This dataset contains extensive health information for 2,149 patients, each uniquely identified with IDs ranging from 4751 to 6900. The dataset includes demographic details, lifestyle factors, medical history, clinical measurements, cognitive and functional assessments, symptoms, and a diagnosis of Alzheimer's Disease. The data is ideal for researchers and data scientists looking to explore factors associated with Alzheimer's, develop predictive models, and conduct statistical analyses.

Table of Contents

1. Patient Information
   • Patient ID
   • Demographic Details
   • Lifestyle Factors
2. Medical History
3. Clinical Measurements
4. Cognitive and Functional Assessments
5. Symptoms
6. Diagnosis Information
7. Confidential Information

Patient Information

Patient ID

• PatientID: A unique identifier assigned to each patient (4751 to 6900).

Demographic Details

• Age: The age of the patients ranges from 60 to 90 years.
• Gender: Gender of the patients, where 0 represents Male and 1 represents Female.
• Ethnicity: The ethnicity of the patients, coded as follows:
  • 0: Caucasian
  • 1: African American
  • 2: Asian
  • 3: Other
• EducationLevel: The education level of the patients, coded as follows:
  • 0: None
  • 1: High School
  • 2: Bachelor's
  • 3: Higher

Lifestyle Factors

• BMI: Body Mass Index of the patients, ranging from 15 to 40.
• Smoking: Smoking status, where 0 indicates No and 1 indicates Yes.
• AlcoholConsumption: Weekly alcohol consumption in units, ranging from 0 to 20.
• PhysicalActivity: Weekly physical activity in hours, ranging from 0 to 10.
• DietQuality: Diet quality score, ranging from 0 to 10.
• SleepQuality: Sleep quality score, ranging from 4 to 10.

Medical History

• FamilyHistoryAlzheimers: Family history of Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.
• CardiovascularDisease: Presence of cardiovascular disease, where 0 indicates No and 1 indicates Yes.
• Diabetes: Presence of diabetes, where 0 indicates No and 1 indicates Yes.
• Depression: Presence of depression, where 0 indicates No and 1 indicates Yes.
• HeadInjury: History of head injury, where 0 indicates No and 1 indicates Yes.
• Hypertension: Presence of hypertension, where 0 indicates No and 1 indicates Yes.

Clinical Measurements

• SystolicBP: Systolic blood pressure, ranging from 90 to 180 mmHg.
• DiastolicBP: Diastolic blood pressure, ranging from 60 to 120 mmHg.
• CholesterolTotal: Total cholesterol levels, ranging from 150 to 300 mg/dL.
• CholesterolLDL: Low-density lipoprotein cholesterol levels, ranging from 50 to 200 mg/dL.
• CholesterolHDL: High-density lipoprotein cholesterol levels, ranging from 20 to 100 mg/dL.
• CholesterolTriglycerides: Triglycerides levels, ranging from 50 to 400 mg/dL.

Cognitive and Functional Assessments

• MMSE: Mini-Mental State Examination score, ranging from 0 to 30. Lower scores indicate cognitive impairment.
• FunctionalAssessment: Functional assessment score, ranging from 0 to 10. Lower scores indicate greater impairment.
• MemoryComplaints: Presence of memory complaints, where 0 indicates No and 1 indicates Yes.
• BehavioralProblems: Presence of behavioral problems, where 0 indicates No and 1 indicates Yes.
• ADL: Activities of Daily Living score, ranging from 0 to 10. Lower scores indicate greater impairment.

Symptoms

• Confusion: Presence of confusion, where 0 indicates No and 1 indicates Yes.
• Disorientation: Presence of disorientation, where 0 indicates No and 1 indicates Yes.
• PersonalityChanges: Presence of personality changes, where 0 indicates No and 1 indicates Yes.
• DifficultyCompletingTasks: Presence of difficulty completing tasks, where 0 indicates No and 1 indicates Yes.
• Forgetfulness: Presence of forgetfulness, where 0 indicates No and 1 indicates Yes.

