Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth.
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ABSTRACT:
SUBMITTER: Hedderich DM
PROVIDER: S-EPMC8047054 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and purposeQualitative studies about the abnormalities appreciated on routine magnetic resonance imaging (MRI) sequences in prematurely born adults are lacking. This article aimed at filling this knowledge gap by (1) qualitatively describing routine imaging findings in prematurely born adults, (2) evaluating measures for routine image interpretation and (3) investigating the impact of perinatal variables related to premature birth.MethodsIn this study two board-certified radiologists assessed T1-weighted and FLAIR-weighted images of 100 prematurely born adults born very preterm (VP <32 weeks) and/or at very low birth weight (VLBW <1500 g) and 106 controls born at full term (FT) (mean age 26.8 ± 0.7 years). The number of white matter lesions (WML) was counted according to localization. Lateral ventricle volume (LVV) was evaluated subjectively and by measurements of Evans' index (EI) and frontal-occipital-horn ratio (FOHR). Freesurfer-based volumetry served as reference standard. Miscellaneous incidental findings were noted as free text.ResultsThe LVV was increased in 24.7% of VP/VLBW individuals and significantly larger than in FT controls. This was best identified by measurement of FOHR (AUC = 0.928). Ventricular enlargement was predicted by low gestational age (odds ratio: 0.71, 95% CI 0.51-0.98) and presence of neonatal intracranial hemorrhage (odds ratio: 0.26, 95% CI 0.07-0.92). The numbers of deep and periventricular WML were increased while subcortical WMLs were not.ConclusionEnlargement of the LVV and deep and periventricular WMLs are typical sequelae of premature birth that can be appreciated on routine brain MRI. To increase sensitivity of abnormal LVV detection, measurement of FOHR seems feasible in clinical practice.
| S-EPMC8211575 | biostudies-literature
Project description:IntroductionSystemic lupus erythematosus (SLE) is an autoimmune connective tissue disease affecting multiple organs in the human body, including the central nervous system. Recently, an artificial intelligence method called BrainAGE (Brain Age Gap Estimation), defined as predicted age minus chronological age, has been developed to measure the deviation of brain aging from a healthy population using MRI. Our aim was to evaluate brain aging in SLE patients using a deep-learning BrainAGE model.MethodsSeventy female patients with a clinical diagnosis of SLE and 24 healthy age-matched control females, were included in this post-hoc analysis of prospectively acquired data. All subjects had previously undergone a 3 T MRI acquisition, a neuropsychological evaluation and a measurement of neurofilament light protein in plasma (NfL). A BrainAGE model with a 3D convolutional neural network architecture, pre-trained on the 3D-T1 images of 1,295 healthy female subjects to predict their chronological age, was applied on the images of SLE patients and controls in order to compute the BrainAGE. SLE patients were divided into 2 groups according to the BrainAGE distribution (high vs. low BrainAGE).ResultsBrainAGE z-score was significantly higher in SLE patients than in controls (+0.6 [±1.1] vs. 0 [±1.0], p = 0.02). In SLE patients, high BrainAGE was associated with longer reaction times (p = 0.02), lower psychomotor speed (p = 0.001) and cognitive flexibility (p = 0.04), as well as with higher NfL after adjusting for age (p = 0.001).ConclusionUsing a deep-learning BrainAGE model, we provide evidence of increased brain aging in SLE patients, which reflected neuronal damage and cognitive impairment.
| S-EPMC10622955 | biostudies-literature
Project description:Importance:Few tools exist for assessing the risk for early atherosclerotic cardiovascular disease (ASCVD) events in young adults. Objective:To assess the performance of the Healthy Heart Score (HHS), a lifestyle-based tool that estimates ASCVD events in older adults, for ASCVD events occurring before 55 years of age. Design, Setting, and Participants:This prospective cohort study included 4893 US adults aged 18 to 30 years from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants underwent measurement of lifestyle factors from March 25, 1985, through June 7, 1986, and were followed up for a median of 27.1 years (interquartile range, 26.9-27.2 years). Data for this study were analyzed from February 24 through December 12, 2016. Exposures:The HHS includes age, smoking status, body mass index, alcohol intake, exercise, and a diet score composed of self-reported daily intake of cereal fiber, fruits and/or vegetables, nuts, sugar-sweetened beverages, and red and/or processed meats. The HHS in the CARDIA study was calculated using sex-specific equations produced by its derivation cohorts. Main Outcomes and Measures:The ability of the HHS to assess the 25-year risk for ASCVD (death from coronary heart disease, nonfatal myocardial infarction, and fatal or nonfatal ischemic stroke) in the total sample, in race- and sex-specific subgroups, and in those with and without clinical ASCVD risk factors at baseline. Model discrimination was assessed with the Harrell C statistic; model calibration, with Greenwood-Nam-D'Agostino statistics. Results:The study population of 4893 participants included 2205 men (45.1%) and 2688 women (54.9%) with a mean (SD) age at baseline of 24.8 (3.6) years; 2483 (50.7%) were black; and 427 (8.7%) had at least 1 clinical ASCVD risk factor (hypertension, hyperlipidemia, or diabetes types 1 and 2). Among these participants, 64 premature ASCVD events occurred in women and 99 in men. The HHS showed moderate discrimination for ASCVD risk assessment in this diverse population of mostly healthy young adults (C statistic, 0.71; 95% CI, 0.66-0.76); it performed better in men (C statistic, 0.74; 95% CI, 0.68-0.79) than in women (C statistic, 0.69; 95% CI, 0.62-0.75); in white (C statistic, 0.77; 95% CI, 0.71-0.84) than in black (C statistic, 0.66; 95% CI, 0.60-0.72) participants; and in those without (C statistic, 0.71; 95% CI, 0.66-0.76) vs with (C statistic, 0.64; 95% CI, 0.55-0.73) clinical risk factors at baseline. The HHS was adequately calibrated overall and within each subgroup. Conclusions and Relevance:The HHS, when measured in younger persons without ASCVD risk factors, performs moderately well in assessing risk for ASCVD events by early middle age. Its reliance on self-reported, modifiable lifestyle factors makes it an attractive tool for risk assessment and counseling for early ASCVD prevention.
| S-EPMC5710563 | biostudies-literature
Project description:Birth characteristics, including weight and gestational age, may be associated with risk of Barrett's esophagus (BE), the only known precursor for esophageal adenocarcinoma; however, data are limited.To examine associations between various birth characteristics and BE, and whether these associations are mediated by known risk factors for BE.Data were obtained from a cross-sectional study among eligible Veterans Affairs patients scheduled for an upper endoscopy, and a sample identified from primary care clinics. Participants underwent an esophagogastroduodenoscopy and completed a survey that captured information on sociodemographic and clinical factors, as well as birth information. We compared 263 patients with histologically confirmed BE to 1416 controls without BE on endoscopy. Odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated using multivariate logistic regression.Premature birth was independently associated with risk of BE after adjusted by age, sex, race, and other birth characteristics (OR 3.28, 95 % CI 1.22-8.79). On the other hand, large for gestational age was inversely associated with risk of BE (OR 0.46, 95 % CI 0.21-0.98). These effects were stronger for patients with long-segment BE than with short-segment BE. The associations were not mediated by gastroesophageal reflux disease symptoms, use of proton pump inhibitors, Helicobacter Pylori infection, waist-hip-ratio, height or the presence of hiatus hernia.Premature birth and large for gestational age may be associated with risk of BE in adults. These associations do not appear to be mediated through known risk factors for BE; however, additional studies are required to confirm our findings.
| S-EPMC4791183 | biostudies-other
Project description:Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging. In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging. Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (n = 20; age mean 23.1 years, range 19.8-27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m2, 21.5-47.7; n = 19 paternal chromosome 15q11-13 deletion) and age- and sex-matched controls (n = 40; age 22.9 years, 19.6-29.0; 65.0% male; BMI 24.1 kg/m2, 19.2-34.2) adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls (effect size mean ± SEM +7.24 ± 2.20 years [95% CI 2.83, 11.63], P = .002). Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5-33.7 kg/m2, and n = 29 controls with BMI 21.7-34.2 kg/m2 (effect size +5.51 ± 2.56 years [95% CI 3.44, 10.38], P = .037). In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive behaviours. A 24.5 year old man (BMI 36.9 kg/m2) with PWS from a SNORD116 microdeletion also had increased brain PAD of 12.87 years, compared to 0.84 ± 6.52 years in a second control adult cohort (n = 95; age mean 34.0 years, range 19.9-55.5; 38.9% male; BMI 28.7 kg/m2, 19.1-43.1). This increase in brain-PAD in adults with PWS indicates abnormal brain structure that may reflect premature brain aging or abnormal brain development. The similar finding in a rare patient with a SNORD116 microdeletion implicates a potential causative role for this PWS region gene cluster in the structural brain abnormalities associated primarily with the syndrome and/or its complications. Further longitudinal neuroimaging studies are needed to clarify the natural history of this increase in brain age in PWS, its relationship with obesity, and whether similar findings are seen in those with PWS from maternal uniparental disomy.
| S-EPMC6412082 | biostudies-literature
Project description:Age-related variations in resting state connectivity of the human brain were examined from young adulthood through middle age. A voxel-based network measure, degree, was used to assess age-related differences in tissue connectivity throughout the brain. Increases in connectivity with age were found in paralimbic cortical and subcortical regions. Decreases in connectivity were found in cortical regions, including visual areas and the default mode network. These findings differ from those of recent developmental studies examining earlier growth trajectories, and are consistent with known changes in cognitive function and emotional processing during mature aging. The results support and extend previous findings that relied on a priori definitions of regions of interest for their analyses. This approach of applying a voxel-based measure to examine the functional connectivity of individual tissue elements over time, without the need for a priori region of interest definitions, provides an important new tool in brain science.
