ABC Study Protocol Full Study Title: The Aboriginal Birth Cohort Protocol version: 1.1 Date: February 23, 2012

Transcription

1 ABC Study Protocol Full Study Title: The Aboriginal Birth Cohort Protocol version: 1.1 Date: February 23, 2012 Prinicipal Investigator: Dr. Sonia Anand Co-Investigators Dr. Rebecca Anglin Dr. Joseph Beyene Dr. Allison Holloway Dr. Sarah McDonald Dr. David Meyre Ms. Ruby Miller Dr. Katherine Morrison Dr. Guilaume Pare Dr. Ravi Retnakaran Dr. Koon Teo Dr. Gita Wahi Ms. Julie Wilson 1

2 Table of Contents 1.0 Introduction: Hypothesis to be Tested: Objectives: Knowledge to Date: Aboriginal people of the Six Nations: Early Origins of Adiposity and Related Metabolic Changes: Rationale for the creation of an Aboriginal Birth Cohort: Birth Weight and Future Cardio-Metabolic Risk Factors: Postnatal factors and adiposity: Contextual factors which influence adiposity: Genetics and Epigenetics: Methods to be Used: Data Collection Stage 1: Antenatal data Data Collection Stage 2: Delivery Data Collection Stage 3: Follow-up after Delivery Statistical Considerations: Possible Problems: Investigator Roles: Knowledge Translation: Anticipated Results and Conclusions: References: Tables and Figures

3 1.0 Introduction: People of Aboriginal ancestry are a rapidly growing population in Canada. There are over 1.2 million Aboriginal people living in Canada, and the birth rate among Aboriginal people is 1.5 times that of the general population. 1 Aboriginal adults suffer a high prevalence of obesity, type 2 diabetes, and cardiovascular disease (CVD) compared to their non-aboriginal counterparts. 2 Furthermore, the prevalence of type 2 diabetes among Aboriginal youth is disproportionately higher compared to other youth in Canada. 3 The increased prevalence of obesity and diabetes among Aboriginal people is highly correlated with their adoption of western lifestyle practices (i.e. high energy intake and low physical activity). However, emerging evidence suggests that the propensity to develop obesity and type 2 diabetes is influenced by both the pre and postnatal environment which shapes developmental growth trajectories throughout the offsprings life. 4 Interestingly risk factors for insulin resistance in childhood, and type 2 diabetes in adulthood, include elevated maternal pre-pregnancy body mass index (BMI), excess pregnancy weight gain, exposure of the fetus to maternal and paternal cigarette smoking, and genetic factors. 5,6 To better understand the pre and postnatal influences on the development of adiposity (i.e. body fat) and related cardio-metabolic factors (i.e. abnormal glucose, insulin, blood pressure, and lipids) in Aboriginal people, we propose to recruit and follow prospectively 1000 Aboriginal pregnant mothers and their offspring whose maternal lineage originates from the Six Nations Reserve, near Brantford, Ontario. This Aboriginal Birth Cohort (ABC) will enhance our understanding of the determinants of adiposity, type 2 diabetes and related cardio-metabolic factors in Aboriginal people, with the ultimate goal of developing chronic disease prevention strategies for this high-risk group. 2.0 Hypothesis to be Tested: Adiposity is partly programmed in-utero, and is influenced by maternal, paternal, and fetal factors. At birth, the programmed fetus is exposed to a new external environment, and interactions between the programmed newborn and the new environment lead to excess adiposity and related cardio-metabolic risk factors during early childhood. (Figure 1) 3.0 Objectives: The overarching objective of this study is to determine the primary causes of adiposity (as measured by skin-fold thickness) and related cardio-metabolic factors among Aboriginal children from birth to 3 years of age. Primary objectives: Specifically, we propose to: 1. Determine the major antenatal maternal factors (e.g. pre-pregnancy weight, dietary intake, physical activity, and smoking exposure), selected paternal factors (e.g. cigarette smoking), and pregnancy factors (e.g. maternal weight gain, smoking exposure, glucose intolerance, and pregnancy-induced hypertension) which are associated with the newborn s adiposity and cardio-metabolic factors at birth, and annually for the first three years of life. 2. Determine the association between early feeding practices (i.e. exclusivity of breastfeeding, formula feeding, type, frequency and duration of breast/bottle feeding, and growth after weaning), sleep patterns and activity on newborn s adiposity, and related cardio-metabolic factors annually for the first 3 years of life. 3. Determine the impact of the home environment, including socio-economic status, social support, and psychosocial factors of the mother on newborn s adiposity at birth and annually for the first three years of life. Secondary Objectives: 4. To determine if there are differences in birth weight and adiposity (corrected for gestational age and sex) comparing Aboriginal newborns to an existing white Caucasian birth cohort (FAMILY study) in 3

4 Hamilton, Ontario, Canada (n=901 babies), and to an ongoing South Asian birth cohort underway in Peel Region, Ontario (START study). 5. To study the association between selected genetic variants of the mother and offspring on the offspring s adiposity and related cardio-metabolic factors. We anticipate requiring 1,000 babies of Aboriginal origin to have high power to study genetic associations of adiposity, and are applying to other funding agencies to recruit an additional 700 mother-baby dyads in the future. 4.0 Knowledge to Date: 4.1 Aboriginal people of the Six Nations: Aboriginal adults living in Canada suffer a high prevalence of overweight, obesity, and type 2 diabetes. 2,7 The increased prevalence of obesity and diabetes among Aboriginal people is highly correlated with their adoption of western lifestyle practices, and is dominated by high energy intake and low physical activity. 3,7 Recently, type 2 diabetes has been reported to be disproportionately high among Aboriginal youth. 3 This indicates that the consequences of increasing overweight and obesity are not limited to adults and will likely dramatically impact the long-term health of Aboriginal youth. The Six Nations Reserve in Brant County, Ontario, Canada took its present form of 20,000 hectares in 1847, and is now home to over 12,000 Six Nations people. 8 The traditional lifestyle of the Six Nations people included agricultural farming, hunting and fishing but the increase in permanent settlements during the second half of the 20th century led to their growing dependence on store-bought foods, and an increased dependence on automobiles and other energy-saving devices. Since 1998, our research group worked closely with the people of the Six Nations to document CVD risk factors, and subsequently to facilitate interventions to reduce these risks for CVD. 2,8-16 In a previous study we conducted among the Six Nations adults, 60% of men and 55% of women were obese (BMI ( 30 kg/m 2 ), compared to 32% and 24% of non-aboriginal men and women in Canada respectively. 2 In addition, the prevalence of glucose intolerance, dyslipidemia, tobacco use, and CVD was significantly higher among Six Nation adults compared to age and sex matched Canadians of European origin. 2 More recently our investigations have focused on children and youth as they represent the group in which chronic disease prevention may have the greatest impact Early Origins of Adiposity and Related Metabolic Changes: Childhood obesity is primarily attributed to environmental changes leading to increased energy intake and lower physical activity. 5 Despite significant population-level changes, there is increasing evidence that a complex interplay of genetics, epigenetics, and non-genetic factors also interact to program a newborn to be more or less prone to develop excess adiposity depending on the environment to which it is exposed. 18 The major lines of evidence which support the concept of fetal programming and adiposity include the observations that: 1) low birth weight babies have a higher risk of adult onset diabetes and CVD compared to normal birth weight babies, 2) maternal behaviours and metabolic characteristics (i.e. nutrition, pre-pregnancy obesity, weight gain and glucose status) are associated with newborn adiposity and adult diabetes in the offspring, 3) infant feeding and post natal growth are associated with adolescent and adult adiposity and metabolic abnormalities, and 4) genetic and possibly epigenetic imprinting of the fetus are associated with adiposity and insulin resistance in the growing offspring. The focus of this protocol is to determine the key exposures (before, during, and after pregnancy) which strongly influence the offspring s weight and adiposity from birth until early childhood. 4

