25 research outputs found

    Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations

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    Circulating plasma proteins play key roles in human health and can potentially be used to measure biological age, allowing risk prediction for age-related diseases, multimorbidity and mortality. Here we developed a proteomic age clock in the UK Biobank (n = 45,441) using a proteomic platform comprising 2,897 plasma proteins and explored its utility to predict major disease morbidity and mortality in diverse populations. We identified 204 proteins that accurately predict chronological age (Pearson r = 0.94) and found that proteomic aging was associated with the incidence of 18 major chronic diseases (including diseases of the heart, liver, kidney and lung, diabetes, neurodegeneration and cancer), as well as with multimorbidity and all-cause mortality risk. Proteomic aging was also associated with age-related measures of biological, physical and cognitive function, including telomere length, frailty index and reaction time. Proteins contributing most substantially to the proteomic age clock are involved in numerous biological functions, including extracellular matrix interactions, immune response and inflammation, hormone regulation and reproduction, neuronal structure and function and development and differentiation. In a validation study involving biobanks in China (n = 3,977) and Finland (n = 1,990), the proteomic age clock showed similar age prediction accuracy (Pearson r = 0.92 and r = 0.94, respectively) compared to its performance in the UK Biobank. Our results demonstrate that proteomic aging involves proteins spanning multiple functional categories and can be used to predict age-related functional status, multimorbidity and mortality risk across geographically and genetically diverse populations

    Recontacting biobank participants to collect lifestyle, behavioural and cognitive information via online questionnaires : lessons from a pilot study within FinnGen

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    OBJECTIVES: To recontact biobank participants and collect cognitive, behavioural and lifestyle information via a secure online platform. DESIGN: Biobank-based recontacting pilot study. SETTING: Three Finnish biobanks (Helsinki, Auria, Tampere) recruiting participants from February 2021 to July 2021. PARTICIPANTS: All eligible invitees were enrolled in FinnGen by their biobanks (Helsinki, Auria, Tampere), had available genetic data and were >18 years old. Individuals with severe neuropsychiatric disease or cognitive or physical disabilities were excluded. Lastly, 5995 participants were selected based on their polygenic score for cognitive abilities and invited to the study. Among invitees, 1115 had successfully participated and completed the study questionnaire(s). OUTCOME MEASURES: The primary outcome was the participation rate among study invitees. Secondary outcomes included questionnaire completion rate, quality of data collected and comparison of participation rate boosting strategies. RESULTS: The overall participation rate was 18.6% among all invitees and 23.1% among individuals aged 18-69. A second reminder letter yielded an additional 9.7% participation rate in those who did not respond to the first invitation. Recontacting participants via an online healthcare portal yielded lower participation than recontacting via physical letter. The completion rate of the questionnaire and cognitive tests was high (92% and 85%, respectively), and measurements were overall reliable among participants. For example, the correlation (r) between self-reported body mass index and that collected by the biobanks was 0.92. CONCLUSION: In summary, this pilot suggests that recontacting FinnGen participants with the goal to collect a wide range of cognitive, behavioural and lifestyle information without additional engagement results in a low participation rate, but with reliable data. We suggest that such information be collected at enrolment, if possible, rather than via post hoc recontacting.publishedVersionPeer reviewe

    Detection of long non-coding RNAs in human breastmilk extracellular vesicles: Implications for early child development

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    <p>Breastmilk has many documented beneficial effects on the developing human infant, but the components of breastmilk that influence these developmental pathways have not been fully elucidated. Increasing evidence suggests that non-coding RNAs encapsulated in extracellular vesicles (EVs) represent an important mechanism of communication between the mother and child. Long non-coding RNAs (lncRNAs) are of particular interest given their key role in gene expression and development. However, it is not known whether breastmilk EVs contain lncRNAs. We used qRT-PCR to determine whether EVs isolated from human breastmilk contain lncRNAs previously reported to be important for developmental processes. We detected 55 of the 87 screened lncRNAs in EVs from the 30 analyzed breastmilk samples, and CRNDE, DANCR, GAS5, SRA1 and ZFAS1 were detected in >90% of the samples. GAS5, SNHG8 and ZFAS1 levels were highly correlated (Spearman's rho > 0.9; <i>P</i> < 0.0001), which may indicate that the loading of these lncRNAs into breastmilk EVs is regulated by the same pathways. The detected lncRNAs are important epigenetic regulators involved in processes such as immune cell regulation and metabolism. They may target a repertoire of recipient cells in offspring and could be essential for child development and health. Further experimental and epidemiological studies are warranted to determine the impact of breastmilk EV-encapsulated lnRNAs in mother to child signaling.</p

    Second trimester extracellular microRNAs in maternal blood and fetal growth: An exploratory study

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    Healthy feto-maternal communication is critical during pregnancy and is orchestrated by the placenta. Dysfunction of the placenta leads to fetal growth complications; however, the underlying biological mechanisms have yet to be fully elucidated. Circulating extracellular microRNAs (exmiRNAs) in the blood have been implicated in cell-to-cell communication. Therefore, exmiRNAs may provide useful biological information about communication between the mother, the fetus, and the placenta during pregnancy. We used logistic regression to determine the association of exmiRNAs with abnormal fetal growth by comparing mothers of infants classified as small-for-gestational age (SGA) (n = 36) and large-for-gestational age (LGA) (n = 13) to appropriate-for-gestational age (AGA), matched by gestational age at delivery and infant sex. In addition, we used linear regression to determine associations between exmiRNAs and birth weight-for-gestational age (BWGA) z-score (n = 100), adjusting for maternal age, body mass index, and parity. We found that higher levels of miR-20b-5p, miR-942-5p, miR-324-3p, miR-223-5p, and miR-127-3p in maternal serum were associated with lower odds for having a SGA vs. AGA infant, and higher levels of miR-661, miR-212-3p, and miR-197-3p were associated with higher odds for having a LGA vs. AGA infant. We also found associations between miR-483-5p, miR-10a-5p, miR-204-5p, miR-202-3p, miR-345-5p, miR-885-5p, miR-127-3p, miR-148b-3p, miR-324-3p, miR-1290, miR-597-5p, miR-139-5p, miR-215-5p, and miR-99b-5p and BWGA z-score. We also found sex-specific associations with exmiRNAs and fetal growth. Our findings suggest that exmiRNAs circulating in maternal blood at second trimester are associated with fetal growth. Validation of our findings may lead to the development of minimally-invasive biomarkers of fetal growth during pregnancy
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