Whole-genome sequencing analysis reveals new susceptibility loci and structural variants associated with progressive supranuclear palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based on genotype array, therefore, were inadequate for the analysis of rare variants as well as larger mutations, such as small insertions/deletions (indels) and structural variants (SVs). METHOD: In this study, we performed whole genome sequencing (WGS) and conducted association analysis for single nucleotide variants (SNVs), indels, and SVs, in a cohort of 1,718 cases and 2,944 controls of European ancestry. Of the 1,718 PSP individuals, 1,441 were autopsy-confirmed and 277 were clinically diagnosed. RESULTS: Our analysis of common SNVs and indels confirmed known genetic loci at MAPT, MOBP, STX6, SLCO1A2, DUSP10, and SP1, and further uncovered novel signals in APOE, FCHO1/MAP1S, KIF13A, TRIM24, TNXB, and ELOVL1. Notably, in contrast to Alzheimer's disease (AD), we observed the APOE ε2 allele to be the risk allele in PSP. Analysis of rare SNVs and indels identified significant association in ZNF592 and further gene network analysis identified a module of neuronal genes dysregulated in PSP. Moreover, seven common SVs associated with PSP were observed in the H1/H2 haplotype region (17q21.31) and other loci, including IGH, PCMT1, CYP2A13, and SMCP. In the H1/H2 haplotype region, there is a burden of rare deletions and duplications (P = 6.73 × 10-3) in PSP. CONCLUSIONS: Through WGS, we significantly enhanced our understanding of the genetic basis of PSP, providing new targets for exploring disease mechanisms and therapeutic interventions.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations.

Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. We employ an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compare this model with APOE and polygenic risk score models across genetic ancestry groups (Hispanic Latino American sample: 610 patients with 126 cases; African American sample: 440 patients with 84 cases; East Asian American sample: 673 patients with 75 cases), using electronic health records from UCLA Health for discovery and the All of Us cohort for validation. Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 31-84% (Wilcoxon signed-rank test p-value <0.05) and the area-under-the-receiver-operating characteristic by 11-17% (DeLong test p-value <0.05) compared to the APOE and the polygenic risk score models. We identify shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. Our study highlights the benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Association of Structural Forms of 17q21.31 with the Risk of Progressive Supranuclear Palsy and MAPT Sub-haplotypes.

Importance: The chromosome 17q21.31 region, containing a 900 Kb inversion that defines H1 and H2 haplotypes, represents the strongest genetic risk locus in progressive supranuclear palsy (PSP). In addition to H1 and H2, various structural forms of 17q21.31, characterized by the copy number of α, ß, and γ duplications, have been identified. However, the specific effect of each structural form on the risk of PSP has never been evaluated in a large cohort study. Objective: To assess the association of different structural forms of 17q.21.31, defined by the copy numbers of α, ß, and γ duplications, with the risk of PSP and MAPT sub-haplotypes. Design setting and participants: Utilizing whole genome sequencing data of 1,684 (1,386 autopsy confirmed) individuals with PSP and 2,392 control subjects, a case-control study was conducted to investigate the association of copy numbers of α, ß, and γ duplications and structural forms of 17q21.31 with the risk of PSP. All study subjects were selected from the Alzheimer's Disease Sequencing Project (ADSP) Umbrella NG00067.v7. Data were analyzed between March 2022 and November 2023. Main outcomes and measures: The main outcomes were the risk (odds ratios [ORs]) for PSP with 95% CIs. Risks for PSP were evaluated by logistic regression models. Results: The copy numbers of α and ß were associated with the risk of PSP only due to their correlation with H1 and H2, while the copy number of γ was independently associated with the increased risk of PSP. Each additional duplication of γ was associated with 1.10 (95% CI, 1.04-1.17; P = 0.0018) fold of increased risk of PSP when conditioning H1 and H2. For the H1 haplotype, addition γ duplications displayed a higher odds ratio for PSP: the odds ratio increases from 1.21 (95%CI 1.10-1.33, P = 5.47 × 10-5) for H1ß1γ1 to 1.29 (95%CI 1.16-1.43, P = 1.35 × 10-6) for H1ß1γ2, 1.45 (95%CI 1.27-1.65, P = 3.94 × 10-8) for H1ß1γ3, and 1.57 (95%CI 1.10-2.26, P = 1.35 × 10-2) for H1ß1γ4. Moreover, H1ß1γ3 is in linkage disequilibrium with H1c (R2 = 0.31), a widely recognized MAPT sub-haplotype associated with increased risk of PSP. The proportion of MAPT sub-haplotypes associated with increased risk of PSP (i.e., H1c, H1d, H1g, H1o, and H1h) increased from 34% in H1ß1γ1 to 77% in H1ß1γ4. Conclusions and relevance: This study revealed that the copy number of γ was associated with the risk of PSP independently from H1 and H2. The H1 haplotype with more γ duplications showed a higher odds ratio for PSP and were associated with MAPT sub-haplotypes with increased risk of PSP. These findings expand our understanding of how the complex structure at 17q21.31 affect the risk of PSP.

