Dietary intakes of branch chained amino acids and obesity risk among Chinese gestational diabetes women.

Introduction: Epidemiological studies have assessed the correlation between daily dietary branch chain amino acid (BCAA) intakes and the risk of obesity, however, the findings from these studies were inconsistent and investigations among GDM women were few. Objective: The present study was to investigate the associations of daily BCAA intakes with the risks of overweight and abdominal obesity among women with prior gestational diabetes mellitus (GDM) postpartum. Method: We performed a cross-sectional study of 1,263 women with prior GDM at 1-5 years post-delivery. Logistic regression models were used to estimate the associations of daily dietary intakes of BCAAs with the risks of overweight and abdominal obesity. Results: The multivariable-adjusted odds ratios (ORs) across quartiles of daily BCAA intakes postpartum were 1.42 (95% confidence interval [CI] 1.02-1.97), 1.00 (reference), 1.21 (95% CI 0.88-1.68), and 1.31 (95% CI 0.95-1.81) for general overweight, and 1.38 (95% CI 0.99-1.90), 1.00, 1.19 (95% CI 0.86-1.64), and 1.43 (95% CI 1.04-1.98) for abdominal obesity, respectively. Women with the lowest quartile of daily BCAA intakes significantly increased the risks of general overweight (OR 1.49; 95 %CI 1.06-2.09) and abdominal obesity (OR 1.50; 95 %CI 1.08-2.11) compared with women at quartile 2 of daily BCAA intakes after further adjustment of daily energy intake. Conclusion: The present study indicated that daily lower BCAA intakes were associated with increased risks of general overweight and abdominal obesity among women with prior GDM.

Body mass index trajectories and time in target range after delivery and long-term type 2 diabetes risk in women with a history of gestational diabetes mellitus.

AIMS: This study aims to determine whether postpartum body mass index (BMI) trajectories and its time in target range (TTR) are associated with long-term type 2 diabetes risk in women with a history of gestational diabetes mellitus (GDM). MATERIALS AND METHODS: The present study included 1057 women with a history of GDM who participated in the Tianjin Gestational Diabetes Mellitus Prevention Program (TGDMPP). Oral glucose tolerance tests or physician-diagnosed information were used to diagnose type 2 diabetes after a median follow-up period of 8.47 years. Latent class modelling was applied to identify trajectories of BMI after delivery. TTR was defined as the proportion of time that BMI was within the standard range (18.5 ≤ BMI < 24.0 kg/m2). The associations of BMI trajectories and TTR with type 2 diabetes risk were analysed using multivariable Cox modelling. RESULTS: Five distinct trajectories of postpartum BMI were identified. Compared with low-stable class, the multivariable-adjusted hazard ratios of type 2 diabetes were 2.02 (95% confidence interval 0.99-4.10) for median-stable class, 3.01 (1.17-7.73) for high-stable class, 2.15 (0.63-7.38) for U-shape class and 7.15 (2.08-24.5) for inverse U-shape class (p for trend = 0.012), respectively. Multivariable-adjusted hazard ratios of type 2 diabetes associated with postpartum BMI TTR of 100%, >43.4%-<100%, >0%-≤43.4% and 0% were 1.00, 1.84 (0.72-4.73), 2.75 (1.23-6.15) and 2.31 (1.05-5.08) (p for trend = 0.039), respectively. CONCLUSIONS: Postpartum BMI trajectories of high-stable and inverse U-shape class as well as lower TTR were associated with an increased risk of type 2 diabetes among women with a history of GDM. Reducing BMI to a normal range in the early postpartum period and maintaining stable over time could attenuate the development of long-term type 2 diabetes.

Construction of 2-azidacetic acid functionalized high-crystallinity fluorescent covalent organic framework: Applications in mitoxantrone and Fe3+ sensing and adsorption.

