Genetic insights into resting heart rate and its role in cardiovascular disease

Funding Information: We thank all participants for their participation and valuable contributions. This research has been conducted using the UK Biobank Resource under application number 12010. The work of N.V. was supported by NWO VENI grant 016.186.125. We thank 23andMe and the 23andMe Research Team for their contribution sharing their data and performing the GWAS analysis in the 23andMe cohort. P.V. received an unrestricted grant from GlaxoSmithkline to build the CoLaus study. N.J.T. is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215-2001), the MRC Integrative Epidemiology Unit (MC_UU_00011/1) and works within the CRUK Integrative Cancer Epidemiology Program (C18281/A29019). A detailed list of acknowledgements and funding is provided in Supplementary Data per cohort. We also thank all individuals that contributed to the generation of software programs, algorithms and genetic summary statistics. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. Authors involved in the funding of the cohorts are listed below. Meta-analyses, Lifelines, PREVEND, UK Biobank: P.v.d.H.; ADDITION-PRO: T.H.; ADVANCE: C.I.; ADVANCE: T.A.; AGES: L.Launer, V.G.; ASCOT: P.Sever, P.B.M.; BC1936: N.G.; BioMe: E.P.B., R.J.F.L.; BRIGHT: P.B.M.; CHS: B.M.P.; CoLaus: P.V.; Croatia-Korcula: O.P., C.H.; DCCT/EDIC: D.R., The DCCT/EDIC Research Group, A.D.P.; DESIR: B.B., P.F.; DGI: L.G.; EPIC-Norfolk: N.J.W.; ERF: C.M.v.D.; Fenland: N.J.W.; FINCAVAS: M.K.; Finrisk: M.P.; FUSION: M.B.; GENOA: P.A.P., S.L.R.K.; GerMIFSs: H.Schunkert, J.E.; GoDARTS: C.N.A.P; GOOD: M.L., C.O.; HBCS: J.G.E.; HERITAGE: T.R., D.C.R., C.B.; HPFS / NHS: P.K.; HRS: D.R.Weir; HYPERGENES: K.S.S., D.C.; InCHIANTI: S.Bandinelli, L.Ferrucci; INGI-CARL: M.P.C.; INGI-FVG: G.G.; JHS: A.Correa; KORA F3: T.M., S.K.; KORA S4: K.Strauch., A.P.; LOLIPOP: J.C.C., J.S.K.; LURIC: W.M.; MESA: J.I.R.; MICROS: A.H.; MPP: O.M.; J.G.S.; NBS: L.A.L.M.K.; NEO: R.d.M.; NESDA: B.W.J.H.P.; NSPHS: Å.J.; ORCADES: J.F.W.; PIVUS: L.L.; PROSPER: P.W.M., J.W.J.; SardiNIA: E.L.L.; SCES: C.Y.C.; SHIP: S.B.F., M.D.; SIMES: T.Y.W.; TRAILS: A.J.O.; TWINS: J.O.; ULSAM: A.P.M., C.Lindgren; YFS: O.T.R., T.L. Funding Information: We thank all participants for their participation and valuable contributions. This research has been conducted using the UK Biobank Resource under application number 12010. The work of N.V. was supported by NWO VENI grant 016.186.125. We thank 23andMe and the 23andMe Research Team for their contribution sharing their data and performing the GWAS analysis in the 23andMe cohort. P.V. received an unrestricted grant from GlaxoSmithkline to build the CoLaus study. N.J.T. is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215-2001), the MRC Integrative Epidemiology Unit (MC_UU_00011/1) and works within the CRUK Integrative Cancer Epidemiology Program (C18281/A29019). A detailed list of acknowledgements and funding is provided in Supplementary Data 1 per cohort. We also thank all individuals that contributed to the generation of software programs, algorithms and genetic summary statistics. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. Authors involved in the funding of the cohorts are listed below. Meta-analyses, Lifelines, PREVEND, UK Biobank : P.v.d.H.; ADDITION-PRO : T.H.; ADVANCE : C.I.; ADVANCE : T.A.; AGES : L.Launer, V.G.; ASCOT : P.Sever, P.B.M.; BC1936 : N.G.; BioMe : E.P.B., R.J.F.L.; BRIGHT : P.B.M.; CHS : B.M.P.; CoLaus : P.V.; Croatia-Korcula : O.P., C.H.; DCCT/EDIC : D.R., The DCCT/EDIC Research Group, A.D.P.; DESIR : B.B., P.F.; DGI : L.G.; EPIC-Norfolk : N.J.W.; ERF : C.M.v.D.; Fenland : N.J.W.; FINCAVAS : M.K.; Finrisk : M.P.; FUSION : M.B.; GENOA : P.A.P., S.L.R.K.; GerMIFSs : H.Schunkert, J.E.; GoDARTS : C.N.A.P; GOOD : M.L., C.O.; HBCS : J.G.E.; HERITAGE : T.R., D.C.R., C.B.; HPFS / NHS: P.K.; HRS : D.R.Weir; HYPERGENES : K.S.S., D.C.; InCHIANTI : S.Bandinelli, L.Ferrucci; INGI-CARL: M.P.C.; INGI-FVG: G.G.; JHS: A.Correa; KORA F3: T.M., S.K.; KORA S4 : K.Strauch., A.P.; LOLIPOP : J.C.C., J.S.K.; LURIC : W.M.; MESA : J.I.R.; MICROS : A.H.; MPP : O.M.; J.G.S.; NBS : L.A.L.M.K.; NEO : R.d.M.; NESDA : B.W.J.H.P.; NSPHS : Å.J.; ORCADES : J.F.W.; PIVUS : L.L.; PROSPER : P.W.M., J.W.J.; SardiNIA : E.L.L.; SCES : C.Y.C.; SHIP : S.B.F., M.D.; SIMES : T.Y.W.; TRAILS : A.J.O.; TWINS : J.O.; ULSAM : A.P.M., C.Lindgren; YFS : O.T.R., T.L. Publisher Copyright: © 2023, The Author(s).Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.Peer reviewe

