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Summary: In multivariate recurrent event data regression, observation of recurrent events is usually terminated by other events that are associated with the recurrent event processes, resulting in informative censoring. Additionally, some covariates could be measured with errors. In some applications, an instrumental variable is observed in a subsample, namely a calibration sample, which can be applied for bias correction. In this article, we develop two non‐parametric correction approaches to simultaneously correct for the informative censoring and measurement errors in the analysis of multivariate recurrent event data. A shared frailty model is adopted to characterize the informative censoring and dependence among different types of recurrent events. To adjust for measurement errors, a non‐parametric correction method using the calibration sample only is proposed. In the second approach, the information from the whole cohort is incorporated by the generalized method of moments. The proposed methods do not require the Poisson‐type assumption for the multivariate recurrent event process and the distributional assumption for the frailty. Moreover, we do not need to impose any distributional assumption on the underlying covariates and measurement error. Both methods perform well, but the second approach improves efficiency. The proposed methods are applied to the Nutritional Prevention of Cancer trial to assess the effect of selenium treatment on the recurrences of basal cell carcinoma and squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

Under suitable assumptions and by exploiting the independence between inherited genetic susceptibility and treatment assignment, the case-only design yields efficient estimates for subgroup treatment effects and gene-treatment interaction in a Cox model. However it cannot provide estimates of the genetic main effect and baseline hazards, that are necessary to compute the absolute disease risk. For two-arm, placebo-controlled trials with rare failure time endpoints, we consider augmenting the case-only design with random samples of controls from both arms, as in the classical case-cohort sampling scheme, or with a random sample of controls from the active treatment arm only. The latter design is motivated by vaccine trials for cost-effective use of resources and specimens so that host genetics and vaccine-induced immune responses can be studied simultaneously in a bigger set of participants. We show that these designs can identify all parameters in a Cox model and that the efficient case-only estimator can be incorporated in a two-step plug-in procedure. Results in simulations and a data example suggest that incorporating case-only estimators in the classical case-cohort design improves the precision of all estimated parameters; sampling controls only in the active treatment arm attains a similar level of efficiency. [ABSTRACT FROM AUTHOR]

Background: Telomeres protect cells from genomic instability. We examined telomere length and lung cancer risk prospectively in heavy smokers.Methods: In a nested case-control study with 709 cases and 1313 controls, conditional logistic regression was used to evaluate associations between telomere length (global, chromosome 5p, and 13q) and lung cancer risk by histotype, controlling for detailed smoking history.Results: Risks of overall lung cancer and adenocarcinoma were suggestively elevated among individuals with telomere length in the longest tertile. No clear patterns were observed for other histotypes, or for chromosome 5p or 13q telomere length. Associations with adenocarcinoma were strongest among (OR, 95% CI for longest versus shortest tertile): former smokers (2.26, 1.03-4.96), individuals <65 years (2.22, 1.13-4.35), and women (2.21, 0.99-4.93).Conclusions: Our large study of heavy smokers adds additional evidence that long telomere length prior to diagnosis is associated with risk of lung adenocarcinoma, but not other histotypes. [ABSTRACT FROM AUTHOR]

