Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT)

Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT) Chih-Fu Wei, Lois Tang, Po-Jen Lin, Tai-Lin Lee, Stephanie Wu, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5952290/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tuvalu has one of the highest obesity prevalence rates globally, and is a Pacific Island nation facing significant climate change challenges. Altered rainfall pattern, as a part of climate change, may influence obesity risk during the critical developmental periods. This study investigated the associations between rainfall exposure during prenatal, pre-pregnancy periods and adult obesity in Tuvalu. A nationwide survey was conducted between February and May 2022, which included 892 adults from Tuvalu. Rainfall data was obtained from ECMWF Reanalysis v5 based on participants’ birth year and birthplace. Rainfall exposure during the first year of birth, the year before birth, and two years before birth was analyzed, and rainfall exposure between three to five years before birth were included as negative control periods. Obesity and severe obesity were defined based on body mass index (BMI) upon the survey, according to the World Health Organization criteria. The results showed association between higher rainfall before birth increased BMI and greater odds of adulthood obesity. These associations were more pronounced among male participants. No significant associations were observed for rainfall three to five years before birth. In conclusion, prenatal exposure to higher rainfall during the year and two years before birth are associated with increased obesity risk in adulthood, reflecting prenatal environmental influences on developmental periods. These findings emphasize the importance of understanding climate-related health exposures and the need for targeted interventions in climate change-vulnerable populations. Further research should explore heterogeneity across Pacific Island nations and the mechanisms linking rainfall, birth weight, and obesity. Health sciences/Health care/Public health/Epidemiology Health sciences/Risk factors Obesity Developmental Programming Climate Change Pacific Islands Figures Figure 1 INTRODUCTION People in Tuvalu have observed dramatic climate change, including increasing temperatures, elevated sea levels and fluctuating rainfall 1 , 2 . Previous studies have demonstrated Pacific Island countries are more vulnerable to climate change-related health effects, especially obesity 2 , 3 . A previous study revealed that climate change has influenced land availability and food security in Tuvalu 4 , 5 . Furthermore, climate change may increase the vulnerability of the healthcare infrastructure, making it more difficult to address major health problems such as obesity in Tuvalu. Our previous results, aligned with the World Health Organization (WHO) results, revealed a high prevalence of obesity in Tuvalu 1 , 6 . For Tuvalu's inhabitants, this is the compounded result of (1) the complex relationship between shifting from nutrient-rich local foods to imported staples and highly processed products following globalization and (2) the sequelae related to climate change 1 , 6 . Early-life exposures and experiences can have long-term effects on an individual’s obesity throughout their lifespan 7 . One crucial aspect of this theory is the impact of climate factors on metabolic health. Previous studies have shown that extreme ambient temperatures during pregnancy can influence fetal development and lead to adverse birth outcomes and increased mortality risk in infancy 8 , 9 . More recently, studies have increasingly focused on rainfall as a significant climate factor related to health outcomes. For instance, higher rainfall exposure increases mortality risk 10 , and those living in areas with greater rainfall are more likely to having obesity. Flood-related events can disrupt food availability and accessibility, whereas increased stress may alter eating behaviors and reduce physical activity, potentially contributing to higher obesity rates in affected communities 11 , 12 . Understanding these developmental and climate-related associations are crucial for designing targeted interventions to improve long-term health outcomes and reduce health disparities. Climate factors are contributing to many climate-sensitive health outcomes in Pacific Island countries 2 , 13 . Among these countries, Tuvalu faces significant challenges because health facilities are located in low-lying coastal areas, making them particularly vulnerable to climate-related risks 13 . Climate change in Tuvalu could impact food security and increase the risk of noncommunicable diseases, such as obesity, through alterations in natural, social, economic, and built environments 5 . In Funafuti, the main island of Tuvalu, most flood events occurred between January and March, when rainfall was relatively high 14 . Higher rainfall may exacerbate the health impacts of other climate change factors and further worsen the health impacts of climate change 15 . However, there is limited research on the link between elevated climate exposures during or prior to pregnancy and adult obesity, particularly in Tuvalu, a low-lying island nation prone to flooding and that has a high prevalence of obesity. Therefore, this study examined the associations between rainfall exposure during the prenatal and pre-pregnancy periods, and the probabilities of having obesity in adults using data from a nationwide survey population in Tuvalu. We examined rainfall exposure during the birth year (late pregnancy), the year before birth (early pregnancy), and two years before birth (pre-pregnancy), and focused on BMI as primary outcome, waist circumference and non-communicable diseases as secondary outcomes. METHODS Study population This study analyzed the de-identified dataset collected from Community Behavior and Attitude Survey in Tuvalu (COMBAT). COMBAT was conducted in Funafuti, the main island, and eight outlying islands in Tuvalu between February and May 2022. The study was part of a collaborative project on horticulture expansion between the Tuvalu government and the Taiwan International Cooperation and Development Fund (ICDF). Trained interviewers traveled to the outlying islands from Funafuti via scheduled passenger ships. The study regions were grouped on the basis of their geographical location and ship availability: (1) Nanumea, Nanumaga, and Niutao as the northern island group; (2) Vaitupu, Nukufetau, and Nui as the middle island group; and (3) Nukulaelae and Niulakita as the southern island group. Face‒to-face interviews were conducted in households by trained local interviewers. To ensure balanced representation, interviewers visited households in the seven villages on the Fongafale islet in Funafuti because people who migrated to Funafuti live with people from the same place of origin but in different communities. On the outlying islands, households were visited in the main residential area near the port. We used convenience sampling to select one or two study participants aged over 18 years from each household. The interview questionnaire was designed collaboratively by the local study team and external experts. Information on the participants’ year of birth, childhood place of residence, socio-demographics, health behaviors, and self-reported medical history was collected. Informed consent was obtained from each participant, and only Tuvaluan citizens, including native Tuvaluans and immigrants, were invited to participate. The survey was designed in compliance with the guidelines of the Declaration of Helsinki, and the protocol was approved by the Tuvalu Department of Health. Exposure assessment The interviewers collected information on the participants’ year of birth and the island where they resided before the age of 18, which was utilized as the proxy for their birthplace. Subsequently, the participants' birth year and birthplace were linked to the available climate reanalysis data from ECMWF Reanalysis v5 (ERA5) by the European Centre for Medium-Range Weather Forecasts (ECWMF). ERA5 is an atmospheric climate dataset that assimilates historical observations into global climate models to produce historical climate data from 1940 to the present 16 . We aggregated the monthly average rainfall with a spatial resolution of 0.25 degrees longitude times 0.25 degrees latitude. The amount of rainfall over each island was retrieved from the corresponding grid points within the reanalysis data. This study specifically focused on the anomaly of rainfall (the deviation of the absolute value of each month from the climatology) to remove the influence of seasonality. We also collected corresponding climate information during the dry season. In Tuvalu, people regard May to October as the dry season, when agriculture is more vulnerable to rainfall variability. Rainfall data was also collected from four weather stations at Funafuti, Nanumea, Nui and Niulakita, with which we performed Spearman correlation analysis to assess the correspondence between the modeled data and the measurement results. In this study, we used the rainfall during the year of birth, one year before birth (unit: mm/day, as the surrogate for prenatal exposure) and two years before birth (as the surrogate for preconception exposure) as the primary exposure. We utilized rainfall levels from three to five years prior to birth as a negative control exposure, given that these years share similar climate patterns but fall outside the critical windows of susceptibility 17 . Outcome assessment During the interviews, trained local research staff