Back

Frequency of lipoprotein(a) testing and its levels in Pakistani population

Batool, H.; Khan, M.; Ain, Q.; Chughtai, O. R.; Khan, M. D.; Khan, M. I.; Sadiq, F.

2024-02-11 cardiovascular medicine
10.1101/2024.02.09.24302487 medRxiv
Show abstract

BackgroundLipoprotein(a) [Lp(a)] is a highly atherogenic particle identified as an independent risk factor for the development of atherosclerotic cardiovascular disease (ASCVD). This study aimed to investigate the frequency of Lp(a) testing and incidence of elevated Lp(a) in the Pakistani population. MethodsFor this observational study, Lp(a) and lipid profile data from five years (June 2015 to October 2020) were acquired from the electronic patient records of a diagnostic laboratory (Chughtai Laboratories, Lahore). The association of age, total cholesterol (TC), high-density lipoprotein (HDL), low-density lipoprotein cholesterol (LDL-C), non-HDL, and triglyceride (TG) levels with two thresholds for Lp(a), that is, <30 mg/dL and [&ge;]30 mg/dL, was calculated using the Kruskal Wallis test, while the association between Lp(a) levels and lipid variables was calculated using Spearman correlation. ResultsFor five years, 1060 tests were conducted, averaging 212 tests per year. Of these tests, 37.2% showed Lp(a) levels above 30 mg/dL. There were no significant differences observed in the results between males and females. However, younger individuals displayed significantly higher Lp(a) levels. Additionally, there was only a weak correlation between Lp(a) levels and other lipid variables. ConclusionDespite being recognized as a risk factor for ASCVD in the Pakistani population, only a small proportion of the large population had their Lp(a) tested. Moreover, a significant proportion of the population lies above the threshold.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.