Predicting Progression of Alzheimer’s Disease Using Ordinal Regression

We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression – the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = –0.64, ADNI and ρ = –0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12–24 months) and late converters (24–36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.     

Regression of diet-induced atherosclerosis in G?ttingen miniature swine

The regression of diet-induced atherosclerosis in G?ttingen Miniature Swine was investigated after a 6-month induction period. At 1 month after feeding a high-cholesterol and high-fat diet, levels of beta-lipoprotein, total cholesterol, free fatty acids and phospholipid had increased rapidly and the high levels were maintained throughout the 6 month induction period. Morphological features at 6 months showed fatty streaks in the thoracic aorta and fibrous plaques in the abdominal aorta. After return to the conventional diet at 6 months, serum lipids decreased rapidly and maintained the baseline level throughout the 9 month regression period. Histopathological findings showed the regression of fatty streaks but the fibrous plaques did not regress. The present study therefore confirms the regression of fatty streaks in the aorta of G?ttingen Miniature Swine by the administration of a cholesterol lowering diet.     

A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer''s disease (AD) measured by amyloid-β deposits. The proposed GRM regards ¹¹C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1-regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.     

Consistent Condom Use Increases the Regression Rate of Cervical Intraepithelial Neoplasia 2–3

Objective

Cervical intraepithelial neoplasia grades 2-3 (CIN2-3) are usually treated by cone excision, although only 30% progress to cancer and 6–50% regress spontaneously. The aim of this study was to examine the influence of clinical factors like smoking habits, number of lifetime sexual partners, age at first sexual intercourse, sexual activity span and hormonal versus non-hormonal contraception type on the regression rate of CIN2-3.

Methods

In this prospective population-based cohort study 170 women aged 25–40 with abnormal cytology and colposcopy-directed biopsies showing first time onset CIN2-3 were consecutively included. The interval between biopsy and cone excision was standardized to minimum 12 weeks. Regression was defined as ≤CIN1 in the cone biopsy.

Results

The regression rate was 22%. Consistent condom use, defined as those women whose partners used condoms for all instances of sexual intercourse, was infrequent (n = 20, 12%). In univariate analysis consistent condom use, hormonal contraception and age at first sexual intercourse significantly predicted regression. In a multivariate analysis only consistent condom use remained as an independent predictor of regression (regression rate 55%, p = 0.001, hazard ratio = 4.4).

Conclusion

Consistent condom use between punch biopsy and cone excision in first-time onset CIN2-3 patients significantly increases the regression rate.     

Malignant Lymphatic and Hematopoietic Neoplasms Mortality in Serbia, 1991–2010: A Joinpoint Regression Analysis

Background

Limited data on mortality from malignant lymphatic and hematopoietic neoplasms have been published for Serbia.

Methods

The study covered population of Serbia during the 1991–2010 period. Mortality trends were assessed using the joinpoint regression analysis.

Results

Trend for overall death rates from malignant lymphoid and haematopoietic neoplasms significantly decreased: by −2.16% per year from 1991 through 1998, and then significantly increased by +2.20% per year for the 1998–2010 period. The growth during the entire period was on average +0.8% per year (95% CI 0.3 to 1.3). Mortality was higher among males than among females in all age groups. According to the comparability test, mortality trends from malignant lymphoid and haematopoietic neoplasms in men and women were parallel (final selected model failed to reject parallelism, P = 0.232). Among younger Serbian population (0–44 years old) in both sexes: trends significantly declined in males for the entire period, while in females 15–44 years of age mortality rates significantly declined only from 2003 onwards. Mortality trend significantly increased in elderly in both genders (by +1.7% in males and +1.5% in females in the 60–69 age group, and +3.8% in males and +3.6% in females in the 70+ age group). According to the comparability test, mortality trend for Hodgkin''s lymphoma differed significantly from mortality trends for all other types of malignant lymphoid and haematopoietic neoplasms (P<0.05).

Conclusion

Unfavourable mortality trend in Serbia requires targeted intervention for risk factors control, early diagnosis and modern therapy.     

Does Raising Type 1 Error Rate Improve Power to Detect Interactions in Linear Regression Models? A Simulation Study

Introduction

Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model.

Methods

A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations.

Results

In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified.

Conclusions

Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.     

