinking greater than 4 cups of coffee per day.6
BMI was found to be inversely associated with SHBG concentrations, both in pre-and postmenopausal women. At the time, BMI was thought to be the major determinant of
SHBG though no mechanisms have been described. However, recent studies found that liver fat content had bigger impact on SHBG levels than BMI. Alcohol consumption and cigarette smoking had no significant correlation with SHBG concentration based on the cross-sectional study on postmenopausal women. Those findings were in line with each other though limited to postmenopausal women.9,10,54
SHBG and weight related factors
It is well known that physical activity can modify insulin levels, improve insulin sensitivity and loss of adiposity, potentially affecting SHBG and the biologically available levels of androgens. 48 Weight loss by exercise and/or caloric restriction has been found to increase SHBG levels in postmenopausal women.8 Inactive postmenopausal women 6 who are obese have a higher risk of developing breast cancer.7,8 The age-related increases of SHBG also had to do with weight change and the decline in insulin sensitivity.9,52 Both overweight sedentary postmenopausal women and obese women with polycystic ovary syndrome (PCOS) were found to have significantly lower levels of
SHBG, the levels of which were inversely related to those of insulin.9,17 Weight reduction intervention resulted in a fall in insulin and a rise in SHBG for middle-aged women with PCOS.17,47 Same results happened with both obese men and postmenopausal women with or without hormone replacement therapy, going under three-week low-fat, high fiber diet combined with aerobic exercise intervention.18, 52 The mechanisms behind those lifestyle factors remains unclear, but be consistently proved to relate to insulin levels. Each of these factors was associated with effects on both SHBG concentration and on insulin resistance according to both longitudinal and cross-sectional studies.11
SHBG and dietary factors
Dietary factors, including fat and fiber, have been reported to play a role in the development of hormone-related cancer such as breast cancer mediated by changes in estradiol.19,55 Diet were also considered to be directly associated with SHBG concentrations, and also play a role in regulating enterohepatic metabolism as well as influencing the bioavailability of levels of sex hormone such as estradiol in human body.
12,19,,52,53
7
SHBG and restricted diet
Regardless of the types of nutrients, previous intervention studies had already examined the effect of calorie restriction on SHBG and found rise in SHBG and fall in free testosterone concentrations in both normal women and patients with PCOS.48 Insulin and insulin-like growth factor-I play the roles as mediator in this matter, as a negative correlation of SHBG with fasting and glucose simulated insulin levels had been reported and confirmed by multiple studies. 16,17,49
SHBG and fiber intake
Later research came in detail to study the correlations between SHBG and the nutrients.
Fiber intake itself was found to be positively correlated with SHBG concentrations in a previous study in men,16 but another study failed to observe a similar direct correlation in postmenopausal women.17 Nevertheless, fiber intake had correlation with excretion of lignans in urine.51,52 Based on studies among Finish and Boston women including breast cancer patients, fiber intake coming from berries and fruits correlated with excretion of lignans, while fiber intake from vegetables correlated with excretion of Da.
High level of excretion of lignans and Da, which had positive correlation with plasma level of SHBG, indirectly showed evidence to association between fiber intake and increased SHBG levels in women.12, 53 The group of breast cancer patients had lowest fiber intake and lignans excretion with significant difference comparing with other groups.12 Combined with earlier studies, lifestyle intervention including high fiber diet and exercise should reduce the risk for breast cancer in women.12,19,46 8
SHBG and fat intake
It has been suggested that fat intake may be related to SHBG levels.24 However, there was not enough evidence on the impact of fat intake to SHBG levels. J F Doegan et al. found mean plasma concentration of total and SHBG-bound testosterone to be higher with high-fat and low-fiber diet in a controlled feeding study in men.21,25 In the
Massachusetts Male Aging study, the association between animal fat and SHBG no longer remains after controlling for confounding factors, even though the simple correlation was significant.16 We mentioned before about finding liver fat content to be have big impact on SHBG concentration. Low fat diet with rich soy products was found to have lower mortality rate in hormone-dependent cancer in Japanese women with excretion of higher lignans and isoflavonoids.14, 46, 47 Future studies on fat intake and
SHBG level should be warranted after adjusting for potential confounding factors.
SHBG and meat consumption
Higher SHBG value and lower risk for hormone-dependent cancers were observed in vegetarians consuming fiber-rich and low fat diet in men, pre-and postmenopausal women, suggesting that nutritional factors specific to a vegan diet my be important determinants of circulating SHBG levels.12,20,49 To support the suggestion, vegetarian based-protein diet was found to increase SHBG without decreasing total testosterone among middle-aged women.12
9
SHBG and protein intake
Evidence on the association between protein intake and SHBG levels showed conflicting and poor results. High protein diet was found to increase SHBG levels in obese men by
Vermuelen et al., while Longcope et al. reported findings on significantly negative correlation between SHBG levels and protein intake from Massachusetts Male Aging study.16,55 In a dietary intervention study, lower serum SHBG concentrations were observed among participants on a conventional high glycemic load diet, while the SHBG concentrations increased among those on a high-protein low glycemic load diet.15
Longcope C. et al. also reported that a diet low in protein resulted in a marked increase in SHBG levels in male rabbits, which is consistent with the authors’ findings in men.16
Therefore, future intervention studies, along with better control of other dietary factors as potential confounding factors, are required to validate the previous results.
SHBG and sugar intake
Very few studies examined the effect of sugar intake on SHBG concentrations.
Overconsumption of sugar also decreases SHBG in the blood based on recent reviews.7
Moreover, although an inverse association between monosaccharides and SHBG production was reported previously in transgenic mice and hepatic cell models,18 a recent large scaled cross-sectional study of postmenopausal women found that sweets intake might negatively correlate with SHBG concentrations, although the result was not significant.9,13 Based on the close relationship between insulin and SHBG, future studies 10 investigating the direct impact on SHBG from sugar consumption are warranted.
