A cross-sectional examination: impact of diet and physical activity on plasma glucose metabolism, insulin sensitivity and pancreatic β-cell function in Hispanic women with histories of gestationa...

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Content A CROSS-SECTIONAL EXAMINATION: IMPACT OF DIET AND PHYSICAL ACTIVITY ON PLASMA GLUCOSE METABOLISM, INSULIN SENSITIVITY AND PANCREATIC Β-CELL FUNCTION IN HISPANIC WOMEN WITH HISTORIES OF GESTATIONAL DIABETES MELLITUS by Xinwen Liu A Thesis Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (BIOSTATISTICS) December 2009 Copyright 2009 Xinwen Liu ii Dedication This work is dedicated to my wonderful parents, LIU Yuanfu and GUO Lanzhi. iii Acknowledgments I would like to express my utmost gratitude to Dr. Anny Xiang, Dr. Stanley Azen and Dr. Thomas Buchanan for their encouragement, guidance and support. I also wish to extend my heartfelt gratitude to all the research participants for their participation and cooperation. Last but not least, I thank my family and all my friends for their love and support. iv Table of Contents Dedication ii Acknowledgments iii List of Tables v Abstract vi Introduction 1 Materials and methods 3 Results 10 Discussion 24 References 27 v List of Tables Table 1: Baseline characteristics of study subjects by non-GDM and GDM groups 11 Table 2-1: Spearman correlations among diet variables at baseline (n=127) 13 Table 2-2: Spearman correlations (r s ) among physical activity variables at baseline (n=127) 14 Table 2-3: Spearman correlations (r s ) of total calories and physical activities at baseline (n=127) 14 Table 3-1: Spearman correlations between lifestyle and glucose metabolic measures in all subjects at baseline (n=127) 16 Table 3-2: Spearman correlations between lifestyle and glucose metabolic measures in GDM subjects at baseline (n=102) 17 Table 4-1: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in all subjects at baseline (n=127) * 20 Table 4-2: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in GDM subjects at baseline (n=102) * 22 vi Abstract The association between diet or physical activity (PA) and insulin sensitivity (IS), as well as β-cell function (BCF), has not been adequately examined. The purpose of the study is to cross-sectionally investigate this association in women with a history of gestational diabetes mellitus (GDM). Demographics, diet and PA data were collected using questionnaires; glucose metabolic measures (GMM) by OGTT and IVGTT. Among the 127 Hispanic women included in this report, we observed statistically significant differences in BMI and GMM between the 102 GDM patients and 25 controls; however, no differences were found in diet/PA. Diets high in carbohydrate, zinc, calcium and vitamin D seemed to be correlated with improved glucose control, IS and BCF. In addition, these beneficial effects were seen for exercise alone. These results highlight the importance of exercise in improving IS and BCF in women with a history of GDM to prevent the development of T2DM. 1 Introduction In the United States, gestational diabetes mellitus (GDM) is one of the most common complications in pregnancy, occurring in 7% of pregnant women [1]. It is characterized by decreased insulin sensitivity, increased insulin resistance, hyperlipidemia, mild inflammation and endothelial dysfunction [2]. GDM is considered a precursor of type 2 diabetes mellitus (T2DM) as it is associated with elevated risk of developing T2DM later in life [3]. An increased prevalence of GDM and T2DM was observed in Hispanic women, who are 2-4 times more likely to be diagnosed with GDM compared to non-Hispanic white women; it was estimated that about 50% of Hispanic women with a history of GDM would develop T2DM within 5 years of their initial GDM [4-6]. Previous studies showed that proper diet and physical activity remains the cornerstone of early treatment for GDM and T2DM in that it reduces glucose levels and prevents T2DM in adult women, whereas unhealthy dieting and physical inactivity constitutes risk factors, although findings regarding the relationship of diet and physical activity with β-cell function have been somewhat controversial [7-12]. Specifically, most findings consistently illustrated that a proper diet regimen and certain types of exercise would promote weight control, glucose metabolism and insulin sensitivity, however, this positive association was rarely studied with β-cell function. Among studies that looked at this association with β-cell function, one study found that diet-induced weight control was associated with improved β-cell function in older men, while this association was absent in Japanese Americans with impaired glucose tolerance [11, 12]. It was also 2 shown by studies that physical activity before or during pregnancy has beneficial effects on weight control and may reduce risk for GDM, even though with to some extent inconsistent findings; worse yet, usually the recommended activities may be perceived as either inappropriate or not feasible given the pregnancy status [13, 14]. Very few studies have researched specifically among Hispanic women with a history of GDM on the association of modifiable lifestyle factors, such as diet and physical activity, with plasma glucose metabolism, especially with insulin sensitivity and β-cell function. The purpose of the present study is to investigate the impact of lifestyle elements such as diet and physical activity on glucose metabolism, insulin sensitivity and β-cell function in women with high risk of developing T2DM after their initial episode of GDM, by performing a cross-sectional examination of this impact in a cohort of Hispanic women with diagnosis of GDM during the index pregnancy. We hypothesized that diet and physical activity, especially regular moderate, vigorous or strenuous exercise, would substantially improve glucose metabolism, insulin sensitivity and β-cell function for women who were at high risk of developing T2DM. 3 Materials and Methods Study Population Subjects in the present report are a subset from a cohort of 150 Hispanic women who participated in a longitudinal study researching the pathogenesis of T2DM after prior GDM, which was described in detail elsewhere [15-17]. Briefly, between August 1993 and March 1995, all Hispanic-American/Latino women that were referred to Los Angeles County Women’s Hospital for management of GDM were asked to participate in the longitudinal study, if all of the following inclusion criteria were met: gestational age between 28 and 34 weeks; with occurrence of GDM but no T2DM diagnosed during pregnancy; no current or prior insulin therapy; no other significant complications in pregnancy; both parents and at least three out of four grandparents from Mexico, Guatemala, or El Salvador. Glucose metabolic measurements were taken during their 6 month postpartum visits from a 75g oral glucose tolerance test (OGTT); and an OGTT and an intravenous glucose tolerance test (IVGTT) were done at their 15 month postpartum visits and every visit afterwards at an interval of 15 months. Subjects remained in the cohort unless they withdrew from study, were lost to follow-up, or were