Search

Objectives: The purpose of the present study was to explore the latent growth trajectory of body mass index (BMI) from birth to 24 months and comprehensively analyze body composition development influencing factor in preschool children., Methods: This ambidirectional cohort study was conducted in Tianjin, China, from 2017 to 2020, and children's regular medical check-up data from birth to 24 months were retrospectively collected. The growth models were used to fit BMI z-score trajectories for children aged 0-24 months. Crossover analysis and interaction model were used to explore the interaction of influencing factors., Results: We analyzed the growth trajectories of 3217 children, of these, 1493 children with complete follow-up data were included in the influencing factors analysis. Trajectories and parental prepregnancy BMI (ppBMI) were independent factors influencing children's body composition. When paternal ppBMI ≥24 kg/m 2 , regardless of maternal ppBMI, the risk of overweight and obesity in senior-class children was increased. The high trajectories played a partial mediating role in the association between paternal ppBMI and body composition in preschool children., Conclusions: BMI growth in children aged 0-24 months can be divided into three latent trajectories: low, middle, and high. These trajectories and parental ppBMI were independent and interactive factors influencing children's body composition. The high trajectories played a partial mediating role in the association between paternal ppBMI and body composition in preschool children. It is necessary to pay attention to the BMI growth level of children aged 0-24 months, which plays an important role in the development of body fat in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aims: Aim of this study is to assess dyslipidemia risk between children exposed to maternal gestational diabetes mellitus (GDM) and those not exposed., Methods: We recruited 1144 mother-child pairs (572 GDM and 572 non-GDM women matched by their offspring's age and sex). The age of offspring ranged from 3 to 9 years old. We used general linear models to compare mean values of different lipid profiles among children born to mothers with and without GDM. Logistic regression models were used to assess associations of maternal GDM with abnormal lipid profiles in offspring., Results: After adjustment for maternal and children's characteristics, children born to mothers with GDM had lower mean values of high-density-lipoprotein (HDL) cholesterol (1.40 ± 0.01 vs. 1.50 ± 0.01; p < 0.001) and higher mean levels of triglycerides/HDL cholesterol ratio (0.37 ± 0.01 vs. 0.35 ± 0.01; p < 0.05) in comparison with their counterparts born to mothers without GDM. Multivariate-adjusted odds ratios among children exposed to mothers with GDM compared with the counterparts were 2.11 (95% confidence interval [CI 1.15-3.88]) for low HDL cholesterol and 1.35 (95% CI 1.00-1.81) for high triglycerides/HDL cholesterol ratio, respectively., Conclusions: Maternal GDM was associated with an increased risk of hyperlipidemia in the offspring during early childhood aged from 3 to 9 years old., (© 2021 Diabetes UK.)

