Abstract
Objectives
to identify the optimal erythrocyte omega-3 index cut-off for predicting cognitive decline and/or polyunsaturated fatty acid (PUFA) treatment response, in order to better define the target population for future dementia prevention trials.
Design & Setting
Secondary exploratory analysis of the randomized controlled MAPT prevention trial.
Participants
724 dementia-free subjects aged 70 or older with subjective memory complaints, limitations in one instrumental activity of daily living, and/or slow gait speed.
Intervention
800mg docosahexaenoic acid (DHA) and 225mg eicosapentaenoic acid (EPA) daily versus placebo.
Measurements
Erythrocyte omega-3 index was measured at baseline. Cognition was measured over 3 years with a composite cognitive score (mean of 4 z-scores).
Results
Placebo group subjects in the lowest quartile of baseline erythrocyte omega-3 index (i.e. ≤4.83%) underwent significantly more 3-year cognitive decline than the other quartiles (mean composite score difference 0.14, 95%CI [0.00, 0.28], p=0.048). In a ROC curve analysis, the optimal omega-3 index cut-off for predicting notable cognitive decline was 5.3%. There was a consistent but non-significant difference in 3-year cognitive decline of approximately 0.12 points between PUFA-treated and placebo subjects with “low baseline omega-3 index when the cut-off was set at ≤5.27%.
Conclusions
Dementia-free older adults with an omega-3 index below approximately 5% are at increased risk of cognitive decline, and could be a good target population for testing the cognitive effects of PUFA supplementation.
Key words: Cognition, omega-3, trial design, prevention, tailored therapy
Introduction
The omega-3 polyunsaturated fatty acids (PUFAs) docosahexaenoic (DHA) and eicosapentaenoic (EPA) acid, provided in the human diet essentially by consumption of oily fish, are important phospholipid membrane components in the brain. PUFAs may help to maintain cognition in later life and various mechanisms of action have been suggested, including maintenance of membrane integrity and neuronal function, mediation of inflammation, and modification of gene expression (1, 2).
However, results from clinical studies have been conflicting. Some observational studies have suggested that higher fish and/or PUFA intakes are associated with a decreased risk of cognitive decline or dementia (3, 4, 5), and erythrocyte or serum PUFA levels are associated with amyloid deposition (6) or brain volume (7). Randomized controlled trials (RCTs) of PUFA supplementation in healthy elderly populations (8, 9, 10, 11), and mild to moderate Alzheimer's disease (12, 13) have mostly been negative however, although a 24-week trial of DHA in individuals with subjective memory complaints and agerelated cognitive decline demonstrated significant benefit on visuospatial learning and episodic memory (14).
One factor which could influence supplementation efficacy is the susceptibility of the study population to benefit from the intervention (15, 16). A 6 month trial in young adults (aged 18-45) with low dietary DHA intake found that DHA supplementation improved memory and reaction times (17), but no trial has specifically assessed the effects of omega-3 supplementation on cognitive decline in healthy elderly individuals with low PUFA levels. A subgroup analysis of the 3-year MAPT (Multidomain Alzheimer's Preventive Trial) study suggested that elderly subjects in the lowest quartile of baseline DHA and EPA underwent more cognitive decline than those with higher levels, but although PUFA supplementation appeared to improve cognition in this small subgroup, the effect was not statistically significant (9).
It could therefore be pertinent to consider testing PUFA supplementation for the prevention of cognitive decline in older individuals with low DHA and EPA levels in a trial specifically designed for this purpose. However, it is unclear how to define “low DHA and EPA, particularly in the context of cognitive aging. To our knowledge, this research question has not been previously studied. The primary aim of this analysis therefore was to identify the optimal cut-off of erythrocyte DHA+EPA composition (also known as the “omega-3 index) for predicting cognitive decline and/or treatment response to PUFA supplementation using data from the MAPT trial.
