COVID-19 vaccine reactogenicity among participants enrolled in the GENCOV study.

BACKGROUND: GENCOV is a prospective, observational cohort study of COVID-19-positive adults. Here, we characterize and compare side effects between COVID-19 vaccines and determine whether reactogenicity is exacerbated by prior SARS-CoV-2 infection. METHODS: Participants were recruited across Ontario, Canada. Participant-reported demographic and COVID-19 vaccination data were collected using a questionnaire. Multivariable logistic regression was performed to assess whether vaccine manufacturer, type, and previous SARS-CoV-2 infection are associated with reactogenicity. RESULTS: Responses were obtained from n = 554 participants. Tiredness and localized side effects were the most common reactions across vaccine doses. For most participants, side effects occurred and subsided within 1-2 days. Recipients of Moderna mRNA and AstraZeneca vector vaccines reported reactions more frequently compared to recipients of a Pfizer-BioNTech mRNA vaccine. Previous SARS-CoV-2 infection was independently associated with developing side effects. CONCLUSIONS: We provide evidence of relatively mild and short-lived reactions reported by participants who have received approved COVID-19 vaccines.

A marginalized two-part joint model for a longitudinal biomarker and a terminal event with application to advanced head and neck cancers.

The sum of the longest diameter (SLD) of the target lesions is a longitudinal biomarker used to assess tumor response in cancer clinical trials, which can inform about early treatment effect. This biomarker is semicontinuous, often characterized by an excess of zeros and right skewness. Conditional two-part joint models were introduced to account for the excess of zeros in the longitudinal biomarker distribution and link it to a time-to-event outcome. A limitation of the conditional two-part model is that it only provides an effect of covariates, such as treatment, on the conditional mean of positive biomarker values, and not an overall effect on the biomarker, which is often of clinical relevance. As an alternative, we propose in this article, a marginalized two-part joint model (M-TPJM) for the repeated measurements of the SLD and a terminal event, where the covariates affect the overall mean of the biomarker. Our simulation studies assessed the good performance of the marginalized model in terms of estimation and coverage rates. Our application of the M-TPJM to a randomized clinical trial of advanced head and neck cancer shows that the combination of panitumumab in addition with chemotherapy increases the odds of observing a disappearance of all target lesions compared to chemotherapy alone, leading to a possible indirect effect of the combined treatment on time to death.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications.

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

THE SCALABLE BIRTH-DEATH MCMC ALGORITHM FOR MIXED GRAPHICAL MODEL LEARNING WITH APPLICATION TO GENOMIC DATA INTEGRATION.

Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprecedented depth and scale. A large amount of genomic data is now distributed through consortia like The Cancer Genome Atlas (TCGA), where specific types of biological information on specific type of tissue or cell are available. In cancer research, the challenge is now to perform integrative analyses of high-dimensional multi-omic data with the goal to better understand genomic processes that correlate with cancer outcomes, e.g. elucidate gene networks that discriminate a specific cancer subgroups (cancer sub-typing) or discovering gene networks that overlap across different cancer types (pan-cancer studies). In this paper, we propose a novel mixed graphical model approach to analyze multi-omic data of different types (continuous, discrete and count) and perform model selection by extending the Birth-Death MCMC (BDMCMC) algorithm initially proposed by Stephens (2000) and later developed by Mohammadi and Wit (2015). We compare the performance of our method to the LASSO method and the standard BDMCMC method using simulations and find that our method is superior in terms of both computational efficiency and the accuracy of the model selection results. Finally, an application to the TCGA breast cancer data shows that integrating genomic information at different levels (mutation and expression data) leads to better subtyping of breast cancers.

Large-scale whole exome sequencing studies identify two genes,CTSL and APOE, associated with lung cancer.

Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Biobank (European, 630 cases vs. 172 864 controls), the replication study. After controlling for the false discovery rate, we found two genes, CTSL and APOE, significantly associated with lung cancer in both studies. Single variant tests in UK Biobank identified 4 RVs (3 missense variants) in CTSL and 2 RVs (1 missense variant) in APOE stongly associated with lung cancer (OR between 2.0 and 139.0). The role of these genetic variants in the regulation of CTSL or APOE expression remains unclear. If such a role is established, this could have important therapeutic implications for lung cancer patients.

Characterizing Risk Factors for Hospitalization and Clinical Characteristics in a Cohort of COVID-19 Patients Enrolled in the GENCOV Study.

