Al jaren wordt getracht om de heterogeniteit van type 2 diabetes beter in kaart te brengen. Recentelijk zijn verschillende manieren van subtypering van type 2 diabetes gepubliceerd. De meeste aandacht gaat naar een onderverdeling in 5 subtypen, vlak na het stellen van de diagnose. Twee van deze subtypen worden gekenmerkt door insulinedeficiëntie; circa de helft van deze patiënten moet relatief vroeg beginnen met insuline. Eén subtype wordt gekenmerkt door ernstige insulineresistentie en een verhoogd risico op nierschade en niet-alcoholische leverziekte.
Subtypering kan leiden tot beter inzicht in de pathofysiologische achtergronden bij een specifiek individu, tot een beter onderbouwde aanpassing van de leefstijl, een betere inschatting van het risico op late complicaties en persoonsgerichte medicamenteuze behandeling. Er is ook meer aandacht voor specifieke factoren die de individuele respons op medicatie beïnvloeden, zoals geslacht, leeftijd en relatief lichaamsgewicht.
Het is te hopen dat deze ontwikkelingen in de nabije toekomst leiden tot een beter op het individu gerichte begeleiding en behandeling van type 2 diabetes.
Het volledige artikel vindt u HIER. (mogelijk moet u eerst inloggen)
Introduction: SURE Netherlands (NCT03929679) evaluated the use of once-weekly (OW) semaglutide, a glucagon-like peptide 1 receptor agonist (GLP-1RA), in routine clinical care for individuals with type 2 diabetes (T2D).
Methods: Adults (age ≥ 18 years) with T2D were enrolled into the single-arm study. The primary endpoint was change from baseline to end of study (EOS; approx. 30 weeks) in glycated haemoglobin (HbA1c). Secondary endpoints were change from baseline to EOS in body weight (BW) and waist circumference (WC). Proportions of participants achieving predefined HbA1c targets and weight-loss responses at EOS, safety, health-related quality of life (HRQoL) and treatment satisfaction were assessed.
Results: In total, 211 participants (mean age 60.5 years; diabetes duration 13.3 years) initiated semaglutide; most were receiving metformin (82.9%) and/or basal insulin (59.2%) at baseline, and 6.2% switched from another GLP-1RA. Mean baseline HbA1c, BW and WC were 8.6%, 105.2 kg and 118.8 cm. In the 186 (88.2%) participants receiving semaglutide at EOS, mean reduction in HbA1c with semaglutide was – 1.2%-points (95% [confidence interval] CI – 1.3; – 1.0; p < 0.0001), with 124 (70.5%), 95 (54.0%) and 65 (36.9%) participants achieving HbA1c targets of < 8.0%, < 7.5% and < 7.0%, respectively. Mean reduction in BW was – 7.8 kg [95% CI – 8.7; – 6.8; p < 0.0001], corresponding to relative reduction of – 7.5% [95% CI – 8.4; – 6.6; p < 0.0001]. Improvements in WC (- 8.8 cm [95% CI – 10.4; – 7.2; p < 0.0001]), HRQoL and treatment satisfaction were observed, including across most Short-Form 36 Health Survey domains. One serious adverse drug reaction (cholecystitis) was reported. Eight participants (all receiving concomitant insulin) experienced severe or documented hypoglycaemia.
Conclusion: Individuals with T2D treated with OW semaglutide experienced significant and clinically relevant improvements in glycaemic control and BW from baseline. These results from a diverse real-world population in the Netherlands support the use of OW semaglutide in treating adults with T2D in routine clinical practice.
You can read the full paper for free at https://link.springer.com/article/10.1007/s12325-022-02385-x
The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.
You can read the full paper at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941118/pdf/41598_2023_Article_27637.pdf
Aims/hypothesis: Optimal diabetes care and risk factor management are important to delay micro- and macrovascular complications in individuals with type 1 diabetes (T1D). Ongoing improvement of management strategies requires the evaluation of target achievement and identification of risk factors in individuals who do (or do not) achieve these targets.
