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  • principal component analysis x
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V G Pluimakers Princess Máxima Centre for Paediatric Oncology, Utrecht, The Netherlands

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M van Waas Department of Paediatric Oncology/Haematology, Erasmus MC–Sophia Children’s Hospital, Rotterdam, The Netherlands

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C W N Looman Department of Public Health, Erasmus MC, Rotterdam, The Netherlands

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M P de Maat Department of Haematology, Erasmus MC, Rotterdam, The Netherlands

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R de Jonge Department of Clinical Chemistry, Erasmus MC, Rotterdam, The Netherlands

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P Delhanty Section Endocrinology, Department of Medicine, Erasmus MC, Rotterdam, The Netherlands

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M Huisman Section Endocrinology, Department of Medicine, Erasmus MC, Rotterdam, The Netherlands

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F U S Mattace-Raso Section Geriatric Medicine, Department of Medicine, Erasmus MC, Rotterdam, The Netherlands

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M M van den Heuvel-Eibrink Princess Máxima Centre for Paediatric Oncology, Utrecht, The Netherlands

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S J C M M Neggers Princess Máxima Centre for Paediatric Oncology, Utrecht, The Netherlands
Section Endocrinology, Department of Medicine, Erasmus MC, Rotterdam, The Netherlands

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, based on principal component analysis and vascular ultrasound measurements. Patients and methods Patients Patients were actively recruited as described before ( 3 ). Briefly, all long-term (5 or more years after treatment) adult survivors of

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-20-0144
Volume/Issue:
Volume 9: Issue 7
Open access
Jia Li Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China
Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, China

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Yan Zhao Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China

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Caoxin Huang Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China

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Zheng Chen Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China

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Xiulin Shi Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China

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Long Li Institute of Drug Discovery Technology, Ningbo University, Ningbo, China

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Zhong Chen Department of Electronic Science, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, China

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Xuejun Li Xiamen Diabetes Institute, The First Affiliated Hospital of Xiamen University, Xiamen, China

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. The XCMS software was employed to convert each data file into a matrix of detected peaks. Differences in metabolic profiles on LC-MS were determined by principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-19-0023
Volume/Issue:
Volume 8: Issue 4
Open access
Li Jing College of Physical Education, Chaohu University, Anhui Province, China

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Wang Chengji College of Physical Education, Chaohu University, Anhui Province, China

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out PCA analysis, the results shown in Fig. 3 , the first principal component (PC1) and the second principal component (PC2) contain information amount of 21.3 and 12.3%, respectively. As can be seen from Fig. 3 , the control group and the DM group

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-19-0012
Volume/Issue:
Volume 8: Issue 6
Open access
Yang Yu Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Hairong Hao Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Linghui Kong Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Jie Zhang Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Feng Bai Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Fei Guo Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Pan Wei Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Rui Chen Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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Wen Hu Department of Endocrinology and Metabolism, Huai’an Hospital Affiliated to Xuzhou Medical University and Huai’an Second People’s Hospital, Huai’an, Jiangsu, China

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the relative abundance of each metabolite type in each sample. (D) 2D principal component score plots of the analysed samples and box plots corresponding to the principal component scores. (E) Partial least square-discriminant analysis (PLS-DA) score

Article Type:
Research Article
Collections:
Metabolic Syndrome and Diabetes
DOI:
https://doi.org/10.1530/EC-23-0079
Volume/Issue:
Volume 12: Issue 10
Open access
Ju-shuang Li Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Tao Wang Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Jing-jing Zuo Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Cheng-nan Guo Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Fang Peng Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Shu-zhen Zhao Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Hui-hui Li Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Xiang-qing Hou Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China

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Yuan Lan Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China
Department of Ophthalmology, Pingxiang People’s Hospital of Southern Medical University, Pingxiang, Jiangxi, China

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Ya-ping Wei Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China

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Chao Zheng The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

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Guang-yun Mao Division of Epidemiology and Health Statistics, Department of Preventive Medicine, School of Public Health & Management, Wenzhou Medical Unviersity, Wenzhou, Zhejiang, China
Center on Evidence-Based Medicine & Clinical Epidemiological Research, School of Public Health & Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
Center on Clinical Research, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, Zhejiang, China

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, we additionally performed a multiple principal component analysis (PCA) on n-6 PUFAs to total n-3 PUFAs. As we could see in Fig. 2 , the top two principle components (PC) could explain 80.8% (62.0 and 18.8% for the 1st and 2nd PC, respectively) of

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-20-0370
Volume/Issue:
Volume 9: Issue 12
Open access
Charlotte Höybye Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden
Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden

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Erik Wahlström Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden

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Petra Tollet-Egnell Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden

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Gunnar Norstedt Department of Endocrinology, Department of Molecular Medicine and Surgery, Metabolism and Diabetology, Karolinska University Hospital, 171 76 Stockholm, Sweden

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custom scripts. Statistical analyses The values are presented as median and range or means± s.e.m . For univariate data Student's t -test was used. All multivariate statistical analyses, i.e. principal component analysis (PCA) and orthogonal projections

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-14-0098
Volume/Issue:
Volume 3: Issue 4
Open access
Ya-Fen Hu Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
Department of Endocrinology, The People’s Hospital of Daxing District, Beijing, China

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Lin Hua Department of Mathematics, School of Biomedical Engineering, Capital Medical University, Beijing, China

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Xiu Tuo Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Ting-Ting Shi Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Yi-Lin Yang Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Yun-Fu Liu Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Zhong-Yu Yan Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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Zhong Xin Department of Endocrinology, Beijing Tongren Hospital, Capital Medical University, Beijing, China

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analysis was performed to find the association between the gene block and the CT index block ( Fig. 3A ). The loadings of the gene markers indicated the association strength to the first principal component (named as comp 1) of the CT index block. The X

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-20-0147
Volume/Issue:
Volume 9: Issue 7
Open access
Lei Lei Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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Yi-Hua Bai Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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Hong-Ying Jiang Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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Ting He Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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Meng Li Department of Nephrology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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Jia-Ping Wang Department of Radiology, The Second Hospital Affiliated to Kunming Medical University, Kunming, Yunnan, China

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peaks between T2D and control. (B) The top 50 up- and downregulated differential peaks genes from A. The red-colored genes were upregulated and the blue-colored genes were downregulated. (C) Principle component analysis (PCA) for the m6A methylome

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-21-0328
Volume/Issue:
Volume 10: Issue 10
Open access
Sandra N Slagter Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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Robert P van Waateringe Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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André P van Beek Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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Melanie M van der Klauw Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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Bruce H R Wolffenbuttel Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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Jana V van Vliet-Ostaptchouk Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

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Introduction The metabolic syndrome (MetS) is nowadays frequently used to identify individuals at higher risk for future type 2 diabetes (T2D) and cardiovascular disease (CVD) ( 1 ). Recognized metabolic risk components are abdominal obesity

Article Type:
Research Article
DOI:
https://doi.org/10.1530/EC-17-0011
Volume/Issue:
Volume 6: Issue 4
Open access
Shanlee M Davis Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA

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Rhianna Urban Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

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Angelo D’Alessandro Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA

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Julie A Reisz Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, USA

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Christine L Chan Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA

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Megan Kelsey Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA

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Susan Howell Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA

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Nicole Tartaglia Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA

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Philip Zeitler Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
eXtraOrdinarY Kids Clinic, Children's Hospital Colorado, Aurora, Colorado, USA

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Peter Baker II Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA

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used to generate a volcano plot identifying metabolites with a fold change >1.5 and FDR <0.05. To determine if a unique metabolome profile is present in KS, partial least squares – discriminant analysis (PLS-DA), a variant of principal component

Article Type:
Research Article
Collections:
Reproduction
DOI:
https://doi.org/10.1530/EC-22-0523
Volume/Issue:
Volume 12: Issue 5
Open access