An estimation of the consequences of reinforcing the 2016 and 2019 ESC/EAS guidelines on current lipid-lowering treatment in patients with type 2 diabetes in tertiary care - a SwissDiab study.

BACKGROUND In 2019, the ESC/EAS updated the 2016 guidelines for the management of dyslipidaemias recommending more stringent LDL-cholesterol (LDL-C) targets in diabetes mellitus type 2 (DM2). Based on a real-world patient population, this study aimed to determine the feasibility and cost of attaining guideline-recommended LDL-C targets, and assess cardiovascular benefit. METHODS The Swiss Diabetes Registry is a multicentre longitudinal observational study of outpatients in tertiary diabetes care. Patients with DM2 and a visit 01.01.2018-31.08.2019 that failed the 2016 LDL-C target were identified. The theoretical intensification of current lipid-lowering medication needed to reach the 2016 and 2019 LDL-C target was determined and the cost thereof extrapolated. The expected number of MACE prevented by treatment intensification was estimated. RESULTS 294 patients (74.8%) failed the 2016 LDL-C target. The percentage of patients that theoretically achieved the 2016 and 2019 target with the indicated treatment modifications were: high-intensity statin, 21.4% and 13.3%; ezetimibe, 46.6% and 27.9%; PCSK9 inhibitor (PCSK9i), 30.6% and 53.7%; ezetimibe and PCSK9i, 1.0% and 3.1%, whereas one (0.3%) and five patients (1.7%) failed to reach target, respectively. Achieving the 2016 versus 2019 target would reduce the estimated 4-year MACE from 24.9 to 18.6 versus 17.4 events, at an additional annual cost of medication of 2,140 CHF versus 3,681 CHF per patient, respectively. CONCLUSIONS For 68% of the patients, intensifying statin treatment and/or adding ezetimibe would be sufficient to reach the 2016 target, whereas 57% would require cost-intensive PCSK9i therapy to reach the 2019 target, with limited additional medium-term cardiovascular benefit

Smoking Status, Snus Use, and Variation at the CHRNA5-CHRNA3-CHRNB4 Locus in Relation to Obesity: The GLACIER Study

A genetic variant within the CHRNA5-CHRNA3-CHRNB4 region (rs1051730), previously associated with smoking quantity, was recently shown to interact with smoking on obesity predisposition. We attempted to replicate this finding in the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk (GLACIER) Study, a prospective cohort study of adults from northern Sweden (n = 16,426). We also investigated whether a similar interaction is apparent between rs1051730 and snus, a type of moist oral tobacco, to determine whether this interaction is driven by factors that cigarettes and snus have in common, such as nicotine. Main effects of smoking, snus, and the rs1051730 variant and pairwise interaction terms (smoking x rs1051730 and snus x rs1051730) were tested in relation to body mass index (BMI; calculated as weight (kg)/height (m)(2)) through the use of multivariate linear models adjusted for age and sex. Smoking status and BMI were inversely related (beta = -0.46 kg/m(2), standard error (SE) = 0.08; P 0.05); the T allele was associated with lower BMI in the overall cohort (beta = -0.10 kg/m(2), SE = 0.05; P = 0.03) and with smoking quantity in those in whom this was measured (n = 5,304) (beta = 0.08, SE = 0.01; P < 0.0001). Neither smoking status (P-interaction = 0.29) nor snus use (P-interaction = 0.89) modified the association between the rs1051730 variant and BMI

Genetic Predisposition to Long-Term Nondiabetic Deteriorations in Glucose Homeostasis: Ten-Year Follow-Up of the GLACIER Study

Objective: To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits. Research Design and Methods: Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059). Results: Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 × 10$^{−6}$) greater elevation in fasting glucose and a 64% (95% CI: 33–201%) higher risk of developing IFG during 10 years of follow-up. Conclusions: Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant

Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 Swedish cohorts.

BACKGROUND: The consumption of sugar-sweetened beverages (SSBs), which has increased substantially during the last decades, has been associated with obesity and weight gain. OBJECTIVE: Common genetic susceptibility to obesity has been shown to modify the association between SSB intake and obesity risk in 3 prospective cohorts from the United States. We aimed to replicate these findings in 2 large Swedish cohorts. DESIGN: Data were available for 21,824 healthy participants from the Malmö Diet and Cancer study and 4902 healthy participants from the Gene-Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk Study. Self-reported SSB intake was categorized into 4 levels (seldom, low, medium, and high). Unweighted and weighted genetic risk scores (GRSs) were constructed based on 30 body mass index [(BMI) in kg/m(2)]-associated loci, and effect modification was assessed in linear regression equations by modeling the product and marginal effects of the GRS and SSB intake adjusted for age-, sex-, and cohort-specific covariates, with BMI as the outcome. In a secondary analysis, models were additionally adjusted for putative confounders (total energy intake, alcohol consumption, smoking status, and physical activity). RESULTS: In an inverse variance-weighted fixed-effects meta-analysis, each SSB intake category increment was associated with a 0.18 higher BMI (SE = 0.02; P = 1.7 × 10(-20); n = 26,726). In the fully adjusted model, a nominal significant interaction between SSB intake category and the unweighted GRS was observed (P-interaction = 0.03). Comparing the participants within the top and bottom quartiles of the GRS to each increment in SSB intake was associated with 0.24 (SE = 0.04; P = 2.9 × 10(-8); n = 6766) and 0.15 (SE = 0.04; P = 1.3 × 10(-4); n = 6835) higher BMIs, respectively. CONCLUSIONS: The interaction observed in the Swedish cohorts is similar in magnitude to the previous analysis in US cohorts and indicates that the relation of SSB intake and BMI is stronger in people genetically predisposed to obesity

Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity.

INTRODUCTION: Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment. METHODS: An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN+]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22-65 yr), and wrist in 63 women (20-35 yr) in whom daily activity-related energy expenditure (PAEE) was available. RESULTS: In the robot experiment, HFEN+ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN). CONCLUSION: In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity

Genetic Determinants of Long-Term Changes in Blood Lipid Concentrations: 10-Year Follow-Up of the GLACIER Study

Recent genome-wide meta-analyses identified 157 loci associated with cross-sectional lipid traits. Here we tested whether these loci associate (singly and in trait-specific genetic risk scores [GRS]) with longitudinal changes in total cholesterol (TC) and triglyceride (TG) levels in a population-based prospective cohort from Northern Sweden (the GLACIER Study). We sought replication in a southern Swedish cohort (the MDC Study; N = 2,943). GLACIER Study participants (N = 6,064) were genotyped with the MetaboChip array. Up to 3,495 participants had 10-yr follow-up data available in the GLACIER Study. The TC- and TG-specific GRSs were strongly associated with change in lipid levels (β = 0.02 mmol/l per effect allele per decade follow-up, P = 2.0×10−11 for TC; β = 0.02 mmol/l per effect allele per decade follow-up, P = 5.0×10−5 for TG). In individual SNP analysis, one TC locus, apolipoprotein E (APOE) rs4420638 (β = 0.12 mmol/l per effect allele per decade follow-up, P = 2.0×10−5), and two TG loci, tribbles pseudokinase 1 (TRIB1) rs2954029 (β = 0.09 mmol/l per effect allele per decade follow-up, P = 5.1×10−4) and apolipoprotein A-I (APOA1) rs6589564 (β = 0.31 mmol/l per effect allele per decade follow-up, P = 1.4×10−8), remained significantly associated with longitudinal changes for the respective traits after correction for multiple testing. An additional 12 loci were nominally associated with TC or TG changes. In replication analyses, the APOE rs4420638, TRIB1 rs2954029, and APOA1 rs6589564 associations were confirmed (P≤0.001). In summary, trait-specific GRSs are robustly associated with 10-yr changes in lipid levels and three individual SNPs were strongly associated with 10-yr changes in lipid levels

Comprehensive Analysis of Established Dyslipidemia-Associated Loci in the Diabetes Prevention Program

Background-We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial. Methods and Results-We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre-diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at PP>1.1×10-16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (β=-0.11 μmol/L per genetic risk scores risk allele; 95% confidence interval,-0.188 to-0.033; P=5×10-3; Pinteraction=1×10-3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits. Conclusions-Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits. Clinical Trial Registration-URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992

Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden

Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21–1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation

Comparison of abdominal adiposity and overall obesity in relation to risk of small intestinal cancer in a European Prospective Cohort

Published version. Source at http://dx.doi.org/10.1007/s10552-016-0772-z Background: The etiology of small intestinal cancer (SIC) is largely unknown, and there are very few epidemiological studies published to date. No studies have investigated abdominal adiposity in relation to SIC. Methods: We investigated overall obesity and abdominal adiposity in relation to SIC in the European Prospective Investigation into Cancer and Nutrition (EPIC), a large prospective cohort of approximately half a million men and women from ten European countries. Overall obesity and abdominal obesity were assessed by body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR). Multivariate Cox proportional hazards regression modeling was performed to estimate hazard ratios (HRs) and 95 % confidence intervals (CIs). Stratified analyses were conducted by sex, BMI, and smoking status. Results: During an average of 13.9 years of follow-up, 131 incident cases of SIC (including 41 adenocarcinomas, 44 malignant carcinoid tumors, 15 sarcomas and 10 lymphomas, and 21 unknown histology) were identified. WC was positively associated with SIC in a crude model that also included BMI (HR per 5-cm increase = 1.20, 95 % CI 1.04, 1.39), but this association attenuated in the multivariable model (HR 1.18, 95 % CI 0.98, 1.42). However, the association between WC and SIC was strengthened when the analysis was restricted to adenocarcinoma of the small intestine (multivariable HR adjusted for BMI = 1.56, 95 % CI 1.11, 2.17). There were no other significant associations. Conclusion: WC, rather than BMI, may be positively associated with adenocarcinomas but not carcinoid tumors of the small intestine. Impact: Abdominal obesity is a potential risk factor for adenocarcinoma in the small intestine