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The Journal of Headache and Pain Oct 2023Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging...
INTRODUCTION
Neuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.
METHODS
We trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).
RESULTS
CM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.
CONCLUSION
The brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
Topics: Humans; Migraine Disorders; Magnetic Resonance Imaging; Brain; Neuroimaging; Biomarkers
PubMed: 37798720
DOI: 10.1186/s10194-023-01670-6 -
IEEE Transactions on Pattern Analysis... Dec 2023In this work, we present a Deep Learning approach to estimate age from facial images. First, we introduce a novel attention-based approach to image...
In this work, we present a Deep Learning approach to estimate age from facial images. First, we introduce a novel attention-based approach to image augmentation-aggregation, which allows multiple image augmentations to be adaptively aggregated using a Transformer-Encoder. A hierarchical probabilistic regression model is then proposed that combines discrete probabilistic age estimates with an ensemble of regressors. Each regressor is adapted and trained to refine the probability estimate over a given age range. We show that our age estimation scheme outperforms current schemes and provides a new state-of-the-art age estimation accuracy when applied to the MORPH II and CACD datasets. We also present an analysis of the biases in the results of the state-of-the-art age estimates.
PubMed: 37751349
DOI: 10.1109/TPAMI.2023.3319472 -
Heliyon Aug 2023Hemorrhage is a common complication of trauma. We evaluated age and sex differences in treatment with blood product transfusions and massive transfusions as well as...
OBJECTIVES
Hemorrhage is a common complication of trauma. We evaluated age and sex differences in treatment with blood product transfusions and massive transfusions as well as in-hospital mortality following trauma at a Level 1 Trauma Center.
METHODS
This cross-sectional study evaluated trauma data from a Level 1 trauma center registry from January 2013 to December 2017. The primary outcome was amount of blood products (packed red blood cells (PRBCs), plasma, platelets), and massive transfusion (MT) by biological sex and by age group: 16-24 (youth), 25-59 (middle age), and >=60 (older age) The secondary outcome was in-hospital mortality to hospital discharge.
RESULTS
There were 13596 trauma patients in the registry, mean age was 48 years, 4589 (34%) female and 9007 (66%) male, and median ISS of 9. Male patients received significantly more PRBC transfusions than female patients within 4-hours 6.6% vs 4.4%, and 24-hours 6.7% vs 4.5% respectively. Older patients received significantly fewer PRBC transfusions within 4-hours and 24-hours than their younger counterparts, with 6.9% in the youth group, 6.8% in the middle age group, and 3.9% in the older group (p<0.001). When adjusted for injury severity, the odds of receiving a blood transfusion within 4 hours of injury was significantly lower in older females. Using multivariate analysis, predictors of mortality included (in order of significance) injury severity, older age, transfusion within 4 hours of injury, penetrating trauma, and male sex.
CONCLUSION
In this large trauma cohort, older female trauma patients were less likely to receive blood products compared to younger females and to their older male counterparts, even after adjusting for injury severity. Predictors of mortality included injury severity, older age, early transfusion, penetrating trauma, and male sex. Following trauma, older women appear vulnerable to undertreatment. Further study is needed to determine the reasons for these differences and their impact on patient outcomes.
PubMed: 37583761
DOI: 10.1016/j.heliyon.2023.e18890 -
Zeitschrift Fur Gerontologie Und... Jul 2023Impaired hearing is associated with disadvantages in developmental outcomes, such as compromised everyday social communication or reduced well-being. Hearing impairment... (Review)
Review
BACKGROUND
Impaired hearing is associated with disadvantages in developmental outcomes, such as compromised everyday social communication or reduced well-being. Hearing impairment might also have an impact on how individuals evaluate their own aging as deterioration in hearing can be interpreted as being age-related and as a phenomenon individuals attribute to getting older.
OBJECTIVE
This study investigated how self-reported hearing is related to awareness of age-related change (AARC).
MATERIAL AND METHODS
AARC is a multidimensional construct comprising perceived age-related gains and losses in general as well as across five functional domains (health and physical functioning, cognitive functioning, interpersonal relations, social cognitive and social emotional functioning, lifestyle and engagement). A sample of 423 individuals (age range 40-98 years; mean age, M = 62.9 years; standard deviation (SD) = 11.8 years) was assessed up to 3 times over approximately 5 years.
RESULTS
Based on longitudinal multilevel regression models, controlling for age, gender, subjective health and education, it was found that poorer self-reported hearing was associated with more perceived general AARC losses as well as with more AARC losses in health and physical functioning and in cognitive functioning at baseline. With an older age at baseline, poorer self-reported hearing was associated with a steeper decline in AARC gains regarding interpersonal relations over time, whereas in those who were younger at baseline poorer hearing was related to fewer gains in social cognitive and social emotional functioning at baseline.
