Next Article in Journal
Rice Hull Extract (RHE) Suppresses Adiposity in High-Fat Diet-Induced Obese Mice and Inhibits Differentiation of 3T3-L1 Preadipocytes
Next Article in Special Issue
Energy Intake and Severity of Dementia Are Both Associated with Health-Related Quality of Life among Older Long-Term Care Residents
Previous Article in Journal
Dietary Intakes of Professional and Semi-Professional Team Sport Athletes Do Not Meet Sport Nutrition Recommendations—A Systematic Literature Review
Previous Article in Special Issue
Ginsenosides Rg5 and Rk1 Enriched Cultured Wild Ginseng Root Extract Bioconversion of Pediococcus pentosaceus HLJG0702: Effect on Scopolamine-Induced Memory Dysfunction in Mice
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 11
Issue 5
10.3390/nu11051161
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Associations of AD Biomarkers and Cognitive Performance with Nutritional Status: The NUDAD Project

by
Astrid S. Doorduijn
1,2,*,
Marjolein Visser
3,
Ondine van de Rest
4,
Maartje I. Kester
2,
Francisca A. de Leeuw
2,5,
Sanne Boesveldt
4,
Jay L. P. Fieldhouse
2,
Ellen G. H. M. van den Heuvel
6,
Charlotte E. Teunissen
5,
Philip Scheltens
2,
Wiesje M. van der Flier
2 and
Marian A. E. de van der Schueren
1,7
1
Department of Nutrition and Dietetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, 1081HV Amsterdam, The Netherlands
2
Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081HZ Amsterdam, The Netherlands
3
Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam and the Amsterdam Public Health Research Institute, 1081HV Amsterdam, The Netherlands
4
Division of Human Nutrition and Health, Wageningen University & Research, 6708WE Wageningen, The Netherlands
5
Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081HZ Amsterdam, The Netherlands
6
FrieslandCampina, 3818LE Amersfoort, The Netherlands
7
Department of Nutrition and Health, HAN University of Applied Sciences, 6525EJ Nijmegen, The Netherlands
*
Author to whom correspondence should be addressed.
Nutrients 2019, 11(5), 1161; https://doi.org/10.3390/nu11051161
Submission received: 9 May 2019 / Revised: 15 May 2019 / Accepted: 21 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Nutrition in Age-Related Cognitive Impairment, Dementia, and Alzheimer's Disease)
Versions Notes

Abstract

:
As malnutrition is common in patients with Alzheimer’s disease (AD), we evaluated nutritional status and body composition of patients with AD, mild cognitive impairment (MCI) and controls, and studied associations of AD biomarkers and cognitive performance with nutritional status and body composition. We included 552 participants, of which 198 patients had AD, 135 patients had MCI and 219 controls. We assessed nutritional status (mini nutritional assessment (MNA)) and body composition (body mass index (BMI), fat-free mass (FFM) and waist circumference). Linear regression analyses (adjusted for age, gender and education where appropriate) were applied to test associations of AD biomarkers and cognitive performance on five domains with nutritional parameters (dependent). Patients with MCI and AD had a lower BMI and MNA score than controls. Worse performance in all cognitive domains was associated with lower MNA score, but not with body composition. AD biomarkers were associated with MNA score, BMI and waist circumference, and associations with MNA score remained after adjustment for cognitive performance. Both AD biomarkers and cognitive performance were associated with nutritional status, associations with AD biomarkers remained after adjustment for cognition. Our data suggest that malnutrition is not only related to impaired cognition but also to AD pathology.

1. Introduction

Unintended weight loss and protein energy malnutrition are common features in patients with moderate to severe Alzheimer’s disease (AD) dementia. The prevalence of malnutrition is reported to range from 0%–13% in community-dwelling patients [1,2] to 30%–60% in institutionalized patients [3,4]. Malnutrition in patients with AD is associated with an accelerated progression of disease and increased morbidity and mortality [5,6,7]. Much less investigated is the prevalence of malnutrition in patients with mild cognitive impairment (MCI). The few available studies showed that patients with MCI are at higher risk of malnutrition than cognitively healthy adults, albeit at lower risk than patients with AD [8,9,10]. Population-based studies in non-demented adults found that weight loss is a predictor for incident MCI and dementia [6,11,12], and may be one of the first signs of cognitive problems.
Several hypotheses have been proposed to explain how weight loss is associated with AD. One potential explanation is poor nutritional intake caused by a decline in cognitive functioning. Some examples are forgetting to eat, no longer being able to use eating utensils, chewing problems or dysphagia [13,14]. Second, biological changes in the brain might play a role in changes in the nutritional status of patients with AD [15]. Cerebrospinal fluid (CSF) β-amyloid 42 (Aβ42), total tau (tau) and tau phosphorylated at threonine 181 (p-tau) are considered to reflect AD pathophysiology and make it possible to measure AD pathology ‘in vivo’ [16]. AD pathology has been hypothesized to elevate metabolism and therefore energy expenditure, or to alter the uptake of nutrients leading to malnutrition [15]. It has, however, not yet been studied whether AD pathology or cognitive performance in different domains are associated with nutritional status or body composition. The aim of this study was to compare the nutritional status and body composition of patients with AD and MCI to cognitively normal controls. Furthermore, we studied associations of AD biomarkers and cognitive performance with nutritional status and body composition.

