Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types.

Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible. To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains after parabiosis. For each cell type, we cataloged alterations in gene expression, molecular pathways, transcriptional networks, ligand-receptor interactions and senescence status. Our analyses identified gene signatures, demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function. These findings suggest new strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors.

Disease Burden of Spinal Muscular Atrophy: A Comparative Cohort Study Using Insurance Claims Data in the USA.

BACKGROUND: Spinal muscular atrophy (SMA) is a neuromuscular disease caused by homozygous deletion or loss-of-function mutations of the survival of motor neuron 1 (SMN1) gene, resulting in reduced levels of SMN protein throughout the body. Patients with SMA may have multiple tissue defects, which could present prior to neuromuscular symptoms. OBJECTIVE: To assess the signs, comorbidities and potential extraneural manifestations associated with SMA in treatment-naïve patients. METHODS: This observational, retrospective and matched-cohort study used secondary insurance claims data from the US IBM® MarketScan® Commercial, Medicaid and Medicare Supplemental databases between 01/01/2000 and 12/31/2013. Treatment-naïve individuals aged≤65 years with≥2 International Classification of Diseases, Ninth Revision (ICD-9) SMA codes were stratified into four groups (A-D), according to age at index (date of first SMA code recorded) and type of ICD-9 code used, and matched with non-SMA controls. The occurrence of ICD-9 codes, which were converted to various classifications (phecodes and system classes), were compared between groups in pre- and post-index periods. RESULTS: A total of 1,457 individuals with SMA were included and matched to 13,362 controls. Increasing numbers of SMA-associated phecodes and system classes were generally observed from pre- to post-index across all groups. The strongest associations were observed in the post-index period for the youngest age groups. Endocrine/metabolic disorders were associated with SMA in almost all groups and across time periods. CONCLUSIONS: This exploratory study confirmed the considerable disease burden in patients with SMA and identified 305 unique phecodes associated with SMA, providing a rationale for further research into the natural history and progression of SMA, including extraneural manifestations of the disease.

Accelerating diagnosis of Parkinson's disease through risk prediction.

BACKGROUND: Characterization of prediagnostic Parkinson's Disease (PD) and early prediction of subsequent development are critical for preventive interventions, risk stratification and understanding of disease pathology. This study aims to characterize the role of the prediagnostic period in PD and, using selected features from this period as novel interception points, construct a prediction model to accelerate the diagnosis in a real-world setting. METHODS: We constructed two sets of machine learning models: a retrospective approach highlighting exposures up to 5 years prior to PD diagnosis, and an alternative model that prospectively predicted future PD diagnosis from all individuals at their first diagnosis of a gait or tremor disorder, these being features that appeared to represent the initiation of a differential diagnostic window. RESULTS: We found many novel features captured by the retrospective models; however, the high accuracy was primarily driven from surrogate diagnoses for PD, such as gait and tremor disorders, suggesting the presence of a distinctive differential diagnostic period when the clinician already suspected PD. The model utilizing a gait/tremor diagnosis as the interception point, achieved a validation AUC of 0.874 with potential time compression to a future PD diagnosis of more than 300 days. Comparisons of predictive diagnoses between the prospective and prediagnostic cohorts suggest the presence of distinctive trajectories of PD progression based on comorbidity profiles. CONCLUSIONS: Overall, our machine learning approach allows for both guiding clinical decisions such as the initiation of neuroprotective interventions and importantly, the possibility of earlier diagnosis for clinical trials for disease modifying therapies.

Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles.

Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small molecule retrieval task, designed to force a model to learn to associate the structure of a small molecule with the transcriptional change it induces. We develop this task formally as multi-view alignment problem, and present a coordinated deep learning approach that jointly optimizes representations of both chemical structure and perturbational gene expression profiles. We benchmark our results against oracle models and principled baselines, and find that cell line variability markedly influences performance in this domain. Our work establishes the feasibility of this new task, elucidates the limitations of current data and systems, and may serve to catalyze future research in small molecule representation learning.

Temporal bias in case-control design: preventing reliable predictions of the future.

One of the primary tools that researchers use to predict risk is the case-control study. We identify a flaw, temporal bias, that is specific to and uniquely associated with these studies that occurs when the study period is not representative of the data that clinicians have during the diagnostic process. Temporal bias acts to undermine the validity of predictions by over-emphasizing features close to the outcome of interest. We examine the impact of temporal bias across the medical literature, and highlight examples of exaggerated effect sizes, false-negative predictions, and replication failure. Given the ubiquity and practical advantages of case-control studies, we discuss strategies for estimating the influence of and preventing temporal bias where it exists.

