Assessment of Disordered Linker Predictions in the CAID2 Experiment.

Disordered linkers (DLs) are intrinsically disordered regions that facilitate movement between adjacent functional regions/domains, contributing to many key cellular functions. The recently completed second Critical Assessments of protein Intrinsic Disorder prediction (CAID2) experiment evaluated DL predictions by considering a rather narrow scenario when predicting 40 proteins that are already known to have DLs. We expand this evaluation by using a much larger set of nearly 350 test proteins from CAID2 and by investigating three distinct scenarios: (1) prediction residues in DLs vs. in non-DL regions (typical use of DL predictors); (2) prediction of residues in DLs vs. other disordered residues (to evaluate whether predictors can differentiate residues in DLs from other types of intrinsically disordered residues); and (3) prediction of proteins harboring DLs. We find that several methods provide relatively accurate predictions of DLs in the first scenario. However, only one method, APOD, accurately identifies DLs among other types of disordered residues (scenario 2) and predicts proteins harboring DLs (scenario 3). We also find that APOD's predictive performance is modest, motivating further research into the development of new and more accurate DL predictors. We note that these efforts will benefit from a growing amount of training data and the availability of sophisticated deep network models and emphasize that future methods should provide accurate results across the three scenarios.

Composite healthy lifestyle, socioeconomic deprivation, and mental well-being during the COVID-19 pandemic: a prospective analysis.

The adverse psychological and social impacts of COVID-19 pandemic are well characterized, but the role of composite, modifiable lifestyle factors that may interact to mitigate these impacts is not. The effect of socioeconomic deprivation on these lifestyle risks also remains unclear. Based on a nationally representative, longitudinal cohort, we assessed the association between a combination of pre-pandemic lifestyle factors and mental health conditions during pandemic, and the contribution of deprivation to it. Composite lifestyle factors included BMI, smoking status, alcohol consumption, physical activity, sedentary time, sleep duration, and fruit and vegetable intake, with lifestyle scores and lifestyle categories calculated for each participant. Symptoms of depression and anxiety, and personal well-being were assessed by validated scales during the pandemic. Socioeconomic deprivation was characterized by both individual-level (income, wealth, and education) and group-level factors (Index of Multiple Deprivation). Of the 5049 eligible participants (mean [SD] age, 68.1 [10.9] years; 57.2% were female) included in the study, 41.6% followed a favorable lifestyle, 48.9% followed an intermediate lifestyle, and 9.5% followed an unfavorable lifestyle. Compared with favorable lifestyle category, participants in the intermediate and unfavorable lifestyle category were at increased risk of mental health conditions, with the hazard ratio (HR) for trend per increment change towards unfavorable category of 1.17 (95% CI 1.09-1.26) for depression, 1.23 (1.07-1.42) for anxiety, and 1.39 (1.20-1.61) for low well-being. A significant trend of lower risk for mental health conditions with increasing number of healthy lifestyle factors was observed (P < 0.001 for trend). There were no significant interactions between lifestyle factors and socioeconomic deprivation for any of the outcomes, with similar HRs for trend per one increment change in lifestyle category observed in each deprivation group. Compared with those in the least deprived group with favorable lifestyle, participants in the most deprived group adherent to unfavorable lifestyle had the highest risk of mental health outcomes. These results suggest that adherence to a broad combination of healthy lifestyle factors was associated with a significantly reduced risk of mental health conditions during the COVID-19 pandemic. Lifestyle factors, in conjunction with socioeconomic deprivation, independently contribute to the risk of mental health issues. Although further research is needed to assess causality, the current findings support public health strategies and individual-level interventions that provide enhanced support in areas of deprivation and target multiple lifestyle factors to reduce health inequalities and promote mental well-being during the ongoing COVID-19 pandemic.

Trends of hypertensive disorders of pregnancy among the Medicaid population before and during COVID-19.

