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Neurobiology of Aging 56 (2017) 33e40

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Neurobiology of Aging

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The frequency and influence of dementia risk factors in prodromal Alzheimer’s disease

Isabelle Bos a,*, Stephanie J. Vos a, Lutz Frölich b,c, Johannes Kornhuber b,d, Jens Wiltfang b,e, Wolfgang Maier b,f, Oliver Peters b,g, Eckhart Rüther b,h, Sebastiaan Engelborghs i, j, Ellis Niemantsverdriet j, Ellen Elisa De Roeck j,k, Magda Tsolaki l, Yvonne Freund-Levi m,n, Peter Johannsen o, Rik Vandenberghe p,q, Alberto Lleó r, Daniel Alcolea r, Giovanni B. Frisoni s,t,u, Samantha Galluzzi u, Flavio Nobili s,v, Silvia Morbelli s,w, Alexander Drzezga s,x, Mira Didic s,y,z, Bart N. van Berckel s,aa, Eric Salmon bb,cc, Christine Bastin cc, Solene Dauby bb, Isabel Santana bb, Inês Baldeiras dd, Alexandre de Mendonça ee, Dina Silva ee, Anders Wallin ff, Arto Nordlund ff, Preciosa M. Coloma gg, Angelika Wientzek hh,ii, Myriam Alexander hh, Gerald P. Novak jj, Mark Forrest Gordon kk, the Alzheimer’s Disease Neuroimaging Initiative1, Åsa K. Wallin ll, Harald Hampelmm, Hilkka Soininen nn, Sanna-Kaisa Herukka nn, Philip Scheltens oo, Frans R. Verhey a, Pieter Jelle Visser a,oo

aDepartment of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, Netherlands bOn behalf of German Dementia Competence Network cDepartment of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany dDepartment of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany eDepartment of Psychiatry and Psychotherapy, University Medical Center (UMC), Georg-August-University, Göttingen, Germany fDepartment of Psychiatry and Psychotherapy, University of Bonn, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany gDepartment of Psychiatry and Psychotherapy, Charité Berlin, Berlin, Germany hDepartment of Psychiatry and Psychotherapy, University of Göttingen, Göttingen, Germany iDepartment of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium jReference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium kDepartment of Clinical and Lifespan Psychology, Vrije Universiteit Brussel, Brussels, Belgium l 3rd Department of Neurology, Aristotle University of Thessaloniki, Memory and Dementia Center, “G Papanicolau” General Hospital, Thessaloniki, Greece mDivision of Clinical Geriatrics, Department of Neurobiology, Caring Sciences and Society (NVS), Karolinska Institutet, Huddinge, Sweden nDepartment of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden oDanish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark pDepartment of Neurology, University of Hospital Leuven, Leuven, Belgium q Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Belgium rHospital de la Santa Creu i Sant Pau, Barcelona, Spain sOn behalf of the EADC-PET consortium tGeneva Neuroscience Center, University Hospital and University of Geneva, Geneva, Switzerland u IRCCS San Giovanni di Dio Fatebenefratelli, Brescia, Italy vClinical Neurology, Department of Neurosciences (DINOGMI), University of Genoa and IRCCS AOU San Martino-IST, Genoa, Italy wNuclear Medicine, Department of Health Science (DISSAL), University of Genoa IRCCS AOU San Martino-IST, Genoa, Italy xDepartment of Nuclear Medicine, University of Cologne, Cologne, Germany yAP-HM Hôpitaux de la Timone, Service de Neurologie et Neuropsychologie, Marseille, France zAix-Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France aaDepartment of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands bbDepartment of Neurology and Memory Clinic, CHU Liège, Liège, Belgium ccGIGA-CRC in vivo Imaging, University of Liège, Liège, Belgium ddCenter for Neuroscience and Cell Biology, Faculty of Medicine, Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal ee Institute of Molecular Medicine and Faculty of Medicine, University of Lisbon, Portugal

* Corresponding author at: Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University Universiteitssingel 40, Box 34, P.O. Box 616, 6200 MD Maastricht, the Netherlands. Tel.: þ31 (0)43 38 84113; fax: þ31 (0)43 38 75444.

