scholarly journals Identifying novel metabolite biomarkers of adherence to a cluster analysis-derived healthy dietary pattern

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Shirin Macias ◽  
Brian D. Green ◽  
Jayne V. Woodside
Keyword(s):  
Citric Acid ◽  
Vitamin C ◽  
Clustering Analysis ◽  
Blood Metabolites ◽  
Whole Grain ◽  
Dietary Biomarkers ◽  
Sweet Foods ◽  
Carbonated Drinks ◽  
Carotene Beta ◽  
Cluster 2

AbstractThe MEDDINI intervention study investigated how advice improved the adoption of a Mediterranean diet (MD) in cardiovascular disease patients. Earlier research profiled the levels of blood metabolites in MEDDINI participants, in the process discovering a number dietary biomarkers indicative of a MD. However, a potential limitation of this approach is that MD scores are semi-quantitative, and don't reflect the absolute amounts of food consumed. Therefore, the present study identified distinct dietary patterns based on quantified food diary data from 58 MEDDINI participants by applying k-means clustering analysis. Previously measured blood metabolites (90) using targeted and untargeted methods were then assessed for their performance as dietary biomarkers. After careful standardisation (z-scores), optimisation and cross-validation dietary data were reduced to 6 specific food groups and this led to the formation of two clusters. Cluster 1 included participants who had the lowest intakes of fruit and vegetables, legumes, fish and whole grain cereals and the highest intake of meat and sweet foods (including carbonated drinks). Cluster 2 comprised the participants with highest intake of fruit and vegetables, legumes, fish and whole grain cereals and the lowest intake of meat and sweet foods (including carbonated drinks). Discriminatory metabolites (p derived from untargeted analysis included Citric acid, Tyrosine, Malonate, Pyroglutamic acid, Succinate, Betaine, L-asparagine and Fumaric acid which were significantly increased in cluster 2, and 2-Hydroxybutyric acid and Pyruvic acid which were significantly decreased in cluster 2. Targeted biomarker analysis showed 8 discriminatory metabolites which were significantly (p increased in cluster 2. These were Docosahexaenoic acid (DHA), alpha-Carotene, beta-Carotene, beta-Cryptoxanthin, Vitamin C, Lutein, alpha-Linolenic acid and Lycopene. Conversely Osbond acid, Cholesterol and Dihomo-γ-linolenic acid (DGLA) were significantly lower in cluster 2. Metabolites significantly correlated with some of the 6 groups in the clusters. For example, Citric acid, Betaine and Vitamin C positively correlated with combined fruit, fruit juice and vegetable intake: (r = 0.20, p = 0.018; r = 0.20, p = 0.02 and r = 0.34, p = 5.7E-5 respectively). DHA, alpha-Carotenoid and beta-Carotenoid significantly correlated with fish intake (r = 0.58, p = 1.94E-13; r = 0.40, p = 2E-6 and r = 0.30, p = 3.5E-4 respectively). The present study demonstrates the utility of clustering analysis for effectively assessing adherence to healthy dietary patterns and the discovery of novel dietary biomarkers.

scholarly journals Application of 1H-NMR Metabolomics for the Discovery of Blood Plasma Biomarkers of a Mediterranean Diet

Metabolites ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 201 ◽  
Author(s):  
Shirin Macias ◽  
Joseph Kirma ◽  
Ali Yilmaz ◽  
Sarah E. Moore ◽  
Michelle C. McKinley ◽  
...  
Keyword(s):  
Citric Acid ◽  
Mediterranean Diet ◽  
1H Nmr ◽  
Pyruvic Acid ◽  
Plasma Metabolites ◽  
Dietary Biomarkers ◽  
Metabolomic Profiling ◽  
1H Nuclear Magnetic Resonance ◽  
Sweet Foods ◽  
The Mediterranean

