Normal fasting plasma glucose predicts type 2 diabetes and cardiovascular disease in elderly population in Taiwan

Machine learning-based prediction model for myocardial ischemia under high altitude exposure: a cohort study

High altitude exposure increases the risk of myocardial ischemia (MI) and subsequent cardiovascular death. Machine learning techniques have been used to develop cardiovascular disease prediction models, but no reports exist for high altitude induced myocardial ischemia. Our objective was to establish a machine learning-based MI prediction model and identify key risk factors. Using a prospective cohort study, a predictive model was developed and validated for high-altitude MI. We consolidated the health examination and self-reported electronic questionnaire data (collected between January and June 2022 in 920th Joint Logistic Support Force Hospital of china) of soldiers undergoing high-altitude training, along with the health examination and second self-reported electronic questionnaire data (collected between December 2022 and January 2023) subsequent to their completion on the plateau, into a unified dataset. Participants were subsequently allocated to either the training or test dataset in a 3:1 ratio using random assignment. A predictive model based on clinical features, physical examination, and laboratory results was designed using the training dataset, and the model's performance was evaluated using the area under the receiver operating characteristic curve score (AUC) in the test dataset. Using the training dataset (n = 2141), we developed a myocardial ischemia prediction model with high accuracy (AUC = 0.86) when validated on the test dataset (n = 714). The model was based on five laboratory results: Eosinophils percentage (Eos.Per), Globulin (G), Ca, Glucose (GLU), and Aspartate aminotransferase (AST). Our concise and accurate high-altitude myocardial ischemia incidence prediction model, based on five laboratory results, may be used to identify risks in advance and help individuals and groups prepare before entering high-altitude areas. Further external validation, including female and different age groups, is necessary.

J-shape relationship between normal fasting plasma glucose and risk of type 2 diabetes in the general population: results from two cohort studies

BACKGROUND

Previous studies have reported that high fasting plasma glucose (FPG), even that within the normal range, is associated with the risk of type 2 diabetes (T2D). Nevertheless, these findings are limited to specific populations. Thus, studies in the general population are imperative.

METHODS

This study included two cohorts comprising 204 640 individuals who underwent physical examinations at the Rich Healthcare Group present at 32 locations in 11 cities of China from 2010 to 2016 and 15 464 individuals who underwent physical tests at the Murakami Memorial Hospital in Japan. Cox regression, restricted cubic spline (RCS), Kaplan-Meier (KM) curves, and subgroup analysis were used to determine the relationship between FPG and T2D. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of FPG for T2D.

RESULTS

The mean age of the 220 104 participants (204 640 Chinese and 15 464 Japanese participants) was 41.8 years (41.7 years for the Chinese and 43.7 years for the Japanese participants). During follow-up, 2611 individuals developed T2D (2238 Chinese and 373 Japanese participants). The RCS demonstrated a J-shaped relationship between FPG and T2D risk, with inflexion points of 4.5 and 5.2 for the Chinese and Japanese populations, respectively. Multivariate-adjusted hazard ratio (HR) was 7.75 for FPG and T2D risk after the inflexion point (7.3 for Chinese and 21.13 for Japanese participants).

CONCLUSIONS

In general Chinese and Japanese populations, the normal baseline FPG range showed a J-shaped relationship with the risk of T2D. Baseline FPG levels help identify individuals at high risk of T2D and may enable early primary prevention to improve their outcomes.

Decreased Threshold of Fasting Serum Glucose for Cardiovascular Events: MASHAD Cohort Study

Background

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally, and specifically in Iran. Generally, diabetes mellitus is the result of impaired glucose tolerance which together with dyslipidemia are considered as important risk factors of CVD. The aim of this study was to determine the relationship between fasting serum glucose (FSG), lipid profile and CVD endpoints, and to establish an optimal FSG cut-off in the MASHAD cohort study after nearly 6 years of follow-up.

Methods

All the participants of MASHAD study were followed up for 6 years to determine their cardiovascular status. FSG, fasting lipids, and physical examinations were all recorded. To identify the optimal cut- off point of FSG, we carried out receiver operating curve (ROC) analysis.

