Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS).

BACKGROUND: Between 5-10% of patients discontinue statin therapy due to statin-associated adverse reactions, primarily statin associated muscle symptoms (SAMS). The absence of a clear clinical phenotype or of biomarkers poses a challenge for diagnosis and management of SAMS. Similarly, our incomplete understanding of the pathogenesis of SAMS hinders the identification of treatments for SAMS. Metabolomics, the profiling of metabolites in biofluids, cells and tissues is an important tool for biomarker discovery and provides important insight into the origins of symptomatology. In order to better understand the pathophysiology of this common disorder and to identify biomarkers, we undertook comprehensive metabolomic and lipidomic profiling of plasma samples from patients with SAMS who were undergoing statin rechallenge as part of their clinical care. METHODS AND FINDINGS: We report our findings in 67 patients, 28 with SAMS (cases) and 39 statin-tolerant controls. SAMS patients were studied during statin rechallenge and statin tolerant controls were studied while on statin. Plasma samples were analyzed using untargeted LC-MS metabolomics and lipidomics to detect differences between cases and controls. Differences in lipid species in plasma were observed between cases and controls. These included higher levels of linoleic acid containing phospholipids and lower ether lipids and sphingolipids. Reduced levels of acylcarnitines and altered amino acid profile (tryptophan, tyrosine, proline, arginine, and taurine) were observed in cases relative to controls. Pathway analysis identified significant increase of urea cycle metabolites and arginine and proline metabolites among cases along with downregulation of pathways mediating oxidation of branched chain fatty acids, carnitine synthesis, and transfer of acetyl groups into mitochondria. CONCLUSIONS: The plasma metabolome of patients with SAMS exhibited reduced content of long chain fatty acids and increased levels of linoleic acid (18:2) in phospholipids, altered energy production pathways (ß-oxidation, citric acid cycle and urea cycles) as well as reduced levels of carnitine, an essential mediator of mitochondrial energy production. Our findings support the hypothesis that alterations in pro-inflammatory lipids (arachidonic acid pathway) and impaired mitochondrial energy metabolism underlie the muscle symptoms of patients with statin associated muscle symptoms (SAMS).

Pleiotropic influence of DNA methylation QTLs on physiological and ageing traits.

DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more 'aged methylome' for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein-protein interaction enrichment analyses identified the hepatic nuclear factor, Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.

BulkLMM: Real-time genome scans for multiple quantitative traits using linear mixed models.

Genetic studies often collect data using high-throughput phenotyping. That has led to the need for fast genomewide scans for large number of traits using linear mixed models (LMMs). Computing the scans one by one on each trait is time consuming. We have developed new algorithms for performing genome scans on a large number of quantitative traits using LMMs, BulkLMM, that speeds up the computation by orders of magnitude compared to one trait at a time scans. On a mouse BXD Liver Proteome data with more than 35,000 traits and 7,000 markers, BulkLMM completed in a few seconds. We use vectorized, multi-threaded operations and regularization to improve optimization, and numerical approximations to speed up the computations. Our software implementation in the Julia programming language also provides permutation testing for LMMs and is available at https://github.com/senresearch/BulkLMM.jl.

Matrix Linear Models for connecting metabolite composition to individual characteristics.

High-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

Changes in Dietary Intake of Methionine, Folate/Folic Acid and Vitamin B12 and Survival in Postmenopausal Women with Breast Cancer: A Prospective Cohort Study.

