and over, fifty-four participants aged 60 to over under 70 were included in the study. Subjects with dementia or major mental disorders were excluded. We assessed cognitive functions using the Korean version of the Consortium to Establish a Registry for Alzheimer Disease neuropsychological battery (CERAD-NP), digit span forward (DS-F), digit span backward (DS-B) and the Frontal Assessment Battery (FAB) within 24 hours after quitting. The score of 1 or more standard deviation below appropriate norms of each test was classified as impairment. Results: All participants were male, and the mean age of each group was 45.962.0 years, 54.263.1 years and 64.662.8 years. There was no difference of the daily smoking amount (mean number ¼ 21.668.2) and urinary cotinine levels at the beginning of smoking cessation (mean ¼ 3491.2 6 2743.4 ng/ml) between the age groups. Impairment of the DS-B (18.2%) and constructional recall (18.2%) were the most frequent, followed by the impairment of word list recall (12.2%), constructional praxis (11.5%), and verbal fluency (10.7%). Among them, abnormalities of constructional recall, word list recall, and constructional praxis were different according to the age group and these impairments were more frequent as the age increased. Digit backward abnormality was seen in all age group without group differences. Conclusions: The effect of varenicline on the nicotine-withdrawal cognitive impairment may vary depending on subgroups, such as age. The future research is needed to understand the underlying mechanisms and improve the nicotine withdrawal-related cognitive deficits in treatment-seeking smokers of various age groups.
Background Alzheimer's disease (AD) is a neurodegenerative disorder, clinically defined by a progressive loss of memory and other cognitive and functional abilities. One of the most studied phases in the prognosis of AD is the Mild cognitive impairment (MCI) since it entails a higher risk of developing this type of dementia. The majority longitudinal studies from MCI to AD utilize both a reduce number of potential prediction markers and a shorten length of follow‐up. Therefore, the present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological (i.e. magnetoencephalography, MEG), and neuroanatomical (i.e. magnetic resonance imaging (MRI) volumetry) factors may predict differences in time to progression from MCI to AD during an extended follow‐up. Method To this end, a sample of 121 MCIs was followed‐up during a 5‐years period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the “progressive” MCI (pMCI; n= 46); and (ii) the “stable” MCI group (sMCI; n= 75). Kaplan‐Meier survival analyses were applied to explore each variable’s relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model that may allow the estimation of differences in time to progression. Result Results indicated that the final model included three variables (in order of relevance): Left parahippocampal volume (corrected by intracranial volume, LP_ ICV), Delayed recall (DR), and Left Inferior Occipital lobe individual alpha peak frequency (LIOL_IAF). Those MCIs with LP_ ICV volume, DR score and LIOL_IAPF value lower than the defined cutoff had 6‐times, 5.5‐times and 3‐times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were “unfavourable” (i.e. values below the cutoff), a 100% of cases progressed to AD at the end of follow‐ up, while a combination of “favourable” categories yielded a 94.7% of stable cases at the end of follow‐up. Conclusion Our results highlighted the relevance of neurophysiological markers as predictors of conversion, and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.
Background Physical activity (PA) has been identified as one of the most relevant modifiable lifestyle factors against Alzheimer's disease (AD). Magnetoencephalography has proven to be a useful tool to predict future risk of AD. In particular, decreased power in the alpha band has been associated with pathological aging. We had already reported how PA was significantly associated with higher alpha peak frequencies in older adults, especially among those at standard genetic risk for AD (APOE ε4 non‐carriers). Recent literature suggest that age could play an important role in the emergence of these relationships, a role that has not been explored yet. Method We used a sample of 112 cognitively normal individuals aging <50 who had worn an accelerometer for one week, divided into four carefully matched groups: younger adults at increased risk (age <60;APOE ε3ε4;n=20), older adults at increased risk (age >60;APOE ε3ε4;n=15), younger adults at standard risk (age <60;APOE ε3ε3;n=44) and older adults at standard risk (age>60;APOE ε3ε3;n=33). Each subject underwent four minutes of closed eyes resting state MEG recording. The aim was to detect any robust correlation between power values derived from clusters of nodes localized in certain brain regions and TPA, using network‐based statistics. Clusters consisted of several adjacent nodes, which systematically showed a significant partial correlation (with age as covariate) in at least 3 consecutive frequency steps between their corresponding power values and TPA (spearman correlation p‐value<0.01). Result A significant cluster was found in the frequency interval 10.75–13Hz involving mainly posterior brain regions (rho=0.360;p=0.0001;see Figure 1/Table 1). This correlation remained significant for both ε3ε3 carriers (rho=0.326;p=0.004;n=77;) and ε3ε4 carriers (rho=0.442;p=0.007;n=35;see Figure 2). It also remained significant for younger adults (rho=0.456;p>0.001) but not for older adults (Figure 3). Interestingly, when looking at the four groups individually, only younger ε4 non‐carriers (rho=0.487;p>0.001) and older ε4 carriers (rho=0.603;p=0.013) show this association. Conclusion Greater PA seems to be associated with increased alpha power in posterior brain regions, a robust biomarker of brain health which is negatively affected in AD. However, the time window when this association can be observed seems to differ between ε4 carriers and non‐carriers.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.