Trajectory Of Disease Progression Research Articles

The extracellular vesicles in HIV infection and progression: mechanisms, and theranostic implications.

Extracellular vesicles (EVs), these minute yet mighty cellular messengers are redefining our understanding of a spectrum of diseases, from cancer to cardiovascular ailments, neurodegenerative disorders, and even infectious diseases like HIV. Central to cellular communication, EVs emerge as both potent facilitators and insightful biomarkers in immune response and the trajectory of disease progression. This review ventures deep into the realm of EVs in HIV-unraveling their pivotal roles in diagnosis, disease mechanism unravelling, and therapeutic innovation. With a focus on HIV, we will highlights the transformative potential of EVs in both diagnosing and treating this formidable virus. Unveiling the intricate dance between EVs and HIV, the review aims to shed light on novel therapeutic strategies that could significantly benefit HIV therapy, potentially even leading to the eradication of HIV.

Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment.

Progression analysis versus traditional methods to quantify slowing of disease progression in Alzheimer’s disease

BackgroundThe clinical meaningfulness of the effects of recently approved disease-modifying treatments (DMT) in Alzheimer’s disease is under debate. Available evidence is limited to short-term effects on clinical rating scales which may be difficult to interpret and have limited intrinsic meaning to patients. The main value of DMTs accrues over the long term as they are expected to cause a delay or slowing of disease progression. While awaiting such evidence, the translation of short-term effects to time delays or slowing of progression could offer a powerful and readily interpretable representation of clinical outcomes.MethodsWe simulated disease progression trajectories representing two arms, active and placebo, of a hypothetical clinical trial of a DMT. The placebo arm was simulated based on estimated mean trajectories of clinical dementia rating scale–sum of boxes (CDR-SB) recordings from amyloid-positive subjects with mild cognitive impairment (MCI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The active arm was simulated to show an average slowing of disease progression versus placebo of 20% at each visit. The treatment effects in the simulated trials were estimated with a progression model for repeated measures (PMRM) and a mixed model for repeated measures (MMRM) for comparison. For PMRM, the treatment effect is expressed in units of time (e.g., days) and for MMRM in units of the outcome (e.g., CDR-SB points). PMRM results were implemented in a health economics Markov model extrapolating disease progression and death over 15 years.ResultsThe PMRM model estimated a 19% delay in disease progression at 18 months and 20% (~ 7 months delay) at 36 months, while the MMRM model estimated a 25% reduction in CDR-SB (~ 0.5 points) at 36 months. The PMRM model had slightly greater power compared to MMRM. The health economic model based on the estimated time delay suggested an increase in life expectancy (10 months) without extending time in severe stages of disease.ConclusionPMRM methods can be used to estimate treatment effects in terms of slowing of progression which translates to time metrics that can be readily interpreted and appreciated as meaningful outcomes for patients, care partners, and health care practitioners.

Astroglial activation: Current concepts and future directions.

Astrocytes are abundantly and ubiquitously expressed cell types with diverse functions throughout the central nervous system. Astrocytes show remarkable plasticity and exhibit morphological, molecular, and functional remodeling in response to injury, disease, or infection of the central nervous system, as evident in neurodegenerative diseases. Astroglial mediated inflammation plays a prominent role in the pathogenesis of neurodegenerative diseases. This review focus on the role of astrocytes as essential players in neuroinflammation and discuss their morphological and functional heterogeneity in the normal central nervous system and explore the spatial and temporal variations in astroglial phenotypes observed under different disease conditions. This review discusses the intimate relationship of astrocytes to pathological hallmarks of neurodegenerative diseases. Finally, this review considers the putative therapeutic strategies that can be deployed to modulate the astroglial functions in neurodegenerative diseases. HIGHLIGHTS: Astroglia mediated neuroinflammation plays a key role in the pathogenesis of neurodegenerative diseases. Activated astrocytes exhibit diverse phenotypes in a region-specific manner in brain and interact with β-amyloid, tau, and α-synuclein species as well as with microglia and neuronal circuits. Activated astrocytes are likely to influence the trajectory of disease progression of neurodegenerative diseases, as determined by the stage of disease, individual susceptibility, and state of astroglial priming. Modulation of astroglial activation may be a therapeutic strategy at various stages in the trajectory of neurodegenerative diseases to modify the disease course.

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings.

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Patient Portal Use Among Family Caregivers of Individuals With Dementia and Cancer: Regression Analysis From the National Study of Caregiving.

