Immunomodulation and metabolism: possible role of lactoferrin

To gain insight in the relationship between innate immune system and metabolic disease, we aimed to investigate the effects of lactoferrin in obesity-related metabolic disturbances. Circulating lactoferrin concentration was significantly decreased in subjects with altered glucose tolerance (AGT) and associated negatively with obesity-related metabolic disturbances. The SNPs-induced aminoacidic changes in lactoferrin N-terminus region were associated with a low atherogenic lipid profile. Lactoferrin production in neutrophils decreased significatively in aging, chronic low-grade inflammation and type 2 diabetes. In vitro, lactoferrin increased insulin signaling pathway, even under insulin resistance conditions and displayed dual effects on adipogenesis (antiadipogenic in 3T3-L1 and adipogenic in human adipocytes). In conclusion, lactoferrin might play a potential protective role against insulin resistance and obesity related metabolic disturbances.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.