Characteristics of included randomized controlled trials.

<p>I: intensity; F: frequency; T: time of each walking bout; PRE; perceived rate of exertion; VO_2max, maximal oxygen consumption; METs, metabolic equivalents.</p>#<p>Age was represented as mean (SD), or mean if SD was not provided, or imputed with a mean.</p>§<p>Characteristics of walking training described did not include warm-up or cool-down periods unless indicated.</p>$<p>Duration meant length of walking intervention in this meta-analysis.</p><p>^*The same study which included 2 different walking groups: “a” was a continuous walking training group; “b” was an energy expenditure–matched interval-walking training group.</p><p>^**The same study which included 2 different walking groups: “a” was a continuous walking training group; “b” was a total oxygen consumption-matched interval-walking training group.</p><p>Characteristics of included randomized controlled trials.</p

Impact of Walking on Glycemic Control and Other Cardiovascular Risk Factors in Type 2 Diabetes: A Meta-Analysis

<div><p>Background</p><p>Walking is the most popular and most preferred exercise among type 2 diabetes patients, yet compelling evidence regarding its beneficial effects on cardiovascular risk factors is still lacking. The aim of this meta-analysis of randomized controlled trials (RCTs) was to evaluate the association between walking and glycemic control and other cardiovascular risk factors in type 2 diabetes patients.</p><p>Methods</p><p>Three databases were searched up to August 2014. English-language RCTs were eligible for inclusion if they had assessed the walking effects (duration ≥8 weeks) on glycemic control or other cardiovascular risk factors among type 2 diabetes patients. Data were pooled using a random-effects model. Subgroup analyses based on supervision status and meta-regression analyses of variables regarding characteristics of participants and walking were performed to investigate their association with glycemic control.</p><p>Results</p><p>Eighteen studies involving 20 RCTs (866 participants) were included. Walking significantly decreased glycosylated haemoglobin A1c (HbA1c) by 0.50% (95% confidence intervals [CI]: −0.78% to −0.21%). Supervised walking was associated with a pronounced decrease in HbA1c (WMD −0.58%, 95% CI: −0.93% to −0.23%), whereas non-supervised walking was not. Further subgroup analysis suggested non-supervised walking using motivational strategies is also effective in decreasing HbA1c (WMD −0.53%, 95% CI: −1.05% to −0.02%). Effects of covariates on HbA1c change were generally unclear. For other cardiovascular risk factors, walking significantly reduced body mass index (BMI) and lowered diastolic blood pressure (DBP), but non-significantly lowered systolic blood pressure (SBP), or changed high-density or low-density lipoprotein cholesterol levels.</p><p>Conclusions</p><p>This meta-analysis supports that walking decreases HbA1c among type 2 diabetes patients. Supervision or the use of motivational strategies should be suggested when prescribed walking to ensure optimal glycemic control. Walking also reduces BMI and lowers DBP, however, it remains insufficient regarding the association of walking with lowered SBP or improved lipoprotein profiles.</p><p>Trial Registration</p><p>PROSPERO CRD42014009515</p></div

MOESM1 of Exercise training and endothelial function in patients with type 2 diabetes: a meta-analysis

Additional file 1: Table S1. Search strategies. Table S2. Quality assessment

Using Serum Advanced Glycation End Products-Peptides to Improve the Efficacy of World Health Organization Fasting Plasma Glucose Criterion in Screening for Diabetes in High-Risk Chinese Subjects

