Does angiotensin-1 converting enzyme genotype influence motor or cognitive development after pre-term birth?

BACKGROUND: Raised activity of the renin-angiotensin system (RAS) may both amplify inflammatory and free radical responses and decrease tissue metabolic efficiency and thus enhance cerebral injury in the preterm infant. The angiotensin-converting enzyme (ACE) DD genotype is associated with raised ACE and RAS activity as well as potentially adverse stimuli such as inflammation. The DD genotype has been associated with neurological impairments in the elderly, and thus may be also associated with poorer motor or cognitive development amongst children born preterm prematurely. METHODS: The association of DD genotype with developmental progress amongst 176 Caucasian children born at less than 33 weeks gestation (median birthweight 1475 g, range 645–2480 g; gestation 30 weeks, range 22–32; 108 male) was examined at 2 and 5 1/2 years of age. Measured neuro-cognitive outcomes were cranial ultrasound abnormalities, cerebral palsy, disability, Griffiths Developmental Quotient [DQ] at 2 yrs, and General Cognitive Ability [British Ability Scales-11] and motor performance [ABC Movement], both performed at 5 1/2 yrs. All outcomes were correlated with ACE genotype. RESULTS: The DD genotype was not associated with lower developmental quotients even after accounting for important social variables. CONCLUSION: These data do not support either a role for ACE in the development of cognitive or motor function in surviving infants born preterm or inhibition of ACE as a neuroprotective therapy

Association between plasma activities of semicarbazide-sensitive amine oxidase and angiotensin-converting enzyme in patients with type 1 diabetes mellitus

Aims/hypothesis: Plasma semicarbazide-sensitive amine oxidase (SSAO) is elevated in patients with type 1 and type 2 diabetes and has been implicated in the pathophysiology of diabetic late complications. The regulation of SSAO production remains unknown. We studied correlations between plasma SSAO activity and parameters associated with diabetic late complications. Methods: Plasma SSAO was measured in a well-characterised group of 287 patients with type 1 diabetes. Standard statistical methods were used to investigate correlations with clinical parameters and components of the renin-angiotensin system. Results: Overall, plasma SSAO was elevated, at 693±196 mU/l (mean±SD; normal controls 352±102 mU/l). Plasma SSAO was higher in the group with late complications or hypertension, and in patients treated with ACE-inhibitors. In univariate analysis a significant positive correlation (p<0.001, r=0.27) was found between plasma SSAO and serum ACE activity in patients untreated with ACE inhibitors or angiotensin II receptor antagonists (n=221), but plasma SSAO did not differ by ACE I/D genotype. Plasma SSAO correlated positively with duration of diabetes, HbA1c and plasma renin, and negatively with plasma angiotensinogen and body mass index. A multiple regression analysis including these variables resulted in serum ACE activity (p<0.001), ACE genotype (negatively, p<0.001) and HbA 1c (p=0.023) as explaining variables. Conclusions/interpretation: Results suggest that a common factor is involved in the regulation of both plasma SSAO and serum ACE, which is different from the genetic determination of ACE activity

Effectiveness of a self-management intervention with personalised genetic and lifestyle-related risk information on coronary heart disease and diabetes-related risk in type 2 diabetes (CoRDia): study protocol for a randomised controlled trial

Background Many patients with type 2 diabetes fail to achieve good glycaemic control. Poor control is associated with complications including coronary heart disease (CHD). Effective self-management and engagement in health behaviours can reduce risks of complications. However, patients often struggle to adopt and maintain these behaviours. Self-management interventions have been found to be effective in improving glycaemic control. Recent developments in the field of genetics mean that patients can be given personalised information about genetic- and lifestyle-associated risk of developing CHD. Such information may increase patients’ motivation to engage in self-management. The Coronary Risk in Diabetes (CoRDia) trial will compare the effectiveness of a self-management intervention, with and without provision of personalised genetic- and lifestyle-associated risk information, with usual care, on clinical and behavioural outcomes, the cognitive predictors of behaviour, and psychological wellbeing. Methods/Design Participants will be adults aged 25–74 years registered with general practices in the East of England, diagnosed with type 2 diabetes, with no history of heart disease, and with a glycated haemoglobin level of ≥6.45 % (47 mmol/mol). Consenting participants will be randomised to one of three arms: usual care control, group self-management only, group self-management plus personalised genetic- and lifestyle-associated risk information. The self-management groups will receive four weekly 2-hour group sessions, focusing on knowledge and information sharing, problem solving, goal setting and action planning to promote medication adherence, healthy eating, and physical activity. Primary outcomes are glycaemic control and CHD risk. Clinical data will be collected from GP records, including HbA1c, weight, body mass index, blood pressure, and HDL and total cholesterol. Self-reported health behaviours, including medication adherence, healthy eating and physical activity, and cognitive outcomes will be assessed by questionnaire. Measures will be taken at baseline, 3 months (questionnaire only), 6 months and 12 months post-baseline. Discussion This study will determine whether the addition of personalised genetic- and lifestyle-associated CHD risk information to a group self-management intervention improves diabetes control and CHD risk compared with group self-management and usual care. Effectiveness of the combined intervention on health behaviours cognitions theorised to predict them, and psychological outcomes will also be investigated. Trial registration This study has been registered at ClinicalTrials.gov; registration identifier NCT01891786, registered 28 June 2013

A 19-SNP coronary heart disease gene score profile in subjects with type 2 diabetes: the coronary heart disease risk in type 2 diabetes (CoRDia study) study baseline characteristics

Background The coronary risk in diabetes (CoRDia) trial (n = 211) compares the effectiveness of usual diabetes care with a self-management intervention (SMI), with and without personalised risk information (including genetics), on clinical and behavioural outcomes. Here we present an assessment of randomisation, the cardiac risk genotyping assay, and the genetic characteristics of the recruits. Methods Ten-year coronary heart disease (CHD) risk was calculated using the UKPDS score. Genetic CHD risk was determined by genotyping 19 single nucleotide polymorphisms (SNPs) using Randox’s Cardiac Risk Prediction Array and calculating a gene score (GS). Accuracy of the array was assessed by genotyping a subset of pre-genotyped samples (n = 185). Results Overall, 10-year CHD risk ranged from 2–72 % but did not differ between the randomisation groups (p = 0.13). The array results were 99.8 % concordant with the pre-determined genotypes. The GS did not differ between the Caucasian participants in the CoRDia SMI plus risk group (n = 66) (p = 0.80) and a sample of UK healthy men (n = 1360). The GS was also associated with LDL-cholesterol (p = 0.05) and family history (p = 0.03) in a sample of UK healthy men (n = 1360). Conclusions CHD risk is high in this group of T2D subjects. The risk array is an accurate genotyping assay, and is suitable for estimating an individual’s genetic CHD risk. Trial registration This study has been registered at ClinicalTrials.gov; registration identifier NCT0189178

Uncoupling proteins, dietary fat and the metabolic syndrome

There has been intense interest in defining the functions of UCP2 and UCP3 during the nine years since the cloning of these UCP1 homologues. Current data suggest that both UCP2 and UCP3 proteins share some features with UCP1, such as the ability to reduce mitochondrial membrane potential, but they also have distinctly different physiological roles. Human genetic studies consistently demonstrate the effect of UCP2 alleles on type-2 diabetes. Less clear is whether UCP2 alleles influence body weight or body mass index (BMI) with many studies showing a positive effect while others do not. There is strong evidence that both UCP2 and UCP3 protect against mitochondrial oxidative damage by reducing the production of reactive oxygen species. The evidence that UCP2 protein is a negative regulator of insulin secretion by pancreatic β-cells is also strong: increased UCP2 decreases glucose stimulated insulin secretion ultimately leading to β-cell dysfunction. UCP2 is also neuroprotective, reducing oxidative stress in neurons. UCP3 may also transport fatty acids out of mitochondria thereby protecting the mitochondria from fatty acid anions or peroxides. Current data suggest that UCP2 plays a role in the metabolic syndrome through down-regulation of insulin secretion and development of type-2 diabetes. However, UCP2 may protect against atherosclerosis through reduction of oxidative stress and both UCP2 and UCP3 may protect against obesity. Thus, these UCP1 homologues may both contribute to and protect from the markers of the metabolic syndrome

Networks in coronary heart disease genetics as a step towards systems epidemiology

We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.British Heart Foundation; European Commission; British Medical Research Council; the US National Institutes of Health and Du Pont Pharma, Wilmington