Due to the dual functions of fluorescence detection and adsorption, fluorescent covalent organic frameworks (COFs) have attracted significant attention. However, common fluorescent COFs often exhibit unsatisfactory fluorescence properties and selectivity, coupled with poor solution dispersibility, which limit their effectiveness in detection and adsorption applications. In response, a novel post-modified fluorescent COF (named AZC-COF) was synthesized by connecting a fluorescent COF (COF-TB) with 2-azidacetic acid through a copper-catalyzed aide-alkyne cycloaddition (CuAAC) reaction. AZC-COF demonstrated excellent solution dispersibility and robust green fluorescence, boasting an absolute fluorescence quantum yield (QY) of 7.58%, which was 13.5 times higher than that of COF-TB. Furthermore, leveraging the active carboxylic acid and triazole sites, AZC-COF exhibited remarkable binding abilities for mitoxantrone (MIX) and Fe3+, enabling sensitive detection and efficient adsorption of them. In contrast, due to the absence of these functional sites, COF-TB showed poor detection and enrichment capabilities for MIX and Fe3+. The impressive detection and adsorption efficiencies of MIX and Fe3+ in environmental water, aquatic organism (fish) and plasma samples underscore the potential of AZC-COF as a detection-adsorption platform. Additionally, AZC-COF demonstrated low toxicity and hemolytic activity, alongside promising potential for cell imaging and detection of MIX and Fe3+, highlighting its considerable application prospect in biological systems.

Identification and Potential Clinical Utility of Common Genetic Variants in Gestational Diabetes among Chinese Pregnant Women.

Background: The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications. Methods: We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants. Results: Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals. Conclusion: Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning.

Systemic amyloidosis involves the deposition of misfolded proteins in organs/tissues, leading to progressive organ dysfunction and failure. Congo red is the gold-standard chemical stain for visualizing amyloid deposits in tissue, showing birefringence under polarization microscopy. However, Congo red staining is tedious and costly to perform, and prone to false diagnoses due to variations in amyloid amount, staining quality and manual examination of tissue under a polarization microscope. We report virtual birefringence imaging and virtual Congo red staining of label-free human tissue to show that a single neural network can transform autofluorescence images of label-free tissue into brightfield and polarized microscopy images, matching their histochemically stained versions. Blind testing with quantitative metrics and pathologist evaluations on cardiac tissue showed that our virtually stained polarization and brightfield images highlight amyloid patterns in a consistent manner, mitigating challenges due to variations in chemical staining quality and manual imaging processes in the clinical workflow.

Light-field tomographic fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging technique that enables the visualization of biological samples at the molecular level by measuring the fluorescence decay rate of fluorescent probes. This provides critical information about molecular interactions, environmental changes, and localization within biological systems. However, creating high-resolution lifetime maps using conventional FLIM systems can be challenging, as it often requires extensive scanning that can significantly lengthen acquisition times. This issue is further compounded in three-dimensional (3D) imaging because it demands additional scanning along the depth axis. To tackle this challenge, we developed a computational imaging technique called light-field tomographic FLIM (LIFT-FLIM). Our approach allows for the acquisition of volumetric fluorescence lifetime images in a highly data-efficient manner, significantly reducing the number of scanning steps required compared to conventional point-scanning or line-scanning FLIM imagers. Moreover, LIFT-FLIM enables the measurement of high-dimensional data using low-dimensional detectors, which are typically low cost and feature a higher temporal bandwidth. We demonstrated LIFT-FLIM using a linear single-photon avalanche diode array on various biological systems, showcasing unparalleled single-photon detection sensitivity. Additionally, we expanded the functionality of our method to spectral FLIM and demonstrated its application in high-content multiplexed imaging of lung organoids. LIFT-FLIM has the potential to open up broad avenues in both basic and translational biomedical research.

Serum CD44 levels in early pregnancy and its genetic variants for increased risk of gestational diabetes mellitus in Chinese pregnant women.

This study aimed to explore associations of serum cluster of differentiation 44 (CD44) levels and its genetic variants in early pregnancy with gestational diabetes mellitus (GDM). We conducted a 1:1 case-control study (n = 414) nested in a prospective cohort of 22,302 pregnant women recruited from 2010 to 2012 in Tianjin, China. Blood samples were collected at the first antenatal care visit (at a median of 10th gestational week). Binary conditional logistic regressions were performed to examine associations of serum CD44 levels and its genetic variants with increased risk of GDM. In this study, we found that serum CD44 levels in early pregnancy was associated with GDM risk in a U-shaped manner. High serum CD44 levels and its genetic risk score in early pregnancy were associated with markedly increased risk of GDM after adjustment for traditional confounders (OR: 1.95, 95%CI: 1.12-3.40 & 1.95, 1.05-3.61). Furthermore, after adjustment for serum CD44 levels, the OR of CD44 genetic risk score for GDM was slightly attenuated but not significant (1.84, 0.98-3.48). In conclusion, serum CD44 levels and its genetic variants in early pregnancy were associated with GDM risk in Chinese pregnant women, with the effect of CD44 genetic variants being accounted for by serum CD44. SIGNIFICANCE: Recent studies suggested that pregnant women with GDM may have abnormal levels of CD44 and abnormal expression of CD44 gene, but it is uncertain whether abnormal CD44 plays a causal role in occurrence of GDM. Specifically, it remains unknown whether serum CD44 levels in early pregnancy and its genetic variants can predict the later occurrence of GDM. In this study, we found that high serum CD44 levels in early pregnancy and its genetic variants were associated with markedly increased risk of GDM in Chinese pregnant women, with the effect of CD44 genetic variants being largely accounted for by serum CD44 levels. Our study is the first reporting that serum CD44 levels and its genetic variants were associated with markedly increased risk of GDM. These multi-omics risk markers may be useful for identification of women at high risk of GDM in early pregnancy. Our findings also provide new insights into the disease mechanisms.

Insertable Glucose Sensor Using a Compact and Cost-Effective Phosphorescence Lifetime Imager and Machine Learning.

Optical continuous glucose monitoring (CGM) systems are emerging for personalized glucose management owing to their lower cost and prolonged durability compared to conventional electrochemical CGMs. Here, we report a computational CGM system, which integrates a biocompatible phosphorescence-based insertable biosensor and a custom-designed phosphorescence lifetime imager (PLI). This compact and cost-effective PLI is designed to capture phosphorescence lifetime images of an insertable sensor through the skin, where the lifetime of the emitted phosphorescence signal is modulated by the local concentration of glucose. Because this phosphorescence signal has a very long lifetime compared to tissue autofluorescence or excitation leakage processes, it completely bypasses these noise sources by measuring the sensor emission over several tens of microseconds after the excitation light is turned off. The lifetime images acquired through the skin are processed by neural network-based models for misalignment-tolerant inference of glucose levels, accurately revealing normal, low (hypoglycemia) and high (hyperglycemia) concentration ranges. Using a 1 mm thick skin phantom mimicking the optical properties of human skin, we performed in vitro testing of the PLI using glucose-spiked samples, yielding 88.8% inference accuracy, also showing resilience to random and unknown misalignments within a lateral distance of ∼4.7 mm with respect to the position of the insertable sensor underneath the skin phantom. Furthermore, the PLI accurately identified larger lateral misalignments beyond 5 mm, prompting user intervention for realignment. The misalignment-resilient glucose concentration inference capability of this compact and cost-effective PLI makes it an appealing wearable diagnostics tool for real-time tracking of glucose and other biomarkers.

Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling.

Objective and Impact Statement: Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis. Here, we introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained BC tissue images. Introduction: Accurate assessment of IHC-stained tissue slides for HER2 expression levels is essential for both treatment guidance and understanding of cancer mechanisms. Nevertheless, the traditional workflow of manual examination by board-certified pathologists encounters challenges, including inter- and intra-observer inconsistency and extended turnaround times. Methods: Our deep learning-based method analyzes morphological features at various spatial scales, efficiently managing the computational load and facilitating a detailed examination of cellular and larger-scale tissue-level details. Results: This approach addresses the tissue heterogeneity of HER2 expression by providing a comprehensive view, leading to a blind testing classification accuracy of 84.70%, on a dataset of 523 core images from tissue microarrays. Conclusion: This automated system, proving reliable as an adjunct pathology tool, has the potential to enhance diagnostic precision and evaluation speed, and might substantially impact cancer treatment planning.

Pyramid diffractive optical networks for unidirectional image magnification and demagnification.

Diffractive deep neural networks (D2NNs) are composed of successive transmissive layers optimized using supervised deep learning to all-optically implement various computational tasks between an input and output field-of-view. Here, we present a pyramid-structured diffractive optical network design (which we term P-D2NN), optimized specifically for unidirectional image magnification and demagnification. In this design, the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification. This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction, while inhibiting the image formation in the opposite direction-achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume. Furthermore, the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength. We also designed a wavelength-multiplexed P-D2NN, where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions, at two distinct illumination wavelengths. Furthermore, we demonstrate that by cascading multiple unidirectional P-D2NN modules, we can achieve higher magnification factors. The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination, successfully matching our numerical simulations. P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.