SHBG and carbohydrates intake
Increasing attention has been attracted to the impact of dietary factors on plasma SHBG levels. Emerging evidence also shows that different types of dietary carbohydrates may have heterogeneous associations with SHBG levels.9-13 Nevertheless, studies examining the effect of quality and quantity of dietary carbohydrates are still scarce and inconclusive. Longcope C. et al. found the intake of calories, fat from animal or vegetable and carbohydrate not related to SHBG in Massachusetts Male Aging Study.16
Very few studies had been done to determine the role of the intake of carbohydrates and carb-rich foods on SHBG levels in women. Therefore, we proposed to conduct a systematical examination of the relation between dietary carbohydrates and plasma
SHBG in a large-scale population-based setting.
11
Part 2: Research Paper – Relation of Dietary Carbohydrates Intake to Circulating
Sex Hormone-Binding Globulin Levels in Postmenopausal Women
Abstract
Background: Low circulating levels of sex hormone-binding globulin (SHBG) have been shown to be a direct and strong risk factor for type 2 diabetes, cardiovascular diseases, and hormone-dependent cancers, although the relation between various aspects of dietary carbohydrates and SHBG levels remains unexplored in population studies.
Methods: Among postmenopausal women with available SHBG measurements at baseline (n=11,159) in the Women’s Health Initiative, we conducted a comprehensive assessment of total dietary carbohydrates, glycemic load (GL), glycemic index (GI), fiber, sugar, and various carbohydrate-abundant foods in relation to circulating SHBG using multiple linear regressions adjusting for potential covariates. Linear trend was tested across quartiles of dietary variables. We used Benjamini and Hochberg’s procedure in calculating the false discovery rate (FDR) for multiple comparisons.
Results: Higher dietary GL based on total and available carbohydrates, dietary GI based on total and available carbohydrates, and higher intake of sugar and sugar sweetened beverages were associated with lower concentrations of circulating SHBG (all Ptrend <
0.05; q-value after FDR correction = 0.04, 0.01, 0.07, 0.10, 0.01, <.0001, respectively). In contrast, women with greater intake of dietary fiber tended to have elevated SHBG levels
(Ptrend = 0.01, q-value after FDR correction = 0.04). There was no significant association 12 of total carbohydrates or other carbohydrate-abundant foods with SHBG concentrations.
Conclusions: These findings suggest that low GL/GI diets with low sugar and high fiber content may be associated with higher serum SHBG concentrations among postmenopausal women. Future studies investigating whether lower GL/GI diets increase SHBG concentrations are warranted.
The significant finding (s) of the study:
This large study of postmenopausal women with comprehensive assessment of diet and serum SHBG levels indicated that low GL/GI, and low sugar, but high dietary fiber were associated with elevated levels of circulating SHBG.
This study adds:
Our findings support the notion that SHBG may be affected by dietary GL, GI, sugar, and fiber, and may also be an important mediator by which various dietary carbohydrates influence risks of type 2 diabetes, cardiovascular disease, and hormone-dependent cancers.
Keywords: dietary carbohydrates, glycemic load, glycemic index, sex hormone binding globulin (SHBG), type 2 diabetes
13
Introduction
Sex hormone-binding globulin (SHBG) is a serum protein synthesized by the liver that binds to both androgens and estrogens, with higher affinity to androgens.1,2 SHBG was originally thought to primarily regulate the amount of sex hormones that are bioavailable to the cells. However, recent epidemiological studies consistently show that low SHBG concentrations are strongly associated with the development of insulin resistance, type 2 diabetes, cardiovascular diseases (CVD), hormone-dependent cancers, as well as hip fractures, either indirectly by modulating the biologic effects of testosterone or exert more direct effects through its own SHBG receptor.1,3-10 Mendelian randomization analyses using single nucleotide polymorphisms within or near the SHBG gene as instrumental variables for blood SHBG concentrations also provided supporting evidence to the causal relationship between SHBG and the risk of type 2 diabetes.3,11_ENREF_11
Given the role SHBG may play in the etiologies of type 2 diabetes, CVD, and hormone-dependent cancers, investigating the determinants of blood SHBG concentrations is of great importance. In addition to several common variants identified within or near the SHBG gene,3,11 lifestyle factors, especially dietary factor, may have a direct effect on circulating concentrations of free endogenous sex hormones through the regulation of SHBG concentrations.12 Physical activity, regular coffee consumption, as well as weight loss by exercise and/or caloric restriction has been found to increase
SHBG concentrations in postmenopausal women.13,14_ENREF_14 Emerging evidence also shows that different types of dietary carbohydrates may have heterogeneous associations with SHBG concentrations.2,15-18 In a dietary intervention study, lower serum 14
SHBG concentrations were observed among participants on a conventional high glycemic load diet, while the SHBG concentrations increased among those on a high-protein low glycemic load diet.15 Fiber intake was found to be positively correlated with SHBG concentrations in a previous study in men,16 but another study failed to observe a similar correlation in postmenopausal women.17 Moreover, although an inverse association between monosaccharides and SHBG production was reported previously in transgenic mice and hepatic cell models,18 a more recent study found that sweets intake may positively correlate with SHBG concentrations, although the result was not significant.2 Increasing attention has been attracted to the impact of dietary factors on circulating SHBG concentrations. Nevertheless, studies examining the effect of quality and quantity of dietary carbohydrates on SHBG are still scarce and inconclusive, and very few have studied foods that are abundant in carbohydrates.
