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THE INTERRELATIONSHIP BETWEEN INSULIN-LIKE GROWTH FACTOR 1, APOLIPOPROTEIN E Ε4, LIFESTYLE FACTORS, AND THE AGING BODY AND BRAIN

 

S.A. Galle1,2,*, I.K. Geraedts1,*, J.B. Deijen1,3, M.V. Milders1, M.L. Drent1,4

 

1. Department of Clinical, Neuro- & Developmental Psychology, Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; 2. Department of Epidemiology, Erasmus Medical Centre, Rotterdam, the Netherlands; 3. Hersencentrum Mental Health Institute, Amsterdam, The Netherlands; 4. Department of Internal Medicine, Section of Endocrinology, Amsterdam UMC, location VUmc, Amsterdam, The Netherlands * These authors contributed equally to this work

Corresponding Author: Sara A. Galle, Department of Clinical, Neuro- & Developmental Psychology, Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Van der Boechorststraat 7-9, 1081 BT Amsterdam, The Netherlands, T: 0031205988769, E-mail: s.a.galle@vu.nl

J Prev Alz Dis 2020;4(7):265-273
Published online March 2, 2020, http://dx.doi.org/10.14283/jpad.2020.11

 


Abstract

Aging is associated with a decrease in body and brain function and with a decline in insulin-like growth factor 1 levels. The observed associations between alterations in insulin-like growth factor 1 levels and cognitive functioning and Mild Cognitive Impairment suggest that altered insulin-like growth factor 1 signaling may accompany Alzheimer’s disease or is involved in the pathogenesis of the disease. Recent animal research has suggested a possible association between insulin-like growth factor 1 levels and the Apolipoprotein E ε4 allele, a genetic predisposition to Alzheimer’s disease. It is therefore hypothesized that a reduction in insulin-like growth factor 1 signaling may moderate the vulnerability to Alzheimer’s disease of human Apolipoprotein E ε4 carriers. We address the impact of age-related decline of insulin-like growth factor 1 levels on physical and brain function in healthy aging and Alzheimer’s disease and discuss the links between insulin-like growth factor 1 and the Apolipoprotein E ε4 polymorphism. Furthermore, we discuss lifestyle interventions that may increase insulin-like growth factor 1 serum levels, including physical activity and adherence to a protein rich diet and the possible benefits to the physical fitness and cognitive functioning of the aging population.

Key words: Insulin-like growth factor, Alzheimer’s disease, ApoE-ε4 allele, physical activity, diet, aging.


 

Introduction

It is well known that the process of aging is associated with physical and mental changes. In the body, normal aging is primarily associated with a decrease in muscle mass and strength. In the brain, normal aging is mainly characterized by metabolic changes in the prefrontal cortex and associated with a decrease in brain size and synaptic plasticity (1). These changes in body and brain lead to alterations in physical, as well as cognitive functioning in elderly people, such as increased frailty and decreased cognitive performance (1, 2).
When age-related cognitive decline becomes qualitatively severe and progresses rapidly, it is likely to progress into a clinical diagnosis of dementia. The most common form of dementia is Alzheimer’s disease (AD). While there are some medications that decelerate the neuropathological progression of AD or offer some symptomatic relief, there is no cure available. In the absence of a cure for AD, research has focused on the most common risk factors and preventive strategies. Important non-modifiable risk factors for AD that have been investigated include age and genetics. Potentially modifiable factors are risk factors that are associated with lifestyle like socioeconomic factors, diet, cerebrovascular disease, and physical inactivity (3).
In the development of preventive strategies, it is important to understand the interplay between
neurobiological and lifestyle factors. One important factor that is both influenced by lifestyle factors like physical activity and diet (4, 5) and plays a role in the maintenance of physical fitness (6) and cognitive functioning (7) is insulin-like growth factor 1 (IGF-1). This review will discuss the impact of age-related decline of IGF-1 levels on physical and cognitive functioning in healthy aging and AD. In addition, we discuss the possible link between IGF-1 and ApoE-ε4. Furthermore, we explore how lifestyle interventions focusing on physical activity and diet may be useful to improve physical fitness and cognitive functioning by increasing IGF-1 serum levels.
Insulin-like growth factor 1 is a peptide growth hormone, with a structure similar to insulin, encoded by the IGF-1 gene located on chromosome 12. As part of the growth hormone (GH)/ IGF-1 axis, IGF-1 plays an essential role in growth of the body and development of the brain. IGF-1 is mainly produced in the liver, stimulated by GH, which is secreted from the anterior pituitary gland. IGF-1 can also be produced in local peripheral tissues such as muscle and bone tissue when GH binds to its Growth Hormone Receptor (GHR) (8). As IGF-1 is GH dependent and, unlike GH, circulating IGF-1 levels do not fluctuate widely over time, IGF-1 is a more reliable measure and appropriate marker for GH status (9). Therefore, this review focuses on neurobiological processes and lifestyle factors related to IGF-1.

 

IGF-1 and the aging body

Throughout the body, IGF-1 regulates the development and function of cells. It promotes cell growth and contributes to cell proliferation, stress resistance and survival in many cell types (10). IGF-1 can bind with high affinity to the IGF-1 receptor (IGF-1R), but also to the insulin receptor (11) as its structure is closely related to insulin. The IGF-1R is expressed in many distinct tissues in the body. For this reason IGF-1 can have different effects, such as the promotion of neuronal survival in the central nervous system and the facilitation of peripheral muscle regeneration (12). Because of the essential role of IGF-1 in muscle growth and the involvement of IGF-1 in many mechanisms and functions of the body, IGF-1 is an important factor for embryonic and childhood growth (13) and anabolic processes in adults (14).
Aging is associated with a decline in IGF-1 (10). The progressive decline has been termed the ‘somatopause’, which may be caused by potential alterations of the hypothalamic regulation of GH secretion, in particular an age-dependent decrease in endogenous hypothalamic GHRH output, contributing to the age-associated GH and IGF-1 decline (15). Moreover, low physical fitness and higher adiposity in older individuals also contribute to the decreased GH secretion and associated IGF-1 decline (16). Low levels of IGF-1 are associated with decreased skeletal muscle mass and function (17). Studies have shown that IGF-1 serum levels are positively associated with muscular strength and walking speed and are negatively associated with self-reported difficulty in mobility tasks (18). Systemic infusion of GH over 8 hours led to increased GH and IGF-1 concentration levels and increased muscle protein synthesis in eight healthy young adults aged 18 to 24 years (19). In addition, Rudman et al. (20) demonstrated increased lean body mass, average vertebral bone density, IGF-1 levels, and decreased body fat following GH administration over 6 months in nine healthy adults that were not observed in 12 untreated healthy adult men. Mauras et al. (14]) used recombinant human IGF-1 (rhIGF-1) treatment to increase IGF-1 plasma levels in 10 patients with Laron’s syndrome, characterized by GH receptor deficiency, and showed that increased IGF-1 plasma levels were associated with increased lean body mass and decreased fat mass. Furthermore, Dik et al. (21) demonstrated that higher IGF-1 serum levels were associated with fewer functional limitations (e.g. difficulties with climbing stairs, cutting toenails, use of public transport) in 1318 healthy participants aged 65 to 88 years. This association suggests that reduced IGF-1 levels in older people might make them more prone to these functional limitations.
The influence of IGF-1 on bone development has been demonstrated using mouse models. Bikle et al. (22) found a 24% decrease in cortical bone size and reduced femoral lengths, but increased connectivity and trabecular bone density, in IGF 1 deficient (Igf-1 -/-) mice. In addition, a study by Courtland et al. (23) used inducible liver IGF-1 deficient mice to deplete IGF-1 serum levels at varying times in mice development and demonstrated that depletion of serum IGF-1 levels at four weeks in male mice resulted in reduced trabecular and cortical bone acquisition by 16 weeks. Depletion of serum IGF-1 levels in mice of eight weeks resulted in decreased cortical bone properties at 32 weeks, whereas depletion of IGF-1 serum levels after peak bone acquisition at 16 weeks did not lead to detrimental effects on bone.
Finkenstedt et al. (24) demonstrated that 12 months of recombinant human GH (rhGH) treatment of 18 adult male and female patients, with adult onset GH deficiency, and an average age of 44 years, resulted in increased markers of bone formation and resorption and elevated IGF-1 levels compared to the untreated group. Following rhGH treatment for 12 months, markers for bone turnover, including bone formation and resorption, increased relative to baseline in those patients who were treated with rhGH. In addition, after 12 months, IGF-1 was significantly increased in all patients treated with rhGH, and bone mineral density in the lumbar and proximal spine was increased in this group, particularly in patients with low bone mass. Furthermore, one month of recombinant human GH administration in 10 healthy older men, with an average age of 68 years, led to improved balance and stair climb time as well as increased muscle IGF-1 gene expression (25). Ohlsson et al. (26) also showed that low IGF-1 serum levels in elderly men were associated with increased risk of bone fractures (e.g. hip, spine), which are partly caused by falls and are a clear marker of physical frailty. Muscle weakness, functional limitations, and age are substantial contributors to the risk of falls in elderly and these factors are all associated with a decrease in IGF-1. Hence, the age-related decrease in IGF-1 may play an important role in the increased incidence of falls in elderly.

 

IGF-1 and the aging brain

IGF-1 produced by the liver has the ability to cross the blood-brain barrier and can subsequently bind to IGF-1 receptors expressed throughout the brain. High densities of IGF-1 receptors are observed in various brain areas including the amygdala, thalamic nuclei, hippocampus, superficial and deep cortical layers, olfactory bulb, cerebellum, cerebral cortex, caudate nucleus, frontal cortex and the putamen (27). In addition, IGF-1 is also produced in brain tissues and can thereby act locally via paracrine or autocrine mechanisms. IGF-1 plays an important role in neuronal growth, the maintenance of synapses and the protection of neurons in the brain (28). Furthermore, IGF-1 has been found to enhance and maintain myelination, essential for the propagation of neuronal impulses, in the central nervous system (CNS) as well as in the peripheral nervous system.
Age-related decline of IGF-1 levels is associated with altered brain function. Sonntag et al. (29) showed age-related decreases in IGF-1 receptor density in hippocampal and cortical regions in rats. The authors found that IGF-1 mRNA levels were reduced in the cerebellum in older rats, compared to younger ones. This decline was associated with an increase in cell death (30). As IGF-1 is involved in maintaining myelination in the CNS, age-related IGF-1 decline may be associated with the breakdown of myelination which in turn may have a negative impact on cognition in humans (31). This age-related breakdown of myelin can lead to decreased signal transmission speed in neurons, essential for integration of information between highly distributed neural networks that underlie higher cognitive functions, such as executive processing (32).

 

IGF-1, cognition and MCI

Evidence thus far has supported the idea that IGF-1 plays an essential role in cognition. In healthy men and women, IGF-1 serum levels have been shown to be positively related to working memory (33), selective attention, executive function (34), verbal fluency and performance on the Mini-Mental State Examination (MMSE) (35). A recent study by Maass et al. (36) demonstrated that an increase in IGF-1 serum levels was positively associated with hippocampal volume and verbal memory recall in a population of healthy elderly. In childhood-onset GH deficient men GH substitution improved both mood and memory. These improvements were maintained during the 10 year follow-up period (37).
With respect to pathological cognitive aging, IGF-1 levels have been found to be reduced in people with MCI compared to cognitively healthy people. MCI is associated with reduced performance in various cognitive domains, including attention, executive function, processing speed, visuospatial skill and memory. Doi et al. (38) conducted a population survey in 3355 participants with an average age of 71.4 years and found that people with MCI showed decreased IGF-1 serum levels compared to cognitively healthy people. Furthermore, Calvo et al. (39) showed a positive association between IGF-1 serum levels and cognitive performance, mainly in the domains of learning and memory, in elderly people with MCI, suggesting IGF-1 may be neuroprotective in elderly people susceptible to AD. This notion is supported by the finding that the cognitive impairments in AD may be partly related to reduced IGF-1 serum levels (40).

 

IGF-1 and AD

At a neurobiological level AD is characterized by several neurotoxic effects caused by senile plaques (SPs) and neurofibrillary tangles (NFTs) that lead to synaptic dysfunction, neuronal cell death and cerebral atrophy, mainly in the hippocampus and temporal and parietal lobes. The main elements of SPs are beta-amyloid (Aβ) aggregates. These Aβ aggregates form plaques outside neurons that intervene with communication between neurons at synapses and contribute to neuronal cell death. NFTs, on the other hand, are primarily composed of hyperphosphorylated tau protein. Deviant abnormal tau proteins inside neurons (tau tangles) block the transports of essential molecules, such as nutrients in the neuron, thereby contributing to cell death. The abundance of NFTs is positively associated with the severity of AD (41). These brain alterations impede the transfer of information between synapses and cause a reduction in the number of synapses. The progression of the disease eventually leads to neuronal cell death causing a substantial shrinkage of the brain.
In 2007, Alvarez et al. (40) showed subnormal IGF-1 levels in adults diagnosed with AD. Additionally, Westwood et al. (42) showed that lower IGF-1 serum levels are associated with an increased risk of developing AD in older- and middle-aged people. This study also demonstrated that higher levels of IGF-1 are associated with greater brain volumes, even among cognitively healthy older and middle-aged people, suggesting a protective effect of IGF-1 against neurodegeneration. Recent evidence showed that IGF-1 resistance in the brain is increased in AD (43). Moloney et al. (44) demonstrated that alterations in IGF-1 receptors (IGF-1Rs) in the AD temporal cortex, including reduced expression as well as an aberrant distribution of IGF-1Rs in the neurons, contribute to impaired IGF-1R signaling in AD neurons. The deviant distribution of IGF-1Rs in neurons away from the plasma membrane suggests that IGF-1Rs are less able to respond to extracellular IGF-1 in AD, contributing to possible IGF-1R signaling resistance in neurons that degenerate (44). A decrease in IGF-1 signaling can contribute to loss of myelin function, which is thought to result in nerve fiber conduction delays found in people with AD (45). Furthermore, deficits in IGF-1 signaling have been related directly to AD pathology like increased accumulation of Aβ, phosphorylated tau, increased neuro-inflammation and apoptosis (28), suggesting that impaired IGF-1 signaling plays a role in the pathogenesis of AD. In contrast to this idea it has also been suggested that downregulation of IGF-1 signaling is a consequence of neuropathology and alterations in IGF-1 signaling could be seen as a compensatory response to attenuate the effects of aging and neurodegeneration. This idea is supported by the assocation between suppression of IGF-1 signaling and longevity in humans (46) and the observation that low IGF-1 levels predict life expectancy in exceptionally long-lived individuals (47).
In model organisms in which IGF-1 signaling was attenuated increased lifespan and a delayed process of aging has been observed (48, 49). For instance, in AD mouse models the long-term suppression of IGF-1 signaling reduced neuronal loss and increased resistance to oxidative stress and neuro-inflammation. In line with these findings, lowerd IGF-1 serum levels in transgenic mouce models, induced by a protein restriction diet, alleviated AD pathology (50).
In human observational studies, a recent meta-analysis by Ostrowski and colleagues could not confirm the hypothesized association between serum IGF-1 and AD. From 3540 studies that analyzed the relation between IGF-1 and AD, only 10 studies provided serum IGF-1 values. These 10 studies included 850 AD patients and 871 controls. From these studies 5 reported that AD subjects had higher IGF-1 levels, 2 reported no difference in IGF-1 levels and 3 reported lower IGF-1 levels in AD. The authors conclude that serum IGF-1 may be a personalized factor reflecting differential influence of genetic polymorphisms, age of onset or disease progression of AD patients (51). It is important to note that the number of included studies poses limitations to the generalizability of the results and more studies are needed to clarify the possible relationship between IGF-1 levels and AD.

 

Potential interactions of IGF-1 and ApoE-ε4 in the development of AD

The Apolipoprotein E gene, APOE, is the largest genetic risk factor associated with cognitive decline in late-onset AD (52). ApoE is involved in lipid transport in the central and peripheral nervous system, and brain injury repair. The three most common alleles of APOE (ε2, ε3, ε4) encode for the three major isoforms (ApoE-ε2, ApoE-ε3, ApoE-ε4) of the apolipoprotein E (ApoE), a protein that plays a central role in brain injury repair, lipid transport and metabolism. The ε2, ε3 and ε4 alleles have a worldwide frequency of 8.4%, 77.9% and 13.7%, respectively (53).
The strength of the effects of the different APOE genotypes on AD risk differs between ethnic groups. In the present study, we will focus on Caucasians. ApoE-ε3 is often considered the neutral allele with regard to AD risk. Compared to the ApoE-ε3, ApoE-ε4 is associated with both an increased incidence rate and an earlier onset of AD. One copy of ApoE-ε4 increases the risk of developing AD threefold, while those who are homozygous for ε4 have an approximately 13-fold increased risk (54). ApoE-ε4 carriers also have an enhanced risk for developing vascular dementia and mild cognitive impairment (MCI) (55) and studies have shown that the ApoE-ε4 allele is involved in the acceleration of cognitive decline (56). The accelerated cognitive decline observed in ApoE-ε4 carriers could be an important clinical precursor of AD. It has been shown that ApoE promotes the proteolytic breakdown of the Aβ aggregates appearing in AD, whereas the isoform ApoE-ε4 is less effective in enhancing this breakdown (57). Moreover, Kumar et al. (58) demonstrated that neurofibrillary tangle density was increased in ApoE-ε4 carriers relative to non-carriers of the allele. Hence, carrying the ApoE-ε4 allele increases the vulnerability of the brain to AD pathology.
As described earlier, IGF-1 has an opposite effect to ApoE-ε4 on N-methyl-D-aspartate receptor (NMDAR) signaling and Aβ clearance in the brain (59). With respect to NMDAR signaling, Liu et al. (60) demonstrated that the ApoE-ε4 allele enhanced an age-related decline in cognitive function in mice by decreasing NR2B subunit levels which in turn down-regulates the NMDAR pathway. Specifically, NR2B may play a role in spatial learning and long-term potentiation (61, 62). In contrast, IGF-1 has been found to positively affect the NMDARr pathway in rats by increasing NR2B subunits (62).
Impairments in Aβ clearance are a major hallmark in early as well as late AD. People carrying the ApoE-ε4 allele are more vulnerable to disturbances in Aβ clearance than people not carrying this allele (63). IGF-1 supports Aβ clearance in the healthy brain (64).
A recent study by Keeney et al. (65) was the first to report a direct association between the three isoforms of ApoE (ε2, ε3 and ε4) and IGF-1 by demonstrating deficient IGF-1 gene expression and reduced IGF-1 protein level in mice carrying the human ApoE-ε3 and ApoE-ε4, compared to mice carrying the human ApoE-ε2 allele. This association indicates that the three isoforms of ApoE affect IGF-1 signaling differently, suggesting a potential mechanism that might contribute to the differences in AD risk of ApoE isoforms (65).
Moderation of the association between IGF-1 signaling and AD by APOE genotype has previously been suggested in experimental studies. Using microarray analysis of the astrocyte transcriptome, Simpson and colleagues demonstrated that as AD pathology progresses, downregulation of gene transcription in astrocytes leads to a reduction in the expression of intra-cellular insulin and IGF signaling pathways, particularly in individuals expressing the ApoE-ε4 allele (66). Impaired IGF-1 signaling in human astrocytes is associated with a reduced ability to protect neurons from oxidative stress, which has been identified as an important factor in the promotion of tau and Aβ pathology in AD (67).
Therapeutic approaches targeting insulin resistance by increasing IGF-1, insulin, or insulin sensitivity have been promising, but do suggest differential effects in people with or without genetic susceptibility to AD. More specifically, intravenous and intranasal insulin administration in patients with AD, reduced amyloid precursor protein (APP) levels and improved memory scores only in those without the ApoE-ε4 allele (68, 69).
Previously, our group reported tentative evidence of an interaction between the ApoE-ε4 allele and IGF-1 receptor stimulating activity in an elderly cohort (59). IGF-1 receptor stimulating activity in the median and top tertiles was related with increased dementia incidence in hetero- and homozygotes of the ApoE-ε4 allele, but did not show any association with dementia risk in people without the ApoE-ε4 allele (59). The observed elevation in IGF-1 receptor stimulating activity may have marked a compensatory response to neuropathological changes associated with the ApoE-ε4 genotype. Additionally, we found that the ApoE-ε4 homozygotes, with a lifetime risk of Alzheimer’s Disease of 80% (70), have the lowest IGF-1 levels (59). Similarly, a genome-wide association study on longevity by Deelen et al. (2001) showed that the ApoE-ε4 allele was related to lower IGF-1 levels in middle-aged women. Hence, the increased risk of developing AD in ApoE-ε4 carriers might partially be attributed to alterations in IGF-1 signaling (71).

 

Physical activity and IGF-1

As mentioned earlier, IGF-1 serum levels can be influenced by lifestyle factors, such as physical activity (5). Aerobic and anaerobic exercise interventions have been shown to influence IGF-1 levels. The positive effect of aerobic exercise on IGF-1 levels has been shown in a mouse study that demonstrated upregulated mRNA levels of IGF-1 in mice after 15 days of voluntary wheel running. Protein levels of IGF-1 in the dentate gyrus had also increased (72). Replication of these results in human participants was provided by several studies that showed an increase in IGF-1 serum levels following aerobic exercise in adults (73, 74). Likewise, a study concerning the effect of anaerobic exercise on IGF-1 serum levels reported positive effects of anaerobic training on IGF-1 levels in healthy older men (75). There is, however, still much controversy concerning the association between physical exercise and IGF-1 levels. A systematic review of experimental studies on the effect of physical activity on measures of IGF-1 and cognitive functioning in healthy elderly concluded moderate intensity aerobic training and moderate and high intensity resistance training may improve circulating IGF-1 and cognition, depending on the sex of the participant and duration of the training. However, disparities in the type of exercise, protocols and samples hinder comparison of the results and the establishment of consensus (76).
Furthermore, negative associations between IGF-1 levels and physical activity, could also be explained by favorable neuromuscular anabolic adaption, which is a normal short-term adaptive response of the body to increased physical exercise (Rarick et al., 2007). It has been thought that during episodes of active muscle building IGF-1 serum levels decrease (78), but local muscle gene expression and production of IGF-1 increase (79). Longitudinal studies on exercise interventions indicate that IGF-1 serum levels may only decline temporarily and may increase after longer duration of intensive training and are maintained when training is reduced (74). The long-term effect of physical activity on IGF-1 levels may be explained by epigenetic alterations. It is known that physical activity can contribute to changes in various physiological systems by epigenetic mechanisms (80). Physical activity may induce epigenetic modifications to the IGF-1 gene, leading to sustained increased IGF-1 levels (6, 80). There is evidence showing that these types of alterations can be inherited (81). In light of epigenetics and the influence of prolonged physical activity on IGF-1 levels, the current decrease in the number of physically active people, mainly in high-income countries, is alarming.
Regular engagement in physical activity could be of special importance to those with a genetic susceptibility to AD. Several studies have indicated that the negative association between regular physical activity and cognitive decline is limited to those with one or more copies of the ApoE-ε4 allele. Schuit et al. registered engagement in physical activity in a group of elderly Dutch men and found that while risk of cognitive decline did not differ between active and inactive ApoE-ε4 non-carriers the risk was 4 times higher in inactive ApoE-ε4 carriers compared to active ApoE-ε4 carriers (82). A similar finding, indicating that inactivity is especially detrimental to cognitive abilities for ApoE-ε4 carriers, was reported in a longitudinal study in a Finnish cohort (83). Rovio et al. found a significant relationship between physical activity at midlife and risk of developing AD at a 21-year follow-up for ApoE-ε4 carriers, but not for ApoE-ε4 non-carriers. Additionally, Kivipelto et al. (84) demonstrated that physical inactivity increased the risk of AD mainly among ApoE-ε4 carriers.
Several brain-imaging studies have reported support for these findings. Deeny et al. found that in the middle-aged, sedentary ApoE-ε4 carriers exhibited lower activity levels in the temporal lobe, a region known to be vulnerable to early decline in AD, relative to active ApoE-ε4 carriers, while activity level did not distinguish between AD risk for ApoE-ε4 non carriers (85). In 2012 Head et al. demonstrated that in cognitively normal older adults those who were sedentary and ApoE-ε4 carriers showed more Aβ deposition than active ApoE-ε4 carriers, whereas this association was not present in non-carriers (86). Subsequently, Smith et al. observed that the hippocampal volume of those ApoE-ε4 carriers that displayed low levels of physical activity was on average 3% lower in comparison to non-carriers, and in comparison to ApoE-ε4 carriers who displayed high levels of physical activity (87), indicating that physical inactivity may be related to brain atrophy in ApoE-ε4 carriers. Together, these studies suggest that ApoE-ε4 carriers may be more susceptible to the negative effects of physical inactivity, and that sedentary ApoE-ε4 carriers may be at increased risk of developing AD.
In contrast, in a functional MRI study Smith et al. observed that among ApoE-ε4 carriers being engaged in higher levels of physical activity was associated with greater regional brain activation during a semantic memory task in comparison to non-carriers and ApoE-ε4 carriers who displayed lower levels of physical activity (88), suggesting that ApoE-ε4 carriers do not suffer more from inactivity than any other group but do experience more benefits from physical activity.
On the other hand, studies have shown that the interaction between physical activity and cognitive decline is restricted to ApoE-ε4 non-carriers. In a prospective study among older adults Podewils et al. found an inverse association between physical activity and risk of AD after a 5 year follow-up that was confined to ApoE-ε4 non-carriers, indicating that benefits of exercise may be confined only to ε4 non-carriers (89). A similar finding was reported after a 5 year follow-up in cognitively healthy elders (90). Fenesi et al. found a significant protective effect of physical activity regarding dementia risk in ApoE-ε4 non-carriers, and no significant effect in ApoE-ε4 carriers. One randomly controlled trial supported these two observational studies (91). Lautenschlager et al. studied the effect of an exercise intervention on cognitive functioning in a randomized trial in healthy older adults with subjective memory impairment. The researchers found a modest improvement in cognitive functioning in those treated with the intervention. In a post-hoc comparison, treatment response interacted with APOE genotype, as ApoE-ε4 non-carriers showed a significantly larger improvement compared to both carriers and non-carriers in the control condition, while no other significant differences were found (91).
One study did not find a significant interaction effect between physical activity and cognition and ApoE-ε4 carrier status (92). Luck et al. failed to find an interaction between physical activity in late life and risk of AD in an observational study after a 4.5-year follow-up in a group of healthy elderly aged 75 years and over. However, the authors did note that the interaction between ApoE-ε4 and low physical activity for AD risk verged on the border of significance.
With regard to physical fitness, it has been found that the presence of the ApoE-ε4 allele is associated with motor decline (e.g. motor performance) in older people (93) and the strength of this relationship increases with age. Further analysis showed that this association was mainly due to a greater age-related decrease in upper and lower limb muscle strength in people carrying the ApoE-ε4 allele. This study showed that ApoE-ε4 carriers are at greater risk of rapid motor decline relative to non-carriers, particularly later in life. Considering that limited physical activity is associated with motor decline, and physical activity is potentially protective against cognitive decline, physical activity is argued to be especially relevant to ApoE-ε4 carriers (86, 93).

 

Diet and IGF-1

In addition to the effect of physical activity on IGF-1 levels, diet is an important lifestyle factor affecting IGF-1 levels. Norat et al. (4) demonstrated that protein intake was positively associated with IGF-1 serum levels. This study showed that intake of milk, calcium, magnesium, phosphorus, potassium, vitamin B6, and vitamin B2 was positively related to IGF-1 serum levels and that the intake of vegetables and beta-carotene was negatively associated with IGF-1 serum levels in women. In line with this study, a study by Allen et al. (94) demonstrated that in adult women aged 20 to 70 a plant-based (vegan) diet was related to lower IGF-1 serum levels compared to women with a meat-eating or lacto-ovo-vegetarian diet. The difference in IGF-1 serum levels between the groups was mainly explained by protein intake consisting of essential amino acids. Long-term caloric restriction for a duration of 1 and 6 years was not associated with with reduced IGF-1 serum levels in healthy middle aged men and women, if protein intake is high (95). In addition, a recent study by Fontana et al. (96) showed that 2 years of caloric restriction did not affect IGF-1 serum levels in healthy non-obese young and middle-aged men and women, suggesting no sustained effects of caloric restriction on IGF-1 serum levels. Though, other studies demonstrated that short term caloric restriction for 6 days lowers IGF-1 serum levels (97), indicating that particularly short term fasting lowers IGF-1 serum levels.

 

Exercise combined with diet and IGF-1

Few studies have examined the influence of physical activity combined with a specific diet on IGF-1 levels. A negative caloric balance induced by physical exercise or caloric restriction, were both associated with equivalent decline in IGF-1 levels (98). Smith et al. (98) concluded that a decline in IGF-1 levels is mainly explained by an energy deficit, irrespective whether this deficit was induced by caloric restriction or physical exercise. A study by Rarick et al. (77) demonstrated a decline in IGF-1 serum levels after 7 days of increased physical activity in healthy men. However, the decrease in IGF-1 serum level was not moderated by fitness intensity, energy balance, or dietary protein intake. This study therefore challenges the concept of Smith et al. (98)and suggests that yet unknown mechanisms related to physical activity, such as enhanced energy flux, may affect IGF-1 levels independently.

 

IGF-1 in relation to other AD risk factors

When investigating the association between IGF-1 and Alzheimer’s disease it is important to consider the limited role of epidemiological evidence in causal inference and the possible confounding influence of a myriad of factors that are related to both AD risk and altered IGF-1 signaling. Among these potential confounders are lifestyle factors, like alcohol and nicotine consumption (99–101), and several conditions associated with alterations in insulin or IGF-1 signaling such as type 2 diabetes, obesity, cardiovascular disease, cerebral infarcts (102–107) and depression (108, 109). These cross-links between altered IGF-1 signaling and increased risk of AD highlight the importance of experimental and meta-analytic evidence, replication studies and a thorough consideration of potential confounders in the association between IGF-1 signaling and Alzheimer’s disease.

 

Conclusion and future perspectives

Although there are contradictory findings on the association between physical exercise, diet and IGF-1 it can be argued that promoting physical activity and a protein rich diet could be promising interventions that may increase IGF-1 levels, thereby increasing physical fitness and counteracting age-related neurodegeneration and AD. Further research, including experimental, epidemiological and multi-omic approaches (110), is warranted to investigate the prospective value of different biomarker profiles for future dementia risk. Findings can be applied to improve early diagnostics and to increase the efficiency of lifestyle interventions targeting IGF-1 signaling to delay or prevent the development of physical and cognitive decline, in particular for those most vulnerable for AD.

 

Highlights

– IGF-1 is associated with cognitive deficits and pathological alterations in the brain that accompany AD
– Decreased IGF-1 levels are a possible moderator of genetic vulnerability to AD
– Increasing physical activity and adherence to a protein rich diet may be useful interventions to increase IGF- serum levels, thereby increasing physical fitness and cognitive functioning

 

Funding: The authors received no financial support for the research, authorship or publication of this manuscript.

Conflict of interests: All authors declare that they have no conflict of interest.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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CORTICAL Β-AMYLOID IN OLDER ADULTS IS ASSOCIATED WITH MULTIDOMAIN INTERVENTIONS WITH AND WITHOUT OMEGA 3 POLYUNSATURATED FATTY ACID SUPPLEMENTATION

 

C. Hooper1, N. Coley2,3, P. De Souto Barreto1,2, P. Payoux4,5, A.S. Salabert4,5, S. Andrieu2,3, M. Weiner6, B. Vellas1,2 for the MAPT/DSA study group

1. Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France; 2. UMR1027, Université de Toulouse, UPS, INSERM, Toulouse, France; 3. Department of Epidemiology and Public Health, CHU Toulouse, Toulouse, France; 4. UMR 1214, Toulouse Neuroimaging Center, University of Toulouse III, Toulouse, France; 5. Department of Nuclear Medicine, University Hospital of Toulouse (CHU-Toulouse), Toulouse, France; 6. University of California San Francisco, School of Medicine, 4150 Clement Street, San Francisco, California. USA.

Corresponding Author: Claudie Hooper, Gérontopôle, Department of Geriatrics, CHU Toulouse, Purpan University Hospital, Toulouse, France , Tel  : +33 (5) 61 77 64 25, Fax : +33 (5) 61 77 64 75 claudie28@yahoo.com

J Prev Alz Dis 2020;
Published online February 5, 2020, http://dx.doi.org/10.14283/jpad.2020.4


Abstract

Multidomain lifestyle interventions (including combinations of physical exercise, cognitive training and nutritional guidance) are attracting increasing research attention for reducing the risk of Alzheimer’s disease (AD). Here we examined for the first time the cross-sectional relationship between cortical β-amyloid (Aβ) and multidomain lifestyle interventions (nutritional and exercise counselling and cognitive training), omega 3 polyunsaturated fatty acid (n-3 PUFA) supplementation or their combination in 269 participants of the Multidomain Alzheimer Preventive Trial (MAPT). In adjusted multiple linear regression models, compared to the control group (receiving placebo alone), cortical Aβ, measured once during follow-up (mean 512.7 ± 249.6 days post-baseline), was significantly lower in the groups receiving multidomain lifestyle intervention + placebo (mean difference, -0.088, 95 % CI, -0.148,-0.029, p = 0.004) or multidomain lifestyle intervention + n-3 PUFA (-0.100, 95 % CI, -0.160,-0.041, p = 0.001), but there was no difference in the n-3 PUFA supplementation alone group (-0.011, 95 % CI, -0.072,0.051, p = 0.729).  Secondary analysis provided mixed results. Our findings suggest that multidomain interventions both with and without n-3 PUFA supplementation might be associated with lower cerebral Aβ. Future trials should investigate if such multidomain lifestyle interventions are causally associated with a reduction or the prevention of the accumulation of cerebral Aβ.

Key words: Multidomain lifestyle intervention, β-amyloid, physical activity, cognitive activity, nutrition, Alzheimer’s disease.


 

Introduction

Evidence suggests that the individual components of multidomain lifestyle interventions, including cognitive activity (1, 2), physical activity (3, 4) and nutrition (5, 6) are associated with reduced cerebral β-amyloid (Aβ). Physical activity has been cross-sectionally associated with reduced central Aβ in cognitively normal older adults (4) as well as in autosomal dominant  (early onset familial) cases of Alzheimer’s disease (AD) (3, 7). A less active lifestyle has been associated with more cerebral Aβ in apolipoprotein E (ApoE) ε4 carriers (8) and the association of physical activity with reduced cerebral Aβ appears to be more prominent in ApoE ε4 carriers (4, 9). Furthermore, long-term treadmill exercise reduces Aβ in murine models of AD possibly through reduced amyloidogenic-cleavage (10, 11) and/or increased Aβ degradation (10).
Lifetime cognitive activity has been cross-sectionally associated with reduced cerebral Aβ in human subjects (1) and in another cross-sectional study it was shown that Aβ was diminished in ApoE ε4 carriers that reported higher cognitive activity over the course of life (2). Moreover, lifetime intellectual enrichment (high education, high midlife cognitive activity) has been associated with lower cortical Aβ deposition longitudinally in ApoE ε4 carriers (12). However, there is in vitro and animal data to indicate that neural activity increases the secretion of Aβ (13, 14), which might lead to enhanced deposition if clearance mechanisms failed. Nevertheless, consistent with our hypothesis, transgenic Aβ-expressing mice exposed to enriched environments deposit less Aβ than control animals (15).
In terms of nutrition and Aβ, increased cerebral Aβ has been associated with a high glycaemic diet (16) and a lack of adherence to a Mediterranean style diet (17) and vitamin B12 as well as vitamin D (5) have been inversely associated with cerebral Aβ. Cell culture and animal models suggest that docosahexaenoic acid (DHA), the predominant omega (n-3) polyunsaturated fatty acid (PUFA) in the brain, might reduce Aβ production (18-20) and serum DHA has been inversely associated with brain Aβ cross-sectionally in older adults (21). To the contrary, however, we have previously reported that erythrocyte membrane DHA, eicosapentaenoic acid (EPA) as well as total n-3 PUFA were not cross-sectionally associated with cortical Aβ in participants of the placebo group of Multidomain Alzheimer Preventive Trial (MAPT) (22).
Using similar multidomain lifestyle interventions to those used in MAPT (nutritional and exercise counselling and cognitive training), other trials have explored the effects of multidomain interventions targeting a healthier lifestyle on cognitive function in older adults (23-26). However, to the best of our knowledge no information is available on the relationship between multidomain lifestyle interventions and cerebral Aβ burden. Hence we explored the cross-sectional relationship between cortical Aβ and multidomain lifestyle interventions, n-3 PUFA supplementation or their combination in 269 participants of the MAPT trial who underwent voluntary [18F] florbetapir positron emission tomography (PET). We hypothesised that multidomain lifestyle intervention might be associated with reduced cerebral Aβ and that this association might be potentiated by n-3 PUFA supplementation.

 

Methods

The Multidomain Alzheimer Preventive Trial (MAPT) and ethical approval

Data were obtained from a [18F] florbetapir PET study carried out as an ancillary project to MAPT (registration: NCT00672685), a large multicentre, phase III, randomized, placebo-controlled trial (RCT) which has already been described in detail (26). MAPT subjects (n=1680) were randomized to one of the four following arms: n-3 PUFA supplementation alone, multidomain lifestyle intervention (involving nutritional and exercise counselling and cognitive training) + placebo, multidomain lifestyle intervention + n-3 PUFA supplementation, or placebo alone (control group). Both MAPT and the PET sub-study were approved by the ethics committee in Toulouse (CPP SOOM II) and written consent was obtained from all participants.

Participants

At inclusion, subjects were community-dwelling men and women without dementia, aged ≥ 70 years, and who met at least one of the following criteria: spontaneous memory complaints, limitation in executing ≥ 1 Instrumental Activity of Daily Living, or slow gait speed (< 0.8 meters/sec). Participants of the study described here were 269 individuals who had data on cortical Aβ (excluding two participants who developed dementia as assessed at the clinical evaluation closest to PET-scan (Clinical Dementia Rating (CDR) ≥ 1)). MAPT participants who were not assessed for cerebral Aβ (n = 1408) were similar to the participants in the PET sub-study (n = 269) (Table S1).

The Multidomain Alzheimer Preventive Trial interventions

The MAPT multidomain lifestyle intervention was comprised of cognitive training, nutritional counselling and physical activity counselling (26). Group-based 2-hour sessions were performed twice a week during the first four weeks of the trial, once a week for the following four weeks and then once a month for the remainder of the trial’s 3-year follow-up period. The sessions comprised: one hour of cognitive training (memory and reasoning), 15 minutes of nutritional advice (based on guidelines established by the Programme National Nutrition Santé, the French National Nutrition Health Programme (27)) and 45 minutes of physical activity counselling. An exercise program was designed for each individual and participants were advised to increase the physical activity to the equivalent of at least 30 minutes walking per day 5 days a week. Two 2-hour reinforcement sessions were performed at 12 and 24 months to boost the effects of the interventions. Preventive consultations were also performed (at baseline, 12 and 24 months) with a physician to optimize the management of cardiovascular risk factors and detect functional impairments. All participants were also asked to consume two soft capsules daily as a single dose, containing either a placebo, or a total of 800 mg of DHA and 225 mg of EPA per day. The trial was double-blind for all subjects for n-3 PUFA supplementation or placebo allocation.  No lifestyle interventions were provided to participants in the placebo alone or n-3 PUFA alone groups.

[18F] Florbetapir Positron Emission Tomography (PET)

PET-scans as a measure of cortical Aβ were performed using [18F] florbetapir as previously described (28, 29). PET data acquisitions commenced 50 minutes after injection of a mean of 4 MBq/kg weight of [18F]-Florbetapir. Radiochemical purity of [18F]-Florbetapir was superior to 99.5 %. Regional standard uptake value ratios (SUVRs) were generated from semi-automated quantitative analysis with the whole cerebellum used as the reference region. Cortical-to-cerebellar SUVRs (cortical-SUVRs) were obtained using the mean signal of the following predefined cortical regions: frontal, temporal, parietal, precuneus, anterior cingulate, and posterior cingulate as previously described (30). A Quality Control procedure was carried out using a semi-quantification-based method. PET-scans were performed throughout the 3 year period of MAPT: the mean time of PET-scan acquisition (standard deviation, SD) was 512.7 ± 249.6 days after study baseline. There was no significant difference (p = 0.223 according to a one way analysis of variance: ANOVA) between the time interval between baseline and PET-scan in subjects allocated to the 4 MAPT groups (placebo: 464.9 ± 2.62.0 days, n-3 PUFA group: 501.8 ± 232.4 days, multidomain + placebo: 544.4 ± 237.3 days, multidomain + n-3 PUFA: 536.8 ± 259.8 days). Very few subjects were scanned before 6 months: 22 out of 269 (8.2 %).

Covariates

We selected the following covariates on the basis of data availability and the literature on AD (31-33): age at PET-scan assessment, gender, educational level, cognitive status assessed at the clinical visit closest to PET-scan [Clinical dementia rating (CDR): scores 0 or 0.5], time interval between baseline and PET-scan (in days), physical activity assessed at the clinical visit closest to PET-scan [measured in metabolic equivalent tasks – minutes per week (MET-min/week)] and ApoE ε4 genotype (carriers of at least one ε4 allele versus non-carriers).

Statistical Analysis

Descriptive statistics are presented as mean ± (SD) or absolute values/percentages. Clinical and demographic characteristics were compared between the participants in each group using chi squared tests for categorical variables and one-way ANOVA for continuous variables. This was a post-hoc analysis since the association between cortical Aβ burden and the MAPT interventions was not an a priori hypothesis of the MAPT study. We used multiple linear regression models to compare cortical Aβ levels (measured once per subject, at any time during follow-up, as described above) across the four MAPT randomization groups (placebo alone, n-3 PUFA supplementation alone, multidomain lifestyle intervention + placebo, and multidomain lifestyle intervention + n-3 PUFA supplementation) adjusting for all covariates. Next we dichotomized the dependent variable, cortical Aβ, with a positivity threshold of mean cortical SUVR ≥ 1.17 (28, 34) then performed logistic regression adjusting for all covariates. We ran three sensitivity analyses in order to substantiate our main analysis. Firstly, we ran a multiple linear regression adjusting for all covariates including only those participants who had their PET-scan ≥ 12 months post-baseline and hence had received MAPT interventions for ≥ 12 months. Secondly, we ran a multiple linear regression adjusting for all covariates including only those participants who had their PET-scan < 12 months post-baseline. Thirdly, we ran a multiple linear regression adjusting for all covariates after combining the MAPT data into 2 groups according to allocation to multidomain lifestyle intervention (placebo plus n-3 PUFA supplementation versus multidomain lifestyle intervention + placebo plus multidomain lifestyle intervention + n-3 PUFA supplementation). To explore the role of adherence to intervention, we ran multiple linear regression restricted to subjects in the multidomain groups adjusted for age, sex and ApoE ε4 carrier status to assess the association between cortical Aβ and adherence to the multidomain lifestyle intervention or adherence to the multidomain lifestyle intervention + n-3 PUFA supplementation. Adherence was defined as ≥ 75 % attendance to the sessions over the 3-year period of MAPT including participation in the two boost sessions at the 12 and 24 month follow-ups (reference: non-adherent < 75 % attendance) (5). Lastly, to explore the role of time on the association of MAPT intervention with cortical Aβ we ran a multiple linear regression analysis (adjusted for all covariates) with the introduction of an interaction term between MAPT group allocation and time between PET-scan and baseline (in days).  Due to the exploratory nature of the study there was no correction for multiple comparisons: P < 0.05 was considered statistically significant.  Statistical analyses were performed using Stata version 14 (Stata Corp., College Station, TX, USA).

 

Results

Sample characteristics

Demographic and clinical characteristics of the participants included in this study are shown in Table 1. There were no significant between-group differences in age, gender, cognitive status, time interval between baseline and PET-scan, physical activity and number of ApoE ε4 carriers. However, educational level differed significantly between groups as did cortical SUVR as a measure of Aβ burden. There was less cortical SUVR present in participants receiving multidomain intervention + placebo or multidomain + n-3 PUFA. The mean age of the participants was approximately 76 years, and around 60 % of the population were female. Participants exhibited a high level of education and almost half of the participants had a CDR score of 0.5 and approximately one third of the subjects carried at least one ApoE ε4 allele.

Table 1. Participant characteristics

Table 1. Participant characteristics

Age, CDR score, and MET-min/week reported at the clinical visit closest to the PET scan are presented. Data is expressed as mean ± standard deviation or as absolute values/percentages. Clinical and demographic characteristics were compared between the participants in each group using chi squared tests for categorical variables and one way analysis of variance (ANOVA) for continuous variables. Abbreviations: ApoE, apolipoprotein E; CDR, clinical dementia rating; MET-min/week, metabolic equivalent tasks – minutes per week; n-3, omega 3 polyunsaturated fatty acid supplementation; MI, multidomain intervention; SUVR, standard uptake ratio values.

 

Main analysis

In the adjusted multiple linear regression model, cortical Aβ was significantly lower in the multidomain lifestyle intervention + placebo group (mean difference, -0.088, 95 % CI, -0.148,-0.029, p = 0.004) and the multidomain lifestyle intervention + n-3 PUFA group (mean difference, -0.100, 95 % CI, -0.160,-0.041, p = 0.001), compared to the placebo alone group (table 2), but there was no difference between the placebo alone and n-3 PUFA supplementation alone groups (mean difference, -0.011, 95 % CI, -0.072,0.051, p = 0.729).  ApoE ε4 carrier status was also significantly associated with cortical Aβ in the model (mean difference, 0.118, 95 % CI, 0.071,0.166, p < 0.001) with ApoE ε4 carriers having greater SUVR compared to non-carriers, as expected. None of the other demographic and clinical co-variates were significantly associated with cortical Aβ.

Table 2. Multiple linear regressions examining the cross-sectional associations between cortical β-amyloid load and the MAPT interventions

Table 2. Multiple linear regressions examining the cross-sectional associations between cortical β-amyloid load and the MAPT interventions

The adjusted model contained fewer subjects due to missing data on covariates (age at PET-scan assessment, ApoE ε4 genotype, gender, educational level, time interval between baseline and PET-scan, and Clinical dementia rating (CDR) and physical activity assessed at the clinical visit closest to PET-scan). B-coefficients represent the mean difference in SUVR between the placebo and intervention. Mean SUVR (95 % CI) for the placebo group in the unadjusted model and as predicted from the adjusted model are 1.23 (1.19,1.27) and 1.33 (0.94,1.72) respectively. Abbreviations: B-coeff, B-coefficient; CI, confidence intervals; n-3 PUFA, omega 3 polyunsaturated fatty acid; p, probability; SUVR, standard uptake ratio values.

 

In the adjusted logistic regression analysis, compared to the placebo alone group, the odds of cortical amyloid positivity (defined as SUVR ≥ 1.17) were significantly lower in the multidomain lifestyle intervention + n-3 PUFA group (odds ratio (OR), 0.31, 95 % CI, 0.133,0.699, p = 0.005), but not in the multidomain lifestyle intervention + placebo group, although they were numerically lower (OR 0.61, 95 % CI, 0.272,1.345, p = 0.218) (Table 3).

Table 3. Logistic regression examining the cross-sectional associations between cortical β-amyloid and MAPT interventions

Table 3. Logistic regression examining the cross-sectional associations between cortical β-amyloid and MAPT interventions

β-amyloid positivity was defined with a threshold of mean cortical SUVR ≥ 1.17. Abbreviations: CI, confidence intervals; n-3 PUFA, omega 3 polyunsaturated fatty acid; p, probability.

 

Sensitivity analysis

In multiple linear regression amongst participants who had their PET scans ≥ 12 months post-baseline, and therefore had received intervention for ≥ 12 months, results were similar to the main analysis (Table 4). Amongst participants who had their PET scans < 12 months post-baseline cortical Aβ was still significantly lower in the multidomain lifestyle intervention + n-3 group compared to the placebo alone group (B-coefficient, -0.126, 95 % CI, -0.252,-0.001, p = 0.048), but the difference between the multidomain lifestyle intervention + placebo group and the placebo alone group was not significant (B-coefficient, -0.099, 95 % CI, -0.227,0.029, p = 0.127) (Table 5). Dividing the participants into two groups according to whether the subjects received multidomain lifestyle intervention or not gave similar results to the main analysis (B-coefficient, -0.089, 95 % CI, -0.132, -0.046, p <0.001: reference = placebo alone and n-3 PUFA supplementation alone groups combined).

Table 4. Sensitivity analysis in subjects having their PET-scan ≥ 12 months

Table 4. Sensitivity analysis in subjects having their PET-scan ≥ 12 months

The adjusted model contained fewer subjects due to missing data on confounders. B-coefficients represent the mean difference in SUVR between the placebo and intervention. Mean SUVR (95 % CI) for the placebo group in the unadjusted model and as predicted from the adjusted model are 1.22 (1.17,1.27) and 1.28 (0.83,1.73) respectively. Abbreviations: B-coeff, B-coefficient; CI, confidence intervals; n-3 PUFA, omega 3 polyunsaturated fatty acid; p, probability; SUVR, standard uptake ratio values.

Table 5. Sensitivity analysis in subjects having their PET-scan < 12 months

Table 5. Sensitivity analysis in subjects having their PET-scan < 12 months

The adjusted model contained fewer subjects due to missing data on confounders. B-coefficients represent the mean difference in SUVR between the placebo and intervention. Mean SUVR (95 % CI) for the placebo group in the unadjusted model and as predicted from the adjusted model are 1.24 (1.18,1.30) and 1.55 (0.63,2.48) respectively. Abbreviations: B-coeff, B-coefficient; CI, confidence intervals; n-3 PUFA, omega 3 polyunsaturated fatty acid; p, probability; SUVR, standard uptake ratio values.

 

Exploratory analysis

Cortical Aβ was not associated with multidomain intervention adherence in the multidomain lifestyle intervention + placebo group (mean difference between adherent and non-adherent subjects , -0.019, 95 % CI, -0.106,0.068, p = 0.668) nor in the multidomain lifestyle intervention + n-3 PUFA group (mean difference, 0.038, 95 % CI, -0.025, 0.102, p = 0.228). Furthermore, the interaction between MAPT group allocation and time between PET scan and baseline was not significantly associated with cortical Aβ in the model (p < 0.05).

 

Discussion

We have observed that assignment to multidomain lifestyle intervention with and without n-3 PUFA supplementation were similarly associated with less cortical Aβ load in older adults at risk of dementia. In contrast, n-3 PUFA supplementation alone was not associated with cortical Aβ. It should be noted, however that a significant association between the multidomain lifestyle intervention + n-3 PUFA group and cortical Aβ was also observed in a sensitivity analysis restricted to those subjects who received a PET-scan < 12 months post-baseline (although the majority of these subjects would still have received the intervention for at least 6 months prior to having their PET scan). Moreover, because it would be expected that the longer participants were exposed to the multidomain lifestyle intervention, the lower the cortical Aβ burden would be, we performed an exploratory analysis for an interaction between MAPT group allocation and time between PET scan and baseline. We found no significant interaction with time. What is more, exploratory analysis showed that cortical Aβ was not significantly associated with adherence to the multidomain lifestyle interventions. Collectively, these findings cast some doubt on our main analysis hence further validation studies are required.
In the primary analysis of MAPT, no significant effects of any of the interventions (multidomain lifestyle intervention + placebo; n-3 PUFA supplementation; multidomain lifestyle intervention + n-3 PUFA supplementation) were found on a composite cognitive score, compared to placebo alone, after adjustment for multiple testing (26). However, significantly less cognitive decline during follow-up was noted in the combined intervention group and in the multidomain intervention plus placebo group than in the placebo group in the subgroup of Aβ positive participants (26, 35). These findings suggest that multidomain intervention might work through the reduction of cerebral Aβ therefore providing indirect evidence to support to the main findings of the analysis presented here. Furthermore, there is a growing body of evidence to suggest that physical activity (4, 7, 8), cognitive activity (1, 2) and nutrition (5, 6) are independently associated with cerebral Aβ levels and thus collectively these elements might offer a synergistic effect on reducing cerebral Aβ.
In the short-term, analysis of existing longitudinal observational studies with data on cerebral Aβ in which two or more components of the MAPT multidomain lifestyle intervention could be operationalized might shed more light on our preliminary findings. Whilst, in the longer-term, our study specifically begs the question ‘Does multidomain lifestyle intervention reduce cortical Aβ?’. Further research in the form of a large RCT, in which cerebral Aβ is measured before and after the intervention, is required to respond to this question. Establishing the correct level of multidomain lifestyle intervention also remains to be determined. In terms of physical activity and cognitive training is sustained activity or activity of increasing difficulty required?  Which nutrients are more important for healthy aging, fats, specific vitamins or the correct dietary balance? Another important question to answer is: What is the best time window to administer a multi-domain intervention?  Cerebral Aβ accrual is believed to occur over a protracted period accounting for the long pro-dromal phase of AD (36); therefore, it is possible that mid-life interventions might be required to prevent future pathological changes. The duration of a lifestyle intervention is another important determinant of efficacy that requires investigation.
In conclusion we present here some evidence that  multidomain lifestyle intervention both with and without n-3 PUFA supplementation were similarly associated with less cortical Aβ in older adults at risk of dementia. Further validation studies are required to either support or refute our preliminary findings and to assess whether any relationships between multidomain interventions and cortical Aβ are causal.

 

Conflicts of Interest: The authors declare that they have no conflict of interest.

Sources of funding: “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, Avid Radiopharmaceuticals Inc and in part by a grant from the French National Agency for Research called “Investissements d’Avenir” n°ANR-11-LABX-0018-01. 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”.

Sponsor’s role: None.

MAPT/DSA Group refers to 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) (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: Bertrand Perret, Claire Vinel, Sylvie Caspar-Bauguil (Toulouse). Safety management : Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, Nicola Coley.

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SUPPLEMENTARY MATERIAL

 

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COGNITIVE PERFORMANCE DOES NOT LIMIT PHYSICAL ACTIVITY PARTICIPATION IN THE LIFESTYLE INTERVENTIONS AND INDEPENDENCE FOR ELDERS PILOT STUDY (LIFE-P)

K.F. Reid1, M.P. Walkup2, J.A. Katula3, K.M. Sink4, S. Anton5, R. Axtell6, D.R. Kerwin7, A.C. King8, A.F. Kramer9, M.E. Miller2, V. Myers10, C. Rosano11, S.A. Studenski12, O.L. Lopez13, J. Verghese14, R.A. Fielding1, J. Williamson4

1. Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; 2. Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston Salem, NC, USA; 3. Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC, USA; 4. Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University, Winston-Salem, NC, USA; 5. Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, USA; 6. Exercise Science Department, Southern Connecticut State University, New Haven, CT, USA; 7. Texas Alzheimer’s and Memory Disorders, Texas Health Presbyterian Hospital, Dallas, TX, USA; 8. Department of Health Research & Policy and Medicine, Stanford University School of Medicine, Stanford, CA, USA; 9. Department of Psychology, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA; 10. Klein Buendel, Inc., Golden, CO, USA; 11. Department of Epidemiology, School of Public Health, University of Pittsburgh, PA , USA; 12. Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA; 13. Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; 14. Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA

Corresponding Author: Kieran F. Reid, PhD, MPH, Nutrition, Exercise Physiology and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington Street, Boston, MA 02111, USA. Email: kieran.reid@tufts.edu

J Prev Alz Dis 2017;4(1):44-50
Published online August 9, 2016, http://dx.doi.org/10.14283/jpad.2016.107

 


Abstract

Objectives: We examined whether multiple domains of baseline cognitive performance were associated with prospective physical activity (PA) adherence in the Lifestyle Interventions and Independence for Elders Pilot study (LIFE-P).
Design, Setting, Participants: The LIFE-P study was a single-blind, multicenter, randomized controlled trial of a PA intervention compared to a successful aging educational intervention in sedentary, mobility-limited older adults.
Intervention: A 12-month structured, moderate-intensity, multi-modal PA program that included walking, resistance training, and flexibility exercises. For the first 2 months (adoption), 3 center-based exercise sessions (40–60 min) / week were conducted. During the next 4 months (transition), center-based sessions were conducted 2 times / week. The subsequent maintenance phase consisted of optional once-to-twice-per-week center-based sessions and home-based PA.
Measurements: Tests of executive and global cognitive functioning, working memory and psychomotor speed were administered at baseline. Median test scores were used to dichotomize participants into low or high cognitive performance groups.
Results: 52 mobility-limited older adults (age: 76.9 ±5 yrs) were randomized to the PA arm of LIFE-P. Compared to participants with high cognitive performance, participants with low performance had similar PA adherence rates (all P ≥ 0.34). Furthermore, weak and non-significant univariate relationships were elicited between all measures of cognition and overall PA adherence levels (r values ranged: -0.20 to 0.12, P ≥ 0.12).
Conclusion: These data suggest that cognitive performance does not limit long-term PA adherence in mobility-limited older adults. Additional studies in larger cohorts are warranted to verify these findings.

Key words: Cognition, physical activity, adherence, mobility-limited.


 

Introduction

Participation in regular physical activity is now recognized as a critical health behavior for the prevention and management of chronic disease among older adults (1-4). While a significant amount of research has been conducted to explore factors related to the adoption and maintenance of physical activity in middle-aged and younger adults, few studies have examined the factors that influence physical activity participation among adults aged 65 years and older (1, 5, 6). Furthermore, little is known about the major determinants of adherence to physical activity among older adults during interventions over prolonged durations (> 6 months).
The Lifestyle Interventions and Independence for Elders Pilot study (LIFE-P) was conducted to examine the feasibility of conducting a large multi-center clinical trial on the effects of increasing physical activity in sedentary, older individuals at risk for mobility disability (7). Independent factors previously shown to influence adherence to this long-term (12 month) physical activity intervention include chronic disease burden and self-reported symptoms of chronic disease (6, 8). The potential influence of baseline cognitive function on subsequent adherence to the LIFE-P physical activity intervention was not examined in either of these previous investigations. However, recent studies have shown that older adults with lower cognitive function (reduced executive functioning) were less adherent to a 3-month exercise-based cardiac rehabilitation program. Importantly, participants with low adherence had poorer outcomes following their exercise intervention (9). Similarly, Tiedemann et al. demonstrated that impaired global cognitive function (assessed using the Mini-Mental State Examination) was a significant independent predictor of low physical activity adherence during a 6-12 month intervention among older retirement village residents (10).
The purpose of this study was to investigate whether measures of baseline cognitive function predict subsequent adherence to the LIFE-P physical activity intervention (PA). Data were examined from the cognitive sub-study of LIFE-P and four domains of cognitive function (global cognition, executive functioning, psychomotor speed and working memory) were evaluated (11). We hypothesized that, in mobility-limited older adults, lower levels of cognitive performance would be associated with lower levels of subsequent PA adherence.

Methods

Study design

The LIFE-P study was a single-blind, multicenter, randomized controlled trial of a PA intervention compared to a successful aging (SA) educational intervention in sedentary older adults. The study was designed to help plan a definitive phase 3 randomized controlled trial to examine the efficacy of a program of physical activity, compared with SA, on the incidence of major mobility disability in older adults. Complete descriptions of the LIFE-P study design and the primary and cognitive outcomes have been reported previously (7, 11, 12).  Briefly, the study was conducted at four field centers across the United States (Cooper Institute, Stanford University, University of Pittsburgh, and Wake Forest University). The LIFE-P cognitive sub-study was conducted at Stanford University and Wake Forest University (11).

Study Participants

Participants were recruited in the age range of 70–89 years. Additional inclusion criteria included a sedentary life style (< 20 min/wk spent in structured PA), able to walk 400 m within 15 minutes without sitting and without use of any assistive device, and Short Physical Performance Battery (SPPB) score 9 or less (of 12). Participants with severe heart failure, uncontrolled angina, severe pulmonary disease, severe arthritis, cancer requiring treatment in the past 3 years, Parkinson’s disease or other serious neurological disorders, life expectancy of less than 12 months, or a Mini-Mental State Examination score less than 21, or a diagnosis of dementia, were ineligible.
Eligible participants received detailed instructions for a 1-week to 2-week behavioral run-in, during which they were asked to self-monitor specific behaviors and to complete forms related to these behaviors. Participants who successfully completed the behavioral run-in received additional baseline assessments and were randomized to the study interventions via a web-based system. Of the 3141 persons who were initially screened by phone, a total of 424 (13.5%) were ultimately randomized to LIFE-P across the four field centers. For the cognitive sub-study, the first 50 participants at the Stanford University and Wake Forest University field centers were administered a cognitive assessment battery at baseline.

Physical Activity Intervention

The PA intervention consisted of a combination of aerobic, strength, balance, and flexibility exercises. The intervention was divided into three phases: adoption (weeks 1–8), transition (weeks 9–24), and maintenance (week 25 to the end of the trial). Each participant in the PA group received a 45-minute individualized, introductory session to describe the intervention and to provide individual counseling to optimize safety and participation. For the first 2 months (adoption), three center-based exercise sessions (40–60 min) per week were conducted in a supervised setting. During the next 4 months (transition), the number of center-based sessions was reduced (2/week) and home-based endurance/strengthening/flexibility exercises (≥3/week) were started. The subsequent maintenance phase consisted of the home-based intervention, optional once-to-twice-per-week center-based sessions, and monthly telephone contacts. The PA intervention included group-based behavioral counseling sessions (1 each week for the first 10 weeks) that focused on PA participation and disability prevention, and on encouraging participants to increase all forms of PA.

Outcome Measures

Cognitive Assessment Battery

The assessment battery was adapted from the Action to Control Cardiovascular Risk in Diabetes (ACCORD)—Memory in Diabetes trial (13). This battery was developed specifically for the purpose of incorporating cognitive assessment as a secondary outcome in a large cardiovascular clinical trial (ACCORD). The LIFE study investigators selected the cognitive assessment battery based on the numerous domains of cognition likely to be affected by the LIFE-P intervention, in addition to experience gained from the ACCORD study on the feasibility of administering this battery in a large clinical trial. The cognitive battery consisted of four primary components:
Modified Mini-Mental State Examination (3MSE) is a widely used measure of global cognitive functioning (14). The 3MSE is an expanded 100-point version of the original Folstein Mini-Mental State Examination (15).
Modified Stroop test (Stroop) was utilized as a measure of processing speed, cognitive flexibility, and inhibition or disinhibition. The Stroop test consists of three subtasks: color word naming, color naming, and naming of color words printed in a different color from the color word (interference component). Participant’s score on this test is the difference between scores on tests 2 and 3.
Digit Symbol Substitution Test (DSST) was used as a measure of psychomotor speed and working memory (16). The DSST has proven to be feasible in aging studies and large multicenter clinical trials (17). Participants are given a series of numbered symbols and then asked to draw the appropriate symbols below a list of random numbers. The score is the number of correctly made matches in 1 minute.
The Rey Auditory Verbal Learning Test (RAVLT), a test of short- and long-term verbal memory, was used to assess the ability to learn a list of 15 common words(18). The study participant is read this list five times, and after each time, he or she immediately recalls as many words as possible. Following the fifth recall, an interference list is presented after which the participant is asked to spontaneously recall words from the original list. Then, a 10-minute interval passes and he or she is asked again to remember spontaneously as many words as possible from the first list (delayed recall). Scoring is based on total correct words across all components.

Measures of adherence

Attendance at center-based physical activity sessions was reported as the percentage of attended sessions relative to the total number of possible sessions in each study phase, excluding facility closings (e.g., holidays, weather emergencies, etc.). During maintenance, adherence was also assessed by completion of the home activity logs.

Statistical Analysis

Data analysis was performed using SAS statistical software (Version 9.4, SAS Institute Inc., Cary, North Carolina). Statistical significance was set at P ≤ 0.05 and results are reported as mean ± SD. Univariate correlation analyses (Pearson and Spearman’s rank) were performed to examine the associations between baseline measures of cognitive function and subsequent PA adherence. Separate analyses were conducted for each of the adoption, transition and maintenance phases of the intervention. In the adoption and transition phases, percent attendance per participant was calculated by dividing the number of sessions attended by the expected number of sessions. For the maintenance phase, the total number of sessions attended was used as the index of PA adherence. To further examine the influence of baseline cognition on subsequent PA adherence, independent samples T tests were used to determine whether group differences in PA adherence were evident for participants with low or high baseline cognition performance in the adoption, transition, and the average of adoption and transition phases of PA. The median test score on the respective cognitive assessment was used as the cut point to define low and high cognitive performance, and differences were adjusted for gender and site. Linear regression models were used to examine the relationships between baseline cognitive function and home log completion rates throughout PA.

Results

A total of 102 participants were administered the cognitive battery at the baseline examination in LIFE-P. From these participants a total of 52 participants were randomized to the PA intervention. The baseline characteristics are shown in Table 1.

Table 1. Baseline characteristics of PA participants (n = 52)

Table 1. Baseline characteristics of PA participants (n = 52)

Values are mean ± SD. BMI: body mass index; SPPB: Short Physical Performance Battery; 3MSE: Modified Mini-Mental State Examination; DSST: Digit Symbol Substitution Test; Stroop: Modified Stroop test; RAVLT: Rey Auditory Verbal Learning Test.

Results of the correlation analysis between the four domains of cognitive performance and subsequent level of PA adherence are provided in Table 2 and Figure 1. No significant relationships were found between any measure of cognitive function and subsequent level of PA adherence at any phase in LIFE-P (all P ≥ 0.12). Figure 1 displays scatterplots of the relationships between the four cognitive performance domains and PA adherence (combined for first 6 months of intervention).

 

Table 2. Correlation coefficients between domains of cognitive performance and subsequent adherence to PA during LIFE–P

^ = Spearman’s rank correlation coefficient 

Figure 1. Relationships between cognitive performance domains and PA adherence (combined for first 6 months of intervention)

^ = Spearman’s rank correlation coefficient

 

Table 3 compares group differences in subsequent PA adherence according to low or high baseline cognitive performance (participants dichotomized into groups based on median score on each measure). The median cut point scores for the four respective cognitive performance domains were as follows: 3MSE: 91.0; DSST: 48.0; Stroop: 33.0; RAVLT: 6.0. Compared to participants with high cognitive performance at baseline, lower cognitive performance was not associated with a significant reduction in PA adherence throughout the adoption or transition phases of the intervention (all P ≥ 0.34). Similarly, no association between any domain of cognitive function and the number of home logs completed was evident (data not shown).

Table 3. Baseline measures of cognitive performance (dichotomized into Low (n = 31) and High (n = 21) groups based on median score) vs. PA adherence

Values mean ± SD, P values (Low vs. High) are adjusted for clinical site and gender; ^Adoption phase of the intervention is weeks 1-8; ^Transition phase of the intervention is weeks 9-24; ^Combined is the adoption + transition (weeks 1-24)

Discussion

The major finding of this study is that cognitive performance did not limit the adoption of, and participation in, a 12 month intervention of multi-modal physical activity in LIFE-P. We demonstrated that, within this population of mobility-limited older adults with mild cognitive deficits, baseline cognitive performance assessed across multiple domains including executive functioning, global cognition and short and long term working memory, was not predictive of subsequent adherence to PA.
Although our primary observations were contrary to our initial hypothesis, there are important considerations associated with our investigation. Our null findings suggest that inherent aspects of the LIFE-P study design may have been important for limiting the potential influence of cognitive performance and subsequent participation in PA. In particular, the PA intervention was complimented with weekly closed-group behavioral counseling sessions that focused on physical activity adherence and the prevention of physical disability. Such behavioral group sessions have been shown to be effective for older adults in promoting commitment to physical activity and as a strategy to cope with the process of physical disablement (19). Previous studies that have identified cognitive function as a significant determinant of activity adherence in cardiac rehabilitation patients and among older persons in retirement villages did not include any additional behavioral counseling sessions to support physical activity adherence (9, 10). In addition, throughout LIFE-P, a Lifestyle Resource Core of behavioral scientists, geriatricians and exercise therapists closely monitored PA attendance, reviewed adherence issues and disseminated strategies to problem-solve and consistently promote strong participation in PA.  The use of participant proxy representatives in LIFE-P may have also diminished any overriding influence of cognitive performance deficits on subsequent PA attendance.
Several other factors could have influenced the present study findings. Due to the small sample size, the current analysis may have been underpowered to detect a meaningful relationship between baseline cognition and subsequent physical activity adherence. In addition, the 12 month duration of PA may not have been of sufficient duration for any potential baseline cognitive impairments to manifest into PA non-adherence. We also did not evaluate the influence of numerous covariates (e.g. education, dietary intake, comorbidities etc.) that may have influenced any potential association between cognitive performance and physical activity adherence in this study (20-22). In addition, previous studies have demonstrated that the relationship between cognition and physical activity adherence may be mediated through self-efficacy (23). The results of this study are also limited to older adults who successfully completed a behavioral run-in prior to study enrollment and were motivated to volunteer for a long-term randomized controlled trial comparing two distinct behavioral interventions. In addition, the external validity of our findings are further limited as we used distribution based cut-points, rather than established population-based norm values, to dichotomize participants into low vs. high cognitive performance groups.

Conclusion

The results of the study demonstrate that, among mobility limited older adults, cognitive performance does not limit the subsequent adoption of, and participation in, a year-long PA intervention (LIFE-P). Study design aspects, including inherent components of the PA intervention that targeted physical activity adherence, may have influenced any potential relationship between cognitive function and prospective activity adherence. These findings are both positive and encouraging as they demonstrate that older adults with mobility limitations, and some with coexisting cognitive performance deficits, can successfully adhere to a long-term multi modal exercise intervention. Additional investigations, in studies with larger sample sizes and longer durations of PA (24), are warranted to further examine cognitive function as a determinant of physical activity adherence in mobility-limited older adults.

Ethical Standards: This study was in accordance with the Declaration of Helsinki for human studies.  

Trial Registration: clinicaltrials.gov Identifier: NCT00116194.

 

Appendix. Research Investigators for Pilot Phase of LIFE

 

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