UNASSIGNED: Waist circumference (WC) and fasting plasma glucose (FPG) have been demonstrated as risk factors for type 2 diabetes mellitus (T2DM). Evidence is limited regarding the association of the combination of WC and FPG (WyG) with the risk of T2DM. The primary aim of the study was to investigate the relationship between WyG and T2DM.
UNASSIGNED: The current study was a population-based cohort study using data from the NAGALA database. Participants were divided into tertiles based on WyG. Cox proportional hazard regression model was applied to identify the association of WyG with T2DM.
UNASSIGNED: During a median follow-up of 6.19 years in the normoglycemia group and 5.58 years in the prediabetes group, respectively, 88 and 285 individuals in the two groups received a diagnosis of T2DM. After full adjustment, risk of T2DM increased in step-wise fashion with increasing tertiles of WyG. For a per-SD increase in WyG, the hazard ratios for T2DM were 3.05 (95% CI 2.64 - 3.51) in all populations, 1.94 (95% CI 1.46 - 2.58) in the normoglycemia group and 1.63 (95% CI 1.40 - 1.90) in the prediabetes group. The interaction between WyG and fatty liver on T2DM was statistically significant in the prediabetes group (P for interaction = 0.034).
UNASSIGNED: Elevated WyG was independently associated with incident T2DM in Japan. Baseline WyG help identify individuals at high risk of T2DM and implement effective preventive measures.

An increasing number of studies have reported the close relation of the hemoglobin glycation index (HGI) with metabolism, inflammation, and disease prognosis. However, the prognostic relationship between the HGI and patients with sepsis remains unclear. Thus, this study aimed to analyze the association between the HGI and all-cause mortality in patients with sepsis using data from the MIMIC-IV database. In this study, 2605 patients with sepsis were retrospectively analyzed. The linear regression equation was established by incorporating glycated hemoglobin (HbA1c) and fasting plasma glucose levels. Subsequently, the HGI was calculated based on the difference between the predicted and observed HbA1c levels. Furthermore, the HGI was divided into the following three groups using X-tile software: Q1 (HGI ≤  - 0.50%), Q2 (- 0.49% ≤ HGI ≤ 1.18%), and Q3 (HGI ≥ 1.19%). Kaplan-Meier survival curves were further plotted to analyze the differences in 28-day and 365-day mortality among patients with sepsis patients in these HGI groups. Multivariate corrected Cox proportional risk model and restricted cubic spline (RCS) were used. Lastly, mediation analysis was performed to assess the factors through which HGI affects sepsis prognosis. This study included 2605 patients with sepsis, and the 28-day and 365-day mortality rates were 19.7% and 38.9%, respectively. The Q3 group had the highest mortality risk at 28 days (HR = 2.55, 95% CI: 1.89-3.44, p < 0.001) and 365 days (HR = 1.59, 95% CI: 1.29-1.97, p < 0.001). In the fully adjusted multivariate Cox proportional hazards model, patients in the Q3 group still displayed the highest mortality rates at 28 days (HR = 2.02, 95% CI: 1.45-2.80, p < 0.001) and 365 days (HR = 1.28, 95% CI: 1.08-1.56, p < 0.001). The RCS analysis revealed that HGI was positively associated with adverse clinical outcomes. Finally, the mediation effect analysis demonstrated that the HGI might influence patient survival prognosis via multiple indicators related to the SOFA and SAPS II scores. There was a significant association between HGI and all-cause mortality in patients with sepsis, and patients with higher HGI values had a higher risk of death. Therefore, HGI can be used as a potential indicator to assess the prognostic risk of death in patients with sepsis.

This study aimed to evaluate the associations of fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c) levels at <24 weeks of gestation with hypertensive disorders of pregnancy (HDP) and compare the strengths of the associations of HDP with FPG and HbA1c levels. Totally, 1,178 participants were included in this prospective cohort study. HDP, FPG, HbA1c, and potential confounding factors were included in multiple logistic regression models. The number of HDP cases was 136 (11.5%). When FPG and HbA1c were included in the model separately, quartile 4 (Q4) of FPG (87-125 mg/dL) and HbA1c (5.2-6.3% [33-45 mmol/mol]) levels had higher odds of HDP than quartile 1. The odds ratios (ORs) were 1.334 (95% confidence interval [CI]: 1.002-1.775) for Q4 of FPG and 1.405 (95% CI: 1.051-1.878) for Q4 of HbA1c. When the participants were divided into two categories based on the cut-off value with the maximum Youden Index of FPG or HbA1c, the ORs for high FPG (≥84 mg/dL) or high HbA1c (≥5.2% [33 mmol/mol]) were 1.223 (95% CI: 1.000-1.496) and 1.392 (95% CI: 1.122-1.728), respectively. When both FPG and HbA1c were included in the model simultaneously, the statistical significance of Q4 of FPG disappeared, whereas that of HbA1c remained. In two-category models, the same results were obtained. High FPG and HbA1c levels at <24 weeks of gestation were risk factors for HDP in pregnant Japanese women. In addition, high HbA1c levels were more strongly associated with HDP than high FPG levels.

BACKGROUND: Undiagnosed diabetes poses significant public health challenges in Ghana. Numerous factors may influence the prevalence of undiagnosed diabetes among adults, and therefore, using a model that takes into account the intricate network of these relationships should be considered. Our goal was to evaluate fasting plasma levels, a critical indicator of diabetes, and the associated direct and indirect associated or protective factors.
METHODS: This research employed a cross-sectional survey to sample 1200 adults aged 25-70 years who perceived themselves as healthy and had not been previously diagnosed with diabetes from 13 indigenous communities within the Cape Coast Metropolis, Ghana. Diabetes was diagnosed based on the American Diabetes Association (ADA) criteria for fasting plasma glucose, and lipid profiles were determined using Mindray equipment (August 2022, China). A stepwise WHO questionnaire was used to collect data on sociodemographic and lifestyle variables. We analyzed the associations among the exogenous, mediating, and endogenous variables using a generalized structural equation model (GSEM).
RESULTS: Overall, the prevalence of prediabetes and diabetes in the Cape Coast Metropolis was found to be 14.2% and 3.84%, respectively. In the sex domain, females had a higher prevalence of prediabetes (15.33%) and diabetes (5.15%) than males (12.62% and 1.24%, respectively). Rural areas had the highest prevalence, followed by peri-urban areas, whereas urban areas had the lowest prevalence. In the GSEM results, we found that body mass index (BMI), triglycerides (TG), systolic blood pressure (SBP), gamma-glutamyl transferase (GGT), and female sex were direct predictive factors for prediabetes and diabetes, based on fasting plasma glucose (FPG) levels. Indirect factors influencing diabetes and prediabetes through waist circumference (WC) included childhood overweight status, family history, age 35-55 and 56-70, and moderate and high socioeconomic status. High density lipoprotein (HDL) cholesterol, childhood overweight, low physical activity, female sex, moderate and high socioeconomic status, and market trading were also associated with high BMI, indirectly influencing prediabetes and diabetes. Total cholesterol, increased TG levels, WC, age, low physical activity, and rural dwellers were identified as indirectly associated factors with prediabetes and diabetes through SBP. Religion, male sex, and alcohol consumption were identified as predictive factors for GGT, indirectly influencing prediabetes and diabetes.
CONCLUSIONS: Diabetes in indigenous communities is directly influenced by blood lipid, BMI, SBP, and alcohol levels. Childhood obesity, physical inactivity, sex, socioeconomic status, and family history could indirectly influence diabetes development. These findings offer valuable insights for policymakers and health-sector stakeholders, enabling them to understand the factors associated with diabetes development and implement necessary public health interventions and personalized care strategies for prevention and management in Ghana.

OBJECTIVE: To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual health checkup records.
METHODS: We included 46,247 patients (32,372 and 13,875 in training and validation sets, respectively) from a national health checkup center database. Univariate and multivariate Cox analyses were performed to identify factors influencing T2DM risk. Extreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), and random forest (RF) models were trained to predict 5-year T2DM risk. Model performances were analyzed using receiver operating characteristic (ROC) curves for discrimination and calibration plots for prediction accuracy.
RESULTS: Key variables included fasting plasma glucose, age, and sedentary time. The LR model showed good accuracy with respective areas under the ROC (AUCs) of 0.914 and 0.913 in training and validation sets; the RF model exhibited favorable AUCs of 0.998 and 0.838. In calibration analysis, the LR model displayed good fit for low-risk patients; the RF model exhibited satisfactory fit for low- and high-risk patients.
CONCLUSIONS: LR and RF models can effectively predict T2DM risk in the Chinese population. These models may help identify high-risk patients and guide interventions to prevent complications and disabilities.

