Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions

Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration

BACKGROUND

Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.

METHODS

We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).

RESULTS

The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.

CONCLUSION

These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets

Variability in case severity and in the range of symptoms experienced has been apparent from the earliest months of the COVID-19 pandemic. From a clinical perspective, symptom variability might indicate various routes/mechanisms by which infection leads to disease, with different routes requiring potentially different treatment approaches. For public health and control of transmission, symptoms in community cases were the prompt upon which action such as PCR testing and isolation was taken. However, interpreting symptoms presents challenges, for instance, in balancing the sensitivity and specificity of individual symptoms with the need to maximise case finding, whilst managing demand for limited resources such as testing. For both clinical and transmission control reasons, we require an approach that allows for the possibility of distinct symptom phenotypes, rather than assuming variability along a single dimension. Here we address this problem by bringing together four large and diverse datasets deriving from routine testing, a population-representative household survey and participatory smartphone surveillance in the United Kingdom. Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.

Toward a new nosology of neurodegenerative diseases

New "omic" technologies are revealing shared and distinct biological pathways within and across neurodegenerative diseases (NDDs), allowing a better understanding of endophenotypes that exceeds the boundaries of the current diagnostic criteria. Moreover, a diagnostic framework is needed that can accommodate the co-pathology and the clinical overlap and heterogeneity of NDDs. Apart from dissecting the reasons for a revolution in how we conceive NDD, this article aims to prompt a change in how we diagnose and classify NDD, drafting a general scheme for a new nosology. As identifying a cause is the key to using the term "disease" properly, we propose using a tridimensional classification based on three axes: (1) etiology or pathogenic mechanism, (2) pathology markers and molecular biomarkers, (3) anatomic-clinical; and three hierarchical levels of etiology: (1) genetic/sporadic (2) cellular pathways and processes, and function of fluidic brain systems, and (3) risk factors.

Four trajectories of 24-hour urine protein levels in real-world lupus nephritis cohorts

OBJECTIVES

A 24-hour urine protein (24hUP) is a key measurement in the management of lupus nephritis (LN); however, trajectories of 24hUP in LN is poorly defined.

METHODS

Two LN cohorts that underwent renal biopsies at Renji Hospital were included. Patients received standard of care in a real-world setting and 24hUP data were collected over time. Trajectory patterns of 24hUP were determined using the latent class mixed modelling (LCMM). Baseline characters were compared among trajectories and multinomial logistic regression was used to determine independent risk factors. Optimal combinations of variables were identified for model construction and user-friendly nomograms were developed.

RESULTS

The derivation cohort composed of 194 patients with LN with 1479 study visits and a median follow-up of 17.5 (12.2-21.7) months. Four trajectories of 24hUP were identified, that is, Rapid Responders, Good Responders, Suboptimal Responders and Non-Responders, with the KDIGO renal complete remission rates (time to complete remission, months) of 84.2% (4.19), 79.6% (7.94), 40.4% (not applicable) and 9.8% (not applicable), respectively (p<0.001). The 'Rapid Responders' distinguish itself from other trajectories and a nomogram, composed of age, systemic lupus erythematosus duration, albumin and 24hUP yielded C-indices >0.85. Another nomogram to predict 'Good Responders' yielded C-indices of 0.73~0.78, which composed of gender, new-onset LN, glomerulosclerosis and partial remission within 6 months. When applied to the validation cohort with 117 patients and 500 study visits, nomograms effectively sorted out 'Rapid Responders' and 'Good Responders'.

CONCLUSION

Four trajectories of LN shed some light to guide the management of LN and further clinical trials design.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

Drug Discovery and Development Targeting Dementia

Dementia, most often associated with neurodegenerative diseases, affects millions of people worldwide, predominantly the elderly. Unfortunately, no treatment is still available. Therefore, there is an urgent need to address this situation. This review presents the state of the art of drug discovery and developments in targeting dementia. Several approaches are discussed, such as drug repurposing, the use of small molecules, and phosphodiesterase inhibitors. Furthermore, the review also provides insights into clinical trials of these molecules. Emphasis has been placed on small molecules and multi-target-directed ligands, as well as disease-modifying therapies. Finally, attention is drawn to the possibilities of applications of nanotechnology in managing dementia.

Latent class trajectory modelling: impact of changes in model specification

Latent class trajectory models (LCTMs) are often used to identify subgroups of patients that are clinically meaningful in terms of longitudinal exposure and outcome, e.g. drug response patterns. These models are increasingly applied in medicine and epidemiology. However, in many published studies, it is not clear whether the chosen models, where subgroups of patients are identified, represent real heterogeneity in the population, or whether any associations with clinically meaningful characteristics are accidental. In particular, we note an apparent over-reliance on lowest AIC or BIC values. While these are objective measures of goodness of fit, and can help identify the optimal number of subgroups, they are not sufficient on their own to fully evaluate a given trajectory model. Here we demonstrate how longitudinal latent class models can substantially change by making small modifications in model specification, and the impact of this on the relationship to clinical outcomes. We show that the predicted trajectory patterns and outcome probabilities differ when pre-specified cubic versus linear shapes are tested on the same data. However, both could be interpreted to be the "correct" model. We emphasise that LCTMs, like all unsupervised approaches, are hypotheses generating, and should not be directly implemented in clinical practice without significant testing and validation.

Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort

A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease

BACKGROUND

In Alzheimer's disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology.

METHODS

A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts.

RESULTS

A β pathology status was determined based on the A β₄₂/A β₄₀ ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R² score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F₁ score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R² score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R² score of 0.228.

CONCLUSION

Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

Classification, Prediction, and Concordance of Cognitive and Functional Progression in Patients with Mild Cognitive Impairment in the United States: A Latent Class Analysis

Background:

Progression trajectories of patients with mild cognitive impairment (MCI) are currently not well understood.

Objective:

To classify patients with incident MCI into different latent classes of progression and identify predictors of progression class.

Methods:

Participants with incident MCI were identified from the US National Alzheimer’s Coordinating Center Uniform Data Set (09/2005-02/2019). Clinical Dementia Rating (CDR^®) Dementia Staging Instrument-Sum of Boxes (CDR-SB), Functional Activities Questionnaire (FAQ), and Mini-Mental State Examination (MMSE) score longitudinal trajectories from MCI diagnosis were fitted using growth mixture models. Predictors of progression class were identified using multivariate multinomial logistic regression models; odds ratios (ORs) and 95% confidence intervals (CIs) were reported.

Results:

In total, 21%, 22%, and 57% of participants (N = 830) experienced fast, slow, and no progression on CDR-SB, respectively; for FAQ, these figures were 14%, 23%, and 64%, respectively. CDR-SB and FAQ class membership was concordant for most participants (77%). Older age (≥86 versus≤70 years, OR [95% CI] = 5.26 [1.78–15.54]), one copy of APOE ɛ4 (1.94 [1.08–3.47]), higher baseline CDR-SB (2.46 [1.56–3.88]), lower baseline MMSE (0.85 [0.75–0.97]), and higher baseline FAQ (1.13 [1.02–1.26]) scores were significant predictors of fast progression versus no progression based on CDR-SB (all p < 0.05). Predictors of FAQ class membership were largely similar.

Conclusion:

Approximately a third of participants experienced progression based on CDR-SB or FAQ during the  4-year follow-up period. CDR-SB and FAQ class assignment were concordant for the vast majority of participants. Identified predictors may help the selection of patients at higher risk of progression in future trials.

Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review

OBJECTIVE

Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia.

MATERIALS AND METHODS

We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus.

RESULTS

There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values).

DISCUSSION

Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research.

Quantifying the heterogeneity of cognitive functioning in Alzheimer's disease to extend the placebo-treatment dichotomy: Latent class analysis of individual-participant data from five pivotal randomized clinical trials of donepezil

BACKGROUND

The extent and profiles of heterogeneity in cognitive functioning among participants in clinical trials of antidementia medication are unknown. We aimed to quantify and identify profiles of heterogeneity of cognition in Alzheimer's disease.

