Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records may provide a resource to assess the clinical relevance of rare variants.
To determine the clinical phenotypes from electronic medical records in individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes.
Design, Setting and Participants
This prospective cohort study included 2022 individuals recruited for non-antiarrhythmic drug exposure phenotypes from 10/5/2012 to 9/30/2013 for the Electronic Medical Records and Genomics Network Pharmacogenomics project from seven US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada Syndromes, were assessed for potential pathogenicity by three laboratories with ion channel expertise and the ClinVar database. Relevant phenotypes were determined from electronic medical records, with data available through 9/10/2014.
One or more variants designated as pathogenic in SCN5A or KCNH2.
Main Outcome Measures
Arrhythmia or electrocardiographic (ECG) phenotypes defined by ICD9 codes, ECG data, and manual electronic medical record review.
Among 2022 study participants (median age, 61 years [interquartile range, 56–65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen κ = 0.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, −5% to +13%; P = .35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, −10 milliseconds; 95% CI, −16 to +3 milliseconds; P = .17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds.
Conclusions and Relevance
Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.
Observational research shows that higher body mass index (BMI) increases Alzheimer’s disease (AD) risk, but it is unclear whether this association is causal. We applied genetic variants that predict BMI in Mendelian Randomization analyses, an approach that is not biased by reverse causation or confounding, to evaluate whether higher BMI increases AD risk. We evaluated individual level data from the AD Genetics Consortium (ADGC: 10,079 AD cases and 9,613 controls), the Health and Retirement Study (HRS: 8,403 participants with algorithm-predicted dementia status) and published associations from the Genetic and Environmental Risk for AD consortium (GERAD1: 3,177 AD cases and 7,277 controls). No evidence from individual SNPs or polygenic scores indicated BMI increased AD risk. Mendelian Randomization effect estimates per BMI point (95% confidence intervals) were: ADGC OR=0.95 (0.90, 1.01); HRS OR=1.00 (0.75, 1.32); GERAD1 OR=0.96 (0.87, 1.07). One subscore (cellular processes not otherwise specified) unexpectedly predicted lower AD risk.
obesity; dementia; Alzheimer’s Disease; Mendelian randomization
No studies have examined prescription opioids’ long-term cognitive effects.
To determine whether opioid use is associated with higher dementia risk or greater cognitive decline.
Prospective cohort study
Group Health, an integrated healthcare delivery system
People age 65 and older, community dwelling and nondemented, with at least 10 years of Group Health enrollment at baseline
The Cognitive Abilities Screening Instrument (CASI) was administered every 2 years. Low scores triggered detailed evaluation, and a multidisciplinary committee assigned dementia diagnoses. From computerized pharmacy data, cumulative opioid exposure was defined as total standardized doses (TSD) dispensed over 10 years (excluding the most recent 1 year because of possible prodromal symptoms). For comparison, we examined use of nonsteroidal anti-inflammatory drugs (NSAIDs), characterized similarly. Analyses of dementia risk used Cox proportional hazards models and of CASI trajectory, linear regression models and generalized estimating equations.
Among 3,434 participants (median age 74), 797 (23%) developed dementia over a mean follow-up of 7.3 years. 637 (19%) had possible or probable Alzheimer’s disease. For cumulative opioid use, the hazard ratios (HRs) for dementia were: 11–30 TSD, HR 1.06 (95% confidence interval [CI] 0.88–1.26); 31–90 TSD, HR 0.88 (0.70–1.09); and 91+ TSD, HR 1.29 (1.02–1.62), compared to 0–10 TSD. A similar pattern was seen for NSAID use. Heavier opioid use was not associated with more rapid cognitive decline.
People with the heaviest opioid or NSAID use had slightly higher dementia risk than people with little or no use. These results may reflect an effect of chronic pain on cognition or residual confounding. While opioids convey other risks, we found little evidence of long-term cognitive harm specific to opioids.
opioids; non-steroidal anti-inflammatory drugs; dementia; cognitive decline; chronic pain
Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).
Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.
Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.
Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.
electronic health records; phenotype algorithms; computable representation; phenotype standardization; data models
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging given the syndromic nature of HF and the need to distinguish HF with preserved or reduced ejection fraction. Using a gold standard cohort of manually abstracted cases, an EMR-driven phenotype algorithm based on structured and unstructured data was developed to identify all the cases. The resulting algorithm was executed in two cohorts from the Electronic Medical Records and Genomics (eMERGE) Network with a positive predictive value of > 95%. The algorithm was expanded to include three hierarchical definitions of HF (i.e., Definite, Probable, Possible) based on the degree of confidence of the classification to capture HF cases in a whole population whereby increasing the algorithm utility for use in e-Epidemiologic research.
Heart failure; Ventricular ejection fraction; Electronic medical records; Natural language processing; Phenotyping
To investigate associations between acute care and critical illness hospitalizations and performance on physical functional measures and activities of daily living (ADLs).
Prospective cohort study.
Large health maintenance organization.
2926 Participants in Adult Changes in Thought, a study of aging enrolling dementia-free individuals aged 65 and older not living in a nursing home from 1994 to September 30, 2008 (N=2,926).
The exposure of interest was hospitalization during study participation, subdivided by presence of critical illness. Outcomes included gait speed, grip strength, chair stand speed, and difficulty and dependence in performing ADLs measured at biennial visits.
Median time between hospital discharge and the next study visit was 311 days (interquartile range (IQR) 151–501 days) after acute care hospitalization and 359 days (IQR 181–420 days) after critical illness hospitalization. Gait speed was slower after acute care (–0.05 m/s, 95% confidence interval (CI)=0.01–0.04 m/s slower, P< .001) and critical illness (–0.16 m/s, 95% CI=–0.22 to –0.10, P< .001). Grip was weaker after acute care hospitalization (–0.8 kg, 95% CI=–1.0 to –0.6, P<.001) but not significantly different after critical illness hospitalization. Chair-stand speed was slower after acute care hospitalization (–0.04 stands/s, 95% CVI=–0.05 to –0.04, P< .001) and critical illness hospitalization (–0.09, 95% CI=–0.15 to –0.03, P=.003). The odds of difficulty with (odds ratio (OR)=1.4, 95% CI=1.2–1.6, P<.001) or dependence in (OR=2.0, 95% CI=1.2–3.2, P=.006) one or more ADLs was higher after acute care hospitalization, as were the odds of difficulty with (OR=1.9, 95% CI=1.1–3.6, P=.03) or dependence in (OR=7.9, 95% CI=2.5–25.7, P=.001) one or more ADLs after critical illness.
In older adults, hospitalization, especially for critical illness, was associated with clinically relevant decline in gait and chair stand speed and strongly associated with difficulty with and dependence in ADLs.
activities of daily living; critical illness; disability; long-term outcomes; gait speed
Effective prevention of Alzheimer’s disease (AD) requires the development of risk prediction tools permitting preclinical intervention. We constructed a genetic risk score (GRS) comprising common genetic variants associated with AD, evaluated its association with incident AD and assessed its capacity to improve risk prediction over traditional models based on age, sex, education, and APOE
ε4. In eight prospective cohorts included in the International Genomics of Alzheimer’s Project (IGAP), we derived weighted sum of risk alleles from the 19 top SNPs reported by the IGAP GWAS in participants aged 65 and older without prevalent dementia. Hazard ratios (HR) of incident AD were estimated in Cox models. Improvement in risk prediction was measured by the difference in C-index (Δ–C), the integrated discrimination improvement (IDI) and continuous net reclassification improvement (NRI>0). Overall, 19,687 participants at risk were included, of whom 2,782 developed AD. The GRS was associated with a 17% increase in AD risk (pooled HR = 1.17; 95%CI = [1.13–1.21] per standard deviation increase in GRS; p-value = 2.86 × 10−16). This association was stronger among persons with at least one APOE
ε4 allele (HRGRS = 1.24; 95%CI = [1.15–1.34]) than in others (HRGRS = 1.13; 95%CI = [1.08–1.18]; pinteraction = 3.45 × 10−2). Risk prediction after seven years of follow-up showed a small improvement when adding the GRS to age, sex, APOE
ε4, and education (Δ–Cindex = 0.0043 [0.0019–0.0067]). Similar patterns were observed for IDI and NRI>0. In conclusion, a risk score incorporating common genetic variation outside the APOE
ε4 locus improved AD risk prediction and may facilitate risk stratification for prevention trials.
Alzheimer’s disease; clinical utility; cohort studies; genetic risk score; IGAP; meta-analysis; risk prediction
Genetic variation can affect drug response in multiple ways, though it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE-PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE-PGx data release includes sequence-derived data from ~5000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled CADD score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.
Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative SNP-SNP models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. 84 SNP-SNP models replicated in the independent dataset at LRT p < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1) – rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 84 replicating SNP-SNP models, which included immune system, signal transduction, and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any GWAS dataset.
association; genetic interaction; complex disease
Late–onset Alzheimer's disease (AD) is heritable with 20 genes showing genome wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease we extended these genetic data in a pathway analysis.
The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain.
ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (p = 3.27×10-12 after multiple testing correction for pathways), regulation of endocytosis (p = 1.31×10-11), cholesterol transport (p = 2.96 × 10-9) and proteasome-ubiquitin activity (p = 1.34×10-6). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected p 0.002 – 0.05).
The immune response, regulation of endocytosis, cholesterol transport and protein ubiquitination represent prime targets for AD therapeutics.
Alzheimer's disease; dementia; neurodegeneration; immune response; endocytosis; cholesterol metabolism; uniquitination; pathway analysis; ALIGATOR; Weighted gene coexpression network analysis
The most common side effect of angiotensin converting enzyme inhibitor drugs (ACEi) is a cough. We conducted a genome wide association study (GWAS) of ACEi-induced cough among 7,080 subjects of diverse ancestries in the eMERGE network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1,595 cases and 5,485 controls) identified associations on chromosome 4 in an intron of KCNIP4. The strongest association was at rs145489027 (MAF=0.33, OR=1.3 [95%CI: 1.2–1.4], p=1.0×10−8). Replication for six SNPs in KCNIP4 was tested in a second eMERGE population (n=926) and in the GoDARTS cohort (n=4,309). Replication was observed at rs7675300 (OR=1.32 [1.01–1.70], p=0.04) in eMERGE and rs16870989 and rs1495509 (OR=1.15 [1.01–1.30], p=0.03 for both) in GoDARTS. The combined association at rs1495509 was significant (OR=1.23 [1.15–1.32], p=1.9×10−9). These results indicate that SNPs in KCNIP4 may modulate ACEi-induced cough risk.
ACE inhibitor; angiotensin converting enzyme inhibitor; GWAS; KCNIP4; Drug Related Side Effects and Adverse Reactions; pharmacogenetics
APOE ε4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer’s Project (IGAP) Consortium in APOE ε4+ (10,352 cases and 9,207 controls) and APOE ε4− (7,184 cases and 26,968 controls) subgroups as well as in the total sample testing for interaction between a SNP and APOE ε4 status. Suggestive associations (P<1x10−4) in stage 1 were evaluated in an independent sample (stage 2) containing 4,203 subjects (APOE ε4+: 1,250 cases and 536 controls; APOE ε4-: 718 cases and 1,699 controls). Among APOE ε4− subjects, novel genome-wide significant (GWS) association was observed with 17 SNPs (all between KANSL1 and LRRC37A on chromosome 17 near MAPT) in a meta-analysis of the stage 1 and stage 2 datasets (best SNP, rs2732703, P=5·8x10−9). Conditional analysis revealed that rs2732703 accounted for association signals in the entire 100 kilobase region that includes MAPT. Except for previously identified AD loci showing stronger association in APOE ε4+ subjects (CR1 and CLU) or APOE ε4− subjects (MS4A6A/MS4A4A/ MS4A6E), no other SNPs were significantly associated with AD in a specific APOE genotype subgroup. In addition, the finding in the stage 1 sample that AD risk is significantly influenced by the interaction of APOE with rs1595014 in TMEM106B (P=1·6x10−7) is noteworthy because TMEM106B variants have previously been associated with risk of frontotemporal dementia. Expression quantitative trait locus analysis revealed that rs113986870, one of the GWS SNPs near rs2732703, is significantly associated with four KANSL1 probes that target transcription of the first translated exon and an untranslated exon in hippocampus (P≤1.3x10−8), frontal cortex (P≤1.3x10−9), and temporal cortex (P≤1.2x10−11). Rs113986870 is also strongly associated with a MAPT probe that targets transcription of alternatively spliced exon 3 in frontal cortex (P=9.2x10−6) and temporal cortex (P=2.6x10−6). Our APOE-stratified GWAS is the first to show GWS association for AD with SNPs in the chromosome 17q21.31 region. Replication of this finding in independent samples is needed to verify that SNPs in this region have significantly stronger effects on AD risk in persons lacking APOE ε4 compared to persons carrying this allele, and if this is found to hold, further examination of this region and studies aimed at deciphering the mechanism(s) are warranted.
