In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics ...may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39–82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
•Menopause, depression, and APOE ε4 all influence biological aging in females•Postmenopause and older age at menopause are linked to longer LTL and lower BAG•Surgical menopause and longer reproductive span are associated with longer LTL•Adding depression and APOE ε4 explained the most variance across proxies of aging•Results are potentially influenced by a healthy volunteer bias
•The study investigates the mediating role of BAG in hearing and cognition link.•Poorer hearing ability was related to the lower cognitive performance.•BAG partially mediated the relationship between ...ARHL and cognitive decline.
In recent years, the relationship between age-related hearing loss, cognitive decline, and the risk of dementia has garnered significant attention. The significant variability in brain health and aging among individuals of the same chronological age suggests that a measure assessing how one’s brain ages may better explain hearing-cognition links. The main aim of this study was to investigate the mediating role of Brain Age Gap (BAG) in the association between hearing impairment and cognitive function.
This research included 185 participants aged 20–79 years. BAG was estimated based on the difference between participant’s brain age (estimated based on their structural T1-weighted MRI scans) and chronological age. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA) test while hearing ability was measured using pure-tone thresholds (PTT) and words-in-noise (WIN) perception. Mediation analyses were used to examine the mediating role of BAG in the relationship between age-related hearing loss as well as difficulties in WIN perception and cognition.
Participants with poorer hearing sensitivity and WIN perception showed lower MoCA scores, but this was an indirect effect. Participants with poorer performance on PTT and WIN tests had larger BAG (accelerated brain aging), and this was associated with poorer performance on the MoCA test. Mediation analyses showed that BAG partially mediated the relationship between age-related hearing loss and cognitive decline.
This study enhances our understanding of the interplay among hearing loss, cognition, and BAG, emphasizing the potential value of incorporating brain age assessments in clinical evaluations to gain insights beyond chronological age, thus advancing strategies for preserving cognitive health in aging populations.
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•A global tectono-magmatic lull (TML) at 2365–2235 Ma is verified by new data.•There is a global decrease in number of zircon and large igneous province ages during the ...TML.•Paleomagnetic data imply the existence of plate tectonics during the TML.•Average minimal plate speed decreased during the TML.
There is a 87–86% drop in frequency in both detrital and igneous zircon U/Pb ages during the Tectono-Magmatic Lull (TML, 2365–2235 Ma) and a significant decrease in frequency of granitoids, but no recognized age gap. The TML is transferred in detrital zircon ages through all younger depositional time windows, indicating it is a globally robust feature. This seems to require a decrease in production rate of felsic magma during the TML, and possibly also recycling of crust of this age into the mantle. However, εHf zircon and εNd whole-rock felsic igneous data show that juvenile continental crust continued to be produced and that the ratio of juvenile/reworked crust remained about the same or increased relative to the ratio before and after the lull. During the TML there is a large decrease in the frequency of LIPs, with only four that fall in the 2365–2235 Ma time frame, and there is a global LIP age gap at 2340–2260 Ma. Komatiite frequency in greenstones decreases rapidly at 2600–2000 Ma and enrichment in incompatible elements in greenstone basalts at 2700–2400 Ma reflects increasing enriched components in mantle sources. Although 13 orogens, all with convergent margin characteristics, are recognized between 2400 and 2200 Ma, only four are known with major deformation in the TML. Paleomagnetic data indicate that the relative positions of the Superior, Kaapvaal and India cratons changed significantly between 2435 and 2175 Ma, implying the existence of plate tectonics during this time interval. Also, paleomagnetic positions from several cratons confirm that average minimal plate speeds are relatively low during the TML (<8 cm/yr) and that there are no fast plates (>10 cm/yr) between 2.35 and 2.25 Ga.
We propose a new testable model for the TML related to a mantle overturn event at 2.7 Ga that initiated widespread subduction. Sinking slabs covered the core-mantle boundary decreasing the rate of mantle convection so that oceanic lithosphere was consumed faster than it was produced. This led to a slow-down in plate speeds some 400 Myr later during the TML, with consequent decreases in magma production, mantle plume generation and orogenic activity.
•Accurate infant age predictions can be made using 20 min resting state EEG from a single channel.•The deep learning age prediction model generalises to two independent datasets from two different ...clinical sites.•The magnitude of the brain age gap differs between infant groups with different Bayley Scale outcomes.
Electroencephalography (EEG) can be used to estimate neonates’ biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates’ brain age gap due to their dependency on relatively large data and pre-processing requirements.
We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites.
In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04).
These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model’s brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes.
The magnitude of neonates’ brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value.
is a widely used index for quantifying individuals' brain health as deviation from a normative brain aging trajectory. Higher-than-expected
is thought partially to reflect above-average rate of brain ...aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank main and Lifebrain replication; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of
models were estimated in two different training datasets (n ≈ 38,000 main and 1800 individuals replication) based on brain structural features. The results showed no association between cross-sectional
and the rate of brain change measured longitudinally. Rather,
in adulthood was associated with the congenital factors of birth weight and polygenic scores of
assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.
Neurofilament light chain protein (NfL) is a marker of neuronal injury and neurodegeneration. Typically assessed in cerebrospinal fluid, recent advances have allowed this biomarker to be more easily ...measured in plasma. This study assesses plasma NfL in people with HIV (PWH) compared with people without HIV (PWoH), and its relationship with cognitive impairment, cardiovascular risk, and a neuroimaging metric of brain aging brain-age gap (BAG).