5 4.3 Rationale for the creation of an Aboriginal Birth Cohort: There is increasing evidence to support the concept that adiposity and related cardio-metabolic factors may be influenced by factors which program the developing fetus. Programming refers to metabolic events which occur during critical time periods of antenatal and postnatal development which have moderating effects on health in later life. 4,18-20 There are numerous birth cohorts underway around the world, and most are being conducted among white Caucasian populations. Our research team has led the development of two birth cohorts in Ontario, i. FAMILY is a birth cohort primarily composed of white Caucasians in which 901 mother-babys were recruited in the Hamilton Ontario region and are currently being followed prospectively. (Progress Report) 20, and ii. the South Asians Birth Cohort Study (START) is a CIHR/HSFO funded birth cohort just underway in the Peel Region, Ontario among mothers/offspring of South Asian origin. There are no Aboriginal birth cohorts in Canada, and in addition to the PIMA Indian studies in the US, 21 we only identified one other birth cohort study being conducted in indigenous people in Australia. 22 Creation of an Aboriginal specific birth cohort is important for three reasons: 1) Among children and youth of Canada, Aboriginal children have the highest rates of type 2 diabetes, and are therefore are a high risk population, 2) Robust health information derived from within Aboriginal communities is needed to provide them culturally specific information they need to plan prevention programs, 23 and 3) Multi-ethnic comparisons between high risk and low risk groups lead to new understandings of disease pathogenesis as we have demonstrated among adults. 14 To begin to prepare for the development of ABC, we recently conducted a detailed chart review of 453 babies delivered from 2005 to 2010 at the Six Nations Birthing Centre. (Table 1 and Progress Report) We observed that compared to non-aboriginal women from the FAMILY study, the Six Nations mothers had a greater pre-pregnancy BMI (28.3 (6.6) vs 26.7 (6.4), P=0.0001) had slightly more weight gain in pregnancy (14.9 (8.9) vs 14.1 (5.6) kg, P=0.11), and were more likely to be current smokers (35% vs 14%, P=0.0001). Among full term newborns, the average birth weight among Aboriginal newborns was significantly higher than non-aboriginal newborns (3677 g (SD: 481) vs 3359 g (SD: 645), P=0.0001). Furthermore some intriguing population differences seem to exist between Aboriginal and South Asian newborns in Canada. Both ancestral groups, Aboriginal and South Asians have high rates of adult onset type 2 diabetes, yet high birth weight appears to be a risk state among Aboriginal people whereas low birth weight newborns are at increased risk for type 2 diabetes among South Asians populations. 20,24,25 This U shaped relationship between newborn birth weight and future risk of cardiometabolic risk factors has also been observed within a well-nourished population of white Caucasian newborns from the Raine cohort in Australia, and a population of Aboriginal Pima Indians in Arizona. 21,26 Furthermore, children born to smoking mothers were more likely to be in the high risk metabolic syndrome-traits cluster by age 8 years compared to offspring of women who did not smoke during pregnancy (1.82; 95% CI: , P=0.03). This association was especially apparent among high birth weight offspring (odds ratio 14.0; 95 % CI: 3.8 to 51.1). Thus, studying populations at the extreme ends of the birthweight distribution, with significant variation in antenatal exposures such as cigarette smoking, will enhance our understanding of the high risk phenotypes which predispose offspring to develop cardio-metabolic risk factors in early childhood. 4.4 Birth Weight and Future Cardio-Metabolic Risk Factors: Population, clinical and animal studies reported associations between low birth weight and the future risk of dysglycemia, insulin resistance, central obesity, hypertension, and CVD in adulthood. 4,18,24 On the other hand newborns born large for gestational age (LGA) have an increased risk of developing childhood obesity, insulin resistance, and cardio-metabolic risk factors as children and obesity, type 2 diabetes and CVD as adults Key maternal factors associated with birth weight include pre-existing maternal diseases (e.g. renal disease), maternal pre-pregnancy weight, weight gain during pregnancy, gestational diabetes, hypertension, nutrition, exposure to cigarette smoke, and psychosocial stress. 5

6 Below we review the relevant literature Maternal weight pre-pregnancy and weight gain: Pre-pregnancy maternal obesity, maternal weight gain during pregnancy, and maternal glucose control during pregnancy are all associated with increased adiposity among offspring. Prior studies have shown that mothers with a low BMI have an increased risk of poor pregnancy outcomes including preterm birth, having a small-for gestational age (SGA) infant. 36,37 On the other hand a high maternal BMI increases the risk for hypertension, preeclampsia, gestational diabetes and giving birth to a macrosomic infant. 38 Greater gestational weight gain is associated with increased pre-term birth, BMI, adiposity and systolic blood pressure in the offspring Furthermore infants of women who gained more than 24 kg during pregnancy are g ( ) heavier at birth than were infants of women who gained 8-10 kg. 29 There are limited data on pre-pregnancy BMI and weight gain among Aboriginal women. Our literature review suggests that the average pre-pregnancy BMI is between 2 to 4 BMI units higher among Aboriginal women compared to non-aboriginal women. 42 Furthermore, 55% of Aboriginal women gained more than recommended weight during pregnancy compared to 44% of non-aboriginal women. 42 Therefore, given that prepregnancy weight and pregnancy weight gain strongly influences newborns birth weight and growth of the offspring, these parameters will be carefully assessed and related to newborn birth weight and adiposity in the ABC Maternal Glycemic Status: The effect of maternal gestational dysglycemia (defined as elevated glucose which includes gestational impaired glucose tolerance, gestational diabetes, and pre-gestational diabetes) on both intrauterine growth and postnatal growth patterns is considerable. Children born to a mother with gestational diabetes 26 or even those with mildly impaired glucose tolerance 43 are at increased risk for overweight 44 and obesity. 45 Gestational diabetes increases the concentration of glucose, free fatty acids, and amino acids, lowers adiponectin in maternal blood, and results in fetal hyperinsulinemia, which in turn results in a larger adipose tissue mass in the newborn In addition, elevated glucose in the non-diabetic range is also associated with increased infant birth weight and increased adipose tissue mass. 48 Our co-investigator Retnakaran has recently shown that maternal insulin sensitivity during pregnancy is a predictor of infant weight gain and adiposity in the first year postpartum. 49 The effect of dysglycemia during pregnancy may be particularly relevant to Aboriginal women for whom the prevalence of gestational diabetes is believed to be high, although there is a paucity of data from this group. In Canada previous studies have estimated the rate of gestational diabetes in Aboriginal people ranges from 8.1% to 11.7% compared to 3.0% to 4.8% in non Aboriginal people. 51,52 However most of these studies relied on self report or physicians report of gestational diabetes and did not perform direct measurement. Furthermore, the impact of glucose intolerance on the newborn varies depending on the health behaviours and/or medication use of the mothers during pregnancy. Thus it is critical to determine the level of maternal glycemic control and hence the degree to which mothers with gestational diabetes receive glucose lowering treatments during pregnancy, and/or modify their diet and activity, as failure to consider these factors may confound associations between maternal glucose status and newborn body composition. 53 Therefore in ABC we will administer a 75 gram oral glucose tolerance test at approximately 28 weeks of pregnancy and record dietary intake, physical activity, and drug use. This information is needed to precisely document the burden of gestational dysglycemia and its association with newborn body composition in Aboriginal people Maternal nutrition: The dietary intake of mothers before and during pregnancy influences fetal growth, and has been linked to newborn adiposity. Fetal undernutrition leads to metabolic and structural changes, which may be initially beneficial for early survival but may increase the risk of type 2 diabetes in adulthood Undernutrition does not appear to be a problem most Aboriginal women in Canada 6

7 face. 21 This is based on the observation of higher than normal pre-pregnancy BMI and excessive pregnancy weight gain reported among Aboriginal women in Canada. 42 However despite apparent overnutrition, the micronutrient profile of the diet of Aboriginal mothers has not been well characterized. Gray-Donald reported that iron deficiency anemia is more common in Aboriginal women compared to their non-aboriginal counterparts, but little other information exists on micronutrients including B12, folate, vitamin D, and calcium status among pregnant Aboriginal women. 59 A dietary assessment we conducted among adults living on the Six Nations Reserve between showed their usual diet is high in processed foods, desserts, and sugary beverage consumption 8 which is reflected in their macronutrient profile which is high in carbohydrates (50% of total calories), and fat (36% of total calories) and lower in protein (14.6% of total calories). Micronutrient analysis revealed while their daily energy-adjusted iron and B12 intake was higher, their daily folate, B6 and calcium intake was lower compared to European Canadian women. Detailed measurement of dietary macro and micronutrients will be undertaken in the ABC Maternal Exposure to tobacco: Exposure to tobacco smoke during pregnancy is considered to be a leading cause of intrauterine growth retardation and lower birth weight newborns. 60,61 However the long-term consequences to the offspring of fetal exposure to tobacco include obesity and associated cardio-metabolic risk factors later in life The pooled odds ratio for development of obesity in childhood after maternal exposure to smoking is 1.64 (95% CI: ) based on 16 studies. 65 This effect exists even after accounting for birth weight, fetal growth, and post-natal weight gain. Recently Syme et al demonstrated that fetal prenatal exposure to smoking increased the accelerated weight gain that occurs normally during late puberty, and showed that this is associated with increased intraabdominal adiposity, a characteristic which is prominent among Aboriginal people, and is a risk factor for the development of cardio-metabolic risk factors. 63 The prevalence of smoking during pregnancy in Aboriginal women ranges from 35-61%, and is substantially higher than among non aboriginal women (i.e %). 69,70 While one may expect that this will result in more SGA newborns, this has not been observed in Aboriginal populations. The association between the effects of smoking and birth weight may be confounded by the likely higher prevalence of gestational diabetes among Aboriginal women, yet only a prospective evaluation with direct measures of these exposures and other potential confounders can enable a more precise determination of risk. 4.5 Postnatal factors and adiposity: At birth, the immediate environment faced by the newborn changes dramatically from that of the intrauterine environment. Prior studies suggest that infant nutrition exposure, breast-feeding, formula-feeding, the energy density of the feeds, activity levels and sleep patterns all influence the development of adiposity and related metabolic changes in the growing offspring and should be carefully measured in order to study the post-natal determinants of adiposity Postnatal catch up growth and adiposity: Infants who are growth restricted during fetal life but subsequently grow rapidly are at a higher risk to develop adiposity during childhood and in adult life. 75,76 Data from high income countries suggest that the children who are most vulnerable to developing obesity are light and thin at birth who then experience a period of rapid growth in the first 7 years of life. 77 However, large birth weight newborns are also at increased risk for childhood obesity. In the Raine cohort from Australia, a U shaped distribution between birth weight and subsequent risk of cardio-metabolic traits at age 8 years was observed, and weight gain after the first year of life was a significant predictor of this high risk state Breastfeeding and adiposity: Meta-analyses support a significant positive effect of breast feeding on later health outcomes Infant feeding practices have been implicated in modifying the risk of developing insulin resistance, obesity and increased blood 7

8 pressure. 81 Some studies have shown a dose dependent relationship between duration of breast feeding and the risk of later development of obesity, while others have shown that breast feeding is protective against development of metabolic syndrome associated traits by age 8 years (odds ratio 0.60; 95% CI: 0.37 to 0.97). 82 The potential pathways that link breast feeding and protection against obesity include reduced early postnatal weight gain, a better learned self-regulation of food intake, and the presence of leptin in breast milk. 83 Higher pre-pregnancy BMI, central adiposity, low educational attainment, and socio-cultural factors are associated with delayed lactogenesis and early cessation of breastfeeding, There are limited data regarding the prevalence, amount and duration of breastfeeding among Aboriginal mothers. Aboriginal mothers living off-reserve were less likely to breast feed (81.5 vs. 88.3%) and fewer breast fed for more than six months duration (17.5 vs. 23.5%) compared to non-aboriginal mothers. 87,88 Furthermore some reserves in Quebec have reported that the consumption of formula and cows milk is high in the first year of life and is associated with a higher than normal prevalence of anemia among Aboriginal children. 89 Thus in ABC, we anticipate that the prevalence of breastfeeding initiation will be lower than the national average, and of variable duration Infant Activity and Sleep Patterns: Children's daytime activity levels, sleep patterns, and sedentary behaviours including hours of television viewed are also associated with adiposity among pre-school children, and may all influence the longterm regulation of energy balance. 90,91 The data regarding activity patterns in the Aboriginal communities in Canada are sparse and mostly anecdotal. In ABC we will carefully characterize early infant feeding practices, the child s dietary intake, infant activity and sleep patterns, and relate them to adiposity and growth trajectories. Identical measurement tools as those used in the FAMILY and START birth cohorts will be used to enable direct comparisons between Aboriginal, white Caucasian and South Asian newborns in Canada. 4.6 Contextual factors which influence adiposity: The Six Nations community suffers from extensive socioeconomic hardship with high rates of unemployment, low income, and only a small proportion of community members have post-secondary education. 12 This social disadvantage is strongly associated with obesity, tobacco use, diabetes and CVD among the Six Nations people. 12 Socioeconomic status (SES) also strongly influences the home environment provided to the newborn and is likely associated with health behaviours including breast feeding, dietary intake, tobacco use, and activity patterns. SES can affect birth outcomes including preterm birth, and postnatal death. 92 Low SES is also associated with maternal health post partum including mental health conditions (i.e. depression and anxiety), as well as domestic violence. 93,94 Antenatal psychosocial stressors including poor marital adjustment, recent life stressors, maternal mental health and intimate partner violence are more common among low SES mothers, and are also associated with adverse postpartum family outcomes including postpartum depression, child abuse and child illness. 95 Furthermore post partum depression has been associated with higher overall offspring adiposity among a cohort of 838 mother-child dyads participating in the Project Viva Birth cohort. 96 On the other hand, having a social support network available, and being able to elicit and receive effective social support has consistently been found to predict improved physical and mental health, and can reduce the negative impact of stress and improve pregnancy outcomes. Aboriginal women in Canada are much more likely to have low household incomes, greater social disadvantage, and greater psychosocial stressors compared to non Aboriginal women, and there is sparse data regarding social support. 97 It is likely that low SES interacts with other risk factors, i.e. diet, activity, alcohol intake, and smoking which together contribute to adverse birth outcomes and increased infant morbidity. 98 Low SES, high levels of stress and low social support have also been associated with poorer parenting behaviours, which may in turn create a stressful environment for the infant and affect infant feeding, activity, and cognitive development. 99 In ABC we will measure key contextual factors (i.e. SES, antenatal and post partum depression, domestic violence, and social support) and test their association with feeding, activity and 8

9 adiposity in the growing offspring. 4.7 Genetics and Epigenetics: Prior studies strongly suggest that consideration of both maternal and fetal genotypes are important in understanding the genetic determinants of birth weight and newborn adiposty There is also emerging evidence from model systems and human placentas that early exposure to environmental factors including the in-utero environment produce epigenetic modifications leading to changes in gene expression, metabolic profile, and infant growth. 103 Creation of the ABC with its longitudinal assessment of nutritional and metabolic risk factors during pregnancy (i.e. fetal environment), at birth, and up to 3 years of age for the index child, provides a unique opportunity to characterize how early environmental exposures interact with genetic variants that in turn program lifelong adverse health trajectories. To our knowledge the contribution of common genetic polymorphisms to the development of adiposity and cardio-metabolic traits has not been comprehensively investigated among Aboriginal newborns. While such investigations will require at least 1,000 subjects, in the first 300 mother-infant dyads we will collect consent for future genetic and epigenetic analyses, a buffy coat for future DNA extraction, and placental biopsies to enable future studies of gene expression and epigenetic marks. Summary: We propose to create a prospective Aboriginal Birth Cohort designed to identify the major determinants of adiposity and associated cardio-metabolic risk factors among Aboriginal offspring. As highlighted above, fetal programming may be influenced by maternal undernutrition and placental insufficiency and/or by maternal overnutrition, weight gain, gestational glucose intolerance, tobacco exposure, and psychosocial stress factors. These factors together with the maternal and fetal genotype and postnatal environments (i.e. feeding, sleep, home environment) may contribute to the development of early adiposity and associated cardio-metabolic risk factors of the growing offspring. To unravel the various pathways by which this high risk group develops excess adiposity and cardio-metabolic risk factors detailed phenotyping of Aboriginal mothers during pregnancy and their offspring from birth to early childhood is required. 5.0 Methods to be Used: We propose to recruit 300 Aboriginal pregnant mothers and their newborns from the Six Nations Reserve, and follow them prospectively to the age of 3 years. (Figure 2) Although our ultimate goal is to enroll 1,000 mother-baby dyads and follow all subjects to 10 years of age, additional funding from alternative sources will be sought for this expansion and extension. Inclusion Criteria: Women of Aboriginal ancestry between years of age, and who are in their 2 nd trimester of pregnancy. Exclusion Criteria: Women who conceived the fetus using artificial methods including in-vitro fertilization or intrauterine insemination, women carrying more than one fetus, surrogate mothers, women who suffer from severe chronic medical conditions including active cancer, severe infectious diseases including HIV, hepatitis B or C, or who are VDRL positive, will be excluded. Recruitment Centres: Aboriginal women will be recruited from local general practitioners (GPs), obstetricians, and midwives at the Birthing centre on the Six Nations Reserve. From the Band list statistics we anticipate that there are 400 new births per year among Aboriginal women living on the reserve. 5.1 Data Collection Stage 1: Antenatal data Pregnant mothers will be recruited during their antenatal visits to their health care providers. Consecutive patients will be approached, a log of all potentially eligible subjects will be kept, and the main reasons for exclusion or refusal to participate will be recorded. Pregnant mothers will be provided 9