0197-4580/$ e see front matter � 2017 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2017.03.034

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e4034

ffDepartment of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden ggReal World Data Science (RWD-S) Neuroscience and Established Products, F. Hoffmann-La Roche Ltd. Pharmaceuticals Division, Basel, Switzerland hh PDB RWD (Real World Data) Team, Roche Products Limited, Welwyn Garden City, UK ii Epidemiologische Beratung und Literatur-Recherche “conepi”, Herrsching, Germany jj Janssen Pharmaceutical Research and Development, Titusville, NJ, USA kkBoehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA llDepartment of Clinical Sciences Malmö, Lund University, Clinical Memory Research Unit, Lund, Sweden mm Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, AXA Research Fund & UPMC Chair, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), Département de Neurologie, Hôpital de la Pitié-Salpétrière, 47 Boulevard de l’Hôpital, Paris, CEDEX 13, France nn Institute of Clinical Medicine, Neurology, University of Eastern Finland and Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland ooAlzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands

a r t i c l e i n f o

Article history: Received 9 November 2016 Received in revised form 29 March 2017 Accepted 31 March 2017 Available online 8 April 2017

Keywords: Alzheimer’s disease Risk factors IWG-2 criteria NIA-AA criteria Biomarkers Prognosis

1 Data used in preparation of this article were Alzheimer’s Disease Neuroimaging Initiative (ADNI) da such, the investigators within the ADNI contributed to tion of ADNI and/or provided data but did not participat report. A complete listing of ADNI investigators can be edu/wpcontent/uploads/ how_to_apply/ADNI_Acknow

a b s t r a c t

We investigated whether dementia risk factors were associated with prodromal Alzheimer’s disease (AD) according to the International Working Group-2 and National Institute of Aging-Alzheimer’s Association criteria, and with cognitive decline. A total of 1394 subjects with mild cognitive impairment from 14 different studies were classified according to these research criteria, based on cognitive performance and biomarkers. We compared the frequency of 10 risk factors between the subgroups, and used Cox- regression to examine the effect of risk factors on cognitive decline. Depression, obesity, and hyper- cholesterolemia occurred more often in individuals with low-AD-likelihood, compared with those with a high-AD-likelihood. Only alcohol use increased the risk of cognitive decline, regardless of AD pathology. These results suggest that traditional risk factors for AD are not associated with prodromal AD or with progression to dementia, among subjects with mild cognitive impairment. Future studies should validate these findings and determine whether risk factors might be of influence at an earlier stage (i.e., pre- clinical) of AD.

� 2017 Elsevier Inc. All rights reserved.

1. Introduction

Various risk factors have been associated with an increased risk for Alzheimer’s disease (AD; Breteler, 2000; de Bruijn and Ikram, 2014). Recently, research criteria have been proposed to identify AD in subjects with mild cognitive impairment (MCI) by their biomarker status, referred to as prodromal AD by international working group-2 (IWG-2; Dubois et al., 2014) and MCI due to AD by the National Institute of Aging-Alzheimer Association (NIA-AA; Albert et al., 2011). It remains uncertain whether risk factors are associated with prodromal AD/MCI due to AD, and whether they influence the rate of cognitive decline. This information could improve early diagnosis and lead to new targets for secondary prevention strategies.

Among the best-validated risk factors for AD are atherosclerosis, depression, diabetes mellitus, hypercholesterolemia, hypertension, lacunar infarcts, stroke, obesity, smoking, and alcohol consumption (Breteler, 2000; de Bruijn and Ikram, 2014; Deckers et al., 2015). Diabetes mellitus, depression, hypertension, stroke, and cardio- vascular diseases have also been associated with an increased risk of progressing from cognitively normal to MCI (Pankratz et al., 2015; Roberts et al., 2015). Moreover, an association with cogni- tive decline has been found in both cognitively normal and MCI subjects (Jefferson et al., 2015; Kaffashian et al., 2013). Therefore, we hypothesize that risk factors will occur more frequently in in- dividuals with prodromal AD/MCI due to AD. We also expect that risk factors will increase the risk of progression to dementia.

partially obtained from the tabase (adni.loni.usc.edu). As the design and implementa- e in analysis orwriting of this found at: http://adni.loni.usc. ledgement_List.pdf.

We aim to investigate the frequency of several risk factors in individuals with prodromal AD/MCI due to AD, classified according to the IWG-2 and NIA-AA criteria, relative to subjects who do not meet these criteria. Secondly, we aim to examine whether risk factors influence the rate of cognitive decline.

2. Methods

2.1. Subjects

Subjects were recruited from 5 multicenter memory-clinic based studies: DESCRIPA (Visser et al., 2008), German Dementia Competence Network (Kornhuber et al., 2009), EDAR (www. edarstudy.eu), the European Alzheimer’s Disease Consortium (EADC)-PET study (Morbelli et al., 2012), and American Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) study (Mueller et al., 2005; Supplemental Text 1); and 9 centers of the EADC and/or EuropeanMedical Information Framework (EMIF)eAD: Amsterdam (van der Flier et al., 2014), Antwerp (Somers et al., 2016), Barcelona (Alcolea et al., 2014), Brescia (Frisoni et al., 2009), Coimbra (Baldeiras et al., 2008), Gothenburg (Wallin et al., 2016), Kuopio (Seppala et al., 2011), Liège (Bastin et al., 2010), and Lisbon (Maroco et al., 2011). For subjects who participated in more than one study, we used data from the study with the longest follow-up.

Inclusion criteria consisted of baseline diagnosis of MCI ac- cording to the criteria of Petersen (Petersen, 2004), and at least one of the following biomarkers available at baseline: amyloid-beta (Ab) 1-42 and tau (total tau and/or phosphorylated tau) in CSF, hippocampal volume on magnetic resonance imaging (MRI), or cerebral glucose metabolism on [18F]FDG-PET of the brain. More- over, baseline data had to be available on at least one of the selected risk factors, as well as information on educational level and at least one clinical follow-up assessment. Exclusion criteria were diagnosis of dementia at baseline.

Table 1 Classification of subjects according to IWG-2 and NIA-AA criteria

IWG-2 groups Amyloid marker: CSF Ab 1-42

Neuronal injury marker: CSF t-tau or p-tau

No prodromal AD Abnormal Normal Normal Abnormal Normal Normal

Prodromal AD Abnormal Abnormal

NIA-AA groups Amyloid Marker: CSF Ab 1-42

Neuronal injury markers: CSF t-tau or p-tau/MTA on MRI/FDG-PET

Low-AD-likelihood Normal All normal High-AD-likelihood Abnormal At least one abnormal IAP Abnormal All normal SNAP Normal At least one abnormal Intermediate-AD-likelihood Unknown At least one abnormal Inconclusive/uninformative Unknown All normal

Key: Ab, amyloid-beta; AD, Alzheimer’s disease; CSF, cerebrospinal fluid; FDG-PET, fluorodeoxyglucose-positron emission tomography; IAP, isolated amyloid pathology; IWG, International Working Group; MRI, magnetic resonance imaging; MTA, medial temporal lobe atrophy;NIA-AA,National InstituteofAging-Alzheimer’sAssociation; p- tau, phosphorylated tau; SNAP, non-Alzheimer pathophysiology; t-tau, total tau.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 35

2.2. Clinical assessment

The clinical assessment is described in detail by Vos et al. (2015). In short, clinical assessment was performed at each site according to local routine protocol. Cognitive impairment was defined as Z- score<�1.5 SD on at least one neuropsychological test, which could be a memory or nonmemory test.

2.3. Outcome at follow-up

Cognitive decline was defined as progression to dementia ac- cording to the Diagnostic and Statistical Manual of Mental Disor- ders (APA,1994), or a decline on theMini-Mental State Examination (MMSE) of at least 3 points at follow-up. We used a combination of these 2 measures, as for a subgroup (n¼ 17), no clinical diagnosis at follow-up was available. For sub analyses, diagnosis of AD-type dementia at follow-up was made according to the National Institute of Neurological and Communicative Disorders and StrokeeAlzheimer’s Disease and Related Disorders Association criteria (McKhann et al., 1984).

The medical ethics committee at each site approved the study. All subjects provided informed consent.

2.4. Biomarker assessment

Biomarker assessment was performed according to the routine protocol at each site and center-specific cut-offs were used to define abnormality, as described elsewhere (Vos et al., 2015). Examination of medial temporal lobe atrophy on MRI and cerebral glucose metabolism on FDG-PET were performed through visual assessment.

2.5. Subject classification

Subjects were classified as having prodromal AD according to the IWG-2 criteria using CSF Ab1-42 and tau biomarkers (Table 1). The NIA-AA criteria distinguish between 6 groups that indicate the likelihood that MCI is due to AD, based on combinations of amyloid and neuronal injury markers. We used CSF Ab1-42 as amyloid marker and CSF total tau, CSF phosphorylated tau, cerebral glucose metabolism on FDG-PET, hippocampal volume, or medial temporal lobe atrophy on MRI as neuronal injury markers (Table 1).

