Body Weight Variation Patterns as Predictors of Cognitive Decline over a 5 Year Follow-Up among Community-Dwelling Elderly (MAPT Study)

Understanding exogenous factors and biological mechanisms for cognitive frailty: A multidisciplinary scoping review

Cognitive frailty (CF) is the conjunction of cognitive impairment without dementia and physical frailty. While predictors of each element are well-researched, mechanisms of their co-occurrence have not been integrated, particularly in terms of relationships between social, psychological, and biological factors. This interdisciplinary scoping review set out to categorise a heterogenous multidisciplinary literature to identify potential pathways and mechanisms of CF, and research gaps. Studies were included if they used the definition of CF OR focused on conjunction of cognitive impairment and frailty (by any measure), AND excluded studies on specific disease populations, interventions, epidemiology or prediction of mortality. Searches used Web of Science, PubMed and Science Direct. Search terms included "cognitive frailty" OR (("cognitive decline" OR "cognitive impairment") AND (frail*)), with terms to elicit mechanisms, predictors, causes, pathways and risk factors. To ensure inclusion of animal and cell models, keywords such as "behavioural" or "cognitive decline" or "senescence", were added. 206 papers were included. Descriptive analysis provided high-level categorisation of determinants from social and environmental through psychological to biological. Patterns distinguishing CF from Alzheimer's disease were identified and social and psychological moderators and mediators of underlying biological and physiological changes and of trajectories of CF development were suggested as foci for further research.

Modifiable dementia risk factors and AT(N) biomarkers: findings from the EPAD cohort

Introduction

Modifiable risk factors account for a substantial proportion of Alzheimer's disease (AD) cases and we currently have a discrete AT(N) biomarker profile for AD biomarkers: amyloid (A), p-tau (T), and neurodegeneration (N). Here, we investigated how modifiable risk factors relate to the three hallmark AT(N) biomarkers of AD.

Methods

Participants from the European Prevention of Alzheimer's Dementia (EPAD) study underwent clinical assessments, brain magnetic resonance imaging, and cerebrospinal fluid collection and analysis. Generalized additive models (GAMs) with penalized regression splines were modeled in the AD Workbench on the NTKApp.

Results

A total of 1,434 participants were included (56% women, 39% APOE ε4+) with an average age of 65.5 (± 7.2) years. We found that modifiable risk factors of less education (t = 3.9, p < 0.001), less exercise (t = 2.1, p = 0.034), traumatic brain injury (t = -2.1, p = 0.036), and higher body mass index (t = -4.5, p < 0.001) were all significantly associated with higher AD biomarker burden.

Discussion

This cross-sectional study provides further support for modifiable risk factors displaying neuroprotective associations with the characteristic AT(N) biomarkers of AD.

Dynamic Features of Body Mass Index in Late Life Predict Cognitive Trajectories and Alzheimer's Disease: A Longitudinal Study

Background

The causal relationships of late-life body mass index (BMI) with Alzheimer's disease (AD) remains debated.

Objective

We aimed to assess the associations of dynamic BMI features (ΔBMIs) with cognitive trajectories, AD biomarkers, and incident AD risk.

Methods

We analyzed an 8-year cohort of 542 non-demented individuals who were aged ≥65 years at baseline and had BMI measurements over the first 4 years. ΔBMIs were defined as changing extent (change ≤ or >5%), variability (standard deviation), and trajectories over the first 4 years measured using latent class trajectory modeling. Linear mixed-effect models were utilized to examine the influence of ΔBMIs on changing rates of AD pathology biomarkers, hippocampus volume, and cognitive functions. Cox proportional hazards models were used to test the associations with AD risk. Stratified analyzes were conducted by the baseline BMI group and age.

Results

Over the 4-year period, compared to those with stable BMI, individuals who experienced BMI decreases demonstrated accelerated declined memory function (p = 0.006) and amyloid-β deposition (p = 0.034) while BMI increases were associated with accelerated hippocampal atrophy (p = 0.036). Three BMI dynamic features, including stable BMI, low BMI variability, and persistently high BMI, were associated with lower risk of incident AD (p < 0.005). The associations were validated over the 8-year period after excluding incident AD over the first 4 years. No stratified effects were revealed by the BMI group and age.

Conclusions

High and stable BMI in late life could predict better cognitive trajectory and lower risk of AD.

Longitudinal body weight dynamics in relation to cognitive decline over two decades: A prospective cohort study

OBJECTIVE

The aim of this study was to investigate the associations of body weight change (BWC) and body weight variability (BWV) with changes in cognitive function.

METHODS

In 10,340 Health and Retirement Study participants (mean age: 68.0 years), body weight was reported biennially from 1993/1994 to 2016, and cognitive function was measured biennially from 1998 to 2016. We calculated BWC and BWV as the slope and root-mean-square error by regressing body weight on time for each individual. BWC was categorized by quintiles (Q): stable weight (Q2 to Q4), weight loss (Q1), and weight gain (Q5). BWV was categorized by tertiles. We used linear mixed regression models to assess associations with cognitive change.

RESULTS

Compared with stable weight (median: 0 kg/y), weight loss (median: -1.3 kg/y) predicted faster cognitive decline as demonstrated by mean difference of -0.023 (95% CI: -0.027 to -0.019) in cognitive change z score per year, whereas weight gain (median: 1 kg/y) was related to slower cognitive decline (β = 0.006; 95% CI: 0.003 to 0.009). Larger BWV was also associated with faster cognitive decline (β comparing the top with bottom tertile = -0.003; 95% CI: -0.006 to -0.0002). Similar associations were observed for episodic and working memory.

CONCLUSIONS

Weight loss and large BWV over a long time independently predicted faster cognitive decline in middle-aged and older adults, underscoring the importance of long-term dynamic body weight monitoring.

Weight loss, visit-to-visit body weight variability and cognitive function in older individuals

OBJECTIVE

to investigate the association between variability and loss of body weight with subsequent cognitive performance and activities of daily living in older individuals.

DESIGN

cross-sectional cohort study.

SETTING

PROspective Study of Pravastatin in the Elderly at Risk, multicentre trial with participants from Scotland, Ireland and the Netherlands.

SUBJECTS

4,309 participants without severe cognitive dysfunction (mean age 75.1 years, standard deviation (SD) = 3.3), at higher risk for cardiovascular disease (CVD).

METHODS

body weight was measured every 3 months for 2.5 years. Weight loss was defined as an average slope across all weight measurements and as ≥5% decrease in baseline body weight during follow-up. Visit-to-visit variability was defined as the SD of weight measurements (kg) between visits. Four tests of cognitive function were examined: Stroop test, letter-digit coding test (LDCT), immediate and delayed picture-word learning tests. Two measures of daily living activities: Barthel Index (BI) and instrumental activities of daily living (IADL). All tests were examined at month 30.

RESULTS

both larger body weight variability and loss of ≥5% of baseline weight were independently associated with worse scores on all cognitive tests, but minimally with BI and IADL. Compared with participants with stable weight, participants with significant weight loss performed 5.83 seconds (95% CI 3.74; 7.92) slower on the Stroop test, coded 1.72 digits less (95% CI -2.21; -1.13) on the LDCT and remembered 0.71 pictures less (95% CI -0.93; -0.48) on the delayed picture-word learning test.

CONCLUSION

in older people at higher risk for CVD, weight loss and variability are independent risk-factors for worse cognitive function.

Independent effect of body mass index variation on amyloid-β positivity

Objectives

The relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.

Methods

We retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.

Results

Decreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.

Conclusion

Our findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.

Interactions Between Weight Loss and Plasma Neurodegenerative Markers for Determining Cognitive Decline Among Community-Dwelling Older Adults

This study aimed to investigate the interaction between weight loss (WL) and plasma amyloid-β 42/40 (Aβ 42/40), neurofilament light chain (NfL), progranulin, and their association with cognitive decline over time among older adults. This 5-year observational approach included 470 participants from the Multidomain Alzheimer Preventive Trial, mean age 76.8 years (SD = 4.5), 59.4% women. WL was defined as ≥5% decrease over the first year. Biomarkers were measured at 12 months. Cognitive function was assessed yearly from 12 months onward by Mini-Mental State Examination (MMSE); Clinical Dementia Rating sum of boxes (CDR-SB); a composite score based on Category Naming Test; Digit Symbol Substitution Test; 10 MMSE orientation items (MMSEO) and free and total recall of the Free and Cued Selective Reminding test; and these tests individually. Twenty-seven participants (5.7%) presented WL. In adjusted analyses, combined WL + lower Aβ 42/40 (≤0.103, lowest quartile) was related with more pronounced 4-year cognitive decline according to CDR-SB (p < .0001) and MMSEO (p = .021), compared with non-WL + higher Aβ 42/40. WL + higher NfL (>94.55 pg/mL, highest quartile) or progranulin (>38.4 ng/mL, 3 higher quartiles) were related with higher cognitive decline according to CDR-SB, MMSE, MMSEO, and composite score (all p < .03), compared with non-WL + lower NfL or higher progranulin. Regrouping progranulin quartiles (Q1-Q3 vs Q4) revealed higher cognitive decline among the WL + lower progranulin group compared with non-WL + lower progranulin. In conclusion, 1-year WL was associated with subsequent higher 4-year cognitive decline among older adults presenting low Aβ 42/40 or high NfL. Future studies combining plasma biomarker assessments and body weight surveillance may be useful for identifying people at risk of cognitive impairment. Clinical trial number: NCT00672685.

Relationship between obesity and structural brain abnormality: Accumulated evidence from observational studies

We aimed to evaluate the relationship between obesity and structural brain abnormalities assessed by magnetic resonance imaging using data from 45 observational epidemiological studies, where five articles reported prospective longitudinal results. In cross-sectional studies' analyses, the pooled weighted mean difference for total brain volume (TBV) and gray matter volume (GMV) in obese/overweight participants was -11.59 (95 % CI: -23.17 to -0.02) and -10.98 (95 % CI: -20.78 to -1.18), respectively. TBV was adversely associated with BMI and WC, GMV with BMI, and hippocampal volume with BMI, WC, and WHR. WC/WHR are associated with a risk of lacunar and white matter hyperintensity (WMH). In longitudinal studies' analyses, BMI was not statistically associated with the overall structural brain abnormalities (for continuous BMI: RR = 1.02, 95 % CI: 0.94-1.12; for categorial BMI: RR = 1.18, 95 % CI: 0.75-1.85). Small sample size of prospective longitudinal studies limited the power of its pooled estimates. A higher BMI is associated with lower brain volume while greater WC/WHR, but not BMI, is related to a risk of lacunar infarct and WMH. Future longitudinal research is needed to further elucidate the specific causal relationships and explore preventive measures.

The role of diet on the risk of dementia in the oldest old: The Monzino 80-plus population-based study

BACKGROUND & AIMS

Longevity also carries its dark side of age-related chronic diseases, dementia being one of the worst and the most prevalent. Since dementia lacks effective treatments, preventing or delaying it is highly desirable. Dietary habits and nutrition have been found to be important modifiable risk factors for many chronic diseases, but evidence on the role of diet on the risk of dementia is still limited, particularly among the very old. Aim of the present work is to study the association of the Mediterranean diet and its components with prevalent and incident dementia in the oldest-old.

METHODS

We analyzed data from the Monzino 80-plus study, a population-based study in subjects 80 years or older in the Varese province, Italy. A validated food frequency questionnaire was used to collect information on 23 different foods consumed in the previous year. A Mediterranean diet score was calculated and its components were classified into tertiles. Multivariable models for dementia prevalence and incidence were adjusted for demographic and clinical characteristics.

RESULTS

Information on nutrition was available for 1390 subjects in the cross-sectional study and 512 subjects in the longitudinal study, mean respective ages 93 and 92. Greater adherence to Mediterranean diet, greater consumption of eggs, fruits and vegetables, carbohydrates, and greater food intake were associated with a lower prevalence of dementia. Increasing number of portions per week and consumption of legumes significantly decreased the incidence of dementia during the 3.6 year mean follow-up: corresponding hazard ratios of highest vs. lowest tertiles (95% confidence intervals) were 0.66 (0.46-0.95) and 0.68 (0.47-0.97), respectively.

CONCLUSION

Oldest-old eating less and having diets with less variety and nutrient density were more frequent among subjects with dementia. The longitudinal analysis confirmed oldest-old subjects who eat more portions, as well as those who have a higher intake of legumes, are at decreased risk of developing dementia even though reverse causality cannot be completely ruled out.

Body weight changes and longitudinal associations with cognitive decline among community-dwelling older adults

INTRODUCTION

We aim to investigate the longitudinal associations between changes in body weight (BW) and declines in cognitive function and risk of mild cognitive impairment (MCI)/dementia among cognitively normal individuals 65 years or older.

METHODS

Data from the Age Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik Study) including 2620 participants, were examined using multiple logistic regression models. Cognitive function included speed of processing (SP), executive function (EF), and memory function (MF). Changes in BW were classified as; weight loss (WL), weight gain (WG), and stable weight (SW).

RESULTS

Mean follow-up time was 5.2 years and 61.3% were stable weight. Participants who experienced WL (13.4%) were significantly more likely to have declines in MF and SP compared to the SW group. Weight changes were not associated with EF. WL was associated with a higher risk of MCI, while WG (25.3%) was associated with a higher dementia risk, when compared to SW.

DISCUSSION

Significant BW changes in older adulthood may indicate impending changes in cognitive function.

Association of weight change with cerebrospinal fluid biomarkers and amyloid positron emission tomography in preclinical Alzheimer's disease

Background

Recognizing clinical manifestations heralding the development of Alzheimer’s disease (AD)-related cognitive impairment could improve the identification of individuals at higher risk of AD who may benefit from potential prevention strategies targeting preclinical population. We aim to characterize the association of body weight change with cognitive changes and AD biomarkers in cognitively unimpaired middle-aged adults.

Methods

This prospective cohort study included data from cognitively unimpaired adults from the ALFA study (n = 2743), a research platform focused on preclinical AD. Cognitive and anthropometric data were collected at baseline between April 2013 and November 2014. Between October 2016 and February 2020, 450 participants were visited in the context of the nested ALFA+ study and underwent cerebrospinal fluid (CSF) extraction and acquisition of positron emission tomography images with [¹⁸F]flutemetamol (FTM-PET). From these, 408 (90.1%) were included in the present study. We used data from two visits (average interval 4.1 years) to compute rates of change in weight and cognitive performance. We tested associations between these variables and between weight change and categorical and continuous measures of CSF and neuroimaging AD biomarkers obtained at follow-up. We classified participants with CSF data according to the AT (amyloid, tau) system and assessed between-group differences in weight change.

Results

Weight loss predicted a higher likelihood of positive FTM-PET visual read (OR 1.27, 95% CI 1.00–1.61, p = 0.049), abnormal CSF p-tau levels (OR 1.50, 95% CI 1.19–1.89, p = 0.001), and an A+T+ profile (OR 1.64, 95% CI 1.25–2.20, p = 0.001) and was greater among participants with an A+T+ profile (p < 0.01) at follow-up. Weight change was positively associated with CSF Aβ42/40 ratio (β = 0.099, p = 0.032) and negatively associated with CSF p-tau (β = − 0.141, p = 0.005), t-tau (β = − 0.147 p = 0.004) and neurogranin levels (β = − 0.158, p = 0.002). In stratified analyses, weight loss was significantly associated with higher t-tau, p-tau, neurofilament light, and neurogranin, as well as faster cognitive decline in A+ participants only.

Conclusions

Weight loss predicts AD CSF and PET biomarker results and may occur downstream to amyloid-β accumulation in preclinical AD, paralleling cognitive decline. Accordingly, it should be considered as an indicator of increased risk of AD-related cognitive impairment.

Trial registration

NCT01835717, NCT02485730, NCT02685969.