Multimodal functional and anatomical imaging identifies preclinical microvascular abnormalities in type 1 diabetes mellitus

Structural and functional change in the microcirculation in type 1 diabetes mellitus predicts future end-organ damage and macrovascular events. We explored the utility of novel signal processing techniques to detect and track change in ocular hemodynamics in patients with this disease. 24 patients with uncomplicated type 1 diabetes mellitus, and 18 age-and-sex matched control subjects were studied. Doppler ultrasound was used to interrogate the carotid and ophthalmic arteries and digital photography to image the retinal vasculature. Frequency analysis algorithms were applied to quantify velocity waveform structure and retinal photographic data at baseline and following inhalation of 100% oxygen. Frequency data was compared between groups. No significant differences were found in the resistive index between groups at baseline or following inhaled oxygen. Frequency analysis of the Doppler flow velocity waveforms identified significant differences in bands 3-7 between patients and controls in data captured from the ophthalmic artery (p<0.01 for each band). In response to inhaled oxygen, changes in the frequency band amplitudes were significantly greater in control subjects compared with patients (p<0.05). Only control subjects demonstrated a positive correlation (R=0.61) between change in retinal vessel diameter and frequency band amplitudes derived from ophthalmic artery waveform data. The use of multimodal signal processing techniques applied to Doppler flow velocity waveforms and retinal photographic data identified preclinical change in the ocular microcirculation in patients with uncomplicated diabetes mellitus. An impaired autoregulatory response of the retinal microvasculature may contribute to the future development of retinopathy in such patients.

General practice-recorded depression and antidepressant use in young people with newly diagnosed Type 1 diabetes: a cohort study using the Clinical Practice Research Datalink

Aims - To investigate whether young people with Type 1 diabetes have an increased rate of depression andantidepressant use and whether their risk varies by age group, time from diabetes diagnosis, calendar period ofdiagnosis or complications status. Methods - A cohort of incident cases of patients with Type 1 diabetes diagnosed before 35 years of age (n = 5548) wasidentified within the Clinical Practice Research Datalink and individually age and sex matched with up to two controlsubjects without diabetes (n = 10 657). Patients with depression were identified through general practice-recordeddepression codes and antidepressant prescriptions. Cox regression models gave hazard ratios for depression in peoplewith Type 1 diabetes compared with control subjects. Results - People with Type 1 diabetes were twice as likely to have a record of antidepressant use and generalpractice-diagnosed depression as their matched control subjects (hazard ratio 2.08, 95% CI 1.73–2.50, P < 0.001).These associations varied by time from diagnosis, with marked increases observed within the first 5 years of diagnosis(hazard ratio 2.14, 95% CI 1.51–3.03, P < 0.001), and by age at diabetes diagnosis, with excesses noted even in the 10-to 19-year age group (hazard ratio 1.45, 95% CI 1.06–1.98, P = 0.02). Conclusions - This population-based study shows that people with Type 1 diabetes have higher rates of generalpractice-recorded depression and antidepressant use. The excess is present within 5 years of diabetes diagnosis,suggesting psychological input for patients is warranted in the early years of their condition.

SORBS1 gene, a new candidate for diabetic nephropathy: results from a multi-stage genome-wide association study in patients with type 1 diabetes

Aims/hypothesis

Excess mortality in Type 1 diabetes diagnosed in childhood and adolescence: a systematic review of population-based cohorts

Aims: Systematic review of mortality in childhood-/adolescent-diagnosed Type 1 diabetes and examination of factors explaining the mortality variation between studies. 
Methods: Relevant studies were identified from systematic searches of MEDLINE and EMBASE. Observed and expected numbers of deaths were extracted, and standardised mortality ratios (SMRs) and 95 % confidence intervals (CIs) were calculated. Negative binomial regression was used to investigate association between mortality and study/country characteristics.
Results: Thirteen relevant publications with mortality data were identified describing 23 independent studies. SMRs varied markedly ranging from 0 to 854 (chi-squared = 70.68,df = 21, p<0.0001). Significant associations were observed between SMR and mid-year of follow-up [incidence rate ratio (IRR) 0.95, 95 % CI 0.91–0.99 equivalent to a 5 % decrease per year], between SMR and infant mortality rate (IRR 1.07, 95 % CI 1.02–1.12, a 7 % increase for each death per 1,000 live births) and, after omitting an outlier, between SMR and health expenditure as a percentage of gross domestic product (GDP) (IRR 0.79, 95 % CI 0.68–0.93, a 21 % decrease for each one percent increase in GDP). No relationship was detected between SMR and a country’s childhood diabetes incidence rate or GDP.
Conclusions: Excess mortality in childhood-/adolescent diagnosed Type 1 diabetes is apparent across countries worldwide. Excesses were less marked in more recent studies and in countries with lower infant mortality and higher health expenditure.

Genetic risk factors affecting mitochondrial function are associated with kidney disease in people with Type 1 diabetes

AIM: To evaluate the association with diabetic kidney disease of single nucleotide polymorphisms (SNPs) that may contribute to mitochondrial dysfunction.

METHODS: The mitochondrial genome and 1039 nuclear genes that are integral to mitochondrial function were investigated using a case (n=823 individuals with diabetic kidney disease) vs. control (n=903 individuals with diabetes and no renal disease) approach. All people included in the analysis were of white European origin and were diagnosed with Type 1 diabetes before the age of 31 years. Replication was conducted in 5093 people with similar phenotypes to those of the discovery collection. Association analyses were performed using the plink genetic analysis toolset, with adjustment for relevant covariates.

RESULTS: A total of 25 SNPs were evaluated in the mitochondrial genome, but none were significantly associated with diabetic kidney disease or end-stage renal disease. A total of 38 SNPs in nuclear genes influencing mitochondrial function were nominally associated with diabetic kidney disease and 16 SNPS were associated with end-stage renal disease, secondary to diabetic kidney disease, with meta-analyses confirming the same direction of effect. Three independent signals (seven SNPs) were common to the replication data for both phenotypes with Type 1 diabetes and persistent proteinuria or end-stage renal disease.

CONCLUSIONS: Our results suggest that SNPs in nuclear genes that influence mitochondrial function are significantly associated with diabetic kidney disease in a white European population

Distinct methylation patterns in genes that affect mitochondrial function are associated with kidney disease in blood-derived DNA from individuals with Type 1 diabetes

AIMS: Epigenetic modifications, such as DNA methylation, can influence the risk of developing kidney disease. We studied methylation profiles in genes related to mitochondrial function to assess whether differences in these epigenetic features were associated with diabetic kidney disease in people with Type 1 diabetes.

METHODS: A case-control association study was undertaken (n = 196 individuals with diabetic kidney disease vs. n = 246 individuals without renal disease). Participants were White and diagnosed with Type 1 diabetes before 31 years of age. Genes that encode mitochondrial proteins (n = 780) were downloaded from mitoproteome. org. DNA methylation profiles from blood-derived DNA were generated using the Illumina Infinium HumanMethylation450 (262 samples) and Illumina Infinium HumanMethylation27 (192 samples) arrays. Beta values (β) were calculated and quality control was conducted, including evaluating blind duplicate DNA samples.

RESULTS: Fifty-four Cytosine-phosphate-Guanine probes across 51 unique genes were significantly associated (P ≤ 10(-8) ) with diabetic kidney disease across both the 450K and the 27K methylation arrays. A subanalysis, employing the 450K array, identified 755 Cytosine-phosphate-Guanine probes in 374 genes that were significantly associated (P ≤ 10(-8) ) with end-stage renal disease. Forty-six of the top-ranked variants for diabetic kidney disease were also identified as being differentially methylated in individuals with end-stage renal disease. The largest change in methylation (Δβ = 0.2) was observed for cg03169527 in the TAMM41 gene, chromosome 3p25.2. Three genes, PMPCB, TSFM and AUH, were observed with differential methylation at multiple Cytosine-phosphate-Guanine sites each (P < 10(-12) ).

