scholarly journals Metabolic Syndrome Derived from Principal Component Analysis and Incident Cardiovascular Events: The Multi Ethnic Study of Atherosclerosis (MESA) and Health, Aging, and Body Composition (Health ABC)

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Subhashish Agarwal ◽  
David R. Jacobs ◽  
Dhananjay Vaidya ◽  
Christopher T. Sibley ◽  
Neal W. Jorgensen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Metabolic Syndrome ◽  
Principal Component Analysis ◽  
Body Composition ◽  
African Americans ◽  
Principal Component ◽  
Component Analysis ◽  
Continuous Measure ◽  
Cut Point ◽  
Health Aging

Background. The NCEP metabolic syndrome (MetS) is a combination of dichotomized interrelated risk factors from predominantly Caucasian populations. We propose a continuous MetS score based on principal component analysis (PCA) of the same risk factors in a multiethnic cohort and compare prediction of incident CVD events with NCEP MetS definition. Additionally, we replicated these analyses in the Health, Aging, and Body composition (Health ABC) study cohort.Methods and Results. We performed PCA of the MetS elements (waist circumference, HDL, TG, fasting blood glucose, SBP, and DBP) in 2610 Caucasian Americans, 801 Chinese Americans, 1875 African Americans, and 1494 Hispanic Americans in the multiethnic study of atherosclerosis (MESA) cohort. We selected the first principal component as a continuous MetS score (MetS-PC). Cox proportional hazards models were used to examine the association between MetS-PC and 5.5 years of CVD events (n=377) adjusting for age, gender, race, smoking and LDL-C, overall and by ethnicity. To facilitate comparison of MetS-PC with the binary NCEP definition, a MetS-PC cut point was chosen to yield the same 37% prevalence of MetS as the NCEP definition (37%) in the MESA cohort. Hazard ratio (HR) for CVD events were estimated using the NCEP and Mets-PC-derived binary definitions. In Cox proportional models, the HR (95% CI) for CVD events for 1-SD (standard deviation) of MetS-PC was 1.71 (1.54–1.90) (P<0.0001) overall after adjusting for potential confounders, and for each ethnicity, HRs were: Caucasian, 1.64 (1.39–1.94), Chinese, 1.39 (1.06–1.83), African, 1.67 (1.37–2.02), and Hispanic, 2.10 (1.66-2.65). Finally, when binary definitions were compared, HR for CVD events was 2.34 (1.91–2.87) for MetS-PC versus 1.79 (1.46–2.20) for NCEP MetS. In the Health ABC cohort, in a fully adjusted model, MetS-PC per 1-SD (Health ABC) remained associated with CVD events (HR=1.21, 95%CI 1.12–1.32) overall, and for each ethnicity, Caucasian (HR=1.24, 95%CI 1.12–1.39) and African Americans (HR=1.16, 95%CI 1.01–1.32). Finally, when using a binary definition of MetS-PC (cut point 0.505) designed to match the NCEP definition in terms of prevalence in the Health ABC cohort (35%), the fully adjusted HR for CVD events was 1.39, 95%CI 1.17–1.64 compared with 1.46, 95%CI 1.23–1.72 using the NCEP definition.Conclusion. MetS-PC is a continuous measure of metabolic syndrome and was a better predictor of CVD events overall and in individual ethnicities. Additionally, a binary MetS-PC definition was better than the NCEP MetS definition in predicting incident CVD events in the MESA cohort, but this superiority was not evident in the Health ABC cohort.

scholarly journals Multivariate Analysis of Risk Factors of the COVID-19 Pandemic in the Community of Madrid, Spain

2021 ◽  
Vol 18 (17) ◽  
pp. 9227
Author(s):  
Víctor Pérez-Segura ◽  
Raquel Caro-Carretero ◽  
Antonio Rua
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Linear Models ◽  
Principal Component ◽  
Scientific Work ◽  
Component Analysis ◽  
Incidence Rates ◽  
Transmission Risk ◽  
Linear Regression Models ◽  
Multivariate Statistical

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world’s most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.

scholarly journals Using compositional principal component analysis to describe children's gut microbiota in relation to diet and body composition

2019 ◽  
Author(s):  
Claudia Leong ◽  
Jillian J Haszard ◽  
Anne-Louise M Heath ◽  
Gerald W Tannock ◽  
Blair Lawley ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Body Composition ◽  
Gut Microbiota ◽  
Principal Component ◽  
Component Analysis ◽  
Z Score ◽  
Large Numbers ◽  
Nonstarch Polysaccharides ◽  
Z Scores ◽  
Duration Of Breastfeeding

