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BACKGROUND AND OBJECTIVES: The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease. METHODS: The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time. RESULTS: The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (ß = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (ß = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (ß = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (ß = -1.75, p < 0.05). DISCUSSION: This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.
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Envejecimiento , Apolipoproteína E4 , Índice de Masa Corporal , Encéfalo , Caracteres Sexuales , Humanos , Masculino , Femenino , Apolipoproteína E4/genética , Anciano , Estudios Longitudinales , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagen , Encéfalo/crecimiento & desarrollo , Envejecimiento/genética , Presión Sanguínea/fisiología , Imagen por Resonancia Magnética , Estudios de Cohortes , Reino Unido/epidemiología , Factores de Riesgo CardiometabólicoRESUMEN
BACKGROUND: Different types of early-life adversity have been associated with children's brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge. METHODS: This study investigates the neural correlates of 10 robust dimensions of early-life adversity identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study. Brain age models were trained, validated, and tested separately on T1-weighted (T1; N = 9524), diffusion tensor (DTI; N = 8834), and resting-state functional (rs-fMRI; N = 8233) magnetic resonance imaging (MRI) data from two time points (mean age = 10.7 years, SD = 1.2, range = 8.9-13.8 years). RESULTS: Bayesian multilevel modelling supported distinct associations between different types of early-life adversity exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support, and lack of caregiver supervision, were associated with lower brain age gaps (BAGs), i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socio-economic disadvantage and neighbourhood safety were associated with higher BAGs, i.e., older-looking brains. CONCLUSIONS: The findings suggest that dimensions of early-life adversity are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.
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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.
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Envejecimiento , Apolipoproteína E4 , Menopausia , Adulto , Anciano , Anciano de 80 o más Años , Femenino , Humanos , Persona de Mediana Edad , Envejecimiento/genética , Envejecimiento/fisiología , Apolipoproteína E4/genética , Encéfalo/metabolismo , Estudios de Cohortes , Depresión/genética , Menopausia/genética , Menopausia/fisiología , Biobanco del Reino Unido , Reino UnidoRESUMEN
Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.
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Encéfalo , Senescencia Celular , Imagen por Resonancia Magnética , Padres , Humanos , Femenino , Masculino , Encéfalo/metabolismo , Encéfalo/diagnóstico por imagen , Adulto , Persona de Mediana Edad , Anciano , Imagen por Resonancia Magnética/métodos , Anciano de 80 o más Años , Senescencia Celular/fisiología , Redes Neurales de la Computación , Envejecimiento/fisiología , Telómero/metabolismo , Biomarcadores/análisis , Leucocitos/metabolismo , Responsabilidad ParentalRESUMEN
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
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Envejecimiento , Aprendizaje Automático , Imagen por Resonancia Magnética , Humanos , Persona de Mediana Edad , Anciano , Adulto , Masculino , Envejecimiento/fisiología , Femenino , Anciano de 80 o más Años , Encéfalo/diagnóstico por imagen , Encéfalo/fisiología , Composición Corporal/fisiología , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/anatomía & histología , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/anatomía & histología , Teorema de BayesRESUMEN
Oxytocin is a neuropeptide associated with both psychological and somatic processes like parturition and social bonding. Although oxytocin homologs have been identified in many species, the evolutionary timeline of the entire oxytocin signaling gene pathway has yet to be described. Using protein sequence similarity searches, microsynteny, and phylostratigraphy, we assigned the genes supporting the oxytocin pathway to different phylostrata based on when we found they likely arose in evolution. We show that the majority (64%) of genes in the pathway are 'modern'. Most of the modern genes evolved around the emergence of vertebrates or jawed vertebrates (540 - 530 million years ago, 'mya'), including OXTR, OXT and CD38. Of those, 45% were under positive selection at some point during vertebrate evolution. We also found that 18% of the genes in the oxytocin pathway are 'ancient', meaning their emergence dates back to cellular organisms and opisthokonta (3500-1100 mya). The remaining genes (18%) that evolved after ancient and before modern genes were classified as 'medium-aged'. Functional analyses revealed that, in humans, medium-aged oxytocin pathway genes are highly expressed in contractile organs, while modern genes in the oxytocin pathway are primarily expressed in the brain and muscle tissue.
