Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

Beck, Dani; Lange, Ann-Marie G. de; Alnæs, Dag; Maximov, Ivan I.; Pedersen, Mads Lund; Leinhard, Olof Dahlqvist; Linge, Jennifer; Simon, Rozalyn; Richard, Geneviève; Ulrichsen, Kristine M.; Dørum, Erlend S.; Kolskår, Knut K.; Sanders, Anne-Marthe; Winterton, Adriano; Gurholt, Tiril P.; Kaufmann, Tobias; Steen, Nils Eiel; Nordvik, Jan Egil; Andreassen, Ole A.

doi:10.1016/j.nicl.2022.102949

Cited by 31 publications

(41 citation statements)

References 96 publications

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“…We regard the normal distribution of the age prediction errors of the ANN as consistent with the idea that both brain age and the "brain age gap" can be regarded as biomarkers (Franke and Gaser, 2019). Some lifestyle and biographical factors such as alcohol consumption (McEvoy et al, 2018), body composition (Beck et al, 2022), or previous childbirths (Voldsbekk et al, 2021) are associated with white matter alterations. If a detailed description of these factors is available in a given study population, this information could be included in the model and corrections for factors other than age could be possible.…”

Section: Artificial Neural Network Explained About 50% Of the Subject...supporting

confidence: 57%

“…Some lifestyle and biographical factors such as alcohol consumption ( McEvoy et al, 2018 ), body composition ( Beck et al, 2022 ), or previous childbirths ( Voldsbekk et al, 2021 ) are associated with white matter alterations. If a detailed description of these factors is available in a given study population, this information could be included in the model and corrections for factors other than age could be possible.…”

Section: Discussionmentioning

confidence: 99%

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Artificial neural networks for non-linear age correction of diffusion metrics in the brain

Kocar

Behler

Leinert

et al. 2022

Front. Aging Neurosci.

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Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [−0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

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Section: Artificial Neural Network Explained About 50% Of the Subject...supporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Artificial neural networks for non-linear age correction of diffusion metrics in the brain

Kocar

Behler

Leinert

et al. 2022

Front. Aging Neurosci.

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“…Considerable evidence implies that excessive weight accelerates normal aging 11,12 , a process that is also manifested in brain aging 38 . In the current study, we examined for the first time, as far as we can say, whether a lifestyle dietary intervention may attenuate the effect of obesity on the brain aging trajectory.…”

Section: Discussionmentioning

confidence: 99%

The effect of 18 months lifestyle intervention on brain age assessed with resting-state functional connectivity

Levakov

Kaplan

Meir

et al. 2022

Preprint

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Obesity negatively impacts multiple bodily systems, including the central nervous system. Retrospective studies that estimated chronological age from neuroimaging have found accelerated brain aging in obesity, but it is unclear how this estimation would be affected by lifestyle intervention. In a sub-study of 102 participants of the DIRECT-PLUS (dietary-intervention-randomized-controlled-trial polyphenol-unprocessed) trial, we tested the effect of 18 months of lifestyle intervention on predicted brain age, based on MRI-assessed resting-state functional connectivity (RSFC). We further examined how dynamics in multiple health factors, including anthropometric measurements, blood biomarkers, and fat deposition, can account for changes in brain age. To establish our method, we first demonstrated that our model could successfully predict chronological age from RSFC in three cohorts (n=291;358;102). We then found that among the DIRECT-PLUS participants, 1% of body weight loss resulted in an 8.9 months attenuation of brain age. Attenuation of brain age was significantly associated with improved liver biomarkers, decreased liver fat, and visceral and deep subcutaneous adipose tissues after 18m of intervention. Finally, we showed that lower consumption of processed food, sweets, and beverages were associated with attenuated brain age. These results suggest that lifestyle intervention has beneficial effects on the trajectory of brain aging.

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“…Brain aging is associated with a natural ‘wear and tear’, but is highly influenced by environmental factors throughout life. Although linked to genetics (Kaufmann et al, 2019; Cole et al, 2017) and birth weight (Vidal-Pineiro et al, 2021), brain structure and related brain age is associated with a range of proxies for general and cardiometabolic health (Montine et al, 2019; Beck et al, 2022a; Beck et al, 2022b) and life-style choices such as unhealthy diet (Onaolapo et al, 2019), lack of physical exercise (Stillmann et al, 2020; Sanders et al, 2021) and heavy alcohol use (Sullivan & Pfefferbaum, 2019). In line with our current findings, a younger appearing brain is not only associated with lower risk of cognitive deficits, but also increases resilience to consequences after injury, such as a stroke (Montine et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Longitudinal brain age prediction and cognitive function after stroke

Aamodt

Alnæs

Lange

et al. 2022

Preprint

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BackgroundAdvanced age and poor brain health have been associated with higher risk for more severe clinical and cognitive outcomes following stroke, but more accurate models for clinical prediction are needed. Machine learning based on brain scans can be used to estimate brain age of individual patients, and the corresponding difference from chronological age, the brain age gap (BAG) has been investigated in a range of clinical conditions. Yet, the predictive value for post stroke NCD has not been established. To this end, using longitudinal data we aimed to investigate the association between BAG and post-stroke neurocognitive disorder (NCD) over time.Methods269 stroke survivors (55.4% women, mean (SD) age = 71 (11) years) were included from the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study. MRI and clinical data were collected shortly after the acute stroke (neuropsychological data first after 3 months) and at 18- and 36 months follow-up. The brain age model was trained in an independent sample using established protocols based on machine learning and brain structural features from Freesurfer and applied to the current dataset. We used linear mixed effects (LME) models and survival analysis to assess the associations between cognitive status and longitudinal brain age.ResultsLMEs revealed a main effect of BAG on post stroke NCD across time, confirming our main hypothesis that a younger appearing brain may protect against post stroke NCD. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post stroke NCD at 18 and 36 months.ConclusionA younger appearing brain is associated with lower risk of post stroke NCD up to 36 months after a stroke, even among those showing no evidence of impairments 3 months after hospital admission. Brain age prediction based on brain scans provides a reliable assessment of brain structure and is sensitive to post stroke NCD with predictive value for cognitive decline among patients with no impairment at the initial assessment.

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Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

Cited by 31 publications

References 96 publications

Artificial neural networks for non-linear age correction of diffusion metrics in the brain

Artificial neural networks for non-linear age correction of diffusion metrics in the brain

The effect of 18 months lifestyle intervention on brain age assessed with resting-state functional connectivity

Longitudinal brain age prediction and cognitive function after stroke

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