MARBLES (Markers of Autism Risk in Babies: Learning Early Sign) Enriched Cohort Study:Internal Metabolomic Biomarker Exposome and Development Disorders (IMBEDD) (part 2)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Blood
DISEASE(S): Autism
PROVIDER: ST001193 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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| ST001209 | MetabolomicsWorkbench
Project description:BackgroundHuman ageing is a complex, multifactorial process and early developmental factors affect health outcomes in old age.MethodsMetabolomic profiling on fasting blood was carried out in 6055 individuals from the UK. Stepwise regression was performed to identify a panel of independent metabolites which could be used as a surrogate for age. We also investigated the association with birthweight overall and within identical discordant twins and with genome-wide methylation levels.ResultsWe identified a panel of 22 metabolites which combined are strongly correlated with age (R(2) = 59%) and with age-related clinical traits independently of age. One particular metabolite, C-glycosyl tryptophan (C-glyTrp), correlated strongly with age (beta = 0.03, SE = 0.001, P = 7.0 × 10(-157)) and lung function (FEV1 beta = -0.04, SE = 0.008, P = 1.8 × 10(-8) adjusted for age and confounders) and was replicated in an independent population (n = 887). C-glyTrp was also associated with bone mineral density (beta = -0.01, SE = 0.002, P = 1.9 × 10(-6)) and birthweight (beta = -0.06, SE = 0.01, P = 2.5 × 10(-9)). The difference in C-glyTrp levels explained 9.4% of the variance in the difference in birthweight between monozygotic twins. An epigenome-wide association study in 172 individuals identified three CpG-sites, associated with levels of C-glyTrp (P < 2 × 10(-6)). We replicated one CpG site in the promoter of the WDR85 gene in an independent sample of 350 individuals (beta = -0.20, SE = 0.04, P = 2.9 × 10(-8)). WDR85 is a regulator of translation elongation factor 2, essential for protein synthesis in eukaryotes.ConclusionsOur data illustrate how metabolomic profiling linked with epigenetic studies can identify some key molecular mechanisms potentially determined in early development that produce long-term physiological changes influencing human health and ageing.
| S-EPMC3781000 | biostudies-literature
Project description:BackgroundADHD is associated with multiple adverse outcomes and early identification is important. The present study sets out to identify early markers and developmental characteristics during the first 30 months of life that are associated with ADHD 6 years later.Methods9201 participants from the prospective Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort were included. Outcome measures were parent-rated ADHD symptom scores (Strengths and Difficulties Questionnaire, SDQ) and ADHD diagnosis (Development and Wellbeing Assessment, DAWBA) at age 7. Seventeen putative markers were identified from previous literature and included: pre- and peri-natal risk factors, genetic liability (ADHD polygenic risk scores, PRS), early development, temperament scores and regulatory problems. Associations were examined using regression analysis.ResultsUnivariable regression analysis showed that multiple early life factors were associated with future ADHD outcomes, even after controlling for sex and socio-economic status. In a multivariable linear regression model; temperament activity scores (B = 0.107, CI = 0.083-0.132), vocabulary delay (B = 0.605, CI = 0.211-0.988), fine motor delay (B = 0.693, CI = 0.360-1.025) and ADHD PRS (B = 0.184, CI = 0.074-0.294) were associated with future symptoms (R 2 = 10.7%). In a multivariable logistic regression model, ADHD PRS (OR = 1.39, CI = 1.10-1.77) and temperament activity scores (OR = 1.09, CI = 1.04-1.16) showed association with ADHD diagnosis.ConclusionAs well as male sex and lower socio-economic status, high temperament activity levels and motor and speech delays in the first 30 months of life, are associated with childhood ADHD. Intriguingly, given that genetic risk scores are known to explain little of the variance of ADHD outcomes, we found that ADHD PRS added useful predictive information. Future research needs to test whether predictive models incorporating aspects of early development and genetic risk scores are useful for predicting ADHD in clinical practice.
| S-EPMC9716640 | biostudies-literature
Project description:BackgroundMaternal prepregnancy obesity is an important risk factor for offspring obesity, which may partially operate through prenatal programming mechanisms.ObjectivesThe study aimed to systematically identify prenatal metabolomic profiles mediating the intergenerational transmission of obesity.MethodsWe included 450 African-American mother-child pairs from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) Study pregnancy cohort. LC-MS was used to conduct metabolomic profiling on maternal plasma samples of the second trimester. The childhood growth outcomes of interest included BMI trajectories from birth to age 4 y (rising-high-, moderate-, and low-BMI trajectories) as well as overweight/obesity (OWO) risk at age 4 y. Mediation analysis was conducted to identify metabolite mediators linking maternal OWO and childhood growth outcomes. The potential causal effects of maternal OWO on metabolite mediators were examined using the Mendelian randomization (MR) method.ResultsAmong the 880 metabolites detected in the maternal plasma during pregnancy, 14 and 11 metabolites significantly mediated the effects of maternal prepregnancy OWO on childhood BMI trajectories and the OWO risk at age 4 y, respectively, and 5 mediated both outcomes. The MR analysis suggested 6 of the 20 prenatal metabolite mediators might be causally influenced by maternal prepregnancy OWO, most of which are from the pathways related to the metabolism of amino acids (hydroxyasparagine, glutamate, and homocitrulline), sterols (campesterol), and nucleotides (N2,N2-dimethylguanosine).ConclusionsOur study provides further evidence that prenatal metabolomic profiles might mediate the effect of maternal OWO on early childhood growth trajectories and OWO risk in offspring. The metabolic pathways, including identified metabolite mediators, might provide novel intervention targets for preventing the intrauterine development of obesity in the offspring of mothers with obesity.
