PubMed · 14101039
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D J OTTENBERG, F M RICHARDSON. 1963. HEMOPTYSIS.. https://pubmed.ncbi.nlm.nih.gov/14101039/
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Data have been accumulating that indicate that matrix metalloproteinase (MMP) gene polymorphisms contribute to inter-individual differences in susceptibility to and outcome of cardiovascular disease. This is currently best exemplified by the MMP3 gene 5A/6A polymorphism which has an effect on MMP3 expression and has been shown to be associated with coronary stenosis, myocardial infarction, coronary artery calcification, post-angioplasty coronary restenosis, carotid atherosclerosis, stroke, arterial stiffness, and blood pressure. Functional polymorphisms in the MMP1, MMP2, MMP7, MMP9, MMP12, and MMP13 genes have also been related to coronary artery disease, arterial stiffness, and/or abdominal aortic aneurysm. These genetic findings support the notion that MMPs play important roles in the pathogenesis of these conditions. There is also some evidence suggesting that MMP genotyping could aid in identifying patients who are likely to have unfavourable prognosis and/or adverse response to treatment.
OBJECTIVE: To provide an update on lipid management and recent modifications in cholesterol guidelines for use of hydroxymethylglutaryl coenzyme A reductase inhibitors (statins), specifically in patients with diabetes. DATA SOURCES: Studies and guidelines were identified through a MEDLINE search (1996-April 2005). STUDY SELECTION AND DATA EXTRACTION: Studies were selected for review if the primary treatment intervention was a statin, at least 4% of the study population held a diagnosis of diabetes, and diabetes subgroup analysis was available. DATA SYNTHESIS: The Heart Protection Study demonstrated an approximately 25% relative risk reduction of a first coronary event in patients with diabetes, a reduction similar to those without diabetes. In subjects with diabetes, a significant reduction in coronary events was noted regardless of the baseline cholesterol level. The Collaborative Atorvastatin Diabetes Study demonstrated a 37% relative risk reduction in the primary prevention of cardiovascular morbidity in patients with diabetes. CONCLUSIONS: Based on the current literature, a low-density lipoprotein cholesterol (LDL-C) level <100 mg/dL remains an appropriate goal for patients with diabetes in the absence of established cardiovascular disease. For higher-risk patients, such as those with diabetes and a history of cardiovascular disease, a more stringent LDL-C goal of <70 mg/dL is an option according to current clinical trial evidence. At least a 30-40% reduction in the LDL-C level is advisable when initiating statin therapy.
BACKGROUND: To evaluate gene-disease associations, genetic epidemiologists collect information on the disease risk in subjects with different genotypes (for a bi-allelic polymorphism: gg, Gg, GG). Meta-analyses of such studies usually reduce the problem to a single comparison, either by performing two separate pairwise comparisons or by assuming a specific underlying genetic model (recessive, co-dominant, dominant). A biological justification for the choice of the genetic model is seldom available. METHODS: We present a genetic model-free approach, which does not assume that the underlying genetic model is known in advance but still makes use of the information available on all genotypes. The approach uses OR(GG), the odds ratio between the homozygous genotypes, to capture the magnitude of the genetic effect, and lambda, the heterozygote log odds ratio as a proportion of the homozygote log odds ratio, to capture the genetic mode of inheritance. The analysis assumes that the same unknown genetic model, i.e. the same lambda, applies in all studies, and this is investigated graphically. The approach is illustrated using five examples of published meta-analyses. RESULTS: Analyses based on specific genetic models can produce misleading estimates of the odds ratios when an inappropriate model is assumed. The genetic model-free approach gives appropriately wider confidence intervals than genetic model-based analyses because it allows for uncertainty about the genetic model. In terms of assessment of model fit, it performs at least as well as a bivariate pairwise analysis in our examples. CONCLUSIONS: The genetic model-free approach offers a unified approach that efficiently estimates the genetic effect and the underlying genetic model. A bivariate pairwise analysis should be used if the assumption of a common genetic model across studies is in doubt.