Efficacy of lifestyle interventions on clinical and neuroimaging outcomes in elderly

Ageing Research Reviews 25 (2016) 1–12

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Ageing Research Reviews journal homepage: www.elsevier.com/locate/arr

Review

Efficacy of lifestyle interventions on clinical and neuroimaging outcomes in elderly Elena Rolandi a , Giovanni Battista Frisoni a,b , Enrica Cavedo a,c,∗ a

Laboratory of Alzheimer’s Neuroimaging and Epidemiology, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy Memory Clinic and Laboratory of Neuroimaging of Ageing - LANVIE, University Hospitals and University of Geneva, Geneva, Switzerland Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Département de Neurologie, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France and the CATI multicenter neuroimaging platform (cati-neuroimaging.com), France b c

a r t i c l e

i n f o

Article history: Received 22 April 2015 Received in revised form 30 October 2015 Accepted 9 November 2015 Available online 14 November 2015 Keywords: Alzheimer’s disease Prevention trials Neuroimaging Lifestyle Nonpharmacological interventions Clinical outcomes

a b s t r a c t The prevalence of Alzheimer’s disease (AD) is constantly growing worldwide in absence of any effective treatment. Methodology and technique advancements facilitated the early diagnosis of AD leading to a shift toward preclinical AD stages investigation in order to delay the disease onset in individuals at risk for AD. Recent evidence demonstrating the aging related multifactorial nature of AD supported the hypothesis that modifiable environmental factors can accelerate or delay the disease onset. In particular, healthy dietary habits, constant physical and cognitive activities are associated with reduced brain atrophy, amyloid load and incidence of AD cases. Due to these promising results, an emerging field of studies is currently investigating the efficacy of interventions addressing different lifestyle habits in cognitive intact elderly individuals as a potential preventive strategy against AD onset. We provide a critical overview of the current evidence on nonpharmacologic treatments in elderly individuals, discussing their efficacy on clinical and neuroimaging outcomes and identifying current methodological issues. Future perspectives, relevant for the scientific community and the worldwide public health institutes will be further discussed. © 2015 Elsevier B.V. All rights reserved.

Contents 1. 2.

3.

4. 5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Single domain lifestyle interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1. Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2. Physical activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2. Cognitive activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Multi domain lifestyle interventions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 3.1. Combined physical and cognitive training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2. Combined physical activity and nutritional intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3. Alzheimer’s disease prevention studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Conclusions and future perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1. Introduction ∗ Corresponding author at: IRCCS Centro San Giovanni di Dio Fatebenefratelli, via Pilastroni 4, 25125 Brescia,Italy. Fax: +39 030 3501592 E-mail addresses: [email protected] (E. Rolandi), [email protected] (G.B. Frisoni), [email protected] (E. Cavedo). http://dx.doi.org/10.1016/j.arr.2015.11.003 1568-1637/© 2015 Elsevier B.V. All rights reserved.

Alzheimer’s disease (AD) represents one of the most common forms of dementia. The global prevalence of AD cases has been estimated to nearly quadruple by 2050 (Brookmeyer et al., 2007; Prince

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et al., 2013) increasing significantly the social and the economic burden of the disease (Wimo et al., 2013). Indeed, the estimated annual worldwide costs of dementia are US$604 billion, considering direct medical and social care costs and informal care costs (Wimo et al., 2013). In the last few decades, the concept of ADonset underwent consistent modifications and evolutions. The AD diagnosis, originally established by the presence of specific clinical symptoms (McKhann et al., 1984) and post mortem verification, was reconceptualized thanks to the implementation of in vivo markers able to detect the two main neuropathological mechanism of AD: amyloid plaques and neurofibrillary tangles formation. In particular, brain amyloid deposition is revealed in vivo by decreased concentration of cerebrospinal fluid (CSF) amyloid ␤1-42 protein or increased cortical retention of amyloid ligands with positron emission tomography (PET), while neurodegeneration is confirmed by increased concentrations of CSF total-tau (t-tau) and phosphorylated-tau (p-tau) proteins, cortical hypometabolism on fluorodeoxyglucose (FDG) PET and hippocampal atrophy on magnetic resonance imaging (MRI) (Dubois et al., 2007; McKhann et al., 2011). As a result of these technological advancements and according to the recently updated Alzheimer’s disease research diagnostic criteria, is currently possible to diagnose preclinical stage of AD by the presence of in vivo markers of neuropathology (Sperling et al., 2011; Jack and Holtzman, 2013; Dubois et al., 2014). This new conceptualization of AD has shifted toward the study of preclinical AD stages offering the opportunity to investigate possible strategy to delay cognitive impairment, leading to the emerging field of AD prevention. Noteworthy, it was estimated that even a 1 year delay in AD onset would result in fewer 11.8 million incident cases worldwide (Brookmeyer et al., 2007). In addition, Norton and colleagues showed that one-third of AD cases might be attributable to modifiable risk factors, such as diabetes, midlife hypertension and obesity, physical inactivity, smoking, depression and low educational attainment (Norton et al., 2014). Moreover, observational studies showed consistent associations between some lifestyle habits, such as high levels of physical (Hamer and Chida, 2009; Sofi et al., 2011) and cognitive activities (Vemuri et al., 2014) or high adherence to specific dietary patterns (Singh et al., 2014; Tangney, 2014), and decreased risk to develop cognitive decline and dementia These observations lead to growing interest by scientific community and public health professionals to investigate nonpharmacological interventions aimed to promote healthy lifestyle habits as a preventive strategy against cognitive decline and AD (Andrieu et al., 2011; Richard et al., 2012; Solomon et al., 2014; Lista et al., 2015). Large randomized trials, aimed to demonstrate the efficacy of dementia prevention by multi domain lifestyle interventions, are currently ongoing (Richard et al., 2009; Kivipelto et al., 2013; Vellas et al., 2014) and recently post-intervention results of one of these trials have been published (Ngadu et al., 2015). The assumptions underlying these approaches is that interventions addressing multiple risk factors simultaneously could lead to greater effects on cognitive and functional status, supporting healthy cognitive aging (Schneider and Yvon, 2013) and being more appropriate to delay the onset of multifactorial disorders such as AD (Richard et al., 2012; Solomon et al., 2014). Although conceptually sound, the associations between healthy lifestyle habits and decreased risk of AD, described in observational studies, need to be supported by interventional studies results (Thiese, 2014). Aim of the present study is to discuss and critically revise the current evidence on the efficacy of individual and multiple nonpharmacological interventions on neuroimaging and clinical outcomes in elderly individuals.

