intCC: An efficient weighted integrative consensus clustering of multimodal data.

High throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case study on the TCGA pan cancer datasets. An R package intCC implementing our proposed method is available at https://github.com/candsj/intCC.

Glial suppression and post-traumatic stress disorder: A cross-sectional study of 1,520 world trade center responders.

Background: Chronically re-experiencing the memory of a traumatic event might cause a glial response. This study examined whether glial activation would be associated with PTSD in a study of responders present after the 9/11 World Trade Center attacks without comorbid cerebrovascular disease. Methods: Plasma was retrieved from 1,520 WTC responders and stored for a cross-sectional sample of responders of varying levels of exposure and PTSD. Plasma levels (pg/ml) of glial fibrillary acidic protein (GFAP) were assayed. Because stroke and other cerebrovascular diseases cause distributional shifts in GFAP levels, multivariable-adjusted finite mixture models analyzed GFAP distributions in responders with and without possible cerebrovascular disease. Results: Responders were aged 56.3 years and primarily male; 11.07% (n = 154) had chronic PTSD. Older age was associated with increased GFAP, whereas higher body mass was associated with decreased GFAP. Multivariable-adjusted finite mixture models revealed that severe re-experiencing trauma from 9/11 was associated with lower GFAP (B = -0.558, p = 0.003). Conclusion: This study presents evidence of reduced plasma GFAP levels among WTC responders with PTSD. Results suggest re-experiencing traumatic events might cause glial suppression.

InterMEL: An international biorepository and clinical database to uncover predictors of survival in early-stage melanoma.

INTRODUCTION: We are conducting a multicenter study to identify classifiers predictive of disease-specific survival in patients with primary melanomas. Here we delineate the unique aspects, challenges, and best practices for optimizing a study of generally small-sized pigmented tumor samples including primary melanomas of at least 1.05mm from AJTCC TNM stage IIA-IIID patients. We also evaluated tissue-derived predictors of extracted nucleic acids' quality and success in downstream testing. This ongoing study will target 1,000 melanomas within the international InterMEL consortium. METHODS: Following a pre-established protocol, participating centers ship formalin-fixed paraffin embedded (FFPE) tissue sections to Memorial Sloan Kettering Cancer Center for the centralized handling, dermatopathology review and histology-guided coextraction of RNA and DNA. Samples are distributed for evaluation of somatic mutations using next gen sequencing (NGS) with the MSK-IMPACTTM assay, methylation-profiling (Infinium MethylationEPIC arrays), and miRNA expression (Nanostring nCounter Human v3 miRNA Expression Assay). RESULTS: Sufficient material was obtained for screening of miRNA expression in 683/685 (99%) eligible melanomas, methylation in 467 (68%), and somatic mutations in 560 (82%). In 446/685 (65%) cases, aliquots of RNA/DNA were sufficient for testing with all three platforms. Among samples evaluated by the time of this analysis, the mean NGS coverage was 249x, 59 (18.6%) samples had coverage below 100x, and 41/414 (10%) failed methylation QC due to low intensity probes or insufficient Meta-Mixed Interquartile (BMIQ)- and single sample (ss)- Noob normalizations. Six of 683 RNAs (1%) failed Nanostring QC due to the low proportion of probes above the minimum threshold. Age of the FFPE tissue blocks (p<0.001) and time elapsed from sectioning to co-extraction (p = 0.002) were associated with methylation screening failures. Melanin reduced the ability to amplify fragments of 200bp or greater (absent/lightly pigmented vs heavily pigmented, p<0.003). Conversely, heavily pigmented tumors rendered greater amounts of RNA (p<0.001), and of RNA above 200 nucleotides (p<0.001). CONCLUSION: Our experience with many archival tissues demonstrates that with careful management of tissue processing and quality control it is possible to conduct multi-omic studies in a complex multi-institutional setting for investigations involving minute quantities of FFPE tumors, as in studies of early-stage melanoma. The study describes, for the first time, the optimal strategy for obtaining archival and limited tumor tissue, the characteristics of the nucleic acids co-extracted from a unique cell lysate, and success rate in downstream applications. In addition, our findings provide an estimate of the anticipated attrition that will guide other large multicenter research and consortia.

Plasma amyloid beta 40/42, phosphorylated tau 181, and neurofilament light are associated with cognitive impairment and neuropathological changes among World Trade Center responders: A prospective cohort study of exposures and cognitive aging at midlife.

Introduction: World Trade Center (WTC) responders are experiencing a high risk of mild cognitive impairment (MCI) and dementia, though the etiology remains inadequately characterized. This study investigated whether WTC exposures and chronic post-traumatic stress disorder (PTSD) were correlated with plasma biomarkers characteristic of Alzheimer's disease (AD) neuropathology. Methods: Eligible participants included WTC-exposed individuals with a baseline cognitive assessment and available plasma sample. We examined levels of the amyloid beta (Aß)40/42 ratio, phosphorylated tau 181 (p-tau181), and neurofilament light chain (NfL) and associations with a WTC exposures (duration on site ≥15 weeks, dust cloud), the PTSD Symptom Checklist for Diagnostic and Statistical Manual of Mental Disorders, 4th edition PTSD, and classification of amyloid/tau/neurodegeneration (AT[N]) profiles. Multinomial logistic regressions assessed whether biomarkers predicted increased risk of MCI or dementia. Results: Of 1179 eligible responders, 93.0% were male, mean (standard deviation) age 56.6 years (7.8). Aß40/42, p-tau181, and NfL intercorrelated and increased with age. In subgroup analyses of responders with available neuroimaging data (n = 75), Aß40/42 and p-tau181 were further associated with decreased hippocampal volume (Spearman's ρ = -0.3). Overall, 58.08% of responders with dementia had ≥1 elevated biomarker, and 3.45% had elevations across all biomarkers. In total, 248 (21.05%) had MCI and 70 (5.94%) had dementia. Increased risk of dementia was associated with plasma AT(N) profile T+ or A+N+. Exposure on site ≥15 weeks was independently associated with T+ (adjusted risk ratio [aRR] = 1.03 [1.01-1.05], P = 0.009), and T+N+ profile (aRR = 2.34 [1.12-4.87]). The presence of PTSD was independently associated with risk of A+ (aRR = 1.77 [1.11-2.82]). Discussion: WTC exposures and chronic PTSD are associated with plasma biomarkers consistent with neurodegenerative disease.

Genetic Liability, Exposure Severity, and Post-Traumatic Stress Disorder Predict Cognitive Impairment in World Trade Center Responders.

BACKGROUND: There is a high incidence of cognitive impairment among World Trade Center (WTC) responders, comorbid with post-traumatic stress disorder (PTSD). Yet, it remains unknown whether genetic liability for Alzheimer's disease, PTSD, educational attainment, or for a combination of these phenotypes, is associated with cognitive impairment in this high-risk population. Similarly, whether the effects of genetic liability are comparable to PTSD and indicators of exposure severity remains unknown. OBJECTIVE: In a study of 3,997 WTC responders, polygenic scores for Alzheimer's disease, PTSD, and educational attainment were used to test whether genome-wide risk for one or more of these phenotypes is associated with cognitive impairment, controlling for population stratification, while simultaneously estimating the effects of demographic factors and indicators of 9/11 exposure severity, including symptoms of PTSD. RESULTS: Polygenic scores for Alzheimer's disease and educational attainment were significantly associated with an increase and decrease, respectively, in the hazard rate of mild cognitive impairment. The polygenic score for Alzheimer's disease was marginally associated with an increase in the hazard rate of severe cognitive impairment, but only age, exposure severity, and symptoms of PTSD were statistically significant predictors. CONCLUSION: These results add to the emerging evidence that many WTC responders are suffering from mild cognitive impairments that resemble symptoms of Alzheimer's disease, as genetic liability for Alzheimer's disease predicted incidence of mild cognitive impairment. However, compared to polygenic scores, effect sizes were larger for PTSD and the type of work that responders completed during rescue and recovery efforts.

Methylation of nonessential genes in cutaneous melanoma - Rule Out hypothesis.

Differential methylation plays an important role in melanoma development and is associated with survival, progression and response to treatment. However, the mechanisms by which methylation promotes melanoma development are poorly understood. The traditional explanation of selective advantage provided by differential methylation postulates that hypermethylation of regulatory 5'-cytosine-phosphate-guanine-3' dinucleotides (CpGs) downregulates the expression of tumor suppressor genes and therefore promotes tumorigenesis. We believe that other (not necessarily alternative) explanations of the selective advantages of methylation are also possible. Here, we hypothesize that melanoma cells use methylation to shut down transcription of nonessential genes - those not required for cell survival and proliferation. Suppression of nonessential genes allows tumor cells to be more efficient in terms of energy and resource usage, providing them with a selective advantage over the tumor cells that transcribe and subsequently translate genes they do not need. We named the hypothesis the Rule Out (RO) hypothesis. The RO hypothesis predicts higher methylation of CpGs located in regulatory regions (CpG islands) of nonessential genes. It also predicts the higher methylation of regulatory CpGs linked to nonessential genes in melanomas compared to nevi and lower expression of nonessential genes in malignant (derived from melanoma) versus normal (derived from nonaffected skin) melanocytes. The analyses conducted using in-house and publicly available data found that all predictions derived from the RO hypothesis hold, providing observational support for the hypothesis.

A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers.

Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However the original definition of HUM involves multiple integration and thus a medical investigation for multi-class receiver operating characteristic (ROC) analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this article. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

Discovery and replication of blood-based proteomic signature of PTSD in 9/11 responders.

Proteomics provides an opportunity to develop biomarkers for the early detection and monitoring of post-traumatic stress disorder (PTSD). However, research to date has been limited by small sample sizes and a lack of replication. This study performed Olink Proseek Multiplex Platform profiling of 81 proteins involved in neurological processes in 936 responders to the 9/11 disaster (mean age at blood draw = 55.41 years (SD = 7.93), 94.1% white, all men). Bivariate correlations and elastic net regressions were used in a discovery subsample to identify concurrent associations between PTSD symptom severity and the profiled proteins, and to create a multiprotein composite score. In hold-out subsamples, nine bivariate associations between PTSD symptoms and differentially expressed proteins were replicated: SKR3, NCAN, BCAN, MSR1, PVR, TNFRSF21, DRAXIN, CLM6, and SCARB2 (|r| = 0.08-0.17, p < 0.05). There were three replicated bivariate associations between lifetime PTSD diagnosis and differentially expressed proteins: SKR3, SIGLEC, and CPM (OR = 1.38-1.50, p < 0.05). The multiprotein composite score retained 38 proteins, including 10/11 proteins that replicated in bivariate tests. The composite score was significantly associated with PTSD symptom severity (ß = 0.27, p < 0.001) and PTSD diagnosis (OR = 1.60, 95% CI: 1.17-2.19, p = 0.003) in the hold-out subsample. Overall, these findings suggest that PTSD is characterized by altered expression of several proteins implicated in neurological processes. Replicated associations with TNFRSF21, CLM6, and PVR support the neuroinflammatory signature of PTSD. The multiprotein composite score substantially increased associations with PTSD symptom severity over individual proteins. If generalizable to other populations, the current findings may inform the development of PTSD biomarkers.

Evidence synthesis analysis with prioritized benefit outcomes in oncology clinical trials.

Overall survival, progression-free survival, objective response/complete response, and duration of (complete) response are frequently used as the primary and secondary efficacy endpoints for designs and analyses of oncology clinical trials. However, these endpoints are typically analyzed separately. In this article, we introduce an evidence synthesis approach to prioritize the benefit outcomes by applying the generalized pairwise comparisons (GPC) method, and use win statistics (win ratio, win odds and net benefit) to quantify treatment benefit. Under the framework of GPC, the main advantage of this evidence synthesis approach is the ability to combine relevant outcomes of various types into a single summary statistic without relying on any parametric assumptions. It is particularly relevant since health authorities and the pharmaceutical industry are increasingly incorporating structured quantitative methodologies in their benefit-risk assessment. We apply this evidence synthesis approach to an oncology phase 3 study in first-line renal cell carcinoma to assess the overall effect of an investigational treatment by ranking the most clinically relevant endpoints in cancer drug development. This application and a simulation study demonstrate that the proposed approach can synthesize the evidence of treatment effect from multiple prioritized benefit outcomes, and has substantial advantage over conventional methods that analyze each individual endpoint separately. We also introduce a newly developed R package WINS for statistical inference based on win statistics.

A systematic assessment of cell type deconvolution algorithms for DNA methylation data.

We performed systematic assessment of computational deconvolution methods that play an important role in the estimation of cell type proportions from bulk methylation data. The proposed framework methylDeConv (available as an R package) integrates several deconvolution methods for methylation profiles (Illumina HumanMethylation450 and MethylationEPIC arrays) and offers different cell-type-specific CpG selection to construct the extended reference library which incorporates the main immune cell subsets, epithelial cells and cell-free DNAs. We compared the performance of different deconvolution algorithms via simulations and benchmark datasets and further investigated the associations of the estimated cell type proportions to cancer therapy in breast cancer and subtypes in melanoma methylation case studies. Our results indicated that the deconvolution based on the extended reference library is critical to obtain accurate estimates of cell proportions in non-blood tissues.

Polygenic association of glomerular filtration rate decline in world trade center responders.

BACKGROUND: The factors associated with estimated glomerular filtrate rate (eGFR) decline in low risk adults remain relatively unknown. We hypothesized that a polygenic risk score (PRS) will be associated with eGFR decline. METHODS: We analyzed genetic data from 1,601 adult participants with European ancestry in the World Trade Center Health Program (baseline age 49.68 ± 8.79 years, 93% male, 23% hypertensive, 7% diabetic and 1% with cardiovascular disease) with ≥ three serial measures of serum creatinine. PRSs were calculated from an aggregation of single nucleotide polymorphisms (SNPs) from a recent, large-scale genome-wide association study (GWAS) of rapid eGFR decline. Generalized linear models were used to evaluate the association of PRS with renal outcomes: baseline eGFR and CKD stage, rate of change in eGFR, stable versus declining eGFR over a 3-5-year observation period. eGFR decline was defined in separate analyses as "clinical" (> -1.0 ml/min/1.73 m2/year) or "empirical" (lower most quartile of eGFR slopes). RESULTS: The mean baseline eGFR was ~ 86 ml/min/1.73 m2. Subjects with decline in eGFR were more likely to be diabetic. PRS was significantly associated with lower baseline eGFR (B = -0.96, p = 0.002), higher CKD stage (OR = 1.17, p = 0.010), decline in eGFR (OR = 1.14, p = 0.036) relative to stable eGFR, and the lower quartile of eGFR slopes (OR = 1.21, p = 0.008), after adjusting for established risk factors for CKD. CONCLUSION: Common genetic variants are associated with eGFR decline in middle-aged adults with relatively low comorbidity burdens.

Landscape of mutations in early stage primary cutaneous melanoma: An InterMEL study.

It is unclear why some melanomas aggressively metastasize while others remain indolent. Available studies employing multi-omic profiling of melanomas are based on large primary or metastatic tumors. We examine the genomic landscape of early-stage melanomas diagnosed prior to the modern era of immunological treatments. Untreated cases with Stage II/III cutaneous melanoma were identified from institutions throughout the United States, Australia and Spain. FFPE tumor sections were profiled for mutation, methylation and microRNAs. Preliminary results from mutation profiling and clinical pathologic correlates show the distribution of four driver mutation sub-types: 31% BRAF; 18% NRAS; 21% NF1; 26% Triple Wild Type. BRAF mutant tumors had younger age at diagnosis, more associated nevi, more tumor infiltrating lymphocytes, and fewer thick tumors although at generally more advanced stage. NF1 mutant tumors were frequent on the head/neck in older patients with severe solar elastosis, thicker tumors but in earlier stages. Triple Wild Type tumors were predominantly male, frequently on the leg, with more perineural invasion. Mutations in TERT, TP53, CDKN2A and ARID2 were observed often, with TP53 mutations occurring particularly frequently in the NF1 sub-type. The InterMEL study will provide the most extensive multi-omic profiling of early-stage melanoma to date. Initial results demonstrate a nuanced understanding of the mutational and clinicopathological landscape of these early-stage tumors.

Metabolomics analysis of post-traumatic stress disorder symptoms in World Trade Center responders.

Metabolomics has yielded promising insights into the pathophysiology of post-traumatic stress disorder (PTSD). The current study expands understanding of the systems-level effects of metabolites by using global metabolomics and complex lipid profiling in plasma samples from 124 World Trade Center responders (56 PTSD, 68 control) on 1628 metabolites. Differential metabolomics analysis identified hexosylceramide HCER(26:1) associated with PTSD at FDR < 0.1. The multi-metabolite composite score achieved an AUC of 0.839 for PTSD versus unaffected control classification. Independent component analysis identified three metabolomic modules significantly associated with PTSD. These modules were significantly enriched in bile acid metabolism, fatty acid metabolism and pregnenolone steroids, which are involved in innate immunity, inflammatory process and neuronal excitability, respectively. Integrative analysis of metabolomics and our prior proteomics datasets on subsample of 96 responders identified seven proteomic modules significantly correlated with metabolic modules. Overall, our findings shed light on the molecular alterations and identify metabolomic-proteomic signatures associated with PTSD by using machine learning and network approaches to enhance understanding of the pathways implicated in PTSD. If present results are confirmed in follow-up studies, they may inform development of novel treatments.

Reduced cerebellar cortical thickness in World Trade Center responders with cognitive impairment.

Prior research has demonstrated high levels of cognitive and physical functional impairments in World Trade Center (WTC) responders. A follow-up neuroimaging study identified changes to white matter connectivity within the cerebellum in responders with cognitive impairment (CI). In the first study to examine cerebellar cortical thickness in WTC responders with CI, we fielded a structural magnetic resonance imaging protocol. WTC responders (N = 99) participated in a structural magnetic resonance imaging (MRI) study, of whom 48 had CI. Participants with CI did not differ demographically or by intracranial volume when compared to cognitively unimpaired participants. MRIs were processed using the CERES imaging pipeline; bilateral cortical thickness in 12 cerebellar lobules was reported. Analyses were completed comparing mean cerebellar cortical thickness across groups. Lobules were examined to determine the location and functional correlates of reduced cerebellar cortical thickness. Multivariable-adjusted analyses accounted for the false discovery rate. Mean cerebellar cortical thickness was reduced by 0.17 mm in responders with CI. Decrements in cerebellar cortical thickness were symmetric and located in the Cerebellar Crus (I and II), and in Lobules IV, VI, VIIb, VIIIa, VIIIb, and IX. Cerebellar cortical thickness was associated with episodic memory, response speed, and tandem balance. WTC responders with CI had evidence of reduced cerebellar cortical thickness that was present across lobules in a pattern unique to this cohort.

Efficient gradient boosting for prognostic biomarker discovery.

MOTIVATION: A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. RESULTS: In this paper, we present a new R package 'Xsurv' as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package 'gbm'. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research. AVAILABILITY AND IMPLEMENTATION: 'Xsurv' is freely available as an R package at: https://github.com/topycyao/Xsurv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Early fish domestication affects methylation of key genes involved in the rapid onset of the farmed phenotype.