| S-EPMC9630879 | biostudies-literature
Project description:Metastasis development represents an important threat for melanoma patients, even when diagnosed at early stages and upon removal of the primary tumor. In this scenario, determination of prognostic biomarkers would be of great interest. Serum contains information about the general status of the organism and therefore represents a valuable source for biomarkers. Thus, we aimed to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict metastatic events on the early-stage population of patients. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with interleukin 4 (IL-4), IL-6, IL-10, IL-17A, interferon γ (IFN-γ), transforming growth factor-β (TGF- β), and granulocyte-macrophage colony-stimulating factor (GM-CSF). We initially recruited 448 melanoma patients, 323 of whom were diagnosed as stages I-II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex, and obtained data were analyzed employing machine learning and Kaplan-Meier techniques to define an algorithm capable of accurately classifying early-stage melanoma patients with a high and low risk of developing metastasis. The results show that in early-stage melanoma patients, serum levels of the cytokines IL-4, GM-CSF, and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis.
| S-EPMC7400797 | biostudies-literature
Project description:Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.
| S-EPMC4618700 | biostudies-literature
Project description:IntroductionTargeted proteomic assays may be useful for diagnosing and staging Alzheimer's disease and related dementias (ADRD). We evaluated the performance of a 120-marker central nervous system (CNS) NUcleic acid-Linked Immuno-Sandwich Assay (NULISA) panel in samples spanning the AD spectrum.MethodsCross-sectional plasma samples (n=252) were analyzed using Alamar's NULISAseq CNS panel. ROC analyses demonstrated NULISAseq-pTau217 accuracy in detecting amyloid (A) and tau (T) PET positivity. Differentially expressed proteins were identified using volcano plots.ResultsNULISAseq-pTau217 accurately classified A/T PET status with ROC AUCs of 0.92/0.86. pTau217 was upregulated in A+, T+, and impaired groups with log2-fold changes of 1.21, 0.57 and 4.63, respectively, compared to A-. Interestingly, pTDP43-409 was also upregulated in the impaired group and correlated with declining hippocampal volume and cognitive trajectories.DiscussionThis study shows the potential of a targeted proteomics panel for characterizing brain changes pertinent to ADRD. The promising pTDP43-409 findings require further replication.
| S-EPMC11623751 | biostudies-literature
Project description:BackgroundAccurate estimates of gestational age (GA) at birth are important for preterm birth surveillance but can be challenging to obtain in low income countries. Our objective was to develop machine learning models to accurately estimate GA shortly after birth using clinical and metabolomic data.MethodsWe derived three GA estimation models using ELASTIC NET multivariable linear regression using metabolomic markers from heel-prick blood samples and clinical data from a retrospective cohort of newborns from Ontario, Canada. We conducted internal model validation in an independent cohort of Ontario newborns, and external validation in heel prick and cord blood sample data collected from newborns from prospective birth cohorts in Lusaka, Zambia and Matlab, Bangladesh. Model performance was measured by comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound.ResultsSamples were collected from 311 newborns from Zambia and 1176 from Bangladesh. The best-performing model accurately estimated GA within about 6 days of ultrasound estimates in both cohorts when applied to heel prick data (MAE 0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for Bangladesh), and within about 7 days when applied to cord blood data (1.02 weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).ConclusionsAlgorithms developed in Canada provided accurate estimates of GA when applied to external cohorts from Zambia and Bangladesh. Model performance was superior in heel prick data as compared to cord blood data.
| S-EPMC9987787 | biostudies-literature
Project description:BackgroundDecisions about when to start an antiretroviral therapy (ART) are normally based on CD4 cell counts and viral load (VL). However, these measurements require equipment beyond the capacity of most laboratories in low-income and middle-income settings. Thus, there is an urgent need to identify and test simple markers to guide the optimal time for starting and for monitoring the effect of ART in developing countries.Objectives(1) To evaluate anthropometric measurements and measurement of plasma-soluble form of the urokinase plasminogen activator receptor (suPAR) levels as potential risk factors for early mortality among HIV-infected patients; (2) to assess whether these markers could help identify patients to whom ART should be prioritised and (3) to determine if these markers may add information to CD4 cell count when VL is not available.DesignAn observational study.SettingThe largest ART centre in Bissau, Guinea-Bissau.Participants1083 ART-naïve HIV-infected patients.Outcome measuresAssociations between baseline anthropometric measurements, CD4 cell counts, plasma suPAR levels and survival were examined using Cox proportional hazards models.ResultsLow body mass index (BMI≤18.5 kg/m(2)), low mid-upper-arm-circumference (MUAC≤250 mm), low CD4 cell count (≤350 cells/μl) and high suPAR plasma levels (>5.3 ng/ml) were independent predictors of death. Furthermore, mortality among patients with low CD4 cell count, low MUAC or low BMI was concentrated in the highest suPAR quartile.ConclusionsIrrespective of ART initiation and baseline CD4 count, MUAC and suPAR plasma levels were independent predictors of early mortality in this urban cohort. These markers could be useful in identifying patients at the highest risk of short-term mortality and may aid triage for ART when CD4 cell count is not available or when there is shortness of antiretroviral drugs.
| S-EPMC3532999 | biostudies-literature
Project description:BackgroundConsumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized.ObjectiveWe aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk.MethodsWe introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events.ResultsWe found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers.ConclusionsHigh-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.
| S-EPMC9377462 | biostudies-literature