confirmed to have developed diabetes. A food/physical activity questionnaire was provided to and self-administered by study participants who had provided a written informed consent and agreed to fill out the questionnaire. In addition to the GDM subjects, a control group composed of 25 non-GDM pregnant women was included in this report. Controls were frequency matched to GDM subjects based on their age, regular BMI (when not pregnant), and gestational age at 4 the time of testing. They were contacted and selected from the obstetrical clinics where GDM subjects were referred. Women were eligible to be controls if they fulfilled all of the following criteria: a plasma glucose concentration <130 mg/dl 1 hour after a 50-g glucose challenge beyond 24 weeks’ gestation; no personal or family history of diabetes or glucose intolerance; with previous uncomplicated singleton pregnancies; both parents and at least three out of four grandparents from Mexico, Guatemala, or El Salvador. The study was approved by the Institutional Review Board of University of Southern California (USC), USC Medical Center and the Los Angeles County; all subjects provided written informed consent for participation in the study. For the present report, we are interested in assessing the cross-sectional relationship of diet and physical activity with glucose metabolism, insulin sensitivity and β-cell function. Subjects who had completed baseline assessment of OGTT, IVGTT, as well as food and physical activity questionnaire were included, along with the 25 frequency-matched controls. Testing Protocol OGTTs and IVGTTs strictly followed the University of Southern California General Clinical Center guideline. For each participant, an OGTT and an IVGTT were performed on two different days that were at least 48 hours apart, after at least 3 days of unrestricted diet and then 8-12 hours of overnight fasting. OGTT was performed in the morning on one day, when subjects drank 75g dextrose, and blood was drawn after 15, 30, 60, 90, 120, and 180 minutes; IVGTT was performed on the other day, when dextrose (300mg/kg) was injected intravenously over 1 minute, followed by a 5 minute 5 infusion of crystalline human insulin (0.03 unit/kg) 20 minutes later; blood was drawn both before the dextrose injection and 240 minutes after. Collected blood was preserved on iced conditions, and plasma was separated in no more than 20 minutes after blood drawn and then stored at -80°C. Laboratory analysis was done to obtain glucose metabolic readings. Glucose was measured by glucose oxidase (Glucose Analyzer II; Beckman, Brea, CA). A radioimmunoassay (Novo Pharmaceuticals, Danbury, CT) was performed to measure insulin levels. Questionnaire The questionnaire used in the present study was adopted from the Hawaii-Los Angeles Multiethnic Cohort Study (MEC), which was described in detail elsewhere [18]. In brief, MEC recruited 215,251 adult men and women aged from 45 to 75 years at baseline, residing in Hawaii and California (primarily Los Angeles County). The participants were composed of five major ethnic groups in the United States: Hispanic, African American, Japanese American, Native Hawaiian and Caucasian, which included the ethnic group of interest in the present report. With diet information as the primary interest, the 26-page self-administered questionnaire contained well-structured questions on food categories, frequency, and amount of consumption so as to accurately configure the eating patterns of each and every ethnic group of interest. Specifically, with the help of photographs clearly demonstrating food serving sizes, subjects indicated what food was consumed, the usual frequency each food item was consumed, and what amount of the food was consumed. In order to obtain absolute macro- and micro-nutrient intakes, 6 quantity of each food item was calculated as the product of frequency and serving size on a daily basis, and then nutrient contents of various kinds of food were determined from a customizable food composition database, which complies with related government guidelines and was validated by laboratory analysis [18]. Physical activity information was captured by questions asking on a daily or weekly basis the frequency and duration of moderate, vigorous or strenuous sports, as well as of sitting in cars, at work, in front of TV and so on. In addition, subjects were asked on average how many times they worked up a sweat per week. Information on demographic characteristics, and brief medical and reproductive history was collected along side. The reference period to fill out the questionnaire was the entire year before completing the questionnaire. Subsequently, a calibration study used a sub-sample from MEC and served as a validation [19]. Briefly, in order to assess if the questionnaire captured dietary intake differently in each ethnic-sex group, a random sample of approximately 260 subjects from each of the eight subgroups defined by ethnicity and sex (Hispanic, African American, Japanese American and Caucasian) was included in the calibration study. These subjects were then asked to complete three 24-hour recalls at one-month intervals and fill out an additional questionnaire (identical to the original questionnaire) about 4-6 weeks later. Study results showed that after energy adjustment the correlations between the 24-hour recalls and the second questionnaire were generally highly satisfactory. In the present report, original questionnaire from MEC was used, and participants indicated in the past year before their baseline visit their diet patterns, physical activity routines, along with demographic and brief medical/reproductive history information. 7 MET Score Creation A paper published in February 2004 showed that a quantitative approach was used to determine the activities that contribute to total energy expenditure in the U.S, as well as the ranking of the activities by individual contribution [20]. Each activity category was assigned the appropriate metabolic expenditure values (METs), where 1 MET is equal to 1 kcal per kg of body weight per hour, therefore, MET is a measure of physical activity intensity by activity duration at an individual’s level. MET score was created by multiplying duration of activity (hour), intensity of activity (MET value), and individual’s body weight (kg). It was worth mentioning that sedentary activities were the number 1 frequent activity in the U.S., in addition to sleeping. In the present study, we adopted a MET value of 4 for moderate activity, 8 for vigorous activity, and 10 for strenuous activity, in combination with each subjects body weight in kg and hours spent on each level of activity per day, to create each subject’s MET score for total physical activity on a daily basis. Statistical Analysis The demographic variables included in this report were age, weight, height, BMI and waist circumference. The list of diet variables were total calories, carbohydrate, protein, fat, saturated fat, cholesterol, dietary fiber, glucose, zinc, selenium, calcium, magnesium, sodium, potassium, phosphorus and vitamin D. Physical activity variables examined were total sedentary hours