DISCUSSION
Self-reported hearing reveals differential associations with AARC domains; however, changes in most AARC domains of gains and losses seem to be only weakly related to subjective hearing.
Topics: Humans; Aged; Aged, 80 and over; Awareness; Self Report; Aging; Cognition; Hearing
PubMed: 36988667
DOI: 10.1007/s00391-023-02171-6 -
Estimating age and gender from electrocardiogram signals: A comprehensive review of the past decade.Artificial Intelligence in Medicine Dec 2023Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and... (Review)
Review
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
Topics: Humans; Aged; Signal Processing, Computer-Assisted; Electrocardiography; Machine Learning; Heart Rate; Probability
PubMed: 38042607
DOI: 10.1016/j.artmed.2023.102690 -
Meat Science Oct 2023Carcass characteristics were studied in 80 young Hungarian red deer in different ages (12, 15, 18 and 20 months of age). In all age group 10 male and 10 female were...
Carcass characteristics were studied in 80 young Hungarian red deer in different ages (12, 15, 18 and 20 months of age). In all age group 10 male and 10 female were slaughtered. The dressed carcass weight in skin varied between 53.72 and 65.66% of live weight. The first class lean meat varied between 14.3 and 16.6% of live weight. The live weight, carcass weight increased with the age and differed also between sexes. The highest dressing percentages were found at 20 months of age in both sexes (♂: 65.7%, ♀: 62.5%). Mainly the hinds had higher loin, leg and shoulder proportions of carcass at 20 months of age than the stags. The carcass muscle, bone and fat content were measured by computer tomography. These traits were grown and their proportion changed with the age. The bone to muscle ratio gradually decreased with the age while the fat to muscle ratio increased after 15 months of age (♂: from 0.13 to 0.17, ♀: from 0.15 to 0.18). At 18 month of age the hinds had higher bone to muscle and fat to muscle ratio than the stags'. The fat percentage of carcasses increased with the age in both sexes (♂: from 8.01% to 11.04%, ♀: from 8.40% to 11.28%). The hinds had higher values than the stags but it was significant just at 20 months of age. From the meat quality attributes there were found differences between ages in the case of pH, lightness, drip loss, cooking loss and shear force. The highest pH was found at 12 months of age in both sexes. There were found 5% intermediate pH (6.2 < pH < 5.8) at 12 and 15 months of age, all of them were male. The highest lightness values were observed at 18 months of age in both sexes (♂: 13.47, ♀: 14.90). There were differences between sexes in pH at all ages, except 15 months of age, and at 18 months of age in redness and lightness. Based on our results, the optimal slaughtering time for Hungarian red deer is 20 months of age, because this is when the dressing percentage is the best for both sexes. Meat quality traits changed with age, and gender differences sexes were the most pronounced for these traits at 18 months of age.
Topics: Animals; Male; Female; Hungary; Deer; Body Composition; Muscles; Cooking; Meat; Body Weight
PubMed: 37531899
DOI: 10.1016/j.meatsci.2023.109290 -
Journal of Aging Studies Dec 2023In this paper, I develop features of a material gerontology which are summarised in the concept of "distributed age(ing);" that is, age(ing) that is distributed across...
In this paper, I develop features of a material gerontology which are summarised in the concept of "distributed age(ing);" that is, age(ing) that is distributed across and co-constituted through meanings, roles, and identities, as well as human and non-human forms of materiality, their productive dimensions and their relations to each other. The starting point is the critique of the human-centredness of gerontological approaches and, thus, the lack of a systematic conceptual consideration of non-human forms of materiality and agency in the context of age(ing). To overcome this problem, I propose the following shifts in perspective that are inspired by actor-network theory: from human-centredness to the recognition and consideration of the material diversity of age(ing); from the critique of subject/object dualism to the symmetrisation of materialities; from the seemingly given ontology of the ageing body to the re-ontologisation of age(ing); from the critique of intentional and causal determinants to embodiment and relationality; from linearity and chronology to the plural temporalities of age(ing). I will explain these features in more detail by using breathing as an example. I will show that the concept of distributed age(ing) allows for both the generation of new insights on age(ing) by asking how, where and when age(ing) takes place and reflection on presumptions, determinants and reductions of approaches belonging to social and cultural gerontology.
Topics: Humans; Geriatrics; Aging
PubMed: 38012945
DOI: 10.1016/j.jaging.2023.101185 -
Age Estimation and Gender Attribution in Typically Developing Children and Children With Dysarthria.American Journal of Speech-language... May 2024The purposes of this study were (a) to investigate adult listeners' perceptions of age and gender in typically developing children and children with dysarthria and (b)...
