Citations

Abstract Background and Aims Truncating variants in the TTN gene (TTNtv) are the most common genetic cause of dilated cardiomyopathy (DCM) but also occur as incidental findings in the general population. This study investigated factors associated with the clinical manifestation of TTNtv. Methods An international multicentre retrospective observational study was performed in families with TTNtv-related DCM. Shared frailty models were used to estimate associations of variant characteristics with lifetime risk of DCM, and logistic regression to estimate odds ratios (ORs) for individual-level clinical risk factor profiles (cardiac conditions, cardiovascular comorbidities, lifestyle) and DCM. Results A total of 3158 subjects in 1043 families with TTNtv-related DCM were studied. TTNtv-positive subjects were 21-fold more likely to develop DCM [OR, 21.21; 95% confidence interval (CI), 14.80–30.39]. Disease onset was earlier in males, but was similar for TTNtv of different types and locations. The presence of clinical risk factors was associated with earlier DCM onset (OR, 3.41; 95% CI, 2.06–5.64), with a prior history of atrial fibrillation having a two-fold increased odds of DCM (OR, 2.05; 95% CI, 1.27–3.32). The prevalence of clinical risk factors increased with age; however, the strength of the DCM association was greatest for young-onset (<30 years) disease (OR, 4.75; 95% CI, 2.35–9.60). Administration of beta-adrenergic receptor or renin-angiotensin system-blocking drugs prior to overt DCM was associated with 87% reduced odds of DCM (OR, .13; 95% CI, .08–.23). Conclusions Disease onset in TTNtv-associated familial DCM is dependent on individual patient context and is potentially modifiable by risk factor management and prophylactic therapeutic intervention.

AbstractBackgroundPrevious publications have described the presence and severity of insomnia amongst those with persistent post‐traumatic headache (PPTH), but there has been a paucity of studies investigating insomnia during the acute phase of post‐traumatic headache (APTH). The primary study objective was to investigate insomnia symptoms and their severity in individuals with APTH due to mild traumatic brain injury (mTBI), and the secondary objective was to assess how psychological factors, sensory hypersensitivities, headache burden, and cognitive performance might associate with insomnia.MethodsIn this clinic‐based, cross‐sectional study, 82 individuals with APTH due to mTBI and 82 healthy controls (HC) were included. Participants completed the Insomnia Severity Index (ISI), Trail Making Test (TMT A and B), 12‐item Allodynia Symptom Checklist (ASC‐12), State–Trait Anxiety Inventory, Photosensitivity Assessment Questionnaire (PAQ), Beck Depression Inventory (BDI), Hyperacusis Questionnaire (HQ), Rey Auditory Verbal Learning Test (RAVLT), and a detailed headache characteristics questionnaire within 59 days of their mTBI to assess insomnia severity, psychological features, sensory hypersensitivities, headache symptoms, and cognitive performance. The questionnaire and test results were compared between APTH and HC groups. Within the APTH group, ISI scores were correlated with the other assessments to determine underlying clinical relationships.ResultsParticipants with APTH, evaluated an average of 27.2 days after their mTBI, had significantly higher ISI scores (median [interquartile range, IQR] score 12 [6–17]) compared to HC (0 [1.3–6], p < 0.001). Twenty‐four (29%) individuals with APTH had ISI scores categorized as moderate clinical insomnia compared to two (2%) in the HC group. Additionally, seven (9%) in the APTH group and none of the HC had ISI scores indicative of severe clinical insomnia. Those with APTH significantly differed from the HC on the BDI (median [IQR] score APTH: 9 [5–16.8] vs. HC: 2 [0–4]; p < 0.001), state anxiety (median [IQR] score APTH: 36.5 [26–45] vs. HC: 23 [20–27.5]; p < 0.001), trait anxiety (median [IQR] score APTH: 38 [27.3–46.8] vs. HC: 26 [23–30]; p < 0.001), photosensitivity (median [IQR] PAQ score APTH: 2 [1–4] vs. HC: 0 [0–1]; p < 0.001), allodynia (median [IQR] ASC‐12 score APTH: 2 [0–5.8] vs. HC: 0 [0–0], p < 0.001), hyperacusis (median [IQR] HQ score APTH: 15.5 [7–23] vs. HC: 4 [2.3–7], p < 0.001), and RAVLT‐delayed recall (median [IQR] score APTH: −0.8 [−1.5 to 0] vs. HC: −0.1 [−0.9 to 0.7], p = 0.019). For participants with APTH, the ISI was most significantly associated with the BDI (standardized regression coefficient (RC) [95% confidence interval, CI] 0.68 [0.52–0.84], p < 0.001), followed by state anxiety (RC [95% CI] 0.60 [0.42–0.78], p < 0.001), trait anxiety (RC [95% CI] 0.56 [0.37–0.74], p < 0.001), headache severity (RC [95% CI] 0.40 [0.19–0.60], p < 0.001), HQ (RC [95% CI] 0.36 [0.16–0.57], p = 0.001), and PAQ (RC [95% CI] 0.35 [0.15–0.56], p = 0.001).ConclusionsIndividuals with APTH have more symptoms of insomnia compared to HC with 38% of individuals with APTH experiencing moderate or severe insomnia symptoms. Insomnia symptoms may be associated with more severe depression, anxiety, photophobia, hyperacusis, headache severity, and cognitive impairment. To address the overall well‐being of patients with APTH, clinicians should screen for and appropriately manage insomnia along with other symptoms following mTBI.