measured the participants' height in centimeters (to the nearest 0.1 cm) via a tape measure and recorded weight via an electronic scale (Samlux SYES-301). The scale had a maximum weight measurement of 180 kilograms, and weight was recorded to one decimal place in kilograms. The research team calculated the body mass index (BMI) for each individual using the collected height and weight. BMI was computed by dividing the weight in kilograms by height squared in meters. We defined obesity as BMI >30 kg/m 2 and severe obesity as BMI >40 kg/m 2 following the WHO criteria 18 . We also collected information on waist circumference as an indicator of abdominal obesity, which was classified as high waist circumference status for males ≥90 cm and females ≥80 cm. Physician-diagnosed, self-reported hypertension, diabetes and dyslipidemia data was also obtained from participants. Covariates Trained interviewers conducted the survey and collected information on various covariates. The selection of these covariates was based on our understanding and previous studies of obesity-related outcomes in Tuvalu. The covariates included sex (male or female), age (grouped into ten-year intervals), education level (high school or higher), noncommunicable disease diagnosis (hypertension, diabetes, or dyslipidemia), income (below or equal to 200 Australian dollars or greater than), and smoking status (yes or no). Statistical analysis Baseline characteristics of the study population and the distribution of health-related factors were described using summary statistics. Categorical variables are presented as frequencies and percentages, whereas continuous variables are reported as the means and standard deviations. The primary analysis examined the associations between average rainfall from the year of birth to five years before birth and BMI, obesity, and severe obesity using generalized linear regressions in both unadjusted and adjusted models, as shown in Table 3. Using the same models, we also examined average dry season rainfall (April-September) as the exposure, which considered rainfall variations within a year. A subgroup analysis for males and females determined whether the associations differed between sexes. The same models were used to examine the associations between rainfall and the secondary outcomes. Statistical significance was determined via two-sided p- values, with a threshold of less than 0.05 considered statistically significant. The above analyses were performed using R software (version 4.0.4). RESULTS Descriptive characteristics The demographic characteristics of the 892 adults in our study were shown in Table 1: the mean age was 41.2 years, with 479 participants (53.7%) being female. The average monthly income was $178.08, with significant variation, which is consistent with our prior findings and governmental survey 1,6,19,20 . With respect to education level, 37.3% of the participants had completed elementary school, 47.3% had finished high school, and 12.9% held a college degree or higher. Additionally, 312 participants (35.1%) were smokers. While most participants were interviewed in Funafuti, 324 (36.3%) participants were interviewed on the outlying islands. In terms of place of residence prior to age 18, most of the participants were from Funafuti (466 participants, 52.2%), followed by Vaitupu (90 participants, 10.1%), Nanumea (76 participants, 8.5%) and other outlying islands. This distribution reflects the population concentration in Funafuti, the capital and most urbanized island. Moreover, more dispersed populations live on smaller, more rural outlying islands. Health-related parameters showed the average BMI was 34.55 kg/m 2 in the study population, with 633 (71.0%) individuals having obesity and 184 (20.7%) individuals having severe obesity. The mean waist circumference was 102.89 cm (male: 103.00 cm, and female: 102.80 cm), with 742 (83.2%, 432 females and 310 males) participants having a high waist circumference. The prevalence rates of hypertension, diabetes and dyslipidemia were 17.5%, 11.0% and 3.1%, respectively (Table 2). Climate factors We observed consistent rainfall patterns across the nine islands, with lower rainfall from May to October and higher rainfall during the remainder of the year. However, the amount of rainfall varied by island group, showing an increasing trend from north to south. Participants from the northern islands, such as Nanumea, Namumaga, Niutao, and Nui, experienced lower rainfall, whereas participants from southern islands, such as Funafuti, Nukulaelae, and Niulakita, experienced greater rainfall (Figure 1). The correlation coefficients between the reanalysis data and weather station measurements ranged between 0.65 and 0.79 across the four islands with weather stations (Funafuti, Nanumea, Nui and Niulakita), suggesting strong consistency between our models and direct measurements in Tuvalu (Supplemental Figure 1). Associations between climate factors and obesity We identified associations between elevated BMI and rainfall during the year before birth (unadjusted estimate: 0.45 kg/m 2 per 1 mm/day increase in annual rainfall, 95% CI: 0.14 to 0.76, p= 0.005; adjusted estimate: 0.35 kg/m 2 , 95% CI: 0.03 to 0.67, p= 0.031) and two years before birth (unadjusted estimate: 0.64 kg/m 2 , 95% CI: 0.33 to 0.95, p< 0.001; adjusted estimate: 0.52 kg/m 2 , 95% CI: 0.20 to 0.83, p= 0.001). Although we did not observe an association during the year of birth, the association was observed when rainfall was averaged between the one- and two-years preceding birth. Similarly, we observed an association between obesity and rainfall during the year before birth (unadjusted odds ratio [OR] per 1 mm/day increase in annual precipitation: 1.12, 95% CI: 1.03 to 1.22, p= 0.009; adjusted OR: 1.10, 95% CI: 1.01 to 1.21, p= 0.034) and two years before birth (unadjusted OR: 1.19, 95% CI: 1.10 to 1.30, p< 0.001; adjusted OR: 1.16, 95% CI: 1.06 to 1.27, p= 0.001). This association was less evident for severe obesity and rainfall (unadjusted OR per 1 mm/day increase in annual rainfall two years before birth: 1.11, 95% CI: 1.01 to 1.22, p= 0.042; adjusted OR: 1.10, 95% CI: 0.99 to 1.22, p= 0.072; Table 3). Similar results were observed for rainfall during the dry season. Each centimeter of increased rainfall during the year before birth was associated with a greater odd of obesity in the study population (unadjusted OR: 1.08, 95% CI: 0.99 to 1.18, p= 0.099; adjusted OR: 1.11, 95% CI: 1.01 to 1.23, p= 0.039). Moreover, increased rainfall during the two years before birth was associated with a higher BMI during adulthood (unadjusted estimate: 0.45 kg/m 2 , 95% CI: 0.14 to 0.77, p= 0.005; adjusted estimate: 0.41 kg/m 2 , 95% CI: 0.07 to 0.74, p= 0.017). Furthermore, increased average rainfall during the dry season between 0-2 years before birth was associated with increased BMI during adulthood (unadjusted estimate: 0.49 kg/m 2 , 95% CI: 0.00 to 0.97, p= 0.049; adjusted estimate: 0.62 kg/m 2 , 95% CI: 0.08 to 1.16, p= 0.025; Table 4). We used rainfall exposure during the period 3-5 years before birth as a negative control exposure. No significant associations were detected between rainfall exposure during this period and increased BMI or between elevated prevalence of obesity and severe obesity (Supplemental Tables 1 and 2). The association between prenatal rainfall and BMI was stronger in male participants, specifically for the rainfall in the one year and two years before birth (for the year before birth: unadjusted estimate: 0.65 kg/m 2 , 95% CI: 0.23 to 1.08, p= 0.003; adjusted estimate: 0.55 kg/m 2 , 95% CI: 0.13 to 0.98, p= 0.011; for two years before birth: unadjusted estimate: 0.70 kg/m 2 , 95% CI: 0.29 to 1.11, p= 0.001; adjusted estimate: 0.56 kg/m 2 , 95% CI: 0.15 to 0.97, p= 0.008). Similarly, we observed an association between increased odds of obesity and rainfall in the one year and two years before birth among male participants (Supplemental Table 3). In contrast, the associations between rainfall exposure before birth and increased BMI or elevated risk of obesity and severe obesity were fewer in female participants (Supplemental Table 4). Average rainfall exposure in the year before birth was associated with increased waist circumference, but was not significantly associated with high waist circumference, hypertension, or diabetes (Supplemental Table 5). DISCUSSION This study revealed that higher average rainfall during the year and two years prior to birth were associated with an increased likelihood of having obesity and severe obesity in Tuvalu. Similar associations were observed for rainfall during the dry season and the two years prior to birth. These findings suggest that environmental exposure before conception and during the prenatal period may influence obesity risk in adulthood. An increasing number of studies have suggested a potential link between rainfall before birth and obesity, highlighting the importance of understanding the underlying mechanisms 21 , 22 . First, heavy rainfall has been associated with increased sedentary time, an indicator of lower physical activity, as suggested by systematic reviews on weather and physical activity in children and adolescents 21 . Moreover, reductions in physical activity are well-established risk factors for obesity 22 . Second, changes in rainfall during this period may alter the living environment and food availability, influencing maternal nutritional status. Increased rainfall could increase crop yields and food resources, potentially altering maternal nutrition and contributing to increased birth weight 7 , 23 – 25 . Meanwhile, crop quality decreases after heavy rainfall, which also increased the obesity risk, potentially through epigenetic changes in germ cells 12 , 26 , 27 . This has been evidenced by a study among young children in Burkina Faso, which showed associations between rainfall variability, dietary patterns and increased weight-for-height value at 7–60 months of age 12 . Finally, newborns and infants exposed to relatively high amounts of rainfall may face increased infection and mortality risks, prompting energy-conserving adaptations 10 , 28 . While these changes support early survival, they may increase the risk of metabolic syndrome, including obesity later in life, as suggested by Barker’s hypothesis 7 . The observed associations between rainfall and obesity situated in the year preceding birth reflects prenatal exposure, and two years before birth reflects periconceptional exposure. At the same time, the association was not observed for the year of birth, likely due to uncertainty in exposure assignment. Exposure during the birth year may partly reflect postnatal exposure, particularly for individuals born early in the year. These findings are aligned with Barker’s hypothesis, also known as the developmental origins of health and disease theory 7 , 29 . Adverse prenatal environmental conditions, such as changes in crop quality related to climate-induced rainfall variations, could affect maternal nutrition and fetal development 24 , 27 . Animal studies have shown that prenatal exposure to nutritionally imbalanced diets leads to metabolic disturbances and increased obesity risk in offspring. For example, rodent models exposed to altered diets during pregnancy are more prone to obesity and metabolic disorders in their offspring 30 . Climate change would impact the availability, access and utilization of food, which increased food security and related health risks 27 . Human studies also support this connection, showing that men under prenatal exposure to Dutch famine (Hunger Winter) had 1.3-fold increased risk of overweight and obesity during early adulthood 24 . Other studies also showed that children born under these conditions exhibit increased risks of obesity and chronic diseases in adulthood 7 , 29 . Birth weight may serve as a critical link between prenatal rainfall exposure and adult obesity, as higher birth weight is associated with an increased risk of obesity later in life 25 . Although birth weight data were not available in this study, future studies are needed to illustrate the role of birth weight on the risk of overweight and obesity later in life 25 , 31 . There are several limitations to this study. First, the study is semi-ecological in design, and the conclusions may not infer individual attributions to the probability of having obesity. Second, there could be measurement error for the exposure of interest. The temporal resolution of the data is limited to yearly scales, which may introduce a nondifferential misclassification, biasing the results toward the null. This bias is likely minimal because the sizes of islands in Tuvalu are small and there is limited regional variability within each island. Also, the primary residence before age 18 may differ from the birthplace, although it is not common in Tuvalu to move to other islands before adulthood. Finally, there could be other unmeasured mediators that would partially explain the associations of our observations. They could be targets of future studies but would not bias the associations in this study because they were in the causal path that occurred after the exposure of interest. Meanwhile, there are multiple strengths of this study. First, while only four of the regions in Tuvalu have direct observations of rainfall data, we used ERA5 reanalysis data to reconstruct historical records of rainfall anomalies over each island 16 .The strong correlation between the weather station observations and reanalysis data suggests the validity of our approach and the potential for future applications. Second, we were able to include participants from all islands in Tuvalu, which increased the generalizability of the findings by offering a broader representation of the population and environmental conditions. Third, we assessed exposure during critical periods of development and obesity, a prevalent health condition in Tuvalu, where people face the threat of climate change and where the risk factors for obesity remain understudied. CONCLUSION In conclusion, this study revealed that higher rainfall exposure before birth was associated with increased odds of obesity and severe obesity in adulthood in Tuvalu. These findings align with the developmental origins of health and disease theory, highlighting the potential impact of early-life environmental exposure on long-term health outcomes, including obesity. Further research is needed to explore rainfall trends in the Pacific region and examine heterogeneity among island populations to better understand the specific pathways linking this environmental factor and obesity. These findings could inform public health strategies aimed at mitigating the effects of climate-related exposures on adult health. Abbreviations Body mass index (BMI); Confidence interval (CI); COMmunity-based Behavior and Attitude (COMBAT); developmental origins of health and disease (DOHaD); ECMWF Reanalysis v5 (ERA5); European Centre for Medium-Range Weather Forecasts (ECWMF); International Cooperation and Development Fund (ICDF); odds ratio (OR). Declarations Ethics approval and consent to participate : This study is approved by Tuvalu ministry of health. Consent for publication : Not applicable. Availability of data and materials : The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests : The authors declared no competing interests. Funding: The community behavior and attitude survey was funded through a collaborative project between the Taiwan ICDF (Fruit and Vegetable Production and Nutrition Enhancement Project) and the Tuvalu government. The funding source had no role in the design, conduct, analysis, or interpretation of the study, and no influence on the content of the manuscript or the decision to publish. Authors' contributions : PJL, CFW and YHL initiated the study, and the study data collection is supported by CWS, ST, VS, PPM, and MT. LT curated the climate data and conducted formal analysis with CFW. LT and CFW interpreted the data and wrote the manuscript with PJL, YTH, YHL, MSH, JFLG, and IL. All authors read and approved the final manuscript. Acknowledgements: The authors want to acknowledge the survey support from the Taiwan ICDF and Tuvalu government from their Fruit and Vegetable Production and Nutrition Enhancement Project. 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Demographic characteristics of the study population (In n [%] or mean [SD]) Study population (n=892) Baseline characteristics Age (years, mean [SD]) 41.20 (16.12) Female, No. (%) 479 (53.7) Income (AUD, mean [SD]) 178.08 (487.49) Education (%) Below elementary school 22 (2.5) Elementary school 332 (37.3) High school 421 (47.3) College or above 115 (12.9) Living in outlying islands (%) 324 (36.3) Residence before 18 years old Funafuti 466 (52.2) Nanumaga 48 (5.4) Nanumea 76 (8.5) Niulakita 5 (0.6) Niutao 65 (7.3) Nui 59 (6.6) Nukufetau 57 (6.4) Nukulaelae 26 (2.9) Vaitupu 90 (10.1) Smoking (%) Daily 258 (29.0) Occasional 54 (6.1) Never 577 (64.9) Table 2. Distribution of health-related factors in the study population (In n [%] or mean [SD]) Study population (n=892) Body height (cm) 169.44 (10.28) Body weight (kg) 98.48 (21.07) BMI (kg/m 2 ) 34.55 (8.15) BMI trajectories Overweight (BMI: 25-30) 181 (20.4) Obesity: Class I (BMI: 30-35) 266 (30.0) Obesity: Class II (BMI: 35-40) 183 (20.6) Obesity: Class III (BMI: >40) 184 (20.7) Waist circumference (cm) 102.89 (16.77) High waist circumference (%) 742 (83.2) Hypertension 156 (17.5) Diabetes 98 (11.0) Dyslipidemia 28 (3.1) Table 3. Multivariable regressions between precipitation exposure before birth on BMI, obesity and severe obesity prevalence, shown in estimate (kg) or odds ratio, 95% confidence interval and p-value a Unadjusted model Fully adjusted model b (a) BMI Year of birth 0.08 (-0.25 to 0.41, p=0.642) 0.03 (-0.30 to 0.36, p=0.842) 1 year before birth 0.45 (0.14 to 0.76, p=0.005) 0.35 (0.03 to 0.67, p=0.031) 2 years before birth 0.64 (0.33 to 0.94, p<0.001) 0.52 (0.20 to 0.83, p=0.001) Average: 0-2 year before birth 1.09 (0.56 to 1.62, p<0.001) 0.90 (0.35 to 1.45, p=0.001) (b) Obesity Year of birth 0.94 (0.85-1.02, p=0.146) 0.93 (0.84-1.02, p=0.112) 1 year before birth 1.12 (1.03-1.22, p=0.009) 1.10 (1.01-1.21, p=0.034) 2 years before birth 1.19 (1.10-1.30, p<0.001) 1.16 (1.06-1.27, p=0.001) Average: 0-2 year before birth 1.24 (1.07-1.43, p=0.003) 1.20 (1.02-1.40, p=0.024) (c) Severe obesity Year of birth 1.00 (0.91-1.11, p=0.997) 0.98 (0.89-1.09, p=0.748) 1 year before birth 1.07 (0.98-1.19, p=0.150) 1.06 (0.96-1.17, p=0.286) 2 years before birth 1.11 (1.00-1.22, p=0.042) 1.10 (0.99-1.22, p=0.072) Average: 0-2 year before birth 1.17 (0.99-1.40, p=0.070) 1.14 (0.95-1.36, p=0.162) a. Obesity was defined as BMI >30 kg/m 2 , and severe obesity as BMI >40 kg/m 2 . b. Adjusted for gender (male or female), age (grouped in ten years), education level (high school or above), income (>200 AUD or not), and smoking. Abbreviation: AUD, Australian dollar; BMI, body mass index; NCD, non-communicable disease. Table 4. Multivariable regressions between precipitation exposure during dry season before birth on BMI, obesity and severe obesity prevalence, shown in estimate (kg) or odds ratio, 95% confidence interval and p-value a Unadjusted model Fully adjusted model b (a) BMI Year of birth -0.06 (-0.37 to 0.26, p=0.729) 0.02 (-0.30 to 0.35, p=0.902) 1 year before birth 0.24 (-0.08 to 0.56, p=0.138) 0.29 (-0.05 to 0.63, p=0.092) 2 years before birth 0.45 (0.14 to 0.77, p=0.005) 0.41 (0.07 to 0.74, p=0.017) Average: 0-2 year before birth 0.49 (0.00 to 0.97, p=0.049) 0.62 (0.08 to 1.16, p=0.025) (b) Obesity Year of birth 0.92 (0.85-1.00, p=0.060) 0.94 (0.86-1.03, p=0.200) 1 year before birth 1.08 (0.99-1.18, p=0.099) 1.11 (1.01-1.23, p=0.039) 2 years before birth 1.06 (0.97-1.16, p=0.171) 1.04 (0.95-1.15, p=0.409) Average: 0-2 year before birth 1.04 (0.91-1.18, p=0.568) 1.07 (0.92-1.25, p=0.384) (c) Severe obesity Year of birth 0.99 (0.90-1.09, p=0.875) 1.00 (0.91-1.11, p=0.960) 1 year before birth 1.00 (0.91-1.11, p=0.951) 1.01 (0.91-1.12, p=0.793) 2 years before birth 1.07 (0.97-1.18, p=0.161) 1.08 (0.97-1.19, p=0.168) Average: 0-2 year before birth 1.05 (0.90-1.22, p=0.533) 1.08 (0.91-1.27, p=0.388) a. Obesity was defined as BMI >30 kg/m 2 , and severe obesity as BMI >40 kg/m 2 . b. Adjusted for gender (male or female), age (grouped in ten years), education level (high school or above), NCD diagnosis (having hypertension, diabetes or dyslipidemia), income (>200 AUD or not), and smoking. Abbreviation: AUD, Australian dollar; BMI, body mass index; NCD, non-communicable disease. Additional Declarations Yes there is potential conflict of interest. Supplementary Files LTSupplementalTableFigure18Jan2025.docx Supplemental Tables and Figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5952290","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411488496,"identity":"e959f05a-b20a-4cfd-81b7-73d75cd8c09a","order_by":0,"name":"Chih-Fu 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Tupulaga","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Malo","middleName":"","lastName":"Tupulaga","suffix":""},{"id":411488509,"identity":"df4b42c8-0d29-4ffc-8a29-cf59a36967c3","order_by":13,"name":"José Francisco López-Gil","email":"","orcid":"https://orcid.org/0000-0002-7412-7624","institution":"Universidad de Murcia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Francisco","lastName":"López-Gil","suffix":""},{"id":411488510,"identity":"0e04c864-49c4-4e8b-882a-0c44f9c1919b","order_by":14,"name":"Maria Hershey","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Hershey","suffix":""},{"id":411488511,"identity":"e664ae66-93f0-4602-bd3e-e679357b5df4","order_by":15,"name":"Yu-Tien Hsu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yu-Tien","middleName":"","lastName":"Hsu","suffix":""}],"badges":[],"createdAt":"2025-02-03 15:40:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5952290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5952290/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75896035,"identity":"97b4c85c-5c4f-4740-ada9-74320d177902","added_by":"auto","created_at":"2025-02-10 10:29:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRainfall distributions in different islands of Tuvalu\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5952290/v1/6c3058f0980952bcde1929a4.png"},{"id":79859049,"identity":"61b5ec11-2f6e-4b24-916c-34f3385b5d09","added_by":"auto","created_at":"2025-04-03 16:35:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1018838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5952290/v1/3388f64b-959c-465c-aa83-6a2fd791191d.pdf"},{"id":75896039,"identity":"6052cb55-6e88-4055-91ed-45143d56189c","added_by":"auto","created_at":"2025-02-10 10:29:31","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":280999,"visible":true,"origin":"","legend":"Supplemental Tables and Figures","description":"","filename":"LTSupplementalTableFigure18Jan2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-5952290/v1/68b834aa83841e6c389e355d.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePeople in Tuvalu have observed dramatic climate change, including increasing temperatures, elevated sea levels and fluctuating rainfall \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Previous studies have demonstrated Pacific Island countries are more vulnerable to climate change-related health effects, especially obesity \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A previous study revealed that climate change has influenced land availability and food security in Tuvalu \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Furthermore, climate change may increase the vulnerability of the healthcare infrastructure, making it more difficult to address major health problems such as obesity in Tuvalu. Our previous results, aligned with the World Health Organization (WHO) results, revealed a high prevalence of obesity in Tuvalu \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For Tuvalu's inhabitants, this is the compounded result of (1) the complex relationship between shifting from nutrient-rich local foods to imported staples and highly processed products following globalization and (2) the sequelae related to climate change \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly-life exposures and experiences can have long-term effects on an individual\u0026rsquo;s obesity throughout their lifespan \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. One crucial aspect of this theory is the impact of climate factors on metabolic health. Previous studies have shown that extreme ambient temperatures during pregnancy can influence fetal development and lead to adverse birth outcomes and increased mortality risk in infancy \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. More recently, studies have increasingly focused on rainfall as a significant climate factor related to health outcomes. For instance, higher rainfall exposure increases mortality risk \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and those living in areas with greater rainfall are more likely to having obesity. Flood-related events can disrupt food availability and accessibility, whereas increased stress may alter eating behaviors and reduce physical activity, potentially contributing to higher obesity rates in affected communities \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Understanding these developmental and climate-related associations are crucial for designing targeted interventions to improve long-term health outcomes and reduce health disparities.\u003c/p\u003e \u003cp\u003eClimate factors are contributing to many climate-sensitive health outcomes in Pacific Island countries \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Among these countries, Tuvalu faces significant challenges because health facilities are located in low-lying coastal areas, making them particularly vulnerable to climate-related risks \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Climate change in Tuvalu could impact food security and increase the risk of noncommunicable diseases, such as obesity, through alterations in natural, social, economic, and built environments \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In Funafuti, the main island of Tuvalu, most flood events occurred between January and March, when rainfall was relatively high \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Higher rainfall may exacerbate the health impacts of other climate change factors and further worsen the health impacts of climate change \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, there is limited research on the link between elevated climate exposures during or prior to pregnancy and adult obesity, particularly in Tuvalu, a low-lying island nation prone to flooding and that has a high prevalence of obesity. Therefore, this study examined the associations between rainfall exposure during the prenatal and pre-pregnancy periods, and the probabilities of having obesity in adults using data from a nationwide survey population in Tuvalu. We examined rainfall exposure during the birth year (late pregnancy), the year before birth (early pregnancy), and two years before birth (pre-pregnancy), and focused on BMI as primary outcome, waist circumference and non-communicable diseases as secondary outcomes.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzed the de-identified dataset collected from\u0026nbsp;Community Behavior and Attitude Survey in Tuvalu (COMBAT). COMBAT\u0026nbsp;was conducted in Funafuti, the main island, and eight outlying islands in Tuvalu between February and May 2022. The study was part of a collaborative project on horticulture expansion between the Tuvalu government and the Taiwan International Cooperation and Development Fund (ICDF). Trained interviewers traveled to the outlying islands from Funafuti via scheduled passenger ships. The study regions were grouped on the basis of their geographical location and ship availability: (1) Nanumea, Nanumaga, and Niutao as the northern island group; (2) Vaitupu, Nukufetau, and Nui as the middle island group; and (3) Nukulaelae and Niulakita as the southern island group.\u003c/p\u003e\n\u003cp\u003eFace‒to-face interviews were conducted in households by trained local interviewers. To ensure balanced representation, interviewers visited households in the seven villages on the Fongafale islet in Funafuti because people who migrated to Funafuti live with people from the same place of origin but in different communities. On the outlying islands, households were visited in the main residential area near the port. We used convenience sampling to select one or two study participants aged over 18 years from each household.