Increasing evidence suggests epidemiological and pathological links between Alzheimer's disease (AD) and ischemic stroke (IS). We investigated the evidence that shared genetic factors underpin the two diseases.
Using genome‐wide association study (GWAS) data from METASTROKE + (15,916 IS cases and 68,826 controls) and the International Genomics of Alzheimer's Project (IGAP; 17,008 AD cases and 37,154 controls), we evaluated known associations with AD and IS. On the subset of data for which we could obtain compatible genotype‐level data (4,610 IS cases, 1,281 AD cases, and 14,320 controls), we estimated the genome‐wide genetic correlation (rG) between AD and IS, and the three subtypes (cardioembolic, small vessel, and large vessel), using genome‐wide single‐nucleotide polymorphism (SNP) data. We then performed a meta‐analysis and pathway analysis in the combined AD and small vessel stroke data sets to identify the SNPs and molecular pathways through which disease risk may be conferred.
We found evidence of a shared genetic contribution between AD and small vessel stroke (rG [standard error] = 0.37 [0.17]; p = 0.011). Conversely, there was no evidence to support shared genetic factors in AD and IS overall or with the other stroke subtypes. Of the known GWAS associations with IS or AD, none reached significance for association with the other trait (or stroke subtypes). A meta‐analysis of AD IGAP and METASTROKE + small vessel stroke GWAS data highlighted a region (ATP5H/KCTD2/ICT1) associated with both diseases (p = 1.8 × 10−8). A pathway analysis identified four associated pathways involving cholesterol transport and immune response.
Our findings indicate shared genetic susceptibility to AD and small vessel stroke and highlight potential causal pathways and loci. Ann Neurol 2016;79:739–747
Background and objective
We designed an algorithm to identify abdominal aortic aneurysm cases and controls from electronic health records to be shared and executed within the “electronic Medical Records and Genomics” (eMERGE) Network.
Materials and methods
Structured Query Language, was used to script the algorithm utilizing “Current Procedural Terminology” and “International Classification of Diseases” codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site. Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites.
The algorithm demonstrated positive predictive values (PPV) of 92.8% (CI: 86.8-96.7) and 100% (CI: 97.0-100) for cases and controls, respectively. It performed well also outside the eMERGE Network. Implementation of the transportable executable algorithm as a Konstanz Information Miner workflow required much less effort than implementation from pseudo code, and ensured that the logic was as intended.
Discussion and conclusion
This ePhenotyping algorithm identifies abdominal aortic aneurysm cases and controls from the electronic health record with high case and control PPV necessary for research purposes, can be disseminated easily, and applied to high-throughput genetic and other studies.
Electronic health records; Electronic medical record; Case-Control study; ICD-9; Computing methodologies; KNIME; Aortic aneurysm
Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts.
To minimize pathologic heterogeneity in genetic studies of Parkinson disease (PD), the Autopsy-Confirmed Parkinson Disease Genetics Consortium conducted a genome-wide association study using both patients with neuropathologically confirmed PD and controls.
Four hundred eighty-four cases and 1,145 controls met neuropathologic diagnostic criteria, were genotyped, and then imputed to 3,922,209 variants for genome-wide association study analysis.
A small region on chromosome 1 was strongly associated with PD (rs10788972; p = 6.2 × 10−8). The association peak lies within and very close to the maximum linkage peaks of 2 prior positive linkage studies defining the PARK10 locus. We demonstrate that rs10788972 is in strong linkage disequilibrium with rs914722, the single nucleotide polymorphism defining the PARK10 haplotype previously shown to be significantly associated with age at onset in PD. The region containing the PARK10 locus was significantly reduced from 10.6 megabases to 100 kilobases and contains 4 known genes: TCEANC2, TMEM59, miR-4781, and LDLRAD1.
We confirm the association of a PARK10 haplotype with the risk of developing idiopathic PD. Furthermore, we significantly reduce the size of the PARK10 region. None of the candidate genes in the new PARK10 region have been previously implicated in the biology of PD, suggesting new areas of potential research. This study strongly suggests that reducing pathologic heterogeneity may enhance the application of genetic association studies to PD.
Objective To determine whether higher cumulative use of benzodiazepines is associated with a higher risk of dementia or more rapid cognitive decline.
Design Prospective population based cohort.
Setting Integrated healthcare delivery system, Seattle, Washington.
Participants 3434 participants aged ≥65 without dementia at study entry. There were two rounds of recruitment (1994-96 and 2000-03) followed by continuous enrollment beginning in 2004.
Main outcomes measures The cognitive abilities screening instrument (CASI) was administered every two years to screen for dementia and was used to examine cognitive trajectory. Incident dementia and Alzheimer’s disease were determined with standard diagnostic criteria. Benzodiazepine exposure was defined from computerized pharmacy data and consisted of the total standardized daily doses (TSDDs) dispensed over a 10 year period (a rolling window that moved forward in time during follow-up). The most recent year was excluded because of possible use for prodromal symptoms. Multivariable Cox proportional hazard models were used to examine time varying use of benzodiazepine and dementia risk. Analyses of cognitive trajectory used linear regression models with generalized estimating equations.
Results Over a mean follow-up of 7.3 years, 797 participants (23.2%) developed dementia, of whom 637 developed Alzheimer’s disease. For dementia, the adjusted hazard ratios associated with cumulative benzodiazepine use compared with non-use were 1.25 (95% confidence interval 1.03 to 1.51) for 1-30 TSDDs; 1.31 (1.00 to 1.71) for 31-120 TSDDs; and 1.07 (0.82 to 1.39) for ≥121 TSDDs. Results were similar for Alzheimer’s disease. Higher benzodiazepine use was not associated with more rapid cognitive decline.
Conclusion The risk of dementia is slightly higher in people with minimal exposure to benzodiazepines but not with the highest level of exposure. These results do not support a causal association between benzodiazepine use and dementia.
We used a novel approach for molecular quantification in standard fixed and embedded tissue to measure Aβ42 and paired helical filament-τ) (PHF-τ) in frontal, temporal, and parietal cortex from 325 consecutive brain autopsies collected as part of a population-based study of brain aging and incident dementia in the Seattle area. We observed significant effects of APOE ε4 on Aβ42 levels in both diagnostic groups by disease stage and region. In contrast, we did not observe a significant effect of APOE ε4 on PHF-τ levels by disease stage in any region. Aβ42 and PHF-τ levels in cerebral cortex were correlated more strongly in the Dementia group, and these measures had independent explanatory power for dementia beyond those of standard neuropathologic indices. Associations between Lewy body disease and levels of Aβ42 or PHF-τ and between Aβ42 levels and microvascular brain injury suggested that these co-morbid diseases enhanced the penetrance of AD. Our novel approach brings additional insights into the molecular pathogenesis of common causes of dementia and may serve as a platform for future studies that pursue associations between molecular changes of AD and genetic or environmental risk.
Amyloid beta; Apolipoprotein E; Neurodegeneration; Neuropathology; Paired helical filament tau
Recently, a rare variant in the amyloid precursor protein gene (APP) was described in a population from Iceland. This variant, in which alanine is replaced by threonine at position 673 (A673T), appears to protect against late-onset Alzheimer disease (AD). We evaluated the frequency of this variant in AD cases and cognitively normal controls to determine whether this variant will significantly contribute to risk assessment in individuals in the United States.
To determine the frequency of the APP A673T variant in a large group of elderly cognitively normal controls and AD cases from the United States and in 2 case-control cohorts from Sweden.
DESIGN, SETTING, AND PARTICIPANTS
Case-control association analysis of variant APP A673T in US and Swedish white individuals comparing AD cases with cognitively intact elderly controls. Participants were ascertained at multiple university-associated medical centers and clinics across the United States and Sweden by study-specific sampling methods. They were from case-control studies, community-based prospective cohort studies, and studies that ascertained multiplex families from multiple sources.
