Nutrition and Metabolic Profiles in the Natural History of Dementia: Recent Insights from Systems Biology and Life Course Epidemiology

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study

Background

By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory.

Methods

Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age.

Findings

A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages.

Interpretation

The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively.

Funding

The Research Grants Council of Hong Kong under the Early Career Scheme 27110519.

Diet patterns and cognitive performance in a UK Female Twin Registry (TwinsUK)

BACKGROUND

Plant-based diets may provide protection against cognitive decline and Alzheimer's disease, but observational data have not been consistent. Previous studies include early life confounding from socioeconomic conditions and genetics that are known to influence both cognitive performance and diet behaviour. This study investigated associations between Mediterranean (MED) diet and MIND diets and cognitive performance accounting for shared genotype and early-life environmental exposures in female twins.

METHODS

Diet scores were examined in 509 female twins enrolled in TwinsUK study. The Cambridge Neuropsychological Test Automated Battery was used to assess cognition at baseline and 10 years later (in n = 275). A co-twin case-control study for discordant monozygotic (MZ) twins examined effects of diet on cognitive performance independent of genetic factors. Differences in relative abundance of taxa at 10-year follow-up were explored in subsamples.

RESULTS

Each 1-point increase in MIND or MED diet score was associated with 1.75 (95% CI: - 2.96, - 0.54, p = 0.005 and q = 0.11) and 1.67 (95% CI: - 2.71, - 0.65, p = 0.002 and q = 0.02) fewer respective errors in paired-associates learning. Within each MZ pair, the twin with the high diet score had better preservation in spatial span especially for MED diet (p = 0.02). There were no differences between diet scores and 10-year change in the other cognitive tests. MIND diet adherence was associated with higher relative abundance of Ruminococcaceae UCG-010 (0.30% (95% CI 0.17, 0.62), q = 0.05) which was also associated with less decline in global cognition over 10 years (0.22 (95% CI 0.06, 0.39), p = 0.01).

CONCLUSIONS

MIND or MED diets could help to preserve some cognitive abilities in midlife, particularly episodic and visuospatial working memory. Effects may be mediated by high dietary fibre content and increased abundance of short-chain fatty acid producing gut bacteria. Longer follow-up with repeated measures of cognition will determine whether diet can influence changes in cognition occurring in older age.

Plasma metabolic profiles predict future dementia and dementia subtypes: a prospective analysis of 274,160 participants

BACKGROUND

Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) and assessed their predictive potential.

METHODS

This study included 274,160 participants from the UK Biobank. Cox proportional hazard models were employed to investigate longitudinal associations between metabolites and dementia. The importance of these metabolites was quantified using machine learning algorithms, and a metabolic risk score (MetRS) was subsequently developed for each dementia type. We further investigated how MetRS stratified the risk of dementia onset and assessed its predictive performance, both alone and in combination with demographic and cognitive predictors.

RESULTS

During a median follow-up of 14.01 years, 5274 participants developed dementia. Of the 249 metabolites examined, 143 were significantly associated with incident ACD, 130 with AD, and 140 with VaD. Among metabolites significantly associated with dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, and branched-chain amino acids ranked top in importance. Individuals within the top tertile of MetRS faced a significantly greater risk of developing dementia than those in the lowest tertile. When MetRS was combined with demographic and cognitive predictors, the model yielded the area under the receiver operating characteristic curve (AUC) values of 0.857 for ACD, 0.861 for AD, and 0.873 for VaD.

CONCLUSIONS

We conducted the largest metabolome investigation of dementia to date, for the first time revealed the metabolite importance ranking, and highlighted the contribution of plasma metabolites for dementia prediction.

Measures of MRI Brain Biomarkers in Middle Age According to Average Modified Mediterranean Diet Scores Throughout Young and Middle Adulthood

BACKGROUND

The Mediterranean diet (MedDiet) has been linked with better cognitive function and brain integrity.

OBJECTIVE

To examine the association of modified Mediterranean diet (mMedDiet) scores from early through middle adulthood in relation to volumetric and microstructural midlife MRI brain measures. Assess the association of mMedDiet and brain measures with four cognitive domains. If variables are correlated, determine if brain measures mediate the relationship between mMedDiet and cognition.

METHODS

618 participants (mean age 25.4±3.5 at year 0) of the Coronary Artery Risk Development in Young Adults (CARDIA) study were included. Cumulative average mMedDiet scores were calculated by averaging scores from years 0, 7, and 20. MRI scans were obtained at years 25 and 30. General linear models were used to examine the association between mMedDiet and brain measures.

RESULTS

Higher cumulative average mMedDiet scores were associated with better microstructural white matter (WM) integrity measured by fractional anisotropy (FA) at years 25 and 30 (all ptrend <0.05). Higher mMedDiet scores at year 7 were associated with higher WM FA at year 25 (β= 0.003, ptrend = 0.03). Higher mMedDiet scores at year 20 associated with higher WM FA at years 25 (β= 0.0005, ptrend = 0.002) and 30 (β= 0.0003, ptrend = 0.02). mMedDiet scores were not associated with brain volumes. Higher mMedDiet scores and WM FA were both correlated with better executive function, processing speed, and global cognition (all ptrend <0.05). WM FA did not mediate the association between mMedDiet scores and cognition.