Diagnosis Information

• Diagnosis: Diagnosis status for Alzheimer's Disease, where 0 indicates No and 1 indicates Yes.

Confidential Information

• DoctorInCharge: This column contains confidential information about the doctor in charge, with "XXXConfid" as the value for all patients.

Conclusion

This dataset offers extensive insights into the factors associated with Alzheimer's Disease, including demographic, lifestyle, medical, cognitive, and functional variables. It is ideal for developing predictive models, conducting statistical analyses, and exploring the complex interplay of factors contributing to Alzheimer's Disease.

Citation

If you use this dataset in your work, please cite it as follows:

@misc{rabie_el_kharoua_2024,
  title={Alzheimer's Disease Dataset},
  url={https://www.kaggle.com/dsv/8668279},
  DOI={10.34740/KAGGLE/DSV/8668279},
  publisher={Kaggle...

Introduction

Alzheimer MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

'0': Mild_Demented

'1': Moderate_Demented

'2': Non_Demented

'3': Very_Mild_Demented

Dataset Information

Train split:

Name: train

Number of bytes: 22,560,791.2

Number of examples: 5,120

Test split:

Name: test

Number of bytes: 5,637,447.08

Number of examples: 1,280

Download size: 28,289,848 bytes

Dataset size: 28,198,238.28 bytes

Source

https://huggingface.co/datasets/Falah/Alzheimer_MRI

Citation

If you use this dataset in your research or health medicine applications, we kindly request that you cite the following publication:

@dataset{alzheimer_mri_dataset,
 author = {Falah.G.Salieh},
 title = {Alzheimer MRI Dataset},
 year = {2023},
 publisher = {Hugging Face},
 version = {1.0},
 url = {https://huggingface.co/datasets/Falah/Alzheimer_MRI}
}

The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.

For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

With this comprehensive dataset, the project aims to explore various neural network models and achieve optimal results in Alzheimer's disease detection and analysis.

Acknowledgments: “Data were provided 1-12 by OASIS-1: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382”

Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498

If you are looking for processed NifTi image version of this dataset please click here

🧠 About the Dataset

This dataset contains resting-state EEG recordings (eyes open and eyes closed) from 53 participants aged 56–85, collected in Sulaymaniyah, Kurdistan Region, Iraq. Participants were recruited from Shar Hospital, Shorsh Hospital, Anwar Shekha Private Hospital, Dr. Abbas Nariman Clinic, and Dr. Sarwer Jamal Al-Bajalan Clinic.

Gender distribution: 9 males, 44 females

Age distribution: 2 in their 50s, 9 in their 60s, 32 in their 70s, 10 in their 80s

🎧 EEG Recordings

Device: Compumedics Profusion EEG system

Electrodes: 40-channel setup (19 standard 10–20 positions + 21 additional electrodes for higher spatial resolution)

Sampling Rate: 250 Hz

Recording Duration: ~30 minutes per participant

Electrode Placement: International 10–20 system

File Formats: .edf (raw), .csv (processed), and a structured file HMMS

Preprocessing included:

Bandpass filtering (1–30 Hz) and 50 Hz notch filtering

Artifact removal (Independent Component Analysis, Automatic Subspace Reconstruction)

Normalization & alignment (40 features per recording)

Label encoding: 0 = Healthy, 1 = Mild, 2 = Moderate, 3 = Severe

📂 Dataset Structure

The dataset contains 53 subjects divided into four groups:

Healthy (23 subjects) – Cognitively normal controls

Mild (8 subjects) – Early Alzheimer’s stage

Moderate (12 subjects) – Mid-stage Alzheimer’s

Severe (10 subjects) – Advanced Alzheimer’s

Total dataset size: 15,820,760 EEG samples.

In addition, a structured subset file named HMMS (Harmonized Machine-learning-Ready EEG Structure) is provided:

Contains a cleaned and class-balanced subset of the data

Built by selecting representative sample ranges (e.g., rows 15,000–30,000) per subject to minimize class imbalance.

Ensures 40-channel consistency and a ready-to-use format for ML/DL pipelines.

