Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research.

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management.

BACKGROUND: The use of artificial intelligence (AI) to optimize medication therapy management (MTM) in identifying drug interactions may potentially improve MTM efficiency. ChatGPT, an AI language model, may be applied to identify medication interventions by integrating patient and drug databases. ChatGPT has been shown to be effective in other areas of clinical medicine, from diagnosis to patient management. However, ChatGPT's ability to manage MTM related activities is little known. OBJECTIVES: To evaluate the effectiveness of ChatGPT in MTM services in simple, complex, and very complex cases to understand AI contributions in MTM. METHODS: Two clinical pharmacists rated and validated the difficulty of patient cases from simple, complex, and very complex. ChatGPT's response to the cases was assessed based on 3 criteria: the ability to identify drug interactions, precision in recommending alternatives, and appropriateness in devising management plans. Two clinical pharmacists validated the accuracy of ChatGPT's responses and compared them to actual answers for each complexity level. RESULTS: ChatGPT 4.0 accurately solved 39 out of 39 (100 %) patient cases. ChatGPT successfully identified drug interactions, provided therapy recommendations and formulated general management plans, but it did not recommend specific dosages. Results suggest it can assist pharmacists in formulating MTM plans to improve overall efficiency. CONCLUSION: The application of ChatGPT in MTM has the potential to enhance patient safety and involvement, lower healthcare costs, and assist healthcare providers in medication management and identifying drug interactions. Future pharmacists can utilize AI models such as ChatGPT to improve patient care. The future of the pharmacy profession will depend on how the field responds to the changing need for patient care optimized by AI and automation.

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWASs) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display extensive results, we developed a web-based tool GnuMoM2b (https://gnumom2b.cumcobgyn.org/) for searching, visualizing, and sharing results from a GWAS of 479 pregnancy traits as well as phenome-wide association studies of more than 17 million single nucleotide polymorphisms. The genetic results from 3 ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWAS) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display the extensive results, we developed a web-based tool GnuMoM2b ( https://gnumom2b.cumcobgyn.org/ ) for searching, visualizing, and sharing results from GWAS of 479 pregnancy traits as well as phenome-wide association studies (PheWAS) of more than 17 million single nucleotide polymorphisms (SNPs). The genetic results from three ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.

Polygenic prediction of preeclampsia and gestational hypertension.

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.

Phenotypic Heterogeneity among GBA p.R202X Carriers in Lewy Body Spectrum Disorders.

We describe the clinical and neuropathologic features of patients with Lewy body spectrum disorder (LBSD) carrying a nonsense variant, c.604C>T; p.R202X, in the glucocerebrosidase 1 (GBA) gene. While this GBA variant is causative for Gaucher's disease, the pathogenic role of this mutation in LBSD is unclear. Detailed neuropathologic evaluation was performed for one index case and a structured literature review of other GBA p.R202X carriers was conducted. Through the systematic literature search, we identified three additional reported subjects carrying the same GBA mutation, including one Parkinson's disease (PD) patient with early disease onset, one case with neuropathologically-verified LBSD, and one unaffected relative of a Gaucher's disease patient. Among the affected subjects carrying the GBA p.R202X, all males were diagnosed with Lewy body dementia, while the two females presented as PD. The clinical penetrance of GBA p.R202X in LBSD patients and families argues strongly for a pathogenic role for this variant, although presenting with a striking phenotypic heterogeneity of clinical and pathological features.

Confirming Pathogenicity of the F386L PSEN1 Variant in a South Asian Family With Early-Onset Alzheimer Disease.

OBJECTIVES: The F386L PSEN1 variant has been reported in 1 Japanese family with limited clinical information. We aimed to prove that F386L is pathogenic by demonstrating that it segregates with early-onset Alzheimer disease (AD). METHODS: Eight individuals in a South Asian family provided DNA for genetic testing and underwent a neurologic examination. RESULTS: The female proband was diagnosed with AD at age 45 years and died at age 49 years. She had a CSF biomarker profile consistent with AD, and her florbetaben PET scan was amyloid positive with high uptake in the striatum. Her MRI showed no prominent white matter disease. Her affected relatives had an age at onset range of 38-57 years and had imaging and biomarker profiles similar to hers. DISCUSSION: The results presented here, in conjunction with the prior report, confirm the pathogenicity of F386L. Furthermore, our study highlights the importance of studying families from underrepresented populations to identify or confirm the pathogenicity of rare variants that may be specific to certain genetic ancestries.

Novel ultra-rare exonic variants identified in a founder population implicate cadherins in schizophrenia.

The identification of rare variants associated with schizophrenia has proven challenging due to genetic heterogeneity, which is reduced in founder populations. In samples from the Ashkenazi Jewish population, we report that schizophrenia cases had a greater frequency of novel missense or loss of function (MisLoF) ultra-rare variants (URVs) compared to controls, and the MisLoF URV burden was inversely correlated with polygenic risk scores in cases. Characterizing 141 "case-only" genes (MisLoF URVs in ≥3 cases with none in controls), the cadherin gene set was associated with schizophrenia. We report a recurrent case mutation in PCDHA3 that results in the formation of cytoplasmic aggregates and failure to engage in homophilic interactions on the plasma membrane in cultured cells. Modeling purifying selection, we demonstrate that deleterious URVs are greatly overrepresented in the Ashkenazi population, yielding enhanced power for association studies. Identification of the cadherin/protocadherin family as risk genes helps specify the synaptic abnormalities central to schizophrenia.

Recent Consanguinity and Outbred Autozygosity Are Associated With Increased Risk of Late-Onset Alzheimer's Disease.

Prior work in late-onset Alzheimer's disease (LOAD) has resulted in discrepant findings as to whether recent consanguinity and outbred autozygosity are associated with LOAD risk. In the current study, we tested the association between consanguinity and outbred autozygosity with LOAD in the largest such analysis to date, in which 20 LOAD GWAS datasets were retrieved through public databases. Our analyses were restricted to eight distinct ethnic groups: African-Caribbean, Ashkenazi-Jewish European, European-Caribbean, French-Canadian, Finnish European, North-Western European, South-Eastern European, and Yoruba African for a total of 21,492 unrelated subjects (11,196 LOAD and 10,296 controls). Recent consanguinity determination was performed using FSuite v1.0.3, according to subjects' ancestral background. The level of autozygosity in the outbred population was assessed by calculating inbreeding estimates based on the proportion (FROH) and the number (NROH) of runs of homozygosity (ROHs). We analyzed all eight ethnic groups using a fixed-effect meta-analysis, which showed a significant association of recent consanguinity with LOAD (N = 21,481; OR = 1.262, P = 3.6 × 10-4), independently of APOE ∗4 (N = 21,468, OR = 1.237, P = 0.002), and years of education (N = 9,257; OR = 1.274, P = 0.020). Autozygosity in the outbred population was also associated with an increased risk of LOAD, both for F ROH (N = 20,237; OR = 1.204, P = 0.030) and N ROH metrics (N = 20,237; OR = 1.019, P = 0.006), independently of APOE ∗4 [(F ROH, N = 20,225; OR = 1.222, P = 0.029) (N ROH, N = 20,225; OR = 1.019, P = 0.007)]. By leveraging the Alzheimer's Disease Sequencing Project (ADSP) whole-exome sequencing (WES) data, we determined that LOAD subjects do not show an enrichment of rare, risk-enhancing minor homozygote variants compared to the control population. A two-stage recessive GWAS using ADSP data from 201 consanguineous subjects in the discovery phase followed by validation in 10,469 subjects led to the identification of RPH3AL p.A303V (rs117190076) as a rare minor homozygote variant increasing the risk of LOAD [discovery: Genotype Relative Risk (GRR) = 46, P = 2.16 × 10-6; validation: GRR = 1.9, P = 8.0 × 10-4]. These results confirm that recent consanguinity and autozygosity in the outbred population increase risk for LOAD. Subsequent work, with increased samples sizes of consanguineous subjects, should accelerate the discovery of non-additive genetic effects in LOAD.

