The Mass General Brigham Biobank Portal: an i2b2-based data repository linking disparate and high-dimensional patient data to support multimodal analytics

Prevalence of autoimmune diseases in functional neurological disorder: influence of psychiatric comorbidities and biological sex

BACKGROUND

Functional neurological disorder (FND) is a common and disabling neuropsychiatric condition, which disproportionally affects women compared with men. While the etiopathogenesis of this disorder remains elusive, immune dysregulation is emerging as one potential mechanism. To begin to understand the role of immune dysfunctions in FND, we assessed the prevalence of several common autoimmune diseases (ADs) in a large cohort of patients with FND and examined the influence of psychiatric comorbidities and biological sex.

METHODS

Using a large biorepository database (Mass General Brigham Biobank), we obtained demographic and clinical data of a cohort of 643 patients diagnosed with FND between January 2015 and December 2021. The proportion of ADs was calculated overall, by sex and by the presence of psychiatric comorbidities.

RESULTS

The overall prevalence of ADs in our sample was 41.9%, with connective tissue and autoimmune endocrine diseases being the most commonly observed ADs. Among patients with FND and ADs, 27.7% had ≥2 ADs and 8% met criteria for multiple autoimmune syndrome. Rates of ADs were significantly higher in subjects with comorbid major depressive disorder and post-traumatic stress disorder (p= 0.02). Women represented the largest proportion of patients with concurrent ADs, both in the overall sample and in the subgroups of interest (p's < 0.05).

CONCLUSIONS

This study is unique in providing evidence of an association between FND and ADs. Future studies are needed to investigate the mechanisms underlying this association and to understand whether FND is characterised by distinct dysregulations in immune response.

Untargeted metabolomic analysis reveals different metabolites associated with response to mepolizumab and omalizumab in asthma

Background

There is limited evidence on biomarkers associated with response to the monoclonal antibodies currently approved for asthma treatment. We sought to identify circulatory metabolites associated with response to treatment with mepolizumab or omalizumab.

Methods

We conducted global metabolomic profiling of pre-treatment plasma samples from 100 patients with moderate-to-severe asthma who initiated mepolizumab (n=31) or omalizumab (n=69). The primary outcome was the change in exacerbations within 12 months of therapy. Negative binomial models were used to assess the association between each metabolite and exacerbations, adjusting for age, sex, body mass index, baseline exacerbations and inhaled corticosteroid use. Chemical similarity enrichment analysis (ChemRICH) was conducted to identify chemical subclasses associated with treatment response.

Results

The mean age of the mepolizumab group was 58.7 years with on average 2.9 exacerbations over the year prior to initiation of biologic therapy. The mean age in the omalizumab group was 48.8 years with 1.5 exacerbations in the preceding year. Patients with higher levels of two tocopherol metabolites were associated with more exacerbations on mepolizumab (δ-carboxyethyl hydroxychroman (CEHC) (p=2.65E-05, false discovery rate (FDR=0.01) and δ-CEHC glucuronide (p=2.47E-06, FDR=0.003)). Higher levels of six androgenic steroids, three carnitine metabolites and two bile acid metabolites were associated with decreased exacerbations in the omalizumab group. In enrichment analyses, xanthine metabolites (cluster FDR=0.0006) and tocopherol metabolites (cluster FDR=0.02) were associated with worse mepolizumab response, while androgenic steroids (cluster FDR=1.9E-18), pregnenolone steroids (cluster p=3.2E-07, FDR=1.4E-05) and secondary bile acid metabolites (cluster p=0.0003, FDR=0.006) were the top subclasses associated with better omalizumab response.

Conclusion

This study identifies distinct metabolites associated with response to mepolizumab and omalizumab, with androgenic steroids associated with response to both mepolizumab and omalizumab.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility

OBJECTIVES

Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility.

MATERIALS AND METHODS

We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts.

RESULTS

EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science.

DISCUSSION

As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied.

