Association of birth weight with general obesity, central obesity, and hepatic steatosis in a nationally representative sample of US adolescents: evidence from NHANES 1999-2020

Association of birth weight with general obesity, central obesity, and hepatic steatosis in a nationally representative sample of US adolescents: evidence from NHANES 1999-2020 | 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 of birth weight with general obesity, central obesity, and hepatic steatosis in a nationally representative sample of US adolescents: evidence from NHANES 1999-2020 jinjin He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6238043/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 Background Birth weight (BW) may influence subsequent risk of obesity and hepatic steatosis; however, the conclusions are controversial and lack exploration in US adolescents. We aimed to explore the association of BW (including low BW [LBW], normal BW [NBW], and high BW [HBW]) with body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and fatty liver index (FLI), as well as general obesity, central obesity, and hepatic steatosis, in adolescents using NHANES 1999–2020. Methods BW was obtained from participants' self-reports. Obesity and hepatic steatosis were diagnosed based on their respective specific cutoff values in adolescents. Multivariate linear regression and logistic regression analyses were used to explore these associations and calculate β and odds ratios (OR). Results A total of 6867 adolescent participants were enrolled. After adjusting for all confounders, BW was positively associated with BMI, WC, WHtR, and FLI (β of 0.639, 1.872, 0.005, and 2.128, respectively). Compared to NBW, HBW was associated with significantly increased BMI, WC, WHtR, and FLI (β of 1.205, 3.387, 0.012, and 4.745, respectively), whereas LBW was not. Similarly, compared to NBW, HBW was associated with significantly increased odds of general obesity, central obesity (as defined by WC/WHtR, respectively), and hepatic steatosis (OR 2.629, 1.713, 1.618, and 1.960, respectively). However, LBW was not significantly associated with obesity and steatosis. Race/ethnicity partially influenced these associations. Conclusions HBW, but not LBW, was associated with increased prevalence of general obesity, central obesity, and hepatic steatosis among U.S. adolescents. These findings underscore that adolescents with HBW are at risk for obesity and steatosis and may require early screening and intervention, especially among other Hispanic ethnic groups. Health sciences/Risk factors Health sciences/Health care/Paediatrics birth weight general obesity central obesity hepatic steatosis NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Birth weight (BW) is one of the most important clinical indicators in neonatology, and it is an important determinant of the health status of newborns. Low birth weight (LBW) is associated with an increased risk of neonatal mortality; moreover, a large body of well-established epidemiologic research suggests that LBW is associated with poor health outcomes in later childhood, adolescence, and/or adulthood[ 1 – 3 ]. In addition, accumulating clinical evidence also suggests that high birth weight (HBW) may predict certain health trajectories later in life, such as increased risk of bone tumors, obesity, and autonomic nervous system disorders[ 4 – 6 ]. In addition, the association of HBW or LBW with subsequent health outcomes in life may shift with age[ 7 – 9 ], emphasizing that the impact of abnormal BW on health outcomes requires an age-specific perspective. Obesity is one of the major health problems among adolescents. The prevalence of obesity among adolescents has now reached epidemic proportions, with the prevalence of severe obesity increasing at least fourfold over the past few decades[ 10 ]. Adolescent obesity is associated with the development of multiple short- and long-term complications, such as psychosocial stress and obesity-related metabolic disorders and cardiovascular disease[ 11 , 12 ]. In addition, 80% of obesity in adolescence can be maintained into adulthood, leading to increased metabolic and cardiovascular risk and higher mortality in adulthood[ 13 , 14 ]. Early identification of adolescents at risk for obesity and timely intervention can help reduce subsequent adverse obesity-related health outcomes. A previously published systematic review and meta-analysis demonstrated that HBW was associated with a significantly increased risk of obesity in pre-school children, school-aged children, and adolescents compared to control populations, whereas LBW was associated with a decreased risk of obesity[ 15 ]. More recent observational clinical studies have suggested that HBW may be associated with an increased risk of general obesity and abdominal obesity in children and adolescents; however, the association of LBW with the prevalence of obesity in pediatric or adolescent populations is controversial[ 16 – 21 ]. Several limitations were present in the previous evidence. First, there was significant heterogeneity in the definitions of LBW, NBW, and HBW among these studies, and the lack of standardized definitions may have hindered conclusions. Second, most of the observational studies were from Asian cohorts and there was a lack of exploration in other countries/regions such as the United States. In addition, most of the studies included children and adolescent populations, and studies focusing on adolescent samples were lacking. Liver steatosis is another emerging health concern in the adolescent population[ 22 ]. The prevalence of obesity-associated nonalcoholic fatty liver disease (NAFLD) in children and adolescents is estimated to be 36.1%[ 23 ]. Interestingly, some studies have shown that LBW and/or HBW compared to NBW are associated with increased prevalence of NAFLD or hepatic steatosis in adults, however controversial findings exist[ 24 – 27 ]. Some sparse observational evidence suggested an association of LBW and/or HBW with hepatic steatosis in children and adolescents, with similarly inconsistent findings[ 28 – 30 ]. Whether LBW/HBW is associated with the prevalence of hepatic steatosis in US adolescents remains largely unexplored. In this study, we utilized a nationally representative sample of adolescents from the National Health and Nutrition Examination Survey (NHANES) to explore the association of LBW and HBW with the prevalence of general obesity, central obesity, and hepatic steatosis. Exploring these associations could help reveal whether abnormal BW contributes to early detection, risk stratification, and timely intervention for obesity and hepatic steatosis among adolescents. 2. METHODS Study design and population NHANES is a large, national, population-based, multiracial series of cross-sectional surveys. Since 1999, NHANES has been conducted in biennial cycles, sampling approximately 5,000 cases per year to assess the health and nutritional status of non-institutionalized children and adults in the U.S. NHANES is a major program of the National Center for Health Statistics (NCHS), and all cycles were approved by the NCHS Ethics Review Board and written informed consent was obtained from participants. We included all 9,812 adolescent participants aged 12–15 years from the NHANES 1999–2020 March. Participants with missing BMI data (n = 461), WC data (n = 83), FLI data (n = 802), BW information (n = 470), and covariate information (n = 1129) were excluded. A total of 6867 eligible adolescent participants were included in further analyses (Fig. 1 ). BW Assessment BW data were obtained from the NHANES Early Childhood Questionnaire, which was available to children and adolescents ≤ 15 years of age in the US[ 31 – 33 ]. Participants were asked, “How much did you weigh at birth?” and self-reported their weight at birth (pounds or ounces). This question was asked by trained interviewers to home-based participants through the computer-assisted personal interview system[ 32 ]. We converted participants' self-reported BW (pounds or ounces) to grams and categorized them according to World Health Organization criteria as LBW (BW 4000 g)[ 31 , 32 ]. Assessment of outcomes Study outcomes included body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and fatty liver index (FLI). BMI was used to define overall obesity. WC and WHtR were used to define central obesity. FLI was used to define hepatic steatosis. The FLI was calculated using the formula FLI = (e 0.953*ln (TG) + 0.139*BMI + 0.718*ln (GGT) + 0.053*WC − 15.745 ) / (1 + e 0.953*ln (TG) + 0.139*BMI + 0.718*ln (GGT) + 0.053*WC − 15.745 ) × 100[ 34 ]. These markers were first explored as continuous variables. Second, they were used as categorical variables using age- and sex-specific cutoffs for diagnosis of the disease of interest. BMI-defined overall obesity was defined as an individual's BMI ≥ the age- and sex-specific 95% percentile[ 35 ]. WC > 75th percentile for age and sex suggested central obesity[ 36 ]. WHtR ≥ 0.5 indicated central obesity[ 37 ]. FLI ≥ 60 suggested the presence of hepatic steatosis[ 38 ]. Covariates Several important covariates including age, gender, race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, or other races), household income-poverty ratio (PIR), mother's age when born, maternal smoking history during pregnancy, total daily dietary energy intake, serum triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were included. Mother's age when born and maternal smoking history during pregnancy was obtained from self-reports on the Early Childhood Questionnaire[ 33 ]. Daily energy intake was derived from dietary interview data, which was calculated from the USDA's Food and Nutrient Database for Dietary Studies. Serum lipid/lipoprotein data were obtained from NHANES laboratory tests. Statistical analysis Data processing and statistical analyses were performed using R (version 4.2.3) and EmpowerStats, and a two-sided P value of less than 0.05 reported statistical significance. All analyses were properly weighted according to the NHANES analytic guidelines to consider the sophisticated study design of NHANES. In the baseline analyses, we conducted baseline analyses based on the BW status of the included adolescent population. Data for continuous variables were reported as mean ± standard error and analyzed using weighted ANOVA, and categorical variables were reported as number (percentage) and analyzed using weighted chi-square tests. Multivariate linear regression analyses were performed to explore the association of LBW and HBW (compared with NBW) with BMI, WC, WHtR, and FLI among US adolescents aged 12–15 years and to calculate β and 95% confidence intervals (95% CIs). For BMI, WC, and WHtR, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For FLI, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake. We used multivariate logistic regression analyses to explore the association of LBW/HBW with general obesity as defined by BMI, central obesity as defined by WC/WHtR, and hepatic steatosis as defined by FLI and to calculate odds ratio (OR) and 95% CI. These logistic regression models were consistent with the adjustment variables used in the respective linear regressions. Restricted cubic spline (RCS) was applied to explore nonlinear associations between BW (continuous) and BMI, WC, WHtR, and FLI in adolescents and to pick suitable knots for smoothed curve fitting. Stratified analyses were conducted to explore whether these associations (between BW and BMI, WC, WHtR, and FLI) differed within subgroups (gender and race/ethnicity) and to identify effect modifiers through interaction analyses. We also explored whether the association of BW with obesity and hepatic steatosis remained stable across subgroups. 