overlap between vad vad and ad and ad overlap between an
play

EMEA 2nd workshop on neurodegenerative diseases:* Focus on Dementia Overlap between VaD VaD and AD: and AD: Overlap between an epidemiological perspective an epidemiological perspective Miia Kivipelto Kivipelto, MD, PhD , MD, PhD Miia

0 downloads 1 Views 644 KB Size Report
  1. EMEA 2nd workshop on neurodegenerative diseases:* Focus on Dementia Overlap between VaD VaD and AD: and AD: Overlap between an epidemiological perspective an epidemiological perspective Miia Kivipelto Kivipelto, MD, PhD , MD, PhD Miia Associate professor Associate professor Aging Research Centre, Karolinska Karolinska Institutet Institutet and and Karolinska Karolinska Aging Research Centre, University Hospital, Stockholm University Hospital, Stockholm

  2. � VaD vs. (?) AD � Risk & protective factors for dementia � Dementia Risk Score � Future directions Results from the CAIDE study and Kungsholmen Project

  3. Brief historical overview Brief historical overview The beginning Cerebral arteriosclerosis – the major cause of dementia Late 1960´s � AD-type pathology - very common in elderly patients with dementia � Attempts to make a sharp distinction between degenerative and vascular diseases Nowadays The relationship between AD and VaD appears to be complex: a considerable overlap in risk factors, clinical features and neuropathology of AD and VaD

  4. Epidemiology of vascular cognitive impairment Epidemiology of vascular cognitive impairment � 1/3 of individuals will experience a stroke, dementia or both (Seshadri et al., Stroke 2006) � After stroke, up to 64% of persons have some degree of cognitive impairment, with up to 30% developing frank dementia (Hachinski et al,. Stroke 2006)

  5. Obscurities in VaD research � Definition of dementia requires memory impairment - often misses the executive dysfunction typical for VCI � VaD is a heterogeneous group (sub-cortical VaD might be more homogeneous) � Focus on demetia even though patients with VCI without dementia might be better candidates for clinical trials (earlier phase of the disease) � None of the current stroke scales used in clinical trials measure cognition

  6. Rethinking the classification of degenerative and vascular cases Rethinking the classification of degenerative and vascular cases Kungsholmen Project ‘Pure AD’ 12% 5% 6% AD with severe cerebral amyloid DSM III-R angiopathy criteria Mild AD with vascular involvement 77% AD with vascular lesions AD VaD Mixed Other AD with cerebrovascular disease Reclassified VD with AD changes 55% VD with small-vessel disease 6% 3% 36% ‘Pure VD’ Kalaria R et al. Alzheimer Dis Assoc Disord 1999

  7. SILENT BRAIN INFARCTS AND RISK OF DEMENTIA Risk of Risk of dementia Alzheimer’s disease HR (95% CI) HR (95% CI) Silent brain infarct † 2.3 (1.1-4.7) 2.6 (1.2-5.7) Silent brain infarct ‡ 2.0 (0.9-4.4) 2.6 (1.1-6.0) † Adjusted for age, sex, and education. ‡ Additionally adjusted for subcortical atrophy, and periventricular white matter lesions. Vermeer S et al. NEJM 2003;348:1215-22

  8. The Nun Study Dementia in individuals with AD neuropathology No infarcts 57% 1-2 lacunar 93% Large infarcts 75% Snowdon et al JAMA 1997

  9. Vascular related risk/protective factors for dementia/AD/VaD Vascular related risk/protective factors for dementia/AD/VaD Protective factors Protective factors Risk factors Risk factors • High education • Cerebrovascular disorders • Physical activity • Hypertension • Active lifestyle • Hypercholesterolemia • Alcohol consumption • Obesity • Antioxidants • Diabetes mellitus • Fish oils • Homocysteine • Antihypertensives • Smoking • Statins • Depression • NSAIDs? • Estrogen?

  10. Midlife risk factors for dementia/AD later in life Main findings from the CAIDE study Vascular: � High midlife cholesterol Kivipelto et al, BMJ 2001, � High midlife systolic BP Ann Intern Med 2002 � Obesity - Kivipelto et al., Arch Neurol 2005 Lifestyle-related (especially among the ApoE4 carriers) � Use of saturated / lack of polyunsaturated fatty acids - Laitinen et al, 2005 � Frequent alcohol drinking - Anttila et al, BMJ 2004 � Physical inactivity - Rovio et al, Lancet Neurology 2005

  11. APOE ε 4 non-carriers Active APOE ε 4 carriers Sedentary Physical activity Active 5.5 ** Sedentary IV III II PUFA intake-quartiles I IV 4 * III II 5 * I I II III SFA intake - quartiles IV I 7.1 ** II III 7.1 * IV Non-drinkers Infrequent Frequent Alcohol drinking Non-drinkers Infrequent 3.8 * Frequent Non-smokers Smokers Smoking ORs for dementia Non-smokers 3.2 * Smokers 0 1 2 3 4 5 6 7 8

  12. Possible processes for the development of AD Possible processes for the development of AD Physical High inactivity fat intake Frequent alcohol Various Injurious Agents drinking High High High BP cholesterol BMI Smoking Vascular Insults ApoE4: Neuronal Damage Poor Repair/ Protection Neurodegeneration

  13. CAIDE Dementia Risk Score CAIDE Dementia Risk Score Age < 47 years 0 3 47-53 years 4 >53 years Formal education ≥ 10 years 0 7-9 years 2 0-6 years 3 Sex Women 0 Men 1 ≤ Systolic BP 140 mm Hg 0 > 140 mm Hg 2 ≤ BMI 30 kg/m2 0 > 30 kg/m2 2 Kivipelto et al., Lancet ≤ Total cholesterol 6.5 mmol/l 0 Neurology > 6.5 mmol/l 2 2006 Physical activity Active 0 Inactive 1

  14. Probability of dementia in late- -life according life according Probability of dementia in late to the risk score category in middle age to the risk score category in middle age The overall occurrence of dementia 4.4% The overall occurrence of dementia 4.4% SCORE All /Demented, n % Risk (95% CI) 0-5 401 / 4 1.0 (0.0- -2.0) 2.0) 1.0 (0.0 6-7 270 / 5 1.9 (0.2- -3.5) 3.5) 1.9 (0.2 8-9 312 / 13 4.2 (1.9- 4.2 (1.9 -6.4) 6.4) 10-11 245 / 18 7.4 (4.1- -10.6) 10.6) 7.4 (4.1 12-15 122 / 20 16.4 (9.7- -23.1) 23.1) 16.4 (9.7 Kivipelto et al., Lancet Neurology 2006

  15. Performance of the Dementia Risk Score in Performance of the Dementia Risk Score in predicting the risk of dementia in 20 years predicting the risk of dementia in 20 years 1 Cutpoint: score > >9 9 Cutpoint: score 0 9 , 0 8 , (39 % of population) (39 % of population) y t i 0 7 , v i t i 0 6 s , n e 0 5 , S Sensitivity = 0.77 0 4 AUC 0.77 (0.71-0.83) , 0 3 , Specificity = 0.63 0 2 , 0 1 PPV = 0.09 , 0 NPV = 0.98 0 0 5 1 , 1 p e s c c y - f t i i i

  16. The CAIDE Risk Score in the Kaiser Study Overall AUC .74 Asian: 0.813 Black: 0.751 White: 0.737

  17. Minding heart health protects the brain Minding heart health protects the brain Dementia Risk Score highlights the role of vascular factors in the development of dementia (AD, VaD and mixed), and may help to identify high risk individuals who might benefit from intensive lifestyle consultations and pharmacological interventions

  18. Multi-domain intervention study as a next step? � For persons at an increased risk of dementia � Several outcomes measures: Sensitive measures for executive functions – Depression, ADL and IADL functions, – disability

  19. Target population in VaD/VCI trials? � Sub-cortical VaD? � VCI (VCI Harmonization criteria)? Neurpsychological tests – Neuroimaging – Biomarkers (e.g. CSF albumin index, – sulfatide, neurofilament, metalloproteases ) New Pre-AD criteria Lancet Neurology 2007

  20. Pushing our research to the limits of our disciplines…and beyond: integrated approach to stroke and dementia Thinking and remembering brain as an end-organ: Moving from ”stroke brain” to ”network brain” Erkinjuntti, Alhainen, Kivipelto

  21. The brain functions with complexity but fails through common basic pathophysiological mechanisms. Hachinski V, Stroke 2007

  22. Miia Kivipelto Ingemar Kåreholt Jaakko Tuomilehto Jaakko Tuomilehto Suvi Rovio Aulikki Nissinen Aulikki Nissinen Laura Fratiglioni Bengt Winblad Hilkka Soininen Alina Solomon Marjo Eskelinen Minna Rusanen Jaakko Tuomilehto Aulikki Nissinen Tiina Laatikainen Tiia Ngandu

Recommend Documents


cloud layer overlap and the influence of vertical and
Cloud Layer Overlap and the Influence

Cloud Layer Overlap and the Influence of Vertical and Temporal Resolution of

mononucleosis mimicking malignancy
MONONUCLEOSIS MIMICKING MALIGNANCY

MONONUCLEOSIS MIMICKING MALIGNANCY Laura Saldivar, MD Introduction - Symptom

overlap of black petrel distributions with new zealand
Overlap of black petrel distributions

Overlap of black petrel distributions with New Zealand fisheries Edward

cloud cover and overlap parameterizations
Cloud cover and overlap

Cloud cover and overlap parameterizations Adrian Tompkins, ICTP

hierarchical overlap graph
Hierarchical Overlap Graph B. Cazaux

Hierarchical Overlap Graph B. Cazaux and E. Rivals LIRMM & IBC,

overlap graph and clumps
Overlap Graph and Clumps Mireille R

Overlap Graph and Clumps Mireille R egnier LIX and INRIA

corrected network measures
Corrected network measures

Corrected network measures V. Batagelj Corrected network measures

modeling semantic overlap
Modeling Semantic Overlap Over the

Reasons to avoid Reasoning: Where does NLP stop and AI Begin? Bill Dolan

fast convolutions via the overlap
Fast Convolutions Via the Overlap-

Fast Convolutions Via the Overlap- and-Save Method Using Shared Memory FFT

1
1 Some overlap with all modules

1 Some overlap with all modules particularly modules 3, 6 and 7. LDF/plan

hypertension in patients with type 2 diabetes mellitus
Hypertension in patients with Type 2

Hypertension in patients with Type 2 Diabetes Mellitus why are we failing to

falls prevention
Falls prevention Martin Littleton

Falls prevention Martin Littleton CPPE Local Tutor 1 Welcome and

high level overview
High Level Overview Partnerships

7/19/2017 Session Overview Component Breakout Session 3: High Level Overview

disclaimer
DISCLAIMER: Video will be taken at

. DISCLAIMER: Video will be taken at this clinic and potentially used in

management of acute
MANAGEMENT OF ACUTE ISCHEMIC STROKE

UPDATES IN THE EARLY MANAGEMENT OF ACUTE ISCHEMIC STROKE Michelle Jakubovics

hypertensive emergencies case and discussion
Hypertensive Emergencies Case and

Hypertensive Emergencies Case and discussion Laura Kuyper R1 Boot Camp July

multi domain interventions to prevent
Multi-domain Interventions to Prevent

Multi-domain Interventions to Prevent Cognitive Impairment and Alzheimers

mitigating risks while optimizing the benefits of
Mitigating Risks While Optimizing the

Mitigating Risks While Optimizing the Benefits of Pharmacologic Agents to

professor dyfrig hughes bangor university
Professor Dyfrig Hughes, Bangor

8 th December 2011 European Parliament Building, Brussels, Belgium Professor

patient population characteristics
Patient Population Characteristics N =

Patient Population Characteristics N = 965 Mean age (years SD):

choosing the right similarity measure
Choosing the Right Similarity Measure

Choosing the Right Similarity Measure John Holliday, University of Sheffield,

stenosis
stenosis Obiagwu P 1 , Gajjar P 1 ,

Salvageability of renal function following renal revascularization in children