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1.
PLoS Med ; 16(10): e1002937, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31626644

RESUMO

BACKGROUND: The role of urate in cardiovascular diseases (CVDs) has been extensively investigated in observational studies; however, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance. METHODS AND FINDINGS: A phenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic model (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 unrelated White British individuals (54% female) in the UK Biobank who were aged 40-69 years (mean age, 56.87; SD, 7.99) when recruited from 2006 to 2010. Mendelian randomization (MR) analyses were performed to replicate significant findings using various genome-wide association study (GWAS) consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for urate. PheWAS analysis, examining the association with 1,431 disease outcomes, identified 13 distinct phecodes representing 4 disease groups (inflammatory polyarthropathies, hypertensive disease, circulatory disease, and metabolic disorders) and 9 disease outcomes (gout, gouty arthropathy, pyogenic arthritis, essential hypertension, coronary atherosclerosis, ischemic heart disease, chronic ischemic heart disease, myocardial infarction, and hypercholesterolemia) that were associated with genetically determined serum urate levels after multiple testing correction (p < 3.35 × 10-4). TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases, and blood lipid levels but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with CVDs. The main limitations of this study relate to possible bias from pleiotropic effects of the considered genetic variants and possible misclassification of cases for mild disease that did not require hospitalization. CONCLUSION: In this study, high serum urate levels were found to be associated with increased risk of different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a common mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

2.
World J Surg ; 2019 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-31605180

RESUMO

BACKGROUND: The extent to which obesity and genetics determine postoperative complications is incompletely understood. METHODS: We performed a retrospective study using two population cohorts with electronic health record (EHR) data. The first included 736,726 adults with body mass index (BMI) recorded between 1990 and 2017 at Vanderbilt University Medical Center. The second cohort consisted of 65,174 individuals from 12 institutions contributing EHR and genome-wide genotyping data to the Electronic Medical Records and Genomics (eMERGE) Network. Pairwise logistic regression analyses were used to measure the association of BMI categories with postoperative complications derived from International Classification of Disease-9 codes, including postoperative infection, incisional hernia, and intestinal obstruction. A genetic risk score was constructed from 97 obesity-risk single-nucleotide polymorphisms for a Mendelian randomization study to determine the association of genetic risk of obesity on postoperative complications. Logistic regression analyses were adjusted for sex, age, site, and race/principal components. RESULTS: Individuals with overweight or obese BMI (≥25 kg/m2) had increased risk of incisional hernia (odds ratio [OR] 1.7-5.5, p < 3.1 × 10-20), and people with obesity (BMI ≥ 30 kg/m2) had increased risk of postoperative infection (OR 1.2-2.3, p < 2.5 × 10-5). In the eMERGE cohort, genetically predicted BMI was associated with incisional hernia (OR 2.1 [95% CI 1.8-2.5], p = 1.4 × 10-6) and postoperative infection (OR 1.6 [95% CI 1.4-1.9], p = 3.1 × 10-6). Association findings were similar after limitation of the cohorts to those who underwent abdominal procedures. CONCLUSIONS: Clinical and Mendelian randomization studies suggest that obesity, as measured by BMI, is associated with the development of postoperative incisional hernia and infection.

3.
JMIR Med Inform ; 2019 Sep 24.
Artigo em Inglês | MEDLINE | ID: mdl-31553307

RESUMO

BACKGROUND: The PheCode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) in the electronic health record (EHR). OBJECTIVE: Here, we present our work on the development and evaluation of maps from ICD-10 and ICD-10-CM codes to PheCodes. METHODS: We mapped ICD-10 and ICD-10-CM codes to PheCodes using a number of methods and resources, such as concept relationships and explicit mappings from the Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM→PheCode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS: We mapped >75% of ICD-10-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47). CONCLUSIONS: This study introduces the initial "beta" versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR.

