Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Publication of Results of Registered Trials With Published Study Protocols, 2011-2022.

Importance: Publishing study protocols might reduce research waste because of unclear methods or incomplete reporting; on the other hand, there might be few additional benefits of publishing protocols for registered trials that are never completed or published. No study has investigated the proportion of published protocols associated with published results. Objective: To estimate the proportion of published trial protocols for which there are not associated published results. Design, Setting, and Participants: This cross-sectional study used stratified random sampling to identify registered clinical trials with protocols published between January 2011 and August 2022 and indexed in PubMed Central. Ongoing studies and those within 1 year of the primary completion date on ClinicalTrials.gov were excluded. Published results were sought from August 2022 to March 2023 by searching ClinicalTrials.gov, emailing authors, and using an automated tool, as well as through incidental discovery. Main Outcomes and Measures: The primary outcome was a weighted estimate of the proportion of registered trials with published protocols that also had published main results. The proportion of trials with unpublished results was estimated using a weighted mean. Results: From 1500 citations that were screened, 308 clinical trial protocols were included, and it was found that 87 trials had not published their main results. Most included trials were investigator-initiated evaluations of nonregulated products. When published, results appeared a mean (SD) of 3.4 (2.0) years after protocol publications. With the use of a weighted mean, an estimated 4754 (95% CI, 4296-5226) eligible clinical trial protocols were published and indexed in PubMed Central between 2011 and 2022. In the weighted analysis, 1708 of those protocols (36%; 95% CI, 31%-41%) were not associated with publication of main results. In a sensitivity analysis excluding protocols published after 2019, an estimated 25% (95% CI, 20%-30%) of 3670 (95% CI, 3310-4032) protocol publications were not associated with publication of main results. Conclusions and Relevance: This cross-sectional study of clinical trial protocols published on PubMed Central between 2011 and 2022 suggests that many protocols were not associated with subsequent publication of results. The overall benefits of publishing study protocols might outweigh the research waste caused by unnecessary protocol publications.

Racial and skin color mediated disparities in pulse oximetry in infants and young children.

Race-based and skin pigmentation-related inaccuracies in pulse oximetry have recently been highlighted in several large electronic health record-based retrospective cohort studies across diverse patient populations and healthcare settings. Overestimation of oxygen saturation by pulse oximeters, particularly in hypoxic states, is disparately higher in Black compared to other racial groups. Compared to adult literature, pediatric studies are relatively few and mostly reliant on birth certificates or maternal race-based classification of comparison groups. Neonates, infants, and young children are particularly susceptible to the adverse life-long consequences of hypoxia and hyperoxia. Successful neonatal resuscitation, precise monitoring of preterm and term neonates with predominantly lung pathology, screening for congenital heart defects, and critical decisions on home oxygen, ventilator support and medication therapies, are only a few examples of situations that are highly reliant on the accuracy of pulse oximetry. Undetected hypoxia, especially if systematically different in certain racial groups may delay appropriate therapies and may further perpetuate health care disparities. The role of biological factors that may differ between racial groups, particularly skin pigmentation that may contribute to biased pulse oximeter readings needs further evaluation. Developmental and maturational changes in skin physiology and pigmentation, and its interaction with the operating principles of pulse oximetry need further study. Importantly, clinicians should recognize the limitations of pulse oximetry and use additional objective measures of oxygenation (like co-oximetry measured arterial oxygen saturation) where hypoxia is a concern.

Systematic Review and Meta-Analysis to Estimate a Reference Range for Circulating Ionized Magnesium Concentrations in Adult Populations.