Objective: To analyze the interactions between maternal gestational diabetes mellitus (GDM) and genetically determined maternal body mass index (BMI) during pregnancy on offspring childhood obesity., Research Design and Methods: A total of 1114 Chinese mother-child pairs (560 GDM and 554 non-GDM) were included between August 2009 and July 2011. Maternal genetic risk score (GRS) of BMI during pregnancy was derived on the basis of 12 single nucleotide polymorphisms identified from a genome-wide association study. Offspring's BMI, BMI-for-age z score, weight, weight-for-age z score, waist circumference, sum of skinfolds, and body fat percentage during childhood were measured or calculated., Results: Maternal GRS of BMI during pregnancy significantly interacted with maternal GDM status on childhood risks of overweight and obesity (all P for interaction <.05). After multivariable adjustment, per unit of GRS was associated with a 24% (P<.001) and a 28% (P<.001) increased risk of overweight and obesity among children of GDM mothers, whereas no significant associations were observed among children of mothers without GDM. In addition, per unit GRS of BMI during pregnancy was significantly associated with 0.16 kg/m 2 higher BMI (P=.002), 0.09 higher BMI-for-age z score (P=.002), 0.24 kg higher weight (P=.04), 0.06 higher weight-for-age z score (P=.02), 0.28 cm higher waist circumference (P=.03), 0.94 mm higher sum of skinfolds (P=.004), and 0.37% higher body fat percentage (P=.03) among children of GDM mothers. There were no significant associations between maternal GRS of BMI during pregnancy and offspring's obesity-related outcomes among children of mothers without GDM., Conclusion: Our findings for the first time indicate that maternal GDM status may modify the relation between genetically determined maternal BMI during pregnancy and offspring's obesity risk during childhood., (Copyright © 2020 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Aims: To examine associations between the indicators of socio-economic status (SES) and gestational diabetes mellitus (GDM)., Methods: From 2010 to 2012, 17 659 women underwent glucose challenge test (GCT) and oral glucose tolerance test if GCT ≥ 7.8 mmol/L at 24-28 gestational weeks in 6 urban districts of Tianjin, China. Binary logistic regression was used to obtain adjusted odds ratio (OR) of SES for GDM, as defined by education attainment and family monthly income., Results: A total of 1264 women (7.2%) were found to have GDM. If the women with low-middle income and high school or below used as the reference group, the middle-high income group and the high income group were associated with decreased risks of GDM (OR: 0.85, 95%CI: 0.71-1.00 & 0.80, 0.65-0.98) while tertiary education attainment was associated with decreased risk of GDM (0.75, 0.58-0.97). Women with higher income and/or higher education attainment tended to have a decreased risk of GDM (P for trend: 0.0105). All these significant ORs were attenuated to be non-significant by adjustment for pre-pregnancy body mass index (BMI), but not by adjustment for gestational weight gain (GWG)., Conclusions: In urban Tianjin, indicators of high SES were associated with decreased risk of GDM via decreased pre-pregnancy BMI., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objective: Gestational weight gain (GWG) is associated with infant birth weight and childhood obesity; however, the patterns of GWG on infant birth weight are poorly understood., Methods: This analysis in 16,218 mother-child dyads from Tianjin, China, determined the risk of infant size at birth according to GWG occurring throughout the first and second trimester (early GWG) or during the third trimester (late GWG), according to maternal prepregnancy BMI and the 2009 Institute of Medicine recommendations., Results: Excessive GWG in early and late pregnancy had an increased risk for large-for-gestational-age (LGA) infants (odds ratio [OR]: 2.4; 95% confidence interval [CI]: 1.5-4.0, P < 0.001). Regardless of prepregnancy BMI, excessive GWG early in pregnancy (< 24 weeks) was associated with an increased risk of LGA infants (OR: 2.5; 95% CI: 2.1-3.1, P < 0.001), and inadequate early GWG was associated with a higher risk of small-for-gestational-age (SGA) infants (OR: 1.4; 95% CI: 1.2-1.7, P < 0.001)., Conclusions: The pattern of GWG early in pregnancy, regardless of GWG later in pregnancy, had the greatest impact on infant size at birth. Interventions initiated early in pregnancy may facilitate better adherence to the GWG guidelines and minimize the risk of LGA and SGA infants, a potential precursor for childhood obesity., (© 2017 The Obesity Society.)

Objective . To examine the relative impact of maternal prepregnancy body mass index (BMI) and glucose level at 24-28 gestational weeks on offspring's overweight status from birth to 3 years of age in China. Methods . Health care records of 21,354 mother-child pairs were collected. The single and joint associations of maternal prepregnancy BMI and glucose level at 24-28 gestational weeks with 0-3-year-old offspring's overweight status were assessed. Results . The odds ratios (95% confidence intervals) of offspring's macrosomia at birth and overweight/obesity at the 12th month, 24th month, and 36th month were 1.12 (1.11-1.13), 1.05 (1.04-1.06), 1.07 (1.06-1.08), and 1.11 (1.10-1.12) for each 1-unit increase (km/m 2 ) in maternal prepregnancy BMI and 1.13 (1.10-1.17), 1.01 (0.99-1.03), 0.99 (0.96-1.01), and 1.00 (0.97-1.02) for each 1-unit increase (mmol/L) in maternal glucose level at 24-28 gestational weeks, respectively. The positive association of maternal glucose level with macrosomia at birth was similar between prepregnancy normal weight (BMI < 24 kg/m 2 ) and overweight (BMI ≥ 24 kg/m 2 ); however, the positive association of high maternal glucose level with childhood overweight was only seen among prepregnancy normal weight mothers but not among overweight mothers. Conclusions . The impact of maternal gestational hyperglycemia on offspring's overweight before 3 years of age can be modified by prepregnancy BMI., Competing Interests: All authors declare that they have no conflict of interests relevant to this article to disclose.

