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Epidemiology-Genetic Epidemiology

Title: Genetic Epidemiology of Sleep Apnea and Comorbidities in Biobanks

Site PI: H. Lester Kirchner, PhD (PI: Brian Cade, Brigham and Women’s Hospital)
Funder: NHLBI
Grant Number: R01 HL153805
Dates: 08/16/2021 – 07/31/2026

Sleep apnea (SA) and insomnia are the two most common sleep disorders, and both contribute individually and jointly to the risk of cardiopulmonary, metabolic, and psychiatric diseases. Despite their high prevalence, treatments for SA and insomnia remain suboptimal. SA and insomnia are thought to be comprised of distinct subtypes, which remain poorly defined and may contribute to differing risks for health outcomes. Our goal is to use machine learning to apply precise phenotyping to biobanks to identify the genetic bases of SA and insomnia and discover SA and insomnia subtypes based on genetics and comorbidities in order to reduce phenotype heterogeneity, guide patient stratification and aid in the discovery of more personalized treatments. Our approach is to combine health care system biobank data with polysomnography (PSG) to achieve statistical power to discover genetic variants for SA and insomnia-related phenotypes and characterize their associated clinical outcomes and endophenotypes. We will use advanced natural language processing (NLP) methods to substantially improve the accuracy of SA and insomnia phenotyping. Polygenic risk scores derived from our results can be used to quantify sleep disorder risk, even among those without sleep phenotypes. Our specific aims are: 1) to construct advanced SA and insomnia phenotyping algorithms across diverse demographic groups and sites; 2) to identify and characterize the genetic associations with SA and insomnia; and 3) to identify and characterize distinct SA and insomnia patient subgroups based on related comorbidity profiles.

Title: Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk.

MPI: Anne Justice, MA, PhD
Grant Number: R01 MH 122686-01
Funder: NIMH
Dates: 02/01/2021 – 12/31/2025

Obesity and related metabolic consequences contribute substantially to accelerated aging and reduce lifespan in psychiatrically ill individuals. These adverse metabolic consequences, particularly antipsychotic-induced weight gain, are thought to be highly genetic, but our understanding of the genetic contributors and mechanisms remains limited. The discovery of genetic variation influencing antipsychotic-induced weight gain has the potential to identify the pathways leading to poor outcomes, suggesting avenues for alternative treatments with less metabolic impact. Our study aims to identify and functionally validate key genetic determinants of antipsychotic-induced increases in adiposity and cardiometabolic risk across. We are implementing population-based genetics, existing biospecimen with linked clinical data including precisely measured adiposity and insulin sensitivity, and advanced molecular tools to identify and functionally validate key genetic determinants of antipsychotic-induced weight gain and cardiometabolic risk across the age-span. This approach leverages 1) existing population-level data from large biobanking initiatives and epidemiological studies with genetic and relevant phenotypic data, 2) existing clinical and biospecimen data from NIH funded randomized clinical trials characterizing the metabolic effects of antipsychotics in children, adults and older adults with direct and precise measures of body fat, and 3) CRISPR based in vitro drug exposure, followed by cellular functional assays to characterize molecular mechanisms impacted by antipsychotic medications. This combined set of molecular techniques will allow us to build on known genetic associations, while discovering new genes and genetic variants that are associated with the greatest risk for treatment-related fat gain in psychiatrically ill patients.

Title: Integrative approaches to identifying function and clinical significance of adiposity susceptibility genes

MPI: Anne Justice, MA, PhD
Grant Number: R01 DK 122503
Funder: NIDDK
Dates: 09/22/2020 – 07/31/2025

Large scale genetic studies have laid the foundation for later investigations into disease progression and public health applications, including for obesity. While hundreds of genetic associations with obesity have been identified, further work is needed to narrow in on the causal factors contributing to these associations. This study leverages collaborations in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), TransOMICs for Precision Medicine (TOPMed) Program, the Genome Sequencing Project (GSP), and the EHR database from the Geisinger MyCODE Community Health Initiative study (MyCode) to narrow in on genes underlying GWAS signals, perform clinical characterization, and conduct in vitro functional studies to characterize the molecular underpinnings and biological mechanisms of obesity-risk loci. To accomplish these goals, we are 1) integrating multiple genomic, methylomic, transcriptomic, and metabolomic data resources from the TOPMed Program to identify causal genes/variants underlying known obesity-risk loci, 2) evaluating the clinical significance these genes/variants in the clinical phenome of the MyCode patient population to develop a more comprehensive picture of obesity-related disease etiology, and 3) confirming the functional role of these targets through experiments in relevant human cell lines. Our approach will substantially move the field away from tag variants and loci to causal variants, genes, and mechanisms. We anticipate that this work will generate fundamental and important insights into the underlying etiology of obesity and ultimately point the way forward towards prevention and treatment.