| S-EPMC3439483 | biostudies-literature
Project description:Using data (2655 observations from 928 participants) from the Chronic Kidney Disease in Children Study, we developed and internally validated new glomerular filtration rate estimating equations for clinical use in children and young adults: two forms of K × [heigh(ht) / serum creatinine(sCr)] and two forms of K × [1 / cystatin C(cysC)]. For each marker, one equation used a sex-dependent K; in the other, K is sex-and age-dependent. Glomerular filtration rate (GFR) was measured directly by plasma iohexol disappearance. The equations using ht⁄sCr had sex-specific constants of 41.8 for males and 37.6 for females. In the age- dependent models, K increased monotonically for children 1-18 years old and was constant for young adults 18-25 years. For males, K ranged from 35.7 for one-year-olds to 50.8 for those 18 and older. For females, the values of K ranged from 33.1 to 41.4. Constant K values for cystatin-C equations were 81.9 for males and 74.9 for females. With age-dependency, K varied non-monotonically with the highest values at age 15 for males (K of 87.2) and 12 years for females (K of 79.9). Use of an age-dependent K with ht/sCr models reduced average bias, notably in young children and young adults; age-dependent cystatin-C models produced similar agreement to using a constant K in children under 18 years, but reduced bias in young adults. These age-dependent proposed equations were evaluated alongside estimated GFRs from 11 other published equations for pediatrics and young adults. Only our proposed equations yielded non- significant bias and within 30% accuracy values greater than 85% in both the pediatric and young adult subpopulations.
| S-EPMC9083470 | biostudies-literature
Project description:Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.
| S-EPMC6208001 | biostudies-literature
Project description:To estimate differences in skeletal maturity and stature from birth to age 18 years between individuals who are overweight vs normal weight in young adulthood.Weight, length and height, and relative skeletal age (skeletal-chronological age) were assessed annually from birth to age 18 years in 521 subjects (255 women) in the Fels Longitudinal Study who were overweight or obese (body mass index (BMI) >25 kg?m(-2), n=131) or normal weight (n=390) in young adulthood (18-30 years). Generalized estimating equations were used to test for skeletal maturity and stature differences by young adult BMI status.Differences in height increased during puberty, being significant for girls at ages 10 to 12 years, and for boys at ages 11 to 13 years (P-values<0.001), with overweight or obese adults being ?3 cm taller at those ages than normal weight adults. These differences then diminished so that by age 18 years, overweight or obese adults were not significantly different in stature to their normal weight peers. Differences in skeletal maturity were similar, but more pervasive; overweight or obese adults were more skeletally advanced throughout childhood. Skeletal maturity differences peaked at chronological age 12 in boys and 14 in girls (P-values<0.001), with overweight or obese adults being ?1 year more advanced than normal weight adults.This descriptive study is the first to track advanced skeletal maturity and linear growth acceleration throughout infancy, childhood and adolescence in individuals who become overweight, showing that differences occur primarily around the time of the pubertal growth spurt. Increased BMI in children on a path to becoming overweight adults precedes an advancement in skeletal development and subsequently tall stature during puberty. Further work is required to assess the predictive value of accelerated pubertal height growth for assessing obesity risk in a variety of populations.
| S-EPMC3312969 | biostudies-literature
Project description:Sleep loss pervasively affects the human brain at multiple levels. Age-related changes in several sleep characteristics indicate that reduced sleep quality is a frequent characteristic of aging. Conversely, sleep disruption may accelerate the aging process, yet it is not known what will happen to the age status of the brain if we can manipulate sleep conditions. To tackle this question, we used an approach of brain age to investigate whether sleep loss would cause age-related changes in the brain. We included MRI data of 134 healthy volunteers (mean chronological age of 25.3 between the age of 19 and 39 years, 42 females/92 males) from five datasets with different sleep conditions. Across three datasets with the condition of total sleep deprivation (>24 h of prolonged wakefulness), we consistently observed that total sleep deprivation increased brain age by 1-2 years regarding the group mean difference with the baseline. Interestingly, after one night of recovery sleep, brain age was not different from baseline. We also demonstrated the associations between the change in brain age after total sleep deprivation and the sleep variables measured during the recovery night. By contrast, brain age was not significantly changed by either acute (3 h time-in-bed for one night) or chronic partial sleep restriction (5 h time-in-bed for five continuous nights). Together, the convergent findings indicate that acute total sleep loss changes brain morphology in an aging-like direction in young participants and that these changes are reversible by recovery sleep.SIGNIFICANCE STATEMENT Sleep is fundamental for humans to maintain normal physical and psychological functions. Experimental sleep deprivation is a variable-controlling approach to engaging the brain among different sleep conditions for investigating the responses of the brain to sleep loss. Here, we quantified the response of the brain to sleep deprivation by using the change of brain age predictable with brain morphologic features. In three independent datasets, we consistently found increased brain age after total sleep deprivation, which was associated with the change in sleep variables. Moreover, no significant change in brain age was found after partial sleep deprivation in another two datasets. Our study provides new evidence to explain the brainwide effect of sleep loss in an aging-like direction.
| S-EPMC10039745 | biostudies-literature