Common variants of the liver fatty acid binding protein gene influence the risk of type 2 diabetes and insulin resistance in Spanish population

SUMMARY The main objective was to evaluate the association between SNPs and haplotypes of the FABP1-4 genes and type 2 diabetes, as well as its interaction with fat intake, in one general Spanish population. The association was replicated in a second population in which HOMA index was also evaluated. METHODS 1217 unrelated individuals were selected from a population-based study [Hortega study: 605 women; mean age 54 y; 7.8% with type 2 diabetes]. The replication population included 805 subjects from Segovia, a neighboring region of Spain (446 females; mean age 52 y; 10.3% with type 2 diabetes). DM2 mellitus was defined in a similar way in both studies. Fifteen SNPs previously associated with metabolic traits or with potential influence in the gene expression within the FABP1-4 genes were genotyped with SNPlex and tested. Age, sex and BMI were used as covariates in the logistic regression model. RESULTS One polymorphism (rs2197076) and two haplotypes of the FABP-1 showed a strong association with the risk of DM2 in the original population. This association was further confirmed in the second population as well as in the pooled sample. None of the other analyzed variants in FABP2, FABP3 and FABP4 genes were associated. There was not a formal interaction between rs2197076 and fat intake. A significant association between the rs2197076 and the haplotypes of the FABP1 and HOMA-IR was also present in the replication population. CONCLUSIONS The study supports the role of common variants of the FABP-1 gene in the development of type 2 diabetes in Caucasians.

Telemedicine influence on the follow-up of type 2 diabetes patients.

OBJECTIVE This study was designed to evaluate the impact of a teleassistance system on the metabolic control of type 2 diabetes patients. RESEARCH DESIGN AND METHODS We conducted a 1-year controlled parallel-group trial comparing patients randomized (1) to an intervention group, assigned to a teleassistance system using real-time transmission of blood glucose results, with immediate reply when necessary, and telephone consultations, or (2) to a control group, being regularly followed-up at their healthcare center. Study subjects were type 2 diabetes patients >30 years of age followed in the primary care setting. RESULTS A total of 328 type 2 diabetes patients were recruited from 35 family practices in the province of Málaga, Spain. There was a reduction in hemoglobin A1c after 12 months from 7.62 +/- 1.60% to 7.40 +/- 1.43% (P = 0.027) in the intervention group and from 7.44 +/- 1.31% to 7.35 +/- 1.38% (P = 0.303) in the control group. The difference in the change between groups was not statistically significant. There was also a significant decrease in systolic and diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and body mass index in the intervention group. In the control group, the only significant decline was in low-density lipoprotein cholesterol. CONCLUSIONS A teleassistance system using real-time transmission of blood glucose results with an option to make telephone consultations is feasible in the primary care setting as a support tool for family physicians in their follow-up of type 2 diabetes patients.

The association between dietary energy density and type 2 diabetes in Europe: results from the EPIC-InterAct Study

BACKGROUND Observational studies implicate higher dietary energy density (DED) as a potential risk factor for weight gain and obesity. It has been hypothesized that DED may also be associated with risk of type 2 diabetes (T2D), but limited evidence exists. Therefore, we investigated the association between DED and risk of T2D in a large prospective study with heterogeneity of dietary intake. METHODOLOGY/PRINCIPAL FINDINGS A case-cohort study was nested within the European Prospective Investigation into Cancer (EPIC) study of 340,234 participants contributing 3.99 million person years of follow-up, identifying 12,403 incident diabetes cases and a random subcohort of 16,835 individuals from 8 European countries. DED was calculated as energy (kcal) from foods (except beverages) divided by the weight (gram) of foods estimated from dietary questionnaires. Prentice-weighted Cox proportional hazard regression models were fitted by country. Risk estimates were pooled by random effects meta-analysis and heterogeneity was evaluated. Estimated mean (sd) DED was 1.5 (0.3) kcal/g among cases and subcohort members, varying across countries (range 1.4-1.7 kcal/g). After adjustment for age, sex, smoking, physical activity, alcohol intake, energy intake from beverages and misreporting of dietary intake, no association was observed between DED and T2D (HR 1.02 (95% CI: 0.93-1.13), which was consistent across countries (I(2) = 2.9%). CONCLUSIONS/SIGNIFICANCE In this large European case-cohort study no association between DED of solid and semi-solid foods and risk of T2D was observed. However, despite the fact that there currently is no conclusive evidence for an association between DED and T2DM risk, choosing low energy dense foods should be promoted as they support current WHO recommendations to prevent chronic diseases.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.