Background: Although epidemiologic studies have shown associations between sedentary behavior and mortality, few have focused on older women with adequate minority representation and few have controlled for both physical activity and functional status.Purpose: The objective of this study was to determine the relationship between sedentary time and total; cardiovascular disease (CVD); coronary heart disease (CHD); and cancer mortality in a prospective, multiethnic cohort of postmenopausal women.Methods: The study population included 92,234 women aged 50-79 years at baseline (1993-1998) who participated in the Women's Health Initiative Observational Study through September 2010. Self-reported sedentary time was assessed by questionnaire and examined in 4 categories (≤4, >4-8, ≥8-11, >11 hours). Mortality risks were examined using Cox proportional hazard models adjusting for confounders. Models were also stratified by age, race/ethnicity, body mass index, physical activity, physical function, and chronic disease to examine possible effect modification. Analyses were conducted in 2012-2013.Results: The mean follow-up period was 12 years. Compared with women who reported the least sedentary time, women reporting the highest sedentary time had increased risk of all-cause mortality in the multivariate model (HR=1.12, 95% CI=1.05, 1.21). Results comparing the highest versus lowest categories for CVD, CHD, and cancer mortality were as follows: HR=1.13, 95% CI=0.99, 1.29; HR=1.27, 95% CI=1.04, 1.55; and HR=1.21, 95% CI=1.07, 1.37, respectively. For all mortality outcomes, there were significant linear tests for trend.Conclusions: There was a linear relationship between greater amounts of sedentary time and mortality risk after controlling for multiple potential confounders. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Antidepressants may attenuate the effects of diet and exercise programs. We compared adherence and changes in body measures and biomarkers of glucose metabolism and inflammation between antidepressant users and non-users in a 12-month randomized controlled trial. Methods: Overweight or obese, postmenopausal women were assigned to: diet (10% weight loss goal, N=118); moderate-to-vigorous aerobic exercise (225min/week, N=117); diet+exercise (N=117); and control (N=87) in Seattle, WA 2005–2009. Women using antidepressants at baseline were classified as users (N=109). ANCOVA and generalized estimating equation approaches, respectively, were used to compare adherence (exercise amount, diet session attendance, and changes in percent calorie intake from fat, cardiopulmonary fitness, and pedometer steps) and changes in body measures (weight, waist and percent body fat) and serum biomarkers (glucose, insulin, homeostasis assessment-insulin resistance, and high-sensitivity C-reactive protein) between users and non-users. An interaction term (intervention×antidepressant use) tested effect modification. Results: There were no differences in adherence except that diet session attendance was lower among users in the diet+exercise group (P<0.05 vs. non-users). Changes in body measures and serum biomarkers did not differ by antidepressant use (Pinteraction >0.05). Conclusion: Dietary weight loss and exercise improved body measures and biomarkers of glucose metabolism and inflammation independent of antidepressant use. [Copyright &y& Elsevier]

Abstract: Urinary sugars excretion has been proposed as a potential biomarker for intake of sugars. In this study, we compared 2 analytical methods (gas chromatography [GC] and enzymatic reactions—UV absorption) for quantifying urinary fructose and sucrose using 24-hour urine samples from a randomized crossover controlled feeding study. All samples were successfully quantified by the GC method; however, 21% and 1.9% of samples were below the detection limit of the enzymatic method for sucrose and fructose, respectively. Although the correlation between the 2 methods was good for fructose (Pearson correlation, 0.71), the correlation was weak for sucrose (Pearson correlation, 0.27). We favor the GC method because of its better sensitivity, simplicity, and the ability to quantify fructose and sucrose directly in the same run. Of the 106 samples from 53 participants with complete urine collection after 2 study diets, 24-hour urinary fructose excretion was significantly associated with fructose intake. The sum of 24-hour urinary fructose and sucrose was significantly associated with total sugars consumption. However, variation in intakes of sugars explained only a modest amount of variation in urinary sugars excretion. In the unadjusted models, fructose intake explained 24.3% of urinary fructose excretion, and intake of total sugars explained 16.3% of the sum of urinary fructose and sucrose. The adjusted models explained 44.3% of urinary fructose excretion and 41.7% of the sum of urinary fructose and sucrose. Therefore, we caution using these biomarkers to predict sugars consumption before other factors that determine urinary sugars excretion are understood. [Copyright &y& Elsevier]

Abstract: Lifestyle-based interventions, which typically promote various behavior modification strategies, can serve as a setting for evaluating specific behaviors and strategies thought to promote or hinder weight loss. The aim of our study was to test the associations of self-monitoring (ie, self-weighing and food journal completion) and eating-related (ie, dietary intake, diet-related weight-control strategies, and meal patterns) behaviors with weight loss in a sample of postmenopausal overweight-to-obese women enrolled in a 12-month dietary weight loss intervention. Changes in body weight and adoption of self-monitoring and eating-related behaviors were assessed in 123 participants. Generalized linear models tested associations of these behaviors with 12-month weight change after adjusting for potential confounders. Mean percent weight loss was 10.7%. In the final model, completing more food journals was associated with a greater percent weight loss (interquartile range 3.7% greater weight loss; P<0.0001), whereas skipping meals (4.3% lower weight loss; P<0.05) and eating out for lunch (at least once a week, 2.5% lower weight loss; P<0.01) were associated with a lower amount of weight loss. These findings suggest that a greater focus on dietary self-monitoring, home-prepared meals, and consuming meals at regular intervals may improve 12-month weight loss among postmenopausal women enrolled in a dietary weight loss intervention. [Copyright &y& Elsevier]

N6-methyladenosine (m6A) RNA modification orchestrates cellular epitranscriptome through tuning the homeostasis of transcript stability, translation efficiency, and the transcript affinity toward RNA-binding proteins (RBPs). An aberrant m6A deposition on RNA can lead toward oncogenic expression profile (mRNA), impaired mitochondrial metabolism (mtRNA), and translational suppression (rRNA) of tumor suppressor genes. In addition, non-coding RNAs (ncRNAs), such as X-inactive specific transcript (XIST), miRNAs, and α-ketoglutarate-centric metabolic transcripts are also regulated by the m6A epitranscriptome. Notably, recent studies had uncovered a myriad of m6A-modified transcripts the center of hematopoietic stem cell (HSC) regulation, in which m6A modification act as a context dependent switch to the on and off of hematopoietic stem cell (HSC) maintenance, lineage commitment and terminal differentiation. In this review, we sequentially unfold the m6A mediated epithelial-to-hematopoietic transition in progenitor blood cell production, lymphocytic lineage expansion (T cells, B cells, NK cells, and non-NK ILCs), and the m6A crosstalk with the onco-metabolic prospects of leukemogenesis. Together, an encompassing body of evidence highlighted the emerging m6A significance in the regulation of HSC biology and leukemogenesis., (© 2024. The Author(s).)

Background: Eating frequency (EF) focuses on the total number of eating occasions per day and may influence metabolic health., Objectives: We sought to examine the effect of high compared with low EF on appetite regulation and inflammatory biomarkers among healthy adults., Methods: Data are from a randomized, crossover trial (the Frequency of Eating and Satiety Hormones study). Participants (n = 50) completed 2 isocaloric 21-d study periods of low EF (3 eating occasions/d) and high EF (6 eating occasions/d) in random order with a 14-d washout period in between. Participants were free-living and consumed their own food, using study-directed, structured meal plans with identical foods and total energy in both study periods. On days 1 and 21 of each EF period, fasting blood was collected during in-person clinic visits to assess plasma concentrations of ghrelin, leptin, adiponectin, and high-sensitivity C-reactive protein (hs-CRP). Linear mixed models with EF, diet sequence, and period as fixed effects and participant as random effect were used to estimate the intervention effect. Interaction effects between EF and body fat percentage were examined., Results: Among the 50 participants who completed the trial, 39 (78%) were women, 30 (60%) were Non-Hispanic White, and 40 (80%) had a body mass index of <25 kg/m 2 , and the mean age was 32.1 y. The differences between high and low EF in fasting ghrelin (geometric mean difference: 17.76 ng/mL; P = 0.60), leptin (geometric mean difference: 2.09 ng/mL; P = 0.14), adiponectin (geometric mean difference: 381.7 ng/mL; P = 0.32), and hs-CRP (geometric mean difference: -0.018 mg/dL; P = 0.08) were not statistically significant. No significant interaction was observed between EF and body fat percentage on appetite regulation and inflammatory biomarkers., Conclusions: No differences was observed in fasting ghrelin, leptin, adiponectin, and hs-CRP comparing high and low EF. Future studies are needed to understand the physiology of EF and appetite as they relate to metabolic health. This trial was registered at clinicaltrials.gov as NCT02392897., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Missing data in covariates can result in biased estimates and loss of power to detect associations. We consider Cox regression in which some covariates are subject to missing. The inverse probability weighted approach is often applied to regression analysis with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood-based estimators, but in general are more robust against model misspecification. In this article, we propose a robust best linear weighted estimator for Cox regression with missing covariates. Our proposed estimator is the projection of the simple inverse probability weighted estimator onto the orthogonal complement of the score space based on a working regression model of the observed data. The efficiency gain is from the use of the association between the survival outcome variable and the available covariates, which is the working regression model. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a colorectal cancer study to assess the association of the microsatellite instability status with colorectal cancer-specific mortality., (© 2024 John Wiley & Sons Ltd.)

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)

Epidemiological studies often encounter a challenge due to exposure measurement error when estimating an exposure-disease association. A surrogate variable may be available for the true unobserved exposure variable. However, zero-inflated data are encountered frequently in the surrogate variables. For example, many nutrient or physical activity measures may have a zero value (or a low detectable value) among a group of individuals. In this paper, we investigate regression analysis when the observed surrogates may have zero values among some individuals of the whole study cohort. A naive regression calibration without taking into account a probability mass of the surrogate variable at 0 (or a low detectable value) will be biased. We developed a regression calibration estimator which typically can have smaller biases than the naive regression calibration estimator. We propose an expected estimating equation estimator which is consistent under the zero-inflated surrogate regression model. Extensive simulations show that the proposed estimator performs well in terms of bias correction. These methods are applied to a physical activity intervention study., Competing Interests: Conflicts of Interest: The authors did not have any conflicts of interest.

A biomarker is a measurable indicator of the severity or presence of a disease or medical condition in biomedical or epidemiological research. Biomarkers may help in early diagnosis and prevention of diseases. Several biomarkers have been identified for many diseases such as carbohydrate antigen 19-9 for pancreatic cancer. However, biomarkers may be measured with errors due to many reasons such as specimen collection or day-to-day within-subject variability of the biomarker, among others. Measurement error in the biomarker leads to bias in the regression parameter estimation for the association of the biomarker with disease in epidemiological studies. In addition, measurement error in the biomarkers may affect standard diagnostic measures to evaluate the performance of biomarkers such as the receiver operating characteristic (ROC) curve, area under the ROC curve, sensitivity, and specificity. Measurement error may also have an effect on how to combine multiple cancer biomarkers as a composite predictor for disease diagnosis. In follow-up studies, biomarkers are often collected intermittently at examination times, which may be sparse and typically biomarkers are not observed at the event times. Joint modeling of longitudinal and time-to-event data is a valid approach to account for measurement error in the analysis of repeatedly measured biomarkers and time-to-event outcomes. In this article, we provide a literature review on existing methods to correct for estimation in regression analysis, diagnostic measures, and joint modeling of longitudinal biomarkers and survival outcomes when the biomarkers are measured with errors. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Robust MethodsStatistical and Graphical Methods of Data Analysis > EM AlgorithmStatistical Models > Survival Models., Competing Interests: CONFLICT OF INTEREST STATEMENT The authors have declared no conflicts of interest for this article.

We consider the proportional hazards model in which the covariates include the discretized categories of a continuous time-dependent exposure variable measured with error. Naively ignoring the measurement error in the analysis may cause biased estimation and erroneous inference. Although various approaches have been proposed to deal with measurement error when the hazard depends linearly on the time-dependent variable, it has not yet been investigated how to correct when the hazard depends on the discretized categories of the time-dependent variable. To fill this gap in the literature, we propose a smoothed corrected score approach based on approximation of the discretized categories after smoothing the indicator function. The consistency and asymptotic normality of the proposed estimator are established. The observation times of the time-dependent variable are allowed to be informative. For comparison, we also extend to this setting two approximate approaches, the regression calibration and the risk-set regression calibration. The methods are assessed by simulation studies and by application to data from an HIV clinical trial., (© 2021 The International Biometric Society.)

Diagnostic biomarkers are often measured with errors due to imperfect lab conditions or analytic variability of the assay. The ability of a diagnostic biomarker to discriminate between cases and controls is often measured by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, among others. Ignoring measurement error can cause biased estimation of a diagnostic accuracy measure, which results in misleading interpretation of the efficacy of a diagnostic biomarker. Existing assays available are either research grade or clinical grade. Research assays are cost effective, often multiplex, but they may be associated with moderate measurement errors leading to poorer diagnostic performance. In comparison, clinical assays may provide better diagnostic ability, but with higher cost since they are usually developed by industry. Correction for attenuation methods are often valid when biomarkers are from a normal distribution, but may be biased with skewed biomarkers. In this paper, we develop a flexible method based on skew-normal biomarker distributions to correct for bias in estimating diagnostic performance measures including AUC, sensitivity, and specificity. Finite sample performance of the proposed method is examined via extensive simulation studies. The methods are applied to a pancreatic cancer biomarker study., Competing Interests: Conflicts of Interest: The authors did not have any conflicts of interest.

Postmenopausal women with overweight or obesity have an increased risk of developing breast cancer but many of the mechanisms underlying this association remain to be elucidated. MicroRNAs (miRNAs), short non-coding single-stranded RNAs, regulate many physiological processes by controlling post-transcriptional regulation of mRNA. We measured circulating miRNA from 192 overweight/obese postmenopausal women (50-75 years) who were part of a randomized controlled trial, comparing independent and combined effects of a 12-month reduced-calorie weight-loss diet and exercise programme, versus control. RNA was extracted from stored plasma samples, and 23 a priori selected miRNA targets related to aetiology of breast cancer or obesity were measured using NanoString nCounter miRNA Expression assays. Changes from baseline to 12-months between controls and women in the diet/exercise weight loss arms were analysed using generalized estimating equations modification of linear regression, adjusted for confounders. We next examined changes in levels of circulating miRNA by amount of weight loss (0-10% versus ≥10%). Participants randomized to weight-loss interventions had statistically significantly greater reductions in miR-122 (-7.25%), compared to controls (+ 33.5%, P = 0.009), and miR-122 levels were statistically significantly correlated with weight loss (rho = 0.24; P = 0.001) Increasing weight loss was associated with greater reductions in miR-122 vs. controls (-11.7% (≥10% weight loss); +2.0% (0-10% weight loss) +33.5% (controls); P trend  = 0.006), though this was not significant after correction for multiple testing (P = 0.05/23) Our study supports the effect of weight loss on regulation of miRNA.

Studies in the field of neuroscience and psychology have hypothesized that a causal association exists between atopic diseases and attention-deficit/hyperactivity disorder (ADHD). Previous systematic reviews and meta-analyses have reported a higher risk of ADHD in children with atopic diseases; however, the relationship between ADHD symptoms and atopic diseases remains unclear. We systematically reviewed observational cross-sectional and longitudinal studies to investigate the relationship between atopic diseases and ADHD symptom severity (hyperactivity/impulsivity and inattention). The majority of studies showed a statistically significant association between atopic diseases and both ADHD symptoms, with substantial heterogeneity in the outcome of hyperactivity/impulsivity. Remarkably decreased heterogeneity and statistical significance were observed in the second meta-analysis of ADHD-related behavior symptoms in atopic patients without ADHD. Our study indicated that atopic diseases not only associated with ADHD but also ADHD symptoms severity. This association was even observed in children with subthreshold ADHD, indicating that atopic diseases may play a role in the spectrum of ADHD symptom severity. Trial registration: This study was registered on PROSPERO (registration ID: CRD42020213219)., (© 2022. The Author(s).)

Background: Influenza is a major cause of acute respiratory infection burden worldwide, leading to many hospitalizations. An annual influenza vaccine is believed to be the best way to prevent influenza-related illnesses. We focused on the efficacies of other possible preventive measures such as increasing sun exposure time and dietary supplements to prevent these illnesses., Methods: We conducted a matched-pair case-control study along with the Taiwan Pediatric Infectious Disease Alliance. We included influenza-related hospitalized patients with age ranging from 6 months to 5 years during the 2012-2013, 2013-2014, 2014-2015, and 2015-2016 influenza seasons. The controls were comparable to cases in age, sex, and residential area and had no influenza-related hospitalization records in the same season. We extracted data from vaccination histories and got the patients' guardians to complete questionnaires. Data were analyzed using conditional logistic regression., Results: We enrolled 1514 children (421 influenza-infected cases and 1093 controls) in the study. We found seasonal influenza vaccination to be an independent protective factor against hospitalizations owing to influenza [p < 0.01; odds ratio (OR), 0.427; 95% confidence interval (CI), 0.306-0.594]. Children with mean sun exposure time of >7 h/week had a significantly lower risk of influenza-related hospitalizations than those with the mean sun exposure time of ≤7 h/week (p < 0.05; OR, 0.667; 95% CI, 0.491-0.906)., Conclusions: Seasonal influenza vaccination effectively prevents influenza-related hospitalizations in children aged ≤5 years. Besides, >7 h of sun exposure/week may also be associated with lower risk of influenza-related hospitalizations in children., (Copyright © 2019. Published by Elsevier B.V.)

Background: Certain eating behaviors are common among women with obesity. Whether these behaviors influence outcomes in weight loss programs, and whether such programs affect eating behaviors, is unclear., Methods: Our aim was to examine the effect of baseline eating behaviors on intervention adherence and weight among postmenopausal women with overweight or obesity, and to assess intervention effects on eating behaviors. Four hundred and 39 women (BMI ≥25 kg/m 2 ) were randomized to 12 months of: i) dietary weight loss with a 10% weight loss goal ('diet'; n = 118); ii) moderate-to-vigorous intensity aerobic exercise for 225 mins/week ('exercise'; n = 117); iii) combined dietary weight loss and exercise ('diet + exercise'; n = 117); or iv) no-lifestyle change control (n = 87). At baseline and 12 months, restrained eating, uncontrolled eating, emotional eating and binge eating were measured by questionnaire; weight and body composition were assessed. The mean change in eating behavior scores and weight between baseline and 12 months in the diet, exercise, and diet + exercise arms were each compared to controls using the generalized estimating equation (GEE) modification of linear regression adjusted for age, baseline BMI, and race/ethnicity., Results: Baseline restrained eating was positively associated with change in total calories and calories from fat during the dietary intervention but not with other measures of adherence. Higher baseline restrained eating was associated with greater 12-month reductions in weight, waist circumference, body fat and lean mass. Women randomized to dietary intervention had significant reductions in binge eating (- 23.7%, p = 0.005 vs. control), uncontrolled eating (- 24.3%, p < 0.001 vs. control), and emotional eating (- 31.7%, p < 0.001 vs. control) scores, and a significant increase in restrained eating (+ 60.6%, p < 0.001 vs. control); women randomized to diet + exercise reported less uncontrolled eating (- 26.0%, p < 0.001 vs. control) and emotional eating (- 22.0%, p = 0.004 vs. control), and increased restrained eating (+ 41.4%, p < 0.001 vs. control). Women randomized to exercise alone had no significant change in eating behavior scores compared to controls., Conclusions: A dietary weight loss intervention helped women modify eating behaviors. Future research should investigate optimal behavioral weight loss interventions for women with both disordered eating and obesity., Trial Registration: NCT00470119 (https://clinicaltrials.gov). Retrospectively registered May 7, 2007.

Objective: We tested the effects of weight loss on serum estradiol, estrone, testosterone, and sex hormone-binding globulin (SHBG) in overweight/obese women 18 months after completing a year-long, 4-arm, randomized-controlled dietary weight loss and/or exercise trial., Methods: From 2005 to 2008, 439 overweight/obese, postmenopausal women (BMI >25 kg/m), 50 to 75 years, were randomized to a year-long intervention: diet (reduced calorie, 10% weight loss, N = 118), exercise (225 min/wk moderate-to-vigorous activity, N = 117), combined diet + exercise (N = 117), or control (N = 87). At 12 months, 399 women provided blood; of these, 156 returned at 30 months and gave a blood sample. Hormones and SHBG were measured by immunoassay. Changes were compared using generalized estimating equations, adjusting for confounders., Results: At 30 months, participants randomized to the diet + exercise intervention had statistically significant increases in SHBG levels versus controls (P = 0.001). There was no statistically significant change in SHBG in the exercise or diet intervention arms. Hormone levels did not vary by intervention arm from baseline to 30 months. Participants who maintained weight loss at 30 months had statistically significantly greater decreases in free estradiol and free testosterone (Ptrend = 0.02 and Ptrend = 0.04, respectively) and increases in SHBG (Ptrend < 0.0001) versus those who did not have sustained weight loss. Levels of other analytes did not vary by weight loss at 30 months., Conclusions: Sustained weight loss results in reductions in free estradiol and testosterone and increases in SHBG 18-month post-intervention.

Many survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association 95, 1209-1219; 2006, Statistica Sinica 16, 861-881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided., (© 2017, The International Biometric Society.)

Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses.

Background: Mexican immigrants are disproportionally affected by diet-related risk of metabolic dysfunction. Whether adhering to a traditional Mexican diet or adopting a US diet contributes to metabolic changes associated with future risk of type 2 diabetes and other chronic diseases has not been investigated., Objective: The purpose of this study was to test in a randomized crossover feeding trial the metabolic responses to a Mexican diet compared with a commonly consumed US diet., Design: First- and second-generation healthy women of Mexican descent (n = 53) were randomly assigned in a crossover design to consume a Mexican or US diet for 24 d each, separated by a 28-d washout period. Diets were eucaloric and similar in macronutrient composition. The metabolic responses to diets were assessed by measuring fasting serum concentrations of glucose, insulin, insulin-like growth factor 1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), adiponectin, C-reactive protein (CRP), and interleukin 6 (IL-6), as well as the homeostasis model assessment of insulin resistance (HOMA-IR) at the beginning and end of each period. Linear mixed models tested the intervention effect on the biomarkers, while adjusting for diet sequence, feeding period, baseline and washout biomarker concentrations, age, acculturation, and BMI., Results: Compared with the US diet, the Mexican diet reduced insulin by 14% [geometric means (95% CIs): 9.3 (8.3, 10.3) compared with 8.0 (7.2, 8.9) μU/mL; P = 0.02], HOMA-IR by 15% [2.0 (1.8, 2.3) compared with 1.7 (1.6, 2.0); P = 0.02], and IGFBP-3 by 6% (mean ± SEM: 2420 ± 29 compared with 2299 ± 29 ng/mL; P < 0.01) and tended to reduce circulating concentrations of IGF-1 by 4% (149 ± 2.6 compared with 144 ± 2.5 ng/mL; P = 0.06). There was no significant intervention effect on serum concentrations of glucose, adiponectin, CRP, or IL-6 in the US compared with the Mexican diet., Conclusion: Compared with the commonly consumed US diet, the traditional Mexican diet modestly improved insulin sensitivity under conditions of weight stability in healthy women of Mexican descent, while having no impact on biomarkers of inflammation. This trial was registered at clinicaltrials.gov as NCT01369173., (© 2016 American Society for Nutrition.)

In biomedical studies, covariates with measurement error may occur in survival data. Existing approaches mostly require certain replications on the error-contaminated covariates, which may not be available in the data. In this paper, we develop a simple nonparametric correction approach for estimation of the regression parameters in the proportional hazards model using a subset of the sample where instrumental variables are observed. The instrumental variables are related to the covariates through a general nonparametric model, and no distributional assumptions are placed on the error and the underlying true covariates. We further propose a novel generalized methods of moments nonparametric correction estimator to improve the efficiency over the simple correction approach. The efficiency gain can be substantial when the calibration subsample is small compared to the whole sample. The estimators are shown to be consistent and asymptotically normal. Performance of the estimators is evaluated via simulation studies and by an application to data from an HIV clinical trial. Estimation of the baseline hazard function is not addressed.

Background: Vitamin D deficiency is associated with obesity; whether repletion supports weight loss and changes obesity-related biomarkers is unknown., Objective: We compared 12 mo of vitamin D3 supplementation with placebo on weight, body composition, insulin, and C-reactive protein (CRP) in postmenopausal women in a weight-loss intervention., Design: A total of 218 overweight/obese women (50-75 y of age) with serum 25-hydroxyvitamin D [25(OH)D] ≥10 ng/mL but <32 ng/mL were randomly assigned to weight loss + 2000 IU oral vitamin D3/d or weight loss + daily placebo. The weight-loss intervention included a reduced-calorie diet (10% weight loss goal) and 225 min/wk of moderate-to-vigorous aerobic activity. Mean 12-mo changes in weight, body composition, serum insulin, CRP, and 25(OH)D were compared between groups (intent-to-treat) by using generalized estimating equations., Results: A total of 86% of participants completed the 12-mo measurements. The mean (95% CI) change in 25(OH)D was 13.6 (11.6, 15.4) ng/mL in the vitamin D3 arm compared with -1.3 (-2.6, -0.3) ng/mL in the placebo arm (P < 0.0001). Changes in weight [-7.1 (-8.7, -5.7) compared with -7.4 (-8.1, -5.4) kg], body mass index (in kg/m(2): both -2.8), waist circumference [-4.9 (-6.7, -2.9) compared with -4.5 (-5.6, -2.6) cm], percentage body fat [-4.1 (-4.9, -2.9) compared with -3.5 (-4.5, -2.5)], trunk fat [-4.1 (-4.7, -3.0) compared with -3.7 (-4.3, -2.9) kg], insulin [-2.5 (-3.4, -1.7) compared with -2.4 (-3.3, -1.4) μU/mL], and CRP [-0.9 (-1.2, -0.6) compared with -0.79 (-0.9, -0.4) mg/L] [corrected] were similar between groups (all P > 0.05). Compared with women who achieved 25(OH)D <32 ng/mL, women randomly assigned to vitamin D who became replete (ie, 25(OH)D ≥32 ng/mL) lost more weight [-8.8 (-11.1, -6.9) compared with -5.6 (-7.2, -5.0) kg; P = 0.05], waist circumference [-6.6 (-9.3, -4.3) compared with -2.5 (-4.6, -2.0) cm; P = 0.02], and percentage body fat [-4.7 (-6.1, -3.5) compared with -2.6 (-3.9, -2.2); P = 0.04]. Among women with complete pill counts (97% adherence), the mean decrease in CRP was 1.18 mg/mL (46%) in the vitamin D arm compared with 0.46 mg/mL (25%) in the placebo arm (P = 0.03)., Conclusions: Vitamin D3 supplementation during weight loss did not increase weight loss or associated factors compared with placebo; however, women who became replete experienced greater improvements. This trial was registered at clinicaltrials.gov as NCT01240213.