10 an information package describing the study and consent to have the recruitment team contact them will be obtained. An initial visit between weeks of pregnancy will be scheduled at the SHARE-AP Research Unit located in the Health Services building on the Reserve. This unit is equipped with the equipment necessary to complete the baseline and follow-up visits including a centrifuge and freezer to store biospecimens. Information on age, parity, medical and pregnancy history, cigarette smoking exposure of mother, father and family members, drugs and alcohol exposure, family structure (i.e. marital status, and number of children in the house), community of birth, mother tongue, cultural practices, psychosocial characteristics, as well as socioeconomic factors (i.e. household income, education, and employment) will be collected. (Table 3) The pregnant mother will also have a number of anthropometric measurements taken during this visit. A digital scale will be used to record body weight to the nearest 100 g. Height will be measured using a stadiometer to the nearest 1 cm and mid upper arm circumference to the nearest 0.1 cm using a plastic measuring tape. Maternal BMI will be calculated using weight height at baseline (kg/m 2 ). The mother s pre-pregnancy weight will be recorded. Skinfold thickness (triceps and subscapular) will be measured to the nearest 0.2 mm, using skinfold calipers (Holtain, UK), for the prediction of body fat using prediction equations. 104 Systolic and diastolic blood pressure will be measured using an automated BP monitor (OMRON Intelli Sense, Model HEM-757). (Table 4) As it is routine practice for women to have an ultrasound at weeks. All ultrasound reports will be obtained and used to establish gestational age and to assess fetal growth characteristics Dietary and Physical Activity assessment: We have previously developed and validated an FFQ for Aboriginal people in Canada as part of SHARE-AP. 11 However, FFQs are subject to recall bias, and are not sensitive to recent changes in diet that may occur in pregnancy or early post partum. For these reasons we will assess mother s macro and micronutrient profile in pregnancy, and in follow-up with a combination of the SHARE-AP FFQ and 24 hour diet recalls. The 24-hour diet recall will be used to assess the immediate and recent diet intake i.e. over the last 24 hours and will be administered during the 2nd trimester visit, as well as at 6 months and 1 year postpartum. (Table 5) Information on maternal activity during pregnancy will be collected for activities in 5 domains occupational, discretionary exercise, household chores, sedentary activities, hobbies and sleep, as will sedentary behaviours, such as screen time per day (computer, television, video games). Both will be collected at baseline, and at each annual visit Psychosocial Assessment: SES will be assessed by recording the annual household income, employment, education and marital status. Information will be gathered about chronic stressors in the home, workplace and community and stressful life events. Adequacy of social support to the mother will be measured using a questionnaire to evaluate the emotional, instrumental, informational, and appraisal components of social support. Depression in the mother will be assessed by the Kessler-10 scale (K-10) which is a 10-item scale with five response categories ranked on a 5-point scale. 105 Intimate partner violence will be assessed using the 2-item Woman Abuse Screening Tool short version. 106 All psychosocial questionnaires will be administered at the baseline visit, at 6 months postpartum and annually thereafter. (Table 6) Laboratory Assessments: The diagnosis of maternal glycemic status is critical to determine glucose-metabolic status during the second trimester of pregnancy. All non-diabetic mothers will undergo the 75 gram oral glucose tolerance test (OGTT) between weeks of gestational age. This test is chosen to avoid the high false negative rate using the 50 gram Glucose Challenge Test among some non-white populations. 107 Three blood samples will be collected: Fasting, 60, and 120 minutes. 108 Some local analysis will be performed immediately (i.e. glucose, complete blood count) using standardized assays, and the remainder will be processed, shipped, and stored at the Clinical Trials Research Laboratory (Hamilton Health Sciences) for the future analysis (i.e. adiponectin, insulin, the 10

11 buffy coat for DNA extraction). (Table 7a) Insulin resistance will be measured by using the HOMA-IR a validated index which uses the fasting glucose and insulin values Data Collection Stage 2: Delivery At the time of delivery, details including birth outcomes for the mother and baby (e.g. type of delivery, Apgar scores, problems during delivery, length of stay) will be collected. A cord blood sample for basic biochemistry (i.e. glucose, insulin, lipids, leptin), DNA and additional serum and plasma aliquots for future analysis will be taken from each baby. (Table 7b) The method of cord blood collection is found in Supplementary 1. A section of the placenta will be collected and stored in RNAlater - a solution which preserves the tissue RNA for future analysis. (Supplementary 2) Based on our chart review we anticipate that babies will be delivered at the birthing centre, at home, and local hospitals (i.e. Brantford General Hospital, and St. Josephs Hospital and McMaster Children s hospital in Hamilton). All delivery sites will be instructed in collection of cord blood and pre-delivery charts will be flagged. The midwife attending home deliveries will also be trained to collect cord blood which will be taken to the SHARE- AP Research Unit for processing. The newborn s physical characteristics- birth weight, skin fold thickness, length, abdominal, head, and arm circumference will be measured by the midwife or trained research assistant within 72 hours after delivery Assessment of Body Composition in Newborn and Infants: In infants, percent body fat can also be estimated by a prediction equation derived from four skinfold measures. 110 This method has been validated against DA in newborns 111 and among children aged 4-10 years. 112 The correlation coefficient of equation-estimated percent body fat in newborns compared to DA is 0.92 and among children aged 4-10 years, The reliability of these estimates range from 99.5 to 99.8% In the ABC (as in FAMILY and START) all newborns and infants will have skinfold thickness measured (biceps, triceps, subscapular, and suprailiac) at birth and at each annual visit. 5.3 Data Collection Stage 3: Follow-up after Delivery After delivery, mother and child dyad will be further followed by or telephone at 1 and 6 months to collect information on the infants weight and feeding practices, and in a face to face clinic visit at the SHARE-AP Research Unit at 1, 2, and 3 years after birth. An annual blood sample will be collected from the infant to measure the complete blood count to screen for iron deficiency anemia, and for analysis of glucose, insulin, and lipids. We will offer use of a secured study website for participants to enter the baby s weight, length, and head circumference at 1 and 6 months. A detailed schedule outlining study visits and the information collected at each visit for mothers and babies is found in Table Assessment of growth and body composition of the infant: Anthropometric measurements of the child will be made annually. Infants will be weighed to the nearest 10 g on an electronic scale; length will be measured on an infantometer. Head, chest and mid upper arm circumference of the baby will be measured to the nearest 0.1 cm using a plastic measuring tape. Skinfold measurements will be measured to the nearest 0.2 mm, using skinfold calipers (Holtain, UK) for prediction of body composition. (Table 4) All measures will be done by trained personnel, and inter-observer reliability testing will be conducted. Crown-heel length which will be measured using a regularly maintained and calibrated length board until 18 months of age, and height will be measured using a Harpenden stadiometer after 18 months of age. Weight will be measured with an electronic scale. 11

12 5.3.2 Breastfeeding practices, Infant Diet recall, and Activity Assessment: Information on infant feeding practices will be collected at 1 and 6 months, and then annually by interviewing the mother of the infant/child. Information on initiation of breastfeeding, exclusivity of breastfeeding, duration of breast feeding, and introduction of complementary foods will be collected. A validated Infant Feeding Form will be used and is a closed ended questionnaire with information about breastfeeding, other feeds and complementary feeds taken during last 7 days (Table 5). 113 During the final visit at 3 years, the mother will complete a 24 hour recall for the child. An activity assessment in the growing child at each annual visit will be performed using a 24 hour activity recall developed and validated for use in young children. (Table 5) 5.4 Genetic Analysis: Mother s and newborn s buffy coats will be stored for future DNA extraction and genetic analysis. RNA from leukocytes and placenta will be stored for future gene expression analysis. We have recently piloted this protocol to optimize collection, sample size collection and storage. (Supplementary 3) 6.0 Statistical Considerations: Statistical Power: We estimate that 300 babies and mothers will provide moderate power to address the main objectives of this study. The primary outcome of the study is newborn adiposity measured by skin fold thickness from 4 locations (triceps, biceps, subscapular, and suprailiac). Primary Objectives: For Objectives 1-3: For continuous predictors, with 300 newborns we have >80% power to detect an absolute change in percent body fat of 0.82 per 1 SD increase in a given predictor (i.e. maternal weight gain) (two-tailed alpha=0.05), and we have >90% power to detect an absolute change of 0.96 in percent body fat. For categorical predictors (i.e. maternal gestational diabetes), with 300 newborns we have >80% power to detect an absolute difference in percent body fat of 2.53% when at least 20% have the exposure of interest. There is also sufficient power to detect an absolute difference of 3.24% body fat between top and bottom quartile group extremes (e.g., maternal glucose values or dietary factors). Similar high power is present to test postnatal factors against change in adiposity from birth to age 3 years. Secondary Objectives: For Objective 4 comparing body fat percentage in Aboriginal newborns to white Caucasian and South Asian newborns in Canada from the FAMILY and START cohorts respectively, we will calculate body fat from the skin fold thickness measures, and after adjusting for differences in gestational age and sex, we will compare the percent body fat per kg of birth weight. Normalizing body composition measurements (e.g. skinfold thickness and body fat) by birth weight has been used as a standard approach to make comparisons across groups of infants of varying body size, including across ethnic categories and sex. The estimate of percent body fat in the FAMILY study among white Caucasians (n=901) is 17% (SD: 4.3%) or 5.1% (1.5%)/kg of birth weight, and is expected to be 6%/kg birth weight in South Asians. Therefore with 300 Aboriginal newborns, we will have > 80% power to detect an absolute difference in percent body fat/kg birth weight of at least 0.30%, and we have similar high power to detect absolute differences in birth weight as low as 120 grams between Aboriginal newborns and newborns from other ethnic groups. (Table 8a,b) A difference of 0.3%/kg is clinically important as this difference was associated with an increase in insulin resistance of 0.8 units in HOMA in an adolescent cohort, which is equivalent to a 20% increase in risk of incident diabetes after 10 years. 114 Objective 5 - Genetics: We anticipate requiring 1,000 babies of Aboriginal origin to have high power to study associations of selected candidate SNPs with their respective quantitative traits. For example, 1,000 newborns will provide more than 80% power to detect an additive genetic effect as low as 1.2% per allele change in body fat for minor allele frequency as low as 20%, using a type I error threshold of (Table 9) While costs for this sample size and for epigenetics analyses are precluded by the current anticipated budget, DNA, leukocytes, and placental tissue will be collected and stored for future analyses. In addition we are actively applying for additional funds to increase our final 12

13 cohort size to 1,000 mothers and newborns. 6.1 Statistical Analysis: Descriptive statistics characterizing maternal and newborn characteristics will be generated. Continuous variables will be reported as means and SD for the normally distributed variables otherwise median and inter-quartile ranges will be reported. Categorical variables will be reported using percentages. Normality of the variables will be examined and appropriate transformations applied if required. Newborns with severe congenital anomalies whose growth would be expected to differ from average will be excluded. Changes in adiposity measurements over time will be compared by groups adjusting for confounders using linear mixed effects regression models. All analysis will be considered statistically significant at 5% level. As an example of the multiple sources of data we will accrue from this study a detailed statistical analysis plan for the Aboriginal cohort is provided. We propose to study, the contributions of maternal, newborn, and post-natal factors to the offspring s adiposity at birth, and the change over time of body fat in the offspring. To do so we will study the effect of multiple types of data (i.e. maternal characteristics such as diet, hypertension, micronutrient status, maternal weight gain, presence of gestational diabetes, smoking status (i.e. never, former, current smokers), infant characteristics (gestational age, sex, feeding type, amount, duration), certain contextual factors (i.e. socioeconomic status) on the outcome of adiposity, and change in body fat from birth until 3 years. Our goal will be to identify those determinants and their interactions, which predict change in adiposity as the child grows. A two-staged analytic plan will be used to accomplish this goal. Stage 1: Examining the strongest influences (maternal and newborn characteristics) on adiposity: First, the relationship within each group of determinants (maternal and newborn) and adiposity will be assessed using multi-level growth curve models. 116 Measurement of adiposity over time for the same children will be modeled to show trajectories or slopes (linear or non-linear patterns) as a function of strongly associated and significant influences identified within each factor group (i.e. maternal, infant). Potential covariates (i.e. contextual factors) will be examined to identify those that are highly correlated with other potential covariates; and only those which have an independent influence will be included. Covariates known to be associated with adiposity within each factor group will be included a priori and then other covariates will be identified using a backward elimination technique. Once a reasonable predictive model has been chosen, all excluded covariates will be added into the model one at a time to identify any missing confounders. We will also apply a stepwise regression approach to assess robustness of our modeling strategy. Stage 2: Interactions of maternal, infant, and contextual determinants on adiposity: Given that the best predictive model is identified within each group of factors (i.e. maternal, infant, childhood), the final model will include each of the significant determinants from each determinant grouping, and we will examine interactions between these determinants using the same analytic techniques as detailed above. For example, any identified biochemical marker (i.e. maternal fasting glucose) and the incremental contribution of maternal weight gain, and early childhood feeding practices to adiposity over time to age 3 years will be determined using growth curve models to determine main effects and interactions. All models will be validated internally using bootstrap resampling. Previous studies in which models are validated in independent data sets have shown that a fitted regression model is likely to be reliable when the number of independent predictors is less than the total sample size divided by This means that in the ABC with 300 offspring we have high power to test the effects of approximately 30 to 40 independent variables and interactions. For the comparison of adiposity (% body fat/kg birthweight) between Aboriginal and white Caucasian and South Asian newborns will be adjusted for key covariates including gestational age, sex, maternal glucose tolerance, weight gain in pregnancy, and SES. 7.0 Possible Problems: Given that there are over 400 Aboriginal births per year (total of 1200 births over 3 years) to mothers living on the Six Nations Reserve, we are confident enrolling 300 mothers and their offspring can be met 13

14 over the planned three year recruitment period. Our research group has considerable experience conducting large prospective epidemiologic studies and birth cohorts in Canada. For example, we ve recently completed the first phase of the FAMILY study, follow-up visits are ongoing and the attrition rate is low i.e. 10%. Furthermore, we have a record of successfully completing of epidemiology studies and clinical trials in the Six Nations community. 2,8, Sample Selection: Ideally our cohort would include random selection of pregnant Aboriginal women from the Six Nations reserve. However this would be extremely challenging, as there is no comprehensive list of pregnant women in their first trimester on the Reserve. We will attempt to accrue a representative sample from three primary sources: local GP s, obstetricians, and from the Six Nations Birthing Centre. Consecutive subjects will be approached and the main reasons for refusal to participate will be recorded. While we will try our best to achieve a representative sample, nevertheless the results of our exposure-outcomes associations will be internally valid, as the key factor to maintaining high validity of a prospective cohort study is minimizing the lost to follow-up. 118 To ensure this we will maintain frequent contact with study participants via telephone, text messaging, and our study website. Although much effort will be expended to maintain the cohort, we expect that a maximum of 5 10% of the families will not participate in all stages of the study, therefore once we have secured additional funding we will plan to recruit additional mothers/offspring into the study to ensure we maintain study power. 8.0 Investigator Roles: The Principal Investigator (PI) of this proposal is Dr. Sonia Anand, Professor of Medicine and Epidemiology, McMaster University. Dr. Anand has worked with the Six Nations people since 1998 and this collaboration has resulted in 8 peer review funded studies and 15 papers. Dr. Anand is currently the PI of the CIHR/HSFO funded South Asian birth cohort (START) and will coordinate all aspects of ABC. Co-Investigators from McMaster include: Dr. Teo, (Medicine) and PI of the FAMILY birth cohort study. He will advise Dr. Anand on issues on recruitment strategies and study design; Dr. Morrison (Pediatrics) and Dr. McDonald (Obstetrics and Gynecology) both are key investigators in the FAMILY study and will be members of the ABC Operations team which will meet weekly with Dr. Anand to review recruitment and address protocol issues; Dr. Beyene, (Epidemiology and Biostatistics) will provide advice and support for all cohort analysis including genetics; Dr. Meyre, (Genetic Epidemiology) will provide input on the genetic aspects of the study, Dr. Pare (Molecular Epidemiology and Genetics) will oversee the ABC samples shipped to the central laboratory in Hamilton and perform future epigenetic analyses, Dr. Holloway will inform the team of findings from animal models regarding tobacco and adiposity and will develop translation projects within ABC, Dr. Anglin (Psychiatry) has experience working with Aboriginal communities and currently is studying post-partum depression in START, and Dr. Wahi (Pediatrics) has research experience in childhood obesity in Aboriginal communities both will be members of the Operations team. Additional co- Investigators include: Ruby Miller, Director of Health Services at Six Nations, who will lead the study operations on the Six Nations Reserve and Julie Wilson, who heads the Six Nations Birthing Centre will assist with recruitment, and Dr. Retnakaran (Endocrinology) at University of Toronto, will advise the study team regarding measurement of gestational diabetes and analysis of stored bloods as related to insulin sensitivity. Thus our team is multidisciplinary in nature, includes senior and new investigators, and has been convened to address the full spectrum of the potential determinants of adiposity from genetics to psychosocial determinants. 9.0 Knowledge Translation: We have worked with the Six Nations people since 1998, and have a long-track record of dialogue with this community. We have partnered with the Six Nations Health Services leadership with whom we have 14

