Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort.

Aims/hypothesis: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits. Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO2max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R2) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM. Results: Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R2 of truncal fat and VO2max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM. Conclusions/Interpretation: Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort.

Background: While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD. Method: Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis. Result: The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD. Conclusion: A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

The clinical and genomic features of seizures in meningiomas.

Meningiomas are the most common central nervous system tumors. Although these tumors are extra-axial, a relatively high proportion (10%-50%) of meningioma patients have seizures that can substantially impact the quality of life. Meningiomas are believed to cause seizures by inducing cortical hyperexcitability that results from mass effect and cortical irritation, brain invasion, or peritumoral brain edema. In general, meningiomas that are associated with seizures have aggressive features, with risk factors including atypical histology, brain invasion, and higher tumor grade. Somatic NF2 mutated meningiomas are associated with preoperative seizures, but the effect of the driver mutation is mediated through atypical features. While surgical resection is effective in controlling seizures in most patients with meningioma-related epilepsy, a history of seizures and uncontrolled seizures prior to surgery is the most significant predisposing factor for persistent postoperative seizures. Subtotal resection (STR) and relatively larger residual tumor volume are positive predictors of postoperative seizures. Other factors, including higher WHO grade, peritumoral brain edema, and brain invasion, are inconsistently associated with postoperative seizures, suggesting they might be crucial in the development of an epileptogenic focus, but do not appear to play a substantial role after seizure activity has been established. Herein, we review and summarize the current literature surrounding meningioma-related epilepsy and underscore the interaction of multiple factors that relate to seizures in patients with meningioma.

Surgical strategies for older patients with glioblastoma.

OBJECTIVE: While adjuvant treatment regimens have been modified for older patients with glioblastoma (GBM), surgical strategies have not been tailored. METHODS: Clinical data of 48 consecutive patients aged 70 years or older, who underwent surgical resection for GBM with intraoperative ultrasonography (IoUS) alone or combination with intraoperative MRI (IoMRI) at Yale New Haven Hospital were retrospectively reviewed. Variables were analyzed, and comparative analyses were performed. RESULTS: The addition of IoMRI was not superior to IoUS alone in terms of overall survival (OS) (P = 0.306), Karnofsky Performance Score (KPS) at postoperative 6 weeks (P = 0.704) or extent of resection (P = 0.263). Length of surgery (LOSx), however, was significantly longer (P = 0.0002) in the IoMRI group. LOSx (P = 0.015) and hospital stay (P = 0.025) were predictors of postoperative complications. Increased EOR (GTR or NTR) (P = 0.030), postoperative adjuvant treatment (P < 0.0001) and postoperative complications (P = 0.006) were predictive for OS. Patients with relatively lower preoperative KPS scores (<70) showed significant improvement at postoperative 6 weeks (P<0.0001). Patients with complications (P = 0.038) were more likely to have lower KPS at postoperative 6 weeks. CONCLUSIONS: Aggressive management with surgical resection should be considered in older patients with GBM, even those with relatively poor KPS. The use of ioMRI in this population does not appear to confer any measurable benefit over ioUS in experienced hands, but prolongs the length of surgery significantly, which is a preventable prognostic factor for impeding care.

Type of bony involvement predicts genomic subgroup in sphenoid wing meningiomas.

PURPOSE: As sphenoid wing meningiomas (SWMs) are associated with varying degrees of bony involvement, we sought to understand potential relationships between genomic subgroup and this feature. METHODS: Patients treated at Yale-New Haven Hospital for SWM were reviewed. Genomic subgroup was determined via whole exome sequencing, while the extent of bony involvement was radiographically classified as no bone invasion (Type I), hyperostosis only (Type II), tumor invasion only (Type III), or both hyperostosis and tumor invasion (Type IV). Among additional clinical variables collected, a subset of tumors was identified as spheno-orbital meningiomas (SOMs). Machine-learning approaches were used to predict genomic subgroups based on pre-operative clinical features. RESULTS: Among 64 SWMs, 53% had Type-II, 9% had Type-III, and 14% had Type-IV bone involvement; nine SOMs were identified. Tumors with invasion (i.e., Type III or IV) were more likely to be WHO grade II (p: 0.028). Additionally, tumors with invasion were nearly 30 times more likely to harbor NF2 mutations (OR 27.6; p: 0.004), while hyperostosis only were over 4 times more likely to have a TRAF7 mutation (OR 4.5; p: 0.023). SOMs were a significant predictor of underlying TRAF7 mutation (OR 10.21; p: 0.004). CONCLUSIONS: SWMs with invasion into bone tend to be higher grade and are more likely to be NF2 mutated, while SOMs and those with hyperostosis are associated with TRAF7 variants. Pre-operative prediction of molecular subtypes based on radiographic bony characteristics may have significant biological and clinical implications based on known recurrence patterns associated with genomic drivers and grade.

Tick hypersensitivity and human tick-borne diseases.

Immune-mediated hypersensitivity reactions to ticks and other arthropods are well documented. Hypersensitivity to ixodid (hard bodied) ticks is especially important because they transmit infection to humans throughout the world and are responsible for most vector-borne diseases in the United States. The causative pathogens of these diseases are transmitted in tick saliva that is secreted into the host while taking a blood meal. Tick salivary proteins inhibit blood coagulation, block the local itch response and impair host anti-tick immune responses, which allows completion of the blood meal. Anti-tick host immune responses are heightened upon repeated tick exposure and have the potential to abrogate tick salivary protein function, interfere with the blood meal and prevent pathogen transmission. Although there have been relatively few tick bite hypersensitivity studies in humans compared with those in domestic animals and laboratory animal models, areas of human investigation have included local hypersensitivity reactions at the site of tick attachment and generalized hypersensitivity reactions. Progress in the development of anti-tick vaccines for humans has been slow due to the complexities of such vaccines but has recently accelerated. This approach holds great promise for future prevention of tick-borne diseases.

Clinical and genomic factors associated with seizures in meningiomas.

OBJECTIVE: The association of seizures with meningiomas is poorly understood. Moreover, any relationship between seizures and the underlying meningioma genomic subgroup has not been studied. Herein, the authors report on their experience with identifying clinical and genomic factors associated with preoperative and postoperative seizure presentation in meningioma patients. METHODS: Clinical and genomic sequencing data on 394 patients surgically treated for meningioma at Yale New Haven Hospital were reviewed. Correlations between clinical, histological, or genomic variables and the occurrence of preoperative and postoperative seizures were analyzed. Logistic regression models were developed for assessing multiple risk factors for pre- and postoperative seizures. Mediation analyses were also conducted to investigate the causal pathways between genomic subgroups and seizures. RESULTS: Seventeen percent of the cohort had presented with preoperative seizures. In a univariate analysis, patients with preoperative seizures were more likely to have tumors with a somatic NF2 mutation (p = 0.020), WHO grade II or III tumor (p = 0.029), atypical histology (p = 0.004), edema (p < 0.001), brain invasion (p = 0.009), and worse progression-free survival (HR 2.68, 95% CI 1.30-5.50). In a multivariate analysis, edema (OR 3.11, 95% CI 1.46-6.65, p = 0.003) and atypical histology (OR 2.00, 95% CI 1.03-3.90, p = 0.041) were positive predictors of preoperative seizures, while genomic subgroup was not, such that the effect of an NF2 mutation was indirectly mediated through atypical histology and edema (p = 0.012). Seizure freedom was achieved in 83.3% of the cohort, and only 20.8% of the seizure-free patients, who were more likely to have undergone gross-total resection (p = 0.031), were able to discontinue antiepileptic drug use postoperatively. Preoperative seizures (OR 3.54, 95% CI 1.37-9.12, p = 0.009), recurrent tumors (OR 2.89, 95% CI 1.08-7.74, p = 0.035), and tumors requiring postoperative radiation (OR 2.82, 95% CI 1.09-7.33, p = 0.033) were significant predictors of postoperative seizures in a multivariate analysis. CONCLUSIONS: Seizures are relatively common at meningioma presentation. While NF2-mutated tumors are significantly associated with preoperative seizures, the association appears to be mediated through edema and atypical histology. Patients who undergo radiation and/or have a recurrence are at risk for postoperative seizures, regardless of the extent of resection. Preoperative seizures may indeed portend a more potentially aggressive molecular entity and challenging clinical course with a higher risk of recurrence.