Type 2 diabetes prevention programs - from proof-of-concept trials to national intervention and beyond

The prevention of type 2 diabetes (T2D) in high-risk people with lifestyle interventions has been demonstrated by several randomized controlled trials. The intervention effect has sustained up to 20 years in post-trial monitoring of T2D incidence. In 2000, Finland launched the national T2D prevention plan. For screening for high T2D risk, the non-laboratory Finnish Diabetes Risk Score was developed and widely used, also in other countries. The incidence of drug-treated T2D has decreased steadily since 2010. The US congress authorized public funding for a national diabetes prevention program (NDPP) in 2010. It was built around a 16-visit program that relies on referral from primary care and self-referral of persons with either prediabetes or by a diabetes risk test. The program uses a train-the-trainer program. In 2015 the program started the inclusion of online programs. There has been limited implementation of nationwide T2D prevention programs in other countries. Despite the convincing results from RCTs in China and India, no translation to the national level was introduced there. T2D prevention efforts in low-and middle-income countries are still limited, but results have been promising. Barriers to efficient interventions are greater in these countries than in high-income countries, where many barriers also exist. Health disparities by socioeconomic status exist for T2D and its risk factors and form a challenge for preventive interventions. It seems that a stronger commitment to T2D prevention is needed, such as the successful WHO Framework Convention on Tobacco Control, which legally binds the countries to act. </p

Baseline correlations and associations between absolute changes in CMPF and glucose parameters adjusted for effect of intervention group (n = 106).

<p>¹ Area under the curve in 2-hour oral glucose tolerance test</p><p>² Homeostasis model of insulin resistance, HOMA IR = (fasting glucose mmol/l x fasting insulin mU/l) / 22.5</p><p>³ Insulinogenic index, IGI = (insulin 30 min—insulin 0 min, pmol/l) / (glucose 30 min – glucose 0 min, mmol/l)</p><p>⁴ Quantitative insulin sensitivity check index, Quicky = 1 / (lg10(insulin 0 min, mU/l) + lg10(glucose 0 min, mg/dl))</p><p>⁵ DI = IGI x Quicky</p><p>Baseline correlations and associations between absolute changes in CMPF and glucose parameters adjusted for effect of intervention group (n = 106).</p

CMPF concentrations before and after the intervention (0wk and 12 wk) in the Healthy Diet, WGED and Control groups.

<p>Changes between the groups were tested using ANOVA and Bonferroni’s post-hoc tests.</p

Absolute changes in glucose parameters according to quartiles¹ based on absolute changes in CMPF (n = 106).

<p>¹ Average changes in quartiles: quartile 1: -7.6 μmol/l; quartile 2: -2.2 μmol/l; quartile 3: 0.89 μmol/l; quartile 4: 7.7 μmol/l</p><p>² Area under the curve in 2-hour oral glucose tolerance test</p><p>³ Homeostasis model of insulin resistance, HOMA IR = (fasting glucose mmol/l x fasting insulin mU/l) / 22.5</p><p>⁴ Insulinogenic index, IGI = (insulin 30 min—insulin 0 min, pmol/l) / (glucose 30 min – glucose 0 min, mmol/l)</p><p>⁵ Quantitative insulin sensitivity check index, Quicky = 1 / (lg10(insulin 0 min, mU/l) + lg10(glucose 0 min, mg/dl))</p><p>⁶ IGI x Quicky</p><p>Absolute changes in glucose parameters according to quartiles<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124379#t002fn001" target="_blank">¹</a> based on absolute changes in CMPF (n = 106).</p

Mean level and number of measurements of FPG, HbA1c, 1-hour OGTT and 2-h OGTT during the 10-year follow-up.

<p>Mean level and number of measurements of FPG, HbA1c, 1-hour OGTT and 2-h OGTT during the 10-year follow-up.</p

Primary Vitamin D Target Genes Allow a Categorization of Possible Benefits of Vitamin D₃ Supplementation

<div><p>Vitamin D deficiency has been associated with an increased risk of developing a number of diseases. Here we investigated samples from 71 pre-diabetic individuals of the VitDmet study, a 5-month high dose vitamin D₃ intervention trial during Finnish winter, for their changes in serum 25-hydroxyvitamin D₃ (25(OH)D₃) concentrations and the expression of primary vitamin D target genes in peripheral blood mononuclear cells and adipose tissue. A negative correlation between serum concentrations of parathyroid hormone and 25(OH)D₃ suggested an overall normal physiological vitamin D response among the participants. The genes <i>CD14</i> and thrombomodulin (<i>THBD</i>) are up-regulated primary vitamin D targets and showed to be suitable gene expression markers for vitamin D signaling in both primary tissues. However, in a ranking of the samples concerning their expected response to vitamin D only the top half showed a positive correlation between the changes of <i>CD14</i> or <i>THBD</i> mRNA and serum 25(OH)D₃ concentrations. Interestingly, this categorization allows unmasking a negative correlation between changes in serum concentrations of 25(OH)D₃ and the inflammation marker interleukin 6. We propose the genes <i>CD14</i> and <i>THBD</i> as transcriptomic biomarkers, from which the effects of a vitamin D₃ supplementation can be evaluated. These biomarkers allow the classification of subjects into those, who might benefit from a vitamin D₃ supplementation, and others who do not.</p></div