3. RESULTS Baseline characteristics A total of 6867 adolescent participants with a mean age of 13.508 years were enrolled. There were 881, 5342, and 644 participants in LBW, NBW, and HBW, respectively. As BW increased, participants had higher PIR, energy intake, and mother's age when born, lower HDL-C, and were more likely to be male, non-Hispanic White, and have no history of maternal smoking during pregnancy. Participants with HBW had higher BMI, WC, WHtR, and FLI (continuous and categorical variables) compared to participants with LBW (Table 1 ). Table 1 Baseline analysis of adolescent participants according to BW status, NHANES 1999–2020. Variables Total (n = 6867) LBW(n = 881) NBW(n = 5342) HBW(n = 644) P value Age, year 13.508 ± 0.017 13.522 ± 0.050 13.501 ± 0.019 13.550 ± 0.063 0.702 PIR 2.591 ± 0.038 2.267 ± 0.070 2.588 ± 0.040 2.972 ± 0.098 < 0.0001 Energy intake, kcal/day 2142.944 ± 18.709 2033.530 ± 44.434 2146.053 ± 20.012 2240.743 ± 63.115 0.014 mother's age when born 26.671 ± 0.143 26.223 ± 0.308 26.529 ± 0.155 28.296 ± 0.341 < 0.0001 TG, mmol/L 1.040 ± 0.013 1.053 ± 0.037 1.040 ± 0.013 1.031 ± 0.027 0.893 TC, mmol/L 4.072 ± 0.014 4.094 ± 0.042 4.077 ± 0.015 4.005 ± 0.044 0.229 HDL, mmol/L 1.324 ± 0.005 1.337 ± 0.014 1.328 ± 0.006 1.283 ± 0.015 0.013 BMI 22.577 ± 0.100 22.711 ± 0.285 22.447 ± 0.110 23.460 ± 0.292 0.002 WC, cm 78.804 ± 0.266 78.645 ± 0.778 78.443 ± 0.290 81.846 ± 0.796 < 0.001 WhtR 0.484 ± 0.002 0.488 ± 0.004 0.482 ± 0.002 0.493 ± 0.004 0.023 FLI 15.931 ± 0.418 17.033 ± 1.269 15.284 ± 0.442 19.823 ± 1.373 0.003 High BMI < 0.0001 No 6527(96.084) 844(95.803) 5099(96.585) 584(92.431) Yes 340(3.916) 37(4.197) 243(3.415) 60(7.569) High WC 0.005 No 5151(77.170) 700(77.272) 4022(78.014) 429(70.375) Yes 1716(22.830) 181(22.728) 1320(21.986) 215(29.625) High WHTR 0.032 No 4377(67.016) 597(66.626) 3398(67.414) 382(64.301) Yes 2490(32.984) 284(33.374) 1944(32.586) 262(35.699) High FLI < 0.001 No 6209(91.989) 812(90.776) 4856(92.771) 541(87.150) Yes 658(8.011) 69(9.224) 486(7.229) 103(12.850) Sex < 0.0001 male 3505(51.884) 403(48.882) 2685(50.269) 417(68.023) female 3362(48.116) 478(51.118) 2657(49.731) 227(31.977) Race/ethnicity < 0.0001 Mexican American 1930(12.616) 196(12.528) 1525(12.613) 209(12.741) Non-Hispanic Black 1881(13.425) 354(23.362) 1407(12.688) 120(8.139) Non-Hispanic White 1957(59.590) 193(50.651) 1541(59.807) 223(67.872) Other Hispanic 514(7.117) 56(6.226) 412(7.416) 46(5.749) Other Race 585(7.252) 82(7.233) 457(7.477) 46(5.499) Maternal smoking history during pregnancy < 0.0001 No 5914(83.587) 701(74.979) 4619(83.826) 594(91.324) Yes 953(16.413) 180(25.021) 723(16.174) 50(8.676) Data for continuous variables were reported as mean ± standard error and analyzed using weighted ANOVA, and categorical variables were reported as number (percentage) and analyzed using weighted chi-square tests. Association of BW with BMI, WC, WHtR, and FLI in adolescents In fully adjusted model 2, BW was significantly and positively associated with BMI, WC, WHtR, and FLI among adolescents (BMI: β = 0.639, 95% CI = 0.343–0.934, p < 0.0001; WC: β = 1.872, 95% CI = 1.051–2.693, p < 0.0001; WHtR: β = 0.005, the 95% CI = 0.001–0.010, p = 0.0207; FLI: β = 2.128, 95% CI = 0.861–3.394, p = 0.0012). Notably, compared to NBW, HBW was associated with significantly increased BMI (β = 1.205), WC (β = 3.387), WHtR (β = 0.012), and FLI (β = 4.745), whereas there was no significant association for LBW (Table 2 ). Table 2 Association of BW with BMI, WC, WHtR, and FLI among adolescents. Crude Model β(95%CI) P-value Model 1 β(95%CI) P-value Model 2 β(95%CI) P-value BMI BW (kg) 0.441 (0.098, 0.783) 0.0125 0.669 (0.335, 1.004) 0.0001 0.639 (0.343, 0.934) < 0.0001 BW LBW 0.264 (-0.348, 0.877) 0.3987 -0.005 (-0.621, 0.610) 0.9871 -0.174 (-0.721, 0.372) 0.5330 NBW Ref. Ref. Ref. HBW 1.013 (0.442, 1.585) 0.0006 1.321 (0.759, 1.882) < 0.0001 1.205 (0.696, 1.714) < 0.0001 P for trend 0.0963 0.0031 0.0008 WC BW (kg) 1.799 (0.870, 2.729) 0.0002 1.958 (1.039, 2.876) < 0.0001 1.872 (1.051, 2.693) < 0.0001 BW LBW 0.202 (-1.435, 1.839) 0.8093 -0.062 (-1.690, 1.566) 0.9405 -0.490 (-1.925, 0.945) 0.5043 NBW Ref. Ref. Ref. HBW 3.404 (1.816, 4.992) < 0.0001 3.681 (2.104, 5.257) < 0.0001 3.387 (1.951, 4.822) < 0.0001 P for trend 0.0088 0.0024 0.0007 WHtR BW (kg) 0.003 (0.000, 0.008) 0.0449 0.006 (0.000, 0.011) 0.0329 0.005 (0.001, 0.010) 0.0207 BW LBW 0.005 (-0.004, 0.014) 0.2591 0.004 (-0.006, 0.013) 0.4388 0.001 (-0.007, 0.009) 0.8540 NBW Ref. Ref. Ref. HBW 0.007 (0.001, 0.016) 0.0201 0.013 (0.005, 0.022) 0.0033 0.012 (0.004, 0.020) 0.0034 P for trend 0.8037 0.1762 0.0800 FLI BW (kg) 1.801 (0.316, 3.285) 0.0185 2.100 (0.606, 3.593) 0.0065 2.128 (0.861, 3.394) 0.0012 BW LBW 1.749 (-0.911, 4.409) 0.1992 1.200 (-1.454, 3.853) 0.3768 0.229 (-2.040, 2.497) 0.8435 NBW Ref. Ref. Ref. HBW 4.540 (1.847, 7.233) 0.0012 4.819 (2.136, 7.502) 0.0006 4.745 (2.444, 7.047) 0.0001 P for trend 0.0047 0.0049 0.0135 For BMI, WC, and WHtR, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For FLI, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake. Association of BW with general obesity, central obesity, and hepatic steatosis among adolescents Similarly, in Model 2, BW was positively associated with BMI-defined general obesity. WC-defined central obesity, WHtR-defined central obesity, and FLI-defined hepatic steatosis (ORs of 1.473, 1.277, 1.169, and 1.311, respectively). Compared to NBW, HBW was associated with an increased prevalence of overall obesity, central obesity, and hepatic steatosis (overall obesity: OR 2.629, 95% CI 1.831–3.776; central obesity [WC]: OR 1.713, 95% CI 1.212–2.421; central obesity [WHtR]: OR 1.618, 95% CI 1.252–2.092; hepatic steatosis: OR 1.960, 95% CI 1.436–2.677). However, LBW was not associated with odds of obesity and hepatic steatosis (Table 3 ). Table 3 Association of BW with general obesity, central obesity, and hepatic steatosis among adolescents. Crude Model OR (95%CI) P-value Model 1 OR (95%CI) P-value Model 2 OR (95%CI) P-value General obesity BW (kg) 1.398 (1.045, 1.870) 0.0253 1.440 (1.082, 1.918) 0.0135 1.473 (1.108, 1.957) 0.0083 BW LBW 1.101 (0.675, 1.794) 0.7009 1.067 (0.662, 1.720) 0.7900 1.021 (0.634, 1.645) 0.9314 NBW Ref. Ref. Ref. HBW 2.412 (1.708, 3.407) < 0.0001 2.569 (1.799, 3.669) < 0.0001 2.629 (1.831, 3.776) < 0.0001 P for trend 0.0082 0.0042 0.0022 Central obesity (WC) BW (kg) 1.211 (1.050, 1.398) 0.0095 1.246 (1.077, 1.441) 0.0035 1.277 (1.102, 1.480) 0.0014 BW LBW 1.029 (0.805, 1.315) 0.8194 0.998 (0.780, 1.276) 0.9863 1.059 (0.826, 1.357) 0.6537 NBW Ref. Ref. Ref. HBW 1.512 (1.187, 1.926) 0.0010 1.590 (1.238, 2.041) 0.0004 1.713 (1.212, 2.421) 0.0027 P for trend 0.0329 0.0128 0.0045 Central obesity (WHtR) BW (kg) 1.104 (1.001, 1.259) 0.0398 1.139 (0.996, 1.303) 0.0589 1.169 (1.021, 1.338) 0.0250 BW LBW 1.031 (0.815, 1.304) 0.8014 0.997 (0.786, 1.265) 0.9814 0.945 (0.737, 1.211) 0.6537 NBW Ref. Ref. Ref. HBW 1.251 (1.001, 1.579) 0.0014 1.321 (1.037, 1.682) 0.0253 1.618 (1.252, 2.092) 0.0003 P for trend 0.0082 0.0282 0.034 Hepatic steatosis BW (kg) 1.246 (0.980, 1.584) 0.0741 1.288 (1.013, 1.637) 0.0402 1.311 (1.029, 1.671) 0.0299 BW LBW 1.270 (0.858, 1.880) 0.2343 1.217 (0.822, 1.803) 0.3282 1.171 (0.790, 1.737) 0.4331 NBW Ref. Ref. Ref. HBW 1.823 (1.348, 2.465) 0.0001 1.936 (1.424, 2.631) < 0.0001 1.960 (1.436, 2.677) < 0.0001 P for trend 0.1728 0.0970 0.0698 For general obesity and central obesity, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For hepatic steatosis, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake. RCS analysis RCS analysis showed that BW was nonlinearly correlated with BMI, WC, WHtR, and FLI among adolescents (p for nonlinearity < 0.0001, < 0.0001, 0.0403, and 0.0001, respectively) (Fig. 2 A-D). Similarly, BW was nonlinearly associated with the odds of general obesity, central obesity (WC), central obesity (WHtR), and hepatic steatosis among adolescents (p for nonlinearity was 0.004, 0.0027, 0.0027, and 0.0005, respectively) (Fig. 3 A-D). Threshold effect analyses indicated that the association of BW with BMI, WC, WHtR, and FLI was only present for BW > 3 kg (β of 1.453, 4.088, 0.016, and 5.598, respectively) ( Table S1 ). Similarly, the associations of BW with overall obesity, central obesity, and hepatic steatosis were significant after their respective inflection points (ORs of 1.915, 1.430, 1.237, and 1.719, respectively) ( Table S2 ). Stratified analysis We selected gender and race/ethnicity for stratified analysis. Interaction analyses indicated race/ethnicity was a significant effect modifier, influencing the association of BW with BMI, WHtR, and FLI (p for interaction 0.037, 0.026, and 0.022, respectively) (Fig. 4 ). Interestingly, race/ethnicity also influenced the association of BW with central obesity (WC and WHtR) ( Figure S1 ). 4. DISCUSSION BW was positively and nonlinearly associated with BMI, WC, WHtR, and FLI as well as general obesity, central obesity, and hepatic steatosis among U.S. adolescents in a national, large-sample, multiethnic, serial cross-sectional study. Compared with NBW, HBW was associated with significantly increased prevalence of BMI, WC, WHtR, and FLI, as well as obesity and hepatic steatosis, whereas LBW was not significantly associated. Race/ethnicity partially influenced these associations. These findings emphasize that U.S. adolescents with HBW are at risk for overall obesity, central obesity, and hepatic steatosis, and require early monitoring and timely prevention of obesity and steatosis. In addition, early maternal prevention of HBW may help reduce the risk of obesity and hepatic steatosis in subsequent adolescence. To our knowledge, this is the first time that the association of LBW/HBW with general obesity, central obesity, and hepatic steatosis among adolescents has been explored in the national, large-sample, multiethnic NHANES database. A large body of observational research has suggested that HBW is associated with an increased risk of overall obesity and central obesity among children and adolescents. A meta-analysis published in 2011 that included 20 observational studies showed that HBW (compared to BW ≤ 4000 g) was positively associated with the risk of obesity among children and adolescents (OR 2.07, 95% CI 1.91–2.24), whereas LBW (compared to BW ≥ 2500 g) was associated with a decreased risk of obesity (OR 0.61, 95% CI 0.46–0.80)[ 15 ]. Interestingly, when NBW was used as a reference, the inverse correlation between LBW and obesity risk disappeared, while the positive correlation for HBW was maintained[ 15 ]. A large-sample cross-sectional analysis from China demonstrated that HBW (defined as ≥ 3000 g) was associated with significantly increased odds of central obesity (as defined by WHtR) in children and adolescents (6–17 years old) compared