brain and blood metabolite signatures of pathology and...
TRANSCRIPT
OMICS in the context of Aging and Alzheimer's disease
Brain and Blood Metabolite Signatures of Pathology and Progression in Alzheimer's Disease
Madhav Thambisetty, MD, PhD
Unit of Clinical and Translational Neuroscience
Laboratory of Behavioral NeuroscienceNational Institute on Aging (NIA)National Institutes of Health (NIH)
Outline
• 1. The need for diversity in the search for AD treatments.
• 2. A new paradigm for a diverse and integrated approach for discovering therapeutic mechanisms.
• 3. Understanding the metabolic basis of pathology and progression in AD
• 4. Translational implications
"A characteristic disease of the cerebral cortex” (Über eine eigenartige Erkrankung der Hirnrinde, 1907
37th meeting of SouthWest German psychiatrists, Tübingen, Germany; 1906
Symptomatic AD treatments
Drug Name Brand Name Approved for FDA Approved
Donepezil Aricept All stages 1996
Rivastigmine Exelon All stages 2000
Galantamine Razadyne Mild-Moderate 2001
Memantine Namenda All stages 2003
Lack of diversity in the AD therapeutics pipeline
1. The trial was terminated before completion.
2. Worsening cognition, increased risk of skin cancer, infections and weight loss in the treatment groups.
N Engl J Med 369;4, 2013 N Engl J Med 370;4, 2014
1. No significant improvement in cognition or function
“shifting the focus of AD research
to human biology may hasten
development of improved
strategies to prevent, detect,
ameliorate, and possibly
cure this devastating disease.”
A diverse and integrated approach to AD therapeutic mechanisms
1. What is your risk factor(s) of interest?
ENVIRONMENTAL/LIFESTYLE
•Obesity
•Inflammation
•Insulin resistance
•Vit-D deficiency
•Anticholinergic medications
•GENETIC
•CLU
•CR1
•PICALM
A diverse and integrated approach to AD therapeutic mechanisms
2. What are the phenotypes associated with the risk factor?
•Deep and broad phenotyping of longitudinal human physiology
Susan Resnick ; BLSA-NI
Marilyn Albert, BIOCARD
• Approximately 50% enrolment into autopsy and detailed neuropathological assessment at death
A diverse and integrated approach to AD therapeutic mechanisms
3. Does the risk factor(s) alter gene expression in the brain and periphery?
A diverse and integrated approach to AD therapeutic mechanisms
4. Is the risk factor(s) associated with altered protein/metabolite levels in the brain?
Mass spectrometry-based proteomics and metabolomics
• Absolute quantification of >600 metabolites
Biogenic amines, Acylcarnitines,
Sphingolipids, Ceramides, Fatty Acids, Oxysterols,
Bile Acids, Hexoses, Glycerophospholipids
• Label free quantification of approximately
1000 proteins
Neuritic Plaques (NP) and Neurofibrillary Tangles (NFTs) are “Pathological Hallmarks” of AD, BUT…
• A substantial number of cognitively normal older individuals have significant AD pathology at death.
David Bennett Neurology. 2006;
Neuropathology of older persons without cognitive impairment from two community-based studies.
• Iacono et al. J Neuropathol Exp Neurol. 2014;73(4):295-304 Mild cognitive impairment and Asymptomatic Alzheimer disease subjects: equivalent β-amyloid and tau loads with divergent cognitive outcomes.
A diverse and integrated approach to AD therapeutic mechanisms
5. Do the same protein/metabolite levels change in the blood prior to AD?
Mass spectrometry-based proteomics and metabolomics
• Absolute quantification of >600 metabolites
Biogenic amines, Acylcarnitines,
Sphingolipids, Ceramides, Fatty Acids,
Bile Acids, Hexoses, Glycerophospholipids
• Label free quantification of approximately
1000 proteins
A diverse and integrated approach to AD therapeutic mechanisms
6. Are the same protein/metabolite implicated in preclinical AD models?
Understanding the metabolic basis of Alzheimer’s disease
1. How do systemic abnormalities in metabolism mediate AD pathogenesis?
2. Do peripheral metabolic signals reflect those in the brain?
3. Can we relate peripheral signatures of abnormal metabolism to severity of AD pathology and progression?
An integrated metabolomics approach to AD
• Small biochemicals are the end result of all the regulatory complexity present in the cell, tissue, or organism, including transcriptional regulation, translational regulation, and post-translational modification
• Metabolic changes are the most proximal reporters of alterations in the body in response to a disease process
Lewis GD, Asnani A, Gerszten RE. Application of Metabolomics to Cardiovascular Biomarker and Pathway
Discovery. Journal of the American College of Cardiology. 2008;52(2):117-123..
Study Design
Brain Cohort
Blood Cohort
BLSA:bloodstudysample
*p<0.05comparingnon-convertersandconvertersatbaseline(bothsamplesnormalcognitionatbaseline)Alzheimer’sDiseaseNeuroimagingInitiative(ADNI):bloodstudysample
MCI=MildCognitiveImpairment;AD=Alzheimer’sdisease;*p<0.05comparingMCIorADtocontrolgroup
Demographicvariables
TotalSampleN=207
Non-convertersN=116
ConvertersN=89
Age(mean,SD) 78.47(6.96) 77.76(7.30) 79.41(6.41)Sex,n(%female) 107(51.69) 56(48.28) 51(56.04)Race,n(%white) 172(83.09) 90(77.59) 82(90.11)*APOEe4-carrier,n(%) 8(5.52) 1(1.19)* 7(11.48)*
DemographicVariables
TotalSampleN=767
NormalN=216
MCIN=366
ADN=185
Age,mean(SD) 75.19(6.82) 75.98(5.05) 74.69(7.35)* 75.26(7.46)Sex,n(%female) 327(42.63) 105(48.61) 132(36.07)* 90(48.65)Race,n(%white) 713(92.96) 199(92.13) 341(93.17) 173(93.51)APOEe4-carrier,n(%) 381(49.67) 58(26.85) 200(54.64)* 123(66.49)*
BLSA-Preclinical AD cohort; N=207
ADNI-Prodromal AD cohort; N=767
STEP 1: Machine learning analyses
Which metabolites discriminate between cases and controls in the BLSA brain autopsy study?
Random Forest (RF) Support Vector Machine (SVM)
Inferior temporal gyrus (ITG)
Accuracy 70.00% Accuracy 83.33% Sensitivity 66.70% Sensitivity 86.67% Specificity 73.30% Specificity 80.00%
Brain metabolite signature of AD
STEP 2: Brain Metabolite concentrations and brain pathology
STEP 3: Blood Metabolite concentrations and AD endophenotypes (BLSA)
STEP 4a: Blood Metabolite concentrations and AD endophenotypes (ADNI)SPARE-AD index (AD-like patterns of atrophy)
STEP 4b: Blood Metabolite concentrations and AD endophenotypes (ADNI)CSF Aβ1-42, t-tau, p-tau
Step 5: Heat map and Endophenotype Association Score in Early Alzheimer’s Disease (EASE-AD) scores of signature metabolites
Endophenotype Association Score in Early Alzheimer’s Disease
EASE-AD
Blood And Brain Endophenotype Score forAlzheimer’s Disease
BABES for Alzheimer’s Disease
Step 6: Mapping metabolites onto pathways related to AD pathology
Summary
• Novel study design: quantitative and targeted metabolomic analyses of both brain and blood tissue (N>900 samples) to identify metabolite signatures associated with AD
• Distinct metabolites belonging to the sphingolipid, glycerophospholipid and acylcarnitine classes are related to severity of AD pathology and progression during preclinical stages
• EASE-AD score represents the cumulative associations of each metabolite with outcome measures related to AD pathology and progression in two independent cohorts
Colleagues and collaborators
Unit of Clinical & Translational NeuroscienceMadhav ThambisettyVijay VarmaAlexandra KueiderBrittany SimpsonYi-Fang ChuangSahba Seddighi (post-bac IRTA; from July 2016)
HiThru AnalyticsSudhir Varma
Wake Forest UniversityRamon Casanova
Johns Hopkins University SOMMarilyn Albert Richard O’BrienAbhay Moghekar
We are grateful to the BIOCARD and BLSA participants for their invaluable contributions
Post-doctoral positions available
Unit of Clinical and Translational Neuroscience, Laboratory of Behavioral Neuroscience, National Institute on Aging,
National Institutes of Health
http://www.irp.nia.nih.gov/branches/lpc/ctnu.htm
• Integrated systems-level understanding of Neurodegeneration
• Biomarker discovery in Alzheimer’s disease
Please contact Madhav Thambisetty, MD, PhD