Hypertensive disorders of pregnancy (HDP) are a group of high blood pressure disorders during pregnancy that are a leading cause of maternal and infant morbidity and mortality. The trend of HDP among the Medicaid population during the coronavirus disease of 2019 (COVID-19) is severely lacking. To determine the trends in the annual prevalence of HDP among Louisiana Medicaid pregnant women before and during the COVID-19 pandemic (2016-2021), a total of 113,776 pregnant women aged 15-50 years was included in this study. For multiparous individuals, only the first pregnancy was used in the analyses. Women with a diagnosis of each type-specific HDP were identified by using the ICD-10 codes. The prevalence of HDP increased from 10.5% in 2016 to 17.7% in 2021. The highest race/ethnicity-specific incidence of HDP was seen in African American women (13.1%), then white women (9.4%), followed by other women (7.9%). HDP remains as a very prevalent and significant global health issue, especially in African American women. Obesity and physical inactivity are major risk factors of HDP, which became amplified during the COVID-19 pandemic and led to a higher prevalence of HDP. Severe HDP substantially increases the risk of mortality in offspring and long-term issues in both the mother and infant. This is very pertinent to the Medicaid population due to the disparities and barriers that diminish the quality of healthcare they receive.

Evolutionary assessment of SQUAMOSA PROMOTER BINDING PROTEIN-LIKE genes in citrus relatives with a specific focus on flowering.

Phase transition and floral induction in citrus requires several years of juvenility after germination. Such a long period of juvenility has been a major hindrance to its genetic improvement program. Studies have shown that miR156 along with its downstream genes SQUAMOSA PROMOTER BINDING PROTEINS (SBP) and SBP-LIKE (SPL) mediate the phase transition and floral induction process in plants. Our current study has systematically analyzed SPLs in 15 different citrus-related species, systematically annotated them based on their close homology to their respective Arabidopsis orthologs, and confirmed the functional attributes of the selected members in floral precocity. The majority of the species harbored 15 SPLs. Their cis-element assessment suggested the involvement of the SPLs in diverse developmental and physiological processes in response to different biotic and abiotic cues. Among all, SPL5, SPL9, and SPL11 stood out as consistently differentially expressed SPLs in the adult and young tissues of different citrus-related species. Independent overexpression of their F. hindsii orthologs (FhSPL5, FhSPL9, and FhSPL11) brought an enhanced expression of endogenous FLOWERING LOCUS T leading to the significantly precocious flowering in transgenic Arabidopsis lines. Future study of the genes in the citrus plant itself is expected to conclude the assessments made in the current study.

The Impact of Reimbursement for Non-Face-to-Face Chronic Care Management on Comprehensive Metabolic Biomarkers Among Multimorbid Patients With Type 2 Diabetes.

AIMS: We evaluated the impact of reimbursement for non-face-to-face chronic care management (NFFCCM) on comprehensive metabolic risk factors among multimorbid Medicare beneficiaries with type 2 diabetes in Louisiana. MATERIALS AND METHODS: We implemented a propensity score method to obtain comparable treatment (n=1501 with NFFCCM) and control (n=17,524 without NFFCCM) groups. Patients with type 2 diabetes were extracted from the electronic health records stored in REACHnet. The study period was from 2013 to February 2020. The comprehensive metabolic risk factors included the primary outcome of glycated hemoglobin (HbA1c) (as the primary outcome) and the secondary outcomes of body mass index (BMI), systolic blood pressure (BP), and low-density lipoprotein cholesterol. RESULTS: Receiving any NFFCCM was associated with improvement in all outcomes measures: a reduction in HbA1c of 0.063% (95% CI: 0.031%-0.094%; P <0.001), a reduction in BMI of 0.155 kg/m 2 (95% CI: 0.029-0.282 kg/m 2 ; P =0.016), a reduction in systolic BP of 0.816 mm Hg (95% CI: 0.469-1.163 mm Hg; P <0.001), and a reduction in low-density lipoprotein cholesterol of 1.779 mg/dL (95% CI: 0.988 2.570 mg/dL; P <0.001). Compared with the control group, the treatment group had 1.6% more patients with HbA1c <7% (95% CI: 0.3%-2.9%; P =0.013). CONCLUSIONS: Patients with diabetes in Louisiana receiving NFFCCM experienced better control of HbA1c, BMI, BP, and low-density lipoprotein outcomes.