Improving genetic risk modeling of dementia from real-world data in underrepresented populations.

BACKGROUND: Genetic risk modeling for dementia offers significant benefits, but studies based on real-world data, particularly for underrepresented populations, are limited. METHODS: We employed an Elastic Net model for dementia risk prediction using single-nucleotide polymorphisms prioritized by functional genomic data from multiple neurodegenerative disease genome-wide association studies. We compared this model with APOE and polygenic risk score models across genetic ancestry groups, using electronic health records from UCLA Health for discovery and All of Us cohort for validation. RESULTS: Our model significantly outperforms other models across multiple ancestries, improving the area-under-precision-recall curve by 21-61% and the area-under-the-receiver-operating characteristic by 10-21% compared to the APOE and the polygenic risk score models. We identified shared and ancestry-specific risk genes and biological pathways, reinforcing and adding to existing knowledge. CONCLUSIONS: Our study highlights benefits of integrating functional mapping, multiple neurodegenerative diseases, and machine learning for genetic risk models in diverse populations. Our findings hold potential for refining precision medicine strategies in dementia diagnosis.

Multi-class Modeling Identifies Shared Genetic Risk for Late-onset Epilepsy and Alzheimer's Disease.

Background: Previous studies have established a strong link between late-onset epilepsy (LOE) and Alzheimer's disease (AD). However, their shared genetic risk beyond the APOE gene remains unclear. Our study sought to examine the shared genetic factors of AD and LOE, interpret the biological pathways involved, and evaluate how AD onset may be mediated by LOE and shared genetic risks. Methods: We defined phenotypes using phecodes mapped from diagnosis codes, with patients' records aged 60-90. A two-step Least Absolute Shrinkage and Selection Operator (LASSO) workflow was used to identify shared genetic variants based on prior AD GWAS integrated with functional genomic data. We calculated an AD-LOE shared risk score and used it as a proxy in a causal mediation analysis. We used electronic health records from an academic health center (UCLA Health) for discovery analyses and validated our findings in a multi-institutional EHR database (All of Us). Results: The two-step LASSO method identified 34 shared genetic loci between AD and LOE, including the APOE region. These loci were mapped to 65 genes, which showed enrichment in molecular functions and pathways such as tau protein binding and lipoprotein metabolism. Individuals with high predicted shared risk scores have a higher risk of developing AD, LOE, or both in their later life compared to those with low-risk scores. LOE partially mediates the effect of AD-LOE shared genetic risk on AD (15% proportion mediated on average). Validation results from All of Us were consistent with findings from the UCLA sample. Conclusions: We employed a machine learning approach to identify shared genetic risks of AD and LOE. In addition to providing substantial evidence for the significant contribution of the APOE-TOMM40-APOC1 gene cluster to shared risk, we uncovered novel genes that may contribute. Our study is one of the first to utilize All of Us genetic data to investigate AD, and provides valuable insights into the potential common and disease-specific mechanisms underlying AD and LOE, which could have profound implications for the future of disease prevention and the development of targeted treatment strategies to combat the co-occurrence of these two diseases.

Whole-Genome Sequencing Analysis Reveals New Susceptibility Loci and Structural Variants Associated with Progressive Supranuclear Palsy.

Background: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disease characterized by the accumulation of aggregated tau proteins in astrocytes, neurons, and oligodendrocytes. Previous genome-wide association studies for PSP were based on genotype array, therefore, were inadequate for the analysis of rare variants as well as larger mutations, such as small insertions/deletions (indels) and structural variants (SVs). Method: In this study, we performed whole genome sequencing (WGS) and conducted association analysis for single nucleotide variants (SNVs), indels, and SVs, in a cohort of 1,718 cases and 2,944 controls of European ancestry. Of the 1,718 PSP individuals, 1,441 were autopsy-confirmed and 277 were clinically diagnosed. Results: Our analysis of common SNVs and indels confirmed known genetic loci at MAPT, MOBP, STX6, SLCO1A2, DUSP10, and SP1, and further uncovered novel signals in APOE, FCHO1/MAP1S, KIF13A, TRIM24, TNXB, and ELOVL1. Notably, in contrast to Alzheimer's disease (AD), we observed the APOE ε2 allele to be the risk allele in PSP. Analysis of rare SNVs and indels identified significant association in ZNF592 and further gene network analysis identified a module of neuronal genes dysregulated in PSP. Moreover, seven common SVs associated with PSP were observed in the H1/H2 haplotype region (17q21.31) and other loci, including IGH, PCMT1, CYP2A13, and SMCP. In the H1/H2 haplotype region, there is a burden of rare deletions and duplications (P = 6.73×10-3) in PSP. Conclusions: Through WGS, we significantly enhanced our understanding of the genetic basis of PSP, providing new targets for exploring disease mechanisms and therapeutic interventions.

The contributions of rare inherited and polygenic risk to ASD in multiplex families.

Autism spectrum disorder (ASD) has a complex genetic architecture involving contributions from both de novo and inherited variation. Few studies have been designed to address the role of rare inherited variation or its interaction with common polygenic risk in ASD. Here, we performed whole-genome sequencing of the largest cohort of multiplex families to date, consisting of 4,551 individuals in 1,004 families having two or more autistic children. Using this study design, we identify seven previously unrecognized ASD risk genes supported by a majority of rare inherited variants, finding support for a total of 74 genes in our cohort and a total of 152 genes after combined analysis with other studies. Autistic children from multiplex families demonstrate an increased burden of rare inherited protein-truncating variants in known ASD risk genes. We also find that ASD polygenic score (PGS) is overtransmitted from nonautistic parents to autistic children who also harbor rare inherited variants, consistent with combinatorial effects in the offspring, which may explain the reduced penetrance of these rare variants in parents. We also observe that in addition to social dysfunction, language delay is associated with ASD PGS overtransmission. These results are consistent with an additive complex genetic risk architecture of ASD involving rare and common variation and further suggest that language delay is a core biological feature of ASD.

Disease risk and healthcare utilization among ancestrally diverse groups in the Los Angeles region.

An individual's disease risk is affected by the populations that they belong to, due to shared genetics and environmental factors. The study of fine-scale populations in clinical care is important for identifying and reducing health disparities and for developing personalized interventions. To assess patterns of clinical diagnoses and healthcare utilization by fine-scale populations, we leveraged genetic data and electronic medical records from 35,968 patients as part of the UCLA ATLAS Community Health Initiative. We defined clusters of individuals using identity by descent, a form of genetic relatedness that utilizes shared genomic segments arising due to a common ancestor. In total, we identified 376 clusters, including clusters with patients of Afro-Caribbean, Puerto Rican, Lebanese Christian, Iranian Jewish and Gujarati ancestry. Our analysis uncovered 1,218 significant associations between disease diagnoses and clusters and 124 significant associations with specialty visits. We also examined the distribution of pathogenic alleles and found 189 significant alleles at elevated frequency in particular clusters, including many that are not regularly included in population screening efforts. Overall, this work progresses the understanding of health in understudied communities and can provide the foundation for further study into health inequities.

Defining the distance between diseases using SNOMED CT embeddings.

Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients and identifying common time courses of disease onset. Distance metrics developed in prior work focused on smaller, targeted disease sets. Distance metrics covering all diseases have not yet been defined, which limits the applications to a broader disease spectrum. Our current study defines disease distances for all disease pairs within the International Classification of Diseases, version 10 (ICD-10), the diagnostic classification system universally used in electronic health records. Our proposed distance is computed based on a biomedical ontology, SNOMED CT (Systemized Nomenclature of Medicine, Clinical Terms), which can also be viewed as a structured knowledge graph. We compared the knowledge graph-based metric to three other distance metrics based on the hierarchical structure of ICD, clinical comorbidity, and genetic correlation, to evaluate how each may capture similar or unique aspects of disease relationships. We show that our knowledge graph-based distance metric captures known phenotypic, clinical, and molecular characteristics at a finer granularity than the other three. With the continued growth of using electronic health records data for research, we believe that our distance metric will play an important role in subgrouping patients for precision health, and enabling individualized disease prevention and treatments.

Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes.

To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance (P < 2.5 × 10-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1 and HNRNPUL2). The association of NAV3 with autism risk is primarily driven by rare inherited loss-of-function (LoF) variants, with an estimated relative risk of 4, consistent with moderate effect. Autistic individuals with LoF variants in the four moderate-risk genes (NAV3, ITSN1, SCAF1 and HNRNPUL2; n = 95) have less cognitive impairment than 129 autistic individuals with LoF variants in highly penetrant genes (CHD8, SCN2A, ADNP, FOXP1 and SHANK3) (59% vs 88%, P = 1.9 × 10-6). Power calculations suggest that much larger numbers of autism cases are needed to identify additional moderate-risk genes.

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer's Disease in Electronic Health Records.

Alzheimer's disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth's bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

Tau interactome maps synaptic and mitochondrial processes associated with neurodegeneration.

Tau (MAPT) drives neuronal dysfunction in Alzheimer disease (AD) and other tauopathies. To dissect the underlying mechanisms, we combined an engineered ascorbic acid peroxidase (APEX) approach with quantitative affinity purification mass spectrometry (AP-MS) followed by proximity ligation assay (PLA) to characterize Tau interactomes modified by neuronal activity and mutations that cause frontotemporal dementia (FTD) in human induced pluripotent stem cell (iPSC)-derived neurons. We established interactions of Tau with presynaptic vesicle proteins during activity-dependent Tau secretion and mapped the Tau-binding sites to the cytosolic domains of integral synaptic vesicle proteins. We showed that FTD mutations impair bioenergetics and markedly diminished Tau's interaction with mitochondria proteins, which were downregulated in AD brains of multiple cohorts and correlated with disease severity. These multimodal and dynamic Tau interactomes with exquisite spatial resolution shed light on Tau's role in neuronal function and disease and highlight potential therapeutic targets to block Tau-mediated pathogenesis.

Pre-existing conditions in Hispanics/Latinxs that are COVID-19 risk factors.

Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9, 2020, and August 31, 2020. Of 9,287 HL tested for SARS-CoV-2, 562 were positive. HL constituted an increasing percentage of all COVID-19 positive individuals as disease severity escalated. Multiple risk factors identified in Non-Hispanic/Latinx whites (NHL-W), like renal disease, also conveyed risk in HL. Pre-existing nonrheumatic mitral valve disorder was a risk factor for HL hospitalization but not for NHL-W COVID-19 or HL influenza hospitalization, suggesting it may be a specific HL COVID-19 risk. Admission laboratory values also suggested that HL presented with a greater inflammatory response. COVID-19 risk factors for HL can help guide equitable government policies and identify at-risk populations.

Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression.

To understand how neural-immune-associated genes and pathways contribute to neurodegenerative disease pathophysiology, we performed a systematic functional genomic analysis in purified microglia and bulk tissue from mouse and human AD, FTD, and PSP. We uncover a complex temporal trajectory of microglial-immune pathways involving the type 1 interferon response associated with tau pathology in the early stages, followed by later signatures of partial immune suppression and, subsequently, the type 2 interferon response. We find that genetic risk for dementias shows disease-specific patterns of pathway enrichment. We identify drivers of two gene co-expression modules conserved from mouse to human, representing competing arms of microglial-immune activation (NAct) and suppression (NSupp) in neurodegeneration. We validate our findings by using chemogenetics, experimental perturbation data, and single-cell sequencing in post-mortem brains. Our results refine the understanding of stage- and disease-specific microglial responses, implicate microglial viral defense pathways in dementia pathophysiology, and highlight therapeutic windows.

Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System.

With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9th, 2020 and June 14th, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical comorbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 x 10-10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 x 10-6) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 x 10-6). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2x10-4) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7x10-4). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3x10-4), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8x10-5). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.

Identification of Conserved Proteomic Networks in Neurodegenerative Dementia.

Data-driven analyses are increasingly valued in modern medicine. We integrate quantitative proteomics and transcriptomics from over 1,000 post-mortem brains from six cohorts representing Alzheimer's disease (AD), asymptomatic AD, progressive supranuclear palsy (PSP), and control patients from the Accelerating Medicines Partnership - Alzheimer's Disease consortium. We define robust co-expression trajectories related to disease progression, including early neuronal, microglial, astrocyte, and immune response modules, and later mRNA splicing and mitochondrial modules. The majority of, but not all, modules are conserved at the transcriptomic level, including module C3, which is only observed in proteome networks and enriched in mitogen-activated protein kinase (MAPK) signaling. Genetic risk enriches in modules changing early in disease and indicates that AD and PSP have distinct causal biological drivers at the pathway level, despite aspects of similar pathology, including synaptic loss and glial inflammatory changes. The conserved, high-confidence proteomic changes enriched in genetic risk represent targets for drug discovery.

Physicians Voluntarily Using an EHR-Based CDS Tool Improved Patients' Guideline-Related Statin Prescription Rates: A Retrospective Cohort Study.

BACKGROUND: In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) released a revised guideline on statin therapy initiation. The guideline included a 10-year risk calculation based on regression modeling, which made hand calculation infeasible. Compliance to the guideline has been suboptimal, as many patients were recommended but not prescribed statin therapy. Clinical decision support (CDS) tools may improve statin guideline compliance. Few statin guideline CDS tools evaluated clinical outcome. OBJECTIVES: We determined if use of a CDS tool, the statin macro, was associated with increased 2013 ACC/AHA statin guideline compliance at the level of statin prescription versus no statin prescription. We did not determine if each patient's statin prescription met ACC/AHA 2013 therapy intensity recommendations (high vs. moderate vs. low). METHODS: The authors developed a clinician-initiated, EHR-embedded statin macro command ("statin macro") that displayed the 2013 ACC/AHA statin guideline recommendation in the electronic health record documentation. We included patients who had a primary care visit during the study period (January 1-June 30, 2016), were eligible for statin therapy based on the ACC/AHA guideline prior to the study period, and were not prescribed statin therapy prior to the study period. We tested the association of macro usage and statin therapy prescription during the study period using relative risk and mixed effect logistic regression. RESULTS: Subjects included 11,877 patients seen in primary care, who were retrospectively recommended statin therapy at study initiation based on the ACC/AHA guideline, but who had not received statin therapy. During the study period, 125 clinicians used the statin macro command for 389 of the 11,877 patients (3.2%). Of the 389 patients for whom that statin macro was used, 108 patients (28%) had a statin prescribed during the study period. Of the 11,488 for whom the statin macro was not used, 1,360 (13%) patients received a clinician-prescribed statin (relative risk 2.3, p < 0.001). Controlling for patient covariates and clinicians, statin macro usage was significantly associated with statin therapy prescription (odds ratio 2.86, p < 0.001). CONCLUSION: Although the statin macro had low uptake, its use was associated with a greater rate of statin prescriptions (dosage not determined) for patients whom 2013 ACC/AHA guidelines required statin therapy.

Optimizing Effect Sizes With Imaging Enrichment and Outcome Choices for Mild Alzheimer Disease Clinical Trials.

Recent clinical trials in mild Alzheimer disease (AD) have enriched for amyloid-specific positron emission tomography (PET) imaging and used extended versions of the AD Assessment Scale-Cognitive Subscale (ADAS-Cog) in an effort to increase the sensitivity to detect treatment effects. We used data from mild AD participants in the AD Neuroimaging Initiative to model trial effect sizes for 12- and 24-month trials using 3 versions of the ADAS-Cog and increased standardized uptake value ratio (SUVR) cutoffs for amyloid imaging inclusion criteria. For 12-month trials, extended ADAS-Cog versions improved effect sizes. The ADAS-Cog11 elicited larger effect sizes when enriching for SUVR 1.1 only, whereas the ADAS-Cog12 and ADAS-Cog13 were associated with larger effect sizes with higher SUVR thresholds. For 24-month trials, extended ADAS-Cog versions increased effect sizes for trials not enriched for amyloid and trials enriched for SUVR 1.1. Only enriching for higher SUVR thresholds (1.3 and 1.4, not 1.1) increased trial power. We conclude that extended versions of the ADAS-Cog improve mild AD trial effect sizes for both 12- and 24-month long studies, whereas amyloid imaging criteria may be most valuable for 12-month trials.