Results

We determined MASHAD cutoff point of blood glucose as 90 mg/dl predicting the CVD outcome. The sensitivity and specificity of the FSG criterion were 54.34% and 71.68%, respectively. The AUC was 0.665 (95% CI 0.656-0.675, P< 0.0001). The adjusted hazard ratio show that FSG is associated with 2.34 increase in CVD risk using MASHAD cutoff point (HR 2.34, 95% 1.73-3.17, P< 0.001).

Conclusion

These findings suggest that not only FSG and lipid profile are related to CVD outcome in the MASHAD study, but also elevated fasting glucose levels is strongly associated with cardiovascular events in this population. Besides, the fasting glucose at a threshold of 90 mg/dl can be used for screening cardiovascular events among the Iranian population.

Trajectories of Long-Term Normal Fasting Plasma Glucose and Risk of Coronary Heart Disease: A Prospective Cohort Study

BACKGROUND

Fasting plasma glucose (FPG) levels can vary over time and its longitudinal changing patterns may predict cardiometabolic risk. We aim to identify different trajectories of FPG in those who remained normoglycemic and investigate the association between trajectory groups and coronary heart disease risk in a large prospective cohort study.

METHODS AND RESULTS

A total of 20 514 subjects between ages 20 and 80 years were included at baseline. All participants had maintained normal FPG throughout an average follow-up period of 5.8 years. We identified 3 distinct trajectories using a group-based trajectory model, labeled by initial value and changing pattern: low-increasing (n=12 694), high-increasing-decreasing (n=5330), and high-decreasing-increasing (n=2490). The coronary heart disease incidence density among these 3 groups (3.00, 4.05, and 3.26 per 1000 person-years, respectively) was significantly different (P=0.038). The high-increasing-decreasing group was characterized by a starting FPG of 4.80 mmol/L, and increased up to 5.42 mmol/L at age 55, then decreased thereafter. Treating the low-increasing group as the reference, the age- and sex-adjusted hazard ratio was 1.58 (95% confidence interval, 1.23-2.02) for the high-increasing-decreasing group by Cox proportional hazard regression. After adjustment for other potential confounding factors, the hazard ratio is 1.40 (95% confidence interval, 1.08-1.81). The association persisted after adjustment for baseline FPG, mean, or SD of FPG.

CONCLUSIONS

Distinct trajectories of long-term normal FPG are associated with the development of coronary heart disease, which is independent of other metabolic factors including FPG levels. These findings have implications for intervention and prevention of coronary heart disease among individuals who are normoglycemic.

Predictive Value of Serum Gamma-glutamyltranspeptidase for Future Cardiometabolic Dysregulation in Adolescents- a 10-year longitudinal study

Serum gamma-glutamyltransferase (γ-GT) is implicated in the pathogenesis of atherosclerosis and metabolic syndrome (MetS) in adults. The relationships between γ-GT and cardiometabolic dysregulation remains unclear in adolescents. We enrolled 7,072 Taiwanese adolescents and followed them for a median of 6.8 years. The optimal cut-off values (CoVs) of baseline γ-GT to predict future MetS, hypertension (HTN), and type 2 diabetes (T2DM) were determined by receiving operating characteristic (ROC) curve. Using these CoVs, the participants were divided into normal- and high-level groups. Cox proportional hazard analysis was used to calculate hazard ratios (HRs) for the subjects with a high level of γ-GT for the risk of future cardiometabolic dysregulation. Serum γ-GT was significantly higher in the subjects with MetS than in those without MetS at baseline (p < 0.001). The optimal CoVs of γ-GT were 12 U/L for boys and 11 U/L for girls. In multivariate Cox regression analysis, a higher serum γ-GT level increased the risk of future MetS (HRs 1.98 and 2.85 for boys and girls, respectively, both p < 0.001), but not new onset HTN and T2DM. In conclusion, serum γ-GT levels not only demonstrated an excellent correlation with the presence of MetS and also in predicting future MetS in adolescents.