Seroprevalence and risk factors of hepatitis B and C viruses among diabetes mellitus patients in Duhok province, Iraqi Kurdistan

Metabolic disorders and hepatitis: Insights from a Mendelian randomization study

BACKGROUND

Hepatitis is a systemic disease that often results in various comorbidities. Meta-bolic disorders, the most common comorbidities in clinical practice, were selected for this study.

AIM

To investigate the causal relationship between comorbidities and hepatitis trea-tment outcomes.

METHODS

A total of 23583378 single nucleotide polymorphisms from 1248743 cases and related summaries of genome-wide association studies were obtained from online public databases. A two-sample Mendelian randomization (MR) was performed to investigate causality between exposure [type 2 diabetes mellitus (T2D), hyperlipidemia, and hypertension] and outcome (chronic hepatitis B or C in-fections).

RESULTS

The data supported the causal relationship between comorbidities and hepatitis infections, which will affect the severity of hepatitis progression and will also provide a reference for clinical researchers. All three exposures showed a link with progression of both hepatitis B (T2D, P = 0.851; hyperlipidemia, P = 0.596; and hypertension, P = 0.346) and hepatitis C (T2D, P = 0.298; hyperlipidemia, P = 0.141; and hypertension, P = 0.035).

CONCLUSION

The results of MR support a possible causal relationship between different ex-posures (T2D, hyperlipidemia, and hypertension) and chronic hepatitis progression; however, the potential mechanisms still need to be elucidated.

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

Highly prevalent hepatitis B and hepatitis C virus (HBV and HCV) infections have been reported among individuals with diabetes. Given the frequently asymptomatic nature of hepatitis and the challenges associated with screening in some vulnerable populations such as diabetes patients, we conducted an investigation into the performance of various machine learning models for the identification of hepatitis in diabetic patients while also evaluating the significance of features. Analyzing NHANES data from 2013 to 2018, machine learning models were evaluated; random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and least absolute shrinkage and selection operator (LASSO) along with stacked ensemble model. We performed hyperparameter tuning to improve the performance of the model, and selected important predictors using the best performance model. LASSO showed the highest predictive performance (AUC-ROC = 0.810) rather than other models. Illicit drug use, poverty, and race were highly ranked as predictive factors for developing hepatitis in diabetes patients. Our study demonstrated that a machine-learning-based model performed optimally in the detection of hepatitis among diabetes patients, achieving high performance. Furthermore, models and predictors evaluated from the current study, we expect, could be supportive information for developing screening or treatment methods for hepatitis care in diabetes patients.

Molecular evaluation of hepatitis B virus infection and predominant mutations of pre-core, basal core promoter and S regions in an Iranian population with type 2 diabetes mellitus: a case-control study

Background

This study was designed to evaluate the prevalence, genotypic patterns, and predominant mutations of hepatitis B virus (HBV) infection among diabetic patients.

Methods

Serum samples were obtained from 733 patients with type 2 diabetes mellitus and 782 non-diabetic controls. The presence of HBsAg and HBcAb was determined by ELISA. Nested PCR, targeting S and pre-core regions of the HBV genome, followed by sequencing was carried out to determine HBV genotypes and predominant mutations in the S, basal core promoter (BCP), and pre-core regions of the HBV genome.

Results

Of 733 diabetic patients, 94 cases (12.82%) were positive for HBcAb, 28 cases (3.82%) were positive for HBsAg, and 19 cases (2.59%) had HBV-DNA with genotype D, sub-genotype D1/D3 and subtype ayw2. An occult HBV infection was found in one of the HBV DNA-positive samples, which was positive for HBcAb but negative for HBsAg. P120T/G145R, G1896A/G1899A, and A1762T/G1764T were the most frequent point substitution mutations detected in the S, pre-core, and BCP regions of the HBV genome, respectively. P120T and G145R mutations were associated with low levels or undetectable levels of HBsAg in serum. Therefore, routine tests based on HBsAg detection cannot detect HBsAg-negative infected patients.

Conclusions

Relatively high prevalence of HBV infection was found in diabetic patients, while all of the HBV-infected patients were unaware of their infection. Therefore, screening for HBV infection should be included in the management program of diabetes for timely diagnosis and treatment of infected but asymptomatic patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07528-7.