\u003c/p\u003e\n\u003cp\u003eThe interview questionnaire was designed collaboratively by the local study team and external experts. Information on the participants’ year of birth, childhood place of residence, socio-demographics, health behaviors, and self-reported medical history was collected. Informed consent was obtained from each participant, and only Tuvaluan citizens, including native Tuvaluans and immigrants, were invited to participate. The survey was designed in compliance with the guidelines of the Declaration of Helsinki, and the protocol was approved by the Tuvalu Department of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interviewers collected information on the participants’ year of birth and the island where they resided before the age of 18, which was utilized as the proxy for their birthplace. Subsequently, the participants' birth year and birthplace were linked to the available climate reanalysis data from ECMWF Reanalysis v5 (ERA5)\u0026nbsp;by the European Centre for Medium-Range Weather Forecasts (ECWMF).\u003c/p\u003e\n\u003cp\u003eERA5 is an atmospheric climate dataset that assimilates historical observations into global climate models to produce historical climate data from 1940 to the present\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e. We aggregated the monthly average rainfall with a spatial resolution of 0.25 degrees longitude times 0.25 degrees latitude.\u0026nbsp;The amount of\u0026nbsp;rainfall over each island\u0026nbsp;was\u0026nbsp;retrieved from the corresponding grid points within the reanalysis data. This study specifically focused on the anomaly of rainfall\u0026nbsp;(the deviation of the absolute value of each month from the climatology)\u0026nbsp;to remove the influence of seasonality.\u0026nbsp;We also collected corresponding climate information during the dry season.\u0026nbsp;In Tuvalu,\u0026nbsp;people\u0026nbsp;regard May to October as the dry season, when agriculture is more vulnerable to rainfall variability.\u0026nbsp;Rainfall data was also collected from four weather stations at Funafuti, Nanumea, Nui and Niulakita, with which we performed Spearman correlation analysis to assess the correspondence between the modeled data and the measurement results.\u003c/p\u003e\n\u003cp\u003eIn this study, we used the rainfall during the year of birth, one year before birth (unit: mm/day, as the surrogate for prenatal exposure) and two years before birth (as the surrogate for preconception exposure) as the primary exposure. We utilized rainfall levels from three to five years prior to birth as a negative control exposure, given that these years share similar climate patterns but fall outside the critical windows of susceptibility \u003csup\u003e17\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the interviews, trained local research staff measured the participants' height in centimeters (to the nearest 0.1 cm) via a tape measure and recorded weight via an electronic scale (Samlux SYES-301).\u0026nbsp;The scale had a maximum weight measurement of 180 kilograms, and weight was recorded to one decimal place in kilograms. The research team calculated the body mass index (BMI) for each individual using the collected height and weight. BMI was computed by dividing the weight in kilograms by height squared in meters. We defined obesity as BMI \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e and severe obesity as BMI \u0026gt;40 kg/m\u003csup\u003e2\u003c/sup\u003e following the WHO criteria\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e.\u0026nbsp;We also collected information on waist circumference as an indicator of abdominal obesity, which was classified as high waist circumference status for males ≥90 cm and females ≥80 cm. Physician-diagnosed, self-reported hypertension, diabetes and dyslipidemia data was also obtained from participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrained interviewers conducted the survey and collected information on various covariates. The selection of these covariates was based on our understanding and\u0026nbsp;previous studies\u0026nbsp;of obesity-related outcomes in Tuvalu. The covariates included sex (male or female), age (grouped into ten-year intervals), education level (high school or higher), noncommunicable disease diagnosis (hypertension, diabetes, or dyslipidemia), income (below or equal to 200 Australian dollars or greater than), and smoking status (yes or no).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the study population and the distribution of health-related factors were described using summary statistics. Categorical variables are presented as frequencies and percentages, whereas continuous variables are reported as the means and standard deviations. The primary analysis examined the associations between average rainfall from the year of birth to five years before birth and BMI, obesity, and severe obesity using generalized linear regressions in both unadjusted and adjusted models, as shown in Table 3. Using the same models, we also examined average dry season rainfall (April-September) as the exposure, which considered rainfall variations within a year. A subgroup analysis for males and females determined whether the associations differed between sexes. The same models were used to examine the associations between rainfall and the secondary outcomes. Statistical significance was determined via two-sided \u003cem\u003ep-\u003c/em\u003evalues, with a threshold of less than 0.05 considered statistically significant. The above analyses were performed using R software (version 4.0.4). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDescriptive characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic characteristics of the 892 adults in our study were shown in Table 1: the mean age was 41.2 years, with 479 participants (53.7%) being female. The average monthly income was $178.08, with significant variation, which is consistent with our prior findings and governmental survey\u0026nbsp;\u003csup\u003e1,6,19,20\u003c/sup\u003e. With respect to education level, 37.3% of the participants had completed elementary school, 47.3% had finished high school, and 12.9% held a college degree or higher. Additionally, 312 participants (35.1%) were smokers.\u003c/p\u003e\n\u003cp\u003eWhile most participants were interviewed in Funafuti,\u0026nbsp;324\u0026nbsp;(36.3%)\u0026nbsp;participants were\u0026nbsp;interviewed\u0026nbsp;on\u0026nbsp;the\u0026nbsp;outlying islands. In terms of place of residence prior to age 18, most of the participants were from Funafuti (466 participants, 52.2%), followed by Vaitupu (90 participants, 10.1%), Nanumea (76 participants, 8.5%) and other outlying islands. This distribution reflects the population concentration in Funafuti, the capital and most urbanized island. Moreover, more dispersed populations live on smaller, more rural outlying islands.\u003c/p\u003e\n\u003cp\u003eHealth-related parameters showed the average BMI was 34.55 kg/m\u003csup\u003e2\u003c/sup\u003e in the study population, with 633 (71.0%) individuals having obesity and 184 (20.7%) individuals having severe obesity. The mean waist circumference was 102.89 cm (male: 103.00 cm, and female: 102.80 cm), with 742 (83.2%, 432 females and 310 males) participants having a high waist circumference. The prevalence rates of hypertension, diabetes and dyslipidemia were 17.5%, 11.0% and 3.1%, respectively (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed consistent rainfall patterns across the nine islands, with lower rainfall from May to October and higher rainfall during the remainder of the year. However, the amount of rainfall varied by island group, showing an increasing trend from north to south. Participants from the northern islands, such as Nanumea, Namumaga, Niutao, and Nui, experienced lower rainfall, whereas participants from southern islands, such as Funafuti, Nukulaelae, and Niulakita, experienced greater rainfall (Figure 1). The correlation coefficients between the reanalysis data and weather station measurements ranged between 0.65 and 0.79 across the four islands with weather stations (Funafuti, Nanumea, Nui and Niulakita), suggesting strong consistency between our models and direct measurements in Tuvalu (Supplemental Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between climate factors and obesity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified associations between elevated BMI and rainfall during the year before birth (unadjusted\u0026nbsp;estimate: 0.45 kg/m\u003csup\u003e2\u003c/sup\u003e per 1 mm/day increase in annual rainfall, 95% CI: 0.14 to 0.76, \u003cem\u003ep=\u003c/em\u003e0.005; adjusted estimate: 0.35\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.03 to 0.67, \u003cem\u003ep=\u003c/em\u003e0.031) and two years before birth (unadjusted\u0026nbsp;estimate:\u0026nbsp;0.64\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.33 to 0.95, \u003cem\u003ep\u0026lt;\u003c/em\u003e0.001; adjusted estimate: 0.52\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.20 to 0.83, \u003cem\u003ep=\u003c/em\u003e0.001). Although we did not observe an association during the year of birth, the association was observed when\u0026nbsp;rainfall was averaged between the one- and two-years preceding birth. Similarly, we observed an association between obesity and\u0026nbsp;rainfall during the year before birth (unadjusted odds ratio [OR]\u0026nbsp;per 1 mm/day increase in annual precipitation:\u0026nbsp;1.12, 95% CI: 1.03 to 1.22, \u003cem\u003ep=\u003c/em\u003e0.009; adjusted OR:\u0026nbsp;1.10, 95% CI: 1.01 to 1.21, \u003cem\u003ep=\u003c/em\u003e0.034) and two years before birth (unadjusted OR:\u0026nbsp;1.19, 95% CI: 1.10 to 1.30, \u003cem\u003ep\u0026lt;\u003c/em\u003e0.001; adjusted OR:\u0026nbsp;1.16, 95% CI: 1.06 to 1.27, \u003cem\u003ep=\u003c/em\u003e0.001). This association was less evident for severe obesity and rainfall (unadjusted OR\u0026nbsp;per 1 mm/day increase in annual rainfall two years before birth: 1.11, 95% CI:\u0026nbsp;1.01 to 1.22, \u003cem\u003ep=\u003c/em\u003e0.042; adjusted\u0026nbsp;OR: 1.10, 95% CI: 0.99 to 1.22, \u003cem\u003ep=\u003c/em\u003e0.072; Table 3).