2. Single domain lifestyle interventions In order to design cost effective preventive lifestyle interventions in elderly, the identification of the best combination of interventions able to promote significant cognitive improvements and structural or functional brain changes is needed. Nutrition, physical and cognitive activities are the main potential areas of interventions aimed to promote lifestyle changes in elderly. In the present section we are going to present evidence from nonpharmacological treatments separately addressing the above described potential areas in improving cognitive and brain health in cognitive intact elderly individuals. 2.1. Nutrition Changes in nutritional intake may be promoted by adding individual dietary components with nutritional supplementation or by improving adherence to specific dietary guidelines. Several studies investigating the efficacy of nutritional supplementation to improve cognitive health found inconsistent results (Jia et al., 2008; Malouf and Grimley Evans, 2008; van de Rest et al., 2015). More recently, a multicomponent nutritional approach was suggested to be more effective as a preventive strategy against cognitive decline (Shea and Remington, 2015). The Mediterranean Diet (MeDi) and the Dietary Approaches to Prevent and Treat Hypertension (DASH) represent the two main dietary patterns associated with better cognitive outcomes and decreased AD incidence in observational studies (Singh et al., 2014; Tangney, 2014; van de Rest et al., 2015). The MeDi is characterized by high consumption of fruit, vegetables, legumes, cereals, nuts, fish, olive oil, low to moderate intake of dairy products, regular but modest intake of alcohol, together with low consumption of meat and saturated fatty acids (Willett et al., 1995). Highintake of omega-3 poliunsaturated fatty acids (PUFA), polyphenols, folates and vitamins preserves brain health and reduces vascular risk factors (Frisardi et al., 2010). The DASH, recommended especially to individuals affected by hypertension (Appel et al., 2006), is composed by nearly the same prescription of MeDi, except for the recommendations of olive oil and moderate alcohol consumption (Tangney, 2014). High MeDi adherence during life was found to be associated with lower AD incidence with a possible dose-response effect (Scarmeas et al., 2006). A meta-analyses of selected longitudinal studies further underlined that individuals with lower levels of MeDi adherence had an increased risk of cognitive impairment, mild cognitive impairment (MCI) and AD incidence (Singh et al., 2014; Feart et al., 2015), even if more recent studies have found inconstant results (Feart et al., 2015). Few studies recently investigated the protective effects of DASH diet on cognitive decline with longitudinal assessments reporting convergent evidence of better cognitive outcomes in individuals with high DASH adherence score (Tangney, 2014). Further, recent evidence showed the association between reported nutrients intake and neuroimaging and biological markers of AD in cognitively healthy individuals (Gu et al., 2012; Titova et al., 2013; Mosconi et al., 2014). Negative association between levels of self reported meat intake and total brain volumes was found in elderly, but no associations were found between other individual nutrients intake or a global measure of MeDi like dietary habits and gray or white matter volumes (Titova et al., 2013). Significant association between nutrients biomarkers were further found between omega-3 PUFA dietary intake and reduced plasma levels of amyloid beta (Gu et al., 2012). Mosconi and colleagues (Mosconi et al., 2014) found a significant association between increased intake of Vitamin B12 , D and omega-3 PUFA and reduced brain amyloid burden, while higher ␤-carotene and folate consumption was associated

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with highbrain glucose metabolism in key brain regions typically involved in AD. Moreover, the association between nutrients and AD-biomarkers was stronger considering nutrients from food, compared to nutrients from food and supplementation sources. This association was further mediated by several risk factors such as: female gender, family history of AD and Apolipoprotein (APOE) ␧4 genotype (Mosconi et al., 2014). Finally, Bowman and colleagues (Bowman et al., 2012) objectively measured nutrients intake from plasma concentrations in older adults at risk for dementia, investigating potential associations between specific nutrient biomarker patterns and neuroimaging biomarkers, such as total cerebral brain volume and white matter hyperintensityvolumes. The authors found an association between high vitamins intake and brain volumes, while levels of marine omega-3 intake were associated with decreased vascular lesions, suggesting that specific nutrients have a differential on brain health (Bowman et al., 2012). Although these promising results, evidence from RCT are needed to rule out possible confounding factors and explore the opportunity to implement MeDi or DASH as an effective preventive strategy against cognitive decline. To our knowledge, only one RCT showed an association between MeDi assumption and a measure of global cognition as secondary outcome, preventing from a baseline comparison and reducing the scientific relevance of the results obtained in the study (Martínez-Lapiscina et al., 2013). A further RCT study, with a crossover design and, focusing on foods with established potential to reduce inflammation and cardio metabolic risk, found improvement in cognitive functions after 4 weeks of an active dietcompared to 4 weeks of a control diet (Nilsson et al., 2013). In summary, nutritional habits seem to play a significant role in supporting cognitive function and in promoting detectable brain changes. However, further evidence are needed to investigate the effect of nutritional intervention on cognitive and neuroimaging measures in elderly. 2.2. Physical activity In a lifetime prospective, physical activity (PA) was recently found to be related to better subsequent cognitive outcomes at any adulthood stages, from early adulthood to middle-age (Zhu et al., 2014), from middle age to older ages (Tolppanen et al., 2015) and even in elderly leisure time PA was associated with a decreased risk of dementia related mortality (Rosness et al., 2014). A systematic meta-analysis on prospective cohort studies with follow up periods ranging from 1 to 12 years, showed that nondemented older adults physically active at baseline had a significantly reduced risk of developing cognitive decline (Sofi et al., 2011). The neuroprotective effect of PA was revealed by neuroimaging findings showing brain structural and functional plastic changes (Voss et al., 2010; Erickson et al., 2014). Several cross-sectional studies consistently reported that higher levels of cardiovascular fitness and self reported physical activity were associated with increased volumes of brain regions susceptible to aging and AD neuropathological processes such as the prefrontal cortex and the hippocampus (for a review (Erickson et al., 2014)). These results suggest that plastic brain changes promoted by lifetime physical engagement might mediate the positive effects found in cognitive outcomes. The protective effects against neurodegeneration were further confirmed by longitudinal observations with follow-upof physical activity assessment from 9 to 21 years (Erickson et al., 2010; Rovio et al., 2010). Two RCT studies showed that intensive aerobic fitness training in sedentary community dwelling elderly lasting at least 6 months, compared to a well matched stretching and tonic program, was more effective to promote plastic changes in the prefrontal (Colcombe et al., 2006) and temporal cortices (Colcombe et al.,

3

2006; Erickson et al. 2011). In the study by Erickson et al., however, both groups improved on a spatial memory task and a significant positive correlation was found between these improvements and increased hippocampal volumes in the aerobic exercise group (Erickson et al., 2011). Conversely, one RCT found that increasing total physical activity, irrespective of intensity levels of the training programs, was positively associated with both increased volumes of the prefrontal and cingulate cortices and improvement of episodic memory performance (Ruscheweyh et al., 2011). In addition, both a walking aerobic training and a flexibility, toning and balance training lasting 1 year, were effective in enhancing functional connectivity in the Default Mode Network (DMN) and improving executive function in older adults(Voss et al., 2010). Physical activity levels, moreover, have shown to be associated with microstructural white matter integrity (Gons et al., 2013), therefore regular physical exercises should be effective in contrasting brain aging by preventing vascular diseases. A large single blind RCT in the context of Australian Imaging Biomarkers and Lifestyle Flagship Study of Aging (AIBL, (Cyarto et al., 2012)) is currently ongoing. The study aimed to demonstrate the efficacy of 24 months home based physical activity program in delaying the progression of white matter changes on MRI in individuals at risk of AD (mild cognitive impaired and subjective memory complainers). To date 2 RCT, investigating the effects of incremented PA as a preventive strategy in older adults at risk for cognitive decline, showed beneficial effects on cognitive measures (Lautenschlager et al., 2008; Maki et al., 2012) and functional abilities such as quality of life, social interaction and motor function (Maki et al., 2012). Noteworthy, the programs implemented were respectively an home based individualized and self administered PA program (Lautenschlager et al., 2008) and in the other case a group walking program on a weekly basis (Maki et al., 2012). Both programs, even if unstructured, demonstrated how low intensity PA programs can improve cognitive and functional status in elderly. We can state that current evidence offer reliable data about the protective effect of PA on brain and cognitive health, warranting the effectiveness of intervention and educational program aimed to increase physical activity as a cost effective preventive strategy against cognitive decline. 2.2. Cognitive activity Studies investigating lifetime cognitive activity have found a significant association between reduced cognitive decline (Vemuri et al., 2012; Vemuri et al., 2014) although results regarding the direct association between lifestyle activities and AD neuroimaging markers remain contrasting (Vemuri et al., 2012). These observational findings highlighted the potential implementation of cognitive training as prevention treatment of cognitive decline (Papp et al., 2009). Based on current literature, cognitive interventions can be briefly classified in three main types: (i) cognitive trainings (CT), consisting of repetitive practice on standard tasks addressed to improve specific cognitive domains using restorative strategies, (ii) cognitive stimulation primarily aimed to improve socialization by means of more unstructured activities and (iii) cognitive rehabilitation, in which mainly compensatory strategies to improve autonomy in everyday activities are promoted (Clare et al., 2003; Buschert et al., 2010). Among them, CT represents a suitable option for primary prevention of cognitive decline (Gates and Sachdev, 2014). The three main interventions applied during CT might be classified in (Papp et al., 2009; Martin et al., 2011): (i) memory training, which generally comprise meta-cognitive interventions to explicitly teach memory strategies combined with practical exercises, (ii) speed of processing training, usually involving nonverbal computer

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based exercises to improve processing speed, and (iii) reasoning training, focused on learning of strategies to solve problems with a sequential structure. A Cochrane systematic review investigating the effect of cognitive interventions conducted in cognitive intact elderly and mild cognitive impaired individuals between 1970 and 2007, found that memory training compared to no treatment conditions, lead to improvements in memory performances (Martin et al., 2011). However, when memory training interventions were compared to an active control condition, such as an alternative training with the same characteristics in terms of dose and social contact, there was no differences in effect size measured (Martin et al., 2011). A further meta-analysis investigating the effects of a specific computer based training aimed to improve speed of processing in older adults, found improvements in the cognitive abilities trained during the programs and iin everyday functional abilities (Ball et al., 2007). Greater improvements were observed in those studies enrolling participants with baseline poor speed of processing and customizing tasks to individual ability in each training session (Roenker et al., 2003; Edwards et al., 2005). In comparison to memory training studies, speed of processing training studies offered more reliable findings thanks to the homogeneity of interventions and outcome measures. Both approached showed methodological issues such as small sample size and short follow-up. Several studies showed evidence of increased brain plasticity after CT in healthy older adults (Belleville and Bherer, 2012). A verbal memory training lasting 8 weeks in middle aged and older adults lead to increased cortical thickness in right fusiform and lateral orbitofrontal cortex (Engvig et al., 2010) and increased fractional anisotropy in anterior white matter tracts (Engvig et al., 2012). These changes further positively correlated with improvement in memory performance. Furthermore, gray (Boyke et al., 2008) and white matter (Lövdén et al., 2010) changes promoted by different types of CT in healthy older adults were similar to those observed in younger adults. Recently, results from a well designed RCT exploring short (Smith et al., 2009) and long term effects (Willis et al., 2006; Rebok et al., 2015) of CT in community dwelling elderly have been reported. The ACTIVE study (Willis et al., 2006; Rebok et al., 2015) is a multi site single blind RCT in 2832 cognitive intact individuals, with a subgroup followed up for 10 years. The study evaluated the effects of the three main types of CT (memory, speed of processing, reasoning) compared to no treatment condition. All training groups compared to control group showed improvements in the cognitive domain trained, lasting 5 years for memory training (Willis et al., 2006) and 10 years for speed of processing and reasoning training (Rebok et al., 2015). Further participants to all types of trainings showed fewer declines in self reported instrumental activity of daily living even at 10 years follow-up evaluation (Rebok et al., 2015). In the IMPACT study (Smith et al. 2009), 487 community dwelling elderly were randomly assigned in a double blind design either to a self-administered at home computerized cognitive training to improve the speed and accuracy of auditory and information processing or to a matched active control condition (watching educational DVD). After 1 h daily session training for 5 days per week for 8 weeks, the participants in the intervention group showed significant improvement in standardized tests of memory and attention. A recent meta-analysis of RCT studies, comprehensively evaluated the effects of computerized cognitive training (CCT) on different cognitive domains in healthy older adults, with the additional aim to identify the moderating effect of several study features on outcomes, such as session length and frequency, total duration of the training, type of control condition (active or passive) and whether the training was delivered in group with supervision or managed independently at home by participants. CCT showed

a modest effect in improving specific cognitive functions, such as nonverbal memory, processing speed, working memory and visuospatial abilities. Moreover, The moderator analyses showed improved efficacy of the group-based interventions compared to the home-based ones and the frequency of 1–3 sessions per week to be optimal (Lampit et al., 2014). Overall, the effects of cognitive trainings in healthy elderly have shown to be highly dependent on the cognitive domain trained (Martin et al., 2011; Rebok et al., 2015). Some degree of generalization to every day cognitive abilities has been consistently reported for speed of processing training (Ball et al., 2007; Smith et al., 2009; Rebok et al., 2015). Performance gains were consistently observed immediately after intervention (Ball et al., 2007; Smith et al., 2009; Martin et al., 2011), with long term effects investigated in few studies, which showed detectable differences after 5 (Willis et al., 2006) and 10 years from training cessation (Rebok et al., 2015). Finally, considering the great heterogeneity of cognitive training protocols proposed by different studies, the identification of the specific features of effective intervention is needed (Lampit et al., 2014). 3. Multi domain lifestyle interventions Non pharmacological treatments presented in Section 2 could be combined to simultaneously address different lifestyle habits in elderly. Findings from the 11 completed studies investigating the short term effects of combined treatments are presented (Sections 3.1 and 3.2). Furthermore, the designs of the main AD prevention studies ongoing in Europe are presented and compared in the last paragraph of the present section (paragraph 3.3.). 3.1. Combined physical and cognitive training So far, 9 studies investigated cognitive changes after combined physical and cognitive training in elderly (Table 1). Interventions combining physical exercises and leisure activities, compared to a no treatment group, lead to improved outcome in cognitive and functional measures in two small samples of healthy elderly (Pieramico et al., 2012; Kamegaya et al., 2014). Furthermore, in the study by Pieramico and colleagues., in the trained group was found a change in the strength of functional connectivity within the Default Mode Network (DMN) and the Dorsal Attention Network (DAN) after 6 months. These changes on functional connectivity further correlated with cognitive and occupational improvements (Pieramico et al., 2012). Two further studies directly compared cognitive outcomes after combined or single intervention (Anderson-Hanley et al., 2012; Rahe et al., 2015). Anderson-Hanley and colleagues found greater improvements on measures of executive functions when physical exercises were performed in a cognitive stimulating environment compared to physical training alone (Anderson-Hanley et al., 2012). In addition, Rahe and colleagues reported similar improvements in both groups immediately after intervention, while after one year of training exclusively the group receiving the combined treatment showed further improvements in executive functions (Rahe et al., 2015). Five further multi domain studies, including a study design with both single domain interventions and control conditions, provided contrasting results (Fabre et al., 2002; Oswald et al., 2006; Legault et al., 2011; Barnes et al., 2013; Shatil, 2013). Two studies showed that in healthy elderly combined intervention lead to greater improvements in cognitive functions than the single domain trainings (Fabre et al., 2002; Oswald et al., 2006). Furthermore, Oswald and colleagues, reported that participants who engaged in a single or multi domain interventions showed persistent better cognitive performance after 5 years from training

Table 1 Characteristics of studies investigating the combined effect of physical and cognitive training. Design

Participants N

Population

Age

N

Content

Dose

N

Content

Dose

Kamegaya et al. (2014)

RCT

43

community dwelling elderly

65–87

19

1x/week 3 months

24

No treatment

n.a.

Five-Cog test

Pieramico et al. (2012)

Non-RCT

30

healthy elderly

60–75

15

5x/week 6 months

15

No treatment

n.a.

Neuropsychological tests scores Resting state networks Cortical thickness

AndersonHanley et al. (2012)

Cluster-RCT

79

elderly in indipendent living facilities

58–89

38

2,5x/week 3 months

41

PT

2,5x/week 3 months

Executive function composite score

Rahe et al. (2015)

Non-RCT

30

healthy elderly

60–76

15

2x/week 2 months

15

CT: 90 min

2x/week 2 months

Neuropsychological tests scores

Fabre et al. (2002)

Non-RCT

32

healthy elderly

60–76

8

PT: Stretching and aerobic exercises + CT: leisure activities (cooking, handcrafts, competitive games) PT: Aerobic training + CT: brain training, exposure to musical stimulation and in group fun-recreation program every 14 days PT: cycle on a stationary bike + CT: virtual reality display (cybercycle). PT: 20 min physical exercises + CT: 70 min of structured and unstructured cognitive activities PT: Brisk walking and/or jogging + CT: 15-min explanation of memory mechanism followed by exercises on a specific theme (8 different themes)

4x/week 2 months

8 8

No treatment PT

8

CT

n.a. 2x/week 2 months 2x/week 2 months

Questionnaire to detect cognitive problems Wechler memory scale

103 115

n.a. 1x/week 12 months

Composite measure of different cognitive domains

32

No treatment ET: lectures on everyday life demands. PT

57

CT

36

PT + ET

Oswald et al. (2006)

Non-RCT

375

Multi domain intervention

independent living elderly

75–93

32

Control/Single domain intervention

PT: balance, 1x/week perceptual and 12 months motor coordination, flexibility + CT: visual search task, memory tasks and memory strategies

Outcome measures

E. Rolandi et al. / Ageing Research Reviews 25 (2016) 1–12

Study

1x/week 12 months 1x/week 12 months 1x/week 12 months

5

6

Table 1 (Continued) Study

Design

Legault et al. (2011)

RCT

Shatil (2013)

RCT

RCT

N

Population

Age

N

Content

73

community dwelling elderly

70–85

19

PT: aerobic (walking 4x/week or stationary cycling) 4 months and flexibility training + CT: 30 words-list learning, recognition and recall

126

122

inactive elderly with memory complaints

healthy elderly

> 65

65–93

32

29

Dose

PT: Aerobic exercises 6x/week 3 (standard months dance-based aerobic format) + CT: at home computer-based games to enhance speed and accuracy of visual and auditory processing.

PT: Mild-aerobic exercises for seniors in groups + CT: home-based personalized cognitive training on different tasks (CogniFit program).

6x/week 4 months

RCT: Randomized controlled trial; PT: Physical training; CT: Cognitive training; ET: Educational training; x/week: sessions per week; n.a: not applicable.

N

Content

Dose

17

n.a

18

ET: health education lectures (Healthy Aging Education) pt

18

ct

32

Stretching and toning exercises + ET: watching DVDs of educational lectures PT + ET

31

29

Stretching and toning exercises + CT ET: reading a book on active living and weekly book club PT

31

CT

31

79

2x/week 4 months 2x/week 4 months 6x/week 3 months

Outcome measures

Executive function and episodic memory composite score

Neuropsychological battery composite score

6x/week 3 months 6x/week 3 months 1x/week 4 months

3x/week 4 months 3x/week 4 months

CogniFit neuropsychological evaluation

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Barnes et al. (2013)

Control/Single domain intervention

Multi domain intervention

Participants

E. Rolandi et al. / Ageing Research Reviews 25 (2016) 1–12

in comparison to control group., The best improvements were observed in the combined physical and cognitive training group. Despite their results, these studies revealed methodological weakness, such as the absence of randomization between groups (Fabre et al., 2002; Oswald et al., 2006), a very small sample size (Fabre et al., 2002) and the selection of a no treatment group as control group instead of an active control condition (Fabre et al., 2002; Oswald et al., 2006). Conversely more recent studies, failed to find an additive effect on cognitive functions improvements of the combined intervention compared to the cognitive and physical training delivered alone (Legault et al., 2011; Barnes et al., 2013; Shatil, 2013). These latter studies were RCT and included educational training as active control conditions instead of no treatment condition (Legault et al., 2011; Barnes et al., 2013; Shatil, 2013), overcoming some methodological weakness observed in previous studies (Fabre et al., 2002; Oswald, et al. 2006). Accordingly to the results described by Barnes and colleagues, the amount of activity is more important in improving cognitive functions than the content and type of trainings(Barnes et al., 2013). 3.2. Combined physical activity and nutritional intervention Three RCT investigated the effects of multi domain interventions simultaneously addressing physical activity and nutritional habits (Table 2). De Jong et al. (2001) found that regular intake of nutrient dense products, alone or in association with increased physical activity, did not lead to detectable improvements in performance on specific psychomotor tasks. Conversely, two more recent studies showed improvements in psychomotor speed when a physical training program were added to the nutritional intervention such as DASH diet (Smith et al., 2010) or protein supplementation (van de Rest et al., 2014). 3.3. Alzheimer’s disease prevention studies In the context of the European Dementia Prevention Initiative (EDPI) three large randomized multi domain lifestyle interventions primarily aimed to prevent dementia are ongoing (Table 3). Data and outcomes derived from these initiatives will be shared to provide practical and methodological recommendations to design future dementia prevention trials (Richard et al., 2012). To date, all the trials reached the target samples sizes (Richard et al., 2010; Vellas et al., 2014), moreover results on the FINGER intervention were recently presented (Ngandu et al., 2015). Authors reported that an intensive multi domain intervention lasting 2 years, compared to regular health care, lead to significant improvements in a composite measure of cognitive functions, specifically in processing speed and executive function domains, but not in memory domain. Although the intervention effects were smaller than expected, the authors claimed that results should be interpreted in a public health context, in which small long term effects on common disorders could have high relevance (Ngandu et al., 2015). The planned 5 years extended follow-up will further clarify the efficacy of this type of interventions in reducing AD and dementia incidence. Some considerations regarding study designs and methodological issues related to dementia prevention initiative above described could be discussed (Richard et al., 2012; Solomon et al., 2014). Indeed, the designs of the selected trials present some similarities, as well as significant differences. All of these studies are well designed multi site randomized controlled trial (RCT), with large samples size predefined by power calculations. Regarding the group allocation, in FINGER and MAPT participants were randomly allocated to groups, while in preDIVA

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are the general practitioner who delivered the intervention to be randomized instead of study participants. Further FINGER and MAPT enrolled cognitive intact individuals at increased risk for dementia, while the preDIVA study enrolled all nondemented elderly. Since, target population and intervention timing represent the main issues to take into account when planning primary prevention trials. The challenge of prevention initiatives should be to identify the optimal time window to maximize the effect of the intervention and, meanwhile, be able to detect the eventual decreased incidence or delayed onset of the disease (Richard et al., 2012), which are the outcomes needed to demonstrate efficacy of preventive interventions. In AD prevention this might be achieve using enrichment strategies to select individuals at increased risk for AD and dementia, such as selection based on AD biomarkers positivity (Lista et al., 2015) or on risk factors as in the FINGER and the MAPT studies. Regarding control groups selection, the FINGER and the preDIVA studies compare a multi domain intervention with usual care, while the MAPT is the only study comparing a multi domain intervention with a single domain intervention and placebo conditions. The direct comparison between the efficacy of single and multi domain interventions is necessary to design cost-effective prevention trials for AD. To date no clear evidence exist about the effects of these interventions on cognitive function and in vivo disease biomarkers of AD. Finally, in the selected RCT designs, primary and secondary outcome measures were clearly predefined based on clinical relevance to identify conversion to dementia. However, in elderly cognitive intact individuals, as those selected for primary prevention trials, the identification of clinical and functional changes is often difficult (Richard et al., 2012). Therefore, primary prevention trials should consider as primary or secondary endpoints more sensitive clinical measures such as neuropsychological tests able to detect subclinical cognitive changes (Vellas et al., 2008; Solomon et al., 2014), and further include neuroimaging markers able to track both disease progression (Dubois et al., 2014) and the eventual brain plastic changes driven by the intervention (Belleville and Bherer, 2012).

4. Discussion Due to the absence of an effective treatment and the dramatic increase in average life expectancy, worldwide dementia prevalence has been estimated to duplicate every 20 years (Brookmeyer et al., 2007; Prince et al., 2013), leading to enormous direct and indirect economic costs (Wimo et al., 2013). Lifestyle modifiable risk factors associated with AD and dementia incidence could be easily dealt with individual or combined non pharmacological treatments, however the efficacy of these interventions in delay disease onset hasnot been firmly demonstrate yet. Based on the evidence described in the present manuscript, several issues regarding multi domain non pharmacological treatments remain largely unsolved, such as:

(i) Issue 1: Which are the underlying mechanisms accounting for the associations found in observational studies between modifiable risk factors and AD incidence? (ii) Issue 2: Multi domain interventions effectively lead to significant improvement of cognitive functions compared to single domain interventions? (iii) Issue 3: Short term positive effects on cognitive functions were confirmed at long term follow-up? (iv) Issue 4: Is improvement of cognitive functions by multi domain interventions an effective strategy to prevent cognitive decline and AD onset in community dwelling elderly?

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Table 2 Characteristics of studies investigating the combined effect of physical training and nutrition intervention. Study

Design

de Jong et al. (2001)

RCT

Multi domain intervention

Participants

Control/Single domain intervention

Outcome measures

Population

Age

N

Content

Dose

N

Content

Dose

165

frail elderly

>70

33

NI: daily consumption of 2 enriched nutrient-dense products (1 fruit and 1 fruit-based dairy product) + PT: walking, stooping, chair stands.

2x/week 4 months

30

Social program Daily consumption of 2 regular products NI + social program PT + daily consumption of 2 regular products No treatment NI

1x/week 4 months

n.a. n.a

Executive functionmemory-learning, Psychomotor speed

Placebo drink PT + placebo drink NI

n.a 2x/week 6 months n.a

Composite measure of different cognitive domains

36 31

Smith et al. (2010)

RCT

124

Overweight sedentary, with high blood pressure

Mean: 52

43

NI: instruction to 3x/week meet DASH-diet 4 months guidelines + PT:supervised aerobic exercises and weekly in group counseling sessions focused on teaching behavioral strategies for weight loss.

38 38

van de Rest et al. (2014)

RCT

127

Pre-frail and frail elderly

>65

31

NI: proteinsupplemented beverage twice daily + PT: resistance-type exercise training.

31 31

2x/week 6 months

RCT: Randomized controlled trial; PT: Physical training; NI: Nutrition intervention; x/week: sessions per week; n.a: not applicable.

34

Neuropsychological indexes (psychomotor speed)

1x/week 4 months 2x/week 4 months

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N

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Table 3 Study designs of non-pharmacological Alzheimer’s disease prevention trials currently ongoing in Europe. Study

FINGER

MAPT

preDIVA

Status Design Participants

Post intervention results RCT 1260 elderly with CAIDE dementia risk score >6

Baseline data Cluster-RCT 3535 nondemented elderly

Age Multi-domain intervention

60–77 years (1) nutritional guidance + (2) physical exercise + (3) cognitive training and social activity + (4) intensive monitoring and management of metabolic and vascular risk factors Regular health advices

Baseline data RCT 1680 elderly at risk for cognitive decline (spontaneous memory complaint, limitation in one IADL, slow walking speed) >70 years (1) omega-3 supplementation or placebo + (2) nutritional counseling + (3) physical exercise + (4) cognitive stimulation.

Control Duration Outcome measures

Outcomes evaluations Biomarkers

2 years Primary: NTB composite score, Stroop test, Trial Making Test. Secondary: dementia, AD, cognitive measures, cardiovascular disease, disability, physical function, depression, dietary intake and markers, quality of life, health resources utilization. 6, 12, 24 months from baseline + 5 years extended follow-up MRI, FDG-PET, Amyloid-PET (in a subgroup)

(1) Omega-3 supplementation; (2) Placebo 3 years Primary: Free and Cued Selective Reminding test Secondary: cognitive measures, physical function, frailty syndrome assessment, depression

6, 12, 24, 36 from baseline + 2 years extended follow-up MRI, FDG-PET, Amyloid-PET (in a subgroup)

70–78 years Intensive vascular care: practice nurse visits every 4 months to monitor vascular risk factors and deliver individualized nutritional and exercise advices. Usual care in accordance with Dutch GP-guidelines 6 years Primary: dementia, disability Secondary: mortality, cardiovascular events, depression, cognitive measures (MMSE, VAT).

2, 4, 6 years from baseline Blood sample stored for DNA isolation; MRI (in a subgroup)

FINGER: Finnish geriatric intervention study to prevent cognitive impairment and disability; MAPT: Multidomain Alzheimer prevention study; PreDIVA: Prevention of dementia by intensive vascular care; RCT: Randomized controlled trial; CAIDE: Cardiovascular risk factors, aging and dementia; IADL: Instrumental activity of daily living; GP: General practitioner; NTB: Neuropsychological test battery; AD: Alzheimer’s disease, MMSE: Mini Mental state examination; VAT: Visual association test; MRI: Magnetic resonance imaging; FDG-PET: Fluorodeoxyglucose PET.

Very few studies in literature investigated the effects of nonpharmacological treatments on AD biomarkers or brain plasticity measures (Issue 1). In nondemented elderly, the presence of neuroimaging markers such as in vivo brain amyloidosis and neurodegeneration predict subsequent cognitive decline (Villemagne et al., 2008; Jack et al., 2010; Lim et al., 2014), underlining that such measures are sensitive and specific enough to detect subclinical changes in elderly without cognitive impairment. Several studies have highlighted the association between healthy lifestyle habits and reduced brain atrophy and amyloid load or increased brain plasticity and cognitive performance (Gu et al., 2012; Landau et al., 2012; Vemuri et al., 2012; Titova et al., 2013; Erickson et al. 2014; Mosconi et al., 2014). Thus, multimodal, well designed lifestyle interventions might strengthen the brain and cognitive reserve in facing the neurodegeneration processes due to normal aging or to the eventual development of neurodegenerative diseases in cognitive intact elderly. Further inclusion of biological or neuroimaging measures in prospective lifestyle intervention studies should clarify the underlying mechanisms responsible of cognitive improvements observed. To date, based on the available literature on interventional studies, is almost impossible to provide a detailed mechanistic hypothesis of which neurobiological process drive the positive clinical changes observed. The results obtained from the FINGER study offered the first evidence on the efficacy of multi domain lifestyle intervention in improving cognitive functions in elderly at risk for AD, compared to regular care. However, to address Issue 2, multiple control groups should be included in the study design, such as a no treatment condition or an active control condition and at least one single domain intervention group, as already stated by a recent review (Schneider and Yvon, 2013). The main ongoing dementia prevention studies were underpowered to address this issue, lacking of multiple control and comparison groups (Richard et al., 2010; Vellas et al., 2014; Ngandu et al., 2015). Several evidence from not randomized controlled studies showed that combined physical and cognitive

