03/2019 journal articles
What next in ad drug development?
EDITORIAL: FAILURE AFTER FAILURE. WHAT NEXT IN AD DRUG DEVELOPMENT?
J Prev Alz Dis 2019;6(3):150Show summaryHide summary
P.S. Aisen (2019): Editorial: Failure After Failure. What Next in AD Drug Development?. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.23
EDITORIAL: TAU BASED THERAPEUTICS: ALTERNATIVE APPROACHES IN THE WAR ON ALZHEIMER’S DISEASE
J Prev Alz Dis 2019;6(3):151-152Show summaryHide summary
M. Grundman (2019): Editorial: Tau Based Therapeutics: Alternative Approaches in the War on Alzheimer’s Disease. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.13
EDITORIAL: IS NOW THE TIME FOR COMBINATION THERAPIES FOR ALZHEIMER DISEASE?
J Prev Alz Dis 2019;6(3):153-154Show summaryHide summary
J.C. Morris (2019): Editorial: Is Now the Time for Combination Therapies for Alzheimer Disease?. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.15
EDITORIAL: BLOOD TESTS FOR ALZHEIMER’S DISEASE AND RELATED DISORDERS
J Prev Alz Dis 2019;6(3):155-156Show summaryHide summary
E.M. Reiman (2019): Editorial: Blood Tests for Alzheimer’s Disease and Related Disorders. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.20
ANTI-TAU TRIALS FOR ALZHEIMER’S DISEASE: A REPORT FROM THE EU/US/CTAD TASK FORCE
J. Cummings, K. Blennow, K. Johnson, M. Keeley, R.J. Bateman, J.L. Molinuevo, J. Touchon, P. Aisen, B. Vellas, and the EU/US/CTAD Task Force
J Prev Alz Dis 2019;6(3):157-163Show summaryHide summary
Efforts to develop effective disease-modifying treatments for Alzheimer’s disease (AD) have mostly targeted the amyloid β (Aβ) protein; however, there has recently been increased interest in other targets including phosphorylated tau and other forms of tau. Aggregated tau appears to spread in a characteristic pattern throughout the brain and is thought to drive neurodegeneration. Both neuropathological and imaging studies indicate that tau first appears in the entorhinal cortex and then spreads to the neocortex. Anti-tau therapies currently in Phase 1 or 2 trials include passive and active immunotherapies designed to prevent aggregation, seeding, and spreading, as well as small molecules that modulate tau metabolism and function. EU/US/CTAD Task Force members support advancing the development of anti-tau therapies, which will require novel imaging agents and biomarkers, a deeper understanding of tau biology and the dynamic interaction of tau and Aβ protein, and development of multiple targets and candidate agents addressing the tauopathy of AD. Incorporating tau biomarkers in AD clinical trials will provide additional knowledge about the potential to treat AD by targeting tau.
J. Cummings ; K. Blennow ; K. Johnson ; M. Keeley ; R.J. Bateman ; J.L. Molinuevo ; J. Touchon ; P. Aisen ; B. Vellas ; and the EU/US/CTAD Task Force (2019): Anti-Tau Trials for Alzheimer’s Disease: A Report from the EU/US/CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.14
COMBINATION THERAPY FOR ALZHEIMER’S DISEASE: PERSPECTIVES OF THE EU/US CTAD TASK FORCE
S. Gauthier, J. Alam, H. Fillit, T. Iwatsubo, H. Liu-Seifert, M. Sabbagh, S. Salloway, C. Sampaio, J.R. Sims, B. Sperling, R. Sperling, K.A. Welsh-Bohmer, J. Touchon, B. Vellas, P. Aisen, and the EU/US/CTAD Task Force
J Prev Alz Dis 2019;6(3):164-168Show summaryHide summary
Combination therapy is expected to play an important role for the treatment of Alzheimer’s disease (AD). In October 2018, the European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) met to discuss scientific, regulatory, and logistical challenges to the development of combination therapy for AD and current efforts to address these challenges. Task Force members unanimously agreed that successful treatment of AD will likely require combination therapy approaches that target multiple mechanisms and pathways. They further agreed on the need for global collaboration and sharing of data and resources to accelerate development of such approaches.
S. Gauthier ; J. Alam ; H. Fillit ; T. Iwatsubo ; H. Liu-Seifert ; M. Sabbagh ; S. Salloway ; C. Sampaio ; J.R. Sims ; B. Sperling ; R. Sperling ; K.A. Welsh-Bohmer ; J. Touchon ; B. Vellas ; P. Aisen ; and the EU/US/CTAD Task Force (2019): Combination Therapy for Alzheimer’s Disease: Perspectives of the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.12
PLASMA BIOMARKERS OF AD EMERGING AS ESSENTIAL TOOLS FOR DRUG DEVELOPMENT: AN EU/US CTAD TASK FORCE REPORT
R.J. Bateman, K. Blennow, R. Doody, S. Hendrix, S. Lovestone, S. Salloway, R. Schindler, M. Weiner, H. Zetterberg, P. Aisen, B. Vellas, and the EU/US CTAD Task Force
J Prev Alz Dis 2019;6(3):169-173Show summaryHide summary
There is an urgent need to develop reliable and sensitive blood-based biomarkers of Alzheimer’s disease (AD) that can be used for screening and to increase the efficiency of clinical trials. The European Union-North American Clinical Trials in Alzheimer’s Disease Task Force (EU/US CTAD Task Force) discussed the current status of blood-based AD biomarker development at its 2018 annual meeting in Barcelona, Spain. Recent improvements in technologies to assess plasma levels of amyloid beta indicate that a single sample of blood could provide an accurate estimate of brain amyloid positivity. Plasma neurofilament light protein appears to provide a good marker of neurodegeneration, although not specific for AD. Plasma tau shows some promising results but weak or no correlation with CSF tau levels, which may reflect rapid clearance of tau in the bloodstream. Blood samples analyzed using -omics and other approaches are also in development and may provide important insight into disease mechanisms as well as biomarker profiles for disease prediction. To advance these technologies, international multidisciplinary, multi-stakeholder collaboration is essential.
R.J. Bateman ; K. Blennow ; R. Doody ; S. Hendrix ; S. Lovestone ; S. Salloway ; R. Schindler ; M. Weiner ; H. Zetterberg ; P. Aisen ; B. Vellas ; and the EU/US CTAD Task Force (2019): Plasma Biomarkers of AD Emerging as Essential Tools for Drug Development: An EU/US CTAD Task Force Report. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.21
COMMENTARY: TREATING ALZHEIMER’S DISEASE : COMBINE OR FAIL ?
J Prev Alz Dis 2019;6(3):174-176Show summaryHide summary
S. Bakchine (2019): Commentary: Treating Alzheimer’s Disease : Combine or Fail ? . The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.16
COMMENTARY: OPPORTUNITIES FOR COMBINATION TRIALS
C. Ballard, A. Corbett
J Prev Alz Dis 2019;6(3):177-178Show summaryHide summary
C. Ballard ; A. Corbett (2019): Commentary: Opportunities for Combination Trials. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.17
COMMENTARY: COMBINATION THERAPY FOR ALZHEIMER’S DISEASE – THE NEXT STEP
J Prev Alz Dis 2019;6(3):179Show summaryHide summary
F. Jessen (2019): Commentary: Combination Therapy for Alzheimer’s Disease – The Next Step. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.18
COMMENTARY: COMBINATION THERAPY FOR ALZHEIMER’S DISEASE: PERSPECTIVES OF THE EU/US CTAD TASK FORCE
J Prev Alz Dis 2019;6(3):180-181Show summaryHide summary
E. Siemers (2019): Commentary: Combination Therapy for Alzheimer’s Disease: Perspectives of the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.19
COMMENTARY: DEVELOPMENT OF THE BLOOD-BASED ALZHEIMER’S DISEASE LIQUID BIOPSY
H. Hampel, S. Lista, A. Vergallo, for the Alzheimer Precision Medicine Initiative (APMI)
J Prev Alz Dis 2019;6(3):182-184Show summaryHide summary
H. Hampel ; S. Lista ; A. Vergallo ; for the Alzheimer Precision Medicine Initiative (APMI) (2019): Commentary: Development of the Blood-Based Alzheimer’s Disease Liquid Biopsy. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.22
MACHINE LEARNING ALGORITHM HELPS IDENTIFY NONDIAGNOSED PRODROMAL ALZHEIMER’S DISEASE PATIENTS IN THE GENERAL POPULATION
O. Uspenskaya-Cadoz, C. Alamuri, L. Wang, M. Yang, S. Khinda, Y. Nigmatullina, T. Cao, N. Kayal, M. O’Keefe, C. Rubel
J Prev Alz Dis 2019;6(3):185-191Show summaryHide summary
Background: Recruiting patients for clinical trials of potential therapies for Alzheimer’s disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R&D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care providers, who may lack time, diagnostic skills and awareness of local clinical trials. Machine learning and predictive analytics offer promise to boost enrollment by predicting which patients have prodromal AD, and which will go on to develop AD.
Objectives: The authors set out to develop a machine learning predictive model that identifies prodromal AD patients in the general population, to aid early AD detection by primary care physicians and timely referral to expert sites for biomarker confirmation of diagnosis and clinical trial enrollment.
Design: The authors use a classification machine learning algorithm to extract patterns within healthcare claims and prescription data three years prior to AD diagnosis/AD drug initiation.
Setting: The study focused on subjects included within proprietary IQVIA US data assets (claims and prescription databases). Patient information was extracted from January 2010 to July 2018, for cohorts aged between 50 and 85 years.
Participants: A total of 88,298,289 subjects aged between 50 and 85 years were identified. For the positive cohort, 667,288 subjects were identified who had 24 months of medical history and at least one record with AD or AD treatment. For the negative cohort, 3,670,254 patients were selected who had a similar length of medical history and who were matched to positive cohort subjects based on the prevalence rate. The scoring cohort was selected based on availability of recent medical data of 2-5 years and included 72,670,283 subjects between the ages of 50 and 85 years.
Intervention (if any): None.
Measurements: A list of clinically-relevant and interpretable predictors was generated and extracted from the data sets for each subject, including pharmacological treatments (NDC/product), office/specialist visits (specialty), tests and procedures (HCPCS and CPT), and diagnosis (ICD). The positive cohort was defined as patients who have AD diagnosis/AD treatment with a 3 years offset as an estimate for prodromal AD diagnosis. Supervised ML techniques were used to develop algorithms to predict the occurrence of prodromal AD cases. The sample dataset was divided randomly into a training dataset and a test dataset. The classification models were trained and executed in the PySpark framework. Training and evaluation of LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier were executed using PySpark’s mllib module. The area under the precision-recall curve (AUCPR) was used to compare the results of the various models.
Results: The AUCPRs are 0.426, 0.157, 0.436, and 0.440 for LogisticRegression, DecisionTreeClassifier, RandomForestClassifier, and GBTClassifier, respectively, meaning that GBTClassifier (Gradient Boosted Tree) outperforms the other three classifiers. The GBT model identified 222,721 subjects in the prodromal AD stage with 80% precision. Some 76% of identified prodromal AD patients were in the primary care setting.
Conclusions: Applying the developed predictive model to 72,670,283 U.S. residents, 222,721 prodromal AD patients were identified, the majority of whom were in the primary care setting. This could drive major advances in AD research by enabling more accurate and earlier prodromal AD diagnosis at the primary care physician level , which would facilitate timely referral to expert sites for in-depth assessment and potential enrolment in clinical trials.
O. Uspenskaya-Cadoz ; C. Alamuri ; L. Wang ; M. Yang ; S. Khinda ; Y. Nigmatullina ; T. Cao ; N. Kayal ; M. O’Keefe ; C. Rubel (2019): Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer’s Disease Patients in the General Population . The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.10
TREATMENT EFFECTS OF VORTIOXETINE ON COGNITIVE FUNCTIONS IN MILD ALZHEIMER’S DISEASE PATIENTS WITH DEPRESSIVE SYMPTOMS: A 12 MONTH, OPEN-LABEL, OBSERVATIONAL STUDY
E. Cumbo, S. Cumbo, S. Torregrossa, D. Migliore
J Prev Alz Dis 2019;6(3):192-197Show summaryHide summary
BACKGROUND/OBJECTIVES:depressive symptoms are common in Alzheimer’s disease(AD). Aim of the study was to investigate the efficacy of vortioxetine compared with other conventional antidepressants on cognitive functions in AD patients with depressive symptoms.
DESIGN: Prospective, randomized, 12 month, parallel-group study.
SETTING: All participants were evaluated on-site at Neurodegenerative Disorders Unit, ASP2 Caltanissetta(Italy).
PARTICIPANTS: 108(71 female, 37 male) AD patients with depression(mean age 76.7± 4.3).