Impact of changes in metabolic control on progression to photocoagulation for clinically significant macular oedema:a 20 year study of type 1 diabetes

AIMS/HYPOTHESIS: Although increasing hyperglycaemia, arterial hypertension and longer duration of diabetes raise the risk of progression of diabetic retinopathy, short-term benefits in terms of improved metabolic control and lowered blood pressure have not been demonstrated. We therefore examined the effect of changes in glycaemia and arterial blood pressure on the incidence of clinically significant macular oedema in a population of diabetic patients. METHODS: We performed a retrospective review of all patients with type 1 diabetes who attended the retinopathy screening clinic at the Steno Diabetes Center from 1988 to 2008, using the endpoint referral to first photocoagulation treatment for clinically significant diabetic macular oedema. The analysis included 1,878 patients (median observation, 8 years). Changes were defined as the inter-visit change; in the case of an event the last event-free interval before referral, where the median screening interval was 6 months. RESULTS: Risk of progression to photocoagulation for macular oedema increased with duration of diabetes (p < 0.001), current HbA(1c) (p < 0.0001) and with the magnitude of changes in HbA(1c) (p = 0.0002) and systolic blood pressure (p < 0.0001) in a multiple regression model. A recent decrease of ≥0.5 percentage points or an increase in HbA(1c) of >0.5 percentage points per 6 months was associated with HRs of 3.04 and 1.28, respectively, compared with lesser changes in HbA(1c). CONCLUSIONS/INTERPRETATION: In this study, large recent changes in metabolic control and systolic blood pressure, irrespective of direction, were independent risk factors for progression to photocoagulation for diabetic macular oedema. The effects of metabolic and haemodynamic stability on diabetic retinopathy should be examined in prospective studies

Skin collagen advanced glycation endproducts (AGEs) and the long-term progression of sub-clinical cardiovascular disease in type 1 diabetes

