Evidence map›Paper›PMID 42542102›Full record

ArticleEnvironment international2026

Exploring the exposome and unexplained variance in epigenetic ageing - Insights from a longitudinal twin study in adolescence and early adulthood.

Annika Opperbeck, Zhiyang Wang, Ilkka Rautiainen, Aino Heikkinen, Miina Ollikainen, Sylvain Sebert, Elina Sillanpää

Abstract read
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In one paragraph

Article in Environment international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Annika OpperbeckFaculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland. Electronic address: annika.a.opperbeck@jyu.fi.
Zhiyang WangPostdoctoral Research Station in Public Health and Preventive Medicine, School of Public Health, Xinjiang Medical University, Urumqi 830017, China; Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, P.O. Box 20, FI-00014 Helsinki, Finland. Electronic address: wangzhiyang@xjmu.edu.cn.
Ilkka RautiainenFaculty of Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland. Electronic address: ilkka.t.rautiainen@jyu.fi.
Aino HeikkinenInstitute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, P.O. Box 20, FI-00014 Helsinki, Finland; Minerva Foundation Institute for Medical Research, Biomedicum Helsinki 2U, Tukholmankatu 8, FI-00290 Helsinki, Finland. Electronic address: aino.heikkinen@helsinki.fi.
Miina OllikainenInstitute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, P.O. Box 20, FI-00014 Helsinki, Finland; Minerva Foundation Institute for Medical Research, Biomedicum Helsinki 2U, Tukholmankatu 8, FI-00290 Helsinki, Finland. Electronic address: miina.ollikainen@helsinki.fi.
Sylvain SebertResearch Unit of Population Health, Faculty of Medicine, University of Oulu, P.O. Box 5000, FI-90014, Finland. Electronic address: sylvain.sebert@oulu.fi.
Elina SillanpääFaculty of Sport and Health Sciences, Gerontology Research Center, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland; The Wellbeing Services County of Central Finland, Hoitajantie 3, FI-40620 Jyväskylä, Finland. Electronic address: elina.sillanpaa@jyu.fi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biological ageing begins before birth, with early-life exposures shaping late-life health. These exposures drive health inequities early, yet specific exposures and the composition of the ageing exposome remain largely undefined. This gap may persist as the field lacks agnostic investigations accounting for non-linearity, interactions and subtle signals. We aimed to identify exposures predictive of epigenetic ageing accumulated during childhood and adolescence and explore the composition of the "missing" exposome. In the FinnTwin12 cohort (847 participants measured at ages 12, 14, 17, and 22), 186 unique exposures (including lifestyle, green environments, air pollutants, and demographic factors) were analysed using exposome-wide association studies and data-driven ML models (Knockoff Boosted Tree, sNPLS and Boruta). Epigenetic age (blood DNA methylation at age 22) was estimated using PCGrimAge and DunedinPACE. Our exposure set explains ∼ 28% of the variance in epigenetic age (R

Indexed as

AgingEnvironmental ExposureEpigenesis, GeneticExposomeAdolescentChildDNA MethylationFemaleHumansLife StyleLongitudinal Studiesdata-driven MLEarly-life exposuresEnvironmental exposuresEpigenetic clocksFinnTwin12LifestyleTwin modelling

Identifiers

PMID42542102

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.