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Oxitocina , Receptores de Oxitocina , Animales , Humanos , Anciano , Oxitocina/metabolismo , Receptores de Oxitocina/genética , Transducción de Señal , Encéfalo/metabolismoRESUMEN
Background and Objectives: Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle- to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes. Methods: A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status. Results: We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (~9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons. Discussion: Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.
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The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
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Lateralidad Funcional , Sustancia Blanca , Masculino , Femenino , Humanos , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen por Resonancia Magnética/métodos , Imagen de Difusión por Resonancia Magnética , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patologíaRESUMEN
Global prevalence of Alzheimer's Disease has a strong sex bias, with women representing approximately two-thirds of the patients. Yet, the role of sex-specific risk factors during midlife, including hormone replacement therapy (HRT) and their interaction with other major risk factors for Alzheimer's Disease, such as apolipoprotein E (APOE)-e4 genotype and age, on brain health remains unclear. We investigated the relationship between HRT (i.e., use, age of initiation and duration of use) and brain health (i.e., cognition and regional brain volumes). We then consider the multiplicative effects of HRT and APOE status (i.e., e2/e2, e2/e3, e3/e3, e3/e4 and e4/e4) via a two-way interaction and subsequently age of participants via a three-way interaction. Women from the UK Biobank with no self-reported neurological conditions were included (N = 207,595 women, mean age = 56.25 years, standard deviation = 8.01 years). Generalised linear regression models were computed to quantify the cross-sectional association between HRT and brain health, while controlling for APOE status, age, time since attending centre for completing brain health measure, surgical menopause status, smoking history, body mass index, education, physical activity, alcohol use, ethnicity, socioeconomic status, vascular/heart problems and diabetes diagnosed by doctor. Analyses of structural brain regions further controlled for scanner site. All brain volumes were normalised for head size. Two-way interactions between HRT and APOE status were modelled, in addition to three-way interactions including age. Results showed that women with the e4/e4 genotype who have used HRT had 1.82% lower hippocampal, 2.4% lower parahippocampal and 1.24% lower thalamus volumes than those with the e3/e3 genotype who had never used HRT. However, this interaction was not detected for measures of cognition. No clinically meaningful three-way interaction between APOE, HRT and age was detected when interpreted relative to the scales of the cognitive measures used and normative models of ageing for brain volumes in this sample. Differences in hippocampal volume between women with the e4/e4 genotype who have used HRT and those with the e3/e3 genotype who had never used HRT are equivalent to approximately 1-2 years of hippocampal atrophy observed in typical health ageing trajectories in midlife (i.e., 0.98%-1.41% per year). Effect sizes were consistent within APOE e4/e4 group post hoc sensitivity analyses, suggesting observed effects were not solely driven by APOE status and may, in part, be attributed to HRT use. Although, the design of this study means we cannot exclude the possibility that women who have used HRT may have a predisposition for poorer brain health.
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Enfermedad de Alzheimer , Masculino , Humanos , Femenino , Persona de Mediana Edad , Biobanco del Reino Unido , Bancos de Muestras Biológicas , Estudios Transversales , Apolipoproteínas E/genética , Encéfalo/diagnóstico por imagen , Genotipo , Terapia de Reemplazo de Hormonas , Apolipoproteína E4/genética , Apolipoproteína E3/genética , Apolipoproteína E2/genéticaRESUMEN
There are prominent sex/gender differences in the prevalence, expression, and life span course of mental health and neurodiverse conditions. However, the underlying sex- and gender-related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those with mental health problems. Therefore, we set up a cocreation session in a 1-week workshop with a multidisciplinary team of 25 researchers, clinicians, and policy makers to identify the main barriers in sex and gender research in the neuroscience of mental health. Based on this work, here we provide recommendations for methodologies, translational research, and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex- and gender-related mechanisms in neuroscience may benefit public health because this is an important step toward precision medicine and may function as an archetype for studying diversity.
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Despite widespread sex differences in prevalence and presentation of numerous illnesses affecting the human brain, there has been little focus on the effect of endocrine ageing. Most preclinical studies have focused on males only, and clinical studies often analyse data by covarying for sex, ignoring relevant differences between the sexes. This sex- (and gender)-neutral approach is biased and contributes to the absence of targeted treatments and services for all sexes (and genders). Female health has been historically understudied, with grave consequences for their wellbeing and health equity. In this Review, we spotlight female brain health across the lifespan by informing on the role of sex steroids, particularly oestradiol, on the female brain and on risk for diseases more prevalent in females, such as depression and Alzheimer's disease.
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Enfermedad de Alzheimer , Femenino , Humanos , Masculino , Enfermedad de Alzheimer/epidemiología , Longevidad , Depresión/epidemiología , Encéfalo , Caracteres Sexuales , EsteroidesRESUMEN
BACKGROUND: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. OBJECTIVE: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. METHODS: We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. FINDINGS: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). CLINICAL IMPLICATIONS: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations.
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Bancos de Muestras Biológicas , Demencia , Humanos , Australia , Factores de Riesgo , Demencia/diagnóstico , Reino Unido/epidemiologíaRESUMEN
Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all.
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Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6-82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions' influence on brain age in future studies.
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Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6-82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.
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Sustancia Blanca , Humanos , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Imagen de Difusión por Resonancia Magnética/métodos , Envejecimiento , Cuerpo CallosoRESUMEN
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
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Trastorno Bipolar , Trastorno Depresivo Mayor , Trastornos Mentales , Humanos , Preescolar , Niño , Adolescente , Adulto Joven , Adulto , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Trastorno Depresivo Mayor/genética , Estudio de Asociación del Genoma Completo , Trastornos Mentales/genética , Encéfalo , Trastorno Bipolar/genéticaRESUMEN
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Enfermedades Cardiovasculares , Vaina de Mielina , Humanos , Masculino , Persona de Mediana Edad , Anciano , Anciano de 80 o más Años , Vaina de Mielina/patología , Enfermedades Cardiovasculares/etiología , Estudios Transversales , Factores de Riesgo , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Envejecimiento/patología , Factores de Riesgo de Enfermedad Cardiaca , AguaRESUMEN
The temporal characteristics of adolescent neurodevelopment are shaped by a complex interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset of children aged 9-13 from the Adolescent Brain Cognitive Development (ABCD) study we tested the associations between pubertal status and brain maturation. Brain maturation was assessed using brain age prediction based on convolutional neural networks and minimally processed T1-weighted structural MRI data. Brain age prediction provided highly accurate and reliable estimates of individual age, with an overall mean absolute error of 0.7 and 1.4 years at the two timepoints respectively, and an intraclass correlation of 0.65. Linear mixed effects (LME) models accounting for age and sex showed that on average, a one unit increase in pubertal maturational level was associated with a 2.22 months higher brain age across time points (ß = 0.10, p < .001). Moreover, annualized change in pubertal development was weakly related to the rate of change in brain age (ß = .047, p = 0.04). These results demonstrate a link between sexual development and brain maturation in early adolescence, and provides a basis for further investigations of the complex sociobiological impacts of puberty on life outcomes.
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Encéfalo , Pubertad , Niño , Humanos , Adolescente , Lactante , Estudios Longitudinales , Maduración SexualRESUMEN
Objective: Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. Methods: Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). Results: Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, ß = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, ß = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (ß = -0.14, t = -1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (ß = -0.3, t = -3.43, p = .0007). This association was found within healthy controls (t = -3.72, p = .0003) and patients with bipolar disorder (t = -2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. Conclusion: Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.