Data Harmonization Research Articles

Critical Data for Critical Care: A Primer on Leveraging Electronic Health Record Data for Research From Society of Critical Care Medicine’s Panel on Data Sharing and Harmonization

A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research. This article reviews and illustrates six organizing topics (data domains and common data elements; data harmonization; data quality; data interoperability and digital infrastructure; data access, sharing, and governance; and ethics and equity) as a data science primer for critical care researchers, laying a foundation for future publications from the SCCM Discovery Data Harmonization and Sharing Guiding Principles Panel.

Evaluating the current methodological practices and issues in existing literature in pooling complex surveys: a systematic review.

Pooling data from complex survey designs is increasingly used in the health and medical sciences. However, current methodological practices are not well documented in theliterature while performing the pooling strategy. We aimed to review related pooling studies and evaluate the quality of pooling within the framework of specific methodological guidelines, particularly when combining complex surveys such as Demographic & Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). We performed a systematic literature search focusing on studies utilizing the pooling method with DHS and MICS survey data. These studies were selected from those published between 2010 and 2021 and were retrieved from electronic databases (PubMed and Scopus) in accordance with pre-defined inclusion criteria. Then, we extracted355 studies for the final review and evaluated the reporting quality of the pooling strategy while considering some methodological issues. The majority of studies (81.4%) reported using a pooled (one-stage) approach, while 11.8% used a separate (two-stage) approach, and 6.8% used both approaches. Approximately 63.3% of studies did not clearly describe their pooling strategy. Only 3.4% of the studies mentioned the variable harmonization process, while 66.9% addressed dealing with heterogeneity between surveys. All studies that used the separate (two-stage) approach conducted a meta-analytic procedure, while 38.1% of studies using the pooled approach employed a multilevel model. More than half of the studies (55.6%) mentioned the use of clustered standard errors. The Delta method, Bootstrap, and Taylor linearization were each applied in 11.1% of the studies for variance estimation. Survey weights, primary sampling unit (PSU) or cluster, and strata were used together in 30.5% of the studies. Survey weights were employed by 69.8%, PSU or cluster by 43.8%, and the strata variable by 31.7%. Sensitivity analysis was conducted in 16% of the studies. Our study revealed that fundamental methodological issues associated with pooling complex survey databases, such as the selection of pooling procedures, data harmonization, accounting for cycle effects, quality control checks, addressing heterogeneity, selecting model effects, utilizing survey design variables, and dealing with missing values, etc., were inadequately reported in the included studies. We recommend authors, readers, reviewers, and editors examine pooling studies more attentively and utilize the customized checklist developed by our study to assess the quality of future pooling studies.

The EURISCO-EVA Information System, an innovative approach to the data management of multi-site crop evaluation data

This paper introduces EURISCO-EVA, an extension of the European Search Catalogue for Plant Genetic Resources (EURISCO), facilitating standardized data collection, sharing, and analysis for plant genetic resources for food and agriculture (PGRFA) characterization and evaluation. In the framework of the European Evaluation Network (EVA), a private-public partnership aiming at the evaluation of crop accessions conserved in European genebanks, this information system addresses the need for a standardized data repository for multi-site evaluations of different crops. Through centralizing metadata maintenance, EURISCO-EVA ensures uniformity in trait definitions, experimental designs, and passport data, promoting efficient exchange of observed phenotypic data. EURISCO-EVA currently stores more than half a million phenotypic data points for 4,845 PGRFA accessions from six genera and 17 species, collected through 382 phenotypic experiments conducted at 115 experimental locations across 33 countries, involving 89 project partners. This platform offers a user-friendly web interface, empowering its users with features such as map-based filtering of trial locations, statistical overviews, and customizable reports. EURISCO-EVA’s robust administrative functionalities, coupled with standardization efforts, enhance data quality and harmonization, providing a robust tool for storage of and access to crop evaluation data that could be further enhanced by adding analysis modules. EURISCO-EVA also formed the basis for the data management of two research projects (AGENT and INCREASE) under the Horizon 2020 funding program, providing the background organization of complex datasets used to address future challenges in European agriculture.

