- Email: [email protected]
The relation between eeg spectral analysis and clinical progression in non-demented, amyloid-positive subjects
Podium Presentations: Tuesday, July 21, 2015
P255
Table 1 Classification of MCI patients at baseline using individual biomarkers, NIA-AA criteria and the PredictAD tool Data required for analysis Individual biomarker; AD like: CSF amyloid beta 1-42 CSF total tau CSF p tau MTA NIA-AA criteria: Uninformative; Conflicting, unavailable, or only one marker present butintermediate likelihood; Only amyloid or neuronal injury marker present but + High likelihood; Amyloid and neuronal injury marker + Low likelihood; Amyloid and neuronal injury marker PredictAD tool: Using demographics, MRI and CSF data DSI 0.5 Using only MRI data DSI 0.5 Using only CSF data DSI 0.5 O3-14-03
Matching criteria
Stable at follow up
Dementia due to AD at follow up
AUC
sensitivity
specificity
137 (65%) 137 (65%) 137 (65%) 171 (81%)
72 (53%) 89 (65%) 97 (71%) 52 (30%)
14 17 22 10
58 72 75 42
0.81 0.86 0.82 0.67
68 85 88 40
73 67 58 57
103 (49%)
103 (49%)
39
64
-
-
-
18 (8%)
18 (8%)
3
15
-
-
-
67 (32%)
11
56
93
63
23 (11%)
19
4
-
211 (100%)
135 (64%)
25
110
0.81
80 (79-81)
66 (64-67)
211 (100%)
85 (50%)
20
66
0.70
62 (61-63)
71 (69-72)
211 (100%)
89 (65%)
14
75
0.86
89 (88-89)
72 (70-73)
90 (43%)
NOVEL FLUID BIOMARKERS FOR BRAIN AMYLOID AND DEMENTIA RISK IN PRESYMPTOMATIC ALZHEIMER DISEASE
Richard J. Perrin1,2,3, Hua Weng1, Kelly R. Bales4, John C. Morris1,5, Tammie L.S. Benzinger1,5, Anne M. Fagan5,6,7, Chengjie Xiong1,2, David M. Holtzman1,2,3, 1Washington University School of Medicine, Saint Louis, MO, USA; 2Knight Alzheimer’s Disease Research Center, Saint Louis, MO, USA; 3Hope Center for Neurological Disorders, Saint Louis, MO, USA; 4Pfizer, Inc, Groton, CT, USA; 5Knight Alzheimer’s Disease Research Center, St. Louis, MO, USA; 6Washington University School of Medicine, St. Louis, MO, USA; 7Hope Center for Neurological Disorders, St. Louis, MO, USA. Contact e-mail: [email protected] Background: For clinical trials targeting Ab production or clearance in presymptomatic Alzheimer disease (AD), additional biomarkers beyond CSF Ab42 and amyloid imaging will be useful. This study sought CSF and plasma proteins that can detect and predict preclinical amyloid, predict subsequent dementia, and provide insight into presymptomatic AD pathophysiology. Methods: Paired CSF and plasma samples, collected within 2.5 years of PET-PIB (Positron emission tomography with Pittsburgh compound B) from 118 Knight ADRC participants with normal cognition (Clinical Dementia Rating [CDR] 0), were analyzed using the 190MAP biomarker panel at Myriad/RBM. All participants were APOE-Ɛ3/-Ɛ3 or -Ɛ3/Ɛ4. Mean cortical PIB binding potential [MCBP] above/below 0.18 (PIB-POS/PIB-NEG) was treated as a categorical variable. Among N¼28 PIB-POS at baseline, N¼13 developed dementia (CDR>0); N¼12 of 67 PIB-NEG participants with multiple scans later became PIB-POS. Receiver operating characteristic (ROC) and C-index analyses were applied with age, gender, education, body mass index (BMI), and APOE-Ɛ4 status as covariates; biomarker combinations (Ab42-free) were evaluated by logistic regression. Results: BMI and MCBP were negatively correlated (p¼ 0.03). For detecting PIB-POS, covariate-adjusted ROC analyses identified 7 plasma and 7 CSF proteins with significant areas under the curve (AUC) (each >0.627; max 0.736 & 0.687). CSF tau/Ab42 yielded
AUC¼0.923; optimal biomarker panels yielded AUCs of 0.837 (CSF) and 0.814 (plasma). For predicting conversion to PIBPOS, covariate-adjusted ROC analyses yielded significant AUCs for 3 CSF and 9 plasma proteins (each >0.685; max 0.779 and 0.810); optimal panels yielded AUCs of 0.827 and 0.955. For predicting dementia (CDR>0) among PIB-POS, C-index analyses identified 6 CSF and 9 plasma proteins that individually contributed improvements to ‘covariates-only’ models (each >0.778; max 0.825 and 0.873); similar improvements by optimal biomarker panels yielded C-indices of 0.884 and 0.968. Conclusions: Many novel fluid proteins have potential for detecting and predicting presymptomatic brain amyloid, and for predicting subsequent dementia. These candidate biomarkers may have value for clinical prevention trials and provide pathophysiological insights into preclinical AD. Broadly, these results associate presymptomatic amyloid with lower BMI, low insulin, an anti-inflammatory peripheral milieu, and a limited neuroinflammatory response; more robust neuroinflammation at baseline is a harbinger of cognitive decline. O3-14-04
THE RELATION BETWEEN EEG SPECTRAL ANALYSIS AND CLINICAL PROGRESSION IN NON-DEMENTED, AMYLOID-POSITIVE SUBJECTS
Alida A. Gouw1,2, Astrid M. Alsema1,2, Betty M. Tijms1,2, Philip Scheltens1,2, Cornelis J. Stam1,2, Wiesje M. van der Flier1,2, 1VU University Medical Center, Amsterdam, Netherlands; 2Neuroscience Campus Amsterdam, Amsterdam, Netherlands. Contact e-mail: aa.gouw@ vumc.nl Background: Amyloid-beta concentration in cerebrospinal fluid (CSF) is an accurate marker for diagnosis of Alzheimer’s disease, but the prognostic value of this marker is limited, as it does not predict time to clinical progression. Here, we studied if electroencephalography (EEG) derived measures of brain electrical activity are related to clinical progression in non-demented, amyloid positive
P256
Podium Presentations: Tuesday, July 21, 2015
subjects. Methods: We included 202 non-demented (CDR 0.5), amyloid-positive (CSF amyloid-b1-42< 640pg/ml) patients from the Amsterdam Dementia Cohort with available clinical followup (1 year) and baseline 21-channel resting-state EEG. Fast Fourier Transformation was performed on five 8.2-sec epochs per subject to calculate parieto-occipital peak frequency and global relative power of each frequency band (delta 0.5–4Hz, theta 4–8Hz, alpha 8–13Hz, beta 13–30Hz). Outcome measure was clinical progression, defined as CDR change 0.5. Cox proportional hazard analyses were performed for each box-cox and z-transformed spectral parameter with age and gender as covariates. Results: Mean age of the cohort was 67.767.8SD and 49% was female. 105(52%) patients showed clinical progression after a median follow-up of 2.0 years (range 0.4– 6.3). Follow-up for stable subjects was 2.1(1.0– 8.1) years. Median (range) relative power was 0.30(0.59) vs 0.30(0.47) in the delta band, 0.12(0.30) vs 0.12(0.42) in the theta band, 0.27(0.51) vs 0.28(0.63) in the alpha band, and 0.19(0.36) vs 0.18(0.40) in the beta band for subjects who clinically progressed vs stable subjects. Median (range) peak frequency was 9.5(6.7) for subjects who clinically progressed and 9.5(5.3) who remained stable. In the total sample, EEG spectral parameters were not related to clinical progression. When we stratified for CDR, higher relative theta power was related to clinical progression in patients with CDR 0 (n¼ 60; HR[CI95%]¼ 1.4 [1.0 – 2.2]), but not in patients with CDR 0.5 (HR[CI95%]¼ 1.0 [0.9 – 1.3). This effect seemed largely attributable to the younger individuals (CDR 0, age65; n¼ 24; HR[CI95%]¼ 6.4[1.4– 29.2]; compared to CDR 0, age>65; HR[CI95%]¼ 1.2[0.7 – 2.1]). Conclusions: Higher EEG-derived relative theta power was related to clinical progression in amyloid positive, cognitively normal individuals younger than 65 years old. In future studies, we will focus on connectivity based analysis of the EEG signal. O3-14-05
HOW COULD POTENTIAL SELECTION BIAS IMPACT THE ANALYSIS OF CORRELATES OF CEREBROSPINAL FLUID BIOMARKERS? THE MEMENTO COHORT
Genevieve Chene1,2,3, Audrey Gabelle4, Vincent Bouteloup1,5, Bruno Dubois6,7,8,9, Bruno Vellas10, Marie Chupin11,12,13,14,15, Olivier Hanon16, Jean-Franc¸ois Dartigues3,17,18, Florence Pasquier19, Mathieu Ceccaldi20, Olivier Beauchet21, Frederic Blanc22, Pierre Krolak Salmon23, Renaud David24, Olivier Rouaud25, Olivier Godefroy26, Catherine Belin27, Nicolas Auguste28, David Wallon29, Chabha Azouani30, Athanase Benetos31, Marc Paccalin32, Mathilde Sauvee33, Caroline Hommet34, Franc¸ois Sellal35, Martine Vercelletto36, Ludovic Fillon30, Isabelle Jalenques37, Armelle Gentric38, Pierre Vandel39, Helen Savarieau40, JeanFrancois Mangin41, Claire Paquet42, Carole Dufouil3,40,43, The Memento Study Group,1Bordeaux University Hospital, Bordeaux, France; 2Inserm, Bordeaux, France; 3Bordeaux University, Bordeaux, France; 4CHRU Gui de Chauliac Hospital, Montpellier, France; 5CIC1401, Clinical Epidemiology, Bordeaux, France; 6INSERM-Universite Pierre et Marie Curie, Paris 6, IHU-ICM, Paris, France; 7APHP- Groupe Hospitalier Pitie Salpetriere, Paris, France; 8APHP, Paris, France; 9Sorbonne Universites, Universite Pierre et Marie Curie-Paris 6, Paris, France; 10CHU Toulouse, Toulouse, France; 11CNRS, UMR 7225 ICM, Paris, France; 12Inria, Aramis project-team, Centre de Recherche Paris-Rocquencourt, Paris, France; 13 Sorbonne Universites, UPMC Univ Paris 06, UMR S 1127, F-75013, ere, ICM, Paris, France; 14Institut du Cerveau et de la Moelle Epini F-75013, Paris, France; 15Inserm, U1127, F-75013, Paris, France; 16 AP-HP, Paris, France; 17INSERM U897, Bordeaux, France; 18Memory Consultation, CHU Bordeaux, Bordeaux, France; 19Universite de Lille, Inserm U1171, CHU, Centre Memoire, Lille, France; 20H^opital de la Timone, Marseille, France; 21University of Angers, Angers, France;
22
Hospital Hautepierre, Strasbourg, France; 23Charpennes Hopspital Hopitaux Civils de Lyon, Villeurbanne, France; 24Centre Memoire de Ressources et de Recherche, CHU de Nice, Nice, France; 25H^opital General, Dijon, France; 26CHU Amiens, Amiens, France; 27Memery Clinic-Avicenne Hospital, Bobigny, France; 28CHU Saint-Etienne, Saint-Etienne, France; 29Rouen Hospital & Memeory Clinic, Rouen, France; 30CATI Project, Paris, France; 31Hopital Brabois and Memory Clinic, Nancy, France; 32CHU Poitiers and Memory Clinic, Poitiers, France; 33Grenoble Hospital & Memory Clinic, Grenoble, France; 34CHU Tours and Memory Clinic, Tours, France; 35Hopitaux Civils de Colmar, Colmar, France; 36CHU Nantes, Nantes, France; 37CHU Clermont-Ferrand, Clermont-Ferrand, France; 38Brest Hospital, Brest, France; 39CHU Besancon, Besancon, France; 40Bordeaux Hospital, Bordeaux, France; 41Neurospin-Institut d’Imagerie BioMedicale-Commissariat a l’Energie Atomique, GIF/Yvette, France; 42 CMRR, Paris, France; 43Inserm u897, Bordeaux, France. Contact e-mail: [email protected] Background: There is increasing interest in whether cerebrospinal fluid (CSF) biomarkers improve diagnostic accuracy in dementia and lead to earlier diagnosis of Alzheimer’s disease. However, the absence of information on the selection process of studied samples may point to weaknesses of prior studies. We aimed at assessing the selection process in an individual cohort and its potential impact on prediction models. Methods: The Memento cohort is a longitudinal study of the determinants of cognitive evolution (including dementia) of patients consecutively enrolled in French memory clinics, presenting either isolated cognitive complaints or MCI. Throughout France, 2319 participants have been enrolled and will be followed at least five years: at least yearly for clinical examinations and every two-years for brain MR imaging (mandatory) and lumbar puncture (LP) (optional). Logistic regression was used to assess whether LP participation differed by center, age, gender, education, informant’s presence, score at memory tests and memory complaints severity. We computed linear regression to identify factors associated with memory score among center, age, gender, education and hippocampal volume (Hv) (estimated by SACHA software) in the full and LP samples. Results: Mean age of participants was 70.4 years old (Standard Deviation¼8.7), 62% were women and 40% had a CDR scored 0. On 31/12/2014, 356 participants had had a LP procedure. LP was more likely to be undertaken if participants were younger (Odds-Ratio (OR)¼0.98 per year, p<0.0001), male (OR¼1.73, p<0.0001), had an amnestic-MCI profile (vs. normal cognition) (OR¼2.13, p¼0.007), had higher levels of memory subjective complaints (OR¼1.11, p¼0.03) or if an informant was present (OR¼1.37, p<0.001). Linear regression models showed much higher effect sizes in the associations with memory score in the LP sample than the full sample (Effects size 180% and 82% higher for age and Hv respectively). Conclusions: In our cohort, individuals who consent to LP differ by demographic and cognitive profiles and notably, are more likely to have memory impairments. Predictors of cognition differed between those who consented to LP and those who did not. Studies on CSF biomarkers need to take into account selection (through inverse probability weighting for example) in order to provide valid results. O3-14-06
ASSESSMENT OF THE INCREMENTAL PROGNOSTIC UTILITY OF CLINICAL DIAGNOSIS IN EQUATIONS FOR DISEASE PROGRESSION IN ALZHEIMER’S DISEASE
K. Jack Ishak1, Anuraag R. Kansal2, Stanimira Krotneva1, Laura Tarko3, Evidera, Montreal, QC, Canada; 2Evidera, Bethesda, MD, USA; 3Evidera, Lexington, MA, USA. Contact e-mail: [email protected] 1
P255
Table 1 Classification of MCI patients at baseline using individual biomarkers, NIA-AA criteria and the PredictAD tool Data required for analysis Individual biomarker; AD like: CSF amyloid beta 1-42 CSF total tau CSF p tau MTA NIA-AA criteria: Uninformative; Conflicting, unavailable, or only one marker present butintermediate likelihood; Only amyloid or neuronal injury marker present but + High likelihood; Amyloid and neuronal injury marker + Low likelihood; Amyloid and neuronal injury marker PredictAD tool: Using demographics, MRI and CSF data DSI 0.5 Using only MRI data DSI 0.5 Using only CSF data DSI 0.5 O3-14-03
Matching criteria
Stable at follow up
Dementia due to AD at follow up
AUC
sensitivity
specificity
137 (65%) 137 (65%) 137 (65%) 171 (81%)
72 (53%) 89 (65%) 97 (71%) 52 (30%)
14 17 22 10
58 72 75 42
0.81 0.86 0.82 0.67
68 85 88 40
73 67 58 57
103 (49%)
103 (49%)
39
64
-
-
-
18 (8%)
18 (8%)
3
15
-
-
-
67 (32%)
11
56
93
63
23 (11%)
19
4
-
211 (100%)
135 (64%)
25
110
0.81
80 (79-81)
66 (64-67)
211 (100%)
85 (50%)
20
66
0.70
62 (61-63)
71 (69-72)
211 (100%)
89 (65%)
14
75
0.86
89 (88-89)
72 (70-73)
90 (43%)
NOVEL FLUID BIOMARKERS FOR BRAIN AMYLOID AND DEMENTIA RISK IN PRESYMPTOMATIC ALZHEIMER DISEASE
Richard J. Perrin1,2,3, Hua Weng1, Kelly R. Bales4, John C. Morris1,5, Tammie L.S. Benzinger1,5, Anne M. Fagan5,6,7, Chengjie Xiong1,2, David M. Holtzman1,2,3, 1Washington University School of Medicine, Saint Louis, MO, USA; 2Knight Alzheimer’s Disease Research Center, Saint Louis, MO, USA; 3Hope Center for Neurological Disorders, Saint Louis, MO, USA; 4Pfizer, Inc, Groton, CT, USA; 5Knight Alzheimer’s Disease Research Center, St. Louis, MO, USA; 6Washington University School of Medicine, St. Louis, MO, USA; 7Hope Center for Neurological Disorders, St. Louis, MO, USA. Contact e-mail: [email protected] Background: For clinical trials targeting Ab production or clearance in presymptomatic Alzheimer disease (AD), additional biomarkers beyond CSF Ab42 and amyloid imaging will be useful. This study sought CSF and plasma proteins that can detect and predict preclinical amyloid, predict subsequent dementia, and provide insight into presymptomatic AD pathophysiology. Methods: Paired CSF and plasma samples, collected within 2.5 years of PET-PIB (Positron emission tomography with Pittsburgh compound B) from 118 Knight ADRC participants with normal cognition (Clinical Dementia Rating [CDR] 0), were analyzed using the 190MAP biomarker panel at Myriad/RBM. All participants were APOE-Ɛ3/-Ɛ3 or -Ɛ3/Ɛ4. Mean cortical PIB binding potential [MCBP] above/below 0.18 (PIB-POS/PIB-NEG) was treated as a categorical variable. Among N¼28 PIB-POS at baseline, N¼13 developed dementia (CDR>0); N¼12 of 67 PIB-NEG participants with multiple scans later became PIB-POS. Receiver operating characteristic (ROC) and C-index analyses were applied with age, gender, education, body mass index (BMI), and APOE-Ɛ4 status as covariates; biomarker combinations (Ab42-free) were evaluated by logistic regression. Results: BMI and MCBP were negatively correlated (p¼ 0.03). For detecting PIB-POS, covariate-adjusted ROC analyses identified 7 plasma and 7 CSF proteins with significant areas under the curve (AUC) (each >0.627; max 0.736 & 0.687). CSF tau/Ab42 yielded
AUC¼0.923; optimal biomarker panels yielded AUCs of 0.837 (CSF) and 0.814 (plasma). For predicting conversion to PIBPOS, covariate-adjusted ROC analyses yielded significant AUCs for 3 CSF and 9 plasma proteins (each >0.685; max 0.779 and 0.810); optimal panels yielded AUCs of 0.827 and 0.955. For predicting dementia (CDR>0) among PIB-POS, C-index analyses identified 6 CSF and 9 plasma proteins that individually contributed improvements to ‘covariates-only’ models (each >0.778; max 0.825 and 0.873); similar improvements by optimal biomarker panels yielded C-indices of 0.884 and 0.968. Conclusions: Many novel fluid proteins have potential for detecting and predicting presymptomatic brain amyloid, and for predicting subsequent dementia. These candidate biomarkers may have value for clinical prevention trials and provide pathophysiological insights into preclinical AD. Broadly, these results associate presymptomatic amyloid with lower BMI, low insulin, an anti-inflammatory peripheral milieu, and a limited neuroinflammatory response; more robust neuroinflammation at baseline is a harbinger of cognitive decline. O3-14-04
THE RELATION BETWEEN EEG SPECTRAL ANALYSIS AND CLINICAL PROGRESSION IN NON-DEMENTED, AMYLOID-POSITIVE SUBJECTS
Alida A. Gouw1,2, Astrid M. Alsema1,2, Betty M. Tijms1,2, Philip Scheltens1,2, Cornelis J. Stam1,2, Wiesje M. van der Flier1,2, 1VU University Medical Center, Amsterdam, Netherlands; 2Neuroscience Campus Amsterdam, Amsterdam, Netherlands. Contact e-mail: aa.gouw@ vumc.nl Background: Amyloid-beta concentration in cerebrospinal fluid (CSF) is an accurate marker for diagnosis of Alzheimer’s disease, but the prognostic value of this marker is limited, as it does not predict time to clinical progression. Here, we studied if electroencephalography (EEG) derived measures of brain electrical activity are related to clinical progression in non-demented, amyloid positive
P256
Podium Presentations: Tuesday, July 21, 2015
subjects. Methods: We included 202 non-demented (CDR 0.5), amyloid-positive (CSF amyloid-b1-42< 640pg/ml) patients from the Amsterdam Dementia Cohort with available clinical followup (1 year) and baseline 21-channel resting-state EEG. Fast Fourier Transformation was performed on five 8.2-sec epochs per subject to calculate parieto-occipital peak frequency and global relative power of each frequency band (delta 0.5–4Hz, theta 4–8Hz, alpha 8–13Hz, beta 13–30Hz). Outcome measure was clinical progression, defined as CDR change 0.5. Cox proportional hazard analyses were performed for each box-cox and z-transformed spectral parameter with age and gender as covariates. Results: Mean age of the cohort was 67.767.8SD and 49% was female. 105(52%) patients showed clinical progression after a median follow-up of 2.0 years (range 0.4– 6.3). Follow-up for stable subjects was 2.1(1.0– 8.1) years. Median (range) relative power was 0.30(0.59) vs 0.30(0.47) in the delta band, 0.12(0.30) vs 0.12(0.42) in the theta band, 0.27(0.51) vs 0.28(0.63) in the alpha band, and 0.19(0.36) vs 0.18(0.40) in the beta band for subjects who clinically progressed vs stable subjects. Median (range) peak frequency was 9.5(6.7) for subjects who clinically progressed and 9.5(5.3) who remained stable. In the total sample, EEG spectral parameters were not related to clinical progression. When we stratified for CDR, higher relative theta power was related to clinical progression in patients with CDR 0 (n¼ 60; HR[CI95%]¼ 1.4 [1.0 – 2.2]), but not in patients with CDR 0.5 (HR[CI95%]¼ 1.0 [0.9 – 1.3). This effect seemed largely attributable to the younger individuals (CDR 0, age65; n¼ 24; HR[CI95%]¼ 6.4[1.4– 29.2]; compared to CDR 0, age>65; HR[CI95%]¼ 1.2[0.7 – 2.1]). Conclusions: Higher EEG-derived relative theta power was related to clinical progression in amyloid positive, cognitively normal individuals younger than 65 years old. In future studies, we will focus on connectivity based analysis of the EEG signal. O3-14-05
HOW COULD POTENTIAL SELECTION BIAS IMPACT THE ANALYSIS OF CORRELATES OF CEREBROSPINAL FLUID BIOMARKERS? THE MEMENTO COHORT
Genevieve Chene1,2,3, Audrey Gabelle4, Vincent Bouteloup1,5, Bruno Dubois6,7,8,9, Bruno Vellas10, Marie Chupin11,12,13,14,15, Olivier Hanon16, Jean-Franc¸ois Dartigues3,17,18, Florence Pasquier19, Mathieu Ceccaldi20, Olivier Beauchet21, Frederic Blanc22, Pierre Krolak Salmon23, Renaud David24, Olivier Rouaud25, Olivier Godefroy26, Catherine Belin27, Nicolas Auguste28, David Wallon29, Chabha Azouani30, Athanase Benetos31, Marc Paccalin32, Mathilde Sauvee33, Caroline Hommet34, Franc¸ois Sellal35, Martine Vercelletto36, Ludovic Fillon30, Isabelle Jalenques37, Armelle Gentric38, Pierre Vandel39, Helen Savarieau40, JeanFrancois Mangin41, Claire Paquet42, Carole Dufouil3,40,43, The Memento Study Group,1Bordeaux University Hospital, Bordeaux, France; 2Inserm, Bordeaux, France; 3Bordeaux University, Bordeaux, France; 4CHRU Gui de Chauliac Hospital, Montpellier, France; 5CIC1401, Clinical Epidemiology, Bordeaux, France; 6INSERM-Universite Pierre et Marie Curie, Paris 6, IHU-ICM, Paris, France; 7APHP- Groupe Hospitalier Pitie Salpetriere, Paris, France; 8APHP, Paris, France; 9Sorbonne Universites, Universite Pierre et Marie Curie-Paris 6, Paris, France; 10CHU Toulouse, Toulouse, France; 11CNRS, UMR 7225 ICM, Paris, France; 12Inria, Aramis project-team, Centre de Recherche Paris-Rocquencourt, Paris, France; 13 Sorbonne Universites, UPMC Univ Paris 06, UMR S 1127, F-75013, ere, ICM, Paris, France; 14Institut du Cerveau et de la Moelle Epini F-75013, Paris, France; 15Inserm, U1127, F-75013, Paris, France; 16 AP-HP, Paris, France; 17INSERM U897, Bordeaux, France; 18Memory Consultation, CHU Bordeaux, Bordeaux, France; 19Universite de Lille, Inserm U1171, CHU, Centre Memoire, Lille, France; 20H^opital de la Timone, Marseille, France; 21University of Angers, Angers, France;
22
