Tag: Biomarker

Posted on

AI-Powered Pan-Cancer Map Reveals Tertiary Lymphoid Structures

Researchers at The University of Texas MD Anderson Cancer Center have developed a spatial atlas of specialized immune structures known as tertiary lymphoid structures (TLSs), across multiple cancer types, revealing how key features vary across tumor types and influence patient outcomes. Led by Linghua Wang, MD, PhD, professor of genomic medicine, executive director and head of the Center for Cellular Language Intelligence, associate member of the James P. Allison Institute™, and focus area co-lead with the Institute for Data Science in Oncology at UT MD Anderson, the team developed scalable artificial intelligence (AI) frameworks to detect, profile and classify TLSs from spatial omics data and routine pathology slides.

Tumors can contain TLSs with very different levels of organization, cellular composition and spatial relationships within tumor cells and the researchers’ newly reported study showed that these differences carry important biological and clinical information. The team suggests that their first-of-its-kind atlas indicates that TLS maturation state, spatial location, and composition within tumors may provide clinically meaningful information about cancer prognosis and treatment response. They also created a composite scoring system to more effectively stratify patients by prognosis and treatment response across different cancer types and treatment contexts.

“Prior to this study, most of the focus on TLSs as biomarkers was simply on whether or not they were present and, in some cases, whether they were mature,” Wang said. “Here, we show that we can go much deeper. TLSs in tumor tissues are much more complex than that. Their maturation state, spatial location and composition within tumors can tell us critical information about the tumor immune microenvironment, treatment response and clinical outcomes.”

Wang is senior author of the team’s published paper in Science, Titled “Pan-cancer spatial atlas of tertiary lymphoid structures.” In their paper the team concluded, “Together, this work provides a comprehensive landscape of TLS heterogeneity across cancers and establishes spatially defined TLS features and artificial intelligence (AI)–driven TLS classification as scalable tools for precision immuno-oncology.”

The immune system’s response to a tumor is a highly coordinated effort taking place within the tumor microenvironment (TME), the authors explained. In some tumors, immune cells come together to form organized structures called tertiary lymphoid structures, or TLSs. These structures operate as local immune “hubs,” bringing together B cells, T cells, antigen-presenting cells and other supporting cells that help coordinate antitumor immune responses. “TLSs frequently develop within the tumor microenvironment (TME) and have been observed across a broad range of human solid tumors, where they contribute to lymphocyte activation, B cell immunity, and regulation of antitumor immune responses,” they noted.

Previous studies have shown that TLSs—particularly those that are more mature—are often associated with better patient outcomes and improved responses to immunotherapy across a variety of cancer types. “The presence of TLSs has been linked to favorable responses to immune checkpoint blockade (ICB) and prolonged survival across multiple cancer types, fueling interest in TLSs as predictive biomarkers, prognostic indicators, and potential therapeutic targets. However, the presence of TLSs alone does not tell the whole story,” the scientists noted. “While it is well acknowledged that TLSs are important in cancer, our understanding of their cellular and molecular heterogeneity has remained limited, especially in their natural spatial context across large cohorts of human tumor samples.”

“Although TLS presence has been associated with enhanced immune activity and improved outcomes in several settings, their maturation states, spatial locations relative to tumors, and context-dependent associations have not been systematically characterized at a pan-cancer scale, limiting a unified view of TLS biology and clinical utility,” they stated.

For their reported study the team developed scalable computational frameworks to precisely detect, comprehensively profile and classify TLSs from spatial omics data. Leveraging this framework, the team built a pan-cancer spatial atlas of TLSs across 340 samples from 12 cancer types. This atlas allowed them to examine the TLS landscape in tumor tissues, to define how TLSs vary in key features, and identify transcriptional programs associated with TLS maturation. “By integrating transcriptomic, spatial, histopathological, and clinical data, we systematically characterized TLS abundance, spatial distribution, size, maturation states, and transcriptomic programs in 340 ST samples across 12 cancer types and examined their interactions with tumor cells and the surrounding TME,” they wrote in summary.

The study found that TLSs vary substantially across tissues. As TLSs mature, they become more organized and undergo coordinated changes in immune, stromal, and vascular components. Further, their proximity to tumor cells is associated with spatial gradients of tumor signaling. These findings suggest that TLS maturation and spatial context are linked to distinct tumor signaling environments and may reflect important features of the tumor immune microenvironment.

To make these insights more scalable, the team developed an AI framework to rapidly identify and classify TLSs from hematoxylin and eosin (H&E) whole-slide images (WSIs), pathology images that are routinely used in daily clinical care. Training this AI model makes the process of analyzing TLSs more easily translatable to the clinic, while also making the process significantly faster and more scalable. The AI framework enabled the researchers to go one step further, evaluating 25,088 TLSs from more than 3,000 whole-slide images across 10 independent cohorts and developing a TLS “composition score” for a given patient’s tumor. “By developing a scalable AI-enabled framework to detect and classify TLSs directly from routine H&E WSIs, we have extended TLS analysis from limited spatial datasets to thousands of tumors,” the team noted.

This composition score captures not only the number of TLSs, but also their maturation states within a tumor. This method significantly outperformed conventional TLS measures in stratifying patients by prognosis and treatment response, suggesting that a more detailed view of TLS biology, accounting for maturation state, may provide more clinically meaningful information than TLS presence alone. “… we developed a data-driven, unsupervised TLS-based patient stratification framework that outperformed existing approaches in prognostic evaluation,” they commented.

The TLS composite scoring approach must be validated in prospective clinical trials. If successful, the framework could support broader integration of TLS profiling into routine pathology workflows, since it uses routine pathology images. “Together, this work establishes generalizable and clinically scalable frameworks for TLS profiling and highlights TLS state composition as a key dimension of tumor immune organization with translational relevance. It also provides a foundation for prospective evaluation of TLS-informed biomarkers in clinical settings,” they stated.

The findings raise important biological and therapeutic questions, the researchers suggest. One important observation from the study is that many TLSs in tumor tissues remain immature, and some are located away from tumor regions rather than within or adjacent to tumor cells. This suggests that future studies should investigate how to promote TLSs toward more mature and functional states, and how to enhance their spatial interaction with tumor cells and the broader tumor microenvironment.

These efforts may help identify therapeutic strategies to promote effective TLS formation and maturation and enhance TLS-associated anti-tumor immune responses. In their paper the team concluded, “Prospective studies should test whether TLS composition improves risk and response modelling beyond established clinicopathologic and molecular predictors, and whether TLS-informed stratification can guide clinical trial design or therapeutic modulation strategies.”

The post AI-Powered Pan-Cancer Map Reveals Tertiary Lymphoid Structures appeared first on GEN – Genetic Engineering and Biotechnology News.

Posted on
<![CDATA[DSM’s future adds biomarkers and context, yet validity remains unresolved—are diagnoses real? Critics urge an evidence-based overhaul.]]>
Posted on

Pain-side-specific alteration of structural networks in trigeminal neuralgia: a connectome analysis

ObjectivesTrigeminal neuralgia (TN) involves disruption in the integrity of the white matter, the side-specific pain topology of these alterations at the network has yet to be defined. In this study, we investigated the lateralization of structural network architecture and nodal characteristics in TN patients.MethodsWhole-brain structural networks (90 × 90 connectivity matrices) were reconstructed from diffusion tensor imaging (DTI) tractography data of 30 TN patients and 20 matched controls. We applied Network-Based Statistics (NBS) to detect altered connectivity sub-networks, and graph theoretical analysis to profile global and nodal properties. Our analysis aimed to delineate changes that were specific to the painful side.ResultsNBS analysis revealed that structural connectivity formed subnetworks involving multiple functional networks. A subnetwork involving the anterior cingulate gyrus (ACG) and postcentral gyrus (S1) was identified on the painful side, indicating that TN stimulation may enhance structural connectivity between regions related to salience and somatosensory processing, thereby facilitating the acceleration of pain perception and response. On the non-pain side, we observed enhanced structural connections between visual and attention-related regions. The third subnetwork was characterized by widespread and non-focal reductions in fiber tract connectivity. However, despite these localized alterations, the global network properties of the brain in TN patients remained stable, with node-specific properties undergoing alterations in multiple brain regions, including the cuneus, inferior parietal lobule, and superior frontal gyrus.ConclusionHerein, we applied NBS and graph theoretical analysis to investigate changes in the structural brain networks of patients with TN. Analysis revealed that specific subnetworks and key nodes can be affected by TN. We also confirmed obvious differences in the involved subnetworks between pain and non-pain sides in TN patients. These findings suggest that these specific subnetworks and nodes could represent valuable biomarkers for clinical evaluation and intervention in TN patients.
Posted on

Detecting Alzheimer’s Early by Brain Scan or Blood

While Alzheimer’s disease accounts for about 60-70% of dementia diagnoses, the timing of these diagnoses occur late in disease progression. Identifying early signs of Alzheimer’s may allow individuals time to address potential risk factors in their lives.

“Alzheimer’s disease pathology begins years before symptoms emerge,” said Kristine Yaffe, MD, vice chair in the UCSF Department of Psychiatry and Behavioral Sciences. Yaffe and her team have spent years researching modifiable risk factors that may play a role in dementia development, including physical and cognitive activity, depression, smoking, and cardiovascular health.

A current trajectory of research into Alzheimer’s disease is focusing on ways to detect the disease early, before major symptoms manifest. Two new studies published in The Lancet present different methods for detecting Alzheimer’s disease that converge in their early detection ability.

Alzheimer’s disease is characterized by increased amounts of tau and amyloid-β plaques. Growing research indicates the importance of tau pathology and amyloid-β  accumulation at initiating changes to the brain associated with Alzheimer’s disease.

“Tau is the biology most closely tied to symptoms and future decline,” said Tharick Pascoal, MD, PhD, associate professor of psychiatry and neurology at the University of Pittsburgh and a behavioral neurologist at University of Pittsburgh School of Medicine. “If we can detect tau earlier and stage it more precisely, we can make better decisions about who is truly on an Alzheimer’s trajectory, which matters for clinical trials now and could shape clinical decision-making as new therapies emerge.”

Researchers at the University of Pittsburgh led by Guilherme Povala, PhD, and Bruna Bellaver, PhD, have focused their work on brain scan imaging to detect tau both more effectively, and earlier.

The team’s recently published work compared the ability of two tracers used in non-invasive PET scans to visualize Alzheimer’s-associated tangles of tau protein. To do this, participants underwent paired tau PET scans, the first using a standard tau tracer, Flortaucipir, or a new tracer, MK6240. Within 45 days of the PET scan, participants also received an amyloid-β PET scan and detailed cognitive assessments. Of the 775 initial participants, 682 completed all of the study components.

“Because participants received both tracer scans within a short window, we’re looking at the same moment in the disease course, so differences we see reflect the tracers, not changes over time,” explained co-lead author, Guilherme Povala, PhD, postdoctoral associate at the University of Pittsburgh, about the experimental design.

Analysis showed that imaging with the MK6240 tracer detected more tau than tracing with Flortaucipir. In participants with cognitive impairment and positive amyloid-β results, MK6240 identified move than twice as many tau-positive cases, 15% compared with six percent, which was an additional 23 patients per 100. Tracing with MK6240 also identified increased tau, 28% vs. 16% with Flortaucipir, in patients with mild cognitive impairment and dementia, resulting in 15 or 21 additional cases per 100 participants scanned.

“People typically seek evaluation because they have memory concerns or other symptoms,” pointed out co-lead author, Bruna Bellaver, PhD, research assistant professor of psychiatry at the University of Pittsburgh. “Tau PET is one tool that can help clinicians stage disease biology and make more informed decisions.”

Across the country in California, researchers from University of California, San Francisco, have drawn on their experience with blood biomarkers in an effort to develop a method for detecting Alzheimer’s before major symptoms develop.

The west coast study included 1,350 participants (out of an initial 2248) who were retained in the U.S. Coronary Artery Risk Development in Young Adults (CARDIA) study. Participant blood plasma was tested from samples that were collected at project year 35 (2020-2022) and five years prior. Plasma biomarkers for amyloid-β (Aβ42 and Aβ40) and tau (p-tau217) were measured and used to calculate p-tau217-to-Aβ42 and Aβ42-to-Aβ40 ratios and then those were compared to the cognitive assessment scores that were measured at the same time period. Any participants missing materials were excluded from the initial cohort. During analysis, participants were also classified by Alzheimer’s disease neuropathy status of negative, intermediate and positive based on amyloid PET-validated cutpoints for each marker.

Results from the initial analysis showed that participants with high biomarkers also had both lower processing speeds and lower executive function in the first time point. After five years, the group that initially had high levels of biomarkers now had a 2.5 to 4 times increased risk of rapid decline in verbal memory, and three to four times the risk for rapid decline in processing speed. Together, these data suggest a connection between the observed cognitive decline and increase in biomarkers.

While brain scans, and spinal fluid tests are often used and widely available, blood tests are less invasive and relatively inexpensive, potentially enabling more patients access to diagnostics along with early detection prior to symptom development. While there is potential for measuring blood biomarkers for clinical use, senior author, Yaffe, prescribes cautious optimism. “There’s a possibility of false positives and [these markers] can only be used for Alzheimer’s, not other dementias, meaning about 60% to 70% of all dementia cases,” she said. “But for some people who discover they have the biomarkers, testing could open a window to embark on interventions that may postpone Alzheimer’s onset.” More work is still needed to clarify the utility of both early detection methods in the clinic.

Overall, both studies show significant progress in developing strategies to identify dementia, and specifically Alzheimer’s disease, earlier. Earlier diagnosis may lead to earlier intervention and potentially more positive outcomes for patients. As Yaffee concluded, “Detecting the disease early means patients can target modifiable risk factors and maybe seek other care.”

The post Detecting Alzheimer’s Early by Brain Scan or Blood appeared first on Inside Precision Medicine.

Posted on

Plasma microRNA signatures in drug-naïve Romanian adolescents with first-episode psychosis

Psychotic disorders are a group of severe psychiatric conditions with onset typically occurring in adolescence or early adulthood. Despite significant efforts to identify clinically useful candidates, no validated biomarkers for psychiatric disorders currently exist. The diagnosis of psychotic disorders is exclusively based on clinical assessment, significantly affected by individual differences and symptom overlap. Although circulating microRNAs (miRNAs) have emerged as potential peripheral biomarkers for early diagnosis and disease evolution, most studies concentrate on adult, medicated cohorts. Studies on miRNA profiles in drug-naïve adolescents with first-episode psychosis (FEP) are scarce. This study aims to identify the plasma miRNA profile in treatment-naïve Romanian adolescents with first-episode psychosis and to compare it with that of age- and sex-matched healthy controls. The plasma from 14 adolescents, seven drug-naïve FEP and seven and age-matched controls (CTRL) aged 15–18 years was collected. Psychiatric symptoms were assessed using PANSS, HAM-D, and YMRS scales. The levels of 179 miRNAs were assessed using qRT-PCR. A case-control analysis on miRNAs levels between FEP and CTRL was performed, as well as correlations with clinical measures. Twenty-one miRNAs showed significantly lower levels and two higher levels in FEP patients compared to controls. After adjustment for multiple comparisons, miR-125a-5p, miR-205-5p, miR-145-5p, miR-363-3p, and miR-23b-3p remained statistically significant (FDR<0.05). Notably, miR-125a-5p, miR-23b-3p, and miR-146a-5p levels negatively correlated with psychotic, depressive, and manic symptom severity, while miR-16-5p and miR-363-3p positively correlated with symptom scores. Comparison with previous studies indicated limited overlap, reflecting potential influences of age, treatment status, and genetic or environmental factors. This work demonstrates that Romanian treatment-naïve adolescents with first-episode psychosis had a unique circulating miRNA profile correlated with symptom severity, indicating their potential as early-stage biomarkers. The results underscore the necessity of accounting for age, treatment status, and environmental variables in the interpretation of miRNA modifications in psychotic illnesses.
Posted on

AI Model Predicts Cancer Treatment Response from Tumor Genotype

Researchers at University of California, San Diego have developed a new artificial intelligence (AI) model that can translate a tumor’s complex genetic profile into predictions about how that cancer may respond to treatment. The foundation model, called MutationProjector, was trained on genomic data from more than 30,000 tumors across 10 solid cancer types, and validated through testing across multiple independent patient cohorts. Led by Trey Ideker, PhD, professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford, the researchers say the model offers a new framework for connecting cancer mutations to the biological pathways that drive treatment response.