Validating a Practical Correction for Intravenous Contrast on Computed Tomography–Based Muscle Density

Objective Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements. While this correction was validated internally, the objective of this study was to obtain external validation using different patient cohorts, muscle regions, and CT series. Methods CT images from 109 patients with kidney tumors who received abdominal CT scans with a multiphase intravenous contrast protocol were analyzed. Paraspinal muscle density measurements taken during noncontrast, venous phase, and delayed phase contrast scans were collected. An a priori correction of −7.5 Hounsfield units (HU) was applied to muscle measurements. Equivalence testing was utilized to determine statistical similarity. Results In the sample of 109 patients (mean age: 63 years [SD: 14.3]; 41.3% female), densities in smaller regions of interest within the paraspinal muscles and the entire paraspinal muscle density (PS) in venous and delayed phase contrast scans were higher than in noncontrast. Equivalence testing showed that average corrected contrast and noncontrast muscle densities were within 3 HU for both muscle measures for the total patient sample, and for a majority of male and female subsamples. The correction is suitable for regions of interests of venous contrast (90% CI: −1.90, −0.69 HU) and delayed contrast scans (90% CI: 0.075, 1.29 HU) and within the PS measures of venous contrast (90% CI: −2.04, −0.94 HU) and delayed contrast scans (90% CI: −0.11, 0.89 HU) Conclusions The previously established correction for contrast of −7.5 HU was applied in a new patient population, axial muscle region, muscle measurement size, and expanded on previously studied contrast phases. The correction produced contrast-corrected muscle densities that were statistically equivalent to noncontrast muscle densities. The simplicity of the correction gives clinicians a tool that seamlessly integrates into practice or research to improve harmonization of data between contrast and noncontrast scans.

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity.

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7000 publications for >15000 traits, from which more than 625000 lead associations have been curated. Additionally, 85 000 full genome-wide summary statistics datasets-containing association data for all variants in the analysis-are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

Microplastics and Trash Cleaning and Harmonization (MaTCH): Semantic Data Ingestion and Harmonization Using Artificial Intelligence.

With the rapid expansion of microplastic research and reliance on semantic descriptors, there is an increasing need for plastic pollution data harmonization. Data standards have been developed but are seldom implemented across research sectors, geographic regions, environmental media, or size classes of plastic pollution. Harmonization of existing data is currently hindered by increasingly large datasets using thousands of different categorical variable descriptors, as well as various metrics used to describe particle abundance and differing size ranges studied across groups. For this study, we used manually developed relational databases to build an algorithm utilizing artificial intelligence capable of automatically curating harmonized, more usable datasets describing micro to macro plastic pollution in the environment. The study algorithm MaTCH (microplastics and trash cleaning and harmonization) can harmonize datasets with different formats, nomenclature, methods, and measured particle characteristics with an accuracy of 71-94% when matching semantically. All other non-semantic corrections are reported within a 95% confidence interval and with model uncertainty. All steps of the algorithm are integrated in an open-source software tool for the benefit of the scientific community and ease of integration for all plastic pollution data.

Adapting the open-source Gen3 platform and kubernetes for the NIH HEAL IMPOWR and MIRHIQL clinical trial data commons: Customization, cloud transition, and optimization

ObjectiveThis study aims to provide the decision-making framework, strategies, and software used to successfully deploy the first combined chronic pain and opioid use data clinical trial data commons using the Gen3 platform. Materials and MethodsThe approach involved adapting the open-source Gen3 platform and Kubernetes for the needs of the NIH HEAL IMPOWR and MIRHIQL networks. Key steps included customizing the Gen3 architecture, transitioning from Amazon to Google Cloud, adapting data ingestion and harmonization processes, ensuring security and compliance for the Kubernetes environment, and optimizing performance and user experience. ResultsThe primary result was a fully operational IMPOWR data commons built on Gen3. Key features include a modular architecture supporting diverse clinical trial data types, automated processes for data management, fine-grained access control and auditing, and researcher-friendly interfaces for data exploration and analysis. DiscussionThe successful development of the Wake Forest IDEA-CC data commons represents a significant milestone for chronic pain and addiction research. Harmonized, FAIR data from diverse studies can be discovered in a secure, scalable repository. Challenges remain in long-term maintenance and governance, but the commons provides a foundation for accelerating scientific progress. Key lessons learned include the importance of engaging both technical and domain experts, the need for flexible yet robust infrastructure, and the value of building on established open-source platforms. ConclusionThe WF IDEA-CC Gen3 data commons demonstrates the feasibility and value of developing a shared data infrastructure for chronic pain and opioid use research. The lessons learned can inform similar efforts in other clinical domains.

