Health Literacy and Its Associations with Understanding and Perception of Front-of-Package Nutrition Labels among Higher Education Students

peer reviewed(1) Background: Nutrition labels on the front of food packages have increasingly become the focus of research. However, too few studies have placed special emphasis on nutritionally at-risk subpopulations, such as young adults or those with low literacy/numeracy skills. The present study aimed to assess both the perception and objective understanding of three front-of-package labeling (FOPL) formats currently in use on the Belgian market, i.e., the Nutri-Score, Reference Intakes, and Multiple Traffic Lights, among students of varying health literacy (HL) levels. (2) Methods: A web-based survey was carried out among 2295 students of tertiary education in the province of Liège, Belgium. The questionnaire included questions related to general characteristics, objective understanding, and perception in response to the assigned FOPL format and level of HL. (3) Results: With respect to objective understanding, the Nutri-Score outperformed all other labels across all HL levels, and it was similarly understood in students of varying HL levels. Several students’ characteristics appeared to be associated with each cluster of perception, with the Nutri-Score cluster having the highest percentages of disadvantaged students, i.e., those with inadequate HL, from non-university institutions, with low self-estimated nutrition knowledge, and with low self-estimated diet quality. (4) Conclusion: Overall, the findings supported the Nutri-Score as particularly effective in guiding students in their food choices. Of particular importance is the fact that the summarized and graded color-coded nutritional label would be a useful strategy for those disadvantaged by limited HL.4. Quality educatio

Comprehensive Cluster Analysis for COPD Including Systemic and Airway Inflammatory Markers

Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. The main purpose of the present study was to identify clinical phenotypes through cluster nalysis in adults suffering from COPD. A retrospective study was conducted on 178 COPD patients in stable state recruited from ambulatory care at University hospital of Liege. All patients were above 40 years, had a smoking history of more than 20 pack years, post bronchodilator FEV1/FVC <70% and denied any history of asthma before 40 years. In this study, the patients were described by a total of 84 mixed sets of variables with some missing values. Hierarchical clustering on principal components (HCPC) was applied on multiple imputation. In the final step, patients were classified into homogeneous distinct groups by consensus clustering. Three different clusters, which shared similar smoking history were found. Cluster 1 included men with moderate airway obstruction (n¼67) while cluster 2 comprised men who were exacerbation-prone, with severe airflow limitation and intense granulocytic airway and neutrophilic systemic inflammation (n¼56). Cluster 3 essentially included women with moderate airway obstruction (n¼55). All clusters had a low rate of bacterial colonization (5%), a low median FeNO value (<20 ppb) and a very low sensitization rate toward common aeroallergens (0-5%). CAT score did not differ between clusters. Including markers of systemic airway inflammation and atopy and applying a comprehensive cluster analysis we provide here evidence for 3 clusters markedly shaped by sex, airway obstruction and neutrophilic inflammation but not by symptoms and T2 biomarkers

Cluster analysis on emergency COVID-19 data: A result-based imputation method for missing data

editorial reviewedBackground and Objective In 2020, hospitals have been confronted with an influx of COVID-19 confirmed patients. Grouping patients based on clinical features could help clinicians to identify a structure of patients who needs more attention. The present study considers cluster analysis to identify different clinical phenotypes with similar properties while accounting for the presence of missing data. Although several frameworks exist for handling missing data in cluster analysis, in this study, a new perspective was introduced for multiple imputation in cluster analysis that focused on the result of clustering. Method To handle the uncertainty of missing values, m imputed datasets were generated. The model-based clustering strategy was applied on the imputed datasets. Based on BIC criterion, the best method and the best number of groups were defined for all imputed datasets. Subsequently, the most repetitive number of groups and types was fixed. In the next step, cluster analysis was re-applied on m imputed datasets by the fixed number of clusters and type. The results of the statistical analysis were reported for each of the groups in imputed datasets. According to Rubin’s rules, in the pooled step, the final results were combined by mean and the statistical inferences were applied by considering between and within variance. Results The performance of the proposed framework was compared and assessed in several scenarios. The proposed method with 20 clinical features was performed on 628 confirmed COVID-19 patients who presented at University Hospital of Liege from March to May 2020. Based on model-based clustering and BIC criterion for multiple imputation, the patients were classified into four clusters. The rate of hospitalization in Cluster2 with older patients was higher than those in Cluster1. The oldest patients were assigned to Cluster3 and Cluster 4. The rate of comorbidity was almost close to 100% in Cluster 4 and percentage of infectious disease in cluster3 was less than Cluster4; however, Cluster3 had a higher rate of hospitalization than Cluster4. Conclusions The proposed method handled cluster analysis on missing data by multiple imputations. Also, the present study identified four clusters of patients confirmed with COVID-19 and the corresponding rate of hospitalization based on clinical features

Are type-2 biomarkers of any help in asthma diagnosis?

Peer reviewe

A clustering analysis of eosinophilic asthmatics: Two clusters with sharp differences in atopic status and disease severity.

peer reviewe

Predictors of asthma-related quality of life in a large cohort of asthmatics : A cross-sectional study in a secondary care center

peer reviewe

Evidence for two clusters among non-eosinophilic asthmatics.

peer reviewed[en] BACKGROUND: Although asthma is often seen as an eosinophilic disease associated with atopy non eosinophilic asthmatics represent a substantial part of asthmatic population. OBJECTIVE: Here we have applied an unsupervised clustering method on a cohort of 588 non eosinophilic asthmatics (sputum eosinophils <3%) recruited from an asthma clinic of a secondary care center. METHODS: Our cluster analysis of the whole cohort identified two subgroups as cluster 1 (n=417) and cluster 2 (n=171). RESULTS: Cluster 1 consisted of a dominant female group with a late disease onset, a low proportion of atopy (24%) and a substantial smoking history (53%). In this cluster, treatment burden was low (<50% of ICS users), asthma control and quality of life was poor with median ACT, ACQ and AQLQ of 16 and 1,7 and 4,5 respectively whereas lung function was preserved with a median post bronchodilation FEV1 of 93% predicted. Cluster 2 was a dominant male group, almost exclusively composed of atopic patients (99%) with early disease onset and a moderate treatment burden (median ICS dose 800 µg/d equivalent beclomethasone). In cluster 2 asthma was partially controlled with median ACT and ACQ reaching 18 and 1.3 respectively and lung function well preserved with a median post bronchodilation 95% predicted. While systemic and airway neutrophilic inflammation was the dominant pattern in cluster 1, cluster 2 essentially comprised paucigranulocytic asthma with moderately elevated FeNO. CONCLUSION: Non eosinophilic asthma splits in two clusters distinguishing by disease onset, atopic status, smoking history, systemic and airway inflammation and disease control and quality of life

Contribution to Cluster Analysis in Chronic Obstructive Airway Diseases

Chronic obstructive pulmonary disease (COPD) and asthma are complex, multidimensional, and heterogeneous diseases, that represent an important burden for the public health expenditure in western world. In literature, patients are divided into several different groups according to the combination of clinical, biological, and physiological characteristics, and these groups are called phenotypes. Understanding the phenotype of each patient is the first step toward effective personalized management and treatment. The common statistical approach to determine the phenotypes is cluster analysis. Cluster analysis is a well-known unsupervised learning methodology that considers multiple variables in order to create coherent subsets among a large group of patients. In this thesis, one of the most competitive and complex statistical analysis frameworks for applying cluster analysis in incomplete large datasets was introduced. In this framework, in addition to handling the missing values by multiple imputation, the dimensions of variables were reduced, and after performing the clustering method, the final result of clustering was achieved using a novel and efficient mixture multivariate multinomial model (4M) method. The efficiency of the proposed framework was evaluated and compared using several scenarios on simulated datasets with different competitive methods for each step. The new framework was applied to three novel specific populations of COPD and asthma. The first study was conducted on 178 stable COPD patients with the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) post bronchodilation less than 70%, age above 40 years, and smoking history of at least 20 pack years and no clinical history of asthma before the age of 40 years. As a result, three different clusters were found, which shared similar smoking history. Including markers of systemic and airway inflammation and atopy and applying a comprehensive cluster analysis, we provide here evidence for 3 clusters markedly shaped by sex, airway obstruction, and neutrophilic inflammation but not by symptoms and T2 biomarkers. In the next study, 426 eosinophilic patients which were defined by a sputum eosinophil count >=3% were considered. On the whole cohort, cluster analysis revealed two groups identified as cluster 1 (n=276) and cluster 2 (n=150) with cluster 1 being highly atopic with achievable control of the disease with ICS in most of the cases whereas cluster 2 featured a more aggressive disease, largely non-atopic with mixed granulocytic inflammation often resisting to ICS or oral Corticosteroids (OCS). Finally, the framework was applied to a large group of asthmatics (n=588) who were non-eosinophilic (sputum eosinophils <3%). The analysis of the whole cohort revealed two groups identified as cluster 1 (n=417) and cluster 2 (n=171) with cluster 1 displaying a low treatment burden and proportion of atopy, a neutrophilic airway inflammation, a frequent smoking history with preserved lung function but poor asthma control and quality of life while the cluster 2 essentially featured atopic patients with paucigranulocytic and partly controlled asthma. In conclusion, our proposed framework has an effective performance compared to competing methods based on the designed scenarios on these simulated datasets. By including airway inflammatory parameters among the variables, we have provided original data on cohorts of COPD and eosinophilic and non-eosinophilic asthmatics, which indicate substantial heterogeneity between clusters and, in asthma, in particular, great differences inside each airway inflammatory phenotype. Our findings should be confirmed in multicentric studies and their clinical value assessed on longitudinal studies looking at mortality and hospitalization in COPD and exacerbation rate and lung function decline in asthmatics