ABSTRACT Background Gut microbiota data obtained by DNA sequencing are complex and compositional because of large numbers of detectable taxa, and because microbiota characteristics are described in relative terms. Nutrition researchers use principal component analysis (PCA) to derive dietary patterns from food data. Although compositional PCA methods are not commonly used to describe patterns from complex microbiota data, this approach would be useful for identifying gut microbiota patterns associated with diet and body composition. Objectives To use compositional PCA to describe the principal components (PCs) of gut microbiota in 5-y-old children and explore associations between microbiota components, diet, and BMI z-score. Methods A fecal sample was provided by 319 children aged 5 y. Their primary caregiver completed a validated 123-item quantitative FFQ. Body composition was determined using DXA, and a BMI z-score was calculated. Compositional PCA identified characterizing taxa and weightings for calculation of gut microbiota PC scores at the genus level, and was examined in relation to diet and body size. Results Three gut microbiota PCs were found. PC1 (negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was related to lower BMI z-scores and longer duration of breastfeeding (per month) (β = −0.14; 95% CI: −0.26, −0.02; and β = 0.02; 95% CI: 0.003, 0.34, respectively). PC2 (positive loadings on Fusicatenibacter and Bifidobacterium; negative loadings on Bacteroides) was associated with a lower intake of nuts, seeds, and legumes (β = −0.05 per gram; 95% CI: −0.09, −0.01). When adjusted for fiber intake, PC2 was also associated with higher BMI z-scores (β = 0.12; 95% CI: 0.01, 0.24). PC3 (positive loadings on Faecalibacterium, Eubacterium, and Roseburia) was associated with higher intakes of fiber (β = 0.02 per gram; 95% CI: 0.003, 0.04) and total nonstarch polysaccharides (β = 0.02 per gram; 95% CI: 0.003, 0.04). Conclusions Our results suggest that specific gut microbiota components determined using compositional PCA are associated with diet and BMI z-score. This trial was registered at clinicaltrials.gov as NCT00892983.

scholarly journals Cardiovascular primary prevention risk factors in a nationwide survey, ABC (atrial fibrillation, high blood pressure and high cholesterol) risk factors in the LIPIDOGRAM2015 study

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
O Greaves ◽  
S L Harrison ◽  
D A Lane ◽  
M Banach ◽  
M Mastej ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Blood Pressure ◽  
Principal Component Analysis ◽  
Statistical Significance ◽  
Principal Component ◽  
Component Analysis ◽  
Venn Diagram ◽  
P Value ◽  
High Cholesterol

Abstract Background The National Health Service in England “Long Term Plan” aims to prevent 150,000 strokes and myocardial infarctions over the next 10 years. To achieve this, resources are being allocated to improve early detection of conditions strongly associated with cardiovascular disease. This includes working towards people routinely knowing their “ABC” risk factors (“A”: atrial fibrillation (AF), “B': hypertension and “C”: high cholesterol) (1). Purpose The aims of this study were to: 1) determine the proportion of participants with “A”, “B”, and “C” criteria; and 2) to identify risk factors for patients fulfilling any of these criteria. Methods LIPIDOGRAM2015 was a nationwide cross-sectional survey for adults in Poland. Adults were recruited in 2015 and 2016 by 438 family physicians. For the ABC criteria, “A” was defined as AF identified in the medical records of the participant, “B” was defined as either systolic blood pressure greater than 140mmHg or diastolic blood pressure greater than 90mmHg or both, and “C” was defined as total cholesterol greater than 200mg/dL (5.17mmol/L). The scaled and centred dataset underwent principal component analysis using singular value decomposition to achieve dimensionality reduction. K-means clustering was used to stratify patients with Hartigan's rule being used to identify optimal K number (2–4). The p-value for statistical significance used in this study was p&lt;0.01 unless otherwise specified. Results 13,724 patients were included in the study. 71.0% (n=9,747) of participants fulfilled the criteria for one or more of the “A”, “B” or “C” components (Fig. 1). 26 variables were used in this analysis with Principal Component Analysis showing 7 principal components explaining over 50% of the variance with 20 components explaining over 90%. K-means clustering was also performed, finding 39 separate clusters. Correlations and statistical significance tests showed a high degree of variability between variables. Participants with AF were older (mean (SD) 67.7 (9.5) vs 55.7 (13.7), p&lt;0.0001), with higher prevalence of concomitant coronary heart disease (CHD) (OR 6.73, 95% CL 5.75, 7.87) and ischaemic stroke (OR 13.45, 95% CL 7.66, 23.6). Participants with hypertension were older (mean (SD) 60.1 (SD 12.4) vs 53.8 (14.0), p&lt;0.0001), with a higher BMI (mean (SD) 29.9 (5.1) vs 27.5 (4.8), p&lt;0.0001) and resting heart rate (mean (SD) 75.7 (10.7) vs 72.7 (8.9), p&lt;0.0001), more likely to be male (OR 1.42, 95% CL 1.32, 1.53) and have diabetes (OR 1.61, 95% CL 1.46, 1.78). Participants with high cholesterol showed an inverse correlation with prevalence of both concomitant diabetes (OR 0.85, 95% CL 0.77, 0.94) and CHD (OR 0.85, 95% CL 0.76, 0.94) (Fig. 2). Conclusion Simple demographic and clinical variables could be used to guide targeted screening to increase population awareness of “ABC” status, allowing for a greater proportion of the population to be appropriately managed with cardiovascular prevention strategies. FUNDunding Acknowledgement Type of funding sources: None. “ABC” Venn diagram Correlogram and significance plot

scholarly journals Principal Component Analysis and Risk Factors for Acute Mountain Sickness upon Acute Exposure at 3700 m

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142375 ◽  
Author(s):  
Shi-Zhu Bian ◽  
Jun Jin ◽  
Ji-Hang Zhang ◽  
Qian-Ning Li ◽  
Jie Yu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Acute Mountain Sickness ◽  
Principal Component ◽  
Component Analysis ◽  
Acute Exposure ◽  
Mountain Sickness
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