Hazard ratios of CVD for 1 unit increase in FPG, 1 hPG, 2 hPG and HbA1c.

<p>Model 1 Covariates: Age, Gender, Smoking.</p><p>Model 2 Covariates: Age, Gender, Smoking, BMI, Physical Activity.</p><p>Model 3 Covariates: Age, Gender, Smoking, BMI, Physical Activity, SBP, HDLC, LDLC.</p><p>Model 4 Covariates: Age, Gender, Smoking, BMI, Physical Activity, SBP, HDLC, LDLC, Previous CVD, Previous Cancer.</p><p>Hazard ratios of CVD for 1 unit increase in FPG, 1 hPG, 2 hPG and HbA1c.</p

Updated mean and updated value of FPG, 1 hPG, 2 hPG and HbA1c in relation to CVD by 1 unit higher SD of the variable.

<p>Model 1 Covariates: Age, Gender, Smoking.</p><p>Model 2 Covariates: Age, Gender, Smoking, BMI, Physical Activity.</p><p>Model 3 Covariates: Age, Gender, Smoking, BMI, Physical Activity, SBP, HDLC, LDLC.</p><p>Model 4 Covariates: Age, Gender, Smoking, BMI, Physical Activity, SBP, HDLC, LDLC, Previous CVD, Previous Cancer.</p><p>Updated mean and updated value of FPG, 1 hPG, 2 hPG and HbA1c in relation to CVD by 1 unit higher SD of the variable.</p

Relevance of Vitamin D Receptor Target Genes for Monitoring the Vitamin D Responsiveness of Primary Human Cells

<div><p>Vitamin D₃ has transcriptome- and genome-wide effects and activates, via the binding of its metabolite 1α,25-dihydroxyvitamin D₃ to the transcription factor vitamin D receptor (VDR), several hundred target genes. Using samples from a 5-month vitamin D₃ intervention study (VitDmet), we recently reported that the expression of 12 VDR target genes in peripheral blood mononuclear cells (PBMCs) as well as 12 biochemical and clinical parameters of the study participants are significantly triggered by vitamin D₃. In this study, we performed a more focused selection of further 12 VDR target genes and demonstrated that changes of their mRNA expression in PBMCs of VitDmet subjects significantly correlate with alterations of 25-hydroxyvitamin D3 serum levels. Network and self-organizing map analysis of these datasets together with that of the other 24 parameters was followed by relevance calculations and identified changes in parathyroid hormone serum levels and the expression of the newly selected genes <i>STS</i>, <i>BCL6</i>, <i>ITGAM</i>, <i>LRRC25</i>, <i>LPGAT1</i> and <i>TREM1</i> as well as of the previously reported genes <i>DUSP10</i> and <i>CD14</i> as the most relevant parameters for describing vitamin D responsiveness <i>in vivo</i>. Moreover, parameter relevance ranking allowed the segregation of study subjects into high and low responders. Due to the long intervention period the vitamin D response was not too prominent on the level of transcriptional activation. Therefore, we performed in the separate VitDbol trial a short-term but high dose stimulation with a vitamin D3 bolus. In PBMCs of VitDbol subjects we observed direct transcriptional effects on the selected VDR target genes, such as an up to 2.1-fold increase already one day after supplementation onset. In conclusion, both long-term and short-term vitamin D₃ supplementation studies allow monitoring the vitamin D responsiveness of human individuals and represent new types of human <i>in vivo</i> vitamin D₃ investigations.</p></div

Changes of 25(OH)D₃ serum concentrations correlate with gene expression in PBMCs and adipocytes.

<p>The study participants were ranked for their changes of <i>CD14</i> and <i>THBD</i> mRNA expression in PBMCs and adipocytes. For the top half of the PBMC donors (35 individuals, A and B) and also for the top half of the adipose tissue donors (23 åindividuals, C and D) changes of the mRNA expression of <i>CD14</i> (A, C) and <i>THBD</i> (B, D) from start to end of the study positively correlate with the respective change of 25(OH)D₃ serum concentrations.</p