BACKGROUND: Previous experimental studies showed that limiting methionine in the diet of animals or in cell culture media suppresses mammary cancer cell proliferation or metastasis. However, no previous study has investigated the associations of changes in methionine intake with survival among breast cancer survivors. We aimed to examine the association between changes in dietary intake of methionine, folate/folic acid, and vitamin B12 from before to after diagnosis of breast cancer, and mortality among breast cancer survivors. METHODS: We included 1553 postmenopausal women from the Women's Health Initiative who were diagnosed with invasive breast cancer and completed a food frequency questionnaire both before and after breast cancer diagnosis. Multivariable Cox proportional hazards regression models were used to estimate adjusted hazard ratios (HRs) and 95% confidence (CIs) of all-cause and breast cancer mortality associated with changes in methionine intake and changes in folate/folic acid and vitamin B12 intake. RESULTS: Relative to pre-diagnosis, 28% of women decreased methionine intake by ≥20%, 30% of women increased methionine intake by ≥20%, and 42% of women had a relatively stable methionine intake (±19.9%) following breast cancer diagnosis. During a mean 16.1 years of follow up, there were 772 deaths in total, including 195 deaths from breast cancer. Compared to women with relatively stable methionine intake, women with decreased methionine intake had lower risks of all-cause (HR 0.78, 95% CI 0.62-0.97) and breast cancer mortality (HR 0.58, 95% CI 0.37-0.91) in fully adjusted models. In contrast, increased methionine intake or changes in folate/folic acid or vitamin B12 intake were not associated with all-cause or breast cancer mortality. CONCLUSIONS: Among breast cancer survivors, decreased methionine intake after breast cancer diagnosis was associated with lower risk of all-cause and breast cancer mortality.

Sex- and age-dependent genetics of longevity in a heterogeneous mouse population.

DNA variants that modulate life span provide insight into determinants of health, disease, and aging. Through analyses in the UM-HET3 mice of the Interventions Testing Program (ITP), we detected a sex-independent quantitative trait locus (QTL) on chromosome 12 and identified sex-specific QTLs, some of which we detected only in older mice. Similar relations between life history and longevity were uncovered in mice and humans, underscoring the importance of early access to nutrients and early growth. We identified common age- and sex-specific genetic effects on gene expression that we integrated with model organism and human data to create a hypothesis-building interactive resource of prioritized longevity and body weight genes. Finally, we validated Hipk1, Ddost, Hspg2, Fgd6, and Pdk1 as conserved longevity genes using Caenorhabditis elegans life-span experiments.

Preoperative opioid usage predicts markedly inferior outcomes 2 years after reverse total shoulder arthroplasty.

BACKGROUND: Reverse total shoulder arthroplasty (RTSA) has proved to be a highly effective treatment for rotator cuff-deficient conditions and other end-stage shoulder pathologies. With value-based care emerging, identifying predictive factors of outcomes is of great interest. Although preoperative opioid use has been shown to predict inferior outcomes after anatomic total shoulder arthroplasty and rotator cuff repair, there is a paucity of data regarding its effect on outcomes after RTSA. We analyzed a series of RTSAs to determine the influence of preoperative opioid use on clinical and radiographic outcomes at a minimum of 2 years' follow-up. METHODS: A retrospective review of primary RTSA patient data revealed 264 patients with ≥2 years of clinical and radiographic follow-up. Patients were classified as preoperative opioid users (71 patients) if they had taken narcotic pain medication for a minimum of 3 months prior to surgery or as opioid naive (193 patients) at the time of surgery. Assessments included preoperative and postoperative visual analog scale pain scores, American Shoulder and Elbow Surgeons scores, strength, and range of motion, as well as complications and revisions. Radiographs were analyzed for signs of loosening or mechanical failure. The Mann-Whitney U and Fisher exact tests were used for comparisons between groups. Statistical significance was set at P < .05. RESULTS: The mean patient age was 69.9 years, and the mean follow-up time was 2.8 years. Opioid users were significantly younger (66.1 years vs. 70.7 years, P < .001) at the time of surgery and had significantly higher preoperative rates of mood disorders, chronic pain disorders, and disability status (all P < .05). Postoperatively, opioid users had inferior visual analog scale pain scores (2.59 vs. 1.25, P < .001), American Shoulder and Elbow Surgeons scores (63.2 vs. 75.2, P < .001), active forward elevation (P < .001), and internal and external rotational shoulder strength (all P < .05) compared with opioid-naive patients. Periprosthetic radiolucency (8.45% vs. 2.07%, P = .026) and subsequent revision arthroplasty (14.1% vs. 4.66%, P = .014) occurred more frequently in opioid users than in opioid-naive patients. Both groups improved from baseline preoperatively to most recent follow-up in terms of functional outcomes and pain. CONCLUSION: Preoperative opioid use portended markedly inferior clinical outcomes in patients undergoing RTSA. Additionally, opioid users had significantly increased rates of periprosthetic radiolucency and revision. Preoperative opioid use appears to be a significant marker for adverse outcomes after RTSA.

