DNA methylation offers an objective method to assess the impact of smoking. In this work, we conduct a Bayesian EWAS of smoking pack years (n = 17,865, ~850k sites, Illumina EPIC array) and extend it by analysing whole genome data of smokers and non-smokers from Generation Scotland (n = 46, ~4-21 million sites via TWIST and Oxford Nanopore sequencing). We develop mCigarette, an epigenetic biomarker of smoking, and test it in two British cohorts. Results of brain- and blood-based EWAS (nbrain=14, nblood = 882, >450k sites, Illumina arrays) reveal several loci with near-perfect discrimination of smoking status, but which do not overlap across tissues. Furthermore, we perform a GWAS of epigenetic smoking, identifying several smoking-related loci. Overall, we improve smoking-related biomarker accuracy and enhance the understanding of the effects of smoking by integrating DNA methylation data from multiple tissues and cohorts.

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN—a cardiovascular risk prediction tool recommended for use in Scotland—was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort. METHODS: Previously generated DNA methylation–derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (n cases ≥1274; n controls ≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI ( P <0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P =3.7×10 − 3 ; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.

Background Self-reported smoking is often incorporated into disease prediction tools but suffers from recall bias and does not capture passive exposure. Blood-based DNA methylation (DNAm) is an objective way to assess smoking. However, studies have not fully explored tissue-specificity or epigenome-wide coverage beyond array data. Here, we update the existing biomarkers of smoking and conduct a detailed analysis of the associations between blood DNAm and self-reported smoking. Methods and Findings A blood-based Bayesian epigenome-wide association study (EWAS) of smoking was carried out in 17,865 Generation Scotland individuals at ∼850k CpG sites (Illumina EPIC array). For 24 pairs of smokers and non-smokers a high-resolution approach was implemented (∼4 million sites, TWIST methylome panel). A DNAm-derived biomarker of smoking (mCigarette) was tested in the independent Lothian Birth Cohort 1936 (n=882, Illumina 450k array) and in the ALSPAC parents and offspring at four time points (range n=496–1,207). To explore tissue specific signals, EWASs of smoking were run across five brain regions for 14 individuals using DNAm from the EPIC array. Lastly, genome-wide association studies (GWASs) of smoking pack years and an epigenetic score for smoking (GrimAge DNAm pack years) were conducted (n=17,105). The primary EWAS analyses identified two novel genome-wide significant loci, mapping to genes related to addiction and carcinogenesis. Associations with CpG sites which are currently absent from methylation arrays were identified by the high resolution EWAS of smoking (n=48). The mCigarette pack years biomarker showed excellent discrimination across all smoking categories (current, former, never), and outperformed existing predictors in associations with pack years in an external test dataset (Pearson r=0.75). Several CpGs showed near-perfect discrimination of smoking status in both blood and brain, but these loci did not overlap across tissues. The GWAS of DNAm (but not self-reported) pack years identified novel and established smoking-related loci. However, the self-reported phenotype GWAS had a higher genetic correlation with a large meta-analysis GWAS of self-reported pack years. Among the study shortcomings are its potential lack of generalizability to non-Europeans and the absence of serum cotinine data. Conclusion A multi-tissue, multi-cohort analysis of the relationship between smoking, DNA and DNAm (assessed via arrays and targeted sequencing) has improved our understanding of the biological consequences of smoking. ### Competing Interest Statement R.E.M has received a speaker fee from Illumina and is an advisor to the Epigenetic Clock Development Foundation. R.F.H. has received consultant fees from Illumina. R.E.M and R.F.H. have received consultant fees from Optima partners. All other authors declare no competing interests. ### Funding Statement Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping and DNA methylation profiling of the Generation Scotland samples was carried out by the Genetics Core Laboratory at the