📊 Benchmark Results

This dataset has been evaluated with 20 ML/DL models, providing baseline benchmarks:

Random Forest (ML): 96.85% accuracy

Deep Neural Networks (DL): 96.05% accuracy

Other models tested include SVM, CNN, LSTM, GRU, Bi-LSTM, CNN-SVM, CNN-DT, and VGG16-like CNNs.

🌍 Significance

First public EEG dataset for Alzheimer’s Disease in Iraq

Includes high-quality recordings with extensive preprocessing

Enables research on EEG biomarkers, AD classification, and disease progression

Suitable for machine learning, deep learning, and signal processing tasks

DementiaBank is a medical domain task. It contains 117 people diagnosed with Alzheimer Disease, and 93 healthy people, reading a description of an image, and the task is to classify these groups. This release contains only the audio part of this dataset, without the text features.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('dementiabank', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Alzheimer’s disease (AD) is the most common form of global dementia, characterized by an irreversible decline in cognitive function. The pathogenesis of several neurodegenerative disorders has been linked to dysbiosis of the gut microbiota through the gut-brain axis. We set out to establish by case-control study methodology whether there were any differences in the composition and/or function of the gut microbiota between older adults resident in care homes with or without an AD diagnosis.Based on the results of 16S rRNA gene profiling we established that AD sufferers had increased levels of Escherichia/Shigella and Clostridium_sensu_stricto_1, and decreased levels of Bacteroides, Faecalibacterium, Blautia and Roseburia species. The increased levels of potentially pro-inflammatory bacteria were consistent with slightly higher concentrations of calprotectin, a biomarker of gut inflammation. Levels of most microbial metabolites measured were similar across groups, although participants with AD had significantly increased proportions of the branched chain fatty acid, iso-butyrate and a decreased proportion of the specific short-chain fatty acid butyrate. Taken together these results show that participants with Alzheimer’s disease have several key differences within their gut microbiota profile, in contrast to care-home residents with no Alzheimer’s disease. The dysbiotic microbiome included both compositional and functional changes linked to poorer health and gut inflammation.

The data set contains the data associated with the study on "Macro-event recognition in healthy aging, Alzheimer's disease, and mild cognitive impairment". The files include the javascript codes for running the experiments, the data, and example stimuli.

For current version see:

https://data.sandiegocounty.gov/Health/2021-Alzheimer-s-Disease-and-Related-Dementias/6gup-43ea

Basic Metadata

*Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

*API: Asian/Pacific Islander.

*AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - Alzheimer's Facts and Figures Report Alzheimer's Association. ICD-10CM - Alzheimer's Facts and Figures Report Alzheimer's Association. ICD-10 Mortality - Alzheimer's Facts and Figures Report Alzheimer's Association; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook:

https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20CodebookData%20GuideMetadata_10.2.19.xlsx

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Background Patients with Alzheimer's disease experience a progressive loss of cognitive function, and the ability to independently perform activities of daily life. Sometimes a dependent stage is reached quite early in the disease, when caregivers decide that the patients can no longer be left alone safely. This is an important aspect of Alzheimer's for patients, their families, and also health care providers. Understanding the relationship between a patient's current cognitive status and their need for care may assist clinicians when recommending an appropriate management plan. In this study, we investigated the relationship of cognitive function to dependence on caregivers before the patients reach a severe stage of the disease.

   Methods
   Data were obtained on 1,289 patients with mild-to-moderate Alzheimer's disease studied in two randomised clinical trials of galantamine (Reminyl®). Cognition was assessed using the cognitive part of the Alzheimer's Disease Assessment Scale (ADAS-cog) and Mini-Mental State Examination (MMSE). Patients were considered dependent if they required >12 hours of supervision each day or had high care needs. The Disability Assessment for Dementia (DAD) scale was also used as a measure of dependence. Disability was predicted directly using MMSE and ADAS-cog and compared to predictions from converted scores.