to the reference (BW in the range of 2,500-2,999 g), while LBW (< 2,500 g) was not associated with the likelihood of central obesity[ 16 ]. Another cross-sectional study from China that included 6561 participants showed that HBW increased the odds of general obesity in children and adolescents, while LBW decreased the prevalence of overweight[ 17 ]. A cross-sectional analysis including Brazilian school-based adolescents aged 10–17 years suggested that BW was significantly and positively associated with BMI and WC, and that biological maturation partially mediated these associations[ 18 ]. A retrospective cohort study from rural India enrolling 756 children aged 7–10 years showed that HBW (> 3500g) was associated with significantly increased BMI in childhood compared to BW at 2500-2999g[ 19 ]. Shi et al. included 10041 children and adolescents aged 7–17 years in a large-sample cross-sectional analysis from China demonstrating a significant increase in the prevalence of general obesity in both participants with a BW ≥ 3500 g and a BW of 2500–2999 g compared to the reference (BW of 3000–3499 g)[ 20 ]. In a large cross-sectional analysis including 16,580 Chinese children and adults aged 7–17 years, Yuan et al. demonstrated that participants with HBW (BW of 3500-5000g) had a significantly increased risk of general obesity compared to those with BW of 3000-3499g, and that those with BW of 4000-4499g also had a significantly increased odds of central obesity (WHtR)[ 21 ]. Interestingly, participants with very low BW (< 1500g) had the highest risk of central obesity (OR = 2.03)[ 21 ]. A national cross-sectional analysis from Korea revealed a higher prevalence of general obesity in female, but not male, adolescents with BW > 75% compared to the reference with BW at 25–75% (OR = 2.13, 95% CI 1.03–4.41)[ 39 ]. Zou et al. similarly showed in a cross-sectional analysis in China that HBW compared to NBW was associated with a significantly higher prevalence of general obesity among participants aged 6–18 years (OR 1.611)[ 40 ]. However, another retrospective cohort study from Australia indicated that BW was not associated with BMI in adolescents[ 41 ]. Finally, a national cross-sectional analysis from Iran suggested that compared to NBW (2500–4000 g), HBW (> 4000 g) was associated with increased odds of general obesity and central obesity in children and adolescents aged 6–18 years, whereas LBW was associated with a decreased prevalence of general obesity[ 42 ]. Overall, these studies collectively suggest that higher BW is associated with an increased prevalence of overall and central obesity in children and adolescents, whereas the association of LBW with obesity risk in the adolescent population remains controversial. Using standard BW classification criteria, our study demonstrated for the first time in a nationally representative sample of adolescents in the U.S. that BW was positively and nonlinearly associated with both BMI, WC, and WHtR as well as general and central obesity. Compared with NBW, HBW was associated with significantly increased BMI, WC, and WHtR and prevalence of obesity, whereas LBW was not significantly associated. These findings were consistent with some of the previous studies in demonstrating that attention to obesity risk and early prevention is required among adolescents with HBW. In addition, our study provided the first indication that race/ethnicity may influence these associations, and the positive associations were more significant in other Hispanic ethnic populations, suggesting the need for individualized screening and prevention strategies. The association of BW with hepatic steatosis in adolescents remains understudied. A multicenter cross-sectional study demonstrated that HBW was associated with an increased prevalence of biopsy-proven severe steatosis in participants < 21 years of age (OR 1.82, 95% CI 1.15–2.88), whereas LBW was associated with an increased odds of advanced liver fibrosis (OR 2.23, 95% CI 1.08–4.62)[ 28 ]. However, another biopsy-proven NAFLD cohort demonstrated that small for gestational age was significantly associated with severe steatosis in children and adolescents aged 6–17 years (OR 4.0, 95% CI 1.43–10.9), whereas the prevalence of moderate steatosis was not significantly different between the groups[ 30 ]. A population-based cohort study from Australia demonstrated that BW was not associated with the risk of NAFLD (diagnosed by questionnaire and ultrasound) in adolescents aged 17 years[ 29 ]. Similar to obesity indicators, our results suggested that HBW was associated with increased odds of hepatic steatosis among adolescents, whereas LBW was not significantly associated, suggesting that a history of HBW may require prompt attention as a group at risk for hepatic steatosis. Several mechanisms may explain the association of HBW with obesity and hepatic steatosis in adolescents. HBW may affect cardiac and circulatory health, leading to metabolic disorders and adipose tissue accumulation. Maternal blood glucose and subsequent fetal hyperinsulinemia may promote accelerated fetal growth[ 5 ]. BW can be assumed to reflect the in-utero environment. Overnutrition during pregnancy affects BW and can lead to persistent epigenomic alterations, resulting in an increased risk of obesity in later life[ 16 , 43 ]. Some genetic factors may also contribute to these associations[ 44 ]. In addition, HBW may lead to higher levels of growth factors, which may affect the risk of obesity in later life[ 20 ]. These mechanisms may confirm the developmental origins of health and disease, i.e., fetal programming permanently shapes the structure, function, and metabolism of the body and leads to subsequent disease[ 45 ]. The strength of our study lies mainly in the fact that it is a national, multiethnic, large-sample, population-based study, making the findings potentially generalizable. In addition, this is the first study to explore the association of LBW/HBW with general obesity, central obesity, and hepatic steatosis in a representative sample of U.S. adolescents, which filled a research gap and has potential clinical value. However, there are limitations to our study. It was a cross-sectional analysis, and therefore could not draw causal associations and still could not adequately adjust for confounding factors. Due to database limitations, some important influences such as maternal obesity status, genetic susceptibility, and history of catch-up growth were not available, which may have affected the findings. The diagnosis of hepatic steatosis was based on noninvasive serologic markers rather than imaging or biopsy, which may have compromised accuracy. However, the FLI has proven its reliability in numerous NHANES and other population-level studies. Future large-sample well-characterized prospective cohort studies are needed to confirm these findings. 5. CONCLUSIONS In a national cross-sectional analysis, BW was positively and nonlinearly associated with BMI, WC, WHtR, and FLI, as well as the prevalence of general obesity, central obesity, and hepatic steatosis among US adolescents. HBW but not LBW was associated with significantly increased odds of obesity and hepatic steatosis compared with NBW. These findings underline that adolescents with HBW may be at risk of developing obesity and hepatic steatosis and require early detection and intervention. Declarations Funding: None. Conflict of interest : None. Acknowledgments : None. Consent for publication: Not applicable Author contributions: JJH designed and developed the study. JJH reviewed literature. JJH composed the manuscript, and edited it. Data availability: This study analyzed publicly available datasets and can be found at https://www.cdc.gov/nchs/nhanes/ . Ethics statement : All protocols were approved by the NCHS Ethics Review Board, and participants have provided written informed consent. References Mugnier A, Chastant S, Lyazrhi F, Saegerman C, Grellet A. Definition of low birth weight in domestic mammals: a scoping review. Anim Health Res Rev. 2022;23(2):157-64. doi: 10.1017/s146625232200007x. Mebrahtu TF, Feltbower RG, Greenwood DC, Parslow RC. Birth weight and childhood wheezing disorders: a systematic review and meta-analysis. J Epidemiol Community Health. 2015;69(5):500-8. doi: 10.1136/jech-2014-204783. Mu M, Ye S, Bai MJ, Liu GL, Tong Y, Wang SF, et al. Birth weight and subsequent risk of asthma: a systematic review and meta-analysis. Heart Lung Circ. 2014;23(6):511-9. doi: 10.1016/j.hlc.2013.11.018. Chen S, Yang L, Pu F, Lin H, Wang B, Liu J, et al. 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J Pediatr Endocrinol Metab. 2022;35(12):1487-96. doi: 10.1515/jpem-2021-0430. Werneck AO, Silva DRP, Collings PJ, Fernandes RA, Ronque ERV, Coelho ESMJ, et al. Birth weight, biological maturation and obesity in adolescents: a mediation analysis. J Dev Orig Health Dis. 2017;8(4):502-7. doi: 10.1017/s2040174417000241. Kumar D, Sharma S, Raina SK. Risk of Childhood Obesity in Children With High Birth Weight in a Rural Cohort of Northern India. Indian Pediatr. 2023;60(1):103-7. Shi J, Guo Q, Fang H, Cheng X, Ju L, Wei X, et al. The Relationship between Birth Weight and the Risk of Overweight and Obesity among Chinese Children and Adolescents Aged 7-17 Years. Nutrients. 2024;16(5). doi: 10.3390/nu16050715. Yuan ZP, Yang M, Liang L, Fu JF, Xiong F, Liu GL, et al. Possible role of birth weight on general and central obesity in Chinese children and adolescents: a cross-sectional study. Ann Epidemiol. 2015;25(10):748-52. doi: 10.1016/j.annepidem.2015.05.011. Mann JP, Valenti L, Scorletti E, Byrne CD, Nobili V. Nonalcoholic Fatty Liver Disease in Children. Semin Liver Dis. 2018;38(1):1-13. doi: 10.1055/s-0038-1627456. Shaunak M, Byrne CD, Davis N, Afolabi P, Faust SN, Davies JH. Non-alcoholic fatty liver disease and childhood obesity. Arch Dis Child. 2021;106(1):3-8. doi: 10.1136/archdischild-2019-318063. Amadou C, Nabi O, Serfaty L, Lacombe K, Boursier J, Mathurin P, et al. Association between birth weight, preterm birth, and nonalcoholic fatty liver disease in a community-based cohort. Hepatology. 2022;76(5):1438-51. doi: 10.1002/hep.32540. Breij LM, Kerkhof GF, Hokken-Koelega AC. Accelerated infant weight gain and risk for nonalcoholic fatty liver disease in early adulthood. J Clin Endocrinol Metab. 2014;99(4):1189-95. doi: 10.1210/jc.2013-3199. Suomela E, Oikonen M, Pitkänen N, Ahola-Olli A, Virtanen J, Parkkola R, et al. Childhood predictors of adult fatty liver. The Cardiovascular Risk in Young Finns Study. J Hepatol. 2016;65(4):784-90. doi: 10.1016/j.jhep.2016.05.020. Sipola-Leppänen M, Vääräsmäki M, Tikanmäki M, Matinolli HM, Miettola S, Hovi P, et al. Cardiometabolic risk factors in young adults who were born preterm. Am J Epidemiol. 2015;181(11):861-73. doi: 10.1093/aje/kwu443. Newton KP, Feldman HS, Chambers CD, Wilson L, Behling C, Clark JM, et al. Low and High Birth Weights Are Risk Factors for Nonalcoholic Fatty Liver Disease in Children. J Pediatr. 2017;187:141-6.e1. doi: 10.1016/j.jpeds.2017.03.007. Ayonrinde OT, Olynyk JK, Marsh JA, Beilin LJ, Mori TA, Oddy WH, et al. Childhood adiposity trajectories and risk of nonalcoholic fatty liver disease in adolescents. J Gastroenterol Hepatol. 