Effect of transforming growth factor-beta on lipoprotein lipase in rat mesenchymal heart cell cultures

Plasma tumour and metabolism related biomarkers AMBP, LPL and Glyoxalase I differentiate heart failure with preserved ejection fraction with pulmonary hypertension from pulmonary arterial hypertension

BACKGROUND

Discrimination of heart failure with preserved ejection fraction with pulmonary hypertension (HFpEF-PH) from pulmonary arterial hypertension (PAH) is crucial for clinical management but may be challenging due to similarities in clinical and comorbid characteristics. We aimed to investigate tumour and metabolism related proteins in differentiating HFpEF-PH from PAH.

METHODS

Sixty-nine tumour and metabolism plasma proteins were analysed with proximity extension assay in heathy controls (n = 20), patients with PAH (n = 48) and LHF-PH (n = 67) [HFpEF-PH (n = 31) and HF reduced EF-PH (n = 36)]. Haemodynamics were assessed with right heart catheterization.

RESULTS

The plasma levels of alpha-1-microglobulin/bikunin precursor (AMBP) and lipoprotein lipase (LPL), were higher in HFpEF-PH compared to healthy controls (p < 0.01), HFrEF-PH (p < 0.05), and PAH (p < 0.001). Glyoxalase I levels were higher in HFpEF-PH and HFrEF-PH compared to controls (p < 0.001) and PAH (p < 0.001). Each of plasma AMBP, LPL, and glyoxalase I, adjusted for age and sex in multivariable logistic regression models, could differentiate HFpEF-PH from PAH, with areas under the receiver operating characteristic curve (AUC) of 0.81, 0.84 and 0.79, respectively. The combination of AMBP, LPL and glyoxalse I yielded the largest AUC of 0.87 [95% confidence interval (0.79-0.95)] in discriminating HFpEF-PH from PAH, with a sensitivity of 87.1% and a specificity of 85.4%. In HFpEF-PH, the plasma levels of AMBP correlated with pulmonary arterial wedge pressure (r_s = -0.42, p = 0.018).

CONCLUSIONS

Plasma AMBP, LPL and glyoxalase I may facilitate the distinction of HFpEF-PH from PAH. Larger clinical studies are encouraged to confirm and validate our findings.

Lipoprotein lipase as a candidate target for cancer prevention/therapy

Epidemiological studies have shown that serum triglyceride (TG) levels are linked with risk of development of cancer, including colorectal and pancreatic cancers, and their precancerous lesions. Thus, it is assumed that serum TG plays an important role in carcinogenesis, and the key enzyme lipoprotein lipase (LPL), which catalyzes the hydrolysis of plasma TG, may therefore be involved. Dysregulation of LPL has been reported to contribute to many human diseases, such as atherosclerosis, chylomicronaemia, obesity, and type 2 diabetes. Recently, it has been reported that LPL gene deficiency, such as due to chromosome 8p22 loss, LPL gene polymorphism, and epigenetic changes in its promoter region gene, increases cancer risk, especially in the prostate. In animal experiments, high serum TG levels seem to promote sporadic/carcinogen-induced genesis of colorectal and pancreatic cancers. Interestingly, tumor suppressive effects of LPL inducers, such as PPAR ligands, NO-1886, and indomethacin, have been demonstrated in animal models. Moreover, recent evidence that LPL plays important roles in inflammation and obesity implies that it is an appropriate general target for chemopreventive and chemotherapeutic agents.

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases.