Impact of sustained calorie restriction and weight cycling on body composition in high-fat diet-fed male and female C57BL/6J mice.

OBJECTIVE: The objective of this study was to investigate body composition changes with weight cycling (WC) among adult C57BL/6J mice with diet-induced obesity. METHODS: A total of 555 single-housed mice were fed a high-fat diet ad libitum (AL) from 8 to 43 weeks of age. The 200 heaviest mice of each sex were randomized to the following four groups: ever obese (EO, continued AL feeding); obese weight loser (OWL, calorie-restricted); obese weight loser moderate (OWLM, body weight halfway between EO and OWL); and WC (diet restricted to OWL followed by AL refeeding cycles). Body weight and composition data were collected. Linear regression was used to calculate residuals between predicted and observed fat mass. Linear mixed models were used to compare diet groups. RESULTS: Although weight loss and regain resulted in changes in body weight and composition, fat mass, body weight, and relative body fat were not significantly greater for the WC group compared with the EO group. During long-term calorie restriction, males (but not females) in the OWLM group remained relatively fatter than the EO group. CONCLUSIONS: WC did not increase body weight or relative fat mass for middle-aged, high-fat diet-fed adult mice. However, long-term moderate calorie restriction resulted in lower body weight but greater "relative" fat in male mice.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.