4.
Int J Epidemiol ; 2019 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-31518429

RESUMO

BACKGROUND: Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. METHODS: We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization-Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. RESULTS: The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. CONCLUSIONS: We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

5.
JAMA Netw Open ; 2(9): e1911130, 2019 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-31509211

RESUMO

Importance: Whether the PCSK9 gene is associated with the progress from infection to sepsis is unknown to date. Objective: To test the associations between PCSK9 genetic variants, a PCSK9 genetic risk score (GRS), or genetically estimated PCSK9 expression levels and the risk of sepsis among patients admitted to a hospital with infection. Design, Setting, and Participants: This retrospective cohort study used deidentified electronic health records to identify patients admitted to Vanderbilt University Medical Center, Nashville, Tennessee, with infection. Patients were white adults, had a code indicating infection from the International Classification of Diseases, Ninth Revision, Clinical Modification, or the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification, and received an antibiotic within 1 day of hospital admission (N = 61 502). Data were collected from January 1, 1993, through December 31, 2017, and analyzed from April 1, 2018, to March 16, 2019. Exposures: Four known PCSK9 functional variants, a GRS for PCSK9, and genetically estimated PCSK9 expression. Main Outcomes and Measures: The primary outcome was sepsis; secondary outcomes included cardiovascular failure and in-hospital death. Results: Of patients with infection, genotype information was available in 10 922 white patients for PCSK9 functional variants (5628 men [51.5%]; mean [SD] age, 60.1 [15.7] years), including 7624 patients with PCSK9 GRS and 6033 patients with estimated PCSK9 expression. Of these, 3391 developed sepsis, 835 developed cardiovascular failure, and 366 died during hospitalization. None of the 4 functional PCSK9 variants were significantly associated with sepsis, cardiovascular failure, or in-hospital death, with or without adjustment for (1) age and sex or (2) age, sex, and Charlson-Deyo comorbidities (in model adjusted for age, sex, and comorbidities, odds ratios for any loss-of function variant were 0.96 [95% CI, 0.88-1.04] for sepsis, 1.05 [95% CI, 0.90-1.22] for cardiovascular failure, and 0.89 [95% CI, 0.72-1.11] for death). Similarly, neither the PCSK9 GRS nor genetically estimated PCSK9 expression were significantly associated with sepsis, cardiovascular failure, or in-hospital death in any of the analysis models. For GRS, in the full model adjusted for age, sex, and comorbidities, the odds ratios were 1.01 for sepsis (95% CI, 0.96-1.06; P = .70), 1.03 for cardiovascular failure (95% CI, 0.95-1.12; P = .48), and 1.05 for in-hospital death (95% CI, 0.92-1.19; P = .50). For genetically estimated PCSK9 expression, in the full model adjusted for age, sex, and comorbidities, the odds ratios were 1.01 for sepsis (95% CI, 0.95-1.06; P = .86), 0.96 for cardiovascular failure (95% CI, 0.88-1.05; P = .41), and 0.99 for in-hospital death (95% CI, 0.87-1.14; P = .94). Conclusions and Relevance: In this study, PCSK9 genetic variants were not significantly associated with risk of sepsis or the outcomes of sepsis in patients hospitalized with infection.

6.
J Biomed Inform ; 99: 103293, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31542521

RESUMO

BACKGROUND: Implementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms. METHODS: We conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category. RESULTS: A total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ±â€¯1.38. Specifically, the average knowledge (K) score is 0.64 ±â€¯0.66, interpretation (I) score is 0.33 ±â€¯0.55, and programming (P) score is 0.40 ±â€¯0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks. CONCLUSION: This study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.

7.
J Biomed Inform ; 98: 103270, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31445983

RESUMO

OBJECTIVE: Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery. METHODS: In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis. RESULTS: From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD. CONCLUSION: This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.

8.
J Biomed Inform ; 96: 103253, 2019 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-31325501

RESUMO

BACKGROUND: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. METHODS: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. RESULTS: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. CONCLUSION: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

9.
BMC Med ; 17(1): 135, 2019 07 17.
Artigo em Inglês | MEDLINE | ID: mdl-31311600

RESUMO

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. METHODS: First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). RESULTS: Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10- 20). This effect was consistent in both pediatric (p = 9.92 × 10- 6) and adult (p = 9.73 × 10- 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10- 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10- 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10- 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10- 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. CONCLUSIONS: In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.


Assuntos
Hepatopatia Gordurosa não Alcoólica/genética , Adulto , Idoso , Índice de Massa Corporal , Estudos de Casos e Controles , Redes Comunitárias/organização & administração , Redes Comunitárias/estatística & dados numéricos , Progressão da Doença , Registros Eletrônicos de Saúde/organização & administração , Registros Eletrônicos de Saúde/estatística & dados numéricos , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Genômica/organização & administração , Genômica/estatística & dados numéricos , Humanos , Lipase/genética , Masculino , Proteínas de Membrana/genética , Pessoa de Meia-Idade , Morbidade , Hepatopatia Gordurosa não Alcoólica/epidemiologia , Fenótipo , Polimorfismo de Nucleotídeo Único , Transdução de Sinais/genética
10.
Circulation ; 140(4): 270-279, 2019 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-31234639

RESUMO

BACKGROUND: Drug effects can be investigated through natural variation in the genes for their protein targets. The present study aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are among the most commonly used medications worldwide. METHODS: Genetic proxies for the effect of antihypertensive drug classes were identified as variants in the genes for the corresponding targets that associated with systolic blood pressure at genome-wide significance. Mendelian randomization estimates for drug effects on coronary heart disease and stroke risk were compared with randomized, controlled trial results. A phenome-wide association study in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University biobank (BioVU) and in observational analysis of the UK Biobank. RESULTS: Suitable genetic proxies for angiotensin-converting enzyme inhibitors, ß-blockers, and calcium channel blockers (CCBs) were identified. Mendelian randomization estimates for their effect on coronary heart disease and stroke risk, respectively, were comparable to results from randomized, controlled trials against placebo. A phenome-wide association study in the UK Biobank identified an association of the CCB standardized genetic risk score with increased risk of diverticulosis (odds ratio, 1.02 per standard deviation increase; 95% CI, 1.01-1.04), with a consistent estimate found in BioVU (odds ratio, 1.01; 95% CI, 1.00-1.02). Cox regression analysis of drug use in the UK Biobank suggested that this association was specific to nondihydropyridine CCBs (hazard ratio 1.49 considering thiazide diuretic agents as a comparator; 95% CI, 1.04-2.14) but not dihydropyridine CCBs (hazard ratio, 1.04; 95% CI, 0.83-1.32). CONCLUSIONS: Genetic variants can be used to explore the efficacy and side effects of antihypertensive medications. The identified potential effect of nondihydropyridine CCBs on diverticulosis risk could have clinical implications and warrants further investigation.

11.
Clin Pharmacol Ther ; 106(3): 623-631, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30924126

RESUMO

Allopurinol, which lowers uric acid (UA) concentration, is increasingly being recognized for its benefits in cardiovascular and renal disease. However, response to allopurinol is variable. We gathered samples from 4,446 multiethnic subjects for a genome-wide association study of allopurinol response. Consistent with previous studies, we observed that the Q141K variant in ABCG2 (rs2231142), which encodes the efflux pump breast cancer resistance protein (BCRP), associated with worse response to allopurinol. However, for the first time this association reached genome-wide level significance (P = 8.06 × 10-11 ). Additionally, we identified a novel association with a variant in GREM2 (rs1934341, P = 3.22 × 10-6 ). In vitro studies identified oxypurinol, the active metabolite of allopurinol, as an inhibitor of the UA transporter GLUT9, suggesting that oxypurinol may modulate UA reabsorption. These results provide strong evidence for a role of BCRP Q141K in allopurinol response, and suggest that allopurinol may have additional hypouricemic effects beyond xanthine oxidase inhibition.

