Daily Energy Intake Distribution and Cognitive Performance in Non-Demented Individuals
Dora BrikouSokratis CharisisArchontoula DroukaStavroula Myrto ChristodoulakouEva NtanasiEirini MamalakiVasilios C. ConstadinidesNikolaos ScarmeasMary Yannakoulia
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Cognitive disorders have become important public health issues around the world. Studies evaluating the association between cognitive decline and food timing are lacking. The objective of this study was to examine the potential association between energy intake distribution during the day and cognitive performance in cognitively healthy and mildly cognitive impaired individuals. Data were derived from the ongoing Albion study which includes people aged 40 years or older who have a positive family history of cognitive disorder or concern about their cognitive status. A thorough dietary and cognitive assessment was performed. Participants consuming low energy intake at the beginning of the day or high energy at the end of the day had higher cognitive function compared to participants characterized by the opposite pattern. This trend remained statistically significant even after adjustment for potential confounders (p = 0.043). This study suggests that individuals with worse cognitive function may choose to eat earlier during the day, when cognitive performance is better, and it might be hypothesized that a meal pattern characterized by high energy consumption at the beginning of the day or low energy at the end of the day could be a marker of cognitive impairment.Keywords:
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Background: This paper briefly summarizes recent evidence on the nature of cognitive decline, the variability in individual responses to ageing, and risk factors known to affect the rate of cognitive decline. Method: Data from the Canberra Longitudinal Study were used to examine the course and diversity of cognitive decline. The sample consisted of 887 participants aged 70–93 years, who were examined in 1991 and followed up in 1994 and 1998. Data summarizing significant risk factors for cognitive decline were based on a review of 34 studies that examined the following predictors: education, the APOE ∊4 allele, health, activity and blood pressure. Results: Using data from participants living in the community, there is evidence that cognitive speed and memory performance decline with age, but that crystallized abilities remain largely intact in those who survive for long-term follow-up. Variability in test scores for memory and speed increases with age. There is evidence that poor health, fewer years of education, lower activity, the presence of the APOE ∊4 allele, and blood pressure predict faster cognitive decline. Conclusions: The diversity in cognitive ageing suggests that more than one process may be operating to produce the observed cognitive outcomes. Education, good health, absence of the APOE ∊4 allele, and activity may be protective of cognitive decline. Preventative strategies for sustaining high intellectual performance in later life may therefore be possible. These findings also speak to the need to extend analysis beyond examining mean changes in cognitive performance to an analysis based on individual differences in change.
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Main Outcome Measures: The cognitive test battery was composed of tests of memory, vocabulary, executive function (composed of 1 reasoning and 2 fluency tests), and a global cognitive score summarizing performance across all 5 tests. Smoking status was assessed over the entire study period. Linear mixed models were used to assess the association between smoking history and 10-year cognitive decline, expressed as z scores. Results: In men, 10-year cognitive decline in all tests except vocabulary among never smokers ranged from a quarter to a third of the baseline standard deviation. Faster cognitive decline was observed among current smokers compared with never smokers in men (mean difference in 10-year decline in global cognition=−0.09 [95% CI, −0.15 to −0.03] and executive function=−0.11 [95% CI, −0.17 to −0.05]). Recent ex-smokers had greater decline in executive function (−0.08 [95% CI, −0.14 to −0.02]), while the decline in long-term ex-smokers was similar to that among never smokers. In analyses that additionally took dropout and death into account, these differences were 1.2 to 1.5 times larger. In women, cognitive decline did not vary as a function of smoking status.
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Abstract Background Recently, disturbed slow wave sleep (SWS) has been identified as an early, modifiable risk factor for dementia. SWS is crucial for memory and metabolic clearance functions, and lack of SWS causes these important functions to suffer, which in turn worsens cognitive decline. A vicious cycle forms between cognitive decline and loss of SWS. Improving SWS could be a way to break this cycle by providing the much‐needed opportunity for the brain to recuperate and ameliorate cognitive decline. We aim to enhance slow wave activity in older adults using phase‐locked auditory stimulation (PLAS). Our laboratory‐based results show that the magnitude of the physiological response to PLAS predicts improvement of memory functions and metabolic clearance. However, larger, long‐term studies in an ecologically valid setting are needed to assess the efficacy of PLAS for the improvement of sleep, memory, and metabolic clearance with the goal of preventing cognitive decline. Laboratory‐based studies quickly become economically and logistically unfeasible to achieve this goal. Method We propose a blinded, sham‐controlled cross‐over study utilizing home‐use devices to study the effect of PLAS on memory functions in 60 older adults with MCI or SCD. Participants will undergo verum‐ and sham‐PLAS in the comfort of their own home across a 12‐week study period. Cognitive performance will be assessed using engaging “serious games”, and blood will be sampled before‐ and after each experimental period to test levels of dementia‐related biomarkers (amyloid beta, pTau181 and 217, GFAP and NfL) and their response to PLAS. Result We expect PLAS to enhance sleep, which will lead to down‐stream effects on memory performance and metabolic clearance. Using a novel approach allowing brain‐age estimation from sleep‐electrophysiology, we hypothesize to see a “rejuvenating” effect of PLAS, restoring an electrophysiological profile typically seen in younger brains. Conclusion This study could pave the way for PLAS‐capable home‐use devices as an affordable, non‐invasive tool to combat cognitive decline, and could lead to novel preventative applications for memory clinics, relieving their clinical burden and improving public health.