2.6. Risk factors

We assessed the following risk factors at baseline: atheroscle- rotic disease, depression, diabetes, hypercholesterolemia, hyper- tension, lacunar infarct, obesity, stroke, current smoking, and current alcohol use. Not all risk factors were available for each subject. Supplemental Table 1 provides an overview of the available risk factors for each center. The risk factor definitions are described by center in Supplemental Table 2. For all risk factors occurrence in medical history was used as a standard. For some risk factors, we used additional definitions based on rating scales, physical mea- surements or medication use, based on availability (Supplemental Table 2).

2.7. Statistical analyses

Baseline differences between the biomarker profile groups were analyzed using ANOVA for continuous variables and c2 test for categorical variables. The relation of risk factors with prodromal AD/MCI due to AD was tested with logistic regression (IWG-2 criteria) or multinomial regression (NIA-AA criteria). Cox propor- tional hazards models were used to test the effect of each risk factor

on the rate of cognitive decline in the total sample, and for the IWG- 2 and NIA-AA biomarker subgroups. All analyses were adjusted for age, sex, education, and center. Statistical analyses were performed using SPSS version 22.0 with the significance level set at p < 0.05. We corrected for multiple comparisons, using the false discovery rate (FDR) adjustment (Benjamini and Hochberg, 1995), taking into account the testing of 10 risk factors. In tables, we reported un- corrected p-values and we indicated which associations were sig- nificant after correction for multiple comparisons in tables and the text.

3. Results

3.1. Subject characteristics

We included 1394 individuals (mean age ¼ 69.7, SD 8.3; 51% female). Seven hundred and fifty-eight subjects had data available on both amyloid and neuronal injury markers, whereas 636 sub- jects only had data on a neuronal injury marker (medial temporal lobe atrophy n ¼ 528, FDG-PET n ¼ 108). Five hundred and eighty individuals (42%) showed cognitive decline after an average follow- up time of 2.3 (SD 1.2) years. Table 2 shows the characteristics of the subjects classified according to the IWG-2 and the NIA-AA criteria.

3.2. Analyses in subjects with both amyloid and neuronal injury markers

Based on the IWG-2 criteria, 302 subjects (40%) were classified as prodromal AD. Individuals with prodromal AD were older (p < 0.001), showed a lower score on the MMSE at follow-up (p< 0.001) and were more likely to progress to AD-type dementia at follow-up (p < 0.001) compared with subjects without prodromal AD. Ac- cording to the NIA-AA criteria, 142 individuals (10%) were classified in the low-AD-likelihood group, 356 (26%) in the high-AD- likelihood group, 54 (4%) in the isolated amyloid pathology group, and 206 (15%) in the Suspected Non-AD Pathophysiology group. Subjects in the high-AD-likelihood group were older and most likely to progress to AD-type dementia, compared with all other groups (Table 2).

3.2.1. Frequency of risk factors Table 3 shows the frequency of AD risk factors for the NIA-AA

groups with both amyloid and neuronal injury data. Compared

Table 2 Demographics and clinical outcome according to IWG-2 and NIA-AA criteria

Characteristics IWG-2 criteria NIA-AA criteriadamyloid and neuronal injury markers NIA-AA criteriadonly neuronal injury markers

No prodromal AD (N ¼ 456)

Prodromal AD (N ¼ 302) Low-AD-likelihood

(N ¼ 142) High-AD-likelihood (N ¼ 356)

IAP (N ¼ 54) SNAP

(N ¼ 206)

Uninformative/Inconclusive (N ¼ 286)

Intermediate-AD-likelihood (N ¼ 350)

Age, y 67.3 (8.6) 71.3 (7.5)e 63.4 (8.9)g,i 71.3 (7.5)f,h,i 66.2 (7.5)g 69.2 (8.0)f,g 67.8 (8.4) 73.1 (7.2)j

Female, n 207 (45%) 148 (49%) 64 (45%) 170 (48%) 25 (46%) 96 (46%) 183 (64%) 177 (50%)j

Education, y 10.4 (3.8) 11.7 (4.3)e 10.3 (3.2)g 11.6 (4.3)f,i 11.0 (4.0) 10.2 (3.9)g 9.9 (4.6) 10.7 (4.2)j

Follow-up, y 2.3 (1.2) 2.3 (1.1) 2.1 (0.9) 2.3 (1.2) 2.3 (1.2) 2.4 (1.3) 2.6 (1.4) 2.3 (1.3)j

APOE-ε4a 138 (35%) 184 (68%)e 37 (30%)g 205 (65%)f,i 22 (47%) 58 (32%)g 81 (36%) 141 (52%)j

MMSE at baseline 27.0 (2.3) 26.1 (2.5)e 27.5 (2.9)g,i 26.2 (2.5)f,h 27.3 (2.4)g 26.7 (2.3)f 27.5 (2.3) 26.6 (2.2)j

Decline on MMSE at follow-upb

58 (25%) 114 (51%)e 8 (13%)g 127 (49%)f,h,i 6 (16%)g 31 (30%)g 329 (38%) 106 (45%)j

Progression to AD-type dementia at follow-upc

86 (20%) 172 (59%)e 6 (5%)g,i 193 (56%)f,h,i 10 (20%)f,g 49 (24%)f,g 56 (19%) 167 (47%)j

Progression to non-AD dementia at follow-upd

38 (9%) 6 (2%)e 12 (9%) 11 (3%) 1 (2%)i 20 (10%)h 10 (4%) 22 (6%)

Results are mean (SD) or continuous variables or frequency (%). Key: AD, Alzheimer’s disease; APOE, apolipoprotein E; IAP, isolated amyloid pathology; IWG, International Working Group; MMSE, MinieMental State Exanimation (range 0e30); NIA-AA, National Institute of Aging and Alzheimer’s Association; SNAP, Suspected non-Alzheimer Pathophysiology.

a APOE genotype was only available in a subgroup of the sample: IWG-2 no prodromal AD n ¼ 397, prodromal AD n ¼ 271; NIA-AA low-AD-likelihood n ¼ 123, high-AD-likelihood n ¼ 316, IAP n ¼ 47, SNAP n ¼ 182, uninformative/inconclusive n ¼ 226, intermediate-AD-likelihood n ¼ 271.

b Decline on MMSE at follow-up was defined as a difference of 3 points or more and was available in a subgroup of the samples: IWG-2 no prodromal AD n ¼ 237, prodromal AD n ¼ 223; NIA-AA low-AD-likelihood n ¼ 61, high-AD-likelihood n ¼ 259, IAP n ¼ 37, SNAP ¼ 103, uninformative/inconclusive n ¼ 184, intermediate-AD-likelihood n ¼ 233.

c Progression to AD-type dementia at follow-upwas available in a subgroup of the sample: IWG-2 no prodromal AD n¼ 435, prodromal AD n¼ 293, NIA-AA low-AD-likelihood n¼ 134, high-AD-likelihood n¼ 344, IAP n¼ 49, SNAP n ¼ 201, uninformative/inconclusive n ¼ 286, intermediate-AD-likelihood n ¼ 350.

d Progression to non-AD dementia at follow-upwas available in a subgroup of the samples: IWG-2 no prodromal AD n¼ 433, prodromal AD n¼ 293, NIA-AA low-AD-likelihood n¼ 133, high-AD-likelihood n¼ 343, IAP n¼ 50, SNAP n ¼ 200, uninformative/inconclusive n ¼ 286, intermediate-AD-likelihood n ¼ 350.

e p < 0.05 compared with the no prodromal Alzheimer’s disease after FDR correction. f p < 0.05 compared with low-AD-likelihood after FDR correction. g p < 0.05 compared with high-AD-likelihood after FDR correction. h p < 0.05 compared with IAP after FDR correction. i p < 0.05 compared with SNAP after FDR correction. j p < 0.05 compared with the uninformative/inconclusive after FDR correction.

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Table 3 Frequency of risk factors for NIA-AA groups

Risk factors Low-AD- likelihood N ¼ 142

High-AD- likelihood N ¼ 356

IAP N ¼ 54

SNAP N ¼ 206

p-value, low vs. high

Atherosclerotic disease (n ¼ 1002) 4% 10% 5% 9% 0.277 Depression (n ¼ 1129) 46% 17%a 27% 29% 0.004 Diabetes (n ¼ 914) 8% 9% 14% 15% 0.960 Hypercholesterolemia (n ¼ 1001) 43% 27%a 38% 38% 0.009 Hypertension (n ¼ 1346) 50% 47% 54% 47% 0.038 Lacunar infarct (n ¼ 497) 29% 23% 18% 30% 0.133 Stroke (n ¼ 1013) 3% 4% 6% 5% 0.787 Obesity (n ¼ 993) 21% 8%a 8% 18% 0.004 Smoking (n ¼ 1195) 53% 36% 40% 42% 0.076 Alcohol use (n ¼ 973) 42% 50% 50% 42% 0.352

Comparisons were corrected for baseline age, gender, years of education and center. Key: AD, Alzheimer’s disease; IAP, isolated amyloid pathology; SNAP, suspected non-Alzheimer pathophysiology.

a p < 0.05 after FDR correction.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 37

with the high-AD-likelihood group, subjects in the low-AD- likelihood group had a higher frequency of depression (46% vs. 17%, p ¼ 0.004, FDR p ¼ 0.020), obesity (21% vs. 8%, p ¼ 0.004, FDR p ¼ 0.020), and hypercholesterolemia (43% vs. 27%, p ¼ 0.009, FDR p ¼ 0.030). No differences were found between the groups for the other risk factors (Table 3).

Supplemental Table 3 shows the frequency of risk factors for the groups according to the IWG-2 criteria. In the group without pro- dromal AD, we found higher frequencies of depression (34% vs. 16%, p ¼ 0.009, FDR p ¼ 0.045) and obesity (17% vs. 8%, p ¼ 0.007, FDR p ¼ 0.045) compared with the group with prodromal AD (Supplemental Table 3).

3.2.2. Effect of risk factors on cognitive decline In the total group of subjects with both amyloid and neuronal

injury markers, alcohol use was associated with a higher risk of cognitive decline (HR ¼ 1.5, p ¼ 0.003, FDR p ¼ 0.030, Table 4). There were no significant interactions between risk factors and NIA-AA group classification, indicating that the effect of risk factors was similar for all groups. Using the IWG-2 classification, the effects of depression, hypercholesterolemia, and smoking were different between the 2 groups, but these differences were no longer sta- tistically significant after adjusting for multiple testing (Table 4).

3.3. Analyses in subjects with only neuronal injury markers

Table 2 shows the characteristics of the 258 (21%) subjects classified as uninformative/inconclusive and the 350 (25%) included in the intermediate-AD-likelihood group according to the NIA-AA

Table 4 Effects of risk factors on cognitive decline

Risk factors Main effect risk factors Inter

HR 95% CI p-value HR

Atherosclerotic disease 1.1 0.7e1.7 0.825 0.5 Depression 0.7 0.5e0.9 0.022 0.5 Diabetes 1.0 0.7e1.6 0.907 1.1 Hypercholesterolemia 0.8 0.6e1.0 0.080 2.2 Hypertension 0.7 0.4e1.1 0.103 0.7 Lacunar infarct 0.8 0.3e2.1 0.603 0.8 Stroke 1.0 0.6e1.9 0.899 0.5 Obesity 0.7 0.4e1.1 0.111 1.0 Smoking 1.2 0.9e1.5 0.214 1.8 Alcohol use 1.5 1.2e2.0 0.003a 0.8

Cognitive decline is defined as progression to dementia or 3 points decline onMMSE at fol corrected for baseline age, gender, years of education and center. Key: CI, confidence interval; HR, hazard ratio; IWG, International Working Group; NIA-A

a p < 0.05 after FDR correction.

criteria. The subjects in the intermediate-AD-likelihood differed on all characteristics from the uninformative/inconclusive group.

The frequency of risk factors for the subjects who had only neuronal injury markers available is described in Supplemental Table 4. In the intermediate-AD-likelihood-group lacunar infarcts occurred more frequently (40%), compared with the uninformative/ inconclusive group (16%, p < 0.001). There were no differences for the other risk factors (Supplemental Table 4).

In the subjects with only neuronal injury markers available, none of the risk factors increased the risk of cognitive decline. Also, there was no difference between the 2 NIA-AA groups in the rate of cognitive decline (Supplemental Table 5).

3.4. Post-hoc analysesdprogression to AD-type dementia

When we repeated the analyses with only progression to AD- type dementia as an outcome in subjects with both amyloid and neuronal injury markers available (n ¼ 725), alcohol use was no longer associated with an increased risk of progression (HR ¼ 1.3, 95% CI: 0.9e1.8, p ¼ 0.164).

Since the ADNI cohort excluded subjects with depressive symptoms (GDS >6), we repeated the analyses concerning depression without ADNI subjects. This did not influence the results.

