Biology subjects

Santos Ferreira, D. L.

Publications and source records attributed to Santos Ferreira, D. L..

3 recordsLinked to original sources

bioRxiv2019

The effect of pre-analytical conditions on blood metabolomics in epidemiological studies

BackgroundSerum and plasma are commonly used biofluids for large-scale metabolomic-epidemiology studies. Their metabolomic profile is susceptible to changes due to variability in pre-analytical conditions and the impact of this is unclear.\n\nMethodsParticipant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (N=151 traits). Metabolic concentrations were compared between reference (pre-storage: 4{degrees}C, 1.5h; post-storage: no sample preparation or NMR-analysis delays) and four, pre-storage, blood processing conditions, where samples were incubated at (i) 4{degrees}C, 24h; (ii) 4{degrees}C, 48h; (iii) 21{degrees}C, 24h; (iv) 21{degrees}C, 48h, before centrifugation; and two, post-storage, sample processing conditions in which samples (i) thawed overnight, then left for 24h before addition of sodium buffer followed by immediate NMR analysis; (ii) thawed overnight, addition of sodium buffer, then left for 24h before profiling. Linear regression models with random-intercepts were used to assess the impact of these six pre-analytical conditions on EDTA-plasma/serum metabolome.\n\nResultsFatty acids, beta-hydroxybutyrate, glycoprotein-acetyls and most lipid-related traits, in serum and plasma, were robust to the tested pre and post-storage conditions. Pre-storage conditions impacted concentrations of glycolysis metabolites, acetate, albumin and amino-acids by levels that could potentially bias research results (up to 1.4SD difference compared with reference). Post-storage conditions affected histidine, phenylalanine and LDL-particle-size, with differences up to 1.4SD.\n\nConclusionsMost metabolic traits are robust to the pre- and post-storage conditions tested here and that may commonly occur in large-scale cohorts. However, concentrations of glycolysis metabolites, and amino-acids may be compromised.\n\nKey messagesO_LIIn large scale epidemiological studies, blood processing delays, incubation at high temperature prior to long term storage, and NMR profiling delays after long term storage, may occur.\nC_LIO_LIConcentrations of fatty acids, beta-hydroxybutyrate, glycoprotein acetyls and most lipid-related traits are robust to variations in pre-storage temperature and duration of incubation (4{degrees}C or 21{degrees}C for up to 48h prior to centrifugation) and post-storage sample handling (24h delay in sample preparation or NMR profiling).\nC_LIO_LIGlycolytic metabolite concentrations are altered by pre-storage conditions and amino-acids, particularly histidine and phenylalanine, by both, pre and post-storage conditions.\nC_LI

epidemiology↗

bioRxiv2017

Metabolic characterization of menopause: cross-sectional and longitudinal evidence

BackgroundIt remains elusive whether the changes in cardiometabolic biomarkers during the menopausal transition are due to ovarian aging or chronological aging. Well-conducted longitudinal studies are required to determine this. The aim of this study was to explore the cross-sectional and longitudinal associations of reproductive status defined according to the 2012 Stages of Reproductive Aging Workshop criteria with 74 metabolic biomarkers, and establish whether any associations are independent of age related changes.\n\nMethodsWe determined cross-sectional associations of reproductive status with metabolic profiling in 3,312 UK midlife women. In a subgroup of 1,492 women who had repeat assessments after 2.5 years, we assessed how change in reproductive status was associated with the changes in metabolic biomarkers. Metabolic profiles were measured by high-throughput quantitative serum NMR metabolomics. In longitudinal analyses, we compared the change in metabolic biomarkers for each reproductive status category change to that in the reference of being pre-menopausal at both time points. As all women aged by a similar amount during follow-up, these analyses contribute to distinguish age related changes from those related to change in reproductive status.\n\nResultsConsistent cross-sectional and longitudinal associations of menopause with a wide range of metabolic biomarkers were observed, suggesting transition to menopause induces multiple metabolic changes independent of chronological aging. The metabolic changes included increased concentrations of very small VLDL, IDL and LDL subclasses, remnant and LDL cholesterol, and reduced LDL particle size, all towards an atherogenic lipoprotein profile. Increased inflammation was suggested via an inflammatory biomarker, glycoprotein acetyls, but not via C-reactive protein. Also, levels of glutamine and albumin were increased during the transition. Most of these metabolic changes seen at the time of becoming post-menopausal remained or became slightly stronger during the post-menopausal years.\n\nConclusionsTransition to post-menopause has effects on multiple circulating metabolic biomarkers, over and above the underlying age trajectory. The adverse changes in multiple apolipoprotein-B containing lipoprotein subclasses and increased inflammation may underlie womens increased cardiometabolic risk in post-menopausal years.\n\nAbbreviations

epidemiology↗

bioRxiv2017

Association of pre-pregnancy body mass index with future offspring metabolic profile: findings from three independent European birth cohorts

BackgroundA high proportion of women start pregnancy overweight/obese. According to the developmental overnutrition hypothesis, this could lead offspring to have metabolic disruption throughout their lives, and, thus perpetuate the obesity epidemic across generations. Concerns about this hypothesis are influencing antenatal care. However, it is unknown whether maternal pregnancy adiposity is associated with long-term risk of adverse metabolic profiles in offspring, and if so, whether this association is causal, via intrauterine mechanisms, or explained by shared familial (genetic, lifestyle, socioeconomic) characteristics. We aimed to determine if associations between maternal body mass index (BMI) with offspring systemic metabolite profile are causal via intrauterine mechanisms or familial factors.\n\nMethods and FindingsWe used one and two-stage individual participant data (IPD) metaanalysis, and a negative-control (paternal BMI) to examine the association between maternal prepregnancy BMI and offspring serum metabolome from three European birth cohorts (offspring age at metabolite assessment 16, 17 and 31 years). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics. Results from one-stage IPD meta-analysis (N=5327 to 5377 mother-father-offspring trios) showed that increasing maternal and paternal BMI was associated with an adverse cardio-metabolic profile in offspring. We observed strong positive associations with VLDL-lipoproteins, VLDL-C, VLDL-triglycerides, VLDL-diameter, branched/aromatic amino acids, glycoprotein acetyls, and triglycerides, and strong negative associations with HDL-lipoprotein, HDL-diameter, HDL-C, HDL2-C and HDL3-C (all P<0.003). Stronger magnitudes of associations were present for maternal compared with paternal BMI across these associations, however there was no strong statistical evidence for heterogeneity between them (all bootstrap P >0.003, equivalent to 0.05 after accounting for multiple testing). Results were similar in each individual cohort, and in the two-stage analysis. Offspring BMI showed similar patterns of crosssectional association with metabolic profiles as for parental pre-pregnancy BMI associations, but with greater magnitudes. Adjustment of the parent BMI-offspring metabolite associations for offspring BMI suggested the parental associations were largely due to the association of parental BMI measures with offspring BMI.\n\nConclusionOur findings suggest that maternal BMI-offspring metabolome associations are likely to be largely due to shared genetic or familial lifestyle confounding, rather than intrauterine programming mechanisms. They do not support the introduction of measures to reduce maternal BMI in order to prevent adverse offspring cardio-metabolic health.

epidemiology↗