Therefore, we conducted a comprehensive examination of the relations between various measures of dietary carbohydrates and concentrations of circulating SHBG among a subsample from the large-scale national Women’s Health Initiative study.19
Methods
Study Subjects
The Women's Health Initiative (WHI) is a long-term national health study that focused on strategies for preventing heart diseases, breast and colorectal cancer, and osteoporotic fractures in postmenopausal women. The original WHI study included 161,808 postmenopausal women enrolled between 1993 and 1998 in two major parts: a partial 15 factorial randomized Clinical Trial (CT) and an Observational Study (OS); both were conducted at 40 Clinical Centers nationwide. The CT enrolled 68,132 postmenopausal women between the ages of 50 to 79 into trials testing three prevention strategies. The
OS examined the relationship between lifestyle, environmental, medical and molecular risk factors and specific measures of health or disease outcomes. This component involved tracking the medical history and health habits of 93,676 women not participating in the CT. The current analysis included an initial total of 13,955 unique participants from either the WHI-CT or the WHI-OS whose blood samples from baseline had been measured for serum SHBG in the following ancillary studies: AS90 (400 hip fracture cases and 400 controls), AS110 (385 coronary heart disease cases and 385 controls),
AS167 (311 breast cancer cases and 592 controls), AS238 (700 type 2 diabetes cases and 1,400 controls), BA7 (422 venous thromboembolism, 534 stroke, 753 CHD, 204 spine fracture, 830 non-hip-or-spine fracture cases, and 1,576 controls), BA9 (1,132 fracture cases and 1,132 controls), BA21 (400 colorectal cancer cases and 800 controls),
W5 (300 controls), W9 (750 hip fracture cases and 750 controls), W10 (755 breast cancer cases and 755 controls), and W18 (240 controls). Participants were excluded if they had implausible total energy intake (< 600 or > 5000 kcal/day) as determined by the food frequency questionnaire (n = 711 excluded), self-reported diabetes at baseline (n =
1072 further excluded), or if they had missing information in important covariates such as age (no missing), race/ethnicity (n = 27), body mass index (n = 74), smoking status (n =
170), physical activity (n = 768), and hormone therapy use (n = 6) (since there were overlap, this step excluded 1013 observations). No missing in dietary measurements were observed after applying the above exclusion criteria. 16
Measurement of Serum SHBG Concentrations
For each study participant, blood was collected at the baseline visit after at least a
12-hour fast and then stored at −80 °C to -70 °C. Samples used for the hormone measurements were taken from these baseline specimens. The serum SHBG concentrations were measured using an electrochemiluminescence immunoassay (ECL) in AS238, a solid-phase, two-site chemiluminescent immunoassay (solid-phase, two-site
CIA) in AS90, AS110, AS167, BA7, BA9, BA21, W9, W10, and W18), or an immunoradiometric assay (IRMA) in W5. The inter-assay coefficients of variation ranged between 3.7% and 17.7%.2
Dietary Measurements
The methods of data collection and validation have been reported previously.19,20
Participants completed at baseline a 122-item standardized food frequency questionnaire (FFQ) developed for the WHI to estimate average daily dietary intake over the past 3 months.21 The FFQ was based on instruments used in the WHI feasibility studies and the original National Cancer Institute/Block FFQ.22-24 The dietary database, linked to the University of Minnesota Nutrition Coordinating Center Nutrition Data System for Research (Nutrition Coordinating Center, Minneapolis, MN, USA), is based on the
U.S. Department of Agriculture standard reference releases and manufacturer information.25 The detailed description of the methods used to calculate GI and GL values can be found elsewhere.26 In summary, GI values based on food consumption or 17 expert judgment were assigned to each food items that contained at least five grams of carbohydrates, and then for each FFQ line item GL values were calculated by multiplying
GI by intake frequencies and portion sizes. Both total carbohydrates and available carbohydrates (total carbohydrates minus total fiber) were used to calculate GI and GL values. In addition, dietary intakes of total carbohydrates, total sugar, and total fiber were included in the analyses in separate models. As a secondary analysis, the associations of different carbohydrates abundant food items (daily servings of white bread, dark bread, rice grains and noodles, potato, cereal, fruits, beans, sugar sweetened beverages, pasta, and whole grains) with serum SHBG concentrations were also examined. The potato variable included French fries, potato salad, sweet potatoes and yams, and other potato/cassava/yucca. The cereal variable included cold and cooked cereals. The beans variable included green or string beans, English peas, refried beans, all other beans, and bean soup. The sugar sweetened beverage variable included regular soft drinks (not diet), orange or grapefruit juice, other fruit juice, and fruit drinks. The pasta variable included macaroni and cheese, lasagna, or noodles with a cream sauce, spaghetti with meat sauce, and spaghetti with tomato sauce. All other variables were pre-calculated by the WHI. This FFQ has demonstrated reasonably good validity as a measurement of dietary intake compared with 24-hour dietary recalls and food records.21
Statistical Analysis
Baseline characteristics were summarized according to SHBG quartiles. Continuous variables were presented as means ± standard deviations, and categorical variables were presented in percentages. The statistical significances of differences among SHBG 18 quartiles were tested by
ANOVA for continuous variables and by chi-square test for categorical variables.
Individual level data from different ancillary studies were pooled and analyzed to assess the associations between measures of carbohydrate intakes and natural-log-transformed
SHBG concentrations using linear multivariable models. Dietary carbohydrate intakes were each analyzed in quartiles. We adjusted for potential confounding factors including total energy intake, total carbohydrates intake (except when total carbohydrates intake was exposure of interest), ancillary study indicators, case/control status in each ancillary study, age (continuous), ethnicity, body mass index (BMI, continuous), cigarette smoking
(never, past, or current), alcohol consumption (continuous), physical activity (metabolic equivalent of tasks per week, continuous), and hormone therapy use (never, past, or current user of unopposed estrogen and/or estrogen plus progesterone). From this model, we calculated the adjusted geometric means of SHBG concentrations for each quartile of the carbohydrate of interest by exponentiating the estimated mean log SHBG concentrations evaluated at the mean of each continuous variable and averaged over the groups of each categorical variable in the model. We also performed a linear trend analysis for each measure of carbohydrate by assigning the median of each quartile to each observation and using the resulting continuous variable as the independent variable in the model. In order to address multiple testing issue, Benjamini and
Hochberg’s procedure for controlling the false discovery rate (FDR) was performed with the results of the trend analysis.27 Measures of carbohydrate intake with q-value below
0.05 were considered statistically significant, which corresponded to less than one false positive result per 20 comparisons. 19
Sensitivity analyses were performed by (a) restricting to only controls of each ancillary study, (b) using linear mixed effects models to pool the estimates from each ancillary study, (c) additionally adjust for dietary total protein, fruits and vegetables intake, and baseline history of cardiovascular diseases and cancer, and (d) restricting to studies that measured SHBG using solid-phase, two-site CIA (nine out of eleven studies).
Furthermore, since SHBG concentrations has been inversely linked to the risk of type 2 diabetes previously,1,3,5 we hypothesized that dietary carbohydrates may influence the risk of type 2 diabetes through affecting serum SHBG concentrations. Thus, we performed exploratory mediation analyses within the type 2 diabetes case control study in our sample (AS238, n = 1,586 after applying exclusion criteria), with quartiles of dietary carbohydrate measures as the exposure (contrasting the highest and the lowest quartile), SHBG concentrations as the mediator, and case control status as the outcome.28-30 The average causal mediation effects, average direct effects, the proportion mediated, and their respective confidence intervals were quantified. All statistical analyses were conducted using R version 3.3.1 (The R Foundation for
Statistical Computing, Vienna, Austria).31
Results
We included a total of 11,159 postmenopausal women in the current analysis, with a median serum SHBG concentration of 47.3 nmol/L and interquartile range of 33.0 – 68.8 nmol/L. This group of participants were on average 65.3 years old (SD = 7.5), had an average BMI of 28.6 kg/m2 (SD = 6.1), an average total energy intake of 1,617.5 kcal per 20 day (SD = 660.6), and an average total carbohydrates intake of 201.4 grams per day (SD
= 80.9). Sixty-eight percent of them were white and 8.6% were smokers at study baseline. When comparing women across SHBG quartiles, those within higher quartiles of SHBG concentrations tended to be older, less likely to be white and more likely to be black or Asian, had lower BMI, and were more likely to be current smokers. Women with higher concentrations of serum SHBG also had lower intake of total energy, total carbohydrates, protein, and fat, as well as sugar, GL, GI, and fruits and vegetables, while they had similar intake of fiber compared to women with lower concentrations of serum
SHBG (Table 1).
Since the original continuous SHBG variable was skewed to the right, we performed natural logarithm transformation and used the log-transformed SHBG variable as the dependent variable in the subsequent linear regression analyses. After adjusting for total energy intake, total carbohydrates (except when total carbohydrates was the exposure of interest), age, race, BMI, smoking status, physical activity, alcohol intake, hormone therapy use, ancillary study indicator, and case-control status in each ancillary study, higher dietary GL based on both total carbohydrates and available carbohydrates were significantly associated with lower concentrations of serum SHBG (P-value for trend =
0.01 and 0.002, q-value = 0.04 and 0.01, respectively). Women within the lowest quartile of dietary GL based on available carbohydrates had an adjusted average SHBG of 56.7 nmol/L (95% CI: 54.6, 58.9), while women within the highest quartile of dietary GL had an adjusted average SHBG of 52.6 nmol/L (95% CI: 50.5, 54.7), and results were very similar for GL based on total carbohydrates. Similar trend was observed for dietary GI 21 based on total and available carbohydrates (P-value for trend = 0.02 and 0.04, q-value =
0.07 and 0.10, respectively) and dietary sugar intake (P-value for trend < 0.001, q-value
= 0.01), for which the lowest intake quartile had an average SHBG concentration of 56.2 nmol/L (95% CI: 54.3, 58.1), while the highest intake quartile had an average of 52.1 nmol/L (95% CI: 50.2, 54.1). We also found a positive trend for fiber, where higher intake of fiber was associated with higher SHBG concentrations (P-value for trend = 0.01, q-value = 0.04). No significant findings were observed for total carbohydrates intake
(Table 2).
For analyses regarding carbohydrate-abundant food items, we found a significant inverse relationship between quartiles of sugar sweetened beverages and circulating
SHBG concentrations (P-value for trend < 0.001, q-value < 0.001). The lowest intake quartile corresponded to an adjusted average SHBG concentration of 56.7 nmol/L (95%
CI: 55.0, 58.6), while the highest intake quartile corresponded to an average of 52.7 nmol/L (95% CI: 51.1, 54.4). Interestingly, we also observed borderline inverse associations for potatoes intake (P-value for trend = 0.07, q-value = 0.16) and beans intake (P-value for trend = 0.10, q-value = 0.19). Other food items were not significantly associated with circulating SHBG concentrations (Table 3).