trainings lead to greater improvements in cognitive functions than the single trainings alone (Fabre et al., 2002; Oswald et al., 2006; Anderson-Hanley et al., 2012; Rahe et al., 2015). However when active control conditions were included in the design these results were not confirmed, suggesting unspecific effects of the trainings performed (Legault et al., 2011; Barnes et al., 2013) or the lack of an additive effect of the combined interventions compared to the single one (Shatil, 2013). Few studies further investigated the effects of physical exercise combined to different type of nutrition interventions on cognitive outcomes, preventing from drawing any conclusions (de Jong et al., 2001; Smith et al., 2010; van de Rest et al., 2014). So far, long term effects of single and multi domain non pharmacological treatments (Issue 3) were investigated in 3 studies, showing long term effects of cognitive training (Rebok et al., 2015) and of combined physical and cognitive trainings (Oswald et al., 2006; Rahe et al., 2015) on cognitive performance. In order to confirm these preliminary results, studies with longer term follow-up are essential to test the efficacy of combined interventions in delaying cognitive impairment and AD onset. Finally, to date, direct evidence of multi domain interventions efficacy as a preventive strategy against AD and dementia incidence, is completely lacking (Issue 4). In recent years 3 large randomized controlled trials started in Europe with the primary aim to address this issue (Richard et al., 2010; Vellas et al., 2014; Ngandu et al., 2015), however no results are currently available. As previously underlined, 1 year delay in AD onset would reduce the disease prevalence by 11.8 million cases worldwide (Brookmeyer et al., 2007), with an estimated annual cost per person with dementia of 32.865 US$ in high income regions (Wimo et al., 2013), making dementia one of the priorities of world public health in next years. On December 2013 during the G8 countries meeting in London countries agreed upon an international approach to dementia research and strategies to face the predicted pandemic of

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E. Rolandi et al. / Ageing Research Reviews 25 (2016) 1–12

the disease (Fox and Petersen, 2013; The Lancet Neurology 2014). Furthermore the 2014 World Alzheimer Report, revising current evidence on protective and modifiable factors for dementia, proposed an integrated approach to the prevention of dementia and other chronic diseases sharing several risk factors (Prince et al., 2014). Indeed, while no clear evidence currently exists regarding the efficacy of non pharmacologic treatments as AD preventive strategies, the implementation of healthy lifestyle could be a desirable outcome for a wide range of comorbid conditions with no or minor side effects. Accordingly to the evidence presented, this is the right moment to address significant efforts to non pharmacological prevention trial initiatives in order to move research results from observation to action. 5. Conclusions and future perspectives In conclusion, the results presented along this review highlighted how single and multi-domain lifestyle interventions frequently promoted short term cognitive improvements in older adults without cognitive impairment. However, the generalizability and specificity of the results observed were often reduced by methodological weakness. Combination of well designed studies, in term of: (i) large and selected sample of cognitive intact individuals at risk of AD, based on AD biomarkers positivity or on other established risk factors; (ii) randomized allocation of participants to treatment groups; (iii) stratification of treatments according to different levels of intensity; (iv) promising outcome measures such as advanced imaging markers (tractography and functional connectivity) able to detect light brain pathological changes, will allow to effectively demonstrate the appropriateness of non pharmacological treatments. If positive results will be confirmed public health organizations will be able to plan interventions aimed at preventing cognitive decline in at risk elderly individuals reducing the burden of illness on society and the costs of public health care system. Acknowledgements E.C. and E.R. are supported by the grant Conv. n.130/GR-201102350494 funded by the Italian Ministry of Health (Bando Giovani Ricercatori 2011-2012). References Anderson-Hanley, C., Arciero, P.J., Brickman, A.M., Nimon, J.P., Okuma, N., Westen, S.C., Merz, M.E., Pence, B.D., Woods, J.A., Kramer, A.F., Zimmerman, E.A., 2012. Exergaming and older adult cognition: a cluster randomized clinical trial. Am. J. Prev. Med. 42, 109–119. Andrieu, S., Aboderin, I., Baeyens, J.P., Beard, J., Benetos, A., Berrut, G., Brainin, M., Cha, H.B., Chen, L.K., Du, P., Forette, B., Forette, F., Franco, A., Fratiglioni, L., Gillette-Guyonnet, S., Gold, G., Gomez, F., Guimaraes, R., Gustafson, D., Khachaturian, A., Luchsinger, J., Mangialasche, F., Mathiex-Fortunet, H., Michel, J.P., Richard, E., Schneider, L.S., Solomon, A., Vellas, B., 2011. IAGG workshop: health promotion program on prevention of late onset dementia. J. Nutr. Health Aging 15, 562–575. Appel, L.J., Brands, M.W., Daniels, S.R., Karanja, N., Elmer, P.J., Sacks, F.M., Association, A.H., 2006. Dietary approaches to prevent and treat hypertension: a scientific statement from the American Heart Association. Hypertension 47, 296–308. Ball, K., Edwards, J.D., Ross, L.A., 2007. The impact of speed of processing training on cognitive and everyday functions. J. Gerontol. B Psychol. Sci. Soc. Sci. 62, 19–31, Spec No1. Barnes, D.E., Santos-Modesitt, W., Poelke, G., Kramer, A.F., Castro, C., Middleton, L.E., Yaffe, K., 2013. The mental activity and exercise (MAX) trial: a randomized controlled trial to enhance cognitive function in older adults. JAMA Intern. Med. 173, 797–804. Belleville, S., Bherer, L., 2012. Biomarkers of cognitive training effects in aging. Curr. Transl. Geriatr. Exp. Gerontol. Rep. 1, 104–110. Bowman, G.L., Silbert, L.C., Howieson, D., Dodge, H.H., Traber, M.G., Frei, B., Kaye, J.A., Shannon, J., Quinn, J.F., 2012. Nutrient biomarker patterns, cognitive function, and MRI measures of brain aging. Neurology 78, 241–249.

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