Citations

Abstract Repetitive head impacts are associated with structural brain changes and an increased risk for chronic traumatic encephalopathy, a progressive neurodegenerative disease that can only be diagnosed after death. Chronic traumatic encephalopathy is defined by the abnormal accumulation of phosphorylated tau protein, particularly at the depths of the superior frontal sulci, suggesting that sulcal morphology may serve as a relevant structural biomarker. Contact sport athletes, such as former football players, are at elevated risk due to their prolonged exposure to repetitive head impacts. Cortical atrophy linked to underlying tau accumulation may result in shallower and wider sulci, potentially making sulcal morphology an imaging marker for identifying individuals at risk for this disease. This study investigated sulcal morphological differences in former football players and examined associations with age, football-related exposure, clinical diagnosis of traumatic encephalopathy syndrome, levels of certainty for chronic traumatic encephalopathy pathology, neuropsychological performance, and positron emission tomography imaging using flortaucipir. We analysed structural magnetic resonance imaging data from 169 male former football players (mean age 57.2 (8.2) years, range 45–74) and 54 age-matched, unexposed asymptomatic male controls (mean age 59.4 (8.5) years, range 45–74). Sulcal depth and width were quantified using the CalcSulc, focusing on two regions in each hemisphere commonly affected by chronic traumatic encephalopathy pathology: the superior frontal and occipitotemporal sulci. Generalized least squares models were used to assess group differences and interactions with age and football exposure variables, including age of first exposure, total years played, and cumulative head impact exposure. An analysis of covariance evaluated relationships between sulcal morphology, clinical measures, and flortaucipir uptake, adjusting for age, race, body mass index, education, imaging site, apolipoprotein E4 status, and total intracranial volume. Former football players demonstrated significantly shallower sulcal depth in the left superior frontal sulcus compared to unexposed controls. Earlier age of first exposure and longer football careers were associated with greater widening of the left occipitotemporal sulcus. Higher cumulative head impact exposure was linked to reduced sulcal depth in the left superior frontal region. However, sulcal morphology was not associated with clinical diagnosis, levels of certainty, neuropsychological test performance, or flortaucipir imaging. These findings suggest that sulcal morphology may reflect cumulative exposure to repetitive head impacts, particularly in brain regions vulnerable to chronic traumatic encephalopathy pathology. Future ante- and post-mortem validation studies are needed to determine whether sulcal morphology can serve as a reliable in vivo biomarker of risk.

Abstract We investigated the influence of 55 583 autophagy-related single-nucleotide polymorphisms (SNPs) on chronic lymphocytic leukemia (CLL) risk across 4 independent populations comprising 5472 CLL cases and 726 465 controls. We also examined their impact on overall survival (OS), time to first treatment (TTFT), autophagy flux, and immune responses. A meta-analysis of the 4 populations identified, to our knowledge, for the first time, significant associations between CDKN2A (rs3731204) and BCL2 (rs4940571, rs12457371, and rs1026825) SNPs and CLL risk, with CDKN2A showing the strongest association (P = 1.57 × 10−12). We also validated previously reported associations for FAS, BCL2, and BAK1 SNPs with CLL risk (P = 4.73 × 10−21 to 3.39 × 10−9). The CDKN2Ars3731204 and FASrs1926194 SNPs associated with increased CDKN2A and ACTA2 messenger RNA expression levels in the whole blood and/or lymphocytes (P = 5.1 × 10−7, P = 1.58 × 10−21, and P = 7.8 × 10−41), although no significant effect on autophagy flux was observed. However, associations were found between CDKN2A, BCL2, and FAS SNPs and various T-cell subsets, cytokine production, and circulating concentrations of interferon gamma, tumor necrosis factor–related apoptosis-inducing ligand, CD40, chemokine ligand 20, and interleukin-2 receptor subunit β proteins (P ≤ .005). No significant association was detected between autophagy variants and OS or TTFT, suggesting that these variants drive disease initiation rather than progression. In conclusion, this study identified 4 novel associations for CLL and provided insights into the biological pathways that influence CLL development.

Abstract Post-translational modifications (PTM) of tau are implicated in Alzheimer disease (AD) progression and are established biomarkers in cerebrospinal fluid and plasma; however, the labor-intensive nature of conventional proteomics limits their investigation in histology-specific contexts. We describe the findings of an artificial intelligence-guided laser capture microdissection (LCM) pipeline for harvesting neurofibrillary tangles (NFTs) by maturation level into pretangles (pre-NFTs), mature tangles (iNFTs), and ghost tangles (eNFTs) using 38 cases obtained from 2 independent biobanks. We evaluated the performance characteristics of proprietary machine learning algorithms for the subclassification of these NFT categories in anti-pTau217-stained whole slide images of entorhinal cortex, hippocampus, frontal, and parietal cortex sections. Overall precision/recall/F1 scores were highest for Classifier A (0.6/0.46/0.5). The best performing algorithm was used to guide LCM capture and inform NFT enrichment. Targeted proteomics on tau signature peptides (pTau181, pTau217, and TauMTBR) was performed on approximately 1250 NFT collections. The results demonstrated that their abundance increased from pretangles to mature tangles (∼2-11-fold increase), and that this was followed by a sharp reduction in ghost tangles (∼3-116-fold decrease), with pTau217 showing the most drastic change. Pathologist-trained NFT classifiers represent an objective albeit imperfect means to enrich specific morphologic forms permitting coupled LCM-MS (mass spectrometry) to investigate AD-associated PTM.

Abstract Purpose: Germline pathogenic variants (PV) in ATM increase the risk of pancreatic ductal adenocarcinoma (PDAC), but the underlying tumor biology of PDAC associated with germline PV in ATM has not been adequately explored. Experimental Design: Whole-genome, whole-exome, and RNA sequencing were performed on PDAC tumors from 25 germline ATM PV carriers diagnosed at Mayo Clinic between 2007 and 2017. Somatic and copy-number alterations, mutational signatures, transcriptomic subtypes, and the immune landscape were evaluated. Results: High-quality whole-exome and whole-genome sequencing were obtained from 21 and 15 tumors, respectively. Biallelic inactivation of ATM was observed in 87%, KRAS PV in 90%, CDKN2A homozygous loss in 60%, and TP53 alterations in <10% of these tumors. A predominant clock-like mutational signature was present in all samples. Whole-transcriptome analysis identified that the aberrantly differentiated endocrine exocrine subtype accounted for 18% of PDAC and was consistently associated with >5-year overall survival. In addition, a 28-gene expression–based signature associated with overall survival was identified and further validated in The Cancer Genome Atlas cohort. Immune landscape analysis through CODEX identified enriched CD4 T-helper cell/tumor interactions and reduced B7H3-high cell/tumor interactions in ATM PV carriers compared with noncarriers. Conclusions: The observed absence of TP53 PV and enrichment for CDKN2A alterations in ATM tumors, along with differences in the mutational signatures, transcriptomic subtypes and immune landscape, improve our understanding of the mechanistic pathways involved in PDAC development in germline ATM PV carriers and help identify potential targeted therapeutic strategies.

Significance: Hepatic encephalopathy (HE) occurs in 20–80% of patients with liver cirrhosis, impacting attention, memory, processing speed, and visuospatial skills. HE standard-of-care psychometric assessments are time-consuming and require staff training. Oculometrics may provide a fast, non-invasive brain health assessment that can be self-administered in a medical environment. Purpose: We investigated whether an oculometric assessment could measure the severity of HE as accurately as standard-of-care psychometric methods. Methods: Forty-eight participants (19 with decompensated cirrhosis, 10 with compensated cirrhosis, 19 controls) completed a previously validated five-minute oculometric test and the standard-of-care psychometric hepatic encephalopathy (PHE) battery. The oculometric test consists of following a dot as it moves across a computer screen and generates 10 metrics including a summary score called nFit. The PHE battery entails five standard cognitive tests, generating seven metrics including a PHE composite score (PHES). Results: The oculometric summary score, nFit, correlated with the current diagnostic standard, the PHES (r = 0.51, p < 0.001), the presence or absence of HE as determined by PHES composite (r = −0.44, p < 0.001), as well as the severity of cirrhosis (r = −0.59, p < 0.001). Additionally, performance on both nFit and PHES distinguished compensated (ROC: nFit: 0.71, PHES: 0.68) and decompensated (ROC: nFit: 0.88, PHES: 0.85) patient groups from control participants comparably. Finally, compared to participants with decompensated cirrhosis, control participants had better scores for almost all oculometrics: acceleration, catch-up saccade amplitude, proportion smooth, direction noise, and speed noise. Conclusions: Patients with liver disease showed impairment on multiple aspects of visual processing compared to a control group. These functional visual processing impairments correlate with the presence or absence of HE, showing significant sensitivity in distinguishing people with HE from controls. Oculometric tests provide a quick, non-invasive functional assessment of brain health in patients with liver disease, with sensitivity indistinguishable from standard-of-case psychometric tests.

Background: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL reconstruction (ACLR) compared with nonoperative management on a patient-specific level can inform surgical decision-making. Hypothesis: Unsupervised machine learning can identify distinct patient subgroups based on outcome achievement after ACL injury, and ACLR will exert a protective effect on the development of posttraumatic osteoarthritis (PTOA) over nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: A longitudinal populational registry identified patients with ACL injuries from 1990 to 2016 with a minimum 7.5-year follow-up. An unsupervised random forest algorithm was utilized to develop and validate patient subgroups. Treatment effects of ACLR on outcomes were analyzed using a machine learning causal inference estimator. Patient subgroup membership was incorporated into a multivariable logistic regression to identify factors predictive of optimal outcomes. Results: A total of 923 patients (785 primary ACLR, 138 nonoperative) were included. The random forest algorithm arrived at an optimal partition of 2 subgroups, with 653 patients in the optimal outcome subgroup (368 male [56.4%]; mean age, 26.0 ± 10.2 years; mean body mass index [BMI], 26.5 ± 4.30) and 270 patients in the suboptimal outcome subgroup (152 male [56.3%]; mean age, 35.0 ± 10.1 years; mean BMI, 30.5 ± 5.54). The latter group demonstrated significantly increased rates of secondary meniscal injury, development of symptomatic PTOA, and progression to total knee arthroplasty (TKA) at the final follow-up (all P < .01). In the optimal outcome subgroup, ACLR had significantly protective treatment effects on the risk of secondary meniscal injury (average treatment effect [ATE], 61%), contralateral ACL injury (ATE, 8%), symptomatic PTOA (ATE, 16%), and progression to TKA (ATE, 6%) (all P < .01). Conversely, in the suboptimal outcome subgroup, ACLR only protected against symptomatic PTOA (ATE, 11%) and progression to TKA (ATE, 8%) (both P < .01). Conclusion: Two clinically meaningful subgroups were identified from retrospectively collected data and found to experience differential treatment responses after ACL injuries. ACLR decreased the rate of development of PTOA and TKA in both subgroups but was not as effective in preventing secondary meniscal injuries or contralateral ACL injuries in patients who were older, heavier, or had concomitant medial meniscus injuries.

Abstract Changes in the clinical symptoms of acute graft-versus-host disease (GVHD) are currently used to assess treatment responses. The Mount Sinai Acute GVHD International Consortium (MAGIC) consortium has recently revealed that the integration of serum biomarkers with clinical symptoms at the onset of treatment in a MAGIC composite score (MCS) more accurately predicts treatment response and 6-month nonrelapse mortality (NRM) than clinical symptoms alone. In this study, we evaluated whether the integration of serum biomarkers and clinical symptoms on day 28 (D28) would also better predict NRM than clinical response only (CRO). We analyzed data from 1135 patients receiving systemic treatment for acute GVHD and created a fourth MCS category for patients with complete resolution of symptoms and low-risk clinical biomarkers on D28. Using a classification and regression tree model with 6-month NRM as the end point, we identified status of MCS 0 or MCS 1 at D28 as responses, which we termed the MAGIC composite response (MCR). In the validation cohort (n = 309), MCR more accurately predicted 6-month NRM than CRO (area under the curve: 0.77 vs 0.69; P = .014) and demonstrated higher negative and positive predictive values. MCR correctly reclassified both clinical nonresponders and responders: 28 of 213 clinical responders (13%) became nonresponders with fivefold higher NRM (34.3% vs 6.8%, P &lt; .001) and a larger group (29/96, 30%) of clinical nonresponders became responders with sixfold lower NRM (7.6% vs 50.7%, P &lt; .001). These findings support the use of MCR as a superior surrogate end point for long-term GVHD control and survival in future clinical trials.

Abstract Objectives Evaluation of differences in the intestinal microbiome and resistome among high-risk patients (i.e. intensive care, oncology, transplant recipients) who are and are not colonized with carbapenem-resistant Enterobacterales (CRE). Methods One hundred and twelve rectal swabs were obtained from 85 patients with known CRE colonization status and cohorted. Long-read metagenomic next-generation sequencing (mNGS) was performed on rectal swabs. Microbiome and resistome analysis were performed by assessing α-diversity, β-diversity, relative abundance assessment and linear discriminant analysis effect size (LEfSe), comparing patients colonized (CRE positive) and not colonized (CRE negative) with CRE. Longitudinal analysis of sequential swabs collected over multiple hospital encounters on a subset of patients was performed at the patient level. Results The microbiomes of cohorts were similar when comparing α- and β-diversity measures and relative abundance. LEfSe analysis identified Gram-negative pathobionts enriched among CRE-positive samples and Gram-positive taxa enriched among CRE-negative samples. α-Diversity of the resistome differed at class, gene and allele levels. Relative abundance and LEfSe analysis demonstrated enrichment of genes conferring β-lactam resistance among CRE-positive patients; LEfSe also demonstrated enrichment of antimicrobial resistance genes to multiple antimicrobial classes. At the patient level, fluctuations in the microbiome and resistome among longitudinally collected swabs were associated with antibiotic exposure. Conclusions Differences between the microbiomes of CRE-positive- and CRE-negative-colonized patients at the cohort level were relatively muted, whereas statistically significant differences were observed among their resistomes. In patients followed longitudinally, shifts in microbiome and resistome composition were dramatic in between encounters and antibiotic exposures.