Citations

Abstract Approximately 17% of people aged 65 years and older are anemic, and 10% of death certificates report anemia as a secondary cause of death in the United States. Nonetheless, anemia remains unexplained in 30%-50% of older adults. This unexplained anemia of aging (UAA) is a diagnosis of exclusion. The mechanism, impact, and progression of UAA remain unknown. At older ages, anemia adds to pre-existing co-morbidities with significant adverse health consequences, representing a compelling unmet clinical concern. The National Institute on Aging held a workshop in 2024 to discuss current knowledge and research opportunities. Topics included the epidemiology of anemia at older age and its clinical implications; probable mechanism(s) underlying UAA, that is, low-grade inflammation’s effects on erythropoiesis; the role of microbiota in iron regulation in bone marrow; the importance of ruling out a diagnosis of leukemic clonal hematopoiesis (CH), which is more prevalent in older age; the role of senescence and aging governing hematopoiesis; and the effects of sex hormones on hematopoietic stem cell aging. Understanding the roles of these factors could reduce the proportion of the older anemic population whose anemia remains unexplained and offer insights into new potential diagnostic and intervention strategies. Speakers reviewed previous clinical trials in patients with UAA and CH. They discussed lessons learned and future research priorities, including efforts to develop new diagnostic algorithms and potential uses of machine learning.

Abstract This brief report details the initial findings from a Phase 1b/2 trial of TN-201, an adeno-associated virus serotype 9 (AAV9) gene therapy for MYBPC3-associated hypertrophic cardiomyopathy (HCM), a condition with significant morbidity, increased risk of mortality, and no approved therapy for the majority of patients. TN-201 was well tolerated, and changes to the management of potential immune responses resulted in a shorter period of immunosuppression. These results show consistent transduction and expression of TN-201 in cardiomyocytes, corresponding with increases in MyBP-C levels, reductions or stabilization of cardiac biomarkers, and reductions in key measures of left ventricular (LV) hypertrophy.

Abstract Background Elevated left ventricular filling pressure (LVFP) is associated with an increased risk of heart failure and mortality. Pulmonary artery wedge pressure (PAWP) is considered the gold standard for assessing LVFP. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) to assess LV diastolic function (LVDF) and LVFP. Herein, we compare AI-ECG with PAWP in predicting long-term mortality. Methods We included adults who had comprehensive echocardiography with available ECG within 14-days and right-heart-catheterization (RHC) within 30-days of echocardiography. Elevated-LVFP was defined as PAWP>15 mmHg by RHC, grades 2-3 LVDF by echocardiography, or as adjudicated by the AI-ECG algorithm. Prognosis by AI-ECG was compared to that by echocardiography and PAWP. Results Among 4,168 patients (median age 64 years, 47.7% female), 1,841 had normal-LVFP, and 2,327 had elevated-LVFP by RHC. Echocardiography classified 1,502 patients as normal-LVFP, 1,643 as elevated-LVFP, and 1,023 as indeterminate-LVFP. AI-ECG demonstrated an area-under-the-curve of 0.879 (95%-confidence-interval 0.867-0.891) for predicting elevated-LVFP as determined by echocardiography and 0.742 (95%-confidence-interval 0.724-0.759) for predicting elevated-LVFP as determined by RHC. Over a median follow-up of 7.7 years, 2,735 (34.4%) patients died. In multivariable models, elevated-LVFP by any modality was associated with an increased risk of death (AI-ECG: adjusted-hazard-ratio 1.80, 95% confidence-interval 1.55-2.09; echocardiography: adjusted-hazard-ratio 1.74, 95% confidence-interval 1.51-2.02; PAWP: adjusted-hazard-ratio 1.26, 95% confidence-interval 1.11-1.43). AI-ECG-derived LVFP provided incremental prognostic discrimination compared to echocardiography- or RHC-derived LVFP. Conclusions AI-ECG-derived LVFP offers an accessible, affordable, and noninvasive alternative for detecting elevated LVFP, with potential independent prognostic value beyond traditional echocardiographic and invasive assessments.Survival across LVFP concordance

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.

BACKGROUND AND OBJECTIVES: The Patient-Generated Subjective Global Assessment (PG-SGA) is a standardized tool for assessing malnutrition in patients with cancer. The primary aim of this study was to assess the impact of preoperative nutritional status as measured by PG-SGA on survival in patients requiring surgical intervention and/or radiotherapy for spinal metastases. METHODS: Patients with spinal metastases who underwent surgery and/or radiation therapy for symptomatic spinal metastases were enrolled in the AO Spine Metastatic Tumor Research and Outcomes Network, a prospective international multicenter research registry, between September 2017 and August 2022. Using the PG-SGA, nutritional status was classified into 3 categories: A, well nourished; B, moderately malnourished; and C, severely malnourished. RESULTS: A total of 589 patients met the inclusion criteria; 362 were classified as well nourished (61%), 159 were moderately malnourished (27%), and 68 were severely malnourished (12%). The median survival was 491 days, 328 days, and 117 days for well-nourished, moderately malnourished, and severely malnourished patients, respectively. In the multivariate analyses, severe malnourishment (HR 2.5 95% CI 1.4-4.3, P < .01) and an ECOG performance status of 3 or 4 (HR 2.7 95% CI 1.2-6.0) remained associated with significantly worse survival. CONCLUSION: Malnutrition as measured by the PG-SGA demonstrated to be significantly and independently associated with postoperative survival. The PG-SGA is a simple and useful tool to identify spinal metastases patients at risk of early postoperative mortality, and inclusion in the preoperative evaluation of these patients should be considered.

Abstract Repetitive head impacts are common in contact and collision sports and are linked to structural brain changes and an elevated risk of neurodegenerative diseases such as Chronic Traumatic Encephalopathy. Identifying early in vivo structural markers remains challenging. Although diagnosis currently requires post-mortem confirmation, clinical symptoms, including cognitive impairment and behavioural changes, are reflected in the diagnosis of Traumatic Encephalopathy Syndrome. These symptoms align with dysfunction in key brain regions—amygdala, hippocampus and thalamus—which support memory, emotion and behaviour and commonly show tau pathology in Chronic Traumatic Encephalopathy. This study uses shape analysis to examine structural differences in these regions between former American football players and unexposed asymptomatic controls and evaluates the influence of age, head impact exposure and clinical diagnosis on brain structure. We analyzed brain morphology in former American football players (n = 163) and unexposed, asymptomatic controls (n = 53). Structural segmentation was performed with FreeSurfer 7.1, and the shape analysis pipeline was used to generate subregional reconstructions. Vertex-level morphometry, based on the logarithm of the Jacobian determinant and radial distance, quantified local surface area dilation and thickness. Group differences were examined with covariate-adjusted linear regression models contrasting football players and controls, as well as participants with and without a Traumatic Encephalopathy Syndrome diagnosis. Partial correlations examined the influence of age, age of first football exposure and cumulative head impact index metrics, including frequency, linear acceleration and rotational force. Models were adjusted accordingly for age, body mass index, education, race, imaging site, apolipoprotein ϵ4 status and total intracranial volume. Former football players exhibited bilateral surface area contractions in the hippocampus and amygdala, along with reduced amygdala thickness, compared to controls. Older age was associated with widespread surface contractions and thinning across all regions, except for preserved thickness in the left hippocampus. An earlier age of first exposure to football correlated with surface contractions in the thalamus and left hippocampus. Greater cumulative linear acceleration was linked to bilateral hippocampal surface contractions and reduced thickness in the left thalamus, while greater rotational force exposure was associated with hippocampal thinning. No significant structural differences were found between players with and without a diagnosis of Traumatic Encephalopathy Syndrome. These findings extend volume-based research by revealing localized alterations in surface area dilation and thickness and emphasize the roles of age and repetitive head impact exposure in long-term brain changes.

Abstract Background and Aims Decrease in pulmonary vascular resistance (PVR) during exercise confers favourable prognosis in biventricular physiology, but its role in Fontan circulation remains unknown. This study aimed to evaluate the prognostic value of PVR index (PVRI) response to exercise in adults post-Fontan palliation. Methods Retrospective cohort of 88 adults (age ≥18 years) post-Fontan undergoing exercise (supine cycling) cardiac catheterization. Patients were categorized into absence (n = 23) or presence (n = 65) of exercise-induced reduction in PVRI. The clinical endpoint was a composite of death or transplantation. Results Age at catheterization was 32.2 ± 8.8 years, and 45.5% were female. Resting and exercise PVRI were 1.9 ± 0.9 and 1.2 (interquartile range [IQR] 0.8–2.1) WU·m2, respectively. Clinical, biochemical and echocardiographic parameters were not associated with PVRI response to exercise. During a follow-up of 2.2 ± 1.4 (IQR 1.2–2.6) years, resting PVRI was not associated with the clinical endpoint (hazard ratio [HR] 0.95 per WU·m2, 95% confidence interval [CI] 0.52–1.74; P = 0.86), whereas exercise PVRI was (HR 2.15 per WU·m2, 95% CI 1.24–3.73; P = .007). Exercise PVRI remained predictive of outcomes after adjusting for exercise pulmonary artery wedge pressure (HR 2.08 per WU·m2, 95% CI 1.19–3.62; P = .01) or clinical risk factors individually. Two-year event-free survival was lower in the no PVRI reduction than in the reduction group (67% vs 95%; P &lt; .001). Conclusions In adults post-Fontan, PVRI response to exercise is a robust predictor of clinical outcomes and offers additive prognostic information beyond resting invasive haemodynamic data and clinical risk factors, underscoring the importance of invasive exercise testing in this population.