A Pragmatic Intervention Using Financial Incentives for Pregnancy Weight Management: Feasibility Randomized Controlled Trial.

BACKGROUND: Excessive gestational weight gain (GWG) is common and can result in maternal and child health complications. Pragmatic behavioral interventions that can be incorporated into standard obstetric care are needed, and financial incentives are a promising approach. OBJECTIVE: The aim of this study is to evaluate the feasibility of recruitment, randomization, and retention, as well as treatment engagement and intervention satisfaction, in a behavioral program. The program provided small incentives for meeting behavioral goals of self-weighing and physical activity as well as larger outcome incentives for meeting GWG goals. METHODS: We recruited 40 adult women in their first trimester of pregnancy from February 2019 to September 2019 at an obstetric clinic. Participants were randomized to 3 intervention components using a 2×2×2 factorial design: daily incentives for self-weighing (lottery vs certain loss), incentives for adhering to the Institute of Medicine's GWG guidelines based on BMI category (monthly vs overall), and incentives for reaching physical activity goals (yes vs no). Participants were asked to complete daily weigh-ins using the Withings Body wireless scale provided by the study, as well as wear a physical activity tracker (Fitbit Flex 2). Feasibility outcomes of recruitment, randomization, and retention, as well as treatment engagement and intervention satisfaction, were assessed. Weight assessments were conducted at baseline, 32-week gestation, and 36-week gestation. RESULTS: Participants were enrolled at, on average, 9.6 (SD 1.8) weeks' gestation. Of the 39 participants who were oriented to their condition and received the intervention, 24 (62%) were Black or African American, 30 (77%) were not married, and 29 (74%) had an annual household income of less than US $50,000. Of the 39 participants, 35 (90%) completed the follow-up data collection visit. Participants were generally quite positive about the intervention components, with a particular emphasis on the helpfulness of, and the enjoyment of using, the e-scale in both the quantitative and qualitative feedback. Participants who received the loss incentive, on average, had 2.86 times as many days of self-weighing as those who received the lottery incentive. Participants had a relatively low level of activity, with no difference between those who received a physical activity incentive and those who did not. CONCLUSIONS: A financial incentive-based pragmatic intervention was feasible and acceptable for pregnant women for promoting self-weighing, physical activity, and healthy GWG. Participants were successfully recruited early in their first trimester of pregnancy and retained for follow-up data collection in the third trimester. Participants demonstrated promising engagement in self-weighing, particularly with loss-based incentives, and reported finding the self-weighing especially helpful. This study supports further investigation of pragmatic, clinic-based financial incentive-based interventions for healthy GWG behaviors. TRIAL REGISTRATION: ClinicalTrials.gov NCT03834194; https://clinicaltrials.gov/ct2/show/NCT03834194.

Speeding up eQTL scans in the BXD population using GPUs.

The BXD family of mouse strains are an important reference population for systems biology and genetics that have been fully sequenced and deeply phenotyped. To facilitate interactive use of genotype-phenotype relations using many massive omics data sets for this and other segregating populations, we have developed new algorithms and code that enable near-real-time whole-genome quantitative trait locus (QTL) scans for up to one million traits. By using easily parallelizable operations including matrix multiplication, vectorized operations, and element-wise operations, our method is more than 700 times faster than a R/qtl linear model genome scan using 16 threads. We used parallelization of different CPU threads as well as GPUs. We found that the speed advantage of GPUs is dependent on problem size and shape (the number of cases, number of genotypes, and number of traits). Our approach is ideal for interactive web services, such as GeneNetwork.org that need to display results in real-time. Our implementation is available as the Julia language package LiteQTL at https://github.com/senresearch/LiteQTL.jl.

Gene-by-environment modulation of lifespan and weight gain in the murine BXD family.