When restricting to just controls from ancillary studies, this subgroup included 5,457 participants. Without correction for multiple comparison, results were similar to those obtained from the primary analyses in the whole group (Table 4 and 5). Dietary GL based on total and available carbohydrates, fiber, sugar, and sugar sweetened beverages 22 remained significantly associated with SHBG concentrations after the FDR procedure, and the effect sizes were also similar. The discrepancy was that the P-values for trend for dietary GI based on total and available carbohydrates were significant before and after the FDR procedure in the controls, while in the whole sample they were only significant before multiple testing correction. From the second sensitivity analyses we performed, the linear mixed effects models where ancillary study indicators were treated as random effects yielded very similar results to the primary analyses (data not shown).
In the third sensitivity analysis, when additionally adjusting for dietary total protein, fruits and vegetables intake, and baseline history of cardiovascular diseases and cancer, the results were virtually the same with those from the primary analysis (for details see
Supplementary Table 1). From the fourth sensitivity analysis where we restricted to studies that measured SHBG using solid-phase, two-site CIA (nine out of eleven studies), the estimated adjusted means of SHBG concentrations were overall lower than what we estimated in the original analytic sample, but the results regarding the trends across quartiles of dietary intake and their significance were similar to the results of our primary analysis (for details see Supplementary Table 2). The exploratory mediation analyses did not find significant average causal mediation effects or average direct effects, possibly due to the fact that one single case control study was not powered enough to detect significant mediation effects (for details see Supplementary Table 3).
Discussion
In this cross-sectional analysis of 11,159 non-diabetic postmenopausal women enrolled in the WHI, positive associations with SHBG concentrations were observed for total 23 dietary fiber intake. Total dietary sugar intake and dietary GL based on total and available carbohydrates were observed to be significantly associated with reduced serum concentrations of SHBG, before and after correction for multiple comparisons. Dietary GI based on total and available carbohydrates were also associated with lower levels of
SHBG before multiple testing correction. In addition, significant association between sugar-sweetened beverages and decreased concentrations of serum SHBG was demonstrated based on analyses regarding carbohydrate-abundant food items, corroborating our results for total sugar intake.
Our finding of the inverse relationship between dietary GL based on total and available carbohydrates and serum SHBG concentrations was consistent with results from a previous dietary intervention trial where SHBG was considerably lowered after a
12-week high GL diet compared to the baseline, and also significantly decreased compared to those on a low GL diet,15 although another study contrasting low GI and high GI diet did not find significant difference in SHBG after an 8-week intervention.32
This somewhat contradicted the fact that in our analysis dietary GI based on total or available carbohydrates were significantly associated with SHBG concentrations, although only before correction for multiple comparisons. Mechanistically, it has been suggested that high GL/GI diet induced greater insulin production, and insulin could act as an inhibitor of hepatic synthesis of SHBG.18,33-35 Dietary sugar, which is usually high in glycemic index and glycemic load, was found to be significantly and inversely associated with serum SHBG. This result was in line with biological evidence from human-SHBG-transgenic mice and human hepatic cells, where glucose and fructose 24 reduced human SHBG production by hepatocytes via the downregulation of hepatocyte nuclear factor-4α, which was independent of the actions of insulin.18 Sugar also likely contributed to the relation between dietary GL/GI and SHBG concentrations. The null relationship we observed between total carbohydrates intake and SHBG was consistent with observations from another previous study.16 Collectively, these results suggested that the quality of dietary carbohydrates might be of greater importance than quantity in affecting circulating SHBG levels, given that our analyses with respect to GL and GI were adjusted for the total amount of dietary carbohydrates.
Dietary fiber, which did not contribute to GL or GI based on available carbohydrates, was positively associated with serum SHBG in our analysis. Previous findings from the WHI
Dietary Modification trial associated a low fat dietary pattern with significant reduction in
SHBG after 1 year of intervention, which were thought to be partially contributed by the concurrent increase in fiber intake as well as weight loss.36 Even though an early study found no correlation between dietary fiber and SHBG,17 a more recent investigation with regression modelling did reveal significant positive relations between the two, accounting for age, BMI, and other covariates.16 The biomarker of lignans intake was also found to be positively related to SHBG levels, albeit the null association between dietary fiber and
SHBG in the same study.37 The mechanism by which fiber intake may be a controlling factor on SHBG is not yet well-understood, but it is possible that it acts through modulating glucagon-like peptide-1 and insulin secretion.38
We also systematically examined the primary carbohydrate-abundant food items that 25 might be responsible for the dietary effects on SHBG concentrations in these data. A significant inverse relationship between sugar sweetened beverages and circulating
SHBG concentrations was discovered, and this mirrored our findings in the relationship between total sugar intake and SHBG concentrations. The significant inverse associations between total sugar intakes, sugar sweetened beverages intakes and plasma SHBG concentrations illustrated the detrimental effect of excessive sugar consumptions, which indicated that cutting down sugar intake may be an important intervention to increase SHBG concentrations. We also identified 2 categories of foods, potatoes and beans, which were borderline significantly inversely associated with SHBG concentrations, albeit the complete null association after correction for multiple comparison or in controls only. Physiological studies show that most potatoes are of high
GI regardless of cooking method, which over the long term may increase the risk of obesity and chronic diseases such as type 2 diabetes and cardiovascular diseases.39
The cross-sectional nature of the current investigation raises concern over the temporality of the associations that we observed. However, the WHI food frequency questionnaire inquired dietary intakes during the period of 3 months prior to study baseline, while the blood samples from which SHBG was measured were taken at baseline. Thus, the temporality between dietary carbohydrates intake and serum SHBG concentrations can be established to a certain extent. Another limitation of this study is that measurements of SHBG from different ancillary studies of the WHI were included in order to boost the power in detecting the associations, especially with the moderately large number of testings in the current analysis. Heterogeneity among these studies in 26 measuring serum SHBG, as well as different criteria for choosing study participants may introduce bias into our results. We attempted to address this issue by including both the indicators of ancillary studies and indicators of case or control status in each ancillary study in our statistical modelling. To evaluate the extent of bias, we also performed sensitivity analyses in controls only, as well as using linear mixed effects models, and our results were largely robust to the different methods used. Adiposity could also potentially influence both dietary intake and blood SHBG concentrations.2 Although we controlled for BMI in our analyses, we could not rule out the possibility of residual confounding as it is not a perfect measure of adiposity. Finally, while a large national sample of postmenopausal women participated in the WHI studies, which was broader and more representative than those in studies based on samples of convenience, the findings presented here can only be generalized to postmenopausal women, which is another limitation of this investigation.