CONCLUSIONS: Differential methylation in genes that influence mitochondrial function are associated with kidney disease in individuals with Type 1 diabetes. 

Glycemic targets in the second and third trimester of pregnancy for women with type 1 diabetes

OBJECTIVE: To assess the relationship between second and third trimester glycemic control and adverse outcomes in pregnant women with type 1 diabetes, as uncertainty exists about optimum glycemic targets.

RESEARCH DESIGN AND METHODS: Pregnancy outcomes were assessed prospectively in 725 women with type 1 diabetes from the Diabetes and Pre-eclampsia Intervention Trial. HbA1c (A1C) values at 26 and 34 weeks' gestation were categorized into five groups, the lowest, <6.0% (42 mmol/mol), being the reference. Average pre- and postprandial results from an eight-point capillary glucose profile the previous day were categorized into five groups, the lowest (preprandial <5.0 mmol/L and postprandial <6.0 mmol/L) being the reference.

RESULTS: An A1C of 6.0-6.4% (42-47 mmol/mol) at 26 weeks' gestation was associated with a significantly increased risk of large for gestational age (LGA) (odds ratio 1.7 [95% CI 1.0-3.0]) and an A1C of 6.5-6.9% (48-52 mmol/mol) with a significantly increased risk of preterm delivery (odds ratio 2.5 [95% CI 1.3-4.8]), pre-eclampsia (4.3 [1.7-10.8]), need for a neonatal glucose infusion (2.9 [1.5-5.6]), and a composite adverse outcome (3.2 [1.3-8.0]). These risks increased progressively with increasing A1C. Results were similar at 34 weeks' gestation. Glucose data showed less consistent trends, although the risk of a composite adverse outcome increased with preprandial glucose levels between 6.0 and 6.9 mmol/L at 34 weeks (3.3 [1.3-8.0]).

CONCLUSIONS: LGA increased significantly with an A1C ≥6.0 (42 mmol/mol) at 26 and 34 weeks' gestation and with other adverse outcomes with an A1C ≥6.5% (48 mmol/mol). The data suggest that there is clinical utility in regular measurement of A1C during pregnancy.

Seasonal variation in month of diagnosis in children with type 1 diabetes registered in 23 European centers during 1989-2008: little short-term influence of sunshine hours or average temperature

BACKGROUND: The month of diagnosis in childhood type 1 diabetes shows seasonal variation.

OBJECTIVE: We describe the pattern and investigate if year-to-year irregularities are associated with meteorological factors using data from 50 000 children diagnosed under the age of 15 yr in 23 population-based European registries during 1989-2008.

METHODS: Tests for seasonal variation in monthly counts aggregated over the 20 yr period were performed. Time series regression was used to investigate if sunshine hour and average temperature data were predictive of the 240 monthly diagnosis counts after taking account of seasonality and long term trends.

RESULTS: Significant sinusoidal pattern was evident in all but two small centers with peaks in November to February and relative amplitudes ranging from ±11 to ±38% (median ±17%). However, most centers showed significant departures from a sinusoidal pattern. Pooling results over centers, there was significant seasonal variation in each age-group at diagnosis, with least seasonal variation in those under 5 yr. Boys showed greater seasonal variation than girls, particularly those aged 10-14 yr. There were no differences in seasonal pattern between four 5-yr sub-periods. Departures from the sinusoidal trend in monthly diagnoses in the period were significantly associated with deviations from the norm in average temperature (0.8% reduction in diagnoses per 1 °C excess) but not with sunshine hours.

CONCLUSIONS: Seasonality was consistently apparent throughout the period in all age-groups and both sexes, but girls and the under 5 s showed less marked variation. Neither sunshine hour nor average temperature data contributed in any substantial way to explaining departures from the sinusoidal pattern.