“Genetic sequencing is already routine in cancer care, but we still struggle to fully interpret the many mutations found in a patient’s tumor,” said Ideker, who also holds a second appointment at UC San Diego Jacobs School of Engineering and is a member of UC San Diego Moores Cancer Center. “Our goal with MutationProjector was to build a general-purpose model that can learn from tens of thousands of tumor genomes and turn those mutation patterns into more precise predictions about treatment response.”

Ideker is co-senior and co-corresponding author of the team’s published paper in Cancer Discovery, titled “A foundation model of cancer genotype enables precise predictions of therapeutic response,” in which the authors stated, “These results establish a unifying framework for connecting tumor genotypes to biological mechanisms and therapeutic outcomes.”

Following a cancer diagnosis, one of the next steps is often genetic testing, which helps doctors classify the tumor and decide which treatments to pursue. “DNA sequencing panels—and in particular those that broadly identify alterations in cancer-associated genes—have been widely adopted in the clinic due to their relatively low cost, rapid turnaround, and established relevance to treatment outcomes,” the authors explained.

Trey Ideker is a professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford. [Erik Jepsen / UC San Diego]
Trey Ideker is a professor of medicine at UC San Diego School of Medicine and director of the Big Data Institute at the University of Oxford. [Erik Jepsen / UC San Diego]

However, while genetic testing is relatively low cost, fast, and has a strong track record in cases where validated genetic biomarkers are available, those cases remain limited, because this type of treatment stratification is currently based on only a small number of known biomarkers. Today, only about 8% of cases are successfully matched to an FDA-approved therapy on the basis of genetics and usually on the basis of a single gene, the team continued. “While this situation may reflect the incomplete scope of genes covered by current sequencing panels, it clearly also reflects a fundamental lack of knowledge about how gene mutations should be interpreted.”

They suggest that an “average” tumor has approximately 11 distinct genetic alterations identified by clinical sequencing, representing a potentially rich source of molecular information, if this information could be used to help select treatment. One of the challenges to matching cancer mutations with treatment outcomes is that most mutations are rare, the investigators pointed out. Another is that individual biomarkers do not function in isolation, but act together to influence drug response.

Unlike existing approaches that rely on a small number of biomarkers, MutationProjector analyzes the broader combination of genetic alterations present in a tumor. The model then uses this information to generate a compact representation of the tumor’s biological state, helping researchers interpret which molecular pathways may be disrupted and, by extension, which treatments may be most effective. “Foundation models, which are pre-trained on large datasets and then applied to solve diverse new challenges with relatively few samples, are especially well positioned to advance precision oncology,” Ideker and team noted.

The investigators trained their foundational model, MutationProjector, using genetic profile data from more than 30,000 tumors samples across different cancer types. They then showed that across several independent cohorts of cancer patients, including those with bladder cancer, lung cancer and melanoma, MutationProjector matched or exceeded existing methods for predicting response to common immunotherapy and chemotherapy treatments. The model also identified both known and unexpected biomarkers associated with treatment outcomes, which could help improve current approaches to genetic testing and patient stratification.

“When applied to predict immunotherapy or chemotherapy resistance across multiple cancer types and cohorts, MutationProjector achieves or exceeds state-of-the-art performance in all contexts,” the team wrote. “It identifies unexpected biomarkers, including KMT2D mutation in immunotherapy sensitivity and joint alteration of SMARCA4 and STK11 in immunotherapy resistance.”

JungHo Kong, PhD, first author of the study and a postdoctoral researcher in the department of medicine at UC San Diego School of Medicine, said, “Many cancer mutations are individually rare, which makes them difficult to study one at a time. By pretraining on a large collection of tumors and integrating molecular network knowledge, MutationProjector can detect patterns that would be easy to miss with conventional biomarker approaches. That gives us a way to move from long lists of mutations toward a more functional understanding of the tumor.”

