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1.
Proc Natl Acad Sci U S A ; 120(52): e2300842120, 2023 Dec 26.
Artículo en Inglés | MEDLINE | ID: mdl-38127979

RESUMEN

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.


Asunto(s)
Neoplasias Encefálicas , Imagen por Resonancia Magnética , Humanos , Imagen por Resonancia Magnética/métodos , Encéfalo/patología , Mapeo Encefálico/métodos , Genómica , Neoplasias Encefálicas/patología
2.
Cereb Cortex ; 34(7)2024 Jul 03.
Artículo en Inglés | MEDLINE | ID: mdl-39077916

RESUMEN

The lifetime effects of repetitive head impacts have captured considerable public and scientific interest over the past decade, yet a knowledge gap persists in our understanding of midlife neurological well-being, particularly in amateur level athletes. This study aimed to identify the effects of lifetime exposure to sports-related head impacts on brain morphology in retired, amateur athletes. This cross-sectional study comprised of 37 former amateur contact sports athletes and 21 age- and sex-matched noncontact athletes. High-resolution anatomical, T1 scans were analyzed for the cortical morphology, including cortical thickness, sulcal depth, and sulcal curvature, and cognitive function was assessed using the Dementia Rating Scale-2. Despite no group differences in cognitive functions, the contact group exhibited significant cortical thinning particularly in the bilateral frontotemporal regions and medial brain regions, such as the cingulate cortex and precuneus, compared to the noncontact group. Deepened sulcal depth and increased sulcal curvature across all four lobes of the brain were also notable in the contact group. These data suggest that brain morphology of middle-aged former amateur contact athletes differs from that of noncontact athletes and that lifetime exposure to repetitive head impacts may be associated with neuroanatomical changes.


Asunto(s)
Atletas , Corteza Cerebral , Imagen por Resonancia Magnética , Humanos , Masculino , Femenino , Corteza Cerebral/diagnóstico por imagen , Corteza Cerebral/patología , Corteza Cerebral/anatomía & histología , Estudios Transversales , Persona de Mediana Edad , Traumatismos en Atletas/patología , Traumatismos en Atletas/diagnóstico por imagen , Anciano , Conmoción Encefálica/patología , Conmoción Encefálica/diagnóstico por imagen , Cognición/fisiología
3.
Int Psychogeriatr ; : 1-12, 2024 Jan 25.
Artículo en Inglés | MEDLINE | ID: mdl-38268483

RESUMEN

OBJECTIVES: Late-life depression (LLD) is common and frequently co-occurs with neurodegenerative diseases of aging. Little is known about how heterogeneity within LLD relates to factors typically associated with neurodegeneration. Varying levels of anxiety are one source of heterogeneity in LLD. We examined associations between anxiety symptom severity and factors associated with neurodegeneration, including regional brain volumes, amyloid beta (Aß) deposition, white matter disease, cognitive dysfunction, and functional ability in LLD. PARTICIPANTS AND MEASUREMENTS: Older adults with major depression (N = 121, Ages 65-91) were evaluated for anxiety severity and the following: brain volume (orbitofrontal cortex [OFC], insula), cortical Aß standardized uptake value ratio (SUVR), white matter hyperintensity (WMH) volume, global cognition, and functional ability. Separate linear regression analyses adjusting for age, sex, and concurrent depression severity were conducted to examine associations between anxiety and each of these factors. A global regression analysis was then conducted to examine the relative associations of these variables with anxiety severity. RESULTS: Greater anxiety severity was associated with lower OFC volume (ß = -68.25, t = -2.18, p = .031) and greater cognitive dysfunction (ß = 0.23, t = 2.46, p = .016). Anxiety severity was not associated with insula volume, Aß SUVR, WMH, or functional ability. When examining the relative associations of cognitive functioning and OFC volume with anxiety in a global model, cognitive dysfunction (ß = 0.24, t = 2.62, p = .010), but not OFC volume, remained significantly associated with anxiety. CONCLUSIONS: Among multiple factors typically associated with neurodegeneration, cognitive dysfunction stands out as a key factor associated with anxiety severity in LLD which has implications for cognitive and psychiatric interventions.