Title: Integrated trajectory data and machine learning approaches for precision obesity medicine

PI: Anne Justice, MA, PhD
Grant Number: SAP-4100085740
Funder: Pennsylvania Department of Health
Dates: 06/01/2020 – 05/31/2024

The overall goal of this project is to develop a novel integrated framework including statistical and machine learning approaches for characterizing patterns and subtypes of longitudinal body mass index (BMI) change in adults, identifying its genetic antecedents and its influence on downstream influence on type 2 diabetes (T2D) and related comorbidities. Thus, the findings from this investigation will improve early detection of disease risk and reveal subgroups of individuals at higher risk for disease spectrum to be targeted for early intervention and monitoring, ultimately moving precision health forward. Specifically, this project is 1) identifying subtypes of body mass change by characterizing common mathematical features of BMI trajectories in adults and machine learning approaches for identifying distinct trajectory patterns for BMI; 2) identifying significant associations between BMI change subtypes and genetic variants and T2D related comorbidities; and 3) developing group-specific polygenic risk score models for associated T2D outcomes. This project will develop a novel integrated framework with statistical and machine learning approaches for characterizing patterns and subtypes of longitudinal body mass index change, identifying its genetic antecedents and its influence on downstream influence on type 2 diabetes and related comorbidities. The long-term benefits of this project include new improved methods for early detection of high-risk individuals that may benefit from weight loss intervention or weight gain monitoring.

Title: Genetic contributions to post-treatment Lyme disease syndrome

PI: Annemarie G. Hirsch, PhD, MPH
Funder: Pennsylvania Department of Health
Dates: 6/2019 – 5/2023

With funding from the Pennsylvania Department of Health, we are studying an important public health problem in Pennsylvania – Lyme disease – and one of its long-term consequences, post-treatment Lyme disease syndrome (PTLDS). Pennsylvania has more cases of Lyme disease than any other state. PTLDS, defined by fatigue, musculoskeletal pain, and cognitive complaints lasting at least six months (but often lasting many years) occurs in 15-20% of Lyme disease cases. PTLDS is associated with significant health care utilization and impacts quality of life and daily functioning. Understanding the causes of PTLDS is a critical first step in its prevention. The project team brings together expertise in Lyme disease and genetic epidemiology and has access to valuable data assets that will be leveraged for this project, including extensive genetic data available from the Geisinger MyCodeTM Community Health Initiative. This project will provide greater understanding of the etiology of PTLDS by using electronic health record and existing single nucleotide polymorphism data to complete the first genome-wide association study of PTLDS.

Title: Assessing the Burden of Diabetes by Type in Children, Adolescents, and Young Adults (DiCAYA).

PI: Annemarie G. Hirsch, PhD, MPH
Funding agency: CDC
Project number: U18DP006509-01-00
Dates: 09/2020 – 09/2025

Geisinger is part of the CDC-funded network DiCAYA (www.dicaya.org) with the goal to advance diabetes surveillance efforts of youth and young adults through the use of large-volume electronic health record data across eight US-based centers. Diabetes prevalence and incidence in the US is increasing in youth and young adults and younger onset is associated with greater risk of morbidity and mortality. Traditional survey-based surveillance is limited in its ability to distinguish diabetes types and achieve the scalability needed to generate valid estimates for uncommon diseases such as pediatric diabetes. Moreover, surveys and conventional disease registries are costly and can be inefficient. EHRs provide a novel approach to conducting timely surveillance. The DiCAYA network is spread across the US, with half the centers conducting surveillance in youth (0-17 years), and the other half conducting surveillance in young adults (18-45 years), and a coordinating center. Geisinger is one of the sites studying young adults.

The DiCAYA network has three primary aims: 1) develop EHR-based computable phenotype algorithms for the accurate identification of incident and prevalent type 1 and type 2 diabetes in youth and young adults; 2) estimate the incidence and prevalence of diabetes in these age groups between 2018 and 2024, by type, sex, race/ethnicity, and geography; and 3) evaluate the DiCAYA network’s EHR surveillance of diabetes on key surveillance attributes (e.g., data quality and accuracy). DiCAYA’s methodology will translate to diverse settings because its centers include a mix of health systems, including a membership-based health system, and geographically-based centers. DiCAYA’s work will inform public health and health services strategies, improve national assessments of the burden of diabetes, and advance nascent EHR-based surveillance methods for other illnesses.

Title: Chronic Rhinosinusitis Integrative Studies Program (CRISP)

Site PI: Annemarie G. Hirsch, PhD, MPH (PI: Robert P. Schleimer, PhD; Project Leader: Brian S. Schwartz, MD, MS)
Funder: National Institute of Allergy and Infectious Diseases (NIAID)
CRISP1: first six years of funding (with no cost extension): U19 AI106683, 2013 to 2019
CRISP2: competitive renewal: P01 AI145818, 2019 to 2024

CRISP1: In CRISP1 we studied a large sample of subjects, representative of the general population in 38 counties in central and northeastern Pennsylvania, across the full spectrum of CRS. We conducted the first U.S. longitudinal study of nasal and sinus symptoms lasting three months, following 7847 subjects over 16 months with six questionnaires assessing symptoms, co-morbidities, risk factors, and seasonal exacerbations. We then conducted the first study of sinus inflammation by CT scan in a general population, on 646 subjects selected from baseline questionnaire respondents enriched for sinus symptoms. In parallel studies, using electronic health record (EHR) diagnoses, we conducted longitudinal analysis to identify incident diagnoses that predated and occurred after CRS diagnosis. CRISP1 was a critical advance in the epidemiologic understanding of CRS; prior studies were limited to tertiary care, representing only the most severe patients; did not objectively measure sinus inflammation; and/or lacked the longitudinal data needed to assess the temporal relations between CRS and other diseases.

We made several important and novel observations. CRS identified with symptoms was common in the general population at 12% and quite disabling. We found heterogeneity in symptom combinations and stability over time and identified risk factors for these symptom patterns. We identified five symptom clusters in those with chronic sinonasal symptoms, with three predicting workplace absence, lost productivity, and/or sinus opacification. Analysis of sinus opacification suggested that current methods of measuring opacification may need to be revised; we used statistical methods to identify localized and diffuse patterns of opacification. There were prominent sex differences in degree and location of sinus opacification and also in symptom patterns. Our EHR-based and longitudinal questionnaire studies both provided preliminary evidence that patients with CRS without nasal polyps are at greater risk of other airway diseases. Studies are now needed that can identify the presence of inflammation earlier than sinus CT; using novel approaches to identification of CRS and new phenotypes; with additional longitudinal information; to address risk of long-term outcomes; and to further evaluate key sex differences.

CRISP2: We have four primary specific aims in CRISP2. First, we want to develop new approaches to CRS phenotyping: We hypothesize that patterns of longitudinal symptoms, longitudinal medical history, and CT scan findings can be used to identify new CRS phenotypes that will be associated with 1) natural history; 2) quality of life; 3) health care utilization; and 4) workplace productivity. Second, we want to evaluate if CRS endotypes, defined with nasal lavage fluid biomarkers identifying type 1, type 2, type 3, and mixed patterns of inflammation, epithelial-mesenchymal transition, and fibrin deposition, are associated with phenotype, natural history, clinical outcomes, quality of life, utilization, and workplace productivity. Third, we will evaluate CRS as antecedent risk for other diseases: we hypothesize that newly identified phenotypes and endotypes will be associated with increased risk for development of asthma and bronchiectasis. Finally, we will assess sex differences: we hypothesize that all specific aims will show sex differences. CRISP1 found that different diagnostic standards for CRS by sex are needed and CRS in women may be sub-optimally managed.

Publications

  1. Tan BK, Chandra RK, Pollak J, Kato A, Conley DB, Peters AT, Grammer LC, Avila PC, Kern RC, Stewart WF, Schleimer RP, Schwartz BS. Incidence and associated premorbid diagnoses of patients with chronic rhinosinusitis. J Allergy Clin Immunol 2013; 131: 1350-60.
  2. Tan BK, Kern RB, Schleimer RP, Schwartz BS. Chronic rhinosinusitis: the unrecognized epidemic [editorial]. AJRCCM 2013; 188: 1275-7.
  3. Sundaresan A, Hirsch AG, Storm M, Tan BK, Kennedy TL, Greene JS, Schwartz BS. Occupational and environmental risk factors for chronic rhinosinusitis: a systematic review. Int Forum Allerg Rhinol 2015; 5: 996-1003.
  4. Hirsch AG, Yan X, Sundaresan A, Tan BK, Schleimer RP, Kern RC, Kennedy TL, Greene JS, Schwartz BS. Five-year risk of incident disease following a diagnosis of chronic rhiosinusitis. Allergy 2015; 70: 1613-21.
  5. Tustin AW, Hirsch AG, Rasmussen SG, Casey JA, Bandeen-Roche K, Schwartz BS. Associations between unconventional natural gas development and nasal and sinus, migraine headache, and fatigue symptoms in Pennsylvania. Environ Health Perspect 2017; 125: 189-97.
  6. Hirsch AG, Stewart WF, Sundaresan AS, Young AJ, Kennedy TL, Greene JS, Feng W, Tan BK, Schleimer RP, Kern RC, Lidder A, Schwartz BS. Nasal and sinus symptoms and chronic rhinosinusitis in a population-based sample. Allergy 2017; 72: 274-281.
  7. Stevens WW, Peters AT, Hirsch AG, Nordberg CM, Schwartz BS, Mercer DG, Mahdavinia M, Grammer LC, Hulse KE, Kern RC, Avila P, Schleimer RP. Clinical characteristics of patients with chronic rhinosinusitis with nasal polyps, asthma, and aspirin-exacerbated respiratory disease. J Allergy Clin Immunol Pract 2017; 5: 1061-1070.
  8. Sundaresan AS, Hirsch AG, Young AJ, Pollak J, Tan BK, Schleimer RP, Kern RC, Kennedy TL, Greene JS, Stewart WF, Bandeen-Roche K, Schwartz BS. Longitudinal evaluation of chronic rhinosinusitis symptoms in a population-based sample. J Allergy Clin Immunol Pract 2018; 6: 1327-35.
  9. Kuiper JR, Hirsch AG, Bandeen-Roche K, Sundaresan AS, Tan BK, Schleimer RP, Kern RC, Stewart WF, Schwartz BS. Prevalence and risk factors for acute exacerbations of nasal and sinus symptoms: a population-based, longitudinal cohort study in Pennsylvania. Allergy 2018; 73: 1244-53.
  10. Cole M, Bandeen-Roche K, Hirsch AG, Kuiper JR, Sundaresan AS, Kern RC, Tan BK, Schleimer RP, Schwartz BS. Longitudinal evaluation of clustering of sinonasal and related symptoms using exploratory factor analysis. Allergy 2018; 73: 1715-23.
  11. Casey JA, Wilcox HC, Hirsch AG, Pollak J, Schwartz BS. Associations of unconventional natural gas development with depression symptoms and disordered sleep in Pennsylvania. Scientific Reports 2018; 8: 11375, 10 pages.
  12. Kuiper JR, Hirsch AG, Bandeen-Roche K, Sundaresan AS, Tan BK, Kern RC, Schleimer RP, Schwartz BS. Workplace indirect cost impacts of nasal and sinus symptoms and related diagnoses. J Occup Environ Med 2019; 61: e333-e339.
  13. Hirsch AG, Nordberg C, Bandeen-Roche K, Tan BK, Schleimer RP, Kern RC, Sundaresan AS, Pinto JM, Kennedy TL, Greene S, Kuiper JR, Schwartz BS. Radiologic sinus inflammation and symptoms of chronic rhinosinusitis in a population-based sample. Allergy 2020; 75: 911-20.
  14. Kuiper JR, Hirsch AG, Bandeen-Roche K, Sundaresan AS, Tan BK, Kern RC, Schleimer RP, Schwartz BS. A new approach to categorization of radiologic inflammation in chronic rhinosinusitis. PLoS One 2020; 15: e0235432: 1-15.
  15. Soliai M, Sundaresan AS, Morin A, Hirsch AG, Stanhope C, Kuiper J, Schwartz BS, Ober C, Pinto JM. Two-stage genome-wide association study of chronic rhinosinusitis and disease subphenotypes highlights mucosal immunity contributing to risk. Int Forum Allergy Rhinol 2021; 11: 814-7.
  16. Schwartz BS, Al-Sayouri SA, Pollak JS, Hirsch AG, Kern R, Tan B, Kato A, Schleimer RP, Peters AT. Strong and consistent associations of precedent chronic rhinosinusitis with risk of non-cystic fibrosis bronchiectasis. J Allergy Clinical Immunology 2022; in press (NIHMS 1790755).

Title: Chronic Kidney Disease Prognosis Consortium

Site PI: Alex Chang, MD, MS
Grant Number: R01DK100446
Funder: NIDDK
Dates: 5/2018 – 3/2023

In this grant, we aim to determine risk of incident CKD and CKD progression associated with clinical markers across a range of patient populations, evaluate the risk of non- kidney outcomes associated with eGFR, and albuminuria, and develop risk calculators for CKD incidence, CKD progression, and ESRD.

Publications:

  1. Shin JI, Chang AR* (co-first-author), Grams ME, Coresh J, Ballew SH, Surapaneni A, Matsushita K, Bilo HJG, Carrero JJ, Chodick G, Daratha KB, Jassal SK, Nadkarni GN, Nelson RG, Nowak C, Stempniewicz N, Sumida K, Traynor JP, Woodward M, Sang Y, Gansevoort RT; CKD Prognosis Consortium. Albuminuria Testing in Hypertension and Diabetes: An Individual-Participant Data Meta-Analysis in a Global Consortium. Hypertension. 2021 Sep;78(4):1042-1052. doi: 10.1161/HYPERTENSIONAHA.121.17323. Epub 2021 Aug 9. PMID: 34365812; PMCID: PMC8429211.
  2. Sumida K, Nadkarni GN, Grams ME, Sang Y, Ballew SH, Coresh J, Matsushita K, Surapaneni A, Brunskill N, Chadban SJ, Chang AR, Cirillo M, Daratha KB, Gansevoort RT, Garg AX, Iacoviello L, Kayama T, Konta T, Kovesdy CP, Lash J, Lee BJ, Major RW, Metzger M, Miura K, Naimark DMJ, Nelson RG, Sawhney S, Stempniewicz N, Tang M, Townsend RR, Traynor JP, Valdivielso JM, Wetzels J, Polkinghorne KR, Heerspink HJL. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis: An Individual Participant-Based Meta- analysis. Ann Intern Med. 2020 Jul 14. doi: 10.7326/M20-0529. Epub ahead of print. PMID: 32658569.
  3. Chang AR, Grams ME, Ballew SH, Bilo H, Correa A, Evans M, Gutierrez OM, Hosseinpanah F, Iseki K, Kenealy T, Klein B, Kronenberg F, Lee BJ, Li Y, Miura K, Navaneethan SD, Roderick PJ, Valdivielso JM, Visseren FLJ, Zhang L, Gansevoort RT, Hallan SI, Levey AS, Matsushita K, Shalev V, Woodward M; CKD Prognosis Consortium (CKD-PC). Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium. BMJ. 2019 Jan 10;364:k5301.
  4. Evans M, Grams ME, Sang Y, Astor BC, Blankestijn PJ, Brunskill NJ, Collins JF, Kalra PA, Kovesdy CP, Levin A, Mark PB, Moranne O, Rao P, Rios PG, Schneider MP, Shalev V, Zhang H, Chang AR, Gansevoort RT, Matsushita K, Zhang L, Eckardt KU, Hemmelgarn B, Wheeler DC. Risk Factors for Prognosis in Patients With Severely Decreased GFR. Kidney Int Rep. 2018 Jan 11;3(3):625-637. doi: 10.1016/j.ekir.2018.01.002. eCollection 2018 May. PubMed PMID: 29854970; PubMed Central PMCID: PMC5976849.

Title: National Kidney Foundation – Patient Network

Site PI: Alex Chang, MD, MS
Funder: National Kidney Foundation
Dates: 9/2019 – 8/2023

In this grant, the goal is to create an interactive community of patients throughout the continuum of CKD that links patient entered data with clinical data from electronic health records.