per day, total hours of exercise per day, total MET score of exercise per day, and number of times a subject works up a sweat per week. For glucose metabolic measures, we analyzed fasting glucose concentration, 2 hour 8 glucose concentration, fasting insulin, insulin sensitivity (Si), acute insulin response (AIRg), and disposition index (Si ×AIRg). Si was calculated by minimal model analysis of IVGTT insulin and glucose data. AIRg was calculated as incremental insulin area during first 10 min of IVGTT. Disposition index is a measure of β-cell function/compensation for insulin resistance, calculated as the product of Si and AIRg. Firstly, demographics, diet, physical activity and glucose metabolic measures were compared between GDM subjects and non-GDM controls to examine similarities and differences between the two groups. All variables were non-normally distributed, and we preserved diet and physical activity variables in their original form but log transformed all glucose metabolic variables to meet the normal distribution assumption. Therefore, a two-group t-test was used for analyzing glucose metabolic variables, and for the rest of the variables, Wilcoxon’s rank-sum test was performed. Furthermore, due to the fact that we categorized physical activity variables (except for sedentary hours) into two levels: no exercise at all (exercise time equals zero) and at least some exercise (exercise time greater than zero), a Chi-square test was used for these categorical variables. Secondly, Spearman correlations of all diet variables, of all physical activity variables, and of total calories with physical activity were separately assessed. Thirdly, Spearman correlations were cross-checked between lifestyle (diet and physical activity) and glucose metabolic measures in all subjects combined and in GDM subjects only. Last but not least, we utilized tertile cutoff points for age, BMI, and all diet variables to further define them as categorical variables (special cutoff points were 9 used on physical activity variables), and compared all six glucose metabolic measures across the strata of upper against lower tertile of any one of the above categorical variables in order to investigate whether there was any difference. A two-group t-test was used to obtain p-values. 10 Results A total of 127 subjects met the inclusion criteria for the present report, of which 25 were non-GDM controls and 102 were GDM patients. Table 1 shows baseline characteristics of all 127 study subjects by non-GDM control group and GDM group. Median age of non-GDM controls was 30.7 years, comparably, it was 32.0 of GDM subjects (p=0.53). BMI was statistically significantly different between the two groups (p=0.0083), with median BMI 27.1 kg/m 2 in control group and 30.4 kg/m 2 in GDM group, while waist circumferences were similar in both groups (p=0.13). Overall, diet patterns were assessed as equivalent in both groups, as daily intake of all the main components of diet and selected minerals in the present report was similar (most p-values ranging from 0.14 to 0.77). However, median glucose intake in control group was marginally significantly higher than in GDM group (p=0.06). Physical activity pattern was not different between the two groups either, with median sedentary hours per day being both 5.0 hours, and the majority of subjects in either group led a physically-inactive lifestyle (72% in control group and 67.6%-79.4% in GDM group had no exercise routine at all; p=0.42-0.67). As expected, all glucose metabolic measures were statistically significantly different between the two groups (all p<0.001). Median fasting glucose and 2-hour glucose were 86.0 mg/dl and 114.0 mg/dl in controls, and 98.5 mg/dl and 159.0 mg/dl in GDM subjects, respectively (p<0.0001). Median fasting insulin was 11.0 µU/ml in controls and 17.5 µU/ml in GDMs (p=0.0007); median Si was 2.5 ×10 -4 ·µU -1 ·ml·min -1 in controls and 1.3 ×10 -4 ·µU -1 ·ml·min -1 in GDMs (p=0.0003); and median AIRg was 736.0 µU·ml -1 ·min in controls and 391.0 µU·ml -1 ·min in GDMs (p=0.0005). Median DI was 1995.6 in controls and 584.5 in GDM subjects, respectively (p<0.0001). 11 Table 1: Baseline characteristics of study subjects by non-GDM and GDM groups Non-GDM Controls (n=25) GDM (n=102) Median (IQR) / Frequency (%) Median (IQR) / Frequency (%) P value * Age (years) 30.7 (28.1, 33.0) 32.0 (28.0, 36.0) 0.53 Weight (kg) 66.6 (59.3, 75.0) 70.3 (64.1, 78.2) 0.16 Height (m) 1.6 (1.5, 1.6) 1.5 (1.5, 1.6) 0.0095 BMI (kg/m 2 ) 27.1 (23.9, 31.2) 30.4 (27.5, 32.9) 0.0083 Waist circumference (cm) 84.0 (72.4, 86.5) 88.8 (83.5, 96.5) 0.13 DIET VARIABLES Total calories (Kcal/day) 2182.5 (1816.5, 2914.0) 2013.6 (1311.2, 2855.9) 0.20 Carbohydrate (g/day) 303.5 (227.8, 398.0) 265.0 (161.0, 364.1) 0.14 Protein (g/day) 86.7 (63.1, 112.9) 80.6 (51.6, 118.0) 0.48 Fat (g/day) 75.9 (59.2, 105.8) 69.2 (44.7, 104.6) 0.23 Saturated fat (g/day) 25.2 (19.3, 39.1) 24.1 (14.1, 36.5) 0.32 Cholesterol (mg/day) 234.0 (158.4, 295.1) 228.7 (133.9, 347.7) 0.51 Dietary fiber (g/day) 28.6 (23.8, 37.5) 30.1 (18.4, 41.5) 0.65 Glucose (g/day) 25.9 (19.8, 38.4) 19.8 (10.0, 29.6) 0.06 Zinc (mg/day) 17.2 (11.4, 22.9) 13.9 (8.6, 21.0) 0.18 Selenium (mcg/day) 98.3 (83.3, 138.7) 93.7 (64.8, 145.0) 0.48 Calcium (mg/day) 1212.6 (816.9, 1776.3) 1025.7 (636.2, 1672.6) 0.21 Magnesium (mg/day) 358.2 (254.9, 432.6) 337.7 (199.3, 471.2) 0.47 Sodium (mg/day) 4234.2 (2863.5, 4901.5) 3592.1 (2156.4, 5332.0) 0.39 Potassium (mg/day) 3211.1 (2547.8, 4451.3) 3320.3 (1954.0, 4764.4) 0.70 Phosphorus (mg/day) 1608.2 (1186.2, 1882.3) 1414.1 (911.1, 2253.7) 0.34 Vitamin D (IU/day) 142.4 (85.7, 199.1) 135.7 (59.2, 215.4) 0.77 PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day 5.0 (4.5, 8.0) 5.0 (3.5, 6.5) 0.63 Total hours of exercise per day 0 (0, 0.1) 0 (0, 0.1) 0.87 Total MET score of exercise per day 0 (0, 59.6) 0 (0, 33.0) 0.90 # of times subject works up a sweat per week 0 (0, 2) 0 (0, 0) 0.19 # (%) of subjects with daily exercise hours>0 7 (28.0%) 33 (32.4%) 0.67 # (%) of subjects with daily MET score of exercise>0 7 (28.0%) 33 (32.4%) 0.67 # (%) of subjects with weekly workout times>0 7 (28.0%) 21 (20.6%) 0.42 GLUCOSE METABOLIC MEASURES Fasting glucose (mg/dl) 86.0 (84.0, 91.0) 98.5 (92.0, 110.0) <0.0001 2-h glucose (mg/dl) 114.0 (101.0, 133.0) 159.0 (132.0, 185.0) <0.0001 Fasting insulin (µU/ml) 11.0 (7.5, 15.5) 17.5 (13.5, 25.5) 0.0007 Si ( × 10 -4 ·µU -1 ·ml·min -1 ) 2.5 (1.4, 3.2) 1.3 (0.8, 2.0) 0.0003 AIRg (µU·ml -1 ·min) 736.0 (590.5, 1179.5) 391.0 (145.0, 715.3) 0.0005 Disposition index ( × 10 -4 ) 1995.6 (1019.6, 2875.5) 584.5 (158.6, 1029.3) <0.0001 *P-values were obtained by Wilcoxon’s Rank-sum test for non-normally distributed continuous variables or by two- group t-test for normally distributed continuous variables; P-values were obtained by Chi-square test for categorical variables 12 Correlations among all diet variables were examined and presented in Table 2-1, where it is obvious that each selected diet component was highly correlated with one another (all p-values <0.0001). The Spearman correlations were further studied after adjustment for age and BMI, or age, BMI and physical activity, respectively, which demonstrated same high correlations. The inner correlations among all physical activity variables were shown in Table 2-2, where sedentary time was not correlated with exercise variables: r s equal to -0.06 with total hours of exercise per day, -0.07 with total MET score of exercise per day, and -0.08 with number of times subject works up a sweat per week (p=0.39-0.47); while all three exercise variables were highly correlated with one another despite different extraction/formation mechanisms (p<0.0001). After adjusting for age and BMI, or age, BMI and total daily calories, respectively, we saw no change in these correlations. Furthermore, correlations between diet (total calories) and physical activity variables were explored and no significant correlations were observed (p=0.15- 0.94; Table 2-3). 