In conclusion, our study found that dietary fiber intake, sugar intake, and GL/GI based on total and available carbohydrates have significant associations with serum SHBG concentrations, thus supporting a role of diet in influencing blood levels of SHBG, which is in turn an important protective factor probably associated with insulin resistance, type
2 diabetes, cardiovascular disease, and hormone-dependent cancers. Further studies are needed to better elucidate the biological mechanisms underlying the associations between dietary carbohydrates and circulating SHBG concentrations, and mediation analyses with sufficient power are also needed to evaluate whether these possible effects of dietary carbohydrates on SHBG extend to the ultimate cardio-metabolic 27 disease risk, which will contribute to a better understanding of the mechanisms of action underlying the effect of diets, particularly of high GL/GL diets rich in refined carbohydrates.
28
Acknowledgments
The WHI program is funded by the National Heart, Lung, and Blood Institute, National
Institutes of Health, U.S. Department of Health and Human Services through contracts
HHSN268201100046C, HHSN268201100001C, HHSN268201100002C,
HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C.
We would like to acknowledge the investigators of each of the ancillary studies (AS90,
AS110, AS167, AS238, BA7, BA9, BA21, W5, W9, W10, and W18) and the following
WHI investigators:
Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland)
Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller Clinical
Coordinating Center: Clinical Coordinating Center: (Fred Hutchinson Cancer Research
Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles
Kooperberg
Investigators and Academic Centers: (Brigham and Women's Hospital, Harvard Medical
School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard
University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center,
Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca
Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at
Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida,
Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport,
IA) Jennifer Robinson; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake
Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (University of Nevada, Reno, NV) Robert Brunner; (University of Minnesota, Minneapolis, MN) 29
Karen L. Margolis
Women’s Health Initiative Memory Study: (Wake Forest University School of Medicine,
Winston-Salem, NC) Mark Espeland
30
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37
Tables
Table 1. Baseline characteristics by quartiles of untransformed plasma SHBG concentrations in a subpopulation of the postmenopausal women from the Women’s Health Initiative (n = 11,159)
SHBG P-value Q1 Q2 Q3 Q4 Number of participants 2801 2794 2776 2788 Median (nmol/L) 25.7 40.0 56.3 92.0 (Interquartile range) (21.0, 29.7) (36.5, 43.5) (51.7, 62.0) (78.0, 125.0) Age (years) [mean (SD)] 63.7 (7.1) 65.6 (7.3) 66.5 (7.5) 65.6 (7.8) <.001 BMI (kg/m2) [mean (SD)] 31.6 (5.9) 29.5 (6.0) 27.3 (5.5) 26.0 (5.5) <.001 Race/ethnicity [n (%)] White 1930 (68.9) 1997 (71.5) 1972 (71.0) 1734 (62.2) Black/African American 502 (17.9) 456 (16.3) 443 (16.0) 592 (21.2) <.001 Hispanic/Latino 198 (7.1) 190 (6.8) 183 (6.6) 227 (8.1) Asian/Pacific Islander 124 (4.4) 102 (3.7) 137 (4.9) 196 (7.0) Other 47 (1.7) 49 (1.8) 41 (1.5) 39 (1.4) Smoking status [n (%)] Never 1444 (51.6) 1495 (53.5) 1457 (52.5) 1471 (52.8) 0.02 Former 1144 (40.8) 1067 (38.2) 1076 (38.8) 1040 (37.3) Current 213 (7.6) 232 (8.3) 243 (8.8) 277 (9.9) Total energy (kcal/d) [mean (SD)] 1715.1 (694.6) 1625.0 (665.1) 1573.7 (627.5) 1555.4 (641.5) <.001 Alcohol intake (g/d) [mean (SD)] 4.5 (11.0) 5.1 (11.6) 5.1 (10.5) 4.4 (11.2) <.001 GL (total CHO) [mean (SD)] 110.4 (46.8) 104.9 (43.5) 104.0 (43.7) 103.2 (43.3) <.001 GL (available CHO) [mean (SD)] 102.9 (44.3) 97.5 (41.0) 96.5 (41.1) 95.8 (40.8) <.001 GI (total CHO) [mean (SD)] 52.6 (3.7) 52.1 (3.9) 52.0 (3.9) 52.1 (3.8) <.001 GI (available CHO) [mean (SD)] 53.0 (3.7) 52.5 (3.9) 52.4 (3.9) 52.5 (3.7) <.001 Total fiber (g/d) [mean (SD)] 15.4 (6.9) 15.4 (6.7) 15.7 (7.0) 15.7 (6.9) 0.13 Total sugar (g/d) [mean (SD)] 103.2 (51.0) 98.7 (47.1) 97.9 (45.6) 96.9 (46.2) <.001 38