BACKGROUND: There is a lack of consensus on a reference range for ionized magnesium (iMg2+) in blood as a measure of the status of circulating iMg2+ for the screening of populations. OBJECTIVES: We estimated the reference range of iMg2+ levels for healthy adult populations and the ranges for populations with cardiovascular disease (CVD), type 2 diabetes, hypertension, and renal disease. We also estimated 95% ranges for circulating magnesium (Mg) in healthy and those with cardiometabolic diseases. METHODS: We searched Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Embase through 24 July, 2020 to identify articles. We included English, peer-reviewed, randomized controlled trials, prospective and retrospective cohort studies, case-control studies, and cross-sectional studies that measured iMg2+ in blood or circulating Mg at baseline. The protocol was registered on PROSPERO (CRD42020216100). Estimated ranges were calculated by employing a frequentist random-effects model using extracted (or calculated) means and SDs from each included study. We determined the 95% confidence interval of the pooled mean. RESULTS: A total of 95 articles were included with 53 studies having data for healthy participants and 42 studies having data for participants with cardiometabolic diseases. The estimated reference range for iMg2+ for healthy populations was 0.40-0.68 mmol/L, 0.38-0.64 mmol/L for CVD, 0.34-0.66 mmol/L for type 2 diabetes, 0.39-1.04 mmol/L for hypertension, and 0.40-0.76 mmol/L for renal disease. For circulating Mg, the estimated range was 0.72-1.0 mmol/L for healthy adults, 0.56-1.05 mmol/L for CVD, 0.58-1.14 mmol/L for type 2 diabetes, 0.60-1.08 mmol/L for hypertension, and 0.59-1.26 mmol/L for renal disease. CONCLUSIONS: Estimated reference ranges for cardiometabolic disease states for both iMg2+ and circulating Mg were broad and overlapped with the estimated range for healthy populations (0.40-0.68 mmol/L). Further studies should evaluate whether iMg2+ can be used as a biomarker of cardiometabolic disease.

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. [...].

Perspective: A Research Roadmap about Ultra-Processed Foods and Human Health for the United States Food System: Proceedings from an Interdisciplinary, Multi-Stakeholder Workshop.

Our objective was to convene interdisciplinary experts from government, academia, and industry to develop a Research Roadmap to identify research priorities about processed food intake and risk for obesity and cardiometabolic diseases (CMD) among United States populations. We convened attendees at various career stages with diverse viewpoints in the field. We held a "Food Processing Primer" to build foundational knowledge of how and why foods are processed, followed by presentations about how processed foods may affect energy intake, obesity, and CMD risk. Breakout groups discussed potential mechanistic and confounding explanations for associations between processed foods and obesity and CMD risk. Facilitators created research questions (RQs) based on key themes from discussions. Different breakout groups convened to discuss what is known and unknown for each RQ and to develop sub-RQs to address gaps. Workshop attendees focused on ultra-processed foods (UPFs; Nova Group 4) because the preponderance of evidence is based on this classification system. Yet, heterogeneity and subjectivity in UPF classification was a challenge for RQ development. The 6 RQs were: 1) What objective methods or measures could further categorize UPFs, considering food processing, formulation, and the interaction of the two? 2) How can exposure assessment of UPF intake be improved? 3) Does UPF intake influence risk for obesity or CMDs, independent of diet quality? 4) What, if any, attributes of UPFs influence ingestive behavior and contribute to excess energy intake? 5) What, if any, attributes of UPFs contribute to clinically meaningful metabolic responses? 6) What, if any, external environmental factors lead people to consume high amounts of UPFs? Uncertainty and complexity around UPF intake warrant further complementary and interdisciplinary causal, mechanistic, and methodological research related to obesity and CMD risk to understand the utility of applying classification by degree of processing to foods in the United States.

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.

Comment on Qiu et al. Effect of Protein-Rich Breakfast on Subsequent Energy Intake and Subjective Appetite in Children and Adolescents: Systematic Review and Meta-Analysis of Randomized Controlled Trials. Nutrients 2021, 13, 2840.

In reading Qiu et al. [...].

Bias in nutrition-health associations is not eliminated by excluding extreme reporters in empirical or simulation studies.

Self-reported nutrition intake (NI) data are prone to reporting bias that may induce bias in estimands in nutrition studies; however, they are used anyway due to high feasibility. We examined whether applying Goldberg cutoffs to remove 'implausible' self-reported NI could reliably reduce bias compared to biomarkers for energy, sodium, potassium, and protein. Using the Interactive Diet and Activity Tracking in the American Association of Retired Persons (IDATA) data, significant bias in mean NI was removed with Goldberg cutoffs (120 among 303 participants excluded). Associations between NI and health outcomes (weight, waist circumference, heart rate, systolic/diastolic blood pressure, and VO2 max) were estimated, but sample size was insufficient to evaluate bias reductions. We therefore simulated data based on IDATA. Significant bias in simulated associations using self-reported NI was reduced but not completely eliminated by Goldberg cutoffs in 14 of 24 nutrition-outcome pairs; bias was not reduced for the remaining 10 cases. Also, 95% coverage probabilities were improved by applying Goldberg cutoffs in most cases but underperformed compared with biomarker data. Although Goldberg cutoffs may achieve bias elimination in estimating mean NI, bias in estimates of associations between NI and outcomes will not necessarily be reduced or eliminated after application of Goldberg cutoffs. Whether one uses Goldberg cutoffs should therefore be decided based on research purposes and not general rules.

