The association of dietary and nutrient patterns on neurocognitive decline: A systematic review of MRI and PET studies

Daily fat intake is associated with basolateral amygdala response to high-calorie food cues and appetite for high-calorie food

OBJECTIVES

Animal studies have indicated that fat intake mediates amygdala activation, which in turn promotes fat intake, while amygdala activation increases the preference for fat and leads to increased fat intake. However, the association among fat intake, amygdala activation, and appetite for high-calorie foods in humans remains unclear. Thus, to examine this association, we conducted a functional magnetic resonance imaging (fMRI) experiment.

METHODS

Fifty healthy-weight adults (18 females; mean age: 22.9 ± 3.02 years) were included. Participants were shown images of high-calorie and low-calorie foods and were instructed to rate their desire to eat the food items during fMRI. All participants provided information on their daily fat intake using a self-reported questionnaire. Associations among fat intake, the desire to eat high-calorie or low-calorie food items, and amygdala responses to food items were examined.

RESULTS

The basolateral amygdala (BLA) response was positively associated with fat intake ([x, y, z] = [24, -6, -16], z = 3.91, pFWE-corrected = 0.007) and the desire to eat high-calorie food items ([26, -4, -16], z = 3.75, pFWE-corrected = 0.010). Structural equation modeling showed that the desire for high-calorie food items was predicted by BLA response to high-calorie food items (p = 0.013, β = 3.176), and BLA response was predicted by fat intake (p < 0.001, β = 0.026).

DISCUSSION

Fat intake influences BLA response to high-fat food, which in turn increases the desire to eat palatable high-fat food. This may lead to additional fat intake and increase the risk of weight gain.

Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?

BACKGROUND

Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzheimer's disease (AD) using data from the Finnish national registers.

METHODS

We conducted a case-control study using data from the Finnish MEDALZ (Medication use and Alzheimer's disease) study. Altogether 56,741 individuals with incident AD diagnosis (age ≥ 65 years at diagnosis and born after 1922) and their 1:1 age-, sex-, and region of residence-matched controls were included. The association of risk factors, evaluated at different age periods (45-54, 55-64, 65+), and AD were assessed with logistic regression. Predictive accuracies of logistic regressions were compared with seven machine learning models (L1-regularized logistic regression, Naive bayes, Decision tree, Random Forest, Multilayer perceptron, XGBoost, and LightGBM).

FINDINGS

63.5 % of cases and controls were females and the mean age was 79.1 (SD = 5.1). The strongest associations with AD were observed for head injuries at age 55-64 (OR, 95 % CI 1.33, 1.19-1.48) and 65+ (1.31, 1.23-1.40), followed by antidepressant use (1.30, 1.22-1.38) at 55-64 and antipsychotic use (1.27, 1.19-1.35) at 65+. The predictive accuracies of all models were low, with the best performance (AUC 0.603) observed in Random Forest for predicting AD onset at age 65-69.

INTERPRETATION

Although significant associations were identified between many risk factors and AD, the low predictive accuracies suggest that specialised healthcare diagnosis data is not sufficient for predicting AD and linkage with other data sources is needed.

Adherence to unhealthy diets is associated with altered frontal gamma-aminobutyric acid and glutamate concentrations and grey matter volume: preliminary findings

OBJECTIVES

Common mental disorders (CMD) are associated with impaired frontal excitatory/inhibitory (E/I) balance and reduced grey matter volume (GMV). Larger GMV (in the areas that are implicated in CMD-pathology) and improved CMD-symptomatology have been observed in individuals who adhere to high quality diets. Moreover, preclinical studies have shown altered neurometabolites (primarily gamma-aminobutyric acid: GABA and glutamate: GLU) in relation to diet quality. However, neurochemical correlates of diet quality and how these neurobiological changes are associated with CMD and with its transdiagnostic factor, rumination, is unknown in humans. Therefore, in this study, we examined the associations between diet quality and frontal cortex neuro-chemistry and structure, as well as CMD and rumination in humans.

METHODS

Thirty adults were classified into high and low diet quality groups and underwent 1H-MRS to measure medial prefrontal cortex (mPFC) metabolite concentrations and volumetric imaging to measure GMV.

RESULTS

Low (vs High) diet quality group had reduced mPFC-GABA and elevated mPFC-GLU concentrations, as well as reduced right precentral gyrus (rPCG) GMV. However, CMD and rumination were not associated with diet quality. Notably, we observed a significant negative correlation between rumination and rPCG-GMV and a marginally significant association between rumination and mPFC-GLU concentrations. There was also a marginally significant association between mPFC-GLU concentrations and rPCG-GMV.

DISCUSSION

Adhering to unhealthy dietary patterns may be associated with compromised E/I balance, and this could affect GMV, and subsequently, rumination.

Towards consistency in dietary pattern scoring: standardising scoring workflows for healthy dietary patterns using 24-h recall and two variations of a food frequency questionnair

Healthy dietary patterns such as the Mediterranean diet (MeDi), Dietary Approaches to Stop Hypertension (DASH) and the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) have been evaluated for their potential association with health outcomes. However, the lack of standardisation in scoring methodologies can hinder reproducibility and meaningful cross-study comparisons. Here we provide a reproducible workflow for generating the MeDi, DASH and MIND dietary pattern scores from frequently used dietary assessment tools including the 24-h recall tool and two variations of FFQ. Subjective aspects of the scoring process are highlighted and have led to a recommended reporting checklist. This checklist enables standardised reporting with sufficient detail to enhance the reproducibility and comparability of their outcomes. In addition to these aims, valuable insights in the strengths and limitations of each assessment tool for scoring the MeDi, DASH and MIND diet can be utilised by researchers and clinicians to determine which dietary assessment tool best meets their needs.