A Bayesian semi-parametric scalar-on-function regression with measurement error using instrumental variables.

Wearable devices such as the ActiGraph are now commonly used in research to monitor or track physical activity. This trend corresponds with the growing need to assess the relationships between physical activity and health outcomes, such as obesity, accurately. Device-based physical activity measures are best treated as functions when assessing their associations with scalar-valued outcomes such as body mass index. Scalar-on-function regression (SoFR) is a suitable regression model in this setting. Most estimation approaches in SoFR assume that the measurement error in functional covariates is white noise. Violating this assumption can lead to underestimating model parameters. There are limited approaches to correcting measurement errors for frequentist methods and none for Bayesian methods in this area. We present a non-parametric Bayesian measurement error-corrected SoFR model that relaxes all the constraining assumptions often involved with these models. Our estimation relies on an instrumental variable allowing a time-varying biasing factor, a significant departure from the current generalized method of moment (GMM) approach. Our proposed method also permits model-based grouping of the functional covariate following measurement error correction. This grouping of the measurement error-corrected functional covariate allows additional ease of interpretation of how the different groups differ. Our method is easy to implement, and we demonstrate its finite sample properties in extensive simulations. Finally, we applied our method to data from the National Health and Examination Survey to assess the relationship between wearable device-based measures of physical activity and body mass index in adults in the United States.

Recommendations on statistical approaches to account for dose uncertainties in radiation epidemiologic risk models.

PURPOSE: Epidemiological studies of stochastic radiation health effects such as cancer, meant to estimate risks of the adverse effects as a function of radiation dose, depend largely on estimates of the radiation doses received by the exposed group under study. Those estimates are based on dosimetry that always has uncertainty, which often can be quite substantial. Studies that do not incorporate statistical methods to correct for dosimetric uncertainty may produce biased estimates of risk and incorrect confidence bounds on those estimates. This paper reviews commonly used statistical methods to correct radiation risk regressions for dosimetric uncertainty, with emphasis on some newer methods. We begin by describing the types of dose uncertainty that may occur, including those in which an uncertain value is shared by part or all of a cohort, and then demonstrate how these sources of uncertainty arise in radiation dosimetry. We briefly describe the effects of different types of dosimetric uncertainty on risk estimates, followed by a description of each method of adjusting for the uncertainty. CONCLUSIONS: Each of the method has strengths and weaknesses, and some methods have limited applicability. We describe the types of uncertainty to which each method can be applied and its pros and cons. Finally, we provide summary recommendations and touch briefly on suggestions for further research.

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.

A Dietary Model of Partial Meat Replacement with Walnuts Demonstrates Changes in the Nutrient Profile and Quality of the United States Population's Diet.

The purpose of the study is to assess the impact of partial meat replacement with walnuts using a dose-escalation approach on nutrient intake and diet quality in the usual US diet. Food modeling was implemented using the nationally representative 2015-2018 National Health and Examination Survey (NHANES), with a focus on non-nut consumers, which included 2707 children and adolescents and 5190 adults. Walnuts replaced meat in a dose-escalating manner (0.5, 1, 1.5, and 2 oz walnuts per day replaced 1, 2, 3, and 4 oz meat, respectively). Diet quality was estimated using the population ratio method of the 2015 Healthy Eating Index. The usual intake of nutrients was estimated using the National Cancer Institute method. Significant differences were determined using non-overlapping 95% confidence intervals. The partial replacement of meat with walnuts demonstrated significant increases in the mean intake of fiber, magnesium, and omega-3 fatty acids and significant decreases in cholesterol and vitamin B12 in the modeled diets for children, adolescents, and adults. Additionally, the partial replacement of meat with walnuts improved overall diet quality. Walnut consumption at 1-2 oz as a replacement for some meat may improve nutrient intake and diet quality across age groups.

Dietary supplementation with L-leucine reduces nitric oxide synthesis by endothelial cells of rats.

