Use of telomere length (TL) as a biomarker for various environmental exposures and diseases has increased in recent years. Various methods have been developed to measure telomere length. Polymerase chain reaction (PCR)-based methods remain wide-spread for population-based studies due to the high-throughput capability. While several studies have evaluated the repeatability and reproducibility of different TL measurement methods, the results have been variable. We conducted a literature review of TL measurement cross-method comparison studies that included a PCR-based method published between January 1, 2002 and May 25, 2020. A total of 25 articles were found that matched the inclusion criteria. Papers were reviewed for quality of methodologic reporting of sample and DNA quality, PCR assay characteristics, sample blinding, and analytic approaches to determine final TL. Overall, methodologic reporting was low as assessed by two different reporting guidelines for qPCR-based TL measurement. There was a wide range in the reported correlation between methods (as assessed by Pearson’s r) and few studies utilized the recommended intra-class correlation coefficient (ICC) for assessment of assay repeatability and methodologic comparisons. The sample size for nearly all studies was less than 100, raising concerns about statistical power. Overall, this review found that the current literature on the relation between TL measurement methods is lacking in validity and scientific rigor. In light of these findings, we present reporting guidelines for PCR-based TL measurement methods and results of analyses of the effect of assay repeatability (ICC) on statistical power of cross-sectional and longitudinal studies. Additional cross-laboratory studies with rigorous methodologic and statistical reporting, adequate sample size, and blinding are essential to accurately determine assay repeatability and replicability as well as the relation between TL measurement methods.

Objective: To determine associations between central adiposity, cognitive function, and randomized menopausal hormone therapy (MHT) in a reanalysis of the Kronos Early Estrogen Prevention Study-Cognitive and Affective (KEEPS-Cog) sub-study participants. Methods: KEEPS randomized 727 women (ages 42-58) who were 35 kg/m ² , coronary artery calcium score >50 Agatston Units, and other cardiometabolic disease risk indicators were excluded from enrollment. In the ancillary KEEPS-Cog study, cognitive tests were completed at baseline, 18-, 36-, and 48-month post-randomization. In these analyses, cognitive variables were summarized as four cognitive domain-specific factor scores: verbal learning and memory, auditory attention and working memory, visual attention and executive function, and speeded language and mental flexibility. Waist-hip-ratio (WHR), an indicator of central adiposity, was measured at screening (baseline) and modeled as a covariate in linear latent growth models assessing associations of MHT with cognitive functions at baseline and over time. Results: Higher baseline WHR was associated with poorer performance on all domain-specific cognitive outcomes at baseline and with changes in visual attention and executive function across time. Models including interaction effects were not significant for either o-CEE x WHR or t-E2 x WHR. Conclusion: Central adiposity is a risk factor for domain-specific cognitive decline, and thus, cognitive health effects should be investigated in early postmenopausal women, even in women with low cardiovascular risk statuses.

Use of telomere length (TL) as a biomarker for various environmental exposures and diseases has increased in recent years. Various methods have been developed to measure telomere length. PCR-based methods remain wide-spread for population-based studies due to the high-throughput capability. While several studies have evaluated TL measurement methods, the results have been variable. We conducted a literature review of TL measurement cross-method comparison studies that included a PCR-based method published between January 1, 2002 and May 25, 2020. A total of 25 articles were found that matched the inclusion criteria. Papers were reviewed for quality of methodologic reporting of sample and DNA quality, PCR assay characteristics, sample blinding, and analytic approaches to determine final TL. Overall, methodologic reporting was low as assessed by two different reporting guidelines for qPCR-based TL measurement. There was a wide range in the reported correlation between methods (as assessed by Pearson’s r) and few studies utilized the recommended intra-class correlation coefficient (ICC) for assessment of assay repeatability and methodologic comparisons. The sample size for nearly all studies was less than 100, raising concerns about statistical power. Overall, this review found that the current literature on the relation between TL measurement methods is lacking in validity and scientific rigor. In light of these findings, we present reporting guidelines for PCR-based TL measurement methods and results of analyses of the effect of assay repeatability (ICC) on statistical power of cross-sectional and longitudinal studies. Additional cross-laboratory studies with rigorous methodologic and statistical reporting, adequate sample size, and blinding are essential to accurately determine assay repeatability and replicability as well as the relation between TL measurement methods.

Access and the ability to work with Tribal data can vastly improve the ability of Tribal Nations to support their citizens’ health and well-being. In this commentary, we expand on previous calls for state and federal public health agencies to share data with Tribes by default. Previous research has described the legal and ethical lay of the land concerning public health data sharing while underscoring the importance of respect for Tribal sovereignty. In this commentary, we expand on this argument by proposing additional pathways through which data can benefit Tribes and identifying critical steps for Tribes to fully benefit from Tribal data. Specifically, we argue for (1) renewed interest and investment in Tribal data science education; (2) proactive data practices, laws, and policies that support long-term health and well-being; and (3) the federal government honoring its trust responsibility to support Tribal data resources.