Call to action for a life course approach.

Gestational diabetes remains the most common medical disorder in pregnancy, with short-term and long-term consequences for mothers and offspring. New insights into pathophysiology and management suggest that the current gestational diabetes treatment approach should expand from a focus on late gestational diabetes to a personalised, integrated life course approach from preconception to postpartum and beyond. Early pregnancy lifestyle intervention could prevent late gestational diabetes. Early gestational diabetes diagnosis and treatment has been shown to be beneficial, especially when identified before 14 weeks of gestation. Early gestational diabetes screening now requires strategies for integration into routine antenatal care, alongside efforts to reduce variation in gestational diabetes care, across settings that differ between, and within, countries. Following gestational diabetes, an oral glucose tolerance test should be performed 6-12 weeks postpartum to assess the glycaemic state. Subsequent regular screening for both dysglycaemia and cardiometabolic disease is recommended, which can be incorporated alongside other family health activities. Diabetes prevention programmes for women with previous gestational diabetes might be enhanced using shared decision making and precision medicine. At all stages in this life course approach, across both high-resource and low-resource settings, a more systematic process for identifying and overcoming barriers to preventative care and treatment is needed to reduce the current global burden of gestational diabetes.

Long-term risk of overweight in offspring of Chinese women with gestational diabetes defined by IADPSG's but not by WHO's criteria.

AIMS: To examine long-term risk of overweight in offspring of women with gestational diabetes mellitus (GDM) defined by the International Association of Diabetes and Pregnancy Study Group (IADPSG)'s criteria but not by the 1999 World Health Organization (WHO)'s criteria. METHODS: We followed up 1681 mother-child pairs for 8 years in Tianjin, China. Overweight in children aged 1-5 and 6-8 were respectively defined as body mass index-for-age and -sex above the 2 z-score and 1 z-score curves of the WHO's child growth standards. Logistic regression was performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs) of hyperglycemia indices at oral glucose tolerance test and GDMs defined by different criteria for offspring overweight at different ages. RESULTS: Offspring of women with fasting plasma glucose ≥5.1 mmol/L were at increased risk of overweight at 6-8 years old (OR:1.45, 95% CI: 1.09-1.93). GDM defined by the IADPSG's criteria only was associated with increased risk of childhood overweight at 6-8 years old (1.65, 1.13-2.40), as compared with non-GDM by either of the two sets of criteria. CONCLUSIONS: Newly defined GDM by the IADPSG's criteria increased the risk of offspring overweight aged 6-8 years.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms.

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms.

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

[Facial nerve protection in surgery for rare tumors of the internal auditory canal].

Objective:To explore strategies for preserving facial nerve function during surgeries for rare tumors of the internal auditory canal. Methods:A total of 235 cases of internal auditory canal tumors treated between 2010 and 2023 were included, encompassing vestibular schwannomas, cavernous hemangiomas, meningiomas, and other rare tumors. Various data, including clinical presentations, imaging classifications, and treatment processes, were meticulously analyzed to delineate the characteristics of rare tumors and assess pre-and postoperative facial nerve function. Results:Among all internal auditory canal tumors, vestibular schwannomas accounted for 91.9%. In rare tumors, facial nerve schwannomas constituted 5.3%, cavernous hemangiomas 26.3%, meningiomas 15.8%, and arterial aneurysms 10.5%. Significantly, patients with cavernous hemangiomas displayed pronounced invasion of the facial nerve by the tumor, in contrast to other tumor types where clear boundaries with the facial nerve were maintained. During surgery, individualized approaches and strategies for facial nerve protection were implemented for different tumor types, involving intraoperative dissection, tumor excision, and facial nerve reconstruction. Conclusion:Preservation of the facial nerve is crucial in the surgical management of rare tumors of the internal auditory canal. Accurate preoperative diagnosis, appropriate timing of surgery, selective surgical approaches, and meticulous intraoperative techniques can maximize the protection of facial nerve function. Personalized treatment plans and strategies for facial nerve functional reconstruction are anticipated to enhance surgical success rates, reduce the risk of postoperative facial nerve dysfunction, and ultimately improve the quality of life for patients.