2. Materials and Methods

2.1. Study Population

The NUDAD (Nutrition, the Unrecognized Determinant in Alzheimer’s Disease) study is a prospective cohort studying nutritional determinants in AD and pre-dementia stages, with three-year clinical follow-up. Here, we present cross-sectional baseline data of all participants enrolled in NUDAD. NUDAD is a subsample of the Amsterdam Dementia Cohort, existing of patients who visited our Alzheimer center between September 2015 and August 2017 and were diagnosed with AD, MCI or subjective cognitive decline (SCD) and had a mini-mental state examination (MMSE) score >16 [17]. Patients underwent a standardized dementia screening, including extensive neuropsychological assessment, neurological examination and laboratory tests [18]. Clinical diagnosis of MCI and AD was established by consensus in a multidisciplinary meeting according to the National Institute on Aging-Alzheimer’s Association criteria [19,20]. As a control group, we used subjects with SCD who presented with memory complaints but appeared normal on all clinical examinations, i.e., criteria for MCI, dementia or psychiatric diagnosis were not fulfilled [18]. In total 552 participants were included, 198 patients with AD, 135 patients with MCI and 219 controls. Informed consent was obtained from all participants and the protocol was approved by the Ethics Committee of the Amsterdam UMC(2015.457).
Descriptive characteristics included: age, gender, MMSE score, level of education, living situation (with partner/children, alone, nursing home) and smoking status (current, former, never). Level of education was assessed using the Verhage classification system [21], which we categorized into low (score 1–3), intermediate (score 4 and 5) and high (score 6 and 7). Furthermore, the presence of diabetes mellitus, hypertension, hypercholesterolemia and the history of myocardial infarct were retrieved from medical records. Advanced glycation endproduct (AGE) score, a measure of cardiovascular risk, was measured via skin autofluorescence with the AGE reader [22]. Apolipoprotein E (APOE) ϵ4 status was dichotomized into carrier (1 or 2 ϵ4 alleles) or non-carrier [23].

2.2. Body Composition

Body mass index (BMI), also known as the Quetelet Index, was calculated by dividing the measured body weight by the squared measured height (kg/m2). Participants were classified as: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), or obese (BMI ≥ 30 kg/m2). All circumferences (cm) were measured with a measuring tape in standing position: arm at the mid-upper left arm hanging loosely by the side, calf at the broadest point, waist at the smallest part between the lowest rib and the hip, and the hip at the broadest part [24]. Fat-free mass (FFM, kg) was estimated using multi-frequency bio-electrical impedance analysis (Bodystat Quadscan 4000) and the formula of Kyle [25]. The availability for body composition parameters ranged from 78% for FFM to 100% for BMI.

2.3. Nutritional Status

Nutritional status was evaluated with the Mini Nutritional Assessment (MNA) [26,27]. Scores ranged from 0 to 30 with a higher score indicating a better nutritional status. Participants were classified as: malnourished (MNA score <17), at risk of malnutrition (MNA score 17–23.5) or well-nourished (MNA score >23.5) [28]. To avoid that differences in MNA score were driven by differences in cognitive performance, we also analyzed a modified MNA score, leaving out the question about neuropsychological problems. MNA was available in 65% of our study population.

2.4. Neuropsychological Assessment

Cognitive performance was measured using a standardized neuropsychological test battery, covering five domains. The domain memory included: total recall on visual association test (VAT) and total immediate and delayed recall of the Dutch version of the Rey auditory verbal learning task [29,30]. The domain attention included: trail making test (TMT) part A, forward condition of digit span, and Stroop test word and color subtasks [31,32,33]. The domain executive functioning included: frontal assessment battery, backward condition of digit span, Stroop test color-word subtask and letter fluency [32,33,34,35]. The domain language included: category fluency (animal naming) and the naming condition of the VAT [29,36]. Finally, the domain visuospatial ability included: dot counting, fragmented letters and number location [37]. Raw test scores were converted into z-scores using the mean and SD of our study population. The test scores for TMT A were log-transformed because they were not normally distributed. Z-scores for TMT A and Stroop were inverted, such that lower scores indicate worse cognitive performance for all cognitive tests. Domain scores were calculated by averaging z-scores of the individual tests within that domain if at least two tests were available. Availability of the domain scores ranged from 93% for visuospatial ability to 98% for memory.

2.5. AD Biomarkers

CSF was obtained by lumbar puncture using a 25-gauge needle and collected in 10 mL polypropylene tubes (Sarstedt) following standardized protocols [38]. Aβ42, tau and p-tau concentrations were determined with sandwich Innotest ELISAs (Fujirebio, Ghent, Belgium) [39] and available in 393 participants (71%). Aβ42 concentrations were adjusted for the drift that occurred over the years [40].

2.6. Statistical Analyses

Between-diagnosis group differences in participant characteristics, nutritional status and body composition were tested using analysis of variance (ANOVA) with post-hoc LSD (least significant difference), adjusted t-test for continuous variables and chi-square-test for categorical variables. ANOVAs of nutritional status and body composition were adjusted for age, gender and education. Analyses with FFM were additionally adjusted for height and fat mass. Within the total cohort, we used linear regression analyses to evaluate associations between AD biomarkers in CSF or cognitive domains (independent variables) with nutritional status and body composition variables (dependent variables). All descriptive variables in Table 1 that changed the regression coefficient ≥ 10% were regarded as confounders and included in the model. First, we assessed the association of AD biomarkers with nutritional parameters adjusted for age and gender (Table 3, model 1), followed by additional adjustment for all cognitive domain scores to examine the association of biomarkers independent of cognitive performance (Table 3, model 2). Similarly, the association of cognitive domains with nutritional parameters were adjusted for age, gender and education (Table 4, model 1), followed by additional adjustment for both Aβ42 and tau levels to examine the association of cognitive performance independent of AD biomarkers (Table 4, model 2). To test the assumptions of the regression analyses, we plotted and checked residuals of all models, which were all normally distributed. Furthermore, in each model, we checked tolerance values, variance inflation factors and correlations between variables and did not observe multicollinearity. Significance was set at p <0.05. All analyses were performed with SPSS version 22 (released 2013, IBM SPSS Statistics for Windows, Armonk, NY, USA).

3. Results

Patients with MCI and AD were older, had a lower MMSE score and had a lower level of education than controls (Table 1). Groups did not differ in vascular risk factors. Patients with MCI and AD were more often APOE ϵ4 carriers and had lower Aβ42 levels and higher tau and p-tau levels than controls. As expected, cognitive performance on all domains differed between groups, with controls scoring highest and patients with AD scoring lowest.
ANCOVAs of body composition showed that patients with MCI and AD had a lower BMI, and were less likely to be obese than controls (Table 2). In addition, patients with MCI had a lower FFM, adjusted for height and fat mass, compared to patients with AD and controls. Patients with AD had a smaller waist and hip circumference compared to controls. There was no interaction with gender for any nutritional parameter. Groups did not differ in arm or calf circumference. Analyzing nutritional status, both full MNA and modified MNA score differed between groups, with patients with AD scoring lowest. Four patients with AD, one patient with MCI and one control were classified as malnourished. More participants were at risk of malnutrition, 38 patients with AD, 17 patients with MCI and 12 controls (p = 0.001).
Adjusted linear regression analyses showed that higher levels of tau and p-tau were related to lower BMI, FFM, waist circumference and lower MNA scores. Lower Aβ42 levels were related to lower BMI, waist circumference and full MNA score (Table 3, model 1). In the analyses adjusted for cognitive performance, the association of both tau and p-tau with the full MNA score remained whereas the associations with Aβ42 lost significance (Table 3, model 2). Furthermore, the association of p-tau with the modified MNA score remained, while the association of tau with this score lost significance.
Adjusted linear regression analyses of cognitive performance revealed associations of all cognitive domains with the full MNA score, with lower domain scores being related to lower full MNA score (Table 4, model 1). Similarly, poorer performance on the domains attention, executive functioning, language and visuospatial ability, but not memory, was associated with a lower modified MNA score. Additionally adjusted for AD biomarker levels, the association of all cognitive domain scores with full MNA score remained, while the association with the modified MNA remained for the domains executive functioning and visuospatial ability only (Table 4, model 2). There were no associations of cognitive domain scores with BMI, FFM and waist circumference.

4. Discussion

The main finding of this study is that lower Aβ42 and higher tau and p-tau levels were associated with poorer nutritional status and body composition. Moreover, poorer cognitive performance in all domains was associated with poorer nutritional status, but not with body composition.
Consistent with the literature, patients with AD and MCI had lower BMI and smaller waist and hip circumferences than controls [9,10], while the MNA malnutrition score of patients with MCI was in between the score of controls and patients with AD [8,9,41]. We extend on existing literature by adding the modified MNA score (leaving the question on neuropsychological problems out) showing that patients with AD still scored lower, indicating that differences in MNA score cannot solely be accounted for by neuropsychological problems. These findings are strengthened by the associations of poorer performance in all cognitive domains in relation with lower MNA score, independent of AD biomarker levels. It is conceivable that patients with cognitive impairment forget to eat, which leads to malnutrition. Independent of assessment method, other studies also found that with poorer cognitive performance, the nutritional status is worse as well [42,43,44]. In general, older adults that live alone are at higher risk of malnutrition, however, we did not found an association of living situation with nutritional status in our population [45].
Our results also implicate a biological pathway, since more abnormal AD biomarker levels were associated with lower BMI, waist circumference and MNA score. Even after adjusting for cognitive performance, the associations with the MNA score remained, indicating that malnutrition is also directly related to AD pathology, independent of cognitive decline within AD. The biological connection between AD pathology and malnutrition might translate into an elevated metabolism, due to disease-related specific changes in lipid metabolism, and therefore a higher energy expenditure [15,46]. This needs to be confirmed in future studies in our cohort. Another possible explanation for this biological link is malabsorption of nutrients, which is supported by previous studies showing changes in the microbiome in AD [47,48], also subject for future studies. The downstream processes within the AD pathological cascade seem most important for nutritional status, since more abnormal tau and p-tau levels, but not Aβ42, were also associated with lower fat-free mass and lower modified MNA score.
Among the strengths of our study is the availability of both AD biomarkers in CSF and neuropsychological tests in different cognitive domains in a large study population covering participants from the complete AD spectrum. Furthermore, we assessed nutritional status and body composition using a set of concise and objective assessments, and not merely (self-reported) body weight or BMI. This study has some limitations. First, the MNA score was missing in 35% of the study population. However, this percentage was similar across all diagnosis groups and there was no difference in BMI between participants with and without MNA (mean ± SD BMI participants with MNA 25.8 ± 4.1 kg/m2, without MNA 26.0 ± 4.1 kg/m2; p = 0.498) indicating the sample was representative for the study population. Second, the control group consisted of patients diagnosed with subjective cognitive decline (SCD). They visited the clinic because of memory complaints, which were not objectified by extended neuropsychological assessment. Associations might be even stronger if a control group without any cognitive complaints would have been included since persons with SCD are at increased risk of developing AD [49]. Third, we did not have data on physical activity, which might explain differences in FFM. Fourth, this study had a cross-sectional design and therefore causal inferences cannot be established. We cannot exclude that the direction of the associations is different than hypothesized and that, due to reduced dietary intake and therefore poorer nutritional status, cognitive function and biomarker levels are affected. Another option might be that AD biomarkers influence nutritional status, which in turn affects cognitive performance. We are currently following our participants longitudinally with yearly neuropsychological testing and assessment of nutritional status. These longitudinal data will enable us to further investigate the pathways involved.
In conclusion, nutritional status and body composition are poorer in patients with AD compared to controls, and already appear affected in patients with MCI. Both AD biomarkers in CSF and cognitive performance are associated with nutritional status, suggesting that malnutrition and to a lesser extent poorer body composition are not only related to impaired cognition but also directly to AD pathology. These insights advocate monitoring the nutritional status of patients with AD pathology even if they do not yet have severe cognitive impairment.

Author Contributions

Conceptualization, A.S.D., M.V., O.v.d.R., M.I.K., F.A.d.L., S.B., J.L.P.F., E.G.H.M.v.d.H., C.E.T., P.S., W.M.v.d.F. and M.A.E.d.v.d.S.; methodology, A.S.D., M.V., O.v.d.R. and M.A.E.d.v.d.S.; formal analysis, A.S.D., M.V. and M.A.E.d.v.d.S.; investigation, A.S.D., F.A.d.L. and J.L.P.F.; data curation, A.S.D., M.V. and M.A.E.d.v.d.S.; writing—original draft preparation, A.S.D., M.V., O.v.d.R., M.I.K., F.A.d.L., S.B., J.L.P.F., W.M.v.d.F.and M.A.E.d.v.d.S.; writing—review and editing, A.S.D., M.V., O.v.d.R., M.I.K., F.A.d.L., S.B., J.L.P.F., E.G.H.M.v.d.H., C.E.T., P.S., W.M.v.d.F.and M.A.E.d.v.d.S.; visualization, A.S.D.; supervision, M.V., O.v.d.R., P.S., W.M.v.d.F.and M.A.E.d.v.d.S.; project administration, A.S.D., M.V., O.v.d.R., M.I.K., F.A.d.L., J.L.P.F. and M.A.E.d.v.d.S.; funding acquisition, W.M.v.d.F.and M.A.E.d.v.d.S..

Funding

Alzheimer Center Amsterdam is supported by Alzheimer Nederland and Stichting VUmc fonds. Research of the Alzheimer Center Amsterdam is part of the neurodegeneration research program of Neuroscience Amsterdam. Astrid Doorduijn, Jay Fieldhouse and Francisca de Leeuw are appointed on an NWO-FCB grant, NUDAD (project number 057-14-004).