Cultural Considerations in Intelligence Test Adaptations: a Critical Review of the WAIS-IV India and Its U.K. and U.S. Counterparts.

This study critically examined the adaptation and normative processes of the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV)India. WAIS-IV U.K. Edition Administration and Scoring Manual, WAIS-IVIndia Administration and Scoring Manual, and WAIS-IV Administration and Scoring Manual and Technical and Interpretive Manuals were used to extract test development and adaptation process data. The analyses were descriptive and qualitative. A review of the manuals showed that the normative sample were fluent English speakers with high education levels, which is not representative of India's multilingual and educational diversity. The study identified minimal cultural and linguistic adaptations in subtests, which raised concern about potential biases in the test adaptation process across cultures. The lack of clarity in item selection, demographic data, sample selection, and psychometric properties raised questions about the test's reliability and validity. Issues related to education, age, gender, and regional distribution are discussed, emphasizing the test's limitations in capturing the diverse characteristics of the heterogeneous Indian population. WAIS-IV India may not adequately represent India's population. More careful consideration of socioecological and psychometric factors when adapting intelligence (IQ) tests and use of advanced data harmonization methods can provide robust cross-national statistical harmonization in the future along with more ecologically valid local test development efforts. This can allow consideration of social determinants of health and other factors that can inform a more nuanced approach to IQ test development in non-western contexts.

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

DGRPool, a web tool leveraging harmonized Drosophila Genetic Reference Panel phenotyping data for the study of complex traits.

Genome-wide association studies have advanced our understanding of complex traits, but studying how a GWAS variant can affect a specific trait in the human population remains challenging due to environmental variability. Drosophila melanogaster is in this regard an excellent model organism for studying the relationship between genetic and phenotypic variation due to its simple handling, standardized growth conditions, low cost, and short lifespan. The Drosophila Genetic Reference Panel (DGRP) in particular has been a valuable tool for studying complex traits, but proper harmonization and indexing of DGRP phenotyping data is necessary to fully capitalize on this resource. To address this, we created a web tool called DGRPool (dgrpool.epfl.ch), which aggregates phenotyping data of 1034 phenotypes across 135 DGRP studies in a common environment. DGRPool enables users to download data and run various tools such as genome-wide (GWAS) and phenome-wide (PheWAS) association studies. As a proof-of-concept, DGRPool was used to study the longevity phenotype and uncovered both established and unexpected correlations with other phenotypes such as locomotor activity, starvation resistance, desiccation survival, and oxidative stress resistance. DGRPool has the potential to facilitate new genetic and molecular insights of complex traits in Drosophila and serve as a valuable, interactive tool for the scientific community.

Optimal Instruments for Measurement of Dietary Intake, Physical Activity, and Sleep Among Adults in Population-Based Studies: Report of a National Heart, Lung, and Blood Institute Workshop.

The National Heart, Lung, and Blood Institute convened a virtual workshop in September 2022 to discuss "Optimal Instruments for Measurement of Diet, Physical Activity, and Sleep." This report summarizes the proceedings, identifying current research gaps and future directions for measuring different lifestyle behaviors in adult population-based studies. Key discussions centered on integrating report-based methods, like questionnaires, with device-based assessments, including wearables and physiological measures such as biomarkers and omics to enhance self-reported metrics and better understand the underlying biologic mechanisms of chronic diseases. Emphasis was placed on the need for data harmonization, including the adoption of standard terminology, reproducible metrics, and accessible raw data, to enhance the analysis through artificial intelligence and machine learning techniques. The workshop highlighted the importance of standardizing procedures for integrated behavioral phenotypes using time-series data. These efforts aim to refine data accuracy and comparability across studies and populations, thereby advancing our understanding of lifestyle behaviors and their impact on chronic disease outcomes over the life course.

A new AI-assisted data standard accelerates interoperability in biomedical research.

In this paper, we leveraged Large Language Models(LLMs) to accelerate data wrangling and automate labor-intensive aspects of data discovery and harmonization. This work promotes interoperability standards and enhances data discovery, facilitating AI-readiness in biomedical science with the generation of Common Data Elements (CDEs) as key to harmonizing multiple datasets. Thirty-one studies, various ontologies, and medical coding systems served as source material to create CDEs from which available metadata and context was sent as an API request to 4th-generation OpenAI GPT models to populate each metadata field. A human-in-the-loop (HITL) approach was used to assess quality and accuracy of the generated CDEs. To regulate CDE generation, we employed ElasticSearch and HITL to avoid duplicate CDEs and instead, added them as potential aliases for existing CDEs. The generated CDEs are foundational to assess the interoperability potential of datasets by determining how many data set column headers can be correctly mapped to CDEs as well as quantifying compliance with permissible values and data types. Subject matter experts reviewed generated CDEs and determined that 94.0% of generated metadata fields did not require manual revisions. Data tables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Global Parkinson's Genetic Program (GP2) were used as test cases for interoperability assessments. Column headers from all test cases were successfully mapped to generated CDEs at a rate of 32.4% via elastic search.The interoperability score, a metric for dataset compatibility to CDEs and other connected datasets, based on relevant criteria such as data field completeness and compliance with common harmonization standards averaged 53.8 out of 100 for test cases. With this project, we aim to automate the most tedious aspects of data harmonization, enhancing efficiency and scalability in biomedical research while decreasing activation energy for federated research.

P02.15.A INVESTIGATING THE ROLE OF CELLULAR SENESCENCE IN BRIDGING GLIOBLASTOMA AND ALZHEIMER’S DISEASE

Abstract BACKGROUND With changing demographics, age-related diseases like neurodegenerative disorders and cancer are becoming increasingly prevalent. We previously demonstrated that over 50% of glioblastoma (GB) patients show Alzheimer’s Disease (AD)-like changes in the tumor-adjacent cortex. However, whether these diseases are biologically linked remains unclear. Here, we investigate cellular senescence, a hallmark of aging, as a shared mechanism. MATERIAL AND METHODS We utilized single cell and single nucleus RNA sequencing datasets from 110 GB patients (GBmap core atlas) and 89 individuals with varying stages of AD (SEA-AD MTG atlas). Following data integration, we assessed senescent cells using three established gene lists, performed differential gene expression and conducted gene set enrichment analysis. Senescence scores were compared across different cell types and patients. Co-expression modules with senescence-associated genes were identified using weighted gene co-expression network analysis (WGCNA). Quantitative neuropathological analysis of published measurements (Iba1, AT8, 6E10) complemented the molecular findings. RESULTS Senescence was found in both GB and AD patient cells including cell types of the microenvironment. Notably, the variability in scores per patient was significantly higher in GB as compared to AD cell types, indicating greater inter-patient heterogeneity. Microglia were specifically enriched for senescence gene sets among glial cells. Subset analysis of microglia through data harmonization identified a proinflammatory GB microglia subtype that shared high similarity with AD microglia and had highest senescence scores. Notably, the increase in senescent microglia was associated with progression to advanced disease stages in both diseases. WGCNA identified 3 consensus modules of senescence-associated genes that were coexpressed with disease-specific genes, like PTEN implicated phosphorylation in the AD module and SPP1 for extracellular matrix remodeling in the GB module. All senescence associated modules showed increased expression in more advanced disease stages. Quantitative analysis of immunohistochemistry measurements in the AD dataset confirmed that samples with a high senescence score showed a significantly higher density of tau pathology and an altered microglia morphology. CONCLUSION Our study identifies cellular senescence as one connection between GB and AD pathology by utilizing atlas-level transcriptomic datasets. Particularly microglia upregulate senescence signatures with disease progression. The co-expression of senescence-associated genes within disease-specific modules underscores the complexity of senescence in this context.

Database of European vascular plants red lists as a contribution to more coherent plant conservation

We introduce the database of European vascular plant red lists, a compilation of red list categories designated to taxa during in-country conservation assessments. Version 1.0 of the database is a standalone static dataset with open access in an end-user friendly format. Its aim is to fulfil the objectives of European Cooperation in Science and Technology (COST) Action 18201, ConservePlants. The database synthesizes data across 42 red lists from 41 countries, with participation of 39 out of a total of 44 European countries and two additional Mediterranean countries. The database contains 51,109 records representing 21,481 original taxonomic names with 37 different red list categories. During data harmonisation, 20,312 of the original taxonomic names were assigned to 17,873 unique accepted taxonomic names with scientific authorships across 184 families, 1650 genera and 15,593 species; and red list categories were standardised to 13 unique categories. We see this database as a source of information in diverse plant conservation activities and suitable for various stakeholders.

Blue-cloud DAB: developing a platform to harmonize, assess and disseminate marine metadata collections

AbstractThe integration and harmonization of marine data from diverse sources are vital for advancing global oceanographic research and ensuring seamless discovery and access of critical datasets. This paper presents a comprehensive analysis of the metadata harmonization efforts within the Blue-cloud 2026 project, which brokers data from numerous Blue Data Infrastructures (BDIs), leveraging the Discovery and Access Broker technology. The platform enables discovery and analysis of marine data collections while facilitating interoperability with other components of the marine digital ecosystem, such as virtual laboratories and the Semantic Analyzer. It also supports the flow of Blue-cloud information to other initiatives like the Global Earth Observations System of Systems. For data managers, the findings emphasize the importance of enhancing metadata quality, revealing discrepancies in core metadata elements, and the need for more consistent use of controlled vocabularies. For cyberinfrastructure developers, the study details the challenges of accommodating a wide array of interfaces from different data systems, highlighting the adoption of an extensible brokering architecture that harmonizes metadata models and protocols. The study also emphasizes the importance of metadata analysis in ensuring effective searches for end users, highlighting challenges in aggregating diverse sources, where data providers may have structured the content with different objectives compared to those of the system of systems. End users will gain insights into the current metadata content of Blue-cloud, enabling them to search and access data from multiple BDIs with an understanding of the technical complexities behind the scenes.

PRECISE-GBM: A RADIOGENOMIC MACHINE LEARNING BASED STUDY TO IDENTIFY PREDICTIVE RADIOMICS FOR EVALUATION OF CANCER IMMUNE SIGNATURE IN IDHW GLIOBLASTOMA

Abstract AIMS The aim of the study is to identify machine learning model based on radiological biomarkers that can predict immune status within tumor microenvironment of IDH wildtype glioblastoma. METHOD This is a retrospective multicenter machine learning based study utilizing open access anonymized matched radiogenomic data of TCGA-GBM, CPTAC, Ivy GAP and REMBRANDT datasets. REMBRANDT data acted as hold out dataset. Imaging data consisted of MRI based radiomics features extracted from deep learning based segmented tumors and transcriptomic (RNA seq/microarray) data consisted of immune scores extracted using CIBERSORTx. Outliers were identified and filtered using principal component analysis. Combat and neuroCombat were utilized for data harmonization for each data type respectively. Radiomic feature selection was performed using LASSO technique with cross validation. Support vector machine (SVM) was trained and tested with 80:20 radiomics data split and a median based binary label of immune score (low and high). Validation of the trained model was performed with hold out dataset. Data query and analysis was performed using python and R statistical software. RESULTS One hundred one patients were included in the study with 15 being on the hold out set. Trained SVM model predicted the immune score label with AUC of 0.79 and balanced accuracy of 0.76 (recall 0.75, F1 score 0.75, precision 0.75) in the test data. In the holdout set, the model predicted the immune score with AUC of 0.70 with specificity of 75% in predicting the cold immune cases. The radiomic signature that acted as the label for this model were a group of first order and second order radiomic features. CONCLUSION Radiogenomic SVM model non-invasively predicts immune status in wildtype glioblastoma microenvironment with good accuracy. This model has potential to be utilized in a prospective glioblastoma clinical trial for immunotherapy.

Incidence and prevalence of Lewy body dementia in India: A systematic review

With increasing life expectancy in India, the prevalence of age-related disorders, such as dementia has also increased. Health and social care resources for each state are allocated based on their inhabitants’ age, sex, education, and urban/rural status but not on the dementia subtype, which can significantly influence prognosis, healthcare utilization, and quality of life. Herein, we aimed to systematically review studies investigating the prevalence of the Lewy body dementia (LBD) subtype in India. We conducted a systematic review of EMBASE, MEDLINE, and APA PsychINFO databases on June 22, 2023. Two independent reviewers performed screening and full-text review, with a third reviewer resolving any disputes. Quality was assessed for each extracted paper. Of 1372 identified studies, full-text reviews were conducted on 399 and data were extracted from 4. Two studies included prevalence data on dementia with Lewy bodies (DLB), one on Parkinson’s disease dementia and one on LBD. DLB or LBD has been reported to represent 1.0 – 8.9% of dementia diagnoses. Methodological heterogeneity was characterized by study design, access to biomarkers, diagnostic criteria, and use of cognitive tools. No studies reported incidence data. A paucity of research on LBD epidemiology in India is compounded by methodological heterogeneity, poorly representative cohorts, and varying access to biomarkers. Consensus guidelines may support data harmonization and the creation of multisite consortia, which could redress the under-representation of Central Asian data in epidemiological and genetic LBD studies.

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies.

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

6846 Rare Variants in SEMA3A in Individuals with Constitutional Delay of Puberty (CDP)

Abstract Disclosure: W. He: None. E.C. Schafer: None. R. Brauner: None. A. Delaney: None. L. Dunkel: None. R. Grinspon: None. J.E. Hall: None. J.N. Hirschhorn: None. S. Howard: None. A. Latronico: None. A.A. Jorge: None. K. McElreavey: None. V. Mericq: None. P.M. Merino: None. M.R. Palmert: None. R.A. Rey: None. R.C. Rezende: None. S.B. Seminara: None. N. Shaw: None. T. Delayed Puberty Genetics Consortium: None. Y. Chan: None. Background: Constitutional delay of puberty (CDP), also known as self-limited delayed puberty, refers to late onset of puberty with relatively normal progression afterward. In contrast, idiopathic hypogonadotropic hypogonadism (IHH) results in a long-lasting defect in reproductive endocrine activity. Both CDP and IHH are highly heritable and are sometimes observed in different family members in the same pedigree. Previous work by our group and others has suggested genetic overlap between the two conditions. In this study, we aimed to identify specific IHH-related genes that are also genetic causes of CDP. Methods: The Delayed Puberty Consortium assembled exome data for 233 individuals of diverse ancestries with CDP, with onset of puberty between 12 and 18 y in girls and between 13.5 and 18 y in boys. For control data, we used exome sequencing data from gnomAD v2.1.1 (n=125,748). After data harmonization and quality control, we used a modification of the “TRAPD” method to perform gene-based burden testing for 63 genes associated with IHH. We analyzed rare variants (minor allele frequency < 0.01) with high impact (premature stop, frameshift, start-lost, stop-lost) in all individuals across ancestries (n=233), as allele frequencies for high-impact variants are unlikely to vary widely across populations. We also analyzed rare variants with moderate impact (missense, inframe insertions/deletions) predicted to be deleterious by the Primate AI algorithm (score >0.6) in non-Finnish European (NFE) individuals with CDP (n=99), then meta-analyzed the results for high- and moderate-impact variants. We considered p-values < 7.9 x 10-4 (0.05 with Bonferroni adjustment for 63 genes) significant. Results: Of the 63 genes analyzed, SEMA3A (which encodes semaphorin-3A) exhibited significant enrichment for moderate-impact variants in the NFE CDP cohort compared to controls (variant frequency in CDP vs control: 0.064 vs 0.0076, p = 1.3 x 10-4) and in the meta-analysis of both high- and moderate-impact variants (p = 3.5 x 10-6). Novel SEMA3A variants identified exclusively in CDP individuals but not in controls were a frameshift variant, p.F546fsX2, and a missense variant, I252R. Also, three missense variants in SEMA3A were observed at a higher frequency in NFE CDP individuals compared with controls: p.R66T, p.V461A, and p.T717I. No other IHH genes showed significant enrichment of rare variants in the CDP cohort. Conclusion: Rare variants in SEMA3A contribute to both CDP and IHH. While functional studies are required to assess the effects of these specific variants on protein function, previous in vitro studies of similar SEMA3A variants demonstrated defects in secretion or signaling activity. Subsequent studies employing individual-level ancestry-matched controls and larger CDP cohorts are planned to identify additional genes that contribute to CDP. Presentation: 6/1/2024