12.
Pac Symp Biocomput ; 24: 272-283, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30864329

RESUMO

The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.

13.
PLoS One ; 14(2): e0212112, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30759150

RESUMO

Genome-wide and phenome-wide association studies are commonly used to identify important relationships between genetic variants and phenotypes. Most studies have treated diseases as independent variables and suffered from the burden of multiple adjustment due to the large number of genetic variants and disease phenotypes. In this study, we used topic modeling via non-negative matrix factorization (NMF) for identifying associations between disease phenotypes and genetic variants. Topic modeling is an unsupervised machine learning approach that can be used to learn patterns from electronic health record data. We chose the single nucleotide polymorphism (SNP) rs10455872 in LPA as the predictor since it has been shown to be associated with increased risk of hyperlipidemia and cardiovascular diseases (CVD). Using data of 12,759 individuals with electronic health records (EHR) and linked DNA samples at Vanderbilt University Medical Center, we trained a topic model using NMF from 1,853 distinct phenotypes and identified six topics. We tested their associations with rs10455872 in LPA. Topics enriched for CVD and hyperlipidemia had positive correlations with rs10455872 (P < 0.001), replicating a previous finding. We also identified a negative correlation between LPA and a topic enriched for lung cancer (P < 0.001) which was not previously identified via phenome-wide scanning. We were able to replicate the top finding in a separate dataset. Our results demonstrate the applicability of topic modeling in exploring the relationship between genetic variants and clinical diseases.


Assuntos
Biologia Computacional/métodos , Doença/genética , Lipoproteína(a)/genética , Modelos Estatísticos , Fenótipo , Polimorfismo de Nucleotídeo Único , Adulto , Registros Eletrônicos de Saúde , Feminino , Predisposição Genética para Doença/genética , Estudo de Associação Genômica Ampla , Humanos , Masculino , Pessoa de Meia-Idade
14.
Sci Rep ; 9(1): 717, 2019 Jan 24.
Artigo em Inglês | MEDLINE | ID: mdl-30679510

RESUMO

Current approaches to predicting a cardiovascular disease (CVD) event rely on conventional risk factors and cross-sectional data. In this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort included 109, 490 individuals. In the first experiment, we extracted aggregated and longitudinal features from EHR. We applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (CNN) and recurrent neural networks with long short-term memory (LSTM) units. In the second experiment, we applied a late-fusion approach to incorporate genetic features. We compared the performance with approaches currently utilized in routine clinical practice - American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equation. Our results indicated that incorporating longitudinal feature lead to better event prediction. Combining genetic features through a late-fusion approach can further improve CVD prediction, underscoring the importance of integrating relevant genetic data whenever available.

15.
JAMA Netw Open ; 2(1): e187223, 2019 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-30657536

RESUMO

Importance: Whether low levels of low-density lipoprotein cholesterol (LDL-C) are associated with increased risk of sepsis and poorer outcomes is unknown. Objective: To examine the association between LDL-C levels and risk of sepsis among patients admitted to the hospital with infection. Design, Setting, and Participants: Cohort study in which deidentified electronic health records were used to define a cohort of patients admitted to Vanderbilt University Medical Center, Nashville, Tennessee, with infection. Patients were white adults, had a code indicating infection from the International Classification of Diseases, Ninth Revision, Clinical Modification, and received an antibiotic within 1 day of hospital admission (N = 61 502). Data were collected from January 1, 1993, through December 31, 2017, and analyzed from January 24 through October 31, 2018. Interventions: Clinically measured LDL-C levels (excluding measurements <1 year before hospital admission and those associated with acute illness) and a genetic risk score (GRS). Main Outcomes and Measures: The primary outcome was sepsis; secondary outcomes included admission to an intensive care unit (ICU) and in-hospital death. Results: Among the 3961 patients with clinically measured LDL-C levels (57.8% women; mean [SD] age, 64.1 [15.9] years) and the 7804 with a GRS for LDL-C (54.0% men; mean [SD] age, 59.8 [15.2] years), lower measured LDL-C levels were significantly associated with increased risk of sepsis (odds ratio [OR], 0.86; 95% CI, 0.79-0.94; P = .001) and ICU admission (OR, 0.85; 95% CI, 0.76-0.96; P = .008), but not in-hospital mortality (OR, 0.80; 95% CI, 0.63-1.00; P = .06); however, none of these associations were statistically significant after adjustment for age, sex, and comorbidity variables (OR for risk of sepsis, 0.96 [95% CI, 0.88-1.06]; OR for ICU admission, 0.94 [95% CI, 0.83-1.06]; OR for in-hospital death, 0.97 [95% CI, 0.76-1.22]; P > .05 for all). The LDL-C GRS correlated with measured LDL-C levels (r = 0.24; P < 2.2 × 10-16) but was not significantly associated with any of the outcomes. Conclusions and Relevance: Results of this study suggest that lower measured LDL-C levels were significantly associated with increased risk of sepsis and admission to ICU in patients admitted to the hospital with infection; however, this association was due to comorbidities because both clinical models adjusted for confounders, and the genetic model showed no increased risk. Levels of LDL-C do not appear to directly alter the risk of sepsis or poor outcomes in patients hospitalized with infection.

16.
Anim Reprod Sci ; 201: 1-11, 2019 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-30587384

RESUMO

Knowledge of conditions affecting sperm quality is essential for efficient culture of fish for commercial purposes and conservation of species. Two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time of flight mass spectrometry were used to characterize the proteomic profile of Acipenser dabryanus spermatozoa relative to motility and fertilization capacity. There were differential amounts of protein in 313 spots in spermatozoa of males classified to have relatively greater or lesser spermatozoa quality. The functions of 43 of 50 selected proteins were identified. The proteins in 14 spots were involved in metabolism, and of these, proteins in 11 spots were highly abundant in spermatozoa of males categorized to have spermatozoa of greater quality, including pyruvate kinase, enolase B, phosphoglycerate kinase, lactate dehydrogenase, cytosolic malate dehydrogenase, brain creatine kinase b, Ckmb protein, and nucleoside diphosphate kinase. The proteins involved in mechanics of flagellum movement were identified, including the dynein intermediate chain, radial spoke head 1 homolog; ropporin-1-like, Bardet-Biedl syndrome 5, ADP-ribosylation factor-like protein 3, tektin-4, gamma-actin, and tubulin cytoskeleton proteins to be differentially abundant in spermatozoa that were classified relatively greater or lesser quality. Heat shock proteins, copper/zinc superoxide dismutase and peroxiredoxins, which are involved in stress response were of differential abundance in spermatozoa from males with spermatozoa in the two different classification groups. Proteins were also detected that are involved in protein folding and binding, or hydrolase activity. The results are valuable for the prediction of sperm quality and for reproduction management in A. dabryanus and other threatened species.


Assuntos
Proteínas de Peixes/metabolismo , Peixes/fisiologia , Espermatozoides/metabolismo , Animais , Proteínas de Peixes/química , Masculino , Proteômica , Motilidade Espermática , Espermatozoides/química
17.
Clin Pharmacol Ther ; 2018 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-30223305

RESUMO

Tizanidine, a widely used muscle relaxant that can lower blood pressure, is metabolized by the cytochrome P450 1A2 (CYP1A2). We studied 1,626 patients prescribed tizanidine and 5,012 prescribed cyclobenzaprine concurrently with a strong CYP1A2 inhibitor. The primary outcome was severe hypotension, defined as systolic blood pressure (SBP) ≤ 70 mmHg during periods of drug co-exposure. Severe hypotension occurred more often in the tizanidine group (2.03%; n = 33) than the cyclobenzaprine group (1.28%; n = 64); odds ratio (OR) = 1.60; P = 0.029. This difference remained statistically significant after adjustment for a log-transformed propensity score that included age, sex, race, Charlson's comorbidity index, and concurrent use of antihypertensive medications (OR = 1.57; P = 0.049). A sensitivity analysis that defined hypotension as SBP < 90 mmHg also yielded higher rates of hypotension among patients prescribed tizanidine. In conclusion, CYP1A2 inhibition increases the risk of hypotensive episodes associated with the use of tizanidine in routine clinical practice.

18.
Nat Commun ; 9(1): 3522, 2018 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-30166544

RESUMO

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.

19.
Nat Genet ; 50(9): 1335-1341, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30104761

RESUMO

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

20.
PLoS Med ; 15(8): e1002642, 2018 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-30153257

RESUMO

BACKGROUND: Observations from statin clinical trials and from Mendelian randomization studies suggest that low low-density lipoprotein cholesterol (LDL-C) concentrations may be associated with increased risk of type 2 diabetes mellitus (T2DM). Despite the findings from statin clinical trials and genetic studies, there is little direct evidence implicating low LDL-C concentrations in increased risk of T2DM. METHODS AND FINDINGS: We used de-identified electronic health records (EHRs) at Vanderbilt University Medical Center to compare the risk of T2DM in a cross-sectional study among individuals with very low (≤60 mg/dl, N = 8,943) and normal (90-130 mg/dl, N = 71,343) LDL-C levels calculated using the Friedewald formula. LDL-C levels associated with statin use, hospitalization, or a serum albumin level < 3 g/dl were excluded. We used a 2-phase approach: in 1/3 of the sample (discovery) we used T2DM phenome-wide association study codes (phecodes) to identify cases and controls, and in the remaining 2/3 (validation) we identified T2DM cases and controls using a validated algorithm. The analysis plan for the validation phase was constructed at the time of the design of that component of the study. The prevalence of T2DM in the very low and normal LDL-C groups was compared using logistic regression with adjustment for age, race, sex, body mass index (BMI), high-density lipoprotein cholesterol, triglycerides, and duration of care. Secondary analyses included prespecified stratification by sex, race, BMI, and LDL-C level. In the discovery cohort, phecodes related to T2DM were significantly more frequent in the very low LDL-C group. In the validation cohort (N = 33,039 after applying the T2DM algorithm to identify cases and controls), the risk of T2DM was increased in the very low compared to normal LDL-C group (odds ratio [OR] 2.06, 95% CI 1.80-2.37; P < 2 × 10-16). The findings remained significant in sensitivity analyses. The association between low LDL-C levels and T2DM was significant in males (OR 2.43, 95% CI 2.00-2.95; P < 2 × 10-16) and females (OR 1.74, 95% CI 1.42-2.12; P = 6.88 × 10-8); in normal weight (OR 2.18, 95% CI 1.59-2.98; P = 1.1× 10-6), overweight (OR 2.17, 95% CI 1.65-2.83; P = 1.73× 10-8), and obese (OR 2.00, 95% CI 1.65-2.41; P = 8 × 10-13) categories; and in individuals with LDL-C < 40 mg/dl (OR 2.31, 95% CI 1.71-3.10; P = 3.01× 10-8) and LDL-C 40-60 mg/dl (OR 1.99, 95% CI 1.71-2.32; P < 2.0× 10-16). The association was significant in individuals of European ancestry (OR 2.67, 95% CI 2.25-3.17; P < 2 × 10-16) but not in those of African ancestry (OR 1.09, 95% CI 0.81-1.46; P = 0.56). A limitation was that we only compared groups with very low and normal LDL-C levels; also, since this was not an inception cohort, we cannot exclude the possibility of reverse causation. CONCLUSIONS: Very low LDL-C concentrations occurring in the absence of statin treatment were significantly associated with T2DM risk in a large EHR population; this increased risk was present in both sexes and all BMI categories, and in individuals of European ancestry but not of African ancestry. Longitudinal cohort studies to assess the relationship between very low LDL-C levels not associated with lipid-lowering therapy and risk of developing T2DM will be important.

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