Background: Epidemiological data on the association between body mass index (BMI) and heart failure (HF) risk among diabetic patients are rare., Methods and Results: We performed a prospective cohort study of risk for HF among 31 155 patients with type 2 diabetes mellitus (11 468 men and 19 687 women). Cox proportional hazards regression models were used to estimate the association of different levels of BMI with HF risk. During a mean follow-up of 7.8 years, 5834 subjects developed HF (2379 men and 3455 women). The multivariable-adjusted (age, race, smoking, income, and type of insurance) hazard ratios of HF associated with BMI levels (18.5-22.9, 23-24.9, 25-29.9 [reference group], 30-34.9, 35-39.9, and ≥40 kg/m(2)) at baseline were 0.95, 1.00, 1.00, 1.16, 1.64, and 2.02 (Ptrend<0.001) for men and 1.16, 1.16, 1.00, 1.23, 1.55, and 2.01 (Pnonlinear<0.001) for women, respectively. When we used an updated mean value of BMI, the association of HF risk with BMI did not change. When stratified by age, race, smoking status, and use of antidiabetic drugs, the positive associations among men and the J-shaped associations among women were still present., Conclusions: Our study suggests a positive association between BMI and HF risk among men and a J-shaped association between BMI and HF risk among women with type 2 diabetes mellitus., (© 2015 American Heart Association, Inc.)

Background and Purpose: Previous studies have evaluated the association of body mass index (BMI) with the risk of all-cause and cardiovascular disease mortality among diabetic patients, and results were controversial. No studies have focused on the association between BMI and stroke risk among diabetic patients. We aimed to examine the association of BMI with stroke risk among diabetic patients., Methods: We performed a prospective cohort study with 29,554 patients with type 2 diabetes mellitus. Cox proportional hazards regression models were used to estimate the association of different levels of BMI with stroke risk., Results: During a mean follow-up period of 8.3 years, 2883 participants developed stroke (2821 ischemic and 109 hemorrhagic). The multivariable-adjusted (age, sex, race, smoking, income, and type of insurance) hazard ratios associated with different levels of BMI at baseline (18.5-24.9 [reference group], 25-29.9, 30-34.9, 35-39.9, and ≥40 kg/m(2)) were 1.00, 0.86, 0.83, 0.76, and 0.70 (Ptrend<0.001) for total stroke, 1.00, 0.87, 0.85, 0.78, and 0.72 (Ptrend <0.001) for ischemic stroke, and 1.00, 0.76, 0.72, 0.54, and 0.53 (Ptrend=0.034) for hemorrhagic stroke, respectively. When we used an updated mean or the last visit value of BMI, the inverse association of BMI with stroke risk did not change. This inverse association was consistent among patients of different races, sex, ages, HbA1c levels, never and current smoking, and patients with and without using glucose-lowering, cholesterol-lowering, or antihypertensive agents., Conclusions: The present study demonstrates an inverse association between BMI and stroke risk among patients with type 2 diabetes mellitus., (© 2014 American Heart Association, Inc.)