CONCLUSION

This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Ratio of plasma IL-13/TNF- ∝ and CXCL10/CCL17 predicts mepolizumab and omalizumab response in asthma better than eosinophil count or immunoglobulin E level

To date, most studies to identify biomarkers associated with response to the anti-interleukin 5 agent, mepolizumab, and to the anti-immunoglobulin E agent, omalizumab have focused on clinically available biomarkers, such as the peripheral blood eosinophil counts (BEC) and total immunoglobulin E (IgE). However, these biomarkers often have low predictive accuracy, with many patients with eosinophilic or allergic asthma failing to demonstrate clinical response to mepolizumab or omalizumab respectively. In this study, we evaluated the association of baseline pre-biologic plasma levels of 26 cytokines and chemokines, including T-helper 1 (Th1)-, Th2-, Th17-related cytokines, and their ratios with subsequent clinical response to mepolizumab or omalizumab. We defined clinical response as a reduction in the baseline annual exacerbation rate by half or more over the one-year period following initiation of the biologic. Baseline levels of plasma IL-13 were differentially elevated in responders versus non-responders to mepolizumab and plasma CXCL10 levels were differentially elevated in responders to omalizumab. The ratio of IL-13/TNF-α had the best sensitivity and specificity in predicting response to mepolizumab and CXCL10/CCL17 to omalizumab, and these performed better as predictive biomarkers of response than BEC and IgE. Cytokines and chemokines associated with airway eosinophilia, allergic inflammation, or Th2 inflammation, such as IL-13 and CXCL10, may be better predictors of clinical response to mepolizumab and omalizumab, than IL-5 or IgE, the targets of mepolizumab and omalizumab.

Multi-ancestry polygenic mechanisms of type 2 diabetes

Type 2 diabetes (T2D) is a multifactorial disease with substantial genetic risk, for which the underlying biological mechanisms are not fully understood. In this study, we identified multi-ancestry T2D genetic clusters by analyzing genetic data from diverse populations in 37 published T2D genome-wide association studies representing more than 1.4 million individuals. We implemented soft clustering with 650 T2D-associated genetic variants and 110 T2D-related traits, capturing known and novel T2D clusters with distinct cardiometabolic trait associations across two independent biobanks representing diverse genetic ancestral populations (African, n = 21,906; Admixed American, n = 14,410; East Asian, n =2,422; European, n = 90,093; and South Asian, n = 1,262). The 12 genetic clusters were enriched for specific single-cell regulatory regions. Several of the polygenic scores derived from the clusters differed in distribution among ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a body mass index (BMI) of 30 kg m-2 in the European subpopulation and 24.2 (22.9-25.5) kg m-2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg m-2 in the East Asian group. Thus, these multi-ancestry T2D genetic clusters encompass a broader range of biological mechanisms and provide preliminary insights to explain ancestry-associated differences in T2D risk profiles.

Cumulative Diastolic Blood Pressure Burden in Normal Systolic Blood Pressure and Cardiovascular Disease

BACKGROUND

The clinical significance of isolated diastolic hypertension defined by the 2017 American College of Cardiology/American Heart Association blood pressure (BP) guidelines remains inconsistent. We examined whether long-term diastolic burden predicts the first major adverse cardiovascular event in participants with sustained and untreated normal systolic BP.

METHODS

The Mass General Brigham Biobank is a New England health care-based cohort recruited between 2010 and 2021. A total of 15 979 participants aged 18 to 64 years and without prior cardiovascular disease, antihypertensives, or high systolic BP were studied. The cumulative diastolic burden was determined as the area under the curve for diastolic BP (DBP) ≥80 mm Hg over 5 years before enrollment. Major adverse cardiovascular event was defined as a composite of first incident ischemic heart disease, stroke, heart failure, or all-cause death.

RESULTS

Of the 15 979 participants, mean (SD) age at enrollment was 47.6 (14.3) years, 11 950 (74.8%) were women, and the mean (SD) systolic BP and DBP were 118.0 (12.9) and 72.2 (9.3) mm Hg, respectively. Over a median (interquartile range) follow-up of 3.5 (1.8-5.4) years, 2467 (15.4%) major adverse cardiovascular events occurred. Using Cox proportional hazards regression, each SD increase in cumulative DBP was independently associated with a hazard ratio (95% CI) of 1.06 (1.02-1.10) without effect modification by sex (P=0.65), age (P=0.46), or race/ethnicity (P=0.24). In addition to traditional risk factors, cumulative DBP modestly improved the discrimination C index (95% CI) from 0.74 (0.72-0.75) to 0.75 (0.74-0.76; likelihood ratio test, P=0.037).

