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Insulin-Like Growth Factor Binding Protein 2 (IGFBP-2) and the Risk of Developing Type 2 Diabetes
(2019)
Recent studies suggest that insulin-like growth factor binding protein 2 (IGFBP-2) may protect against type 2 diabetes, but population-based human studies are scarce. We aimed to investigate the prospective association of circulating IGFBP-2 concentrations and of differential methylation in the IGFBP-2 gene with type 2 diabetes risk.
Aims/hypothesis This study aimed to evaluate associations of height as well as components of height (sitting height and leg length) with risk of type 2 diabetes and to explore to what extent associations are explainable by liver fat and cardiometabolic risk markers. Methods A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study comprising 26,437 participants who provided blood samples was designed. We randomly selected a subcohort of 2500 individuals (2029 diabetes-free at baseline and with anamnestic, anthropometrical and metabolic data for analysis). Of the 820 incident diabetes cases identified in the full cohort during 7 years of follow-up, 698 remained for analyses after similar exclusions. Results After adjustment for age, potential lifestyle confounders, education and waist circumference, greater height was related to lower diabetes risk (HR per 10 cm, men 0.59 [95% CI 0.47, 0.75] and women 0.67 [0.51, 0.88], respectively). Leg length was related to lower risk among men and women, but only among men if adjusted for total height. Adjustment for liver fat and triacylglycerols, adiponectin and C-reactive protein substantially attenuated associations between height and diabetes risk, particularly among women. Conclusions/interpretation We observed inverse associations between height and risk of type 2 diabetes, which was largely related to leg length among men. The inverse associations may be partly driven by lower liver fat content and a more favourable cardiometabolic profile.
Background: Epidemiological studies suggest that an increased red meat intake is associated with a higher risk of type 2 diabetes, whereas an increased fiber intake is associated with a lower risk. Objectives: We conducted an intervention study to investigate the effects of these nutritional factors on glucose and lipid metabolism, body-fat distribution, and liver fat content in subjects at increased risk of type 2 diabetes. Methods: This prospective, randomized, and controlled dietary intervention study was performed over 6 mo. All groups decreased their daily caloric intake by 400 kcal. The "control" group (N = 40) only had this requirement. The "no red meat" group (N = 48) in addition aimed to avoid the intake of red meat, and the "fiber" group (N = 44) increased intake of fibers to 40 g/d. Anthropometric parameters and frequently sampled oral glucose tolerance tests were performed before and after intervention. Body-fat mass and distribution, liver fat, and liver iron content were assessed by MRI and single voxel proton magnetic resonance spectroscopy. Results: Participants in all groups lost weight (mean 3.3 +/- 0.5 kg, P < 0.0001). Glucose tolerance and insulin sensitivity improved (P < 0.001), and body and visceral fat mass decreased in all groups (P < 0.001). These changes did not differ between groups. Liver fat content decreased significantly (P < 0.001) with no differences between the groups. The decrease in liver fat correlated with the decrease in ferritin during intervention (r(2) = 0.08, P = 0.0021). This association was confirmed in an independent lifestyle intervention study (Tuebingen Lifestyle Intervention Program, N = 229, P = 0.0084). Conclusions: Our data indicate that caloric restriction leads to a marked improvement in glucose metabolism and body-fat composition, including liver-fat content. The marked reduction in liver fat might be mediated via changes in ferritin levels. In the context of caloric restriction, there seems to be no additional beneficial impact of reduced red meat intake and increased fiber intake on the improvement in cardiometabolic risk parameters. This trial was registered at clinicaltrials.gov as NCT03231839.
Odd-chain fatty acids (OCFA) are inversely associated with type-2-diabetes in epidemiological studies. They are considered as a biomarker for dairy intake because fermentation in ruminants yields high amounts of propionate, which is used as the primer for lipogenesis. Recently, we demonstrated endogenous OCFA synthesis from propionate in humans and mice, but how this is affected by microbial colonization is still unexplored. Here, we investigated the effect of increasing microbiota complexity on hepatic lipid metabolism and OCFA levels in different dietary settings. Germ-free (GF), gnotobiotic (SIH, simplified human microbiota) or conventional (CONV) C3H/HeOuJ-mice were fed a CHOW or high-fat diet with inulin (HFI) to induce microbial fermentation. We found that hepatic lipogenesis was increased with increasing microbiota complexity, independently of diet. In contrast, OCFA formation was affected by diet as well as microbiota. On CHOW, hepatic OCFA and intestinal gluconeogenesis decreased with increasing microbiota complexity (GF > SIH > CONV), while cecal propionate showed a negative correlation with hepatic OCFA. On HFI, OCFA levels were highest in SIH and positively correlated with cecal propionate. The propionate content in the CHOW diet was 10 times higher than that of HFI. We conclude that bacterial propionate production affects hepatic OCFA formation, unless this effect is masked by dietary propionate intake.
Purpose UK guidelines recommend dietary saturated fatty acids (SFAs) should not exceed 10% total energy (%TE) for cardiovascular disease prevention, with benefits observed when SFAs are replaced with unsaturated fatty acids (UFAs). This study aimed to assess the efficacy of a dietary exchange model using commercially available foods to replace SFAs with UFAs. Methods Healthy men (n = 109, age 48, SD 11 year) recruited to the Reading, Imperial, Surrey, Saturated fat Cholesterol Intervention-1 (RISSCI-1) study (ClinicalTrials.Gov n degrees NCT03270527) followed two sequential 4-week isoenergetic moderate-fat (34%TE) diets: high-SFA (18%TE SFAs, 16%TE UFAs) and low-SFA (10%TE SFAs, 24%TE UFAs). Dietary intakes were assessed using 4-day weighed diet diaries. Nutrient intakes were analysed using paired t-tests, fasting plasma phospholipid fatty acid (PL-FA) profiles and dietary patterns were analysed using orthogonal partial least square discriminant analyses. Results Participants exchanged 10.2%TE (SD 4.1) SFAs for 9.7%TE (SD 3.9) UFAs between the high and low-SFA diets, reaching target intakes with minimal effect on other nutrients or energy intakes. Analyses of dietary patterns confirmed successful incorporation of recommended foods from commercially available sources (e.g. dairy products, snacks, oils, and fats), without affecting participants' overall dietary intakes. Analyses of plasma PL-FAs indicated good compliance to the dietary intervention and foods of varying SFA content. Conclusions RISSCI-1 dietary exchange model successfully replaced dietary SFAs with UFAs in free-living healthy men using commercially available foods, and without altering their dietary patterns. Further intervention studies are required to confirm utility and feasibility of such food-based dietary fat replacement models at a population level.
Background: Most studies on food choice have been focussing on the individual level but familial aspects may also play an important role. This paper reports of a novel study that will focus on the familial aspects of the formation of food choice among men and women aged 50-70 years by recruiting spouses and siblings (NutriAct Family Study; NFS). Discussion: Until August 4th 2017, 4783 EPIC-Participants were contacted by mail of which 446 persons recruited 2 to 5 family members (including themselves) resulting in 1032 participants, of whom 82% had started answering or already completed the questionnaires. Of the 4337 remaining EPIC-participants who had been contacted, 1040 (24%) did not respond at all, and 3297 (76%) responded but declined, in 51% of the cases because of the request to recruit at least 2 family members in the respective age range. The developed recruitment procedures and web-based methods of data collection are capable to generate the required study population including the data on individual and inter-personal determinants which will be linkable to food choice. The information on familial links among the study participants will show the role of familial traits in midlife for the adoption of food choices supporting healthy aging.
Dietary linoleic acid: will modifying dietary fat quality reduce the risk of type 2 diabetes?
(2021)
Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.
Aim: Assessment of the feasibility and reliability of immune-inflammatory biomarker measurements. Methods: The following biomarkers were assessed in 207 predominantly healthy participants at baseline and after 4 months: MMF, TGF-beta, suPAR and clusterin. Results: Intraclass correlation coefficients (95% CIs) ranged from good for TGF-beta (0.75 [95% CI: 0.33-0.90]) to excellent for MMF (0.81 [95% CI: 0.64-0.90]), clusterin (0.83 [95% CI: 0.78-0.87]) and suPAR (0.91 [95% CI: 0.88-0.93]). Measurement of TGF-beta was challenged by the large number of values below the detection limit. Conclusion: Single measurements of suPAR, clusterin and MMF could serve as feasible and reliable biomarkers of immune-inflammatory pathways in biomedical research.
Aims:
Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA.
Methods:
We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group).
Results:
In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs >= 5% were below $50,000/QALY gained.
Conclusions:
Intervening in those with >= 7% diabetes risk based on the GDRS or >= 9% on the ARIC 2009 score would be cost-effective. A risk score threshold >= 5% together with elevated FPG would also allow targeting interventions cost-effectively.