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      Correlation between Alzheimer’s disease and type 2 diabetes using non-negative matrix factorization

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      1 , 1 , , the Alzheimer’s Disease Neuroimaging Initiative
      Scientific Reports
      Nature Publishing Group UK
      Alzheimer's disease, Transcription

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          Abstract

          Alzheimer’s disease (AD) is a complex and heterogeneous disease that can be affected by various genetic factors. Although the cause of AD is not yet known and there is no treatment to cure this disease, its progression can be delayed. AD has recently been recognized as a brain-specific type of diabetes called type 3 diabetes. Several studies have shown that people with type 2 diabetes (T2D) have a higher risk of developing AD. Therefore, it is important to identify subgroups of patients with AD that may be more likely to be associated with T2D. We here describe a new approach to identify the correlation between AD and T2D at the genetic level. Subgroups of AD and T2D were each generated using a non-negative matrix factorization (NMF) approach, which generated clusters containing subsets of genes and samples. In the gene cluster that was generated by conventional gene clustering method from NMF, we selected genes with significant differences in the corresponding sample cluster by Kruskal–Wallis and Dunn-test. Subsequently, we extracted differentially expressed gene (DEG) subgroups, and candidate genes with the same regulation direction can be extracted at the intersection of two disease DEG subgroups. Finally, we identified 241 candidate genes that represent common features related to both AD and T2D, and based on pathway analysis we propose that these genes play a role in the common pathological features of AD and T2D. Moreover, in the prediction of AD using logistic regression analysis with an independent AD dataset, the candidate genes obtained better prediction performance than DEGs. In conclusion, our study revealed a subgroup of patients with AD that are associated with T2D and candidate genes associated between AD and T2D, which can help in providing personalized and suitable treatments.

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          Most cited references51

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          Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database.

          The past decade has witnessed hundreds of reports declaring or refuting genetic association with putative Alzheimer disease susceptibility genes. This wealth of information has become increasingly difficult to follow, much less interpret. We have created a publicly available, continuously updated database that comprehensively catalogs all genetic association studies in the field of Alzheimer disease (http://www.alzgene.org). We performed systematic meta-analyses for each polymorphism with available genotype data in at least three case-control samples. In addition to identifying the epsilon4 allele of APOE and related effects, we pinpointed over a dozen potential Alzheimer disease susceptibility genes (ACE, CHRNB2, CST3, ESR1, GAPDHS, IDE, MTHFR, NCSTN, PRNP, PSEN1, TF, TFAM and TNF) with statistically significant allelic summary odds ratios (ranging from 1.11-1.38 for risk alleles and 0.92-0.67 for protective alleles). Our database provides a powerful tool for deciphering the genetics of Alzheimer disease, and it serves as a potential model for tracking the most viable gene candidates in other genetically complex diseases.
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            A flexible R package for nonnegative matrix factorization

            Background Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. Results Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. Conclusions The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN.
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              Type 2 Diabetes and its Impact on the Immune System

              Introduction: Type 2 Diabetes (T2D) is a major health problem worldwide. This metabolic disease is indicated by high blood glucose levels due to insufficient insulin production by the pancreas. An inflammatory response occurs as a result of the immune response to high blood glucose levels as well as the presence of inflammatory mediators produced by adipocytes and macrophages in fat tissue. This low and chronic inflammation damages the pancreatic beta cells and leads to insufficient insulin production, which results in hyperglycemia. Hyperglycemia in diabetes is thought to cause dysfunction of the immune response, which fails to control the spread of invading pathogens in diabetic subjects. Therefore, diabetic subjects are known to more susceptible to infections. The increased prevalence of T2D will increase the incidence of infectious diseases and related comorbidities. Objective: This review provides an overview of the immunological aspect of T2D and the possible mechanisms that result in increased infections in diabetics. Conclusion: A better understanding of how immune dysfunctions occur during hyperglycemia can lead to novel treatments and preventions for infectious diseases and T2D comorbidities, thus improving the outcome of infectious disease treatment in T2D patients.
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                Author and article information