Adverse birth outcomes and childhood overweight at age of 3-8 years in a prospective cohort study in Tianjin, China.

OBJECTIVES: To explore associations between adverse birth outcomes and childhood overweight at 3-8 years of age. DESIGN: A prospective cohort study. SETTING: Six central urban districts of Tianjin, China. PARTICIPANTS: 1681 woman-child pairs. METHODS: 1681 woman-child pairs were followed up for 8 years in Tianjin, China. Demographic and clinical information including birth outcomes was collected longitudinally, commencing from first antenatal care visit till postpartum period. Offspring height and weight were measured at 3-8 years of age. High and low weight/length ratios (WLR) at birth were, respectively, defined as ≥90th and ≤10th gestational week and sex-specific percentiles. Overweight for children at 3-5 and 6-8 years of age were, respectively, defined as body mass index (BMI)-for-age and -sex above the 2 z-score and 1 z-score curves of the WHO's child growth standards. Binary logistic regression analysis was used to obtain ORs and 95% CI with a stepwise backward selection method to select independent predictors. PRIMARY OUTCOMES MEASURES: Childhood overweight. RESULTS: Of 1681 children, 10.7% (n=179) and 27.8% (n=468) developed overweight at 3-5 and 6-8 years of age, respectively. Large for gestational age (LGA) was associated with increased risk of overweight at 3-5 years of age (aOR: 1.86, 95% CI: 1.27 to 2.72) while high WLR at birth was associated with increased risk of overweight at 6-8 years of age (1.82, 1.41 to 2.34). Low WLR at birth was associated with decreased risk of overweight at 6-8 years of age (0.52, 0.30 to 0.90). CONCLUSIONS: LGA and high WLR at birth predicted childhood overweight at 3-5 and 6-8 years of age, respectively. Low WLR at birth was associated with decreased risk of childhood overweight at 6-8 years of age.

Maternal gestational diabetes and childhood adiposity risk from 6 to 8 years of age.

BACKGROUND/OBJECTIVE: Previous studies found conflicting results on the association between maternal gestational diabetes mellitus (GDM) and childhood overweight/obesity. This study was to assess the association between maternal GDM and offspring's adiposity risk from 6 to 8 years of age. METHODS: The present study longitudinally followed 1156 mother-child pairs (578 GDM and 578 non-GDM) at 5.9 ± 1.2 years postpartum and retained 912 mother-child pairs (486 GDM and 426 non-GDM) at 8.3 ± 1.6 years postpartum. Childhood body mass index (BMI), waist circumference, body fat and skinfold were measured using standardized methods. RESULTS: Compared with the counterparts born to mothers with normal glucose during pregnancy, children born to mothers with GDM during pregnancy had higher mean values of adiposity indicators (waist circumference, body fat, subscapular skinfold and suprailiac skinfold) at 5.9 and 8.3 years of age. There was a positive association of maternal GDM with changes of childhood adiposity indicators from the 5.9-year to 8.3-year visit, and ß values were significantly larger than zero: +0.10 (95% CI: 0.02-0.18) for z score of BMI for age, +1.46 (95% CI: 0.70-2.22) cm for waist circumference, +1.78% (95% CI: 1.16%-2.40%) for body fat, +2.40 (95% CI: 1.78-3.01) mm for triceps skinfold, +1.59 (95% CI: 1.10-2.09) mm for subscapular skinfold, and +2.03 (95% CI: 1.35-2.71) mm for suprailiac skinfold, respectively. Maternal GDM was associated with higher risks of childhood overweight/obesity, central obesity, and high body fat (Odd ratios 1.41-1.57 at 5.9 years of age and 1.73-2.03 at 8.3 years of age) compared with the children of mothers without GDM. CONCLUSIONS: Maternal GDM was a risk factor of childhood overweight/obesity at both 5.9 and 8.3 years of age, which was independent from several important confounders including maternal pre-pregnancy BMI, gestational weight gain, children's birth weight and lifestyle factors. This significant and positive association became stronger with age.

Interactive effects of gestational diabetes and high pre-pregnancy body mass index on adverse growth patterns of offspring.

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.