The two remaining clusters assemble genes whose expression is modified by the treatment but not by aging (cluster C, Table S5), and genes modulated by aging but not by the treatment (cluster D, Table S6).
To our knowledge, this is the first study to analyse a nationally representative PMV dataset to estimate the incidence rates, CIR, and life expectancies stratified by age and different clusters of diagnoses.
From P14 to adult, samples were almost perfectly separated into two large clusters by region, and then by age within each regional cluster.
Graphical depictions of clusters by age group can be seen in Fig. 3. Demographic surveillance systems provide a viable method for the collection of reliable data on vital events in rural sub-Saharan Africa, especially in the absence of accurate routine mortality statistics.
When analysed by age group, the main clusters were asthma and a mental health condition for 20-39 year olds (67.1%; 95% CI 53.9 -78.1), arthritis and mental health for 40-59 year olds (38.2%; 95% CI 31.3 -45.6) and arthritis and asthma for 60+ year olds (25.2%; 95% CI 20.7 -30.20.7
The clustering by age was statistically significant (P test in UniFrac: P = 0.007), although the clusters did not represent evolutionary divergence of bacteria between the age classes (Unifrac significance: P = 0.104).
The voluntary group included 42 subjects (22 men, 3 left-handed) who were clustered by age into three subgroups: young (18 35 years old), middle-aged (36 50 years old), and elderly subjects (51 80 years old).
Demographics clustered by age groups with patients' information of age, gender, weight, body size, and injected activity.
Globally, samples in both regions were almost perfectly clustered by age group, reflecting distinct patterns of gene expression at each age.
