Saguenay Youth Study: a multi-generational approach to studying virtual trajectories of the brain and cardio-metabolic health.
Ontology highlight
ABSTRACT:
SUBMITTER: Paus T
PROVIDER: S-EPMC6989769 | biostudies-literature | 2015 Feb
REPOSITORIES: biostudies-literature
Publications
Saguenay Youth Study: a multi-generational approach to studying virtual trajectories of the brain and cardio-metabolic health.
Paus T T Pausova Z Z Abrahamowicz M M Gaudet D D Leonard G G Pike G B GB Richer L L
Developmental cognitive neuroscience 20141023
This paper provides an overview of the Saguenay Youth Study (SYS) and its parental arm. The overarching goal of this effort is to develop trans-generational models of developmental cascades contributing to the emergence of common chronic disorders, such as depression, addictions, dementia and cardio-metabolic diseases. Over the past 10 years, we have acquired detailed brain and cardio-metabolic phenotypes, and genome-wide genotypes, in 1029 adolescents recruited in a population with a known gene ...[more]
Similar Datasets
Project description:The Saguenay Youth Study (SYS) is a two-generational study of adolescents and their parents (n = 1029 adolescents and 962 parents) aimed at investigating the aetiology, early stages and trans-generational trajectories of common cardiometabolic and brain diseases. The ultimate goal of this study is to identify effective means for increasing healthy life expectancy. The cohort was recruited from the genetic founder population of the Saguenay Lac St Jean region of Quebec, Canada. The participants underwent extensive (15-h) phenotyping, including an hour-long recording of beat-by-beat blood pressure, magnetic resonance imaging of the brain and abdomen, and serum lipidomic profiling with LC-ESI-MS. All participants have been genome-wide genotyped (with ? 8 M imputed single nucleotide polymorphisms) and a subset of them (144 adolescents and their 288 parents) has been genome-wide epityped (whole blood DNA, Infinium HumanMethylation450K BeadChip). These assessments are complemented by a detailed evaluation of each participant in a number of domains, including cognition, mental health and substance use, diet, physical activity and sleep, and family environment. The data collection took place during 2003-12 in adolescents (full) and their parents (partial), and during 2012-15 in parents (full). All data are available upon request.
| S-EPMC5837575 | biostudies-literature
Project description:Little is known about body size over the life-course and non-communicable disease risk in low- and middle-income country populations. Our study explored the role of body mass index (BMI) trajectories from infancy through mid-adulthood on cardio-metabolic disease (CMD) risk factors in a prospective cohort of Guatemalan adults. Study participants were born in Guatemala from 1962-77 and have been followed prospectively since participating in a nutrition supplementation trial as children. Sex-specific BMI latent class trajectories were derived using latent class growth modeling from up to 22 possible BMI values from age 1 month to 42 years measured between 1969 and 2004. CMD risk factors were assessed in 2015-17 (at age 37-54 years) using anthropometry, blood glucose and lipids, and blood pressure. We used logistic regression to assess the role of BMI trajectory on CMD risk factors in 510 women and 346 men (N = 856). We identified two BMI latent classes for women (low [n = 287, 56.3%] and high [n = 223, 43.7%]) and three classes for men (low [n = 141, 40.8%], medium [n = 160, 46.2%], and high [n = 45, 13.0%]). Given the small percentage of men in the high BMI latent class, we collapsed the medium and high BMI latent classes for men (n = 205, 59.1%). Among the most prevalent CMD risk factors at ages 37-54 years were abdominal obesity defined by waist-height ratio (99.6% of women and 87.3% of men), obesity defined by percent body fat (96.6% of women and 75.9% of men), low HDL-c (87.5% of women and 74.5% of men), and elevated triglycerides (78.3% of women and 73.6% of men). Except for obesity defined by BMI, we found no associations between BMI latent class and CMD risk factors in women. Among men, BMI latent class was not associated with CMD risk factors after controlling for current BMI. For the CMD risk factors we analyzed, the role of early life BMI on adult CMD appeared to be mediated by adult BMI among men-highlighting the need to establish and maintain healthy body weight over the life course.
