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Higher adolescent BMI linked to faster biological aging decades later (opens in a new tab)

medicalxpress.com · 2026-09-21

Short answerEvidenceSource

Short answer

Mixed

Mixed.

One claim goes further than the study. One other point was not covered by the paper.

  • 3 supported
  • 1 overstated
  • 1 not covered

Checked against the study summary. The full text wasn't available, so some details couldn't be settled either way.

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1
2

NewsLink checks it

Mixed

One claim overstates the study. Three of five check out. One claim the study doesn't address.

  • 3 supported
  • 1 overstated
  • 1 not covered
Open claim evidence
3
Then inspect each claim

Evidence layer

Claim by claim

Each claim gets a verdict. Expand it to see the evidence directly below.

5 claims in this story

Showing all 5 claimsChoose a verdict to focus the list.

Claim 1 of 5OverstatedThe article says adolescents with both a genetic predisposition to high BMI and a high BMI may experience accelerated biological aging, which could increase their risk of obesity-related diseases later in life and premature death.View evidenceHide evidence

Why this verdict

The part about adolescents with genetic susceptibility and higher adolescent BMI showing accelerated epigenetic aging is supported by the profile. However, the claim extends the finding to later obesity-related diseases and premature death, outcomes that the supplied paper profile does not report as measured or tested. Even though the story hedges with 'could,' this extrapolates beyond the abstract-level evidence.

1

Study evidence

Adolescent BMI level partially mediated the association between higher BMI polygenic risk and accelerated epigenetic aging from late adolescence to middle adulthood.

“Participants were from the Young Finns Study (n = 3 596, ages 3–18 at baseline), followed from 1980 to 2018–2020.”
2

Study evidence

MR provided evidence of a positive causal effect of genetically predicted adolescent BMI on later-life biological aging, with DunedinPACE showing consistent causal estimates in 2011 and 2018.DunedinPACE 2011: 0.020 (95% CI 0.008 to 0.031); DunedinPACE 2018: 0.019 (95% CI 0.003 to 0.035)

“The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data.”
Claim 2 of 5Not coveredBiological aging was measured using DNA methylation-based epigenetic clocks and computational models developed using machine learning methods in participants from the Cardiovascular Risk in Young Finns Study, followed for approximately 40 years.View evidenceHide evidence

As statedapproximately 40 years

Why this verdict

The profile supports that biological aging was measured with DNA methylation-based epigenetic clocks, including DunedinPACE and PC-GrimAge, in the Young Finns Study followed from 1980 to 2018–2020, roughly 40 years. However, the supplied abstract-level profile does not verify the story's additional statement that the computational models were developed using machine-learning methods. That subclaim may require full-text or external model-development details.

1

Study evidence

Adolescent BMI level partially mediated the association between higher BMI polygenic risk and accelerated epigenetic aging from late adolescence to middle adulthood.

“Participants were from the Young Finns Study (n = 3 596, ages 3–18 at baseline), followed from 1980 to 2018–2020.”
2

Study evidence

MR provided evidence of a positive causal effect of genetically predicted adolescent BMI on later-life biological aging, with DunedinPACE showing consistent causal estimates in 2011 and 2018.DunedinPACE 2011: 0.020 (95% CI 0.008 to 0.031); DunedinPACE 2018: 0.019 (95% CI 0.003 to 0.035)

“The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data.”
Claim 3 of 5SupportedA study by the Faculty of Sport and Health Sciences at the University of Jyväskylä found that a high adolescent body mass index may be linked to accelerated biological aging in adulthood.View evidenceHide evidence

Why this verdict

The abstract-level profile supports a hedged associational headline that higher adolescent BMI may be linked to accelerated biological aging. The paper reports that higher adolescent BMI level partly mediated the association between BMI genetic liability and accelerated epigenetic aging, and MR supported a positive effect of genetically predicted adolescent BMI on biological aging. The headline is hedged and does not materially outrun the supplied abstract evidence.

1

Study evidence

Adolescent BMI level partially mediated the association between higher BMI polygenic risk and accelerated epigenetic aging from late adolescence to middle adulthood.