CONCLUSIONS

Among individuals with normal systolic BP, cumulative DBP may augment cardiovascular disease risk stratification beyond a single DBP measure and traditional risk factors.

Lower myostatin and higher MUC1 levels are associated with better response to mepolizumab and omalizumab in asthma: a protein-protein interaction analyses

INTRODUCTION

Biomarkers are needed to inform the choice of biologic therapy in patients with asthma given the increasing number of biologics. We aimed to identify proteins associated with response to omalizumab and mepolizumab.

METHODS

Aptamer-based proteomic profiling (SomaScan) was used to assess 1437 proteins from 51 patients with moderate to severe asthma who received omalizumab (n = 29) or mepolizumab (n = 22). Response was defined as the change in asthma-related exacerbations in the 12 months following therapy initiation. All models were adjusted for age, sex, and pre-treatment exacerbation rate. Additionally, body mass index was included in the omalizumab model and eosinophil count in the mepolizumab model. We evaluated the association between molecular signatures and response using negative binomial regression correcting for the false discovery rate (FDR) and gene set enrichment analyses (GSEA) to identify associated pathways.

RESULTS

Over two-thirds of patients were female. The average age for omalizumab patients was 42 years and 57 years for mepolizumab. At baseline, the average exacerbation rate was 1.5/year for omalizumab and 2.4/year for mepolizumab. Lower levels of LOXL2 (unadjusted p: 1.93 × 10E-05, FDR-corrected: 0.028) and myostatin (unadjusted: 3.87 × 10E-05, FDR-corrected: 0.028) were associated with better response to mepolizumab. Higher levels of CD9 antigen (unadjusted: 5.30 × 10E-07, FDR-corrected: 0.0006) and MUC1 (unadjusted: 1.15 × 10E-06, FDR-corrected: 0.0006) were associated with better response to omalizumab, and LTB4R (unadjusted: 1.12 × 10E-06, FDR-corrected: 0.0006) with worse response. Protein-protein interaction network modeling showed an enrichment of the TNF- and NF-kB signaling pathways for patients treated with mepolizumab and multiple pathways involving MAPK, including the FcER1 pathway, for patients treated with omalizumab.

CONCLUSIONS

This study provides novel fundamental data on proteins associated with response to mepolizumab or omalizumab in severe asthma and warrants further validation as potential biomarkers for therapy selection.

Impact of selection bias on polygenic risk score estimates in healthcare settings

BACKGROUND

Hospital-based biobanks are being increasingly considered as a resource for translating polygenic risk scores (PRS) into clinical practice. However, since these biobanks originate from patient populations, there is a possibility of bias in polygenic risk estimation due to overrepresentation of patients with higher frequency of healthcare interactions.

METHODS

PRS for schizophrenia, bipolar disorder, and depression were calculated using summary statistics from the largest available genomic studies for a sample of 24 153 European ancestry participants in the Mass General Brigham (MGB) Biobank. To correct for selection bias, we fitted logistic regression models with inverse probability (IP) weights, which were estimated using 1839 sociodemographic, clinical, and healthcare utilization features extracted from electronic health records of 1 546 440 non-Hispanic White patients eligible to participate in the Biobank study at their first visit to the MGB-affiliated hospitals.

RESULTS

Case prevalence of bipolar disorder among participants in the top decile of bipolar disorder PRS was 10.0% (95% CI 8.8-11.2%) in the unweighted analysis but only 6.2% (5.0-7.5%) when selection bias was accounted for using IP weights. Similarly, case prevalence of depression among those in the top decile of depression PRS was reduced from 33.5% (31.7-35.4%) to 28.9% (25.8-31.9%) after IP weighting.

CONCLUSIONS

Non-random selection of participants into volunteer biobanks may induce clinically relevant selection bias that could impact implementation of PRS in research and clinical settings. As efforts to integrate PRS in medical practice expand, recognition and mitigation of these biases should be considered and may need to be optimized in a context-specific manner.

OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records

Biological aging is a multifactorial process involving complex interactions of cellular and biochemical processes that is reflected in omic profiles. Using common clinical laboratory measures in ~30,000 individuals from the MGB-Biobank, we developed a robust, predictive biological aging phenotype, EMRAge, that balances clinical biomarkers with overall mortality risk and can be broadly recapitulated across EMRs. We then applied elastic-net regression to model EMRAge with DNA-methylation (DNAm) and multiple omics, generating DNAmEMRAge and OMICmAge, respectively. Both biomarkers demonstrated strong associations with chronic diseases and mortality that outperform current biomarkers across our discovery (MGB-ABC, n=3,451) and validation (TruDiagnostic, n=12,666) cohorts. Through the use of epigenetic biomarker proxies, OMICmAge has the unique advantage of expanding the predictive search space to include epigenomic, proteomic, metabolomic, and clinical data while distilling this in a measure with DNAm alone, providing opportunities to identify clinically-relevant interconnections central to the aging process.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity

We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.

A Rapid Review on the Value of Biobanks Containing Genetic Information

OBJECTIVES

Increasing access to health data through biobanks containing genetic information has the potential to expand the knowledge base and thereby improve screening, diagnosis, and treatment options for many diseases. Nevertheless, although privacy concerns and risks surrounding genetic data sharing are well documented, direct evidence in favor of the hypothesized benefits of data integration is scarce, which complicates decision making in this area. Therefore, the objective of this study is to summarize the available evidence on the research and clinical impacts of biobanks containing genetic information, so as to better understand how to quantify the value of expanding genomic data access.

METHODS

Using a rapid review methodology, we performed a search of MEDLINE/PubMed and Embase databases; and websites of biobanks and genomic initiatives published from 2010 to 2022. We classified findings into 11 indicators including outputs (a direct product of the biobank activities) and outcomes (changes in scientific and clinical capacity).

RESULTS

Of 8479 abstracts and 101 gray literature sources were reviewed, 96 records were included. Although most records did not report key indicators systematically, the available evidence concentrated on research indicators such as publications and gene-disorder association discoveries (63% of studies), followed by research infrastructure (26%), and clinical indicators (11%) such as supporting the diagnosis of individual patients.

CONCLUSIONS

Existing evidence on the benefits of biobanks is skewed toward easily quantifiable research outputs. Measuring a comprehensive set of outputs and outcomes inspired by value frameworks is necessary to generate better evidence on the benefits of genomic data sharing.

A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

BACKGROUND

Changes in cell-type composition of tissues are associated with a wide range of diseases and environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect shifts in tissue composition.

METHODS

Here we derive a DNA methylation reference matrix for 12 immune-cell types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix, we perform a directional Stouffer and fixed effects meta-analysis comprising 23,053 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes. In a separate cohort of 4386 blood samples, we assess associations between immune-cell fractions and health outcomes.

RESULTS

Our meta-analysis reveals many associations of cell-type fractions with age, sex, smoking and obesity, many of which we validate with single-cell RNA sequencing. We discover that naïve and regulatory T-cell subsets are higher in women compared to men, while the reverse is true for monocyte, natural killer, basophil, and eosinophil fractions. Decreased natural killer counts associated with smoking, obesity, and stress levels, while an increased count correlates with exercise and sleep. Analysis of health outcomes revealed that increased naïve CD4 + T-cell and N-cell fractions associated with a reduced risk of all-cause mortality independently of all major epidemiological risk factors and baseline co-morbidity. A machine learning predictor built only with immune-cell fractions achieved a C-index value for all-cause mortality of 0.69 (95%CI 0.67-0.72), which increased to 0.83 (0.80-0.86) upon inclusion of epidemiological risk factors and baseline co-morbidity.

CONCLUSIONS

This work contributes an extensively validated high-resolution DNAm reference matrix for blood, which is made freely available, and uses it to generate a comprehensive map of associations between immune-cell fractions and common phenotypes, including health outcomes.

Ten Topics to Get Started in Medical Informatics Research

The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.

Reduced Stress-Related Neural Network Activity Mediates the Effect of Alcohol on Cardiovascular Risk

BACKGROUND

Chronic stress associates with major adverse cardiovascular events (MACE) via increased stress-related neural network activity (SNA). Light/moderate alcohol consumption (ACl/m) has been linked to lower MACE risk, but the mechanisms are unclear.

