Metabolic disorders, such as obesity, elevated blood pressure, dyslipidemias, insulin resistance, hyperglycemia, and hyperuricemia have all been identified as risk factors for an epidemic of important and widespread chronic-degenerative diseases, such as type 2 diabetes and cardiovascular disease, that constitute some of the world's most important public health challenges. Their increasing prevalence can be associated with an aging population and to lifestyles within an obesogenic environment. Taking educational level as a proxy for lifestyle, and using both logistic and linear regressions, we study the relation between a wide set of metabolic biomarkers, and educational level, body mass index (BMI), age, and sex as correlates, in a population of 1,073 students, academic and non-academic staff at Mexico's largest university (UNAM). Controlling for BMI and sex, we consider educational level and age as complementary measures-degree and duration-of exposure to metabolic insults. Analyzing the role of education across a wide spectrum of educational levels (from primary school to doctoral degree), we show that higher education correlates to significantly better metabolic health when compared to lower levels, and is associated with significantly less risk for waist circumference, systolic blood pressure, glucose, glycosylated hemoglobin, triglycerides, high density lipoprotein and metabolic syndrome (all p < 0.05); but not for diastolic blood pressure, basal insulin, uric acid, low density lipoprotein, and total cholesterol. We classify each biomarker, and corresponding metabolic disorder, by its associated set of statistically significant correlates. Differences among the sets of significant correlates indicate various aetiologies and the need for targeted population-specific interventions. Thus, variables strongly linked to educational level are candidates for lifestyle change interventions. Hence, public policy efforts should be focused on those metabolic biomarkers strongly linked to education, while adopting a different approach for those biomarkers not linked as they may be poor targets for educational campaigns.

The dynamic metabolism of membrane phosphoinositide lipids involves several cellular compartments including the ER, Golgi, and plasma membrane. There are cycles of phosphorylation and dephosphorylation and of synthesis, transfer, and breakdown. The simplified phosphoinositide cycle comprises synthesis of phosphatidylinositol in the ER, transport, and phosphorylation in the Golgi and plasma membranes to generate phosphatidylinositol 4,5-bisphosphate, followed by receptor-stimulated hydrolysis in the plasma membrane and return of the components to the ER for reassembly. Using probes for specific lipid species, we have followed and analyzed the kinetics of several of these events during stimulation of M1 muscarinic receptors coupled to the G-protein Gq. We show that during long continued agonist action, polyphosphorylated inositol lipids are initially depleted but then regenerate while agonist is still present. Experiments and kinetic modeling reveal that the regeneration results from gradual but massive up-regulation of PI 4-kinase pathways rather than from desensitization of receptors. Golgi pools of phosphatidylinositol 4-phosphate and the lipid kinase PI4KIIIα (PI4KA) contribute to this homeostatic regeneration. This powerful acceleration, which may be at the level of enzyme activity or of precursor and product delivery, reveals strong regulatory controls in the phosphoinositide cycle.

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, and waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, and creatinine), along with vital signs (axillar temperature, systolic, and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network's topology that reflect changes in the system−wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, i.e., the density of triangles in the network, can provide topological indicators of health that assess the whole state of the system. Overall, our findings show the importance of visualizing health as a network of organs and/or systems, and highlight the importance of triglycerides, insulin, uric acid and glucose as key biomarkers in the prevention of the development of metabolic disorders.

Phosphatidylinositol-4,5-bisphosphate (PIP) is a membrane phosphoinositide that regulates the activity of many ion channels. Influx of calcium primarily through voltage-gated calcium (Ca) channels promotes insulin secretion in pancreatic β-cells. However, whether Ca channels are regulated by PIP, as is the case for some non-insulin-secreting cells, is unknown. The purpose of this study was to investigate whether Ca channels are regulated by PIP depletion in pancreatic β-cells through activation of a muscarinic pathway induced by oxotremorine methiodide (Oxo-M). Ca channel currents were recorded by the patch-clamp technique. The Ca current amplitude was reduced by activation of the muscarinic receptor 1 (MR) in the absence of kinetic changes. The Oxo-M-induced inhibition exhibited the hallmarks of voltage-independent regulation and did not involve PKC activation. A small fraction of the Oxo-M-induced Ca inhibition was diminished by a high concentration of Ca chelator, whereas ≥50% of this inhibition was prevented by diC8-PIP dialysis. Localization of PIP in the plasma membrane was examined by transfecting INS-1 cells with PH-PLCδ1, which revealed a close temporal association between PIP hydrolysis and Ca channel inhibition. Furthermore, the depletion of PIP by a voltage-sensitive phosphatase reduced Ca currents in a way similar to that observed following MR activation. These results indicate that activation of the MR pathway inhibits the Ca channel via PIP depletion by a Ca-dependent mechanism in pancreatic β- and INS-1 cells and thereby support the hypothesis that membrane phospholipids regulate ion channel activity by interacting with ion channels.

Phosphoinositide lipids regulate many cellular processes. They are synthesized by lipid kinases. Type I phosphatidylinositol phosphate 5-kinases (PIP5KIs) generate phosphatidylinositol-4,5-bisphosphate (PtdIns(4,5)P2). Several phosphoinositide-sensitive readouts revealed nonequivalence of overexpressing PIP5KIβ or PIP5KIγ or RASSF4, believed to activate PIP5KIs. Mass spectrometry showed each protein increased total cellular PtdInsP2 and PtdInsP3 at the expense of PtdInsP without changing lipid acyl chains. KCNQ2/3 channels and PH domains confirmed an increase of plasma membrane PtdIns(4,5)P2 with PIP5KIβ or PIP5KIγ overexpression, but RASSF4 required coexpression with PIP5KIγ to increase plasma membrane PtdIns(4,5)P2. Effects on the several steps of store-operated calcium entry (SOCE) were not explained by plasma membrane phosphoinositide increases alone. PIP5KIβ and RASSF4 increased STIM1 proximity to the plasma membrane and accelerated mobilization and faster onset of SOCE. But PIP5KIγ reduced STIM1 recruitment yet did not change induced Ca2+ entry. These differences imply actions through different segregated pools of phosphoinositides and specific protein-protein interactions and targeting.