Expert study: A bolus calculator improves glycemic control and quality of life of type 1 diabetic patients (T1DM) (preliminary results)

Aims: To evaluate the impact on glycemic control and quality of life of a bolus calculator. Methods: Multicentre randomized prospective crosssectional study. Patients were randomized to control phase (3 months; calculation of prandial insulin according to insulinto-carbohydrate ratio and insulin sensitivity factor using a single strip meter) or intervention phase (3 months; calculation of prandial insulin with a bolus advisor), with a washout period (3 months). Patients wore a continuous glucosensor (7 days) and answered a quality of life questionnaire at the beginning and at the end of each phase. A questionnaire of satisfaction was obtained at the end of both phases. Inclusion criteria: Adults; T1DM> 1 year, HbA1c > 7.5%, basal-bolus therapy with insulin analogs, experience with carbohydrate Results: Data from the first 32 subjects with at least 1 ended phase (27 females, age 38 – 11 years, diabetes duration 16.8 – 7.5 years). Basal characteristics were comparable independently of the starting phase. No differences were found between phases in terms of mean blood glucose, standard deviation (from meter neither from sensor) and satisfaction. Conclusions: The use of a bolus calculator improves glycemic control and quality of life of T1DM subjects.

Prevalence and Clinical Characteristics of HIV Type 1-Infected Patients Receiving Dialysis in Spain: Results of a Spanish Survey in 2006: GESIDA 48/05 Study

End-stage renal diseases (ESRD) are becoming more frequent in HIV-infected patients. In Europe there is little information about HIV-infected patients on dialysis. A cross-sectional multicenter survey in 328 Spanish dialysis units was conducted in 2006. Information from 14,876 patients in dialysis was obtained (81.6% of the Spanish dialysis population). Eighty-one were HIV infected (0.54%; 95% CI, 0.43-0.67), 60 were on hemodialysis, and 21 were on peritoneal dialysis. The mean (range) age was 45 (28-73) years. Seventy-two percent were men and 33% were former drug users. The mean (range) time of HIV infection was 11 (1-27) years and time on dialysis was 4.6 (0.4-25) years. ESRD was due to glomerulonephritis (36%) and diabetes (15%). HIV-associated nephropathy was not reported. Eighty-five percent were on HAART, 76.5% had a CD4 T cell count above 200 cells, and 73% had undetectable viral load. Thirty-nine percent of patients met criteria for inclusion on the renal transplant (RT) waiting list but only 12% were included. Sixty-one percent had HCV coinfection. HCV-coinfected patients had a longer history of HIV, more previous AIDS events, parenteral transmission as the most common risk factor for acquiring HIV infection, and less access to the RT waiting list (p < 0.05). The prevalence of HIV infection in Spanish dialysis units in 2006 was 0.54% HCV coinfection was very frequent (61%) and the percentage of patients included on the Spanish RT waiting list was low (12%).

Metabolomic insights into the intricate gut microbial-host interaction in the development of obesity and type 2 diabetes.

Gut microbiota has recently been proposed as a crucial environmental factor in the development of metabolic diseases such as obesity and type 2 diabetes, mainly due to its contribution in the modulation of several processes including host energy metabolism, gut epithelial permeability, gut peptide hormone secretion, and host inflammatory state. Since the symbiotic interaction between the gut microbiota and the host is essentially reflected in specific metabolic signatures, much expectation is placed on the application of metabolomic approaches to unveil the key mechanisms linking the gut microbiota composition and activity with disease development. The present review aims to summarize the gut microbial-host co-metabolites identified so far by targeted and untargeted metabolomic studies in humans, in association with impaired glucose homeostasis and/or obesity. An alteration of the co-metabolism of bile acids, branched fatty acids, choline, vitamins (i.e., niacin), purines, and phenolic compounds has been associated so far with the obese or diabese phenotype, in respect to healthy controls. Furthermore, anti-diabetic treatments such as metformin and sulfonylurea have been observed to modulate the gut microbiota or at least their metabolic profiles, thereby potentially affecting insulin resistance through indirect mechanisms still unknown. Despite the scarcity of the metabolomic studies currently available on the microbial-host crosstalk, the data-driven results largely confirmed findings independently obtained from in vitro and animal model studies, putting forward the mechanisms underlying the implication of a dysfunctional gut microbiota in the development of metabolic disorders.