The relationship between hyperlipidemia and pancreatitis remains obscure in dogs. The aim of the present study was to investigate any possible association between hyperlipidemia and pancreatitis in dogs. In the first part of the study, Miniature Schnauzers with hypertriglyceridemia were found to have significantly higher serum cPLI concentrations than Miniature Schnauzers with normal serum triglyceride concentrations (P=0.0001). Also, Miniature Schnauzers with severe hypertriglyceridemia (>862 mg/dL) had 4.5 times higher odds (P=0.0343) for having a serum cPLI concentration consistent with pancreatitis. In the second part of the study, 17 Miniature Schnauzers prospectively enrolled with a history of pancreatitis were significantly more likely to have hypertriglyceridemia (71 percent) after resolution of pancreatitis than 34 age-matched Miniature Schnauzers without a history of pancreatitis (33 percent; odds ratio=5.02; P=0.0163). For the third part of the study, assessment of the feasibility and usefulness of a novel density gradient ultracentrifugation method using NaBiEDTA for lipoprotein profiling in dogs was attempted. Density gradient ultracentrifugation using NaBiEDTA was found to be useful for the study of lipoprotein profiles in dogs. Significant differences were detected in the lipoprotein profiles (mainly involving TRL and specific LDL fractions) among healthy Miniature Schnauzers, dogs of various other breeds, and hypertriglyceridemic Miniature Schnauzers. In the fourth part of the study, the effect of a commercially available low-fat diet on serum lipid and pancreas-specific lipase (Spec cPL) concentrations and lipoprotein profiles in Miniature Schnauzers with primary hypertriglyceridemia was evaluated. The study diet was found to be effective in significantly reducing serum triglyceride and cholesterol concentrations and changing the lipoprotein profiles of the dogs studied within 2 months. However, there was no significant effect of the study diet on serum Spec cPL concentrations. In the last part of the study, serum triglyceride and cholesterol concentrations and lipoprotein profiles were compared between dogs with naturally occurring pancreatitis and healthy dogs. The majority of dogs with naturally occurring pancreatitis had normal serum triglyceride and cholesterol concentrations. Important differences were identified in lipoprotein profiles between dogs with pancreatitis (higher LDL2, LDL3, and LDL4 fractions and lower TRL, HDL2a, and HDL3c fractions) and healthy control dogs.

Wichtige Marker in der klinischen Routine für die Risikoabschätzung von kardiovaskulären Erkrankungen (CVD) sind Blutcholesterinwerte auf Basis von Lipoproteinklassen wie ''schlechtes'' LDL oder ''gutes'' HDL. Dies vernachlässigt, dass jede Lipoproteinklasse eine nicht-homogene Population von Lipoproteinpartikeln unterschiedlicher Zusammensetzung aus Lipiden und Proteinen bildet. Studien zeigen zudem, dass solche Sub-populationen von Lipoproteinen im Stoffwechsel als auch im Beitrag zu CVD unterschiedlich sind. Mehrwert und routinemäßiger Einsatz einer detaillierteren Auftrennung von Lipoproteinen sind jedoch umstritten, da die experimentelle Fraktionierung und Analyse aufwendig, zeit- und kostenintensiv sind. Die vorliegende Arbeit ''Computational Lipidology'' präsentiert einen neuartigen Modellierungsansatz für die Berechnung von Lipoproteinverteilungen (Lipoproteinprofil) im Blutplasma, wobei erstmals individuelle Lipoproteinpartikel anstelle von Lipoproteinklassen betrachtet werden. Das Modell berücksichtigt elementare Bestandteile (Lipide, Proteine) und Prozesse des Stoffwechsel von Lipoproteinen. Stochastische wie deterministische Simulationen errechnen auf Basis aller Lipoproteinpartikel im System deren Dichteverteilung. Die Modellberechnungen reproduzieren erfolgreich klinisch gemessene Lipoproteinprofile von gesunden Patienten und zeigen Hauptmerkmale von pathologischen Situationen, die durch Störung eines der zugrundeliegenden molekularen Prozesse verursacht werden. Hochaufgelöste Lipoproteinprofile zeigen die Verteilung von sogenannten ''high-resolution density sub-fractions'' (hrDS) innerhalb von Hauptlipoproteinklassen. Die Ergebnisse stimmen mit klinischen Beobachtungen sehr gut überein, was die Arbeit als einen signifikanten Schritt in Richtung Analyse von individuellen Unterschieden, patienten-orientierte Diagnose von Fettstoffwechselstörungen und Identifikation neuer Sub-populationen von potentiell klinischer Relevanz qualifiziert. / Monitoring the major lipoprotein classes, particularly low-density lipoproteins (''bad'' LDL) and high-density lipoproteins (''good'' HDL) for characterizing risk of cardiovascular disease (CVD) is well-accepted and routine in clinical practice. However, it is only one-half of the truth as lipoprotein classes comprise non-homogeneous populations of lipoprotein particles varying significantly in their composition of lipids and apolipoproteins. Various studies have shown differing metabolic behavior and contribution to CVD of individual lipoprotein sub-populations. Nevertheless, the superiority of more detailed lipoprotein fractionation is still a matter of debate because experimental separation and analysis is an elaborate, time-consuming and expensive venture and not yet worthwhile for routine measurements. The present work ''Computational Lipidology'' aims at establishing a novel modeling approach to calculate the distribution of lipoproteins (lipoprotein profile) in blood plasma being the first that settles on individual lipoprotein complexes instead of common lipoprotein classes. Essential lipoprotein constituents and processes involved in the lipoprotein metabolism are taken into account. Stochastic as well as deterministic simulations yield the distribution of lipoproteins over density based on the set of individual lipoprotein complexes in the system. The model calculations successfully reproduce lipoprotein profiles measured in healthy subjects and show main characteristics of pathological situations elicited by disorder in one of the underlying molecular processes. Moreover, the model reveals the distribution of high-resolution lipoprotein sub-fractions (hrDS) within major density classes. The results show satisfactory agreement with clinical observations which qualifies the work as a significant step towards analyzing inter-individual variability, patient-oriented diagnosis of lipid disorders and identifying new sub-fractions of potential clinical relevance.