The GENCOV study aims to identify patient factors which affect COVID-19 severity and outcomes. Here, we aimed to evaluate patient characteristics, acute symptoms and their persistence, and associations with hospitalization. Participants were recruited at hospital sites across the Greater Toronto Area in Ontario, Canada. Patient-reported demographics, medical history, and COVID-19 symptoms and complications were collected through an intake survey. Regression analyses were performed to identify associations with outcomes including hospitalization and COVID-19 symptoms. In total, 966 responses were obtained from 1106 eligible participants (87% response rate) between November 2020 and May 2022. Increasing continuous age (aOR: 1.05 [95%CI: 1.01-1.08]) and BMI (aOR: 1.17 [95%CI: 1.10-1.24]), non-White/European ethnicity (aOR: 2.72 [95%CI: 1.22-6.05]), hypertension (aOR: 2.78 [95%CI: 1.22-6.34]), and infection by viral variants (aOR: 5.43 [95%CI: 1.45-20.34]) were identified as risk factors for hospitalization. Several symptoms including shortness of breath and fever were found to be more common among inpatients and tended to persist for longer durations following acute illness. Sex, age, ethnicity, BMI, vaccination status, viral strain, and underlying health conditions were associated with developing and having persistent symptoms. By improving our understanding of risk factors for severe COVID-19, our findings may guide COVID-19 patient management strategies by enabling more efficient clinical decision making.

Public knowledge of SARS-CoV-2 serological and viral lineage laboratory testing and result interpretation: A GENCOV study cross-sectional survey.

OBJECTIVES: Concepts related to SARS-CoV-2 laboratory testing and result interpretation can be challenging to understand. A cross-sectional survey of COVID-19 positive adults residing in Ontario, Canada was conducted to explore how well people understand SARS-CoV-2 laboratory tests and their associated results. DESIGN AND METHODS: Participants were recruited through fliers or by prospective recruitment of outpatients and hospitalized inpatients with COVID-19. Enrolled participants included consenting adults with a positive SARS-CoV-2 polymerase chain reaction test result. An 11-item questionnaire was developed by researchers, nurses, and physicians in the study team and was administered online between April 2021 to May 2022 upon enrolment into the study. RESULTS: Responses were obtained from 940 of 1106 eligible participants (85% participation rate). Most respondents understood 1) that antibody results should not influence adherence to social distancing measures (n = 602/888, 68%), 2) asymptomatic SARS-CoV-2 infection following test positivity (n = 698/888, 79%), 3) serological test sensitivity in relation to post-infection timeline (n = 540/891, 61%), and 4) limitations of experts' knowledge related to SARS-CoV-2 serology (n = 693/887, 78%). Conversely, respondents demonstrated challenges understanding 1) conflicting molecular and serological test results and their relationship with immune protection (n = 162/893, 18%) and 2) the impact of SARS-CoV-2 variants on vaccine effectiveness (n = 235/891, 26%). Analysis of responses stratified by sociodemographic variables identified that respondents who were either: 1) female, 2) more educated, 3) aged 18-44, 4) from a high-income household, or 5) healthcare workers responded expectedly more often. CONCLUSIONS: We have highlighted concepts related to SARS-CoV-2 laboratory tests and associated results which may be challenging to understand. The findings of this study enable us to identify 1) misconceptions related to various SARS-CoV-2 test results, 2) groups of individuals at risk, and 3) strategies to improve people's understanding of their test results.

Cord blood myostatin concentrations by gestational diabetes mellitus and fetal sex.

Introduction: Myostatin is a member of the transforming growth factor ß superfamily, and is mainly secreted from skeletal muscle. Animal studies have demonstrated that deficiency in myostatin promotes muscle growth and protects against insulin resistance. In humans, gestational diabetes mellitus (GDM) affects fetal insulin sensitivity. Females are more insulin resistant and weigh less than males at birth. We sought to assess whether cord blood myostatin concentrations vary by GDM and fetal sex, and the associations with fetal growth factors. Methods: In a study of 44 GDM and 66 euglycemic mother-newborn dyads, myostatin, insulin, proinsulin, insulin-like growth factor (IGF)-1, IGF-2 and testosterone were measured in cord blood samples. Results: Cord blood myostatin concentrations were similar in GDM vs. euglycemic pregnancies (mean ± SD: 5.5 ± 1.4 vs. 5.8 ± 1.4 ng/mL, P=0.28), and were higher in males vs. females (6.1 ± 1.6 vs. 5.3 ± 1.0 ng/mL, P=0.006). Adjusting for gestational age, myostatin was negatively correlated with IGF-2 (r=-0.23, P=0.02), but not correlated with IGF-1 (P=0.60) or birth weight (P=0.23). Myostatin was strongly correlated with testosterone in males (r=0.56, P<0.001), but not in females (r=-0.08, P=0.58) (test for difference in r, P<0.001). Testosterone concentrations were higher in males vs. females (9.5 ± 6.4 vs. 7.1 ± 4.0 nmol/L, P=0.017), and could explain 30.0% (P=0.039) of sex differences in myostatin concentrations. Discussion: The study is the first to demonstrate that GDM does not impact cord blood myostatin concentration, but fetal sex does. The higher myostatin concentrations in males appear to be partly mediated by higher testosterone concentrations. These findings shed novel insight on developmental sex differences in insulin sensitivity regulation relevant molecules.

