G3: Genes, Genomes, Genetics

Recent studies report associations between the patient genome and the chronic wound microbiome, suggesting a causal relationship. Guided by the expectation that larger genomic perturbations are less likely to be neutral, here we study the association of genomic copy number variants (CNVs) with abundances of wound species. Genomic and metagenomic data were collected from 597 patients with chronic wounds at initial clinical visit. Association studies included 831 CNVs and 68 species, implementing a two-cohort design and study-wide significance threshold. There were 47 CNVs (5.7%) associated with 25 species (36.8%); an additional 1,730 associations achieved study-wide significance in one cohort. Associations with haplotype dosage variation dominated significant associations (69.4%) indicating haplotype dosage is important beyond simply locus copy count. Significant co-occurrences between associated and non-associated species, all involving anaerobes, support a model in which the genome selects for certain species that in turn shape the broader microbiome through ecological interactions. Significant CNVs encompassed 66 genes which were functionally overrepresented for several terms, with extracellular matrix (ECM) and ECM-cell interaction terms emphasized. Four collagen and collagen remodeling genes were distributed across four unlinked significant CNVs, a result that strikingly aligns with the previous studies reporting other collagen or ECM component associations. Related, cytoskeletal dynamics are also strongly implicated. Heritability estimates drawing on all available data and controlling for species correlation and patient factors yielded 30 species with significant heritabilities, 24 of which were greater than 20%. These findings extend the genomic basis of the chronic wound microbiome by emphasizing importance of genomic structural variation and suggest a prominent role for variation across ECM-cellular connections as mechanisms defining microbial selection.

Polygenic scores (PGS) predict an individual's genetic value for a complex trait of interest by aggregating effect size estimates from genome-wide association studies (GWAS). While powerful, this approach assumes that genetic variants affect the phenotype in an additive way, which might be problematic when dominance is part of the genetic architecture. To address this limitation, we developed PGSa,d, a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive (a) and dominance effects (d) directly. We evaluated PGSa,d against the standard GWAS-based PGS (PGS{beta}) in simulations. The results showed that PGSa,d consistently achieved lower root mean squared error (RMSE) and progressively higher R2 as dominance contributed more to the genetic architecture. We then applied the method to 16 blood cell traits, height, and body mass index (BMI) from UK Biobank. PGSa,d showed comparable R2 to PGS{beta} for all traits (including height and BMI whose architecture is predominantly additive) and significantly lower RMSE for four traits after strict Bonferroni correction. Across traits, RMSE improvement from PGSa,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGSa,d. As a byproduct of our analysis, we also identified 141 variants in 39 genomic regions with significant dominance effects after Bonferroni correction across traits, recapitulating known associations and identifying novel ones. Together, these results show that explicitly modeling additive and dominance effects is a robust strategy that can improve individual-level predictions, especially when dominance contributes to complex trait architecture.

Background: Current Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results. Methods: LynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature. Results: In two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing. Discussion: LynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.

Background: Hereditary spastic paraplegia (HSP) comprises a heterogeneous group of neurodegenerative disorders characterized by progressive lower-extremity weakness and spasticity, with or without additional neurological symptoms. Objectives: To characterize the molecular genetic spectrum, inheritance patterns, molecular finding rate, and clinical characteristics of HSP within a multicenter Canadian cohort. Methods: We investigated 671 patients from 423 families who met clinical criteria for HSP identified through the Canadian HSP (CanHSP) registry from 7 medical centers across Canada. Patients were evaluated using known HSP and HSP mimic gene panels, whereas unsolved and more recent patients underwent whole-exome sequencing (WES). 435 individuals underwent gene-panel testing and 485 underwent WES. Copy-number variants (CNVs) were assessed using ExomeDepth, and candidate CNVs using multiplex ligation-dependent probe amplification (MLPA). Results: Molecular findings were identified in 426 of 671 individuals (63.5%): 351 (52.3%) had HSP-associated findings, 70 (10.4%) HSP-mimicking disorders, and five (0.7%) other molecular findings; 245 (36.5%) remained unresolved. Candidate variants were found in 80 relevant genes, 36 of which were HSP-associated genes. The most frequently implicated genes were SPAST (22.5%), SPG7 (5.7%), ATL1 (5.2%), SPG11 (3.6%), and SPG8 (3.3%). Phenotypically, SPAST, ATL1, and SPG8 w[zg1.1]ere predominantly associated with pure HSP, whereas SPG7 and SPG11 more often showed complicated phenotypes, with prominent cerebellar involvement in SPG7 and cognitive and developmental features in SPG11. Conclusion: Patients in CanHSP present with genetic and phenotypic heterogeneity. Reanalysis and complementary approaches, including whole-genome, RNA, and long-read sequencing, may increase diagnostic yield.