How lifespan and body weight vary as a function of diet and genetic differences is not well understood. Here we quantify the impact of differences in diet on lifespan in a genetically diverse family of female mice, split into matched isogenic cohorts fed a low-fat chow diet (CD, n = 663) or a high-fat diet (HFD, n = 685). We further generate key metabolic data in a parallel cohort euthanized at four time points. HFD feeding shortens lifespan by 12%: equivalent to a decade in humans. Initial body weight and early weight gains account for longevity differences of roughly 4-6 days per gram. At 500 days, animals on a HFD typically gain four times as much weight as control, but variation in weight gain does not correlate with lifespan. Classic serum metabolites, often regarded as health biomarkers, are not necessarily strong predictors of longevity. Our data indicate that responses to a HFD are substantially modulated by gene-by-environment interactions, highlighting the importance of genetic variation in making accurate individualized dietary recommendations.

Identification of fibronectin 1 as a candidate genetic modifier in a Col4a1 mutant mouse model of Gould syndrome.

Collagen type IV alpha 1 and alpha 2 (COL4A1 and COL4A2) are major components of almost all basement membranes. COL4A1 and COL4A2 mutations cause a multisystem disorder that can affect any organ but typically involves the cerebral vasculature, eyes, kidneys and skeletal muscles. In recent years, patient advocacy and family support groups have united under the name of Gould syndrome. The manifestations of Gould syndrome are highly variable, and animal studies suggest that allelic heterogeneity and genetic context contribute to the clinical variability. We previously characterized a mouse model of Gould syndrome caused by a Col4a1 mutation in which the severities of ocular anterior segment dysgenesis (ASD), myopathy and intracerebral hemorrhage (ICH) were dependent on genetic background. Here, we performed a genetic modifier screen to provide insight into the mechanisms contributing to Gould syndrome pathogenesis and identified a single locus [modifier of Gould syndrome 1 (MoGS1)] on Chromosome 1 that suppressed ASD. A separate screen showed that the same locus ameliorated myopathy. Interestingly, MoGS1 had no effect on ICH, suggesting that this phenotype could be mechanistically distinct. We refined the MoGS1 locus to a 4.3 Mb interval containing 18 protein-coding genes, including Fn1, which encodes the extracellular matrix component fibronectin 1. Molecular analysis showed that the MoGS1 locus increased Fn1 expression, raising the possibility that suppression is achieved through a compensatory extracellular mechanism. Furthermore, we found evidence of increased integrin-linked kinase levels and focal adhesion kinase phosphorylation in Col4a1 mutant mice that is partially restored by the MoGS1 locus, implicating the involvement of integrin signaling. Taken together, our results suggest that tissue-specific mechanistic heterogeneity contributes to the variable expressivity of Gould syndrome and that perturbations in integrin signaling may play a role in ocular and muscular manifestations.

Assessing the Contribution of Self-Monitoring Through a Commercial Weight Loss App: Mediation and Predictive Modeling Study.

BACKGROUND: Electronic self-monitoring technology has the potential to provide unique insights into important behaviors for inducing weight loss. OBJECTIVE: The aim of this study is to investigate the effects of electronic self-monitoring behavior (using the commercial Lose It! app) and weight loss interventions (with differing amounts of counselor feedback and support) on 4- and 12-month weight loss. METHODS: In this secondary analysis of the Fit Blue study, we compared the results of two interventions of a randomized controlled trial. Counselor-initiated participants received consistent support from the interventionists, and self-paced participants received assistance upon request. The participants (N=191), who were active duty military personnel, were encouraged to self-monitor their diet and exercise with the Lose It! app or website. We examined the associations between intervention assignment and self-monitoring behaviors. We conducted a mediation analysis of the intervention assignment for weight loss through multiple mediators-app use (calculated from the first principal component [PC] of electronically collected variables), number of weigh-ins, and 4-month weight change. We used linear regression to predict weight loss at 4 and 12 months, and the accuracy was measured using cross-validation. RESULTS: On average, the counselor-initiated-treatment participants used the app more frequently than the self-paced-treatment participants. The first PC represented app use frequencies, the second represented calories recorded, and the third represented reported exercise frequency and exercise caloric expenditure. We found that 4-month weight loss was partially mediated through app use (ie, the first PC; 60.3%) and the number of weigh-ins (55.8%). However, the 12-month weight loss was almost fully mediated by 4-month weight loss (94.8%). Linear regression using app data from the first 8 weeks, the number of self-weigh-ins at 8 weeks, and baseline data explained approximately 30% of the variance in 4-month weight loss. App use frequency (first PC; P=.001), self-monitored caloric intake (second PC; P=.001), and the frequency of self-weighing at 8 weeks (P=.008) were important predictors of 4-month weight loss. Predictions for 12-month weight with the same variables produced an R2 value of 5%; only the number of self-weigh-ins was a significant predictor of 12-month weight loss. The R2 value using 4-month weight loss as a predictor was 31%. Self-reported exercise did not contribute to either model (4 months: P=.77; 12 months: P=.15). CONCLUSIONS: We found that app use and daily reported caloric intake had a substantial impact on weight loss prediction at 4 months. Our analysis did not find evidence of an association between participant self-monitoring exercise information and weight loss. As 12-month weight loss was completely mediated by 4-month weight loss, intervention targets should focus on promoting early and frequent dietary intake self-monitoring and self-weighing to promote early weight loss, which leads to long-term success. TRIAL REGISTRATION: ClinicalTrials.gov NCT02063178; https://clinicaltrials.gov/ct2/show/NCT02063178.

