Plasma lipoprotein(a) (Lp(a)) concentrations vary considerably between individuals. To examine the variation for products of the same and different apolipoprotein(a) (apo(a)) alleles, conditions were established whereby phenotyping immunoblots could be used to estimate the concentration of Lp(a) associated with the constituent apo(a) isoforms. In these studies 28 distinct isoforms were identified, each differing by a single kringle IV unit. Tracking the isoforms through 10 families showed that there could be up to 200-fold difference in the Lp(a) concentration associated with the same-sized isoform produced from different alleles. In contrast there was typically < 2.5-fold variation in the Lp(a) concentration associated with the same allele. However, there were four occasions where the concentration associated with a particular allele was reduced below the typical range from one generation to the next. A nonlinear, inverse trend with isoform size was apparently superimposed upon the other factors that determine Lp(a) concentration. Inheritance of familial hypercholesterolemia or familial-defective apoB100 had little consistent effect upon Lp(a) concentration. In both the families and in other unrelated individuals the distribution of isoforms and their associated concentrations provided evidence for the presence of at least two and possibly more subpopulations of apo(a) alleles with different sizes and expression.
Y F Perombelon, A K Soutar, B L Knight
Usage data is cumulative from May 2024 through May 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 251 | 12 |
57 | 25 | |
Figure | 0 | 4 |
Scanned page | 479 | 4 |
Citation downloads | 56 | 0 |
Totals | 843 | 45 |
Total Views | 888 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.