Acknowledgments

We acknowledge members of the NUDAD project team: Amsterdam University Medical Center location VUmc: Wiesje van der Flier, Maartje Kester, Philip Scheltens, Charlotte Teunissen, Marian de van der Schueren, Francien de Leeuw, Astrid Doorduijn, Jay Fieldhouse, José Overbeek, Els Dekkers; Vrije Universiteit Amsterdam: Marjolein Visser; Wageningen University & Research: Ondine van de Rest, Sanne Boesveldt; DSM: Peter van-Dael, Manfred Eggersdorfer; Nutricia Research: John Sijben, Nick van Wijk, Amos Attali, J. Martin Verkuyl: FrieslandCampina: Martijn Veltkamp, Ellen van den Heuvel.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Droogsma, E.; Van Asselt, D.; Scholzel-Dorenbos, C.J.M.; Van Steijn, J.; Van Walderveen, P.E.; Van der Hooft, C. Nutritional status of community-dwelling elderly with newly diagnosed alzheimer’s disease: Prevalence of malnutrition and the relation of various factors to nutritional status. J. Nutr. Health Aging 2013, 17, 606–610. [Google Scholar] [CrossRef] [PubMed]
  2. Gillioz, A.S.; Villars, H.; Voisin, T.; Cortes, F.; Gillette-Guyonnet, S.; Andrieu, S.; Gardette, V.; Nourhashemi, F.; Ousset, P.J.; Jouanny, P.; et al. Spared and impaired abilities in community-dwelling patients entering the severe stage of Alzheimer’s disease. Dement. Geriatr. Cogn. Disord. 2009, 28, 427–432. [Google Scholar] [CrossRef] [PubMed]
  3. Galesi, L.F.; Leandro-Merhi, V.A.; de Oliveira, M.R. Association between indicators of dementia and nutritional status in institutionalised older people. Int. J. Older People Nurs. 2013, 8, 236–243. [Google Scholar] [CrossRef] [PubMed]
  4. Malara, A.; Sgro, G.; Caruso, C.; Ceravolo, F.; Curinga, G.; Renda, G.F.; Spadea, F.; Garo, M.; Rispoli, V. Relationship between cognitive impairment and nutritional assessment on functional status in Calabrian long-term-care. Clin. Interv. Aging 2014, 9, 105–110. [Google Scholar] [PubMed]
  5. Droogsma, E.; van Asselt, D.; van Steijn, J.; Veeger, N.; van Dusseldorp, I.; De Deyn, P.P. Nutritional interventions in community-dwelling Alzheimer patients with (risk of) undernutrition: A systematic review. Int. Psychogeriatr. 2014, 26, 1–9. [Google Scholar] [CrossRef]
  6. Barrett-Connor, E.; Edelstein, S.L.; Corey-Bloom, J.; Wiederholt, W.C. Weight loss precedes dementia in community-dwelling older adults. J. Am. Geriatr. Soc. 1996, 44, 1147–1152. [Google Scholar] [CrossRef] [PubMed]
  7. Lauque, S.; Arnaud-Battandier, F.; Gillette-Guyonnet, S.; Plaze, J.M.; Andrieu, S.; Cantet, C.; Vellas, B. Improvement of weight and fat-free mass with oral nutritional supplementation in patients with alzheimer’s disease at risk of malnutrition: A prospective randomized study. J. Am. Geriatr. Soc. 2004, 52, 1702–1707. [Google Scholar] [CrossRef] [PubMed]
  8. Isaia, G.; Mondino, S.; Germinara, C.; Cappa, G.; Aimonino-Ricauda, N.; Bo, M.; Isaia, G.C.; Nobili, G.; Massaia, M. Malnutrition in an elderly demented population living at home. Arch. Gerontol. Geriatr. 2011, 53, 249–251. [Google Scholar] [CrossRef]
  9. Cova, I.; Pomati, S.; Maggiore, L.; Forcella, M.; Cucumo, V.; Ghiretti, R.; Grande, G.; Muzio, F.; Mariani, C. Nutritional status and body composition by bioelectrical impedance vector analysis: A cross sectional study in mild cognitive impairment and Alzheimer’s disease. PLoS ONE 2017, 12, e0171331. [Google Scholar] [CrossRef]
  10. Besser, L.M.; Gill, D.P.; Monsell, S.E.; Brenowitz, W.; Meranus, D.H.; Kukull, W.; Gustafson, D. Body mass index, weight change, and clinical progression in mild cognitive impairment and Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2014, 28, 36–43. [Google Scholar] [CrossRef]
  11. Stewart, R.; Masaki, K.; Xue, Q.L.; Peila, R.; Petrovitch, H.; White, L.R.; Launer, L.J. A 32-year prospective study of change in body weight and incident dementia: The Honolulu-Asia aging study. Arch. Neurol. 2005, 62, 55–60. [Google Scholar] [CrossRef]
  12. Alhurani, R.E.; Vassilaki, M.; Aakre, J.A.; Mielke, M.M.; Kremers, W.K.; Machulda, M.M.; Geda, Y.E.; Knopman, D.S.; Peterson, R.C.; Roberts, R.O. Decline in weight and incident mild cognitive impairment: mayo clinic study of aging. JAMA Neurol. 2016, 73, 439–446. [Google Scholar] [CrossRef] [PubMed]
  13. Tamura, B.K.; Bell, C.L.; Masaki, K.H.; Amella, E.J. Factors associated with weight loss, low BMI, and malnutrition among nursing home patients: A systematic review of the literature. J. Am. Med. Dir. Assoc. 2013, 14, 649–655. [Google Scholar] [CrossRef] [PubMed]
  14. Chang, C.C.; Roberts, B.L. Feeding difficulty in older adults with dementia. J. Clin. Nurs. 2008, 17, 2266–2274. [Google Scholar] [CrossRef] [PubMed]
  15. De Wilde, M.C.; Vellas, B.; Girault, E.; Yavuz, A.C.; Sijben, J.W. Lower brain and blood nutrient status in Alzheimer’s disease: Results from meta-analyses. Alzheimers Dement. (New York) 2017, 3, 416–431. [Google Scholar] [CrossRef] [PubMed]
  16. Blennow, K.; Hampel, H. CSF markers for incipient Alzheimer’s disease. Lancet Neurol. 2003, 2, 605–613. [Google Scholar] [CrossRef]
  17. Folstein, M.; Folstein, S.; McHugh, P. “Mini-mental state” a practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12, 189–198. [Google Scholar] [CrossRef]
  18. Van der Flier, W.M.; Pijnenburg, Y.A.; Prins, N.; Lemstra, A.W.; Bouwman, F.H.; Teunissen, C.E.; van Berckel, B.N.; Stam, C.J.; Barkhof, F.; Visser, P.J.; et al. Optimizing patient care and research: The Amsterdam Dementia Cohort. J. Alzheimers Dis. 2014, 41, 313–327. [Google Scholar] [CrossRef]
  19. McKhann, G.M.; Knopman, D.S.; Chertkow, H.; Hyman, B.T.; Jack, C.R., Jr.; Kawas, C.H.; Klunk, W.E.; Koroshetz, W.J.; Manly, J.J.; Mayeux, R.; et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011, 7, 263–269. [Google Scholar] [CrossRef]
  20. Albert, M.S.; DeKosky, S.T.; Dickson, D.; Dubois, B.; Feldman, H.H.; Fox, N.C.; Gamst, A.; Holtzman, D.M.; Jagust, W.J.; Petersen, R.C.; et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011, 7, 270–279. [Google Scholar] [CrossRef]