METHODS

Individual-level participant data were analyzed from five pivotal clinical trials of donepezil for Alzheimer's disease (N = 2,919). Based on Alzheimer's Disease Assessment Scale-Cognitive Subscale total scores from baseline up to week 12, a latent class model was used to identify heterogeneous groups. A logistic regression model was used to examine factors associated with group membership. Sensitivity analysis was conducted, restricted to the donepezil, and then the placebo arm.

RESULTS

The latent class model identified three classes labeled as low scorers (i.e., least cognitive impairment; N = 1,666, 76.04%), improvers (N = 27, 1.23%), and high scorers (N = 498, 22.73%). Logistic modeling showed that donepezil compared to placebo was significantly (p < 0.05) positively associated with membership in the improvers class (OR = 6.88, 95% CI = 2.03, 42.95), and negatively with high scorers (OR = 0.79, 95% CI = 0.64, 0.98). Sensitivity analysis restricted to the placebo, then donepezil arms replicated similar heterogeneity patterns.

CONCLUSIONS

Our results inform clinicians regarding the extent of heterogeneity in cognitive functioning during treatment and contribute to trial design considerations.

Do traditional BMI categories capture future obesity? A comparison with trajectories of BMI and incidence of cancer

In 2016, 13 specific obesity related cancers were identified by IARC. Here, using baseline WHO BMI categories, latent profile analysis (LPA) and latent class trajectory modelling (LCTM) we evaluated the usefulness of one-off measures when predicting cancer risk vs life-course changes. Our results in LPA broadly concurred with the three basic WHO BMI categories, with similar stepwise increase in cancer risk observed. In LCTM, we identified 5 specific trajectories in men and women. Compared to the leanest class, a stepwise increase in risk for obesity related cancer was observed for all classes. When latent class membership was compared to baseline BMI, we found that the trajectories were composed of a range of BMI (baseline) categories. All methods reveal a link between obesity and the 13 cancers identified by IARC. However, the additional information included by LCTM indicates that lifetime BMI may highlight additional group of people that are at risk.

Statin therapy and risk of Alzheimer's and age-related neurodegenerative diseases

INTRODUCTION

Establishing efficacy of and molecular pathways for statins has the potential to impact incidence of Alzheimer's and age-related neurodegenerative diseases (NDD).

METHODS

This retrospective cohort study surveyed US-based Humana claims, which includes prescription and patient records from private-payer and Medicare insurance. Claims from 288,515 patients, aged 45 years and older, without prior history of NDD or neurological surgery, were surveyed for a diagnosis of NDD starting 1 year following statin exposure. Patients were required to be enrolled with claims data for at least 6 months prior to first statin prescription and at least 3 years thereafter. Computational system biology analysis was conducted to determine unique target engagement for each statin.

RESULTS

Of the 288,515 participants included in the study, 144,214 patients (mean [standard deviation (SD)] age, 67.22 [3.8] years) exposed to statin therapies, and 144,301 patients (65.97 [3.2] years) were not treated with statins. The mean (SD) follow-up time was 5.1 (2.3) years. Exposure to statins was associated with a lower incidence of Alzheimer's disease (1.10% vs 2.37%; relative risk [RR], 0.4643; 95% confidence interval [CI], 0.44-0.49; P < .001), dementia 3.03% vs 5.39%; RR, 0.56; 95% CI, 0.54-0.58; P < .001), multiple sclerosis (0.08% vs 0.15%; RR, 0.52; 95% CI, 0.41-0.66; P < .001), Parkinson's disease (0.48% vs 0.92%; RR, 0.53; 95% CI, 0.48-0.58; P < .001), and amyotrophic lateral sclerosis (0.02% vs 0.05%; RR, 0.46; 95% CI, 0.30-0.69; P < .001). All NDD incidence for all statins, except for fluvastatin (RR, 0.91; 95% CI, 0.65-1.30; P = 0.71), was reduced with variances in individual risk profiles. Pathway analysis indicated unique and common profiles associated with risk reduction efficacy.

DISCUSSION

Benefits and risks of statins relative to neurological outcomes should be considered when prescribed for at-risk NDD populations. Common statin activated pathways indicate overarching systems required for risk reduction whereas unique targets could advance a precision medicine approach to prevent neurodegenerative diseases.

Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease

The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.

Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Introduction

We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.

Methods

A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance.

Results

Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment.

The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants.

Conclusions

Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.

Good problems to have? Policy and societal implications of a disease-modifying therapy for presymptomatic late-onset Alzheimer's disease

In the United States alone, the prevalence of AD is expected to more than double from six million people in 2019 to nearly 14 million people in 2050. Meanwhile, the track record for developing treatments for AD has been marked by decades of failure. But recent progress in genetics, neuroscience and gene editing suggest that effective treatments could be on the horizon. The arrival of such treatments would have profound implications for the way we diagnose, triage, study, and allocate resources to Alzheimer's patients. Because the disease is not rare and because it strikes late in life, the development of therapies that are expensive and efficacious but less than cures, will pose particular challenges to healthcare infrastructure. We have a window of time during which we can begin to anticipate just, equitable and salutary ways to accommodate a disease-modifying therapy Alzheimer's disease. Here we consider the implications for caregivers, clinicians, researchers, and the US healthcare system of the availability of an expensive, presymptomatic treatment for a common late-onset neurodegenerative disease for which diagnosis can be difficult.

Latent Class Trajectory Modeling of 2-Component Disease Activity Score in 28 Joints Identifies Multiple Rheumatoid Arthritis Phenotypes of Response to Biologic Disease-Modifying Antirheumatic Drugs

OBJECTIVE

To determine whether using a reweighted disease activity score that better reflects joint synovitis, i.e., the 2-component Disease Activity Score in 28 joints (DAS28) (based on swollen joint count and C-reactive protein level), produces more clinically relevant treatment outcome trajectories compared to the standard 4-component DAS28.

METHODS

Latent class mixed modeling of response to biologic treatment was applied to 2,991 rheumatoid arthritis (RA) patients in whom treatment with a biologic disease-modifying antirheumatic drug was being initiated within the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate cohort, using both 4-component and 2-component DAS28 scores as outcome measures. Patient groups with similar trajectories were compared in terms of pretreatment baseline characteristics (including disability and comorbidities) and follow-up characteristics (including antidrug antibody events, adherence to treatments, and blood drug levels). We compared the trajectories obtained using the 4- and 2-component scores to determine which characteristics were better captured by each.

RESULTS

Using the 4-component DAS28, we identified 3 trajectory groups, which is consistent with previous findings. We showed that the 4-component DAS28 captures information relating to depression. Using the 2-component DAS28, 7 trajectory groups were identified; among them, distinct groups of nonresponders had a higher incidence of respiratory comorbidities and a higher proportion of antidrug antibody events. We also identified a group of patients for whom the 2-component DAS28 scores remained relatively low; this group included a high percentage of patients who were nonadherent to treatment. This highlights the utility of both the 4- and 2-component DAS28 for monitoring different components of disease activity.

CONCLUSION

Here we show that the 2-component modified DAS28 defines important biologic and clinical phenotypes associated with treatment outcome in RA and characterizes important underlying response mechanisms to biologic drugs.

Association of combination statin and antihypertensive therapy with reduced Alzheimer's disease and related dementia risk

BACKGROUND

Hyperlipidemia and hypertension are modifiable risk factors for Alzheimer's disease and related dementias (ADRD). Approximately 25% of adults over age 65 use both antihypertensives (AHTs) and statins for these conditions. While a growing body of evidence found statins and AHTs are independently associated with lower ADRD risk, no evidence exists on simultaneous use for different drug class combinations and ADRD risk. Our primary objective was to compare ADRD risk associated with concurrent use of different combinations of statins and antihypertensives.

METHODS

In a retrospective cohort study (2007-2014), we analyzed 694,672 Medicare beneficiaries in the United States (2,017,786 person-years) who concurrently used both statins and AHTs. Using logistic regression adjusting for age, socioeconomic status and comorbidities, we quantified incident ADRD diagnosis associated with concurrent use of different statin molecules (atorvastatin, pravastatin, rosuvastatin, and simvastatin) and AHT drug classes (two renin-angiotensin system (RAS)-acting AHTs, angiotensin converting enzyme inhibitors (ACEIs) or angiotensin-II receptor blockers (ARBs), vs non-RAS-acting AHTs).

FINDINGS

Pravastatin or rosuvastatin combined with RAS-acting AHTs reduce risk of ADRD relative to any statin combined with non-RAS-acting AHTs: ACEI+pravastatin odds ratio (OR) = 0.942 (CI: 0.899-0.986, p = 0.011), ACEI+rosuvastatin OR = 0.841 (CI: 0.794-0.892, p<0.001), ARB+pravastatin OR = 0.794 (CI: 0.748-0.843, p<0.001), ARB+rosuvastatin OR = 0.818 (CI: 0.765-0.874, p<0.001). ARBs combined with atorvastatin and simvastatin are associated with smaller reductions in risk, and ACEI with no risk reduction, compared to when combined with pravastatin or rosuvastatin. Among Hispanics, no combination of statins and RAS-acting AHTs reduces risk relative to combinations of statins and non-RAS-acting AHTs. Among blacks using ACEI+rosuvastatin, ADRD odds were 33% lower compared to blacks using other statins combined with non-RAS-acting AHTs (OR = 0.672 (CI: 0.548-0.825, p<0.001)).

CONCLUSION

Among older Americans, use of pravastatin and rosuvastatin to treat hyperlipidemia is less common than use of simvastatin and atorvastatin, however, in combination with RAS-acting AHTs, particularly ARBs, they may be more effective at reducing risk of ADRD. The number of Americans with ADRD may be reduced with drug treatments for vascular health that also confer effects on ADRD.

Perceptions of precision medicine among diverse dementia caregivers and professional providers

Introduction

Underrepresented groups experience health disparities and a history of exploitation by researchers and the health-care system that may contribute to distrust of new treatments and technologies. This study aims to understand how diverse family caregivers and health-care professionals view the benefits and risks of precision medicine as well as cultural dimensions to consider when developing and implementing precision medicine interventions in dementia care.

Methods

Eight focus group sessions and one individual interview were conducted over a 6-month period. Fifty-four focus group participants included African-American, American Indian, rural Caucasian, Latino, and West African caregivers and health professionals. The majority of participants were female (73%) and were of Hispanic/Latino ethnicity (68%). About a third of participants identified their race as white. Participants were presented with four hypothetical scenarios related to precision medicine diagnostic and treatment approaches in dementia care: (1) genetic testing for dementia risk, (2) health-care informatics to determine individualized medication dosages based on health and family history, (3) a smartphone application providing dementia caregiving tips, and (4) remote activity monitoring technology in the home. Focus groups' responses were coded using thematic analysis.

Results

Participants indicated skepticism regarding the use of precision medicine in their communities. Concerns included cost of precision medicine and insurance coverage; lack of alignment with cultural norms; fraught relationships between communities, health professionals, and researchers; data ownership and privacy; and the trade-off between knowing risk and treatment benefit.

Discussion

Establishing relationships with underserved communities is crucial to advancing precision medicine in dementia care. Appropriate engagement with diverse racial, ethnic, and geographic communities may require significant investment but is necessary to deliver precision medicine effectively.

Clinical trial data reuse - overcoming complexities in trial design and data sharing

There are many acknowledged benefits for the reuse of clinical trial data; from independent verification of published results to the evaluation of new hypotheses. However, the reuse of shared clinical trial data is not without obstacles. Here we present some of the issues and lessons learned from our own experiences in accessing and analyzing trial data; specifically, where we aim to combine and pool data from multiple different trials. In addition to issues around missing annotation and incomplete datasets, we identify trial-design complexity as a potential hurdle that may complicate downstream analyses. We address potential solutions and emphasize the need for benefits of transparent sharing and analysis of participant-level clinical trial data with appropriate risk mitigation, a matter important to efficient clinical research.

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.