Biology subjects

Nettle, D.

Publications and source records attributed to Nettle, D..

4 recordsLinked to original sources

bioRxiv2019

The evolution of life history theory: Bibliometric analysis of an interdisciplinary research area

Life history theory developed as a branch of formal evolutionary theory concerned with the fitness consequences of allocating energy to reproduction, growth and self-maintenance across the life course. More recently, researchers have advocated its relevance to many psychological and social-science questions. As a scientific paradigm expands its range, its parts can become conceptually isolated from one another, so that in the end it is no longer held together by a common core of shared ideas. Here, we investigate the life history theory literature using quantitative bibliometric methods based on patterns of citation. We found that the literature up to and including 2010 was relatively coherent: it drew on a shared body of core references, and had only weak cluster divisions running along taxonomic lines. The post-2010 literature is more fragmented: it has more marked cluster boundaries, including boundaries within the literature on humans. Specifically, there are two clusters of human literature based around the idea of a fast-slow continuum of individual differences in behaviour that are bibliometrically isolated from the rest of the literature. We also find some evidence suggesting a relative decline in formal mathematical modelling. We point out that the human fast-slow continuum literature is conceptually closer to the non-human pace of life literature than to the non-human literature usually referred to as life history theory.

evolutionary biology↗

bioRxiv2018

Consequences of measurement error in qPCR telomere data: A simulation study

The qPCR method provides an inexpensive, rapid method for estimating relative average telomere length across a set of biological samples. Like all laboratory methods, it involves some degree of measurement error. The estimation of relative telomere length is done subjecting the actual measurements made (the Cq values for telomere and a control gene) to non-linear transformations and combining them into a ratio. Here, we use computer simulations, supported by mathematical analysis, to explore how errors in measurement affect qPCR estimates of relative telomere length, both in cross-sectional and longitudinal data. We show that errors introduced at the level of Cq values are magnified when the TS ratio is calculated. If the errors at the Cq level are normally distributed and independent of telomere length, those in the TS ratio are positively skewed and proportional to telomere length. The repeatability of the TS ratio declines abruptly with increasing error in measurement of the telomere sequence and/or the control gene. In simulated longitudinal data, measurement error alone can produce a pattern of low correlation between successive measures of telomere length, coupled with a strong dependency of the rate of change on initial telomere length. Our results illustrate the importance of control of measurement error: a small increase in error in Cq values can have large consequences for the power and interpretability of qPCR estimates of relative telomere length. They also illustrate the importance of characterising the measurement error that exists in each dataset--coefficients of variation are generally unhelpful, and researchers should report standard deviations of Cq values and/or repeatabilities of TS ratios--and allowing for the known effects of measurement error when interpreting patterns of TS ratio change over time.

epidemiology↗

bioRxiv2018

Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis

Telomeres have been proposed as a biomarker that integrates the impacts of different kinds of stress and adversity into a common currency. There has as yet been no overall comparison of how different classes of exposure associate with telomeres. We present a meta-analysis of the literature relating telomere measures to stresses and adversities in humans. The analysed dataset contained 543 associations from 138 studies involving 402,116 people. Overall, there was a weak association between telomere variables and exposures (greater adversity, shorter telomeres: r = -0.15, 95% CI - 0.18 to -0.11). This was not driven by any one type of exposure, since significant associations were found separately for physical diseases, environmental hazards, nutrition, psychiatric illness, smoking, physical activity, psychosocial and socioeconomic exposures. Methodological features of the studies did not explain any substantial proportion of the heterogeneity in association strength. There was, however, evidence consistent with publication bias, with unexpectedly strong negative associations reported by studies with small samples. Restricting analysis to sample sizes greater than 100 attenuated the overall association substantially (r = -0.09, 95% CI -0.13 to -0.05). Most studies were underpowered to detect the typical association magnitude. The literature is dominated by cross-sectional and correlational studies which makes causal interpretation problematic.

epidemiology↗

bioRxiv2016

Detecting telomere elongation in longitudinal datasets: Analysis of a proposal by Simons, Stulp and Nakagawa

Telomere shortening has emerged as an important biomarker of aging. Longitudinal studies consistently find that, although telomere length shortens over time on average, there is a subset of individuals for whom telomere length is observed to increase. This apparent lengthening could either be a genuine biological phenomenon, or simply due to measurement and sampling error. Simons, Stulp and Nakagawa [Biogerontology 15: 99-103, 2014] recently proposed a statistical test for detecting when the amount of apparent lengthening in a dataset exceeds that which should be expected due to error, and thus indicating that genuine elongation may be operative in some individuals. The test is however based on a restrictive assumption, namely that each individuals true rate of telomere change is constant over time. It is not currently known whether this assumption is true. Here we show, using simulated datasets, that with perfect measurement and large sample size, the test has high power to detect true lengthening as long as the true rate of shortening is either constant, or moderately stable, over time. If the true rate of lengthening varies randomly from year to year, the test systematically returns type-II errors. We also consider the impact of measurement error. Using estimates of the magnitude of annual attrition and of measurement error derived from the human telomere literature, we show that power of the test is likely to be low in several empirically-realistic scenarios, even in large samples. Thus, whilst a significant result of the proposed test is likely to indicate that true lengthening is present in a data set, type-II errors are a likely outcome, either if measurement error is substantial, and/or the true rate of attrition varies substantially over time within individuals.

epidemiology↗