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Original Research Article

Dement Geriatr Cogn Disord 2006;21:51–58 DOI: 10.1159/000089515

Conversion of Mild Cognitive Impairment to Dementia: Predictive Role of Mild Cognitive Impairment Subtypes and Vascular Risk Factors

Giovanni Ravaglia a Paola Forti a Fabiola Maioli a Mabel Martelli a Lucia Servadei a Nicoletta Brunetti a Gaia Pantieri a Erminia Mariani b a

Department of Internal Medicine, Cardioangiology and Hepatology, University Hospital S. Orsola-Malpighi, b

Laboratory of Immunology and Genetics, Codivilla Putti Research Institute, Rizzoli Orthopedic Institute, Bologna , Italy

Introduction

Mild cognitive impairment (MCI) was fi rst defi ned by Petersen et al. [1] as an isolated, measurable memory dis-order that can precede dementia (‘amnestic MCI’, aMCI). In a subsequent revision of the MCI concept, however, Petersen hypothesized the existence of other MCI sub-types involving cognitive domains other than memory [2] . This revision was motivated by the criticisms raised against the prominent role attributed to memory in the diagnostic criteria for dementia, which favor the clinical features of Alzheimer’s disease (AD) at the expense of non-Alzheimer dementias [3] . As an opportunity for ear-ly identifi cation of subjects at increased risk of dementia (4–10 times higher than cognitively normal elderly per-sons), MCI is not only an important concept for epidemi-ologists and specialized researchers in the fi eld of cogni-tive disorders, but also a clinical condition whose identi-fi cation and monitoring is recommended in medical practice [4] . However, huge variations in operational cri-teria have for a long time hampered the use of MCI as specifi c diagnostic entity [5] . It is only recently that an international panel of experts [6] approved and recom-mended the newly revised and standardized Petersen’s criteria for diagnosis and classifi cation of aMCI and non-

Key Words Mild cognitive impairment � Vascular risk factors � Atrial fi brillation � Serum folate

Abstract Mild cognitive impairment (MCI) is regarded as a precur-sor to dementia, but not all patients with MCI develop dementia. We followed up 165 elderly outpatients with MCI for a mean of 3 years. The aims were (1) to investi-gate the risk of conversion to dementia for different MCI subtypes diagnosed according to standardized criteria (amnestic; impairment of memory plus other cognitive domains; nonamnestic); (2) to assess whether the risk of conversion was affected by several established and emerging vascular risk factors. Forty-eight subjects (29%) converted to dementia, and the risk of conversion was doubled for amnestic MCI with respect to the other subtypes. Independently of MCI subtype, risk of conver-sion was associated with atrial fi brillation and low serum folate levels. Our results show that current diagnostic criteria for MCI defi ne heterogeneous populations, but some potentially treatable vascular risk factors may be of help in predicting conversion to dementia.

Copyright © 2006 S. Karger AG, Basel

Accepted: July 18, 2005 Published online: November 4, 2005

Prof. Giovanni Ravaglia Department of Internal Medicine, Cardioangiology and Hepatology University Hospital S. Orsola-Malpighi, Via Massarenti 9 IT–40138 Bologna (Italy) Tel. +39 051 6364310, Fax +39 051 340877, E-Mail ravaglia@med.unibo.it

© 2006 S. Karger AG, Basel 1420–8008/06/0211–0051$23.50/0

Accessible online at: www.karger.com/dem

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Dement Geriatr Cogn Disord 2006;21:51–58 52

amnestic MCI (naMCI) subtypes in research and clinical settings [7] .

Another major limitation to the clinical usefulness of MCI is that not all individuals with MCI convert to de-mentia: up to 40–70% of the patients with aMCI improve or stay stable at follow-up [8] , and no data are available for the other MCI subtypes.

Vascular risk factors are associated with both the main causes of dementia, AD and vascular dementia [9] and have also been suggested to contribute to the heterogene-ity of MCI progression [10, 11] . The only two prospective studies performed on this issue came up with negative results [12, 13] , but they considered only a small range of vascular risk factors.

The present investigation examined the effect of sev-eral established and emerging vascular risk factors on conversion to dementia in a large group of elderly patients with MCI diagnosed and classifi ed according to Peter-sen’s revised criteria [7] .

Methods

Identifi cation of MCI Cases at Baseline Participants were recruited among the outpatients seeking med-

ical advice for cognitive complaints at the Center for Physiopathol-ogy of Aging, University of Bologna. Enrollment started in March 1999 and ended in March 2004. All procedures and methods for obtaining informed consent from participants and collateral sourc-es were approved by the local Ethic Committee.