<div><p>The efficacy of using fasting plasma glucose (FPG) alone as a preferred screening test for diabetes has been questioned. This study was aimed to evaluate whether the use of serum advanced glycation end products-peptides (sAGEP) would help to improve the efficacy of FPG in diabetes screening among high-risk Chinese subjects with FPG <7.0 mmol/L. FPG, 2-h plasma glucose (2h-PG), serum glycated haemoglobin A1c (HbA1c), and sAGEP were measured in 857 Chinese subjects with risk factors for diabetes. The areas under receiver operating characteristic (ROC) curves generated by logistic regression models were assessed and compared to find the best model for diabetes screening in subjects with FPG <7.0 mmol/L. The optimal critical line was determined by maximizing the sum of sensitivity and specificity. Among the enrolled subjects, 730 of them had FPG <7.0 mmol/L, and only 41.7% new diabetes cases were identified using the 1999 World Health Organization FPG criterion (FPG ≥7.0 mmol/L). The area under ROC curves generated by the model on FPG-sAGEP was the largest compared with that on FPG-HbA1c, sAGEP, HbA1c or FPG in subjects with FPG <7.0 mmol/L. By maximizing the sum of sensitivity and specificity, the optimal critical line was determined as 0.69×FPG + 0.14×sAGEP = 7.03, giving a critical sensitivity of 91.2% in detecting 2h-PG ≥11.1 mmol/L, which was significantly higher than that of FPG-HbA1c or HbA1c. The model on FPG-sAGEP improves the efficacy of using FPG alone in detecting diabetes among high-risk Chinese subjects with FPG <7.0 mmol/L, and is worth being promoted for future diabetes screening.</p></div

The ROC curves generated by the logistic regression models on FPG, HbA1c, sAGEP, FPG-HbA1c and FPG-sAGEP.

<p>ROC, receiver operating characteristic; FPG, fasting plasma glucose; HbA1c, glycated haemoglobin A1c; sAGEP, serum advanced glycation end products-peptides; FPG-HbA1c, FPG plus HbA1c; FPG-sAGEP, FPG plus sAGEP. The areas under the ROC curves for FPG, HbA1c, sAGEP, FPG-HbA1c and FPG-sAGEP were 0.772, 0.838, 0.894, 0.845, and 0.899, respectively. Comparisons among these areas were as follows: FPG-sAGEP vs. FPG-HbA1c (<i>P</i> = 0.0003), FPG-sAGEP vs. sAGEP (<i>P</i> = 0.58), FPG-sAGEP vs. HbA1c (<i>P</i> <0.001), FPG-sAGEP vs. FPG (<i>P</i> <0.001), FPG-HbA1c vs. FPG (<i>P</i> <0.001), sAGEP vs. HbA1c (<i>P</i> = 0.006), sAGEP vs. FPG (<i>P</i> <0.001).</p

Age-adjusted partial correlations between sAGEP and FPG, 2h-PG, and HbA1c.

<p>sAGEP, serum advanced glycation end products-peptides; FPG, fasting plasma glucose; 2h-PG, 2h-plasma glucose; HbA1c, glycated haemoglobin A1c; NGT, normal glucose tolerance; IGR, impaired glucose regulation</p><p>Data are presented as <i>r (P)</i>.</p><p>Age-adjusted partial correlations between sAGEP and FPG, 2h-PG, and HbA1c.</p

General and clinical characteristics of Chinese subjects.

<p>BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; 2h-PG, 2h-plasma glucose; HbA1c, glycated haemoglobin A1c; sAGEP, serum advanced glycation end products-peptides</p><p>Data are presented as means ± standard deviations.</p><p>^a Compared between men and women.</p><p>^bSamples for measuring sAGEP were taken from 856 subjects with 290 men and 566 women.</p><p>^c Compared between men and women with adjustment for age.</p><p>General and clinical characteristics of Chinese subjects.</p

Flow diagram of the recruitment of Chinese subjects.

<p>Flow diagram of the recruitment of Chinese subjects.</p

Descriptive statistics, comparison with the original DAS-3 and test-retest intraclass correlation coefficients for C-DAS-3.

<p>DAS-3: The third version of diabetes attitude scale.</p><p>Total scale: The average score of the five subscales.</p

Correlations between C-DAS-3 and demographic/medical variables.

<p>Correlations between C-DAS-3 and demographic/medical variables.</p