Methods: Cross-sectional data were collected from adults with T1D visiting six diabetes centers in the Netherlands in 2018. Targets were defined as glycated hemoglobin (HbA1c) <53 mmol/mol, low-density lipoprotein-cholesterol (LDL-c) <2.6 mmoL/L (no cardiovascular disease [CVD] present) or <1.8 mmoL/L (CVD present), or blood pressure (BP) <140/90 mm Hg. Target achievement was compared for individuals with and without CVD.
Results: Data from 1737 individuals were included. Mean HbA1c was 63 mmol/mol (7.9%), LDL-c was 2.67 mmoL/L, and BP 131/76 mm Hg. In individuals with CVD, 24%, 33%, and 46% achieved HbA1c, LDL-c, and BP targets respectively. In individuals without CVD these percentages were 29%, 54%, and 77%, respectively. Individuals with CVD did not have any significant risk factors for HbA1c, LDL-c, and BP target achievement. In comparison, individuals without CVD were more likely to achieve glycemic targets if they were men and insulin pump users. Smoking, microvascular complications, and the prescription of lipid-lowering and antihypertensive medication were negatively associated with glycemic target achievement. No characteristics were associated with LDL-c target achievement. Microvascular complications and antihypertensive medication prescription were negatively associated with BP target attainment.
Conclusion: Opportunities for improvement of diabetes management exist for the achievement of glycemic, lipid, and BP targets but may differ between individuals with and without CVD.
You can read the full article at https://onlinelibrary.wiley.com/doi/10.1111/1753-0407.13368
Fantasie wordt werkelijkheid… introductie van het mylife CamAPS FX systeem in Nederland.
Het totale systeem bestaat -zoals het plaatje aangeeft- uit de myLife YpsoPump insuline pomp, de Dexcom G6 glucose sensor, en het verbindende CamAPS FX software systeem, dat is ontworpen door professor Roman Hovorka, een genie op het gebied van diabetes en closed-loop software.
Kijk hier naar de opnames van dit webinar. De link is: https://www.youtube.com/watch?v=FNzAE-6A1-4
In een recent onderzoek worden de resultaten gepresenteerd van de insuline afgifte na een test maaltijd bij mensen met langer bestaande type 1 diabetes.
Het abstract is als volgt:
Aims: This study aims to evaluate the stability of C-peptide over time and to compare fasting C-peptide and C-peptide response after Mixed-Meal Tolerance Test (MMTT) at T90 or T120 with C-peptide area under the curve (AUC) in long-standing type 1 diabetes.
Methods: We included 607 type 1 diabetes individuals with diabetes duration >5 years. C-peptide concentrations (ultrasensitive assay) were collected in the fasting state, and in a subpopulation after MMTT (T0, just prior to, T30-T60-T90-T120, 30-120 minutes after ingestion of Mixed-meal) (n=168). Fasting C-peptide concentrations (in n=535) at Year 0 and Year 1 were compared. The clinical determinants associated with residual C-peptide secretion and the correspondence of C-peptide at MMTT T90 / T120 and total AUC were assessed.
Results: 153 participants (25%) had detectable fasting serum C-peptide (i.e ≥ 3.8 pmol/L). Fasting C-peptide was significantly lower at Year 1 (P <0.001, effect size = -0.16). Participants with higher fasting C-peptide had a higher age at diagnosis, shorter disease duration and were less frequently insulin pump users. Overall, 109 of 168 (65%) participants had both non-detectable fasting and post-meal serum C-peptide concentrations. The T90 and T120 C-peptide values at MMTT were concordant with total AUC. In 17 (10%) individuals, C-peptide was only detectable at MMTT and not in the fasting state.
Conclusions: Stimulated C-peptide was detectable in an additional 10% of individuals compared with fasting in individuals with >5 years diabetes duration. T90 and T120 MMTT measurements showed good concordance with the MMTT total AUC. Overall there was a decrease of C-peptide at 1-year follow-up.
Het volledige artikel is hier te lezen: https://onlinelibrary.wiley.com/doi/10.1111/dme.15012
Het onderzoek werd mogelijk gemaakt door het JDRF (Juvenile Diabetes Research Foundation) en het DiabetesFonds Nederland.