Body mass index predicts cognitive aging trajectories selectively for females: Evidence from the Victoria Longitudinal Study

Frailty in motion: Amnestic mild cognitive impairment and Alzheimer's disease cohorts display heterogeneity in multimorbidity classification and longitudinal transitions

BACKGROUND

Data-driven examination of multiple morbidities and deficits are informative for clinical and research applications in aging and dementia. Resulting profiles may change longitudinally according to dynamic alterations in extent, duration, and pattern of risk accumulation. Do such frailty-related changes include not only progression but also stability and reversion?

OBJECTIVE

With cognitively impaired and dementia cohorts, we employed data-driven analytics to (a) detect the extent of heterogeneity in frailty-related multimorbidity and deficit burden subgroups and (b) identify key person characteristics predicting differential transition patterns.

METHODS

We assembled baseline and 2-year follow-up data from the National Alzheimer's Coordinating Center for amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) cohorts. We applied factor analyses to 43 multimorbidity and deficit indicators. Latent Transition Analysis (LTA) was applied to the resulting domains in order to detect subgroups differing in transition patterns for multimorbidity and deficit burden. We characterized heterogeneity in change patterns by evaluating key person characteristics as differential predictors.

RESULTS

Factor analyses revealed five domains at two time points. LTA showed that two latent burden subgroups at Time 1 (Low, Moderate) differentiated into an additional two subgroups at Time 2 (adding Mild, Severe). Transition analyses detected heterogeneous changes, including progression, stability, and reversion. Baseline classifications and transitions varied according to clinical cohort, global cognition, sex, age, and education.

CONCLUSIONS

Heterogeneous frailty-related subgroup transitions can be (a) detected in aging adults living with aMCI and AD, (b) characterized as not only progression but also stability and reversion, and (c) predicted by precision characteristics.

Leptin receptor reactivation restores brain function in early-life Lepr-deficient mice

Obesity is a chronic disease caused by excessive fat accumulation that impacts the body and brain health. Insufficient leptin or leptin receptor (LepR) is involved in the disease pathogenesis. Leptin is involved with several neurological processes, and it has crucial developmental roles. We have previously demonstrated that leptin deficiency in early life leads to permanent developmental problems in young adult mice, including an imbalance in energy homeostasis, alterations in melanocortin and the reproductive system and a reduction in brain mass. Given that in humans, obesity has been associated with brain atrophy and cognitive impairment, it is important to determine the long-term consequences of early-life leptin deficiency on brain structure and memory function. Here, we demonstrate that leptin-deficient (LepOb) mice exhibit altered brain volume, decreased neurogenesis and memory impairment. Similar effects were observed in animals that do not express the LepR (LepRNull). Interestingly, restoring the expression of LepR in 10-week-old mice reverses brain atrophy, in addition to neurogenesis and memory impairments in older animals. Our findings indicate that leptin deficiency impairs brain development and memory, which are reversible by restoring leptin signalling in adulthood.

Sex difference in the association between BMI and cognitive impairment in Chinese older adults

BACKGROUND

The association between body mass index (BMI) and cognitive impairment (CI) has been the subject of extensive research, yet the precise dose-response effects remain undefined.

METHODS

Older adults were selected from the 2011/2012 survey at baseline and the new recruits from the 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Multiple logistic regression models were used to evaluate the association between BMI categories and CI, and Restricted Cubic Spline (RCS) was used to explore the nonlinear relationship between BMI and CI.

RESULTS

The study included 29,380 older adults aged from 65 to 117 years, with an average age of 82 years. Of these, 13,465 were men, and 5359 exhibited cognitive impairment. The logistic model indicated that in female participants, being underweight was positively correlated with CI (OR:1.32; 95%CI 1.20-1.46), whereas being overweight was inversely correlated with CI (OR:0.86; 95%CI 0.75-0.99), and we didn't find any association between BMI category and CI in male participants. RCS modeling revealed a U-shaped relationship between BMI and CI. When stratified by sex, men exhibited a similar trend, with the lowest risk at a BMI of 22.774 kg/ m2, while women had the lowest risk of CI at a BMI of 24.817 kg/ m2.

LIMITATION

This was a cross-sectional study, it cannot provide information on causal relationships.

CONCLUSION

A U-shaped relationship was observed between BMI and CI in older adults, more pronounced in the male population, suggesting that male older adults may need to manage their BMI more rigorously.

Cognitive Speed in Neurodegenerative Disease: Comparing Mean Rate and Inconsistency Within and Across the Alzheimer's and Lewy Body Spectra in the COMPASS-ND Study

Background

Alzheimer's disease (AD) and Lewy body disease (LBD) are characterized by early and gradual worsening perturbations in speeded cognitive responses.

Objective

Using simple and choice reaction time tasks, we compared two indicators of cognitive speed within and across the AD and LBD spectra: mean rate (average reaction time across trials) and inconsistency (within person variability).

Methods

The AD spectrum cohorts included subjective cognitive impairment (SCI, n = 28), mild cognitive impairment (MCI, n = 121), and AD (n = 45) participants. The LBD spectrum included Parkinson's disease (PD, n = 32), mild cognitive impairment in PD (PD-MCI, n = 21), and LBD (n = 18) participants. A cognitively unimpaired (CU, n = 39) cohort served as common benchmark. We conducted multivariate analyses of variance and discrimination analyses.

Results

Within the AD spectrum, the AD cohort was slower and more inconsistent than the CU, SCI, and MCI cohorts. The MCI cohort was slower than the CU cohort. Within the LBD spectrum, the LBD cohort was slower and more inconsistent than the CU, PD, and PD-MCI cohorts. The PD-MCI cohort was slower than the CU and PD cohorts. In cross-spectra (corresponding cohort) comparisons, the LBD cohort was slower and more inconsistent than the AD cohort. The PD-MCI cohort was slower than the MCI cohort. Discrimination analyses clarified the group difference patterns.

Conclusions

For both speed tasks, mean rate and inconsistency demonstrated similar sensitivity to spectra-related comparisons. Both dementia cohorts were slower and more inconsistent than each of their respective non-dementia cohorts.

Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function

Introduction

Neuroimaging-based 'brain age' can identify individuals with 'advanced' or 'resilient' brain aging. Brain-predicted age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unknown how individual health and lifestyle factors may modify the relationship between brain-PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain-PAD, and determine if membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years.

Methods

Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) trial. Participants were grouped as having resilient (n=159) or advanced (n=167) brain-PAD, and latent class analysis (LCA) was performed using a set of cognitive, lifestyle, and health measures. We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex and education.

Results

Subgroups of resilient and advanced brain aging were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 were characterized by low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient brain agers, class 1 was associated with a decrease in cognition, and class 2 with an increase over 5 years, though was a small effect that was equivalent to a 0.04 standard deviation difference per year. No significant class distinctions were evident with FI. For advanced brain agers, there was no evidence of an association between class membership and changes in cognition or FI.

Conclusion

These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. In particular, people with age-resilient brains had different trajectories of cognitive change depending on their cognitive and physical health status at baseline. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple lifestyle and health indices alongside brain age.

Weight change across adulthood and accelerated biological aging in middle-aged and older adults

BACKGROUND

Little is known regarding the association between weight change and accelerated aging.

OBJECTIVES

This study aimed to estimate the influence of weight change across adulthood on biological aging acceleration in middle-aged and older adults in the United States.

METHODS

We used data of 5553 adults (40-84 y) from the National Health and Nutrition Examination Survey 1999-2010. Weight change patterns (i.e., stable normal, maximal overweight, obese to nonobese, nonobese to obese, and stable obese) and absolute weight change groups across adulthood (i.e., from young to middle adulthood, young to late adulthood, and middle to late adulthood) were defined. A biological aging measure (i.e., phenotypic age acceleration [PhenoAgeAccel]) at late adulthood was calculated. Survey analysis procedures with the survey weights were performed.

RESULTS

Across adulthood, maximal overweight, nonobese to obese, and stable obesity were consistently associated with higher PhenoAgeAccel. For instance, from young to middle adulthood, compared with participants who had stable normal weight, participants experiencing maximal overweight, moving from the nonobese to obese, and maintaining obesity had 1.71 (standard error [SE], 0.21; P < 0.001), 3.62 (SE, 0.28; P < 0.001), and 6.61 (SE, 0.58; P < 0.001) higher PhenoAgeAccel values, respectively. From young to middle adulthood, relative to absolute weight loss or gain of <2.5 kg, weight loss of ≥2.5 kg was marginally associated with lower PhenoAgeAccel (P = 0.054), whereas an obese to nonobese pattern from middle to late adulthood was associated with higher PhenoAgeAccel (P < 0.001).

CONCLUSIONS

Maximal overweight, nonobese to obese, and stable obesity across adulthood, as well as an obese to nonobese pattern from middle to late adulthood, were associated with accelerated biological aging. In contrast, weight loss from young to middle adulthood was associated with decelerated biological aging. The findings highlight the potential role of weight management across adulthood for aging. Monitoring weight fluctuation may help identify the population at high risk of accelerated aging.

Association of Psychological distress and Physical Health with Subjective and Objective Memory in Older Adults

ObjectivesTo investigate how indicators of psychological stress and physical health differentially influence subjective and objective memory in older adults. Methods: 404 adults aged ≥55 without cognitive impairment participated in remote assessment of physical health (PHY; multimorbidity, body-mass-index), psychological distress (PDS; perceived stress, anxiety, depression), subjective memory complaints (SM), and task-based objective memory performance (OM). Results: Separately, both PHY and PDS significantly predicted SM (p < 0.01), but only PHY was associated with OM (p = 0.05). Combined models showed that PHY and PDS maintained significant association with SM (p < 0.01, R² = 0.30), while only PHY was associated with OM (p = .07, R² = 0.03; for associative OM, p = 0.04). Discussion: SM is associated with participants' psychological profile, highlighting the importance of addressing these factors when assessing SM. The results also reveal that remotely-administered OM tasks are more immune to participants' psychological profile, and support previously-established links between physical health and objective and subjective memory function.

The Molecular Basis of Spinocerebellar Ataxia Type 7

Spinocerebellar ataxia (SCA) type 7 (SCA7) is caused by a CAG trinucleotide repeat expansion in the ataxin 7 (ATXN7) gene, which results in polyglutamine expansion at the amino terminus of the ATXN7 protein. Although ATXN7 is expressed widely, the best characterized symptoms of SCA7 are remarkably tissue specific, including blindness and degeneration of the brain and spinal cord. While it is well established that ATXN7 functions as a subunit of the Spt Ada Gcn5 acetyltransferase (SAGA) chromatin modifying complex, the mechanisms underlying SCA7 remain elusive. Here, we review the symptoms of SCA7 and examine functions of ATXN7 that may provide further insights into its pathogenesis. We also examine phenotypes associated with polyglutamine expanded ATXN7 that are not considered symptoms of SCA7.

Body Mass Index and Memory Across 18 Years in the Wisconsin Longitudinal Study

BACKGROUND

Body weight is a modifiable risk factor for dementia, but results have been mixed as to the ages at which normal body weight is markedly preferable to overweight or obesity. This study assessed the association between change in body mass index (BMI) over 2 periods of the life course with change in memory between the ages of 65 and 72 for males and females.

METHODS

Participants were 3 637 White high school graduates, born in 1939, from the Wisconsin Longitudinal Study. The statistical analyses were fixed-effects regression models, with moderation terms to test for sex differences. One set of models examined change in BMI between ages 54 and 65, and the other set examined change in BMI between ages 65 and 72. In both cases, cognitive change occurred between ages 65 and 72.

RESULTS

Greater increases in BMI were associated with a greater decline in immediate recall for females only, both contemporaneously and following a lag. Increases in BMI were associated with greater contemporaneous-but not lagged-declines in both delayed recall and digit ordering for both males and females.

CONCLUSIONS

The present study adds to the evidence that for White, high school educated Americans, weight gain in midlife and young-old age is a risk factor for memory decline. Results vary according to the timing of the weight gain, the aspect of memory measured, and participant sex.

Risk Factors, Lifestyle Behaviors, and Vascular Brain Health

Although a relationship between traditional cardiovascular risk factors and stroke has long been recognized, these risk factors likely play a role in other aspects of brain health. Clinical stroke is only the tip of the iceberg of vascular brain injury that includes covert infarcts, white matter hyperintensities, and microbleeds. Furthermore, an individual's risk for not only stroke but poor brain health includes not only these traditional vascular risk factors but also lifestyle and genetic factors. The purpose of this narrative review is to summarize the state of the evidence on traditional and nontraditional vascular risk factors and their contributions to brain health. Additionally, we will review important modifiers that interact with these risk factors to increase, or, in some cases, reduce risk of adverse brain health outcomes, with an emphasis on genes and biomarkers associated with Alzheimer disease. Finally, we will consider the importance of social determinants of health in brain health outcomes.

Sex-Specific Patterns of Body Mass Index Relationship with White Matter Connectivity

BACKGROUND

Obesity is an increasingly recognized modifiable risk factor for Alzheimer's disease (AD). Increased body mass index (BMI) is related to distinct changes in white matter (WM) fiber density and connectivity.

OBJECTIVE

We investigated whether sex differentially affects the relationship between BMI and WM structural connectivity.

METHODS

A cross-sectional sample of 231 cognitively normal participants were enrolled from the Knight Alzheimer Disease Research Center. Connectome analyses were done with diffusion data reconstructed using q-space diffeomorphic reconstruction to obtain the spin distribution function and tracts were selected using a deterministic fiber tracking algorithm.

RESULTS

We identified an inverse relationship between higher BMI and lower connectivity in the associational fibers of the temporal lobe in overweight and obese men. Normal to overweight women showed a significant positive association between BMI and connectivity in a wide array of WM fibers, an association that reversed in obese and morbidly obese women. Interaction analyses revealed that with increasing BMI, women showed higher WM connectivity in the bilateral frontoparietal and parahippocampal parts of the cingulum, while men showed lower connectivity in right sided corticostriatal and corticopontine tracts. Subgroup analyses demonstrated comparable results in participants with and without positron emission tomography or cerebrospinal fluid evidence of brain amyloidosis, indicating that the relationship between BMI and structural connectivity in men and women is independent of AD biomarker status.

CONCLUSION

BMI influences structural connectivity of WM differently in men and women across BMI categories and this relationship does not vary as a function of preclinical AD.

Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes

BACKGROUND

Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.

OBJECTIVE

To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.

METHODS

We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.

RESULTS

For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.

CONCLUSION

Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.

Data-driven approaches to executive function performance and structure in aging: Integrating person-centered analyses and machine learning risk prediction

Objective: Executive function (EF) performance and structure in nondemented aging are frequently examined with variable-centered approaches. Person-centered analytics can contribute unique information about classes of persons by simultaneously considering EF performance and structure. The risk predictors of these classes can then be determined by machine learning technology. Using data from the Victoria Longitudinal Study we examined two goals: (a) detect different underlying subgroups (or classes) of EF performance and structure and (b) test multiple risk predictors for best discrimination of these detected subgroups. Method: We used a classification sample (n = 778; M_age = 71.42) for the first goal and a prediction subsample (n = 570; M_age = 70.10) for the second goal. Eight neuropsychological measures represented three EF dimensions (inhibition, updating, shifting). Fifteen predictors represented five domains (genetic, functional, lifestyle, mobility, demographic). Results: First, we observed two distinct classes: (a) lower EF performance and unidimensional structure (Class 1) and (b) higher EF performance and multidimensional structure (Class 2). Second, Class 2 was predicted by younger age, more novel cognitive activity, more education, lower body mass index, lower pulse pressure, female sex, faster balance, and more physical activity. Conclusions: Data-driven modeling approaches tested the possibility of an EF aging class that displayed both preserved EF performance levels and sustained multidimensional structure. The two observed classes differed in both performance level (lower, higher) and structure (unidimensional, multidimensional). Machine learning prediction analyses showed that the higher performing and multidimensional class was associated with multiple brain health-related protective factors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging

Background: Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

The dynamic association between body mass index and cognition from midlife through late-life, and the effect of sex and genetic influences

Body mass index (BMI) is associated with cognitive abilities, but the nature of the relationship remains largely unexplored. We aimed to investigate the bidirectional relationship from midlife through late-life, while considering sex differences and genetic predisposition to higher BMI. We used data from 23,892 individuals of European ancestry from the Health and Retirement Study, with longitudinal data on BMI and three established cognitive indices: mental status, episodic memory, and their sum, called total cognition. To investigate the dynamic relationship between BMI and cognitive abilities, we applied dual change score models of change from age 50 through 89, with a breakpoint at age 65 or 70. Models were further stratified by sex and genetic predisposition to higher BMI using tertiles of a polygenic score for BMI (PGSBMI). We demonstrated bidirectional effects between BMI and all three cognitive indices, with higher BMI contributing to steeper decline in cognitive abilities in both midlife and late-life, and higher cognitive abilities contributing to less decline in BMI in late-life. The effects of BMI on change in cognitive abilities were more evident in men compared to women, and among those in the lowest tertile of the PGSBMI compared to those in the highest tertile, while the effects of cognition on BMI were similar across groups. In conclusion, these findings highlight a reciprocal relationship between BMI and cognitive abilities, indicating that the negative effects of a higher BMI persist from midlife through late-life, and that weight-loss in late-life may be driven by cognitive decline.

Portals to frailty? Data-driven analyses detect early frailty profiles

BACKGROUND

Frailty is an aging condition that reflects multisystem decline and an increased risk for adverse outcomes, including differential cognitive decline and impairment. Two prominent approaches for measuring frailty are the frailty phenotype and the frailty index. We explored a complementary data-driven approach for frailty assessment that could detect early frailty profiles (or subtypes) in relatively healthy older adults. Specifically, we tested whether (1) modalities of early frailty profiles could be empirically determined, (2) the extracted profiles were differentially related to longitudinal cognitive decline, and (3) the profile and prediction patterns were robust for males and females.

METHODS

Participants (n = 649; M age = 70.61, range 53-95) were community-dwelling older adults from the Victoria Longitudinal Study who contributed data for baseline multi-morbidity assessment and longitudinal cognitive trajectory analyses. An exploratory factor analysis on 50 multi-morbidity items produced 7 separable health domains. The proportion of deficits in each domain was calculated and used as continuous indicators in a data-driven latent profile analysis (LPA). We subsequently examined how frailty profiles related to the level and rate of change in a latent neurocognitive speed variable.

RESULTS

LPA results distinguished three profiles: not-clinically-frail (NCF; characterized by limited impairment across indicators; 84%), mobility-type frailty (MTF; characterized by impaired mobility function; 9%), and respiratory-type frailty (RTF; characterized by impaired respiratory function; 7%). These profiles showed differential neurocognitive slowing, such that MTF was associated with the steepest decline, followed by RTF, and then NCF. The baseline frailty index scores were the highest for MTF and RTF and increased over time. All observations were robust across sex.

CONCLUSIONS

A data-driven approach to early frailty assessment detected differentiable profiles that may be characterized as morbidity-intensive portals into broader and chronic frailty. Early inventions targeting mobility or respiratory deficits may have positive downstream effects on frailty progression and cognitive decline.

Effects of Gene and Plasma Tau on Cognitive Impairment in Rural Chinese Population

BACKGROUND

Sufficient attention was not paid to the effects of microtubule-associated protein tau (MAPT) and plasma tau protein on cognition.

OBJECTIVE

A total of 3072 people in rural China were recruited. They were provided with questionnaires, and blood samples were obtained.

METHODS

The MMSE score was used to divide the population into cognitive impairment group and control group. First, logistic regression analysis was used to explore the possible factors influencing cognitive function. Second, 1837 samples were selected for SNP detection through stratified sampling. Third, 288 samples were selected to test three plasma biomarkers (tau, phosphorylated tau, and Aβ-42).

RESULTS

For the MAPT rs242557, people with AG genotypes were 1.32 times more likely to develop cognitive impairment than those with AA genotypes, and people with GG genotypes were 1.47 times more likely to develop cognitive impairment than those with AG phenotypes. The plasma tau protein concentration was also increased in the population carrying G (P = 0.020). The plasma tau protein was negatively correlated with the MMSE score (P = 0.004).

CONCLUSION

The mutation of MAPT rs242557 (A > G) increased the risk of cognitive impairment and the concentration of plasma tau protein.

Neurofilament light plasma concentration positively associates with age and negatively associates with weight and height in the dog

Plasma neurofilament light chain (pNfL) concentration is a biomarker for neuroaxonal injury and degeneration and can be used to monitor response to treatment. Spontaneous canine neurodegenerative diseases are a valuable comparative resource for understanding similar human conditions and as large animal treatment models. The features of pNfL concentration in healthy dogs is not well established. We present data reporting basic pNfL concentration trends in the Labrador Retriever breed. Fifty-five Labrador Retrievers were enrolled. pNfL concentration was measured and correlated to age, sex, neuter status, height, weight, body mass index, and coat color. We found increased pNfL with age (P < 0.0001), shorter stature (P = 0.009) and decreased body weight (P < 0.001). These are similar to findings reported in humans. pNfL concentration did not correlate with sex, BMI or coat color. This data further supports findings that pNfL increase with age in a canine population but highlights a need to consider weight and height when determining normal pNfL concentration in canine populations.

Age- and Sex-Specific Prevalence and Modifiable Risk Factors of Mild Cognitive Impairment Among Older Adults in China: A Population-Based Observational Study

Background

Minimal data are available on the prevalence of mild cognitive impairment (MCI) in older Chinese adults. Moreover, the current information on MCI shows important geographical variations.

Objective

We aimed to assess the prevalence and risk factors for MCI by age and sex among older adults in a North Chinese population.

Methods

In this population-based cross-sectional study, we enrolled a random sample of 4,943 adults aged ≥ 60 years between March 2018 and June 2019 in Tianjin, China. Of these, 312 individuals were excluded due to a lack of data (e.g., fasting blood test). As a result, 4,631 subjects were assessed. Individuals with MCI were identified using neuropsychological assessments, including the Mini-Mental State Examination and Activities of Daily Living scale, based on a modified version of the Petersen’s criteria.

Results

The mean (SD) age of the 4,631 participants was 67.6 (4.89) years, and 2,579 (55.7%) were female. The overall age- and sex-standardized prevalence of MCI in our study population was 10.7%. There were significant associations of MCI with age [65–69 vs. 60–64 years, OR = 0.74; 95% confidence interval (CI): 0.58, 0.96], physical activity (≥23.0 vs. <23.0 MET-hours/week, OR = 0.79; 95% CI: 0.64, 0.96), body mass index (BMI) (OR = 0.92; 95% CI: 0.89, 0.95), grip strength (OR = 0.50; 95% CI: 0.38, 0.67), hypertension (yes vs. no, OR = 1.44; 95% CI: 1.18, 1.77), higher levels of sleepiness (OR = 1.80; 95% CI: 1.36, 2.37), and longer sleep duration (OR = 1.40; 95% CI: 1.14, 1.72). The inverse association between BMI and MCI was stronger in older age groups (P for heterogeneity = 0.003). Moreover, the magnitude of association between triglycerides and MCI was different between the sexes (P for heterogeneity = 0.029).

Conclusion

The age- and sex-standardized prevalence of MCI was 10.7% in the study sample. Physical activity, BMI, grip strength, sleepiness, sleep duration, and hypertension were associated with the prevalence of MCI. Additionally, triglycerides and BMI might be differently associated with the presence of MCI for different sexes and age stages, respectively.

Study protocol of a randomized intervention study to explore effects of a pure physical training and a mind-body exercise on cognitive executive function in independent living adults age 65-85

BACKGROUND

Decline in cognitive function associated with aging is one of the greatest concerns of older adults and often leads to a significant burden for individuals, families, and the health care system. Executive functions are most susceptible to age-related decline. Despite the well-known benefits of regular exercise on cognitive health, older adults tend to be less physically active than other age groups. Thus, there is a need to identify strategies that attract older adults and can enhance cognitive vitality.

AIMS

This article describes the protocol of a study designed to evaluate whether two interventions, a pure physical exercise and a mind-body exercise, can improve cognitive executive function in independent-living older adults. In addition, the study will explore barriers/facilitators related to adherence.

METHODS

After baseline assessment, participants will be randomly assigned to one of three groups (strength training, Awareness Through Movement^®, or a control group). Participants of the two active groups will attend the interventions for 12 weeks. The control group continues with the usual everyday life. Assessments will include three measures of executive function of the NIH Toolbox, and are administered at baseline, post-intervention and at 3-month follow-up. The primary outcomes are the changes in cognitive executive function performances. Secondary outcomes include adherence, self-efficacy for exercise, symptoms of depression, mindfulness and enjoyment. Attendance will be used as a measure of adherence.

DISCUSSION AND CONCLUSION

If successful, the interventions could provide low-cost strategies for older adults to maintain cognitive vitality and has the potential to impact current exercise guidelines.