How to apply the personalized medicine in obesity-associated asthma?

Multivariate analysis and data mining help predict asthma exacerbations

BACKGROUND

Work-related asthma has become a highly prevalent occupational lung disorder.

OBJECTIVE

Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation.

METHOD

We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations.

RESULTS

We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19-85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p < .001), occupational exposure (OR 4.65, p < .001), and lung function impairment (FEV1 < 80%) (OR 1.15, p = .011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT.

CONCLUSIONS

Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation.

The "Asthma-Polycystic Ovary Overlap Syndrome" and the Therapeutic Role of Myo-Inositol

Asthma is a heterogeneous inflammatory disease characterized by abnormalities in immune response. Due to the inherent complexity of the disease and the presence of comorbidities, asthma control is often difficult to obtain. In asthmatic patients, an increased prevalence of irregular menstrual cycles, infertility, obesity, and insulin resistance has been reported. Given that these conditions are also common in patients with polycystic ovary syndrome (PCOS), we propose the definition of "asthma-PCOS overlap syndrome" to indicate a medical condition which shares characteristics of both diseases. The aim of this review is to analyze the links between asthma and PCOS and evaluate the therapeutic role of myo-inositol, a natural compound currently utilized in patients with PCOS, in the management of asthma patients.