This study tested the hypothesis that elevated L-leucine concentrations in plasma reduce nitric oxide (NO) synthesis by endothelial cells (ECs) and affect adiposity in obese rats. Beginning at four weeks of age, male Sprague-Dawley rats were fed a casein-based low-fat (LF) or high-fat (HF) diet for 15 weeks. Thereafter, rats in the LF and HF groups were assigned randomly into one of two subgroups (n = 8/subgroup) and received drinking water containing either 1.02% L-alanine (isonitrogenous control) or 1.5% L-leucine for 12 weeks. The energy expenditure of the rats was determined at weeks 0, 6, and 11 of the supplementation period. At the end of the study, an oral glucose tolerance test was performed on all the rats immediately before being euthanized for the collection of tissues. HF feeding reduced (P < 0.001) NO synthesis in ECs by 21% and whole-body insulin sensitivity by 19% but increased (P < 0.001) glutamine:fructose-6-phosphate transaminase (GFAT) activity in ECs by 42%. Oral administration of L-leucine decreased (P < 0.05) NO synthesis in ECs by 14%, increased (P < 0.05) GFAT activity in ECs by 35%, and reduced (P < 0.05) whole-body insulin sensitivity by 14% in rats fed the LF diet but had no effect (P > 0.05) on these variables in rats fed the HF diet. L-Leucine supplementation did not affect (P > 0.05) weight gain, tissue masses (including white adipose tissue, brown adipose tissue, and skeletal muscle), or antioxidative capacity (indicated by ratios of glutathione/glutathione disulfide) in LF- or HF-fed rats and did not worsen (P > 0.05) adiposity, whole-body insulin sensitivity, or metabolic profiles in the plasma of obese rats. These results indicate that high concentrations of L-leucine promote glucosamine synthesis and impair NO production by ECs, possibly contributing to an increased risk of cardiovascular disease in diet-induced obese rats.

Relationship between short-term self-reported dietary magnesium intake and whole blood ionized magnesium (iMg2+) or serum magnesium (s-Mg) concentrations.

OBJECTIVE: Since we and others have shown that supplemental magnesium raises whole blood ionized magnesium (iMg2+) we investigated the relationships between self-reported dietary magnesium intake and concentrations of whole blood iMg2+ and serum magnesium (s-Mg). METHODS: We obtained whole blood iMg2+ concentrations, as well as s-Mg concentrations, from a pilot, three-arm, randomized, controlled, crossover bioavailability study of magnesium supplements (n = 23; 105 measures). Dietary magnesium intake was assessed using three-day food records and the Nutrition Data System for Research (NDSR, University of Minnesota, MN, USA). Whole blood iMg2+ was measured with an electrode analyser (NOVA Biochemical, Waltham, MA, USA), whereas s-Mg was measured using atomic absorption spectroscopy. A linear mixed-effects model was employed with dietary magnesium as the outcome variable and iMg2+, s-Mg, study treatment and study visit as fixed effects. We adjusted age, gender, race and body mass index covariates. RESULTS: Values for dietary magnesium, iMg2+ and s-Mg were 303.8 ± 118.9 mg/day, 1.3 ± 0.1 mg/dL and 2.2 ± 4.1 mg/dL, respectively. No association was found between dietary magnesium intake and iMg2+ -125 ± 176.95 (p = .49) or s-Mg -9.33 ± 5.04 (p = .08). CONCLUSIONS: Whole blood iMg2+ and s-Mg concentrations do not reflect short-term self-reported dietary intake in adults. Further research is needed to determine whether blood biomarkers of magnesium may reflect dietary magnesium intake.Key messagesDietary intake of magnesium, a shortfall nutrient, may be objectively measured using blood biomarkers of magnesium.Serum magnesium and whole blood iMg2+ were not associated with short-term dietary intake of magnesium.

Parametric and Semiparametric Approaches to Analyzing Device-Based Measures of Energy Expenditure in Zucker Diabetic Fatty Rats.

BACKGROUND: Obesity results from a chronic imbalance between energy intake and energy expenditure. Total energy expenditure for all physiological functions combined can be measured approximately by calorimeters. These devices assess energy expenditure frequently (e.g., in 60-second epochs), resulting in massive complex data that are nonlinear functions of time. To reduce the prevalence of obesity, researchers often design targeted therapeutic interventions to increase daily energy expenditure. METHODS: We analyzed previously collected data on the effects of oral interferon tau supplementation on energy expenditure, as assessed with indirect calorimeters, in an animal model for obesity and type 2 diabetes (Zucker diabetic fatty rats). In our statistical analyses, we compared parametric polynomial mixed effects models and more flexible semiparametric models involving spline regression. RESULTS: We found no effect of interferon tau dose (0 vs. 4 µg/kg body weight/day) on energy expenditure. The B-spline semiparametric model of untransformed energy expenditure with a quadratic term for time performed best in terms of the Akaike information criterion value. CONCLUSIONS: To analyze the effects of interventions on energy expenditure assessed with devices that collect data at frequent intervals, we recommend first summarizing the high dimensional data into epochs of 30 to 60 minutes to reduce noise. We also recommend flexible modeling approaches to account for the nonlinear patterns in such high dimensional functional data. We provide freely available R codes in GitHub.

Adding Walnuts to the Usual Diet Can Improve Diet Quality in the United States: Diet Modeling Study Based on NHANES 2015-2018.

BACKGROUND: The under-consumption of calcium, potassium, fiber, and vitamin D is considered a U.S. public health concern. Shifts in eating patterns that increase the consumption of vegetables, fruits, whole grains, nuts/seeds, and dairy products can help achieve the recommended intakes of these nutrients, leading to healthier diets. OBJECTIVE: We assessed the impact of adding 1 ounce (28.35 g) of walnuts to usual diets on diet quality and nutrients of concern, including magnesium, fiber, and potassium. METHODS: We utilized 24 h dietary recalls obtained from the What We Eat in America, National Health and Nutrition Examination Survey (NHANES) and modeled the addition of 1 ounce (28.35 g) of walnuts to the usual diets of no-nut consumers. No-nut consumers aged ≥4 years (n = 7757) from the 2015-2018 NHANES study were included. Population percentages with intakes below the estimated average requirement (EAR) values for calcium, magnesium, folate, and vitamin E and above the adequate intake (AI) values for potassium and fiber were examined. Diet quality was assessed using the Healthy Eating Index-2015 (HEI-2015). The National Cancer Institute method was used to estimate the usual and modeled intakes. Significant differences between usual (current) and modeled intakes were determined using non-overlapping 95% confidence intervals. All analyses included sample weights to account for the NHANES survey design. RESULTS: Adding 1 ounce (28.35 g) of walnuts to the usual diet resulted in significant reductions in the percentages of adults with intakes below the EAR for magnesium and folate (69.6% vs. 52.0%; 49.2% vs. 40.6%, respectively), and increased the percentage of adults above the AI for potassium (22.8% vs. 26.5%). A similar trend was observed among children (4-18 years). HEI scores improved significantly from 49.1 (95% CI: 48.0-50.4) to 58.5 (95% CI: 57.5-59.6) in children and from 52.4 (95% CI: 51.0-53.8) to 59.2 (95% CI: 58.0-60.5) in adults. CONCLUSIONS: Adding 1 ounce (28.35 g) of walnuts to the usual diet of no-nut consumers improved the diet quality and adequacy of some under-consumed nutrients.

A function-based approach to model the measurement error in wearable devices.

Physical activity (PA) is an important risk factor for many health outcomes. Wearable-devices such as accelerometers are increasingly used in biomedical studies to understand the associations between PA and health outcomes. Statistical analyses involving accelerometer data are challenging due to the following three characteristics: (i) high-dimensionality, (ii) temporal dependence, and (iii) measurement error. To address these challenges we treat accelerometer-based measures of PA as a single function-valued covariate prone to measurement error. Specifically, in order to determine the relationship between PA and a health outcome of interest, we propose a regression model with a functional covariate that accounts for measurement error. Using regression calibration, we develop a two-step estimation method for the model parameters and establish their consistency. A test is also proposed to test the significance of the estimated model parameters. Simulation studies are conducted to compare the proposed methods with existing alternative approaches under varying scenarios. Finally, the developed methods are used to assess the relationship between PA intensity and BMI obtained from the National Health and Nutrition Examination Survey data.