  21. Verhage, F. Intelligence and age in a Dutch sample. Hum. Dev. 1965, 8, 238–245. [Google Scholar] [CrossRef]
  22. Stirban, A.; Heinemann, L. Skin autofluorescence a non-invasive measurement for assessing cardiovascular risk and risk of diabetes. Eur. Endocrinol. 2014, 10, 106–110. [Google Scholar] [CrossRef] [PubMed]
  23. Van Harten, A.C.; Jongbloed, W.; Teunissen, C.E.; Scheltens, P.; Veerhuis, R.; van der Flier, W.M. CSF ApoE predicts clinical progression in nondemented APOEepsilon4 carriers. Neurobiol. Aging 2017, 57, 186–194. [Google Scholar] [CrossRef]
  24. World Health Organization. Waist Circumference and Waist-Hip Ratio; WHO: Geneva, Switzerland, 2008. [Google Scholar]
  25. Kyle, U.G.; Genton, L.; Karsegard, L.; Slosman, D.O.; Pichard, C. Single prediction equation for bioelectrical impedance analysis in adults aged 20–94 years. Nutrition 2001, 17, 248–253. [Google Scholar] [CrossRef]
  26. Vellas, B.; Villars, H.; Abellani, G.; Soto, M.; Rolland, Y.; Guigoz, Y.; Morley, J.; Chumlea, W.; Salva, A.; Rubenstein, L.; et al. Overview of the MNA—Its history and challenges. J. Nutr. Health Aging 2006, 10, 456–465. [Google Scholar] [PubMed]
  27. Vellas, B.; Guigoz, Y.; Garry, P.; Nourhashemi, F.; Bennahum, D.; Lauque, S.; Albaréde, J. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Appl. Nutr. Investig. 1999, 15, 116–122. [Google Scholar] [CrossRef]
  28. Guigoz, Y. The Mini Nutritional Assessment (MNA) review of the literature—What does it tell us? J. Nutr. Health Aging 2006, 10, 466–485. [Google Scholar] [PubMed]
  29. Lindeboom, J.; Schmand, B.; Tulner, L.; Walstra, G.; Jonker, C. Visual association test to detect early dementia of the Alzheimer type. J. Neurol. Neurosurg. Psychiatry 2002, 73, 126–133. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Rey, A. L’Examen Clinique en Psychologie; The clinical psychological examination; Presses Universitaires de France: Paris, France, 1964. [Google Scholar]
  31. Reitan, R. The relation of the trail making test to organic brain damage. J. Consult. Psychol. 1955, 19, 393–394. [Google Scholar] [CrossRef]
  32. Wechsler, D. Adult Intelligence Scale—Administration and Scoring Manual; The Psychological Corporation: San Antonio, TX, USA, 1997. [Google Scholar]
  33. Stroop, R. Studies of interference in serial verbal reactions. J. Exp. Psychol. 1935, 18, 643–662. [Google Scholar] [CrossRef]
  34. Hughes, B. Missile wounds of the brain: A study of psychological deficits. J. Neurol. Neurosurg. Psychiatry 1970, 33, 551. [Google Scholar] [CrossRef]
  35. Dubois, B.; Slachevsky, A.; Litvan, I.; Pillon, B. The FAB a frontal assessment battery at bedside. Neurology 2000, 55, 1621–1626. [Google Scholar] [CrossRef]
  36. Benton, A.L. Differential behavioral effects in frontal lobe disease. Neuropsychologia 1967, 6, 53–60. [Google Scholar] [CrossRef]
  37. Warrington, E.K.; James, M. The Visual Object and Space Perception Battery: VOSP; Pearson: London, UK, 1991. [Google Scholar]
  38. Teunissen, C.; Petzhold, A.; Bennett, J.; Berven, F.; Brundin, L.; Comabella, M.; Franciotta, D.; Frederiksen, J.; Fleming, J.; Furlan, R.; et al. A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking. Neurology 2009, 73, 1914–1922. [Google Scholar] [CrossRef] [Green Version]
  39. Mulder, C.; Verwey, N.A.; van der Flier, W.M.; Bouwman, F.H.; Kok, A.; van Elk, E.J.; Scheltens, P.; Blankenstein, M.A. Amyloid-beta(1-42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. Clin. Chem. 2010, 56, 248–253. [Google Scholar] [CrossRef] [PubMed]
  40. Tijms, B.M.; Willemse, E.A.J.; Zwan, M.D.; Mulder, S.D.; Visser, P.J.; van Berckel, B.N.M.; van der Flier, W.M.; Scheltens, P.; Teunissen, C.E. Unbiased approach to counteract upward drift in cerebrospinal fluid amyloid-beta 1-42 analysis results. Clin. Chem. 2018, 64, 576–585. [Google Scholar] [CrossRef] [PubMed]
  41. Saragat, B.; Buffa, R.; Mereu, E.; Succa, V.; Cabras, S.; Mereu, R.M.; Putzu, P.F.; Marini, E. Nutritional and psycho-functional status in elderly patients with Alzheimer’s disease. J. Nutr. Health Aging 2012, 16, 231–236. [Google Scholar] [CrossRef] [PubMed]
  42. Guerin, O.; Soto, M.; Brocker, P.; Robert, P.; Benoit, M.; Vellas, B. Nutritional status assessment during alzheimer’s disease: Results after one year (the REAL French study group). J. Nutr. Health Aging 2005, 9, 81–84. [Google Scholar] [PubMed]
  43. Roque, M.; Salva, A.; Vellas, B. Malnutrition in community-dwelling adults with dementia (NutriAlz Trial). J. Nutr. Health Aging 2013, 17, 295–299. [Google Scholar] [CrossRef]
  44. Magri, F.; Borza, A.; del Vecchio, S.; Chytiris, S.; Cuzzoni, G.; Busconi, L.; Rebesco, A.; Ferrari, E. Nutritional assessment of demented patients: A descriptive study. Aging Clin. Exp. Res. 2003, 15, 148–153. [Google Scholar] [CrossRef]
  45. Agarwal, E.; Miller, M.; Yaxley, A.; Isenring, E. Malnutrition in the elderly: A narrative review. Maturitas 2013, 76, 296–302. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Toledo, J.B.; Arnold, M.; Kastenmuller, G.; Chang, R.; Baillie, R.A.; Han, X.; Thambisetty, M.; Tenenbaum, J.D.; Suhre, K.; Thompson, J.W.; et al. Metabolic network failures in Alzheimer’s disease: A biochemical road map. Alzheimers Dement. 2017, 13, 965–984. [Google Scholar] [CrossRef]
  47. Pistollato, F.; Sumalla Cano, S.; Elio, I.; Masias Vergara, M.; Giampieri, F.; Battino, M. Role of gut microbiota and nutrients in amyloid formation and pathogenesis of Alzheimer disease. Nutr. Rev. 2016, 74, 624–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Bhattacharjee, S.; Lukiw, W.J. Alzheimer’s disease and the microbiome. Front. Cell. Neurosci. 2013, 7, 153. [Google Scholar] [CrossRef] [PubMed]
  49. Buckley, R.F.; Maruff, P.; Ames, D.; Bourgeat, P.; Martins, R.N.; Masters, C.L.; Rainey-Smith, S.; Lautenschlager, N.; Rowe, C.C.; Savage, G.; et al. Subjective memory decline predicts greater rates of clinical progression in preclinical Alzheimer’s disease. Alzheimers Dement. 2016, 12, 796–804. [Google Scholar] [CrossRef]
Table
Table 1. Characteristics of the study population according to diagnosis group.
Table 1. Characteristics of the study population according to diagnosis group.
ControlsMCIADp-Value
n n n
Age (years)21960.6 ± 7.713566.3 ± 7.7 †19867.4 ± 7.9 †<0.001
Gender, female219103 (47.0)13554 (40.0)19899 (50.0)0.193
MMSE score21929 (27–29)13527 (25–28) †19823 (21–25) †,‡<0.001
Vegetarian dietary pattern9810 (10.2)532 (3.8)7410 (13.5)0.191
Level of education
Low21913 (5.9)13513 (9.6) †19817 (8.6) †0.014
Intermediate79 (36.1)67 (49.6) †93 (47.0) †
High127 (58.0)55 (40.7) †88 (44.4) †
Living situation
With partner/children219163 (74.4)135111 (82.2)198155 (78.3)0.223
Alone56 (25.6)23 (17.0)41 (20.7)
Nursing home0 (0)1 (0.7)2 (1.0)
Smoking status
Current21928 (12.8)13523 (17.0)19826 (13.1)0.759
Former85 (38.8)52 (38.5)73 (36.9)
Never106 (48.4)60 (44.4)99 (50.0)
Vascular risk factors
Diabetes Mellitus21916 (7.3)13518 (13.3)19817 (8.6)0.151
Hypertension21949 (22.4)13536 (26.7)19852 (26.3)0.557
Hypercholesterolemia21921 (9.6)13519 (14.1)19829 (14.6)0.242
Myocardial infarct2194 (1.8)1357 (5.2)1985 (2.5)0.174
AGE score1752.3 ± 0.51132.4 ± 0.61692.4 ± 0.60.157
AD biomarkers
APOE ϵ4 carrier20589 (43.4)12971 (55.0) †190123 (64.7) †,‡<0.001
CSF Aβ42 (pg/mL)1501047 ± 298103856 ± 321 †141592 ± 156 †,‡<0.001
CSF tau (pg/mL)149342 ± 254103516 ± 300 †141547 ± 433 †,‡<0.001
CSF p-tau (pg/mL)14952 ± 3610371 ± 32 †14192 ± 37 †,‡<0.001
Cognitive domain specific z-scores
Memory2160.79 ± 0.56130−0.16 ± 0.58 †193−0.76 ± 0.57 †,‡<0.001
Attention2150.39 ± 0.581320.05 ± 0.59 †196−0.51 ± 0.93 †,‡<0.001
Executive functioning2150.45 ± 0.581320.04 ± 0.54 †188−0.61 ± 0.80 †,‡<0.001
Language2150.43 ± 0.601290.08 ± 0.51 †190−0.52 ± 0.81 †,‡<0.001
Visuospatial ability2100.30 ± 0.281270.17 ± 0.42 †175−0.47 ± 1.11 †,‡<0.001
Data in mean ± SD; n (%); median (interquartile range); AD = Alzheimer’s disease; MCI = mild cognitive impairment; MMSE = mini-mental state examination; AGE = advanced glycation endproduct; APOE = apolipoprotein E; CSF = cerebrospinal fluid; Aβ42 = β-amyloid 42; p-tau = phosphorylated tau; † significantly different from controls upon post-hoc testing; ‡ significantly different from MCI upon post-hoc testing.
Table
Table 2. Nutritional status and body composition per diagnosis group.
Table 2. Nutritional status and body composition per diagnosis group.
ControlsMCIADp-Value
n n n
BMI (kg/m2)21926.7 ± 0.313525.4 ± 0.3 †19825.1 ± 0.3 †0.001
BMI category219 135 198 <0.001
Underweight (BMI < 18.5) 0 (0) 3 (2.2) † 3 (1.5) †
Normal weight (BMI 18.5–24.9) 93 (42.5) 58 (43.0) † 111 (56.1) †
Overweight (BMI 25–29.9) 73 (33.3) 60 (44.4) † 68 (34.3) †
Obese (BMI ≥ 30) 53 (24.2) 14 (10.4) † 16 (8.1) †
FFM # (kg) 18153.5 ± 0.39951.5 ± 0.4 †15252.9 ± 0.3 ‡0.001
Waist circumference (cm) 21092.3 ± 0.812790.6 ± 0.918889.2 ± 0.8 †0.024
Hip circumference (cm) 210102.1 ± 0.6127100.9 ± 0.818899.9 ± 0.7 †0.080
Arm circumference (cm)21029.0 ± 0.212728.2 ± 0.318928.4 ± 0.20.068
Calf circumference (cm)21036.0 ± 0.212735.5 ± 0.318935.8 ± 0.20.367
MNA score13326.0 ± 0.29125.1 ± 0.3 †13424.3 ± 0.2 †,‡<0.001
Modified MNA score13324.3 ± 0.29124.0 ± 0.313423.6 ± 0.2 †0.050
MNA category133 91 134 0.001
Malnourished (MNA < 17) 1 (0.8) 1 (1.1) 4 (3.0) †
At risk of malnutrition (MNA 17–23.5) 12 (9.0) 17 (18.7) 38 (28.4) †
Well-nourished (MNA > 23.5) 120 (90.2) 73 (80.2) 92 (68.7) †
Data is presented as mean ± SE; n (%); MCI = mild cognitive impairment; AD = Alzheimer’s disease; BMI = body mass index; FFM # = fat-free mass, adjusted for height and fat mass; MNA = mini nutritional assessment; all tested using age, gender and education adjusted ANOVA with post-hoc LSD adjusted t-test or chi-square-test; † significantly different from controls upon post-hoc testing; ‡ significantly different from MCI upon post-hoc testing.
Table
Table 3. Associations of AD biomarkers in CSF with nutritional status and body composition.
Table 3. Associations of AD biomarkers in CSF with nutritional status and body composition.
BMI (kg/m2)FFM (kg)Waist Circumference (cm)MNA ScoreModified MNA Score
Aβ42Model 1 0.16 *0.010.17 *0.22 *0.11
Model 2 0.130.11
TauModel 1−0.23 *−0.05 *−0.14 *−0.27 *−0.19 *
Model 2 −0.15 *−0.13
P-tauModel 1−0.22 *−0.05 *−0.14 *−0.28 *−0.21 *
Model 2 −0.17 *−0.15 *
Data presented as β (regression coefficients); * p < 0.05; model 1 adjusted for age and gender; model 2 additionally adjusted for cognitive domain scores (memory, attention, executive functioning, language, visuospatial ability); AD = Alzheimer’s disease; CSF = cerebrospinal fluid; Aβ42 = β-amyloid 42; p-tau = phosphorylated tau; BMI = body mass index; FFM = fat-free mass, adjusted for height and fat mass in both models; MNA = mini nutritional assessment.
Table
Table 4. Associations of cognitive domains with nutritional status and body composition.
Table 4. Associations of cognitive domains with nutritional status and body composition.
BMI (kg/m2)FFM (kg)Waist Circumference (cm)MNA ScoreModified MNA Score
MemoryModel 1 0.07 0.02 0.05 0.21 *0.10
Model 2 0.15 *0.00
AttentionModel 10.01 −0.01 0.00 0.19 *0.12 *
Model 2 0.14 *0.07
Executive functioningModel 10.020.010.020.31 *0.21 *
Model 2 0.23 *0.15 *
LanguageModel 1−0.010.00−0.010.18 *0.11 *
Model 2 0.18 *0.12
Visuospatial abilityModel 10.06−0.010.070.20 *0.12 *
Model 2 0.20 *0.13 *
Data presented as β (regression coefficients); * p < 0.05; model 1 adjusted for age, gender and education; model 2 additionally adjusted for Aβ42 and tau levels; BMI = body mass index; FFM = fat-free mass, adjusted for height and fat mass in both models; MNA = mini nutritional assessment.

Share and Cite

MDPI and ACS Style

Doorduijn, A.S.; Visser, M.; van de Rest, O.; Kester, M.I.; de Leeuw, F.A.; Boesveldt, S.; Fieldhouse, J.L.P.; van den Heuvel, E.G.H.M.; Teunissen, C.E.; Scheltens, P.; et al. Associations of AD Biomarkers and Cognitive Performance with Nutritional Status: The NUDAD Project. Nutrients 2019, 11, 1161. https://doi.org/10.3390/nu11051161

AMA Style

Doorduijn AS, Visser M, van de Rest O, Kester MI, de Leeuw FA, Boesveldt S, Fieldhouse JLP, van den Heuvel EGHM, Teunissen CE, Scheltens P, et al. Associations of AD Biomarkers and Cognitive Performance with Nutritional Status: The NUDAD Project. Nutrients. 2019; 11(5):1161. https://doi.org/10.3390/nu11051161

Chicago/Turabian Style

Doorduijn, Astrid S., Marjolein Visser, Ondine van de Rest, Maartje I. Kester, Francisca A. de Leeuw, Sanne Boesveldt, Jay L. P. Fieldhouse, Ellen G. H. M. van den Heuvel, Charlotte E. Teunissen, Philip Scheltens, and et al. 2019. "Associations of AD Biomarkers and Cognitive Performance with Nutritional Status: The NUDAD Project" Nutrients 11, no. 5: 1161. https://doi.org/10.3390/nu11051161

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop