Cardiovascular disease in retired NFL players: a systematic review.

OBJECTIVE: Despite robust research endeavors exploring post-play health implications in former NFL players, the impact of former-player status on long-term cardiovascular health has not yet been elucidated. The purpose of this systematic review is to describe the available research on the cardiovascular health in former NFL players. METHODS: Relevant studies were included from the PubMed, Scopus, and Embase databases. Studies were evaluated in accordance with PRISMA guidelines. Two independent reviewers conducted the title/abstract screenings and risk of bias determinations. The results of the studies were extracted for inclusion in the review. RESULTS: Sixteen studies met inclusion criteria. Though evidence was discordant among studies, former NFL players appeared to possess more favorable metabolic profiles and decreased mortality compared to community controls. Of note, 90% of former players were found to be overweight or obese. CONCLUSION: Though cardiovascular disease is the leading cause of death among former NFL players, they possess comparable metabolic and cardiovascular profiles to community controls. Further research is necessary to ascertain the impact of NFL play on cardiovascular health and develop tailored preventative care strategies for former players.

Orthopedic consequences of modern gladiators: a systematic review of lower extremity musculoskeletal issues in retired NFL players.

OBJECTIVES: The stressors that National Football League (NFL) athletes face are well-described and documented with regard to multisystem afflictions and injury prevalence. However, the majority of literature discusses the short-term effects rather than long-term outcomes of playing professional football. The purpose of this study was to characterize the long-term musculoskeletal issues in the retired NFL population. METHODS: Publications from CENTRAL, Scopus, Medline, PubMed, Embase, and Google Scholar were searched from database inception to February 2021. A total of 9 cohort studies evaluating lower extremity arthritis in retired NFL athletes were included for review. Two reviewers extracted data from the individual studies, including demographic information (age, body mass index, length of career, position), injury descriptions (location of injury, number of injuries, diagnoses), and procedure (total knee and or hip arthroplasty) frequency. RESULTS: Arthritis in retired NFL players was more than twice as prevalent than the general United States male population (95% CI: 2.1-2.3). Ankle osteoarthritis was directly correlated with the number of foot and ankle injuries. Players <50 years of age had a 16.1 and 13.8 times higher risk of undergoing TKA and THA, respectively, when compared to the general population. In older age groups, this trend held with retired NFL players being at least 4.3 and 4.6 times more likely than members of the general population to undergo TKA and THA, respectively. CONCLUSION: This review demonstrates that the effects of NFL-related lower extremity injuries extend beyond the players' careers and present a higher risk for early-onset osteoarthritis and overall frequency of undergoing total knee and hip arthroplasty.

TMT One-Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform.

In this chapter we describe the workflow we use for labeled quantitative proteomics analysis using tandem mass tags (TMT) starting with the sample preparation and ending with the multivariate analysis of the resulting data. We detail the step-by-step process from sample processing, labeling, fractionation, and data processing using Proteome Discoverer through to data analysis and interpretation in the context of a multi-run experiment. The final analysis and data interpretation rely on an R package we call TMTPrepPro, which are deployed on a local GenePattern server, and used for generating various outputs which are also outlined herein.

Label-free quantitative shotgun proteomics using normalized spectral abundance factors.

In this chapter we describe the workflow used in our laboratory for label-free quantitative shotgun proteomics based on spectral counting. The main tools used are a series of R modules known collectively as the Scrappy program. We describe how to go from peptide to spectrum matching in a shotgun proteomics experiment using the XTandem algorithm, to simultaneous quantification of up to thousands of proteins, using normalized spectral abundance factors. The outputs of the software are described in detail, with illustrative examples provided for some of the graphical images generated. While it is not strictly within the scope of this chapter, some consideration is given to how best to extract meaningful biological information from quantitative shotgun proteomics data outputs.

PloGO: plotting gene ontology annotation and abundance in multi-condition proteomics experiments.

We describe the PloGO R package, a simple open-source tool for plotting gene ontology (GO) annotation and abundance information, which was developed to aid with the bioinformatics analysis of multi-condition label-free proteomics experiments using quantitation based on spectral counting. PloGO can incorporate abundance (raw spectral counts) or normalized spectral abundance factors (NSAF) data in addition to the GO annotation, as well as handle multiple files and allow for a targeted collection of GO categories of interest. Our main aims were to help identify interesting subsets of proteins for further analysis such as those arising from a protein data set partition based on the presence and absence or multiple pair-wise comparisons, as well as provide GO summaries that can be easily used in subsequent analyses. Though developed with label-free proteomics experiments in mind it is not specific to that approach and can be used for any multi-condition experiment for which GO information has been generated.