| S-EPMC7580923 | biostudies-literature
Project description:We investigated body mass index (BMI) trajectories in the first 2 years of life in 1170 children from an Asian mother-offspring cohort in Singapore, and examined their predictors and associations with childhood cardio-metabolic risk measures at 5 years. Latent class growth mixture modelling analyses were performed to identify distinct BMI z-score (BMIz) trajectories. Four trajectories were identified: 73.2%(n = 857) of the children showed a normal BMIz trajectory, 13.2%(n = 155) a stable low-BMIz trajectory, 8.6%(n = 100) a stable high-BMIz trajectory and 5.0%(n = 58) a rapid BMIz gain after 3 months trajectory. Predictors of the stable high-BMIz and rapid BMIz gain trajectories were pre-pregnancy BMI, gestational weight gain, Malay and Indian ethnicity, while predictors of stable low-BMIz trajectory were preterm delivery and Indian ethnicity. At 5 years, children with stable high-BMIz or rapid BMIz gain trajectories had increased waist-to-height ratios [B(95%CI) 0.02(0.01,0.03) and 0.03(0.02,0.04)], sum of skinfolds [0.42(0.19,0.65) and 0.70(0.36,1.03)SD units], fat-mass index [0.97(0.32,1.63)SD units] and risk of obesity [relative risk 3.22(1.73,6.05) and 2.56 (1.19,5.53)], but not higher blood pressure. BMIz trajectories were more predictive of adiposity at 5 years than was BMIz at 2 years. Our findings on BMIz trajectories in the first 2 years suggest important ethnic-specific differences and impacts on later metabolic outcomes.
| S-EPMC5567284 | biostudies-literature
Project description:OBJECTIVES:Leptin is a hormone produced by adipose tissue that promotes satiety, and some evidence suggests that greater early life leptin exposure prevents excessive adiposity gain in later life. However, few studies have analyzed dynamic changes in leptin throughout childhood in relation to later cardio-metabolic health. Our study aims to identify distinct leptin trajectories in childhood, and to examine their associations with cardio-metabolic outcomes in adolescence. METHODS:Among children in the Project Viva cohort born 1999-2002 in Massachusetts, we used latent class growth models to identify leptin trajectories independent of maternal BMI, child sex, race/ethnicity, size at birth and current age and size among 1360 children with leptin measured at least once at birth, early childhood (mean 3.3?±?SD 0.3?years), or mid-childhood (7.9?±?0.8?years). At research visits in early adolescence (13.2?±?0.9?years), we assessed cardio-metabolic outcomes including adiposity measures, fasting biomarkers, and blood pressure among 855 children. We then applied multiple regression models to examine associations of the leptin trajectories with these cardio-metabolic outcomes in early adolescence, adjusting for child age at outcome, maternal age, education, prenatal smoking and glucose, total gestational weight gain and paternal BMI. RESULTS:The latent class growth model identified 3 distinct leptin trajectories: "low stable" (n?=?1031, 75.8%), "high-decreasing" (n?=?219, 16.1%) and "intermediate-increasing" (n?=?110, 8.1%). In adjusted models, the intermediate-increasing leptin trajectory was associated with higher early adolescence adiposity measures (e.g. BMI z-score: 0.62?units; 95% confidence interval: 0.28, 0.96 and odds of obesity: 2.84: 1.17, 6.94), but lower systolic blood pressure (-0.46 z-score units; -0.74, -0.18), compared to the low-stable group. CONCLUSIONS:Our findings on leptin trajectories in childhood suggest important differences and associations with later metabolic outcomes.
| S-EPMC6366620 | biostudies-literature
Project description:BackgroundAs cardiovascular disease is a leading cause of death in cancer survivors, the new subspecialty of Cardio-Oncology has emerged to address prevention, monitoring, and management of cardiovascular toxicities to cancer therapies. During the coronavirus disease of 2019 (COVID-19) pandemic, we developed a Virtual-Hybrid Approach to build a de novo Cardio-Oncology Clinic.MethodsWe conceptualized a Virtual-Hybrid Approach including three arms: information seeking in locations with existing Cardio-Oncology clinics, information gathering at the location for a new clinic, and information sharing to report clinic-building outcomes. A retrospective review of outcomes included collection and synthesis of data from our first 3 months (at pandemic peak) on types of appointments, cancers, drugs, and cardiotoxicities. Data were presented using descriptive statistics.ResultsA de-novo Cardio-Oncology clinic was developed structured from the ground up to integrate virtual and in-person care in a hybrid and innovative model, using the three arms of the Virtual-Hybrid Approach. First, we garnered in-person and virtual preparation through hands-on experiences, training, and discussions in existing Cardio-Oncology Clinics and conferences. Next, we gleaned information through virtual inquiry and niche-building. With partners throughout the institution, a virtual referral process was established for outpatient referrals and inpatient e-consult referrals to actualize a hybrid care spectrum for our patients administered by a multidisciplinary hybrid care team of clinicians, ancillary support staff, and clinical pharmacists. Among the multi-subspecialty clinic sessions, approximately 50% were in Cardio-Oncology, 20% in Preventive Cardiology, and 30% in General Cardiology. In the hybrid model, the Heart & Vascular Center had started to re-open, allowing for 65% of our visits to be in person. In additional analyses, the most frequent cardiovascular diagnosis was cardiomyopathy (34%), the most common cancer drug leading to referral was trastuzumab (29%), and the most prevalent cancer type was breast cancer (42%).ConclusionThis Virtual-Hybrid Approach and retrospective review provides guidance and information regarding initiating a brand-new Cardio-Oncology Clinic during the pandemic for cancer patients/survivors. This report also furnishes virtual resources for patients, virtual tools for oncologists, cardiologists, and administrators tasked with starting new clinics during the pandemic, and innovative future directions for this digital pandemic to post-pandemic era.
