Effect of nutritional supports on malnutrition, cognition, function and biomarkers of Alzheimer's disease: a systematic review

AIM

To summarize the nutritional supplementation on biochemical parameters, cognition, function, Alzheimer's Disease (AD) biomarkers and nutritional status.

MATERIALS AND METHODS

PubMed, Web of Science, Korean Journal Database, Russian Science Citation Index, SciELO Citation Index, Cochrane Library and Scopus databases were searched until 16 April 2021. 22.193 records in total were reached according to inclusion and exclusion criteria. Included Studies were evaluated through the Modified Jadad Scale and gathered under four subheadings.

RESULTS

Forty-eight studies with a total of 7009 AD patients were included. Souvenaid, ONS (368 ± 69 kcal), Vegenat-med, 500 mg Resveratrol, ONS (200 mL) were effective nutritional supplements on promoting weight gain and protecting malnutrition status but showed conflicting results in Body mass index, Mid-Upper-Arm Circumference and Triceps Skin Fold Thickness. ONS and a lyophilized whole supplementation Vegenat-med intake made an increase in MNA scores. While all nutritional supplements showed controversial results in biochemical parameters but caused a decrease in Hcy levels which caused reductions in brain Aβ plaque (increase serum Aβ), p-Tau and cognitive improvement. Folic acid and vitamin D decreased serum APP, BACE1, BACE1mRNA. Resveratrol, Hericium erinaceus mycelia, vitamin D and Betaine supplements improved cognitive, functional prognosis and quality of life unlike other nutritional supplements had no effect on cognitive scales.

CONCLUSIONS

Better designed trials with holistic measures are needed to investigate the effect of nutritional support on the AD biomarkers, cognitive status, biochemical parameters and functional states. Also, more beneficial results can be obtained by examining the simultaneous effects of nutritional supplements with larger sample groups.

Influence of preanalytical and analytical factors on the quantification of six regulatory serum proteins

Background: We analyzed differences in protein concentrations in human blood serum depending on the tube material and the immunoassay platform used. Materials & methods: Blood samples from study participants were collected in glass and polypropylene tubes (n = 292). Serum concentrations of six proteins (BDNF, IGF-1, VEGF-A, TGF-β1, MCP-1 and IL-18) were assessed by using ELISAs (all biomarkers), as well as a novel fully automated immunoassay platform (all but IGF-1, n = 211). Bland-Altman analyses were conducted to investigate intrasample variability of protein concentrations. Results: Tube comparison resulted in mean biases of between -0.45 and -70.64%. Platform comparison revealed mean biases of between 21.04 and -128.10%. Conclusion: Protein concentrations can vary significantly depending on the types of tube and immunoassay used, with protein-specific differences.

Predicting amyloid PET and tau PET stages with plasma biomarkers

Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aβ1-42 and Aβ1-40 (analysed as the Aβ42/Aβ40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Identification of plasma metabolites associated with modifiable risk factors and endophenotypes reflecting Alzheimer's disease pathology

Modifiable factors can influence the risk for Alzheimer's disease (AD) and serve as targets for intervention; however, the biological mechanisms linking these factors to AD are unknown. This study aims to identify plasma metabolites associated with modifiable factors for AD, including MIND diet, physical activity, smoking, and caffeine intake, and test their association with AD endophenotypes to identify their potential roles in pathophysiological mechanisms. The association between each of the 757 plasma metabolites and four modifiable factors was tested in the wisconsin registry for Alzheimer's prevention cohort of initially cognitively unimpaired, asymptomatic middle-aged adults. After Bonferroni correction, the significant plasma metabolites were tested for association with each of the AD endophenotypes, including twelve cerebrospinal fluid (CSF) biomarkers, reflecting key pathophysiologies for AD, and four cognitive composite scores. Finally, causal mediation analyses were conducted to evaluate possible mediation effects. Analyses were performed using linear mixed-effects regression. A total of 27, 3, 23, and 24 metabolites were associated with MIND diet, physical activity, smoking, and caffeine intake, respectively. Potential mediation effects include beta-cryptoxanthin in the association between MIND diet and preclinical Alzheimer cognitive composite score, hippurate between MIND diet and immediate learning, glutamate between physical activity and CSF neurofilament light, and beta-cryptoxanthin between smoking and immediate learning. Our study identified several plasma metabolites that are associated with modifiable factors. These metabolites can be employed as biomarkers for tracking these factors, and they provide a potential biological pathway of how modifiable factors influence the human body and AD risk.

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.

Carboxylic Acid-Functionalized, Graphitic Layer-Coated Three-Dimensional SERS Substrate for Label-Free Analysis of Alzheimer's Disease Biomarkers

Surface-enhanced Raman spectroscopy (SERS)-based protein analysis is a promising alternative to existing early stage diagnoses. However, SERS research conducted thus far accompanies challenges such as nonuniformity of plasmonic nanostructures, irregular coating of analytes, and denaturation of proteins, which seriously limit the practicability of suggested approaches. Here, we introduce a carboxylic acid-functionalized and graphitic nanolayer-coated three-dimensional SERS substrate (CGSS) fabricated by sequential nanotransfer printing. The substrate consists of well-defined, uniform gold nanowire arrays for effective Raman signal enhancement and a strong protein-immobilization layer. With an enhancement factor (EF) of 5.5 × 10⁵, on par with the highest ever reported values, the CGSS allows the detection of protein conformational changes and the determination of protein concentration via Raman measurements. Exploiting the CGSS, we successfully measured the SERS spectra of Alzheimer's biomarkers, tau protein and amyloid β, based on which secondary structural changes were analyzed quantitatively.