\u003c/p\u003e\n\u003cp\u003eSimilar results were observed for rainfall during the dry season. Each centimeter of increased rainfall during the year before birth was associated with a greater odd of obesity in the study population (unadjusted OR: 1.08, 95% CI: 0.99 to 1.18, \u003cem\u003ep=\u003c/em\u003e0.099; adjusted OR:\u0026nbsp;1.11, 95% CI: 1.01 to 1.23, \u003cem\u003ep=\u003c/em\u003e0.039). Moreover, increased rainfall during the two years before birth was associated with a higher BMI during adulthood (unadjusted\u0026nbsp;estimate: 0.45 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.14 to 0.77, \u003cem\u003ep=\u003c/em\u003e0.005; adjusted estimate: 0.41\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.07 to 0.74, \u003cem\u003ep=\u003c/em\u003e0.017). Furthermore,\u0026nbsp;increased average rainfall during the dry season between 0-2 years before birth was associated with increased BMI\u0026nbsp;during adulthood (unadjusted\u0026nbsp;estimate: 0.49 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.00 to 0.97, \u003cem\u003ep=\u003c/em\u003e0.049; adjusted estimate: 0.62\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.08 to 1.16, \u003cem\u003ep=\u003c/em\u003e0.025; Table 4).\u003c/p\u003e\n\u003cp\u003eWe used rainfall exposure during the period 3-5 years before birth as a negative control exposure. No significant associations were detected between rainfall exposure during this period and increased BMI or between elevated prevalence of obesity and severe obesity (Supplemental Tables 1 and 2). The association between prenatal rainfall and BMI was stronger in male participants, specifically for the rainfall in the one year and two years before birth (for the year before birth: unadjusted estimate: 0.65 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.23 to 1.08, \u003cem\u003ep=\u003c/em\u003e0.003; adjusted estimate: 0.55 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.13 to 0.98, \u003cem\u003ep=\u003c/em\u003e0.011; for two years before birth: unadjusted estimate: 0.70 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.29 to 1.11, \u003cem\u003ep=\u003c/em\u003e0.001; adjusted estimate: 0.56 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.15 to 0.97, \u003cem\u003ep=\u003c/em\u003e0.008). Similarly, we observed an association between increased odds of obesity and rainfall in the one year and two years before birth among male participants (Supplemental Table 3). In contrast, the associations between rainfall exposure before birth and increased BMI or elevated risk of obesity and severe obesity were fewer in female participants (Supplemental Table 4). Average rainfall exposure in the year before birth was associated with increased waist circumference, but was not significantly associated with high waist circumference, hypertension, or diabetes (Supplemental Table 5).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study revealed that higher average rainfall during the year and two years prior to birth were associated with an increased likelihood of having obesity and severe obesity in Tuvalu. Similar associations were observed for rainfall during the dry season and the two years prior to birth. These findings suggest that environmental exposure before conception and during the prenatal period may influence obesity risk in adulthood. An increasing number of studies have suggested a potential link between rainfall before birth and obesity, highlighting the importance of understanding the underlying mechanisms \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. First, heavy rainfall has been associated with increased sedentary time, an indicator of lower physical activity, as suggested by systematic reviews on weather and physical activity in children and adolescents \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Moreover, reductions in physical activity are well-established risk factors for obesity \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Second, changes in rainfall during this period may alter the living environment and food availability, influencing maternal nutritional status. Increased rainfall could increase crop yields and food resources, potentially altering maternal nutrition and contributing to increased birth weight \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Meanwhile, crop quality decreases after heavy rainfall, which also increased the obesity risk, potentially through epigenetic changes in germ cells \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This has been evidenced by a study among young children in Burkina Faso, which showed associations between rainfall variability, dietary patterns and increased weight-for-height value at 7\u0026ndash;60 months of age \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Finally, newborns and infants exposed to relatively high amounts of rainfall may face increased infection and mortality risks, prompting energy-conserving adaptations \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. While these changes support early survival, they may increase the risk of metabolic syndrome, including obesity later in life, as suggested by Barker\u0026rsquo;s hypothesis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe observed associations between rainfall and obesity situated in the year preceding birth reflects prenatal exposure, and two years before birth reflects periconceptional exposure. At the same time, the association was not observed for the year of birth, likely due to uncertainty in exposure assignment. Exposure during the birth year may partly reflect postnatal exposure, particularly for individuals born early in the year. These findings are aligned with Barker\u0026rsquo;s hypothesis, also known as the developmental origins of health and disease theory \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Adverse prenatal environmental conditions, such as changes in crop quality related to climate-induced rainfall variations, could affect maternal nutrition and fetal development \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Animal studies have shown that prenatal exposure to nutritionally imbalanced diets leads to metabolic disturbances and increased obesity risk in offspring. For example, rodent models exposed to altered diets during pregnancy are more prone to obesity and metabolic disorders in their offspring \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Climate change would impact the availability, access and utilization of food, which increased food security and related health risks \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Human studies also support this connection, showing that men under prenatal exposure to Dutch famine (Hunger Winter) had 1.3-fold increased risk of overweight and obesity during early adulthood \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Other studies also showed that children born under these conditions exhibit increased risks of obesity and chronic diseases in adulthood \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Birth weight may serve as a critical link between prenatal rainfall exposure and adult obesity, as higher birth weight is associated with an increased risk of obesity later in life \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Although birth weight data were not available in this study, future studies are needed to illustrate the role of birth weight on the risk of overweight and obesity later in life \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, the study is semi-ecological in design, and the conclusions may not infer individual attributions to the probability of having obesity. Second, there could be measurement error for the exposure of interest. The temporal resolution of the data is limited to yearly scales, which may introduce a nondifferential misclassification, biasing the results toward the null. This bias is likely minimal because the sizes of islands in Tuvalu are small and there is limited regional variability within each island. Also, the primary residence before age 18 may differ from the birthplace, although it is not common in Tuvalu to move to other islands before adulthood. Finally, there could be other unmeasured mediators that would partially explain the associations of our observations. They could be targets of future studies but would not bias the associations in this study because they were in the causal path that occurred after the exposure of interest.\u003c/p\u003e \u003cp\u003eMeanwhile, there are multiple strengths of this study. First, while only four of the regions in Tuvalu have direct observations of rainfall data, we used ERA5 reanalysis data to reconstruct historical records of rainfall anomalies over each island \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.The strong correlation between the weather station observations and reanalysis data suggests the validity of our approach and the potential for future applications. Second, we were able to include participants from all islands in Tuvalu, which increased the generalizability of the findings by offering a broader representation of the population and environmental conditions. Third, we assessed exposure during critical periods of development and obesity, a prevalent health condition in Tuvalu, where people face the threat of climate change and where the risk factors for obesity remain understudied.