Methods
Study population
We retrospectively analyzed data from the MAPT trial which has been described previously (9, 18). Briefly, from May 2008 to February 2011, 1680 community-dwelling individuals aged 70 and older with a spontaneously reported memory complaint, a limitation in one instrumental activity of daily living (IADL) (19), and/or slow walking speed (≤ 0.8 m/s) were included. Exclusion criteria included a Mini Mental State Examination (MMSE) score <24 (20), dementia, difficulty in basic activities of daily living (ADL) (21), and use of PUFA supplements. Participants were randomized to one of the following four groups: (i) multidomain lifestyle intervention + PUFA supplementation (2 capsules/day, containing a daily dose of 800mg DHA and a maximum of 225mg EPA); (ii) multidomain lifestyle intervention + placebo; (iii) PUFA supplement alone; (iv) placebo alone. The study was double blind for PUFA/ placebo assignment. For the present analysis, we only included participants from the modified intention-to-treat population (i.e. subjects with a composite score at baseline and at least one post-baseline visit) who were randomized to receive either PUFA or placebo alone, because we did not want our analysis to be affected by the multidomain intervention.
All participants gave written informed consent, and the study protocol was approved by the Toulouse University Hospital Ethical Committee and the French National Agency for Medicines and Health Products Safety. Clinicaltrials.gov identifier: NCT00672685
Assessments
Assessments were performed at baseline, 6 months, and 1, 2 and 3 years by trained geriatricians, neurologists or neuropsychologists. Sociodemographics (age, sex, education) were recorded at baseline and fasting blood samples were taken at baseline and 1 year. Measurement of erythrocyte DHA and EPA content (omega-3 index) has been described previously (18), and results are expressed as % of total fatty acids. APOE genotype was determined for all participants who gave consent for genetic analyses. Cognitive tests included the MMSE (20), Free and Cued Selective Reminding test (FCSRT,(22)), Clinical Dementia Rating (CDR (23)), Digit Symbol Substitution Test (DSST (24)), and Category Naming test (CNT). Other measures included the Short Physical Performance Battery (SPPB, (25)), Fried frailty criteria (26), and the Geriatric Depression Scale (GDS (27)).
The primary outcome measure was a composite cognitive score, calculated as the average of the following z-scores (standardized using baseline means and standard deviations): MMSE orientation items, FCSRT (sum of free and total recall scores), category fluency and DSST. A validation study showed that this score distinguished between subgroups (based on age, APOE, CDR progression, and Alzheimer's dementia status) with different expected rates of cognitive decline, and was highly predictive of future Alzheimer's dementia in a prevention trial setting (28). Furthermore, an initial estimate of the 1-year minimum clinically important difference was −0.3 points (28).
Statistical analysis
The relationship between baseline erythrocyte omega-3 index and cognitive decline was analyzed in placebo group subjects only. Baseline characteristics were compared between included and excluded subjects, and subjects in the lowest omega-3 index quartile (≤4.83%) and those in the other three quartiles (>4.83%) using chi² tests for categorical variables and t-tests or Wilcoxon rank-sum tests for continuous variables. Longitudinal mixed effects models were used to estimate composite cognitive score change from baseline by baseline omega-3 index quartile. Time was included in the models as a categorical variable (i.e. an indicator for each visit), in addition to subject- and center-specific random intercepts. Models were adjusted for age and baseline score.
Two methods were used to identify the optimal cut-off of baseline omega-3 index for predicting cognitive decline. First, linear and restricted cubic spline regression plots were used, since they may provide better insight into dose-response relationships than analyses using categorized variables (29). Linear splines enable the relationship between two variables to be investigated as a piecewise linear function, while restricted cubic splines, which can be more suitable to very curved functions, use both piecewise linear and cubic polynomial splines. All spline regressions were run with 3, 4 and 5 knots.
Second, a ROC curve analysis was performed in order to identify the optimal omega-3 index cut-off for predicting “notable cognitive decline (30), defined as being in the lowest quartile (i.e. ≤-0.227 points) of composite score 3-year change from baseline, estimated for each individual from an age- and baseline score-adjusted mixed model. Omega-3 index values were rounded to the nearest 0.1% and the optimal cut-off was determined using the Youden Index (31).
Finally, using data from both the placebo and PUFA groups, the efficacy of PUFA supplementation was examined in subgroups of individuals with low baseline omega-3 index, using varying definitions of “low levels, based on the 15th, 25th, 35th and 45th percentiles. Treatment effects were assessed using mixed models including all individuals randomized to the placebo or PUFA group. A subgroup*treatment*visit interaction was included in order to estimate treatment effects in the low omega-3 subgroup. Models were adjusted for age and baseline composite score.
