Transitioning from having no metabolic abnormality nor obesity to metabolic impairment in a cohort of apparently healthy adults

Predicting metabolic syndrome: Machine learning techniques for improved preventive medicine

Objectives: Metabolic syndrome (MetS) has a significant impact on health. MetS is the umbrella term for a group of interdependent metabolic threats that contribute to the emergence of diseases that can lead to death. This study was designed to better predict the risks associated with MetS to enable medical personnel to make more optimal preventive medical decisions. Study design: Data from a large hospital survey database was used to train data mining classification techniques to predict patient-level risk subsequent to extensive data engineering that included aggregating predictors from multiple visits. Methods: A prospective group of seemingly healthy volunteers from the database was studied based on data obtained during their regular annual health checkups. Results: After aggregating the variables over time, the findings indicated that the predictive power of our model outperformed methods presented in other studies (AUC = 0.947). Specific lifestyle factors were identified as contributing to MetS. Conclusion: Involvement to avoid recurring diseases can significantly decrease medical problems and treatment expenses. The findings emphasize the importance of using predictive tools in healthcare and preventive medicine. The results can be used for future prevention strategies that encourage lifestyle changes and implement directed medical treatment protocols to decrease the burden of illness.

Blood cell counts and nonalcoholic fatty liver disease: Evidence from Mendelian randomization analysis

BACKGROUND

Previous research has highlighted correlations between blood cell counts and chronic liver disease. Nonetheless, the causal relationships remain unknown.

AIM

To evaluate the causal effect of blood cell traits on liver enzymes and nonalcoholic fatty liver disease (NAFLD) risk.

METHODS

Independent genetic variants strongly associated with blood cell traits were extracted from a genome-wide association study (GWAS) conducted by the Blood Cell Consortium. Summary-level data for liver enzymes were obtained from the United Kingdom Biobank. NAFLD data were obtained from a GWAS meta-analysis (8434 cases and 770180 controls, discovery dataset) and the Fingen GWAS (2275 cases and 372727 controls, replication dataset). This analysis was conducted using the inverse-variance weighted method, followed by various sensitivity analyses.

RESULTS

One SD increase in the genetically predicted haemoglobin concentration (HGB) was associated with a β of 0.0078 (95%CI: 0.0059-0.0096), 0.0108 (95%CI: 0.0080-0.0136), 0.0361 (95%CI: 0.0156-0.0567), and 0.0083 (95%CI: 00046-0.0121) for alkaline phosphatase (ALP), alanine aminotransferase (ALT), aspartate aminotransferase, and gamma-glutamyl transferase, respectively. Genetically predicted haematocrit was associated with ALP (β = 0.0078, 95%CI: 0.0052-0.0104) and ALT (β = 0.0057, 95%CI: 0.0039-0.0075). Genetically determined HGB and the reticulocyte fraction of red blood cells increased the risk of NAFLD [odds ratio (OR) = 1.199, 95%CI: 1.087-1.322] and (OR = 1.157, 95%CI: 1.071-1.250). The results of the sensitivity analyses remained significant.

CONCLUSION

Novel causal blood cell traits related to liver enzymes and NAFLD development were revealed through Mendelian randomization analysis, which may facilitate the diagnosis and prevention of NAFLD.

Alanine transferase levels (ALT) and triglyceride-glucose index are risk factors for type 2 diabetes mellitus in obese patients

AIMS

The role of liver steatosis and increased liver enzymes (ALT) in increasing incident type 2 diabetes mellitus (T2DM) is debated, because of their differential effects on different ethnicities and populations. The aim of this study was to evaluate the role of elevated ALT in the development of T2DM in non-diabetic obese subjects receiving routine medical treatment.

METHODS

A total of 1005 subjects [296 men and 709 women, aged 45.7 ± 13.12 years, body mass index (BMI) 39.5 ± 4.86 kg/m2] were followed for a mean period of 14.3 ± 4.44 years. Subjects were evaluated for several metabolic variables, including the triglyceride-glucose index and the presence of metabolic syndrome (IDF 2005 definition), and were subdivided into ALT quartiles.

RESULTS

T2DM developed in 136 subjects, and the difference was significant between the first and the fourth ALT quartile (p = 0.048). Both at univariate analysis and at stepwise regression, ALT quartiles were associated with incident T2DM. Traditional risk factors for T2DM coexisted, with a somehow greater predictive value, such as triglyceride-glucose index, age, arterial hypertension, LDL-cholesterol, and metabolic syndrome.

CONCLUSIONS

These data suggest an association between elevated ALT levels and the risk of incident T2DM in obesity.