Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology - disclosure of fundamental structural and metabolic relationships
Aims Quantitative lipoprotein analytics by NMR spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilised also in large biobanks. It allows comprehensive characterisation of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualised here in relation to lipoprotein metabolism with particular attention to the fundamental characteristics of subclass particle numbers,
... d concentrations and compositional measures. Methods and Results The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohort 1966 and 1986 with 5,651 and 5,605 participants, respectively. All results were highly coherent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveal that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations. Conclusion The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is consequential for the precise interpretation of novel therapeutic opportunities and for fully utilising the potential of forthcoming analyses of genetic and metabolic data in extensive biobanks.