The Impact of Reimbursement for Non-Face-to-Face Chronic Care Management on Health Utilization Among Patients With Type 2 Diabetes in Louisiana.

OBJECTIVES: We evaluated the impact of reimbursement for non-face-to-face chronic care management (NFFCCM) on healthcare utilization among Medicare beneficiaries with type 2 diabetes in Louisiana. METHODS: We implemented group-based trajectory balancing and propensity score matching to obtain comparable treatment (with NFFCCM) and control (without NFFCCM) groups at baseline. Patients with diabetes with Medicare as their primary payer at baseline were extracted using electronic health records of 3 health systems from Research Action for Health Network, a Clinical Research Network. The study period is from 2013 to early 2020. Our outcomes include general healthcare utilization (outpatient, emergency department, and inpatient encounters) and health utilization related to diabetic complications. We tested each of these outcomes according to multiple treatment definitions and different subgroups. RESULTS: Receiving any NFFCCM was associated with an increase in outpatient visits of 657 (95% confidence interval [CI] 626-687; P < .001) per 1000 patients per month, a decrease in inpatient admissions of 5 (95% CI 2-7; P < .001) per 1000 patients per month, and a decrease in emergency department visits of 4 (95% CI 1-7; P = .005) per 1000 patients per month after 24-month follow-up from initial NFFCCM encounter. Both complex and noncomplex NFFCCM significantly increased visits to outpatient services and inpatient admissions per month. Receiving NFFCCM has a dose-response association with increasing outpatient visits per month. CONCLUSIONS: Patients with diabetes in Louisiana who received NFFCCM had more low-cost primary healthcare and less high-cost healthcare utilization in general. The cost savings of NFFCCM in diabetes management could be further explored in the future.

Epidemiological characteristics of gonorrhea and its influential meteorological factors: a 14-year retrospective assessment in China.

This study aimed to investigate the seasonal variation of gonorrhea in China, and to analyze the relationship between the incidence of gonorrhea and meteorological factors. Data from gonorrhea cases were obtained from the Disease Prevention and Control Bureau and the Data-Center for China Public Health Science, Chinese Center for Disease Control and Prevention, and the incidence of gonorrhea in China from 1 January 2006 to 31 December 2019 was analyzed. Meteorological data from the same period were obtained from the South China Meteorological Data Sharing Center, including the average monthly temperature, relative humidity, atmospheric pressure, sunshine hours, number of rainy days, and precipitation. The ratio-to-moving average method and seasonal subseries plots were used to analyze the seasonality of gonorrhea cases. The distributed lag non-linear model and attribution risks were used to investigate the effects of meteorological indexes on gonorrhea cases. The number of gonorrhea cases showed seasonal variation, with a peak in the third quarter and a decline in the first quarter. The Spearman assay showed that the reported number of patients with gonorrhea was positively correlated with the monthly temperature, relative humidity, precipitation, and number of rainy days. The risk of gonorrhea onset was highest at 6-11 °C, and the highest risk occurred when the average monthly temperature was 7 °C (resulting in an increase of 14.5% compared with 13 °C); a similar result was found regarding the cumulative risk of gonorrhea onset in relation to temperature. The attribution score of temperature to the onset of gonorrhea was 5.02% (95% confidence interval: - 3.84%, 13.88%). The study findings suggest that increased emphasis should be placed on screening for gonorrhea during summer and autumn, and that education on safe sexual behavior should be promoted during these times.

Patient-specific factors associated with use of diabetes self-management education and support programs in Louisiana.