Comprehensive Clustering Analysis for Incomplete COPD Dataset

Introduction : Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. In the past years, application of classification methods in COPD context has been developed based on clinical observation with a limited number of variables on incomplete dataset with missing values. In such studies, selection of variables included in the analysis and dealing with missing data have a high effect on results. The main purpose of this study is to identify clinical phenotypes among adults. In this study, missing data and dimension-reduction, which are present in any large dataset of observational data, were handled. Méthodologie : In this application, 178 patients were described by 86 mixed and huge sets of variables. A common occurrence in clinical study is missing value. In various literature, we can find many methods to deal with missing value. Among several methods for imputing missing values, multiple imputation is widely used to handle missing data. A very limited number of studies combining these two important issues, cluster analysis and multiple imputation. Therefore, in this study, difficulties of multiple imputing missing values in cluster analysis is characterized and in final step, patients are classified into homogeneous distinct groups. After imputation step, the methodology of HCPC (Hierarchical Clustering on Principal Components) is used. Factor analysis of mixed data (FAMD) is applied for reducing the complexity of huge dimensional data. After this step, hierarchical clustering is performed using Ward's criterion on the selected principal components. In the final step, consensus clustering is used to assign each individual to cluster. All statistical analyses were performed using R software. Résultats : Three different phenotypes were defined in COPD. These clusters were identified as: phenotype 1 included women with moderate COPD and mild atopic traits (n=65). phenotype 2 comprised severe men with exacerbation-prone, bacterial colonization/neutrophilic and systemic inflammation (n=52) and phenotype 3 included men moderate COPD with emphysema (n=61). Conclusions : In the past years, classification methods in COPD have been applied based on ignoring missing values with limited or selected number of variables. In this study, these two issues are solved. Then, with advanced statistical methods, patients are divided into three distinct clusters. These clinically meaningful clusters of patients with common characteristics can be used to predict outcomes of patients with COPD, to aid in development of personalized therapy

Contribution to Cluster Analysis in Chronic Obstructive Airway Diseases

Chronic obstructive pulmonary disease (COPD) is a complex, multidimensional and heterogeneous disease. A common objective within this large datasets study is the classification of observations into homogenous groups to identify clinical phenotypes. A retrospective study was conducted on 178 COPD patients in stable state recruited from ambulatory care at University hospital of Liege. In this study, the patients were described by more than 70 mixed sets of variables. The rate of missingness ranged from 0% to 25% and 73% of patients presented at least one missing value. The presesnt study attempts to introduce a new framework for cluster analysis combining multiple imputation and variable reduction. The challenge of missing values was solved by multiple imputation. Factor analysis for mixed data (FAMD) was applied on quantitative and qualitative variables for creating new lower dimensional components. The number of clusters in each imputed dataset was determined using hierarchical clustering, finally K-means was applied for assigning clusters to patients. In the consensus clustering step, final result was achieved by fitting mixed multivariate multinomial model. Two different clusters, which shared similar smoking history were derived. Cluster 1 included men who have received more treatment and have higher symptoms in airway obstruction (n=70) while cluster 2 comprised women who were lower airway and neutrophilic systemic inflammation (n=108). Two clusters had a low rate of bacterial colonization (5%), a low median FeNO value (<20 ppb) and a very low sensitization rate toward common aeroallergens (0-5%). CAT score did not differ between clusters. Including markers of systemic airway inflammation and atopy and applying a comprehensive cluster analysis we provide here evidence for two clusters markedly shaped by sex, airway obstruction and neutrophilic inflammation but not by Eosinophils or Lymphocyte