Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration

Background & Aims: Excess liver iron content is common and is linked to hepatic and extrahepatic disease risk. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals in UK Biobank with MRI quantified liver iron, and validated our findings in an independent cohort (n=1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 29 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 anthropometric traits and diseases. Results: We identified three independent genetic variants (rs1800562 (C282Y) and rs1799945 (H63D) in HFE and rs855791 (V736A) in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p<5x10-8). The two HFE variants account for ~85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P &lt; 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P &lt; 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio &gt;2, P &lt; 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.</p

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk

Predicting glycated hemoglobin levels in the non-diabetic general population:Development and validation of the DIRECT-DETECT prediction model - a DIRECT study

AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. METHODS: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. RESULTS: At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. CONCLUSIONS/INTERPRETATION: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent

Prevalence of Insomnia (Symptoms) in T2D and Association With Metabolic Parameters and Glycemic Control:Meta-Analysis

Objective: We aimed to determine the prevalence of insomnia and insomnia symptoms and its association with metabolic parameters and glycemic control in people with type 2 diabetes (T2D) in a systematic review and meta-analysis. Data Sources: A systematic literature search was conducted in PubMed/Embase until March 2018. Study Selection: Included studies described prevalence of insomnia or insomnia symptoms and/or its association with metabolic parameters or glycemic control in adults with T2D. Data Extraction: Data extraction was performed independently by 2 reviewers, on a standardized, prepiloted form. An adaptation of Quality Assessment Tool for Quantitative Studies was used to assess the methodological quality of the included studies. Data Synthesis: When possible, results were meta-analyzed using random-effects analysis and rated using Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results: A total of 11 329 titles/abstracts were screened and 224 were read full text in duplicate, of which 78 studies were included. The pooled prevalence of insomnia (symptoms) in people with T2D was 39% (95% confidence interval, 34-44) with I 2 statistic of 100% (P < 0.00001), with a very low GRADE of evidence. Sensitivity analyses identified no clear sources of heterogeneity. Meta-analyses showed that in people with T2D, insomnia (symptoms) were associated with higher hemoglobin A1c levels (mean difference, 0.23% [0.1-0.4]) and higher fasting glucose levels (mean difference, 0.40 mmol/L [0.2-0.7]), with a low GRADE of evidence. The relative low methodological quality and high heterogeneity of the studies included in this meta-analysis complicate the interpretation of our results. Conclusions: The prevalence of insomnia (symptoms) is 39% (95% confidence interval, 34-44) in the T2D population and may be associated with deleterious glycemic control

The Association between Eating Traits and Weight Change after a Lifestyle Intervention in People with Type 2 Diabetes Mellitus

Aims. To date, studies on the role of eating traits in weight loss success have only included obese people without type 2 diabetes mellitus (T2DM), thereby disregarding negative effects of T2DM-related metabolic changes. Our aim was to assess the association between eating traits and weight change after a lifestyle intervention in people with T2DM. Methods. For the current study, we reexamined data from a six-month intervention in 120 participants. We determined eating traits at baseline, using the DEBQ, which were used to produce three groups: unsuccessful dietary restrained (high restraint, high emotional/external eating scores), successful dietary restrained (high restraint, low emotional/external eating scores), and reference (low restraint, high or low emotional/external eating scores). Linear regression was used to study the association between the eating trait groups and weight changes after six months, while correcting for possible confounders. Results. On average, the weight loss success was limited, with a third of the participants being weight stable, a third losing weight > −1 kg (average loss −2.6 ± 1.9 kg), and a third gaining weight > +1 kg (average gain +3.3 ± 1.9 kg). When compared to the reference group, the unsuccessful dietary restrained gained weight during the intervention (beta = 1.2 kg, confidence interval (CI)% = 0.1; 2). No significant change was observed in the succesful dietary restrained group. Conclusions. The eating trait of being unsuccessfully dietary restrained is associated with weight-loss failure after a six-month lifestyle intervention in people with T2DM

A prospective study on glucagon responses to oral glucose and mixed meal and 7-year change in fasting glucose

Introduction: The role of insufficient glucagon suppression after an oral load in the development of type 2 diabetes mellitus is unclear. The aim of this study was to examine the association between glucagon responses at baseline and fasting glucose levels 7 years later. Methods: Data of the Hoorn Meal Study were used, an observational cohort study among 121 persons without diabetes with a mean age of 61.1 ± 6.7 years and 50% being female. The glucagon response to an oral glucose tolerance test and mixed meal test was expressed as early and late incremental area under the curve. The association with change in fasting glucose levels at follow-up was assessed by linear regression analysis. Results: The early glucagon response following the mixed meal test was associated with an increase in fasting glucose levels of 0.18 mmol/L (95%-CI: 0.04-0.31, P = 0.01), per unit increase in the incremental area under the curve of glucagon, adjusted for confounders. No significant associations were observed for the late response after the mixed meal test or oral glucose tolerance test. Conclusions: Within a population without diabetes, relative lack of glucagon suppression early after a meal was associated with increased glucose levels over time, suggesting a role of insufficient glucagon suppression in the deterioration of glycaemic control

The association between multiple sleep-related characteristics and the metabolic syndrome in the general population: the New Hoorn study

Background: Previous studies have investigated the association between sleep duration, insomnia, day-time napping and metabolic syndrome individually, but never conjointly. In addition, the association with sleep medication use has yet to be investigated. We aimed to examine the associations between these sleep-related characteristics and the metabolic syndrome, individually and conjointly, in a population-based cohort. Material and methods: We used cross-sectional data of 1679 participants from the New Hoorn study, 52.6% women and age 60.8 + 6.4y. Sleep duration, insomnia, and day-time napping were measured using validated questionnaires. The use of sleep medication was documented by the registration of dispensing labels. The metabolic syndrome was defined according to ATP III. Linear and Poisson regressions were used, and all analyses were adjusted for age, sex, education level, job status, smoking, physical activity, depression and BMI. Results: In our population-based cohort, 447 (26.6%) persons had the metabolic syndrome. Individual associations showed that, after correction, day-time napping for ≤30 min and >30 min was associated with a prevalence ratio for the metabolic syndrome of 1.28 (95% CI: 1.1–1.5) and 1.74 (95% CI: 1.4–2.2), respectively, compared to participants who did not nap. Sleep duration, insomnia, and sleep medication use were not associated with the metabolic syndrome individually. However, conjointly analyses showed that, after correction, having ≥2 sleep-related characteristics was associated with a PR of 1.36 (95% CI: 1.0–1.8) of having the metabolic syndrome, compared to having no sleep-related characteristics. Conclusion: Sleep-related characteristics were associated with a higher prevalence of the metabolic syndrome in the general population

The association between psychosocial stress and mortality is mediated by lifestyle and chronic diseases: The Hoorn Study

Psychosocial stress is associated with chronic disease. We evaluated whether in the general population the number of stressful life events is associated with risk of mortality and whether this association is mediated by behavioral factors and morbidities. We conducted this study in the Hoorn cohort; a population-based cohort study among older men and women. Our main variable of interest was the number of stressful life events experienced during the previous 5 years, which were assessed by questionnaire. We calculated Cox proportional hazard ratios (HRs) for all-cause and cause-specific mortality during follow-up for those who experienced stressful life events compared to those who did not. We included 2385 participants (46% male; 62 +/- 7 years). During 20 years of follow-up 834 (35%) participants died, of whom 239 (28.6%) died of cardiovascular disease. Compared to the group with no stressful life events, the age, sex and socioeconomic status adjusted HRs (with 95% confidence intervals) for all-cause mortality, for the groups who had 1 event, 2 events, 3 events and &gt;= 4 events were 0.89 (0.72-1.09), 1.01 (0.81-1.24), 1.29 (1.00-1.66) and 1.44 (1.08-1.92), respectively. Similar results were observed for cardiovascular mortality. Mediation analysis showed that smoking, prevalent type 2 diabetes and cardiovascular disease were statistically significant mediators of the association between the number of stressful life events and mortality. Having 3 or more stressful life events is associated with a significantly increased risk for mortality in an elderly population-based cohort. This association is mediated by smoking, type 2 diabetes and cardiovascular disease