Estimation of sparse functional quantile regression with measurement error: a SIMEX approach.

Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the method's robustness to distributional assumptions and outliers in the outcome, regression quantiles may be biased in the presence of measurement error in the covariates. The impact of function-valued covariates contaminated with heteroscedastic error has not yet been examined previously; although, studies have investigated the case of scalar-valued covariates. We present a two-stage strategy to consistently fit linear quantile regression models with a function-valued covariate that may be measured with error. In the first stage, an instrumental variable is used to estimate the covariance matrix associated with the measurement error. In the second stage, simulation extrapolation (SIMEX) is used to correct for measurement error in the function-valued covariate. Point-wise standard errors are estimated by means of nonparametric bootstrap. We present simulation studies to assess the robustness of the measurement error corrected for functional quantile regression. Our methods are applied to National Health and Examination Survey data to assess the relationship between physical activity and body mass index among adults in the United States.

Errors in the implementation, analysis, and reporting of randomization within obesity and nutrition research: a guide to their avoidance.

Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research.

Chlamydia trachomatis Is Associated With Medically Indicated Preterm Birth and Preeclampsia in Young Pregnant Women.

BACKGROUND: Studies on Chlamydia trachomatis-associated pregnancy outcomes are largely conflicting, ignoring the heterogeneous natures of pregnancy complications and potential effect modification by maternal age. This study determined if prenatal C. trachomatis infection is associated with preterm birth (PTB) and preeclampsia subtypes. METHODS: A retrospective cohort study was conducted using 22,772 singleton pregnancies with a prenatal C. trachomatis diagnostic test. Spontaneous and medically indicated PTBs, and term and preterm preeclampsia were outcomes. Modified Poisson regression calculated relative risk (RR) and 95% confidence intervals (CI) with propensity score adjustments stratified by maternal ages <25 and ≥25 years. RESULTS: Overall, C. trachomatis was significantly associated with term preeclampsia (adjusted RR [RRadj], 1.88; 95% CI, 1.38-2.57). Among young women (age <25 years), C. trachomatis was significantly associated with medically indicated PTB (RRadj, 2.29; 95% CI, 1.38-3.78) and term preeclampsia (RRadj, 1.57; 95% CI, 1.05-2.36) in propensity-adjusted models. No significant associations in older women were detected. CONCLUSION: C. trachomatis was associated with medically indicated PTB and term preeclampsia in young women. Associations between chlamydia and perinatal outcomes may depend on the subtype of PTB and preeclampsia, which should be investigated through mechanistic studies.

Instrumental variable approach to estimating the scalar-on-function regression model with measurement error with application to energy expenditure assessment in childhood obesity.

Wearable device technology allows continuous monitoring of biological markers and thereby enables study of time-dependent relationships. For example, in this paper, we are interested in the impact of daily energy expenditure over a period of time on subsequent progression toward obesity among children. Data from these devices appear as either sparsely or densely observed functional data and methods of functional regression are often used for their statistical analyses. We study the scalar-on-function regression model with imprecisely measured values of the predictor function. In this setting, we have a scalar-valued response and a function-valued covariate that are both collected at a single time period. We propose a generalized method of moments-based approach for estimation, while an instrumental variable belonging in the same time space as the imprecisely measured covariate is used for model identification. Additionally, no distributional assumptions regarding the measurement errors are assumed, while complex covariance structures are allowed for the measurement errors in the implementation of our proposed methods. We demonstrate that our proposed estimator is L2 consistent and enjoys the optimal rate of convergence for univariate nonparametric functions. In a simulation study, we illustrate that ignoring measurement error leads to biased estimations of the functional coefficient. The simulation studies also confirm our ability to consistently estimate the function-valued coefficient when compared to approaches that ignore potential measurement errors. Our proposed methods are applied to our motivating example to assess the impact of baseline levels of energy expenditure on body mass index among elementary school-aged children.

Illustration of Measurement Error Models for Reducing Bias in Nutrition and Obesity Research Using 2-D Body Composition Data.

OBJECTIVE: This study aimed to illustrate the use and value of measurement error models for reducing bias when evaluating associations between body fat and having type 2 diabetes (T2D) or being physically active. METHODS: Logistic regression models were used to evaluate T2D and physical activity among adults aged 19 to 80 years from the Photobody Study (n = 558). Self-reported T2D and physical activity were categorized as "yes" or "no." Body fat measured by two-dimensional photographs was adjusted for bias using dual-energy x-ray absorptiometry scans as a reference. Three approaches were applied: regression calibration (RC), simulation extrapolation (SIMEX), and multiple imputation (MI). RESULTS: Unadjusted two-dimensional measures of body fat had upward biases of 30% and 233% for physical activity and T2D, respectively. For the physical activity model, RC-adjusted values had a 13% upward bias, whereas MI and SIMEX decreased the bias to 9% and 91%, respectively. For the T2D model, MI reduced the bias to 0%, whereas RC and SIMEX increased the upward bias to > 300%. CONCLUSIONS: Of three statistical approaches to reducing bias due to measurement errors, MI performed best in comparison to RC and SIMEX. Measurement error methods can improve the reliability of analyses that look for relations between body fat measures and health outcomes.

Safety of dietary supplementation with arginine in adult humans.

Previous studies with animals and humans have shown beneficial effects of dietary supplementation with L-arginine (Arg) on reducing white fat and improving health. At present, a long-term safe level of Arg administration to adult humans is unknown. The objective of this study was to conduct a randomized, placebo-controlled, clinical trial to evaluate the safety and tolerability of oral Arg in overweight or obese but otherwise healthy adults with a body mass index of ≥ 25 kg/m2. A total of 142 subjects completed a 7-day wash-in period using a 12 g Arg/day dose. All the remaining eligible 101 subjects who tolerated the wash-in dose (45 men and 56 women) were assigned randomly to ingest 0, 15 or 30 g Arg (as pharmaceutical-grade Arg-HCl) per day for 90 days. Arg was taken daily in at least two divided doses by mixing with a flavored beverage. At Days 0 and 90, blood pressures of study subjects were recorded, their physical examinations were performed, and their blood and 24-h urine samples were obtained to measure: (1) serum concentrations of amino acids, glucose, fatty acids, and related metabolites; and (2) renal, hepatic, endocrine and metabolic parameters. Our results indicate that the serum concentration of Arg in men or women increased (P < 0.05) progressively with increasing oral Arg doses from 0 to 30 g/day. Dietary supplementation with 30 g Arg/day reduced (P < 0.05) systolic blood pressure and serum glucose concentration in females, as well as serum concentrations of free fatty acids in both males and females. Based on physiological and biochemical variables, study subjects tolerated oral administration of 15 and 30 g Arg/day without adverse events. We conclude that a long-term safe level of dietary Arg supplementation is at least 30 g/day in adult humans.

Polyphenol-rich Mango (Mangifera indica L.) Ameliorate Functional Constipation Symptoms in Humans beyond Equivalent Amount of Fiber.

SCOPE: Chronic constipation is a common gastrointestinal condition associated with intestinal inflammation and considerably impaired quality of life, affecting about 20% of Americans. Dietary fiber and laxatives aid in its treatment but do not fully address all symptoms, such as intestinal inflammation. Mango (Mangifera indica L.), a fiber- and polyphenol-rich fruit may provide anti-inflammatory effects in constipation. METHODS AND RESULTS: The 4 week consumption of mango fruit (300 g) or the equivalent amount of fiber is investigated in otherwise healthy human volunteers with chronic constipation who are randomly assigned to either group. Blood and fecal samples and digestive wellness questionnaires are collected at the beginning and end of the study. Results show that mango consumption significantly improve constipation status (stool frequency, consistency, and shape) and increase gastrin levels and fecal concentrations of short chain fatty acid (valeric acid) while lowering endotoxin and interleukin 6 concentrations in plasma. CONCLUSION: In this pilot study, the consumption of mango improves symptoms and associated biomarkers of constipation beyond an equivalent amount of fiber. Larger follow-up studies would need to investigate biomarkers for intestinal inflammation in more detail.