Edinburgh Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award STratifying Resilience and Depression Longitudinally (STRADL; Reference 104036/Z/14/Z). The DNA methylation data assayed for Generation Scotland was partially funded by a 2018 NARSAD Young Investigator Grant from the Brain & Behavior Research Foundation (Ref: 27404; awardee: Dr David M Howard) and by a JMAS SIM fellowship from the Royal College of Physicians of Edinburgh (Awardee: Dr Heather C Whalley). LBC1936: The LBC1936 is supported by the BBSRC, and the Economic and Social Research Council \[BB/W008793/1\] (which supports S.E.H.), Age UK (Disconnected Mind project), the Milton Damerel Trust, the Medical Research Council (MR/M01311/1), and the University of Edinburgh. Methylation typing of LBC1936 was supported by the Centre for Cognitive Ageing and Cognitive Epidemiology (Pilot Fund award), Age UK, The Wellcome Trust Institutional Strategic Support Fund, The University of Edinburgh, and The University of Queensland. Genotyping was funded by the BBSRC (BB/F019394/1). S.R.C. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). ALSPAC: The UK Medical Research Council and Wellcome (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and they will serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the ALSPAC website (). Funding for ALSPAC DNAm measurements were supported by the Wellcome (102215/2/13/2); the University of Bristol; the UK Economic and Social Research Council (ES/N000498/1); the UK Medical Research Council (MC\_UU\_12013/1, MC\_UU\_12013/2); the Biotechnology and Biological Sciences Research Council (BBI025751/1 and BB/I025263/1); and the John Templeton Foundation (60828). P.Y. and M.S. work is supported by the National Institute for Health and Care Research Bristol Biomedical Research Centre, the Medical Research Council Integrative Epidemiology Unit at the University of Bristol (MC\_UU\_00032/3, MC\_UU\_00032/4, MC\_UU\_00032/6), and Cancer Research UK [C18281/A29019, EDDISA-Jan22\100003]. A.D.C. is supported by a Medical Research Council PhD Studentship in Precision Medicine with funding from the Medical Research Council Doctoral Training Program and the University of Edinburgh College of Medicine and Veterinary Medicine. R.F.H is supported by an MRC IEU Fellowship. E.B. and R.E.M. are supported by Alzheimer's Society major project grant AS-PG-19b-010. This research was funded in whole, or in part, by the Wellcome Trust (104036/Z/14/Z, 108890/Z/15/Z, 220857/Z/20/Z, and 221890/Z/20/Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All components of Generation Scotland received ethical approval from the NHS Tayside Committee on Medical Research Ethics (REC Reference Number: 05/S1401/89). All participants provided broad and enduring written informed consent for biomedical research. Generation Scotland has also been granted Research Tissue Bank status by the East of Scotland Research Ethics Service (REC Reference Number: 15/0040/ES), providing generic ethical approval for a wide range of uses within medical research. This study was performed in accordance with the Helsinki declaration. Ethical approval for the LBC1936 study was obtained from the Multi-Centre Research Ethics Committee for Scotland (MREC/01/0/56) and the Lothian Research Ethics committee (LREC/1998/4/183; LREC/2003/2/29). All participants provided written informed consent. These studies were performed in accordance with the Helsinki declaration. Ethical approval for the ALSPAC study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004). Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes According to the terms of consent for Generation Scotland participants, access to data must be reviewed by the Generation Scotland Access Committee. Applications should be made to access{at}generationscotland.org. Lothian Birth Cohort data are available on request from the Lothian Birth Cohort Study, University of Edinburgh (). Lothian Birth Cohort data are not publicly available due to them containing information that could compromise participant consent and confidentiality. ALSPAC data are available on request from bona fide researchers. The study website contains details of all the data that is available through a fully searchable data dictionary and variable search tool (). All custom R (version 4.3.1), Python (version 3.9.7), and bash code is available with open access at the following GitHub repository: GWAS and EWAS summary statistics will be made available on Edinburgh DataShare on publication.

Background: Metabolomics, the study of small molecules in biological systems, can provide valuable insights into kidney dysfunction in people with type 2 diabetes mellitus (T2DM), but prospective studies are scarce. We investigated the association between metabolites and kidney function decline in people with T2DM.Methods: The Edinburgh Type 2 Diabetes Study, a population-based cohort of 1066 men and women aged 60 to 75 years with T2DM. We measured 149 serum metabolites at baseline and investigated individual associations with baseline estimated glomerular filtration rate (eGFR), incident chronic kidney disease [CKD; eGFR <60 mL/min/(1.73 m)(2)], and decliner status (5% eGFR decline per year).Results: At baseline, mean eGFR was 77.5 mL/min/(1.73 m)(2) (n = 1058), and 216 individuals had evidence of CKD. Of those without CKD, 155 developed CKD over a median 7-year follow-up. Eighty-eight metabolites were significantly associated with baseline eGFR (beta range -4.08 to 3.92; P-FDR < 0.001). Very low density lipoproteins, triglycerides, amino acids (AAs), glycoprotein acetyls, and fatty acids showed inverse associations, while cholesterol and phospholipids in high-density lipoproteins exhibited positive associations. AA isoleucine, apolipoprotein A1, and total cholines were not only associated with baseline kidney measures (P-FDR < 0.05) but also showed stable, nominally significant association with incident CKD and decline.Conclusion: Our study revealed widespread changes within the metabolomic profile of CKD, particularly in lipoproteins and their lipid compounds. We identified a smaller number of individual metabolites that are specifically associated with kidney function decline. Replication studies are needed to confirm the longitudinal findings and explore if metabolic signals at baseline can predict kidney decline.

AIMS:Myocardial infarction (MI) is a major cause of death and disability worldwide. Most metabolomics studies investigating metabolites predicting MI are limited by the participant number and/or the demographic diversity. We sought to identify biomarkers of incident MI in the COnsortium of METabolomics Studies. METHODS AND RESULTS:We included 7897 individuals aged on average 66 years from six intercontinental cohorts with blood metabolomic profiling (n = 1428 metabolites, of which 168 were present in at least three cohorts with over 80% prevalence) and MI information (1373 cases). We performed a two-stage individual patient data meta-analysis. We first assessed the associations between circulating metabolites and incident MI for each cohort adjusting for traditional risk factors and then performed a fixed effect inverse variance meta-analysis to pull the results together. Finally, we conducted a pathway enrichment analysis to identify potential pathways linked to MI. On meta-analysis, 56 metabolites including 21 lipids and 17 amino acids were associated with incident MI after adjusting for multiple testing (false discovery rate < 0.05), and 10 were novel. The largest increased risk was observed for the carbohydrate mannitol/sorbitol {hazard ratio [HR] [95% confidence interval (CI)] = 1.40 [1.26-1.56], P < 0.001}, whereas the largest decrease in risk was found for glutamine [HR (95% CI) = 0.74 (0.67-0.82), P < 0.001]. Moreover, the identified metabolites were significantly enriched (corrected P < 0.05) in pathways previously linked with cardiovascular diseases, including aminoacyl-tRNA biosynthesis. CONCLUSIONS:In the most comprehensive metabolomic study of incident MI to date, 10 novel metabolites were associated with MI. Metabolite profiles might help to identify high-risk individuals before disease onset. Further research is needed to fully understand the mechanisms of action and elaborate pathway findings.

AIMS:To identify a group of metabolites associated with incident cardiovascular disease (CVD) in people with type 2 diabetes and assess its predictive performance over-and-above a current CVD risk score (QRISK3). METHODS AND RESULTS:A panel of 228 serum metabolites was measured at baseline in 1066 individuals with type 2 diabetes (Edinburgh Type 2 Diabetes Study) who were then followed up for CVD over the subsequent 10 years. We applied 100 repeats of Cox least absolute shrinkage and selection operator to select metabolites with frequency >90% as components for a metabolites-based risk score (MRS). The predictive performance of the MRS was assessed in relation to a reference model that was based on QRISK3 plus prevalent CVD and statin use at baseline. Of 1021 available individuals, 255 (25.0%) developed CVD (median follow-up: 10.6 years). Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis, and lipoproteins were selected to construct the MRS that showed positive association with 10-year cardiovascular risk following adjustment for traditional risk factors [hazard ratio (HR) 2.67; 95% confidence interval (CI) 1.96, 3.64]. The c-statistic was 0.709 (95%CI 0.679, 0.739) for the reference model alone, increasing slightly to 0.728 (95%CI 0.700, 0.757) following addition of the MRS. Compared with the reference model, the net reclassification index and integrated discrimination index for the reference model plus the MRS were 0.362 (95%CI 0.179, 0.506) and 0.041 (95%CI 0.020, 0.071), respectively. CONCLUSION:Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with type 2 diabetes. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.

Background Ketone bodies (KBs) are an alternative energy supply for brain functions when glucose is limited. The most abundant ketone metabolite, 3-β-hydroxybutyrate (BOHBUT), has been suggested to prevent or delay cognitive impairment, but the evidence remains unclear. We triangulated observational and Mendelian randomization (MR) studies to investigate the association and causation between KBs and cognitive function. Methods In observational analyses of 5506 participants aged ≥ 45 years from the Whitehall II study, we used multiple linear regression to investigate the associations between categorized KBs and cognitive function scores. Two-sample MR was carried out using summary statistics from an in-house KBs meta-analysis between the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium and Kettunen et al. ( N = 45,031), and publicly available summary statistics of cognitive performance and Alzheimer’s disease (AD) from the Social Science Genetic Association Consortium ( N = 257,841), and the International Genomics of Alzheimer’s Project ( N = 54,162), respectively. Both strong ( P < 5 × 10 −8 ) and suggestive ( P < 1 × 10 −5 ) sets of instrumental variables for BOHBUT were applied. Finally, we performed cis -MR on OXCT1 , a well-known gene for KB catabolism. Results BOHBUT was positively associated with general cognitive function ( β = 0.26, P = 9.74 × 10 −3 ). In MR analyses, we observed a protective effect of BOHBUT on cognitive performance (inverse variance weighted: β IVW = 7.89 × 10 −2 , P IVW = 1.03 × 10 −2 ; weighted median: β W-Median = 8.65 × 10 −2 , P W-Median = 9.60 × 10 −3 ) and a protective effect on AD ( β IVW = − 0.31, odds ratio: OR = 0.74, P IVW = 3.06 × 10 −2 ). Cis -MR showed little evidence of therapeutic modulation of OXCT1 on cognitive impairment. Conclusions Triangulation of evidence suggests that BOHBUT has a beneficial effect on cognitive performance. Our findings raise the hypothesis that increased BOHBUT may improve general cognitive functions, delaying cognitive impairment and reducing the risk of AD.

To determine the relationship of dementia with preceding body mass index (BMI), changes in body weight and waist circumference in older people with type 2 diabetes.

Background Higher concentrations of cholesterol-containing low-density lipoprotein (LDL-C) increase the risk of cardiovascular disease (CVD). The association of LDL-C with non-CVD traits remains unclear, as are the possible independent contributions of other cholesterol-containing lipoproteins and apolipoproteins. Methods Nuclear magnetic resonance spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), as well as low-density lipoprotein fractions, the apolipoproteins Apo-A1 and Apo-B, as well as total triglycerides (TG), remnant-cholesterol (Rem-Chol) and total cholesterol (TC). The causal effects of these exposures were assessed against 33 outcomes using univariable and multivariable Mendelian randomization (MR). Results The majority of cholesterol containing lipoproteins and apolipoproteins affect coronary heart disease (CHD), carotid intima-media thickness, carotid plaque, C-reactive protein (CRP) and blood pressure. Multivariable MR indicated that many of these effects act independently of HDL-C, LDL-C and TG, the most frequently measured lipid fractions. Higher concentrations of TG, VLDL-C, Rem-Chol and Apo-B increased heart failure (HF) risk; often independently of LDL-C, HDL-C or TG. Finally, a subset of these exposures associated with non-CVD traits such as Alzheimer’s disease (AD: HDL-C, LDL-C, IDL-C, Apo-B), type 2 diabetes (T2DM: VLDL-C, IDL-C, LDL-C), and inflammatory bowel disease (IBD: LDL-C, IDL-C). Conclusions The cholesterol content of a wide range of lipoprotein and apolipoproteins associate with measures of atherosclerosis, blood pressure, CRP, and CHD, with a subset affecting HF, T2DM, AD and IBD risk. Many of the observed effects appear to act independently of LDL-C, HDL-C, and TG, supporting the targeting of lipid fractions beyond LDL-C for disease prevention.

