Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization

Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning

Alzheimer's disease (AD) is the most common form of dementia. An increasing number of studies have confirmed epigenetic changes in AD. Consequently, a robust phenotyping mechanism must take into consideration the environmental effects on the patient in the generation of phenotypes. Positron Emission Tomography (PET) is employed for the quantification of pathological amyloid deposition in brain tissues. The objective is to develop a new methodology for the hyperparametric analysis of changes in cognitive scores and PET features to test for there being multiple AD phenotypes. We used a computational method to identify phenotypes in a retrospective cohort study (532 subjects), using PET and Magnetic Resonance Imaging (MRI) images and neuropsychological assessments, to develop a novel computational phenotyping method that uses Partial Volume Correction (PVC) and subsets of neuropsychological assessments in a non-biased fashion. Our pipeline is based on a Regional Spread Function (RSF) method for PVC and a t-distributed Stochastic Neighbor Embedding (t-SNE) manifold. The results presented demonstrate that (1) the approach to data-driven phenotyping is valid, (2) the different techniques involved in the pipelines produce different results, and (3) they permit us to identify the best phenotyping pipeline. The method identifies three phenotypes and permits us to analyze them under epigenetic conditions.

Multimodal Phenotyping of Alzheimer's Disease with Longitudinal Magnetic Resonance Imaging and Cognitive Function Data

Alzheimer's disease (AD) varies a great deal cognitively regarding symptoms, test findings, the rate of progression, and neuroradiologically in terms of atrophy on magnetic resonance imaging (MRI). We hypothesized that an unbiased analysis of the progression of AD, regarding clinical and MRI features, will reveal a number of AD phenotypes. Our objective is to develop and use a computational method for multi-modal analysis of changes in cognitive scores and MRI volumes to test for there being multiple AD phenotypes. In this retrospective cohort study with a total of 857 subjects from the AD (n = 213), MCI (n = 322), and control (CN, n = 322) groups, we used structural MRI data and neuropsychological assessments to develop a novel computational phenotyping method that groups brain regions from MRI and subsets of neuropsychological assessments in a non-biased fashion. The phenotyping method was built based on coupled nonnegative matrix factorization (C-NMF). As a result, the computational phenotyping method found four phenotypes with different combination and progression of neuropsychologic and neuroradiologic features. Identifying distinct AD phenotypes here could help explain why only a subset of AD patients typically respond to any single treatment. This, in turn, will help us target treatments more specifically to certain responsive phenotypes.

Comparative Effectiveness of Combination Therapy with Statins and Angiotensin-Converting Enzyme Inhibitors versus Angiotensin II Receptor Blockers in Patients with Coronary Heart Disease: A Nationwide Population-Based Cohort Study in Korea

STUDY OBJECTIVE

Patients with coronary heart disease (CHD) frequently use the combination of a statin and renin-angiotensin-aldosterone system (RAAS) blocker, an angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB), to control lipid levels and blood pressure, respectively, and the use of ARBs is increasing in Korean patients. Few studies are available, however, that have compared combination therapy with statin-ACEIs versus statin-ARBs. The objective of this study was to evaluate whether ARBs are associated with a reduced risk of major adverse cardiovascular and cerebrovascular events (MACCEs) compared with ACEIs when used in combination with statins in patients with established CHD.

DESIGN

Population-based retrospective cohort study.

DATA SOURCE

Korean National Health Insurance Service-National Sample Cohort database.

PATIENTS

A total of 6577 adults who started statin-RAAS blocker combination therapy (1870 in the statin-ACEI cohort and 4707 in the statin-ARB cohort) after being diagnosed with CHD between January 2003 and December 2013 were included. Of these, 3676 propensity score-matched patients (1838 in the statin-ACEI cohort and 1838 in the statin-ARB cohort) were included in the final analyses.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was MACCE (major adverse cardiovascular and cerebrovascular events), a composite of cardiovascular mortality, nonfatal myocardial infarction (MI), ischemic stroke, and revascularization. Incidences and incidence rates of MACCE were calculated to estimate the differences between the two cohorts. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated by using a Cox proportional hazard model. The incidence of MACCE was 19.9% and 14.7%, and incidence rates were 11.7 and 8.1 per 100 person-years in the statin-ACEI and statin-ARB cohorts, respectively. The risk of MACCE was significantly lower in the statin-ARB cohort (adjusted HR 0.69, 95% CI 0.59-0.81).

CONCLUSION

A MACCE was less likely to occur in patients who received a statin-ARB than in those who received a statin-ACEI. Similar trends were seen in cardiovascular mortality and the occurrence of recurrent MI but not stroke. The availability of statin-ARB fixed-dose combinations may have contributed to the improved outcomes in the statin-ARB cohort by reducing pill burden and improving medication adherence. Further research is warranted to validate our findings and to address whether a particular statin-ARB combination is more effective than other combinations.