Total carbohydrates (g/d) [mean (SD)] 208.9 (84.6) 200.4 (79.7) 199.0 (79.4) 197.4 (79.4) <.001 Total protein (g/d) [mean (SD)] 70.7 (30.8) 67.1 (30.2) 64.6 (27.6) 62.9 (282.2) <.001 Total fat (g/d) [mean (SD)] 65.9 (35.6) 60.9 (34.0) 57.1 (31.8) 57.1 (32.2) <.001 Fruits & vegetables (servings/d) [mean 3.9 (2.2) 3.9 (2.1) 4.1 (2.2) 4.1 (2.2) <.001 (SD)] Abbreviations: SHBG: sex hormone-binding globulin; Q: quartile; SD: standard deviation; BMI: body mass index; GL: glycemic load; CHO: carbohydrates; GI: glycemic index. 39
Table 2. Adjusted means of serum SHBG concentrations according to quartiles of dietary glycemic load, glycemic index, and intakes of fiber, sugar, and total carbohydrates
Adjusted 95% CI Ptrend mean* q-value GL (total CHO) Q1 56.4 (54.3, 58.6) Q2 55.6 (53.8, 57.4) 0.01 0.04 Q3 53.0 (51.3, 54.7) Q4 52.9 (50.8, 55.1) GL (available CHO) Q1 56.7 (54.6, 58.9) Q2 55.5 (53.7, 57.3) 0.002 0.01 Q3 53.1 (51.4, 54.8) Q4 52.6 (50.5, 54.7) GI (total CHO) Q1 55.5 (53.7, 57.3) Q2 54.8 (53.1, 56.5) 0.02 0.07 Q3 53.9 (52.3, 55.7) Q4 53.9 (52.2, 55.6) GI (available CHO) Q1 55.3 (53.5, 57.1) Q2 54.9 (53.2, 56.6) 0.04 0.10 Q3 54.1 (52.4, 55.8) Q4 53.9 (52.2, 55.6) Total fiber (g/d) Q1 53.6 (51.9, 55.4) 0.01 0.04 Q2 54.1 (52.5, 55.9) Q3 54.0 (52.3, 55.8) 40
Q4 56.4 (54.5, 58.5) Total sugar (g/d) Q1 56.2 (54.3, 58.1) Q2 55.1 (53.3, 56.8) <.001 0.01 Q3 54.1 (52.4, 55.9) Q4 52.1 (50.2, 54.1) Total carbohydrates
(g/d) 54.9 (53.0, 56.8) Q1 0.37 0.42 Q2 55.2 (53.4, 57.0) Q3 53.4 (51.7, 55.1) Q4 54.3 (52.3, 56.3) Abbreviations: SHBG: sex hormone-binding globulin; CI: confidence interval;
Ptrend: P-value for trend; Q: quartile; GL: glycemic load; CHO: carbohydrates; GI: glycemic index. * Adjusted means were computed by exponentiating the least squares means of estimated log-transformed SHBG concentrations from model including the exposure of interest, total carbohydrates (except when total carbohydrates was the exposure of interest), total energy intake, age, race, BMI, smoking status, physical activity, alcohol intake, hormone therapy use, ancillary study indicator, and case-control status in each ancillary study. Linear mixed model with ancillary study indicator as random effect yielded very similar results.
41
Table 3. Adjusted means of serum SHBG concentrations according to quartiles of intake of carbohydrates abundant food items
Adjusted 95% CI Ptrend mean* q-value White bread (servings/d) Q1 54.5 (52.8, 56.2) Q2 55.2 (53.5, 56.9) 0.13 0.20 Q3 54.5 (52.8, 56.2) Q4 53.7 (52.0, 55.5) Dark bread (servings/d) Q1 54.0 (52.3, 55.6) Q2 54.8 (53.1, 56.6) 0.64 0.68 Q3 54.8 (53.1, 56.6) Q4 54.7 (52.9, 56.5) Rice, grains and plain noodles (servings/d) Q1 54.0 (52.3, 55.6) 0.12 0.20 Q2 54.5 (52.7, 56.4) Q3 53.9 (52.1, 55.7) Q4 55.2 (53.6, 57.0) Potato (servings/d) Q1 55.4 (53.7, 57.1) Q2 54.6 (52.9, 56.3) 0.07 0.16 Q3 53.7 (52.1, 55.5) Q4 53.9 (52.2, 55.7) Cereal (servings/d) Q1 54.5 (52.9, 56.2) Q2 54.8 (53.1, 56.6) 0.92 0.92 Q3 54.0 (52.3, 55.7) Q4 54.8 (53.0, 56.7) 42
Fruits (servings/d) Q1 54.3 (52.6, 56.0) Q2 54.1 (52.4, 55.9) 0.21 0.27 Q3 54.4 (52.7, 56.2) Q4 55.3 (53.5, 57.1) Beans (servings/d) Q1 54.8 (53.1, 56.5) Q2 54.8 (53.1, 56.5) 0.10 0.19 Q3 54.9 (53.2, 56.7) Q4 53.6 (51.9, 55.3) Sugar sweetened beverages (servings/d) Q1 56.7 (55.0, 58.6) <.001 <.001 Q2 55.2 (53.4, 56.9) Q3 54.0 (52.3, 55.7) Q4 52.7 (51.1, 54.4) Pasta (servings/d) Q1 55.0 (53.3, 56.8) Q2 53.7 (52.1, 55.4) 0.20 0.27 Q3 53.8 (52.1, 55.5) Q4 55.5 (53.7, 57.4) Whole grains (servings/d) Q1 54.2 (52.5, 56.0) Q2 54.4 (52.7, 56.1) 0.22 0.27 Q3 54.2 (52.5, 55.9) Q4 55.3 (53.5, 57.1) Abbreviations: SHBG: sex hormone-binding globulin; CI: confidence interval;
Ptrend: P-value for trend; Q: quartile. * Adjusted means were computed by exponentiating the least squares means of estimated log-transformed SHBG concentrations from model including the 43
exposure of interest, total carbohydrates, total energy intake, age, race, BMI, smoking status, physical activity, alcohol intake, hormone therapy use, ancillary study indicator, and case-control status in each ancillary study. Linear mixed model with ancillary study indicator as random effect yielded very similar results.