Animal domestication is a process of environmental modulation and artificial selection leading to permanent phenotypic modifications. Recent studies showed that phenotypic changes occur very early in domestication, i.e., within the first generation in captivity, which raises the hypothesis that epigenetic mechanisms may play a critical role on the early onset of the domestic phenotype. In this context, we applied reduced representation bisulphite sequencing to compare methylation profiles between wild Nile tilapia females and their offspring reared under farmed conditions. Approximately 700 differentially methylated CpG sites were found, many of them associated not only with genes involved in muscle growth, immunity, autophagy and diet response but also related to epigenetic mechanisms, such as RNA methylation and histone modifications. This bottom-up approach showed that the phenotypic traits often related to domestic animals (e.g., higher growth rate and different immune status) may be regulated epigenetically and prior to artificial selection on gene sequences. Moreover, it revealed the importance of diet in this process, as reflected by differential methylation patterns in genes critical to fat metabolism. Finally, our study highlighted that the TGF-ß1 signalling pathway may regulate and be regulated by several differentially methylated CpG-associated genes. This could be an important and multifunctional component in promoting adaptation of fish to a domestic environment while modulating growth and immunity-related traits.

Characterization of the CpG Island Hypermethylated Phenotype Subclass in Primary Melanomas.

Cutaneous melanoma can be lethal even if detected at an early stage. Epigenetic profiling may facilitate the identification of aggressive primary melanomas with unfavorable outcomes. We performed clustering of whole-genome methylation data to identify subclasses that were then assessed for survival, clinical features, methylation patterns, and biological pathways. Among 89 cutaneous primary invasive melanomas, we identified three methylation subclasses exhibiting low methylation, intermediate methylation, or hypermethylation of CpG islands, known as the CpG island methylator phenotype (CIMP). CIMP melanomas occurred as early as tumor stage 1b and, compared with low-methylation melanomas, were associated with age at diagnosis ≥65 years, lentigo maligna melanoma histologic subtype, presence of ulceration, higher American Joint Committee on Cancer stage and tumor stage, and lower tumor-infiltrating lymphocyte grade (all P < 0.05). Patients with CIMP melanomas had worse melanoma-specific survival (hazard ratio = 11.84; confidence interval = 4.65‒30.20) than those with low-methylation melanomas, adjusted for age, sex, American Joint Committee on Cancer stage, and tumor-infiltrating lymphocyte grade. Genes hypermethylated in CIMP compared with those in low-methylation melanomas included PTEN, VDR, PD-L1, TET2, and gene sets related to development/differentiation, the extracellular matrix, and immunity. CIMP melanomas exhibited hypermethylation of genes important in melanoma progression and tumor immunity, and although present in some early melanomas, CIMP was associated with worse survival independent of known prognostic factors.

A deep learning approach for monitoring parietal-dominant Alzheimer's disease in World Trade Center responders at midlife.

Little is known about the characteristics and causes of early-onset cognitive impairment. Responders to the 2001 New York World Trade Center disaster represent an ageing population that was recently shown to have an excess prevalence of cognitive impairment. Neuroimaging and molecular data demonstrate that a subgroup of affected responders may have a unique form of parietal-dominant Alzheimer's Disease. Recent neuropsychological testing and artificial intelligence approaches have emerged as methods that can be used to identify and monitor subtypes of cognitive impairment. We utilized data from World Trade Center responders participating in a health monitoring program and applied a deep learning approach to evaluate neuropsychological and neuroimaging data to generate a cortical atrophy risk score. We examined risk factors associated with the prevalence and incidence of high risk for brain atrophy in responders who are now at midlife. Training was conducted in a randomly selected two-thirds sample (N = 99) enrolled using of the results of a structural neuroimaging study. Testing accuracy was estimated for each training cycle in the remaining third subsample. After training was completed, the scoring methodology that was generated was applied to longitudinal data from 1441 World Trade Center responders. The artificial neural network provided accurate classifications of these responders in both the testing (Area Under the Receiver Operating Curve, 0.91) and validation samples (Area Under the Receiver Operating Curve, 0.87). At baseline and follow-up, responders identified as having a high risk of atrophy (n = 378) showed poorer cognitive functioning, most notably in domains that included memory, throughput, and variability as compared to their counterparts at low risk for atrophy (n = 1063). Factors associated with atrophy risk included older age [adjusted hazard ratio, 1.045 (95% confidence interval = 1.027-1.065)], increased duration of exposure at the WTC site [adjusted hazard ratio, 2.815 (1.781-4.449)], and a higher prevalence of post-traumatic stress disorder [aHR, 2.072 (1.408-3.050)]. High atrophy risk was associated with an increased risk of all-cause mortality [adjusted risk ratio, 3.19 (1.13-9.00)]. In sum, the high atrophy risk group displayed higher levels of previously identified risk factors and characteristics of cognitive impairment, including advanced age, symptoms of post-traumatic stress disorder, and prolonged duration of exposure to particulate matter. Thus, this study suggests that a high risk of brain atrophy may be accurately monitored using cognitive data.

The Association of Posttraumatic Stress Disorder With Longitudinal Change in Glomerular Filtration Rate in World Trade Center Responders.

OBJECTIVE: High levels of psychological distress increase the risk of a wide range of medical diseases. In this study, we investigated the association between posttraumatic stress disorder (PTSD) and kidney disease. METHODS: World Trade Center (WTC) responders were included if they had two or more measures of estimated glomerular filtration rate (eGFR). The PTSD Checklist (PCL) was used to define no PTSD (PCL < 40), "mild" PTSD (40 ≤ PCL <50), and "severe" PTSD (PCL ≥50). Subtypes of PTSD by symptom clusters were analyzed. Multinomial logistic regression was used to estimate the association of PTSD with two GFR change outcomes (decline or increase) compared with the stable GFR outcome. RESULTS: In 2266 participants, the mean age was 53.1 years, 8.2% were female, and 89.1% were White. Individuals with PTSD (n = 373; 16.5%) did not differ in mean baseline GFR from individuals without PTSD (89.73 versus 90.56 mL min-1 1.73 m-2; p = .29). During a 2.01-year mean follow-up, a mean GFR decline of -1.51 mL min-1 1.73 m-2 per year was noted. In multivariable-adjusted models, PTSD was associated with GFR decline (adjusted relative risk [aRR] = 1.74 [1.32-2.30], p < .001) compared with stable GFR, with "hyperarousal" symptoms showing the strongest association (aRR =2.11 [1.40-3.19]; p < .001). Dose-response effects were evident when comparing mild with severe PTSD and comparing PTSD with versus without depression. PTSD was also associated with GFR rise (aRR = 1.47 [1.10-1.97], p < .009). The association between PTSD and GFR change was stronger in participants older than 50 years. CONCLUSIONS: PTSD may be a novel risk factor for exaggerated longitudinal GFR change in young, healthy adults. These findings need to be validated in other cohorts.

Fenchel duality of Cox partial likelihood with an application in survival kernel learning.

The Cox proportional hazard model is one of the most widely used methods in modeling time-to-event data in the health sciences. Due to the simplicity of the Cox partial likelihood function, many machine learning algorithms use it for survival data. However, due to the nature of censored data, the optimization problem becomes intractable when more complicated regularization is employed, which is necessary when dealing with high dimensional omic data. In this paper, we show that a convex conjugate function of the Cox loss function based on Fenchel duality exists, and provide an alternative framework to optimization based on the primal form. Furthermore, the dual form suggests an efficient algorithm for solving the kernel learning problem with censored survival outcomes. We illustrate performance and properties of the derived duality form of Cox partial likelihood loss in multiple kernel learning problems with simulated and the Skin Cutaneous Melanoma TCGA datasets.