One hundred and four PWH (HIV RNA <50 copies/ml) and 42 PWoH provided blood samples and completed a cardiovascular risk score calculator, neuroimaging, and cognitive testing.
Plasma NfL was compared between PWoH and PWH and assessed for relationships with age, HIV clinical markers, cardiovascular disease risk, cognition, and BAG (difference between a brain-predicted age and chronological age).
Plasma NfL was not significantly different between PWoH and PWH. Higher NfL related to increasing age in both groups. Plasma NfL was not associated with typical HIV disease variables. Within PWH, NfL was higher with higher cardiovascular risk, cognitive impairment and a greater BAG.
Virally suppressed PWH who are cognitively normal likely do not have significant ongoing neurodegeneration, as evidenced by similar plasma NfL compared with PWoH. However, NfL may represent a biomarker of cognitive impairment and brain aging in PWH. Further research examining NfL with longitudinal cognitive decline is needed to understand this relationship more fully.
The incidence of kidney failure is known to increase with age. We have previously developed and validated the use of retinal age based on fundus images as a biomarker of aging. However, the ...association of retinal age with kidney failure is not clear. We investigated the association of retinal age gap (the difference between retinal age and chronological age) with future risk of kidney failure.
Prospective cohort study.
11,052 UK Biobank study participants without any reported disease for characterizing retinal age in a deep learning algorithm. 35,864 other participants with retinal images and no kidney failure were followed to assess the association between retinal age gap and the risk of kidney failure.
Retinal age gap, defined as the difference between model-based retinal age and chronological age.
Incident kidney failure.
A deep learning prediction model used to characterize retinal age based on retinal images and chronological age, and Cox proportional hazards regression models to investigate the association of retinal age gap with incident kidney failure.
After a median follow-up period of 11 (IQR, 10.89-11.14) years, 115 (0.32%) participants were diagnosed with incident kidney failure. Each 1-year greater retinal age gap at baseline was independently associated with a 10% increase in the risk of incident kidney failure (HR, 1.10 95% CI, 1.03-1.17; P=0.003). Participants with retinal age gaps in the fourth (highest) quartile had a significantly higher risk of incident kidney failure compared with those in the first quartile (HR, 2.77 95% CI, 1.29-5.93; P=0.009).
Limited generalizability related to the composition of participants in the UK Biobank study.
Retinal age gap was significantly associated with incident kidney failure and may be a promising noninvasive predictive biomarker for incident kidney failure.
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The retina, as part of the central nervous system (CNS), can be the perfect target for
in vivo, in situ
, and noninvasive neuropathology diagnosis and assessment of therapeutic efficacy. It has long ...been established that several age-related brain changes are more pronounced in Alzheimer's disease (AD). Nevertheless, in the retina such link is still under-explored. This study investigates the differences in the aging of the CNS through the retina of 3× Tg-AD and wild-type mice. A dedicated optical coherence tomograph imaged mice's retinas for 16 months. Two neural networks were developed to model independently each group's ages and were then applied to an independent set containing images from both groups. Our analysis shows a mean absolute error of 0.875±1.1 × 10
−2
and 1.112±1.4 × 10
−2
months, depending on training group. Our deep learning approach appears to be a reliable retinal OCT aging marker. We show that retina aging is distinct in the two classes: the presence of the three mutated human genes in the mouse genome has an impact on the aging of the retina. For mice over 4 months-old, transgenic mice consistently present a negative retina age-gap when compared to wild-type mice, regardless of training set. This appears to contradict AD observations in the brain. However, the ‘black-box” nature of deep-learning implies that one cannot infer reasoning. We can only speculate that some healthy age-dependent neural adaptations may be altered in transgenic animals.
Immigrants to Canada increasingly come from regions where child marriage (<18 years) is prevalent. We described the prevalence, demographic characteristics, and reproductive health correlates of ...marriage among births to Canadian-born and foreign-born adolescent mothers. Using Canadian birth registrations from 1990 to 2018, marriage prevalence, parental birth region, and parental age gap were examined by maternal birthplace (Canada and 12 world regions) among births to mothers <18 years. Adjusted odds ratios (AORs) of preterm birth (PTB), small for gestational age (SGA), and repeat birth were estimated for the joint associations of adolescent maternal age group (<18-year, 18–19-year, and 20–24-year), marriage, and nativity status (n = 1,904,200). Depending on maternal birthplace, marital births represented 2.6% to 81.8% of births to mothers <18 years. Marriage among mothers giving birth at <18 years was associated with higher proportions of parents from the same birthplace and larger parental age gaps. AORs of PTB tended to increase with lower maternal age. AORs of SGA were generally higher among births to foreign-born mothers. Marriage was associated with lower AORs of PTB and SGA among births to Canadian-born mothers and PTB among births to foreign-born mothers in the older adolescent age groups, but no association existed in the <18-year group. Marriage was positively associated with repeat birth in all adolescent age groups, with stronger associations in the <18-year group. The reproductive health correlates of marriage are similar between births to Canadian-born and foreign-born mothers <18 years but some differ between births to mothers <18 years and those to older adolescent mothers.
•Immigrants to Canada increasingly come from regions where child marriage is common.•Marriage below age 18 is permitted in Canada, but it is poorly understood.•Marriage is more common in <18-year-old foreign-born than Canadian-born mothers.•Reproductive correlates of marriage are similar by nativity status in <18-year-olds.•Reproductive correlates of marriage differ between <18-year-old and older mothers.
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the ...methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
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