PURPOSE
The purposes of this study were (a) to investigate adult listeners' perceptions of age and gender in typically developing children and children with dysarthria and (b) to identify predictors of their estimates among auditory-perceptual parameters and an acoustic measure of vocal pitch (0). We aimed to evaluate the influence of dysarthria on the listeners' impressions of age and gender against the background of typical developmental processes.
METHOD
In a listening experiment, adult listeners completed age and gender estimates of 144 typically developing children (3-9 years of age) and 25 children with dysarthria (5-9 years of age). The Bogenhausen Dysarthria Scales for Childhood Dysarthria (BoDyS-KiD) were applied to record speech samples and to complete auditory-perceptual judgments covering all speech subsystems. Furthermore, each child's mean 0 was determined from samples of four BoDyS-KiD sentences.
RESULTS
Age estimates for the typically developing children showed a regression to the mean, whereas children with dysarthria were systematically underestimated in their age. The estimates of all children were predicted by developmental speech features; for the children with dysarthria, specific dysarthria symptoms had an additional effect. We found a significantly higher accuracy of gender attribution in the typically developing children than in the children with dysarthria. The prediction accuracy of the listeners' gender attribution in the preadolescent children by the included speech characteristics was limited.
CONCLUSIONS
Children with dysarthria are more difficult to estimate for their age and gender than their typically developing peers. Dysarthria thus alters the auditory-perceptual impression of indexical speech features in children, which must be considered another facet of the communication disorder associated with childhood dysarthria.
Topics: Humans; Dysarthria; Female; Male; Child, Preschool; Child; Speech Acoustics; Speech Perception; Age Factors; Speech Production Measurement; Child Language; Sex Factors; Adult; Voice Quality; Judgment
PubMed: 38416062
DOI: 10.1044/2023_AJSLP-23-00246 -
Computer Methods and Programs in... Oct 2023Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the...
BACKGROUND AND OBJECTIVE
Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the ability to predict biological age can support appropriate and timely active interventions aimed at improving coping with the aging process. However, the aging process is highly complex and multifactorial. Therefore, it is more scientific to construct a prediction model for biological age from multiple dimensions systematically.
METHODS
Physiological and biochemical parameters were evaluated to gage individual health status. Then, age-related indices were screened for inclusion in a model capable of predicting biological age. For subsequent modeling analyses, samples were divided into training and validation sets for subsequent deep learning model-based analyses (e.g. linear regression, lasso model, ridge regression, bayesian ridge regression, elasticity network, k-nearest neighbor, linear support vector machine, support vector machine, and decision tree models, and so on), with the model exhibiting the best ability to predict biological age thereby being identified.
RESULTS
First, we defined the individual biological age according to the individual health status. Then, after 22 candidate indices (DNA methylation, leukocyte telomere length, and specific physiological and biochemical indicators) were screened for inclusion in a model capable of predicting biological age, 14 age-related indices and gender were used to construct a model via the Bagged Trees method, which was found to be the most reliable qualitative prediction model for biological age (accuracy=75.6%, AUC=0.84) by comparing 30 different classification algorithm models. The most reliable quantitative predictive model for biological age was found to be the model developed using the Rational Quadratic method (R=0.85, RMSE=8.731 years) by comparing 24 regression algorithm models.
CONCLUSIONS
Both qualitative model and quantitative model of biological age were successfully constructed from a multi-dimensional and systematic perspective. The predictive performance of our models was similar in both smaller and larger datasets, making it well-suited to predicting a given individual's biological age.
Topics: Humans; Adolescent; Bayes Theorem; Machine Learning; Algorithms; Aging; DNA Methylation
PubMed: 37421874
DOI: 10.1016/j.cmpb.2023.107686 -
Aging & Mental Health 2023The purpose of this research is to explore the relationship between age-friendly environment, social support, sense of community, and loneliness of Korean adults aged 45...
OBJECTIVES
The purpose of this research is to explore the relationship between age-friendly environment, social support, sense of community, and loneliness of Korean adults aged 45 and above.
METHODS
A total of 590 participants from a cross-sectional and secondary data from an age integration survey conducted in 2018 was used for analysis. Structural equation modelling and bootstrapping method were applied to examine the mediating role of social support and sense of community on the relationship between age-friendly environment and loneliness.
RESULTS
Age-friendly environment was positively associated with social support () and sense of community (). Social support was negatively associated with loneliness (). Full mediation effect of social support was observed in the pathway from age-friendly environment to loneliness (95% CI: -0.135 to -0.036).
CONCLUSION
Social support was fundamental in lowering loneliness in an age-friendly environment. There was no significant association linking age-friendly environment, sense of community, and loneliness. The results support the adoption of AFE to protect people at risk of loneliness with social support mediating this relationship.
PubMed: 36036282
DOI: 10.1080/13607863.2022.2116395