13 Table 2-1: Spearman correlations among diet variables at baseline (n=127) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) Total calories (1) 1 Carbohydrate (2) 0.96 1 Protein (3) 0.97 0.89 1 Fat (4) 0.96 0.85 0.97 1 Saturated fat (5) 0.96 0.86 0.96 0.99 1 Cholesterol (6) 0.91 0.81 0.93 0.93 0.93 1 Dietary fiber (7) 0.86 0.89 0.84 0.76 0.76 0.72 1 Glucose (8) 0.79 0.87 0.71 0.67 0.68 0.64 0.80 1 Zinc (9) 0.85 0.83 0.84 0.81 0.81 0.84 0.75 0.67 1 Selenium (10) 0.97 0.89 0.98 0.96 0.95 0.95 0.80 0.69 0.84 1 Calcium (11) 0.88 0.90 0.84 0.80 0.83 0.79 0.80 0.73 0.92 0.83 1 Magnesium (12) 0.95 0.96 0.92 0.86 0.87 0.82 0.95 0.80 0.83 0.90 0.89 1 Sodium (13) 0.94 0.87 0.94 0.93 0.92 0.90 0.80 0.70 0.82 0.94 0.78 0.87 1 Potassium (14) 0.93 0.93 0.92 0.86 0.86 0.82 0.94 0.82 0.81 0.89 0.87 0.98 0.88 1 Phosphorus (15) 0.97 0.95 0.95 0.91 0.92 0.88 0.87 0.75 0.86 0.94 0.93 0.96 0.89 0.93 1 Vitamin D (16) 0.79 0.79 0.76 0.73 0.77 0.74 0.70 0.64 0.79 0.77 0.90 0.81 0.70 0.80 0.86 All p-values are <0.0001 Crude Spearman correlations are shown in table (Adjusted Spearman correlations are omitted from table due to similarities; the following two sets of factors were individually adjusted for: age and BMI, or age, BMI and physical activity) 14 Table 2-2: Spearman correlations (r s ) among physical activity variables at baseline (n=127) Sedentary hours r s (p) Hours of exercise r s (p) MET score r s (p) Total sedentary hours per day 1 Total hours of exercise per day -0.06 (0.47) 1 Total MET score of exercise per day -0.07 (0.42) 0.99 (<0.0001) 1 # of times subject works up a sweat per week -0.08 (0.39) 0.75 (<0.0001) 0.74 (<0.0001) Crude Spearman correlations are shown in table (Adjusted Spearman correlations are omitted from table due to similarities; the following two sets of factors were individually adjusted for: age and BMI, or age, BMI and total calories) Table 2-3: Spearman correlations (r s ) of total calories and physical activities at baseline (n=127) Total calories (Kcal/day) Crude r s (p) Adjusted r s (p) PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day 0.04 (0.67) 0.007 (0.94) Total hours of exercise per day 0.13 (0.15) 0.12 (0.17) Total MET score of exercise per day 0.12 (0.16) 0.12 (0.19) # of times subject works up a sweat per week -0.03 (0.71) -0.05 (0.61) The following factors were adjusted for: age and BMI 15 Correlations between all lifestyle variables against each glucose metabolic measure were investigated, which were showed in the scale of all 127 subjects (Table 3-1) and of 102 GDM subjects (Table 3-2), respectively. Overall, similar patterns emerged in both analyses. In all subjects combined, high carbohydrate intake seemed to be associated with improved AIRg, after adjustment for age and BMI (r s = 0.19, p=0.04). Similarly for zinc with 2-hour glucose (r s = -0.17, p=0.07) and AIRg (r s = 0.19, p=0.04); and for calcium with AIRg (r s = 0.16, p=0.09). In GDM subjects only, high carbohydrate intake also seemed to be correlated with improved AIRg, after the same adjustment as above (r s = 0.17, p=0.09); similarly, zinc, calcium and vitamin D were found to be associated with elevated AIRg after the same adjustment (r s = 0.20, 0.18 and 0.20; p=0.05, 0.07 and 0.05). Interestingly, we also see a similar correlation with daily glucose intake. Generally strong correlations were observed for all exercise variables with improved glucose metabolism, insulin sensitivity and β-cell function. In all subjects combined, “total hours of exercise per day” was associated with fasting insulin, Si and DI both before and after adjustment for age and BMI (|r s | = 0.17-0.21, p=0.02-0.06); similarly, “total MET score of exercise per day” was also found to be associated with fasting insulin, Si and DI both before and after adjustment for age and BMI (|r s | = 0.18-0.20, p=0.03-0.06); “number of times subject works up a sweat” was associated with fasting glucose and Si both before and after adjustment for age and BMI (|r s | = 0.18-0.30, p=0.005-0.05); Daily sedentary time was not found to have any correlations with glucose metabolic measures (|r s | = 0.005-0.04, all p>0.15). In GDM subjects only, associations similar to above mentioned were observed. 16 Table 3-1: Spearman correlations between lifestyle and glucose metabolic measures in all subjects at baseline (n=127) Glucose metabolic measures Fasting glucose 2-h glucose Fasting insulin Si AIRg Disposition index Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s DIET VARIABLES Total Calories 0.01 -0.02 -0.03 -0.07 -0.02 0.02 -0.05 -0.06 0.10 0.14 0.03 0.06 Carbohydrate -0.01 -0.03 -0.09 -0.12 -0.05 -0.01 -0.02 -0.03 0.14 0.19 0.08 0.12 Protein 0.04 0.005 -0.01 -0.05 0.01 0.05 -0.08 -0.09 0.08 0.12 0.01 0.05 Fat 0.01 -0.02 0.01 -0.02 0.02 0.05 -0.07 -0.07 0.04 0.08 -0.02 0.01 Saturated fat 0.004 -0.02 0.01 -0.02 0.02 0.05 -0.06 -0.06 0.05 0.08 -0.01 0.02 Cholesterol 0.01 -0.01 -0.03 -0.05 -0.01 0.03 -0.06 -0.07 0.09 0.12 0.03 0.06 Dietary fiber 0.04 0.02 -0.06 -0.09 0.02 0.07 -0.03 -0.05 0.07 0.12 0.02 0.06 Glucose -0.07 -0.07 -0.17 -0.18 -0.08 -0.03 0.04 0.01 0.18 0.21 0.14 0.15 Zinc -0.02 -0.05 -0.12 -0.17 -0.04 -0.01 -0.04 -0.05 0.15 0.19 0.10 0.14 Selenium 0.03 0.004 -0.02 -0.05 -0.02 0.02 -0.07 -0.08 0.08 0.12 0.01 0.05 Calcium -0.04 -0.07 -0.10 -0.14 -0.08 -0.04 -0.02 -0.03 0.11 0.16 0.07 0.11 Magnesium 0.03 -0.01 -0.06 -0.01 -0.01 0.04 -0.02 -0.04 0.08 0.14 0.04 0.08 Sodium 0.01 -0.02 -0.02 -0.05 0.05 0.09 -0.08 -0.10 0.11 0.14 0.02 0.04 Potassium 0.01 -0.02 -0.07 -0.10 -0.02 0.03 -0.02 -0.04 0.08 0.13 0.04 0.07 Phosphorus 0.02 0.003 -0.04 -0.06 -0.03 0.02 -0.05 -0.07 0.07 0.12 0.02 0.06 Vitamin D -0.01 -0.02 -0.07 -0.08 -0.05 -0.002 -0.04 -0.06 0.10 0.13 0.04 0.06 PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day -0.02 0.01 -0.02 0.01 0.01 0.04 -0.005 -0.03 -0.01 0.01 -0.003 0.005 Total hours of exercise per day -0.14 -0.15 -0.15 -0.11 -0.20 -0.19 0.21 0.18 0.07 0.08 0.19 0.17 Total MET score of exercise per day -0.14 -0.15 -0.14 -0.11 -0.19 -0.20 0.20 0.18 0.08 0.08 0.18 0.18 # of times subject works up a sweat per week -0.20 -0.19 -0.15 -0.10 -0.20 -0.15 0.3 0.18 0.03 0.03 0.19 0.15 p≤0.005 where |r s | ≥0.30; 0.005<p ≤0.05 where 0.18 ≤|r s |<0.30; 0.05<p ≤0.1 where 0.15 ≤|r s |<0.18; 0.1<p ≤0.15 where 0.13 ≤|r s |<0.15; p>0.15 where |r s |<0.13 (r s were bolded where p≤0.15) Spearman correlations are adjusted for age and BMI (spearman correlations were further adjusted for physical activities for diet variables, and total calories for physical activity variables, which showed similar results and were omitted from table) 17 Table 3-2: Spearman correlations between lifestyle and glucose metabolic measures in GDM subjects at baseline (n=102) Glucose metabolic measures Fasting glucose 2-h glucose Fasting insulin Si AIRg Disposition index Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s Crude r s Adj. r s DIET VARIABLES Total Calories 0.08 0.01 0.01 -0.03 0.04 0.09 -0.10 -0.09 0.06 0.12 -0.03 0.02 Carbohydrate 0.05 -0.02 -0.05 -0.09 -0.005 0.04 -0.06 -0.06 0.11 0.17 0.04 0.09 Protein 0.10 0.04 0.01 -0.03 0.06 0.10 -0.12 -0.11 0.05 0.11 -0.04 0.01 Fat 0.10 0.03 0.07 0.03 0.10 0.13 -0.12 -0.09 -0.01 0.03 -0.11 -0.06 Saturated fat 0.08 0.02 0.04 0.01 0.08 0.12 -0.11 -0.08 0.01 0.05 -0.08 -0.03 Cholesterol 0.08 0.02 0.001 -0.03 0.06 0.10 -0.12 -0.11 0.06 0.11 -0.04 0.01 Dietary fiber 0.07 0.02 -0.06 -0.09 0.003 0.06 -0.03 -0.04 0.08 0.13 0.03 0.07 Glucose -0.02 -0.06 -0.13 -0.15 -0.05 -0.005 0.02 0.004 0.14 0.18 0.10 0.13 Zinc 0.05 -0.03 -0.11 -0.17 -0.02 0.02 -0.07 -0.06 0.13 0.20 0.07 0.12 Selenium 0.08 0.02 -0.004 -0.04 0.04 0.09 -0.11 -0.10 0.06 0.12 -0.03 0.02 Calcium 0.01 -0.06 -0.09 -0.13 -0.06 -0.02 -0.05 -0.05 0.13 0.18 0.07 0.12 Magnesium 0.07 0.002 -0.05 -0.09 -0.01 0.05 -0.04 -0.05 0.08 0.14 0.03 0.08 Sodium 0.08 0.02 -0.01 -0.05 0.10 0.15 -0.10 -0.10 0.05 0.10 -0.04 0.001 Potassium 0.04 -0.02 -0.09 -0.13 -0.02 0.03 -0.02 -0.02 0.10 0.15 0.05 0.09 Phosphorus 0.08 0.02 -0.02 -0.05 0.003 0.05 -0.09 -0.09 0.07 0.13 -0.002 0.05 Vitamin D -0.004 -0.06 -0.10 -0.14 -0.05 -0.003 -0.03 -0.04 0.16 0.20 0.08 0.11 PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day 0.04 0.04 0.05 0.05 -0.02 0.007 0.003 -0.01 -0.09 -0.07 -0.08 -0.07 Total hours of exercise per day -0.16 -0.17 -0.15 -0.14 -0.15 -0.17 0.21 0.21 0.09 0.10 0.20 0.21 Total MET score of exercise per day -0.15 -0.17 -0.14 -0.14 -0.15 -0.18 0.20 0.21 0.10 0.11 0.20 0.21 # of times subject works up a sweat per week -0.18 -0.14 -0.10 -0.07 -0.13 -0.10 0.22 0.18 0.002 0.004 0.15 0.13 p≤0.05 where |r s |≥0.20; 0.05<p ≤0.1 where 0.17 ≤|r s |<0.20; 0.1<p ≤0.15 where 0.15 ≤|r s |<0.17; p>0.15 where |r s |<0.15 (r s were bolded where p ≤0.15) Spearman correlations are adjusted for age and BMI (spearman correlations were further adjusted for physical activities for diet variables, and total calories for physical activity variables, which showed similar results and were omitted from table) 18 Table 4-1 (for all 127 subjects) and Table 4-2 (for GDM subjects only) present the comparison of glucose metabolic measures between subjects in the upper and the lower tertile of the distribution of the demographic variables and dietary variables. For physical activity measures, due to highly skewed distribution, total hours of exercise per day was divided into three levels: no exercise, regular exercise (at least an average of 30 minutes of exercise per day), and the category in between; only no exercise and regular exercise were compared. Same categorization approach was used for the variable “total MET score of exercise per day”. For variable “number of times subject works up a sweat per week”, in addition to the category that represented no exercise at all, at least twice of work-outs was expected to be categorized as “regular exercise”. As one would expect, younger GDM subjects seemed to have better AIRg and disposition index than older subjects (p=0.0073-0.07). Consistent with prior findings, subjects with BMI<28.0 kg/m 2 showed statistically significantly better glucose metabolic profile than subjects with BMI>31.6 kg/m 2 (who can be classified as “obese”). There were no differences in glucose metabolic measures (including β-cell function) between subjects whose dietary consumption was in the lower tertile compared to the upper tertile. On the contrary, in all subjects combined, we found that subjects in upper tertile of exercise variables had significantly improved glucose metabolism, insulin sensitivity and β-cell function: median fasting glucose, 2-hour glucose, Si and DI were 96.5, 155.0, 1.2 and 664.0 in the upper tertile of variable “total hours of exercise per day” and 86.5, 133.0, 2.2 and 1094.1 in the lower tertile, and p values were 0.06, 0.09, 0.04 and 0.04, respectively; similar differences were found in the other two exercise variables “total MET score of exercise per day” and “number of times subject works up a sweat per week”. Sedentary time 19 did not influence glucose metabolic readings. The results from these categorical analyses are generally consistent with the correctional analysis obtained on original data. 20 Table 4-1: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in all subjects at baseline (n=127) * Glucose metabolic measures Fasting glucose (mg/dl) 2-h glucose (mg/dl) Fasting insulin (µU/ml) Si ( × 10 -4 ·µU -1 ·ml·min -1 ) AIRg (µU·ml -1 ·min) Disposition index ( × 10 -4 ) Median p Median p Median p Median p Median p Median p Age (in years) <29.0 92.8 141.0 16.0 1.5 515.5 874.9 >35.0 102.5 0.10 146.0 0.40 17.8 0.63 1.4 0.86 304.0 0.0073 538.0 0.0178 BMI <28.0 92.5 119.0 15.0 1.8 510.0 902.5 >31.6 99.0 0.0261 162.0 0.0021 17.8 0.0067 1.2 0.0001 413.5 0.18 566.5 0.0017 DIET VARIABLES Total Calories (Kcal/day) <1633.2 96.3 162.0 16.0 1.4 375.5 709.9 >2551.8 97.5 0.80 146.0 0.60 16.0 0.98 1.4 0.76 597.8 0.31 774.8 0.56 Carbohydrate (g/day) <220.2 95.8 162.0 18.0 1.3 326.8 613.7 >349.4 97.5 0.74 145.0 0.46 17.5 0.78 1.3 0.59 698.8 0.17 841.7 0.35 Protein (g/day) <66.2 96.3 152.5 16.0 1.5 366.0 685.0 >107.4 98.5 0.69 146.0 0.80 16.5 0.91 1.3 0.45 516.0 0.31 703.6 0.72 Fat (g/day) <54.9 96.0 148.0 15.5 1.5 385.0 801.8 >94.0 97.5 0.62 152.0 0.77 17.5 0.54 1.3 0.46 475.8 0.65 683.3 0.89 Saturated fat (g/day) <18.3 96.0 148.0 15.5 1.4 425.5 774.0 >31.1 98.3 0.68 156.0 0.49 17.5 0.44 1.4 0.65 475.8 0.65 683.3 0.98 Cholesterol (mg/day) <176.2 96.0 150.0 15.5 1.4 364.3 771.3 >289.8 98.5 0.84 144.0 0.78 16.0 0.62 1.4 0.51 532.0 0.24 715.5 0.50 Dietary fiber (g/day) <22.5 95.0 162.0 16.0 1.3 425.5 816.7 >37.5 97.8 0.41 145.0 0.65 17.5 0.68 1.3 0.66 622.8 0.50 819.1 0.62 Glucose (g/day) <14.9 96.5 158.5 16.0 1.4 375.5 660.4 >28.7 96.0 0.82 135.0 0.21 16.3 0.61 1.3 0.86 657.0 0.13 857.4 0.28 Zinc (mg/day) <10.3 95.8 152.5 16.0 1.4 334.8 644.1 >18.9 97.5 0.76 143.5 0.52 15.8 0.78 1.3 0.49 532.0 0.17 902.5 0.29 Selenium (mcg/day) <79.1 96.5 162.0 16.0 1.4 343.5 685.0 >125.6 98.8 0.96 149.0 0.46 16.3 0.95 1.4 0.93 500.0 0.28 691.6 0.47 Calcium (mg/day) <802.2 96.0 162.0 16.0 1.4 392.0 642.3 >1407.5 97.5 0.53 146.0 0.81 16.0 0.82 1.3 0.80 556.8 0.45 819.1 0.69 21 Table 4-1: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in all subjects at baseline (n=127) * (Continued) Glucose metabolic measures Fasting glucose (mg/dl) 2-h glucose (mg/dl) Fasting insulin (µU/ml) Si ( × 10 -4 ·µU -1 ·ml·min -1 ) AIRg (µU·ml -1 ·min) Disposition index ( × 10 -4 ) Median p Median p Median p Median p Median p Median p Magnesium (mg/day) <254.9 95.8 152.5 15.8 1.4 366.0 645.8 >394.7 98.8 0.34 150.0 0.95 17.5 0.41 1.3 0.39 538.5 0.51 768.7 0.98 Sodium (mg/day) <2863.5 96.5 150.0 15.5 1.5 385.0 801.8 >4745.1 98.0 0.76 144.0 0.99 18.5 0.36 1.4 0.35 500.0 0.46 715.5 0.82 Potassium (mg/day) <2510.3 96.3 158.5 16.0 1.4 366.0 645.8 >4094.5 97.5 0.82 143.0 0.29 16.5 0.68 1.4 0.94 561.5 0.20 819.1 0.43 Phosphorus (mg/day) <1097.5 96.0 150.0 16.0 1.4 385.0 645.8 >1843.4 97.5 0.93 146.0 0.55 15.8 0.85 1.4 0.97 575.0 0.34 893.9 0.52 Vitamin D (IU/day) <89.6 95.3 146.5 16.0 1.4 500.0 833.9 >174.2 96.8 0.65 141.0 0.88 15.0 0.95 1.4 0.88 556.8 0.82 898.2 0.99 PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day <4.5 96.0 143.0 16.0 1.3 524.3 874.9 >5.5 93.5 0.74 148.0 0.69 18.5 0.86 1.3 0.99 508.5 0.67 682.2 0.73 Total hours of exercise per day 0 (n=87) 96.5 155.0 18.0 1.2 448.8 664.0 >0.5 (n=15) 86.5 0.06 133.0 0.09 14.0 0.20 2.2 0.04 537.5 0.50 1094.1 0.04 Total MET score of exercise per day 0 (n=87) 96.5 155.0 18.0 1.2 448.8 664.0 >176.3 (n=15) 90.5 0.06 135.0 0.11 14.0 0.09 1.7 0.15 639.3 0.37 1094.1 0.06 # of times subject works up a sweat per week 0 (n=99) 97.0 152.0 17.8 1.2 456.0 664.0 ≥2 (n=15) 87.5 0.11 139.0 0.47 14.8 0.20 2.0 0.08 537.5 0.82 1052.3 0.14 *Physical activity variables were not categorized into tertiles; specific cutoff points and sample sizes were given in table P-values were obtained by two group t-test P-values are bolded where p ≤0.15 22 Table 4-2: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in GDM subjects at baseline (n=102) * Glucose metabolic measures Fasting glucose (mg/dl) 2-h glucose (mg/dl) Fasting insulin (µU/ml) Si ( × 10 -4 ·µU -1 ·ml·min -1 ) AIRg (µU·ml -1 ·min) Disposition index ( × 10 -4 ) Median p Median p Median p Median p Median p Median p Age (in years) <29.0 96.0 157.0 16.5 1.2 367.8 532.7 >35.0 104.0 0.21 157.0 0.70 18.0 0.33 1.3 0.99 195.0 0.0327 269.4 0.07 BMI <28.0 96.0 146.0 16.0 1.6 391.0 639.9 >31.6 101.5 0.20 170.0 0.13 20.0 0.0353 1.1 0.0055 375.5 0.61 448.7 0.0564 DIET VARIABLES Total Calories (Kcal/day) <1633.2 96.8 163.0 16.0 1.3 343.5 642.3 >2551.8 101.8 0.41 162.5 0.86 18.5 0.55 1.3 0.58 451.5 0.64 566.5 0.97 Carbohydrate (g/day) <220.2 97.0 163.0 18.0 1.3 304.3 561.0 >349.4 101.8 0.40 158.5 0.78 19.5 0.53 1.1 0.42 575.0 0.17 691.6 0.47 Protein (g/day) <66.2 97.0 163.0 16.0 1.4 307.3 584.5 >107.4 102.8 0.58 158.5 0.71 18.5 0.59 1.3 0.40 451.5 0.40 566.5 0.92 Fat (g/day) <54.9 96.5 155.0 16.0 1.4 354.8 705.8 >94.0 101.5 0.35 169.0 0.52 19.5 0.23 1.3 0.31 429.8 0.93 447.0 0.35 Saturated fat (g/day) <18.3 96.5 162.0 16.0 1.4 375.5 679.5 >31.1 102.0 0.53 169.0 0.55 19.5 0.24 1.3 0.64 429.8 0.92 447.0 0.51 Cholesterol (mg/day) <176.2 96.5 162.0 16.0 1.4 318.0 705.8 >289.8 102.8 0.65 158.5 0.79 17.0 0.34 1.3 0.35 446.0 0.43 566.5 0.99 Dietary fiber (g/day) <22.5 96.3 162.5 18.0 1.2 366.0 645.8 >37.5 99.0 0.31 157.0 0.76 17.5 0.92 1.3 0.84 561.5 0.67 748.2 0.62 Glucose (g/day) <14.9 100.0 163.0 18.0 1.2 338.0 613.7 >28.7 98.0 0.55 149.0 0.41 17.3 0.72 1.3 0.82 413.5 0.33 691.6 0.46 Zinc (mg/day) <10.3 96.0 162.0 18.0 1.4 318.0 613.7 >18.9 101.5 0.46 152.0 0.56 16.5 0.46 1.2 0.34 480.3 0.23 703.6 0.47 Selenium (mcg/day) <79.1 97.8 163.0 16.0 1.3 310.0 584.0 >125.6 102.8 0.78 162.5 0.56 18.5 0.59 1.3 0.61 446.0 0.46 465.0 0.92 Calcium (mg/day) <802.2 96.5 163.0 17.3 1.3 366.0 584.0 >1407.5 98.8 0.49 153.5 0.73 17.0 0.86 1.2 0.69 538.5 0.26 715.5 0.47 23 Table 4-2: Comparison of glucose metabolic measures between lower and upper tertiles of demographic, dietary and physical activity measures in GDM subjects at baseline (n=102) * (Continued) Glucose metabolic measures Fasting glucose (mg/dl) 2-h glucose (mg/dl) Fasting insulin (µU/ml) Si ( × 10 -4 ·µU -1 ·ml·min -1 ) AIRg (µU·ml -1 ·min) Disposition index ( × 10 -4 ) Median p Median p Median p Median p Median p Median p Magnesium (mg/day) <254.9 97.0 163.0 16.0 1.4 307.3 584.5 >394.7 103.5 0.29 160.0 0.99 19.5 0.57 1.3 0.52 491.8 0.53 629.0 0.84 Sodium (mg/day) <2863.5 97.0 162.0 16.0 1.4 354.8 787.9 >4745.1 103.5 0.43 157.0 0.94 20.5 0.16 1.3 0.36 448.8 0.82 515.8 0.71 Potassium (mg/day) <2510.3 97.8 163.5 17.0 1.3 310.0 585.0 >4094.5 100.3 0.71 149.0 0.23 17.5 0.87 1.3 0.72 532.0 0.23 715.5 0.31 Phosphorus (mg/day) <1097.5 97.0 162.0 16.0 1.3 326.8 584.5 >1843.4 99.0 0.90 155.0 0.51 17.5 0.85 1.3 0.93 535.3 0.33 703.6 0.49 Vitamin D (IU/day) <89.6 96.5 162.0 18.3 1.3 489.0 801.8 >174.2 98.8 0.64 147.0 0.64 16.3 0.98 1.3 0.83 538.5 0.43 780.9 0.67 PHYSICAL ACTIVITY VARIABLES Total sedentary hours per day <4.5 96.5 143.0 16.5 1.2 501.0 837.1 >5.5 95.0 0.92 157.0 0.78 19.5 0.82 1.3 0.98 451.5 0.84 585.0 0.75 Total hours of exercise per day 0 (n=69) 101.5 164.0 19.0 1.1 389.0 474.2 >0.5 (n=9) 99.0 0.22 142.0 0.46 18.5 0.53 1.5 0.22 390.0 0.68 780.9 0.15 Total MET score of exercise per day 0 (n=69) 101.5 164.0 19.0 1.1 389.0 474.2 >176.3 (n=9) 97.5 0.22 142.0 0.56 17.5 0.95 1.3 0.69 500.0 0.48 780.9 0.25 # of times subject works up a sweat per week 0 (n=81) 101.5 162.0 19.0 1.2 395.0 481.4 ≥2 (n=8) 99.8 0.56 188.0 0.28 20.5 0.36 1.4 0.66 275.3 0.62 658.7 0.95 *Physical activity variables were not categorized into tertiles; specific cutoff points and sample sizes were given in table P-values were obtained by two group t-test P-values are bolded where p ≤0.15 24 Discussion Previous studies revealed that in addition to ethnicity, advanced maternal age and BMI are established risk factors for GDM and T2DM [7, 21]. Our study, correspondingly, also focused on examine the following demographic variables: age, weight, height, BMI and waist circumference. Our results, coherent with previous findings, showed a significant difference in BMI between GDM subjects and controls, as well as a severe deterioration in glucose metabolism, insulin sensitivity and β-cell function at older age and more obese levels of BMI. The relationship between diet (specifically, certain dietary aspects/components) and development of GDM or T2DM has been in the spotlight for the search of health enhancement and early treatment. Previously, studies informatively but inconclusively showed that diets high in carbohydrate and low in fat were found to be associated with decreased risk of glucose abnormalities or GDM [22]. Other researches illustrated that the minerals zinc and magnesium are thought to play a prominent role in the synthesis and action of insulin, while selenium is found to augment the antioxidant defense, which could improve glucose homeostasis [23-26]. In the present report, diet aspects/components studied were calories, carbohydrate, protein, fat, saturated fat, cholesterol, dietary fiver, glucose, zinc, selenium, calcium, magnesium, sodium, potassium, phosphorus and vitamin D. In the baseline comparison between control and GDM groups, there were no significant differences in diet variables. In addition, diet did not seem to correlate with physical activity levels, neither did glucose metabolism readings differ by comparing diet variables using upper against lower tertile. 