Diagnoses were performed with a three-stage process. First, a geriatrician administered a semistructured interview to each pa-tient and to a knowledgeable informant (usually an immediate rel-ative), along with a physical examination and the Italian version of the Mini-Mental State Examination (MMSE) [14] (for which spe-cifi c age and education adjustments have been validated [15] ), and verifi ed whether the patient met the following general eligibility criteria: (1) age 6 60 years; (2) complaint of cognitive decline ex-pressed by the person or an informant; (3) MMSE 6 24; (4) report of intact activities of daily living [16, 17] by the informant (depen-dency due to physical impairment was not considered); (5) no his-tory of malignant disease, severe organ failure, metabolic or hema-tologic disorders, neurosurgery or neurological conditions such as Parkinson’s disease, epilepsy, postencephalitic and postconcus-sional syndrome; (6) no evidence of depression (score 6 15 on the Geriatric Depression Scale [18] ), other psychiatric disorders or de-lirium; (7) no sensory-motor defi cits affecting performance at neu-ropsychological testing; (8) no use of psychoactive substances; (9) normal routine laboratory tests; (10) willingness to return for follow-up examinations. Subjects for whom a reliable informant was not available were excluded from the study. On the basis of the informant and participant interviews, and after clinical evaluation of six areas of cognitive function (memory, orientation, judgment and problem solving, community affairs, home and hobbies and personal care), the geriatrician also assigned a Clinical Dementia Rating score (CDR) [19] to each eligible subject. Brain scan was

ordered as clinically indicated. Within a few weeks from the fi rst interview, a second geriatrician administered to the subject an ex-tensive battery of neuropsychological tests for evaluation of sev-eral cognitive domains including memory (immediate and delayed recall of Rey’s 15 words and prose memory test), attention (atten-tive matrices), language (Token test), frontal function (phonological word fl uency), abstract reasoning (Raven’s 47 progressive colored matrices) and visuospatial abilities (freehand copying of drawings and copying of drawings with landmarks), for a total of nine test scores. Details on administration procedures, validation in the Ital-ian population and age and education-specifi c norms for all of these tests are described elsewhere [20, 21] . Based on a review of clinical and neuropsychological data, the second geriatrician rendered a CDR rating for each examined subject, independently of the fi rst geriatrician. If their CDR ratings differed, a fi nal CDR rating was rendered by a third senior geriatrician clinician after re-examina-tion of the case record.

According to the revised Petersen’s criteria [7] , a diagnosis of MCI was made only for subjects who in addition to meeting all general eligibility criteria also had: (1) a fi nal CDR rating ̂ 0.5, and (2) objective impairment (at least 1.5 SD below the mean for age- and education-matched norms) in any of the neuropsychological tests administered by the second geriatrician. MCI was further clas-sifi ed into the following subtypes: aMCI, if there was impairment in memory alone; multiple domains MCI with memory impair-ment (mdMCI+a), if there was impairment in memory and at least another cognitive domain; naMCI, if there was impairment in one or more nonmemory cognitive domains.

Identifi cation of Incident Dementia Cases during Follow-Up All participants were scheduled for semestral clinical and neu-

ropsychological evaluations as a part of their regular ambulatory care. For the purposes of this study, diagnosis of dementia was based on a defi cit in two or more cognitive domains severe enough to affect the participant functional abilities [3] . Standard criteria were used for the diagnosis of AD [22] and vascular dementia [23] . September 2004 was chosen as the end date for dementia surveil-lance. Follow-up ended at the diagnosis of dementia, refusal or in-ability of the patient to undergo the next scheduled examination, or death. Patients who, at any time during follow-up, did not pres-ent themselves for the scheduled semestral examination were con-tacted by phone and proposed a new appointment date. All subjects who refused or were unable to return within 1 month of the origi-nally scheduled date were considered lost to follow-up.

Vascular Risk Factors Established Risk Factors. Smoking habit was dichotomized as

never smokers versus ex-smokers and current smokers. Sitting blood pressure measurements were recorded with the participant at rest. Systolic and diastolic fi fth phase blood pressures were mea-sured three times in the right arm, using a standard sphygmoma-nometer and stethoscope. The mean of the last two measurements was used in all analyses. Hypertension was defi ned as a systolic blood pressure 6 140 mm Hg, a diastolic blood pressure 6 90 mm Hg, or the use of antihypertensive medication. Diagnoses of diabe-tes mellitus, cardiovascular disease (history of myocardial infarc-tion, angina, peripheral vascular disease and congestive heart fail-ure), cerebrovascular disease (history of stroke or TIA) and atrial fi brillation were based on medical history as provided by the pa-tients and confi rmed by clinical evaluation. Whenever available,

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Dement Geriatr Cogn Disord 2006;21:51–58 53

previous medical records were reviewed. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. Serum total cholesterol was measured on fresh venous blood samples as previously described [24] .

Emerging Risk Factors. Apolipoprotein E (APOE) is the major lipid-carrier protein in the brain, and several studies provided evi-dence that APOE � 4 allele can be considered a genetic risk factor for vascular diseases and late-onset AD [9] . APO � 4 allele genotyp-ing was performed with polymerase chain reaction as previously described [25] . In the elderly, defi cits of folate and vitamin B 12 are the main cause of mild hyperhomocysteinemia, a condition associ-ated with both vascular disease [26] and cognitive impairment [27] . Levels of these vitamins were measured on fresh serum by immu-noelectrochemiluminescence analysis (Elecsys Folate Immunoas-say and Elecsys B12 Immunoassay for Elecsys 2010 System, Roche Diagnostics Italia S.p.A. Monza, Milan, Italy).

Data Analysis Incidence rates of dementia per 100 person-years were calcu-

lated as the number of new cases divided by the number of person-years at risk. The 95% confi dence intervals (CI) were based on the Poisson distribution. Comparison of converters and nonconverters was performed using the t test and the � 2 test, as appropriate. The risk of conversion to dementia associated with baseline cognitive status (poor global cognitive performance as measured with the MMSE, and MCI subtype) and vascular risk factors was fi rst as-sessed performing, for each considered predictor, a separate Cox proportional hazards model analysis adjusted only for sociodemo-graphic variables (age, gender and education). A conservative p level ( ! 0.20) was chosen for identifi cation of the variables that were subsequently entered in a multivariable backward stepwise regres-sion algorithm, in order to select the best subset of independent predictors. The algorithm started with all the potential predictors included in the model and then proceeded to sequentially remove all the variables that did not reach a p ! 0.05 level when adjusted for each other (except for age, gender and education, which were maintained in the model independently of their statistical signifi -cance). A fi nal Cox proportional hazards model was used to esti-mate the multivariable-adjusted risk of conversion associated with the subset of independent predictors identifi ed by the stepwise al-gorithm. In order to ascertain that the hazard ratio of each covari-ate remained constant across the entire range of values of any oth-er included covariate, we also tested whether the product of any two covariates (interaction) contributed signifi cantly to the model in addition to the covariates themselves. For MCI subtypes, aMCI and mdMCI+a were compared with naMCI used as the reference category. Educational status was categorized as 5 versus 6 or more years of formal education, because only a small number of partici-pants had completed the 5 years of mandatory education provided for in the old Italian school system. The median of the MMSE score distribution ( ̂ 26) was used to defi ne poor global cognition. BMI and serum cholesterol were categorized into quartiles based on their own distribution, and the second quartile was taken as the reference group. The 25th percentile of the corresponding distribution was used to defi ne low folate ( ̂ 10.4 nmol/l) and low vitamin B 12 levels ( ̂ 217 pmol/l). Multivariable-adjusted survival curves were used to illustrate the differences in progression patterns across different categories of selected variables.

Results

There were 165 participants followed up for an aver-age of 2.8 8 1.6 years (range from 6 months to 5 years) and a total of 468.7 years. Mean age was 76.0 8 8.4 years, 51% were women and 78% had ̂ 5 years of schooling. Mean baseline MMSE score was 26.3 8 2.9. A total of 67 eligible individuals received an MCI diagnosis at base-line but refused to undergo any follow-up. With respect to participants, these subjects were older (78.5 8 7.8, p = 0.039), more likely to be women (65.7%, p = 0.040), and had a lower MMSE score (25.1 8 3.0, p = 0.006). No differences with regard to education (p = 0.234) and dis-tribution across MCI subtypes (p = 0.349) were observed. There were 48 incident cases of dementia (29%), with AD accounting for 71% of all cases, all the others being rep-resented by vascular dementia (n = 14). Except for one individual who died, loss to follow-up was due to unwill-ingness or inability to undergo the next scheduled exam-ination (see table 1 for further details on attrition). Over-all rate of conversion was 10.2 (7.7–13.5) per 100 person-years. Over the same interval, 67% of participants remained stable and 4% reverted to normal. Rates of conversion for the specifi c MCI subtypes are reported in table 2 .

