The glycaemic index (GI) concept was originally introduced to classify different sources of carbohydrate (CHO)-rich foods, usually having an energy content of . 80 % from CHO, to their effect on post-meal glycaemia. It was assumed to apply to foods that primarily deliver available CHO, causing hyperglycaemia. Low-GI foods were classified as being digested and absorbed slowly and high-GI foods as being rapidly digested and absorbed, resulting in different glycaemic responses. Low-GI foods were found to induce benefits on certain risk factors for CVD and diabetes. Accordingly it has been proposed that GI classification of foods and drinks could be useful to help consumers make 'healthy food choices' within specific food groups. Classification of foods according to their impact on blood glucose responses requires a standardised way of measuring such responses. The present review discusses the most relevant methodological considerations and highlights specific recommendations regarding number of subjects, sex, subject status, inclusion and exclusion criteria, pre-test conditions, CHO test dose, blood sampling procedures, sampling times, test randomisation and calculation of glycaemic response area under the curve. All together, these technical recommendations will help to implement or reinforce measurement of GI in laboratories and help to ensure quality of results. Since there is current international interest in alternative ways of expressing glycaemic responses to foods, some of these methods are discussed.

Abstract. When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.Abrégé. Le choix de métrique de probabilité est une décision très importante lorsqu'onétudie la convergence des mesures. Nous vous fournissons avec un sommaire de plusieurs métriques/distances de probabilité couramment utilisées par des statisticiens(nes) at par des probabilistes, ainsi que certains nouveaux résultats qui se rapportentà leurs bornes. Avoir connaissance d'autres métriques peut vous fournir avec un moyen de dériver des bornes pour une autre métrique dans un problème appliqué. Le fait de prendre en considération plusieurs métriques vous permettra d'approcher des problèmes d'une manière différente. Ainsi, nous vous démontrons que les taux de convergence peuvent dépendre de façon importante sur votre choix de métrique. Il est donc important de tout considérer lorsqu'on doit choisir une métrique.

In subjects with T2DM managed by diet alone with optimal glycemic control, long-term HbA1c was not affected by altering the GI or the amount of dietary carbohydrate. Differences in total:HDL cholesterol among diets had disappeared by 6 mo. However, because of sustained reductions in postprandial glucose and CRP, a low-GI diet may be preferred for the dietary management of T2DM.

Background: Consumption of 3 g oat b-glucan/d is considered sufficient to lower serum LDL cholesterol, but some studies have shown no effect. LDL cholesterol lowering by oat b-glucan may depend on viscosity, which is controlled by the molecular weight (MW) and amount of oat b-glucan solubilized in the intestine (C). Objectives: Our 2 primary objectives were to determine whether consumption of 3 g high-MW oat b-glucan/d would reduce LDL cholesterol and whether LDL cholesterol lowering was related to the log(MW · C) of oat b-glucan. Design: In a double-blind, parallel-design, multicenter clinical trial, subjects with LDL cholesterol 3.0 and 5.0 mmol/L (n = 786 screened, n = 400 ineligible, n = 19 refused, n = 367 enrolled, and n = 345 completed) were randomly assigned to receive cereal containing wheat fiber (n = 87) or 3 g high-MW (2,210,000 g/mol, n = 86), 4 g medium-MW (850,000 g/mol, n = 67), 3 g medium-MW (530,000 g/mol, n = 64), or 4 g low-MW (210,000 g/mol, n = 63) oat b-glucan/d (divided doses, twice daily) for 4 wk. Results: LDL cholesterol was significantly less with 3 g high-MW, 4 g medium-MW, and 3 g medium-MW oat b-glucan cereals than with the wheat-fiber cereal by 0.21 (5.5%; 95% CI: 20.11, 20.30; P = 0.002), 0.26 (6.5%; 95% CI: 20.14, 20.37; P = 0.0007), and 0.19 (4.7%; 95% CI: 20.08, 20.30; P = 0.01) mmol/L, respectively. However, the effect of 4 g low-MW oat b-glucan/d (0.10 mmol/L) was not significant (2.3%; 95% CI: 0.02, 20.20). By analysis of covariance, log(MW · C) was a significant determinant of LDL cholesterol (P = 0.003). Treatment effects were not significantly influenced by age, sex, study center, or baseline LDL cholesterol. Conclusions: The physicochemical properties of oat b-glucan should be considered when assessing the cholesterol-lowering ability of oat-containing products; an extruded breakfast cereal containing 3 g oat b-glucan/d with a high-MW (2,210,000 g/mol) or a medium-MW (530,000 g/mol) lowered LDL cholesterol similarly by '0.2 mmol/L (5%), but efficacy was reduced by 50% when MW was reduced to 210,000 g/mol. This trial was registered at www.clinicaltrials.gov as NCT00981981.Am J Clin Nutr 2010;92:723-32.

Abstract. When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.Abrégé. Le choix de métrique de probabilité est une décision très importante lorsqu'onétudie la convergence des mesures. Nous vous fournissons avec un sommaire de plusieurs métriques/distances de probabilité couramment utilisées par des statisticiens(nes) at par des probabilistes, ainsi que certains nouveaux résultats qui se rapportentà leurs bornes. Avoir connaissance d'autres métriques peut vous fournir avec un moyen de dériver des bornes pour une autre métrique dans un problème appliqué. Le fait de prendre en considération plusieurs métriques vous permettra d'approcher des problèmes d'une manière différente. Ainsi, nous vous démontrons que les taux de convergence peuvent dépendre de façon importante sur votre choix de métrique. Il est donc important de tout considérer lorsqu'on doit choisir une métrique.

The Berlin questionnaire (BQ) has been used to help identify patients at high risk of having sleep apnea in primary care and atrial fibrillation patients. The BQ may be a useful adjunct in sleep medicine and research, but it has never been validated in a sleep clinic population. The aim of the study is to determine the specificity and sensitivity of the BQ compared to the respiratory disturbance index (RDI) values obtained from two nights of polysomnographic recording in a sleep clinic population. This is a retrospective chart review study of 130 sleep clinic patients. Patients' demographics, BQ scores, RDI measurements, and sleep study parameters were extracted from the patients' chart. Of the 130 charts reviewed, the BQ identified 76 (58.5%) as being at high-risk of having sleep apnea, but overnight polysomnography found only 34 of the 130 patients (26.2%) had an RDI > 10. The BQ performed with 0.62 sensitivity and 0.43 specificity at the RDI > 10 level. Due to the low sensitivity and specificity as well as the large number of false negatives and positives, the Berlin questionnaire is not an appropriate instrument for identifying patients with sleep apnea in a sleep clinic population.

Weekly doses of vitamin D in individuals with suboptimal vitamin D levels who were at risk for type 2 diabetes did not improve oral glucose tolerance or markers of glycaemic status.

Summary Shift work is a ubiquitous phenomenon and its adverse effects on workers’ physical and mental health have been documented. In the sleep literature, differentiating between the symptoms of fatigue and sleepiness, and developing appropriate objective and subjective measures, have become very important endeavors. From such research, fatigue and sleepiness have been shown to be distinct and independent phenomena. However, it is not known whether shift work differentially affects fatigue and sleepiness. In an attempt to answer this question, 489 workers from a major Ontario employer completed a series of subjective, self‐report questionnaires, including the Fatigue Severity Scale (FSS) and the Epworth Sleepiness Scale. Workers were separated into four groups based on the frequency with which they are engaged in shift work (never, fewer than four times per month, 1–2 days per week, 3 days or more per week). The frequency of shift work was found to have a significant effect on subjective fatigue, but not on subjective sleepiness. Compared with the subjects who never had a shift schedule, those who worked in a shift for 3 days or more had significantly higher mean score of the FSS. In agreement with previous results, a low correlation was found between workers’ subjective fatigue and sleepiness scores, providing further support for the concept of fatigue and sleepiness as distinct and independent phenomena. Future research should address the possibility of using the FSS as an indicator when the frequency of shift work has become high enough to adversely affect work performance or cause health problems.