   Results
   The odds ratio of dependence was significantly higher amongst the patients with worse cognitive impairment, adjusting for age, gender and antipsychotic medication use. For example, a 4-point difference in ADAS-cog score was associated with an increase of 17% (95% CI 11–23) in the adjusted odds for >12 hours of supervision, and of 35% (95% CI 28–43) for dependence. Disability predicted directly using actual ADAS-cog and scores converted from MMSE values had close agreement using the models developed.


   Conclusion
   In patients with mild-to-moderate Alzheimer's disease, even relatively small degrees of poorer cognitive function increased the risk of losing the ability to live independently.

This dataset contains gene expression data from individuals with varying degrees of Alzheimer's Disease (AD) and from healthy individuals. The hippocampal gene expression of nine control subjects and 22 AD subjects was analyzed using 31 separate microarrays. The correlation between each gene's expression and the Mini Mental Status Examination (MMSE) and neurofibrillary tangle (NFT) scores was then tested across all 31 subjects, regardless of their diagnosis. These tests revealed a significant transcriptional response involving thousands of genes that were strongly correlated with AD markers. Additionally, several hundred of these genes were also correlated with AD markers in only control and early-stage AD subjects (MMSE > 20). This dataset is valuable for identifying potential genes involved in AD and for diagnosing the disease using highly associated markers. However, the dataset contains 13147 genes (features) instead of just 31 samples, which presents a challenge due to the curse of dimensionality. This makes it difficult to implement machine learning and deep learning models, and dimensional reduction should be applied first.

Missing attribute values: None

Class Distribution: Sever (7/31), Moderate (8/31), Incipient (7/31), Control (9/31)

Contributors: Eric M Blalock, James Geddes, Kuey-Chu Chen, Nada Porter, William Markesbery, Philip Landfield

Database weblink = http://www.ncbi.nlm.nih.gov/geo

Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.

Paper: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: https://doi.org/10.1101/454793.

Details: A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:

Development (Phases I-II). 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).

Hold-out (Phase III). 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.

CERAD-like hold-out. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.

Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.

Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.

Slide source and preparation: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (https://www.ucdmc.ucdavis.edu/alzheimers/). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.

Code: Please visit https://github.com/keiserlab/plaquebox-paper

Background Some alterations of the lipoprotein profile have been associated with cerebrovascular disease. Recently, it has been suggested that cerebrovascular disease might play a role in the pathogenesis of both vascular dementia (VD) and Alzheimer's disease (AD). Nevertheless, the possible association of dyslipidemias with VD or AD is still a controversial issue.

   Methods
   We investigated the lipoprotein profile in 100 older patients with vascular dementia (VD; n°: 60) or Late Onset Alzheimer's Disease (LOAD; n°: 40). The patients were compared with 54 community dwelling non-demented older controls.


   Results
   After adjustment for functional status, blood sedimentation rate, and serum albumin levels, no differences in lipoprotein profile emerged between the three groups, with the exception of HDL-C that was lower in VD compared with controls. Low HDL-C (< 45 mg/dL) was associated with VD (O.R.: 6.52, C.I. 95%: 1.42–30.70 vs controls, and 4.31, C.I. 95%: 0.93–19.82 vs LOAD), after multivariate adjustment. No differences in plasma lipid levels emerged between the three groups after stratification for apo E4 genotype.


   Conclusions
   In this cross-sectional study low HDL-C levels are associated with VD, but not with LOAD, in a sample of older subjects.

This series of code and corresponding data files are intended for use in cognitive decline and Alzheimer’s disease and related dementias (ADRD) research. The files include twelve years of cleaned datasets derived from the 2007-2018 years of the National Health Interview Survey (NHIS). NHIS is a nationally representative study aimed at monitoring the health of the non-institutionalized United States population. The provided datasets include sociodemographic information on respondents’ age, sex, race, and marital status from the Sample Adult Files, cognition variables from the Sample Adult files and, in applicable years, merged cognition data from the Adult Functioning and Disability (AFD) supplement. The files were constructed to allow for users to append multiple years of data for longitudinal analysis. Brief and detailed summaries of the variables available in these datasets along with more detailed descriptions of performed calculations can be found in the provided data dictionaries. Users may also refer to the provided “Overview of variables across years” document to see which variables are available each year. SAS, Stata, and CSV data file formats are provided as are the full coding scripts used in Stata.

The microglial surface protein Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) plays a critical role in mediating brain homeostasis and inflammatory responses in Alzheimer’s disease (AD). The soluble form of TREM2 (sTREM2) exhibits neuroprotective effects in AD, though the underlying mechanisms remain elusive. Moreover, differences in ligand binding between TREM2 and sTREM2, which have major implications for their roles in AD pathology, remain unexplained. To address these knowledge gaps, we conducted the most computationally intensive molecular dynamics simulations to date of human (s)TREM2, exploring their interactions with key damage- and lipoprotein-associated phospholipids and the impact of the AD-risk mutation R47H. Our results demonstrate that the flexible stalk domain of sTREM2 serves as the molecular basis for differential ligand binding between sTREM2 and TREM2, facilitated by its role in modulating the dynamics of the Ig-like domain and altering the accessibility of ..., , # The flexible stalk domain of sTREM2 modulates its interactions with brain-based phospholipids

Dataset DOI: 10.5061/dryad.0000000gn

Description of the data and file structure

MD trajectories, structure files, and analysis files (RMSD, RMSF) as outlined in the associated eLife paper. https://doi.org/10.7554/eLife.102269.2

Files and variables

File: eLife_Data_Availability.zip

Description:Â This directory contains molecular dynamics (MD) trajectories, structure files, and analysis outputs (RMSD and RMSF) corresponding to the simulations described in the associated eLife paper.

Folder Structure

The top-level directory contains two main folders:

• WT/ — Wild-type (s)TREM2 simulations
• R47H/ — R47H mutant (s)TREM2 simulations

Within each of these, subdirectories are organized by the simulated system. For example:

• PSIG/ — Simulations of the Ig-like domain of TREM2 in the presence of pho...,

Analyzing the proteomes of different brain cell types is fundamental for understanding the pathophysiology of Alzheimer's disease (AD). However, spatial analysis of these diverse and limited cell populations poses significant challenges. The GeoMx Digital Spatial Profiler (DSP) platform analyzed protein levels in the prefrontal cortex of AD and non-AD brains. The platform interrogated 76 proteins and used immunofluorescence to distinguish between three cell types. Neprilysin, which promotes Aβ degradation, was significantly higher in AD neurons and microglia. LAMP2A level was higher in neurons of individuals with AD compared to a control group. Additionally, markers of neuroinflammation, such as CD11c, CD11b, and CD163, were also elevated in AD neurons. Our findings indicate that the DSP platform effectively facilitates cell-specific snapshots of the AD brain proteome., , # Cell-specific protein expression in Alzheimer's disease prefrontal cortex

Dataset DOI: https://doi.org/10.5061/dryad.w6m905r12

GENERAL INFORMATION

1. Study Objective: This dataset supports a spatial proteomics study investigating cell-specific alterations in protein expression within the prefrontal cortex (Brodmann area 8/9) in Alzheimer's disease (AD). The analysis aims to characterize how protein signatures differ between AD and control samples at the cellular level, focusing on neurons, astrocytes, and microglia.

2.Authors Information:

• Principal Investigator Contact Information

Name: Mohammad Haeri, MD PhD

 Institution: University Of Kansas Medical Center
 Email: mhaeri@kumc.edu

• First Author Contact Information

   Name: Maryam Gholampour, PharmD
   Institution: University Of Kansas Medical Center
   Email: mgholampour@kumc.edu
  

3. Data Collection Dates: 2022-2023Â

**4. Geographic Locat..., The Kansas University Medical Center Human Subjects Committee (KUMC HSC) issued approval for all involvement of human subjects, and all participants provided informed consent prior to enrollment. This investigation was conducted in accordance with the Code of Ethics set forth by the World Medical Association (the Declaration of Helsinki).