Development and validation of a predictive model for outcomes in shoulder arthroplasty: a multicenter analysis of nearly 2000 patients.

BACKGROUND: Guiding expectations following shoulder arthroplasty is important in improving patient satisfaction. The purpose of this study was to develop a predictive model to calculate 2-year American Shoulder and Elbow Surgeons (ASES) scores in shoulder arthroplasty patients from a comprehensive set of preoperative patient factors and types of arthroplasty performed. METHODS: This retrospective multicenter study included 1947 shoulder arthroplasties performed from 2010 to 2015 at 3 high-volume centers. Twenty-six variables were evaluated for an association with 2-year ASES scores, and variables with P < .20 in our pair-wise analysis were used to develop a predictive model. The prediction root-mean-square error was calculated. External validation was performed using data from 233 patients who underwent shoulder arthroplasty performed by a separate shoulder surgeon at a center not involved with creation of the predictive model. RESULTS: A total of 1947 patients were analyzed, and their data were used to construct the predictive model. Variables most associated with 2-year ASES scores were patient age, preoperative ASES score, disability, chronic obstructive pulmonary disease, alcohol use, anatomic vs. reverse total shoulder arthroplasty, and primary vs. revision shoulder arthroplasty. By use of cross validation, the prediction error was 20.1, the proportion of variance explained was 25.3%, the mean absolute error was 15.9, and the C statistic for the linear regression model was 0.66. After external validation, the mean difference between predicted and actual 2-year ASES scores was 12.7 points, within the accepted minimal clinically important difference after shoulder arthroplasty. DISCUSSION: Data from nearly 2000 shoulder arthroplasties allowed the development and validation of a model to predict 2-year ASES scores following shoulder arthroplasty. The model was accurate within the minimal clinically important difference in 85% of patients.

Organophosphate Flame Retardants, Highly Fluorinated Chemicals, and Biomarkers of Placental Development and Disease During Mid-Gestation.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) and organophosphate flame retardants (OPFRs) are chemicals that may contribute to placenta-mediated complications and adverse maternal-fetal health risks. Few studies have investigated these chemicals in relation to biomarkers of effect during pregnancy. We measured 12 PFASs and four urinary OPFR metabolites in 132 healthy pregnant women during mid-gestation and examined a subset with biomarkers of placental development and disease (n = 62). Molecular biomarkers included integrin alpha-1 (ITGA1), vascular endothelial-cadherin (CDH5), and matrix metalloproteinase-1 (MMP1). Morphological endpoints included potential indicators of placental stress and the extent of cytotrophoblast (CTB)-mediated uterine artery remodeling. Serum PFASs and urinary OPFR metabolites were detected in ∼50%-100% of samples. The most prevalent PFASs were perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), and perfluorooctane sulfonic acid (PFOS), with geometric mean (GM) levels of ∼1.3-2.8 (95% confidence limits from 1.2-3.1) ng/ml compared to ≤0.5 ng/ml for other PFASs. Diphenyl phosphate (DPhP) and bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) were the most prevalent OPFR metabolites, with GMs of 2.9 (95% CI: 2.5-3.4) and 3.6 (95% CI: 2.2-3.1) ng/ml, respectively, compared to <1 ng/ml for bis(2-chloroethyl) phosphate (BCEP) and bis(1-chloro-2-propyl) phosphate (BCIPP). We found inverse associations of PFASs or OPFRs with ITGA1 or CDH5 immunoreactivity and positive associations with indicators of placental stress in multiple basal plate regions, indicating these chemicals may contribute to abnormal placentation and future health risks. Associations with blood pressure and lipid concentrations warrant further examination. This is the first study of these chemicals with placental biomarkers measured directly in human tissues and suggests specific biomarkers are sensitive indicators of exposure during a vulnerable developmental period.

Estimating the Effect of Social Distancing Interventions on COVID-19 in the United States.

Since its global emergence in 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused multiple epidemics in the United States. When medical treatments for the virus were still emerging and a vaccine was not yet available, state and local governments sought to limit its spread by enacting various social-distancing interventions, such as school closures and lockdowns; however, the effectiveness of these interventions was unknown. We applied an established, semimechanistic Bayesian hierarchical model of these interventions to the spread of SARS-CoV-2 from Europe to the United States, using case fatalities from February 29, 2020, up to April 25, 2020, when some states began reversing their interventions. We estimated the effects of interventions across all states, contrasted the estimated reproduction numbers before and after lockdown for each state, and contrasted the predicted number of future fatalities with the actual number of fatalities as a check of the model's validity. Overall, school closures and lockdowns were the only interventions modeled that had a reliable impact on the time-varying reproduction number, and lockdown appears to have played a key role in reducing that number to below 1.0. We conclude that reversal of lockdown without implementation of additional, equally effective interventions will enable continued, sustained transmission of SARS-CoV-2 in the United States.

Importance of Multiple Reinforcing Comments and Areas for Change in Optimizing Dietary and Exercise Self-Monitoring Feedback in Behavioral Weight Loss Programs: Factorial Design.

BACKGROUND: Individualized dietary and physical activity self-monitoring feedback is a core element of behavioral weight loss interventions and is associated with clinically significant weight loss. To our knowledge, no studies have evaluated individuals' perspectives on the composition of feedback messages or the effect of feedback composition on the motivation to self-monitor. OBJECTIVE: This study aims to assess the perceptions of feedback emails as a function of the number of comments that reinforce healthy behavior and the number of areas for change (ie, behavioral changes that the individual might make to have an impact on weight) identified. METHODS: Emailed feedback followed a factorial design with 2 factors (ie, reinforcing comments and areas for change), each with 3 levels (ie, 1, 4, or 8 comments). A total of 250 adults with overweight or obesity who were interested in weight loss were recruited from the Qualtrics research panel. Participants read 9 emails presented in a random order. For each email, respondents answered 8 questions about the likelihood to self-monitor in the future, motivation for behavioral change, and perceptions of the counselor and the email. A mixed effects ordinal logistic model was used to compute conditional odds ratios and predictive margins (ie, average predicted probability) on a 5-point Likert response scale to investigate the optimal combination level of the 2 factors. RESULTS: Emails with more reinforcing comments or areas for change were better received, with small incremental benefits for 8 reinforcing comments or areas for change versus 4 reinforcing comments or areas for change. Interactions indicated that the best combination for 3 of 8 outcomes assessed (ie, motivation to make behavioral changes, counselor's concern for their welfare, and the perception that the counselor likes them) was the email with 8 reinforcing comments and 4 areas for change. Emails with 4 reinforcing comments and 4 areas for change resulted in the highest average probability of individuals who reported being very likely to self-monitor in the future. CONCLUSIONS: The study findings suggest how feedback might be optimized for efficacy. Future studies should explore whether the composition of feedback email affects actual self-monitoring performance.

Racial/ethnic and geographic differences in polybrominated diphenyl ether (PBDE) levels across maternal, placental, and fetal tissues during mid-gestation.

