TY - JOUR
T1 - Measurement of Telomere Length for Longitudinal Analysis
T2 - Implications of Assay Precision
AU - Nettle, Daniel
AU - Gadalla, Shahinaz M.
AU - Lai, Tsung Po
AU - Susser, Ezra
AU - Bateson, Melissa
AU - Aviv, Abraham
N1 - Publisher Copyright:
Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US.
PY - 2021/7/1
Y1 - 2021/7/1
N2 - Researchers increasingly wish to test hypotheses concerning the impact of environmental or disease exposures on telomere length (TL), and they use longitudinal study designs to do so. In population studies, TL is usually measured with a quantitative polymerase chain reaction (qPCR)-based method. This method has been validated by calculating its correlation with a gold standard method such as Southern blotting (SB) in cross-sectional data sets. However, in a cross-section, the range of true variation in TL is large, and measurement error is introduced only once. In a longitudinal study, the target variation of interest is small, and measurement error is introduced at both baseline and follow-up. In this paper, we present results from a small data set (n = 20) in which leukocyte TL was measured twice 6.6 years apart by means of both qPCR and SB. The cross-sectional correlations between qPCR and SB were high at both baseline (r = 0.90) and follow-up (r = 0.85), yet their correlation for TL change was poor (r = 0.48). Moreover, the qPCR data but not the SB data showed strong signatures of measurement error. Through simulation, we show that the statistical power gain from performing a longitudinal analysis is much greater for SB than for qPCR. We discuss implications for optimal study design and analysis.
AB - Researchers increasingly wish to test hypotheses concerning the impact of environmental or disease exposures on telomere length (TL), and they use longitudinal study designs to do so. In population studies, TL is usually measured with a quantitative polymerase chain reaction (qPCR)-based method. This method has been validated by calculating its correlation with a gold standard method such as Southern blotting (SB) in cross-sectional data sets. However, in a cross-section, the range of true variation in TL is large, and measurement error is introduced only once. In a longitudinal study, the target variation of interest is small, and measurement error is introduced at both baseline and follow-up. In this paper, we present results from a small data set (n = 20) in which leukocyte TL was measured twice 6.6 years apart by means of both qPCR and SB. The cross-sectional correlations between qPCR and SB were high at both baseline (r = 0.90) and follow-up (r = 0.85), yet their correlation for TL change was poor (r = 0.48). Moreover, the qPCR data but not the SB data showed strong signatures of measurement error. Through simulation, we show that the statistical power gain from performing a longitudinal analysis is much greater for SB than for qPCR. We discuss implications for optimal study design and analysis.
KW - Southern blot
KW - assay precision
KW - leukocyte telomere length
KW - longitudinal studies
KW - measurement error
KW - quantitative polymerase chain reaction
KW - telomere length
KW - terminal restriction fragment
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U2 - 10.1093/aje/kwab025
DO - 10.1093/aje/kwab025
M3 - Article
C2 - 33564874
AN - SCOPUS:85111789560
VL - 190
SP - 1406
EP - 1413
JO - American Journal of Epidemiology
JF - American Journal of Epidemiology
SN - 0002-9262
IS - 7
ER -