“Participants were from the Young Finns Study (n = 3 596, ages 3–18 at baseline), followed from 1980 to 2018–2020.”
2

Study evidence

MR provided evidence of a positive causal effect of genetically predicted adolescent BMI on later-life biological aging, with DunedinPACE showing consistent causal estimates in 2011 and 2018.DunedinPACE 2011: 0.020 (95% CI 0.008 to 0.031); DunedinPACE 2018: 0.019 (95% CI 0.003 to 0.035)

“The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data.”
Claim 4 of 5SupportedThe researchers used Mendelian randomization to investigate whether adolescent BMI has a causal effect on biological aging, and the results indicate that higher adolescent BMI may be causally associated with accelerated biological aging.View evidenceHide evidence

Why this verdict

The profile explicitly states that the authors used individual-level Mendelian randomisation to examine the causal effect of genetically predicted adolescent BMI on biological aging in adulthood, and that MR supported a positive causal effect, especially for DunedinPACE in 2011 and 2018. The story is hedged with 'may,' which is appropriate, though the precise paper framing is genetically predicted adolescent BMI and abstract-level details on MR assumptions and sensitivity tests are not available.

1

Study evidence

MR provided evidence of a positive causal effect of genetically predicted adolescent BMI on later-life biological aging, with DunedinPACE showing consistent causal estimates in 2011 and 2018.DunedinPACE 2011: 0.020 (95% CI 0.008 to 0.031); DunedinPACE 2018: 0.019 (95% CI 0.003 to 0.035)

“The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data.”
Claim 5 of 5SupportedThe researchers assessed participants' biological age and examined whether adolescent BMI was associated with the rate of biological aging.View evidenceHide evidence

Why this verdict

The profile states that biological aging was assessed using DNA methylation epigenetic clocks and that adolescent BMI was modelled from repeated measures at ages 9, 12, 15, and 18, then used in path/mediation analyses and MR examining links to biological aging. Although the paper focuses on adolescent BMI level/trajectory and genetic liability rather than a simple 'rate' analysis alone, the story's broad description is consistent with the abstract.

1

Study evidence

Adolescent BMI level partially mediated the association between higher BMI polygenic risk and accelerated epigenetic aging from late adolescence to middle adulthood.

“Participants were from the Young Finns Study (n = 3 596, ages 3–18 at baseline), followed from 1980 to 2018–2020.”
2

Study evidence

Latent growth curve modelling of BMI at ages 9, 12, 15 and 18 produced trajectory factors (level/intercept and change/slope) that were used in subsequent analyses linking genetic liability for higher BMI to accelerated epigenetic aging.

“BMI trajectories were modelled from BMI measured at ages 9, 12, 15 and 18 using latent growth curve modelling.”
Then look for missing context

Context layer

What the story left out

Important study details the story did not include.

  • Adolescent BMI trajectory modelling: BMI at ages 9, 12, 15, and 18 was modelled using latent growth curve modelling to derive BMI level and slope/trajectory factors.

    The story broadly says adolescent BMI was examined, but the supplied presentation does not mention the latent growth curve modelling approach or the repeated adolescent BMI measurement ages. This is a material methods detail for interpreting what 'adolescent BMI' represents in the analyses.

    From latent growth curve modelling

  • Attrition/sample limitation: methylation data were available for a smaller sample than the baseline cohort, indicating attrition or reduced analytic sample size.

    The profile notes methylation n=2,045 versus baseline n=3,596. The story mentions long follow-up but not the smaller methylation sample or potential attrition, which is a material limitation for longitudinal interpretation.

    From latent growth curve + path analysis + MR

  • Disease and premature-death outcomes were not reported as directly measured outcomes in the supplied paper profile.

    The story frames accelerated biological aging as potentially increasing obesity-related disease and premature death risk. The supplied profile reports epigenetic aging outcomes, not incident obesity-related diseases or mortality, so this implication is not directly reflected in the paper evidence at abstract depth.

    From latent growth curve + path analysis + MR; individual-level Mendelian randomisation

4 things the story did carry across
  • Core finding: higher adolescent BMI level partly mediated the association between higher BMI polygenic risk and accelerated epigenetic biological aging from late adolescence to middle adulthood.
  • Causal-inference component: individual-level Mendelian randomisation tested genetically predicted adolescent BMI and supported a positive causal effect on biological aging, with more consistent reported estimates for DunedinPACE.
  • Biological aging measures: DNA methylation-based epigenetic clocks DunedinPACE and PC-GrimAge were measured at follow-ups in the Young Finns Study.
  • Caveat about adult BMI continuity: adolescent BMI associations may partly reflect persistence of high BMI into adulthood.
Then read the study layer

Study layer

Study at a glance

Scan the study first. Expand only the parts you want to inspect.