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The aim of this article is to review the association between diabetes mellitus, cognitive decline and dementia, including the effects of cognitive decline and dementia on self management of diabetes. This is a literature review of primary research articles. A number of contemporary research articles that met the inclusion criteria were selected for this review paper. These articles were selected using a number of search strategies and electronic databases, such as EBSCOhost Research and SwetsWise databases. The duration of diabetes, glycated haemoglobin levels and glycaemic fluctuations were associated with cognitive decline and dementia. Similarly, hypoglycaemia was significantly related to increased risk of developing cognitive decline and dementia. Furthermore, cognitive decline and dementia were associated with poorer diabetes management. There is evidence of the association between diabetes, cognitive decline and dementia including the shared pathogenesis between diabetes and Alzheimer’s disease. In addition, the self management of diabetes is affected by dementia and cognitive decline. It could be suggested that the association between diabetes and dementia is bidirectional with the potential to proceed to a vicious cycle. Further studies are needed in order to fully establish the relationship between diabetes, cognitive decline and dementia. Patients who have diabetes and dementia could benefit from structured education strategies, which should involve empowerment programmes and lifestyle changes. The detection of cognitive decline should highlight the need for education strategies.
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Abstract Background Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. This study evaluated a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. Methods 99 participants diagnosed with Dementia, Mild Cognitive Impairment (MCI), or Normal Cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. Results BrainCheck battery performance showed significant differences between the NC, MCI, and Dementia groups, achieving ≥88% sensitivity/specificity for separating NC from Dementia, and ≥77% sensitivity/specificity in separating the MCI group from NC/Dementia groups. Three-group classification found true positive rates ≥80% for the NC and Dementia groups and ≥64% for the MCI group. Conclusions BrainCheck was able to distinguish between diagnoses of Dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.
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<b><i>Introduction:</i></b> Early markers of neurodegeneration provide an opportunity to detect, monitor, and initiate interventions in individuals who have an increased risk of developing dementia. Here, we investigated whether the Timed Up and Go (TUG) test is associated with early brain neurodegeneration and whether the TUG test could be a marker of cognitive decline in people with subjective cognitive decline (SCD). <b><i>Methods:</i></b> This is a longitudinal analysis of the Dementia Disease Initiation Study, a prospective, community-based, cohort study from Norway, designed to investigate early markers of cognitive impairment and dementia. Participants were classified as SCD and healthy controls (HC). The main studied variables were the TUG test and cognition as measured by the Mini-Mental State Examination and the Consortium to Establish a Registry for Alzheimer’s Disease memory composite score. Additionally, we investigated the cross-sectional association of brain morphology with the TUG using 1.5T-MRI. <b><i>Results:</i></b> The sample included 45 participants (SCD = 21, HC = 24) followed during a mean time of 1.50 ± 0.70 years. At baseline, the cognitive performance did not differ between the groups, but TUG was longer in SCD. Slower baseline TUG was associated with a faster cognitive decline in both groups and it was also associated with reduced cortical thickness especially in motor, executive, associative, and somatosensory cortical regions in people with SCD. <b><i>Discussion/Conclusion:</i></b> TUG predicted cognitive change in individuals with SCD, and there was a negative association between TUG and cortical thickness. TUG is a promising cheap and noninvasive marker of early cognitive decline and may help initiate interventions in individuals who have an increased risk of dementia.
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Reports of cognitive decline associated with b-amyloid (Ab) pathology in cognitively normal individuals vary widely. This variation may be explained by differences in study design, length of follow-up, choice of cognitive tests, geographic region, and sampling variation. In this study, we aimed to identify the magnitude and precision of expected cognitive decline in preclinical AD and the study design features that exert influence over the trajectory of cognitive changes. In 1120 cognitively normal individuals from three multicenter studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) in North America, the Australian Imaging, Biomarker & Lifestyle Study (AIBL), and the Swedish BioFINDER study (BioFINDER), we estimated the effect of Ab pathology on decline over a spectrum of cognitive domains. Despite considerable study differences in terms of demographics, recruitment, follow-up schedule, and neuropsychological test battery, the magnitude of the differences in decline between Ab groups was consistent for the cognitive composite (PACC, the Preclinical Alzheimer Cognitive Composite) (Figure 1). To achieve 80% power with 800 subjects per arm in a simulated 4-year treatment trial in preclinical AD, estimates of the required drug effect ranged from 34% to 50%. Ab+ groups declined significantly faster on all cognitive domains in all cohorts compared to the Ab- groups (Figure 2, the only exception was Trails B in BioFINDER). Several baseline factors interacted significantly with Ab to predict cognitive decline including APOE e4-positivity, baseline cognition, and education in AIBL; age in BioFINDER; and sex in ADNI, however these interactions were cohort specific. On average, Ab+ subjects performed similarly to early MCI patients on cognitive tests 6 years after baseline.
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