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, this study revealed that higher rainfall exposure before birth was associated with increased odds of obesity and severe obesity in adulthood in Tuvalu. These findings align with the developmental origins of health and disease theory, highlighting the potential impact of early-life environmental exposure on long-term health outcomes, including obesity. Further research is needed to explore rainfall trends in the Pacific region and examine heterogeneity among island populations to better understand the specific pathways linking this environmental factor and obesity. These findings could inform public health strategies aimed at mitigating the effects of climate-related exposures on adult health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBody mass index (BMI); Confidence interval (CI); COMmunity-based Behavior and Attitude (COMBAT); developmental origins of health and disease (DOHaD); ECMWF Reanalysis v5 (ERA5); European Centre for Medium-Range Weather Forecasts (ECWMF); International Cooperation and Development Fund (ICDF); odds ratio (OR).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study is approved by Tuvalu ministry of health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declared no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe community behavior and attitude survey was funded through a collaborative project between the Taiwan ICDF (Fruit and Vegetable Production and Nutrition Enhancement Project) and the Tuvalu government. The funding source had no role in the design, conduct, analysis, or interpretation of the study, and no influence on the content of the manuscript or the decision to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: PJL, CFW and YHL initiated the study, and the study data collection is supported by CWS, ST, VS, PPM, and MT. LT curated the climate data and conducted formal analysis with CFW. LT and CFW interpreted the data and wrote the manuscript with PJL, YTH, YHL, MSH, JFLG, and IL. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors want to acknowledge the survey support from the Taiwan ICDF and Tuvalu government from their Fruit and Vegetable Production and Nutrition Enhancement Project. We sincerely appreciate every participant\u0026rsquo;s input and feedback on the study, as well as the insightful discussions with Drs. Nile Nair and Linh Bui at the Harvard TH Chan School of Public Health regarding Nutrition and Planetary Health. JFL-G is a Margarita Salas Fellow (Universidad de Castilla-La Mancha \u0026ndash; 2021-MS-20563). Maria Soledad Hershey receives ERC training-grant support (T42 OH008416).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLin P-J, Hershey MS, Lee T-L (Irene), Shih C-W, Tausi S, Sosene V \u003cem\u003eet al.\u003c/em\u003e Temporal trends of food consumption patterns in Tuvalu under the context of climate change: COMmunity-based Behavior and Attitude survey in Tuvalu (COMBAT) since 2020. \u003cem\u003eNutrition\u003c/em\u003e 2024; \u003cstrong\u003e125\u003c/strong\u003e: 112488.\u003c/li\u003e\n\u003cli\u003eMcIver L, Kim R, Woodward A, Hales S, Spickett J, Katscherian D \u003cem\u003eet al.\u003c/em\u003e Health Impacts of Climate Change in Pacific Island Countries: A Regional Assessment of Vulnerabilities and Adaptation Priorities. \u003cem\u003eEnviron Health Perspect\u003c/em\u003e 2016; \u003cstrong\u003e124\u003c/strong\u003e: 1707\u0026ndash;1714.\u003c/li\u003e\n\u003cli\u003eAbarca-G\u0026oacute;mez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C \u003cem\u003eet al.\u003c/em\u003e Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128\u0026middot;9 million children, adolescents, and adults. \u003cem\u003eThe Lancet\u003c/em\u003e 2017; \u003cstrong\u003e390\u003c/strong\u003e: 2627\u0026ndash;2642.\u003c/li\u003e\n\u003cli\u003eFisher PB. 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Climate, Birth Weight, and Agricultural Livelihoods in Kenya and Mali. \u003cem\u003eAm J Public Health\u003c/em\u003e 2018; \u003cstrong\u003e108\u003c/strong\u003e: S144\u0026ndash;S150.\u003c/li\u003e\n\u003cli\u003eLumey LH, Ekamper P, Bijwaard G, Conti G, Van Poppel F. Overweight and obesity at age 19 after pre-natal famine exposure. \u003cem\u003eInt J Obes\u003c/em\u003e 2021; \u003cstrong\u003e45\u003c/strong\u003e: 1668\u0026ndash;1676.\u003c/li\u003e\n\u003cli\u003eSchellong K, Schulz S, Harder T, Plagemann A. Birth Weight and Long-Term Overweight Risk: Systematic Review and a Meta-Analysis Including 643,902 Persons from 66 Studies and 26 Countries Globally. \u003cem\u003ePLoS ONE\u003c/em\u003e 2012; \u003cstrong\u003e7\u003c/strong\u003e: e47776.\u003c/li\u003e\n\u003cli\u003eDu W, FitzGerald GJ, Clark M, Hou X-Y. Health Impacts of Floods. \u003cem\u003ePrehospital Disaster Med\u003c/em\u003e 2010; \u003cstrong\u003e25\u003c/strong\u003e: 265\u0026ndash;272.\u003c/li\u003e\n\u003cli\u003eHadley K, Wheat S, Rogers HH, Balakumar A, Gonzales-Pacheco D, Davis SS \u003cem\u003eet al.\u003c/em\u003e Mechanisms underlying food insecurity in the aftermath of climate-related shocks: a systematic review. \u003cem\u003eLancet Planet Health\u003c/em\u003e 2023; \u003cstrong\u003e7\u003c/strong\u003e: e242\u0026ndash;e250.\u003c/li\u003e\n\u003cli\u003eNagata JM, Hampshire K, Epstein A, Lin F, Zakaras J, Murnane P \u003cem\u003eet al.\u003c/em\u003e Analysis of Heavy Rainfall in Sub-Saharan Africa and HIV Transmission Risk, HIV Prevalence, and Sexually Transmitted Infections, 2005-2017. \u003cem\u003eJAMA Netw Open\u003c/em\u003e 2022; \u003cstrong\u003e5\u003c/strong\u003e: e2230282.\u003c/li\u003e\n\u003cli\u003eGluckman PD, Hanson MA, Buklijas T. A conceptual framework for the developmental origins of health and disease. \u003cem\u003eJ Dev Orig Health Dis\u003c/em\u003e 2010; \u003cstrong\u003e1\u003c/strong\u003e: 6\u0026ndash;18.\u003c/li\u003e\n\u003cli\u003eTaylor PD, Poston L. Developmental programming of obesity in mammals. \u003cem\u003eExp Physiol\u003c/em\u003e 2007; \u003cstrong\u003e92\u003c/strong\u003e: 287\u0026ndash;298.\u003c/li\u003e\n\u003cli\u003eShi J, Guo Q, Fang H, Cheng X, Ju L, Wei X \u003cem\u003eet al.\u003c/em\u003e The Relationship between Birth Weight and the Risk of Overweight and Obesity among Chinese Children and Adolescents Aged 7\u0026ndash;17 Years. \u003cem\u003eNutrients\u003c/em\u003e 2024; \u003cstrong\u003e16\u003c/strong\u003e: 715.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDemographic characteristics of the study population (In n [%] or mean [SD])\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy population (n=892)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBaseline characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eAge (years, mean [SD])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e41.20 (16.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFemale, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e479 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eIncome (AUD, mean [SD])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e178.08 (487.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEducation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eBelow elementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e22 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e332 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e421 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e115 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eLiving in outlying islands (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e324 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eResidence before 18 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFunafuti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e466 (52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNanumaga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e48 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNanumea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e76 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNiulakita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e5 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNiutao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e65 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNui\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e59 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNukufetau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e57 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNukulaelae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e26 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eVaitupu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e90 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e258 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eOccasional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e54 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e577 (64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDistribution of health-related factors in the study population (In n [%] or mean [SD])\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy population (n=892)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eBody height (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e169.44 (10.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eBody weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e98.48 (21.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e34.55 (8.