Relationship Between Body Mass Index and Diagnosis of Obesity in the Military Health System Active Duty Population.

OBJECTIVE: The overall rate of obesity is rising in the USA; this is also reflected in the military population. It is important that providers appropriately diagnose obesity and discuss treatment options with their patients.The purpose of this study was to investigate diagnosis of obesity compared to documented body mass index (BMI) in the military health system. METHODS: Institutional review board approval was obtained by the 59th Medical Wing (Lackland Air Force Base, Texas) as an exempt study. This study included active duty military service members aged 18-65 years who sought outpatient care at a military treatment facility from September 2013 to August 2018 with a weight within the range of 31.8-226.8 kg and height between 121.9 and 215.9 cm. Data were collected from the Clinical Data Repository vitals and M2 encounter data to determine the percentage of each sub-population with a diagnosis of obesity according to BMI (≥30 kg/m2) and International Classification of Diseases diagnosis codes. RESULTS: Using BMI, 19.2% of female and 26.8% of male service members can be diagnosed with obesity; however, only 42.2% and 35.1%, respectively, with a BMI ≥30 was diagnosed as such. This discrepancy was consistent among all service branches and BMI ranges. CONCLUSION: This study demonstrates that obesity is underdiagnosed compared to BMI. This may result in insufficient resources being provided to patients to reduce weight. Further investigation is warranted to identify causes of underdiagnosis and potential barriers to diagnosis.

Diabetes mellitus and arthritis: is it a risk factor or comorbidity?: A systematic review and meta-analysis.

BACKGROUND: Investigators have explored the association between diabetes mellitus and arthritis for a long time; however, there are uncertainties and inconsistencies among various studies. In this study, we tried to explore the relationship between diabetes mellitus and the overall risk of arthritis, as well as the potential modifiers for this relationship. METHODS: We conducted a comprehensive literature search through PubMed and identified 36 eligible studies. The overall analyses, subgroup analyses, as well as sensitivity analyses, were conducted to illustrate the association between diabetes mellitus and arthritis. Study quality was evaluated using the Newcastle-Ottawa Quality Assessment Scale. All statistical analyses were conducted using STATA SE version 13.0. RESULTS: In our study, 36 eligible studies were identified and involved in the meta-analysis. The overall association between diabetes mellitus and arthritis is 1.61 (95% confidence interval [CI]: 1.14-2.28, P = .007). The association exists only in nongouty arthritis, where we observed the estimated odds ratio (OR) 1.33 (95% CI: 1.05-1.67, P < .001). The opposite point estimates from different types of diabetes may indicate possible different associations for type I (OR: 0.98, 95% CI: 0.18-5.39, P = .985) or type II diabetes (OR: 1.28, 95% CI: 0.88-1.84, P = .194). CONCLUSION: Diabetes mellitus performs more likely as a comorbidity of arthritis rather than a risk factor; however, more studies will be helpful to increase the confidence of identifying the association between diabetes and arthritis.

Bioinformatics Analysis of Proteome Changes in Calu-3 Cell Infected by Influenza A Virus (H5N1).

AIM: This paper aimed to identify the differentially expressed proteins (DEPs) in Calu-3 cells infected by influenza A virus (IAV) subtype H5N1. METHODS: We downloaded proteome data (BTO: 0000762) from the Proteomics Identifications database and identified the DEPs in the IAV-infected Calu-3 cells. Then we constructed a protein-protein interaction network and a transcriptional regulatory network of the proteins. Finally, we performed gene ontology (GO) analysis to study the IAV infection at a functional level. RESULTS: A total of 4 protein groups between the normal cells and the Calu-3 cells infected by IAV, severe acute respiratory syndrome or swine influenza were identified. In the networks, we found 5 significant proteins including FAN, CPSF2, AGO1, AGO2 and PAX5. In addition, we demonstrated those proteins were associated with GO terms such as phosphate metabolic process, calcium ion transport, cell division and regulation of cell motion. STAT1, NS2, CD5, NCKX6 and PDGFB were significant DEPs in these GO terms. CONCLUSIONS: By referring to the previous studies, we suggest that proteins including FAN, CPSF2, AGO1, AGO2, PAX5, STAT1 and PDGFB can be used as therapeutic targets of IAV infection.