Abstract Objectives/Background Post‐traumatic headache (PTH) is a common symptom following mild traumatic brain injury (mTBI). Currently, there is no identified way to accurately predict if, when, and at what pace a person will have PTH improvement. In our prior studies, we focused on predicting headache improvement at 3 months post‐mTBI. However, that approach may overlook individual differences in how headaches evolve over time. This study aimed to identify individual subgroups based on their headache trajectories and to develop machine‐learning (ML) models for early prediction of headache evolution. Methods Participants with acute PTH completed a daily electronic headache diary over 3 months, recording their headache‐related symptoms. Tensor decomposition was utilized to extract latent factors underlying the time‐varying symptoms. We applied clustering techniques on the latent factors to identify patient subgroups with varying headache improvement trajectory. Next, we developed an ML method to classify each individual into a headache trajectory subgroup as early as possible within the 3‐month interval. Results Seventy‐three individuals with acute PTH (mean age = 44.8 years, SD = 14.0; 50 females/23 males) were enrolled between 0 and 59 days post‐mTBI. Data from 54 individuals were used as the training cohort for model training, and 19 individuals were used as the test cohort for model evaluation. Tensor decomposition extracted two latent factors: one factor representing the overall state of PTH severity and disability and the other representing the improvement state of these symptoms over 3 months. Clustering identified four patient subgroups with distinct headache evolution trajectories: (1) severe symptoms without improvement, (2) severe symptoms with mild improvement, (3) milder symptoms with substantial improvement, and (4) mildest symptoms with mild improvement. The proposed ML model achieved 0.80 cross‐validation accuracy in classifying individuals with PTH into subgroups for the training cohort and 0.84 accuracy for the test cohort. Notably, the model required only the first 2 weeks of headache data to accurately identify the subgroup with the mildest headaches, 3 additional weeks to identify the subgroup with the most severe headaches and no improvement in 3 months, and 2 additional weeks to distinguish the remaining subgroups. Conclusion This study identified subgroups of individuals with acute PTH with distinct headache improvement trajectories. The proposed ML method accurately classified individuals into these subgroups using the minimally necessary early headache data for each person, including detecting the subgroup with the mildest headaches at 2 weeks. This approach could offer an estimated forecast of headache burden over time and could assist clinicians with determining treatment needs and eligibility for PTH clinical trials.

7049 Background: Odronextamab, an investigational off-the-shelf CD20×CD3 bispecific antibody, demonstrated compelling efficacy and a generally manageable safety profile in heavily pretreated pts with R/R FL in the primary analysis of the Phase 2 ELM-2 study (NCT03888105; Kim TM, et al. Ann Oncol 2024). We present updated efficacy and safety data for odronextamab in pts with R/R FL from ELM-2 after >2 yrs follow-up. Methods: Odronextamab was administered intravenously until disease progression/unacceptable toxicity, with Cycle (C) 1 step-up dosing to help mitigate cytokine release syndrome (CRS) risk, as reported previously. Pts with a complete response (CR) for ≥9 months (mo) switched from maintenance dosing Q2W to Q4W. Primary endpoint: objective response rate (ORR) per Lugano criteria by independent central review (ICR); secondary endpoints: CR rate, duration of response (DOR), progression-free survival (PFS), overall survival (OS). Results: At the updated data cutoff (Aug 15, 2024), 157 pts with centrally confirmed R/R FL Grade (Gr) 1–3a were enrolled. Median no. of treatment cycles: 19.0 (range 0.1–117.3); 96.2% (n = 151) and 82.8% (n = 130) of pts completed C1 and C4, respectively. The global cohort comprised 128 pts evaluable for efficacy. At a median efficacy follow-up of 28.3 mo, ORR was 80.5% (n = 103) by ICR and CR rate was 74.2% (n = 95); 92.2% of responders achieved CR. Responses were durable (median DOR, 26.0 mo; median duration of CR, 32.2 mo). ORR was consistent across high-risk subgroups (Follicular Lymphoma International Prognostic Index score 3–5, 78.4%; progression of disease within 2 yrs of frontline therapy, 81.0%). Median PFS was 23.0 mo (estimated 36-mo PFS rate, 37.5%), and median OS was 54.2 mo (estimated 36-mo OS rate, 62.6%). Median PFS was longer in pts who were minimal residual disease (MRD) negative (42.4 mo) versus MRD positive (21.6 mo) at Week 12. Of 47/128 pts who switched to Q4W dosing, 32 remained in CR. The odronextamab long-term safety profile was consistent with the primary analysis. All 157 pts had TEAEs (Gr ≥3, 86.0%), and 15.3% discontinued treatment due to TEAEs (most common: COVID-19 infection, 2.5%). With 0.7/4/20 mg step-up dosing (n = 89), CRS events were mostly low grade (Gr 1, 46.1%; Gr 2, 13.5%; Gr 3, n = 1; Gr ≥4, n = 0), occurred mostly in C1, and resolved in a median of 8.4 hrs. Immune effector cell-associated neurotoxicity syndrome was reported in one pt (Gr 2). Infections were reported in 79.0% of pts (124/157; Gr ≥3, 42.0%). COVID-19-related infections were reported in 38.2% of pts (Gr 5, 5.7%). Conclusions: With longer follow-up, odronextamab demonstrated durable responses in heavily pretreated pts with R/R FL from ELM-2, with robust efficacy in those with high-risk features, and a generally manageable safety profile. Overall, these compelling results support odronextamab as a potential off-the-shelf treatment option for pts with R/R FL. Clinical trial information: NCT03888105 .

Background Headache is a common symptom following mild traumatic brain injury (mTBI). Post-traumatic headache (PTH), a secondary headache disorder that develops after mTBI, often persists for months or years. To identify potential recovery mechanisms and prognostic biomarkers, the present study investigated whether longitudinal changes in pain-induced brain activation differ between healthy controls (HC) and PTH participants showing headache improvement and those without improvement. Methods Thirty-three participants who met International Classification of Headache Disorders, 3rd edition, criteria for acute PTH within 59 days post-mTBI and 33 HC participants were included with no significant differences in demographics. All participants underwent functional magnetic resonance imaging scans at baseline, four weeks, and 16 weeks post-enrollment using a thermal stimulation paradigm with noxious and non-painful heat stimuli. ‘Painful vs. Non-Painful Heat’ contrasts were generated using SPM12. PTH improvement was assessed at three months post-enrollment via electronic headache diaries. Two-sample t -tests compared the brain activation between HC and PTH at baseline. Linear mixed-effects models examined longitudinal changes for HC, PTH improvement and non-improvement groups across visits. Generalized linear models compared these groups within visits. Results Baseline analysis revealed several regions with significantly higher activation in acute PTH compared to HC, including bilateral postcentral gyrus, right superior temporal gyrus, right middle temporal gyrus, left inferior parietal gyrus, right superior parietal gyrus, left ventral striatum, left olfactory cortex, left gyrus rectus, and left middle occipital gyrus. Over time, the PTH improvement group demonstrated progressive normalization across all identified brain regions, whereas the non-improvement group showed only partial normalization in left ventral striatum, left olfactory cortex, and left gyrus rectus. Sustained elevated activation in specific regions distinguished PTH participants without headache improvement from those with headache improvement, suggesting potential biomarkers for persistent PTH. Conclusions Our findings demonstrate significantly altered pain-induced brain activations in participants with acute PTH compared to HC. Longitudinal analysis revealed distinct recovery trajectories: progressive normalization in the improvement group versus persistent alterations in the non-improvement group. These neuroimaging patterns may serve as biomarkers for identifying individuals at risk for persistent PTH, with implications for early intervention and personalized treatment approaches.