| S-EPMC7803880 | biostudies-literature
Project description:Primary open-angle glaucoma (POAG) represents the leading cause of irreversible blindness worldwide and is a multifactorial, chronic neurodegenerative disease characterized by retinal ganglion cell and visual field loss. There are many factors that are associated with the risk of developing POAG, with increased intraocular pressure being one of the most prevalent. Due to the asymptomatic nature of the disease, the diagnosis of POAG often occurs too late, which necessitates development of new effective screening strategies for early diagnosis of the disease. However, this task still remains unfulfilled. In order to provide further insights into the pathophysiology of POAG, we applied a targeted metabolomics strategy based on a high-throughput screening method for the determination of tear amino acids, free carnitine, acylcarnitines, succinylacetone, nucleosides, and lysophospholipids in naïve to therapy glaucomatous patients and normal controls. Also, we conducted proteomic analyses of the whole lacrimal fluid and purified extracellular vesicles obtained from POAG patients and healthy subjects. This multi-omics approach allowed us to conclude that POAG patients had lower levels of certain tear amino acids and lysophospholipids compared with controls. These targeted analyses also highlighted the low amount of acetylcarnitine (C2) in POAG patient which correlated well with proteomics data. Moreover, POAG tear proteins seemed to derive from extracellular vesicles, which carried a specific pro-inflammatory protein cargo.
| S-EPMC6721157 | biostudies-literature
Project description:Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive picture of a specific disease, multiple studies need to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data "silos", which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.
| S-EPMC7335180 | biostudies-literature
Project description:Objective:Physical inactivity inChinese youth students particularly in senior high schools, who participate inthe National Higher Education Entrance Examination (NCEE) is very common. Inorder to explore the beneficial effects from physical exercise and education afterNCEE, we performed a Physicalexercise Intervention Program in the Youth (PiPy) to evaluate the interaction with PPAR? genetic variants on cardiovascular and metabolicparameters. Methods:A total of 772 freshmen (males 610/females162) from high schools to university were recruited into the PiPy cohort, which was designedaccording to the National Student Health Standards in China. Anthropometric data were collected, whilephysical activities and body composition at the baseline of PiPy cohort weremeasured with SECAprotocols. Eighttagged single nucleotide polymorphisms (SNPs) in the PPAR ? gene were genotyped with TaqMan allelicdiscrimination. Results:After physical exercise intervention forthree months, in parallel with increased physical activities, BMI and skeletalmuscle content in all subjects was enhanced, while heart rate and bloodpressures were decreased. Furthermore, SNPs in 5'-UTR of the PPAR? gene, including rs2920502, rs9817428 and rs2972164, were found to be associated with the changes of BMI. Body weight in the subjects with BMI <18.5and 18.5-23.9 kg/m2 were increased, while the obese subjects (BMI ?24.0 kg/m2) decreased. Conclusion:The present study for the first timedemonstrated that the PiPy could improve cardio-metabolic parameters such asheart rate, blood pressures and BMI for Chinese youth students after NCEE, inwhich the genetic interactive effects of PPAR ? should be included into obesityintervention.
| S-EPMC7173993 | biostudies-literature
Project description:Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.
| S-EPMC6497301 | biostudies-literature
Project description:At every level of the visual system - from retina to cortex - information is encoded in the activity of large populations of cells. The populations are not uniform, but contain many different types of cells, each with its own sensitivities to visual stimuli. Understanding the roles of the cell types and how they work together to form collective representations has been a long-standing goal. This goal, though, has been difficult to advance, and, to a large extent, the reason is data limitation. Large numbers of stimulus/response relationships need to be explored, and obtaining enough data to examine even a fraction of them requires a great deal of experiments and animals. Here we describe a tool for addressing this, specifically, at the level of the retina. The tool is a data-driven model of retinal input/output relationships that is effective on a broad range of stimuli - essentially, a virtual retina. The results show that it is highly reliable: (1) the model cells carry the same amount of information as their real cell counterparts, (2) the quality of the information is the same - that is, the posterior stimulus distributions produced by the model cells closely match those of their real cell counterparts, and (3) the model cells are able to make very reliable predictions about the functions of the different retinal output cell types, as measured using Bayesian decoding (electrophysiology) and optomotor performance (behavior). In sum, we present a new tool for studying population coding and test it experimentally. It provides a way to rapidly probe the actions of different cell classes and develop testable predictions. The overall aim is to build constrained theories about population coding and keep the number of experiments and animals to a minimum.
| S-EPMC3544815 | biostudies-literature