03610nam 2200949z- 450 991067438980332120231214133237.0(CKB)5680000000080873(oapen)https://directory.doabooks.org/handle/20.500.12854/92040(EXLCZ)99568000000008087320202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierThe Role of Nutrition in Cardiometabolic Health: Experimental, Clinical, and Community-Based EvidenceBaselMDPI Books20221 electronic resource (194 p.)3-0365-4838-6 3-0365-4837-8 The purpose of this Special Issue “The Role of Nutrition in Cardiometabolic Health: Experimental, Clinical, and Community-Based Evidence” is to publish a focused, coherent, impactful, and well-cited volume on how nutrition influences diverse cardiometabolic risk factors. Cardiometabolic diseases, such as coronary heart disease, stroke, type 2 diabetes mellitus, and obesity, is the leading cause of death worldwide. In recent years, dietary habits have shifted all over the globe. At the same time, a constantly growing body of evidence demonstrates the role of caloric intake and dietary composition as determinants of cardiometabolic health. Suboptimal diet predisposes to a myriad of cardiometabolic risk factors such as impaired glucose metabolism, insulin resistance, dyslipidaemias, and high blood pressure.Role of Nutrition in Cardiometabolic HealthMedicinebicsscvitamin Dobesitymicrovascularbariatric surgeryweight lossnitric oxidecardiac remodelingcardiac dysfunctionechocardiogramobese ratshigh-fat high-sugar dietvascular stiffnessblood pressurewhey protein isolateolder adultsdietary factorcardiovascular diseaseumbrella reviewlow-carbohydrate diethypocaloricisocaloricwomen healthconduit arterymicrovasculaturecardiovascular risksprimary preventionhomocysteinefolatevitamin B12vascular dysfunctionhepatocyteTGR5glucose regulationhomocysteine and vascular diseaseH3K27me3epigeneticsatherosclerosisMRI (magnetic resonance imaging)livermetabolic regulationlaparoscopic sleeve gastrectomymicronutrientsdeficiencybody mass indexcardiotonic steroidsleft ventricular massmarinobufagenindietary salt intakeyoung adultsMedicineMahmoud Abeer Medt1338458Phillips ShaneedtMahmoud Abeer MothPhillips ShaneothBOOK9910674389803321The Role of Nutrition in Cardiometabolic Health: Experimental, Clinical, and Community-Based Evidence3058534UNINA05565nam 22006974a 450 991083019780332120230617012241.01-280-34400-897866103440000-470-24688-X0-471-46798-70-471-46797-9(CKB)111087027131652(EBL)162765(OCoLC)475872970(SSID)ssj0000231211(PQKBManifestationID)11193164(PQKBTitleCode)TC0000231211(PQKBWorkID)10216963(PQKB)10145144(MiAaPQ)EBC162765(EXLCZ)9911108702713165220030404d2003 uy 0engur|n|---|||||txtccrQuantitative methods in population health[electronic resource] extensions of ordinary regression /Mari PaltaHoboken, N.J. John Wileyc20031 online resource (339 p.)Wiley series in probability and statisticsDescription based upon print version of record.0-471-45505-9 Includes bibliographical references and index.Quantitative Methods in Population Health; List of Figures; List of Tables; Contents; Preface; Acknowledgments; Acronyms; Introduction; I.1 Newborn Lung Project; I.2 Wisconsin Diabetes Registry; I.3 Wisconsin Sleep Cohort Study; Suggested Reading; 1 Review of Ordinary Linear Regression and Its Assumptions; 1.1 The Ordinary Linear Regression Equation and Its Assumptions; 1.1.1 Straight-Line Relationship; 1.1.2 Equal Variance Assumption; 1.1.3 Normality Assumption; 1.1.4 Independence Assumption; 1.2 A Note on How the Least-Squares Estimators are ObtainedOutput Packet I: Examples of Ordinary Regression Analyses2 The Maximum Likelihood Approach to Ordinary Regression; 2.1 Maximum Likelihood Estimation; 2.2 Example; 2.3 Properties of Maximum Likelihood Estimators; 2.4 How to Obtain a Residual Plot with PROC MIXED; Output Packet II: Using PROC MIXED and Comparisons to PROC REG; 3 Reformulating Ordinary Regression Analysis in Matrix Notation; 3.1 Writing the Ordinary Regression Equation in Matrix Notation; 3.1.1 Example; 3.2 Obtaining the Least-Squares Estimator b in Matrix Notation; 3.2.1 Example: Matrices in Regression Analysis3.3 List of Matrix Operations to Know4 Variance Matrices and Linear Transformations; 4.1 Variance and Correlation Matrices; 4.1.1 Example; 4.2 How to Obtain the Variance of a Linear Transformation; 4.2.1 Two Variables; 4.2.2 Many Variables; 5 Variance Matrices of Estimators of Regression Coefficients; 5.1 Usual Standard Error of Least-Squares Estimator of Regression Slope in Nonmatrix Formulation; 5.2 Standard Errors of Least-Squares Regression Estimators in Matrix Notation; 5.2.1 Example; 5.3 The Large Sample Variance Matrix of Maximum Likelihood Estimators5.4 Tests and Confidence Intervals5.4.1 Example-Comparing PROC REG and PROC MIXED; 6 Dealing with Unequal Variance Around the Regression Line; 6.1 Ordinary Least Squares with Unequal Variance; 6.1.1 Examples; 6.2 Analysis Taking Unequal Variance into Account; 6.2.1 The Functional Transformation Approach; 6.2.2 The Linear Transformation Approach; 6.2.3 Standard Errors of Weighted Regression Estimators; Output Packet III: Applying the Empirical Option to Adjust Standard Errors; Output Packet IV: Analyses with Transformation of the Outcome Variable to Equalize Residual VarianceOutput Packet V: Weighted Regression Analyses of GHb Data on Age7 Application of Weighting with Probability Sampling and Nonresponse; 7.1 Sample Surveys with Unequal Probability Sampling; 7.1.1 Example; 7.2 Examining the Impact of Nonresponse; 7.2.1 Example (of Reweighting as Well as Some SAS Manipulations); 7.2.2 A Few Comments on Weighting by a Variable Versus Including it in the Regression Model; Output Packet VI: Survey and Missing Data Weights; 8 Principles in Dealing with Correlated Data; 8.1 Analysis of Correlated Data by Ordinary Unweighted Least-Squares Estimation; 8.1.1 Example8.1.2 Deriving the Variance EstimatorEach topic starts with an explanation of the theoretical background necessary to allow full understanding of the technique and to facilitate future learning of more advanced or new methods and softwareExplanations are designed to assume as little background in mathematics and statistical theory as possible, except that some knowledge of calculus is necessary for certain parts.SAS commands are provided for applying the methods. (PROC REG, PROC MIXED, and PROC GENMOD)All sections contain real life examples, mostly from epidemiologic researchFirst chapter includes a SAS refresherWiley series in probability and statistics.Medical statisticsRegression analysisPopulationHealth aspectsStatistical methodsHealth surveysStatistical methodsMedical statistics.Regression analysis.PopulationHealth aspectsStatistical methods.Health surveysStatistical methods.614.072614.420727Palta Mari1948-1674906MiAaPQMiAaPQMiAaPQBOOK9910830197803321Quantitative methods in population health4040031UNINA