The Mediterranean diet (MD) is a dietary pattern well-known for its benefits in disease prevention. Monitoring adherence to the MD could be improved by discovery of novel dietary biomarkers. The MEDiterranean Diet in Northern Ireland (MEDDINI) intervention study monitored the adherence of participants to the MD for up to 12 months. This investigation aimed to profile plasma metabolites, correlating each against the MD score of participants (n = 58). Based on an established 14-point scale MD score, subjects were classified into two groups (“low” and “high”). 1H-Nuclear Magnetic Resonance (1H-NMR) metabolomic analysis found that citric acid was the most significant metabolite (p = 5.99 × 10−4*; q = 0.03), differing between ‘low’ and ‘high’. Furthermore, five additional metabolites significantly differed (p < 0.05; q < 0.35) between the two groups. Discriminatory metabolites included: citric acid, pyruvic acid, betaine, mannose, acetic acid and myo-inositol. Additionally, the top five most influential metabolites in multivariate models were also citric acid, pyruvic acid, betaine, mannose and myo-inositol. Metabolites significantly correlated with the consumption of certain food types. For example, citric acid positively correlated fruit, fruit juice and vegetable constituents of the diet, and negatively correlated with sweet foods alone or when combined with carbonated drinks. Citric acid was the best performing biomarker and this was enhanced by paired ratio with pyruvic acid. The present study demonstrates the utility of metabolomic profiling for effectively assessing adherence to MD and the discovery of novel dietary biomarkers.

scholarly journals OP0038 SPLICEOSOME ALTERATIONS IN LEUCOCYTES FROM APS, SLE AND SLE+APS PATIENTS ARE CLOSELY RELATED TO THEIR MAIN CLINICAL FEATURES

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 20.2-20
Author(s):  
A. M. Patiño-Trives ◽  
C. Perez-Sanchez ◽  
A. Ibañez-Costa ◽  
P. S. Laura ◽  
M. Luque-Tévar ◽  
...  
Keyword(s):  
Autoimmune Diseases ◽  
Clinical Features ◽  
Antiphospholipid Syndrome ◽  
Immune Cells ◽  
Inflammatory Mediators ◽  
Clustering Analysis ◽  
Renal Involvement ◽  
Molecular Clustering ◽  
Cluster 2 ◽  
Splicing Machinery

Background:To date, although multiple molecular approaches have illustrated the various aspects of Primary Antiphospholipid Syndrome (APS), systemic lupus erythematosus (SLE) and antiphospholipid syndrome plus lupus (APS plus SLE), no study has so far fully characterized the potential role of posttranscriptional regulatory mechanisms such as the alternative splicing.Objectives:To identify shared and differential changes in the splicing machinery of immune cells from APS, SLE and APS plus SLE patients, and their involvement in the activity and clinical profile of these autoimmune disorders.Methods:Monocytes, lymphocytes and neutrophils from 80 patients (22 APS, 35 SLE and 23 APS plus SLE) and 50 healthy donors (HD) were purified by immunomagnetic selection. Then, selected elements of the splicing machinery were evaluated using a microfluidic qPCR array (Fluidigm). In parallel, extensive clinical/serological evaluation was performed, comprising disease activity, thrombosis and renal involvement, along with autoantibodies, acute phase reactants, complement and inflammatory molecules. Molecular clustering analyses and correlation/association studies were developed.Results:Patients with primary APS, SLE and APS plus SLE displayed significant and specific alterations in the splicing machinery components in comparison with HD, that were further specific for each leukocyte subset. Besides, these alterations were associated with distinctive clinical features.Hence, in APS, clustering analysis allowed to identify two sets of patients representing different molecular profile groups with respect to the expression levels of splicing machinery components. Principal component analyses confirmed a clear separation between patients. Clinically, cluster 1 characterized patients with higher thrombotic episodes and recurrences than cluster 2 and displayed a higher adjusted global APS score (aGAPSS). Accordingly, these patients showed higher levels of inflammatory mediators than cluster 2.Similarly, in patients with APS plus SLE, clustering analysis allowed to identify two sets of patients showing differential expression of splicing machinery components. Clinical and laboratory profiles showed that cluster 2 characterized patients that had suffered more thrombotic recurrences, most of them displaying an aGAPSS over 12 points and expressing higher levels of inflammatory mediators than cluster 1. The incidence of lupus nephropathy was similarly represented in both clusters.Lastly, in SLE patients, molecular clustering analysis identified two sets of patients showing distinctive clinical features. One cluster characterized most of the patients positive for anti-dsDNA antibodies, further suffering lupus nephropathy, and a high proportion of them also presenting atheroma plaques and high levels of inflammatory mediators.Correlation studies further demonstrated that several deranged splicing machinery components in immune cells (i.e. SF3B1tv1, PTBP1, PRP8 and RBM17) were linked to the autoimmune profile of the three autoimmune diseases, albeit in a specific way on each disorder. Accordingly, in vitro treatment of HD lymphocytes with aPL-IgG or anti-dsDNA-IgG changed the expression of spliceosome components also found altered in vivo in the three autoimmune diseases. Finally, the induced over/downregulated expression of selected spliceosome components in leukocytes modulated the expression of inflammatory cytokines, changed the procoagulant/adhesion activities of monocytes and regulated NETosis in neutrophils.Conclusion:1) The splicing machinery, profoundly altered in leukocytes from APS, APS plus SLE and SLE patients, is closely related to the activity of these diseases, their autoimmune and inflammatory profiles. 2) The analysis of the splicing machinery allows the segregation of APS, APS plus SLE and SLE, with specific components explaining the CV risk and renal involvement in these highly related autoimmune disorders.Acknowledgements:Funded by ISCIII, PI18/00837 and RIER RD16/0012/0015 co-funded with FEDERDisclosure of Interests:None declared

scholarly journals Pattern of inflammatory immune response determines the clinical course and outcome of COVID-19: unbiased clustering analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eunyoung Emily Lee ◽  
Kyoung-Ho Song ◽  
Woochang Hwang ◽  
Sin Young Ham ◽  
Hyeonju Jeong ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Clustering Analysis ◽  
Validation Cohort ◽  
Inflammatory Responses ◽  
Clinical Course ◽  
Severe Disease ◽  
Outcome Data ◽  
Inflammatory Immune Response ◽  
Cluster 2

AbstractThe objective of the study was to identify distinct patterns in inflammatory immune responses of COVID-19 patients and to investigate their association with clinical course and outcome. Data from hospitalized COVID-19 patients were retrieved from electronic medical record. Supervised k-means clustering of serial C-reactive protein levels (CRP), absolute neutrophil counts (ANC), and absolute lymphocyte counts (ALC) was used to assign immune responses to one of three groups. Then, relationships between patterns of inflammatory responses and clinical course and outcome of COVID-19 were assessed in a discovery and validation cohort. Unbiased clustering analysis grouped 105 patients of a discovery cohort into three distinct clusters. Cluster 1 (hyper-inflammatory immune response) was characterized by high CRP levels, high ANC, and low ALC, whereas Cluster 3 (hypo-inflammatory immune response) was associated with low CRP levels and normal ANC and ALC. Cluster 2 showed an intermediate pattern. All patients in Cluster 1 required oxygen support whilst 61% patients in Cluster 2 and no patient in Cluster 3 required supplementary oxygen. Two (13.3%) patients in Cluster 1 died, whereas no patient in Clusters 2 and 3 died. The results were confirmed in an independent validation cohort of 116 patients. We identified three different patterns of inflammatory immune response to COVID-19. Hyper-inflammatory immune responses with elevated CRP, neutrophilia, and lymphopenia are associated with a severe disease and a worse outcome. Therefore, targeting the hyper-inflammatory response might improve the clinical outcome of COVID-19.

scholarly journals Analisis Pola Masa Studi Mahasiswa Fakultas Teknik Universitas Darma Persada Menggunakan Metode Clustering

2020 ◽  
Vol 7 (4) ◽  
pp. 861
Author(s):  
Ayu Hardianti ◽  
Dewi Agushinta. R
Keyword(s):  
Grade Point Average ◽  
Clustering Analysis ◽  
Study Program ◽  
Grade Point ◽  
Program Manager ◽  
Knowledge Analysis ◽  
Initial Stage ◽  
Study Programs ◽  
Result Interpretation ◽  
Cluster 2

<p class="Abstrak"><span lang="IN">Penelitian ini bertujuan menganalisis pola lama studi mahasiswa fakultas teknik universitas Darma Persada dari</span><span lang="IN">data akademik. Metode yang digunakan adalah <em>clustering</em> algoritma K-Means. Variabel yang dianalisis adalah </span><span lang="IN">jurusan, daerah asal, umur, jenis kelamin, Indeks Prestasi Komulatif (IPK), Satuan Kredit Semester (SKS), tahun masuk, lama studi. Analisis dilakukan menggunakan perangkat lunak WEKA. Penelitian dilakukan melalui pengumpulan data dari arsip atau  <em>database</em> biro Administrasi Akademik yaitu berupa data akademik mahasiswa fakultas teknik Universitas Darma Persada angkatan 2009 sampai 2014. Tahapan selanjutnya adalah <em>preprocessing</em> data yang dilakukan melalui analisis metode <em>clustering</em> menggunakan algoritma K-Means dengan terlebih dahulu menentukan jumlah <em>cluster </em>menggunakan metode Elbow dan interpretasi hasil. Berdasarkan hasil metode Elbow, jumlah <em>cluster</em> sebanyak 4 <em>cluster</em>. Berdasarkan hasil proses K-Means <em>clustering, </em>pembagian data pada masing-masing <em>cluster </em>adalah <em>cluster </em>1 berjumlah 556 data (26%), <em>cluster </em>2 berjumlah 414 data (19%), <em>cluster </em>3 berjumlah 189 data (9%) dan <em>cluster </em>4 berjumlah 1010 data (46%). Selanjutnya, yang memiliki lama studi lebih dari 4 tahun (lebih dari 8 semester) berada pada <em>cluster </em>2, <em>cluster </em>3, <em>cluster </em>4 sedangkan mahasiswa yang memiliki masa studi 4 tahun (8 semester) berada pada <em>cluster </em>1.</span></p><p class="Abstrak"><span lang="IN"><br /></span></p><p class="Abstrak"><em><strong><span lang="IN">Abstract</span></strong></em></p><p class="Judul2"><em>The duration of student study is one of the factors that influence the completing students' timeliness. Based on the policy of the National Accreditation Board of Higher Education (BAN-PT) in Regulation No. 4 of 2017 concerning the Policy for Preparing Accreditation Instruments, the duration of study is one of the benchmarks and evaluation elements in accreditation of study programs. From the Faculty of Engineering academic data, Darma Persada University, many students take more than four years of study. The duration of study is one of the problems of the study program manager in terms of academic performance. This study aims to analyze the old patterns of study by students of the Faculty of Engineering, Darma Persada University from academic data. K-Means algorithm clustering technique is used with the variables are majors, the area of origin, age, gender, Grade Point Average (GPA), Semester Credit Unit (SKS), year of entry and study duration. The Waikato Environment for Knowledge Analysis (WEKA) software is used as an analytic tool. The initial stage of research is through collecting data from archives or Academic sections, namely academic data from students of the Faculty of Engineering, Darma Persada University, 2009 to 2014. The next stage is preprocessing data through K-Means algorithm clustering analysis by first calculating many clusters using the Elbow method and result interpretation. From the Elbow method result, the number of clusters used is 4 (four) clusters. Based on the results of the K-Means clustering process, the data sharing in each cluster is cluster 1 (one) totaling 556 data (26%), cluster 2 (two) totaling 414 data (19%), cluster 3 (three) totaling 189 data (9%) and cluster 4 (four) totaling 1010 data (46%). Furthermore, those who have more than 4 years of study are in cluster 2, cluster 3, cluster 4 and students who have a 4-year study period are in cluster 1.</em></p><p class="Judul2"> </p><p class="Abstrak"><em><strong><span lang="IN"><br /></span></strong></em></p>

scholarly journals Variability of Fruit Quality among 103 Acerola (Malpighia emarginata D. C.) Phenotypes from the Subtropical Region of Brazil

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1078
Author(s):  
Daniela Farinelli ◽  
Silvia Portarena ◽  
Daniel Fernandes da Silva ◽  
Chiara Traini ◽  
Giordana Menegazzo da Silva ◽  
...  
Keyword(s):  
Vitamin C ◽  
Fruit Quality ◽  
Fruit Size ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Pulp Yield ◽  
Subtropical Region ◽  
Breeding Programs ◽  
Malpighia Emarginata ◽  
Cluster 2

Acerola fruit is one of the richest natural sources of ascorbic acid. As a consequence, acerola fruit and its products are in demand worldwide for the production of health supplements and for the development of functional products. Acerola phenotypes (103) were screened in Western Paraná State, in the Southern region of Brazil, and evaluated to obtain information on fruit quality characteristics with the aim of using them in future breeding programs. Principal Component and Hierarchical Cluster analysis were performed on all datasets to explore the variability among samples and to identify the main clusters. A great variability among phenotypes was observed, with potential for use in breeding programs. Seven phenotypes were selected as candidates in the next breeding program, characterized by high vitamin C content and yield, or higher values of fruit size and color parameters. Four belong to cluster 1 and three to cluster 2. Specifically, two phenotypes, belonging to cluster 2, showed the best performance in terms of vitamin C (2150 mg 100 g−1 pulp and 2625 mg 100 g−1 pulp respectively) and pulp yield (74.8% and 82.3% respectively), and one phenotype, belonging to cluster 1, for high pulp yield, fruit size and vitamin C content (80.3% 6.43 g and 2490 mg 100 g−1 pulp).

scholarly journals Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020

2021 ◽  
Vol 22 (1) ◽  
pp. 1
Author(s):  
Febiyanti Alfiah ◽  
Almadayani Almadayani ◽  
Danial Al Farizi ◽  
Edy Widodo
Keyword(s):  
Clustering Analysis ◽  
The Other ◽  
Poor People ◽  
Clustering Method ◽  
Capita Expenditure ◽  
Poor Households ◽  
Per Capita ◽  
Poverty Indicators ◽  
Partition Method ◽  
Cluster 2

 Keberadaan pandemi COVID-19 di Indonesia, mengakibatkan kemiskinan di Indonesia semakin tinggi terutama di Jawa Timur yang menjadi satu diantara provinsi lain dengan kasus COVID-19 tinggi di Indonesia. Tujuan penelitian ini yaitu mengetahui pengelompokan kabupaten/kota di Jawa Timur yang mempunyai kesamaan karakteristik berdasarkan indikator kemiskinan tahun 2020. Penelitian ini menggunakan data yang didapatkan dari Badan Pusat Statistik. Metode yang digunakan ialah metode k-medoids clustering yang merupakan metode partisi clustering guna pengelompokan n objek ke dalam k cluster. Berdasarkan hasil penelitian, diperoleh pengelompokan karakteristik masing-masing cluster yang dibentuk berdasarkan nilai indikator kemiskinan di Jawa Timur tahun 2020 sebanyak 2 cluster. Dimana 30 kabupaten/kota pada cluster 1 dan dan 8 kabupaten/kota pada cluster 2. Cluster 1 memiliki karakteristik Persentase Rumah Tangga yang Mempunyai Sanitasi Layak, Angka Harapan Hidup, dan Persentase Angka Melek Huruf Umur 15-55 Th tinggi. Sedangkan cluster 2 memiliki karakteristik Persentase Rumah Tangga Miskin Penerima Raskin, Persentase Penduduk Miskin, dan Persentase Pengeluaran Perkapita untuk Makanan dengan Status Miskin tinggi. Kata kunci: Clustering; Jawa Timur; K-medoids; kemiskinan  K-Medoids Clustering Analysis Based on Poverty Indicators in East Java in 2020 ABSTRACT The existence of the pandemic COVID-19 in Indonesia has resulted in higher poverty in Indonesia, especially in East Java, which is one of the other provinces with high cases in Indonesia. The purpose of this study is to find out the grouping of regencies/cities in East Java that have similar characteristics based on the poverty indicators in 2020. This study uses data obtained from the Badan Pusat Statistik. The method used is k-medoids clustering method which is a clustering partition method for grouping n objects into k clusters. Based on the results of the study, it was found that the grouping of the characteristics of each cluster formed based on the value of the poverty indicator in East Java in 2020 was 2 clusters. Where 30 regencies/cities in cluster 1 and and 8 regencies/cities in cluster 2. Cluster 1 has the characteristics of the percentage of households that have proper sanitation, life expectancy, and a high percentage of literacy rates aged 15-55 years. While cluster 2 has the characteristics of the percentage of poor households receiving Raskin, the percentage of poor people, and the percentage of per capita expenditure on food with high poor status. Keywords: Clustering; East Java; K-Medoids; poverty

Sign in / Sign up

Export Citation Format

Share Document