Prenatal polybrominated diphenyl ether (PBDE) exposures are a public health concern due to their persistence and potential for reproductive and developmental harm. However, we have little information about the extent of fetal exposures during critical developmental periods and the variation in exposures for groups that may be more highly exposed, such as communities of color and lower socioeconomic status (SES). To characterize maternal-fetal PBDE exposures among potentially vulnerable groups, PBDE levels were examined in the largest sample of matched maternal serum, placenta, and fetal liver tissues during mid-gestation among a geographically, racially/ethnically, and socially diverse population of pregnant women from Northern California and the Central Valley (n = 180; 2014-16). Maternal-fetal PBDE levels were compared to population characteristics using censored Kendall's tau correlation and linear regression. PBDEs were commonly detected in all biomatrices. Before lipid adjustment, wet-weight levels of all four PBDE congeners were highest in the fetal liver (p < 0.001), whereas median PBDE levels were significantly higher in maternal serum than in the fetal liver or placenta after lipid-adjustment (p < 0.001). We also found evidence of racial/ethnic disparities in PBDE exposures (Non-Hispanic Black > Latina/Hispanic > Non-Hispanic White > Asian/Pacific Islander/Other; p < 0.01), with higher levels of BDE-100 and BDE-153 among non-Hispanic Black women compared to the referent group (Latina/Hispanic women). In addition, participants living in Fresno/South Central Valley had 34% (95% CI: - 2.4 to 84%, p = 0.07) higher wet-weight levels of BDE-47 than residents living in the San Francisco Bay Area. PBDEs are widely detected and differentially distributed in maternal-fetal compartments. Non-Hispanic Black pregnant women and women from Southern Central Valley geographical populations may be more highly exposed to PBDEs. Further research is needed to identify sources that may be contributing to differential exposures and associated health risks among these vulnerable populations.

Association of polybrominated diphenyl ether (PBDE) levels with biomarkers of placental development and disease during mid-gestation.

BACKGROUND: Polybrominated diphenyl ether (PBDE) exposures have been associated with adverse pregnancy outcomes. A hypothesized mechanism is via alterations in placental development and function. However, we lack biomarkers that can be used as early indicators of maternal/fetal response to PBDE exposures and/or perturbations in placental development or function. METHODS: To evaluate the relationship between PBDE levels and placental biomarkers during mid-gestation of human pregnancy (n = 62), we immunolocalized three molecules that play key roles in cytotrophoblast (CTB) differentiation and interstitial/endovascular uterine invasion-integrin alpha-1 (ITGA1), vascular endothelial-cadherin (CDH5), and metalloproteinase-1 (MMP1)-and assessed three morphological parameters as potential indicators of pathological alterations using H&E-stained tissues-leukocyte infiltration, fibrinoid deposition, and CTB endovascular invasion. We evaluated associations between placental PBDE levels and of biomarkers of placental development and disease using censored Kendall's tau correlation and linear regression methods. RESULTS: PBDEs were detected in all placental samples. We observed substantial variation in antigen expression and morphological endpoints across placental regions. We observed an association between PBDE concentrations and immunoreactivity of endovascular CTB staining with anti-ITGA1 (inverse) or interstitial CTBs staining with anti-CDH5 (positive). CONCLUSIONS: We found several molecular markers that may be sensitive placental indicators of PBDE exposure. Further, this indicates that placental biomarkers of development and disease could be useful barometers of exposure to PBDEs, a paradigm that could be extended to other environmental chemicals and placental stage-specific antigens.

Matrix Linear Models for High-Throughput Chemical Genetic Screens.

We develop a flexible and computationally efficient approach for analyzing high-throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. Typically, interactions between genes and stresses are detected by grouping the mutants and stresses into categories, and performing modified t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or nonoverlapping annotations (e.g., if conditions have doses or a mutant falls into more than one category simultaneously). We develop a matrix linear model (MLM) framework that allows us to model relationships between mutants and conditions in a simple, yet flexible, multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. We develop a fast estimation algorithm that takes advantage of the structure of MLMs. We evaluate our method's performance in simulations and in an Escherichia coli chemical genetic screen, comparing it with an existing univariate approach based on modified t-tests. We show that MLMs perform slightly better than the univariate approach when mutants and conditions are classified in nonoverlapping categories, and substantially better when conditions can be ordered in dosage categories. Therefore, it is an attractive alternative to current methods, and provides a computationally scalable framework for larger and complex chemical genetic screens. A Julia language implementation of MLMs and the code used for this paper are available at https://github.com/janewliang/GeneticScreen.jl and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

Cleaning Genotype Data from Diversity Outbred Mice.

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.