15 worked over the past 10 years to engage the Aboriginal community. Six Nations Health Services will promote the project itself to help facilitate recruitment into the study, and will assist in disseminating the results of our study through community presentations, brochures, and the local media. Information generated from this Cohort will be made easily accessible to other health professionals, policy makers, and the public through our study website. The website will also be linked to the CIHR website which describes birth cohort studies. The results of our study will be disseminated among academics and policy makers using traditional methods including scientific publications i.e. abstracts, oral presentations, and papers in scientific journals Anticipated Results and Conclusions: There is an urgent need to understand maternal and child factors that underlie the early development of adiposity and type 2 diabetes in Aboriginal people. The information generated from this cohort will increase our understanding of the contribution of pre and post natal factors to childhood overweight/obesity and type 2 diabetes, and assist the Six Nations community in developing interventions to prevent early adiposity in Aboriginal children. 15

16 11.0 References: 1. Statistics Canada Ethnocultural Portrait of Canada Highlight Tables, 2006 Census. Statistics Canada Catalogue no WE Ottawa. Version updated April 2, Ottawa. (accessed February 27). 2. Anand SS, Yusuf S, Jacobs R, Davis AD, Yi Q, Gerstein H, Montague PA, Lonn E. Risk factors, atherosclerosis, and cardiovascular disease among Aboriginal people in Canada: the Study of Health Assessment and Risk Evaluation in Aboriginal Peoples (SHARE-AP). The Lancet 2001;358(9288): Amed S, Dean HJ, Panagiotopoulos C, Sellers EA, Hadjiyannakis S, Laubscher TA, Dannenbaum D, Shah BR, Booth GL, Hamilton JK. Type 2 Diabetes, Medication-Induced Diabetes, and Monogenic Diabetes in Canadian Children. Diabetes Care 2010;33(4): Gluckman PD, Hanson MA, Beedle AS. Early life events and their consequences for later disease: a life history and evolutionary perspective. Am J Hum Biol Jan-Feb;19(1): Ludwig DS. Childhood Obesity The Shape of Things to Come. New England Journal of Medicine 2007;357(23): Schack-Nielsen L, Michaelsen KF, Gamborg M, Mortensen EL, Sorensen TIA. Gestational weight gain in relation to offspring body mass index and obesity from infancy through adulthood. Int J Obes 2009;34(1): Ho L, Gittelsohn J, Sharma S, Cao, Treuth M, Rimal R, Ford E, Harris S. Food-related behavior, physical activity, and dietary intake in First Nations1 a population at high risk for diabetes. Ethnicity & Health 2008;13(4): Anand SS, Davis AD, Ahmed R, Jacobs R, ie C, Hill A, Sowden J, Atkinson S, Blimkie C, Brouwers M, Morrison K, de Koning L, Gerstein H, Yusuf S; SHARE-AP ACTION Investigators. A family-based intervention to promote healthy lifestyles in an aboriginal community in Canada. Can J Public Health 2007;98 (6): Mente A, Razak F, Blankenberg S, Vuksan V, Davis AD, Miller R, Teo K, Gerstein H, Sharma AM, Yusuf S, Anand SS; Study of the Health Assessment And Risk Evaluation; Study of the Health Assessment And Risk Evaluation in Aboriginal Peoples Investigators. Ethnic variation in adiponectin and leptin levels and their association with adiposity and insulin resistance. Diabetes Care Jul;33(7): Merchant AT, Kelemen LE, de Koning L, Lonn E, Vuksan V, Jacobs R, Davis B, Teo KK, Yusuf S, Anand SS; SHARE and SHARE-AP investigators. Interrelation of saturated fat, trans fat, alcohol intake, and subclinical atherosclerosis. Am J Clin Nutr Jan;87(1): Merchant AT, Anand SS, Kelemen LE, Vuksan V, Jacobs R, Davis B, Teo K, Yusuf S; SHARE and SHARE-AP Investigators. Carbohydrate intake and HDL in a multiethnic population. Am J Clin Nutr Jan;85(1): Anand SS, Razak F, Davis AD, Jacobs R, Vuksan V, Teo K, Yusuf S. Social disadvantage and cardiovascular disease: development of an index and analysis of age, sex, and ethnicity effects. Int J Epidemiol Oct;35(5): Merchant AT, Anand SS, Vuksan V, Jacobs R, Davis B, Teo K, Yusuf S; SHARE and SHARE- AP Investigators. Protein intake is inversely associated with abdominal obesity in a multi-ethnic population. J Nutr May;135(5): Razak F, Anand S, Vuksan V, Davis B, Jacobs R, Teo KK, Yusuf S; SHARE Investigators. Ethnic differences in the relationships between obesity and glucose-metabolic abnormalities: a 16

17 cross-sectional population-based study. Int J Obes Jun;29(6): Anand SS, Razak F, Yi Q, Davis B, Jacobs R, Vuksan V, Lonn E, Teo K, McQueen M, Yusuf S. C-reactive protein as a screening test for cardiovascular risk in a multiethnic population. Arterioscler Thromb Vasc Biol Aug;24(8): Anand SS, Yi Q, Gerstein H, Lonn E, Jacobs R, Vuksan V, Teo K, Davis B, Montague P, Yusuf S; Study of Health Assessment and Risk in Ethnic Groups; Study of Health Assessment and Risk Evaluation in Aboriginal Peoples Investigators. Relationship of metabolic syndrome and fibrinolytic dysfunction to cardiovascular disease. Circulation Jul 29;108(4): Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med Feb 11;362(6): Barker D, Osmond C, Golding J, Kuh D, Wadsworth ME. Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. BMJ 1989, 289(6673), Yajnik CS. Fetal Programming of Diabetes: Still So Much to Learn! Diabetes Care 2010;33(5): Morrison K, Atkinson S, Yusuf S, Bourgeois J, McDonald S, McQueen M, Persadie R, Hunter B, Pogue J, Koon T. The Family Atherosclerosis Monitoring In EarLY Life (FAMILY) Study: Rationale, design and baseline data of a study examining the early determinants of atherosclerosis. Am Heart Jour 2009, 158 (4): Lindsay RS, Cook V, Hanson RL, Salbe AD, Tataranni A, Knowler WC. Early Excess Weight Gain of Children in the Pima Indian Population. Pediatrics 2002;109(2):e Langridge AT, Li J, Nassar N, Stanley FJ. Community-level socioeconomic inequalities in infants with poor fetal growth in Western Australia, 1984 to Ann Epidemiol Jul;21(7): Netto G, Bhopal R, Lederle N, Khatoon J, Jackson A. How can health promotion interventions be adapted for minority ethnic communities? Five principles for guiding the development of behavioural interventions. Health Promotion International 2010;25(2): Yajnik CS, Ganpule-Rao AV. Review: The Obesity-Diabetes Association: What Is Different in Indians? The Int. J. of Lower Ext. Wounds 2010;9(3): Ray JG, Jiang D, Sgro M, Shah R, Singh G, Mamdani MM. Thresholds for small for gestational age among newborns of East Asian and South Asian ancestry. J Obstet Gynaecol Can 2009;31(4): Huang RC, Burke V, Newnham JP, Stanley FJ, Kendall GE, Landau LI, Oddy WH, Blake KV, Palmer LJ, Beilin LJ. Perinatal and childhood origins of cardiovascular disease. Int J Obes Feb;31(2): Boney CM, Verma A, Tucker R, Vohr BR. Metabolic Syndrome in Childhood: Association With Birth Weight, Maternal Obesity, and Gestational Diabetes Mellitus. Pediatrics. 2005;115(3):e290-e Parsons TJ, Power C, Logan S, Summerbell CD. Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metab Disord. 1999;23 Suppl 8:S Dietz W. Critical periods in childhood for the development of obesity. The American journal of clinical nutrition. 1994;59(5): Whitaker RC, Dietz WH. Role of the prenatal environment in the development of obesity. The Journal of pediatrics. 1998;132(5):

