scholarly journals Hardware and system architecture for a very large database

1975 ◽  
Author(s):  
Robert Healey ◽  
Bradford Heckman
Keyword(s):  
System Architecture ◽  
Large Database ◽  
Very Large Database

scholarly journals SEASONAL VARIATIONS IN LIPID PROFILES FROM 2.8 MILLION US ADULTS: THE VERY LARGE DATABASE OF LIPIDS (VLDL 14)

2014 ◽  
Vol 63 (12) ◽  
pp. A1458
Author(s):  
Parag Joshi ◽  
Seth Martin ◽  
Michael Blaha ◽  
John McEvoy ◽  
Raul Santos ◽  
...  
Keyword(s):  
Seasonal Variations ◽  
Lipid Profiles ◽  
Large Database ◽  
Very Large Database

scholarly journals ATHEROGENIC LIPID LEVELS IN 662,711 ELDERLY PERSONS: THE VERY LARGE DATABASE OF LIPIDS 10A (VLDL 10A)

2014 ◽  
Vol 63 (12) ◽  
pp. A1459
Author(s):  
Kristopher Swiger ◽  
Seth Martin ◽  
Michael Blaha ◽  
Peter Toth ◽  
Khurram Nasir ◽  
...  
Keyword(s):  
Elderly Persons ◽  
Lipid Levels ◽  
Large Database ◽  
Very Large Database ◽  
Atherogenic Lipid

scholarly journals Modern prevalence of the Fredrickson-Levy-Lees dyslipidemias: findings from the Very Large Database of Lipids and National Health and Nutrition Examination Survey

2020 ◽  
Vol 16 (6) ◽  
pp. 1279-1287 ◽  
Author(s):  
Vasanth Sathiyakumar ◽  
Vincent A. Pallazola ◽  
Jihwan Park ◽  
Rachit M. Vakil ◽  
Peter Toth ◽  
...  
Keyword(s):  
National Health ◽  
Nutrition Examination Survey ◽  
Large Database ◽  
Health And Nutrition ◽  
Very Large Database

scholarly journals Narrowing Sex Differences in Lipoprotein Cholesterol Subclasses Following Mid‐Life: The Very Large Database of Lipids (VLDL‐10B)

2014 ◽  
Vol 3 (2) ◽  
Author(s):  
Kristopher J. Swiger ◽  
Seth S. Martin ◽  
Michael J. Blaha ◽  
Peter P. Toth ◽  
Khurram Nasir ◽  
...  
Keyword(s):  
Sex Differences ◽  
Lipoprotein Cholesterol ◽  
Large Database ◽  
Very Large Database

scholarly journals VITAMIN D DEFICIENCY AND NON-LIPID SERUM MARKERS OF CARDIOVASCULAR RISK: THE VERY LARGE DATABASE OF LIPIDS STUDY

2016 ◽  
Vol 67 (13) ◽  
pp. 1908
Author(s):  
Kamil F. Faridi ◽  
Joshua Lupton ◽  
Seth Martin ◽  
Krishnaji Kulkarni ◽  
Steven Jones ◽  
...  
Keyword(s):  
Vitamin D ◽  
Cardiovascular Risk ◽  
Vitamin D Deficiency ◽  
Serum Markers ◽  
Large Database ◽  
Very Large Database

scholarly journals Deficient serum 25-hydroxyvitamin D is associated with an atherogenic lipid profile: The Very Large Database of Lipids (VLDL-3) study

2016 ◽  
Vol 10 (1) ◽  
pp. 72-81.e1 ◽  
Author(s):  
Joshua R. Lupton ◽  
Kamil F. Faridi ◽  
Seth S. Martin ◽  
Sristi Sharma ◽  
Krishnaji Kulkarni ◽  
...  
Keyword(s):  
Lipid Profile ◽  
Large Database ◽  
Hydroxyvitamin D ◽  
Atherogenic Lipid Profile ◽  
25 Hydroxyvitamin D ◽  
Very Large Database ◽  
Atherogenic Lipid