Background Exercise cardiac catheterization is the gold standard for diagnosing diastolic dysfunction and impaired pulmonary reserve in patients with Fontan circulation. The diagnostic value of noninvasive parameters is unclear. Methods Patients who underwent simultaneous exercise hemodynamic evaluation and peripheral venous pressure (PVP) measurement were included. Echocardiographic surrogates for ventricular filling pressure were compared with invasive measures. Diastolic dysfunction was defined invasively as either resting pulmonary artery wedge pressure ≥12 mm Hg or exercise pulmonary artery wedge pressure >20 mm Hg. Results Twenty‐nine patients were included. Age at catheterization was 34±9 years. Atrioventricular valve systole‐to‐diastole duration ratio (S/D) and resting PVP were significant predictors of resting pulmonary artery wedge pressure (S/D: β=0.71 per 0.1 S/D [95% CI, 0.16–1.25]; P =0.01; PVP: β=0.68 per mm Hg [95% CI, 0.41–0.96]; P <0.001). By contrast, resting PVP was a predictor of diastolic dysfunction, whereas S/D was not. Based on these data, we proposed a 2‐tier assessment algorithm with the first tier considering resting PVP ≤15 mm Hg and S/D ≤1.1 and the second tier considering exercise PVP ≤25 mm Hg in those with 1 elevated resting parameter. This algorithm showed a positive predictive value of 88% (95% CI, 47%–100%), a negative predictive value of 90% (95% CI, 70%–99%), a sensitivity of 78% (95% CI, 40%–97%), and a specificity of 95% (95% CI, 75%–100%). Exercise PVP indexed to peak oxygen consumption also predicted invasively measured exercise pulmonary vascular resistance index (β=0.36 WU·m 2 per mm Hg/100 mL per min [95% CI, 0.08–0.64]; P =0.01). Conclusions This study presents a simple, noninvasive approach for evaluating diastolic function and pulmonary vascular reserve in patients with a Fontan circulation.

Decades of research on biomarker identification and integration in Alzheimer’s disease (AD) have helped to advance biologically driven disease models, disease subtypes, and disease-modifying treatments. Key lessons learned from AD biomarker development include the need for integration of new biological insights, investment in large cross-sectional and longitudinal studies, a multicomponent biomarker approach, and recruitment of clinical trial cohorts and biosamples that are representative of the target population. We describe how these lessons are being applied in Parkinson’s disease, Huntington’s disease, multiple sclerosis, and psychotic disorders as use cases to inform biomarker-based approaches across central nervous system disorders.

Abstract Background Deep learning auto segmentation (DLAS) tools are widely used for radiation therapy planning, yet limited information exists on how patient positioning impacts their performance, particularly in head and neck (HN) computed tomography (CT) imaging. Purpose This study investigates the impact of abnormal neck tilt on the accuracy of DLAS‐generated contours in HN CT scans. We hypothesize that abnormal positioning degrades auto segmentation performance, particularly for organs at risk (OARs) influenced by cervical spine orientation. Methods A total of 35 HN CT scans were retrospectively analyzed. Neck tilt was quantified using principal component analysis of the spinal cord contours from C1 to C4, with patients stratified into normal and abnormal tilt groups based on percentile thresholds. Seven FDA‐cleared DLAS tools were evaluated for OAR segmentation accuracy across brainstem, parotid glands, submandibular glands, brachial plexus, and optic nerves. Gold‐standard contours were curated from manually created clinical contours and compared to DLAS output using Sorenson's volumetric dice similarity coefficient (DSC), surface Dice similarity coefficient with a 2 mm threshold (sDSC), mean distance to agreement (MDA) and difference in mean dose. Statistical differences were assessed using Wilcoxon rank‐sum tests ( p < 0.05). Results Among the evaluated OARs, the parotid glands showed consistent and statistically significant degradation in contouring accuracy for patients with abnormal neck tilt across all metrics (DSC, sDSC, MDA) and across all seven DLAS tools. Changes in contouring accuracy resulted in significant differences in both absolute and signed difference in mean dose. No consistent differences were observed for other structures across multiple DLAS tools. Conclusions Abnormal neck tilt is associated with reduced DLAS accuracy and greater dose variability for parotid gland segmentation in HN radiotherapy planning. These findings underscore the need for enhanced patient quality assurance strategies for patients with abnormal positioning when using clinical DLAS workflows. Future work should explore automated detection of anatomical outliers and site‐specific model retraining to ensure accurate contouring delineation for patients with atypical positioning or anatomy.