Plasma lipoproteins: genetic influences and clinical implications

Lipoprotein (a) in type 2 diabetes mellitus: Relation to LDL:HDL ratio and glycemic control

BACKGROUND

Increased lipoprotein (a) [Lp (a)] concentrations are predictive of coronary artery disease (CAD). Type 2 diabetes mellitus also leads to dyslipidemia, like elevated triglyceride levels and low HDL levels, which are known risk factors for CAD. This study was designed to investigate the levels of Lp (a) in type 2 diabetic patients and their association with LDL: HDL ratio and glycemic control.

MATERIALS AND METHODS

The study included 60 patients of type 2 diabetes and 50 age and sex matched controls. The Lp(a) levels in the diabetic group were compared with the control group and the relationship between the Lp(a) levels and LDL: HDL ratio was evaluated. Diabetic group was further divided into three subgroups according to levels of glycated hemoglobin. Lp(a) levels and glycated hemoglobin in controlled and uncontrolled diabetes mellitus were also compared to find out any correlation between them. Statistical analysis was done using the students 't' test and Chi square test.

RESULTS

Lp(a) levels were found to be significantly increased in the diabetic group as compared to the control group (P< 0.001). LDL: HDL ratio was also increased in the diabetic group as compared to the control group. Lp(a) levels showed no association with LDL: HDL ratio and degree of glycemic control in these patients.

CONCLUSIONS

The results of the present study suggest that Lp(a) levels are increased in type 2 diabetic patients. The elevated Lp(a) levels do not reflect the glycemic status and are also independent of increase in LDL:HDL ratio suggesting different metabolic pathways and the genetic connection for LDL and Lp(a).

Integrating post-genomic approaches as a strategy to advance our understanding of health and disease

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.