Evaluating colonoscopy screening intervals in patients with Lynch syndrome from a large Canadian registry.

BACKGROUND: Lynch syndrome (LS) screening guidelines originally recommended colonoscopy every 1 to 2 years, beginning between the ages of 20 and 25 years. Recent studies have questioned the benefits of these short screening intervals in preventing colorectal cancer (CRC). Our goal is to determine how colonoscopy screening intervals impact CRC in patients with LS. METHODS: We analyzed the demographics, screening practices, and outcomes of patients with LS identified through the clinic based Familial Gastrointestinal Cancer Registry at the Zane Cohen Centre, Sinai Health System, Toronto, Canada. RESULTS: A total of 429 patients with LS were identified with median follow-up of 9.2 years; 44 developed CRC. We found a positive trend between shorter screening intervals and the number of adenomas detected during colonoscopy. Any new adenoma detected at screening decreased 10-year CRC incidence by 11.3%. For MLH1 carriers, a screening interval of 1-2 years vs 2-3 years led to a 20-year cumulative CRC risk reduction of 28% and 14% in females and males, respectively. For MSH2 carriers, this risk reduction was 29% and 17%, respectively, and for male MSH6 carriers 18%. Individuals without any adenomas detected (53.4% of LS carriers) had an increased 20-year CRC risk of 25.7% and 57.2% for women and men, respectively, compared with those diagnosed with adenomas at screening. CONCLUSIONS: The recommended colonoscopy screening interval of 1-2 years is efficient at detecting adenomas and reducing CRC risk. The observation that 53.4% of LS patients never had an adenoma warrants further investigation about a possible adenoma-free pathway.

Bayesian estimation of two-part joint models for a longitudinal semicontinuous biomarker and a terminal event with INLA: Interests for cancer clinical trial evaluation.

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., a large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using the INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.

Genome-wide placental DNA methylations in fetal overgrowth and associations with leptin, adiponectin and fetal growth factors.

BACKGROUND: Fetal overgrowth "programs" an elevated risk of type 2 diabetes in adulthood. Epigenetic alterations may be a mechanism in programming the vulnerability. We sought to characterize genome-wide alterations in placental gene methylations in fetal overgrowth and the associations with metabolic health biomarkers including leptin, adiponectin and fetal growth factors. RESULTS: Comparing genome-wide placental gene DNA methylations in large-for-gestational-age (LGA, an indicator of fetal overgrowth, n = 30) versus optimal-for-gestational-age (OGA, control, n = 30) infants using the Illumina Infinium Human Methylation-EPIC BeadChip, we identified 543 differential methylation positions (DMPs; 397 hypermethylated, 146 hypomethylated) at false discovery rate < 5% and absolute methylation difference > 0.05 after adjusting for placental cell-type heterogeneity, maternal age, pre-pregnancy BMI and HbA1c levels during pregnancy. Twenty-five DMPs annotated to 20 genes (QSOX1, FCHSD2, LOC101928162, ADGRB3, GCNT1, TAP1, MYO16, NAV1, ATP8A2, LBXCOR1, EN2, INCA1, CAMTA2, SORCS2, SLC4A4, RPA3, UMAD1,USP53, OR2L13 and NR3C2) could explain 80% of the birth weight variations. Pathway analyses did not detect any statistically significant pathways after correcting for multiple tests. We validated a newly discovered differentially (hyper-)methylated gene-visual system homeobox 1 (VSX1) in an independent pyrosequencing study sample (LGA 47, OGA 47). Our data confirmed a hypermethylated gene-cadherin 13 (CDH13) reported in a previous epigenome-wide association study. Adiponectin in cord blood was correlated with its gene methylation in the placenta, while leptin and fetal growth factors (insulin, IGF-1, IGF-2) were not. CONCLUSIONS: Fetal overgrowth may be associated with a large number of altered placental gene methylations. Placental VSX1 and CDH13 genes are hypermethylated in fetal overgrowth. Placental ADIPOQ gene methylations and fetal circulating adiponectin levels were correlated, suggesting the contribution of placenta-originated adiponectin to cord blood adiponectin.