RNA virus-mediated changes in organismal oxygen consumption rate in young and old Drosophila melanogaster males.

Aging is accompanied by increased susceptibility to infections including with viral pathogens resulting in higher morbidity and mortality among the elderly. Significant changes in host metabolism can take place following virus infection. Efficient immune responses are energetically costly, and viruses divert host molecular resources to promote their own replication. Virus-induced metabolic reprogramming could impact infection outcomes, however, how this is affected by aging and impacts organismal survival remains poorly understood. RNA virus infection of Drosophila melanogaster with Flock House virus (FHV) is an effective model to study antiviral responses with age, where older flies die faster than younger flies due to impaired disease tolerance. Using this aged host-virus model, we conducted longitudinal, single-fly respirometry studies to determine if metabolism impacts infection outcomes. Analysis using linear mixed models on Oxygen Consumption Rate (OCR) following the first 72-hours post-infection showed that FHV modulates respiration, but age has no significant effect on OCR. However, the longitudinal assessment revealed that OCR in young flies progressively and significantly decreases, while OCR in aged flies remains constant throughout the three days of the experiment. Furthermore, we found that the OCR signature at 24-hours varied in response to both experimental treatment and survival status. FHV-injected flies that died prior to 48- or 72-hours measurements had a lower OCR compared to survivors at 48-hours. Our findings suggest the host's metabolic profile could influence the outcome of viral infections.

The effect of a pharmaceutical ghrelin agonist on lifespan in C57BL/6J male mice: A controlled experiment.

Interventions for animal lifespan extension like caloric restriction (CR) have identified physiologic and biochemical pathways related to hunger and energy-sensing status as possible contributors, but mechanisms have not been fully elucidated. Prior studies using ghrelin agonists show greater food intake but no effect on lifespan in rodent models. This experiment in male C57BL/6J mice tested the influence of ghrelin agonism for perceived hunger, in the absence of CR, on longevity. Mice aged 4 weeks were allowed to acclimate for 2 weeks prior to being assigned (N = 60/group). Prior to lights off daily (12:12 cycle), animals were fed a ghrelin agonist pill (LY444711; Eli Lilly) or a placebo control (Ctrl) until death. Treatment (GhrAg) animals were pair-fed daily based on the group mean food intake consumed by Ctrl (ad libitum feeding) the prior week. Results indicate an increased lifespan effect (log-rank p = 0.0032) for GhrAg versus placebo Ctrl, which weighed significantly more than GhrAg (adjusted for baseline weight). Further studies are needed to determine the full scope of effects of this ghrelin agonist, either directly via increased ghrelin receptor signaling or indirectly via other hypothalamic, systemic, or tissue-specific mechanisms.

Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo.

To date, nutritional epidemiology has relied heavily on relatively weak methods including simple observational designs and substandard measurements. Despite low internal validity and other sources of bias, claims of causality are made commonly in this literature. Nutritional epidemiology investigations can be improved through greater scientific rigor and adherence to scientific reporting commensurate with research methods used. Some commentators advocate jettisoning nutritional epidemiology entirely, perhaps believing improvements are impossible. Still others support only normative refinements. But neither abolition nor minor tweaks are appropriate. Nutritional epidemiology, in its present state, offers utility, yet also needs marked, reformational renovation. Changing the status quo will require ongoing, unflinching scrutiny of research questions, practices, and reporting-and a willingness to admit that "good enough" is no longer good enough. As such, a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal and defense of the status quo" was held from July 15 to August 14, 2020. This virtual symposium focused on: (1) Stronger Designs, (2) Stronger Measurement, (3) Stronger Analyses, and (4) Stronger Execution and Reporting. Participants from several leading academic institutions explored existing, evolving, and new better practices, tools, and techniques to collaboratively advance specific recommendations for strengthening nutritional epidemiology.

Machine learning modeling practices to support the principles of AI and ethics in nutrition research.

BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. METHODS: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. RESULTS: Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. CONCLUSION: The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.

Mixed Random and Nonrandom Allocation, and Group Randomization Have Been Mislabeled and Misanalysed, Necessitating Reanalysis. Comment on Conner et al. KiwiC for Vitality: Results of a Randomized Placebo-Controlled Trial Testing the Effects of Kiwifruit or Vitamin C Tablets on Vitality in Adults with Low Vitamin C Levels. Nutrients 2020, 12, 2898.

We read the report by Conner and colleagues that tested whether kiwifruit or vitamin C affected measures of vitality [...].