Background: Several prospective studies have evaluated the association between body mass index (BMI) and death risk among patients with diabetes mellitus; however, the results have been inconsistent., Methods and Results: We performed a prospective cohort study of 19 478 black and 15 354 white patients with type 2 diabetes mellitus. Cox proportional hazards regression models were used to estimate the association of different levels of BMI stratification with all-cause mortality. During a mean follow-up of 8.7 years, 4042 deaths were identified. The multivariable-adjusted (age, sex, smoking, income, and type of insurance) hazard ratios for all-cause mortality associated with BMI levels (18.5-22.9, 23-24.9, 25-29.9, 30-34.9 [reference group], 35-39.9, and ≥40 kg/m(2)) at baseline were 2.12 (95% confidence interval [CI], 1.80-2.49), 1.74 (95% CI, 1.46-2.07), 1.23 (95% CI, 1.08-1.41), 1.00, 1.19 (95% CI, 1.03-1.39), and 1.23 (95% CI, 1.05-1.43) for blacks and 1.70 (95% CI, 1.42-2.04), 1.51 (95% CI, 1.27-1.80), 1.07 (95% CI, 0.94-1.21), 1.00, 1.07 (95% CI, 0.93-1.23), and 1.20 (95% CI, 1.05-1.38) for whites, respectively. When stratified by age, smoking status, patient type, or the use of antidiabetic drugs, a U-shaped association was still present. When BMI was included in the Cox model as a time-dependent variable, the U-shaped association of BMI with all-cause mortality risk did not change., Conclusions: The present study indicated a U-shaped association of BMI with all-cause mortality risk among black and white patients with type 2 diabetes mellitus. A significantly increased risk of all-cause mortality was observed among blacks with BMI <30 kg/m(2) and ≥35 kg/m(2) and among whites with BMI <25 kg/m(2) and ≥40 kg/m(2) compared with patients with BMI of 30 to 34.9 kg/m(2)., (© 2014 American Heart Association, Inc.)

Objective: The association between obesity and coronary heart disease (CHD) risk remains debatable, and no studies have assessed this association among diabetic patients. The aim of our study was to investigate the association between BMI and CHD risk among patients with type 2 diabetes., Research Design and Methods: The sample included 30,434 diabetic patients (10,955 men and 19,479 women) 30-95 years of age without a history of CHD or stroke in the Louisiana State University Hospital-Based Longitudinal Study., Results: During a mean follow-up period of 7.3 years, 7,414 subjects developed CHD. The multivariable-adjusted hazard ratios for CHD across levels of BMI at baseline (18.5-24.9, 25-29.9, 30-34.9, 35-39.9, and ≥40 kg/m(2)) were 1.00, 1.14 (95% CI 1.00-1.29), 1.27 (1.12-1.45), 1.54 (1.34-1.78), and 1.42 (1.23-1.64) (Ptrend < 0.001) in men and 1.00, 0.95 (0.85-1.07), 0.95 (0.84-1.06), 1.06 (0.94-1.20), and 1.09 (1.00-1.22) (Ptrend < 0.001) in women, respectively. When we used an updated mean or last visit value of BMI, the positive association between BMI and CHD risk did not change in men. However, the positive association of BMI with CHD changed to a U-shaped association in women when we used the last visit value of BMI., Conclusions: Our study suggests that there is a positive association between BMI at baseline and during follow-up with the risk of CHD among patients with type 2 diabetes. We indicate a U-shaped association between BMI at the last visit and the risk of CHD among women with type 2 diabetes., (© 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Objective: To evaluate the effects of prepregnancy BMI and weight change from prepregnancy to postpartum on postpartum type 2 diabetes (T2D) risk among women with gestational diabetes (GDM)., Methods: A retrospective cohort study in 1,263 GDM women at 1-5 years after delivery was performed. Cox proportional hazards regression models were used to evaluate the association of prepregnancy BMI and weight change with T2D and prediabetes risks., Results: The multivariable-adjusted hazard ratios based on different levels of prepregnancy BMI (<23, 23-24.9, 25-29.9, and ≥30 kg/m(2) ) were 1.00, 1.77, 2.35, and 6.54 (Ptrend  < 0.001) for incident T2D, and 1.00, 1.46, 1.87, and 1.79 (Ptrend  < 0.001) for incident prediabetes, respectively. Compared with women with stable weight (±3 kg), those with weight gain ≥7 kg had an 86% and a 32% increased risk of diabetes and prediabetes, and those with weight loss ≥3 kg had a 45% decreased risk of prediabetes. The positive associations of prepregnancy BMI with incident diabetes and prediabetes risk were persistent in women with different levels of weight change (<3 kg and ≥3 kg)., Conclusion: Prepregnancy obesity and excessive weight gain from prepregnancy to postpartum increase postpartum diabetes and prediabetes risks among GDM women., (Copyright © 2014 The Obesity Society.)

Aims: To examine the independent and interactive effects of maternal gestational diabetes mellitus (GDM) and high pre‐pregnancy body mass index (BMI) on the risk of offspring adverse growth patterns. Materials and Methods: One thousand six hundred and eighty one mother‐child pairs were followed for 8 years in Tianjin, China. Group‐based trajectory modelling was used to identify offspring growth patterns. Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs) of GDM and high pre‐pregnancy BMI for offspring adverse growth patterns. Restricted cubic spline was used to identify cut‐off points. Additive interactions and multiplicative interactions were used to test interactive effects between GDM and high pre‐pregnancy BMI for adverse growth patterns. Results: Four distinct growth patterns were identified in offspring, including normal growth pattern, persistent lean growth pattern, late obesity growth pattern (LOGP), and persistent obesity growth pattern (POGP). Maternal high pre‐pregnancy BMI was associated with LOGP and POGP (adjusted OR, 95% CI: 2.38, 1.74–3.25 & 4.92, 2.26–10.73). GDM greatly enhanced the adjusted OR of high pre‐pregnancy BMI for LOGP up to 3.48 (95% CI: 2.25–5.38). Additive interactions and multiplicative interactions between both risk factors were significant for LOGP but not for POGP. Conclusions: Maternal high pre‐pregnancy BMI was associated with increased risk of LOGP and POGP, whereas GDM greatly enhanced the risk of high pre‐pregnancy BMI for LOGP. [ABSTRACT FROM AUTHOR]

To evaluate the independent association of seasonal variation with GDM incidence in Tianjin, China, and to test whether there is an additive interaction between seasonal variation and pre-pregnancy body mass index (BMI) on GDM incidence. A population-based observational cohort study was conducted using the healthcare records data from Tianjin, China. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction between pre-pregnancy BMI groups and seasons was estimated by using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S). Among the 112,639 pregnant women, 20.8% developed GDM at 24–28 weeks of gestation. The multivariable adjusted ORs and 95% CIs were 1.00, 1.00 (0.96–1.05), 1.15 (1.09–1.20) and 1.22 (1.16–1.29) respectively based on seasons (spring, summer, autumn and winter). Compared with the spring/summer and pre-pregnant BMI < 24 kg/m2 group, co-presence of autumn/winter and pre-pregnancy BMI ≥ 24 kg/m2 increased the OR from 1.00 to 2.70 (95% CI 2.28–3.20), with a significant additive interaction: RERI (0.32, 95% CI 0.19–0.45), S (1.21, 95% CI 1.12–1.31) and AP (0.11, 95% CI 0.07–0.16). Autumn/winter is an independent risk factor for GDM incidence, and can significantly amplify the obesity-associated risk for GDM incidence. The underlying mechanism warrants further investigations. We suggest that seasonality is an additional factor when interpreting OGTT results for the diagnosis of GDM. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to develop a machine learning–based prediction model for gestational diabetes mellitus (GDM) in early pregnancy in Chinese women. Materials and methods: We used an established population‐based prospective cohort of 19,331 pregnant women registered as pregnant before the 15th gestational week in Tianjin, China, from October 2010 to August 2012. The dataset was randomly divided into a training set (70%) and a test set (30%). Risk factors collected at registration were examined and used to construct the prediction model in the training dataset. Machine learning, that is, the extreme gradient boosting (XGBoost) method, was employed to develop the model, while a traditional logistic model was also developed for comparison purposes. In the test dataset, the performance of the developed prediction model was assessed by calibration plots for calibration and area under the receiver operating characteristic curve (AUR) for discrimination. Results: In total, 1484 (7.6%) women developed GDM. Pre‐pregnancy body mass index, maternal age, fasting plasma glucose at registration, and alanine aminotransferase were selected as risk factors. The machine learning XGBoost model‐predicted probability of GDM was similar to the observed probability in the test data set, while the logistic model tended to overestimate the risk at the highest risk level (Hosmer–Lemeshow test p value: 0.243 vs. 0.099). The XGBoost model achieved a higher AUR than the logistic model (0.742 vs. 0.663, p < 0.001). This XGBoost model was deployed through a free, publicly available software interface (https://liuhongwei.shinyapps.io/gdm%5frisk%5fcalculator/). Conclusion: The XGBoost model achieved better performance than the logistic model. [ABSTRACT FROM AUTHOR]