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Table 4. Adjusted means of serum SHBG concentrations according to quartiles of dietary glycemic load, glycemic index, and intakes of fiber, sugar, and total carbohydrates in controls of the ancillary studies
Adjusted 95% CI Ptrend mean* q-value GL (total CHO) Q1 55.9 (53.0, 58.9) Q2 55.8 (53.3, 58.4) 0.03 0.06 Q3 52.8 (50.5, 55.2) Q4 51.7 (48.8, 54.7) GL (available CHO) Q1 56.3 (53.4, 59.3) Q2 55.5 (53.0, 58.1) 0.01 0.05 Q3 52.8 (50.5, 55.2) Q4 51.5 (48.6, 54.5) GI (total CHO) Q1 55.7 (53.3, 58.2) Q2 54.7 (52.4, 57.1) 0.001 0.01 Q3 53.3 (51.0, 55.7) Q4 52.5 (50.2, 54.8) GI (available CHO) Q1 55.5 (53.1, 58.0) Q2 54.5 (52.2, 56.9) 0.01 0.02 Q3 53.5 (51.2, 55.9) Q4 52.6 (50.4, 55.0) Total fiber (g/d) Q1 52.0 (49.7, 54.5) Q2 53.5 (51.1, 55.9) <.001 0.004 Q3 53.6 (51.3, 56.0) Q4 57.5 (54.8, 60.4) 45
Total sugar (g/d) Q1 55.5 (52.9, 58.2) Q2 54.8 (52.4, 57.3) 0.02 0.05 Q3 53.9 (51.5, 56.3) Q4 51.5 (48.9, 54.3) Total carbohydrates (g/d) 55.0 (52.4, 57.7) Q1 0.05 0.11 Q2 55.7 (53.2, 58.2) Q3 53.2 (50.9, 55.6) Q4 52.2 (49.6, 55.1) Abbreviations: SHBG: sex hormone-binding globulin; CI: confidence interval;
Ptrend: P-value for trend; Q: quartile; GL: glycemic load; CHO: carbohydrates; GI: glycemic index. * Adjusted means were computed by exponentiating the least squares means of estimated log-transformed SHBG concentrations from model including the exposure of interest, total carbohydrates (except when total carbohydrates was the exposure of interest), total energy intake, age, race, BMI, smoking status, physical activity, alcohol intake, hormone therapy use, and ancillary study indicator. Linear mixed model with ancillary study indicator as random effect yielded very similar results.
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Table 5. Adjusted means of serum SHBG concentrations according to quartiles of intake of carbohydrates abundant food items in controls of the ancillary studies
Adjusted mean* 95% CI Ptrend q-value
White bread (servings/d)
Q1 54.3 (52.0, 56.7)
Q2 53.5 (51.2, 55.9) 0.91 0.92
Q3 54.3 (52.0, 56.7)
Q4 53.8 (51.4, 56.4) Dark bread (servings/d)
Q1 53.7 (51.5, 56.1)
Q2 54.0 (51.7, 56.5) 0.68 0.77
Q3 54.2 (51.8, 56.6)
Q4 54.3 (51.8, 56.9) Rice, grains and plain noodles (servings/d)
Q1 53.0 (50.7, 55.4) 0.10 0.20 Q2 54.0 (51.5, 56.7)
Q3 53.8 (51.5, 56.1)
Q4 55.3 (52.7, 58.1) Potato (servings/d)
Q1 55.5 (53.2, 58.0)
Q2 52.9 (50.7, 55.3) 0.13 0.20
Q3 54.1 (51.7, 56.5)
Q4 53.1 (50.7, 55.6) Cereal (servings/d)
Q1 53.7 (51.4, 56.0)
Q2 54.4 (52.1, 56.8) 0.53 0.75
Q3 52.9 (50.6, 55.4)
Q4 55.0 (52.5, 57.6) 47
Fruits (servings/d)
Q1 53.7 (51.4, 56.2)
Q2 52.6 (50.4, 55.0) 0.13 0.20
Q3 55.1 (52.7, 57.6)
Q4 54.9 (52.5, 57.5) Beans (servings/d)
Q1 53.3 (51.0, 55.7)
Q2 54.1 (51.8, 56.5) 0.68 0.77
Q3 54.8 (52.4, 57.3)
Q4 53.9 (51.6, 56.4) Sugar sweetened beverages (servings/d)
Q1 56.6 (54.1, 59.1) <.001 0.003 Q2 54.6 (52.3, 57.1)
Q3 53.3 (51.0, 55.7)
Q4 52.2 (49.9, 54.6) Pasta (servings/d) Q1 54.6 (52.3, 57.1)
Q2 53.6 (51.4, 56.0) 0.66 0.77
Q3 53.1 (50.8, 55.5)
Q4 54.8 (52.4, 57.4) Whole grains (servings/d)
Q1 54.5 (52.1, 56.9)
Q2 53.2 (51.0, 55.6) 0.92 0.92
Q3 54.1 (51.8, 56.5)
Q4 54.2 (51.8, 56.7) Abbreviations: SHBG: sex hormone-binding globulin; CI: confidence interval;
Ptrend: P-value for trend; Q: quartile. * Adjusted means were computed by exponentiating the least squares means of estimated log-transformed SHBG concentrations from model including the 48
exposure of interest, total carbohydrates, total energy intake, age, race, BMI, smoking status, physical activity, alcohol intake, hormone therapy use, and ancillary study indicator. Linear mixed model with ancillary study indicator as random effect yielded very similar results.