The mediating role of comorbid conditions in the association between type 2 diabetes and cognition: a cross-sectional observational study using the UK Biobank cohort

Aims: Using the UK Biobank cohort, a large sample of middle aged and older adults in the UK, the present study aimed to examine the cross-sectional association between type 2 diabetes and cognition and to assess the hypothesised mediating role of common comorbid conditions, whilst controlling for important demographic and lifestyle factors. Methods: Using regression models and general structural equation models, we examined the cross-sectional association between type 2 diabetes status and: fluid intelligence; reaction time; visual memory; digit span and prospective memory; and the hypothesised mediating role of common comorbid conditions: visceral obesity; sleep problems; macrovascular problems; respiratory problems,; cancer and depressive symptoms in 47,468 participants from the UK Biobank cohort, of whom 1,831 have type 2 diabetes. We controlled for ethnicity, sex, age, deprivation, smoking status, alcohol consumption, physical activity levels and use of diabetes medication. Results: Participants with type 2 diabetes had a significantly shorter digit span, b = -0.14, 99.2% CIs [-0.27, -0.11] than those without type 2 diabetes. Those with type 2 diabetes did not differ from those without type 2 diabetes on fluid intelligence, reaction time, visual memory and prospective memory. The associations that do exist between type 2 diabetes and cognition are consistently mediated via macrovascular problems, depressive symptoms, and to a lesser extent visceral obesity. Respiratory problems, sleep disturbances and cancer did not mediate the association between type 2 diabetes status and measures of cognition. Conclusions: Comorbid conditions explain some of the observed association between type 2 diabetes and cognitive deficits. This suggests that prevention, management or treatment of these comorbid conditions may be important to reduce the likelihood of cognitive decline. Treatment studies with long follow-ups are needed to examine this. Tweet: Comorbid conditions explain the association between type 2 diabetes and cognitive deficits. Prevention, management or treatment of these comorbid conditions may prevent or delay the onset of cognitive decline in people with type 2 diabetes

The role of mental disorders in precision medicine for diabetes: a narrative review

This narrative review aims to examine the value of addressing mental disorders as part of the care of people with type 1 and type 2 diabetes in terms of four components of precision medicine. First, we review the empirical literature on the role of common mental disorders in the development and outcomes of diabetes (precision prevention and prognostics). We then review interventions that can address mental disorders in individuals with diabetes or at risk of diabetes (precision treatment) and highlight recent studies that have used novel methods to individualise interventions, in person and through applications, based on mental disorders. Additionally, we discuss the use of detailed assessment of mental disorders using, for example, mobile health technologies (precision monitoring). Finally, we discuss future directions in research and practice and challenges to addressing mental disorders as a factor in precision medicine for diabetes. This review shows that several mental disorders are associated with a higher risk of type 2 diabetes and its complications, while there is suggestive evidence indicating that treating some mental disorders could contribute to the prevention of diabetes and improve diabetes outcomes. Using technologically enabled solutions to identify mental disorders could help individuals who stand to benefit from particular treatments. However, there are considerable gaps in knowledge and several challenges to be met before we can stratify treatment recommendations based on mental disorders. Overall, this review demonstrates that addressing mental disorders as a facet of precision medicine could have considerable value for routine diabetes care and has the potential to improve diabetes outcomes

Three-dimensional cometary dust coma modelling in the collisionless regime: strengths and weaknesses

Inverse coma and tail modelling of comets based on the method developed by Finson & Probstein is commonly used to analyse cometary coma images. Models of this type often contain a large number of assumptions that may not be constrained unless wide temporal or spectral coverage is available and the comets are bright and at relatively small geocentric distance. They are used to predict physical parameters, such as the mass distribution of the dust, but rarely give assessments of the accuracy of the estimate. A three-dimensional cometary dust coma model in the collisionless regime has been developed to allow the effectiveness of such models to constrain dust coma properties to be tested. The model is capable of simulating the coma morphology for the following input parameters: the comet nucleus shape, size, rotation, emission function (including active fraction and jets), grain velocity distribution (and dispersion), size distribution, dust production rate, grain material and light scattering from the cometary dust. Characterization of the model demonstrates that the mass distribution cannot be well constrained as is often assumed; the cumulative mass distribution index ? can only be constrained to within ±0.15. The model is highly sensitive to the input grain terminal velocity distribution so model input can be tested with a large degree of confidence. Complex secondary parameters such as jets, rotation and grain composition all have an effect on the structure of the coma in similar ways, so unique solutions for these parameters cannot be derived from a single optical image alone. Multiple images at a variety of geometries close in time can help constrain these effects. The model has been applied to photometric observations of comets 126P/IRAS and 46P/Wirtanen to constrain a number of physical properties including the dust production rate and mass distribution index. The derived dust production rate (Qdust) for 46P/Wirtanen was 3+7/1.5 kg s1 at a pre-perihelion heliocentric distance of 1.8 au, and for P/IRAS was 50+100/20 kg s1 at a pre-perihelion heliocentric distance of 1.7 au; both comets exhibited a mass distribution index ? = 0.8 ± 0.15

