Objective:Effectively managing postprandial blood glucose is significant for impaired glucose tolerance (IGT) and type 2 diabetes mellitus (T2DM). We developed a streamlined and real-world framework and evaluated how meal timing and staple food type affect postprandial glycemic responses (PPGRs) and the influencing factors on inter-individual differences of PPGR. Materials and methods:We conducted a prospective observational study involving 33 patients with IGT and T2DM. Over a 1-week free-living period, participants completed 7 standardized meal tests: glucose solutions at breakfast, lunch, and dinner; steamed bread, rice, noodles, and oats at lunch. Linear mixed-effects models were used to compare PPGR differences of meal timing and staple food type effects. Linear regression models were applied to explore factors influencing inter-individual heterogeneity in PPGRs. Results:Participants deemed the framework simple and well tolerated. Meal timing had no significant effect on PPGR at 120 minutes (time effect P = 0.110) or 180 minutes (time effect P = 0.097). HOMA-IR was positively associated with the meal timing variability index (adjusted: β = 9.10, 95% CI: 3.01-15.20, P = 0.005). Staple food types affected 120- and 180-minute incremental area under the curve (iAUC), relative peak glucose, and glucose fluctuation amplitude (staple food type effect P < 0.001), but had no significant effects on time to peak or coefficient of variation (staple food type effect P > 0.05). Those born in northern China had a significantly higher refined staple food sensitivity index (adjusted: β = 267.14, 95% CI: 59.18-475.09, P = 0.014). Conclusion:The framework enables convenient, outpatient-based assessment of PPGRs and is highly acceptable to patients. We need to focus not only on the group-level general characteristics of PPGRs but also analyze their individual-level heterogeneity; this emphasizes the critical role of personalized nutrition.

AIMS:To evaluate the platform-independent robustness of the continuous glucose monitoring (CGM)-derived High Blood Glucose Index (HBGI) and the predictive utility of a single day for progression to type 2 diabetes. METHODS:We analysed 365 valid CGM records from three devices across four independent cohorts. Linear mixed-effects modelling partitioned HBGI variance by device, dataset and metabolic status. Cross-sectional analyses, utilizing full monitoring periods to ensure biological robustness, evaluated HBGI gradients across glycaemic stages. Longitudinal analyses, utilizing single-day recordings, evaluated its predictive accuracy for progression to overt diabetes. RESULTS:Device type accounted for only 3.4% of total HBGI variance, compared with 31.8% for metabolic status. Cross-sectionally, HBGI demonstrated a robust stepwise increase from healthy through prediabetes to diabetes (p < 0.001). Longitudinally, baseline HBGI significantly differentiated metabolic progressors from non-progressors (p = 0.005) and showed moderate predictive accuracy (area under the curve = 0.74; 95% confidence interval: 0.61-0.85). Discriminative performance was comparable to mean glucose and superior to the M-value, time in range (70-180 mg/dL) and coefficient of variation. Notably, among prediabetic individuals with established glycaemic indices within recommended ranges, elevated HBGI (>0.174) identified an occult high-risk subgroup with a 5.39- to 6.13-fold higher progression risk. CONCLUSIONS:HBGI is a device-agnostic metric that effectively stratifies glycaemic severity. A single-day HBGI assessment provides complementary risk stratification by unmasking occult risk in prediabetes with established CGM indices within recommended ranges. However, given the limited progression events, its predictive threshold requires validation in larger, independent cohorts.

CONTEXT:Immune checkpoint inhibitor (ICI)-related hypothyroidism is mostly irreversible and prompt thyroid hormone replacement therapy is crucial, especially for patients undergoing neoadjuvant immunotherapy. OBJECTIVE:This study aimed to propose a novel titration strategy for ICI-related hypothyroidism, evaluate levothyroxine (LT4) dose differences between hypothyroidism patterns, and develop a predictive equation for the optimal LT4 dose. DESIGN:Retrospective study. SETTING:Tertiary academic hospital. PATIENTS:A total of 109 patients with ICI-related hypothyroidism. INTERVENTIONS:Rapid vs conventional titration strategy. MAIN OUTCOME MEASURES:The time to achieve normal free thyroxine and TSH levels. RESULTS:Patients with transient thyrotoxicosis followed by overt hypothyroidism required higher LT4 doses to achieve a euthyroid state compared to isolated overt hypothyroidism, with a mean difference of 0.23 μg/kg/day (95% CI, 0.08-0.38). In patients with ICI-related overt hypothyroidism and no cardiac disease, who had elevated TSH levels within 4 weeks of the last documented low or normal TSH, a rapid titration strategy was implemented. This strategy significantly improved the cumulative incidence of achieving normal free thyroxine and TSH levels compared to conventional titration strategy (hazard ratio, 4.44; 95% CI, 2.24-8.82; and hazard ratio, 4.11; 95% CI, 2.18-7.73, respectively), with a comparable safety profile. Predicted LT4 dose at euthyroid state (µg/kg/day) = (-0.016 × body weight) + (0.109 × baseline TSH level) + 2.661 for patients with thyrotoxicosis followed by overt hypothyroidism. CONCLUSION:LT4 requirements vary depending on the subtype of ICI-related hypothyroidism. The rapid titration strategy reduced the time to achieve a euthyroid state without a significant increase in adverse effects compared to conventional LT4 replacement therapy.

To develop an accurate prediction system for gestational diabetes mellitus (GDM) in women adhering to Institute of Medicine (IOM) weight gain criteria by analyzing early-pregnancy metabolic kinetics and identifying independent risk factors. A prospective two-center cohort study enrolled 1,031 pregnant women meeting IOM guidelines. Clinical, anthropometric, and metabolic parameters (pre-pregnancy BMI, lipid profiles, inflammatory markers) were collected at 6–12 weeks of gestation. Machine learning models were trained on seven core variables identified via logistic regression, with performance evaluated by AUC, sensitivity, and specificity. The GDM group (n = 279) exhibited significantly higher pre-pregnancy weight, BMI, triglycerides (1.21 vs. 0.96 mmol/L, p < 0.001), and inflammatory markers. Multivariate analysis identified parity ≥ 2 (OR = 4.37), family diabetes history (OR = 1.64), third-trimester weight (OR = 1.13), and triglycerides (OR = 1.49) as independent predictors. A neural network model achieved the highest AUC (0.732) with 65.1

Objective:This two-center prospective cohort study aimed to evaluate the predictive value of the Metabolic Score for Insulin Resistance (METS-IR) for gestational diabetes mellitus (GDM) in Chinese women during early pregnancy and compare its performance with conventional insulin resistance (IR) indices. Methods:This prospective investigation evaluated 1450 Chinese gravidas (<12 gestational weeks) without pregestational diabetes from two obstetrical institutions. Baseline clinical-biochemical profiling occurred during the first trimester (6-12 weeks), with GDM confirmation via standardized 75g oral glucose tolerance testing at 24-28 weeks' gestation. Analytical methodologies incorporated multivariable regression modeling and ROC curve optimization to quantify the predictive validity of five metabolic indices (METS-IR, TyG index, TG/HDL-C ratio, HOMA-IR, TyG-BMI) for GDM risk stratification. Results:Among participants, 378 (26.1%) developed GDM. The GDM group (n=378, 26.1%) was older (median age 31.0 vs 30.0 years, p<0.0001) and had higher prepregnancy BMI (22.62 vs 21.32 kg/m², p<0.0001), fasting glucose (4.7 vs 4.5 mmol/L, p<0.0001) compared to NGT. The GDM group exhibited significantly higher METS-IR (31.14 vs 29.03, p <0.001) and other IR indices (p <0.001). In unadjusted models, METS-IR quartile 4 (Q4) was strongly associated with GDM (OR=3.33, 95% CI:2.38-4.70), but this association attenuated after adjusting for age, weight gain, lipids, and insulin (adjusted OR=1.56, 95% CI:1.04-2.35). Comparatively, the TyG index (adjusted OR=3.06, 95% CI:2.03-4.66) and TG/HDL-C ratio (adjusted OR=2.02, 95% CI:1.38-2.99) retained robust predictive power. ROC analysis revealed a moderate discriminative capacity for METS-IR (AUC=0.629 unadjusted; 0.676 fully adjusted), outperformed by HOMA-IR (AUC=0.699) and TyG (AUC=0.699). METS-IR demonstrated high specificity (76.2%) in unadjusted screening but showed dependency on metabolic confounders in adjusted models. Conclusion:METS-IR shows promise for early GDM screening and is outperformed by TyG and HOMA-IR in predictive value. METS-IR in early pregnancy reflects metabolic dysregulation linked to GDM risk, yet its predictive utility is partially mediated by lipid and insulin abnormalities. While METS-IR offers clinical feasibility through routine measurements, TyG and HOMA-IR exhibit superior independent predictive value. These findings highlight the importance of context-specific IR indices for early GDM risk stratification and underscore METS-IR's role as a composite marker of metabolically unhealthy obesity in pregnancy.

Effective interventions to manage postprandial glycemia are critical because postprandial glycemic response (PPGR) is strongly linked to cardiovascular and metabolic disease. Considering the interindividual variability in PPGR, the widespread application of dietary interventions has led to an increasing recognition that a universal, one-size-fits-all approach to dietary intervention is far from ideal. This highlights the need for personalized nutrition plans. In this context, we explored the potential benefits of leveraging machine learning to predict PPGR and guide personalized dietary interventions. We also critically examined the limitations of current approaches and outlined promising future directions for advancing this field.

ABSTRACT Objective To develop and validate an early second‐trimester predictive model integrating body mass index (BMI) trajectories and pathophysiological biomarkers for gestational diabetes mellitus (GDM) risk stratification, with the aim of reducing weight‐monitoring frequency while maintaining predictive accuracy. Methods In this prospective dual‐center cohort study, 1,450 pregnant women (<12 weeks gestation) without preexisting diabetes were enrolled from two Beijing maternal‐child hospitals. Serial anthropometric (BMI at 6–10, 12–14, 15–19, and 24–28 weeks) and metabolic biomarkers (fasting glucose, C‐peptide, lipids, uric acid) were analyzed. GDM was diagnosed via 75 g OGTT at 24–28 weeks. Multivariable logistic regression identified predictors using a 7:3 training‐test split. Model performance was assessed by area under the ROC curve (AUC), calibration, and decision curve analysis. Results GDM incidence was 26.1% (378/1,450). Significant weight/BMI disparities emerged as early as 6–10 weeks (GDM vs NGT: +2.3 kg, P < 0.0001), escalating through gestation. The final model incorporated age (OR = 1.07/year, 95% CI = 1.03–1.11), 12–14‐week BMI (OR = 1.06/kg/m2, 1.01–1.12), fasting glucose (OR = 1.86/mmol/L, 1.45–2.40), fasting C‐peptide (OR = 1.87/ng/mL, 1.28–2.73). The model demonstrated moderate discrimination (training AUC = 0.684; testing AUC = 0.685) with 63.7–67.2% sensitivity and 59.8–61.4% specificity at the optimal threshold (0.237). Negative predictive values exceeded 82%, enabling effective risk exclusion. Conclusions This dual‐center model pioneers GDM risk stratification by 12–14 weeks using clinically accessible metrics, reducing weight‐monitoring frequency without compromising prognostic value. While AUC limitations (0.68–0.69) suggest unmeasured contributors, its operational simplicity and robust negative predictive capacity support implementation in resource‐constrained settings. The findings redefine antenatal care paradigms by shifting focus to early metabolic dysregulation rather than late diagnostic thresholds.

OBJECTIVE:To assess the association between glycated haemoglobin (HbA1c) variability and risk of renal function decline in type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS:A comprehensive search was carried out in PubMed, Embase, Web of Science and the Cochrane Library (until 12 March 2024). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines were followed for this meta-analysis. HbA1c variability was presented as indices of the standard deviation (SD), coefficient of variation (CV), HbA1c variability score (HVS) and haemoglobin glycation index (HGI). This meta-analysis was performed using random-effect models. RESULTS:Eighteen studies met the objectives of this meta-analysis. The analyses showed positive associations between HbA1c variability and kidney function decline, with hazard ratio (HR) 1.26 (95% confidence interval [CI] 1.15-1.38) for high versus low SD groups, HR 1.47 (95% CI 1.30-1.65) for CV groups, HR 1.32 (95% CI 1.10-1.57) for HVS groups and HR 1.53 (95% CI 1.05-2.23) for HGI groups. In addition, each 1% increase in SD and CV was linked to kidney function decline, with HR 1.26 (95% CI 1.17-1.35), and 1.13 (95% CI 1.03-1.23), respectively. Also, each 1-SD increase in SD of HbA1c was associated with deterioration in renal function, with HR 1.17 (95% CI 1.07-1.29). CONCLUSIONS:The four HbA1c variability indicators were all positively associated with renal function decline progression; therefore, HbA1c variability might play an important and promising role in guiding glycaemic control targets and predicting kidney function decline progression in T2DM.

Objective:Gestational diabetes mellitus (GDM) is a condition of glucose intolerance, which may be accompanied with inflammation. The levels of hematological parameters during pregnancy can reflect inflammatory conditions in pregnant women. This study aims to describe the dynamic change of blood cell parameters from the first trimester (6-12 weeks of gestation) to the second trimester (24-28 weeks of gestation) and to investigate the associations of these biomarkers with the risk of GDM.Methods:This study was a prospective double-center study conducted in Beijing, China (clinical trial number: NCT03246295). Hematological parameters were tested four times during the follow-up. Logistic regression analysis and Receiver Operating Characteristic (ROC) curve analysis were used to explore the association and predictive ability of hematological parameters for GDM.Results:There were 258 of 1027 pregnant women in our study developed GDM. Among the 1027 pregnant women, white blood cells (WBC) gradually increased, and red blood cells (RBC), hemoglobin (HGB), and platelet (PLT) tended to decrease from the first trimester to second trimester. After adjusting for confounding factors, higher levels of RBC, HGB, and PLT in both early and middle pregnancy were positively associated with GDM risk, whereas the level of WBC was associated with GDM risk only in early pregnancy. WBC, RBC, HGB, and PLT in early and middle pregnancy were all correlated with fasting insulin (FINS) in early pregnancy.Conclusion:Higher levels of hematological parameters in early and middle pregnancy were associated with glucose metabolism in early pregnancy and the subsequent risk of GDM.

BackgroundGestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy, which increases the risk of other pregnant complications and adverse perinatal outcomes. Thyroid dysfunction is closely with the risk of diabetes mellitus. However, the relationship between euthyroid function in early pregnancy and GDM is still controversial.AimsThis study was to find the relationship between thyroid function within normal range during early pregnancy as well as glucose and lipids metabolisms as well as the risk of subsequent GDM.MethodsA total of 1486 pregnant women were included in this prospective double-center cohort study. Free thyroxine (FT4), thyroid stimulating hormone (TSH) and antithyroid peroxidase antibodies (TPOAb) were tested during 6-12 weeks of gestation and oral glucose tolerance test (OGTT) was conducted during 24-28 weeks to screen GDM. Relative risks (RR) with 95% confidence intervals (CI) for subsequent risk of GDM by thyroid function quartiles were assessed adjusting for major risk factors.ResultsThe incidence of GDM was 23.0% (342/1486). TSH, FT4 and the percentage of positive TPOAb were no significant difference between women with and without GDM, but FT4/TSH ratio was significantly higher in GDM group compared with NGT group [6.97(0.84,10.61) vs. 4.88(0.66,12.44), P=0.025)]. The linear trends of TC, TG, HDL-C, LDL-C, fasting glucose in the first trimester, insulin, C-peptide, HOMA-IR, fasting glucose during OGTT and incidence of GDM according to FT4/TSH ratio were all statistically significant. Further analysis based on fetal sex presented only the third quartile of FT4/TSH ratio in women carrying male fetus was associated with higher incidence of GDM statistically significant [RR (95% CI), 1.917 (1.143,3.216)], rather than in women carrying female fetus.ConclusionsThyroid function even in normal range is closely related to glucose and lipids metabolisms during the first trimester. Unappropriated FT4/TSH ratio in the first trimester is an independent risk factor of GDM in women carrying male fetus.

Aims There were some studies reported inconsistent results on the associations between fetal sex and maternal metabolism. This study aimed to examine the effect of fetal sex on maternal glucose and lipid metabolism and perinatal outcomes in women with gestational diabetes mellitus (GDM) during pregnancy in Chinese population. Methods This was a retrospective cohort study including 134 women diagnosed as GDM. All of them accepted 100g oral glucose tolerance test(OGTT) during 26–29 gestational week because of positive 50g glucose challenge test(GCT) and then had a regular follow-up. The clinical and laboratory data as well as perinatal outcomes were collected from Electronic Medical Record. Results Of 134 pregnant women with GDM, 64(47.76%) delivered a girl and 70(52.24%) delivered a boy. Homeostasis model assessment of β-cell function (HOMA-β) in women carrying a male fetus was significantly lower than in those carrying a female fetus [176(129.09,245.56) vs. 212(150.00,307.5), p = 0.029]. There was no difference between two groups in maternal lipid metabolism. Large-for-gestational-age(LGA) fetus was more likely to happen on male fetus (14.8% vs. 3.1%, p = 0.033), but there were no difference between two groups of the other perinatal outcomes. Higher maternal fasting blood glucose(OR 5.256, 95% CI 1.318,14.469) and lower HDL-C/LDL-C in women carrying male fetus suggested higher risk of LGA. Conclusions Women carrying a male fetus suggested decreased maternal β-cell function and increased percentage of LGA. The different management strategy of women with GDM between male and female fetus during pregnancy is necessary.

CONTEXT:The relationship between vitamin D and thyroid profiles lacks consensus despite extensive investigations. Whether vitamin D levels correlate with thyroid hormone sensitivity remains largely unexplored. OBJECTIVE:To explore the relationship between vitamin D levels and thyroid hormone sensitivity among euthyroid individuals. METHODS:This study involved 6452 euthyroid participants. Clinical parameters, including TSH, free thyroxine, 25-hydroxyvitamin D [25(OH)D], and other relevant indicators were extracted from the National Health and Nutrition Examination Survey 2007-2012. To quantify thyroid hormone sensitivity, we calculated the Thyroid Feedback Quantile-based Index (TFQI), the TSH index (TSHI), and the thyrotropin thyroxine resistance index (TT4RI). RESULTS:Subjects with impaired thyroid hormone sensitivity have decreased 25(OH)D levels (TFQI, TT4RI: P < 0.05; TSHI: P = .05574) following adjustment of confounding variables. Age-specific analysis found negative correlations between thyroid hormone sensitivity indices and 25(OH)D within the 20 to 60 years subgroup, turning positive in the 60 to 80 years subgroup. In females, thyroid hormone sensitivity indices and vitamin D levels were negatively linked, while in males, vitamin D's relationships with TFQI, TT4RI, and TSHI shifted from negative to positive when 25(OH)D levels exceeded 63.5 nmol/L, 56.7 nmol/L, and 56.7 nmol/L, respectively. Stratification by race revealed U-shaped curvilinear patterns resembling those found in the males. In body mass index (BMI) subanalysis, vitamin D had differing associations with thyroid hormone sensitivity indices: negative in the <25 kg/m2 and ≥30 kg/m2 subgroups and U-shaped in the 25-30 kg/m2 subgroup. CONCLUSION:Impaired thyroid hormone sensitivity correlates with decreased vitamin D levels among euthyroid subjects, with associations varying by age, sex, race, and BMI.

The influence of the microbiota on hypoglycemic agents is becoming more apparent. The effects of metformin, a primary anti-diabetes drug, on gut microbiota are still not fully understood. This prospective cohort study aims to investigate the longitudinal effects of metformin on the gut microbiota of 25 treatment-naïve diabetes patients, each receiving a daily dose of 1500 mg. Microbiota compositions were analyzed at baseline, and at 1, 3, and 6 months of medication using 16S rRNA gene sequencing. Prior to the 3-month period of metformin treatment, significant improvements were noted in body mass index (BMI) and glycemic-related parameters, such as fasting blood glucose (FPG) and hemoglobin A1c (HbA1c), alongside homeostasis model assessment indices of insulin resistance (HOMA-IR). At the 3-month mark of medication, a significant reduction in the α-diversity of the gut microbiota was noted, while β-diversity exhibited no marked variances throughout the treatment duration. The Firmicutes to Bacteroidetes ratio. markedly decreased. Metformin treatment consistently increased Escherichia-Shigella and decreased Romboutsia, while Pseudomonas decreased at 3 months. Fuzzy c-means clustering identified three longitudinal trajectory clusters for microbial fluctuations: (i) genera temporarily changing, (ii) genera continuing to decrease (Bacteroides), and (iii) genera continuing to increase(Lachnospiraceae ND3007 group, [Eubacterium] xylanophilum group, Romboutsia, Faecalibacterium and Ruminococcaceae UCG-014). The correlation matrix revealed associations between specific fecal taxa and metformin-related clinical parameters HbA1c, FPG, Uric Acid (UA), high-density lipoproteincholesterol (HDL-C), alanine aminotransferase (ALT), hypersensitive C-reactive protein (hs-CRP), triglyceride (TG) (P < 0.05). Metacyc database showed that metformin significantly altered 17 functional pathways. Amino acid metabolism pathways such as isoleucine biosynthesis predominated in the post-treatment group. Metformin’s role in glucose metabolism regulation may primarily involve specific alterations in certain gut microbial species rather than an overall increase in microbial species diversity. This may suggest gut microbiota targets in future studies on metabolic abnormalities caused by metformin.

AimsTo investigate the effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins in insulin-naive patients with type 2 diabetes mellitus. MethodsMEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched from January 2000 to February 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was adopted. The registration ID is CRD42022319078 in PROSPERO. ResultsAmong 11 163 citations retrieved, 35 publications met the planned criteria. From meta-analyses and network meta-analyses, we found that when injecting basal insulin regimens at bedtime, the optimal choice in order of most to least effective might be glargine U-300 or degludec U-100, glargine U-100 or detemir, followed by neutral protamine hagedorn (NPH). Injecting glargine U-100 in the morning may be more effective (ie, more patients archiving glycated hemoglobin < 7.0%) and lead to fewer hypoglycemic events than injecting it at bedtime. The optimal starting dose for the initiation of any basal insulins can be 0.10-0.20 U/kg/day. There is no eligible evidence to investigate the optimal maintenance dose for basal insulins. ConclusionsThe five basal insulins are effective for the target population. Glargine U-300, degludec U-100, glargine U-100, and detemir lead to fewer hypoglycemic events than NPH without compromising glycemic control.

We aimed to explore the medium- and long-term (≥12 weeks) effects of dapagliflozin on serum uric acid (SUA) level in patients with type 2 diabetes mellitus (T2DM) in the real world study and to explore the influencing factors of dapagliflozin on reducing SUA level. This observational, prospective cohort study was based on the real world. There were 77 patients included in this study. They were divided into two groups. Patients in treatment group (n = 38) were treated as dapagliflozin 10 mg/d combined with therapy of routine glucose-lowering drugs (GLDs), and patients in the control group (n = 39) were treated with their routine GLDs. All measurements of physical examinations, blood, and urine samples, including age, sex, weight, height, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), and SUA, were collected at baseline for all patients in these two groups and repeated after 12, 24, and 48 weeks of therapy. We compared the changes of metabolic indicators including SUA in these two groups to evaluate the effects of dapagliflozin and analyzed its influencing factors. In the dapagliflozin group, mean SUA levels significantly decreased from 334.2 ± 99.1 μmol/L at baseline to 301.9 ± 73.2 μmol/L after 12 weeks therapy (t = 2.378, p = 0.023). There was no significant statistical difference of SUA levels after 24 weeks treatment of dapagliflozin compared with 12-week and 48-week treatment with dapagliflozin (p > 0.05). We found that baseline SUA had a significant impact on the effect of dapagliflozin on reducing SUA (OR 1.014, 95%CI 1.003–1.025, p = 0.014) by logistic regression analysis. Receiver operating characteristic (ROC) curve showed that T2DM patients with SUA level ≥ 314.5 μmol/L had relative accuracy in recognizing the good effects of dapagliflozin on reducing SUA (sensitivity 76.9%, specificity 76.2%). Combination therapy of dapagliflozin with routine blood-glucose-lowering drugs in T2DM patients showed the significant and sustained stable effect of lowering SUA level in this real-world study.

Background For better disease management and improved prognosis, early identification of co-morbid depression in diabetic patients is warranted. the WHO-5 well-being index (WHO-5) has been used to screen for depression in diabetic patients, and its Chinese version (WHO-5-C) has been validated. However, its psychometric properties remain to be further validated in the type 2 diabetes patient population. The aim of our study was to examine the reliability and validity of the WHO-5-C in patients with type 2 diabetes mellitus. Methods The cross-sectional study was conducted on 200 patients from July 2014 to March 2015. All patients should complete the WHO-5-C, the Patient Health Questionnaire-9 (PHQ-9), the 20-item Problem Areas in Diabetes Scale (PAID-20), the Mini International Neuropsychiatric Interview (M.I.N.I), and Hamilton Rating Scale for Depression (HAM-D). Internal consistency of WHO-5 was revealed by Cronbach’s alpha, and constructive validity by confirmatory factor analysis (CFA). Relationship with PHQ-9, HAM-D, and PAID-20 was examined for concurrent validity, and ROC analysis was performed for criterion validity. Results The WHO-5-C presented satisfactory reliability (Cronbach’s alpha = 0.88). CFA confirmed the unidimensional factor structure of WHO-5-C. The WHO-5-C had significant negative correlation with HAM-D ( r = -0.610), PHQ-9 ( r = -0.694) and PAID-20 ( r = -0.466), confirming good concurrent validity. Using M.I.N.I as the gold standard, the cut-off value of WHO-5-C was 42, with a sensitivity of 0.83 and specificity of 0.75. Conclusion The WHO-5-C holds satisfactory reliability and validity that is suitable for depression screening in type 2 diabetes patients as a short and convenient instrument.

The objective of this study was to provide recommendations regarding effectiveness, safety, optimal starting dose, optimal maintenance dose range, and target fasting plasma glucose of five basal insulins (glargine U-300, degludec U-100, glargine U-100, detemir, and insulin protamine Hagedorn) in insulin-naive adult patients with type 2 diabetes in the Asia-Pacific region. Based on evidence from a systematic review, we developed an Asia-Pacific clinical practice guideline through comprehensive internal review and external review processes. We set up and used clinical thresholds of trivial, small, moderate, and large effects for different critical and important outcomes in the overall certainty of evidence assessment and balancing the magnitude of intervention effects when making recommendations, following GRADE methods (Grading of Recommendations, Assessment, Development, and Evaluation). The AGREE (Appraisal of Guidelines, Research and Evaluation) and RIGHT (Reporting Items for practice Guidelines in HealThcare) guideline reporting checklists were complied with. After the second-round vote by the working group members, all the recommendations and qualifying statements reached over 75% agreement rates. Among 44 contacted external reviewers, we received 33 clinicians' and one patient's comments. The overall response rate was 77%. To solve the four research questions, we made two strong recommendations, six conditional recommendations, and two qualifying statements. Although the intended users of this guideline focused on clinicians in the Asia-Pacific region, the eligible evidence was based on recent English publications. We believe that the recommendations and the clinical thresholds set up in the guideline can be references for clinicians who take care of patients with type 2 diabetes worldwide.

This study aimed to develop a simplified screening model to identify pregnant Chinese women at risk of gestational diabetes mellitus (GDM) in the first trimester. This prospective study included 1289 pregnant women in their first trimester (6–12 weeks of gestation) with clinical parameters and laboratory data. Logistic regression was performed to extract coefficients and select predictors. The performance of the prediction model was assessed in terms of discrimination and calibration. Internal validation was performed through bootstrapping (1000 random samples). The prevalence of GDM in our study cohort was 21.1