CONCLUSIONS

mMedDiet scores may be associated with microstructural WM integrity at midlife.

Unlocking Modifiable Risk Factors for Alzheimer's Disease: Does the Oral Microbiome Hold Some of the Keys?

Advancing age is recognized as the primary risk factor for Alzheimer's disease (AD); however approximately one third of dementia cases are attributable to modifiable risk factors such as hypertension, diabetes, smoking, and obesity. Recent research also implicates oral health and the oral microbiome in AD risk and pathophysiology. The oral microbiome contributes to the cerebrovascular and neurodegenerative pathology of AD via the inflammatory, vascular, neurotoxic, and oxidative stress pathways of known modifiable risk factors. This review proposes a conceptual framework that integrates the emerging evidence regarding the oral microbiome with established modifiable risk factors. There are numerous mechanisms by which the oral microbiome may interact with AD pathophysiology. Microbiota have immunomodulatory functions, including the activation of systemic pro-inflammatory cytokines. This inflammation can affect the integrity of the blood-brain barrier, which in turn modulates translocation of bacteria and their metabolites to brain parenchyma. Amyloid-β is an antimicrobial peptide, a feature which may in part explain its accumulation. There are microbial interactions with cardiovascular health, glucose tolerance, physical activity, and sleep, suggesting that these modifiable lifestyle risk factors of dementia may have microbial contributors. There is mounting evidence to suggest the relevance of oral health practices and the microbiome to AD. The conceptual framework presented here additionally demonstrates the potential for the oral microbiome to comprise a mechanistic intermediary between some lifestyle risk factors and AD pathophysiology. Future clinical studies may identify specific oral microbial targets and the optimum oral health practices to reduce dementia risk.

Nutrition state of science and dementia prevention: recommendations of the Nutrition for Dementia Prevention Working Group

Observational studies suggest that nutritional factors have a potential cognitive benefit. However, systematic reviews of randomised trials of dietary and nutritional supplements have reported largely null effects on cognitive outcomes and have highlighted study inconsistencies and other limitations. In this Personal View, the Nutrition for Dementia Prevention Working Group presents what we consider to be limitations in the existing nutrition clinical trials for dementia prevention. On the basis of this evidence, we propose recommendations for incorporating dietary patterns and the use of genetic, and nutrition assessment tools, biomarkers, and novel clinical trial designs to guide future trial developments. Nutrition-based research has unique challenges that could require testing both more personalised interventions in targeted risk subgroups, identified by nutritional and other biomarkers, and large-scale and pragmatic study designs for more generalisable public health interventions across diverse populations.

Personalized nutrition for dementia prevention

The role of nutrition has been investigated for decades under the assumption of one-size-fits-all. Yet there is heterogeneity in metabolic and neurobiological responses to diet. Thus a more personalized approach may better fit biological reality and have increased efficacy to prevent dementia. Personalized nutrition builds on the food exposome, defined as the history of diet-related exposures over the lifetime, and on its interactions with the genome and other biological characteristics (eg, metabolism, the microbiome) to shape health. We review current advances of personalized nutrition in dementia research. We discuss key questions, success milestones, and future roadmap from observational epidemiology to clinical studies through basic science. A personalized nutrition approach based on the best prescription for the most appropriate target population in the most relevant time-window has the potential to strengthen dementia-prevention efforts.

Early signature in the blood lipidome associated with subsequent cognitive decline in the elderly: A case-control analysis nested within the Three-City cohort study

BACKGROUND

Brain lipid metabolism appears critical for cognitive aging, but whether alterations in the lipidome relate to cognitive decline remains unclear at the system level.

METHODS

We studied participants from the Three-City study, a multicentric cohort of older persons, free of dementia at time of blood sampling, and who provided repeated measures of cognition over 12 subsequent years. We measured 189 serum lipids from 13 lipid classes using shotgun lipidomics in a case-control sample on cognitive decline (matched on age, sex and level of education) nested within the Bordeaux study center (discovery, n = 418). Associations with cognitive decline were investigated using bootstrapped penalized regression, and tested for validation in the Dijon study center (validation, n = 314).

FINDINGS

Among 17 lipids identified in the discovery stage, lower levels of the triglyceride TAG50:5, and of four membrane lipids (sphingomyelin SM40:2,2, phosphatidylethanolamine PE38:5(18:1/20:4), ether-phosphatidylethanolamine PEO34:3(16:1/18:2), and ether-phosphatidylcholine PCO34:1(16:1/18:0)), and higher levels of PCO32:0(16:0/16:0), were associated with greater odds of cognitive decline, and replicated in our validation sample.

INTERPRETATION

These findings indicate that in the blood lipidome of non-demented older persons, a specific profile of lipids involved in membrane fluidity, myelination, and lipid rafts, is associated with subsequent cognitive decline.

FUNDING

The complete list of funders is available at the end of the manuscript, in the Acknowledgement section.