Several polyglutamine-encoding repeat expansions cause spinocerebellar ataxias (SCAs) and other movement disorders. SCAs are genetically heterogeneous with a substantial diagnostic gap. We used genome sequencing data from 266 individuals of European and Asian ancestry and screened genome-wide for repeat expansions using STRling, a dedicated repeat expansion caller. The cohort comprised patients with movement disorders, predominantly dystonia and ataxia, as well as unaffected participants. Further, we screened another 436 individuals with ataxia and 427 ethnically matched controls through fragment analysis of the most promising candidate locus in EP400. We applied targeted long-read sequencing to expansion carriers and one control individual to resolve the sequence structure of the repeat locus. Finally, we used microsatellite markers in chromosome 12q24.32-33 to test for a common founder. A genome-wide outlier analysis in STRling revealed a polyglutamine-encoding repeat expansion (GRCh38 chr12:132,062,525-132,062,590) in the E1A binding protein p400 (EP400) gene in two patients with ataxia. Through screening of additional ataxia patients, we identified in total 18 patients from 14 families with heterozygous repeat expansions in EP400. All expansion carriers were from Serbia. The repeat number in affected individuals ranged from 37 to 55, whereas the longest control allele had 32 repeats. We demonstrated that non-expanded alleles carried a CAA interruption within the 5-prime CAG tract; this interruption was lost in expanded alleles. Expansion carriers have pure SCA with onset mainly in their 40s and 50s. There is an inverse correlation between the number of repeats and age at onset. The EP400-encoded protein plays an essential role in chromatin remodeling, epigenetic regulation and DNA repair. Pathogenic sequence variants in EP400 have previously been implicated in a neurodevelopmental disorder. Our study identifies repeat expansions in EP400 as a novel and frequent cause of ataxia in Serbian patients. This finding further increases the diagnostic yield of ataxia testing and warrants investigation in ataxia patients worldwide. We add EP400 to the growing list of ataxia-associated repeat loci, which warrants inclusion in diagnostic ataxia panels.

Mitochondrial disease arises from variants in either the mitochondrial or nuclear genome that impair mitochondrial oxidative phosphorylation. Complex III (ubiquinol:cytochrome c oxidoreductase, CIII2) is a key enzyme of the mitochondrial respiratory chain, catalyzing electron transfer from reduced coenzyme Q10 (ubiquinol) to complex IV, via cytochrome c. CIII2 has one subunit encoded by mitochondrial DNA, the remainder being nuclear encoded. Individuals with CIII2 deficiency present with significant clinical heterogeneity, on account of pathogenic variants in genes encoding structural CIII2 subunits or assembly factors. UQCC1 (GenBank: NM_018244.5) encodes the mitochondrial complex III assembly factor Ubiquinol-cytochrome c reductase complex assembly factor 1, which is currently not associated with human disease. Here we describe nine subjects from eight unrelated families harboring biallelic UQCC1 variants with heterogeneous, early-onset mitochondrial disease presentations and lactic acidosis. Subject-derived fibroblasts demonstrated a decrease in the steady-state levels of UQCC1 and its interacting partner UQCC2, with structural modelling supportive of variant pathogenicity. BN-PAGE and quantitative proteomics demonstrated decreased CIII2 abundance whilst mass spectrometry (MS)-complexome profiling corroborated these findings, identifying impaired respiratory supercomplex formation and a delay in the incorporation of newly synthesized CIII2 subunits into native assemblies. Lentiviral transduction experiments in subject-derived fibroblasts restored the levels of both UQCC1 protein and CIII2 activity. Collectively, these findings provide convergent genetic, biochemical, proteomic and functional evidence that bi-allelic UQCC1 variants disrupt CIII2 assembly, establishing UQCC1 deficiency as a cause of mitochondrial CIII2 deficiency.

The accrual of somatic copy number alterations (CNAs) in the premalignant condition Barrett's oesophagus (BO) predicts transformation to oesophageal adenocarcinoma (OAC). The dynamics of CNA evolution, namely when CNAs first appear and their rate of clonal expansion, are unknown. Here we show that CNAs typically begin to accrue in BO many decades prior to first clinical detection of BO or cancer occurrence, and in some cases during childhood. We constructed a hierarchical Bayesian methodology called CARBINE to calculate the calendar year occurrence time of CNAs from single-timepoint contemporary genetic data. We analysed data from 80 individuals who had received longitudinal endoscopic surveillance of their BO and had known cancer progression status. Loss of heterozygosity (LOH) on chromosome 9p tended to be the first CNA event and often occurred when patients were between 10-30 years old. Genome doubling (GD), known to be a strong risk factor for cancer development, also typically originated multiple decades prior to cancer develop and the GD clone expanded very slowly. Patients who progressed to OAC had an enrichment of CNAs late in life. CNA accrual dynamics were more predictive of cancer risk than CNA burden measurements alone. Analysis of 97 OAC whole genomes, of which 38 had sufficient purity for CNA timing analysis, confirmed the early-life origins of CNAs found in the cancer and indicated that the seeds of OAC exist within an increasingly cancerised BO field for many years. CNA timing shows that the malignant transformation potential of BO may be recorded within the somatic genome in the first decades of life, giving a strong rationale for the effectiveness of risk stratification in early life for Barrett's patients.

Tandem repeat expansions cause more than 60 Mendelian disorders but are difficult to detect and size accurately with short-read sequencing because of their length and repetitive structure. Long-read sequencing (LRS) resolves these loci directly, yet analytic tooling for nanopore sequencing data has lagged behind that available for other long-read platforms. Here we present Mahi-mahi, a tandem repeat genotyper designed for nanopore reads that reports allele-level repeat length, motif composition, interruptions, and read-level flags for systematic basecalling artifacts. Across six Genome in a Bottle samples, whole-genome and adaptive sampling (real-time targeted) data returned calls within one repeat unit of each other for 829 of 832 alleles, and Mahi-mahi calls were within one repeat unit of orthogonal HiFi data at 95.6% of compared alleles after excluding three loci at which the two tools define the repeat differently. In 245 samples from the 1000 Genomes Project, Mahi-mahi calls were within one repeat unit of assembly-derived calls for 98.6% of 10,927 alleles at 57 loci, and Mahi-mahi distinguished pathogenic from nonpathogenic RFC1 alleles of overlapping size by motif. Across 14 Coriell reference lines, Mahi-mahi identified the expected expansion in every line and agreed with orthogonal assays to within two repeat units at HTT, ATXN1, DMPK, and PABPN1. Finally, in 103 samples from three laboratories, Mahi-mahi detected an expanded allele in all samples in which prior testing had reported a pathogenic expansion, assigned the same category as the referring laboratory for 98% of 180 genotypes, and identified known ATXN1 interruption structure. Our results demonstrate that Mahi-mahi accurately identifies repeat expansions at disease-associated loci and could serve as a single assay to evaluate repeat expansions.

Background: School-based implementation of robot-assisted walking may support increased physical and social participation for children with physical disabilities, but evidence from an adequately powered trial is needed. This pilot study evaluated implementation strategies and study procedures to inform a future definitive trial. Methods: Participants were randomized to 12-weeks of robot-assisted walking or control, with subsequent crossover to the other condition. In the intervention arm, targeted dose was 75 minutes of robot-assisted walking, spread across three days/week. Implementation strategies for the child's classroom team included educational materials, centralized technical assistance, school champions, and on-going training. Implementation strategy evaluation focused on intervention protocol feasibility (i.e., weekly device use, troubleshooting) and fidelity (documentation of implementation strategy modifications). Preliminary estimates participation goal changes were obtained using the Canadian Occupational Performance Measure (COPM) 'Performance' scale (/10). Results: Eight children with neurodevelopmental physical disabilities participated (mean age 3.5, SD 0.5 years). Device use varied from 2.1 to 4.3 days/week (median 3.3) with a range of 48 to 96 minutes/week (median 80). Target use of >= 3 days/week was achieved for 66% of weeks and met the >=75-minute target for 33% of weeks. Troubleshooting occurred in 18.5% of sessions. Two implementation strategies were modified to improve feasibility. COPM mean change was 3.56 (SD 1.07) for robot-assisted walking and 2.11 (SD 2.41) for control (dz=0.74). Conclusions: This pilot study provided preliminary evidence regarding both the feasibility of robot-assisted walking implementation in schools and its potential effectiveness for children with physical disabilities. Findings support progression to a definitive trial.

Protein coding variants span the gamut of structural and functional effects on proteins, and they have similarly heterogeneous effects on traits. Their functional effects can be predicted and categorized, particularly using machine learning models, and integrating these predictors benefits gene discovery. However, it remains unclear how well known structural features and predicted functional scores explain the heterogeneous effects of protein coding variants on human complex traits. We introduce the Kernel Allelic Series Model (KASM), which integrates multiple functional annotations to form a kernel describing how allelic effects within a gene covary. We apply it to exome data from 26 traits and 786,871 individuals from UK Biobank and All of Us, integrating 35 annotations describing protein structure and predicting allelic effects. We estimate that rare missense heritability is at least 1.27{+/-}0.09% on average, 1.83{+/-}0.11% under our most flexible model, exceeding pLoF heritability (0.87{+/-}0.05%) and far exceeding missense burden heritability as previously estimated (0.54{+/-}0.04%). AlphaMissense is the single most enriched missense predictor, but a composite score doubles its enrichment. A series of structural features is enriched for heritability, from amino acid chemical features and structural motifs to residue contacts and protein-protein interaction interfaces. Examining pairs of variants, we find that most missense effects are modestly correlated, but an exception is when residues are close in 3D structure but distant in 1D sequence; such pairs also covary across species. Finally, we analyze individual genes and identify gene-specific enrichments, like a 95-fold enrichment among PIEZO1 hydrogen bond residues, that nominate functional hypotheses.

Partitioned-heritability studies have shown which tissues carry the heritability of complex traits, but whether the regulatory layer whose annotations are most informative for heritability within a tissue differs between traits has not been tested, because measured annotations for different layers come from different sets of biosamples. Sequence-to-function models predict the allelic effect of every variant on several regulatory layers with a single model, so that layers can be compared on the same variants within the same tissues. We grouped AlphaGenome Atlas predictions into 12 lineages and six regulatory layers and partitioned the heritability of 11 complex traits with stratified LD score regression. Traits localized largely to the expected lineages, and within these lineages the most informative predicted layer differed between traits (P = 3.6 x 10^-8). Predicted chromatin-accessibility (DNase) effects were the most informative for height and bone mineral density, whereas predicted transcription-initiation (CAGE) effects were the most informative for Alzheimer's disease, most brain traits and cholesterol levels. The difference persisted across sensitivity analyses, and the predicted contrast held for three related traits whose layers had been predicted in advance (P = 2 x 10^-7). Predicted CAGE effects also remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. These results identify differences across traits in the heritability information captured by predicted regulatory layers and offer testable hypotheses for investigating the regulatory basis of complex traits.

BACKGROUND: The chromosome 9p21.3 locus is among the strongest genetic risk loci for cardiovascular disease (CVD), yet the regulatory mechanisms underlying this association remain poorly understood. Previous vascular disease studies focused predominantly on the role of this locus in smooth muscle cell (SMC) dysfunction, whereas its potential role in endothelial cells (ECs) remains less well characterized. Here, we sought to identify candidate regulatory variants at 9p21.3, their distal target genes, and effector transcripts that contribute to CVD-related phenotypes in ECs. METHODS: Promoter capture Hi-C was used to map chromatin interactions at 9p21.3 in primary human ascending aortic SMCs (n=6), supplementing the previously obtained ECs (n=16) dataset. To support the regulatory relevance of these interactions, expression quantitative trait locus analysis was performed in 136 vascular biopsies. Variant effects were prioritized in silico using AlphaGenome. The regulatory activity of the prioritized region was validated using CRISPR activation. RESULTS: Chromatin interaction mapping identified a complex regulatory architecture at the 9p21.3 locus involving 19 putative regulatory elements and 15 genes across ECs and SMCs. MIR31HG showed the most recurrent distal promoter connectivity with the 9p21.3 region. Consistent with the chromatin interaction map, expression of MIR31HG transcripts was associated with genetic variation at the 9p21.3 locus. In ECs, the lncRNA MIR31HG, but not miR-31, was responsive to hemodynamic flow. Depletion of MIR31HG in ECs was associated with endothelial activation. Epigenetic tracks and deep learning predictions highlighted an active enhancer element at 9p21.3, which was subsequently validated by CRISPR activation and increased MIR31HG expression, confirming its capacity to regulate MIR31HG over a large genomic distance of approximately 538 kb. CONCLUSIONS: Altogether, these data implicate lncRNA MIR31HG as a likely effector of the 9p21.3 CVD locus, linking the cis-regulatory element and heritable genetic variation in the respective region to endothelial activation through long-range enhancer regulation.

Objective To examine the association between patient-reported shared decision-making (SDM) and documented advance care planning (ACP) among adults with Alzheimer disease and related dementias (ADRD), and to assess whether this association differed between adults with and without ADRD. Design Retrospective observational study using linked survey and electronic health record (EHR) data. Setting and Participants Participants aged [≥]50 years in the All of Us Research Program who completed a patient-reported SDM measure and had linked EHR records. The primary analytic cohort included 3,635 adults with ADRD; 123,681 adults without ADRD comprised a secondary comparison cohort. Methods SDM was measured by how often clinicians asked participants about their opinions or preferences regarding medical care or treatment and was categorized as high (always/most of the time) or low (some of the time/ None of the time). Documented ACP was identified within 3 years before or after the SDM assessment. Modified Poisson regression with robust variance estimated prevalence ratios (PRs) and adjusted PRs (aPRs). Results Among adults with ADRD, 60.6% reported high SDM. Documented ACP prevalence was 5.1% among participants with high SDM and 3.6% among those with low SDM (P=0.027). High SDM was associated with greater ACP prevalence in the unadjusted model (PR, 1.44; 95% CI, 1.04-1.99); the association remained positive but was attenuated after full adjustment (aPR, 1.28; 95% CI, 0.93-1.77). Among adults without ADRD, high SDM was associated with documented ACP (aPR, 1.17; 95% CI, 1.03-1.33). Conclusions and Implications Among adults with ADRD, high SDM was associated with greater ACP prevalence in unadjusted analyses, although the fully adjusted association was not statistically significant. In contrast, higher SDM remained significantly associated with ACP among those without ADRD. The low prevalence of ACP highlights an important opportunity to expand discussions of current and future care preferences as a routine component of patient-centered care.

Neuropsychiatric symptoms (NPS) are highly prevalent in Mild Cognitive Impairment (MCI), conceptualized as an intermediate clinical state between normal aging and dementia. Although neuroimaging studies have reported associations between NPS and brain alterations in MCI, longitudinal evidence on NPS and their relationship with multimodal MRI measures remains limited. We aimed to investigate (1) longitudinal changes in NPS, volumetric assessment and measurement of regional cerebral blood flow (CBF) within the medial temporal lobe (MTL); (2) associations between longitudinal changes in MRI measures and NPS; (3) whether baseline MRI measures predicted longitudinal changes in NPS. Thirty individuals with MCI aged over 65 years were recruited, of whom eighteen followed up for a mean period of 29 {+/-} 8.9 months. There were significant reductions in total grey matter and white matter volumes, together with volume loss in the bilateral hippocampus, bilateral parahippocampal gyrus and right entorhinal cortex (pFDR[&le;]0.05) over the follow up period. Volume loss in the right parahippocampal gyrus was associated with increase in aggression (pFDR<0.01), and CBF increase in the right hippocampus and parahippocampal gyrus was associated with worsening in agitation (pFDR<0.01). Furthermore, lower baseline volume of the right entorhinal cortex was associated with increase in aggression, and lower baseline CBF in the right hippocampus and parahippocampal gyrus was associated with worsening of agitation (pFDR<0.05). Our findings highlight the importance of identifying NPS in individuals with MCI and the role of structural and perfusion MRI measures as promising biomarkers for monitoring disease progression and improving risk prediction of conversion to dementia.

People experiencing homelessness (PEH) have substantially lower quality-adjusted life expectancy and substantially higher healthcare spending compared to housed populations. California has the largest population of PEH in the United States, and an estimated ten percent of PEH have co-occurring opioid use disorder (OUD). We developed a lifetime, individual-level microsimulation of the 18,928 PEH with OUD in California to evaluate the health and economic effects of expanding access to housing through a Housing First approach, which does not require OUD treatment or abstinence. Increases in permanent housing and shelter access were based on Los Angeles County's Measure A goals, and we accounted for both housing and healthcare costs. Expanding housing access increased mean age at death by 0.34 years, reduced fatal overdoses for the cohort by 39, and produced an incremental cost-effectiveness ratio of $31,083 per quality-adjusted life-year gained, well below conventional willingness-to-pay thresholds. We provide evidence that housing is a high-value health intervention for PEH with OUD in California. Policymakers should support Housing First programs as part of integrated responses to the intertwined crises of homelessness and opioid use.

Background: The relationship between recent viral infection and subsequent susceptibility to, or protection against, other viruses remains unclear, particularly across ages and symptom status. Methods: A prospective cohort study enrolled participants aged 6 months to 49 years in Washington and Oregon, USA, 2022-2024. Weekly nasal swabs were tested for SARS-CoV-2, respiratory syncytial virus (RSV), and influenza A/B; additional swabs were collected during symptomatic illness and were also tested for parainfluenza (PIV) 1-4, endemic human coronaviruses (hCOV), adenovirus, human metapneumovirus, human rhinovirus (hRV), and enteroviruses. Proportional hazards regression models were used to characterize associations between incident symptomatic infection (illness) and history of recent illness due to other viruses (within 1-7, 8-14, 15-30, 31-60, 61-90, 91-180 days), adjusted for geographic region, age, sex, daycare/school/work attendance, household size, and number of children in the household. For SARS-CoV-2, influenza, and RSV, these analyses were repeated to study incidence of infection (including asymptomatic cases). Findings: Of 3,560 participants, 2,969 (83%) had [&ge;]1 illness over 4,299 person-years. Adjusted hazard ratios (aHRs) in the short term ([&le;]2 weeks) after an illness were predominantly below 1, indicating that recent viral illness was often associated with lower risk of subsequent illness with other viruses. Across age groups, recent hRV illness was associated with lower risk of hCOV, PIV, and RSV illness (aHRs 0.07-0.27). SARS-CoV-2 illness was associated with a higher short-term risk of adenovirus and PIV illness (aHRs 3.20-3.33). In children aged 6 months-4 years, recent influenza illness was associated with higher short-term risk of SARS-CoV-2 illness. Including asymptomatic infections generally attenuated these associations. Interpretation: Some viral illnesses may alter short-term susceptibility to subsequent viral illnesses. Interactions may differ by age and symptom status. Funding: US Centers for Disease Control and Prevention; Washington State Department of Health.

Biomedical artificial intelligence (AI) increasingly relies on heterogeneous data sources, yet differences in data structure, terminology, evidence quality, and provenance complicate their integration into reliable knowledge resources. Here, we develop an evidence-aware framework for processing and integrating heterogeneous pharmacovigilance data from FDA Adverse Event Reporting System (FAERS) reports, biomedical literature, clinical trials, and electronic health records in Mass General Brigham. The framework combines large language model (LLM)-based information extraction, ontology-driven semantic normalization, cross-source evidence assessment, statistical and machine learning-based prioritization, and ingredient-based adverse drug event (ADE) relationship inference to transform heterogeneous evidence into a structured and evidence-based knowledge resource. The constructed knowledge system contains 11,588 drug nodes, 44,843 ingredient nodes, 3,275 ADE nodes, 993 indication nodes, and more than 2.47 million relationships, with each drug-ADE relationship assigned a confidence score. We further developed a drug-level web interface for accessing the knowledge resource and validated its utility in downstream AI tasks. Specifically, incorporating the developed knowledge resource improved the comprehensiveness, relevance, and overall quality of LLM-based drug-specific ADE synthesis and improved the precision and F1 score of clinical ADE relation extraction. These results demonstrate that AI-assisted processing combined with evidence-aware curation can transform heterogeneous biomedical evidence into reusable, AI-ready knowledge with explicit provenance and evidence strength information. The framework provides a general strategy for integrating heterogeneous biomedical data to support reproducible knowledge construction and downstream biomedical AI applications.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Background. In people with type 2 diabetes (T2D), postprandial glucose weighs on glycaemic control, and carbohydrate counting is difficult. Patients can describe their meal but rarely quantify it. Objective. To assess (1) whether a small open large language model (LLM), fine-tuned on public data, estimates the carbohydrate content of a meal described in words, and (2) what this carbohydrate adds to the prediction of the postprandial glucose rise in two public cohorts. Methods. Step 1: the Qwen3-4B LLM was fine-tuned with QLoRA on Nutrition5k dishes and compared with an automatically matched USDA food composition table and with Qwen3-4B and Qwen3-8B used without training, on test dishes (internal control) and on 100 real meals of ShanghaiT2DM patients, whose reference carbohydrate was computed from the recorded quantities. Step 2: the maximum glucose rise between 0 and 120 minutes ({Delta}Gmax) was predicted by personalised gradient boosting (M2), compared with a spline linear regression and with the Chronos-2 foundation model, using only information available at meal start and person-level cross-validation, in ShanghaiT2DM (people with T2D recruited in two Shanghai hospitals; carbohydrate estimated by the LLM) and CGMacros (free-living adults in Santa Barbara, California; carbohydrate entered in the dataset, no text). Step 3: the model was transferred between datasets. 95% confidence intervals (CI) were obtained by cluster BCa bootstrap (2,000 resamples). Results. On the 100 real meals, the carbohydrate mean absolute error (MAE) of the fine-tuned model was 12.2 [9.9, 15.2] g, almost half that of the table (22.6 g; paired difference -10.5 [-16.8, -6.0] g) and nearly a third of that of the same model without fine-tuning (34.3 g), with a bias of -10.3 g; on test dishes it was 2.2 [2.0, 2.5] g. Personalised gradient boosting (M2) reached a correlation between predicted and observed rises of r = 0.63 [0.55, 0.69] in CGMacros (1,286 meals, 45 adults) and 0.49 [0.42, 0.54] in ShanghaiT2DM (2,339 meals, 99 people with T2D), about twice the linear reference (r = 0.32 and 0.23); for a rise above 50 mg/dL, its area under the ROC curve (AUROC) was 0.80 and 0.74, versus 0.64 and 0.68 with pre-meal glucose alone. Adding carbohydrate to pre-meal glucose changed r by 0.095 [0.068, 0.128] in CGMacros and by 0.004 [-0.010, 0.017] in ShanghaiT2DM; difference between datasets: 0.091 [0.060, 0.126]. Applied from one dataset to the other, the model lost correlation ({Delta}r = -0.11 [-0.17, -0.05] towards ShanghaiT2DM and -0.12 [-0.19, -0.04] towards CGMacros). The full "text [-&gt;] LLM [-&gt;] glucose" chain was evaluated in ShanghaiT2DM only. Conclusion. An open 4-billion-parameter LLM, fine-tuned with QLoRA on public data and usable offline, almost halves the error of carbohydrate counting for real meals described in words compared with an automatically matched table, with an underestimation of large meals. Personalised gradient boosting, based on pre-meal glucose and the person's usual response, doubles the correlation of a linear reference for predicting the glucose rise in both cohorts. Carbohydrate improved this prediction in CGMacros but not in ShanghaiT2DM; the two datasets differ in many respects, and the cause of this gap is not established.

Background: Plasma biomarkers offer a minimally invasive alternative for detecting Alzheimer disease (AD) pathology, yet the genetic determinants influencing their levels remain largely underexplored. Objective: To identify genetic variants associated with AD-related plasma biomarker levels in a Midwestern Amish cohort. Methods: We analyzed plasma levels of amyloid beta (A{beta}42/A{beta}40), phosphorylated tau 181 (p-tau181), and the p-tau181/A{beta}42 ratio in the Midwestern Amish. Genome-wide association studies (GWAS) were used to examine associations with common genetic variants, while genetic linkage analysis was used to assess the potential impact of rare variants. Results: GWAS found associations between plasma biomarkers and the APOE gene. Additionally, novel candidate genes were implicated, including MME for A{beta}42/A{beta}40 ratio, and VEGFC and LRRTM4 for p-tau181. Linkage analysis identified significant signals for p-tau181 on chromosome 1, potentially involving the CSF1 and AHCYL1genes. Conclusions: The novel genetic variants associated with AD pathology implicate metabolic, synaptic, neuronal, and immune pathways. The use of GWAS and linkage analysis sheds light on the genetic architecture of AD-related plasma biomarkers.