INTRODUCTION: The prevalence of diabetes self-management education and support (DSME/S) use among patients with newly diagnosed type 2 diabetes mellitus (T2DM) and patients with insulin prescription has not been evaluated. It is also unclear what demographic, behavioral, and clinical factors associated with use of DSME/S. RESEARCH DESIGN AND METHODS: This retrospective analysis was based on electronic health records from the Research Action for Health Network (2013-2019). Patients with newly diagnosed T2DM were identified as 35-94 year-olds diagnosed with T2DM≥1 year after the first recorded office visit. Patients with insulin were identified by the first insulin prescription records. DSME/S (Healthcare Common Procedure Coding System G0108 and G0109) codes that occurred from 2 months before the 'new diagnosis date' or first insulin prescription date through 1 year after were defined as use of DSME/S. Age-matched controls (non-users) were identified from the Electronic Health Records (EHR). The date of first DSME/S record was selected as the index date. Logistic regression was used to estimate the associations between patient factors and use of DSME/S. RESULTS: The prevalence of DSME/S use was 6.5% (8909/137 629) among patients with newly diagnosed T2DM and 32.7% (13,152/40,212) among patients with diabetes taking insulin. Multivariable analysis found that among patients with newly diagnosed T2DM, black and male patients were less likely to use DSME/S, while in patients with insulin, they were more likely to use the service compared with white and female counterparts, respectively. Among patients taking insulin, those with private insurance or self-pay status were significantly less likely, while those with Medicaid were more likely to use the service compared with their Medicare counterparts. A strong positive association was found between HbA1c, obesity, and DSME/S use in both cohorts, while hypertension was negatively associated with DSME/S in both cohorts. CONCLUSION: We showed a low rate of DSME/S use in Louisiana, especially in patients with newly diagnosed T2DM. Our findings demonstrated heterogeneity in factors influencing DSME/S use between patients with newly diagnosed T2D and patients with insulin.

Do Human Capital Investment and Technological Innovation Have a Permanent Effect on Population Health? An Asymmetric Analysis of BRICS Economies.

This study examines the asymmetric impact of human capital investment, and technological innovation on population health from the years spanning from 1991 to 2019, by using a panel of the BRICS countries. For this purpose, we have employed the PMG panel NARDL approach, which captures the long-run and short-run dynamics of the concerned variables. The empirical results show that human capital investment and technological innovation indeed happen to exert asymmetric effects on the dynamics of health in BRICS countries. Findings also reveal that increased human capital investment and technological innovation have positive effects on health, while the deceased human capital investment and technological innovation tend to have negative effects on population health in the long run. Based on these revelations, some policy recommendations have been proposed for BRICS economies.

QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores.

A recent advance in the disorder prediction field is the development of the quality assessment (QA) scores. QA scores complement the propensities produced by the disorder predictors by identifying regions where these predictions are more likely to be correct. We develop, empirically test and release a new QA tool, QUARTERplus, that addresses several key drawbacks of the current QA method, QUARTER. QUARTERplus is the first solution that utilizes QA scores and the associated input disorder predictions to produce very accurate disorder predictions with the help of a modern deep learning meta-model. The deep neural network utilizes the QA scores to identify and fix the regions where the original/input disorder predictions are poor. More importantly, the accurate QUATERplus's predictions are accompanied by easy to interpret residue-level QA scores that reliably quantify their residue-level predictive quality. We provide these interpretable QA scores for QUARTERplus and 10 other popular disorder predictors. Empirical tests on a large and independent (low similarity) test dataset show that QUARTERplus predictions secure AUC = 0.93 and are statistically more accurate than the results of twelve state-of-the-art disorder predictors. We also demonstrate that the new QA scores produced by QUARTERplus are highly correlated with the actual predictive quality and that they can be effectively used to identify regions of correct disorder predictions. This feature empowers the users to easily identify which parts of the predictions generated by the modern disorder predictors are more trustworthy. QUARTERplus is available as a convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTERplus/.

Construction of a Non-Mutually Exclusive Decision Tree for Medication Recommendation of Chronic Heart Failure.

