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Schuurmans, I. K.

Publications and source records attributed to Schuurmans, I. K..

3 recordsLinked to original sources

Epigenetic and brain age across development: Performance and associations in the MIND consortium

Understanding how biological age measures perform across development lays the groundwork for investigations into lifespan trajectories of healthy aging. We provide the most comprehensive assessment of epigenetic and brain age models across development (birth to 24 years; [≤]20,917 observations across 15 cohorts), evaluating how these models associate with chronological age and with each other, and how these associations change across development. Chronological age-prediction accuracy of epigenetic and brain age models was modest and varied substantially. Accuracy improved with age and stabilized by middle childhood. Few brain and fewer epigenetic clocks performed stably and well across all developmental stages. Performance was better when age range and tissue corresponded between training and testing data. Associations between epigenetic-brain age residuals were small, and changed little across development, tissues or clock generation. Given this developmentally dynamic system of epigenetic-brain age performances and associations, we give key recommendations to improve developmental research in this field.

neuroscience↗

Development of a microRNA clock for gestational age in the general population

IntroductionDNA methylation (DNAm) has shown promise as a biological marker of gestational age (GA). Here, we aimed to characterize how plasma circulating miRNAs - another epigenetic mechanism regulating gene expression - associate with GA at birth, and to construct a miRNA-based GA clock (miRClock-GA). MethodsWe leveraged 2083 umbilical cord plasma-derived circulating miRNAs from Generation R (N=1695). First, we performed linear regressions to identify miRNome-wide significant miRNAs associated with GA. Second, we applied elastic net regression to construct miRClock-GA. These steps were validated in Gen3G (N=213). Finally, we computed age acceleration (miRClock-AA) and evaluated association of miRClock-GA and miRClock-AA with child developmental outcomes up to 17 years of age, including comparisons with DNAmClocks. FindingsWe identified 123 miRNAs associated with GA, with miR-150-5p showing the strongest positive association ( B=0.244, SE=0.036, P=2.3e-11) and miR-373-3p the strongest negative association ( B=-0.255, SE=0.065, P=8.6e-5). MiRClock-GA correlated consistently with GA in Generation R train (rrange=0.62-0.72) and test sets (rrange=0.45-0.52) and in the independent validation cohort (rGen3G =0.33). Correlations between miRClock-GA and DNAmClocks were weak to moderate (rrange=0.28-0.42). MiRClock-AA explained significant variance in birthweight and childhood BMI beyond clinical GA. ConclusionsThis study reveals widespread associations between circulating miRNAs and GA, supports miRClock-GA as a consistent, well-performing biological marker of GA, with miRClock-AA predicting birthweight and childhood BMI beyond GA itself. Our findings provide a broader perspective on the potential utility of miRNAs as early markers of development. FundingE.U. Horizon Europe Research and Innovation Programme (FAMILY,No.101057529); European Research Council (TEMPO,No.101039672). Full funding in Acknowledgements. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSEpigenetic clocks have emerged as powerful tools for assessing individual differences in biological ageing. Existing epigenetic clocks are typically constructed using DNA methylation (DNAm) data and are developed to estimate whether an individuals epigenetic age deviates relative to their chronological age. In adults, higher predicted biological epigenetic age than chronological age (accelerated epigenetic ageing) is an established indicator of mortality risk and other age-related morbidities. MicroRNAs are another epigenetic mechanism associated with chronological age and age-related phenotypes across species such as C. elegans, mice, primates and humans. Recently, a plasma-derived, cell-free circulating miRNA clock was developed in a sample of older European adults, with accelerated miRNA age predicting biological age-related conditions such as frailty and multi-system blood biomarkers. However, miRNA-based estimations of biological gestational age at birth have not been previously done and their relationship to child health outcomes are unknown. Added value of this studyTo gain a broader perspective on the role of epigenetic markers in biological gestational age at birth and the potential utility of miRNAs as early markers of (altered) development, we leveraged cord blood plasma circulating miRNA data collected at birth in a large population-based cohort. First, we identified 123 miRNAs associated with gestational age. Second, we used this information to construct a miRNA-based epigenetic clock for gestational age (miRClock-GA) and validated this clock in an independent cohort. MiRClock-GA showed moderate correlations with both gestational age and existing DNAm-based gestational clocks. Third, we found that miRClock-GA correlated cross-sectionally with birthweight, as well as prospectively with several adaptive, behavioural, cognitive, and growth outcomes measured up to age 17y, after taking into account maternal influences such as maternal education level, smoking, pre-pregnancy BMI, and age. Accelerated ageing of miRClock-GA (miRClock-AA) explained variance in birthweight and childhood BMI beyond clinical GA, after accounting for maternal influences. Implications of all available evidenceOur study provides the first large-scale evidence that circulating miRNAs in cord blood plasma show widespread associations with gestational age at birth and can be used to derive a robust biological marker of GA (MiRClock-GA), which explains a substantial proportion of the variation in clinical GA. MiRClock-AA provides information beyond GA in some child outcomes, such as BMI. These findings extend adult research, supporting the potential of miRNAs as promising markers for early risk assessment and health monitoring in a developmental context.

genomics↗

The gut microbiome and child mental health: a population-based study

The link between the gut microbiome and the brain has gained increasing scientific and public interest for its potential to explain psychiatric risk. While differences in gut microbiome composition have been associated with several mental health problems, evidence to date has been largely based on animal models and human studies in small sample sizes. Here, we aimed to systematically characterize associations of the gut microbiome with overall psychiatric symptoms as well as with specific domains of emotional and behavioral problems, assessed via the maternally rated Child Behavior Checklist in 1,784 ten-year-old children from the multi-ethnic, population-based Generation R Study. While we observe lower gut microbiome diversity in case of more overall and specific mental health problems, associations were not significant. Likewise, we did not identify any taxonomic feature associated with mental health problems after multiple testing correction, although nominally significant findings indicated depletion of genera previously associated with psychiatric disorders, including Anaerotruncus, Anaeroplasma, and Hungatella. The identified compositional abundance differences were found to be similar across all mental health problems. Finally, we did not find any specific microbial functions that were enriched in relation to children with mental health problems. In conclusion, based on the largest sample examined to date, we found no clear evidence of associations between gut microbiome diversity, taxonomies or functions and mental health problems in the general pediatric population. In future, the use of longitudinal designs with repeated measurements microbiome and psychiatric outcomes will be critical to clarify any emerging associations between the gut microbiome and mental health from early life to adulthood.

microbiology↗