THE RELEVANCE OF SPATIAL STATISTICS ON THE STATISTICAL MODEL BUILDING FOR CORONARY HEART DISEASE

1995 ◽  
Vol 03 (03) ◽  
pp. 661-675
Author(s):  
RITA VASCONCELOS
Keyword(s):  
Spatial Statistics ◽  
Model Building ◽  
Heart Diseases ◽  
Hidden Variables ◽  
Large Database ◽  
Loglinear Models ◽  
Long Time ◽  
Spatial Aspect ◽  
Very Large Database ◽  
Standard Techniques

For a long time, it has been widely acknowledged that putting data on a map underlines important features, and helps in the understanding and interpretation of the real world. Recent and extensive developments of spatial statistics and of geostatistics show the growing importance of this field. Our aim was to help physicians to interpret a very large database on heart diseases (acute myocardial infarction and angina pectoris) on the Madeira Islands. Besides standard techniques, such as loglinear models fitting, we decided to explore the spatial aspect of the question, and to bring in to the analysis recent advances in exploratory and robust data analysis. We show the relevance of spatial statistics on the detection of "hidden" variables.

Lipid phenotypes at the extremes of high-density lipoprotein cholesterol: The very large database of lipids-9

2015 ◽  
Vol 9 (4) ◽  
pp. 511-518.e5 ◽  
Author(s):  
Renato Quispe ◽  
Mohammed Al-Hijji ◽  
Kristopher J. Swiger ◽  
Seth S. Martin ◽  
Mohamed B. Elshazly ◽  
...  
Keyword(s):  
High Density Lipoprotein ◽  
High Density Lipoprotein Cholesterol ◽  
Density Lipoprotein ◽  
High Density ◽  
Lipoprotein Cholesterol ◽  
Large Database ◽  
Very Large Database

Abstract 17033: Inflammation and Lipids Components: Insights From the VLDL Study

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Abdulhamied Alfaddagh ◽  
Renato Quispe ◽  
Steven R Jones
Keyword(s):  
Linear Regression ◽  
Inflammatory Markers ◽  
Lipoprotein Cholesterol ◽  
Large Database ◽  
Second Best ◽  
Very Large Database ◽  
Lipid Parameters ◽  
Lipid Components

Background: Dyslipidemia and inflammation independently contribute to atherosclerosis. The associations between different lipid parameters and inflammatory markers is not fully understood. Hypothesis: LDL-C, triglycerides, and HDL-C do not predict inflammation equally. Methods: We analyzed data from 784,233 patients from the second harvest of the Very Large Database of Lipid study with lipids and hsCRP measured. The prevalence of having hsCRP≥2 mg/L was compared in 20 quantiles of non-HDL-C, LDL-C, HDL-C and triglycerides. Using linear regression, we estimated the correlations between hsCRP and lipids and to what degree individual lipid components explain the variation in hsCRP values. We then examined these association by sex and age (≥65 vs <65 years) categories. Results: The median hsCRP of the population was 2.3 mg/L (IQR, 1.0-5.7). The proportion of patients with hsCRP≥2 mg/L progressively increased with higher non-HDL-C, LDL-C, triglycerides quintiles, but decreased with higher HDL-C quintiles. All lipid measures directly correlated with hsCRP value except HDL-C which was inversely correlated (P<0.001 for all). LDL-C and non-HDL-C values explained very little of the variance seen in hsCRP (univariate R 2 = 0.5% and 0.1%, respectively). Triglyceride levels were the strongest predictor of hsCRP (standardized β, 0.21; P<0.001) and explained 4.5% of variability in hsCRP values. HDL-C was the second best (albeit an inverse) predictor of hsCRP (standardized β, -0.19; P<0.001) and explained 3.8% of its variance. Regardless LDL-C quantile, the prevalence of having hsCRP ≥2 mg/L was lower in those also have low triglyceride or high HDL-C levels (Figure). These associations were consistent by sex and age categories. Conclusion: Of major lipid and lipoprotein cholesterol fractions, triglycerides and HDL-C correlated most strongly and non-HDL-C and LDL-C contributed the least to hsCRP.

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