Objective: Although guidelines have recommended standardized drug treatment for heart failure (HF), there are still many challenges in making the correct clinical decisions due to the complicated clinical situations of HF patients. Each patient would satisfy several recommendations, meaning the decision tree of HF treatment should be nonmutually exclusive, and the same patient would be allocated to several leaf nodes in the decision tree. In the current study, we aim to propose a way to ensemble a nonmutually exclusive decision tree for recommendation system for complicated diseases, such as HF. Methods: The nonmutually exclusive decision tree was constructed via knowledge rules summarized from the HF clinical guidelines. Then similar patients were defined as those who followed the same pattern of leaf node allocation according to the decision tree. The frequent medication patterns for each similar patient were mined using the Apriori algorithms, and we also carried out the outcome prognosis analyses to show the capability for the evidence-based medication recommendations of our nonmutually exclusive decision tree. Results: Based on a large database that included 29,689 patients with 84,705 admissions, we tested the framework for HF treatment recommendation. In the constructed decision tree, the HF treatment recommendations were grouped into two independent parts. The first part was recommendations for new cases, and the second part was recommendations when patients had different historical medication. There are 14 leaf nodes in our decision tree, and most of the leaf nodes had a guideline adherence of around 90%. We reported the top 10 popular similar patients, which accounted for 32.84% of the whole population. In addition, the multiple outcome prognosis analyses were carried out to assess the medications for one of the subgroups of similar patients. Our results showed even for the subgroup of the same similar patients that no one medication pattern would benefit all outcomes. Conclusion: In the present study, the methodology to construct a nonmutually exclusive decision tree for medication recommendations for HF and its application in CDSS was proposed. Our framework is universal for most diseases and could be generally applied in developing the CDSS for treatment.

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type.

With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.

Qualitative Analysis of Health Systems Utilizing Non-Face-to-Face Chronic Care Management for Medicare-Insured Patients With Diabetes.

Chronic diseases impact 117 million adults in the United States and account for $3.3 trillion in health care spending. This qualitative study investigated experiences with reimbursement structure for non-face-to-face (NFF) chronic care management (CCM) through diverse health system approaches in primary care settings in Southeastern Louisiana. In-depth interviews were conducted with 16 purposively selected key informants, and reflexive thematic analysis was utilized to explore findings. Variation in health system approaches for implementing and quality of NFF CCM programs were identified. Results included differences in health system motivation and methods for financial structuring and quality of third-party vendor and alternative models.

Within-trial cost-effectiveness of lifestyle intervention using a 3-tier shared care approach for pregnancy outcomes in Chinese women with gestational diabetes.

This study assessed within-trial cost-effectiveness of a shared care program (SC, n = 339) for pregnancy outcomes compared to usual care (UC, n = 361), as implemented in a randomized trial of Chinese women with gestational diabetes (GDM). SC consisted of an individualized dietary advice and physical activity counseling program. The UC was a one-time group education program. The effectiveness was measured by number needed to treat (NNT) to prevent one macrosomia/large for gestational age (LGA) infant. The cost-effectiveness was measured by incremental cost-effectiveness ratio in terms of cost (2012 Chinese Yuan/US dollar) per case of macrosomia and LGA prevented. The study took both a health care system and a societal perspective. This study found that the NNT was 16/14 for macrosomia/LGA. The incremental cost for treating a pregnant woman was ¥1,877 ($298) from a health care system perspective and ¥2,056 ($327) from a societal perspective. The cost of preventing a case of macrosomia/LGA from the two corresponding perspectives were ¥30,032/¥26,278 ($4,775/$4,178) and ¥32,896/¥28,784 ($5,230/$4,577), respectively. Considering the potential severe adverse health and economic consequences of a macrosomia/LGA infant, our findings suggest that implementing this lifestyle intervention for women with GDM is an efficient use of health care resources.

Prediction of Intrinsic Disorder with Quality Assessment Using QUARTER.

Intrinsically disordered regions (IDRs) are estimated to be highly abundant in nature. While only several thousand proteins are annotated with experimentally derived IDRs, computational methods can be used to predict IDRs for the millions of currently uncharacterized protein chains. Several dozen disorder predictors were developed over the last few decades. While some of these methods provide accurate predictions, unavoidably they also make some mistakes. Consequently, one of the challenges facing users of these methods is how to decide which predictions can be trusted and which are likely incorrect. This practical problem can be solved using quality assessment (QA) scores that predict correctness of the underlying (disorder) predictions at a residue level. We motivate and describe a first-of-its-kind toolbox of QA methods, QUARTER (QUality Assessment for pRotein inTrinsic disordEr pRedictions), which provides the scores for a diverse set of ten disorder predictors. QUARTER is available to the end users as a free and convenient webserver at http://biomine.cs.vcu.edu/servers/QUARTER/ . We briefly describe the predictive architecture of QUARTER and provide detailed instructions on how to use the webserver. We also explain how to interpret results produced by QUARTER with the help of a case study.

Association between liver fibrosis scores and the risk of mortality among patients with coronary artery disease.

BACKGROUND AND AIMS: Fatty liver diseases are highly prevalent in patients with coronary artery disease (CAD) and might progress to irreversible liver fibrosis. Whether baseline liver fibrosis (LF) scores are associated with long-term mortality among patients with CAD requires investigation. METHODS: The analysis was conducted based on a prospective cohort study among 3263 patients with CAD in China. Cox models were used to assess the association of baseline levels of LF scores, including non-alcoholic fatty liver disease fibrosis score (NFS), fibrosis 4 score (FIB-4), aspartate aminotransferase to platelet ratio index (APRI), gamma-glutamyltransferase to platelet ratio (GPR), and Forns score, with the risk of all-cause and cardiovascular mortality among CAD patients. RESULTS: During a median follow-up period of 7.56 (inter-quartile range: 6.86-8.31) years, 538 deaths were identified, 319 of those were due to cardiovascular diseases. Compared with patients with lowest score levels, multivariable-adjusted HRs (95% CI) for those with highest levels of NFS, FIB-4, APRI, GPR and Forns score were 2.89 (2.14-3.91), 2.84 (2.14-3.76), 1.77 (1.33-2.36), 1.47 (1.19-1.83) and 3.10 (1.88-5.11) for all-cause mortality, 3.02 (2.05-4.45), 3.34 (2.29-4.86), 1.99 (1.40-2.83), 1.80 (1.36-2.39) and 2.43 (1.28-4.61) for cardiovascular mortality, respectively. These associations were consistent when we excluded those who died within the first year of follow-up or stratified patients by different sex, age, BMI, diabetes status, metabolic syndrome status, CAD type and hsCRP level. CONCLUSIONS: Higher LF scores are associated with increased risks of all-cause and cardiovascular mortality among CAD patients. LF scores might play a potential role in CAD prognosis prediction.

Construction of Guideline-Based Decision Tree for Medication Recommendation.

Clinical decision support system (CDSS) plays an essential role nowadays and CDSS for treatment provides clinicians with the clinical evidence of candidate prescriptions to assist them in making patient-specific decisions. Therefore, it is essential to find a partition of patients such that patients with similar clinical conditions are grouped together and the preferred prescriptions for different groups are diverged. A comprehensive clinical guideline often provides information of patient partition. However, for most diseases, the guideline is not so detailed that only limited circumstances are covered. This makes it challenging to group patients properly. Here we proposed an approach that combines clinical guidelines with medical data to construct a nested decision tree for patient partitioning and treatment recommendation. Compared with pure data-driven decision tree, the recommendations generated by our model have better guideline adherence and interpretability. The approach was successfully applied in a real-world case study of patients with hyperthyroidism.

Racial differences of incident diabetes postpartum in women with a history of gestational diabetes.

AIMS: The aim of the present study was to investigate the race-specific association between a history of gestational diabetes mellitus (GDM) and incidence of type 2 diabetes and evaluate how the risk changed over different years after delivery. METHODS: We performed two large cohorts - the Coronary Artery Risk Development in Young Adults (CARDIA) cohort and the Tianjin GDM Observational Study. The multivariate cox regression model was used to assess the risk of incident postpartum diabetes between women with and without prior GDM. RESULTS: During a mean follow-up of 13.8 years, 405 women developed type 2 diabetes. After adjustment for multiple confounding factors, Chinese women with GDM had a higher risk of incident diabetes within 5 years postpartum than African Americans with GDM compared with Chinese and African Americans without GDM (Hazard ratio 71.5 in Chinese vs. 9.29 in African Americans). When the risk of incident diabetes was analyzed within 10 years, white women with GDM seemed to have a higher hazard ratio than African American and Chinese women with GDM compared with non-GDM women of different races. In comparison to African American women without GDM, the highest risk of type 2 diabetes over 10 years postpartum appeared in Chinese women with GDM, followed by African American women with GDM, and the smallest risk was seen in white women with GDM. CONCLUSIONS: Different genetic backgrounds and other risk factors among women of different races might contribute to the racial differences in the incidence of diabetes postpartum among women with GDM.

Association between breakfast frequency and physical activity and sedentary time: a cross-sectional study in children from 12 countries.

BACKGROUND: Existing research has documented inconsistent findings for the associations among breakfast frequency, physical activity (PA), and sedentary time in children. The primary aim of this study was to examine the associations among breakfast frequency and objectively-measured PA and sedentary time in a sample of children from 12 countries representing a wide range of human development, economic development and inequality. The secondary aim was to examine interactions of these associations between study sites. METHODS: This multinational, cross-sectional study included 6228 children aged 9-11 years from the 12 International Study of Childhood Obesity, Lifestyle and the Environment sites. Multilevel statistical models were used to examine associations between self-reported habitual breakfast frequency defined using three categories (breakfast consumed 0 to 2 days/week [rare], 3 to 5 days/week [occasional] or 6 to 7 days/week [frequent]) or two categories (breakfast consumed less than daily or daily) and accelerometry-derived PA and sedentary time during the morning (wake time to 1200 h) and afternoon (1200 h to bed time) with study site included as an interaction term. Model covariates included age, sex, highest parental education, body mass index z-score, and accelerometer waking wear time. RESULTS: Participants averaged 60 (s.d. 25) min/day in moderate-to-vigorous PA (MVPA), 315 (s.d. 53) min/day in light PA and 513 (s.d. 69) min/day sedentary. Controlling for covariates, breakfast frequency was not significantly associated with total daily or afternoon PA and sedentary time. For the morning, frequent breakfast consumption was associated with a higher proportion of time in MVPA (0.3%), higher proportion of time in light PA (1.0%) and lower min/day and proportion of time sedentary (3.4 min/day and 1.3%) than rare breakfast consumption (all p ≤ 0.05). No significant associations were found when comparing occasional with rare or frequent breakfast consumption, or daily with less than daily breakfast consumption. Very few significant interactions with study site were found. CONCLUSIONS: In this multinational sample of children, frequent breakfast consumption was associated with higher MVPA and light PA time and lower sedentary time in the morning when compared with rare breakfast consumption, although the small magnitude of the associations may lack clinical relevance. TRIAL REGISTRATION: The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) is registered at (Identifier NCT01722500 ).

Glycemic variation in uncontrolled Graves' disease patients with normal glucose metabolism: Assessment by continuous glucose monitoring.

PURPOSE: To observe the glycemic variation (GV) in uncontrolled Graves' disease (GD) patients with normal glucose metabolism measured by continuous glucose monitoring (CGM). METHODS: This was a single-center, open-label, observational study. From January 2017 to October 2017, 20 GD patients with normal glucose metabolism and 24 healthy control subjects were recruited. Serum samples were obtained at 0, 30, and 120 min after oral glucose loading for glucose, insulin, and C-peptide level measurements. Fasting plasma fasting free triiodothyronine (FT3), free thyroxin (FT4), and thyroid stimulating hormone concentrations were also detected. All participants were subjected to a 3-day CGM after baseline data were collected. The primary endpoint was the difference in the mean amplitude of the glycemic excursions between the two groups. RESULTS: Compared with the healthy subjects, the GD patients had higher mean amplitude of glycemic excursions (MAGE) (P < 0.01). Multiple linear stepwise regression analysis showed that FT4 level was an independent factor for the MAGE. Interestingly, the GD patients had a significant prolongation in the time to peak glucose, especially after breakfast (P < 0.01), and the elevation in the incremental area under the curve of glucose after breakfast till 4 hours later. CONCLUSIONS: Uncontrolled GD patients with normal glucose metabolism had a greater GV, and the FT4 level may contributed to the increased GV.