Diabetes Stops Here will provide snap shots of the people who are committed to putting an end to this disease, from inspiring volunteer stories to moving staff experiences to celebrity stories about how to be successful while living with diabetes. The stories, interviews and news will be shared by the blog’s author, a staff member at the American Diabetes Association, who has lived with type 1 diabetes for nearly ten years. 

To investigate the combined effects of diabetes and smoking on the subgingival microbiome, we compared the microbial assemblages of uncontrolled hyperglycemics with a 10 pack-year smoking history to normoglycemic smokers and hyperglycemic nonsmokers. All these individuals had periodontitis; unlike the smokers, diabetics and controls, we were unable to identify periodontally healthy diabetic smokers. PCoA as well as LDA revealed a significant clustering of the three groups (P=0.001, Figure 2a). Diabetic-smokers also demonstrated significantly lower alpha diversity (median of 150, P=0.0001), a bimodal distribution of the density plot and a significantly greater range in the ACE index when compared to diabetics or smokers (Figure 2b). Additionally, the relative risk for diabetic smokers to demonstrate an ACE index <150 was 11, while it was 2.1 for smokers and 4.7 for diabetics. Diabetic-smokers exhibited lower levels of gram-negative anaerobes and higher levels of gram-negative facultatives when compared to both smokers and diabetics (P<0.05, Figure 2c). The microbiomes of 23 diabetic smokers contained gram-negative facultatives at 10% or more of the total microbiota (and gram-negative anaerobes at 15% or less), while six diabetics and two smokers had the same numbers. These species belonged to Leptotrichia, Pseudomonas, Acinetobacter, Brevundimonas, Enterobacter, Alloprevotella, Bergeyella, Terrahemophilus and Stenotophomonas (P<0.05, Figures 4c and d). The core microbiome in diabetic smokers was the smallest (4% of s-OTUs in diabetic smokers when compared to 30% in controls, 23% in smokers, and 18% in diabetics, Figure 3a). However, microbial species in diabetic smokers demonstrated the most robust co-occurrence patterns, with over 150 s-OTUs contributing to the creation of microbial hubs (Figure 5d). Taken together, the relative risk of having a lower diversity, higher levels of gram-negative facultatives, lower levels of gram-negative anaerobes and a smaller core are much higher in diabetic smokers than would be expected by adding the odds of smokers and diabetics, suggesting that when these presses intersect, their effect is multiplicative, not additive.

The sequences have been deposited in the Sequence Read Archive of the NCBI (Accession number: SRP090878). These studies were funded through National Institute of Dental and Craniofacial Medicine (NIDCR) grant R01-DE022579 and through American Society for Microbiology grant (ASM-IUSSTF Professorship) to Vinayak Joshi. Shareef Dabdoub was supported by NIDCR grant T32-DE014320.
There was a significant reduction in the body mass index (BMI) of patients, an improvement in regular self-checks of blood sugar, dietary regimen, foot care, and exercise and lifestyle behavior following the educational program. It was observed that patients' knowledge of diabetes had improved after exposure to the educational program in the three-time intervals.
Table 6 shows the results of regression model testing for the effect of independent variables, separately/one at a time, on the score of HbA1c of the diabetic patients. Females were 7.386 times more likely than males to have poor HbA1c compared to HbA1c = good. The females were 1.48 times more likely than males to have a HbA1c = fair compared to HbA1c = good.

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The annual incidence of type 1 diabetes in children <15 yr in the Auckland population in 1990–2009 was 16.4/100,000 (95% CI 15.3–17.5). Considering the underlying 36% population growth over the 1990–2009 period, there was still a progressive increase in the incidence of new cases (p<0.0001; Figure 1A). By Poisson regression the type 1 diabetes incidence in children <15 yr in 2009 was 22.5 per 100,000 (95% CI 17.5–28.4), in comparison to 10.9 per 100,000 in 1990 (95% CI 7.0–16.1) (Figure 1A). Overall incidence among males and females across the 20-year period was similar (p = 0.49). The increase in incidence was greatest among children 10–14 yr (average increase of +0.81/year; p<0.0001) and lowest among children 0–4 yr (+0.32/year; p = 0.02); incidences by 2009 were 27.0 (95% CI 18.1–38.8) for children 10–14 yr, 25.4 (95% CI 16.5–37.3; +0.66/year; p = 0.0002) for children 5–9 yr, and 14.9 per 100,000 (95% CI 8.4–24.5) for those aged 0–4 yr (Figure 1B).


Statistical analyses were performed by SAS version 9.4 (SAS Institute). All statistical tests were two sided at a 5% significance level. Analyses were performed on the principle of intention to treat, including all randomised participants who provided at least one valid measure on the primary outcome after randomisation. Demographics and baseline characteristics of all participants were first summarised by treatment group with descriptive statistics. No formal statistical tests were conducted at baseline, because any baseline imbalance observed between two groups could have occurred by chance with randomisation.

From 1994 onwards, anthropometric data were recorded at each clinic visit, and for the purposes of this study we used data from the first post-diagnosis clinic that usually occurred 3–4 months afterwards. Standard deviation scores (SDS) were calculated based on the British 1990 Growth Reference Data [17] to obtain height SDS, weight SDS, and body mass index (BMI) SDS.


Table 6 shows the results of regression model testing for the effect of independent variables, separately/one at a time, on the score of HbA1c of the diabetic patients. Females were 7.386 times more likely than males to have poor HbA1c compared to HbA1c = good. The females were 1.48 times more likely than males to have a HbA1c = fair compared to HbA1c = good.
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We saw no significant interaction between the treatment group and any of the prespecified subgroups: type 1 versus type 2 diabetes (P=0.82), non-Māori/non-Pacific versus Māori/Pacific ethnicity (P=0.60), high urban versus high rural/remote region (P=0.38). Adjusted mean differences on change in HbA1c from baseline to nine months for patients with type 1 and type 2 diabetes were −5.75 mmol/mol (95% confidence interval −10.08 to −1.43, P=0.009) and −3.64 mmol/mol (−7.72 to 0.44, P=0.08), respectively. Adjusted mean differences for non-Māori/non-Pacific and Māori/Pacific people were −4.97 mmol/mol (−8.51 to −1.43, P=0.006) and −3.21 mmol/mol (−9.11 to 2.70, P=0.28), respectively. Adjusted mean differences for participants living in high urban and high rural/remote areas were −4.54 mmol/mol (−8.40 to −0.68, P=0.02) and −3.94 mmol/mol (−9.00 to 1.12, P=0.13), respectively (table 3).
This study shows that the incidence of type 1 diabetes in the Auckland region has increased steadily over the last two decades. However, unlike other studies [3], [4], [5], the rate of increase in incidence has been particularly marked in older children (10–14 yr), which was approximately 2.5-fold greater than that in children 0–4 yr. Interestingly, the incidence of type 1 diabetes in children 0–4 and 10–14 in Auckland are very similar to those reported in Australia, our closest geographical and ethnic neighbours [19], both of which had very high case ascertainment levels (close to 100%).
Pre-diabetes and type 2 diabetes are at epidemic proportions in New Zealand with the Auckland region over represented in certain populations. This programme works with those who have the highest rates of pre-diabetes and type 2 diabetes in Auckland creating that awareness and preventing diabetes where possible that is needed on a more intimate level within the community.

DiabeteSteps Rx® takes an “integrative” approach to health care and diabetes care and provides “Remedies for Reversing Diabetes Naturally.”  By “integrating” both a functional and conventional approach to diabetes care, and its associated medical conditions including high blood pressure, high cholesterol and overweight and obesity, there is the potential to REVERSE these metabolic conditions (and their devastating consequences).
The use of apps to record blood glucose was the most favored function in apps used by people with diabetes, with interest in insulin dose calculating function. HPs do not feel confident in recommending insulin dose calculators. There is an urgent need for an app assessment process to give confidence in the quality and safety of diabetes management apps to people with diabetes (potential app users) and HPs (potential app prescribers).
Similarity in microbial community configuration between deep and shallow sites. (a–d) show the principal coordinate analysis (PCoA) of UniFrac distances between deep and shallow sites in normoglycemic nonsmokers, normoglycemic smokers, hyperglycemic nonsmokers and hyperglycemic smokers. (e–h) show the kernel density plots of alpha diversity (ACE) between the same sites in the same individuals. The peak indicates the median values for each group, and the x axis shows the data range. Neither the PCoA nor the ACE values differed significantly between deep and shallow sites.
Health targets were first introduced in 2007 as a way to highlight priority areas where the government wished to see measurable progress in the health system. [14]   In 2009, Health Minister Hon Tony Ryall announced a smaller set of six health targets in order to tightly focus district health boards’ performance. [15]   One of the targets was that 90% of the eligible population would have had their cardiovascular risk assessed within the last five years (this would include a diabetes test). [16]   Primary health organisations are expected to meet this target by 30 June 2015 as part of their Integrated Performance and Incentive Framework agreement with district health boards. [17]  

Species that were uniquely identified in normoglycemic nonsmokers, normoglycemic smokers, hyperglycemic nonsmokers and hyperglycemic smokers with periodontitis, as well as those that were significantly different between groups (P<0.05, FDR-adjusted Wald Test, DE-Seq2). Species are arranged by phylogeny and the fold differences (log2 scale) are shown.


A longitudinal experimental research design was used. A convenient sample of 150 adult patients diagnosed as type-2 diabetes was drawn from diabetic outpatient clinics of King Fahad University Hospital. Randomization is beneficial because on average it tends to evenly distribute both known and unknown confounding variables between the intervention and control group. However, when the sample size is small, randomization may not adequately accomplish this balance. Thus, alternative design and analytical methods are often used in place of randomization when only small sample sizes are available. Hence, there was no control group in this study.

This study contributes to the evidence around the use of SMS to support diabetes management.131415 The improvements in HbA1c seen in this study are similar to those reported in meta-analyses of SMS interventions in diabetes not limited to those with poor control.141641 Unlike previous studies that typically focus on a particular population defined by diabetes type, age, or treatment, the current study provided an intervention for all adults with either type 1 or type 2 diabetes under any treatment regimen, enhancing potential reach and generalisability. The only limit on the population was the requirement that participants had to have poor diabetes control. This criterion was particularly important given associated costs and debilitating complications of poorly controlled diabetes. Although few trials so far have examined the effectiveness of mHealth interventions in this population,42 this study provides evidence to support the use of this modality to provide diabetes education and support to individuals with poor control.


In our study, patients' compliance to medication adherence was measured at three different time intervals. There was a significant difference among the patients in the three-time periods (before the educational intervention, 3 months later, and at 6 months later). Overall improvement in adherence rate of 78% was observed with a decline of the rate of nonadherence after interventions. This result is in accordance with Al-Hayek et al.,[11] who illustrated a relationship between diabetes education and adherence to medication. They found a significant improvement in patients' adherence to medication regimen after the diabetes education session.
Sexual problems are common in the general population but people with diabetes are at an increased risk. The biological effects of diabetes can affect both men and women although the correlation between diabetes and sexual function in women is poorly understood. It is important to ask both male and female patients if they are experiencing any issues regarding their sexual functioning.