Pieces of work

3

Evidence read

study summary

Lead result

secondary data

1Lead resultsecondary dataTest whether genetic liability to higher BMI is associated with accelerated biological aging across the life course, and whether adolescent BMI level/trajectory mediates that association.latent growth curve + path analysis + MRExpand

In plain English

In the Young Finns Study (n=3,596 at baseline), authors tested whether genetic liability to higher BMI (PRS) is associated with accelerated epigenetic aging and whether adolescent BMI level/trajectory mediates this association. BMI at ages 9, 12, 15, and 18 was modelled with latent growth curves; path analysis examined mediation to DNA methylation–based aging measures (DunedinPACE, PC‑GrimAge) measured at up to three follow-ups (ages ~15–56, methylation n=2,045). Mendelian randomisation using individual-level data provided additional causal inference.

Key findings

  • Adolescent BMI level partially mediated the association between higher BMI polygenic risk and accelerated epigenetic aging from late adolescence to middle adulthood.
  • Mendelian randomisation analyses supported a positive causal effect of genetically predicted adolescent BMI on biological aging.DunedinPACE causal estimate = 0.020 (95% CI 0.008, 0.031) in 2011; 0.019 (95% CI 0.003, 0.035) in 2018
“Participants were from the Young Finns Study (n = 3 596, ages 3–18 at baseline), followed from 1980 to 2018–2020.”
What this piece can’t prove
  • Methylation sample was smaller than baseline cohort (methylation n=2,045 vs baseline n=3,596), indicating attrition; abstract does not detail its potential impact.
  • Abstract does not report covariate adjustment sets, PRS construction/weights, or full statistical model coefficients for the mediation analysis.

1 further detail could not be confirmed from the summary.

2secondary dataEstimate adolescent BMI developmental change (trajectory) from repeated BMI measures in adolescence using latent growth curve modelling.latent growth curve modellingExpand

In plain English

Latent growth curve modelling (LGCM) was applied to repeated BMI measures at ages 9, 12, 15 and 18 to estimate adolescent BMI trajectory factors (level/intercept and change/slope), which were used as derived predictors/mediators in downstream path analyses and Mendelian randomisation examining associations with DNA methylation–based biological aging.

Key findings

  • Latent growth curve modelling of BMI at ages 9, 12, 15 and 18 produced trajectory factors (level/intercept and change/slope) that were used in subsequent analyses linking genetic liability for higher BMI to accelerated epigenetic aging.
“BMI trajectories were modelled from BMI measured at ages 9, 12, 15 and 18 using latent growth curve modelling.”
What this piece can’t prove

4 further details could not be confirmed from the summary.

3secondary dataAssess a causal effect of genetically predicted adolescent BMI on later-life biological aging using individual-level Mendelian randomisation (MR).individual-level Mendelian randomisationExpand

In plain English

Individual-level Mendelian randomisation (MR) using genetic instruments for adiposity (polygenic risk scores/variants) was used to test whether genetically predicted adolescent BMI causally affects later-life biological aging as measured by DNA-methylation epigenetic clocks (DunedinPACE, PC-GrimAge) in the Young Finns Study cohort.

Key findings

  • MR provided evidence of a positive causal effect of genetically predicted adolescent BMI on later-life biological aging, with DunedinPACE showing consistent causal estimates in 2011 and 2018.DunedinPACE 2011: 0.020 (95% CI 0.008 to 0.031); DunedinPACE 2018: 0.019 (95% CI 0.003 to 0.035)
“The causal effect of genetically predicted adolescent BMI on biological aging in adulthood was examined with Mendelian randomisation (MR) using individual-level data.”
What this piece can’t prove

3 further details could not be confirmed from the summary.

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Papers considered

The selected paper, plus nearby candidates.

Crossref, PubMed, Europe PMC · 16 candidate papers

Candidate

Childhood and adolescence body mass index associates with impaired reproductive function - a prospective, population-based cohort study

2018 · Crossref

Source (opens in a new tab)
Candidate

The Development of Body Mass Index from Adolescence to Adulthood: A Genotype-Family Socioeconomic Status Interaction Study

2025 · Crossref

Source (opens in a new tab)

And 10 more candidates considered.