Novel Synthetic Monoketone Transmute Radiation-Triggered NFκB-Dependent TNFα Cross-Signaling Feedback Maintained NFκB and Favors Neuroblastoma Regression

Recently, we demonstrated that radiation (IR) instigates the occurrence of a NFκB-TNFα feedback cycle which sustains persistent NFκB activation in neuroblastoma (NB) cells and favors survival advantage and clonal expansion. Further, we reported that curcumin targets IR-induced survival signaling and NFκB dependent hTERT mediated clonal expansion in human NB cells. Herein, we investigated the efficacy of a novel synthetic monoketone, EF24, a curcumin analog in inhibiting persistent NFκB activation by disrupting the IR-induced NFκB-TNFα-NFκB feedback signaling in NB and subsequent mitigation of survival advantage and clonal expansion. EF24 profoundly suppressed the IR-induced NFκB-DNA binding activity/promoter activation and, maintained the NFκB repression by deterring NFκB-dependent TNFα transactivation/intercellular secretion in genetically varied human NB (SH-SY5Y, IMR-32, SK–PN–DW, MC-IXC and SK–N-MC) cell types. Further, EF24 completely suppressed IR-induced NFκB-TNFα cross-signaling dependent transactivation/translation of pro-survival IAP1, IAP2 and Survivin and subsequent cell survival. In corroboration, EF24 treatment maximally blocked IR-induced NFκB dependent hTERT transactivation/promoter activation, telomerase activation and consequent clonal expansion. EF24 displayed significant regulation of IR-induced feedback dependent NFκB and NFκB mediated survival signaling and complete regression of NB xenograft. Together, the results demonstrate for the first time that, novel synthetic monoketone EF24 potentiates radiotherapy and mitigates NB progression by selectively targeting IR-triggered NFκB-dependent TNFα-NFκB cross-signaling maintained NFκB mediated survival advantage and clonal expansion.     

Can Linear Regression Modeling Help Clinicians in the Interpretation of Genotypic Resistance Data? An Application to Derive a Lopinavir-Score

Background

The question of whether a score for a specific antiretroviral (e.g. lopinavir/r in this analysis) that improves prediction of viral load response given by existing expert-based interpretation systems (IS) could be derived from analyzing the correlation between genotypic data and virological response using statistical methods remains largely unanswered.

Methods and Findings

We used the data of the patients from the UK Collaborative HIV Cohort (UK CHIC) Study for whom genotypic data were stored in the UK HIV Drug Resistance Database (UK HDRD) to construct a training/validation dataset of treatment change episodes (TCE). We used the average square error (ASE) on a 10-fold cross-validation and on a test dataset (the EuroSIDA TCE database) to compare the performance of a newly derived lopinavir/r score with that of the 3 most widely used expert-based interpretation rules (ANRS, HIVDB and Rega). Our analysis identified mutations V82A, I54V, K20I and I62V, which were associated with reduced viral response and mutations I15V and V91S which determined lopinavir/r hypersensitivity. All models performed equally well (ASE on test ranging between 1.1 and 1.3, p = 0.34).

Conclusions

We fully explored the potential of linear regression to construct a simple predictive model for lopinavir/r-based TCE. Although, the performance of our proposed score was similar to that of already existing IS, previously unrecognized lopinavir/r-associated mutations were identified. The analysis illustrates an approach of validation of expert-based IS that could be used in the future for other antiretrovirals and in other settings outside HIV research.     

Simulation Studies as Designed Experiments: The Comparison of Penalized Regression Models in the “Large p,Small n” Setting

New algorithms are continuously proposed in computational biology. Performance evaluation of novel methods is important in practice. Nonetheless, the field experiences a lack of rigorous methodology aimed to systematically and objectively evaluate competing approaches. Simulation studies are frequently used to show that a particular method outperforms another. Often times, however, simulation studies are not well designed, and it is hard to characterize the particular conditions under which different methods perform better. In this paper we propose the adoption of well established techniques in the design of computer and physical experiments for developing effective simulation studies. By following best practices in planning of experiments we are better able to understand the strengths and weaknesses of competing algorithms leading to more informed decisions about which method to use for a particular task. We illustrate the application of our proposed simulation framework with a detailed comparison of the ridge-regression, lasso and elastic-net algorithms in a large scale study investigating the effects on predictive performance of sample size, number of features, true model sparsity, signal-to-noise ratio, and feature correlation, in situations where the number of covariates is usually much larger than sample size. Analysis of data sets containing tens of thousands of features but only a few hundred samples is nowadays routine in computational biology, where “omics” features such as gene expression, copy number variation and sequence data are frequently used in the predictive modeling of complex phenotypes such as anticancer drug response. The penalized regression approaches investigated in this study are popular choices in this setting and our simulations corroborate well established results concerning the conditions under which each one of these methods is expected to perform best while providing several novel insights.     

Measuring Visual Field Progression in the Central 10 Degrees Using Additional Information from Central 24 Degrees Visual Fields and ‘Lasso Regression’

Purpose

To measure progression of the visual field (VF) mean deviation (MD) index in longitudinal 10-2 VFs more accurately, by adding information from 24-2 VFs using Lasso regression.