18 31. Sørensen HT, Sabroe S, Rothman KJ, Gillman M, Fischer P, Sørensen TIA. Relation between weight and length at birth and body mass index in young adulthood: cohort study. BMJ. 1997;315(7116): Oken E, Gillman MW. Fetal Origins of Obesity. Obesity. 2003;11(4): Rich-Edwards JW, Colditz GA, Stampfer MJ, Willett WC, Gillman MW, Hennekens CH, et al. Birthweight and the Risk for Type 2 Diabetes Mellitus in Adult Women. Annals of Internal Medicine. 1999;130(1): Rich-Edwards JW, Stampfer MJ, Manson JE, Rosner B, Hankinson SE, Colditz GA, et al. Birth weight and risk of cardiovascular disease in a cohort of women followed up since BMJ. 1997;315(7105): Huang RC, de Klerk NH, Smith A, Kendall GE, Landau LI, Mori TA, Newnham JP, Stanley FJ, Oddy WH, Hands B, Beilin LJ. Lifecourse childhood adiposity trajectories associated with adolescent insulin resistance. Diabetes Care Apr;34(4): Epub 2011 Mar de Rooij S; Painter R, Holleman F, Bossuyt P, Roseboom T. The metabolic syndrome in adults prenatally exposed to the Dutch famine. Am J Clin Nutr. 2007, 86 (4), Li Y, He Y, Qi L, Jaddoe V, Feskens E, Yang, et al. Exposure to the Chinese famine in early life and the risk of hyperglycemia and type 2 diabetes in adulthood. Diabetes Nohr E, Vaeth M, Baker J, Sorenson T, Olsen J, Rasmussen K. Combined associations of prepregnancy body mass index and gestational weight gain with the outcome of pregnancy. Am J Clin Nutr 2008, 87 (6), Lawlor DA, Smith GD, O'Callaghan M, Alati R, Mamun AA, Williams GM, Najman JM.Epidemiologic evidence for the fetal overnutrition hypothesis: findings from the materuniversity study of pregnancy and its outcomes. Am J Epidemiol Feb 15;165(4): Oken E, Taveras E, Klienman K, Rich-Edwards J, Gillman M. Gestational weight gain and child adiposity at age 3 years. Am J Obstet Gynecol 2007, 196 (4(322)): e McDonald S, Han Z, Mulla S, Beyene J. Overweight and obesity in mothers and risk of preterm birth and low birth weight infants: systematic review and meta-analyses. BMJ 2010, 341 :c Lowell H, Miller DC. Weight gain during pregnancy: adherence to Health Canada's guidelines. Health Rep 2010;21:31-6. Statistics Canada, Catalogue no PE (Accessed Feb 27) 43. Oken E, Gillman M. Fetal Origins of Obesity. Obes Res 2003, 11 (4): Gillman M, Rifas-Shinman S, Berkey C, Field A, Colditz G. Maternal Gestational Diabetes, Birthweight and Adolescent Obesity. Pediatrics 2003, 111 (3):e221-e Wroblewska-Seniuk K, Wender-Ozegowska E, Szczapa J. Long-term effects of diabetes during pregnancy of the offspring. J Pediatr Diabetes 2009, 10 (7): Catalano P, Kirwan J, Haugel-de Mouzon S, King, J. Gestational diabetes and insulin resistance: role in short- and long-term implications for mother and fetus. J Nutr 2003, 133, 1674S-83S. 47. Catalano P, Hoegh M, Minium J, Huston-Presley L, Bernard S, Kalhan S, Hauguel-de Mouzon S. Adiponectin in human pregnancy: implications for regulation of glucose and lipid metabolism. Diabetologia 2006;49 (7): HAPO Study Cooperative Research Group. Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study: associations with neonatal anthropometrics. Diabetes Feb;58(2): Hamilton JK, Odrobina E, Yin J, Hanley AJ, Zinman B, Retnakaran R. Maternal insulin sensitivity during pregnancy predicts infant weight gain and adiposity at 1 year of age. Obesity Feb;18(2):

19 50. Willows ND, Sanou D, Bell RC. Assessment of Canadian Cree infants' birth size using the WHO Child Growth Standards. American Journal of Human Biology 2011;23(1): Aljohani N, Rempel BM, Ludwig S, et al. Gestational diabetes in Manitoba during a twenty-year period. Clin Invest Med 2008;31(3):E Rodrigues S, Robinson EJ, Kramer MS, Gray-Donald K. High Rates of Infant Macrosomia: A Comparison of a Canadian Native and a Non-Native Population. The Journal of Nutrition 2000;130(4): Retnakaran R, Qi Y, Sermer M, Connelly PW, Hanley AJ, Zinman B. Glucose Intolerance in Pregnancy and Future Risk of Pre-Diabetes or Diabetes. Diabetes Care 2008; 31(10): Barker D, Gluckman P, Godfrey K, Harding J, Owens J, Robinson J. Fetal nutrition and cardiovascular disease in adult life. Lancet 1993, 341 (8850): McMillen I, Robinson J. Developmental Origins of the Metabolic Syndrome: Prediction, Plasticity, and Programming. Physiol Rev 2005, 85 (2): Singhal A, Lucas A. Early origins of cardiovascular disease: is there a unifying hypothesis? Lancet 2004, 363 (9421): Fetoui H, Garoui M, Zeghal N. Protein restriction in pregnant-and lactating rats-induced oxidative stress and hypohomocysteinaemia in their offspring. J Anim Physiol Anim Nutr. 2009, 93(2): Chen J, Martin-Gronert M, Tarry-Adkins J, Ozanne S. Maternal protein restriction affects postnatal growth and the expression of key proteins involved in lifespan regulation in mice. Plos One 2009, 4 (3): e Langley-Evans SC, McMullen S. Developmental Origins of Adult Disease. Medical Principles and Practice 2010;19(2): Lumley J, Chamberlain C, Dowswell T, Oliver S, Oakley L, Watson L. Interventions for promoting smoking cessation during pregnancy. Cochrane Database Syst Rev 2009;(3): CD Oken E, Rifas-Shiman SL, Field AE, Frazier AL, Gillman MW. Maternal gestational weight gain and offspring weight in adolescence. Obstet Gynecol 2008;112(5): Ino T. Maternal smoking during pregnancy and offspring obesity: Meta-analysis. Pediatrics International 2010;52: Syme C, Abrahamowicz M, Mahboubi A, et al. Prenatal Exposure to Maternal Cigarette Smoking and Accumulation of Intra-abdominal Fat During Adolescence. Obesity 2010;18: Oken E, Levitan EB, Gillman MW. Maternal smoking during pregnancy and child overweight: systematic review and meta-analysis. Int J Obes 2008;32(2): Montgomery SM, Ekbom A. Smoking during pregnancy and diabetes mellitus in a British longitudinal birth cohort. BMJ 2002;324(7328): First Nations Centre, First Nations Regional Longitudinal Health Survey (RHS) 2002/03: Results for Adults, Youth and Children Living on First Nations Reserves, Ottawa: First Nations Centre, 2005, p ( technical_report.pdf) (accessed Jun 1, 2011) 67. Heaman MI, Chalmers K. Prevalence and Correlates of Smoking During Pregnancy: A Comparison of Aboriginal and Non-Aboriginal Women in Manitoba. Birth 2005 (4);32:

20 68. Wenman WM, Joffres MR, Tataryn IV, Group atepi. A prospective cohort study of pregnancy risk factors and birth outcomes in Aboriginal women. Canadian Medical Association Journal 2004;171: Willows ND, Sanou D, Bell RC. Assessment of Canadian Cree infants' birth size using the WHO Child Growth Standards. American Journal of Human Biology 2011;23 (1): Rodrigues S, Robinson EJ, Kramer MS, Gray-Donald K. High Rates of Infant Macrosomia: A Comparison of a Canadian Native and a Non-Native Population. The Journal of Nutrition 2000;130: Ong K, Ahmed M, Emmett P, Preece M, Dunger D. Association between postnatal catch-up growth and obesity in childhood: prospective cohort study. BMJ. 2000, 320 (7240): Bergmann K, Bergmann R, Von Kries R, Bohm O, Richter R, Dudenhausen J, et al. Early determinants of childhood overweight and adiposity in a birth cohort study: role of breast feeding. Int J Obes Relat Metabol Disord 2003 ;27(2): Ong K, Dunger D. Birth weight, infant growth and insulin resistance. Eur J Endocrinol 2004, 151 (Suppl 3), U Ong K, Loos R. Rapid infancy weight gain and subsequent obesity: systematic review and hopeful suggestions. Acta Pediatr 2006, 95 (8), Fewtrell M, Morley R, Abbott R. Catch up growth in small for gestational age term infants: a randomized trial. AJCN 2001, 74 (4), Owen C, Martin R, Whincup P, Smith G. Effect of Infant feeding on the risk of obesity across the life course: A Quantitative Review of Published Evidence. Pediatrics 2005, 115 (5), Ekelund U, Ong K, Linne Y, Neovius M, Brage S, Dunger D, et al. Upward weight percentile crossing in infancy and early childhood independently predicts fat mass in young adults: the Stockholm Weight Development Study (SWEDES). Am J Clin Nutr Feb;83(2): Arenz S, Von Kries R. Protective effect of breast-feeding against obesity in childhood. Can a meta-analysis of observational studies help to validate the hypothesis? Adv Exp Med Biol 2005, 569: Dewey K. Is breast feeding protective against child obesity? J Hum Lact 2003, 19 (1), Harder T, Bergmann R, Kallischnigg G, Plagemann A. Duration of breastfeeding and risk of overweight: a meta-analysis. Am J Epidemiol 2005, 162 (5): Campbell K; Hesketh K, Crawford D, Salmon J, Ball B, Mccallum Z. The Infant Feeding Activity and Nutrition Trial (INFANT) an early intervention to prevent childhood obesity: Cluster-randomised controlled trial. BMC Public Health 2008, 8: Arenz S, Ruckerl R, Koletzko B, von Kries R. Breast-feeding and childhood obesity -- a systematic review. Int J Obes Relat Metab Disord 2004;28 (10): Miralles O, Sanchez J, Palon A, Pico C. A physiological role of breast milk leptin in body weight control in developing infants. Obesity 2006, 14 (8), Rasmussen KM. Association of Maternal Obesity Before Conception with Poor Lactation Performance. Annual Review of Nutrition 2007;27 (1): Amir L, Donath S. A systematic review of maternal obesity and breastfeeding intention, initiation and duration. BMC Pregnancy and Childbirth 2007;7(1): Heck KE, Braveman P, Cubbin C, Chávez GF, Kiely JL. Socioeconomic Status and Breastfeeding Initiation Among California Mothers. Public Health Rep. 2006; 121(1):