OBJECTIVE: Impaired fasting glucose is a prediabetic condition defined as glucose levels of 100-125 mg/dL and is considered a risk factor for type 2 diabetes. However, this definition does not confer to pregnancy. The significance of first-trimester fasting glucose and future progression to diabetes is not well defined. Therefore, we aimed to evaluate the progression to type 2 diabetes according to first- trimester fasting plasma glucose levels, as compared with gestational diabetes, a well-established risk factor for diabetes, in up to 5-year follow-up postpartum.
METHODS: A retrospective analysis of 69 001 parturients, evaluating fasting plasma glucose levels measured during the first trimester. The primary outcome was the incidence of type 2 diabetes within 5 years post-delivery. Fasting plasma glucose levels were categorized in 10 mg/dL increments. Receiver operating characteristic-area under the curve (ROC-AUC) statistics and the Youden index were employed to identify the optimal fasting plasma glucose cutoff for progression to type 2 diabetes. Survival analysis was applied to calculate the adjusted hazard ratios (aHRs) for type 2 diabetes progression with further stratification to maternal obesity status.
RESULTS: The identified fasting plasma glucose cutoff for progression to type 2 diabetes was 86.5 mg/dL. This cut-off demonstrated superior performance compared with gestational diabetes diagnosis. Stratification by maternal obesity revealed enhanced predictive capabilities for type 2 diabetes, particularly among patients without obesity.
CONCLUSIONS: Increased first-trimester fasting plasma glucose levels are associated with progression to type 2 diabetes, at least as gestational diabetes. For patients without obesity, first-trimester fasting plasma glucose has a more pronounced impact on progression to diabetes.

BACKGROUND: The implication of intermediately elevated fasting plasma glucose (FPG) in the first trimester of pregnancy is uncertain.
OBJECTIVE: The primary outcome of the meta-analysis was to analyze if intermediately elevated first-trimester FPG could predict development of GDM at 24-28 weeks. The secondary outcomes were to determine if the commonly used FPG cut-offs 5.1 mmol/L (92 mg/dL), 5.6 mmol/L (100 mg/dL), and 6.1 mmol/L (110 mg/dL) correlated with adverse pregnancy events.
METHODS: Databases were searched for articles published from 2010 onwards for studies examining the relationship between first-trimester FPG and adverse fetomaternal outcomes.
METHODS: A total of sixteen studies involving 115,899 pregnancies satisfied the inclusion criteria.
METHODS: Women who developed GDM had a significantly higher first-trimester FPG than those who did not [MD 0.29 mmoL/l (5 mg/dL); 95 % CI: 0.21-0.38; P < 0.00001]. First-trimester FPG ≥5.1 mmol/L (92 mg/dL) predicted the development of GDM at 24-28 weeks [RR 3.93 (95 % CI: 2.67-5.77); P < 0.0000], pre-eclampsia [RR 1.55 (95%CI:1.14-2.12); P = 0.006], gestational hypertension [RR1.47 (95%CI:1.20-1.79); P = 0.0001], large-for-gestational-age (LGA) [RR 1.32 (95%CI:1.13-1.54); P = 0.0004], and macrosomia [RR1.29 (95%CI:1.15-1.44); P < 0.001]. However, at the above threshold, the rates of preterm delivery, lower-segment cesarean section (LSCS), small-for gestational age (SGA), and neonatal hypoglycemia were not significantly higher. First-trimester FPG ≥5.6 mmol/L (100 mg/dL) correlated with occurrence of macrosomia [RR1.47 (95 % CI:1.22-1.79); P < 0.0001], LGA [RR 1.43 (95%CI:1.24-1.65); P < 0.00001], and preterm delivery [RR1.51 (95%CI:1.15-1.98); P = 0.003], but not SGA and LSCS.
CONCLUSIONS: Only one study reported outcomes at first-trimester FPG of 6.1 mmol/L (110 mg/dL), and hence was not analyzed.
CONCLUSIONS: The risk of development of GDM at 24-28 weeks increased linearly with higher first-trimester FPG. First trimester FPG cut-offs of 5.1 mmol/L (92 mg/dL) and 5.6 mmol/L (100 mg/dL) predicted several adverse pregnancy outcomes.

OBJECTIVE: Diabetes mellitus (DM) is a global health concern linked to various complications, including cardiovascular disease (CVD). However, long-term follow-up studies on the risk of DM and CVD using different blood glucose assessment methods in the general Korean population are lacking. This study aimed to assess the predictive abilities of fasting plasma glucose (FPG), 2-h oral glucose tolerance test (OGTT), and glycosylated hemoglobin (HbA1c) for new-onset DM and high CVD risk in a middle-aged and older Korean population.
METHODS: This study used data from the Korean Genome and Epidemiology Study, a population-based prospective cohort. Blood sugar measures (FPG, OGTT, and HbA1c) were examined. The primary endpoint was the development of new-onset DM, and CVD risk was evaluated using the Framingham risk score. The predictive abilities for new-onset DM based on glycemic values were evaluated using Harrell\'s Concordance index and 95% confidence intervals.
RESULTS: Among the 10,030 participants, data of 6813 participants without DM at baseline were analyzed. The study revealed that OGTT outperformed FPG and HbA1c in predicting new-onset DM. The combination of FPG and HbA1c did not significantly enhance predictions for DM compared with OGTT alone. OGTT also outperformed FPG and HbA1c in predicting high CVD risk, and this difference remained significant even after adjusting for additional confounders.
CONCLUSIONS: OGTT has superior predictive capabilities in identifying new-onset DM and high CVD risk in the Korean population. This suggests that relying solely on individual blood sugar measures may be insufficient for assessing DM and CVD risks.

The association between fasting plasma glucose (FPG), an important indicator of overall glycemic status, and the risk of cardiovascular mortality has been well investigated. The longitudinal study can repeatedly collect measured results for the variables to be studied and then consider the potential effects of intraindividual changes in measurement. This study aimed to identify long-term FPG trajectories and investigate the association between trajectory groups and cardiovascular and all-cause mortality. A latent class growth mixture modeling (LCGMM) was used to identify FPG trajectories. Cox proportional hazard models were used to estimate associations between FPG trajectories and the risk of all-cause and cardiovascular mortality. A U-shaped relationship between FPG and all-cause and cardiovascular mortality was observed in the restricted cubic spline regression models. Two FPG longitudinal trajectories of low-level (mean FPG = 5.12mmol/L) and high-level (mean FPG = 6.74mmol/L) were identified by LCGMM. After being adjusted for potential confounders, compared with the low-level category, the hazard ratios (HRs) for all-cause and cardiovascular mortality were 1.23(1.16-1.30) and 1.25(1.16-1.35), respectively, for the high-level group. Long-term FPG trajectories are significantly associated with and potentially impact the risk of all-cause and cardiovascular mortality.

BACKGROUND: Gestational diabetes mellitus\'s (GDM\'s) prevalence in Sri Lanka ranges from 5.5% to 11.5%. It is associated with maternal and perinatal complications, emphasizing the need for early screening and intervention. This study aims to determine the predictive effect of early pregnancy lipid profile and fasting plasma glucose for GDM.
METHODS: It is a prospective cohort study of 172 pregnant women attending antenatal clinics at a tertiary hospital in Jaffna, Sri Lanka. Prediction was derived by calculating odds ratios (ORs) and 95% confidence intervals (CIs) in multivariable logistic regression, assessing lipid and glucose effects on GDM risk.
RESULTS: The study included 172 participants (mean age: 29.84±5.38). GDM\'s prevalence was 16.9%, and 57.14% of these mothers were obese. Significant differences in fasting plasma glucose (FPG) values were observed between the first visit and at 24-28 weeks. GDM mothers showed elevated total cholesterol and low-density lipoprotein (LDL) levels. Triglyceride (TG) levels correlated significantly with FPG at the Point of Assessment (POA), identifying a 0.945 mmol/L cutoff with 75% sensitivity and 77.1% specificity. Logistic regression confirmed a significant TG-GDM relationship. There is an association between FPG levels measured in early pregnancy and the likelihood of developing GDM later on. Specifically, when FPG levels in early pregnancy surpass a cutoff value of 3.94 mmol/L, there is an increased risk of GDM, indicated by an OR of 3.81 Conclusion: Early pregnancy FPG and TG levels are potential markers for predicting GDM. FPG shows higher predictive efficacy than TG. Total cholesterol, LDL, and high-density lipoprotein (HDL) lack predictive ability.