How mycobacterium tuberculosis infection could lead to the increasing risks of chronic fatigue syndrome and the potential immunological effects: a population-based retrospective cohort study

Increased risk of chronic fatigue syndrome following infection: a 17-year population-based cohort study

BACKGROUND

Previous serological studies have indicated an association between viruses and atypical pathogens and Chronic Fatigue Syndrome (CFS). This study aims to investigate the correlation between infections from common pathogens, including typical bacteria, and the subsequent risk of developing CFS. The analysis is based on data from Taiwan's National Health Insurance Research Database.

METHODS

From 2000 to 2017, we included a total of 395,811 cases aged 20 years or older newly diagnosed with infection. The cases were matched 1:1 with controls using a propensity score and were followed up until diagnoses of CFS were made.

RESULTS

The Cox proportional hazards regression analysis was used to estimate the relationship between infection and the subsequent risk of CFS. The incidence density rates among non-infection and infection population were 3.67 and 5.40 per 1000 person-years, respectively (adjusted hazard ratio [HR] = 1.5, with a 95% confidence interval [CI] 1.47-1.54). Patients infected with Varicella-zoster virus, Mycobacterium tuberculosis, Escherichia coli, Candida, Salmonella, Staphylococcus aureus and influenza virus had a significantly higher risk of CFS than those without these pathogens (p < 0.05). Patients taking doxycycline, azithromycin, moxifloxacin, levofloxacin, or ciprofloxacin had a significantly lower risk of CFS than patients in the corresponding control group (p < 0.05).

CONCLUSION

Our population-based retrospective cohort study found that infection with common pathogens, including bacteria, viruses, is associated with an increased risk of developing CFS.

Treatments of chronic fatigue syndrome and its debilitating comorbidities: a 12-year population-based study

Background

This study aims to provide 12-year nationwide epidemiology data to investigate the epidemiology and comorbidities of and therapeutic options for chronic fatigue syndrome (CFS) by analyzing the National Health Insurance Research Database.

Methods

6306 patients identified as having CFS during the 2000–2012 period and 6306 controls (with similar distributions of age and sex) were analyzed.

Result

The patients with CFS were predominantly female and aged 35–64 years in Taiwan and presented a higher proportion of depression, anxiety disorder, insomnia, Crohn’s disease, ulcerative colitis, renal disease, type 2 diabetes, gout, dyslipidemia, rheumatoid arthritis, Sjogren syndrome, and herpes zoster. The use of selective serotonin receptor inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), Serotonin antagonist and reuptake inhibitors (SARIs), Tricyclic antidepressants (TCAs), benzodiazepine (BZD), Norepinephrine-dopamine reuptake inhibitors (NDRIs), muscle relaxants, analgesic drugs, psychotherapies, and exercise therapies was prescribed significantly more frequently in the CFS cohort than in the control group.

Conclusion

This large national study shared the mainstream therapies of CFS in Taiwan, we noticed these treatments reported effective to relieve symptoms in previous studies. Furthermore, our findings indicate that clinicians should have a heightened awareness of the comorbidities of CFS, especially in psychiatric problems.

Building a model for predicting metabolic syndrome using artificial intelligence based on an investigation of whole-genome sequencing

Background

The circadian system is responsible for regulating various physiological activities and behaviors and has been gaining recognition. The circadian rhythm is adjusted in a 24-h cycle and has transcriptional–translational feedback loops. When the circadian rhythm is interrupted, affecting the expression of circadian genes, the phenotypes of diseases could amplify. For example, the importance of maintaining the internal temporal homeostasis conferred by the circadian system is revealed as mutations in genes coding for core components of the clock result in diseases. This study will investigate the association between circadian genes and metabolic syndromes in a Taiwanese population.

Methods

We performed analysis using whole-genome sequencing, read vcf files and set target circadian genes to determine if there were variants on target genes. In this study, we have investigated genetic contribution of circadian-related diseases using population-based next generation whole genome sequencing. We also used significant SNPs to create a metabolic syndrome prediction model. Logistic regression, random forest, adaboost, and neural network were used to predict metabolic syndrome. In addition, we used random forest model variables importance matrix to select 40 more significant SNPs, which were subsequently incorporated to create new prediction models and to compare with previous models. The data was then utilized for training set and testing set using five-fold cross validation. Each model was evaluated with the following criteria: area under the receiver operating characteristics curve (AUC), precision, F1 score, and average precision (the area under the precision recall curve).

Results

After searching significant variants, we used Chi-Square tests to find some variants. We found 186 significant SNPs, and four predicting models which used 186 SNPs (logistic regression, random forest, adaboost and neural network), AUC were 0.68, 0.8, 0.82, 0.81 respectively. The F1 scores were 0.412, 0.078, 0.295, 0.552, respectively. The other three models which used the 40 SNPs (logistic regression, adaboost and neural network), AUC were 0.82, 0.81, 0.81 respectively. The F1 scores were 0.584, 0.395, 0.574, respectively.

Conclusions

Circadian gene defect may also contribute to metabolic syndrome. Our study found several related genes and building a simple model to predict metabolic syndrome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03379-7.