MAIN OUTCOMES AND MEASURES
Genotypes for the APP A673T variant were determined using the Infinium HumanExome V1 Beadchip (Illumina, Inc) and by TaqMan genotyping (Life Technologies).
The A673T variant genotypes were evaluated in 8943 US AD cases, 10 480 US cognitively normal controls, 862 Swedish AD cases, and 707 Swedish cognitively normal controls. We identified 3 US individuals heterozygous for A673T, including 1 AD case (age at onset, 89 years) and 2 controls (age at last examination, 82 and 77 years). The remaining US samples were homozygous for the alanine (A673) allele. In the Swedish samples, 3 controls were heterozygous for A673T and all AD cases were homozygous for the A673 allele. We also genotyped a US family previously reported to harbor the A673T variant and found a mother-daughter pair, both cognitively normal at ages 72 and 84 years, respectively, who were both heterozygous for A673T; however, all individuals with AD in the family were homozygous for A673.
CONCLUSIONS AND RELEVANCE
The A673T variant is extremely rare in US cohorts and does not play a substantial role in risk for AD in this population. This variant may be primarily restricted to Icelandic and Scandinavian populations.
Bioinformatics approaches to examine gene-gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge-driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty-three SNP-SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell-to-cell adhesion signaling, cell-cell junction organization, and cell-cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP-SNP models, which included signal transduction and PI3K-Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology-driven method, applicable for any genome-wide association study dataset.
association; genetic interaction; complex disease
Mutations in known causal Alzheimer disease (AD) genes account for only 1% to 3% of patients and almost all are dominantly inherited. Recessive inheritance of complex phenotypes can be linked to long (>1-megabase [Mb]) runs of homozygosity (ROHs) detectable by single-nucleotide polymorphism (SNP) arrays.
To evaluate the association between ROHs and AD in an African American population known to have a risk for AD up to 3 times higher than white individuals.
DESIGN, SETTING, AND PARTICIPANTS
Case-control study of a large African American data set previously genotyped on different genome-wide SNP arrays conducted from December 2013 to January 2015. Global and locus-based ROH measurements were analyzed using raw or imputed genotype data. We studied the raw genotypes from 2 case-control subsets grouped based on SNP array: Alzheimer’s Disease Genetics Consortium data set (871 cases and 1620 control individuals) and Chicago Health and Aging Project–Indianapolis Ibadan Dementia Study data set (279 cases and 1367 control individuals). We then examined the entire data set using imputed genotypes from 1917 cases and 3858 control individuals.
MAIN OUTCOMES AND MEASURES
The ROHs larger than 1 Mb, 2 Mb, or 3 Mb were investigated separately for global burden evaluation, consensus regions, and gene-based analyses.
The African American cohort had a low degree of inbreeding (F ~ 0.006). In the Alzheimer’s Disease Genetics Consortium data set, we detected a significantly higher proportion of cases with ROHs greater than 2 Mb (P = .004) or greater than 3 Mb (P = .02), as well as a significant 114-kilobase consensus region on chr4q31.3 (empirical P value 2 = .04; ROHs >2 Mb). In the Chicago Health and Aging Project–Indianapolis Ibadan Dementia Study data set, we identified a significant 202-kilobase consensus region on Chr15q24.1 (empirical P value 2 = .02; ROHs >1 Mb) and a cluster of 13 significant genes on Chr3p21.31 (empirical P value 2 = .03; ROHs >3 Mb). A total of 43 of 49 nominally significant genes common for both data sets also mapped to Chr3p21.31. Analyses of imputed SNP data from the entire data set confirmed the association of AD with global ROH measurements (12.38 ROHs >1 Mb in cases vs 12.11 in controls; 2.986 Mb average size of ROHs >2 Mb in cases vs 2.889 Mb in controls; and 22% of cases with ROHs >3 Mb vs 19% of controls) and a gene-cluster on Chr3p21.31 (empirical P value 2 = .006-.04; ROHs >3 Mb). Also, we detected a significant association between AD and CLDN17 (empirical P value 2 = .01; ROHs >1 Mb), encoding a protein from the Claudin family, members of which were previously suggested as AD biomarkers.
CONCLUSIONS AND RELEVANCE
To our knowledge, we discovered the first evidence of increased burden of ROHs among patients with AD from an outbred African American population, which could reflect either the cumulative effect of multiple ROHs to AD or the contribution of specific loci harboring recessive mutations and risk haplotypes in a subset of patients. Sequencing is required to uncover AD variants in these individuals.
Older persons account for the majority of hospitalizations in the United States.1 Identifying risk factors for hospitalization among elders, especially potentially preventable hospitalization, may suggest opportunities to improve primary care. Certain factors—for example, living alone—may increase the risk for hospitalization, and their effect may be greater among persons with dementia and the old-old (aged 85+).
To determine the association of living alone and risk for hospitalization, and see if the observed effect is greater among persons with dementia or the old-old.
Retrospective longitudinal cohort study.
2,636 participants in the Adult Changes in Thought (ACT) study, a longitudinal cohort study of dementia incidence. Participants were adults aged 65+ enrolled in an integrated health care system who completed biennial follow-up visits to assess for dementia and living situation.
Hospitalization for all causes and for ambulatory care sensitive conditions (ACSCs) were identified using automated data.
At baseline, the mean age of participants was 75.5 years, 59 % were female and 36 % lived alone. Follow-up time averaged 8.4 years (SD 3.5), yielding 10,431 approximately 2-year periods for analysis. Living alone was positively associated with being aged 85+, female, and having lower reported social support and better physical function, and negatively associated with having dementia. In a regression model adjusted for age, sex, comorbidity burden, physical function and length of follow-up, living alone was not associated with all-cause (OR = 0.93; 95 % CI 0.84, 1.03) or ambulatory care sensitive condition (ACSC) hospitalization (OR = 0.88; 95 % CI 0.73, 1.07). Among participants aged 85+, living alone was associated with a lower risk for all-cause (OR = 0.76; 95 % CI 0.61, 0.94), but not ACSC hospitalization. Dementia did not modify any observed associations.
Living alone in later life did not increase hospitalization risk, and in this population may be a marker of healthy aging in the old-old.
aging; health care delivery; ambulatory care; dementia; social support
We describe here the design and initial implementation of the eMERGE-PGx project. eMERGE-PGx, a partnership of the eMERGE and PGRN consortia, has three objectives : 1) Deploy PGRNseq, a next-generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1–3 year timeframe across several clinical sites; 2) Integrate well-established clinically-validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and assess process and clinical outcomes of implementation; and 3) Develop a repository of pharmacogenetic variants of unknown significance linked to a repository of EHR-based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site-specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to manage incidental findings, and patient and clinician education methods.
pharmacogenetics; pharmacogenomics; next generation sequencing; study design; pre-emptive genotyping
The authors conducted focus groups with patients prescribed antidepressants (pilot session plus 2 focus groups, n = 27); patients prescribed carbamazepine (2 focus groups, n = 17); and healthy patients (2 focus groups, n = 17). Although participants understood the potential advantages of pharmacogenetic testing, many felt that the risks (discrimination, stigmatization, physician overreliance on genomic results, and denial of certain medications) may outweigh the benefits. These concerns were shared across groups but were more strongly expressed among participants with chronic mental health diagnoses.
Pharmacogenetic testing, a form of precision medicine, has the potential to optimize medication choice and dosing. Yet, relatively little is known about the views of patients—particularly those with chronic psychiatric conditions—with respect to such testing.
To explore patients’ beliefs and attitudes regarding pharmacogenetic testing, with the goal of informing policy development and implementation.
Qualitative study design using semistructured focus groups with adults enrolled in Group Health Cooperative, a large health maintenance organization in the Pacific Northwest. We conducted focus groups with patients prescribed antidepressants (pilot session plus 2 focus groups, n = 27); patients prescribed carbamazepine (2 focus groups, n = 17); and healthy patients (2 focus groups, n = 17).
Although participants understood the potential advantages of pharmacogenetic testing, many felt that the risks (discrimination, stigmatization, physician overreliance on genomic results, and denial of certain medications) may outweigh the benefits. These concerns were shared across groups but were more strongly expressed among participants with chronic mental health diagnoses.
Clinical implementation of pharmacogenetic testing must address patient concerns about privacy, discrimination, quality of care, and erosion of the physician-patient relationship.