Michael J. Theodorakis

Objective: The conventional approach to analyzing data from oral glucose tolerance testing (OGTT) requires model identification in each individual separately (standard two stage, STS), ignoring knowledge about the population as a whole. In practice, however, the OGTT is sparsely sampled and individual estimates are often not resolvable from available data. This weakness is often encountered in large scale trials or epidemiological studies, leading to either multiple imputations or simply much less data available for analysis. Methods: We have applied a population approach, nonlinear mixed effects modeling, to plasma glucose, insulin and C-peptide data obtained from a 120 minute OGTT undertaken by 106 subjects with varying glucose tolerance. This method provides estimates of population means, variances and covariances of model parameters and empirical Bayes estimates of individual parameter values, as well as measures of intra-individual (within-subject) and inter-individual (between-subject) variability. The recently developed oral glucose minimal model was used to evaluate insulin sensitivity, and a combined model approach was used to assess β-cell secretion. Results: Applying these models allowed for the reconstruction of insulin secretion and glucose absorption profiles and gave population indexes of insulin sensitivity (S_I = 6.51 ± 1.20 × 10⁻⁴ min⁻¹·μU⁻¹·ml), fractional hepatic extraction of insulin (F = 0.522 ± 0.291) and fractional insulin clearance (k_I = 0.258 ± 0.151 min⁻¹). Whereas the traditional approach to parameter estimation failed to recover estimates in more than one third of the population, the population approach provided individual estimates in all subjects. Examination of the empirical Bayes estimates showed that individual parameter estimates were able to differentiate well between individuals at glucose tolerant states ranging from euglycemia to overt type 2 diabetes. Conclusions: Our findings suggest that population analysis is a powerful tool for obtaining accurate assessments of indexes of insulin sensitivity and β-cell function from the OGTT, especially in epidemiological studies with large numbers of sparsely sampled subjects.

Prevention or delay of onset of type 2 diabetes in individuals at varying risk across the dysglycaemia continuum before overt diabetes becomes clinically manifest constitutes a leading objective of global disease prevention schemes. Pharmacological intervention has been suggested as a means to help prevent diabetes and reduce the global burden of this chronic condition. However, there is no credible evidence that early pharmacological intervention leads to long‐term benefit in reducing diabetes‐related complications or preventing early mortality, compared to treating people with diagnosed diabetes who have crossed the glycaemic threshold. In this review, we examine published evidence from trials using pharmacological agents to delay or prevent progression to diabetes. We also explore the benefit/risk impact of such therapies, safety issues and relevant off‐target effects. Current evidence suggests none of the drugs currently available sustainably lower cumulative diabetes incidence, none provides a durable delay in diabetes diagnosis and none provides a convincing concomitant excess benefit for microvascular or macrovascular risk.

In the emerging landscape of cardiovascular (CV) outcome trials evaluating the effects of blood glucose lowering drugs in individuals with type 2 diabetes, it is becoming increasingly apparent that since the promising signals coming from the United Kingdom Prospective Diabetes Study (UKPDS) no unequivocal benefits have been established for any single therapy thus far. There is an unmet need for introducing an effective pharmacological agent which could target both correlates of glycaemic regulation and CV risk factors, to ameliorate the enormous burden of fatal and non-fatal CV events in diabetic patients. Acarbose, like other alpha-glucosidase inhibitors (AGIs), has been proven to be an effective antidiabetic treatment for decades, but the overall significant impact of this class of drugs on modulating CV risk has only recently been appreciated. Accumulating evidence has shown that apart from its multiple effects on primarily postprandial glucose dysmetabolism, a key component of mechanisms linked to increased incidence of CV events, acarbose therapy also associates with a favorable impact on an array of surrogate markers of CV disease. Data stemming from in vitro testing of human cell lines as well as from preliminary trials in diabetic populations, like the Study to Prevent Non-Insulin-Dependent Diabetes Mellitus (STOP-NIDDM) trial, have highlighted – though not undisputed – the potential beneficial effects of the drug on CV morbidity. Large scale trials, like the ongoing Acarbose Cardiovascular Evaluation (ACE) trial, aim at conclusively establishing such a positive effect in patients with coronary heart disease and impaired glucose tolerance. In view of its usually acceptable level of side effects that are, if they occur, mostly limited to transient gastrointestinal symptoms, acarbose could well be a strong future player in CV disease secondary prevention. Current discouraging results from many trials of antidiabetic medications to significantly lower CV event rates in diabetic patients, should only draw further attention on alternative glucose lowering agents, among which acarbose is indeed promising.