Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

Introduction: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques and mixed findings. The present work aimed to summarise the evidence and critically appraise the methodology.

Methods: 10 databases were searched.

Lifetime asthma incidence is related to age at onset and allergies in western Sweden.

Although asthma is more frequently diagnosed in childhood, a substantial proportion of cases manifests in adulthood. Nonetheless, few studies have comprehensively examined asthma incidence across different ages, genders, and asthma phenotypes. We conducted a retrospective evaluation of asthma incidence from birth to late adulthood, stratified by age, gender, and the presence or absence of allergies.

Physical exercise, systemic inflammation and adult-onset asthma: a 12-year follow-up study.

Physical exercise in treatment of asthma is scarcely studied with no clear exercise guidelines for asthmatics. We aimed to investigate the associations between physical exercise frequency, systemic inflammation and asthma control. This has not been previously studied in adult-onset asthma.

Asthma and allergy trajectories in children based on combined parental report and register data.

Background: Trajectories of asthma and allergy in children are heterogeneous and commonly derived from parental report of disease or clinical records. This study combined parental-reported and register-based dispensed medication data to characterize childhood trajectories of co-existing asthma, allergic rhinitis, and eczema.

Methods: From a Swedish population-based birth cohort (N = 5654), survey responses collected at the age of 1, 4.

Body mass index and risk of over 100 cancer forms and subtypes in 4.1 million individuals in Sweden: the Obesity and Disease Development Sweden (ODDS) pooled cohort study.

Background: Obesity, assessed by body mass index (BMI), is an established risk factor for 13 cancers. We aimed to identify further potential obesity-related cancers and to quantify their association with BMI relative to that of established obesity-related cancers.

Methods: Using Cox regression models on 4,142,349 individuals in Sweden (mean age 27.