- Email: [email protected]
BEHAVIORAL SYMPTOMS ASSOCIATED WITH PROGRESSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER'S DISEASE: A LONGITUDINAL STUDY
P684 P3-151
Poster Presentations: P3 PROGRESSION TO DEMENTIA IN MEXICAN ELDERLY WITH MILD COGNITIVE IMPAIRMENT
Alberto Mimenza Alvarado1, Carolina Bernal Lopez2, Sara Aguilar Navarro1, Guillermo Davila de la Llave1, Alberto Avila2, 1 Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico, D.F., Mexico; 2Instituto Nacional de Ciencias Medicas y Nutrici on Salvador Zubir an, Mexico, Mexico. Contact e-mail: ajmaa@ prodigy.net.mx Background: Mild Cognitive Impairment (MCI) refers to an intermediate state of cognitive decline between normal aging and dementia. Many factors have been identified to play a role in progression from MCI to dementia. The aim of this study was to determinate factors associated with progression to dementia in Mexican elderly with mild cognitive impairment in a Memory Clinic. Methods: We present the retrospective arm of the MCI Cohort from our Memory Clinic. A total of 142 patients with MCI were evaluated. We obtain the sociodemographic information, past clinical history, neuropsychological evaluation and follow up from clinical records of the Memory Clinic inpatients. We analyzed the difference between the MCI progressive and stable by the t test and the x 2 test. Results: 53.5% were women and the average 81.5 (SD +/- 6.5). At baseline, according to the subtypes of MCI, patients were classified as follows: single domain amnestic-MCI (29.3%), multiple domain amnestic-MCI (35.4%), single domain nonamnestic-MCI (13.4%) and multiple domain nonamnesti-MCI (21.9%). At follow up, patients were classified in two groups: progressive MCI (51.3%), for those who convert to dementia, and stable MCI (48.7%), for those who remain as MCI at follow up. Multivariate logistic regression analysis found that progression to dementia in MCI patients was associated with age (OR 1.21, 95%CI 1.15 to 1.31, p ¼0.018), smoking (OR 3.32, 95%CI 2.45 to 7.23, p ¼.043), diabetes (OR 3.54, 95%CI 1.59 to 6.57, p ¼.014), and abnormal verbal fluency test at baseline (OR 4.96, 95%CI 2.98 to 12.34, p ¼.012). Conclusions: Age, vascular risk factors and abnormal verbal fluency test at baseline were associated with progression to dementia in Mexican elderly with MCI. Our results suggest that controlling blood glucose and avoiding smoking could be effective strategies to delay cognitive decline in patients with MCI and prevent conversion to dementia. P3-152
PAIN INTENSITY AND PAIN INTERFERENCE ARE ASSOCIATED WITH TRANSITIONS FROM COGNITIVE NORMALITY, AMNESTIC MCI (AMCI), AND DEMENTIA: RESULTS FROM THE EINSTEIN AGING STUDY (EAS)
Richard B. Lipton, Cuiling Wang, Mindy Joy Katz, Carol A. Derby, Molly E. Zimmerman, Albert Einstein College of Medicine, Bronx, New York, United States. Contact e-mail: [email protected] Background: Pain intensity and pain interference are common in older adults. In the EAS, pain interference is associated with the doubling of risk for incident dementia after adjusting for pain intensity. Herein, we use transition modelsto assess the influence of pain intensity and interference on the transitions among three states: cognitive normality, aMCI and dementia. Methods: Longitudinal data from the EAS were used to fit transition models based on mixed effects logistic regression. The models examined the influence of baseline pain intensity and interference, as measured by questions from the SF-36, on the transitions among normal (absence of dementia or aMCI), aMCI and dementia. All predictors, except time, are at baseline. All models include age, gender, education, years since baseline. Results: Among 1053 eligible participants (mean age at baseline 78.465.4. Pain interference was not significantly associated with transitioning from normal to aMCI but showed strong, borderline significant effects on the transition from normal to dementia (relative to staying at normal, OR¼2.71, P¼0.080). Pain interference was significantly associated with an increased odds, among people with aMCI of remaining aMCI using transitioning back to normal as the reference (OR¼2.67, P¼0.017). Conclusions: Pain interfence is associated with the transition from normal to dementia and also with remaining aMCI in a community
sample of older adults. These results are compatible with both causal and reverse causal hypotheses. P3-153
ADDITIVE VALUE OF BIOMARKERS ON A HIGHLY SENSITIVE COGNITIVE PREDICTOR OF ALZHEIMER’S DEMENTIA IN PATIENTS WITH MILD COGNITIVE IMPAIRMENT
Simone Clara Egli1, Daniela Hirni2, Kirsten I. Taylor2, Manfred Berres3, Andreas U. Monsch2, Achim Gass4, Axel Regeniter5, Marc A. Sollberger6, 1 University Center for Medicine of Aging Basel, Felix Platter-Hospital, Basel, Switzerland; 2University Center for Medicine of Aging Basel, Basel, Switzerland; 3University of Applied Sciences Koblenz, Koblenz, Germany; 4Neurologische Universit€atsklinik, Mannheim, Germany; 5 University Hospital Basel, Basel, Switzerland; 6University Center for Medicine of Aging, Basel, Switzerland. Contact e-mail: Simone.Egli@ fps-basel.ch Background: The combination of cognitive variables and biomarkers best predicts conversion to Alzheimer’s dementia (AD). However, previous studies used non-optimally sensitive cognitive measures of AD, and, correspondingly, results indicated that biomarkers predict conversion better than cognitive measures. This study aimed to a) compare the predictive strengths of a newly established, highly sensitive cognitive measure of AD, CSF and neuroimaging biomarkers, and b) examine the additive predictive strengths of biomarkers on the cognitive measure to predict disease progression (i.e., conversion to AD dementia and cognitive decline) in patients with mild cognitive impairment (MCI). Methods: A Regional Primacy Score (RPS) from a verbal episodic learning test, CSF (t-tau, p-tau 181, Ab 1-42), and neuroimaging (hippocampal and entorhinal cortex T1-weighted mean signal intensities, mean fornix fractional anisotropy) measures were available at baseline from thirty-six MCI patients participating in a longitudinal observational study. Univariate analyses of cognitive, CSF and neuroimaging measures were performed, followed by multivariate cox regression analyses for time to conversion to AD dementia and linear mixed-models for cognitive decline. Results: In contrast to all biomarkers, RPS significantly predicted both outcome measures in multivariate models. Biomarkers significantly enhanced the predictive accuracies of both outcome measures compared with RPS alone. The combination of RPS and CSF Ab 1-42 provided the best multivariate model for time to conversion whereas RPS and fornix integrity best predicted cognitive decline. Conclusions: A highly sensitive cognitive measure of AD is a critical predictor of disease progression above and beyond CSF and MRI biomarkers. The combination of reliable, multimodal predictors enhances predictive accuracy in line with the complementary information they provide. P3-154
BEHAVIORAL SYMPTOMS ASSOCIATED WITH PROGRESSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER’S DISEASE: A LONGITUDINAL STUDY
Stefan Van der Mussele1, Erik Fransen2, Peter Mari€en3, Jos Saerens4, Nore Somers5, Johan Goeman6, Peter Paul De Deyn7, Sebastiaan Engelborghs7, 1Antwerp University, Antwerp (Wilrijk), Belgium; 2 University of Antwerp, Edegem, Belgium; 3Vrije Universiteit Brussel, Brussel, Belgium; 4Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp (Wilrijk), Belgium; 5Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp (Hoboken), Belgium; 6Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp, Belgium; 7 University of Antwerp, Antwerp, Belgium. Contact e-mail: stefan. [email protected] Background: This study would like to contribute to the understanding of the prognostic role of behavioral symptoms in mild cognitive impairment (MCI) for the progression to dementia due to Alzheimer’s disease (AD). Methods: Data were generated through an ongoing prospective longitudinal study on behavioral symptoms in MCI and dementia. Behavioral assessment was performed by means of the MFS, Behave-AD, CMAI, CSDD and Geriatric Depression Scale 30-questions (GDS-30). Cox proportional hazard models were used to test the hypothesis that behavioral symptoms in MCI
Poster Presentations: P3 increase the risk for developing AD. Results: The study population consisted of 183 MCI patients at baseline. At follow-up, 74 patients were stable and 109 patients progressed to AD. The presence of significant depressive symptoms in MCI as measured by the CSDD (HR: 2.06; 95% CI: 1.23 3.44; p¼0.011) and the GDS-30 (HR: 1.77; 95% CI: 1.10 - 2.85; p¼0.025) were associated with an increased the risk of progression to AD. The severity of depressive symptoms as measured by the GDS-30 was a predictor for progression too (HR: 1.06; 95% CI: 1.01 - 1.11; p¼0.020). Furthermore, also the severity of agitated behavior, especially verbal agitation, and the presence of purposeless activity were associated risk factors for progression, whereas diurnal rhythm disturbances in our study was associated with a decreased risk of progression. Conclusions: Depressive symptoms in MCI appear to be associated with an increased risk of progression to AD.
P685
Israel; 2School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; 3IBM Speech Research Lab, Haifa, Israel; 4Nice Memory Clinic, Nice, France; 5Maastricht University, Maastricht, Netherlands; 6MUMC+, Maastricht, Netherlands; 7H^opital de Cimiez, Lyon, France. Contact e-mail: [email protected] Background: Assessment of early stage Alzheimer’s disease (E-AD) and other dementia types, as well as Mild Cognitive Impairment (MCI) is complex; a broad range of heterogeneous assessment methods exist. Various types of dementia and MCI are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progression. Therefore, automatic speech analysis is expected to be a useful tool in providing indicators for assessment and detection of early stage dementia and MCI. Methods: 13 Healthy elderly subjects (HC), 22 MCI patients and 23 E-AD patients were recorded while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description and counting down. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their ’power’ to distinguish between HC, MCI and E-AD. The second step included training automatic classifiers for detecting MCI and E-AD, based on machine learning methods, and testing the detection accuracy. Results: Preliminary results show the value of certain vocal tasks for distiguishing between HC, MCI and E-AD. Using the above data, we demonstrated classification accuracy as follows: between HC and MCI: 82 6 8%, between HC and E-AD: 87 6 5%, and between MCI and E-AD: 81 6 7%. Detailed description will be presented at the AAIC meeting. Conclusions: Decline in cognitive functioning affects speech production in different ways. Preliminary analysis indicates the potential value of vocal cognitive tasks for accurate automatic differentiation between HC, MCI and E-AD. This can provide the clinician with meaningful information for assessment and early diagnosis purposes, based on non-invasive, simple and low-cost method. Investigations of new and improved vocal tasks, signal processing tools and pattern recognition tools, are planned.
P3-156
POOR RENAL FUNCTION IS ASSOCIATED WITH AMCI AT CROSS-SECTION: RESULTS FROM THE EINSTEIN AGING STUDY
Andrea R. Zammit1, Mindy Joy Katz1, Markus Bitzer2, Jennifer Lai3, Richard B. Lipton1, 1Albert Einstein College of Medicine, Bronx, New York, United States; 2University of Michigan, Ann Arbor, Michigan, United States; 3University of Michigan, Ann Arbor, Michigan, United States. Contact e-mail: [email protected]
P3-155
THE DEM@CARE PROJECT SPEECH RECORDING AND AUTOMATIC ANALYSIS FOR THE ASSESSMENT OF ALZHEIMER DISEASE AND RELATED DISORDERS
Aharon Satt1, Alexandra K€onig2, Alexander Sorin1, Orith Toledo-Ronen3, Ron Hoory3, Renaud David4, Frans R.J. Verhey5, Pauline Aalten6, Philippe H. Robert7, 1Speech Technologies IBM Research Lab, Haifa,
Background: Though renal function is associated with cognitive impairment, few studies have examined the association between renal function and amnestic and non-amnsetic mild cognitive impairment (aMCI, naMCI). Further the prevalence of cognitive impairment seems to appear early in the course of renal disease, where i ndividuals with poor renal function experience greater decline in cognitive function. The objective of this study was to determine the association between eGFR and aMCI and naMCI and dementia in a relatively physically healthy community-dwelling sample. Methods: This a cross-sectional analysis conducted in the Einstein Aging Study (EAS). EAS enrolls community dwelling, English-speaking residents of Bronx county New York who are age 70+. Renal function was assessed using the estimated glomerular filtration rate (eGFR) using the CKD-EPI
Poster Presentations: P3 PROGRESSION TO DEMENTIA IN MEXICAN ELDERLY WITH MILD COGNITIVE IMPAIRMENT
Alberto Mimenza Alvarado1, Carolina Bernal Lopez2, Sara Aguilar Navarro1, Guillermo Davila de la Llave1, Alberto Avila2, 1 Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran, Mexico, D.F., Mexico; 2Instituto Nacional de Ciencias Medicas y Nutrici on Salvador Zubir an, Mexico, Mexico. Contact e-mail: ajmaa@ prodigy.net.mx Background: Mild Cognitive Impairment (MCI) refers to an intermediate state of cognitive decline between normal aging and dementia. Many factors have been identified to play a role in progression from MCI to dementia. The aim of this study was to determinate factors associated with progression to dementia in Mexican elderly with mild cognitive impairment in a Memory Clinic. Methods: We present the retrospective arm of the MCI Cohort from our Memory Clinic. A total of 142 patients with MCI were evaluated. We obtain the sociodemographic information, past clinical history, neuropsychological evaluation and follow up from clinical records of the Memory Clinic inpatients. We analyzed the difference between the MCI progressive and stable by the t test and the x 2 test. Results: 53.5% were women and the average 81.5 (SD +/- 6.5). At baseline, according to the subtypes of MCI, patients were classified as follows: single domain amnestic-MCI (29.3%), multiple domain amnestic-MCI (35.4%), single domain nonamnestic-MCI (13.4%) and multiple domain nonamnesti-MCI (21.9%). At follow up, patients were classified in two groups: progressive MCI (51.3%), for those who convert to dementia, and stable MCI (48.7%), for those who remain as MCI at follow up. Multivariate logistic regression analysis found that progression to dementia in MCI patients was associated with age (OR 1.21, 95%CI 1.15 to 1.31, p ¼0.018), smoking (OR 3.32, 95%CI 2.45 to 7.23, p ¼.043), diabetes (OR 3.54, 95%CI 1.59 to 6.57, p ¼.014), and abnormal verbal fluency test at baseline (OR 4.96, 95%CI 2.98 to 12.34, p ¼.012). Conclusions: Age, vascular risk factors and abnormal verbal fluency test at baseline were associated with progression to dementia in Mexican elderly with MCI. Our results suggest that controlling blood glucose and avoiding smoking could be effective strategies to delay cognitive decline in patients with MCI and prevent conversion to dementia. P3-152
PAIN INTENSITY AND PAIN INTERFERENCE ARE ASSOCIATED WITH TRANSITIONS FROM COGNITIVE NORMALITY, AMNESTIC MCI (AMCI), AND DEMENTIA: RESULTS FROM THE EINSTEIN AGING STUDY (EAS)
Richard B. Lipton, Cuiling Wang, Mindy Joy Katz, Carol A. Derby, Molly E. Zimmerman, Albert Einstein College of Medicine, Bronx, New York, United States. Contact e-mail: [email protected] Background: Pain intensity and pain interference are common in older adults. In the EAS, pain interference is associated with the doubling of risk for incident dementia after adjusting for pain intensity. Herein, we use transition modelsto assess the influence of pain intensity and interference on the transitions among three states: cognitive normality, aMCI and dementia. Methods: Longitudinal data from the EAS were used to fit transition models based on mixed effects logistic regression. The models examined the influence of baseline pain intensity and interference, as measured by questions from the SF-36, on the transitions among normal (absence of dementia or aMCI), aMCI and dementia. All predictors, except time, are at baseline. All models include age, gender, education, years since baseline. Results: Among 1053 eligible participants (mean age at baseline 78.465.4. Pain interference was not significantly associated with transitioning from normal to aMCI but showed strong, borderline significant effects on the transition from normal to dementia (relative to staying at normal, OR¼2.71, P¼0.080). Pain interference was significantly associated with an increased odds, among people with aMCI of remaining aMCI using transitioning back to normal as the reference (OR¼2.67, P¼0.017). Conclusions: Pain interfence is associated with the transition from normal to dementia and also with remaining aMCI in a community
sample of older adults. These results are compatible with both causal and reverse causal hypotheses. P3-153
ADDITIVE VALUE OF BIOMARKERS ON A HIGHLY SENSITIVE COGNITIVE PREDICTOR OF ALZHEIMER’S DEMENTIA IN PATIENTS WITH MILD COGNITIVE IMPAIRMENT
Simone Clara Egli1, Daniela Hirni2, Kirsten I. Taylor2, Manfred Berres3, Andreas U. Monsch2, Achim Gass4, Axel Regeniter5, Marc A. Sollberger6, 1 University Center for Medicine of Aging Basel, Felix Platter-Hospital, Basel, Switzerland; 2University Center for Medicine of Aging Basel, Basel, Switzerland; 3University of Applied Sciences Koblenz, Koblenz, Germany; 4Neurologische Universit€atsklinik, Mannheim, Germany; 5 University Hospital Basel, Basel, Switzerland; 6University Center for Medicine of Aging, Basel, Switzerland. Contact e-mail: Simone.Egli@ fps-basel.ch Background: The combination of cognitive variables and biomarkers best predicts conversion to Alzheimer’s dementia (AD). However, previous studies used non-optimally sensitive cognitive measures of AD, and, correspondingly, results indicated that biomarkers predict conversion better than cognitive measures. This study aimed to a) compare the predictive strengths of a newly established, highly sensitive cognitive measure of AD, CSF and neuroimaging biomarkers, and b) examine the additive predictive strengths of biomarkers on the cognitive measure to predict disease progression (i.e., conversion to AD dementia and cognitive decline) in patients with mild cognitive impairment (MCI). Methods: A Regional Primacy Score (RPS) from a verbal episodic learning test, CSF (t-tau, p-tau 181, Ab 1-42), and neuroimaging (hippocampal and entorhinal cortex T1-weighted mean signal intensities, mean fornix fractional anisotropy) measures were available at baseline from thirty-six MCI patients participating in a longitudinal observational study. Univariate analyses of cognitive, CSF and neuroimaging measures were performed, followed by multivariate cox regression analyses for time to conversion to AD dementia and linear mixed-models for cognitive decline. Results: In contrast to all biomarkers, RPS significantly predicted both outcome measures in multivariate models. Biomarkers significantly enhanced the predictive accuracies of both outcome measures compared with RPS alone. The combination of RPS and CSF Ab 1-42 provided the best multivariate model for time to conversion whereas RPS and fornix integrity best predicted cognitive decline. Conclusions: A highly sensitive cognitive measure of AD is a critical predictor of disease progression above and beyond CSF and MRI biomarkers. The combination of reliable, multimodal predictors enhances predictive accuracy in line with the complementary information they provide. P3-154
BEHAVIORAL SYMPTOMS ASSOCIATED WITH PROGRESSION FROM MILD COGNITIVE IMPAIRMENT TO ALZHEIMER’S DISEASE: A LONGITUDINAL STUDY
Stefan Van der Mussele1, Erik Fransen2, Peter Mari€en3, Jos Saerens4, Nore Somers5, Johan Goeman6, Peter Paul De Deyn7, Sebastiaan Engelborghs7, 1Antwerp University, Antwerp (Wilrijk), Belgium; 2 University of Antwerp, Edegem, Belgium; 3Vrije Universiteit Brussel, Brussel, Belgium; 4Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp (Wilrijk), Belgium; 5Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp (Hoboken), Belgium; 6Ziekenhuisnetwerk Antwerpen (ZNA), Antwerp, Belgium; 7 University of Antwerp, Antwerp, Belgium. Contact e-mail: stefan. [email protected] Background: This study would like to contribute to the understanding of the prognostic role of behavioral symptoms in mild cognitive impairment (MCI) for the progression to dementia due to Alzheimer’s disease (AD). Methods: Data were generated through an ongoing prospective longitudinal study on behavioral symptoms in MCI and dementia. Behavioral assessment was performed by means of the MFS, Behave-AD, CMAI, CSDD and Geriatric Depression Scale 30-questions (GDS-30). Cox proportional hazard models were used to test the hypothesis that behavioral symptoms in MCI
Poster Presentations: P3 increase the risk for developing AD. Results: The study population consisted of 183 MCI patients at baseline. At follow-up, 74 patients were stable and 109 patients progressed to AD. The presence of significant depressive symptoms in MCI as measured by the CSDD (HR: 2.06; 95% CI: 1.23 3.44; p¼0.011) and the GDS-30 (HR: 1.77; 95% CI: 1.10 - 2.85; p¼0.025) were associated with an increased the risk of progression to AD. The severity of depressive symptoms as measured by the GDS-30 was a predictor for progression too (HR: 1.06; 95% CI: 1.01 - 1.11; p¼0.020). Furthermore, also the severity of agitated behavior, especially verbal agitation, and the presence of purposeless activity were associated risk factors for progression, whereas diurnal rhythm disturbances in our study was associated with a decreased risk of progression. Conclusions: Depressive symptoms in MCI appear to be associated with an increased risk of progression to AD.
P685
Israel; 2School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands; 3IBM Speech Research Lab, Haifa, Israel; 4Nice Memory Clinic, Nice, France; 5Maastricht University, Maastricht, Netherlands; 6MUMC+, Maastricht, Netherlands; 7H^opital de Cimiez, Lyon, France. Contact e-mail: [email protected] Background: Assessment of early stage Alzheimer’s disease (E-AD) and other dementia types, as well as Mild Cognitive Impairment (MCI) is complex; a broad range of heterogeneous assessment methods exist. Various types of dementia and MCI are manifested as irregularities in human speech and language, which have proven to be strong predictors for the disease presence and progression. Therefore, automatic speech analysis is expected to be a useful tool in providing indicators for assessment and detection of early stage dementia and MCI. Methods: 13 Healthy elderly subjects (HC), 22 MCI patients and 23 E-AD patients were recorded while performing several short vocal cognitive tasks during a regular consultation. These tasks included verbal fluency, picture description and counting down. The voice recordings were processed in two steps: in the first step, vocal markers were extracted using speech signal processing techniques; in the second, the vocal markers were tested to assess their ’power’ to distinguish between HC, MCI and E-AD. The second step included training automatic classifiers for detecting MCI and E-AD, based on machine learning methods, and testing the detection accuracy. Results: Preliminary results show the value of certain vocal tasks for distiguishing between HC, MCI and E-AD. Using the above data, we demonstrated classification accuracy as follows: between HC and MCI: 82 6 8%, between HC and E-AD: 87 6 5%, and between MCI and E-AD: 81 6 7%. Detailed description will be presented at the AAIC meeting. Conclusions: Decline in cognitive functioning affects speech production in different ways. Preliminary analysis indicates the potential value of vocal cognitive tasks for accurate automatic differentiation between HC, MCI and E-AD. This can provide the clinician with meaningful information for assessment and early diagnosis purposes, based on non-invasive, simple and low-cost method. Investigations of new and improved vocal tasks, signal processing tools and pattern recognition tools, are planned.
P3-156
POOR RENAL FUNCTION IS ASSOCIATED WITH AMCI AT CROSS-SECTION: RESULTS FROM THE EINSTEIN AGING STUDY
Andrea R. Zammit1, Mindy Joy Katz1, Markus Bitzer2, Jennifer Lai3, Richard B. Lipton1, 1Albert Einstein College of Medicine, Bronx, New York, United States; 2University of Michigan, Ann Arbor, Michigan, United States; 3University of Michigan, Ann Arbor, Michigan, United States. Contact e-mail: [email protected]
P3-155
THE DEM@CARE PROJECT SPEECH RECORDING AND AUTOMATIC ANALYSIS FOR THE ASSESSMENT OF ALZHEIMER DISEASE AND RELATED DISORDERS
Aharon Satt1, Alexandra K€onig2, Alexander Sorin1, Orith Toledo-Ronen3, Ron Hoory3, Renaud David4, Frans R.J. Verhey5, Pauline Aalten6, Philippe H. Robert7, 1Speech Technologies IBM Research Lab, Haifa,
Background: Though renal function is associated with cognitive impairment, few studies have examined the association between renal function and amnestic and non-amnsetic mild cognitive impairment (aMCI, naMCI). Further the prevalence of cognitive impairment seems to appear early in the course of renal disease, where i ndividuals with poor renal function experience greater decline in cognitive function. The objective of this study was to determine the association between eGFR and aMCI and naMCI and dementia in a relatively physically healthy community-dwelling sample. Methods: This a cross-sectional analysis conducted in the Einstein Aging Study (EAS). EAS enrolls community dwelling, English-speaking residents of Bronx county New York who are age 70+. Renal function was assessed using the estimated glomerular filtration rate (eGFR) using the CKD-EPI