Abstract Rationale: We have previously shown reduced total IgG, IgG subclasses, and levels/function of pneumococcal-specific antibodies are associated with increased risk for exacerbations of COPD (ECOPD). The binding of IgG to its receptor, the Fc receptor for IgG (FcγR) on immune cells, has a critical role in determining antibody levels and function. In addition to our research demonstrating that IgG heavy chain gene (IGHG) variants are associated with ECOPD, single nucleotide variants (SNVs) in genes encoding FcγR (FCGR1-3, which encode receptor subtypes FcγRI, FcγRIIa/b/c, and FcγRIIIa/b) may also result in antibody deficiency and increased exacerbation risk. Due to technical challenges in accurately identifying SNVs in FCGR, this region has not been well-characterized. The limited data that exist are from people of European descent, and prior studies have not included African American participants. We therefore sought to investigate the hypothesis that distinct SNVs in genes encoding FcγR are associated with disease severity and history of exacerbations in African American people with COPD. Methods: We developed a novel pipeline to identify SNVs in the coding (CDS) regions of chromosome 1 using short-read whole genome sequence data. The pipeline was benchmarked using paired long-read samples from Genome in a Bottle. SNVs were called within CDS regions and analyzed in a meta-analysis of self-reported African American participants in SPIROMICS and COPDGene (n=3063). Genetic variants were tested for association with COPD severity (GOLD spirometry stage 1-2 vs 3-4) as well as COPD exacerbations, adjusting for age, sex and principal components controlling for population structure. Results: Eight SNVs were significantly associated with frequent ECOPD (≥2 in one year) in the meta-analysis of African American COPDGene and SPIROMICS participants (Table). Seven of these 8 SNVs were found in FCGR3; 4 in the activating FCGR3A subtype, and 3 in FCGR3B. Seven of 11 SNVs associated with COPD severity were in FCGR3 (4 in FCGR3A; 3 in FCGR3B). One SNV (rs371849) was associated with both frequent exacerbations (P=0.002) and COPD severity (P=0.044). Conclusions: We used a cutting-edge variant calling pipeline to identify multiple SNVs in FCGR, primarily involving FcγRIII receptor subtypes, associated with COPD severity and exacerbations in African American participants in SPIROMICS and COPDGene. FcγRIII variants are known to cause impaired antibody levels and function in people of European descent. The structural and functional importance of newly recognized variants in African American cohorts require further study, as they may represent immunogenetic risk factors for antibody deficiency and exacerbations.

AbstractINTRODUCTIONIdentifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lacking. We developed novel set algorithms using both structured and unstructured electronic health record (EHR) data, applying Diagnostic and Statistical Manual of Mental Disorders criteria for dementia case identification.METHODSOur cohort (n = 1082) included individuals aged ≥ 60 with dementia identified through specialist clinics and a comparison group without dementia. Clinicians from Australia and the United States informed predictor selection. We developed algorithms through a biostatistics stream for structured data and a natural language processing (NLP) stream for text, synthesizing results via logistic regression.RESULTSThe final structured model retained 16 variables (area under the receiver operating characteristic curve [AUC] 0.853, specificity 72.2%, sensitivity 80.6%). NLP classifiers (logistic regression, support vector machine, and random forest models) performed comparably. The final, combined model outperformed all others (AUC = 0.951, P < 0.001 for comparison to structured model).DISCUSSIONEmbedding text‐derived insights within algorithms trained on structured medical data significantly enhances dementia identification capacity.Highlights Algorithmic tools for detection of individuals with dementia are available; however, previous work has used heterogeneous case definitions which are not clinically meaningful, and has relied on proxies such as diagnostic codes or medications for case ascertainment. We used a novel, dual‐stream algorithmic development approach, simultaneously and separately modeling a clinically meaningful outcome (diagnosis of dementia according to specialized clinical impression) using structured and unstructured electronic health record datasets. Our clinically grounded case definition supported the inclusion of key structured variables (such as dementia International Classification of Disease codes and medications) as modeling predictors rather than outcomes. Our algorithms, published in detail to support validation and replication, represent a major step forward in the use of routinely collected data for detection of diagnosed dementia.