Comparison of hypothesis- and data-driven asthma phenotypes in NHANES 2007-2012: the importance of comprehensive data availability

Risk factors for depression in asthmatic individuals: Findings from NHANES (2005-2018)

BACKGROUND

The risk factors for depression in asthma are still unclear. The objective of this study was to identify the risk factors associated with depression in asthmatic individuals.

METHODS

We used data from the 2005-2018 National Health and Nutrition Examination Survey (NHANES). Univariate analysis and multivariate logistic regression analyses were used to identify risk factors for depression and calculate unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

A total of 5,379 asthmatic participants were included. Of these subjects, 767 individuals had depression, and 4,612 individuals had no depression. Univariate analysis and multivariate analyses suggested that asthmatic individuals with smoking (OR 1.98, 95% CI 1.19-3.29), hypertension (OR 2.73, 95% CI 1.48-5.04), and arthritis (OR 2.83, 95% CI 1.53-5.22) were more likely to have depression. Asthmatic individuals who had more than a high school education had lower depression risk than those with less than a high school education (OR 0.55, 95% CI 0.30-0.99). Increasing age was also associated with decreased depression risk (OR 0.97, 95% CI 0.95-0.99).

CONCLUSIONS

Depression was more likely in asthmatic individuals with smoking, hypertension, and arthritis and less likely in individuals with higher education and increasing age. These findings could improve the identification of target populations for effective interventions to improve the mental health of asthmatic individuals.

Association of Depression, Poor Mental Health Status and Asthma Control Patterns in US Adults Using a Data-Reductive Latent Class Method

Objectives To explore the association between depression, poor mental health status, and asthma control patterns among US adults using a latent class analysis (LCA) approach. Methods We used data from 10,337 adults aged 18 years and above from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) Asthma Call-back Survey. Data-reductive LCA was used to derive asthma control patterns in the population using class variables indicative of asthma control. Besides univariate analysis, adjusted and unadjusted logistic regression models were used to examine the association of depression and poor mental health on the derived asthma control patterns.  Results About 27.8% of adults aged <55 reported depression, while 27.3% aged ≥55 years were depressed. The latent class prevalence of asthma control patterns was 42.8%, 31.1%, and 26.1%, corresponding to good, fair, and poor asthma control patterns, respectively. In adults aged <55 years, odds of depression (OR=1.52, 95% CI=1.27-1.82) and poor mental health (OR=1.58, 95% CI=1.27-1.96) were higher in the poor asthma control group compared to the good asthma control group. Odds for depression (OR=1.28, 95% CI=1.06-1.53) were also higher in the moderate asthma control group compared to the good asthma control group. Among those aged ≥55 years, depression odds (OR=1.57, 95% CI=1.31-1.87) were higher in only the poor asthma control group. Conclusions These findings may have public health implications. Detecting, screening, and treating depression and mental health disorders may help improve asthma control in people with asthma.

Comparison of Pulmonary Function and Inflammation in Children/Adolescents with New-Onset Asthma with Different Adiposity Statuses

(1) Background: The relationship between obesity and asthma is still uncertain. This study aimed to investigate the effect of overweight/obesity on the pulmonary function of patients with new-onset pediatric asthma and explore the possible causative factors related to concomitant obesity and asthma. (2) Methods: Patients aged 5 to 17 years old with newly diagnosed mild to moderate asthma were recruited from June 2018 to May 2019, from a respiratory clinic in Shanghai, China. Participants were categorized into three groups: normal weight, overweight, and obese asthma. A family history of atopy and patients’ personal allergic diseases were recorded. Pulmonary function, fractional exhaled nitric oxide (FeNO), eosinophils, serum-specific immunoglobulins E (sIgE), serum total IgE (tIgE), and serum inflammatory biomarkers (adiponectin, leptin, Type 1 helper T, and Type 2 helper T cytokines) were tested in all participants. (3) Results: A total of 407 asthma patients (197 normal weight, 92 overweight, and 118 obese) were enrolled. There was a reduction in forced expiratory volume in the first second (FEV1)/forced vital capacity (FVC), FEV1/FVC%, and FEF25–75% in the overweight/obese groups. No difference was found between the study groups in the main allergy characteristics. Leptin levels were higher while adiponectin was lower in asthmatics with obesity. Higher levels of IL-16 were found in overweight/obese asthmatic individuals than in normal-weight individuals. (4) Conclusions: Obesity may have an effect on impaired pulmonary function. While atopic inflammation plays an important role in the onset of asthma, nonatopic inflammation (including leptin and adiponectin) increases the severity of asthma in overweight/obese patients. The significance of different levels of IL-16 between groups needs to be further studied.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

Highlights and recent developments in allergic diseases in EAACI journals (2019)

The European Academy of Allergy and Clinical Immunology (EAACI) owns three journals: Allergy, Pediatric Allergy and Immunology and Clinical and Translational Allergy. One of the major goals of EAACI is to support health promotion in which prevention of allergy and asthma plays a critical role and to disseminate the knowledge of allergy to all stakeholders including the EAACI junior members. There was substantial progress in 2019 in the identification of basic mechanisms of allergic and respiratory disease and the translation of these mechanisms into clinics. Better understanding of molecular and cellular mechanisms, efforts for the development of biomarkers for disease prediction, novel prevention and intervention studies, elucidation of mechanisms of multimorbidities, entrance of new drugs in the clinics as well as recently completed phase three clinical studies and publication of a large number of allergen immunotherapy studies and meta-analyses have been the highlights of the last year.

Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors

Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–2014). Latent transition analysis (LTA) was performed to detect baseline and 12-month follow-up asthma phenotypes and longitudinal patterns. Risk factors associated with longitudinal patterns were assessed through multinomial logistic regression. Results: LTA detected four longitudinal patterns: persistent asthma diagnosis with symptoms, 27.2%; persistent asthma diagnosis without symptoms, 4.6%; persistent asthma symptoms without diagnosis, 44.0%; and ex -asthma, 24.1%. The longitudinal patterns were differently associated with asthma comorbidities. Persistent asthma diagnosis with symptoms showed associations with passive smoke (OR 2.64, 95% CI 1.10–6.33) and traffic exposure (OR 1.86, 95% CI 1.02–3.38), while persistent asthma symptoms (without diagnosis) with passive smoke (OR 3.28, 95% CI 1.41–7.66) and active smoke (OR 6.24, 95% CI 2.68–14.51). Conclusions: LTA identified three cross-sectional phenotypes and their four longitudinal patterns in a real-life setting. The results highlight the necessity of a careful monitoring of exposure to active/passive smoke and vehicular traffic, possible determinants of occurrence of asthma symptoms (with or without diagnosis). Such information could help affected patients and physicians in prevention and management strategies.

Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

BACKGROUND

Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice.

OBJECTIVE

This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship.

METHODS

In this study, we used Taiwan's National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality.

RESULTS

The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes.

CONCLUSIONS

The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.