Table 2. Incidence of dementia in different MCI subtypes

MCI subtypes Person-years

Cases Rate per 100 person-years (95% CI)

aMCI (n = 50) 127.6 16 12.5 (7.7–20.4)mdMCI+a (n = 67) 206.6 21 10.2 (6.6–15.6)naMCI (n = 48) 134.5 11 8.2 (4.5–14.7)

Table 1. Follow-up data for the 165 patients with MCI

Timeyears

Numberat risk

Incident dementia cases

Lost tofollow-up

0 165 – –1 138 11 162 103 18 71

3 78 4 64 68 1 595 8 0 0

1 One subject died.

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Dement Geriatr Cogn Disord 2006;21:51–58 54

As shown in table 3 , converters to dementia were more likely to be older, men, nonsmokers, and with an APOE � 4 genotype and a lower MMSE score than nonconverters. MCI subtype distribution did not differ between the groups. Converters were also characterized by a higher prevalence of atrial fi brillation and lower blood pressure values, BMI, plasma cholesterol and serum folate.

When adjusting for sociodemographic variables ( table 4 ), the risk of conversion was signifi cantly associated with poor baseline global cognitive performance, MCI sub-type, APOE � 4 genotype, atrial fi brillation and lower val-ues of systolic and diastolic blood pressure, BMI, plasma cholesterol and serum folate. All of these variables, along with smoking (where p reached the required ! 0.20 level) and sociodemographic status, were then included in the stepwise algorithm in order to select the best subgroup of signifi cant predictors. As shown in table 5 , only poor base-line global cognitive performance, MCI subtype, atrial fi brillation, low diastolic blood pressure and low folate

status were maintained in the fi nal Cox model. Interac-tions among these variables did not reach statistical sig-nifi cance and, therefore, were not included in the model. The multivariable-adjusted hazard ratio for aMCI com-pared to mdMCI+a, calculated from the fi nal model esti-mates [28] , was 2.33 (95% CI 1.03–5.27, p = 0.042). Fig-ure 1 shows the corresponding multivariable-adjusted survival curves for selected predictors. Exclusion of pa-tients with non-AD dementia did not signifi cantly affect results (data not shown).

Discussion

This prospective study is one of the fi rst to investigate, in a clinical setting, the outcome of MCI amnestic and nonamnestic subtypes diagnosed according to the recent-ly revised and standardized Petersen’s criteria [7] . Our conversion rate for aMCI (12.3 per 100 person-years) ful-

Nonconverters(n = 117)

Converters(n = 48)

p value

SociodemographicAge, years 74.688.0 79.388.5 <0.001Women, n (%) 60 (51.3) 21 (43.7) <0.001Education 66 years, n (%) 24 (20.5) 13 (27.1) <0.358

Baseline cognitive statusMMSE 26.982.5 24.683.3 <0.001MCI subtype <0.533

aMCI 34 (29.1) 16 (33.3)mdMCI+a 46 (39.3) 21 (43.7)naMCI 37 (31.6) 11 (22.9)

Vascular risk factorsEver smoking, n (%) 40 (34.2) 7 (14.6) <0.011Hypertension, n (%) 43 (36.7) 20 (41.7) <0.555Systolic blood pressure, mm Hg 140818 130814 <0.005Diastolic blood pressure, mm Hg 82810 7686 <0.001Diabetes, n (%) 12 (10.2) 4 (8.3) <0.705Cardiovascular disease, n (%) 24 (20.5) 10 (20.8) <0.963Cerebrovascular disease, n (%) 6 (5.1) 4 (8.3) <0.433

Vascular risk factorsAtrial fi brillation, n (%) 3 (2.6) 8 (16.7) <0.001BMI 28.684.8 26.884.1 <0.023Plasma cholesterol, mmol/l 6.081.0 5.381.2 <0.001APOE�4 genotype, n (%) 6 (5.3) 12 (25) <0.001Serum folate, nmol/l 15.285.4 13.285.4 <0.038Serum vitamin B12, pmol/l 2858119 2848113 <0.961

Values are mean 8 SD unless otherwise indicated.

Table 3. Summary characteristics forconverters and nonconverters

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Dement Geriatr Cogn Disord 2006;21:51–58 55

ly replicates previously reported fi gures [1, 8] , whereas no comparison is possible for conversion rates of the other MCI subtypes due to the lack of specifi c literature data. More than 50% of all conversions occurred within 2 years of observation, but only 40% of our sample had 4 or more years of follow-up, so it cannot be determined whether there is truly a plateau in the number of subjects who de-veloped dementia.

As expected [29] , the risk of dementia was higher for patients with poor global cognitive function as measured with the MMSE. However, aMCI had a higher risk of de-mentia than the other MCI subtypes, suggesting that the current criteria for aMCI defi ne a subgroup at increased

risk of dementia, whereas the other subtypes may include higher proportions of healthy individuals who have long-standing poor cognitive function but do not progress to dementia.

This is in contrast to results from Bozoki et al . [30] , who reported an increased risk of dementia at 2 years of follow-up for nondemented patients with multiple cogni-tive impairments with respect to patients with memory impairment alone. Different diagnostic criteria and bi-ases related to the retrospective design of the study by Bozoki et al. [30] may concur to explain this difference. According to Rasquin et al. [31] , MCI with multiple cog-nitive impairments had a far better prognostic accuracy than aMCI in identifying people who developed dementia during a 2-year follow-up. Their study population, how-ever, was rather heterogeneous and actually included two different samples (patients with memory complaints re-cruited at a memory clinic and a cohort of fi rst-ever stroke patients) with differently scheduled follow-up intervals. Moreover, because of the very low MMSE cut-off of 15 used for screening of individuals with global cognitive im-pairment, their fi nding may partly result from the acciden-tal inclusion of people already in mild dementia stages.

Of all the established vascular risk factors considered in the present investigation, only atrial fi brillation was a

Table 4. Risk factors for conversion to dementia in multivariable analysis adjusted for sociodemographic variables 1

Predictor Hazardratio

95% CI p value

MMSE ^ 26 2.68 1.50–4.79 <0.001MCI subtype

aMCI 2.84 1.21–6.66 <0.016mdMCI+a 1.59 0.74–3.36 <0.229naMCI 1 reference

categoryEver smoking 0.54 0.22–1.32 <0.177Hypertension 1.25 0.70–2.45 <0.453Blood pressure

(10 mm Hg decrement)Systolic 0.81 0.69–0.95 <0.013Diastolic 0.56 0.39–0.80 <0.001

Diabetes 0.75 0.26–2.13 <0.593Cardiovascular disease 0.90 0.44–1.84 <0.780Cerebrovascular disease 2.01 0.69–5.88 <0.200Atrial fi brillation 8.06 3.43–18.94 <0.001BMI

^25.0 2.07 1.04–4.14 <0.03925.1–27.6 1 reference

category27.7–29.9 0.53 0.20–1.37 0.189630.0 0.62 0.26–1.46 0.270

Cholesterol, mmol/l^5.2 1.78 0.85–3.70 0.1235.3–5.9 1 reference

category6.0–6.5 0.38 0.13–1.07 0.06866.6 0.29 0.09–0.87 0.028

APOE�4 genotype 4.22 1.32–13.5 0.015Serum folate ^ 10.4 nmol/l 2.23 1.12–4.43 0.022Serum vitamin B12 ^ 217 pmol/l 0.60 0.26–1.39 0.234

1 Model adjusted for age, gender and education.

Table 5. Risk factors for conversion to dementia in multivariable analysis: fi nal model 1

Predictor Hazardratio1

95% CI p value

MMSE ^ 26 4.22 2.09–8.50 <0.001MCI subtype

aMCI 2.91 1.13–7.51 <0.027mdMCI+a 1.26 0.54–2.95 <0.607naMCI 1 reference

categoryDiastolic blood pressure

(10 mm Hg decrement)0.52 0.32–0.84 <0.008

Atrial fi brillation 4.94 1.89–12.88 <0.001BMI

^25.0 3.07 1.27–7.41 <0.01325.1–27.6 1 reference

category27.7–29.9 0.64 0.21–1.94 <0.429630.0 0.63 0.23–0.21 <0.373

Serum folate ^10.4 nmol/l 3.11 1.49–6.47 <0.002

1 Adjusted for age, gender, education and all the other variables listed in the model.

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Dement Geriatr Cogn Disord 2006;21:51–58 56

predictor of conversion from MCI to dementia. Possible explanatory mechanisms are repeated thromboembolic damage and cerebral hypoperfusion due to fl uctuations in cardiac output [32] . A link between atrial fi brillation and poor cognitive function has been pointed out in non-demented elderly individuals [33, 34] , but this is the fi rst study to investigate this issue in MCI patients. Caution is must be taken, however, because of the very small num-ber of atrial fi brillation cases in this sample.

Moreover, it cannot be excluded that the brief obser-vation period used in this study may have favored iden-tifi cation of mechanisms with quicker progression at the expense of longer-acting factors. For example, hyperten-sion is a known risk factor for dementia [9] , but it did not affect the risk of conversion in this sample of MCI pa-tients, whose risk even decreased with higher diastolic blood pressure values. Blood pressure has been reported to decline before dementia onset in the elderly [35] , but it is not clear whether low blood pressure has a role in dementia etiopathogenesis by decreasing cerebral perfu-sion, or whether pathologic brain changes cause blood pressure dysregulation.

In a similar way, our results do not allow to determine whether the association between risk of conversion and low values of BMI and serum folate indicates an effect of nutritional status on cognitive function or, on the con-trary, that subtle functional alterations may affect dietary intake early in predementia states. Indeed, in an elderly nondemented cohort followed for up to 8 years [36] , the risk of dementia increased for subjects with lower BMI, but the association was no longer signifi cant after exclu-sion of those developing dementia early during follow-up. On the contrary, two prospective studies support the hy-pothesis that lower serum folate levels may be a risk fac-tor for dementia [37, 38] . Possible mechanisms include elevation of plasma homocysteine levels, an acknowl-edged vascular risk factor [26] , as well as homocysteine-mediated neurotoxic damage and impaired cerebral methylation reactions [27] .

Only two other prospective studies examined the issue of MCI conversion in relation to vascular risk factors. In a sample of 52 patients, the risk of progression from MCI to dementia was not affected by either clinical vascular

Fig. 1. Multivariable-adjusted survival curves for patients with MCI stratifi ed according to MCI subtype ( a ), presence of atrial fi -brillation ( b ) and serum folate levels ( c ).

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Dement Geriatr Cogn Disord 2006;21:51–58 57

features or evidence of cerebrovascular disease at mag-netic resonance imaging [12] . This study, however, did not distinguish between different MCI subtypes and de-fi ned clinical vascular features as an arbitrary score of a few selected conditions. Another study from a large Ital-ian population-based cohort also failed to identify any defi nite association between some major vascular risk factors and conversion to dementia [13] . In this study, however, only the aMCI subtype was considered, and di-agnostic work-up did not require testing of any specifi c cognitive domain other than memory. None of these studies, moreover, included atrial fi brillation and serum folate levels among the variables of interest.

The strengths of our study are its prospective design, the relatively large number of MCI patients, the variety of vascular risk factors taken into account and the strict adhesion to internationally approved diagnostic MCI cri-teria. Our study has also several major limitations; be-cause neuroimaging data on cerebrovascular disease were not available for most of the patients, folate status was measured as serum levels, and follow-up was relatively short. Moreover, because of the large number of associa-tions studied, there is an increased risk of false-positive fi ndings, whereas, on the other hand, due to the small number of converters, some associations may have been missed because of insuffi cient statistical power. As an ex-ample, contrary to previous results [39] , APOE � 4 geno-type was not included as a predictor in the fi nal multivari-

able model, although, in our sample, this largely acknowl-edged dementia genetic risk factor had a very low prevalence among nonconverters. Finally, the small num-ber of converters did not allow separate analyses for in-dividuals with shorter and longer follow-up periods.

In conclusion, our fi ndings suggest that the current def-initions for MCI subtypes, particularly those referring to individuals with multiple or nonamnestic cognitive im-pairment, include a substantial number of individuals who may not progress to dementia. The possible role of atrial fi brillation and low folate in the conversion from MCI to dementia could have important implications, be-cause both conditions are easily identifi able and amena-ble to therapeutic options. Moreover, although discourag-ing with respect to the clinical usefulness of currently available MCI criteria, our results raise the possibility that defi ning a protocol of multiple clinical risk factors may be of help in identifying MCI individuals at in-creased risk of conversion. However, before clinical rec-ommendations can be made, our observations need to be confi rmed in other prospective studies.

Acknowledgments

This study was supported by grants from the Italian Ministry of University and Scientifi c Research (60% fund) and from ‘Ricerca Corrente Istituti Ortopedici Rizzoli’.

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