                Contributors
                hyunjulee@gist.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 July 2021
                27 July 2021
                2021
                : 11
                : 15265
                Affiliations
                [1 ]GRID grid.61221.36, ISNI 0000 0001 1033 9831, School of Electrical Engineering and Computer Science, , Gwangju Institute of Science and Technology, ; Gwangju, Korea
                [2 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, UC San Francisco, ; San Francisco, CA 94107 USA
                [3 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, UC San Diego, ; La Jolla, CA 92093 USA
                [4 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Mayo Clinic, ; Rochester, MN USA
                [5 ]GRID grid.47840.3f, ISNI 0000 0001 2181 7878, UC Berkeley, ; Berkeley, San Francisco USA
                [6 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [7 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, USC, ; Los Angeles, CA 90032 USA
                [8 ]GRID grid.27860.3b, ISNI 0000 0004 1936 9684, UC Davis, ; Sacramento, CA USA
                [9 ]GRID grid.38142.3c, ISNI 000000041936754X, Brigham and Women’s Hospital/Harvard Medical School, ; Boston, MA 02215 USA
                [10 ]GRID grid.411377.7, ISNI 0000 0001 0790 959X, Indiana University, ; Bloomington, IN 47405 USA
                [11 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Washington University St. Louis, ; St. Loui, MO 63110 USA
                [12 ]GRID grid.468171.d, Prevent Alzheimer’s Disease, ; Rockville, MD 20850 USA
                [13 ]GRID grid.5406.7, ISNI 000000012178835X, Siemens, ; Erlangen, Germany
                [14 ]GRID grid.422384.b, ISNI 0000 0004 0614 7003, Alzheimer’s Association, ; Chicago, IL 60631 USA
                [15 ]GRID grid.21925.3d, ISNI 0000 0004 1936 9000, University of Pittsburg, ; Pittsburgh, PA 15213 USA
                [16 ]GRID grid.5386.8, ISNI 000000041936877X, Cornell University, ; Ithaca, NY 14853 USA
                [17 ]GRID grid.268433.8, ISNI 0000 0004 1936 7638, Albert Einstein College of Medicine, , Yeshiva University, ; Bronx, NY 10461 USA
                [18 ]AD Drug Discovery Foundation, New York, NY 10019 USA
                [19 ]GRID grid.427650.2, Acumen Pharmaceuticals, ; Livermore, CA 94551 USA
                [20 ]GRID grid.16753.36, ISNI 0000 0001 2299 3507, Northwestern University, ; Chicago, IL 60611 USA
                [21 ]GRID grid.416868.5, ISNI 0000 0004 0464 0574, National Institute of Mental Health, ; Bethesda, MD 20892 USA
                [22 ]GRID grid.40263.33, ISNI 0000 0004 1936 9094, Brown University, ; Providence, RI 02912 USA
                [23 ]GRID grid.34477.33, ISNI 0000000122986657, University of Washington, ; Seattle, WA 98195 USA
                [24 ]GRID grid.4464.2, ISNI 0000 0001 2161 2573, University of London, ; London, UK
                [25 ]GRID grid.239844.0, ISNI 0000 0001 0157 6501, UCLA, ; Torrance, CA 90509 USA
                [26 ]GRID grid.214458.e, ISNI 0000000086837370, University of Michigan, ; Ann Arbor, MI 48109-2800 USA
                [27 ]GRID grid.223827.e, ISNI 0000 0001 2193 0096, University of Utah, ; Salt Lake City, UT 84112 USA
                [28 ]GRID grid.418204.b, ISNI 0000 0004 0406 4925, Banner Alzheimer’s Institute, ; Phoenix, AZ 85006 USA
                [29 ]UUC Irvine, Orange, CA 92868 USA
                [30 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Johns Hopkins University, ; Baltimore, MD 21205 USA
                [31 ]Richard Frank Consulting, New York, USA
                [32 ]GRID grid.419475.a, ISNI 0000 0000 9372 4913, National Institute on Aging, ; Baltimore, MD USA
                [33 ]GRID grid.5288.7, ISNI 0000 0000 9758 5690, Oregon Health and Science University, ; Portland, OR 97239 USA
                [34 ]GRID grid.265892.2, ISNI 0000000106344187, University of Alabama, ; Birmingham, AL USA
                [35 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Mount Sinai School of Medicine, ; New York, NY USA
                [36 ]GRID grid.240684.c, ISNI 0000 0001 0705 3621, Rush University Medical Center, ; Chicago, IL 60612 USA
                [37 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Baylor College of Medicine, ; Houston, TX USA
                [38 ]Wien Center, Miami Beach, FL 33140 USA
                [39 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Columbia University Medical Center, ; New York, NY USA
                [40 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, New York University, ; New York, NY USA
                [41 ]GRID grid.267313.2, ISNI 0000 0000 9482 7121, University of Texas Southwestern Medical School, ; Galveston, TX 77555 USA
                [42 ]GRID grid.189509.c, ISNI 0000000100241216, Duke University Medical Center, ; Durham, NC USA
                [43 ]GRID grid.189967.8, ISNI 0000 0001 0941 6502, Emory University, ; Atlanta, GA 30307 USA
                [44 ]GRID grid.266515.3, ISNI 0000 0001 2106 0692, Medical Center, , University of Kansas, ; Kansas City, KS USA
                [45 ]GRID grid.266539.d, ISNI 0000 0004 1936 8438, University of Kentucky, ; Lexington, KY USA
                [46 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Mayo Clinic, ; Jacksonville, FL USA
                [47 ]GRID grid.412750.5, ISNI 0000 0004 1936 9166, University of Rochester Medical Center, ; Rochester, NY 14642 USA
                [48 ]GRID grid.47100.32, ISNI 0000000419368710, Yale University School of Medicine, ; New Haven, CT USA
                [49 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, McGill Univ. Montreal-Jewish General Hospital, ; Montreal, PQ H3A 2A7 Canada
                [50 ]GRID grid.413104.3, ISNI 0000 0000 9743 1587, Sunnybrook Health Sciences, ; Toronto, ON Canada
                [51 ]U.B.C. Clinic for AD & Related Disorders, Vancouver, BC Canada
                [52 ]Cognitive Neurology - St. Joseph’s, London, ON Canada
                [53 ]GRID grid.239578.2, ISNI 0000 0001 0675 4725, Cleveland Clinic Lou Ruvo Center for Brain Health, ; Las Vegas, NV 89106 USA
                [54 ]GRID grid.477769.c, Premiere Research Inst (Palm Beach Neurology), ; W Palm Beach, FL USA
                [55 ]GRID grid.411667.3, ISNI 0000 0001 2186 0438, Georgetown University Medical Center, ; Washington, DC 20007 USA
                [56 ]GRID grid.168010.e, ISNI 0000000419368956, Stanford University, ; Stanford, CA 94305 USA
                [57 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Boston University, ; Boston, MA USA
                [58 ]GRID grid.257127.4, ISNI 0000 0001 0547 4545, Howard University, ; Washington, DC 20059 USA
                [59 ]GRID grid.67105.35, ISNI 0000 0001 2164 3847, Case Western Reserve University, ; Cleveland, OH 44106 USA
                [60 ]Neurological Care of CNY, Liverpool, NY 13088 USA
                [61 ]GRID grid.416448.b, ISNI 0000 0000 9674 4717, St. Joseph’s Health Care, ; London, ON N6A 4H1 Canada
                [62 ]GRID grid.417854.b, Dent Neurologic Institute, ; Amherst, NY 14226 USA
                [63 ]GRID grid.261331.4, ISNI 0000 0001 2285 7943, Ohio State University, ; Columbus, OH 43210 USA
                [64 ]GRID grid.413558.e, ISNI 0000 0001 0427 8745, Albany Medical College, ; Albany, NY 12208 USA
                [65 ]GRID grid.277313.3, ISNI 0000 0001 0626 2712, Hartford Hospital Olin Neuropsychiatry Research Center, ; Hartford, CT 06114 USA
                [66 ]GRID grid.413480.a, ISNI 0000 0004 0440 749X, Dartmouth-Hitchcock Medical Center, ; Lebanon, NH USA
                [67 ]GRID grid.412860.9, ISNI 0000 0004 0459 1231, Wake Forest University Health Sciences, ; Winston-Salem, NC USA
                [68 ]GRID grid.259828.c, ISNI 0000 0001 2189 3475, Medical University South Carolina, ; Charleston, SC 29425 USA
                [69 ]GRID grid.250263.0, ISNI 0000 0001 2189 4777, Nathan Kline Institute, ; Orangeburg, NY USA
                [70 ]GRID grid.214572.7, ISNI 0000 0004 1936 8294, University of Iowa College of Medicine, ; Iowa City, IA 52242 USA
                [71 ]GRID grid.170693.a, ISNI 0000 0001 2353 285X, USF Health Byrd Alzheimer’s Institute, , University of South Florida, ; Tampa, FL 33613 USA
                Article
                94048
                10.1038/s41598-021-94048-0
                8316581
                34315930
                4290936c-da0a-430e-83f7-3ea59e93fde1
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 January 2021
                : 24 June 2021
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