25 However, we did see certain correlations between carbohydrate, zinc, calcium and vitamin D, and improved insulin sensitivity. Interestingly, daily intake of glucose in controls at baseline was marginally significantly higher than that in GDM subjects (p=0.06). As the non-GDM controls were composed of women within the same age group, same ethnicity, and similar physically inactive lifestyle, compared to GDM subjects in our study, they might tend to go less restricted on dietary glucose intake, thus the inverse difference. Possibly due to the same reason, we further observed the association of higher glucose intake and improved glucose metabolic measures. For physical activity variables, the following categories were selected to represent the physical activity characteristics of the study participants: total sedentary hours per day, total hours of exercise per day, total MET score of exercise per day, and number of times subject works up a sweat per week. Sedentary time was considered an element in the physical activity category, because a recent report showed that objectively measured sedentary time may be predictor of insulin resistance independent of intensity level of physical activity [27]. However, in the present study, we did not observe this association. Possible explanation could be that the sedentary time used in our study was self- estimated and may be inaccurate compared to the cited report, where customized minute- by-minute heart rate monitors were used to ensure accurate measurement of sedentary time. Our results are consistent with most of the previous findings that regular or leisure time physical activity has a protective effect against development of T2DM, moreover, the present report uniquely further assessed this beneficial impact on insulin sensitivity and β-cell function, and provided a first look at this impact in women with history of GDM and genetically more susceptible in developing T2DM. 26 In summary, we did not observe significant effects of diet components on glucose metabolic measures including β-cell function in Hispanic women participated in the current study, although a diet routine of high carbohydrate, zinc, calcium and vitamin D is suggested to be beneficial. Exercise showed statistically significant positive influences on glucose metabolism, insulin sensitivity and β-cell function. Such observation is of great public health advisory importance. Women who are diagnosed with GDM are at high risk of developing T2DM. They are more insulin resistant than normal women. Their β-cell function is deteriorating on the background of chronic insulin resistance. With proper education and rising public awareness, women at high risk of GDM and T2DM can be more motivated to actively adjust their lifestyle to increase exercise time, therefore, improve their long term overall wellness. Further longitudinal analyses are in quest to evaluate changes in β-cell function with physical activity and dietary pattern in the present study cohort. 27 References 25. Agbor, G.A., et al., Effect of selenium- and glutathione-enriched yeast supplementation on a combined atherosclerosis and diabetes hamster model. J Agric Food Chem, 2007. 55(21): p. 8731-6. 23. Beletate, V., R.P. El Dib, and A.N. Atallah, Zinc supplementation for the prevention of type 2 diabetes mellitus. Cochrane Database Syst Rev, 2007(1): p. CD005525. 15. Buchanan, T.A., et al., Antepartum predictors of the development of type 2 diabetes in Latino women 11-26 months after pregnancies complicated by gestational diabetes. Diabetes, 1999. 48(12): p. 2430-6. 21. Buchanan, T.A. and A.H. Xiang, Gestational diabetes mellitus. J Clin Invest, 2005. 115(3): p. 485-91. 11. Carr, D.B., et al., A reduced-fat diet and aerobic exercise in Japanese Americans with impaired glucose tolerance decreases intra-abdominal fat and improves insulin sensitivity but not beta-cell function. Diabetes, 2005. 54(2): p. 340-7. 3. Centers for Disease Control and Prevention. National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2005, in Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. 2005. 4. Dabelea, D., et al., Increasing prevalence of gestational diabetes mellitus (GDM) over time and by birth cohort: Kaiser Permanente of Colorado GDM Screening Program. Diabetes Care, 2005. 28(3): p. 579-84. 20. Dong, L., G. Block, and S. Mandel, Activities Contributing to Total Energy Expenditure in the United States: Results from the NHAPS Study. Int J Behav Nutr Phys Act, 2004. 1(1): p. 4. 26. Erbayraktar, Z., et al., Effects of selenium supplementation on antioxidant defense and glucose homeostasis in experimental diabetes mellitus. Biol Trace Elem Res, 2007. 118(3): p. 217-26. 24. He, K., et al., Magnesium intake and the metabolic syndrome: epidemiologic evidence to date. J Cardiometab Syndr, 2006. 1(5): p. 351-5. 27. Helmerhorst, H.J., et al., Objectively measured sedentary time may predict insulin resistance independent of moderate- and vigorous-intensity physical activity. Diabetes, 2009. 58(8): p. 1776-9. 28 7. Hu, F.B., et al., Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med, 2001. 345(11): p. 790-7. 8. Hu, F.B., et al., Walking compared with vigorous physical activity and risk of type 2 diabetes in women: a prospective study. Jama, 1999. 282(15): p. 1433-9. 9. Hu, F.B., et al., Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. Jama, 2003. 289(14): p. 1785-91. 2. Kaaja, R.J. and I.A. Greer, Manifestations of chronic disease during pregnancy. Jama, 2005. 294(21): p. 2751-7. 5. Kjos, S.L., et al., Predicting future diabetes in Latino women with gestational diabetes. Utility of early postpartum glucose tolerance testing. Diabetes, 1995. 44(5): p. 586-91. 18. Kolonel, L.N., et al., A multiethnic cohort in Hawaii and Los Angeles: baseline characteristics. Am J Epidemiol, 2000. 151(4): p. 346-57. 10. Lindstrom, J., M. Peltonen, and J. Tuomilehto, Lifestyle strategies for weight control: experience from the Finnish Diabetes Prevention Study. Proc Nutr Soc, 2005. 64(1): p. 81-8. 6. NHANES II (second national health and nutrition examination survey). 13. Oken, E., et al., Associations of physical activity and inactivity before and during pregnancy with glucose tolerance. Obstet Gynecol, 2006. 108(5): p. 1200-7. 22. Saldana, T.M., A.M. Siega-Riz, and L.S. Adair, Effect of macronutrient intake on the development of glucose intolerance during pregnancy. Am J Clin Nutr, 2004. 79(3): p. 479-86. 1. Standards of medical care in diabetes--2008. Diabetes Care, 2008. 31 Suppl 1: p. S12-54. 19. Stram, D.O., et al., Calibration of the dietary questionnaire for a multiethnic cohort in Hawaii and Los Angeles. Am J Epidemiol, 2000. 151(4): p. 358-70. 12. Utzschneider, K.M., et al., Diet-induced weight loss is associated with an improvement in beta-cell function in older men. J Clin Endocrinol Metab, 2004. 89(6): p. 2704-10. 29 14. Vanheest, J.L. and C.D. Rodgers, Effects of exercise in diabetic rats before and during gestation on maternal and neonatal outcomes. Am J Physiol, 1997. 273(4 Pt 1): p. E727-33. 16. Xiang, A.H., et al., Multiple metabolic defects during late pregnancy in women at high risk for type 2 diabetes. Diabetes, 1999. 48(4): p. 848-54. 17. Xiang, A.H., et al., Coordinate changes in plasma glucose and pancreatic beta- cell function in Latino women at high risk for type 2 diabetes. Diabetes, 2006. 55(4): p. 1074-9.

