Our study suggests that the association between cytomegalovirus and Type 2 diabetes is explained by age and other risk factors for diabetes.

traditional manual gating strategies are often time-intensive, place a high burden on the analyzer, and are susceptible to bias between analyzers. Several automated gating methods have shown to exceed performance of manual gating for a limited number of cell subsets. However, many of the automated algorithms still require significant manual interventions or have yet to demonstrate their utility in large datasets. therefore, we developed an approach that utilizes a previously published automated algorithm (opencyto framework) with a manually created hierarchically cell gating template implemented, along with a custom developed visualization software (flowAnnotator) to rapidly and efficiently analyze immunophenotyping data in large population studies. This approach allows pre-defining populations that can be analyzed solely by automated analysis and incorporating manual refinement for smaller downstream populations. We validated this method with traditional manual gating strategies for 24 subsets of T cells, B cells, NK cells, monocytes and dendritic cells in 931 participants from the Health and Retirement Study (HRS). our results show a high degree of correlation (r ≥ 0.80) for 18 (78%) of the 24 cell subsets. For the remaining subsets, the correlation was low (<0.80) primarily because of the low numbers of events recorded in these subsets. The mean difference in the absolute counts between the hybrid method and manual gating strategy of these cell subsets showed results that were very similar to the traditional manual gating method. We describe a practical method for standardization of immunophenotyping methods in large scale population studies that provides a rapid, accurate and reproducible alternative to labor intensive manual gating strategies. Flow cytometry (FCM) provides a high dimensional quantitative measure for single cell analysis of the immune system. Manual gating using analyzer-defined boundaries or "gates" to identify cell populations of interest is commonly analyzed using proprietary software (e.g. FlowJo Tree Star Inc version 10). In the context of large-scale epidemiological investigations involving thousands of samples, this time consuming and labor-intensive process depends on the skill of the analyst, thereby introducing subjectivity that can increase variability among analysts and limit reproducibility of flow cytometry assays. Recent technological advancements in computational methods help reduce the subjectivity intrinsic to manual gating for multi-dimensional flow cytometry data and promote standardization of immunophenotyping analysis in large population studies. Several automated analyses tools such as OpenCyto 1 and FLOCK 2,3 and hybrid tools such as DAFi 4 and FlowGM 5 have been validated in recent years with good concordance when compared to manually gated datasets. Additional tools such as CytoML 6 allow data to be shared across platforms, which makes a streamlined analysis using both automated and manual analysis possible. Recently, the Flow Cytometry

Human cytomegalovirus (HCMV) is a highly prevalent herpes virus which persists as a latent infection and has been detected in several different tumor types. HCMV disease is rare but may occur in high-risk settings, often manifesting as a pulmonary infection. To date HCMV has not been investigated in malignant pleural mesothelioma (MPM). In a consecutive case series of 144 MPM patients we evaluated two biomarkers of HCMV: IgG serostatus (defined as positive and negative) and DNAemia (>100 copies/mL of cell free HCMV DNA in serum). Approximately half of the MPM patient population was HCMV IgG seropositive (51%). HCMV DNAemia was highly prevalent (79%) in MPM and independent of IgG serostatus. DNAemia levels consistent with high level current infection (>1000 copies/mL serum) were present in 41% of patients. Neither IgG serostatus nor DNAemia were associated with patient survival. In tissues, we observed that HCMV DNA was present in 48% of tumors (n = 40) and only 29% of normal pleural tissue obtained from individuals without malignancy (n = 21). Our results suggest nearly half of MPM patients have a high level current HCMV infection at the time of treatment and that pleural tissue may be a reservoir for latent HCMV infection. These findings warrant further investigation to determine the full spectrum of pulmonary infections in MPM patients, and whether treatment for high level current HCMV infection may improve patient outcomes.

Cytomegalovirus (CMV) is a highly prevalent human herpes virus that exerts a strong influence on immune repertoire which may influence cancer risk. We have tested whether CMV IgG serostatus is associated with immune cell proportions (n=132 population controls), HPV co-infection and head and neck cancer risk (n=184 cancer cases and 188 controls), and patient survival. CMV status was not associated with the proportion of NK cells, B cells, nor the neutrophil to lymphocyte ratio (NLR). However, CD8+T cells increased with increasing categories of IgG titers (p=1.7 x 10 -10), and titers were inversely associated with the CD4:CD8 ratio (p=5.6 x 10 -5). Despite these differences in T cell proportions, CMV was not associated with HPV16 co-infection. CMV seropositivity was similar in cases (52%) and controls (47%) and was not associated with patient survival (HR 1.14, 95% CI 0.70 - 1.86). However, those patients with the highest titers had the worst survival (HR 1.91, 95% CI 1.13 - 3.23). Tumor-based data from the Cancer Genome Atlas (TCGA) demonstrated that the presence of CMV transcripts was associated with worse patient survival (HR 1.79, 95% CI 0.96 - 2.78). These findings confirm that a past history of CMV infection alters T cell proportions, but this does not translate to HPV16 co-infection or head and neck cancer risk. Our data suggest that high titers and active virus in the tumor environment may confer worse survival.