Highly efficient reprogrammable mouse lines with integrated reporters to track the route to pluripotency.

Revealing the molecular events associated with reprogramming different somatic cell types to pluripotency is critical for understanding the characteristics of induced pluripotent stem cell (iPSC) therapeutic derivatives. Inducible reprogramming factor transgenic cells or animals-designated as secondary (2°) reprogramming systems-not only provide excellent experimental tools for such studies but also offer a strategy to study the variances in cellular reprogramming outcomes due to different in vitro and in vivo environments. To make such studies less cumbersome, it is desirable to have a variety of efficient reprogrammable mouse systems to induce successful mass reprogramming in somatic cell types. Here, we report the development of two transgenic mouse lines from which 2° cells reprogram with unprecedented efficiency. These systems were derived by exposing primary reprogramming cells containing doxycycline-inducible Yamanaka factor expression to a transient interruption in transgene expression, resulting in selection for a subset of clones with robust transgene response. These systems also include reporter genes enabling easy readout of endogenous Oct4 activation (GFP), indicative of pluripotency, and reprogramming transgene expression (mCherry). Notably, somatic cells derived from various fetal and adult tissues from these 2° mouse lines gave rise to highly efficient and rapid reprogramming, with transgene-independent iPSC colonies emerging as early as 1 wk after induction. These mouse lines serve as a powerful tool to explore sources of variability in reprogramming and the mechanistic underpinnings of efficient reprogramming systems.

Genome-Wide Placental Gene Methylations in Gestational Diabetes Mellitus, Fetal Growth and Metabolic Health Biomarkers in Cord Blood.

Gestational diabetes mellitus (GDM) "program" an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. In an epigenome-wide association study using the Infinium MethylationEPIC Beadchip, we sought to identify genome-wide placental differentially methylated genes and enriched pathways in GDM, and to assess the correlations with fetal growth and metabolic health biomarkers in cord blood. The study samples were 30 pairs of term placentas in GDM vs. euglycemic pregnancies (controls) matched by infant sex and gestational age at delivery in the Shanghai Birth Cohort. Cord blood metabolic health biomarkers included insulin, C-peptide, proinsulin, IGF-I, IGF-II, leptin and adiponectin. Adjusting for maternal age, pre-pregnancy BMI, parity, mode of delivery and placental cell type heterogeneity, 256 differentially methylated positions (DMPs,130 hypermethylated and 126 hypomethylated) were detected between GDM and control groups accounting for multiple tests with false discovery rate <0.05 and beta-value difference >0.05. WSCD2 was identified as a differentially methylated gene in both site- and region-level analyses. We validated 7 hypermethylated (CYP1A2, GFRA1, HDAC4, LIMS2, NAV3, PAX6, UPK1B) and 10 hypomethylated (DPP10, CPLX1, CSMD2, GPR133, NRXN1, PCSK9, PENK, PRDM16, PTPRN2, TNXB) genes reported in previous epigenome-wide association studies. We did not find any enriched pathway accounting for multiple tests. DMPs in 11 genes (CYP2D7P1, PCDHB15, ERG, SIRPB1, DKK2, RAPGEF5, CACNA2D4, PCSK9, TSNARE1, CADM2, KCNAB2) were correlated with birth weight (z score) accounting for multiple tests. There were no significant correlations between placental gene methylations and cord blood biomarkers. In conclusions, GDM was associated with DNA methylation changes in a number of placental genes, but these placental gene methylations were uncorrelated to the observed metabolic health biomarkers (fetal growth factors, leptin and adiponectin) in cord blood. We validated 17 differentially methylated placental genes in GDM, and identified 11 differentially methylated genes relevant to fetal growth.

DNA methylation profiles in the blood of newborn term infants born to mothers with obesity.

Maternal obesity is an important risk factor for childhood obesity and influences the prevalence of metabolic diseases in offspring. As childhood obesity is influenced by postnatal factors, it is critical to determine whether children born to women with obesity during pregnancy show alterations that are detectable at birth. Epigenetic mechanisms such as DNA methylation modifications have been proposed to mediate prenatal programming. We investigated DNA methylation signatures in male and female infants from mothers with a normal Body Mass Index (BMI 18.5-24.9 kg/m2) compared to mothers with obesity (BMI≥30 kg/m2). BMI was measured during the first prenatal visit from women recruited into the Ontario Birth Study (OBS) at Mount Sinai Hospital in Toronto, ON, Canada. DNA was extracted from neonatal dried blood spots collected from heel pricks obtained 24 hours after birth at term (total n = 40) from women with a normal BMI and women with obesity matched for parity, age, and neonatal sex. Reduced representation bisulfite sequencing was used to identify genomic loci associated with differentially methylated regions (DMRs) in CpG-dense regions most likely to influence gene regulation. DMRs were predominantly localized to intergenic regions and gene bodies, with only 9% of DMRs localized to promoter regions. Genes associated with DMRs were compared to those from a large publicly available cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC; total n = 859). Hypergeometric tests revealed a significant overlap in genes associated with DMRs in the OBS and ALSPAC cohorts. PTPRN2, a gene involved in insulin secretion, and MAD1L1, which plays a role in the cell cycle and tumor suppression, contained DMRs in males and females in both cohorts. In males, KEGG pathway analysis revealed significant overrepresentation of genes involved in endocytosis and pathways in cancer, including IGF1R, which was previously shown to respond to diet-induced metabolic stress in animal models and in lymphocytes in the context of childhood obesity. These preliminary findings are consistent with Developmental Origins of Health and Disease paradigm, which posits that adverse prenatal exposures set developmental health trajectories.

Building knowledge, optimising physical and mental health and setting up healthier life trajectories in South African women (Bukhali): a preconception randomised control trial part of the Healthy Life Trajectories Initiative (HeLTI).

INTRODUCTION: South Africa's evolving burden of disease is challenging due to a persistent infectious disease, burgeoning obesity, most notably among women and rising rates of non-communicable diseases (NCDs). With two thirds of women presenting at their first antenatal visit either overweight or obese in urban South Africa (SA), the preconception period is an opportunity to optimise health and offset transgenerational risk of both obesity and NCDs. METHODS AND ANALYSIS: Bukhali is the first individual randomised controlled trial in Africa to test the efficacy of a complex continuum of care intervention and forms part of the Healthy Life Trajectories Initiative (HeLTI) consortium implementing harmonised trials in Canada, China, India and SA. Starting preconception and continuing through pregnancy, infancy and childhood, the intervention is designed to improve nutrition, physical and mental health and health behaviours of South African women to offset obesity-risk (adiposity) in their offspring. Women aged 18-28 years (n=6800) will be recruited from Soweto, an urban-poor area of Johannesburg. The primary outcome is dual-energy X-ray absorptiometry derived fat mass index (fat mass divided by height2) in the offspring at age 5 years. Community health workers will deliver the intervention randomly to half the cohort by providing health literacy material, dispensing a multimicronutrient supplement, providing health services and feedback, and facilitating behaviour change support sessions to optimise: (1) nutrition, (2) physical and mental health and (3) lay the foundations for healthier pregnancies and early child development. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Human Ethics Research Committee University of the Witwatersrand, Johannesburg, South Africa (M1811111), the University of Toronto, Canada (19-0066-E) and the WHO Ethics Committee (ERC.0003328). Data and biological sample sharing policies are consistent with the governance policy of the HeLTI Consortium (https://helti.org) and South African government legislation (POPIA). The recruitment and research team will obtain informed consent. TRIAL REGISTRATION: This trial is registered with the Pan African Clinical Trials Registry (https://pactr.samrc.ac.za) on 25 March 2019 (identifier: PACTR201903750173871). PROTOCOL VERSION: 20 March 2022 (version #4). Any protocol amendments will be communicated to investigators, Institutional Review Board (IRB)s, trial participants and trial registries.

Maternal BMI, breastfeeding and perinatal factors that influence early childhood growth trajectories: a scoping review.

Obesity rates among children are rapidly rising internationally and have been linked to noncommunicable diseases in adulthood. Individual preventive strategies have not effectively reduced global obesity rates, leading to a gap in clinical services regarding the development of early perinatal interventions. The objective of this scoping review is to explore the relationship between maternal BMI and breastfeeding behaviors on child growth trajectories to determine their relevance in developing interventions aimed at preventing childhood obesity.The scoping review was guided and informed by the Arksey and O'Malley (2005) framework. A systematic search was performed in four databases. Studies included in the final review were collated and sorted into relevant themes. A systematic search yielded a total of 5831 records (MEDLINE: 1242, EMBASE: 2629, CINAHL: 820, PubMed: 1140). Results without duplicates (n = 4190) were screened based on relevancy of which 197 relevant-full-text articles were retrieved and assessed for eligibility resulting in 14 studies meeting the inclusion criteria. Data were extracted and charted for the studies and six themes were identified: (1) healthy behaviors, lifestyle, and social economic status; (2) parental anthropometrics and perinatal weight status; (3) genetics, epigenetics, and fetal programming; (4) early infant feeding; (5) infant growth trajectories; and (6) targeted prevention and interventions. Early life risk factors for child obesity are multifactorial and potentially modifiable. Several at-risk groups were identified who would benefit from early preventative interventions targeting the importance of healthy weight gain, exclusive breastfeeding to 6 months, and healthy lifestyle behaviors.

Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data.

Joint models for a longitudinal biomarker and a terminal event have gained interests for evaluating cancer clinical trials because the tumor evolution reflects directly the state of the disease. A biomarker characterizing the tumor size evolution over time can be highly informative for assessing treatment options and could be taken into account in addition to the survival time. The biomarker often has a semicontinuous distribution, i.e., it is zero inflated and right skewed. An appropriate model is needed for the longitudinal biomarker as well as an association structure with the survival outcome. In this article, we propose a joint model for a longitudinal semicontinuous biomarker and a survival time. The semicontinuous nature of the longitudinal biomarker is specified by a two-part model, which splits its distribution into a binary outcome (first part) represented by the positive versus zero values and a continuous outcome (second part) with the positive values only. Survival times are modeled with a proportional hazards model for which we propose three association structures with the biomarker. Our simulation studies show some bias can arise in the parameter estimates when the semicontinuous nature of the biomarker is ignored, assuming the true model is a two-part model. An application to advanced metastatic colorectal cancer data from the GERCOR study is performed where our two-part model is compared to one-part joint models. Our results show that treatment arm B (FOLFOX6/FOLFIRI) is associated to higher SLD values over time and its positive association with the terminal event leads to an increased risk of death compared to treatment arm A (FOLFIRI/FOLFOX6).

DNA methylation mediates the association between breastfeeding and early-life growth trajectories.

BACKGROUND: The role of breastfeeding in modulating epigenetic factors has been suggested as a possible mechanism conferring its benefits on child development but it lacks evidence. Using extensive DNA methylation data from the ALSPAC child cohort, we characterized the genome-wide landscape of DNA methylation variations associated with the duration of exclusive breastfeeding and assessed whether these variations mediate the association between exclusive breastfeeding and BMI over different epochs of child growth. RESULTS: Exclusive breastfeeding elicits more substantial DNA methylation variations during infancy than at other periods of child growth. At the genome-wide level, 13 CpG sites in girls (miR-21, SNAPC3, ATP6V0A1, DHX15/PPARGC1A, LINC00398/ALOX5AP, FAM238C, NATP/NAT2, CUX1, TRAPPC9, OSBPL1A, ZNF185, FAM84A, PDPK1) and 2 CpG sites in boys (IL16 and NREP), mediate the association between exclusive breastfeeding and longitudinal BMI. We found enrichment of CpG sites located within miRNAs and key pathways (AMPK signaling pathway, insulin signaling pathway, endocytosis). Overall DNA methylation variation corresponding to 3 to 5 months of exclusive breastfeeding was associated with slower BMI growth the first 6 years of life compared to no breastfeeding and in a dose-response manner with exclusive breastfeeding duration. CONCLUSIONS: Our study confirmed the early postnatal period as a critical developmental period associated with substantial DNA methylation variations, which in turn could mitigate the development of overweight and obesity from infancy to early childhood. Since an accelerated growth during these developmental periods has been linked to the development of sustained obesity later in life, exclusive breastfeeding could have a major role in preventing the risks of overweight/obesity and children and adults through DNA methylation mechanisms occurring early in life.

FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs.

FamEvent is a comprehensive R package for simulating and modelling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an Expectation-Maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.