OBJECTIVES

The purpose of this study was to evaluate whether the association between ACl/m and MACE is mediated by decreased SNA.

METHODS

Individuals enrolled in the Mass General Brigham Biobank who completed a health behavior survey were studied. A subset underwent 18F-fluorodeoxyglucose positron emission tomography, enabling assessment of SNA. Alcohol consumption was classified as none/minimal, light/moderate, or high (<1, 1-14, or >14 drinks/week, respectively).

RESULTS

Of 53,064 participants (median age 60 years, 60% women), 23,920 had no/minimal alcohol consumption and 27,053 ACl/m. Over a median follow-up of 3.4 years, 1,914 experienced MACE. ACl/m (vs none/minimal) associated with lower MACE risk (HR: 0.786; 95% CI: 0.717-0.862; P < 0.0001) after adjusting for cardiovascular risk factors. In 713 participants with brain imaging, ACl/m (vs none/minimal) associated with decreased SNA (standardized beta -0.192; 95% CI: -0.338 to -0.046; P = 0.01). Lower SNA partially mediated the beneficial effect of ACl/m on MACE (log OR: -0.040; 95% CI: -0.097 to -0.003; P < 0.05). Further, ACl/m associated with larger decreases in MACE risk among individuals with (vs without) prior anxiety (HR: 0.60 [95% CI: 0.50-0.72] vs 0.78 [95% CI: 0.73-0.80]; P interaction = 0.003).

CONCLUSIONS

ACl/m associates with reduced MACE risk, in part, by lowering activity of a stress-related brain network known for its association with cardiovascular disease. Given alcohol's potential health detriments, new interventions with similar effects on SNA are needed.

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research

Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation of electronic health record data, live data, and real-time high-resolution device data can be challenging and time-consuming. The development of clinically relevant AI tools requires overcoming challenges in data acquisition, scarce hospital resources, and requirements for data governance. These bottlenecks may result in resource-heavy needs and long delays in research and development of AI systems. We present a system and methodology to accelerate data acquisition, dataset development and analysis, and AI model development. We created an interactive platform that relies on a scalable microservice architecture. This system can ingest 15,000 patient records per hour, where each record represents thousands of multimodal measurements, text notes, and high-resolution data. Collectively, these records can approach a terabyte of data. The platform can further perform cohort generation and preliminary dataset analysis in 2-5 minutes. As a result, multiple users can collaborate simultaneously to iterate on datasets and models in real time. We anticipate that this approach will accelerate clinical AI model development, and, in the long run, meaningfully improve healthcare delivery.

Psychotherapy for Alcohol Use Disorder Is Associated With Reduced Risk of Incident Alcohol-Associated Liver Disease

BACKGROUND & AIMS

Alcohol-associated liver disease (ALD) is a devastating complication of alcohol use disorder (AUD). Once it develops, ALD is exceedingly difficult to treat; it therefore is critical to identify ways to prevent ALD. By treating the causes of increased alcohol consumption, psychotherapy may offer prophylactic benefit against the development of ALD for patients with AUD.

METHODS

In this retrospective cohort study, we used International Classification of Diseases, 9th and 10th revision, codes to identify 9635 patients with AUD in the Mass General Brigham Biobank. The mean follow-up period from AUD diagnosis was 9.2 years. We used Cox regression models to generate hazard ratios (HR) for the development of ALD given the receipt or nonreceipt of psychotherapy, adjusting for a range of other contributors including the receipt of medication-assisted treatment.

RESULTS

In our cohort, 60.4% of patients were men, 83.5% of patients were white, the median age was 57.0 years, and 3544 patients (36.8%) received psychotherapy. ALD developed in 1135 patients (11.8%). In multivariable analysis, psychotherapy was associated with a reduced rate of ALD (HR, 0.59; 95% CI, 0.50-0.71; P < .001). This association held for both individual psychotherapy (HR, 0.70; 95% CI, 0.56-0.86; P < .001) and group psychotherapy (HR, 0.76; 95% CI, 0.61-0.94; P = .01). Among patients with cirrhosis, psychotherapy was associated with a lower rate of hepatic decompensation (HR, 0.68; 95% CI, 0.48-0.95; P = .03).