INTERVENTION: Randomized subjects received vortioxetine, 15 mg/day(n=36) or other common antidepressants(n=72).
MEASURES:primary outcome was change from baseline in the MMSE; secondary outcomes were change in Attentive Matrices, Raven Coloured Progressive Matrices, Digit Span, HAM-D and Cornell scale.
RESULTS: Statistically significant improvement vs. controls was observed for vortioxetine on most of the cognitive tests and showed significantly baseline-to-endpoint reduction in both HAM-D and Cornell total scores.The most commonly reported adverse events were nausea and headache for votioxetine; nausea in the control group.
CONCLUSIONS: Vortioxetine had a beneficial effect on cognition and mood in elderly AD patients and was safe and well tolerated.
E. Cumbo ; S. Cumbo ; S. Torregrossa ; D. Migliore (2019): Treatment Effects of Vortioxetine on Cognitive Functions in Mild Alzheimer’s Disease Patients with Depressive Symptoms: A 12 Month, Open-Label, Observational Study. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.24
ADVANCING ALZHEIMER’S DISEASE TREATMENT: LESSONS FROM CTAD 2018
B. Vellas, L.J. Bain, J. Touchon, P.S. Aisen
J Prev Alz Dis 2019;6(3):198-203Show summaryHide summary
The 2018 Clinical Trials on Alzheimer’s Disease (CTAD) conference showcased recent successes and failures in trials of Alzheimer’s disease treatments. More importantly, the conference provided opportunities for investigators to share what they have learned from those studies with the goal of designing future trials with a greater likelihood of success. Data from studies of novel and non-amyloid treatment approaches were also shared, including neuroprotective and regenerative strategies and those that target neuroinflammation and synaptic function. New tools to improve the efficiency and productivity of clinical trials were described, including biomarkers and machine learning algorithms for predictive modeling.
B. Vellas ; L.J. Bain ; J. Touchon ; P.S. Aisen (2019): Advancing Alzheimer’s Disease Treatment: Lessons from CTAD 2018. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.11
DIETARY FAT INTAKE AND COGNITIVE FUNCTION AMONG OLDER POPULATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS
G.-Y. Cao, M. Li, L. Han, F. Tayie, S.-S. Yao, Z. Huang, P. Ai, Y.-Z. Liu, Y.-H. Hu, B. Xu
J Prev Alz Dis 2019;6(3):204-211Show summaryHide summary
Objective: The associations between dietary fat intake and cognitive function are inconsistent and inconclusive. This study aimed to provide a quantitative synthesis of prospective cohort studies on the relationship between dietary fat intake and cognitive function among older adults.
Methods: PubMed, EMBASE, PsycINFO and Web of Science databases were searched for prospective cohort studies published in English before March 2018 reporting cognitive outcomes in relation to dietary fat intake. Four binary incident outcomes included were mild cognitive impairment (MCI), dementia, Alzheimer disease (AD) and cognitive impairment. The categories of dietary fat intake were based on fat consumption or the percentage of energy from fat consumption, including dichotomies, tertiles, quartiles and quintiles. The relative risk (RR) with the corresponding 95% confidence intervals (CIs) was pooled using a random effects model.
Results: Nine studies covering a total of 23,402 participants were included. Compared with the lowest category of consumption, the highest category of saturated fat intake was associated with an increased risk of cognitive impairment (RR = 1.40; 95% CI: 1.02-1.91) and AD (RR: 1.87, 95% CI: 1.09-3.20). The total and unsaturated fat intake was not statistically associated with cognitive outcomes with significant between-study heterogeneity.
Conclusion: This study reported a detrimental association between saturated fat intake and cognitive impairment and mixed results between unsaturated fat intake and selected cognitive outcomes. Given the substantial heterogeneity in the sample size and methodology used across studies, the evidence presented here should be interpreted with caution.
G.-Y. Cao ; M. Li ; L. Han ; F. Tayie ; S.-S. Yao ; Z. Huang ; P. Ai ; Y.-Z. Liu ; Y.-H. Hu ; B. Xu (2019): Dietary Fat Intake and Cognitive Function among Older Populations: A Systematic Review and Meta-Analysis. The Journal of Prevention of Alzheimer’s Disease (JPAD). http://dx.doi.org/10.14283/jpad.2019.9