BackgroundNeuropsychiatric symptoms (NPSs) lead to myriad poor health outcomes among individuals with Alzheimer’s disease (AD). Prior studies have observed associations between the various aspects of the home environment and NPSs, but macro-level environmental stressors (e.g., neighborhood income) may also disrupt the neuronal microenvironment and exacerbate NPSs. Yet, to our knowledge, no studies have investigated the relationship between the neighborhood environment and NPSs.MethodsUsing 2010 data among older adults with AD collected from a sample of the South Carolina Alzheimer’s Disease Registry, we estimated cross-sectional associations between neighborhood characteristics and NPSs in the overall population and by race/ethnicity. Neighborhood measures (within a 1/2-mile radius of residence) came from the American Community Survey and Rural Urban Commuting Area Code. We categorized median household income into tertiles: < $30,500, $30,500–40,000, and > $40,000, and rurality as: rural, small urban, and large urban. Residential instability was defined as the percent of residents who moved within the past year. NPSs were defined using the Neuropsychiatric Inventory Questionnaire that included the composite measure of all 12 domains. Adjusting for age, sex/gender, race/ethnicity, and caregiver educational attainment, we used negative binomial regression to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for NPSs by neighborhood characteristics.ResultsAmong 212 eligible participants, mean age was 82 ± 8.7 years, 72% were women, and 55% non-Hispanic (NH)-Black. Individuals with AD living in < $30,500 vs. > $40,000 income neighborhoods had a 53% (PR = 1.53; 95% CI = 1.06–2.23) higher prevalence of NPSs while individuals living in rural vs. large urban neighborhoods had a 36% lower prevalence of NPSs (PR = 0.64; 95% CI = 0.45–0.90), after adjustment. We did not observe an association between residential instability and NPSs (PR = 0.92; 95% CI = 0.86–1.00); however, our estimates suggested differences by race/ethnicity where NH-White older adults living in residential instable areas had lower NPSs (PR = 0.89; 95% CI = 0.82–0.96) compared to NH-Black older adults (PR = 0.96; 95% CI = 0.86–1.07).DiscussionAcross racial/ethnic groups, individuals with AD had more symptomology when living in lower income areas. Pending replication, intervention efforts should consider resource allocation to high-need neighborhoods (e.g., lower income), and studies should investigate underlying mechanisms for this relationship.

Supplementary dataset and tutorials for the "Statistical analysis in metabolic phenotyping"

This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed1 and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and 1H NMR, using the methods described by Lewis et al2 and Dona et al. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE3 repository, which can be accessed via the Sage BioNetworks portal (https://doi.org/10.7303/syn22252881). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: https://www.ebi.ac.uk/metabolights/MTBLS719.

1.  Lovestone, S. et al. AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in Annals of the New York Academy of Sciences (2009). doi:10.1111/j.1749-6632.2009.05064.x
   

2.  Lewis, M. R. et al. Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal. Chem. 88, acs.analchem.6b01481 (2016).
   

3.  Birkenbihl, C. et al. ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. medRxiv (2020). doi:10.1101/2020.08.04.20168229
   

There were a total of 451 ICD codes in Sorokdo National Hospital. Therefore, a total of 7603 ICD code persons from 2005 to 2020, when computerization was performed, were the study cohort of Sorok Island. Participants were randomized in a 2:1 ratio to DDS (+) or matching placebo DDS (-). Randomization was unrestricted (no blocking or stratification), and we analysed the ICD codes in databases of Sorokdo National Hospital. A 2:1 allocation ratio was performed because of HD patients taking dapsone for a lifetime. Therefore, it increases participants' probability of receiving the active study drug without compromising statistical power. Furthermore, the 2:1 allocation ratio led to a 10% increase in the overall sample size relative to a 1:1 allocation ratio.

