Re-Analysis and Additional Information Needed to Inform Conclusions. Comment on Halenova et al. Deuterium-Depleted Water as Adjuvant Therapeutic Agent for Treatment of Diet-Induced Obesity in Rats. Molecules 2020, 25, 23.

We were interested to read the report by Halenova et al. [...].

Data anomalies and apparent reporting errors in 'Randomized controlled trial testing weight loss and abdominal obesity outcomes of moxibustion'.

Randomized Controlled Trials (RCTs) are the best method to determine causal effects for treatments if they are well done and well reported. Good evidence about proposed treatments for obesity is needed, and Hsieh et al. (Biomed Eng Online 17:149, 2018) are to be commended for putting moxibustion to the test. However, careful evaluation of the paper reveals inconsistencies and apparent reporting errors, which raise doubts about conclusions from the study.

Body mass index trajectories and mortality risk in Japan using a population-based prospective cohort study: the Japan Public Health Center-based Prospective Study.

BACKGROUND: Recent studies have found that long-term changes in weight during adulthood are associated with a high risk of mortality. The objective of this study was to characterize body mass index (BMI) trajectories during adulthood and to examine the association between BMI trajectories and risk of death in the Japanese population. METHODS: The data were extracted from Japan Public Health Center-based Prospective Study-a population-based prospective cohort study in Japan with participants aged 40-69 years followed over 20 years. The participants were categorized into multiple BMI trajectory groups using the latent class growth model. The Cox proportional-hazards model was conducted using all-cause mortality and cause-specific mortality as outcomes and the identified BMI trajectory groups as a predictor. In total, 65 520 participants were included in the analysis. RESULTS: Six BMI trajectory groups were identified: underweight stable (Group 1), low-to-high normal (Group 2), high-to-low normal (Group 3), normal to overweight (Group 4), overweight to normal (Group 5) and normal to obese (Group 6). Our Cox models showed a higher hazard (risk) of all-cause mortality among participants in the BMI-declining groups [Group 3, adjusted hazard ratio (aHR): 1.10, 95% CI: 1.05-1.16; Group 5, aHR: 1.16, 95% CI: 1.08-1.26], underweight stable group (Group 1, aHR: 1.27, 95% CI: 1.21-1.33) and normal to obese group (Group 6, aHR: 1.22, 95% CI: 1.13-1.33) than Group 2 (low-to-high normal BMI trajectory). CONCLUSIONS: Stable underweight and weight loss were associated with a high risk of mortality, both of which were uniquely observed in a Japanese population.

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.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research.

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.