Genetic study of multimodal imaging Alzheimer's disease progression score implicates novel loci.

Identifying genetic risk factors underpinning different aspects of Alzheimer's disease has the potential to provide important insights into pathogenesis. Moving away from simple case-control definitions, there is considerable interest in using quantitative endophenotypes, such as those derived from imaging as outcome measures. Previous genome-wide association studies of imaging-derived biomarkers in sporadic late-onset Alzheimer's disease focused only on phenotypes derived from single imaging modalities. In contrast, we computed a novel multi-modal neuroimaging phenotype comprising cortical amyloid burden and bilateral hippocampal volume. Both imaging biomarkers were used as input to a disease progression modelling algorithm, which estimates the biomarkers' long-term evolution curves from population-based longitudinal data. Among other parameters, the algorithm computes the shift in time required to optimally align a subjects' biomarker trajectories with these population curves. This time shift serves as a disease progression score and it was used as a quantitative trait in a discovery genome-wide association study with n = 944 subjects from the Alzheimer's Disease Neuroimaging Initiative database diagnosed as Alzheimer's disease, mild cognitive impairment or healthy at the time of imaging. We identified a genome-wide significant locus implicating LCORL (rs6850306, chromosome 4; P = 1.03 × 10-8). The top variant rs6850306 was found to act as an expression quantitative trait locus for LCORL in brain tissue. The clinical role of rs6850306 in conversion from healthy ageing to mild cognitive impairment or Alzheimer's disease was further validated in an independent cohort comprising healthy, older subjects from the National Alzheimer's Coordinating Center database. Specifically, possession of a minor allele at rs6850306 was protective against conversion from mild cognitive impairment to Alzheimer's disease in the National Alzheimer's Coordinating Center cohort (hazard ratio = 0.593, 95% confidence interval = 0.387-0.907, n = 911, PBonf = 0.032), in keeping with the negative direction of effect reported in the genome-wide association study (ßdisease progression score = -0.07 ± 0.01). The implicated locus is linked to genes with known connections to Alzheimer's disease pathophysiology and other neurodegenerative diseases. Using multimodal imaging phenotypes in association studies may assist in unveiling the genetic drivers of the onset and progression of complex diseases.

A variant in PPP4R3A protects against alzheimer-related metabolic decline.

OBJECTIVES: A reduction in glucose metabolism in the posterior cingulate cortex (PCC) predicts conversion to Alzheimer's disease (AD) and tracks disease progression, signifying its importance in AD. We aimed to use decline in PCC glucose metabolism as a proxy for the development and progression of AD to discover common genetic variants associated with disease vulnerability. METHODS: We performed a genome-wide association study (GWAS) of decline in PCC fludeoxyglucose F 18 ([18 F] FDG) positron emission tomography measured in Alzheimer's Disease Neuroimaging Initiative participants (n = 606). We then performed follow-up analyses to assess the impact of significant single-nucleotide polymorphisms (SNPs) on disease risk and longitudinal cognitive performance in a large independent data set (n = 870). Last, we assessed whether significant SNP influence gene expression using two RNA sequencing data sets (n = 210 and n = 159). RESULTS: We demonstrate a novel genome-wide significant association between rs2273647-T in the gene, PPP4R3A, and reduced [18 F] FDG decline (p = 4.44 × 10-8 ). In a follow-up analysis using an independent data set, we demonstrate a protective effect of this variant against risk of conversion to MCI or AD (p = 0.038) and against cognitive decline in individuals who develop dementia (p = 3.41 × 10-15 ). Furthermore, this variant is associated with altered gene expression in peripheral blood and altered PPPP4R3A transcript expression in temporal cortex, suggesting a role at the molecular level. INTERPRETATIONS: PPP4R3A is a gene involved in AD risk and progression. Given the protective effect of this variant, PPP4R3A should be further investigated as a gene of interest in neurodegenerative diseases and as a potential target for AD therapies. Ann Neurol 2017;82:900-911.