4.
J Cardiovasc Nurs ; 39(3): E80-E85, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-39137265

RESUMEN

BACKGROUND: Incidence of cognitive impairment and its consequences have not been fully examined in heart failure (HF). OBJECTIVE: The aim of this study was to examine associations of HF with cognitive decline, frequencies and risks of, and time-to-develop mild cognitive impairment (MCI) or dementia during 15-year follow-up. METHODS: For this retrospective cohort study, data were retrieved from the National Alzheimer's Coordinating Center. Cognitive decline was assessed using the Uniform Data Set neuropsychological battery. Development of MCI and dementia was assessed using clinically diagnosed cognitive status. RESULTS: Compared with participants without HF (n = 12 904), participants with HF (n = 256) had more decline in attention, executive function, and memory while controlling for covariates including apolipoprotein E4. Participants with HF developed MCI or dementia more frequently (44.9% vs 34.4%), developed dementia faster from normal cognition, and had a lower risk of dementia from MCI after controlling for covariates (hazard ratio, 0.71) than participants without HF. CONCLUSIONS: Heart failure was associated with accelerated cognitive decline.


Asunto(s)
Disfunción Cognitiva , Demencia , Insuficiencia Cardíaca , Humanos , Insuficiencia Cardíaca/complicaciones , Insuficiencia Cardíaca/epidemiología , Disfunción Cognitiva/epidemiología , Femenino , Masculino , Anciano , Demencia/epidemiología , Demencia/complicaciones , Estudios Retrospectivos , Anciano de 80 o más Años
5.
Alzheimers Dement ; 2024 May 21.
Artículo en Inglés | MEDLINE | ID: mdl-38770829

RESUMEN

INTRODUCTION: Alzheimer's disease (AD) pathology is defined by ß-amyloid (Aß) plaques and neurofibrillary tau, but Lewy bodies (LBs; 𝛼-synuclein aggregates) are a common co-pathology for which effective biomarkers are needed. METHODS: A validated α-synuclein Seed Amplification Assay (SAA) was used on recent cerebrospinal fluid (CSF) samples from 1638 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, 78 with LB-pathology confirmation at autopsy. We compared SAA outcomes with neuropathology, Aß and tau biomarkers, risk-factors, genetics, and cognitive trajectories. RESULTS: SAA showed 79% sensitivity and 97% specificity for LB pathology, with superior performance in identifying neocortical (100%) compared to limbic (57%) and amygdala-predominant (60%) LB-pathology. SAA+ rate was 22%, increasing with disease stage and age. Higher Aß burden but lower CSF p-tau181 associated with higher SAA+ rates, especially in dementia. SAA+ affected cognitive impairment in MCI and Early-AD who were already AD biomarker positive. DISCUSSION: SAA is a sensitive, specific marker for LB-pathology. Its increase in prevalence with age and AD stages, and its association with AD biomarkers, highlights the clinical importance of α-synuclein co-pathology in understanding AD's nature and progression. HIGHLIGHTS: SAA shows 79% sensitivity, 97% specificity for LB-pathology detection in AD. SAA positivity prevalence increases with disease stage and age. Higher Aß burden, lower CSF p-tau181 linked with higher SAA+ rates in dementia. SAA+ impacts cognitive impairment in early disease stages. Study underpins need for wider LB-pathology screening in AD treatment.

6.
Alzheimers Dement ; 20(4): 2680-2697, 2024 04.
Artículo en Inglés | MEDLINE | ID: mdl-38380882

RESUMEN

INTRODUCTION: Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages. METHODS: Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition. RESULTS: Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures. DISCUSSION: We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials. HIGHLIGHTS: Mutation position influences Aß burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aß burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.


Asunto(s)
Enfermedad de Alzheimer , Amiloidosis , Enfermedades de los Pequeños Vasos Cerebrales , Humanos , Enfermedad de Alzheimer/diagnóstico por imagen , Enfermedad de Alzheimer/genética , Enfermedad de Alzheimer/patología , Enfermedades de los Pequeños Vasos Cerebrales/diagnóstico por imagen , Enfermedades de los Pequeños Vasos Cerebrales/genética , Enfermedades de los Pequeños Vasos Cerebrales/complicaciones , Imagen de Difusión Tensora , Imagen por Resonancia Magnética , Mutación/genética , Presenilina-1/genética
7.
PEC Innov ; 4: 100282, 2024 Dec.
Artículo en Inglés | MEDLINE | ID: mdl-38706495