Hospital Hautepierre, Strasbourg, France; 23Charpennes Hopspital Hopitaux Civils de Lyon, Villeurbanne, France; 24Centre Memoire de Ressources et de Recherche, CHU de Nice, Nice, France; 25H^opital General, Dijon, France; 26CHU Amiens, Amiens, France; 27Memery Clinic-Avicenne Hospital, Bobigny, France; 28CHU Saint-Etienne, Saint-Etienne, France; 29Rouen Hospital & Memeory Clinic, Rouen, France; 30CATI Project, Paris, France; 31Hopital Brabois and Memory Clinic, Nancy, France; 32CHU Poitiers and Memory Clinic, Poitiers, France; 33Grenoble Hospital & Memory Clinic, Grenoble, France; 34CHU Tours and Memory Clinic, Tours, France; 35Hopitaux Civils de Colmar, Colmar, France; 36CHU Nantes, Nantes, France; 37CHU Clermont-Ferrand, Clermont-Ferrand, France; 38Brest Hospital, Brest, France; 39CHU Besancon, Besancon, France; 40Bordeaux Hospital, Bordeaux, France; 41Neurospin-Institut d’Imagerie BioMedicale-Commissariat a l’Energie Atomique, GIF/Yvette, France; 42 CMRR, Paris, France; 43Inserm u897, Bordeaux, France. Contact e-mail: [email protected] Background: There is increasing interest in whether cerebrospinal fluid (CSF) biomarkers improve diagnostic accuracy in dementia and lead to earlier diagnosis of Alzheimer’s disease. However, the absence of information on the selection process of studied samples may point to weaknesses of prior studies. We aimed at assessing the selection process in an individual cohort and its potential impact on prediction models. Methods: The Memento cohort is a longitudinal study of the determinants of cognitive evolution (including dementia) of patients consecutively enrolled in French memory clinics, presenting either isolated cognitive complaints or MCI. Throughout France, 2319 participants have been enrolled and will be followed at least five years: at least yearly for clinical examinations and every two-years for brain MR imaging (mandatory) and lumbar puncture (LP) (optional). Logistic regression was used to assess whether LP participation differed by center, age, gender, education, informant’s presence, score at memory tests and memory complaints severity. We computed linear regression to identify factors associated with memory score among center, age, gender, education and hippocampal volume (Hv) (estimated by SACHA software) in the full and LP samples. Results: Mean age of participants was 70.4 years old (Standard Deviation¼8.7), 62% were women and 40% had a CDR scored 0. On 31/12/2014, 356 participants had had a LP procedure. LP was more likely to be undertaken if participants were younger (Odds-Ratio (OR)¼0.98 per year, p<0.0001), male (OR¼1.73, p<0.0001), had an amnestic-MCI profile (vs. normal cognition) (OR¼2.13, p¼0.007), had higher levels of memory subjective complaints (OR¼1.11, p¼0.03) or if an informant was present (OR¼1.37, p<0.001). Linear regression models showed much higher effect sizes in the associations with memory score in the LP sample than the full sample (Effects size 180% and 82% higher for age and Hv respectively). Conclusions: In our cohort, individuals who consent to LP differ by demographic and cognitive profiles and notably, are more likely to have memory impairments. Predictors of cognition differed between those who consented to LP and those who did not. Studies on CSF biomarkers need to take into account selection (through inverse probability weighting for example) in order to provide valid results. O3-14-06
ASSESSMENT OF THE INCREMENTAL PROGNOSTIC UTILITY OF CLINICAL DIAGNOSIS IN EQUATIONS FOR DISEASE PROGRESSION IN ALZHEIMER’S DISEASE
K. Jack Ishak1, Anuraag R. Kansal2, Stanimira Krotneva1, Laura Tarko3, Evidera, Montreal, QC, Canada; 2Evidera, Bethesda, MD, USA; 3Evidera, Lexington, MA, USA. Contact e-mail: [email protected] 1