Biological Age Predictors

Jylhävä, Juulia; Pedersen, Nancy L.; Hägg, Sara

doi:10.1016/j.ebiom.2017.03.046

Cited by 870 publications

(759 citation statements)

References 78 publications

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“…In parallel with the epigenetic clock (Horvath, 2013;Jones, Goodman, & Kobor, 2015;Jylhävä, Pedersen, & Hägg, 2017), EEG-estimated brain maturation might become a tool that address questions concerning developmental trajectories. In addition, we have shown that the prediction error is not random noise, but moderately stable over a period of about 1.5 years, and to a large extent influenced by the genetic background of the subject.…”

Section: Discussionmentioning

confidence: 99%

EEG‐based age‐prediction models as stable and heritable indicators of brain maturational level in children and adolescents

Vandenbosch

Ent

Boomsma

et al. 2019

Human Brain Mapping

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The human brain shows remarkable development of functional brain activity from childhood to adolescence. Here, we investigated whether electroencephalogram (EEG) recordings are suitable for predicting the age of children and adolescents. Moreover, we investigated whether overestimation or underestimation of age was stable over longer time periods, as stable prediction error can be interpreted as reflecting individual brain maturational level. Finally, we established whether the age‐prediction error was genetically determined. Then, 3 min eyes‐closed resting‐state EEG data from the longitudinal EEG studies of Netherlands Twin Register (NTR; n = 836) and Washington University in St. Louis (n = 702) were used at ages 5, 7, 12, 14, 16, and 18. Longitudinal data were available within childhood (5–7 years) and adolescence (16–18 years). We calculated power in 1 Hz wide bins (1–24 Hz). Random forest (RF) regression and relevance vector machine with sixfold cross‐validation were applied. The best mean absolute prediction error was obtained with RF (1.22 years). Classification of childhood versus puberty/adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5–2.1 years (0.53 < r < 0.74) and signifcantly affected by genetic factors (heritability between 42 and 79%). Our results show that age prediction from low‐cost EEG recordings is comparable in accuracy to those obtained with magnetic resonance imaging. Children and adolescents showed stable overestimation or underestimation of their age, which means that some participants have stable brain activity patterns that reflect those of an older or younger age, and could therefore reflect individual brain maturational level. This prediction error is heritable, suggesting that genes underlie maturational level of functional brain activity. We propose that age prediction based on EEG recordings can be used for tracking neurodevelopment in typically developing children, in preterm children, and in children with neurodevelopmental disorders.

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Section: Discussionmentioning

confidence: 99%

EEG‐based age‐prediction models as stable and heritable indicators of brain maturational level in children and adolescents

Vandenbosch

Ent

Boomsma

et al. 2019

Human Brain Mapping

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“…Biological aging denotes the heterogeneity of different biomarkers, genomic predictors, epigenetic clocks and biological processes in individuals. While biological aging is seen in many chronic diseases, we have little understanding on how it could be utilized as a true biomarker …”

Section: Discussionmentioning

confidence: 99%

Infections after kidney transplantation. Does age matter?

Hemmersbach‐Miller

Alexander

Sudan

et al. 2019

Clinical Transplantation

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Infections threaten successful outcomes after kidney transplantation. Our aim was to determine if the number, types of infections and the risk factors for common infections differed between older compared to younger kidney transplant (KT) recipients in the first year after surgery. We performed a single‐center retrospective cohort study. Between 2011 and 2015, 91 KTs were performed in patients ≥65 years of age; these were matched 1:1 (by year of transplantation, sex and race) to controls aged 40‐60 years. Over 90% of both groups had an infectious complication. Urinary tract infections (UTIs) and cytomegalovirus (CMV) viremia were significantly more frequent in older recipients. Older adults had more late onset UTIs, including after stent removal. CMV viremia was more frequent in older adults in the 1‐6 months post‐transplant period. Due to our center‐specific protocol utilizing pre‐emptive monitoring in the CMV recipient‐seropositive population, the higher CMV incidence in the aged recipient was driven by this subpopulation of older adults. No difference in pneumonias or bloodstream infections were found, nor in surgical complications, rejection or graft loss. Mortality was higher at 1‐year post‐transplant in the older recipients (9.9% vs 1.1%; P = 0.018). Prophylactic and immunosuppressive strategies may need to be altered for older KT recipients.

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“…Based on information in the section above, developing a proxy measure of biological aging for humans still requires work but is a very dynamic and promising area of investigation with strong potential for translation. Some of the measures described-namely mitochondrial function, DNA methylation, and, to a lesser extent, cellular senescence and autophagy-are ready to be implemented based on several epidemiological studies, although refinements are always possible Choi et al, 2016;Cohen, Morissette-Thomas, Ferrucci, & Fried, 2016;Jylhävä, Pedersen, & Hägg, 2017;Jylhävä et al, 2014;Kananen et al, 2016;Kent & Fitzgerald, 2016;Kim & Jazwinski, 2015;Levine et al, 2018;Li et al, 2018;Marioni et al, 2019;Marttila et al, 2015;Putin et al, 2017;Sillanpää et al, 2018). Measures of telomere length are hampered by noise and wide longitudinal variations that cannot be explained by health events and at this stage are not useful for measuring biological age (Arai et al, 2015;Jodczyk, Fergusson, Horwood, Pearson, & Kennedy, 2014;Tomaska & Nosek, 2009).…”

Section: Connec Ting the B I Ology Of Ag Ing With Ag E-a Sso Ciatedmentioning

confidence: 99%

Measuring biological aging in humans: A quest

et al. 2019

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The global population of individuals over the age of 65 is growing at an unprecedented rate and is expected to reach 1.6 billion by 2050. Most older individuals are affected by multiple chronic diseases, leading to complex drug treatments and increased risk of physical and cognitive disability. Improving or preserving the health and quality of life of these individuals is challenging due to a lack of well‐established clinical guidelines. Physicians are often forced to engage in cycles of “trial and error” that are centered on palliative treatment of symptoms rather than the root cause, often resulting in dubious outcomes. Recently, geroscience challenged this view, proposing that the underlying biological mechanisms of aging are central to the global increase in susceptibility to disease and disability that occurs with aging. In fact, strong correlations have recently been revealed between health dimensions and phenotypes that are typical of aging, especially with autophagy, mitochondrial function, cellular senescence, and DNA methylation. Current research focuses on measuring the pace of aging to identify individuals who are “aging faster” to test and develop interventions that could prevent or delay the progression of multimorbidity and disability with aging. Understanding how the underlying biological mechanisms of aging connect to and impact longitudinal changes in health trajectories offers a unique opportunity to identify resilience mechanisms, their dynamic changes, and their impact on stress responses. Harnessing how to evoke and control resilience mechanisms in individuals with successful aging could lead to writing a new chapter in human medicine.

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Biological Age Predictors

Cited by 870 publications

References 78 publications

EEG‐based age‐prediction models as stable and heritable indicators of brain maturational level in children and adolescents

EEG‐based age‐prediction models as stable and heritable indicators of brain maturational level in children and adolescents

Infections after kidney transplantation. Does age matter?

Measuring biological aging in humans: A quest

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