2015;30(1):163-71. doi: 10.1111/jgh.12666. Bugianesi E, Bizzarri C, Rosso C, Mosca A, Panera N, Veraldi S, et al. Low Birthweight Increases the Likelihood of Severe Steatosis in Pediatric Non-Alcoholic Fatty Liver Disease. Am J Gastroenterol. 2017;112(8):1277-86. doi: 10.1038/ajg.2017.140. Huang R, Yang S, Lei Y. Birth weight influences differently on systolic and diastolic blood pressure in children and adolescents aged 8-15. BMC Pediatr. 2022;22(1):278. doi: 10.1186/s12887-022-03346-7. Sanjeevi N, Freeland-Graves JH. Birth weight and prediabetes in a nationally representative sample of US adolescents. Clin Obes. 2022;12(2):e12504. doi: 10.1111/cob.12504. Brathwaite KE, Levy RV, Sarathy H, Agalliu I, Johns TS, Reidy KJ, et al. Reduced kidney function and hypertension in adolescents with low birth weight, NHANES 1999-2016. Pediatr Nephrol. 2023;38(9):3071-82. doi: 10.1007/s00467-023-05958-2. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33. doi: 10.1186/1471-230x-6-33. Lin MS, Lin TH, Guo SE, Tsai MH, Chiang MS, Huang TJ, et al. Waist-to-height ratio is a useful index for nonalcoholic fatty liver disease in children and adolescents: a secondary data analysis. BMC Public Health. 2017;17(1):851. doi: 10.1186/s12889-017-4868-5. Sousa MA, Guimarães IC, Daltro C, Guimarães AC. Association between birth weight and cardiovascular risk factors in adolescents. Arq Bras Cardiol. 2013;101(1):9-17. doi: 10.5935/abc.20130114. Xi B, Mi J, Zhao M, Zhang T, Jia C, Li J, et al. Trends in abdominal obesity among U.S. children and adolescents. Pediatrics. 2014;134(2):e334-9. doi: 10.1542/peds.2014-0970. Arshad T, Paik JM, Biswas R, Alqahtani SA, Henry L, Younossi ZM. Nonalcoholic Fatty Liver Disease Prevalence Trends Among Adolescents and Young Adults in the United States, 2007-2016. Hepatol Commun. 2021;5(10):1676-88. doi: 10.1002/hep4.1760. Kang M, Yoo JE, Kim K, Choi S, Park SM. Associations between birth weight, obesity, fat mass and lean mass in Korean adolescents: the Fifth Korea National Health and Nutrition Examination Survey. BMJ Open. 2018;8(2):e018039. doi: 10.1136/bmjopen-2017-018039. Zou Z, Yang Z, Yang Z, Wang X, Gao D, Dong Y, et al. Association of high birth weight with overweight and obesity in Chinese students aged 6-18 years: a national, cross-sectional study in China. BMJ Open. 2019;9(5):e024532. doi: 10.1136/bmjopen-2018-024532. Stock K, Nagrani R, Gande N, Bernar B, Staudt A, Willeit P, et al. Birth Weight and Weight Changes from Infancy to Early Childhood as Predictors of Body Mass Index in Adolescence. J Pediatr. 2020;222:120-6.e3. doi: 10.1016/j.jpeds.2020.03.048. Ansari H, Qorbani M, Rezaei F, Djalalinia S, Asadi M, Miranzadeh S, et al. Association of birth weight with abdominal obesity and weight disorders in children and adolescents: the weight disorder survey of the CASPIAN-IV Study. J Cardiovasc Thorac Res. 2017;9(3):140-6. doi: 10.15171/jcvtr.2017.24. van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS. Epigenetics and human obesity. Int J Obes (Lond). 2015;39(1):85-97. doi: 10.1038/ijo.2014.34. Ong KK, Dunger DB. Birth weight, infant growth and insulin resistance. Eur J Endocrinol. 2004;151 Suppl 3:U131-9. doi: 10.1530/eje.0.151u131. Wadhwa PD, Buss C, Entringer S, Swanson JM. Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med. 2009;27(5):358-68. doi: 10.1055/s-0029-1237424. 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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-6238043","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432382106,"identity":"3153c77f-b9ba-4336-9b89-49e57a6923f6","order_by":0,"name":"jinjin He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIie3PsQrCMBCA4YRCp5OsCfgQ2YpQ9FVOCuniAzgGCvoK9UWcr7gWXYUu7eKcji5iBh/g3ATzTxnuI3dCpFI/mBVUB9yXoJTnEyfG3i1NS2wicjkdLqX1yCSFpMeI+Q3idzLMOwZZeaotwgBF5jNzOnMWI0KNeoBo82zBJ/YKlpBPnEak70hlkSowbdcwb7n32+n5Wm+Uarowc4jQPX5e0nPmY+pIzMlUKpX6294Q8z6KHNY+gAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7074-3868","institution":"+86 15126515860","correspondingAuthor":true,"prefix":"","firstName":"jinjin","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-03-16 14:16:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6238043/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6238043/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79662532,"identity":"a899d1ac-fc8b-49e2-ba85-f4d7339ae63c","added_by":"auto","created_at":"2025-04-01 09:52:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":320931,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study population selection, NHANES 1999-2020.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/b74ac7199ad729e23e68fe95.jpg"},{"id":79662535,"identity":"2d88c91d-32a1-48b7-8125-d4d808a70940","added_by":"auto","created_at":"2025-04-01 09:52:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":342896,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the association of BW with BMI, WC, WHtR, and FLI in US adolesents.\u003cstrong\u003e A\u003c/strong\u003e: BMI; \u003cstrong\u003eB\u003c/strong\u003e: WC;\u003cstrong\u003e C\u003c/strong\u003e: WHtR; \u003cstrong\u003eD\u003c/strong\u003e: FLI.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/de2fb1903b337c2a1f6b822e.jpg"},{"id":79664174,"identity":"ee980ba3-e1c3-417f-aaa4-25170ebeb379","added_by":"auto","created_at":"2025-04-01 10:00:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":372867,"visible":true,"origin":"","legend":"\u003cp\u003eRCS analysis of the association of BW with obesity and hepatic steatosis in US adolesents.\u003cstrong\u003e A\u003c/strong\u003e: general obesity; \u003cstrong\u003eB\u003c/strong\u003e: central obesity (WC);\u003cstrong\u003e C\u003c/strong\u003e: central obesity (WHtR); \u003cstrong\u003eD\u003c/strong\u003e: hepatic steatosis.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/4ba539aa65b2cd972f5cd18e.jpg"},{"id":79665382,"identity":"29bfab32-a55e-4927-a730-387b00f85b1d","added_by":"auto","created_at":"2025-04-01 10:08:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":276397,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the association of BW with BMI, WC, WHtR, and FLI in US adolesents.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/7f8f29c961ee9e6d86eb42a0.jpg"},{"id":86018607,"identity":"88d22d98-b156-4f2f-9fd6-f7b7439d26b0","added_by":"auto","created_at":"2025-07-04 11:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2639150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/54a767f0-772e-4954-bfe7-4d6f1faa8a6a.pdf"},{"id":79662533,"identity":"4bfaf3a6-7930-4529-b434-3ccc3562e455","added_by":"auto","created_at":"2025-04-01 09:52:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":253329,"visible":true,"origin":"","legend":"supplement tables","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/c43fd4fdfc547160fc9d140f.docx"},{"id":79664172,"identity":"3506eec0-0362-42e8-b3bc-8cad95bfe196","added_by":"auto","created_at":"2025-04-01 10:00:23","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1036679,"visible":true,"origin":"","legend":"raw data","description":"","filename":"weightbmi.rawdata.csv","url":"https://assets-eu.researchsquare.com/files/rs-6238043/v1/5fdd32ba3b20b3aeff7fabd3.csv"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Association of birth weight with general obesity, central obesity, and hepatic steatosis in a nationally representative sample of US adolescents: evidence from NHANES 1999-2020","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eBirth weight (BW) is one of the most important clinical indicators in neonatology, and it is an important determinant of the health status of newborns. Low birth weight (LBW) is associated with an increased risk of neonatal mortality; moreover, a large body of well-established epidemiologic research suggests that LBW is associated with poor health outcomes in later childhood, adolescence, and/or adulthood[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, accumulating clinical evidence also suggests that high birth weight (HBW) may predict certain health trajectories later in life, such as increased risk of bone tumors, obesity, and autonomic nervous system disorders[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, the association of HBW or LBW with subsequent health outcomes in life may shift with age[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], emphasizing that the impact of abnormal BW on health outcomes requires an age-specific perspective.\u003c/p\u003e \u003cp\u003eObesity is one of the major health problems among adolescents. The prevalence of obesity among adolescents has now reached epidemic proportions, with the prevalence of severe obesity increasing at least fourfold over the past few decades[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Adolescent obesity is associated with the development of multiple short- and long-term complications, such as psychosocial stress and obesity-related metabolic disorders and cardiovascular disease[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, 80% of obesity in adolescence can be maintained into adulthood, leading to increased metabolic and cardiovascular risk and higher mortality in adulthood[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Early identification of adolescents at risk for obesity and timely intervention can help reduce subsequent adverse obesity-related health outcomes. A previously published systematic review and meta-analysis demonstrated that HBW was associated with a significantly increased risk of obesity in pre-school children, school-aged children, and adolescents compared to control populations, whereas LBW was associated with a decreased risk of obesity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. More recent observational clinical studies have suggested that HBW may be associated with an increased risk of general obesity and abdominal obesity in children and adolescents; however, the association of LBW with the prevalence of obesity in pediatric or adolescent populations is controversial[\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Several limitations were present in the previous evidence. First, there was significant heterogeneity in the definitions of LBW, NBW, and HBW among these studies, and the lack of standardized definitions may have hindered conclusions. Second, most of the observational studies were from Asian cohorts and there was a lack of exploration in other countries/regions such as the United States. In addition, most of the studies included children and adolescent populations, and studies focusing on adolescent samples were lacking.\u003c/p\u003e \u003cp\u003eLiver steatosis is another emerging health concern in the adolescent population[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The prevalence of obesity-associated nonalcoholic fatty liver disease (NAFLD) in children and adolescents is estimated to be 36.1%[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Interestingly, some studies have shown that LBW and/or HBW compared to NBW are associated with increased prevalence of NAFLD or hepatic steatosis in adults, however controversial findings exist[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Some sparse observational evidence suggested an association of LBW and/or HBW with hepatic steatosis in children and adolescents, with similarly inconsistent findings[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Whether LBW/HBW is associated with the prevalence of hepatic steatosis in US adolescents remains largely unexplored.