Porphyromonas gingivalis, a bacterium associated with periodontal disease, is a suspected cause of Alzheimer’s disease. This bacterium is reliant on gingipain proteases, which cleave host proteins after arginine and lysine residues. To characterize gingipain susceptibility, we performed enrichment analyses of arginine and lysine proportion proteome-wide. Genes differentially expressed in brain samples with detected P. gingivalis reads were also examined. Genes from these analyses were tested for functional enrichment and specific neuroanatomical expression patterns. Proteins in the SRP-dependent cotranslational protein targeting to membrane pathway were enriched for these residues and previously associated with periodontal and Alzheimer’s disease. These ribosomal genes are up-regulated in prefrontal cortex samples with detected P. gingivalis sequences. Other differentially expressed genes have been previously associated with dementia (ITM2B, MAPT, ZNF267, and DHX37). For an anatomical perspective, we characterized the expression of the P. gingivalis associated genes in the mouse and human brain. This analysis highlighted the hypothalamus, cholinergic neurons, and the basal forebrain. Our results suggest markers of neural P. gingivalis infection and link the cholinergic and gingipain hypotheses of Alzheimer’s disease. Code on github: https://github.com/leonfrench/gingipain_releaseSupplement Table 1: Proteome-wide arginine and lysine counts and proportion.Supplement Table 2: Gene Ontology enrichment results for arginine and lysine proportions.Supplement Table 3: SRP-dependent cotranslational protein targeting to membrane amino acid specificity tests. Supplement Table 4: Results of differential expression tests for P. gingivalis detection.Supplement Table 5: Gene Ontology enrichment results for genes associated with P. gingivalis detection.Supplement Table 6: Neuroanatomical enrichment results for genes associated with P. gingivalis detection.Supplement Table 7: Nervous system cell-type enrichment results for genes associated with P. gingivalis detection.Supplement Table 8: Neuroanatomical enrichment results for the SRP-dependent cotranslational protein targeting to membrane genes.Supplement Table 9: Nervous system cell-type enrichment results for the SRP-dependent cotranslational protein targeting to membrane genes.Supplement Figure 1: Gingipain digestion of 70S ribosomes. Fifteen microliter reactions containing 2.5 ug of 70S ribosome and either 120 nM of recombinant Lys-gingipain (rKgp), Arg-gingipains (rRgpA and rRgpB), or about 206 – 412 µM (one unit in a 15 µL reaction volume) of Caspase-3 were incubated at 30°C for 1 h or 3 h. After incubation, results were visualized using SDS PAGE & Coomassie brilliant blue staining. The Precision Plus Protein WesternC Dual Color ladder was used for band sizing. The asterisk (*) to the left of the ribosome only control indicates a faint band present at about 60 kDa. This faint band appeared to be digested by Kgp and RgpB after 1 and 3 hours of incubation, as indicated by carets (^) to the left of the Kgp and RgpB.

BackgroundMitochondrial dysfunction may play a central role in the pathologic process of Alzheimer’s disease (AD), but there is still a scarcity of data that directly links the pathology of AD with the alteration of mitochondrial DNA. This study aimed to provide a comprehensive assessment of mtDNA rearrangement events in AD brain tissue.Patients and MethodsPostmortem frozen human brain cerebral cortex samples were obtained from the Banner Sun Health Research Institute Brain and Body Donation Program, Sun City, AZ. Mitochondria were isolated and direct sequence by using MiSeq®, and analyzed by relative software.ResultsThree types of mitochondrial DNA (mtDNA) rearrangements have been seen in post mortem human brain tissue from patients with AD and age matched control. These observed rearrangements include a deletion, F-type rearrangement, and R-type rearrangement. We detected a high level of mtDNA rearrangement in brain tissue from cognitively normal subjects, as well as the patients with Alzheimer's disease (AD). The rate of rearrangements was calculated by dividing the number of positive rearrangements by the coverage depth. The rearrangement rate was significantly higher in AD brain tissue than in control brain tissue (17.9%versus 6.7%; p = 0.0052). Of specific types of rearrangement, deletions were markedly increased in AD (9.2% versus 2.3%; p = 0.0005).ConclusionsOur data showed that failure of mitochondrial DNA in AD brain might be important etiology of AD pathology.