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eBMI trajectories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eOverweight (BMI: 25-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e181 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eObesity: Class I (BMI: 30-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e266 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eObesity: Class II (BMI: 35-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e183 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eObesity: Class III (BMI: \u0026gt;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e184 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e102.89 (16.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eHigh waist circumference (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e742 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e156 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e98 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40px;\"\u003e\n \u003cp\u003e28 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Multivariable regressions between precipitation exposure before birth on BMI, obesity and severe obesity prevalence, shown in estimate (kg) or odds ratio, 95% confidence interval and p-value \u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFully adjusted model \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(a) BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.08 (-0.25 to 0.41, p=0.642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.03 (-0.30 to 0.36, p=0.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.45 (0.14 to 0.76, p=0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.35 (0.03 to 0.67, p=0.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.64 (0.33 to 0.94, p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.52 (0.20 to 0.83, p=0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.09 (0.56 to 1.62, p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.90 (0.35 to 1.45, p=0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(b) Obesity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.94 (0.85-1.02, p=0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.93 (0.84-1.02, p=0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.12 (1.03-1.22, p=0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.10 (1.01-1.21, p=0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.19 (1.10-1.30, p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.16 (1.06-1.27, p=0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.24 (1.07-1.43, p=0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.20 (1.02-1.40, p=0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(c) Severe obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.00 (0.91-1.11, p=0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.98 (0.89-1.09, p=0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.07 (0.98-1.19, p=0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.06 (0.96-1.17, p=0.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.11 (1.00-1.22, p=0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.10 (0.99-1.22, p=0.072)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.17 (0.99-1.40, p=0.070)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.14 (0.95-1.36, p=0.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003ea. Obesity was defined as BMI \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e, and severe obesity as BMI \u0026gt;40 kg/m\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003eb. Adjusted for gender (male or female), age (grouped in ten years), education level (high school or above), income (\u0026gt;200 AUD or not), and smoking.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAbbreviation: AUD, Australian dollar; BMI, body mass index; NCD, non-communicable disease.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Multivariable regressions between precipitation exposure \u003cstrong\u003eduring dry season\u003c/strong\u003e before birth on BMI, obesity and severe obesity prevalence, shown in estimate (kg) or odds ratio, 95% confidence interval and p-value \u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFully adjusted model \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(a) BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.06 (-0.37 to 0.26, p=0.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.02 (-0.30 to 0.35, p=0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.24 (-0.08 to 0.56, p=0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.29 (-0.05 to 0.63, p=0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.45 (0.14 to 0.77, p=0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.41 (0.07 to 0.74, p=0.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.49 (0.00 to 0.97, p=0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.62 (0.08 to 1.16, p=0.025)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(b) Obesity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.92 (0.85-1.00, p=0.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0.94 (0.86-1.03, p=0.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.08 (0.99-1.18, p=0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.11 (1.01-1.23, p=0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.06 (0.97-1.16, p=0.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.04 (0.95-1.15, p=0.409)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.04 (0.91-1.18, p=0.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.07 (0.92-1.25, p=0.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(c) Severe obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eYear of birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.99 (0.90-1.09, p=0.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.00 (0.91-1.11, p=0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.00 (0.91-1.11, p=0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.01 (0.91-1.12, p=0.793)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2 years before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.07 (0.97-1.18, p=0.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.08 (0.97-1.19, p=0.168)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eAverage: 0-2 year before birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e1.05 (0.90-1.22, p=0.533)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e1.08 (0.91-1.27, p=0.388)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003ea. Obesity was defined as BMI \u0026gt;30 kg/m\u003csup\u003e2\u003c/sup\u003e, and severe obesity as BMI \u0026gt;40 kg/m\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003eb. Adjusted for gender (male or female), age (grouped in ten years), education level (high school or above), NCD diagnosis (having hypertension, diabetes or dyslipidemia), income (\u0026gt;200 AUD or not), and smoking.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAbbreviation: AUD, Australian dollar; BMI, body mass index; NCD, non-communicable disease.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obesity, Developmental Programming, Climate Change, Pacific Islands","lastPublishedDoi":"10.21203/rs.3.rs-5952290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5952290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTuvalu has one of the highest obesity prevalence rates globally, and is a Pacific Island nation facing significant climate change challenges. Altered rainfall pattern, as a part of climate change, may influence obesity risk during the critical developmental periods. This study investigated the associations between rainfall exposure during prenatal, pre-pregnancy periods and adult obesity in Tuvalu. A nationwide survey was conducted between February and May 2022, which included 892 adults from Tuvalu. Rainfall data was obtained from ECMWF Reanalysis v5 based on participants\u0026rsquo; birth year and birthplace. Rainfall exposure during the first year of birth, the year before birth, and two years before birth was analyzed, and rainfall exposure between three to five years before birth were included as negative control periods. Obesity and severe obesity were defined based on body mass index (BMI) upon the survey, according to the World Health Organization criteria. The results showed association between higher rainfall before birth increased BMI and greater odds of adulthood obesity. These associations were more pronounced among male participants. No significant associations were observed for rainfall three to five years before birth. In conclusion, prenatal exposure to higher rainfall during the year and two years before birth are associated with increased obesity risk in adulthood, reflecting prenatal environmental influences on developmental periods. These findings emphasize the importance of understanding climate-related health exposures and the need for targeted interventions in climate change-vulnerable populations. Further research should explore heterogeneity across Pacific Island nations and the mechanisms linking rainfall, birth weight, and obesity.\u003c/p\u003e","manuscriptTitle":"Association between Prenatal, Pre-pregnancy Rainfall and Adult Obesity: Findings from the Community Behavior and Attitude Survey in Tuvalu (COMBAT)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 10:29:27","doi":"10.21203/rs.3.rs-5952290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f86c633d-3a19-4a86-a922-2b9f40cf930e","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43875123,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":43875124,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-03T16:27:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 10:29:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5952290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5952290","identity":"rs-5952290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}