Single-cell transcriptomic profiling of the aging mouse brain.

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain ) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

Applying Deep Neural Network Analysis to High-Content Image-Based Assays.

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control-SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes.

Systemic nature of spinal muscular atrophy revealed by studying insurance claims.

OBJECTIVE: We investigated the presence of non-neuromuscular phenotypes in patients affected by Spinal Muscular Atrophy (SMA), a disorder caused by a mutation in the Survival of Motor Neuron (SMN) gene, and whether these phenotypes may be clinically detectable prior to clinical signs of neuromuscular degeneration and therefore independent of muscle weakness. METHODS: We utilized a de-identified database of insurance claims to explore the health of 1,038 SMA patients compared to controls. Two analyses were performed: (1) claims from the entire insurance coverage window; and (2) for SMA patients, claims prior to diagnosis of any neuromuscular disease or evidence of major neuromuscular degeneration to increase the chance that phenotypes could be attributed directly to reduced SMN levels. Logistic regression was used to determine whether phenotypes were diagnosed at significantly different rates between SMA patients and controls and to obtain covariate-adjusted odds ratios. RESULTS: Results from the entire coverage window revealed a broad spectrum of phenotypes that are differentially diagnosed in SMA subjects compared to controls. Moreover, data from SMA patients prior to their first clinical signs of neuromuscular degeneration revealed numerous non-neuromuscular phenotypes including defects within the cardiovascular, gastrointestinal, metabolic, reproductive, and skeletal systems. Furthermore, our data provide evidence of a potential ordering of disease progression beginning with these non-neuromuscular phenotypes. CONCLUSIONS: Our data point to a direct relationship between early, detectable non-neuromuscular symptoms and SMN deficiency. Our findings are particularly important for evaluating the efficacy of SMN-increasing therapies for SMA, comparing the effectiveness of local versus systemically delivered therapeutics, and determining the optimal therapeutic treatment window prior to irreversible neuromuscular damage.

Toward Precision Medicine for Neurological and Neuropsychiatric Disorders.

The genetic complexity, clinical variability, and inaccessibility of affected tissue in neurodegenerative and neuropsychiatric disorders have largely prevented the development of effective disease-modifying therapeutics. A precision medicine approach that integrates genomics, deep clinical phenotyping, and patient stem cell models may facilitate identification of underlying biological drivers and targeted drug development.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

A look into the future of ALS research.

Although amyotrophic lateral sclerosis (ALS), also referred as 'Lou Gehrig's Disease,' was first described in 1869 and the first disease-associated gene was discovered almost 20 years ago, the disease etiology is still not fully understood and treatment options are limited to one drug approved by the US Food and Drug Administration (FDA). The slow translational progress suggests that current research models are not ideal to study such a complicated disease and need to be re-examined. Progress will require greater insight into human genes and biology involved in ALS susceptibility, as well as a deeper understanding of disease phenotype at the histological and molecular levels. Improving human disease outcome will require directing focus toward improved assessment technologies and innovative approaches.

Real-Time Assessment of Wellness and Disease in Daily Life.

The next frontier in medicine involves better quantifying human traits, known as "phenotypes." Biological markers have been directly associated with disease risks, but poor measurement of behaviors such as diet and exercise limits our understanding of preventive measures. By joining together an uncommonly wide range of disciplines and expertise, the Kavli HUMAN Project will advance measurement of behavioral phenotypes, as well as environmental factors that impact behavior. By following the same individuals over time, KHP will liberate new understanding of dynamic links between behavioral phenotypes, disease, and the broader environment. As KHP advances understanding of the bio-behavioral complex, it will seed new approaches to the diagnosis, prevention, and treatment of human disease.

Rapid and Efficient Generation of Transgene-Free iPSC from a Small Volume of Cryopreserved Blood.

