Regression to the mean, apparent data errors and biologically extraordinary results: letter regarding 'changes in telomere length 3-5 years after gastric bypass surgery'

Changes in leukocyte telomere length among children with obesity participating in a behavioural weight control program

OBJECTIVE

To examine changes in leukocyte telomere length (LTL) during and after a behavioural weight control program for children with obesity.

METHODS

We measured LTL among a cohort of 158 children 8-12 years of age with a body mass index greater than or equal to the 95th percentile for age and sex. Children were 55% female, 29% white, 52% Latinx, 8% Asian and 11% Pacific Islander, other or multiethnic. All children participated in a 6-month, family-based, group behavioural weight control program and were assessed before treatment, after treatment and 1 year after the end of treatment. To test the sample population slope of LTL over the intervention and maintenance time periods, we fit spline mixed-effect regression models.

RESULTS

LTL increased an average of 0.09 T/S units per year (95% confidence interval [CI] 0.04 to 0.13; p = 0.0001) during the weight control program intervention period, followed by an average decline of -0.05 T/S units per year (95% CI -0.08 to -0.03; p < 0.0001) during the 1 year of follow-up after the completion of the intervention. Among 26 social, psychological, behavioural and physiological factors we examined, we did not find any predictors of these changes.

CONCLUSIONS

LTL increased in response to a behavioural weight control program among children with obesity, suggesting an impact on biological health and cellular aging from participation in a behavioural weight control intervention. LTL may be a useful biomarker for assessing changes in response to behavioural interventions.

Measurement of Telomere Length for Longitudinal Analysis: Implications of Assay Precision

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.

Best (but oft-forgotten) practices: identifying and accounting for regression to the mean in nutrition and obesity research

BACKGROUND

Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution).

OBJECTIVES

To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research.

METHODS

We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM.

RESULTS

Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect.

CONCLUSIONS

The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.