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Background: It is unknown whether atrial fibrillation (AF) is associated with an increased risk of sudden cardiac death (SCD) in the general population. This association was examined in 2 population-based cohorts.

Higher serum CML levels in late life are associated with incident disability and prevalent frailty. Further work is needed to understand CML's value as a risk stratifier, biomarker, or target for interventions that promote healthy aging.

Objectives To determine whether lifestyle factors measured late in life could compress the disabled period towards the end of life. Design Community-based cohort study of older adults followed from 1989 to 2015. Setting Four US communities. Participants Men and women ages 65 and older (N= 5248, mean age 72.7 ± 5.5 years, 57% women, 15.2% minority) who were living in the community, not wheel-chair dependent and able to give informed consent at baseline. Measurements Multiple lifestyle factors including smoking, alcohol consumption, physical activity, diet, body mass index (BMI), social networks and social support were measured at baseline. Activities of Daily Living (ADL) were assessed at baseline and throughout follow-up. Years of Life (YoL) was defined as years until death. Years of Able Life (YAL) was defined as years without any ADL difficulty. YAL/YoL%, the proportion of life lived able, was used to indicate the relative compression/expansion of the disabled period. Results The average duration of disabled years was 4.5 (out of 15.4 mean YoL) for women and 2.9 (out of 12.4 mean YoL) for men. In a multivariable model, obesity (compared to normal BMI) was associated with 7.3% (95% CI, 5.4–9.2) lower YAL/YoL%. Scores in the lowest quintile of the Alternate Healthy Eating Index (compared to highest) were associated with a 3.7% (95% CI, 1.6–5.9) lower YAL/YoL%. Every 25 blocks walked in a week was associated with 0.5% (95% CI, 0.3–0.8) higher YAL/YoL%. Conclusion The effects of healthy lifestyle factors on the proportion of future life lived free of disability indicate that the disabled period can be compressed, given the right combination of these factors.

Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has demonstrated considerable promise for numerous clinical intended uses. Successful validation and commercialization of novel ctDNA tests have the potential to improve the outcomes of patients with cancer. The goal of the Blood Profiling Atlas Consortium (BloodPAC) is to accelerate the development and validation of liquid biopsy assays that will be introduced into the clinic. To accomplish this goal, the BloodPAC conducts research in the following areas: Data Collection and Analysis within the BloodPAC Data Commons; Preanalytical Variables; Analytical Variables; Patient Context Variables; and Reimbursement. In this document, the BloodPAC’s Analytical Variables Working Group (AV WG) attempts to define a set of generic analytical validation protocols tailored for ctDNA-based Next-Generation Sequencing (NGS) assays. Analytical validation of ctDNA assays poses several unique challenges that primarily arise from the fact that very few tumor-derived DNA molecules may be present in circulation relative to the amount of nontumor-derived cell-free DNA (cfDNA). These challenges include the exquisite level of sensitivity and specificity needed to detect ctDNA, the potential for false negatives in detecting these rare molecules, and the increased reliance on contrived samples to attain sufficient ctDNA for analytical validation. By addressing these unique challenges, the BloodPAC hopes to expedite sponsors’ presubmission discussions with the Food and Drug Administration (FDA) with the protocols presented herein. By sharing best practices with the broader community, this work may also save the time and capacity of FDA reviewers through increased efficiency.

Various anthropometric measures, including height, have been associated with atrial fibrillation (AF). This raises questions about the appropriateness of using ratio measures such as body mass index (BMI), which contains height squared in its denominator, in the evaluation of AF risk. Among older adults, the optimal anthropometric approach to risk stratification of AF remains uncertain. Anthropometric and bioelectrical impedance measures were obtained from 4,276 participants (mean age = 72.4 years) free of cardiovascular disease in the Cardiovascular Health Study. During follow-up (1989-2008), 1,050 cases of AF occurred. BMI showed a U-shaped association, whereas height, weight, waist circumference, hip circumference, fat mass, and fat-free mass were linearly related to incident AF. The strongest adjusted association occurred for height (per each 1-standard-deviation increment, hazard ratio = 1.38, 95% confidence interval: 1.25, 1.51), which exceeded all other measures, including weight (hazard ratio = 1.21, 95% confidence interval: 1.13, 1.29). Combined assessment of log-transformed weight and height showed regression coefficients that departed from the 1 to -2 ratio inherent in BMI, indicating a loss of predictive information. Risk estimates for AF tended to be stronger for hip circumference than for waist circumference and for fat-free mass than for fat mass, which was explained largely by height. These findings highlight the prominent role of body size and the inadequacy of BMI as determinants of AF in older adults.

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Biomarkers are frequently being included in early-phase clinical trials. This article is meant to introduce clinical investigators to the fundamentals of choosing a biomarker test for use in an early phase trial. Steps to consider are briefly outlined including defining the role of the biomarker in the early phase trial; selecting a fit-for-purpose biomarker test and laboratory; describing the test procedures; carrying out analytical validation testing appropriate for the research objectives and the risk involved in the trial; implementing the test in the trial; and planning for the future. Examples illustrate analytical validation approaches in the context of typical biomarker roles. The importance of collaboration between clinical investigators and laboratory researchers is emphasized.