Missing data were not imputed. However, all available data, including those from participants with incomplete follow-up, were included in the mixed models in which missing data were assumed to be missing at random.
All analyses were performed using Stata version 14.1 (StataCorp LP, College Station, Texas). Since this was an exploratory analysis, no adjustment was made for multiple comparisons and a p-value of 0.05 was considered statistically significant.
Results
362 subjects were included in the analysis of the associations between baseline omega-3 index and cognitive decline, and 724 were included in the analysis of PUFA treatment effects (Figure 1).
Figure 1.
Flowchart
In the placebo group, there were no statistically significant differences between subjects who were included in the analyses and those who were excluded. When the placebo and PUFA groups were combined, compared to included subjects, excluded subjects had significantly poorer cognition (mean MMSE 27.8 ± 1.6 versus 28.1 ± 1.6, p=0.013; mean composite score -0.13 ± 0.6 versus 0.03 ± 0.7, p=0.012) and were significantly more likely to have slow gait speed (defined as ≤0.8m/s; 17.9% versus 10.7%, p=0.024)
Subjects in the placebo group in the lowest omega-3 index quartile were significantly older (75.99 ± 4.35 versus 74.48 ± 4.23 years, p=0.025) than subjects in the other three quartiles, and had significantly poorer cognitive function, as measured by the cognitive composite score (-0.12 ± 0.74 versus 0.07 ± 0.64, p=0.022), but not the MMSE. By definition, they also had lower erythrocyte fatty acid DHA+EPA content (3.97 ± 0.61% versus 6.59 ± 1.21%, p<0.001) but there were no other significant differences in baseline characteristics between the two groups (Table 1).
Table 1.
Baseline characteristics of placebo group participants by erythrocyte omega-3 index (%)
| Total | Q2-4: >4.83% | Q1: ≤4.83% | p | |
|---|---|---|---|---|
| (N=362) | (N=277) | (N=85) | ||
| Variable | Mean (SD) or N (%) | Mean (SD) or N (%) | Mean (SD) or N (%) | |
| Age (years) | 75.11 (4.28) | 74.84 (4.23) | 75.99 (4.35) | 0.025 |
| MMSE | 28.12 (1.53) | 28.18 (1.57) | 27.94 (1.39) | 0.214 |
| Composite score | 0.03 (0.67) | 0.07 (0.64) | -0.12 (0.74) | 0.022 |
| GDS | 3.24 (2.71) | 3.24 (2.70) | 3.21 (2.76) | 0.755 |
| SPPB | 10.68 (1.52) | 10.68 (1.49) | 10.68 (1.61) | 0.714 |
| BMI | 25.96 (3.87) | 25.76 (3.98) | 26.63 (3.41) | 0.069 |
| Female | 246 (68.0%) | 194 (70.0%) | 52 (61.2%) | 0.126 |
| Level of education | 0.221 | |||
| ≤Primary school cert. | 78 (21.8%) | 54 (19.7%) | 24 (28.6%) | |
| Secondary education | 110 (30.7%) | 84 (30.7%) | 26 (31.0%) | |
| High school diploma | 63 (17.6%) | 53 (19.3%) | 10 (11.9%) | |
| > High school diploma | 107 (29.9%) | 83 (30.3%) | 24 (28.6%) | |
| Walking speed ≤0.8 m/s | 36 (10.0%) | 27 (9.8%) | 9 (10.7%) | 0.811 |
| ApoE ε4 positive | 73 (24.5%) | 57 (24.1%) | 16 (26.2%) | 0.724 |
| CDR 0.5 | 152 (42.0%) | 113 (40.8%) | 39 (45.9%) | 0.406 |
| ≥1 Fried Frailty criteria | 153 (44.6%) | 114 (43.2%) | 39 (49.4%) | 0.332 |
Note. ApoE: Apolipoprotein E; BMI: Body Mass Index; CDR: Clinical Dementia Rating; GDS: Geriatric Depression Scale; MMSE: Mini Mental State Examination; Q: quartile; SPPB: Short Physical Performance Battery
Longitudinal association between omega-3 index and cognition
Figure 2 shows the 3-year change from baseline in the cognitive composite score by baseline omega-3 index quartile. In age-and baseline score-adjusted models, there was significantly more cognitive decline on the composite score after 3 years in subjects in the lowest omega-3 quartile compared to quartile 3 (mean difference 0.17, 95%CI [0.00, 0.34], p=0.045) and quartiles 2 to 4 combined (mean difference 0.14, 95%CI [0.00, 0.28], p=0.048) (Table 1).
