Global disease burden and its attributable risk factors of peripheral arterial disease.

Peripheral arterial disease (PAD) is a prevalent subtype of atherosclerotic cardiovascular diseases. It is crucial to assess the PAD-related burden and its attributable risk factors. We use the Global Burden of Disease study 2019 database to calculate the incidence, prevalence, mortality, disability-adjusted life years (DALY), attributable risk factors and estimated annual percentage change. The disease burden of PAD grows significantly with age accompanied by prominent heterogeneity between male and female. Despite the increase in the absolute numbers of disease burden from 1990 to 2019, the global PAD-related age-standardized death rate (ASDR) and age-standardized disability-adjusted life years rate (ASDALYR) have a mild downward trend from 1990 to 2019, which negatively correlated with sociodemographic index (SDI). Smoking and high systolic blood pressure (SBP) were the primary attributable risk factors for males (ASDR: 33.4%; ASDALYR: 43.4%) and females (ASDR: 25.3%; ASDALYR: 27.6%), respectively. High fasting plasma glucose (FPG) had become the second risk factor for ASDR (males: 28.5%; females: 25.2%) and ASDALYR (males: 29.3%; females: 26.3%) with an upward tendency. Low-middle SDI regions were predicted to have the most remarkable upward trend of PAD-related burden caused by high FPG. Smoking caused more disease burden in males before 85-90 years old and females before 65-70 years old, while high FPG and high SBP caused more burden after that. The patterns of PAD-related burden and its attributable risk factors are heterogeneous across ages, genders, and SDI regions. To reduce disease burden, tailored strategies should be implemented.

Dependence among sites in RNA evolution.

Although probabilistic models of genotype (e.g., DNA sequence) evolution have been greatly elaborated, less attention has been paid to the effect of phenotype on the evolution of the genotype. Here we propose an evolutionary model and a Bayesian inference procedure that are aimed at filling this gap. In the model, RNA secondary structure links genotype and phenotype by treating the approximate free energy of a sequence folded into a secondary structure as a surrogate for fitness. The underlying idea is that a nucleotide substitution resulting in a more stable secondary structure should have a higher rate than a substitution that yields a less stable secondary structure. This free energy approach incorporates evolutionary dependencies among sequence positions beyond those that are reflected simply by jointly modeling change at paired positions in an RNA helix. Although there is not a formal requirement with this approach that secondary structure be known and nearly invariant over evolutionary time, computational considerations make these assumptions attractive and they have been adopted in a software program that permits statistical analysis of multiple homologous sequences that are related via a known phylogenetic tree topology. Analyses of 5S ribosomal RNA sequences are presented to illustrate and quantify the strong impact that RNA secondary structure has on substitution rates. Analyses on simulated sequences show that the new inference procedure has reasonable statistical properties. Potential applications of this procedure, including improved ancestral sequence inference and location of functionally interesting sites, are discussed.