To establish the antihyperglycemic mechanisms of metformin in non-insulin-dependent diabetes mellitus (NIDDM) independently of the long-term, aspecific effects of removal of glucotoxicity, 21 NIDDM subjects (14 obese, 7 nonobese) were studied on two separate occasions, with an isoglycemic (plasma glucose approximately 9 mM) hyperinsulinemic (two-step insulin infusion, 2 h each, at the rate of 4 and 40 mU.m-2.min-1) clamp combined with [3-3H]glucose infusion and indirect calorimetry, after administration of either metformin (500 mg per os, at -5 and -1 h before the clamp) or placebo. Compared with placebo, hepatic glucose production (HGP) decreased approximately 30% more after metformin (from 469 +/- 50 to 330 +/- 54 mumol/min), but glucose uptake did not increase. Metformin suppressed free fatty acids (FFAs) by approximately 17% (from 0.42 +/- 0.04 to 0.35 +/- 0.04 mM) and lipid oxidation by approximately 25% (from 4.5 +/- 0.4 to 3.4 +/- 0.4 mumol.kg-1.min-1) and increased glucose oxidation by approximately 16% (from 16.2 +/- 1.4 to 19.3 +/- 1.3 mumol.kg-1.min-1) compared with placebo (P < 0.05), but did not affect nonoxidative glucose metabolism, protein oxidation, or total energy expenditure. Suppression of FFA and lipid oxidation after metformin correlated with suppression of HGP (r = 0.70 and r = 0.51, P < 0.001). The effects of metformin in obese and nonobese subjects were no different. We conclude that the specific, antihyperglycemic effects of metformin in the clinical condition of hyperglycemia in NIDDM are primarily due to suppression of HGP, not stimulation of glucose uptake, and are mediated, at least in part, by suppression of FFA and lipid oxidation.

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Objective Decision support systems (DSS) have been developed and promoted for their potential to improve quality of health care. However, there is a lack of common clinical strategy and a poor management of clinical resources and erroneous implementation of preventive medicine. Methods To overcome this problem, this work proposed an integrated system that relies on the creation and sharing of a database extracted from GPs’ Electronic Health Records (EHRs) within the Netmedica Italian (NMI) cloud infrastructure. Although the proposed system is a pilot application specifically tailored for improving the chronic Type 2 Diabetes (T2D) care it could be easily targeted to effectively manage different chronic-diseases. The proposed DSS is based on EHR structure used by GPs in their daily activities following the most updated guidelines in data protection and sharing. The DSS is equipped with a Machine Learning (ML) method for analyzing the shared EHRs and thus tackling the high variability of EHRs. A novel set of T2D care-quality indicators are used specifically to determine the economic incentives and the T2D features are presented as predictors of the proposed ML approach. Results The EHRs from 41237 T2D patients were analyzed. No additional data collection, with respect to the standard clinical practice, was required. The DSS exhibited competitive performance (up to an overall accuracy of 98%±2% and macro-recall of 96%±1%) for classifying chronic care quality across the different follow-up phases. The chronic care quality model brought to a significant increase (up to 12%) of the T2D patients without complications. For GPs who agreed to use the proposed system, there was an economic incentive. A further bonus was assigned when performance targets are achieved. Conclusions The quality care evaluation in a clinical use-case scenario demonstrated how the empowerment of the GPs through the use of the platform (integrating the proposed DSS), along with the economic incentives, may speed up the improvement of care.

Inasmuch as previous studies have obtained conflicting results on the contribution of obesity to insulin resistance in noninsulin-dependent diabetes mellitus (NIDDM), we studied 10 nonobese and 10 obese NIDDM patients with the isoglycemic-(approximately 10 mmol/L)-hyperinsulinemic clamp (two insulin infusions of 4 and 40 mU/m-2 min-1), combined with [3-3H]glucose infusion and indirect calorimetry. As compared with nonobese patients, obese NIDDM patients had higher baseline peripheral and estimated portal plasma insulin concentrations (113 +/- 18 vs. 46 +/- 3 pmol/L and 288 +/- 53 vs. 98 +/- 6 pmol/L, respectively; P < 0.05) and less suppressed endogenous insulin production during clamp. Hepatic glucose production was greater in obese than in nonobese patients (basal, 16 +/- 1.1 vs. 12 +/- 0.5 mumol/kg-1 fat-free mass (FFM) min-1; clamp, 5.7 +/- 0.5 vs. 2.8 +/- 0.2 mumol/kg-1 FFM min-1, P < 0.05). Glucose utilization increased to a lesser extent in obese than in nonobese patients (49 +/- 5 vs. 73 +/- 7 mumol/kg-1 FFM min-1, P < 0.05) during clamp because of a lower increase in nonoxidative glucose metabolism (30 +/- 5 vs. 50 +/- 7 mumol/kg-1 FFM min-1, P < 0.05). Plasma free fatty acid concentrations and rates of lipid oxidation were greater in obese (P < 0.05) patients and correlated with hepatic glucose production (r = 0.79 and 0.50, P < 0.05). In conclusion, obesity exaggerates hepatic as well as extra-hepatic insulin resistance in NIDDM. The impaired inhibition of pancreatic beta-cell function by exogenous insulin contributes to exaggerated hyperinsulinemia in obese NIDDM.