Figure 2.
3-year change in composite cognitive score by baseline omega-3 index quartile
Determination of the optimal cut-off of baseline erythrocyte omega-3 index for predicting cognitive decline
In the ROC curve analysis aiming to identify the optimal omega-3 index cut-off for predicting notable cognitive decline (i.e. decline of ≤-0.227 points in 3 years), the area under the curve was 0.597 (95%CI [0.528, 0.665]) and the optimal cut-off was 5.3 (sensitivity 51.65%, specificity 69.37%; Supplementary Figure 1, Supplementary Table 1). In sensitivity analyses, the area under the curve was not significantly improved when alternative definitions (decline of ≤-0.3 points or decline by more than the median) of cognitive decline were used (data not shown).
Figure 3 shows linear and restricted cubic spline regression plots of the association between baseline omega-3 index and age-adjusted estimated 3-year cognitive change from baseline. The results of models with 3 or 4 knots at varying positions are shown. From these analyses, no obvious optimal cut-off for predicting cognitive decline in any of the models was observed. In fact, the spline plots suggested a relatively linear positive association between baseline omega-3 index and the rate of 3-year cognitive decline for the majority of DHA+EPA values.
Figure 3.
Spline regression plots of the association between baseline omega-3 index and 3-year cognitive change
Effect of 3-years PUFA supplementation on cognitive decline in subjects with low baseline omega-3 index
Figure 4 shows the adjusted mean difference in 3-year cognitive change from baseline between the PUFA and placebo groups in subjects with low baseline omega-3 index, where “low levels were defined by the 15th (i.e. ≤4.35%), 25th (i.e. ≤4.83%), 35th (i.e. ≤5.27%) and 45th (i.e. ≤5.62%) percentiles of the omega-3 index distribution in the total MAPT population. There were no significant differences in 3-year change from baseline between the PUFA and placebo groups in subjects with “low omega-3 index in any of the analyses, although there was a consistent, but non-significant, between-group difference of approximately 0.12 points when the cut-off was set at the 35th percentile or lower.
Figure 4.
Effect of PUFA treatment on 3-year change from baseline on the composite cognitive score in subjects with low baseline omega-3 index
The 3-year changes from baseline on the composite score in the placebo and PUFA groups by baseline omega-3 index in the 4 models are shown in Supplementary Figure 2.
Discussion
In this study, lower baseline erythrocyte omega-3 index was associated with greater cognitive decline. Amongst subjects with a low omega-3 index, defined by the 35th percentile or lower, PUFA-treated subjects showed less cognitive decline than placebo-treated subjects, but the differences were not statistically significant.
We did not identify a precise omega-3 index cut-off for predicting cognitive decline and/or PUFA treatment effects that performed substantially better than the level of ≤4.83% which was defined a priori using distribution-based methods in the MAPT trial main analyses. We therefore suggest that an erythrocyte omega-3 index of approximately 5% could be a suitable level for identifying the target population for future trials of PUFA supplementation for the prevention of cognitive decline in elderly subjects. This cut-off requires further validation in different samples, and the impact of assay variability should also be considered.
Although two previous studies have found low omega-3 index levels (defined by the lowest quartile (i.e. <4.4%) or tertile (i.e. <5.7%) in the respective study populations) to be associated with cognitive impairment and brain volume in cross-sectional analyses of older adults (5, 32) to our knowledge, this is the first study aiming to identify a specific erythrocyte omega-3 index cut-off for predicting longitudinal cognitive outcomes. This index has been more widely studied in the cardiovascular field where it has been proposed that individuals with an omega-3 index <4% are at high risk of poor cardiovascular outcomes, those with an index between 4-8% are at intermediate risk and that those with levels above 8% are likely to have optimal cardiovascular health (33, 34). It should be noted that these cut-offs were calculated for younger populations than that of our study.
The optimal omega-3 index for cognitive health, as well as the amount of PUFA intake or supplementation required to achieve such a level, remains to be determined. A doseresponse meta-analysis suggested a curvelinear relationship between fish consumption and the risk of AD, and dietary PUFA intake and the risk of MCI (35). Our spline analyses raised the possibility that the relationship between the omega-3 index and cognitive decline could be reversed at higher levels, which could suggest that high dose supplementation and/or supplementing individuals with a moderate to high omega-3 index might not be beneficial, but it should be noted that relatively few subjects had a very high omega-3 index at baseline. Furthermore, we did not observe a detrimental effect of PUFA supplementation in individuals in the highest 25% or 15% of baseline omega-3 index (data not shown). This relationship has not been widely studied in the literature: one meta-analysis suggested that lower dosages of omega-3 fatty acid supplementation may be more beneficial on cognition than higher doses (36), but another suggested that higher PUFA doses may be more beneficial (37). More research, using larger samples, is therefore required to better understand the relationship between the full spectrum of omega-3 index levels and cognition, including further evaluating the possibility of a non-linear relationship. Putative adverse effects associated with high intakes of DHA and EPA in humans include impaired lipid and glucose metabolism (38), both of which could influence the pathology of Alzheimer's disease and therefore affect cognitive function.
Adjustment for additional confounders should also be considered. In our models, additional adjustment for sex, education and ApoE slightly attenuated our estimates (data not shown), but conclusions were hindered by a lack of power since the sample size was reduced by approximately 20%.
The strengths of this study include the standardized and thorough repeated cognitive testing by blinded evaluators, and the relatively long duration of PUFA supplementation. Furthermore, we measured erythrocyte, rather than plasma or dietary, DHA + EPA levels, which are considered a valid marker of omega-3 intake (39), and content of other tissues (40).
The main limitations are the relatively small sample size, and the post-hoc nature of the analysis of PUFA treatment effects. We did not find any statistically significant treatment effects, perhaps because our analysis lacked statistical power, both because of sample sizes, and also because there was relatively little cognitive decline in the placebo group (well below the estimated minimum clinically important difference of -0.3 points). An adequately powered and specifically designed RCT is required to better evaluate the efficacy of PUFA supplementation in individuals with a low omega-3 index. Furthermore, we were not able to define a precise cut-off for predicting cognitive decline, and the area under the curve, sensitivity and specificity values obtained in our ROC curve analysis were well below optimal. Finally, this analysis was conducted on data collected in a randomized prevention trial, and participants of such trials tend to be healthier and more highly educated than the general elderly population.
In conclusion, older adults without dementia who have an omega-3 index below approximately 5% are at increased risk of cognitive decline, and could be a good target population for testing the cognitive effects of PUFA supplementation. Identifying specific groups of individuals who could benefit most from a given intervention could pave the way for “personalized prevention of cognitive decline and Alzheimer's disease.
Acknowledgements
The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), Exhonit Therapeutics SA, and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie d'Alzheimer (AMPA) and the UMR 1027 Unit INSERM-University of Toulouse III. The funders played no role in the study design, collection, analysis or interpretation of data, nor in the writing of this report or the decision to submit the article for publication.
Ethical standards
The research described in this manuscript was conducted in compliance with current French laws.
Conflicts of Interest
None of the authors report any conflicts of interest in relation to this work.
The members of the MAPT/DSA study group are
MAPT Study Group: Principal investigator: Bruno Vellas (Toulouse); Coordination: Sophie Guyonnet; Project leader: Isabelle Carrié; CRA: Lauréane Brigitte; Investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, Hélène Villars; Psychologists: Emeline Combrouze, Carole Badufle, Audrey Zueras; Methodology, statistical analysis and data management: Sandrine Andrieu, Christelle Cantet, Christophe Morin; Multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), Françoise Lala (preventive consultation), Bertrand Fougère (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert and Francine Fontaine from the University of Montreal. Co-Investigators in associated centres : Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristelle Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI group: Carole Dufouil (Bordeaux), Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, Nadège Costa (Toulouse). Biological sample collection: Christian Vincent, Bertrand Perret, Claire Vinel (Toulouse).Safety management : Pascale Olivier-Abbal. DSA Group : Sandrine Andrieu, Christelle Cantet, Nicola Coley
Electronic supplementary material
Supplementary material is available for this article at https://doi.org/10.1007/s12603-018-1052-2 and is accessible for authorized users.
Online supplemental material
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