Neural correlates of top-down guidance of attention to food: an fMRI study

We investigated the neural correlates of working memory guided attentional selection of food versus non-food stimuli in young women. Participants were thirty-two women, aged 20.6y (± 0.5) who were presented with a cue (food or non-food item) to hold in working memory. Subsequently, they had to search for a target in a 2-item display where target and distractor stimuli were each flanked by a picture of a food or a non-food item. The behavioural data showed that attention is particularly efficiently drawn to food stimuli when thinking about food. Using fMRI, we found that holding a non-food versus food stimulus in working memory was associated with increased activity in occipital gyrus, fusiform, inferior and superior frontal gyrus. In the posterior cingulum, retrosplenial cortex, a food item that re-appeared in the search array when it was held in memory led to a reduced response, compared to when it did not re-appear. The reverse effect was found for non-food stimuli. The extent of the reappearance effect correlated with the attentional capture of food as measured behaviourally. In conclusion, these results suggest that holding food in mind may bias attention because thinking of food facilitated neuronal responses to sensory input related to food stimuli and because holding food-related information in mind is less taxing on memory

Sex differences in cardiometabolic risk factors, pharmacological treatment and risk factor control in type 2 diabetes:findings from the Dutch Diabetes Pearl cohort

Introduction Sex differences in cardiometabolic risk factors and their management in type 2 diabetes (T2D) have not been fully identified. Therefore, we aimed to examine differences in cardiometabolic risk factor levels, pharmacological treatment and achievement of risk factor control between women and men with T2D. Research design and methods Cross-sectional data from the Dutch Diabetes Pearl cohort were used (n=6637, 40% women). Linear and Poisson regression analyses were used to examine sex differences in cardiometabolic risk factor levels, treatment, and control. Results Compared with men, women had a significantly higher body mass index (BMI) (mean difference 1.79 kg/m 2 (95% CI 1.49 to 2.08)), while no differences were found in hemoglobin A 1c (HbA 1c) and systolic blood pressure (SBP). Women had lower diastolic blood pressure (-1.94 mm Hg (95% CI -2.44 to -1.43)), higher total cholesterol (TC) (0.44 mmol/L (95% CI 0.38 to 0.51)), low-density lipoprotein cholesterol (LDL-c) (0.26 mmol/L (95% CI 0.22 to 0.31)), and high-density lipoprotein cholesterol (HDL-c) sex-standardized (0.02 mmol/L (95% CI 0.00 to 0.04)), and lower TC:HDL ratio (-0.29 (95% CI -0.36 to -0.23)) and triglycerides (geometric mean ratio 0.91 (95% CI 0.85 to 0.98)). Women had a 16% higher probability of being treated with antihypertensive medication in the presence of high cardiovascular disease (CVD) risk and elevated SBP than men (relative risk 0.84 (95% CI 0.73 to 0.98)), whereas no sex differences were found for glucose-lowering medication and lipid-modifying medication. Among those treated, women were less likely to achieve treatment targets of HbA 1c (0.92 (95% CI 0.87 to 0.98)) and LDL-c (0.89 (95% CI 0.85 to 0.92)) than men, while no differences for SBP were found. Conclusions In this Dutch T2D population, women had a slightly different cardiometabolic risk profile compared with men and a substantially higher BMI. Women had a higher probability of being treated with antihypertensive medication in the presence of high CVD risk and elevated SBP than men, and were less likely than men to achieve treatment targets for HbA 1c and LDL levels

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

High prevalence of impaired awareness of hypoglycemia and severe hypoglycemia among people with insulin-treated type 2 diabetes: The Dutch Diabetes Pearl Cohort

Objective People with type 2 diabetes on insulin are at risk for hypoglycemia. Recurrent hypoglycemia can cause impaired awareness of hypoglycemia (IAH), and increase the risk for severe hypoglycemia. The aim of this study was to assess the prevalence and determinants of self-reported IAH and severe hypoglycemia in a Dutch nationwide cohort of people with insulin-treated type 2 diabetes. Research design and methods Observational study of The Dutch Diabetes Pearl, a cohort of people with type 2 diabetes treated in primary, secondary and tertiary diabetes care centers. The presence of IAH and the occurrence of severe hypoglycemia in the past year, defined as an event requiring external help to re

Plasma Metabolomics Identifies Markers of Impaired Renal Function: A Meta-analysis of 3089 Persons with Type 2 Diabetes

CONTEXT: There is a need for novel biomarkers and better understanding of the pathophysiology of diabetic kidney disease. OBJECTIVE: To investigate associations between plasma metabolites and kidney function in people with type 2 diabetes (T2D). DESIGN: 3089 samples from individuals with T2D, collected between 1999 and 2015, from 5 independent Dutch cohort studies were included. Up to 7 years follow-up was available in 1100 individuals from 2 of the cohorts. MAIN OUTCOME MEASURES: Plasma metabolites (n = 149) were measured by nuclear magnetic resonance spectroscopy. Associations between metabolites and estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), and eGFR slopes were investigated in each study followed by random effect meta-analysis. Adjustments included traditional cardiovascular risk factors and correction for multiple testing. RESULTS: In total, 125 metabolites were significantly associated (PFDR = 1.5×10-32 - 0.046; β = -11.98-2.17) with eGFR. Inverse associations with eGFR were demonstrated for branched-chain and aromatic amino acids (AAAs), glycoprotein acetyls, triglycerides (TGs), lipids in very low-density lipoproteins (VLDL) subclasses, and fatty acids (PFDR < 0.03). We observed positive associations with cholesterol and phospholipids in high-density lipoproteins (HDL) and apolipoprotein A1 (PFDR < 0.05). Albeit some metabolites were associated with UACR levels (P < 0.05), significance was lost after correction for multiple testing. Tyrosine and HDL-related metabolites were positively associated with eGFR slopes before adjustment for multiple testing (PTyr = 0.003; PHDLrelated < 0.05), but not after. CONCLUSIONS: This study identified metabolites associated with impaired kidney function in T2D, implying involvement of lipid and amino acid metabolism in the pathogenesis. Whether these processes precede or are consequences of renal impairment needs further investigation

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

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