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.

Catalyzing communities of research rigour champions.

The biomedical sciences must maintain and enhance a research culture that prioritizes rigour and transparency. The US National Institute of Neurological Disorders and Stroke convened a workshop entitled 'Catalyzing Communities of Research Rigor Champions' that brought together a diverse group of leaders in promoting research rigour and transparency (identified as 'rigour champions') to discuss strategies, barriers and resources for catalyzing technical, cultural and educational changes in the biomedical sciences. This article summarizes 2 days of panels and discussions and provides an overview of critical barriers to research rigour, perspectives behind reform initiatives and considerations for stakeholders across science. Additionally, we describe applications of network science to foster, maintain and expand cultural changes related to scientific rigour and opportunities to embed rigourous practices into didactic courses, training experiences and degree programme requirements. We hope this piece provides a primer for the wider research community on current discussions and actions and inspires individuals to build, join or expand collaborative networks within their own institutions that prioritize rigourous research practices.

Not feeling the heat? Effects of dietary protein on satiation and satiety in mice are not due to its impact on body temperature.

Dietary protein modulates food intake (FI) via unclear mechanism(s). One possibility is that higher protein leads to greater post-ingestive heat production (Specific dynamic action: SDA) leading to earlier meal termination (increased satiation), and inhibition of further intake (increased satiety). The influence of dietary protein on feeding behaviour in C57BL/6J mice was tested using an automated FI monitoring system (BioDAQ), simultaneous to body temperature (Tb). Total FI, inter meal intervals (IMI, satiety) and meal size (MS, satiation) were related to changes in Tb after consuming low (5%, LP), moderate (15%, MP) and high (30%, HP) protein diets. Diets were tested over three conditions: 1) room temperature (RT, 21±1°C), 2) room temperature and running wheels (RTRW) and 3) low temperature (10°C) and running wheels (LTRW). The differences between diets and conditions were also compared using mixed models. Mice housed at RT fed HP diet, reduced total FI compared with LP and MP due to earlier meal termination (satiation effect). FI was lowered in RTRW conditions with no differences between diets. FI significantly increased under LTRW conditions for all diets, with protein content leading to earlier meal termination (satiation) but not the intervals between feeding bouts (satiety). Tb fell immediately after feeding in all conditions. Despite a reduction in total FI in mice fed HP, mediated via increased satiation, this effect was not linked to increased Tb during meals. We conclude effects of dietary protein on intake are not mediated via SDA and Tb.

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.

Design and conduct of a randomized controlled feeding trial in a residential setting with mitigation for COVID-19.

BACKGROUND: Evaluating effects of different macronutrient diets in randomized trials requires well defined infrastructure and rigorous methods to ensure intervention fidelity and adherence. METHODS: This controlled feeding study comprised two phases. During a Run-in phase (14-15 weeks), study participants (18-50 years, BMI, ≥27 kg/m2) consumed a very-low-carbohydrate (VLC) diet, with home delivery of prepared meals, at an energy level to promote 15 ± 3% weight loss. During a Residential phase (13 weeks), participants resided at a conference center. They received a eucaloric VLC diet for three weeks and then were randomized to isocaloric test diets for 10 weeks: VLC (5% energy from carbohydrate, 77% from fat), high-carbohydrate (HC)-Starch (57%, 25%; including 20% energy from refined grains), or HC-Sugar (57%, 25%; including 20% sugar). Outcomes included measures of body composition and energy expenditure, chronic disease risk factors, and variables pertaining to physiological mechanisms. Six cores provided infrastructure for implementing standardized protocols: Recruitment, Diet and Meal Production, Participant Support, Assessments, Regulatory Affairs and Data Management, and Statistics. The first participants were enrolled in May 2018. Participants residing at the conference center at the start of the COVID-19 pandemic completed the study, with each core implementing mitigation plans. RESULTS: Before early shutdown, 77 participants were randomized, and 70 completed the trial (65% of planned completion). Process measures indicated integrity to protocols for weighing menu items, within narrow tolerance limits, and participant adherence, assessed by direct observation and continuous glucose monitoring. CONCLUSION: Available data will inform future research, albeit with less statistical power than originally planned.

Trends in U.S. public confidence in science and opportunities for progress.

In recent years, many questions have been raised about whether public confidence in science is changing. To clarify recent trends in the public's confidence and factors that are associated with these feelings, an effort initiated by the National Academies' Strategic Council for Research Excellence, Integrity, and Trust (the Strategic Council) analyzed findings from multiple survey research organizations. The Strategic Council's effort, which began in 2022, found that U.S. public confidence in science, the scientific community, and leaders of scientific communities is high relative to other civic, cultural, and governmental institutions for which researchers regularly collect such data. However, confidence in these institutions has fallen during the previous 5 years. Science's decline, while real, is similar to or less than that in the other groups. A recent study goes into greater detail by exploring public views of science. From these data, we observe that many of the surveyed U.S. public question the extent to which scientists share their values or overcome personal biases when presenting conclusions. At the same time, large majorities agree on certain types of actions that they want scientists to take. For example, 84% respond that it is "somewhat important" or "very important" for scientists to disclose their funders. Ninety-two percent (92%) offer the same responses to scientists "being open to changing their minds based on new evidence." Collectively, these data clarify how the U.S. public views science and scientists. They also suggest actions that can affect public confidence in science and scientists in the years to come.

Breastfeeding and Once-Daily Small-Volume Formula Supplementation to Prevent Infant Growth Impairment.

BACKGROUND: Randomized controlled trials in Guinea-Bissau and Uganda have revealed that the intensive promotion of exclusive breastfeeding (EBF) impairs growth in early infancy. When newborn growth is impaired, small amounts of formula may be combined with breastfeeding to promote growth. METHODS: To determine if breastfeeding combined with once-daily formula supplementation improves growth among at-risk newborns, we conducted a pilot randomized controlled trial in Bissau, Guinea-Bissau and Kampala, Uganda. We randomly assigned 324 healthy breastfeeding newborns who weighed 2000 g to 2499 g at birth or <2600 g at 4 days old to once-daily formula feeding through 30 days as a supplement to frequent breastfeeding followed by EBF from 31 days through 6 months, or to EBF through 6 months. The primary outcome was weight-for-age z score (WAZ) at 30 days. Other outcomes included weight-for-length z score (WLZ), length-for-age z score (LAZ), breastfeeding cessation, adverse events, and serious adverse events through 180 days. RESULTS: Daily formula consumption in the intervention group was 31.9 ± 11.8 mL. The random assignment did not impact WAZ, WLZ, LAZ, breastfeeding cessation, adverse events, or serious adverse events through 180 days. In the intervention and control groups, 19 (12%) and 35 (21%) infants, respectively, reported nonformula supplementation in the first 30 days (P = .02). CONCLUSIONS: Once-daily formula supplementation for 30 days was well-tolerated, but the small volume consumed did not alter growth through 180 days of age. Further research would be required to determine if larger formula volumes, longer duration of treatment, or more frequent feeding are effective at increasing growth for this at-risk population.

Comment on Marsigliante et al. Effects on Children's Physical and Mental Well-Being of a Physical-Activity-Based School Intervention Program: A Randomized Study. Int. J. Environ. Res. Public Health 2023, 20, 1927.

We conducted a critical review of the article "Effects on Children's Physical and Mental Well-Being of a Physical-Activity-Based School Intervention Program: A Randomized Study", published in the International Journal of Environmental Research and Public Health in 2023 as part of the Special Issue "Psychomotricity and Physical Education in School Health". We identified multiple mistakes in the statistical analyses applied. First, the authors claim to have found a statistically significant association between the proposed intervention and change in body composition (body mass index (BMI) percentiles, relative fat mass, and BMI classes) by way of exhibiting differences in nominal significance between the pre- and post-intervention changes within the control and intervention groups, instead of exhibiting a significant difference between groups. Furthermore, the analysis described fails to account for clustering and nesting in the data. The reporting of the statistical methods and results include multiple elements that are variously incorrect, incoherent, or impossible. Revised statistical analyses are proposed which can render the study's methods valid and its results substantiated, whereas the current methods and results are invalid and unsubstantiated, respectively.

Unreliable Findings Due to Miscalculations and Errors. Comment on Nally et al. The Effectiveness of School-Based Interventions on Obesity-Related Behaviours in Primary School Children: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Children 2021, 8, 489.

Nally et al. [...].

Causal models and causal modelling in obesity: foundations, methods and evidence.

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Incorrect analysis in "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study" has resulted in incorrect conclusions of demonstrated effects where no such effects have been demonstrated.

In their 2023 Nutrition and Health paper "Effects of the application of a food processing-based classification system in obese women: A randomized controlled pilot study", Giacomello et al. investigated the effects of an educational intervention based on the Dietary Guidelines for the Brazilian Population among obese women. The authors concluded that the intervention significantly improved weight loss, quality of life, components of metabolic syndrome, and pain. However, we believe the statistical analysis employed in the study was flawed. The authors used within-group changes to draw conclusions, which is known as a difference in nominal significance error. This error has the potential to inflate Type I error rates substantially. To address this issue, we re-analyzed the data obtained from the authors. We focused on body mass and hip circumference and replicated the incorrectly chosen within-group analyses, which remained significant. However, to properly evaluate the intervention's effectiveness, it is essential to compare the differences between the groups directly. Therefore, we calculated change scores for each participant and used independent samples t-tests and linear mixed models to compare between-group differences. Both methods yielded similar non-significant p-values, indicating that there is no significant effect of treatment on body mass or hip circumference. The original paper's conclusions regarding the effectiveness of the intervention are not supported by the proper statistical analysis. The data should be re-analyzed using appropriate between-group comparisons, and the corrected results should be published, or the incorrect results and original paper should be retracted.

Corrected analysis of "the effects of bright light treatment on affective symptoms in people with dementia: a 24-week cluster randomized controlled trial" that accounts for clustering and nesting verifies conclusions.

In this correspondence, we explain the reasoning for invalidity of the analysis choices by Kolberg et al., and provide the results produced using correct statistical procedures for their study design. Reassuringly, we could verify the original conclusions. That is, results of the corrected statistical models are similar to the results of the original analysis. Regardless of the magnitude of difference that corrected statistical methods make, results and conclusions that are derived from invalid methods are unsubstantiated. By verifying the results, we allow the readers to be assured that the published conclusions in the study by Kolberg et al. now rest on a sound evidential basis.

One Cluster per Condition Is Not a Valid Design in Cluster-Randomized Trials. Comment on Wang et al. The Effect of Different Physical Exercise Programs on Physical Fitness among Preschool Children: A Cluster-Randomized Controlled Trial. Int. J. Environ. Res. Public Health 2023, 20, 4254.

Wang et al. [...].

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.

Unanswered questions about the causes of obesity.

Obesity is now a global pandemic, but there is little consensus about the causes.