Abstract (if available)

Abstract The association between diet or physical activity (PA) and insulin sensitivity (IS), as well as β-cell function (BCF), has not been adequately examined. The purpose of the study is to cross-sectionally investigate this association in women with a history of gestational diabetes mellitus (GDM). Demographics, diet and PA data were collected using questionnaires

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Creator Liu, Xinwen (author)

Core Title A cross-sectional examination: impact of diet and physical activity on plasma glucose metabolism, insulin sensitivity and pancreatic β-cell function in Hispanic women with histories of gestationa...

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 11/30/2009

Defense Date 10/22/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag diet,Exercise,gestational diabetes mellitus,high risk,Hispanic women,insulin sensitivity,OAI-PMH Harvest,pancreatic β-cell function,physical activity,plasma glucose metabolism,sedentary time,type 2 diabetes mellitus

Place Name Los Angeles (counties)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Azen, Stanley Paul (committee chair), Xiang, Anny (committee chair), Buchanan, Thomas A. (committee member)

Creator Email xinwenli@usc.edu,xinwenliu@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2736

Unique identifier UC1462292

Identifier etd-Liu-1771 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-278034 (legacy record id),usctheses-m2736 (legacy record id)

Legacy Identifier etd-Liu-1771.pdf

Dmrecord 278034

Document Type Thesis

Rights Liu, Xinwen

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu