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We have previously developed a bispectral electroencephalography (BSEEG) device, which was shown to be effective in detecting delirium and predicting patient outcomes. In this study we aimed to apply the BSEEG approach for a sepsis. This was a retrospective cohort study conducted at a single center. Sepsis-positive cases were identified based on retrospective chart review. EEG raw data and calculated BSEEG scores were obtained in the previous studies. The relationship between BSEEG scores and sepsis was analyzed, as well as the relationship among sepsis, BSEEG score, and mortality. Data were analyzed from 628 patients. The BSEEG score from the first encounter (1st BSEEG) showed a significant difference between patients with and without sepsis ( p  = 0.0062), although AUC was very small indicating that it is not suitable for detection purpose. Sepsis patients with high BSEEG scores showed the highest mortality, and non-sepsis patients with low BSEEG scores showed the lowest mortality. Mortality of non-sepsis patients with high BSEEG scores was as bad as that of sepsis patients with low BSEEG scores. Even adjusting for age, gender, comorbidity, and sepsis status, BSEEG remained a significant predictor of mortality ( p  = 0.008). These data are demonstrating its usefulness as a potential tool for identification of patients at high risk and management of sepsis.

This article describes a polynomial growth curve quantile regression model that provides a comprehensive assessment about the treatment effects on the changes of the distribution of outcomes over time. The proposed model has the flexibility, as it allows the degree of a polynomial to vary across quantiles. A high degree polynomial model fits the data adequately, yet it is not desirable due to the complexity of the model. We propose the model selection criterion based on an empirical loglikelihood that consistently identifies the optimal degree of a polynomial at each quantile. After the parsimonious model is fitted to the data, the hypothesis test is further developed to evaluate the treatment effects by comparing the growth curves. It is shown that the proposed empirical loglikelihood ratio test statistic follows a chi-square distribution asymptotically under the null hypothesis. Various simulation studies confirm that the proposed test successfully detects the difference between the curves across quantiles. When the empirical loglikelihood is employed, we incorporate the within-subject correlation commonly existing in longitudinal data and gain estimation efficiency of the quantile regression parameters in the growth curve model. The proposed process is illustrated through the analysis of randomized controlled longitudinal depression data.

A repeatedly measured outcome in longitudinal studies allows researchers to monitor how the outcome changes over time. When an intervention affects the outcome and subjects initiate the intervention at different times during the course of a study, it is essential to account for the varying time to intervention (TTI) in models of such changes. In this study, we develop a piecewise polynomial regression model with TTI-varying coefficients that describes the population mean outcome over time. The TTI-varying coefficients in the model enable us to capture the population mean outcome trajectory, affected by both the intervention and the varying TTI. In observational studies, other covariates can confound these effects, leading to estimation bias if not properly accounted for. To mitigate this, we propose a double-weighted estimation procedure based on a kernel function and a generalized propensity score. The proposed estimation procedure effectively corrects the estimation bias of the TTI-varying coefficients and provides valid statistical inferences about the coefficients. We apply our approach to assess changes in the population mean of an inflammation biomarker for HIV-infected adults in Haiti who initiate antiretroviral therapy following the World Health Organization guideline.

Tracking subjects with disease risks at multiple time points is an important objective for disease prevention and preventive medicine. Appropriate statistical tracking models are essential for identifying risk factors that remain persistent over time and the early detection of subjects with high disease risks. Because disease risks are often defined by multivariate response variables, we propose a class of bivariate risk-predictive probability models that quantify the likelihood of an individual’s future disease risk. These models describe the relationships between bivariate risk outcomes at a later time point and covariates at an early time point using a class of conditional quantile-based joint distribution functions. We develop a simulation-based procedure under the stratified bivariate time-varying quantile regression framework to estimate the conditional joint distributions and risk-predictive probabilities. In addition, we use theoretical and simulation studies to show that the estimation procedure yields consistent estimates, and propose a statistical quantity that measures the relative risk to identify high-risk individuals. Finally, we apply the proposed models and procedures to data from the National Growth and Health Study to identify early adolescent girls who are more likely to be diagnosed with hypertension at late adolescence.

The Parkinson's Progression Markers Initiative (PPMI) is an ongoing observational, longitudinal cohort study of participants with Parkinson's disease, healthy controls, and carriers of the most common Parkinson's disease-related genetic mutations, which aims to define biomarkers of Parkinson's disease diagnosis and progression. All participants are assessed annually with a battery of motor and non-motor scales, 123-I Ioflupane dopamine transporter (DAT) imaging, and biological variables. We aimed to examine whether non-manifesting carriers of LRRK2 and GBA mutations have prodromal features of Parkinson's disease that correlate with reduced DAT binding. This cross-sectional analysis is based on assessments done at enrolment in the subset of non-manifesting carriers of LRRK2 and GBA mutations enrolled into the PPMI study from 33 participating sites worldwide. The primary objective was to examine baseline clinical and DAT imaging characteristics in non-manifesting carriers with GBA and LRRK2 mutations compared with healthy controls. DAT deficit was defined as less than 65% of putamen striatal binding ratio expected for the individual's age. We used t tests, χ2 tests, and Fisher's exact tests to compare baseline demographics across groups. An inverse probability weighting method was applied to control for potential confounders such as age and sex. To account for multiple comparisons, we applied a family-wise error rate to each set of analyses. This study is registered with ClinicalTrials.gov, number NCT01141023. Between Jan 1, 2014, and Jan 1, 2019, the study enrolled 208 LRRK2 (93% G2019S) and 184 GBA (96% N370S) non-manifesting carriers. Both groups were similar with respect to mean age, and about 60% were female. Of the 286 (73%) non-manifesting carriers that had DAT imaging results, 18 (11%) LRRK2 and four (3%) GBA non-manifesting carriers had a DAT deficit. Compared with healthy controls, both LRRK2 and GBA non-manifesting carriers had significantly increased mean scores on the Movement Disorders Society Unified Parkinson's Disease Rating Scale (total score 4·6 [SD 4·4] healthy controls vs 8·4 [7·3] LRRK2 vs 9·5 [9·2] GBA, p<0·0001 for both comparisons) and the Scale for Outcomes for PD – autonomic function (5·8 [3·7] vs 8·1 [5·9] and 8·4 [6·0], p<0·0001 for both comparisons). There was no difference in daytime sleepiness, anxiety, depression, impulsive–compulsive disorders, blood pressure, urate, and rapid eye movement (REM) behaviour disorder scores. Hyposmia was significantly more common only in LRRK2 non-manifesting carriers (69 [36%] of 194 healthy controls vs 114 [55%] of 208 LRRK2 non-manifesting carriers; p=0·0003). Finally, GBA but not LRRK2 non-manifesting carriers showed increased DAT striatal binding ratios compared with healthy controls in the caudate (healthy controls 2·98 [SD 0·63] vs GBA 3·26 [0·63]; p<0·0001), putamen (2·15 [0·56] vs 2·48 [0·52]; p<0·0001), and striatum (2·56 [0·57] vs 2·87 [0·55]; p<0·0001). Our data show evidence of subtle motor and non-motor signs of Parkinson's disease in non-manifesting carriers compared with healthy controls that can precede DAT deficit. Longitudinal data will be essential to confirm these findings and define the trajectory and predictors for development of Parkinson's disease. Michael J Fox Foundation for Parkinson's Research.

Background: While there are medications that treat and manage age-related diseases, a compound that prolongs lifespan is yet to be discovered. Nonetheless, metformin, a commonly prescribed anti-diabetic medication, has repeatedly been shown to hinder aging in pre-clinical models and to be associated with lower mortality for humans, even among cancer patients. It is, however, not well understood how metformin can potentially prolong lifespan from a biological standpoint. We hypothesized that metformin's potential mechanism of action for longevity is through its epigenetic modifications. Methods: To test our hypothesis, we conducted a post-hoc analysis of available genome-wide DNA methylation (DNAm) data obtained from whole blood collected from inpatients with and without a history of metformin use. We assessed the methylation profile of 171 patients (first run) and only among 63 diabetic patients (second run) and compared the DNAm rates between metformin users and nonusers. Results: Enrichment analysis from the Kyoto Encyclopedia of Genes and Genome (KEGG) showed pathways relevant to metformin's mechanism of action, such as longevity, AMPK, and inflammatory pathways. We also identified several pathways related to delirium whose risk factor is aging. Moreover, top hits from the Gene Ontology (GO) included HIF-1a pathways. However, no individual CpG site showed genome-wide statistical significance (p<5E-08). Conclusion: This study may elucidate metformin's potential role in longevity through epigenetic modifications and other possible mechanisms of action. Competing Interest Statement Gen Shinozaki is co-founder of Predelix Medical LLC and has pending patents as follows: \"Non-invasive device for predicting and screening delirium\", PCT application no. PCT/US2016/064937 and US provisional patent no. 62/263,325; \"Prediction of patient outcomes with a novel electroencephalography device\", US provisional patent no. 62/829,411; \"Epigenetic Biomarker of Delirium Risk\" in the PCT Application No. PCT/US19/51276, and in U.S. Provisional Patent No. 62/731,599. Pedro S. Marra, Takehiko Yamanashi, Kaitlyn J. Crutchley, Nadia E. Wahba, Zoe-Ella M. Anderson, Manisha Modukuri, Gloria Chang, Tammy Tran, Masaaki Iwata, and Hyunkeun Ryan Cho have declared that no conflict of interest exists.

Background: The authors previously hypothesized the role of epigenetics in pathophysiology of delirium, and tested DNA methylation (DNAm) change among pro-inflammatory cytokines along with aging in blood, glia and neuron. The authors reported that DNAm level of the TNF-alpha decreases along with aging in blood and glia, but not in neuron; however, DNAm differences between delirium cases and non-delirium controls have not been investigated directly. Therefore, in the present study, DNAm differences in blood between delirium patients and controls without delirium were examined. Methods: A case-control study with 92 subjects was conducted. Whole blood samples were collected and genome-wide DNAm was measured by the Infinium HumanMethylationEPIC BeadChip arrays. The correlation between DNAm levels in the TNF-alpha and age, network analysis, and the correlation between age and DNAm age were tested. Results: Only delirium cases showed 3 CpGs sites in the TNF-alpha significantly correlated to age after multiple corrections. A genome-wide significant CpG site near the gene of LDLRAD4 was identified. In addition, network analysis showed several significant pathways with false discovery rate adjusted p-value < 0.05. The top pathway with GO was immune response, and the second top pathway with KEGG was cholinergic synapse. Although there was no statistically significant difference, DNAm age among non-delirium controls showed \"slower aging\" compared to delirium cases. Conclusions: DNAm differences were shown both at gene and network levels between delirium cases and non-delirium controls. This finding indicates that DNAm status in blood has a potential to be used as epigenetic biomarkers for delirium.