Introduction: Cardiovascular disease (CVD) is the leading cause of death for people with type 2 diabetes (T2D). The predictive performance of existing CVD risk scores in T2D populations is suboptimal. Metabolomics is a promising method of identifying novel biomarkers which might improve risk prediction. We aimed to identify a group of metabolites associated with incident CVD in people with T2D and assess its predictive performance over-and-above a current CVD risk score (QRISK3). Methods: In 1,066 individuals with T2D (Edinburgh Type 2 Diabetes Study), a panel of 228 serum metabolites was measured at baseline and incident CVD events were identified over the subsequent 10 years. We applied 100 repeats of Cox LASSO (least absolute shrinkage and selection operator) to select metabolites with frequency >90% as candidate components for a metabolites-based risk score (MRS). The MRS was calculated using the linear predictor in an unpenalized Cox regression model where only candidate metabolites were included. The predictive performance of the MRS was assessed in relation to a reference score which refitted components of QRISK3 plus prevalent CVD and statin use at baseline. Predictive metrics were internally validated using 500-repeat bootstrapping. Results: In 1,021 available individuals (mean age 67.9 years, 51.7% male), 255 people developed CVD (25.0%) during a median of 10.6 years of follow-up. Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis and lipoproteins were selected to construct the MRS. C-statistics were 0.673 (95%CI 0.642, 0.704) for the MRS alone and 0.718 (95%CI 0.689, 0.747) for the reference score, increasing slightly to 0.736 (95%CI 0.707, 0.764) for the combination of the two. The improved prediction by combining the MRS with the reference score was internally validated in bootstrapping samples, where the C-statistics for the reference score and the combination were 0.679 (95%CI 0.650, 0.708) vs. 0.699 (95%CI 0.671, 0.728) separately. Conclusions: Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with T2D. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.

ObjectivesWe investigated whether functional health literacy and cognitive ability were associated with self-reported diabetes.DesignProspective cohort study.SettingData were from waves 2 (2004–2005) to 7 (2014–2015) of the English Longitudinal Study of Ageing (ELSA), a cohort study designed to be representative of adults aged 50 years and older living in England.Participants8669 ELSA participants (mean age=66.7, SD=9.7) who completed a brief functional health literacy test assessing health-related reading comprehension, and 4 cognitive tests assessing declarative memory, processing speed and executive function at wave 2.Primary outcome measureSelf-reported doctor diagnosis of diabetes.ResultsLogistic regression was used to examine cross-sectional (wave 2) associations of functional health literacy and cognitive ability with diabetes status. Adequate (compared with limited) functional health literacy (OR 0.71, 95% CI 0.61 to 0.84) and higher cognitive ability (OR per 1 SD=0.73, 95% CI 0.67 to 0.80) were associated with lower odds of self-reporting diabetes at wave 2. Cox regression was used to test the associations of functional health literacy and cognitive ability measured at wave 2 with self-reporting diabetes over a median of 9.5 years follow-up (n=6961). Adequate functional health literacy (HR 0.64; 95% CI 0.53 to 0.77) and higher cognitive ability (HR 0.77, 95% CI 0.69 to 0.85) at wave 2 were associated with lower risk of self-reporting diabetes during follow-up. When both functional health literacy and cognitive ability were added to the same model, these associations were slightly attenuated. Additionally adjusting for health behaviours and body mass index fully attenuated cross-sectional associations between functional health literacy and cognitive ability with diabetes status, and partly attenuated associations between functional health literacy and cognitive ability with self-reporting diabetes during follow-up.ConclusionsAdequate functional health literacy and better cognitive ability were independently associated with lower likelihood of reporting diabetes.