21 87. Health Canada. Breastfeeding Initiation in Canada: Key Statistics and Graphics ( ). Statistics Canada, Canadian Community Health Survey, (accessed Jun 1, 2011) 88. Health Canada. Duration of Exclusive Breastfeeding in Canada: Key Statistics and Graphics ( ). Statistics Canada, Canadian Community Health Survey, (accessed Jun 1, 2011) 89. Black R, Godwin M, Ponka D. Breastfeeding among the Ontario James Bay Cree: a retrospective study. Can J Public Health 2008;99 (2): Taveras E, Rifas-Shinman S, Oken E. Short sleep duration in infancy and risk of childhood overweight. Arch Pediatr Adolesc Med 2008, 162 (4): Must A, Tybor DJ. Physical activity and sedentary behavior: a review of longitudinal studies of weight and adiposity in youth. Int J Obes Relat Metab Disord 2000;29:S84-S Luo Z-C, Wilkins R, Kramer MS, and for the Fetal and Infant Health Study Group of the Canadian Perinatal Surveillance System. Effect of neighbourhood income and maternal education on birth outcomes: a population-based study. Canadian Medical Association Journal 2006;174 (10): Séguin L, Potvin L, St-Denis M, Loiselle J. Depressive Symptoms in the Late Postpartum Among Low Socioeconomic Status Women. Birth 1999;26 (3): Bohn DK, Tebben JG, Campbell JC. Influences of Income, Education, Age, and Ethnicity on Physical Abuse Before and During Pregnancy. Journal of Obstetric, Gynecologic, & Neonatal Nursing 2004;33 (5): Wilson LM, Reid AJ, Midmer DK, Biringer A, Carroll JC, Stewart DE. Antenatal psychosocial risk factors associated with adverse postpartum family outcomes. CMAJ 1996;154 (6): Ertel KA, Koenen KC, Rich-Edwards JW, Gillman MW. Antenatal and postpartum depressive symptoms are differentially associated with early childhood weight and adiposity. Paediatr Perinat Epidemiol Mar;24(2): Frohlich KL, Ross N, Richmond C. Health disparities in Canada today: Some evidence and a theoretical framework. Health Policy 2006;79 (2-3): Kramer MS, Séguin L, Lydon J, Goulet L. Socio-economic disparities in pregnancy outcome: why do the poor fare so poorly? Paediatric and Perinatal Epidemiology 2000;14 (3): Bradley RH, Corwyn RF. Socioeconomic Status and Child Development. Annual Review of Psychology 2002;53 (1): Elks C, Loos R, Sharp S, Langenberg C, Ring S, Timpson N, et al. Genetic markers of adult obesity risk are associated with greater early infancy weight gain and growth. PLoS Med. 2010, 7 (5) Freathy R, Weedon M, Bennett A, Hypponen E, Relton C, Knight B, et al. Type 2 diabetes TCF7L2 risk genotypes alter birth weight: a study of 24,053 individuals. Am J Hum Genet. 2007, 80 (6) Hattersley A, Tooke J. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet 1999, 353 (9166) Filiberto AC, Maccani MA, Koestler D, Wilhelm-Benartzi C, Avissar-Whiting M, Banister CE, Gagne LA and Marsit CJ Birthweight is associated with DNA promoter methylation of the glucocorticoid receptor in human placenta. Epigenetics 2011 May;6(5): Durnin J, Womersley J. Estimates of total body fat from skinfold thickness: measurements on 481 men and women aged from years. Br J Nutr 1974, 32,

22 105. Kessler R, Andrews G, Colpe L, et al. Short screening scales to monitor population prevalence and trends in non-specific psychological distress. Psychological Medicine 32: ; Brown JB, Lent B, Schmidt G, Sas G. Application of the Woman Abuse Screening Tool (WAST) and WAST-short in the family practice setting. J Fam Pract. 2000; 49(10): HAPO Study Cooperative Research Group, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, Hadden DR, McCance DR, Hod M, McIntyre HD, Oats JJ, Persson B, Rogers MS, Sacks DA. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med May 8;358(19): Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Canadian Diabetes Association 2008 clinical practice guidelines for the prevention and management of diabetes in Canada. Can J Diabetes. 2008;32(suppl 1): S1-S Vogeser M, König D, Frey I, Predel H, Parhofer K, Berg A. Fasting serum insulin and the homeostasis model of insulin resistance (HOMA-IR) in the monitoring of lifestyle interventions in obese persons. Clin Biochem 2007, 40 (13) Slaughter M, Lohman T, Boileau R, Horswill C, Stillman R, Van Loan M.; et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol 1988, 60 (5), Schmelzle H, Fusch C. Body fat in neonates and young infants: validation of skinfold thickness versus dual-energy -ray absorptiometry. Am J Clin Nutr 2002, 76 (5), Shaikh S, Mahalanabis D. Empirically derived new equations for calculating body fat percentage based on skinfold thickness and midarm circumference in preschool Indian children. Am J Hum Biol. 2004, 16 (3), Ness A. The Avon Longitudinal Study of Parents and Children (ALSPAC) a resource for the study of the environmental determinants of childhood obesity. European Journal of Endocrinology 115:U141-U149; Morrison JA, Glueck CJ, Horn PS, Schreiber GB, Wang P. Pre-teen insulin resistance predicts weight gain, impaired fasting glucose, and type 2 diabetes at age y: a 10-y prospective study of black and white girls. Am J Clin Nutr 2008;88: Anand S, ie C, Paré G, Montpetit A, Rangarajan A, McQueen M, et al. Genetic Variants Associated With Myocardial Infarction Risk Factors in Over 8000 Individuals From Five Ethnic Groups. Circulation: Cardiovascular Genetics. 2008, 2, Goldstein H. Multilevel Statistical Models: John Wiley & Sons; Cohen J, Cohen P. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences: L. Erlbaum Associates; Merchant AT, Anand SS, Yusuf S. Commentary: insights from cross-population studies: Rose revisited. Int J Epidemiol Apr;34(2): Epub 2005 Mar

23 12.0 Tables and Figures Figure 1: Figure 2: ObGyn Offices GP Offices Enrol Pregnant mothers N=300 Delivery F/U Birthing Centre Baseline Bloods OGTT results Questionnaires Cord Blood Birth Outcomes Telephone/Web based F/U Annual Visit to 3 years Share-AP Research Unit or Birth Centre Hospital/Home Birth Centre Share-AP Research Unit Table 1: Characteristics of Mothers and Newborns from Six Nations Six Nations Birthing centre N= 453 Maternal Age (SD) 25.1 (6.2) Maternal Pre-pregnancy BMI (SD) 28.3 (6.6) Maternal Weight Gain (SD) 14.9 (8.3) Maternal Smoking during pregnancy 34.9% Maternal Diabetes plus Gestational Diabetes 16/343 (3.5%) Maternal Hypertension 23/444 (5.2%) Baseline Systolic BP mmhg (SD) (8.8) Baseline Diastolic BP mmhg (SD) 64.6 (6.7) NEWBORNS N=364 Female sex no. (%) 172/364 (47.3) Gestational age at delivery wks (SD) 39.5±1.7 Preterm deliveries (< 37 wks gestation) no. (%) 19/362 (5.2) Birth weight g (SD) ±557.3 Birth weight for term deliveries wks (SD) ±481.5 Birth length cm (SD) 52.2±2.3 Head circumference cm (SD) 35.4±1.5 Table 2: Inclusion and Exclusion Criteria for ABC Inclusion Criteria Exclusion Criteria Women Age: years Artificial/Assisted Conception of Fetus, Surrogate Mothers Aboriginal Origin Pregnant with > 1 Fetus > 4 Live Births Chronic Medical Conditions, Active Cancer HIV, Hepatitis B or C, VDRL Positive Rheumatic Heart Disease Seizure Disorder on Medication 23