Identification of AD (Alzheimer's disease)-related genes obtained from blood samples is crucial for early AD diagnosis. We used three public datasets, ADNI, AddNeuroMed1 (ANM1), and ANM2, for this study. Five feature selection methods and five classifiers were used to curate AD-related genes and discriminate AD patients, respectively. In the internal validation (five-fold cross-validation within each dataset), the best average values of the area under the curve (AUC) were 0.657, 0.874, and 0.804 for ADNI, ANMI, and ANM2, respectively. In the external validation (training and test sets from different datasets), the best AUCs were 0.697 (training: ADNI to testing: ANM1), 0.764 (ADNI to ANM2), 0.619 (ANM1 to ADNI), 0.79 (ANM1 to ANM2), 0.655 (ANM2 to ADNI), and 0.859 (ANM2 to ANM1), respectively. These results suggest that although the classification performance of ADNI is relatively lower than that of ANM1 and ANM2, classifiers trained using blood gene expression can be used to classify AD for other data sets. In addition, pathway analysis showed that AD-related genes were enriched with inflammation, mitochondria, and Wnt signaling pathways. Our study suggests that blood gene expression data are useful in predicting the AD classification. Alzheimer's disease (AD), the most common form of dementia, is estimated to affect in 13.8 million individuals in the United States (US), with 7.0 million being aged 85 years or older by 2050 1. Based on the National Institute of Neurological, Communicative Disorders, and Stroke and Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria in 1985, probable or possible AD was diagnosed based on subjective symptoms and questionnaires 2. Recently, the transition from symptom-based to pathophysiology-based AD diagnosis showed that AD diagnosis is mainly based on structural brain changes (MRI), molecular neuroimaging changes (positron emission tomography imaging), and alterations in cerebral spinal fluid biomarkers 3. Although the elucidation of the biological basis of AD has resulted in many advancements 3 , early diagnostic detection of AD remains challenging. Recent advances in biotechnology have led to full-scale analyses of the genome, transcriptome, and epigenome rather than focusing on a few biomarkers. A large-scale genome-wide association study (GWAS) of 2,032 individuals with AD and 5,328 controls was presented in 2009 and it identified variants at CLU and CR1, which were associated with AD 4. Additionally, a meta-analysis of four previously reported GWAS datasets (17,008 AD cases, 37,154 controls) yielded 11 new loci of susceptibility to AD 5. Recently, Xu et al. constructed an AlzData database integrating data from GWAS, eQTL, interactome, and laboratory experiments 6 , which provides all human genes with scores for association with AD, called the Convergent Functional Genomics (CFG) score 7,8. In recent years, two large multi-center studies were conducted to identify biomarkers for early AD diagnosis and MCI progression to AD: the Europe-based ANM and ...

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Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

BackgroundThe association between skipping breakfast and cardio-metabolic syndrome is well known. However, there are very few Korean studies about the habit of eating breakfast and hypertension. The present study aimed to investigate the relationship between the habit of eating breakfast and hypertension in a healthy Korean population.MethodsParticipants in the 2014 Korea National Health and Nutrition Examination Surveys (KNHANES) were enrolled for this study. Medical history, including hypertension, was measured using a 24-hour recall method. The habit of eating breakfast was estimated from self-reported questionnaires and was classified into two groups: the eating breakfast group, defined as those who ate breakfast more than 5 times per week, and the not eating breakfast group, defined as those who did not eat any breakfast for a week.ResultsThe crude odds ratio of skipping breakfast for the prevalence of hypertension was 0.366. However, after adjusting for all considerable confounding factors (age, sex, regular exercise, current smoking, systolic blood pressure, diastolic blood pressure, body mass index, waist circumference, and red blood cell counts), not eating breakfast was associated with a higher risk of HTN (OR = 1.065; 95% CI = 1.057–1.073; p-value < 0.001)ConclusionThe habit of eating breakfast was associated with a lower risk of hypertension among healthy Korean adults.

Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.