Objective: Given that the repetitive loss and regain of body weight, termed weight cycling, is a prevalent phenomenon that has been associated with negative physiological and psychological outcomes, the purpose of this study was to investigate weight change and physiological outcomes in women with a lifetime history of weight cycling enrolled in a 12-month diet and/or exercise intervention., Methods: 439 overweight, inactive, postmenopausal women were randomized to: i) dietary weight loss with a 10% weight loss goal (N=118); ii) moderate-to-vigorous intensity aerobic exercise for 45 min/day, 5 days/week (n=117); ii) both dietary weight loss and exercise (n=117); or iv) control (n=87). Women were categorized as non-, moderate- (≥3 losses of ≥4.5 kg), or severe-cyclers (≥3 losses of ≥9.1 kg). Trend tests and linear regression were used to compare adherence and changes in weight, body composition, blood pressure, insulin, C-peptide, glucose, insulin resistance (HOMA-IR), C-reactive protein, leptin, adiponectin, and interleukin-6 between cyclers and non-cyclers., Results: Moderate (n=103) and severe (n=77) cyclers were heavier and had less favorable metabolic profiles than non-cyclers at baseline. There were, however, no significant differences in adherence to the lifestyle interventions. Weight-cyclers (combined) had a greater improvement in HOMA-IR compared to non-cyclers participating in the exercise only intervention (P=.03), but no differences were apparent in the other groups., Conclusion: A history of weight cycling does not impede successful participation in lifestyle interventions or alter the benefits of diet and/or exercise on body composition and metabolic outcomes., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Regression calibration, refined regression calibration, and conditional scores estimation procedures are extended to a measurement model that is motivated by nutritional and physical activity epidemiology. Biomarker data, available on a small subset of a study cohort for reasons of cost, are assumed to adhere to a classical measurement error model, while corresponding self-report nutrient consumption or activity-related energy expenditure data are available for the entire cohort. The self-report assessment measurement model includes a person-specific random effect, the mean and variance of which may depend on individual characteristics such as body mass index or ethnicity. Logistic regression is used to relate the disease odds ratio to the actual, but unmeasured, dietary or physical activity exposure. Simulation studies are presented to evaluate and contrast the three estimation procedures, and to provide insight into preferred biomarker subsample size under selected cohort study configurations.

Regulation of p53 protein activity is required for normal embryogenesis, tumor suppression, and cellular response to DNA damage. Here we report that loss of mdmx, a p53-binding protein, results in midgestational embryo lethality, a phenotype that is completely rescued by the absence of p53. Mice homozygous for both mdmx and p53 null mutations are viable and appear developmentally normal. Fibroblasts derived from embryos with reduced mdmx expression demonstrate a decreased growth rate and increased UV-induced apoptosis compared with wild-type cells and contain elevated levels of p53 and several p53 target proteins including the proapoptotic bax protein. These observations demonstrate that mdmx functions as a critical negative regulator of p53 in vivo.