BACKGROUND: We recently reported strong associations between eight skin collagen AGEs and two solubility markers from skin biopsies obtained at DCCT study closeout and the long-term progression of microvascular disease in EDIC, despite adjustment for mean glycemia. Herein we investigated the hypothesis that some of these AGEs (fluorescence to be reported elsewhere) correlate with long-term subclinical cardiovascular disease (CVD) measurements, i.e. coronary artery calcium score (CAC) at EDIC year 7-9 (n = 187), change of carotid intima-media thickness (IMT) from EDIC year 1 to year 6 and 12 (n = 127), and cardiac MRI outcomes at EDIC year 15-16 (n = 142). METHODS: Skin collagen AGE measurements obtained from stored specimens were related to clinical data from the DCCT/EDIC using Spearman correlations and multivariable logistic regression analyses. RESULTS: Spearman correlations showed furosine (early glycation) was associated with future mean CAC (p \u3c 0.05) and CAC \u3e0 (p = 0.39), but not with CAC score100. Glucosepane and pentosidine crosslinks, methylglyoxal hydroimidazolones (MG-H1) and pepsin solubility (inversely) correlated with IMT change from year 1 to 6(all P \u3c 0.05). Left ventricular (LV) mass (cMRI) correlated with MG-H1, and inversely with pepsin solubility (both p \u3c 0.05), while the ratio LV mass/end diastolic volume correlated with furosine and MG-H1 (both p \u3c 0.05), and highly with CML (p \u3c 0.01). In multivariate analysis only furosine (p = 0.01) was associated with CAC. In contrast IMT was inversely associated with lower collagen pepsin solubility and positively with glucosepane, CONCLUSIONS: In type 1 diabetes, multiple AGEs are associated with IMT progression in spite of adjustment for A1c implying a likely participatory role of glycation and AGE mediated crosslinking on matrix accumulation in coronary arteries. This may also apply to functional cardiac MRI outcomes, especially left ventricular mass. In contrast, early glycation measured by furosine, but not AGEs, was associated with CAC score, implying hyperglycemia as a risk factor in calcium deposition perhaps via processes independent of glycation. TRIAL REGISTRATION: Registered at Clinical trial reg. nos. NCT00360815 and NCT00360893, http://www.clinicaltrials.gov

Significance of Epicardial and Intrathoracic Adipose Tissue Volume among Type 1 Diabetes Patients in the DCCT/EDIC: A Pilot Study.

Introduction Type 1 diabetes (T1DM) patients are at increased risk of coronary artery disease (CAD). This pilot study sought to evaluate the relationship between epicardial adipose tissue (EAT) and intra-thoracic adipose tissue (IAT) volumes and cardio-metabolic risk factors in T1DM. Method EAT/IAT volumes in 100 patients, underwent non-contrast cardiac computed tomography in the Diabetes Control and Complications Trial /Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study were measured by a certified reader. Fat was defined as pixels’ density of -30 to -190 Hounsfield Unit. The associations were assessed using–Pearson partial correlation and linear regression models adjusted for gender and age with inverse probability sample weighting. Results The weighted mean age was 43 years (range 32–57) and 53% were male. Adjusted for gender, Pearson correlation analysis showed a significant correlation between age and EAT/IAT volumes (both p Conclusion T1DM patients with greater BMI, WTH ratio, weighted HbA1c level, triglyceride level and AER≥300/ESRD had significantly larger EAT/IAT volumes. Larger sample size studies are recommended to evaluate independency

Biomarkers of tubulointerstitial damage and function in type 1 diabetes

Objective To evaluate biomarkers of renal tubulointerstitial damage and function in type 1 diabetes with and without diabetic kidney disease. Research design and methods Cross-sectional case-control study of Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study participants. Cases (N=43) had incident persistent estimated glomerular filtration rate (eGFR) \u3c60 mL/min/1.73 m2 with urinary albumin excretion \u3e300 mg/24 hour. Controls (N=43) had persistent eGFR \u3e90 mL/min/1.73 m2 and urinary albumin excretion \u3c30 mg/24 hour. Urinary and plasma biomarkers reflecting tubular injury, inflammation, fibrosis, secretion, and synthetic function were measured from stored specimens collected at the first study visit with reduced eGFR (for case participants) or the corresponding study year (for control participants). Results Mean (SD) age was 51 (9) and 50 (8) years for case and control participants, and mean (SD) duration of diabetes was 30 (6) and 30 (5) years, respectively. Mean (SD) eGFR was 39 (14) and 103 (9) mL/min/1.73 m2 for case and control participants, and mean (SD) albumin excretion rate was 1978 (2914) and 10 (7) mg/day, respectively. Comparing cases with controls, significant differences were observed in each measured biomarker, including urine epidermal growth factor (mean 5.3 vs 21.2 μg/g creatinine for case vs control participants, respectively), urine monocyte chemoattractant protein-1 (596 vs 123 ng/g creatinine), urine galectin-3 (168 vs 52 μg/g creatinine), plasma soluble tubular necrosis factor receptor-1 (3695 vs 1022 pg/mL), plasma galectin-3 (21.3 vs 11.0 ng/mL), urinary clearances of hippurate (70 vs 167 mL/min) and cinnamoylglycine (77 vs 317 mL/min), and plasma arginine-citrulline ratio (5.6 vs 7.7 μg/μg), each P\u3c0.001. Conclusions Marked abnormalities in biomarkers of kidney tubular injury, inflammation, fibrosis, secretion, and synthetic function accompany reduced eGFR and albuminuria in type 1 diabetes. Trial registration number NCT00360893, NCT00360815