CONCLUSIONS

The receipt of psychotherapy in the setting of AUD is associated with reduced incidence and progression of ALD. Given the safety and potential benefit of psychotherapy, clinicians should consider using it to prevent the development of ALD.

The impact of rare protein coding genetic variation on adult cognitive function

Compelling evidence suggests that human cognitive function is strongly influenced by genetics. Here, we conduct a large-scale exome study to examine whether rare protein-coding variants impact cognitive function in the adult population (n = 485,930). We identify eight genes (ADGRB2, KDM5B, GIGYF1, ANKRD12, SLC8A1, RC3H2, CACNA1A and BCAS3) that are associated with adult cognitive function through rare coding variants with large effects. Rare genetic architecture for cognitive function partially overlaps with that of neurodevelopmental disorders. In the case of KDM5B we show how the genetic dosage of one of these genes may determine the variability of cognitive, behavioral and molecular traits in mice and humans. We further provide evidence that rare and common variants overlap in association signals and contribute additively to cognitive function. Our study introduces the relevance of rare coding variants for cognitive function and unveils high-impact monogenic contributions to how cognitive function is distributed in the normal adult population.

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity

OBJECTIVE

Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity.

RESEARCH DESIGN AND METHODS

Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459).

RESULTS

The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations.

CONCLUSIONS

Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

Measured Blood Pressure, Genetically Predicted Blood Pressure, and Cardiovascular Disease Risk in the UK Biobank

Importance

Hypertension remains the major cardiovascular disease risk factor globally, but variability in measured blood pressure may result in suboptimal management. Whether genetic contributors to elevated blood pressure may complementarily inform cardiovascular disease risk assessment is unknown.

Objective

To examine incident cardiovascular disease by blood pressure polygenic risk score independent of measured blood pressures and antihypertensive medication prescriptions.

Design, Setting, and Participants

The cohort study (UK Biobank) recruited UK residents aged 40 to 69 years between March 2006 and August 2010. Participants without a prior physician diagnosis of cardiovascular disease, including myocardial infarction, stroke, or heart failure, were included. Excluded were individuals with mismatch between self-reported and genotypically inferred sex, sex aneuploidy, missing genotype rates of 1% or greater, and excess genotypic heterozygosity. Data analyses were performed from September 25, 2021, to July 21, 2022.

Exposures

Measured blood pressure and externally derived blood pressure polygenic risk score stratified by hypertension diagnosis and management, which included normal blood pressure (<130/80 mm Hg without antihypertensives), untreated hypertension (systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥80 mm Hg without antihypertensives), and treated hypertension (current antihypertensives prescriptions).

Main Outcomes and Measures

Composite of first incident myocardial infarction, stroke, heart failure, or cardiovascular-related death.

Results

Of the 331 078 study participants included (mean [SD] age at enrollment, 56.9 [8.1] years; 178 824 female [54.0%]), 83 094 (25.1%) had normal blood pressure, 197 597 (59.7%) had untreated hypertension, and 50 387 (15.2%) had treated hypertension. Over a median (IQR) follow-up of 11.1 (10.4-11.8) years, the primary outcome occurred in 15 293 participants. Among those with normal blood pressure, untreated hypertension, and treated hypertension, each SD increase in measured systolic blood pressure was associated with hazard ratios of 1.08 (95% CI, 0.93-1.25), 1.20 (95% CI, 1.16-1.23), and 1.16 (95% CI, 1.11-1.20), respectively, for the primary outcome. Among these same categories, each SD increase in genetically predicted systolic blood pressure was associated with increased hazard ratios of 1.13 (95% CI, 1.05-1.20), 1.04 (95% CI, 1.01-1.07), and 1.06 (95% CI, 1.02-1.10), respectively, for the primary outcome independent of measured blood pressures and other covariates. Findings were similar for measured and genetically predicted diastolic blood pressure.

Conclusions and Relevance

Blood pressure polygenic risk score may augment identification of individuals at heightened cardiovascular risk, including those with both normal blood pressure and hypertension. Whether it may also guide antihypertensive initiation or intensification requires further study.