4. Discussion

We examined the frequency of vascular and lifestyle risk factors in prodromal AD/MCI due to AD, and the influence of these factors on cognitive decline, in subjects with MCI. We

action with IWG-2 groups Interaction with NIA-AA groups

95% CI p-value HR 95% CI p-value

0.2e1.3 0.159 0.6 0.4e1.1 0.119 0.3e0.9 0.048 0.8 0.6e1.2 0.253 0.4e2.8 0.922 1.2 0.7e2.3 0.501 1.2e3.9 0.010 1.1 0.8e1.5 0.624 0.5e1.1 0.103 0.8 0.6e1.1 0.118 0.3e2.1 0.603 0.8 0.4e1.6 0.535 0.1e1.5 0.201 0.7 0.4e1.3 0.263 0.4e2.6 0.993 1.1 0.6e2.1 0.771 1.1e3.0 0.017 1.2 0.9e1.7 0.233 0.5e1.4 0.478 0.9 0.7e1.3 0.624

low-up. Hazard ratios and 95% CIs were calculated using Cox-regression analyses and

A, National Institute of Aging Alzheimer’s Association.

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e4038

found that the frequencies of depression, hypercholesterolemia and obesity were higher in the group without AD pathology compared with the group with AD pathology. Only alcohol increased the risk of cognitive decline, regardless of AD-pathology.

4.1. Frequency of risk factors

Contrary to our hypothesis, we found higher frequencies of depression, hypercholesterolemia, and obesity, in the group without prodromal AD and in the low-AD-likelihood group. This suggests that subjects without prodromal AD or low-AD-likelihood had cognitive impairment due to other causes than AD, such as depression or vascular disorders (DeCarli, 2003; Gorelick et al., 2011). It is also possible that low cholesterol or low body mass in- dex are risk factors for prodromal AD in this elderly sample. Although obesity and hypercholesterolemia in middle age have been shown to be predictive for AD (Deckers et al., 2015; Kivipelto et al., 2005), other studies showed that this association is reversed at older age, such that low body mass index and low cholesterol increase the risk for AD (Anstey et al., 2008; Johnson et al., 2006). When we compared the frequencies observed in the present study to the prevalence of risk factors reported in meta-analyses and population-based cohort studies (Supplemental Table 6), we found that the frequencies of obesity and hypercholesterolemia in the high-AD-likelihood group were decreased, whereas frequencies were similar in subjects with a low-AD-likelihood. On the contrary, the frequency of depression in the high-AD-likelihood group was similar to that in the general population, whereas it was higher in the low-AD-likelihood group compared with the population-based studies. This would suggest that the difference in frequencies of obesity and hypercholesterolemia between the low and high-AD- likelihood in our study results from a decrease of obesity and hy- percholesterolemia in the high-AD-likelihood, rather than from an increase in the low-AD-likelihood. Conversely, the difference in frequency in depression between groups could result from an in- crease in frequency of depression in the low-AD-likelihood. This indicates that depression can be a possible cause of MCI in the studied population (DeCarli, 2003; Defrancesco et al., 2009). Clearly there are methodological differences in the inclusion of subjects, definition, and method of ascertainment of risk factors and age range between the present study and the population-based studies. Studies that directly compare frequency of risk factors in prodromal AD to cognitively normal subjects are needed to further clarify this.

4.2. Influence of risk factors on cognitive decline

Alcohol consumption was associated with an increased risk of cognitive decline, independent of AD-pathology. Although this finding is in line with several previous studies (Deckers et al., 2015; Jauhar et al., 2014) that identified alcohol as a risk factor for cognitive decline, other studies have reported a protective or no relation to alcohol consumption with incident AD (Anstey et al., 2009; Ruitenberg et al., 2002). These conflicting results could be explained by differences in study population (MCI vs. cognitively normal), definitions of alcohol consumption (dichotomous vs. cat- egories based on the amount of alcohol use) and the type of alcohol. When conversion to AD-type dementia was used as outcome instead of conversion to dementia or a decline on the MMSE, we found that the effect of alcohol was no longer significant. This suggests that alcohol consumption mainly has an effect on pro- gression to non-AD types of dementia or cognitive decline in general.

4.3. IWG-2 versus NIA-AA criteria

The results on frequency of risk factors were comparable for the IWG-2 and NIA-AA criteria. Also, when comparing the effect of risk factors on cognitive decline, we found similar outcomeswhen using the 2 sets of criteria. This shows that eventhough the IWG-2 criteria only classify neuronal injury based on tau in CSF, whereas the NIA- AA criteria also include other neuronal injury markers, this did not influence the results. Although the outcomes were similar for the 2 sets of criteria, the NIA-AA criteria provided more insight into which specific biomarker profile was associated with a higher fre- quency of a certain risk factor, which could be useful to give a more refined diagnosis and prognosis of early AD and age-related comorbidities.

4.4. Strengths and limitations of the study

Strengths of the study are the large sample size, the broad spectrum of assessed risk factors, and longitudinal data on clinical outcome. There are also limitations to this study that should be mentioned. Some of the biomarker subgroups were small and some risk factors were only available in a subgroup or had a low fre- quency, which limited statistical power. The data used in this study were contributed by different centers and data were not collected using the same protocol. This might have led to variability, although it does reflect current clinical practice. Furthermore, the use of in- direct measures (e.g., medical history) of risk factors could have introduced heterogeneity in classification. Wewere unable to study the potential interactions between risk factors, as not all centers contributed data on all risk factors. We had only limited data available on medication use, which did not allow us to control for this. Also, we could not correct for the duration and the severity of a risk factor, as we had no information on this. Although the mean follow-up was 2.3 years, some individuals likely would have shown cognitive decline at a later stage. Since these findings are based on clinical research populations, they may not be generalizable to other settings.

5. Conclusion

In summary, we found that dementia risk factors were not associated with prodromal AD/MCI due to AD in subjects with MCI, and only alcohol increased the risk of cognitive decline, regardless of underlying pathology. Moreover, we found that a lower fre- quency of hypercholesterolemia or obesity may be indicative of early AD in an elderly population. Although our findings should be validated in future studies, they could have implications for clinical practice, future scientific studies, as well as for the selection of in- dividuals for participation in clinical trials. Different risk factor profiles in subjects with MCI could be related to distinct etiologies of cognitive dysfunction, and therefore may have different prog- nostic values. Management of alcohol habits could possibly lessen or prevent further cognitive decline. Future studies should focus on the role of risk factors in even earlier stages of AD (e.g., preclinical AD), examine longitudinal biomarkers values, and consider the duration and severity of risk factors. Also, co-occurrence of risk factors and possible synergistic effects on biomarkers should be a topic for future research as we were unable to study this in the current sample.

Acknowledgements

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no 115372, resources of which are composed of

I. Bos et al. / Neurobiology of Aging 56 (2017) 33e40 39

financial contribution from the European Union’s Seventh Frame- work Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The present study was conducted as part of the Project VPH-DARE@IT funded by the European Union Seventh Framework Programme (FP7-ICT-2011-9-601055) under grant agreement no 601055. The Dementia Competence Network (DCN) has been supported by a grant from the German Federal Ministry of Education and Research (BMBF): Kompetenznetz Demenzen (01GI0420). Additional funding related to the randomized clinical trials came from Janssen-Cilag and Merz Pharmaceuticals. The latter funds were exclusively used for personnel, pharmaceuticals, blistering and shipment of medication, monitoring, and as capi- tation fees for recruiting centers. The DESCRIPA study was funded by the European Commission within the 5th framework program (QLRT-2001-2455).

The EDAR study was funded by the European Commission within the 5th framework program (contract # 37670). The Coimbra center was funded by Project PIC/IC/83206/2007 da Fundação para a Ciência e TecnologiaePortugal. Research of the VUmc Alzheimer center is part of the neurodegeneration research program of the Neuroscience Campus Amsterdam. The VUmc Alzheimer Center is supported by Alzheimer Nederland and Stichting VUmc fonds. The clinical database structure was developed with funding from Stichting Dioraphte. The Alz- heimer’s Disease Neuroimaging Initiative (ADNI; National In- stitutes of Health grant U01 AG024904 and DOD ADNI Department of Defense award number W81XWH-12-2-0012) was funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alz- heimer’s Drug Discovery Foundation; BioClinica, Inc; Biogen Idec Inc; Bristol-Myers Squibb Company; Eisai Inc; Elan Pharmaceu- ticals, Inc; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc; GE Healthcare; Innoge- netics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceu- tical Research & Development LLC.; Medpace, Inc; Merck & Co, Inc; Meso Scale Diagnostics, LLC.; NeuroRx Research; Novartis Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Serv- ier; Synarc Inc; and Takeda Pharmaceutical Company. The Cana- dian Institutes of Health Research is providing funds to Rev December 5, 2013 support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee orga- nization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Dis- ease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Statistical analysis and drafting the manuscript and study concept and design is done by Isabelle Bos, Stephanie J. Vos, and Pieter Jelle Visser. All authors contributed to acquisition and/or interpretation of data and critical revision of final draft of manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging. 2017.03.034.

Disclosure statement

The authors have no conflicts of interest to disclose.

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