Background and Aims : The causal relevance of the cholesterol and triglyceride (TG) content of lipoproteins other than low-density lipoproteins (LDL-C) in coronary heart disease (CHD) is uncertain.Results: Univariable MR indicated a causal association with CHD of the TG content of six lipoprotein subfractions and the cholesterol content of 10 lipoprotein subfractions. In MVMR analysis, the TG content of four subfractions displayed associations with CHD independently of the cholesterol content in the same subfraction, while the cholesterol content of 10 subfractions displayed a causal association with CHD independent of the TG content of the corresponding subfractions. The cholesterol but not the triglyceride content in subfractions referred to as triglyceride-rich lipoproteins (TRL) displayed the largest association with CHD (MVMR odds ratio [OR] 2.73 to 14.31 per 1 SD increase in the subfraction), though the large effect estimates may reflect model instability.Conclusions: The cholesterol and TG content of certain lipoprotein subfractions other LDL may contribute to risk of CHD and may be relevant risk factors to target in drug development. Background and Aims : The causal relevance of the cholesterol and triglyceride (TG) content of lipoproteins other than low-density lipoproteins (LDL-C) in coronary heart disease (CHD) is uncertain. Results: Univariable MR indicated a causal association with CHD of the TG content of six lipoprotein subfractions and the cholesterol content of 10 lipoprotein subfractions. In MVMR analysis, the TG content of four subfractions displayed associations with CHD independently of the cholesterol content in the same subfraction, while the cholesterol content of 10 subfractions displayed a causal association with CHD independent of the TG content of the corresponding subfractions. The cholesterol but not the triglyceride content in subfractions referred to as triglyceride-rich lipoproteins (TRL) displayed the largest association with CHD (MVMR odds ratio [OR] 2.73 to 14.31 per 1 SD increase in the subfraction), though the large effect estimates may reflect model instability. Conclusions: The cholesterol and TG content of certain lipoprotein subfractions other LDL may contribute to risk of CHD and may be relevant risk factors to target in drug development.

Background Atherosclerotic cardiovascular diseases (CVD) is the leading cause of death in diabetes, but the full range of biomarkers reflecting atherosclerotic burden and CVD risk in people with diabetes is unknown. Metabolomics may help identify novel biomarkers potentially involved in development of atherosclerosis. We investigated the serum metabolomic profile of subclinical atherosclerosis, measured using ankle brachial index (ABI), in people with type 2 diabetes, compared with the profile for symptomatic CVD in the same population. Methods The Edinburgh Type 2 Diabetes Study is a cohort of 1,066 individuals with type 2 diabetes. ABI was measured at baseline, years 4 and 10, with cardiovascular events assessed at baseline and during 10 years of follow-up. A panel of 228 metabolites was measured at baseline using nuclear magnetic resonance spectrometry, and their association with both ABI and prevalent CVD was explored using univariate regression models and least absolute shrinkage and selection operator (LASSO). Metabolites associated with baseline ABI were further explored for association with follow-up ABI and incident CVD. Results Mean (standard deviation, SD) ABI at baseline was 0.97 (0.18, N = 1025), and prevalence of CVD was 35.0%. During 10-year follow-up, mean (SD) change in ABI was + 0.006 (0.178, n = 436), and 257 CVD events occurred. Lactate, glycerol, creatinine and glycoprotein acetyls levels were associated with baseline ABI in both univariate regression [ β s (95% confidence interval, CI) ranged from − 0.025 (− 0.036, − 0.015) to − 0.023 (− 0.034, − 0.013), all p < 0.0002] and LASSO analysis. The associations remained nominally significant after adjustment for major vascular risk factors. In prospective analyses, lactate was nominally associated with ABI measured at years 4 and 10 after adjustment for baseline ABI. The four ABI-associated metabolites were all positively associated with prevalent CVD [odds ratios (ORs) ranged from 1.29 (1.13, 1.47) to 1.49 (1.29, 1.74), all p < 0.0002], and they were also positively associated with incident CVD [ORs (95% CI) ranged from 1.19 (1.02, 1.39) to 1.35 (1.17, 1.56), all p < 0.05]. Conclusions Serum metabolites relating to glycolysis, fluid balance and inflammation were independently associated with both a marker of subclinical atherosclerosis and with symptomatic CVD in people with type 2 diabetes. Additional investigation is warranted to determine their roles as possible etiological and/or predictive biomarkers for atherosclerotic CVD.

Abstract Aims To provide a comprehensive evaluation of the biomedical effects of circulating concentrations of cholesterol-containing lipoproteins and apolipoproteins. Methods and Results Nuclear magnetic resonance (NMR) spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), and low-density (LDL-C) lipoprotein fractions; apolipoproteins Apo-A1 and Apo-B; as well as total triglycerides (TG), remnant-cholesterol (Rem-chol) and total cholesterol (TC). The causal effects of these exposures were assessed against 33 cardiovascular as well as non-cardiovascular outcomes using two-sample univariable and multivariable Mendelian randomization (MR). We observed that most cholesterol containing lipoproteins and apolipoproteins affected coronary heart disease (CHD), cIMT, carotid plaque, CRP and blood pressure. Through multivariable MR we showed that many of these exposures acted independently of the more commonly measured blood lipids: HDL-C, LDL-C and TG. We furthermore found that HF risk was increased by higher concentrations of TG, VLDL-C, Rem-Chol and Apo-B, often independently of LDL-C, HDL-C or TG. Finally, a smaller subset of these exposures could be robustly mapped to non-CVD traits such as Alzheimer’s disease (AD: HDL-C, LDL-C, IDL-C, Apo-B), type 2 diabetes (T2DM: VLDL-C, IDL-C, LDL-C), and inflammatory bowel disease (IBD: LDL-C, IDL-C). Conclusion The cholesterol content of a wide range of lipoprotein and apolipoproteins affected measures of atherosclerosis and CHD, implicating subfractions beyond LDL-C. Novel findings include cholesterol-containing lipoproteins and apolipoproteins affecting HF, blood pressure, CRP, AD and IBD. Many of the observed effects acted independently of LDL-C, HDL-C, and TG, supporting additional non-LDL-C avenues to disease prevention.

Aims To provide a comprehensive evaluation of the biomedical effects of circulating concentrations of cholesterol-containing lipoproteins and apolipoproteins.Methods and Results Nuclear magnetic resonance (NMR) spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), and low-density (LDL-C) lipoprotein fractions; apolipoproteins Apo-A1 and Apo-B; as well as total triglycerides (TG), remnant-cholesterol (Rem-chol) and total cholesterol (TC). The causal effects of these exposures were assessed against 33 cardiovascular as well as non-cardiovascular outcomes using two-sample univariable and multivariable Mendelian randomization. We observed that most cholesterol containing lipoproteins and apolipoproteins affected coronary heart disease (CHD), cIMT, carotid plaque, CRP and blood pressure. Through MVMR we showed that many of these exposures acted independently of the more commonly measured blood lipids: HDL-C, LDL-C and TG. We furthermore found that HF risk was increased by higher concentrations of TG, VLDL-C, Rem-Chol and Apo-B, often independently of LDL-C, HDL-C or TG. Finally, a smaller subset of these exposures could be robustly mapped to non-CVD traits such as Alzheimer’s disease (HDL-C, LDL-C, IDL-C, Apo-B), type 2 diabetes (VLDL-C, IDL-C, LDL-C), and inflammatory bowel disease (LDL-C, IDL-C).Conclusion The cholesterol content of a wide range of lipoprotein and apolipoproteins affected measures of atherosclerosis and CHD, implicating subfractions beyond LDL-C. Many of the observed effects acted independently of LDL-C, HDL-C, and TG, supporting the potential for additional, non-LDL-C, avenues to disease prevention.### Competing Interest StatementAFS has received Servier funding for unrelated work. DAL has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to that presented here. TRG receives funding from Biogen for unrelated research. DAL Has received support from Roche Diagnostics and Medtronic Ltd for research unrelated to this paper. The views expressed in this article are the personal views of MGM and do not represent the views of her current employer, the European Medicines Agency.### Funding StatementAFS is supported by British Heart Foundation (BHF) grant PG/18/5033837 and the UCL BHF Research Accelerator AA/18/6/34223. CF and AFS received additional support from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. MGM is supported by a BHF Fellowship FS/17/70/33482. ADH and DAL (NF-0616-10102) are an NIHR Senior Investigators. The UCLEB Consortium is supported by a British Heart Foundation Programme Grant (RG/10/12/28456). DAL's contribution to this research is supported by the Bristol BHD Accelerator Award (AA/18/7/34219), her BHF Chair (CH/F/20/90003) and the UK Medical Research Council (MC\_UU\_00011/1-6). MK is supported by the UK Medical Research Council (MRC MR/R024227/1), National Institute on Aging (NIA), US (R01AG056477), and the Wellcome Trust (221854/Z/20/Z). PC is supported by the Thailand Research Fund (MRG6280088). TRG receives funding from the UK Medical Research Council as part of the MRC Integrative Epidemiology Unit (MC\_UU\_00011/4). ADH receives support from the UK Medical Research (MC\_UU\_12019/1). NF received funding from the National Health Institutes (MD012765, DK117445). CG has reveived funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754490 - MINDED project.### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesSummary genetic effect estimates for outcomes were extracted from publicly accessible GWAS on glucose and HbA1c, and C-reactive protein (all from the UKB (nealelab.is/uk-biobank; removing low confidence variants), as well as blood pressure (systolic and diastolic), available from Evangelou et al.23. The CKDGen consortium provided GWAS associations on blood urea nitrogen, estimated glomerular filtration rate, and chronic kidney disease24. Genetic associations with primary biliary cirrhosis were available from Jostins et al25. A meta-analysis of CHARGE26 and UCLEB20 provided genetic associations with carotid artery intima media thickness and plaque. CHD data were available for 42,335 cases from CardiogramplusC4D27; 40,585 stroke cases (including four subtypes) from MEGASTROKE28; 47,309 heart failure cases from HERMES29, 60620 atrial fibrillation cases from Nielson et al30, 74,124 type 2 diabetes31 cases from DIAGRAM, 32,637 cases of inflammatory bowel disease32, 5,956 cases of Crohn's disease33 and 6,687 cases of ulcerative colitis34 from IIBDGC, 29880 rheumatoid arthritis cases from Okada et al35, 14,498 cases of multiple sclerosis36 from the IMSG consortium, 15156 amyotrophic lateral sclerosis cases from Rheenen et al37, 71,880 cases of Alzheimer's disease from Jansen et al38, and 56,306 cases of Parkinson's disease from Nalls et al.39. Finally, we sourced data on pancreatic cancer, colon cancer, rectal cancer, lung cancer and melanoma from Rashkin et al40. Please contact AFS for access to analysis scripts, processed analyses tables are presented in the supplemental, and crude pickled results file have been deposited at XXX.

BACKGROUND: Guidelines recommend measuring blood pressure (BP) in both arms, adopting the higher arm readings for diagnosis and management. Data to support this recommendation are lacking. We evaluated associations of higher and lower arm systolic BPs with diagnostic and treatment thresholds, and prognosis in hypertension, using data from the Inter-arm Blood Pressure Difference—Individual Participant Data Collaboration. METHODS: One-stage multivariable Cox regression models, stratified by study, were used to examine associations of higher or lower reading arm BPs with cardiovascular mortality, all-cause mortality, and cardiovascular events, in individual participant data meta-analyses pooled from 23 cohorts. Cardiovascular events were modelled for Framingham and atherosclerotic cardiovascular disease risk scores. Model fit was compared throughout using Akaike information criteria. Proportions reclassified across guideline recommended intervention thresholds were also compared. RESULTS: We analyzed 53 172 participants: mean age 60 years; 48% female. Higher arm BP, compared with lower arm, reclassified 12% of participants at either 130 or 140 mm Hg systolic BP thresholds (both P<0.001). Higher arm BP models fitted better for all-cause mortality, cardiovascular mortality, and cardiovascular events (all P<0.001). Higher arm BP models better predicted cardiovascular events with Framingham and atherosclerotic cardiovascular disease risk scores (both P<0.001) and reclassified 4.6% and 3.5% of participants respectively to higher risk categories compared with lower arm BPs). CONCLUSIONS: Using BP from higher instead of lower reading arms reclassified 12% of people over thresholds used to diagnose hypertension. All prediction models performed better when using the higher arm BP. Both arms should be measured for accurate diagnosis and management of hypertension. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: CRD42015031227.