Lipoprotein (a) concentration is associated with plasma arachidonic acid in subjects with familial hypercholesterolemia

Elevated lipoprotein (a) (Lp[a]) is associated with cardiovascular disease (CVD) and is mainly genetically determined. Studies suggest a role of dietary fatty acids (FAs) in the regulation of Lp(a), however, no studies have investigated the association between plasma Lp(a) concentration and omega-6 FAs. We aimed to investigate whether plasma Lp(a) concentration was associated with dietary omega-6 FA intake, and plasma levels of arachidonic acid in subjects with familial hypercholesterolemia (FH). We included FH subjects with (n=68) and without (n=77) elevated Lp(a) defined as ≥75 nmol/L, and healthy subjects (n=14). Total fatty acid profile was analyzed by Gas Chromatography-Flame Ionization Detector analysis, and the daily intake of macronutrients (including the sum of omega-6 FAs: 18:2n-6, 20:2n-6, 20:3n-6 and 20:4n-6) were computed from completed food frequency questionnaires. FH subjects with elevated Lp(a) had higher plasma levels of arachidonic acid (AA) compared to FH subjects without elevated Lp(a) (P=0.03). Furthermore, both FH subjects with and without elevated Lp(a) had higher plasma levels of AA compared to controls (P<0.001). The multivariable analyses showed associations between dietary omega-6 FA intake and plasma levels of AA (P=0.02), and between plasma levels of Lp(a) and AA (P=0.006). Our data suggest a novel link between plasma Lp(a) concentration, dietary omega-6 FAs and plasma AA concentration, which may contribute to explain the small diet-induced increase in Lp(a) levels associated with lifestyle changes. Although the increase may not be clinically relevant, this association may be mechanistically interesting in understanding more of the role and regulation of Lp(a)

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of me-teorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric ap-proach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assump-tion is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive es-timation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covari-ance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomia