Serum γ-glutamyltransferase as an independent predictor for incident type 2 diabetes in middle-aged and older adults: Findings from the KoGES over 12 years of follow-up

A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.

Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data

BACKGROUND

An 8-year prediction of the Framingham Diabetes Risk Model (FDRM) was proposed, but the predictor has a gap with current clinical standards. Therefore, we evaluated the validity of the original FDRM in Korean population data, developed a modified FDRM by redefining the predictors based on current knowledge, and evaluated the internal and external validity.

METHODS

Using data from a community-based cohort in Korea (n = 5,409), we calculated the probability of diabetes through FDRM, and developed a modified FDRM based on modified definitions of hypertension (HTN) and diabetes. We also added clinical features related to diabetes to the predictive model. Model performance was evaluated and compared by area under the curve (AUC).

RESULTS

During the 8-year follow-up, the cumulative incidence of diabetes was 8.5%. The modified FDRM consisted of age, obesity, HTN, hypo-high-density lipoprotein cholesterol, elevated triglyceride, fasting glucose, and hemoglobin A1c. The expanded clinical model added γ-glutamyl transpeptidase to the modified FDRM. The FDRM showed an estimated AUC of 0.71, and the model's performance improved to an AUC of 0.82 after applying the redefined predictor. Adding clinical features (AUC = 0.83) to the modified FDRM further improved in discrimination, but this was not maintained in the validation data set. External validation was evaluated on population-based cohort data and both modified models performed well, with AUC above 0.82.

CONCLUSION

The performance of FDRM in the Korean population was found to be acceptable for predicting diabetes, but it was improved when corrected with redefined predictors. The validity of the modified model needs to be further evaluated.

Sex Differences in the Association Between AST/ALT and Incidence of Type 2 Diabetes in Japanese Patients with Nonalcoholic Fatty Liver Disease: A Retrospective Cohort Study

OBJECTIVES/INTRODUCTION

The purpose of the current study was to investigate the association between Aspartate Transaminase (AST)/Alanine transaminase(ALT) and type 2 diabetes (T2DM) in nonalcoholic fatty liver disease (NAFLD) patients and to determine whether there were sex differences.

METHODS

In the retrospective study, we collected data on NAFLD patients (1, 896 men and 465 women) at Murakami Memorial Hospital from 2004 to 2015. Data were stratified by sex to investigate the association between AST/ALT and T2DM incidence by sex. Multiple regression analysis, smooth curve fitting model and subgroup analysis were used to determine the correlation, non-linear relationship and threshold effect between AST/ALT and T2DM.

RESULTS

In our study, 157 men and 40 women developed T2DM at follow-up. After adjusting for risk factors, AST/ALT was significantly associated with T2DM in men with NAFLD but not in women with NAFLD. The risk of T2DM increased as the AST/ALT ratio decreased. Besides, in male NAFLD patients, AST/ALT showed a non-linear relationship with T2DM, with an inflection point value of 0.964. When the AST to ALT ratio was below the threshold (AST/ALT <0.964), AST/ALT was significantly negatively associated with T2DM (HR = 0.177, 95% CI 0.055-0.568; P = 0.0036). In contrast, when AST/ALT >0.964, no significant association was found (HR = 3.174, 95% CI 0.345-29.167; P = 0.3074). Moreover, subgroup analysis showed that GGT could alter the relationship between AST/ALT and T2DM. In the group with GGT ≤ 40, AST/ALT was strongly associated with T2DM (HR = 0.24, 95% CI 0.09-0.66; P = 0.0059).

CONCLUSIONS

These results suggested that there were sex differences in the association between AST/ALT and T2DM in NAFLD participants. A non-linear association between AST/ALT and T2DM was observed in males. AST/ALT in the normal GGT group (GGT ≤40) might better facilitate the early screening of T2DM.

A bidirectional link between metabolic syndrome and elevation in alanine aminotransferase in elderly female: a longitudinal community study

Pre-obesity, as a significant risk factor for the progression of metabolic syndrome (MS), has become a prevalent public health threat globally. In this three-year longitudinal study of pre-obese women at baseline, the goal was to clarify the female-specific bidirectional relationship between the risk of MS and blood alanine aminotransferase. In this manuscript, the MS score was determined using the following equation: MS score = 2*waist/height + fasting glucose/5.6 + TG/1.7 + SBP/130-HDL/1.02 for men and 1.28 for women, which is highly related to the risk of MS. With 2,338 participants, a hierarchical nonlinear model with random effects was utilized to analyze the temporal trends of serum characteristics from 2017 to 2019. A bivariate cross-lagged panel model (CLPM) was employed to estimate the structural relations of frequently measured variables at three different time points to determine the directionality of the relationship between the risk of MS and serum characteristics. MassARRAY Analyzer 4 platforms were used to evaluate and genotype candidate SNPs. In this study, the MS score only rose with age in females; it was positively correlated with serum alanine aminotransferase (ALT) in females; the CLPM revealed that the MS score in 2017 predicted ALT in 2018 (β = 0.066, p < 0.001); and ALT in 2018 predicted an MS score in 2019 (β = 0.037, p < 0.050); both relationships were seen in females. Additionally, the MS score in elderly females with NAFLD was related to the rs295 in the lipoprotein lipase (LPL) gene (p = 0.042). Our work showed that there may be female-specific causal correlations between elevated ALT and risk of MS and that the polymorphism rs295 in LPL may serve as a marker for the prognosis of MS. The genetic roles of rs295 in the LPL gene in the onset of MS and the development of ALT in the elderly Chinese Han population are thus provided by this, offering one potential mechanism.

Serum γ-glutamyltransferase level and incidence risk of metabolic syndrome in community dwelling adults: longitudinal findings over 12 years

PURPOSE

Although a recent meta-analysis demonstrated a positive association between serum γ-glutamyltransferase (GGT) and metabolic syndrome (MetS), sex differences in the relationship between GGT levels and MetS risk were not fully considered. We prospectively examined the relationship between serum GGT levels and incidence risk of MetS.

METHODS

Data were collected from the Korean Genome and Epidemiology Study (KoGES) enrolled in 2001-2002. Among 10,030 total participants, 5960 adults (3130 men and 2830 women) aged 40-69 without MetS were included and divided according to sex-specific quartiles of baseline serum GGT levels and followed up biennially until 2014. The hazard ratios (HRs) with 95% confidence intervals (CIs) for incident MetS were prospectively analyzed using multiple Cox proportional hazards regression analysis models.

RESULTS

Among 5960 participants, 1215 males (38.8%) and 1263 females (44.6%) developed MetS during 12-year follow up. Higher quartiles of GGT showed significantly higher cumulative incidence of MetS in both sexes (log-rank test P < 0.001). The HRs (95% CIs) for incident type 2 diabetes for the highest quartile versus referent lowest quartile for serum GGT levels were 3.01 (2.35-3.76) for men and 1.83 (1.30-2.57) for women after adjusting for age, smoking status, daily alcohol intake (g/day), regular exercise, family history of diabetes, and log-transformed LDL-cholesterol, creatinine, and aminotransferase levels.

CONCLUSION

In conclusion, high levels of GGT were found to be associated with increased risk of Mets in both men and women and the positive associations were stronger in men than in women.

Potential Association of Isolated γ-Glutamyltransferase Elevation with Incident Ischemic Heart Disease in Lean Koreans

Isolated elevation of γ-glutamyltransferase (GGT), a microsomal membrane-bound protein, is commonly observed in non-obese Koreans without diabetes, and its clinical implications are not well-known. Therefore, we aimed to investigate the longitudinal effect of isolated GGT on the incidence of ischemic heart disease (IHD) risk in a large cohort of lean non-diabetic Koreans. Data were obtained from the Health Risk Assessment Study (HERAS) and Korea Health Insurance Review and Assessment (HIRA) datasets. The participants were divided into four groups according to the GGT quartile after the exclusion of those participants with diabetes, a body mass index (BMI) ≥ 25 kg/m2, alanine aminotransferase (ALT) ≥ 40 IU/L, and aspartate aminotransferase (AST)/ALT > 1.5, as well as those positive for hepatitis B surface antigen or hepatitis C antibody. We prospectively assessed the hazard ratios (HRs) with 95% confidence intervals (CIs) for IHD using multivariate Cox proportional hazard regression models over a 50-month period. During the follow-up period, 183 individuals (1.85%) developed IHD. After setting the lowest GGT quartile as a reference group, the HRs of IHD for GGT quartiles 2−4 were 1.66 (95% CI 0.95−2.89), 1.82 (95% CI 1.05−3.16), and 1.98 (95% CI 1.12−3.50), respectively, after adjusting for age, sex, body mass index, smoking status, alcohol consumption, physical activity, mean arterial blood pressure, fasting plasma glucose, and dyslipidemia. An isolated high GGT may be an additional measure for assessing and managing future IHD risks among lean Koreans without diabetes.

Cumulative Exposure to High γ-Glutamyl Transferase Level and Risk of Diabetes: A Nationwide Population-Based Study

BACKGROUND

Elevated γ-glutamyl transferase (γ-GTP) level is associated with metabolic syndrome, impaired glucose tolerance, and insulin resistance, which are risk factors for type 2 diabetes. We aimed to investigate the association of cumulative exposure to high γ-GTP level with risk of diabetes.

METHODS

Using nationally representative data from the Korean National Health Insurance system, 346,206 people who were free of diabetes and who underwent 5 consecutive health examinations from 2005 to 2009 were followed to the end of 2018. High γ-GTP level was defined as those in the highest quartile, and the number of exposures to high γ-GTP level ranged from 0 to 5. Hazard ratio (HR) and 95% confidence interval (CI) for diabetes were analyzed using the multivariable Cox proportional-hazards model.

RESULTS

The mean follow-up duration was 9.2±1.0 years, during which 15,183 (4.4%) patients developed diabetes. There was a linear increase in the incidence rate and the risk of diabetes with cumulative exposure to high γ-GTP level. After adjusting for possible confounders, the HR of diabetes in subjects with five consecutive high γ-GTP levels were 2.60 (95% CI, 2.47 to 2.73) in men and 3.05 (95% CI, 2.73 to 3.41) in women compared with those who never had a high γ-GTP level. Similar results were observed in various subgroup and sensitivity analyses.

CONCLUSION

There was a linear relationship between cumulative exposure to high γ-GTP level and risk of diabetes. Monitoring and lowering γ-GTP level should be considered for prevention of diabetes in the general population.

Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

PURPOSE

The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults.

PATIENTS AND METHODS

This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram.

RESULTS

After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful.

CONCLUSION

Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.