Lifestyle-based subtypes of childhood obesity and their association with cardiometabolic factors: A cluster analysis.

BACKGROUND: Lifestyle factors play an important role in the development and management of childhood obesity and its related cardiometabolic complications. OBJECTIVE/METHODS: We aimed to explore childhood obesity subtypes based on lifestyle factors and examine their association with cardiometabolic health. We included 1550 children with obesity from the National Health and Nutrition Examination Survey. Cluster analysis identified obesity subtypes based on four lifestyle factors (physical activity, diet quality, sedentary time and smoking). Multiple linear regression assessed their association with cardiometabolic factors. RESULTS: Five subtypes of childhood obesity were identified: unhealthy subtype (n = 571; 36.8%), physically active subtype (n = 185; 21.1%), healthy diet subtype (n = 404; 26.1%), smoking subtype (n = 125; 8.1%) and non-sedentary subtype (n = 265; 17.1%). Compared with the unhealthy subtype, the physically active subtype had lower insulin and HOMA-IR levels, and smoking subtype was associated with lower HDL levels. When compared with children with normal weight, all obesity subtypes had worse cardiometabolic profile, except the physically active subtype who had similar DBP, HbA1c and TC levels; smoking subtype who had similar TC levels; and healthy diet and non-sedentary subtypes who had similar DBP levels. CONCLUSION: Children of different lifestyle-based obesity subtypes might have different cardiometabolic risks. Our new classification system might help personalize assessment of childhood obesity.

How to enhance the applicability of a risk prediction model for term small-for-gestational-age neonates in clinical settings?

OBJECTIVE: To construct a simple term small-for-gestational-age (SGA) neonate prediction model that is clinically practical. METHODS: This analysis was based on the Born in Guangzhou Cohort Study (BIGCS). Mothers who had a singleton pregnancy, delivered a term neonate, and had an ultrasonography within 30 + 0 to 32 + 6 weeks of gestation were included. Term SGA was defined with customized population percentiles. Prediction models were constructed with backward selection logistic regression in a four-step approach, where model 1 contained fetal biometrics only, models 2 and 3 included maternal features and a time factor (weeks between ultrasonography and delivery), respectively; and model 4 contained all features mentioned. The prediction performance of individual models was evaluated based on area under the curve (AUC) and a calibration test was performed. RESULTS: The prevalence of SGA in the study population of 21 346 women was 11.5%. With a complete-case analysis approach, data of 19 954 women were used for model construction and validation. The AUC of the four models were 0.781, 0.793, 0.823, and 0.834, respectively, and all were well-calibrated. Model 3 consisted of fetal biometrics and corrected for time to delivery was chosen as the final model to build risk prediction graphs for clinical use. CONCLUSION: A prediction model derived from fetal biometrics in early third trimester is satisfactory to predict SGA.

A method for high-precision measuring differential transformer asymmetry.

The differential transformer is an important component in the front-end electronics of high-precision capacitive position sensing circuits, which are widely employed in space inertial sensors and electrostatic accelerometers. The position sensing offset, one of the space inertial sensors' most critical error sources in the performance range, is dominated by the differential transformer asymmetry and requires a high-precision evaluation. This paper proposes a method to assess differential transformers' asymmetry and realize a prototype circuit to test a transformer sample. The results show that the asymmetry measurement precision can achieve 0.6 ppm for the transformer with an asymmetry level of about -278.2 ppm.

Incremental predictive value of platelet parameters for preeclampsia: results from a large prospective cohort study.

BACKGROUND: Platelet parameters during pregnancy were associated with the risk of preeclampsia (PE), but the predictive value of these parameters for PE remained unclear. Our aim was to clarify the individual and incremental predictive value of platelet parameters, including platelet count (PC), mean platelet volume (MPV), plateletcrit (PCT), and platelet distribution width (PDW) for PE. METHODS: This study was based on the Born in Guangzhou Cohort Study in China. Data on platelet parameters were extracted from medical records of routine prenatal examinations. Receiver operating characteristic (ROC) curve was performed to analyze the predictive ability of platelet parameters for PE. Maternal characteristic factors proposed by NICE and ACOG were used to develop the base model. Detection rate (DR), integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI) were calculated compared with the base model to assess the incremental predictive value of platelet parameters. RESULTS: A total of 30,401 pregnancies were included in this study, of which 376 (1.24%) were diagnosed with PE. Higher levels of PC and PCT were observed at 12-19 gestational weeks in women who developed PE later. However, no platelet parameters before 20 weeks of gestation reliably distinguished between PE complicated pregnancy and non-PE complicated pregnancy, with all values of the areas under the ROC curves (AUC) below 0.70. The addition of platelet parameters at 16-19 gestational weeks to the base model increased the DR for preterm PE from 22.9 to 31.4% at a fixed false positive rate of 5%, improved the AUC from 0.775 to 0.849 (p = 0.015), and yielded a NRI of 0.793 (p < 0.001), and an IDI of 0.0069 (p = 0.035). Less but significant improvement in prediction performance was also observed for term PE and total PE when all the four platelet parameters were added to the base model. CONCLUSIONS: Although no single platelet parameter at the early stage of pregnancy identified PE with high accuracy, the addition of platelet parameters to known independent risk factors could improve the prediction of PE.

Combined effects of air pollutants on gestational diabetes mellitus: A prospective cohort study.

Exposures to multiple air pollutants during pregnancy have been associated with the risk of gestational diabetes mellitus (GDM). However, their combined effects are unclear. We aimed to evaluate the combined associations of five air pollutants from pre-pregnancy to the 2nd trimester with GDM. This study included 20,113 participants from the Born in Guangzhou Cohort Study (BIGCS). The inverse distance-weighted models were used to estimate individual air pollutant exposure, namely ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter less than 10 µm in diameter (PM10), and less than 2.5 µm in diameter (PM2.5). We estimated stage-specific associations of air pollutants with GDM using generalized estimating equation, and departures from additive joint effects were assessed using the relative excess risk (RERI) and the joint relative risk (JRR). Of the 20,113 participants, 3440 women (17.1%) were diagnosed with GDM. In the adjusted model, increased concentrations of O3 and SO2 3-6 months before pregnancy were associated with GDM occurrence, as well as O3 and PM10 in the 1st trimester, the adjusted relative risk (95% confident intervals) [RRs (95%CI)] ranged from 1.05 (1.00, 1.09) to 1.21 (1.04, 1.40). The largest JRR for GDM was the combination of SO2, NO2, and PM10 in the 1st trimester (JRR = 1.32, 95% CI: 1.10, 1.59). The JRR for O3 and SO2 was less than their additive joint effects [RERI = -0.25 (-0.47, -0.04), P for interaction = 0.048]. Associations of air pollutants with GDM differed somewhat by pre-pregnancy BMI and season. This study added new evidence to the current understanding of the combined effects of multiple air pollutants on GDM. Public health strategies were needed to reduce the adverse effects of air pollution exposure on pregnant women.