Family caregivers are often inexperienced and require information from clinic visits to effectively provide care for patients. Despite reported deficiencies, 68% of health systems facilitate sharing information with family caregivers through the patient portal. The patient portal is especially critical in the context of serious illnesses, like advanced cancer and dementia, where caregiving is intense and informational needs change over the trajectory of disease progression. The objective of our study was to analyze a large, nationally representative sample of family caregivers from the National Study of Caregiving (NSOC) to determine individual characteristics and demographic factors associated with patient portal use among family caregivers of persons living with dementia and those living with cancer. We conducted a secondary data analysis using data from the 2020 NSOC sample of family caregivers linked to National Health and Aging Trends Study. Weighted regression analysis by condition (ie, dementia or cancer) was used to examine associations between family caregiver use of the patient portal and demographic variables, including age, race or ethnicity, gender, employment status, caregiver health, education, and religiosity. A total of 462 participants (representing 4,589,844 weighted responses) were included in our analysis. In the fully adjusted regression model for caregivers of persons living with dementia, Hispanic ethnicity was associated with higher odds of patient portal use (OR: 2.81, 95% CI 1.05-7.57; P=.04), whereas qualification lower than a college degree was associated with lower odds of patient portal use by family caregiver (OR 0.36, 95% CI 0.18-0.71; P<.001. In the fully adjusted regression model for caregivers of persons living with cancer, no variables were found to be statistically significantly associated with patient portal use at the .05 level. In our analysis of NSOC survey data, we found differences between how dementia and cancer caregivers access the patient portal. As the patient portal is a common method of connecting caregivers with information from clinic visits, future research should focus on understanding how the portal is used by the groups we have identified, and why.

Alternative Endpoints and Models for AD Prevention Trials

AbstractBackgroundThere are several challenges for AD prevention trials. First, the subtle cognitive change in asymptomatic disease stages requires large sample sizes, e.g. 400‐500/arm for two phase 3 confirmatory trials, or 800‐900/arm for a single phase 3 confirmatory trial, using the traditional MMRM. Second, large, international trials may necessitate the use of different tracers for imaging biomarker assessment due to global availability issues, however, statistical models that harmonize tracer differences within clinical trials are still lacking. Third, primary prevention trials using time to event endpoint demands even larger sample sizes, e.g., 1650/arm for TRAILBLAZER‐ALZ 3 with time‐to‐first‐conversion from CDR global 0 to &gt;0 as the outcome. The percentage slowing relative to placebo decline (e.g., lecanemab led to 27% slowing at 18 months) is a popular concept to describe treatment effect. Instead of estimating the absolute difference using MMRM, we proposed to model this percentage treatment effect directly using the percentage or proportional MMRM (pMMRM). pMMRM can harmonize assessments from different tracers by converting the absolute difference into a shared proportional treatment effect across tracers. When using time‐to‐conversion as the primary outcome, instead of using a single event, Cox proportional hazards models for recurrent events can capture the treatment effect after the first event, where a recurrent event is defined as any increment over the previous visit in CDR global.MethodWe first demonstrated that disease modifying treatment effects are essentially proportional retreatment effects. Then, we evaluated the power advantage of pMMRM over MMRM and Cox recurrent events model over single event model through simulations. Finally, we demonstrated how to harmonize data from different tracers using pMMRM.ResultFigure 1 demonstrates that disease modifying treatment effects are like proportional treatment effects, leading to diverged disease progression trajectories and increased absolute differences. Figure 2 shows the power comparison between MMRM and pMMRM. pMMRM led to a power increase up to 30% over MMRM.ConclusionAlternative models such as pMMRM or Cox recurrent event models can yield greater power and pMMRM can harmonize assessment from different tracers, which may help enable global prevention trials in AD with disease modifying drug effects.

Comparative biology and non-traditional approaches for basic aging research for facilitating translational studies.

As humans, we aspire to healthy aging and ideally reaching our maximal lifespan. That, however, requires optimizing resilience to stressors and minimizing exposure to factors that accelerate aging. Understanding the complexities of aging processes involves characterizing the causal bases of physical, physiological, and cognitive deficits that accumulate over time, eventually culminating in reduced functionality and decreased resistance to disease and environmental stressors. Both the progression of age-related conditions and onset of diseases are affected by environmental stressors; however, the basis for increased susceptibility remains poorly understood. Furthermore, the actions of some environmental stressors, such as endocrine disruptors, can alter both developmental and aging processes, contributing to lifelong issues with inflammatory and neurodegenerative conditions. This manuscript focuses on the comparative biology and evolution of aging and longevity. The status of an array of animal models and potential for specific geroscience translational applications is addressed by asking these questions. What animal models are currently available for aging and translational geroscience? What are the key roadblocks and barriers for studies of healthy aging, and how might specific animal models be useful? Are research tools available? Which vertebrate animal models can specifically address targeted questions in human aging processes? Can information be synthesized for a range of vertebrate species to identify suitable animal models for addressing specific research questions in geroscience, especially relative to basic physiological function, timing and trajectory of disease progression, effects of environmental stressors, and potential for regenerative medicine?

Abstract 17760: Conversion of Dicom ECG Images to Tabular Format for Building Large Language Model in Diagnoses and Disease Progression of Cardiovascular Conditions

Introduction: DICOM Electrocardiography (ECG) images from individuals are routinely stored at the institutional PACS server, including normal and abnormal findings. Hypothesis: The study’s objective is to create a database of ECGs as per Standard Communication Protocol (SCP) and to build an accurate Large Language Model (LLM) across different cardiovascular conditions Methods: DICOM ECGs are retrieved, anonymized and labelled to convert the signals to (x, y) coordinates with the help of PyDicom libraries. The tabular format is then stored with lead definition and dimensions of duration and amplitude, metadata, clinical conditions, and textual diagnoses. Results: In the pilot study, 1308 ECGs from individuals with different age (&lt;45 &amp; &gt;45 years), gender (male &amp; female), binary clinical categories (normal &amp; abnormal) and heart rate (&lt;90 &amp; &gt;90 per min) are selected for analysis and modeling. The DICOM image signal reproducibility following conversion (Figure 1 &amp; 2) shows Cross Correlation Percentage of 0.85 (0.76 - 0.94) (Figure 3). An eXtreme Gradient Boosting (XGBoost) model (6) was trained to predict above variables. The results (Figure 4) show AUC highest for predicting binary clinical categories (normal vs abnormal) at 0.81 (0.7 - 0.9) and lowest for predicting age (&lt;45 &amp; &gt;45 years) at 0.6 (0.46 - 0.7). The accuracies in the leads also show similar trend. The limitations include reproducibility of results in Lead VIII, IX and XI, which are sub average and hence being retrained with optimizers. These are initial results with relatively smaller database where multiclass disease or condition classifications are not performed. Conclusions: To conclude, the methodology possesses opportunities to improve the model with Deep Learning and Entity Disambiguation Techniques (NLP) to build Large Language ECG Models for cardiovascular diagnoses and disease progression trajectories.

Targeting aberrant sialylation and fucosylation in prostate cancer cells using potent metabolic inhibitors.

Aberrant glycosylation is a hallmark of cancer and is not just a consequence, but also a driver of a malignant phenotype. In prostate cancer, changes in fucosylated and sialylated glycans are common and this has important implications for tumor progression, metastasis, and immune evasion. Glycans hold huge translational potential and new therapies targeting tumor-associated glycans are currently being tested in clinical trials for several tumor types. Inhibitors targeting fucosylation and sialylation have been developed and show promise for cancer treatment, but translational development is hampered by safety issues related to systemic adverse effects. Recently, potent metabolic inhibitors of sialylation and fucosylation were designed that reach higher effective concentrations within the cell, thereby rendering them useful tools to study sialylation and fucosylation as potential candidates for therapeutic testing. Here, we investigated the effects of global metabolic inhibitors of fucosylation and sialylation in the context of prostate cancer progression. We find that these inhibitors effectively shut down the synthesis of sialylated and fucosylated glycans to remodel the prostate cancer glycome with only minor apparent side effects on other glycan types. Our results demonstrate that treatment with inhibitors targeting fucosylation or sialylation decreases prostate cancer cell growth and downregulates the expression of genes and proteins important in the trajectory of disease progression. We anticipate our findings will lead to the broader use of metabolic inhibitors to explore the role of fucosylated and sialylated glycans in prostate tumor pathology and may pave the way for the development of new therapies for prostate cancer.

Disease progression in patients with usual interstitial pneumonia and probable UIP patterns on computed tomography with various underlying etiologies: a retrospective cohort study.

Usual interstitial pneumonia (UIP) is a pattern of interstitial pneumonia that is caused by different etiologies. This study aimed to investigate the transplant-free survival (TFS) and the decline in forced vital capacity (FVC) of the patients with UIP and probable UIP patterns on CT caused by various underlying conditions. A retrospective cohort study was conducted, enrolling patients with interstitial lung disease exhibiting a CT pattern consistent with UIP or probable UIP. Clinical and prognostic data of patients categorized by the etiology were compared. A total of 591 patients were included and classified into the following groups: idiopathic pulmonary fibrosis (IPF) (n = 320), connective tissue disease (CTD)-UIP (n = 229), asbestosis-UIP (n = 28), and hypersensitivity pneumonitis (HP)-UIP (n = 14). Advanced age, elevated levels of serum cytokeratin fraction 21-1 and percentage of neutrophils in bronchoalveolar lavage were observed in all groups. IPF patients showed a more rapid decline in FVC (133.9 mL/year) compared to CTD-UIP (24.5 mL/year, p = 0.001) and asbestosis-UIP (61.0 mL/year, p = 0.008) respectively. Sub-analysis of CTD-UIP revealed that patients with rheumatoid arthritis (RA)-UIP (88.1 mL/year) or antineutrophil cytoplasmic antibody-associated vasculitis (AAV)-UIP (72.9 mL/year) experienced a faster deterioration in FVC compared to those with primary Sjögren's syndrome (pSS)-UIP (25.9 mL/year, p < 0.05). Kaplan-Meier curves showed that IPF had the poorest TFS (median 55.9 months), followed by HP-UIP (57.5 months), CTD-UIP (66.7 months), and asbestosis-UIP (TFS not reached). RA-UIP or AAV-UIP did not exhibit any prognostic advantages compared to IPF, while asbestosis-UIP and pSS-UIP showed better survival rates. Patients with UIP caused by different underlying conditions share certain common features, but the trajectories of disease progression and survival outcomes differ.

Magnetic Resonance Spectroscopy Studies of Brain Energy Metabolism in Schizophrenia: Progression from Prodrome to Chronic Psychosis.

Schizophrenia (SZ) is a debilitating mental illness; existing treatments are partially effective and associated with significant side effect burden, largely due to our limited understanding of disease mechanisms and the trajectory of disease progression. Accumulating evidence suggests that metabolic changes associated with glucose metabolism, mitochondrial dysfunction, and redox imbalance play an important role in the pathophysiology of schizophrenia. However, the molecular mechanisms associated with these abnormalities in the brains of schizophrenia patients and the ways in which they change over time remain unclear. This paper aims to review the current literature on molecular mechanisms and in vivo magnetic resonance spectroscopy (MRS) studies of impaired energy metabolism in patients at clinical high risk for psychosis, with first-episode SZ, and with chronic SZ. Our review covers research related to high-energy phosphate metabolism, lactate, intracellular pH, redox ratio, and the antioxidant glutathione. Both first-episode and chronic SZ patients display a significant reduction in creatine kinase reaction activity and redox (NAD + /NADH) ratio in the prefrontal cortex. Chronic, but not first-episode, SZ patients also show a trend toward increased lactate levels and decreased pH value. These findings suggest a progressive shift from oxidative phosphorylation to glycolysis for energy production over the course of SZ, which is associated with redox imbalance and mitochondrial dysfunction. Accumulating evidence indicates that aberrant brain energy metabolism associated with mitochondrial dysfunction and redox imbalance plays a critical role in SZ and will be a promising target for future treatments.

Generation of percentile curves for strength and functional abilities for boys with Duchenne muscular dystrophy.

Considering the heterogeneity of the clinical manifestations of Duchenne muscular dystrophy (DMD), it is important to describe their various clinical profiles. Thus, in this study we aimed to develop percentile curves for DMD using a battery of measures to define the patterns of functional abilities, timed tests, muscle strength, and range of motion (ROM). This retrospective data analysis was based on the records of patients with DMD using the Motor Function Measure (MFM) scale, isometric muscle strength (IS), dorsiflexion ROM, 10-meter walk test (10 MWT), and 6-minute walk test (6 MWT). Percentile curves (25th, 50th, and 75th percentiles) with MFM, IS, ROM, 10 MWT, and 6 MWT on the y axis and patient age on the x axis were constructed using the generalized additive model for location, scale, and shape, with Box-Cox power exponential distribution. There were records of 329 assessments of patients between 4 and 18 years of age. The MFM percentiles showed a gradual reduction in all dimensions. Muscle strength and ROM percentiles showed that the knee extensors were the most affected from 4 years of age, and dorsiflexion ROM negative values were noted from the age of 8 years. The 10 MWT showed a gradual increase in performance time with age. For the 6 MWT, the distance curve remained stable until 8 years, with a subsequent progressive decline. In this study we generated percentile curves that can help health professionals and caregivers follow the trajectory of disease progression in DMD patients.

Meaningful Clinical Changes in Alzheimer Disease Measured With the iADRS and Illustrated Using the Donanemab TRAILBLAZER-ALZ Study Findings.

To provide relevant background of the Integrated Alzheimer's Disease Rating Scale (iADRS), with examples, to assist the reader with the interpretation of iADRS findings from the TRAILBLAZER-ALZ study. The iADRS is an integrated measure of global Alzheimer disease (AD) severity for use in the clinical trial environment. It provides a single score that captures commonalities across cognitive and functional ability domains, reflecting disease-related impairment, while minimizing noise not related to disease progression that may exist within each domain. In AD, disease-modifying therapies (DMTs) are expected to slow the rate of clinical decline, changing the trajectory of disease progression. The overall percent slowing of disease progression with treatment is a more informative outcome of effect than absolute point differences between treatment and placebo groups at any given time point because the latter is influenced by treatment period and disease severity. The TRAILBLAZER-ALZ trial was a phase 2 study designed to evaluate the safety and efficacy of donanemab in participants with early symptomatic AD; the primary outcome measure was the change from baseline to 76 weeks on the iADRS. In the TRAILBLAZER-ALZ study, donanemab slowed disease progression by 32% at 18 months (p = 0.04 vs placebo), demonstrating clinical efficacy. At the patient level, one can assess whether the DMT effect is clinically meaningful by estimating the threshold of change consistent with clinically meaningful worsening; based on the TRAILBLAZER-ALZ findings, treatment with donanemab would delay reaching this threshold by approximately 6 months. The iADRS is capable of accurately describing clinical changes associated with disease progression and detecting treatment effects and is an effective assessment tool for use in clinical trials of individuals with early symptomatic AD.

Clustering and prediction of disease progression trajectories in Huntington's disease: An analysis of Enroll-HD data using a machine learning approach.

Huntington's disease (HD) is a rare neurodegenerative disease characterized by cognitive, behavioral and motor symptoms that progressively worsen with time. Cognitive and behavioral signs of HD are generally present in the years prior to a diagnosis; however, manifest HD is typically assessed by genetic confirmation and/or the presence of unequivocal motor symptoms. Nevertheless, there is a large variation in symptom severity and rate of progression among individuals with HD. In this retrospective study, longitudinal natural history of disease progression was modeled in individuals with manifest HD from the global, observational Enroll-HD study (NCT01574053). Unsupervised machine learning (k-means; km3d) was used to jointly model clinical and functional disease measures simultaneously over time, based on one-dimensional clustering concordance such that individuals with manifest HD (N = 4,961) were grouped into three clusters: rapid (Cluster A; 25.3%), moderate (Cluster B; 45.5%) and slow (Cluster C; 29.2%) progressors. Features that were considered predictive of disease trajectory were then identified using a supervised machine learning method (XGBoost). The cytosine adenine guanine-age product score (a product of age and polyglutamine repeat length) at enrollment was the top predicting feature for cluster assignment, followed by years since symptom onset, medical history of apathy, body mass index at enrollment and age at enrollment. These results are useful for understanding factors that affect the global rate of decline in HD. Further work is needed to develop prognostic models of HD progression as these could help clinicians with individualized clinical care planning and disease management.

Next-Generation Morphometry for pathomics-data mining in histopathology

Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.

Chapter 7 - Lessons from other fields of medicine, Part 1: Breast cancer

Through the understanding of multiple etiologies, pathologies, and disease progression trajectories, breast cancer shifted historically from a singular malignancy of the breast to a complex of molecular/biological entities, translating into individualized disease-modifying treatments. As a result, this led to various de-escalations of treatment compared with the gold standard in the era preceding systems biology: radical mastectomy. Targeted therapies have minimized morbidity from the treatments and mortality from the disease. Biomarkers further individualized tumor genetics and molecular biology to optimize treatments targeting specific cancer cells. Landmark discoveries in breast cancer management have evolved through histology, hormone receptors, human epidermal growth factor, single-gene prognostic markers, and multigene prognostic markers. Relevant to the reliance on histopathology in neurodegenerative disorders, histopathology evaluation in breast cancer can serve as a marker of overall prognosis rather than predict response to therapies. This chapter reviews the successes and failures of breast cancer research through history, with focus on the transition from a universal approach for all patients to divergent biomarker development and individualized targeted therapies, discussing future areas of growth in the field that may apply to neurodegenerative disorders.

Correlations and associations between fluid‐based and morphometric imaging biomarkers through interrogation and integration of data and analysis across two data platforms

AbstractBackgroundThe Global Alzheimer’s Association Interactive Network (GAAIN) and the Critical Path for Alzheimer’s Disease (CPAD) Consortium have undertaken a collaborative effort to establish a mechanism that can help evaluate the relationship between fluid‐based, morphometric imaging biomarkers and clinical trajectories of disease progression, across datasets and analyses pipelines housed by CPAD and GAAIN. Additionally, this collaboration will demonstrate the feasibility of bidirectional integration of data management models and will guide development of a federated image analysis pipeline model across these two platforms.MethodTo enable interoperability, GAAIN’s original architecture is expanded to accommodate the LONI brain structural imaging pipeline. Under this new framework, GAAIN researchers can initiate brain imaging‐related analyses on their servers directly from GAAIN’s graphical interface with the results integrating back into GAAIN for further analyses. Feasibility was demonstrated for the determination of correlations for CPAD’s biomarker and imaging volumetric data. GAAIN’s brain imaging pipeline was executed on CPAD data, allowing for quantification of volumetric images. In turn, blood and volumetric data were inserted in GAAIN allowing further evaluation and analyses.ResultBased on the results, we provide guidelines for a future framework for a federated analysis model that can be utilized from GAAIN and CPAD to establish relationships between blood and brain markers. In this model, for a cohort of interest, analyses of relevant brain marker data can be remotely done in CPAD servers via GAAINs’ federated platform after which results can be inserted back to GAAIN alongside CPAD’s blood marker data. The full capabilities of this federated model will be explored in subsequent work.ConclusionAs data sharing becomes increasingly prominent and mature, the partnership between GAAIN and CPAD will yield ever greater benefits and knowledge and will continue to grow and evolve in response to the changing Alzheimer’s disease landscape. As such, it represents a non‐exclusive door for researchers to make use of the data sets and advanced analytical tools and is intended to leverage the unique competencies of these organizations and lead to the continued evolution of bringing meaningful and high‐impact advances in Alzheimer’s disease.

Event‐related‐potentials are sensitive and reliable EEG biomarkers for mild cognitive impairment: A longitudinal study

AbstractBackgroundEvent‐related‐potentials (ERPs) during cognitive tasks are sensitive measures of cognitive decline [1]‐[2] and could quantitatively measure the trajectory of disease progression or response to clinical interventions. In this longitudinal study, we aimed to compare ERPs between individuals with mild cognitive impairment (MCI) and controls, and to demonstrate the test‐retest reliability of ERP measures to assess their potential utility for future observational studies and treatment trials.MethodsParticipants included individuals with mild cognitive impairment (MCI, n=31) and healthy controls stratified by age: HC2 (n=46, 40‐60 yrs.), HC3 (n=56, 60‐90 yrs.). ERPs during attention, image recognition, and verbal memory tasks were recorded using a wireless EEG to determine amplitudes and latencies of the P200 and late positive potential (LPP) ERP components in response to target (memorized) and non‐target (novel) stimuli. Differences between MCI and HC3 groups were characterized at the initial visit, and longitudinal changes in ERP components were determined at 1‐year follow‐up for both groups. Reliability of ERP measures was demonstrated by computing intraclass correlation coefficient (ICC) measures of test‐retest variability in all healthy controls (HC2 and HC3) from initial visit to 1‐year follow‐up.ResultsAt the initial visit, the MCI group demonstrated reduced LPP amplitude (Hedges‐g=0.8, p=10‐3) and delayed P200‐latency (Hedges‐g=0.89, p=10‐4) when compared to HC3. Reliability in healthy controls was high for LPP (0.85 and 0.75 for target and non‐target stimuli, respectively) and moderate for P200‐latency (0.67 and 0.53 for target and non‐target stimuli, respectively), without longitudinal changes after 1‐year follow‐up. Individuals with MCI demonstrated increased P200‐latency (Hedges‐g= 1.05, p=0.02, df=8).ConclusionsIndividuals with MCI demonstrated quantifiable memory ERP deficits at baseline with detectable change in the P200 component at 1‐year follow‐up, demonstrating that ERPs are sensitive measures for quantifying mild cognitive impairment. Test‐retest reliability of these measures was good or moderate after 1‐year, supporting their feasibility for longitudinal monitoring of patients during disease progression or quantifying the cognitive impact of potential interventions.