Utility of using electrocardiogram measures of heart rate variability as a measure of cardiovascular autonomic neuropathy in type 1 diabetes patients

AIMS/INTRODUCTION: Cardiovascular autonomic neuropathy (CAN) is a predictor of cardiovascular disease and mortality. Cardiovascular reflex tests (CARTs) are the gold standard for the diagnosis of CAN, but might not be feasible in large research cohorts or in clinical care. We investigated whether measures of heart rate variability obtained from standard electrocardiogram (ECG) recordings provide a reliable measure of CAN. MATERIALS AND METHODS: Standardized CARTs (R-R response to paced breathing, Valsalva, postural changes) and digitized 12-lead resting ECGs were obtained concomitantly in Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications participants (n = 311). Standard deviation of normally conducted R-R intervals (SDNN) and the root mean square of successive differences between normal-to-normal R-R intervals (rMSSD) were measured from ECG. Sensitivity, specificity, probability of correct classification and Kappa statistics evaluated the agreement between ECG-derived CAN and CARTs-defined CAN. RESULTS: Participants with CARTs-defined CAN had significantly lower SDNN and rMSSD compared with those without CAN (P \u3c 0.001). The optimal cut-off points of ECG-derived CAN were \u3c17.13 and \u3c24.94 ms for SDNN and rMSSD, respectively. SDNN plays a dominant role in defining CAN, with an area under the curve of 0.73, indicating fair test performance. The Kappa statistic for SDNN was 0.41 (95% confidence interval 0.30-0.51) for the optimal cut-off point, showing fair agreement with CARTs-defined CAN. Combining SDNN and rMSSD optimal cut-off points does not provide additional predictive power for CAN. CONCLUSIONS: These analyses are the first to show the agreement between indices of heart rate variability derived from ECGs and the gold standard CARTs, thus supporting potential use as a measure of CAN in clinical research and clinical care

Caffeine Consumption Contributes to Skin Intrinsic Fluorescence in Type 1 Diabetes.

Background: A variant (rs1495741) in the gene for the N-acetyltransferase 2 (NAT2) protein is associated with skin intrinsic fluorescence (SIF), a noninvasive measure of advanced glycation end products and other fluorophores in the skin. Because NAT2 is involved in caffeine metabolism, we aimed to determine whether caffeine consumption is associated with SIF and whether rs1495741 is associated with SIF independently of caffeine. Materials and Methods: SIF was measured in 1,181 participants with type 1 diabetes from the Epidemiology of Diabetes Interventions and Complications study. Two measures of SIF were used: SIF1, using a 375-nm excitation light-emitting diode (LED), and SIF14 (456-nm LED). Food frequency questionnaires were used to estimate mean caffeine intake. To establish replication, we examined a second type 1 diabetes cohort. Results: Higher caffeine intake was significantly associated with higher SIF1LED 375 nm[0.6, 0.2] (P=2×10−32) and SIF14LED 456 nm[0.4, 0.8] (P=7×10−31) and accounted for 4% of the variance in each after adjusting for covariates. When analyzed together, caffeine intake and rs1495741 both remained highly significantly associated with SIF1LED 375 nm[0.6, 0.2] and SIF14LED 456 nm[0.4, 0.8]. Mean caffeinated coffee intake was also positively associated with SIF1LED 375 nm[0.6, 0.2] (P=9×10−12) and SIF14LED 456 nm[0.4, 0.8] (P=4×10−12), but no association was observed for decaffeinated coffee intake. Finally, caffeine was also positively associated with SIF1LED 375 nm[0.6, 0.2] and SIF14LED 456 nm[0.4, 0.8] (P\u3c0.0001) in the replication cohort. Conclusions: Caffeine contributes to SIF. The effect of rs1495741 on SIF appears to be partially independent of caffeine consumption. Because SIF and coffee intake are each associated with cardiovascular disease, our findings suggest that accounting for coffee and/or caffeine intake may improve risk prediction models for SIF and cardiovascular disease in individuals with diabetes

Haptoglobin Genotype and the Rate of Renal Function Decline in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study

Many patients with type 1 diabetes develop renal disease despite moderately good metabolic control, suggesting other risk factors may play a role. Recent evidence suggests that the haptoglobin (HP) 2-2 genotype, which codes for a protein with reduced antioxidant activity, may predict renal function decline in type 1 diabetes. We examined this hypothesis in 1,303 Caucasian participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. HP genotype was determined by polyacrylamide gel electrophoresis. Glomerular filtration rate was estimated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and albumin excretion based on timed urine samples. Participants were followed up for a mean of 22 years. HP genotype was significantly associated with the development of sustained estimated glomerular filtration rate (GFR) \u3c60 mL/min/1.73 m2and with end-stage renal disease (ESRD), with HP 2-2 having greater risk than HP 2-1 and 1-1. No association was seen with albuminuria. Although there was no treatment group interaction, the associations were only significant in the conventional treatment group, where events rates were much higher. We conclude that the HP genotype is significantly associated with the development of reduced GFR and ESRD in the DCCT/EDIC study

HbA_1c variability is associated with increased mortality and earlier hospital admission in people with Type 1 diabetes

Aim: Despite evidence of morbidity, no evidence exists on the relationship between HbA1c variability and mortality in Type 1 diabetes. We performed an observational study to investigate whether the association between HbA1c variability and mortality exists in a population of people with Type 1 diabetes. As a secondary outcome, we compared onset of first hospital admission between groups. Methods: People with Type 1 diabetes were identified for inclusion from the Scottish Care Information – Diabetes data set. This database includes data of all people known to have diabetes who live within Scotland. A survival analysis was carried out over a 47‐month period comparing two groups; group 1 with a HbA1c coefficient of variation (CV) above the median CV value, and group 2 with a CV below the median value. Time to death or first admission was also analysed. A Cox proportional hazard model was used to compare time to death, adjusting for appropriate covariables. Results: Some 6048 individuals with Type 1 diabetes were included in the analysis. Median HbA1c CV was 7.9. The hazard ratio (HR) for mortality for those with an HbA1c CV above the median value is 1.5 over 47 months of follow‐up (P &lt; 0.001). HR for survival to either the first admission to hospital or death for those with an HbA1c CV above the median value was 1.35 (95% confidence interval 1.25–1.45) over 730 days of follow‐up (P &lt; 0.001). Conclusion: Our results show that people with greater HbA1c variability have a higher rate of mortality and earlier hospital admission in Type 1 diabetes

Quality Control Measures over 30 Years in a Multicenter Clinical Study: Results from the Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study.

Implementation of multicenter and/or longitudinal studies requires an effective quality assurance program to identify trends, data inconsistencies and process variability of results over time. The Diabetes Control and Complications Trial (DCCT) and the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study represent over 30 years of data collection among a cohort of participants across 27 clinical centers. The quality assurance plan is overseen by the Data Coordinating Center and is implemented across the clinical centers and central reading units. Each central unit incorporates specific DCCT/EDIC quality monitoring activities into their routine quality assurance plan. The results are reviewed by a data quality assurance committee whose function is to identify variances in quality that may impact study results from the central units as well as within and across clinical centers, and to recommend implementation of corrective procedures when necessary. Over the 30-year period, changes to the methods, equipment, or clinical procedures have been required to keep procedures current and ensure continued collection of scientifically valid and clinically relevant results. Pilot testing to compare historic processes with contemporary alternatives is performed and comparability is validated prior to incorporation of new procedures into the study. Details of the quality assurance plan across and within the clinical and central reading units are described, and quality outcomes for core measures analyzed by the central reading units (e.g. biochemical samples, fundus photographs, ECGs) are presented