First study author JungHo Kong, shown here, is a postdoctoral researcher at UC San Diego School of Medicine. [UC San Diego Health Sciences]
First study author JungHo Kong, shown here, is a postdoctoral researcher at UC San Diego School of Medicine. [UC San Diego Health Sciences]

The researchers emphasize that the model was designed not only to make predictions, but also to provide insight into why those predictions are made, which could help when refining biomarkers and treatment strategies. This interpretability is especially important in precision oncology, where clinicians need to understand how tumor genotypes relate to treatment decisions.

The team also hopes to expand the model to additional cancer types and data sources, including international cancer genome datasets and other forms of clinical information, such as imaging, transcriptomics, and electronic health records. “While 30,000+ genomes representing 10 solid tumor types were considered in our study, numerous additional tumor samples are available for expansion of MutationProjector to tumor types such as pancreatic cancer, prostate cancer or sarcomas,” the authors said. “Other future studies should explore the extent to which the MutationProject concept can be applied to further clinical tasks of interest, including application to liquid biopsies of circulating tumor DNAs for early cancer detection.”

Ideker added, “Our results suggest that tumor genome foundation models may help extend the clinical value of sequencing beyond a handful of well-known genes. This could support a more comprehensive and biologically grounded approach to precision oncology.”

The post AI Model Predicts Cancer Treatment Response from Tumor Genotype appeared first on GEN – Genetic Engineering and Biotechnology News.

Posted on

Task-state P300 and functional brain network abnormalities in adolescent major depressive disorder: a Stroop paradigm study

BackgroundCognitive control deficits are a core feature of adolescent major depressive disorder (MDD), yet the associated task-state neurophysiological mechanisms remain poorly characterized. This study investigated electrophysiological alterations in MDD using a Stroop color-word task.MethodsTwenty-two adolescents with MDD and fifteen age- and sex-matched healthy controls (HC) completed the task during 32-channel EEG recording. We analyzed P300 amplitude and latency, 1-30Hz power spectral density (PSD) in key cortical regions, and task-based functional connectivity using the phase locking value (PLV). A support vector machine (SVM) classifier with leave-one-out cross-validation was employed to assess the diagnostic utility of the multimodal features.ResultsRelative to HC, the MDD group exhibited significantly prolonged incongruent trial reaction times, reduced P300 amplitude at centro-parietal electrodes (Oz, PO7, O2), and enhanced alpha/beta-band PSD in occipitotemporal regions. Functional connectivity analysis revealed a task-state shift from a frontoparietal to an occipitotemporal network. The multimodal SVM model achieved 86.49% classification accuracy (AUC = 0.86).ConclusionTask-specific P300 hypoactivity, aberrant oscillatory dynamics, and functional network reorganization collectively distinguish adolescent MDD from HC. These findings provide convergent neurophysiological evidence for impaired cognitive control in MDD and highlight the potential of preliminary candidate EEG biomarkers for early identification, prognostic assessment, and monitoring treatment response in adolescent MDD.
Posted on

Machine learning for Alzheimer’s disease progression under extreme class imbalance

BackgroundTimely identification of individuals at risk for Alzheimer’s disease (AD) progression remains a major clinical challenge. Traditional cognitive assessments provide limited prognostic insight, while many machine learning (ML) models rely on costly biomarkers or poorly interpretable algorithms that limit clinical scalability. This study evaluated whether widely available baseline demographic, clinical, and cognitive measures could support short-term progression prediction using interpretable ML methods under extreme class imbalance.MethodsWe analyzed 3,240 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of whom 2,423 had valid 24-month follow-up data. The primary outcome was strict unidirectional diagnostic worsening within 24 months (13 events; 0.5%). Baseline demographic, clinical, and cognitive variables were used to train XGBoost and logistic regression models under natural class imbalance using stratified k-fold cross-validation with out-of-fold predictions. Model performance was evaluated using AUROC, area under the precision-recall curve (AUPRC), calibration analyses, and bootstrap confidence intervals. Sensitivity analyses evaluated cost-sensitive learning, threshold optimization, and alternative imputation strategies (KNN and MICE). Longitudinal mixed-effects modeling was conducted separately to characterize cognitive decline and was not used as input to the predictive models. SHAP (Shapley Additive Explanations) quantified feature contributions.ResultsUnder natural class imbalance, XGBoost achieved AUROC = 0.912 and AUPRC = 0.051, while logistic regression achieved AUROC = 0.787 and AUPRC = 0.038. Although discrimination exceeded baseline prevalence, precision remained low and threshold optimization produced substantial false-positive burdens, limiting immediate clinical applicability. Cost-sensitive learning did not materially improve performance. MICE imputation produced results comparable to median imputation, whereas KNN imputation reduced performance. SHAP analyses identified baseline cognitive severity, functional measures, and diagnostic status as dominant predictors. Mixed-effects modeling confirmed significant cognitive decline over time (β = −0.027 points/month, p < 0.001).ConclusionAccessible baseline clinical and cognitive variables contain measurable but limited predictive signal for short-term AD progression under extreme event scarcity. These findings should be interpreted as an early-stage proof-of-concept rather than a clinically deployable decision-support tool. External validation remains necessary before clinical translation.
Posted on