\u003c/p\u003e \u003cp\u003eIn this study, we utilized a nationally representative sample of adolescents from the National Health and Nutrition Examination Survey (NHANES) to explore the association of LBW and HBW with the prevalence of general obesity, central obesity, and hepatic steatosis. Exploring these associations could help reveal whether abnormal BW contributes to early detection, risk stratification, and timely intervention for obesity and hepatic steatosis among adolescents.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cp\u003e \u003cb\u003eStudy design and population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNHANES is a large, national, population-based, multiracial series of cross-sectional surveys. Since 1999, NHANES has been conducted in biennial cycles, sampling approximately 5,000 cases per year to assess the health and nutritional status of non-institutionalized children and adults in the U.S. NHANES is a major program of the National Center for Health Statistics (NCHS), and all cycles were approved by the NCHS Ethics Review Board and written informed consent was obtained from participants. We included all 9,812 adolescent participants aged 12\u0026ndash;15 years from the NHANES 1999\u0026ndash;2020 March. Participants with missing BMI data (n\u0026thinsp;=\u0026thinsp;461), WC data (n\u0026thinsp;=\u0026thinsp;83), FLI data (n\u0026thinsp;=\u0026thinsp;802), BW information (n\u0026thinsp;=\u0026thinsp;470), and covariate information (n\u0026thinsp;=\u0026thinsp;1129) were excluded. A total of 6867 eligible adolescent participants were included in further analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBW Assessment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBW data were obtained from the NHANES Early Childhood Questionnaire, which was available to children and adolescents\u0026thinsp;\u0026le;\u0026thinsp;15 years of age in the US[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Participants were asked, \u0026ldquo;How much did you weigh at birth?\u0026rdquo; and self-reported their weight at birth (pounds or ounces). This question was asked by trained interviewers to home-based participants through the computer-assisted personal interview system[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We converted participants' self-reported BW (pounds or ounces) to grams and categorized them according to World Health Organization criteria as LBW (BW\u0026thinsp;\u0026lt;\u0026thinsp;2500 g), normal BW (NBW, BW at 2500\u0026ndash;4000 g), and HBW (BW\u0026thinsp;\u0026gt;\u0026thinsp;4000 g)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of outcomes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStudy outcomes included body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and fatty liver index (FLI). BMI was used to define overall obesity. WC and WHtR were used to define central obesity. FLI was used to define hepatic steatosis. The FLI was calculated using the formula FLI = (e\u003csup\u003e0.953*ln (TG) + 0.139*BMI + 0.718*ln (GGT) + 0.053*WC \u0026minus; 15.745\u003c/sup\u003e) / (1\u0026thinsp;+\u0026thinsp;e\u003csup\u003e0.953*ln (TG) + 0.139*BMI + 0.718*ln (GGT) + 0.053*WC \u0026minus; 15.745\u003c/sup\u003e) \u0026times; 100[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These markers were first explored as continuous variables. Second, they were used as categorical variables using age- and sex-specific cutoffs for diagnosis of the disease of interest. BMI-defined overall obesity was defined as an individual's BMI\u0026thinsp;\u0026ge;\u0026thinsp;the age- and sex-specific 95% percentile[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. WC\u0026thinsp;\u0026gt;\u0026thinsp;75th percentile for age and sex suggested central obesity[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. WHtR\u0026thinsp;\u0026ge;\u0026thinsp;0.5 indicated central obesity[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. FLI\u0026thinsp;\u0026ge;\u0026thinsp;60 suggested the presence of hepatic steatosis[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral important covariates including age, gender, race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, other Hispanic, or other races), household income-poverty ratio (PIR), mother's age when born, maternal smoking history during pregnancy, total daily dietary energy intake, serum triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were included. Mother's age when born and maternal smoking history during pregnancy was obtained from self-reports on the Early Childhood Questionnaire[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Daily energy intake was derived from dietary interview data, which was calculated from the USDA's Food and Nutrient Database for Dietary Studies. Serum lipid/lipoprotein data were obtained from NHANES laboratory tests.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData processing and statistical analyses were performed using R (version 4.2.3) and EmpowerStats, and a two-sided P value of less than 0.05 reported statistical significance. All analyses were properly weighted according to the NHANES analytic guidelines to consider the sophisticated study design of NHANES. In the baseline analyses, we conducted baseline analyses based on the BW status of the included adolescent population. Data for continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error and analyzed using weighted ANOVA, and categorical variables were reported as number (percentage) and analyzed using weighted chi-square tests. Multivariate linear regression analyses were performed to explore the association of LBW and HBW (compared with NBW) with BMI, WC, WHtR, and FLI among US adolescents aged 12\u0026ndash;15 years and to calculate β and 95% confidence intervals (95% CIs). For BMI, WC, and WHtR, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For FLI, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake. We used multivariate logistic regression analyses to explore the association of LBW/HBW with general obesity as defined by BMI, central obesity as defined by WC/WHtR, and hepatic steatosis as defined by FLI and to calculate odds ratio (OR) and 95% CI. These logistic regression models were consistent with the adjustment variables used in the respective linear regressions. Restricted cubic spline (RCS) was applied to explore nonlinear associations between BW (continuous) and BMI, WC, WHtR, and FLI in adolescents and to pick suitable knots for smoothed curve fitting. Stratified analyses were conducted to explore whether these associations (between BW and BMI, WC, WHtR, and FLI) differed within subgroups (gender and race/ethnicity) and to identify effect modifiers through interaction analyses. We also explored whether the association of BW with obesity and hepatic steatosis remained stable across subgroups.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e \u003cb\u003eBaseline characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 6867 adolescent participants with a mean age of 13.508 years were enrolled. There were 881, 5342, and 644 participants in LBW, NBW, and HBW, respectively. As BW increased, participants had higher PIR, energy intake, and mother's age when born, lower HDL-C, and were more likely to be male, non-Hispanic White, and have no history of maternal smoking during pregnancy. Participants with HBW had higher BMI, WC, WHtR, and FLI (continuous and categorical variables) compared to participants with LBW (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline analysis of adolescent participants according to BW status, NHANES 1999\u0026ndash;2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;6867)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLBW(n\u0026thinsp;=\u0026thinsp;881)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNBW(n\u0026thinsp;=\u0026thinsp;5342)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHBW(n\u0026thinsp;=\u0026thinsp;644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.508\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.522\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.501\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.550\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.591\u0026thinsp;\u0026plusmn;\u0026thinsp;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.267\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.588\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.972\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnergy intake, kcal/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2142.944\u0026thinsp;\u0026plusmn;\u0026thinsp;18.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2033.530\u0026thinsp;\u0026plusmn;\u0026thinsp;44.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2146.053\u0026thinsp;\u0026plusmn;\u0026thinsp;20.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2240.743\u0026thinsp;\u0026plusmn;\u0026thinsp;63.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003emother's age when born\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.671\u0026thinsp;\u0026plusmn;\u0026thinsp;0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.223\u0026thinsp;\u0026plusmn;\u0026thinsp;0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.529\u0026thinsp;\u0026plusmn;\u0026thinsp;0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.296\u0026thinsp;\u0026plusmn;\u0026thinsp;0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.053\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.031\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.077\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.324\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.337\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.328\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.283\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.577\u0026thinsp;\u0026plusmn;\u0026thinsp;0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.711\u0026thinsp;\u0026plusmn;\u0026thinsp;0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.447\u0026thinsp;\u0026plusmn;\u0026thinsp;0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.460\u0026thinsp;\u0026plusmn;\u0026thinsp;0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC, cm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.804\u0026thinsp;\u0026plusmn;\u0026thinsp;0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.645\u0026thinsp;\u0026plusmn;\u0026thinsp;0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.443\u0026thinsp;\u0026plusmn;\u0026thinsp;0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.846\u0026thinsp;\u0026plusmn;\u0026thinsp;0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.484\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.488\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.493\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.931\u0026thinsp;\u0026plusmn;\u0026thinsp;0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.033\u0026thinsp;\u0026plusmn;\u0026thinsp;1.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.284\u0026thinsp;\u0026plusmn;\u0026thinsp;0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.823\u0026thinsp;\u0026plusmn;\u0026thinsp;1.