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Multiplexing arrays increase the throughput and decrease sample requirements for studies employing multiple biomarkers. The goal of this project was to examine the performance of Multiplex arrays for measuring multiple protein biomarkers in saliva and serum. Specimens from the OsteoPerio ancillary study of the Women's Health Initiative Observational Study were used. Participants required the presence of at least 6 teeth and were excluded based on active cancer and certain bone issues but were not selected on any specific condition. Quality control (QC) samples were created from pooled serum and saliva. Twenty protein markers were measured on five multiplexing array panels. Sample pretreatment conditions were optimized for each panel. Recovery, lower limit of quantification (LLOQ) and imprecision were determined for each analyte. Statistical adjustment at the plate level was used to reduce imprecision estimates and increase the number of usable observations. Sample pre-treatment improved recovery estimates for many analytes. The LLOQ for each analyte agreed with manufacturer specifications except for MMP-1 and MMP-2 which were significantly higher than reported. Following batch adjustment, 17 of 20 biomarkers in serum and 9 of 20 biomarkers in saliva demonstrated acceptable precision, defined as <20% coefficient of variation (<25% at LLOQ). The percentage of cohort samples having levels within the reportable range for each analyte varied from 10% to 100%. The ratio of levels in saliva to serum varied from 1∶100 to 28∶1. Correlations between saliva and serum were of moderate positive magnitude and significant for CRP, MMP-2, insulin, adiponectin, GM-CSF and IL-5. Multiplex arrays exhibit high levels of analytical imprecision, particularly at the batch level. Careful sample pre-treatment can enhance recovery and reduce imprecision. Following statistical adjustments to reduce batch effects, we identified biomarkers that are of acceptable quality in serum and to a lesser degree in saliva using Multiplex arrays.

Aging invokes physiological changes, such as immunosenescence and inflammation, that could increase host susceptibility to oral microbiome shifts that enable periodontitis progression in later life. At present, there is a dearth of studies specifically evaluating the oral microbiome and periodontitis in older adults. We used high-throughput untargeted sequencing methods and functional metagenomic analyses to assess and compare the subgingival biofilm of postmenopausal women (mean age 71 years) according to periodontitis status. Subgingival plaque samples were obtained from 15 postmenopausal women with no periodontitis, and from 15 women with severe periodontitis, determined by probing measures. The 16S rRNA gene (V1–V3 region) was sequenced on the 454 FLX platform. The PICRUSt technique was used to provide information on what the potential functional characteristics of microbiota might be in healthy, compared with diseased, periodontium. The subgingival microbiome associated with periodontitis showed clear differences to that associated with health. Of the 464 species identified, 22.8% had elevated abundance in disease, while only 6.3% had elevated abundance in health. Among the 12 most prevalent organisms in periodontitis, one-half have previously been recognized as periodontal pathogens by other investigators. The subgingival microbiome in periodontitis contained genes that could code for specific activities, including microbial mobility, synthesis of endotoxin, and proteolytic degradation. The healthy microbiome included genes that could code for sustaining microbial life, including encoding for transporters, glycolysis, gluconeogenesis, the Krebs cycle, and protein kinases. In the present study on postmenopausal women, aged 60 and older, the subgingival microbiome differed in composition and potential function between those with and without periodontitis. Studies of functional gene expression, such as transcriptomics, are needed to definitively identify the molecules carrying out functions associated with pathogenic subgingival complexes. This, in turn, could lead to identification of targets for enhanced management of periodontitis and, possibly, other diseases, in later life.

Background: Studies examining the association between periodontal disease and coronary heart disease have shown a consistent but weak to moderate relationship. Limited data have been reported in women and the role of smoking has not been fully clarified. Methods/Results: A population-based case–control study examining the association between periodontal disease (PD) and acute non-fatal myocardial infarction (MI) was conducted in Erie and Niagara counties in Western New York State. Cases (574) were discharged alive from local hospitals with MI diagnosis. Controls (887) were county residents randomly selected from the NY State Department of Motor Vehicles rolls and Health Care Financing Administration files. Periodontal disease was assessed using clinical attachment loss (CAL). Among men (415 cases), the odds ratio (OR) of the association between mean CAL (mm) and MI, adjusting for the effects of age, body mass index (BMI), physical activity, hypertension, cholesterol, diabetes, and total pack-years of cigarette smoking was 1.34 (1.15–1.57). In women (120 cases), the corresponding OR was 2.08 (1.47–2.94). The estimate of this association among non-smokers, also adjusting for age, gender, BMI, physical activity, hypertension, cholesterol, diabetes, and total pack-years of cigarette smoking, was 1.40 (1.06–1.86), while it was 1.49 (1.26–1.77) among smokers. Conclusions: This study provides evidence of an association between PD and incident MI in both genders. This association appears to be independent from the possible confounding effect of smoking.

Background After clinical trials end, continued follow-up of the assembled cohort often is desirable for additional research. Factors influencing participants’ decisions to consent to additional follow-up and how these shape posttrial cohorts have not been broadly studied. Purpose We examined how two re-enrollment campaigns and the passage of time altered features of the posttrial cohorts compared with the original Women’s Health Initiative (WHI) Hormone Therapy clinical trials. Methods We examined associations that markers of sociodemography, health, lifestyle, and on-trial experiences had with re-enrollment and contrasted the characteristics of successive posttrial cohorts with those of the original enrollees. Results The posttrial enrollment campaigns re-enrolled 81.1% and 82.5% of available women, respectively. Women who re-enrolled tended to have better health characteristics than those not re-enrolled. Compared to women of comparable age in the original cohort, women retained for the second posttrial follow-up less often had a history of cardiovascular disease (odds ratio (OR) = 0.36), hypertension (OR = 0.57), diabetes (OR = 0.59), or measured cognitive deficit (OR = 0.40). These women more often had graduated from high school (OR = 1.72) and had participated in other WHI trials (OR = 1.76). Limitations We have examined experience with creating follow-up cohorts from participants in a single study. Thus, our findings may not apply to other cohorts and protocols. Conclusions Posttrial enrollment in follow-up studies can be successful; however, the characteristics of the resulting cohort may differ substantially from the originally assembled group of trial participants. Collection during the original trial of potential predictors of differential re-enrollment may strengthen interpretation of findings.

Multiplexing arrays increase the throughput and decrease sample requirements for studies employing multiple biomarkers. The goal of this project was to examine the performance of Multiplex arrays for measuring multiple protein biomarkers in saliva and serum. Specimens from the OsteoPerio ancillary study of the Women's Health Initiative Observational Study were used. Participants required the presence of at least 6 teeth and were excluded based on active cancer and certain bone issues but were not selected on any specific condition. Quality control (QC) samples were created from pooled serum and saliva. Twenty protein markers were measured on five multiplexing array panels. Sample pretreatment conditions were optimized for each panel. Recovery, lower limit of quantification (LLOQ) and imprecision were determined for each analyte. Statistical adjustment at the plate level was used to reduce imprecision estimates and increase the number of usable observations. Sample pre-treatment improved recovery estimates for many analytes. The LLOQ for each analyte agreed with manufacturer specifications except for MMP-1 and MMP-2 which were significantly higher than reported. Following batch adjustment, 17 of 20 biomarkers in serum and 9 of 20 biomarkers in saliva demonstrated acceptable precision, defined as <20% coefficient of variation (<25% at LLOQ). The percentage of cohort samples having levels within the reportable range for each analyte varied from 10% to 100%. The ratio of levels in saliva to serum varied from 1:100 to 28:1. Correlations between saliva and serum were of moderate positive magnitude and significant for CRP, MMP-2, insulin, adiponectin, GM-CSF and IL-5. Multiplex arrays exhibit high levels of analytical imprecision, particularly at the batch level. Careful sample pre-treatment can enhance recovery and reduce imprecision. Following statistical adjustments to reduce batch effects, we identified biomarkers that are of acceptable quality in serum and to a lesser degree in saliva using Multiplex arrays.