PubMed Health⌕ Search

PubMed · 2082908

Hyperlipidaemia and hypertension.

Abstract

Hyperlipidaemia and hypertension are commonly found in the population and are independent risk factors for cardiovascular complications. The combined presence of hypertension and hypercholesterolaemia increases considerably the risk for cardiovascular complications. These high-risk patients have to be identified early and treated appropriately in order to avoid complications. The goal of antihypertensive therapy is to reduce the average levels of diastolic pressure to below 90 mm Hg and the systolic pressure to below 140 mm Hg. The goals set for lipid levels in these patients are total cholesterol less than or equal to 200 mg/dl, LDL cholesterol less than or equal to 135 mg/dl and triglycerides less than or equal to 200 mg/dl. The first step in the treatment of patients with both hyperlipidaemia and hypertension is to seek underlying causes and to take appropriate therapeutic measures for both diseases. Non-pharmacological treatment of these high-risk patients includes weight reduction, appropriate dietary measures including alcohol and salt restriction, cessation of smoking and increasing physical activity. If pharmacological antihypertensive treatment is needed, drugs without adverse effects on lipid and lipoprotein levels should be preferred. If, in addition to dietary measures, drug treatment is necessary to reduce high cholesterol levels, bile acid sequestrants are the drug of first choice at present. If long-term safety can be established for HMG CoA reductase inhibitors, these drugs will represent a major advance in the therapy of hypercholesterolaemia.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W Krone, D Müller-Wieland. 1990. Hyperlipidaemia and hypertension.. https://doi.org/10.1016/s0950-351x(05)80081-3

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Tree-structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives.

This paper discusses a new class of multiple testing procedures, tree-structured gatekeeping procedures, with clinical trial applications. These procedures arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by Westfall and Krishen (J. Statist. Planning Infer. 2001; 99:25-41) and Dmitrienko et al. (Statist. Med. 2003; 22:2387-2400). The proposed testing methodology is illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.

Antihypertensive Agents↗

Bayesian analysis of latent variable models with non-ignorable missing outcomes from exponential family.

To provide a comprehensive framework for analysing complex non-normal medical and biological data, we propose a Bayesian approach for a non-linear latent variable model with covariates, and non-ignorable missing data, under the exponential family of distributions. The non-ignorable missing mechanism is defined via a logistic regression model. Based on conjugate prior distributions, full conditional distributions for the implementation of Markov chain Monte Carlo methods in simulating observations from the joint posterior distribution are derived. These observations are used in computing the Bayesian estimates, as well as in implementing a path sampling procedure to evaluate the Bayes factor for model comparison. The proposed methods are illustrated using real data from a study on the non-adherence of hypertension patients.

Antihypertensive Agents↗

Revisiting the relation between change and initial value: a review and evaluation.

The relation between initial disease status and subsequent change following treatment has attracted great interest in clinical research. However, statisticians have repeatedly warned against correlating/regressing change with baseline due to two methodological concerns known as mathematical coupling and regression to the mean. Oldham's method and Blomqvist's formula are the two most often adopted methods to rectify these problems. The aims of this article are to review briefly the proposed solutions in the statistical and psychological literature, and to clarify the popular misconception that Blomqvist's formula is superior to Oldham's method. We argue that this misconception is due to a failure to recognize that the heterogeneity of individual responses to treatment is a source of regression to the mean in the analysis of the relation between change and initial value. Furthermore, we demonstrate how each method actually answers different research questions, and how confusion arises when this is not always understood.

Antihypertensive Agents↗