Human peripheral blood and umbilical cord blood represent attractive sources of cells for reprogramming to induced pluripotent stem cells (iPSCs). However, to date, most of the blood-derived iPSCs were generated using either integrating methods or starting from T-lymphocytes that have genomic rearrangements thus bearing uncertain consequences when using iPSC-derived lineages for disease modeling and cell therapies. Recently, both peripheral blood and cord blood cells have been reprogrammed into transgene-free iPSC using the Sendai viral vector. Here we demonstrate that peripheral blood can be utilized for medium-throughput iPSC production without the need to maintain cell culture prior to reprogramming induction. Cell reprogramming can also be accomplished with as little as 3000 previously cryopreserved cord blood cells under feeder-free and chemically defined Xeno-free conditions that are compliant with standard Good Manufacturing Practice (GMP) regulations. The first iPSC colonies appear 2-3 weeks faster in comparison to previous reports. Notably, these peripheral blood- and cord blood-derived iPSCs are free of detectable immunoglobulin heavy chain (IGH) and T cell receptor (TCR) gene rearrangements, suggesting they did not originate from B- or T- lymphoid cells. The iPSCs are pluripotent as evaluated by the scorecard assay and in vitro multi lineage functional cell differentiation. Our data show that small volumes of cryopreserved peripheral blood or cord blood cells can be reprogrammed efficiently at a convenient, cost effective and scalable way. In summary, our method expands the reprogramming potential of limited or archived samples either stored at blood banks or obtained from pediatric populations that cannot easily provide large quantities of peripheral blood or a skin biopsy.

iPSC-derived dopamine neurons reveal differences between monozygotic twins discordant for Parkinson's disease.

Parkinson's disease (PD) has been attributed to a combination of genetic and nongenetic factors. We studied a set of monozygotic twins harboring the heterozygous glucocerebrosidase mutation (GBA N370S) but clinically discordant for PD. We applied induced pluripotent stem cell (iPSC) technology for PD disease modeling using the twins' fibroblasts to evaluate and dissect the genetic and nongenetic contributions. Utilizing fluorescence-activated cell sorting, we obtained a homogenous population of "footprint-free" iPSC-derived midbrain dopaminergic (mDA) neurons. The mDA neurons from both twins had ∼50% GBA enzymatic activity, ∼3-fold elevated α-synuclein protein levels, and a reduced capacity to synthesize and release dopamine. Interestingly, the affected twin's neurons showed an even lower dopamine level, increased monoamine oxidase B (MAO-B) expression, and impaired intrinsic network activity. Overexpression of wild-type GBA and treatment with MAO-B inhibitors normalized α-synuclein and dopamine levels, suggesting a combination therapy for the affected twin.

Specimen collection for induced pluripotent stem cell research: harmonizing the approach to informed consent.

Induced pluripotent stem cells (iPSCs) have elicited excitement in both the scientific and ethics communities for their potential to advance basic and translational research. They have been hailed as an alternative to derivation from embryos that provides a virtually unlimited source of pluripotent stem cells for research and therapeutic applications. However, research with iPSCs is ethically complex, uniquely encompassing the concerns associated with genomics, immortalized cell lines, transplantation, human reproduction, and biobanking. Prospective donation of tissue specimens for iPSC research thus requires an approach to informed consent that is constructed for this context. Even in the nascent stages of this field, approaches to informed consent have been variable in ways that threaten the simultaneous goals of protecting donors and safeguarding future research and translation, and investigators are seeking guidance. We address this need by providing concrete recommendations for informed consent that balance the perspectives of a variety of stakeholders. Our work combines analysis of consent form language collected from investigators worldwide with a conceptual balancing of normative ethical concerns, policy precedents, and scientific realities. Our framework asks people to consent prospectively to a broad umbrella of foreseeable research, including future therapeutic applications, with recontact possible in limited circumstances. We argue that the long-term goals of regenerative medicine, interest in sharing iPSC lines, and uncertain landscape of future research all would be served by a framework of ongoing communication with donors. Our approach balances the goals of iPSC and regenerative medicine researchers with the interests of individual research participants.

Implementation of multi-echo-based correlated spectroscopic imaging and pilot findings in human brain and calf muscle.

PURPOSE: To implement a spatially encoded correlated spectroscopic imaging (COSI) sequence on 3 Tesla (T) MRI/MR spectroscopy scanners incorporating four echoes to collect four phase-encoded acquisitions per repetition time (TR), and to evaluate the performance and reliability of this four-dimensional (4D) multi-echo COSI (ME-COSI) sequence in brain and calf muscle. MATERIALS AND METHODS: Typical scan parameters for the 4D datasets were as follows: repetition time = 1500 ms, 2000 Hz bandwidth, 8 × 8 spatial encoding, one average, 64 Δt(1) increments and the scan duration was 25 min. The performance and test-retest reliability of ME-COSI were evaluated with phantoms and in the occipitoparietal brain tissues and calf of six healthy volunteers (mean age = 32 years old). RESULTS: Regional differences in concentrations of lipids, creatine (Cr), choline (Ch), and carnosine (Car) were observed between spectra from voxels located in tibial marrow, tibialis anterior, and soleus muscle. Diagonal and cross-peak resonances were identified from several brain metabolites including N-acetyl aspartate (NAA), Ch, Cr, lactate (Lac), aspartate (Asp), glutathione (GSH), and glutamine\glutamate (Glx). Coefficients of variation (CV) in metabolite ratios across repeated measurements were <15% for diagonal and <25% for cross-peaks observed in vivo. CONCLUSION: The ME-COSI sequence reliably acquired spatially resolved 2D Correlated Spectroscopy (COSY) spectra demonstrating the feasibility of differentiating spatial variation of metabolites in different tissues. Multi-echo acquisition shortens scan duration to clinically feasible times.

Combined DCE-MRI and single-voxel 2D MRS for differentiation between benign and malignant breast lesions.

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and proton (1H) magnetic resonance spectroscopy (MRS) provide structural and biochemical information, including vascular volume, vascular permeability and tissue metabolism. In this study, we performed analysis of the enhancement characteristic from DCE-MRI and the biochemical information provided by two-dimensional (2D) Localized Correlated Spectroscopy (L-COSY) MRS to determine the sensitivity and specificity of using DCE-MRI alone compared to the combination with 2D MRS. The metabolite ratios from the 2D MRS spectra were analyzed using multivariate statistical analyses to determine a method capable of automatic separation of the patient cohort into malignant and benign lesions. A total of 24 lesions were studied with 21 diagnosed accurately using the enhancement characteristics alone resulting in sensitivity and specificity of 100% and 73%, respectively. Analysis of the 2D MRS data demonstrated a significant difference (p < 0.05) in 12 of 18 metabolite ratios analyzed for malignant compared to benign lesions. Previous research focused on utilizing the choline signal to noise ratio (SNR) as a marker for malignancy has been verified using 2D MRS in this study. Using Fisher's linear discriminant test using water (WAT)/olefinic fat diagonal (UFD), choline (CHO)/fat (FAT), CHO/UFD, and FAT/methyl fat (FMETD) as predictors the sensitivity and specificity increased to 92% and 100%, respectively. Using the Classification and Regression Tree (CART) statistical analysis the resulting sensitivity and specificity were 100% and 91%, respectively, with the most accurate predictor for differentiating malignant and benign determined to be FAT/FMETD. The cases within the study that presented a indeterminate diagnosis using DCE-MRI alone were able to be accurately diagnosed when the metabolic information from 2D MRS was incorporated. The results suggest improved breast cancer detection through the combination of morphological and enhancement information from DCE-MRI and metabolic information from 2D MRS.

Echo planar correlated spectroscopic imaging: implementation and pilot evaluation in human calf in vivo.

Exploiting the speed benefits of echo-planar imaging (EPI), the echo-planar spectroscopic imaging (EPSI) sequence facilitates recording of one spectral and two to three spatial dimensions faster than the conventional magnetic resonance spectroscopic imaging (MRSI). A novel four dimensional (4D) echo-planar correlated spectroscopic imaging (EP-COSI) was implemented on a whole body 3 T MRI scanner combining two spectral with two spatial encodings. Similar to EPSI, the EP-COSI sequence used a bipolar spatial read-out train facilitating simultaneous spatial and spectral encoding, and the conventional phase and spectral encodings for the other spatial and indirect spectral dimensions, respectively. Multiple 2D correlated spectroscopy (COSY) spectra were recorded over the spatially resolved volume of interest (VOI) localized by a train of three slice-selective radiofrequency (RF) pulses (90°-180°-90°). After the initial optimization using phantom solutions, the EP-COSI data were recorded from the lower leg of eight healthy volunteers including one endurance trained volunteer. Pilot results showed acceptable spatial and spectral quality achievable using the EP-COSI sequence. There was a detectable separation of cross peaks arising from the skeletal muscle intramyocellular lipids (IMCLs) and extramyocellular lipids (EMCLs) saturated and unsaturated pools. Residual dipolar interaction between the N-methylene and N-methyl protons of creatine/phosphocreatine (Cr/PCr) was also observed in the tibialis anterior region.