In the article, the authors present their study which examined the effects of lifestyle intervention in maternal gestational diabetes mellitus (GDM) on the growth pattern of newborns before two years of age. In the research, a population-based randomized controlled trial (RCT) was carried out in Tianjin, China. The study compared the effectiveness of intensive care (IC) against usual care (UC) on adverse pregnancy results in patients with GDM.

Objectives: To examine the association of maternal pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) with anthropometry in the offspring of mothers with gestational diabetes mellitus (GDM). Methods: We performed a retrospective cohort study in 1263 GDM mother-child pairs. General linear models and Logistic regression models were used to assess the single and joint associations of maternal pre-pregnancy BMI (normal weight, overweight, and obesity) and GWG (inadequate, adequate and excessive GWG) with anthropometry and overweight status in the offspring from birth to 1-5 years old. Results: Maternal pre-pregnancy BMI and GWG were positively associated with birth weight for gestational age Z score and birth weight for length for gestational age Z score at birth, and weight for age Z score, length/height for age Z score, and weight for length/height Z score at of 1-5 years old offspring. Maternal pre-pregnancy overweight, obesity, and excessive GWG were associated with increased risks of large for gestational age [ORs 95% CIs = 1.87 (1.37-2.55), 2.98 (1.89-4.69), and 2.93 (2.07-4.13), respectively] and macrosomia [ORs 95% CIs = 2.06 (1.50-2.84), 2.89 (1.78-4.70), and 2.84 (1.98-4.06), respectively] at birth and childhood overweight at 1-5 years old [ORs 95% CIs = 1.26 (0.92-1.73), 1.96 (1.24-3.09), and 1.59 (1.15-2.21), respectively]. Conclusions: Offspring born to GDM mothers with pre-pregnancy overweight/obesity or excessive GWG were associated with increased risks of large for gestational age and macrosomia at birth, and childhood overweight at 1-5 years old, compared with those born to GDM mothers with pre-pregnancy normal weight and adequate GWG. [ABSTRACT FROM AUTHOR]

Aims: Very few studies have assessed the association of fasting and 2h glucose, and HbA1c during pregnancy with postpartum diabetes risk among women with prior gestational diabetes mellitus (GDM). We assessed the association of fasting glucose, 2h glucose and HbA1c at 26-30 gestational weeks with postpartum diabetes risk among women with prior GDM.Methods: A cohort study in 1263 GDM women at 1-5 years after delivery was performed. Cox proportional hazards regression models were used to evaluate the association of fasting and 2h plasma glucose, and HbA1c at 26-30 gestational weeks with the risk of diabetes at postpartum.Results: The multivariable-adjusted (age, pre-pregnancy body mass index, weight gain during pregnancy, current body mass index, family history of diabetes, marital status, education, family income, smoking status, passive smoking, leisure-time physical activity, alcohol drinking, and intake of energy, saturated fat, and dietary fiber) hazard ratios of postpartum diabetes were 1.61 (95% confidence interval [CI]: 1.36-1.91) for each 1 mmol/l increase in fasting glucose during pregnancy, 1.63 (95% CI: 1.45-1.84) for each 1 mmol/l increase in 2h glucose during pregnancy, 2.11 (95% CI: 1.50-2.97) for each 1 unit (%) increase in HbA1c during pregnancy. When fasting glucose, 2h glucose and HbA1c during pregnancy were entered multivariable-adjusted model simultaneously, 2h glucose and HbA1c but not fasting glucose remained to be significant and positive predictors for postpartum diabetes.Conclusions: For women with prior GDM, 2h plasma glucose and HbA1c during pregnancy are independent predictors of postpartum diabetes, but fasting plasma glucose during pregnancy is not. [ABSTRACT FROM AUTHOR]