Methods

A training dataset consisted of 138 eyes from 97 patients with glaucoma or ocular hypertension and a testing dataset consisted of 40 eyes from 34 patients with glaucoma or ocular hypertension. The Lasso method was used to predict total deviation (TD) values in training patients’ 10-2 VFs based on information from their 24-2 VFs (52 TD values, foveal sensitivity and mean deviation MD). Then, the MD of each patient’s 10-2 VF was estimated as the average of these Lasso-predicted TD values (10-2 VF ‘Lasso MD’; LMD). Finally, linear regression was applied to each testing patient’s series of longitudinal 10-2 VF MDs with and without additional Lasso-derived LMDs in order to predict future MDs not included in the regression analysis. Absolute prediction errors were compared when only actual 10-2 MDs were regressed against when a combination of actual 10-2 MDs and LMDs were regressed.

Results

The average absolute prediction error was significantly smaller for the novel method incorporating LMDs (range: 1.6 to 1.8 dB) compared with the standard approach (range: 1.7 to 3.4 dB) (p<0.05, ANOVA test).

Conclusions

Deriving 10-2 VF MD values from 24-2 VFs improves the prediction accuracy of progression. This approach will help clinicians to predict patients’ visual function in the parafoveal area.     

Regression analysis of yield stability is strongly affected by companion test varieties and locations — examples from a study of Nordic barley lines

The suitability of regression analysis for studying the phenotypic stability of grain yield was investigated using a collection of 220 Nordic barley lines. Linear regression explained 26–52% of the genotype x environment (GE) interactions in different groupings of the material. The regression coefficient, b _i , measures the yield response of the i-th genotype to improved environmental conditions. Deviations from regression, S _di ² , have been used to estimate Tai's stability parameter, _i , which is a measure of the phenotypic yield stability in the agronomic sense. Repeatability of b _i , _i , and grain yield was studied by means of correlations between estimates obtained in each experimental year. Yield had the highest repeatability, with correlations between years ranging from 0.57 to 0.85. In this study, regression coefficients and _i -values were not repeatable, i.e. genotypes reacted differentially to the yearly climatic variations. Six-rowed (6r) barleys had higher responsiveness, but lower mean yields, than two-rowed (2r) barleys. This is partly due to the history of selection of 6r-barleys, which mainly originate from regions with low potential yield levels, i.e. Finland and Norway. In general, responsiveness and stability were not correlated with yield. The highest-yielding lines had b _i 1. The response pattern of the different types of barleys used in this study show that responsiveness can be changed by recombination.     

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble‐based methods?

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble‐based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30‐day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in‐sample and out‐of‐sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short‐term mortality in population‐based samples of subjects with cardiovascular disease.     

Differential Impact of Two Risk Communications on Antipsychotic Prescribing to People with Dementia in Scotland: Segmented Regression Time Series Analysis 2001–2011

Background

Regulatory risk communications are an important method for disseminating drug safety information, but their impact varies. Two significant UK risk communications about antipsychotic use in older people with dementia were issued in 2004 and 2009. These varied considerably in their content and dissemination, allowing examination of their differential impact.

Methods

Segmented regression time-series analysis 2001–2011 for people aged ≥65 years with dementia in 87 Scottish general practices, examining the impact of two pre-specified risk communications in 2004 and 2009 on antipsychotic and other psychotropic prescribing.

Results

The percentage of people with dementia prescribed an antipsychotic was 15.9% in quarter 1 2001 and was rising by an estimated 0.6%/quarter before the 2004 risk communication. The 2004 risk communication was sent directly to all prescribers, and specifically recommended review of all patients prescribed relevant drugs. It was associated with an immediate absolute reduction in antipsychotic prescribing of 5.9% (95% CI −6.6 to −5.2) and a change to a stable level of prescribing subsequently. The 2009 risk communication was disseminated in a limited circulation bulletin, and only specifically recommended avoiding initiation if possible. There was no immediate associated impact, but it was associated with a significant decline in prescribing subsequently which appeared driven by a decline in initiation, with the percentage prescribed an antipsychotic falling from 18.4% in Q1 2009 to 13.5% in Q1 2011. There was no widespread substitution of antipsychotics with other psychotropic drugs.

Conclusions

The two risk communications were associated with reductions in antipsychotic use, in ways which were compatible with marked differences in their content and dissemination. Further research is needed to ensure that the content and dissemination of regulatory risk communications is optimal, and to track their impact on intended and unintended outcomes. Although rates are falling, antipsychotic prescribing in dementia in Scotland remains unacceptably high.     

The Cytolytic Amphipathic β(2,2)-Amino Acid LTX-401 Induces DAMP Release in Melanoma Cells and Causes Complete Regression of B16 Melanoma

In the present study we examined the ability of the amino acid derivative LTX-401 to induce cell death in cancer cell lines, as well as the capacity to induce regression in a murine melanoma model. Mode of action studies in vitro revealed lytic cell death and release of danger-associated molecular pattern molecules, preceded by massive cytoplasmic vacuolization and compromised lysosomes in treated cells. The use of a murine melanoma model demonstrated that the majority of animals treated with intratumoural injections of LTX-401 showed complete and long-lasting remission. Taken together, these results demonstrate the potential of LTX-401 as an immunotherapeutic agent for the treatment of solid tumors.     

Regression to the Mean and Predictors of MRI Disease Activity in RRMS Placebo Cohorts - Is There a Place for Baseline-to-Treatment Studies in MS?

BackgroundGadolinium-enhancing (GD+) lesions and T2 lesions are MRI outcomes for phase-2 treatment trials in relapsing-remitting Multiple Sclerosis (RRMS). Little is known about predictors of lesion development and regression-to-the-mean, which is an important aspect in early baseline-to-treatment trials.ObjectivesTo quantify regression-to-the-mean and identify predictors of MRI lesion development in placebo cohorts.Methods21 Phase-2 and Phase-3 trials were identified by a systematic literature research. Random-effects meta-analyses were performed to estimate development of T2 and GD+ after 6 months (phase-2) or 2 years (phase-3). Predictors of lesion development were evaluated with mixed-effect meta-regression.ResultsThe mean number of GD+-lesions per scan was similar after 6 months (1.19, 95%CI: 0.87-1.51) and 2 years (1.19, 95%CI: 1.00-1.39). 39% of the patients were without new T2-lesion after 6 month and 19% after 2 years (95%CI: 12-25%). Mean number of baseline GD+-lesions was the best predictor for new lesions after 6 months.ConclusionBaseline GD-enhancing lesions predict evolution of Gd- and T2 lesions after 6 months and might be used to control for regression to the mean effects. Overall, proof-of-concept studies with a baseline to treatment design have to face a regression to 1.2 GD+lesions per scan within 6 months.     

Novel Changes in NF-κB Activity during Progression and Regression Phases of Hyperplasia: ROLE OF MEK,ERK, AND p38*

Utilizing the Citrobacter rodentium-induced transmissible murine colonic hyperplasia (TMCH) model, we measured hyperplasia and NF-κB activation during progression (days 6 and 12 post-infection) and regression (days 20–34 post-infection) phases of TMCH. NF-κB activity increased at progression in conjunction with bacterial attachment and translocation to the colonic crypts and decreased 40% by day 20. NF-κB activity at days 27 and 34, however, remained 2–3-fold higher than uninfected control. Expression of the downstream target gene CXCL-1/KC in the crypts correlated with NF-κB activation kinetics. Phosphorylation of cellular IκBα kinase (IKK)α/β (Ser^176/180) was elevated during progression and regression of TMCH. Phosphorylation (Ser^32/36) and degradation of IκBα, however, contributed to NF-κB activation only from days 6 to 20 but not at later time points. Phosphorylation of MEK1/2 (Ser^217/221), ERK1/2 (Thr²⁰²/Tyr²⁰⁴), and p38 (Thr¹⁸⁰/Tyr¹⁸²) paralleled IKKα/β kinetics at days 6 and 12 without declining with regressing hyperplasia. siRNAs to MEK, ERK, and p38 significantly blocked NF-κB activity in vitro, whereas MEK1/2-inhibitor (PD98059) also blocked increases in MEK1/2, ERK1/2, and IKKα/β thereby inhibiting NF-κB activity in vivo. Cellular and nuclear levels of Ser⁵³⁶-phosphorylated (p65⁵³⁶) and Lys³¹⁰-acetylated p65 subunit accompanied functional NF-κB activation during TMCH. RSK-1 phosphorylation at Thr³⁵⁹/Ser³⁶³ in cellular/nuclear extracts and co-immunoprecipitation with cellular p65-NF-κB overlapped with p65⁵³⁶ kinetics. Dietary pectin (6%) blocked NF-κB activity by blocking increases in p65 abundance and nuclear translocation thereby down-regulating CXCL-1/KC expression in the crypts. Thus, NF-κB activation persisted despite the lack of bacterial attachment to colonic mucosa beyond peak hyperplasia. The MEK/ERK/p38 pathway therefore seems to modulate sustained activation of NF-κB in colonic crypts in response to C. rodentium infection.