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6527(96.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e844(95.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5099(96.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e584(92.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e340(3.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(4.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243(3.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60(7.569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh WC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5151(77.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700(77.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4022(78.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e429(70.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1716(22.830)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(22.728)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1320(21.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e215(29.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh WHTR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4377(67.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e597(66.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3398(67.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e382(64.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2490(32.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284(33.374)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1944(32.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e262(35.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHigh FLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6209(91.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e812(90.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4856(92.771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e541(87.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e658(8.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69(9.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e486(7.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103(12.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3505(51.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403(48.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2685(50.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e417(68.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3362(48.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478(51.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2657(49.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227(31.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1930(12.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196(12.528)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1525(12.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209(12.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1881(13.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354(23.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1407(12.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120(8.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1957(59.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193(50.651)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1541(59.807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e223(67.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e514(7.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(6.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412(7.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(5.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e585(7.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(7.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e457(7.477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(5.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaternal smoking history during pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5914(83.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e701(74.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4619(83.826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e594(91.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e953(16.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180(25.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e723(16.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(8.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData for continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error and analyzed using weighted ANOVA, and categorical variables were reported as number (percentage) and analyzed using weighted chi-square tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of BW with BMI, WC, WHtR, and FLI in adolescents\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn fully adjusted model 2, BW was significantly and positively associated with BMI, WC, WHtR, and FLI among adolescents (BMI: β\u0026thinsp;=\u0026thinsp;0.639, 95% CI\u0026thinsp;=\u0026thinsp;0.343\u0026ndash;0.934, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; WC: β\u0026thinsp;=\u0026thinsp;1.872, 95% CI\u0026thinsp;=\u0026thinsp;1.051\u0026ndash;2.693, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; WHtR: β\u0026thinsp;=\u0026thinsp;0.005, the 95% CI\u0026thinsp;=\u0026thinsp;0.001\u0026ndash;0.010, p\u0026thinsp;=\u0026thinsp;0.0207; FLI: β\u0026thinsp;=\u0026thinsp;2.128, 95% CI\u0026thinsp;=\u0026thinsp;0.861\u0026ndash;3.394, p\u0026thinsp;=\u0026thinsp;0.0012). Notably, compared to NBW, HBW was associated with significantly increased BMI (β\u0026thinsp;=\u0026thinsp;1.205), WC (β\u0026thinsp;=\u0026thinsp;3.387), WHtR (β\u0026thinsp;=\u0026thinsp;0.012), and FLI (β\u0026thinsp;=\u0026thinsp;4.745), whereas there was no significant association for LBW (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of BW with BMI, WC, WHtR, and FLI among adolescents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Model β(95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1 β(95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 β(95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.441 (0.098, 0.783) 0.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.669 (0.335, 1.004) 0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.639 (0.343, 0.934)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.264 (-0.348, 0.877) 0.3987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005 (-0.621, 0.610) 0.9871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.174 (-0.721, 0.372) 0.5330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.013 (0.442, 1.585) 0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.321 (0.759, 1.882)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.205 (0.696, 1.714)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.799 (0.870, 2.729) 0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.958 (1.039, 2.876)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.872 (1.051, 2.693)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202 (-1.435, 1.839) 0.8093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.062 (-1.690, 1.566) 0.9405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.490 (-1.925, 0.945) 0.5043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.404 (1.816, 4.992)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.681 (2.104, 5.257)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.387 (1.951, 4.822)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWHtR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.000, 0.008) 0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006 (0.000, 0.011) 0.0329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005 (0.001, 0.010) 0.0207\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005 (-0.004, 0.014) 0.2591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (-0.006, 0.013) 0.4388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 (-0.007, 0.009) 0.8540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.007 (0.001, 0.016) 0.0201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013 (0.005, 0.022) 0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012 (0.004, 0.020) 0.0034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.801 (0.316, 3.285) 0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.100 (0.606, 3.593) 0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.128 (0.861, 3.394) 0.0012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.749 (-0.911, 4.409) 0.1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.200 (-1.454, 3.853) 0.3768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.229 (-2.040, 2.497) 0.8435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.540 (1.847, 7.233) 0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.819 (2.136, 7.502) 0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.745 (2.444, 7.047) 0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFor BMI, WC, and WHtR, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For FLI, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of BW with general obesity, central obesity, and hepatic steatosis among adolescents\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSimilarly, in Model 2, BW was positively associated with BMI-defined general obesity. WC-defined central obesity, WHtR-defined central obesity, and FLI-defined hepatic steatosis (ORs of 1.473, 1.277, 1.169, and 1.311, respectively). Compared to NBW, HBW was associated with an increased prevalence of overall obesity, central obesity, and hepatic steatosis (overall obesity: OR 2.629, 95% CI 1.831\u0026ndash;3.776; central obesity [WC]: OR 1.713, 95% CI 1.212\u0026ndash;2.421; central obesity [WHtR]: OR 1.618, 95% CI 1.252\u0026ndash;2.092; hepatic steatosis: OR 1.960, 95% CI 1.436\u0026ndash;2.677). However, LBW was not associated with odds of obesity and hepatic steatosis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of BW with general obesity, central obesity, and hepatic steatosis among adolescents.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Model OR (95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1 OR (95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2 OR (95%CI) P-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.398 (1.045, 1.870) 0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.440 (1.082, 1.918) 0.0135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.473 (1.108, 1.957) 0.0083\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.101 (0.675, 1.794) 0.7009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.067 (0.662, 1.720) 0.7900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.021 (0.634, 1.645) 0.9314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.412 (1.708, 3.407)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.569 (1.799, 3.669)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.629 (1.831, 3.776)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral obesity (WC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.211 (1.050, 1.398) 0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.246 (1.077, 1.441) 0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.277 (1.102, 1.480) 0.0014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.029 (0.805, 1.315) 0.8194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998 (0.780, 1.276) 0.9863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.059 (0.826, 1.357) 0.6537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.512 (1.187, 1.926) 0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.590 (1.238, 2.041) 0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.713 (1.212, 2.421) 0.0027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral obesity (WHtR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.104 (1.001, 1.259) 0.0398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.139 (0.996, 1.303) 0.0589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.169 (1.021, 1.338) 0.0250\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.031 (0.815, 1.304) 0.8014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997 (0.786, 1.265) 0.9814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945 (0.737, 1.211) 0.6537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.251 (1.001, 1.579) 0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.321 (1.037, 1.682) 0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.618 (1.252, 2.092) 0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHepatic steatosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.246 (0.980, 1.584) 0.0741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.288 (1.013, 1.637) 0.0402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.311 (1.029, 1.671) 0.0299\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.270 (0.858, 1.880) 0.2343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.217 (0.822, 1.803) 0.3282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.171 (0.790, 1.737) 0.4331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHBW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.823 (1.348, 2.465) 0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.936 (1.424, 2.631)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.960 (1.436, 2.677)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFor general obesity and central obesity, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, total energy intake, TG, TC, and HDL-C. For hepatic steatosis, the crude model did not adjust for any covariates; model 1 partially adjusted for age, sex, race/ethnicity, and PIR; and model 2 adjusted for age, sex, race/ethnicity, PIR, mother's age when born, maternal smoking history during pregnancy, and total energy intake.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRCS analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRCS analysis showed that BW was nonlinearly correlated with BMI, WC, WHtR, and FLI among adolescents (p for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, \u0026lt;\u0026thinsp;0.0001, 0.0403, and 0.0001, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). Similarly, BW was nonlinearly associated with the odds of general obesity, central obesity (WC), central obesity (WHtR), and hepatic steatosis among adolescents (p for nonlinearity was 0.004, 0.0027, 0.0027, and 0.0005, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D). Threshold effect analyses indicated that the association of BW with BMI, WC, WHtR, and FLI was only present for BW\u0026thinsp;\u0026gt;\u0026thinsp;3 kg (β of 1.453, 4.088, 0.016, and 5.598, respectively) (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Similarly, the associations of BW with overall obesity, central obesity, and hepatic steatosis were significant after their respective inflection points (ORs of 1.915, 1.430, 1.237, and 1.719, respectively) (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStratified analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe selected gender and race/ethnicity for stratified analysis. Interaction analyses indicated race/ethnicity was a significant effect modifier, influencing the association of BW with BMI, WHtR, and FLI (p for interaction 0.037, 0.026, and 0.022, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Interestingly, race/ethnicity also influenced the association of BW with central obesity (WC and WHtR) (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eBW was positively and nonlinearly associated with BMI, WC, WHtR, and FLI as well as general obesity, central obesity, and hepatic steatosis among U.S. adolescents in a national, large-sample, multiethnic, serial cross-sectional study. Compared with NBW, HBW was associated with significantly increased prevalence of BMI, WC, WHtR, and FLI, as well as obesity and hepatic steatosis, whereas LBW was not significantly associated. Race/ethnicity partially influenced these associations. These findings emphasize that U.S. adolescents with HBW are at risk for overall obesity, central obesity, and hepatic steatosis, and require early monitoring and timely prevention of obesity and steatosis. In addition, early maternal prevention of HBW may help reduce the risk of obesity and hepatic steatosis in subsequent adolescence.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first time that the association of LBW/HBW with general obesity, central obesity, and hepatic steatosis among adolescents has been explored in the national, large-sample, multiethnic NHANES database. A large body of observational research has suggested that HBW is associated with an increased risk of overall obesity and central obesity among children and adolescents. A meta-analysis published in 2011 that included 20 observational studies showed that HBW (compared to BW\u0026thinsp;\u0026le;\u0026thinsp;4000 g) was positively associated with the risk of obesity among children and adolescents (OR 2.07, 95% CI 1.91\u0026ndash;2.24), whereas LBW (compared to BW\u0026thinsp;\u0026ge;\u0026thinsp;2500 g) was associated with a decreased risk of obesity (OR 0.61, 95% CI 0.46\u0026ndash;0.80)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Interestingly, when NBW was used as a reference, the inverse correlation between LBW and obesity risk disappeared, while the positive correlation for HBW was maintained[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A large-sample cross-sectional analysis from China demonstrated that HBW (defined as \u0026ge;\u0026thinsp;3000 g) was associated with significantly increased odds of central obesity (as defined by WHtR) in children and adolescents (6\u0026ndash;17 years old) compared to the reference (BW in the range of 2,500-2,999 g), while LBW (\u0026lt;\u0026thinsp;2,500 g) was not associated with the likelihood of central obesity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another cross-sectional study from China that included 6561 participants showed that HBW increased the odds of general obesity in children and adolescents, while LBW decreased the prevalence of overweight[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A cross-sectional analysis including Brazilian school-based adolescents aged 10\u0026ndash;17 years suggested that BW was significantly and positively associated with BMI and WC, and that biological maturation partially mediated these associations[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A retrospective cohort study from rural India enrolling 756 children aged 7\u0026ndash;10 years showed that HBW (\u0026gt;\u0026thinsp;3500g) was associated with significantly increased BMI in childhood compared to BW at 2500-2999g[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Shi et al. included 10041 children and adolescents aged 7\u0026ndash;17 years in a large-sample cross-sectional analysis from China demonstrating a significant increase in the prevalence of general obesity in both participants with a BW\u0026thinsp;\u0026ge;\u0026thinsp;3500 g and a BW of 2500\u0026ndash;2999 g compared to the reference (BW of 3000\u0026ndash;3499 g)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In a large cross-sectional analysis including 16,580 Chinese children and adults aged 7\u0026ndash;17 years, Yuan et al. demonstrated that participants with HBW (BW of 3500-5000g) had a significantly increased risk of general obesity compared to those with BW of 3000-3499g, and that those with BW of 4000-4499g also had a significantly increased odds of central obesity (WHtR)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Interestingly, participants with very low BW (\u0026lt;\u0026thinsp;1500g) had the highest risk of central obesity (OR\u0026thinsp;=\u0026thinsp;2.03)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A national cross-sectional analysis from Korea revealed a higher prevalence of general obesity in female, but not male, adolescents with BW\u0026thinsp;\u0026gt;\u0026thinsp;75% compared to the reference with BW at 25\u0026ndash;75% (OR\u0026thinsp;=\u0026thinsp;2.13, 95% CI 1.03\u0026ndash;4.41)[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Zou et al. similarly showed in a cross-sectional analysis in China that HBW compared to NBW was associated with a significantly higher prevalence of general obesity among participants aged 6\u0026ndash;18 years (OR 1.611)[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, another retrospective cohort study from Australia indicated that BW was not associated with BMI in adolescents[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Finally, a national cross-sectional analysis from Iran suggested that compared to NBW (2500\u0026ndash;4000 g), HBW (\u0026gt;\u0026thinsp;4000 g) was associated with increased odds of general obesity and central obesity in children and adolescents aged 6\u0026ndash;18 years, whereas LBW was associated with a decreased prevalence of general obesity[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Overall, these studies collectively suggest that higher BW is associated with an increased prevalence of overall and central obesity in children and adolescents, whereas the association of LBW with obesity risk in the adolescent population remains controversial. Using standard BW classification criteria, our study demonstrated for the first time in a nationally representative sample of adolescents in the U.S. that BW was positively and nonlinearly associated with both BMI, WC, and WHtR as well as general and central obesity. Compared with NBW, HBW was associated with significantly increased BMI, WC, and WHtR and prevalence of obesity, whereas LBW was not significantly associated. These findings were consistent with some of the previous studies in demonstrating that attention to obesity risk and early prevention is required among adolescents with HBW. In addition, our study provided the first indication that race/ethnicity may influence these associations, and the positive associations were more significant in other Hispanic ethnic populations, suggesting the need for individualized screening and prevention strategies.\u003c/p\u003e \u003cp\u003eThe association of BW with hepatic steatosis in adolescents remains understudied. A multicenter cross-sectional study demonstrated that HBW was associated with an increased prevalence of biopsy-proven severe steatosis in participants\u0026thinsp;\u0026lt;\u0026thinsp;21 years of age (OR 1.82, 95% CI 1.15\u0026ndash;2.88), whereas LBW was associated with an increased odds of advanced liver fibrosis (OR 2.23, 95% CI 1.08\u0026ndash;4.62)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, another biopsy-proven NAFLD cohort demonstrated that small for gestational age was significantly associated with severe steatosis in children and adolescents aged 6\u0026ndash;17 years (OR 4.0, 95% CI 1.43\u0026ndash;10.9), whereas the prevalence of moderate steatosis was not significantly different between the groups[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A population-based cohort study from Australia demonstrated that BW was not associated with the risk of NAFLD (diagnosed by questionnaire and ultrasound) in adolescents aged 17 years[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similar to obesity indicators, our results suggested that HBW was associated with increased odds of hepatic steatosis among adolescents, whereas LBW was not significantly associated, suggesting that a history of HBW may require prompt attention as a group at risk for hepatic steatosis.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain the association of HBW with obesity and hepatic steatosis in adolescents. HBW may affect cardiac and circulatory health, leading to metabolic disorders and adipose tissue accumulation. Maternal blood glucose and subsequent fetal hyperinsulinemia may promote accelerated fetal growth[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. BW can be assumed to reflect the in-utero environment. Overnutrition during pregnancy affects BW and can lead to persistent epigenomic alterations, resulting in an increased risk of obesity in later life[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Some genetic factors may also contribute to these associations[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, HBW may lead to higher levels of growth factors, which may affect the risk of obesity in later life[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These mechanisms may confirm the developmental origins of health and disease, i.e., fetal programming permanently shapes the structure, function, and metabolism of the body and leads to subsequent disease[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strength of our study lies mainly in the fact that it is a national, multiethnic, large-sample, population-based study, making the findings potentially generalizable. In addition, this is the first study to explore the association of LBW/HBW with general obesity, central obesity, and hepatic steatosis in a representative sample of U.S. adolescents, which filled a research gap and has potential clinical value. However, there are limitations to our study. It was a cross-sectional analysis, and therefore could not draw causal associations and still could not adequately adjust for confounding factors. Due to database limitations, some important influences such as maternal obesity status, genetic susceptibility, and history of catch-up growth were not available, which may have affected the findings. The diagnosis of hepatic steatosis was based on noninvasive serologic markers rather than imaging or biopsy, which may have compromised accuracy. However, the FLI has proven its reliability in numerous NHANES and other population-level studies. Future large-sample well-characterized prospective cohort studies are needed to confirm these findings.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eIn a national cross-sectional analysis, BW was positively and nonlinearly associated with BMI, WC, WHtR, and FLI, as well as the prevalence of general obesity, central obesity, and hepatic steatosis among US adolescents. HBW but not LBW was associated with significantly increased odds of obesity and hepatic steatosis compared with NBW. These findings underline that adolescents with HBW may be at risk of developing obesity and hepatic steatosis and require early detection and intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflict of interest\u003c/b\u003e: None.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAcknowledgments\u003c/b\u003e: None.\u003c/p\u003e \u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\u003ch2\u003eAuthor contributions:\u003c/h2\u003e \u003cp\u003eJJH designed and developed the study. JJH reviewed literature. JJH composed the manuscript, and edited it.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003eThis study analyzed publicly available datasets and can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthics statement\u003c/b\u003e: All protocols were approved by the NCHS Ethics Review Board, and participants have provided written informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMugnier A, Chastant S, Lyazrhi F, Saegerman C, Grellet A. Definition of low birth weight in domestic mammals: a scoping review. Anim Health Res Rev. 2022;23(2):157-64. doi: 10.1017/s146625232200007x.\u003c/li\u003e\n\u003cli\u003eMebrahtu TF, Feltbower RG, Greenwood DC, Parslow RC. 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J Pediatr. 2017;187:141-6.e1. doi: 10.1016/j.jpeds.2017.03.007.\u003c/li\u003e\n\u003cli\u003eAyonrinde OT, Olynyk JK, Marsh JA, Beilin LJ, Mori TA, Oddy WH, et al. Childhood adiposity trajectories and risk of nonalcoholic fatty liver disease in adolescents. J Gastroenterol Hepatol. 2015;30(1):163-71. doi: 10.1111/jgh.12666.\u003c/li\u003e\n\u003cli\u003eBugianesi E, Bizzarri C, Rosso C, Mosca A, Panera N, Veraldi S, et al. Low Birthweight Increases the Likelihood of Severe Steatosis in Pediatric Non-Alcoholic Fatty Liver Disease. Am J Gastroenterol. 2017;112(8):1277-86. doi: 10.1038/ajg.2017.140.\u003c/li\u003e\n\u003cli\u003eHuang R, Yang S, Lei Y. Birth weight influences differently on systolic and diastolic blood pressure in children and adolescents aged 8-15. BMC Pediatr. 2022;22(1):278. doi: 10.1186/s12887-022-03346-7.\u003c/li\u003e\n\u003cli\u003eSanjeevi N, Freeland-Graves JH. Birth weight and prediabetes in a nationally representative sample of US adolescents. Clin Obes. 2022;12(2):e12504. doi: 10.1111/cob.12504.\u003c/li\u003e\n\u003cli\u003eBrathwaite KE, Levy RV, Sarathy H, Agalliu I, Johns TS, Reidy KJ, et al. Reduced kidney function and hypertension in adolescents with low birth weight, NHANES 1999-2016. Pediatr Nephrol. 2023;38(9):3071-82. doi: 10.1007/s00467-023-05958-2.\u003c/li\u003e\n\u003cli\u003eBedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33. doi: 10.1186/1471-230x-6-33.\u003c/li\u003e\n\u003cli\u003eLin MS, Lin TH, Guo SE, Tsai MH, Chiang MS, Huang TJ, et al. Waist-to-height ratio is a useful index for nonalcoholic fatty liver disease in children and adolescents: a secondary data analysis. BMC Public Health. 2017;17(1):851. doi: 10.1186/s12889-017-4868-5.\u003c/li\u003e\n\u003cli\u003eSousa MA, Guimar\u0026atilde;es IC, Daltro C, Guimar\u0026atilde;es AC. Association between birth weight and cardiovascular risk factors in adolescents. Arq Bras Cardiol. 2013;101(1):9-17. doi: 10.5935/abc.20130114.\u003c/li\u003e\n\u003cli\u003eXi B, Mi J, Zhao M, Zhang T, Jia C, Li J, et al. Trends in abdominal obesity among U.S. children and adolescents. Pediatrics. 2014;134(2):e334-9. doi: 10.1542/peds.2014-0970.\u003c/li\u003e\n\u003cli\u003eArshad T, Paik JM, Biswas R, Alqahtani SA, Henry L, Younossi ZM. Nonalcoholic Fatty Liver Disease Prevalence Trends Among Adolescents and Young Adults in the United States, 2007-2016. Hepatol Commun. 2021;5(10):1676-88. doi: 10.1002/hep4.1760.\u003c/li\u003e\n\u003cli\u003eKang M, Yoo JE, Kim K, Choi S, Park SM. Associations between birth weight, obesity, fat mass and lean mass in Korean adolescents: the Fifth Korea National Health and Nutrition Examination Survey. BMJ Open. 2018;8(2):e018039. doi: 10.1136/bmjopen-2017-018039.\u003c/li\u003e\n\u003cli\u003eZou Z, Yang Z, Yang Z, Wang X, Gao D, Dong Y, et al. 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Int J Obes (Lond). 2015;39(1):85-97. doi: 10.1038/ijo.2014.34.\u003c/li\u003e\n\u003cli\u003eOng KK, Dunger DB. Birth weight, infant growth and insulin resistance. Eur J Endocrinol. 2004;151 Suppl 3:U131-9. doi: 10.1530/eje.0.151u131.\u003c/li\u003e\n\u003cli\u003eWadhwa PD, Buss C, Entringer S, Swanson JM. Developmental origins of health and disease: brief history of the approach and current focus on epigenetic mechanisms. Semin Reprod Med. 2009;27(5):358-68. doi: 10.1055/s-0029-1237424.\u003c/li\u003e\n\u003c/ol\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":"birth weight, general obesity, central obesity, hepatic steatosis, NHANES,","lastPublishedDoi":"10.21203/rs.3.rs-6238043/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6238043/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBirth weight (BW) may influence subsequent risk of obesity and hepatic steatosis; however, the conclusions are controversial and lack exploration in US adolescents. We aimed to explore the association of BW (including low BW [LBW], normal BW [NBW], and high BW [HBW]) with body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), and fatty liver index (FLI), as well as general obesity, central obesity, and hepatic steatosis, in adolescents using NHANES 1999\u0026ndash;2020.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBW was obtained from participants' self-reports. Obesity and hepatic steatosis were diagnosed based on their respective specific cutoff values in adolescents. Multivariate linear regression and logistic regression analyses were used to explore these associations and calculate β and odds ratios (OR).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 6867 adolescent participants were enrolled. After adjusting for all confounders, BW was positively associated with BMI, WC, WHtR, and FLI (β of 0.639, 1.872, 0.005, and 2.128, respectively). Compared to NBW, HBW was associated with significantly increased BMI, WC, WHtR, and FLI (β of 1.205, 3.387, 0.012, and 4.745, respectively), whereas LBW was not. Similarly, compared to NBW, HBW was associated with significantly increased odds of general obesity, central obesity (as defined by WC/WHtR, respectively), and hepatic steatosis (OR 2.629, 1.713, 1.618, and 1.960, respectively). However, LBW was not significantly associated with obesity and steatosis. Race/ethnicity partially influenced these associations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHBW, but not LBW, was associated with increased prevalence of general obesity, central obesity, and hepatic steatosis among U.S. adolescents. These findings underscore that adolescents with HBW are at risk for obesity and steatosis and may require early screening and intervention, especially among other Hispanic ethnic groups.\u003c/p\u003e","manuscriptTitle":"Association of birth weight with general obesity, central obesity, and hepatic steatosis in a nationally representative sample of US adolescents: evidence from NHANES 1999-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 09:52:18","doi":"10.21203/rs.3.rs-6238043/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":"218c3b59-62ec-4d7e-8054-a703642f161b","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46055529,"name":"Health sciences/Risk factors"},{"id":46055530,"name":"Health sciences/Health care/Paediatrics"}],"tags":[],"updatedAt":"2025-07-04T11:15:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 09:52:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6238043","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6238043","identity":"rs-6238043","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}