RESUMEN

Objectives: Lack of awareness of Alzheimer's disease (AD) among Black Americans may undermine their ability to identify potential AD risk. We examined Black Americans' perceptions and knowledge of AD, and views of a healthy brain, which may contribute to the development of effective and culturally sensitive strategies to address racial disparities in AD. Methods: We conducted a mixed-methods study, integrating a cross-sectional survey of 258 older (>55 years) Black participants and qualitative interviews with a sub-sample of N = 29. Both data sets were integrated to inform the results. Results: Participants endorsed having little knowledge of AD. While most participants reported practicing a healthy lifestyle to promote a healthy brain, the range of activities listed were limited. Participants made several suggestions to increase AD awareness, which includes using AD educational materials containing information that would benefit the whole family, not only older adults. Outreach approaches that address both individual behaviors and structural factors were also encouraged. Conclusion: Our findings identify ongoing needs to improve AD awareness among traditionally under-represented groups. Innovation: The study utilized novel approaches to examine participants' perspectives of AD that included a diverse sample of research naïve participants, and integrated exploration of participants' views of AD and brain health.

8.
AMIA Jt Summits Transl Sci Proc ; 2024: 439-448, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38827045

RESUMEN

Over the past decade, Alzheimer's disease (AD) has become increasingly severe and gained greater attention. Mild Cognitive Impairment (MCI) serves as an important prodromal stage of AD, highlighting the urgency of early diagnosis for timely treatment and control of the condition. Identifying the subtypes of MCI patients exhibits importance for dissecting the heterogeneity of this complex disorder and facilitating more effective target discovery and therapeutic development. Conventional method uses clinical measurements such as cognitive score and neurophysical assessment to stratify MCI patients into two groups with early MCI (EMCI) and late MCI (LMCI), which shows their progressive stages. However, such clinical method is not designed to de-convolute the heterogeneity of the disorder. This study uses a data-driven approach to divide MCI patients into a novel grouping of two subtypes based on an amyloid dataset of 68 cortical features from positron emission tomography (PET), where each subtype has a homogeneous cortical amyloid burden pattern. Experimental evaluation including visual two-dimensional cluster distribution, Kaplan-Meier plot, genetic association studies, and biomarker distribution analysis demonstrates that the identified subtypes performs better across all metrics than the conventional EMCI and LMCI grouping.

9.
Res Sq ; 2024 Feb 08.
Artículo en Inglés | MEDLINE | ID: mdl-38405816

RESUMEN

Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a significant set of SNPs associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

10.
iScience ; 27(3): 109212, 2024 Mar 15.
Artículo en Inglés | MEDLINE | ID: mdl-38433927

RESUMEN

Traditional loss functions such as cross-entropy loss often quantify the penalty for each mis-classified training sample without adequately considering its distance from the ground truth class distribution in the feature space. Intuitively, the larger this distance is, the higher the penalty should be. With this observation, we propose a penalty called distance-weighted Sinkhorn (DWS) loss. For each mis-classified training sample (with predicted label A and true label B), its contribution to the DWS loss positively correlates to the distance the training sample needs to travel to reach the ground truth distribution of all the A samples. We apply the DWS framework with a neural network to classify different stages of Alzheimer's disease. Our empirical results demonstrate that the DWS framework outperforms the traditional neural network loss functions and is comparable or better to traditional machine learning methods, highlighting its potential in biomedical informatics and data science.

11.
Mach Learn Med Imaging ; 14349: 144-154, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38463442

RESUMEN

Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net's ability to elucidate biomarker differences across dementia stages.

12.
AMIA Jt Summits Transl Sci Proc ; 2024: 211-220, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38827072

RESUMEN

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.

13.
J Alzheimers Dis ; 99(2): 715-727, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38728189

RESUMEN

Background: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.


Asunto(s)
Enfermedad de Alzheimer , Aprendizaje Profundo , Polimorfismo de Nucleótido Simple , Enfermedad de Alzheimer/genética , Humanos , Polimorfismo de Nucleótido Simple/genética , Redes Reguladoras de Genes/genética
14.
Artículo en Inglés | MEDLINE | ID: mdl-38584725

RESUMEN

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

15.
Alzheimers Res Ther ; 16(1): 5, 2024 01 09.
Artículo en Inglés | MEDLINE | ID: mdl-38195609

RESUMEN

BACKGROUND: Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers. METHODS: We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification. RESULTS: Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs. CONCLUSIONS: Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.


Asunto(s)
Enfermedad de Alzheimer , Disfunción Cognitiva , MicroARNs , Humanos , Enfermedad de Alzheimer/genética , Especies Reactivas de Oxígeno , MicroARNs/genética , Biomarcadores
16.
J Alzheimers Dis ; 98(1): 319-332, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-38393900

RESUMEN

Background: The Cognitive Change Index (CCI) is a widely-used measure of self-perceived cognitive ability and change. Unfortunately, it is unclear if the CCI predicts future cognitive and clinical decline. Objective: We evaluated baseline CCI to predict transition from normal cognition to cognitive impairment in nondemented older adults and in predementia groups including, subjective cognitive decline, motoric cognitive risk syndrome, and mild cognitive impairment. Different versions of the CCI were assessed to uncover any differential risk sensitivity. We also examined the effect of ethnicity/race on CCI. Methods: Einstein Aging Study participants (N = 322, Mage = 77.57±4.96, % female=67.1, Meducation = 15.06±3.54, % non-Hispanic white = 46.3) completed an expanded 40-item CCI version (CCI-40) and neuropsychological evaluation (including Clinical Dementia Rating Scale [CDR], Montreal Cognitive Assessment, and Craft Story) at baseline and annual follow-up (Mfollow - up=3.4 years). CCI-40 includes the original 20 items (CCI-20) and the first 12 memory items (CCI-12). Linear mixed effects models (LME) and generalized LME assessed the association of CCI total scores at baseline with rate of decline in neuropsychological tests and CDR. Results: In the overall sample and across predementia groups, the CCI was associated with rate of change in log odds on CDR, with higher CCI at baseline predicting faster increase in the odds of being impaired on CDR. The predictive validity of the CCI broadly held across versions (CCI-12, 20, 40) and ethnic/racial groups (non-Hispanic black and white). Conclusions: Self-perception of cognitive change on the CCI is a useful marker of dementia risk in demographically/clinically diverse nondemented samples. All CCI versions successfully predicted decline.


Asunto(s)
Trastornos del Conocimiento , Disfunción Cognitiva , Humanos , Femenino , Anciano , Masculino , Disfunción Cognitiva/diagnóstico , Disfunción Cognitiva/psicología , Pruebas Neuropsicológicas , Cognición , Envejecimiento
17.
Med Image Anal ; 97: 103231, 2024 Jun 14.
Artículo en Inglés | MEDLINE | ID: mdl-38941858

RESUMEN

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

18.
medRxiv ; 2024 Jul 11.
Artículo en Inglés | MEDLINE | ID: mdl-39040205

RESUMEN

Identification of genetic alleles associated with both Alzheimer's disease (AD) and concussion severity/recovery could help explain the association between concussion and elevated dementia risk. However, there has been little investigation into whether AD risk genes associate with concussion severity/recovery, and the limited findings are mixed. We used AD polygenic risk scores (PRS) and APOE genotypes to investigate any such associations in the NCAA-DoD Grand Alliance CARE Consortium (CARE) dataset. We assessed six outcomes in 931 total participants. The outcomes were two concussion recovery measures (number of days to asymptomatic status, number of days to return to play (RTP)) and four concussion severity measures (scores on SAC and BESS, SCAT symptom severity, and total number of symptoms). We calculated PRS using a published score [1] and performed multiple linear regression (MLR) to assess the relationship of PRS with the outcomes. We also used t-tests and chi-square tests to examine outcomes by APOE genotype, and MLR to analyze outcomes in European and African genetic ancestry subgroups. Higher PRS was associated with longer injury to RTP in the normal RTP (<24 days) subgroup ( p = 0.024), and one standard deviation increase in PRS resulted in a 9.89 hour increase to the RTP interval. There were no other consistently significant effects, suggesting that high AD genetic risk is not strongly associated with more severe concussions or poor recovery in young adults. Future studies should attempt to replicate these findings in larger samples with longer follow-up using PRS calculated from diverse populations.

19.
JNCI Cancer Spectr ; 8(2)2024 Feb 29.
Artículo en Inglés | MEDLINE | ID: mdl-38556480

RESUMEN

PURPOSE: Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment. METHODS: We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function. RESULTS: Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score. CONCLUSIONS: Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.


Asunto(s)
Neoplasias de la Mama , Supervivientes de Cáncer , Disfunción Cognitiva , Hominidae , Humanos , Femenino , Animales , Anciano , Supervivientes de Cáncer/psicología , Neoplasias de la Mama/complicaciones , Neoplasias de la Mama/psicología , Estudios Prospectivos , Disfunción Cognitiva/diagnóstico , Disfunción Cognitiva/epidemiología , Disfunción Cognitiva/etiología
20.
medRxiv ; 2024 Jan 24.
Artículo en Inglés | MEDLINE | ID: mdl-38313266

RESUMEN

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

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