Redefining meaningful age groups in the context of disease

Abstract

Age is an important factor when considering phenotypic changes in health and disease. Currently, the use of age information in medicine is somewhat simplistic, with ages commonly being grouped into a small number of crude ranges reflecting the major stages of development and aging, such as childhood or adolescence. Here, we investigate the possibility of redefining age groups using the recently developed Age-Phenome Knowledge-base (APK) that holds over 35,000 literaturederived entries describing relationships between age and phenotype. Clustering of APK data suggests 13 new, partially overlapping, age groups. The diseases that define these groups suggest that the proposed divisions are biologically meaningful. We further show that the number of different age ranges that should be considered depends on the type of disease being evaluated. This finding was further strengthened by similar results obtained from clinical blood measurement data. The grouping of diseases that share a similar pattern of disease-related reports directly mirrors, in some cases, medical knowledge of disease-age relationships. In other cases, our results may be used to generate new and reasonable hypotheses regarding links between diseases.

Association of dietary patterns and risk of cardiovascular disease events in theMASHADcohort study

“…Sexadjusted means and proportions for baseline characteristics across each dietary pattern tertile were calculated using analysis of variance and logistic regression, respectively. The age variable was categorised into two groups (35-48 years and ≥48 years) according to the association between age groups and a disease occurrence by the latent Dirichlet allocation, K-means and hierarchical clustering algorithms (35) .…”

“…Hazard ratio (95% confidence intervals) of cardiovascular events according to tertiles of Western dietary pattern HR) was adjusted for age(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) years and ≥49 years), sex, body mass index, smoking status (nonsmoker, ex-smoker and current smoker), education level (low, moderate, high), marital status (married, single, divorced, widow), total energy intake, diabetes mellitus, hypertension, and dyslipidaemia. CI, confidence interval; CVD, cardiovascular disease.…”

Indications for bi-cruciate retaining total knee replacement: An international survey of 346 knee surgeons

“…The upper age limitations considered appropriate for BCR TKA indication were stratified into groups of <45 years (young), 45-64 years (middle-aged), 65-79 years (aged) and 80 years or older using the Medical Subject Headings (MeSH) classification. [11] BMI. The upper body mass index (BMI) limitations considered appropriate for BCR TKA indication were stratified into groups using the World Health Organization (WHO) classification.…”

Depressive and Anxiety Symptoms among Pediatric In-Patients with Dengue Fever: A Case-Control Study

“…and living arrangements. For age, we adjusted the age-disease association data (simple clustering method: k-means) which closely matched the accepted Medical Subject Heading (MeSH) ranges, into three age groups: 8-11, 12-14, and 15-17 based on the age criteria of RCADS-25 [37]. Income of the participants was identified by the help of their parents, guardian or LAR using the clusters from the indicative range of monthly family incomes (for a family of 5) in 2015 and 2017 [38].…”

“…The t-scores were adjusted for gender (girl and boy) and grade level (grade 3 to 12); the child version has different t-scores from the parent version [40]. Both depression and anxiety scales have good internal consistency (Cronbach's alpha) of α = 0.78 and α = 0.88 in clinical and general or school-based populations [37][38][39][40][41]. This tool has been reported to be a cross-culturally reliable measure of depressive and anxiety symptoms for children and adolescents [42].…”