The triple-hit hypothesis of Alzheimer’s disease: blood–brain barrier breakdown, infection, and neuroimmune activation as a unified etiological framework

Studies of Alzheimer’s disease (AD) have long been dominated by the amyloid cascade hypothesis, although mounting evidence suggests that amyloid-β (Aβ) deposition might be a late downstream event, rather than the initiating trigger of AD. Here, I propose a unifying Triple-Hit Hypothesis in which AD develops through a sequential interaction among three causative processes that have been individually implicated before in the onset of Alzheimer’s disease: (1) early blood–brain barrier (BBB) breakdown, (2) entry or reactivation of microbial agents within the brain, and (3) maladaptive innate immune responses that produce chronic neuroinflammation and Aβ accumulation. Converging data from human imaging, neuropathology, infectious disease studies, genetics, and vascular medicine could suggest that these three processes, which were in some previous studies linked to AD, might not be independent but rather that their temporal synergy could drive disease progression. This new conceptual framework integrates long-standing but fragmented lines of evidence and, if confirmed by experimental studies, offers the possibility for the development of new diagnostic biomarkers, new therapeutic entry points, and prevention strategies for AD. I argue that understanding AD as a disorder of the blood-brain barrier, immunity, and host–pathogen interactions should be taken into account in future research on the etiology and clinical progression of AD.
Posted on

Association between neurodegenerative plasma biomarkers and geriatric depression in older adults with and without clinical dementia in Kinshasa, Democratic Republic of the Congo

IntroductionPrevious Western studies have shown an association between neurodegenerative plasma biomarkers and geriatric depression. However, few studies have examined this relationship in sub-Saharan African countries. This article examines this association in older adults with or without clinical dementia in Kinshasa, Democratic Republic of Congo.MethodsThis cross-sectional study included eighty-one people aged 65 years and older assessed for dementia using the Community Dementia Screening Instrument (CSID) and the Alzheimer’s Disease Questionnaire. Depression and cognitive impairment were assessed using the Geriatric Depression Scale and the African Neuropsychological Battery (ANB), respectively. Plasma biomarkers measured included β-amyloid (Aβ) 40/42, phosphorylated tau 181 (p-tau), neurofilament light chain (NfL), glial fibrillar acid protein (GFAP), interleukins (IL-1β, IL-10), and tumor necrosis factor-α (TNF-α). Logistic regression models were used to estimate the likelihood that these biomarkers are associated with depression, adjusting for sex, age, and education level.ResultsThe mean age of the participants was 72.9 years. Of those suspected of suffering from Alzheimer’s disease (AD), 80% had geriatric depression. High blood pressure was common in participants with suspected AD (60%). People with dementia had significantly lower cognitive scores on all ANB tests than those without dementia. GFAP and NfL were significantly associated with higher odds of geriatric depression, with odds ratios of 1.98 (1.17–3.67; p = 0.02) and 1.76 (1.06–3.20; p = 0.04), respectively. Other biomarkers were not associated with depression.ConclusionThis study suggests a potential role of nonspecific biomarkers of AD, neurodegeneration, and astrocytic glial activation in geriatric depression in older adults in sub-Saharan Africa.