{"paper_id":"63a0845f-b064-4385-ab71-7e696c709d09","body_text":"Association between carotid arterial strain and heart rate \nvariability in older age  \n \nMatthew A Stanley a,b*, Massimiliano Fornasiero a,c*, Matt Webber a,b,d, James C \nMoona,d, Peter Friberg f,g, Alun D Hughes a,e, Cristian Topriceanu a,b,e, Gabriella \nCaptura,b,e \n \n*Joint first authors  \n \na) Institute of Cardiovascular Science, University College London, Gower Street, \nLondon WC1E 6BT, UK \nb) The Royal Free Hospital, Centre for Inherited Heart Muscle Conditions, \nCardiology Department, Pond Street, Hampstead, London NW3 2QG, UK  \nc) UCL Medical School, Gower Street, London, WC1E 6BT \nd) Cardiac MRI Unit, Barts Heart Centre, West Smithfield, London, EC1A 7BE \ne) Unit for Lifelong Health and Ageing at UCL, University College London, \nFitzrovia, London WC1E 7HB \nf) Department of Physiology, Institute of Medicine, Sahlgrenska Academy, \nUniversity of Gothenburg, Gothenburg, Sweden \ng) Swedish Institute for Global Health Transformation (SIGHT), Royal Swedish \nAcademy of Sciences, Stockholm, Sweden. \n \n \nShort-running head: Carotid arterial strain and heart rate variability  \n \n \nSources of Support  \n \nG.C. has received support in the form of a programme grant from the British Heart \nFoundation (reference SP/20/2/34841) and by the NIHR UCL Hospitals Biomedical \nResearch Centre. The NSHD cohort is funded by the UK MRC (program codes \nMC_UU_12019/1; MC_UU_12019/4; MC_UU_12019/5). J.C.M. is directly and \nindirectly supported by the UCL Hospitals NIHR BRC and Biomedical Research Unit \nat Barts Hospital respectively. A.H. receives support from the British Heart \nFoundation, the Economic and Social Research Council (ESRC), the Horizon 2020 \nFramework Programme of the European Union, the National Institute on Aging, the \nNational Institute for Health Research University College London Hospitals \nBiomedical Research Centre and the UK MRC.  \n \nCorresponding author  \n \nGabriella Captur  \nConsultant Cardiologist in Inherited Heart Muscle Conditions, Senior Clinical \nLecturer  \nInstitute of Cardiovascular Science,  \nUniversity College London,  \nGower Street,  \nLondon WC1E 6BT, UK \nE-mail: gabriella.captur@ucl.ac.uk,  Phone No: +44 2074600595 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\nWord Count: 5068   \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nAbstract  \n \nBackground: Loss of the normal heart rate variability (HRV) predicts death. We \nhypothesised that impaired arterial compliance and increased vascular stiffness, \ncould inhibit the baroreceptor reflex, resulting in cardiac autonomic dysfunction and \nloss of the normal HRV.  \n \nAims: To investigate the association between carotid arterial strain (CAS) and HRV \nin an older age population-based cohort. \n  \nMethod: Participants (60-64 years) were from the 1946 Medical Research Council, \nNational Survey of Health and Development British birth cohort. Carotid intima media \nthickness (cIMT) and CAS (exposures) were measured by ultrasound and time- and \nfrequency-domain HRV indices (outcomes) by a resting 5-minute 12-lead tachogram. \nGeneralized linear models were used, adjusted for relevant clinic-demographic \nconfounders and subjected to sensitivity analysis in which we re-analyzed \nassociations after additional adjustment for cIMT and after removing participants with \nknown cardiovascular disease. \n  \nResults: 896 participants were included. On univariate analysis CAS was associated \nwith HRV markers: standard deviation of normal-to-normal beats (SDNN), root mean \nsquare of successive differences (RMSDD), HRV triangular index, high- and low-\nfrequency (H/LF) normalised high-frequency power (all p<0.05). Associations \npersisted in fully confound er-adjusted models: SDNN β =0.52 [confidence interval: \n0.2,0.8] p<0.001; RMSDD β =0.59 [0.3,0.9], p<0.001; HRV triangular index β =-0.34 [-\n0.5,-0.1], p<0.001; HF power β =8.33 [2.2,14.4], p=0.007; LF power β=8.47 [1.0,15.9], \np=0.026; normalised HF power β =0.55 [0.2,0.9], p=0.006. Key associations persisted \nin the sensitivity analysis. \n  \nConclusion: Regardless of carotid atherosclerotic vascular disease (indicated by \ncIMT), hypertension or stroke, increased carotid arterial function in older age \nassociates with a loss of HRV, potentially through an impaired baroreceptor \nresponse. \n \n \n  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nKey words  \n \n \nArterial Stiffness \nCarotid Intima-Media Thickness  \nHeart Rate  \n \n    \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nIntroduction \nIt has been suggested that reduced carotid arterial strain (CAS) caused by \natherosclerosis, aging and metabolic factors such as hyperglycaemia 1, reduces the \nsensitivity of carotid baroreceptors to blood pressure changes 2 resulting in cardiac \nautonomic dysfunction. Such autonomic dysfunction is known to manifest amongst \nother things, as a dampening of heart rate variability (HRV). CAS ultimately relates to \nthe quantity and integrity of elastin, and organisation of collagen in the arterial wall. \nIndices of HRV are clinically obtained through time- and frequency-domain analysis \nof electrocardiogram (ECG) traces. Low HRV has been implicated in increased \nmortality, e.g. following myocardial infarction (MI)\n3 and in heart failure4.  \nAn association between carotid stiffening and HRV has been found in young patients \nwith type 1 diabetes\n5, and in young persons free from cardiovascular disease 6, but \nwhether this association persists into older age, at the population level is not known. \nWe sought to investigate whether CAS and carotid intima-media thickness (cIMT) \nwere associated with HRV in an older age British-based cohort.  \n \n \nMethods  \n \nParticipants  \nParticipants were from the Medical Research Council (MRC) National Survey of \nHealth and Development (NSHD)–a birth cohort study comprised of 5,362 individuals \nborn in 1 week in 1946 in Britain. The cohort has been extensively followed up with \nperiodic assessments which have been described elsewhere\n7. Briefly, the cohort has \nbeen evaluated multi-dimensionally: anthropometrically, socio-economically, and in \nterms of life-style choices (e.g., smoking) and health function (e.g., mental health, \ncardiovascular and respiratory function)\n7. The current cross-sectional study uses \ndata collected between 2006-2010 when participants were aged 60-64 years of age. \nWritten, informed consent was obtained from all participants and ethical approval \nwas granted from the Greater Manchester Local Research Ethics Committee and the \nScotland Research Ethics Committee\n7. All procedures were in accordance with the \nethical standards of our institutional and/or national research ethics committees and \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nconformed to the 1964 Helsinki declaration and its later amendments or comparable \nethical standards. \nThe participant selection process is shown in Figure 1. The minimum set of inclusion \ncriteria comprised the availability of bilateral CD ultrasonic data and standard \ndeviation of normal-to-normal beats (SDNN) HRV data. Participants were sent a \npostal invite and pre-assessment questionnaire. The questionnaire collected data on \nsocio-demographic factors, lifestyle and medical history. \n \nOutcome: HRV at 60-64 years  \nHRV was assessed through a 5-minute, 12-lead ECG of the supine, rested \nparticipant. The ECG programme was specifically developed for HRV analysis by a \nmember of the data-gathering team. Recordings were manually cleaned by a \nphysician to remove artefacts, ensure that normal beats were all registered and that \nectopics were discarded. The analysis was completed automatically by \nCardioNavigator Plus (Spacelabs Healthcare Ltd, Snoqualmie, Washington) to \ngenerate values for the following HRV parameters:  SDNN, root mean square of \nsuccessive differences (RMSDD) and high-frequency (HF) power as measures of \nparasympathetic activity; low-frequency (LF) power as a measure of sympathetic \nactivity\n8. Additional indices also included normalised LF, normalised HF power, \nLF/HF ratio, HRV triangular index, total power spectral density (total PSD) and power \nspectral density squared.  \n \nExposures \nCAS and cIMT were measured at the clinic visit with a GE Vivid I ultrasound scanner \n(GE Healthcare; Chalfont St Giles, UK) with a high-resolution probe (12Hz). Clear \nimages of the artery, 1cm proximal to the bifurcation, were obtained. Ten second \ncineloops were recorded in digital imaging and communications in medicine format \nand downloaded for offline analysis by the Vascular Physiology Unit, Institute of \nCardiovascular Science, University College London, using dedicated software \n(Carotid Analyser; Iowa City, Iowa). Images were calibrated and software used to \nautomatically generate diameter measurements. Area strain (given as a percentage) \nwas calculated as the difference between maximum and minimum cross-sectional \narea, as a proportion of the minimum cross-sectional area. Average strain was \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\ncalculated as the mean of left and right strain. cIMT was calculated as previously \ndescribed9. \n \nCovariates   \nCovariates were selected a priori based on their previously published association \nwith HRV and added into our models successively, after centering on age, to help \nwith the interpretation of coefficients. Model 1 adjusted for sex; Model 2 included \nadditional adjustments for SEP; Model 3 added clinical covariates known to be \nassociated with HRV; and Model 4 added cardiac covariates known to be associated \nwith HRV. The same models were used for all the HRV outcomes. \nThe sex of participants was recorded as male or female (1/2). Height and weight \nmeasurements were taken in light, indoor clothing without shoes. Height was \nmeasured to the nearest millimetre using a portable stadiometer with the head in the \nFrankfort plane. Weight measurements to the nearest 0.1kg, were taken to calculate \nbody mass index (BMI). Waist circumference measurements were taken at the \nmidpoint between the costal margin and the iliac crest and hip circumference was \nmeasured at the level of the greater trochanter. The waist-to-hip ratio (WHR) was \nsubsequently derived. Participants’ socio-economic position (SEP) was evaluated \nusing occupational data from 1989, when they were in active working age, according \nto the UK Office of Population Censuses and Surveys Registrar General’s social \nclass, dichotomized as manual or non-manual (0/1). Brachial systolic and diastolic \nblood pressure measurements were taken twice with the participant in a seated \nposition using an Omron HEM-705 sphygmomanometer (OMRON UK Healthcare \nUK Ltd.; Milton Keynes, UK). The second reading (or the first if the second was \nmissing) was used in our analysis. Two-dimensional transthoracic echocardiography \nwas performed to measure left ventricular ejection fraction and mass as previously \ndescribed\n10.  \nInformation about medication usage relevant to HRV, was collected through survey \ninstruments and self-reporting along with other relevant clinical information to \ncapture history of diabetes, heart disease (i.e. ischemic heart disease, myocardial \ninfarction, stroke, heart failure, heart rhythm abnormality, congenital heart disease, \nrheumatic heart disease, and other cardiovascular diseases), hypertension, physical \nactivity (as self-reported activity in average minutes/day spent at a metabolic \nequivalent task of 1.5-2.99 in the last year) and smoking as previously described\n11-13.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nFor biochemical analysis, a 50ml blood sample was collected in clinic using the \nSarstedt system (Sarstedt; Nümbrecht, Germany). Total cholesterol, high-density \nlipoprotein (HDL) cholesterol and triglyceride were measured using a Siemens \nDimension Xpand analyser (Siemens plc Healthcare Sector; Frimley, UK) using the \nmanufacturer’s assays. HbA1c was analysed using a TOSOH G7 analyser (Tosoh \nBioscience Ltd; Redditch, UK). Low-density lipoprotein (LDL) was calculated by the \nFriedewald equation. A participant was defined as hypercholesterolaemic if LDL > \n4.9mmol/L based on guidance from the National Institute of Health and Care \nExcellence\n14.  \n \nStatistical analysis \nStatistical analyses were conducted using R version 3.6.2 (RStudio Team 2020). \nDistribution of data was assessed using Q-Q plots, histograms and the Shapiro-Wilk \ntest. Continuous sample variables are expressed as mean ± 1 standard deviation \n(SD) or median (interquartile range) as appropriate; categorical sample variables, as \ncounts and percent. Differences between groups were tested using analysis of \nvariance (ANOVA) with post-hoc Tukey test or else Kruskal-Wallis with post-hoc \nNemenyi test for normally and non-normally distributed continuous data respectively, \nor with a Chi-square test for categorical data. \nDue to the skewed distribution of HRV parameters, generalized linear models (GLMs) \nwith a gamma distribution and log link were fitted to examine the associations of CD \nand cIMT with HRV. Unless otherwise stated, model coefficients (\nβ ) in results are \nexpressed as percent change per unit increase in exposure (%Δ  per unit), calculated \nas 100×[exp( β )−1]%. To determine whether the associations of CAS with HRV \ndiffered by sex, an interaction term for sex were tested at the 10% significance level \nand no interaction was found to justify stratification by sex. Where more than one \nmeasure of CAS was significant at univariate analysis, average CAS was used in the \nmultivariable model. Model assumptions were verified with regression diagnostics. \nMulti-collinearity between final model variables was excluded by demonstrating \nvariance inflation factors <3. Data missingness was minimal in the study sample \n(Supplementary Table S1 ) so multiple imputation was not required. Strength of \nevidence for an association was assessed on the basis of the size of the regression \ncoefficients, their confidence interval (CI) and the p value. All tests were 2 sided.  \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nWe ran sensitivity analyses in which we re-analyzed the association between CAS \nand HRV biomarkers after removing participants with known cardiovascular disease \nand after additional adjustment for cIMT (Supplementary Tables S2-S3) \n \n \nResults  \n \nParticipant characteristics  \nOf the 5362 originally enrolled into NSHD, 747 were deceased, 570 had emigrated, \n853 had withdrawn and 530 were not contactable, leaving 2662 that were \nsuccessfully interviewed between 2006-2010. Of these 896 had contemporaneous 5-\nminute ECG for SDNN HRV (outcome) and carotid ultrasonography for CAS \n(exposure, Figure 1). Characteristics of study participants are presented in Table 1. \nThe population mean for S DNN was 30.0 (IQR 23.1-39.6) with 46.5% being male. \nParticipants with dampened HRV (lowest SDNN quartile) were more likely to be \nolder, male and smokers, suffering from hypertension, cardiovascular disease, \ndiabetes or hypercholesterolaemia. Data missingness for key covariates used in \nmultivariable models per exposure-outcome pair are presented in Supplementary \nTable S1. \n \nAssociations of CAS with SDNN, RMSDD and HRV triangular index  \nLeft (%\nΔ  per unit=61.6%, 95% confidence interval [35.0% to 101.0% per unit], \np<0.001), right (% Δ  per unit=46.3% [22.1% to 82.2%], p<0.001) and average (% Δ  \nper unit=69.8% [35.0% to 122.5%], p<0.001) cross-sectional CAS showed significant \npositive associations with SDNN on univariate analysis (Table 2), while age (%Δ  per \nunit=–79.0% [–90.0% to –55.1%], p<0.001), sex (% Δ  per unit=–84.0% [–97.3% to \n0.0%], p=0.047), BMI (%Δ  per unit=–20.6% [–33.0% to 0.0%], p=0.025),  triglyceride \nlevels (% Δ  per unit=–76.3% [–90.0% to –33.0%], p=0.008), HbA1c (% Δ  per \nunit=−18.1% [−25.9% to −9.5%], p<0.001) and previous MI or angina (% Δ  per \nunit=−98.3% [−99.9% to −9.5%], p=0.029) showed significant negative associations \nwith SDNN. In fully adjusted multivariable models, average CAS (%Δ  per unit=68.2% \n[22.1% to 122.6%], p<0.001), age (% Δ  per unit=−67.1% [−86.5% to −18.1%], \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\np=0.016) and male sex (%Δ  per unit=−93.6% [−99.3% to −45.1%], p=0.011) retained \nindependent associations with SDNN (Table 3).  \nOn univariate analysis, average, left and right CAS respectively, were all positively \nassociated with RMSDD (% Δ  per unit=69.8% [35.0% to 122.6%], p<0.001; =56.8% \n[22.1% to 101.4%], p<0.001; =49.2% [22.1% to 82.2%], p<0.001;). Triglyceride and \nstroke respectively were negatively associated with RMSDD (% Δ  per unit=−67.4% \n[−83.5% to −18.1%], p=0.019; =−65.7% [−79.8% to −9.5%], p=0.002) ( Table 2 ). In \nfully adjusted multivariable models only average CAS retained an independent \nassociation with RMSDD (%Δ  per unit=80.4% [35.0% to 146.0%], p<0.001, Table 3). \nOn univariate analysis, average, left and right CAS respectively, were all positively \nassociated with HRV triangular index (% Δ  per unit=11.6% [10.5% to 22.1%], \np<0.001; =10.5% [10.5% to 10.5%], p<0.001; =8.3% [0.0% to 10.5%], p =<0.001, \nTable 2 ). Age (% Δ  per unit=-30.9% [-39.4% to -18.1%], p<0.001), BMI (% Δ  per \nunit=-25.9% [-39.4% to -9.5%], p=0.039), triglyceride (% Δ  per unit=-25.9% [-39.4% \nto -9.5%], p=0.009), HbA1c (% Δ  per unit=-4.9% [-9.5% to 0.0%], p<0.001) and \nstroke (%Δ  per unit=-23.7% [-33.0% to 0.0%], p=0.009) were negatively associated \nwith HRV triangular index. In fully adjusted multivariable models, only age and \naverage CAS retained independent associations (%\nΔ  per unit=-28.8% [-39.4% to -\n9.5%], p<0.001; =9.42% [0.0% to 22.1%], p=0.005 Table 3) \n \nAssociations of CAS with HF power, LF power and normalised HF power \nAverage, left and right CAS showed a significant positive association with HF power \nat univariate analysis (respectively %\nΔ  per unit=10.6x104% [3.5X102% to 36.2x106%], \np=0.012; =28.0x10 3% [101% to 66.2x10 5%], p=0.031; =17.3x10 3% [82.2% to \n180.3x104%], p=0.030, Table 2 ). On multivariable analysis, average CAS (% Δ  per \nunit=41.5x104% [802% to 17.9x10 7%], p=0.007) retained a significant association \n(Table 3). \nAverage (%Δ  per unit=49.2x104% [15.4x102% to 26.8x107%], p<0.014) and left (% Δ  \nper unit=21.8x10 5% [8.0x10 3%,10.8x108%], p=0.002) CAS were significantly \nassociated with LF power on univariate analysis. HbA1c negatively associated with \nLF power (%Δ  per unit=-80% [-87.8% to -69.9%], p<0.001), as were age, triglyceride \nblood levels, previous stroke and diagnosis of diabetes mellitus (Table 2 ). On \nmultivariable analysis, average CAS (% Δ  per unit=47.7% [17.1x10 1%,80.4x107%], \np=0.026) was significantly associated with LF power (Table 3). \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nAverage and right CAS were associated with normalised HF power at univariate \nanalysis (%Δ  per unit=41.9% [1.0% to 103.4%], p=0.040; =35.0% [0.0% to 84.0%], \np=0.046). Other associations are summarised in Table 2 . Average CAS and sex \nretained significance in fully adjusted multivariable models (%Δ  per unit=73.3% [22.1% \nto 146.0%], p=0.006; =20.2x104% [12.1x103% to 32.9x105%], p<0.001; Table 3). \nThe association between average CAS with total power spectral density and PSD \nsquared, was significant at univariate analysis but attenuated after multivariable \nadjustment (Supplementary Table  S4-S5). There was no association between \nnormalized LF power and LF/HF ratio with average CAS at univariate analysis.  \n \nSensitivity analysis  \nWhen removing participants with known cardiovascular disease from the analysis, \naverage CAS retained association with SDNN, RMSDD and HRV triangular index \n(%\nΔ  per unit=53.7% [10.5% to 101.4%], p=0.003; =63.2% [22.1% to 122.6%], \np<0.001; =7.25% [0.0% to 10.5%], p=0.021, Table S2) and the same was observed \nafter adjusting for cIMT (Table S3). \n \nDiscussion \nIn a cross-sectional population-based study we found that older persons with stiffer \ncarotids exhibited impairment of normal HRV.  \nOur study data show an independent association between CAS and several HRV \nmeasures including SDNN, RMSDD, HRV triangular index, HF/LF power, and \nnormalised HF power. Results lend credence to our initial theory that reduced CAS \ncould potentially dampen the sensitivity of carotid sinus baroreceptors thus reducing \nHRV. This is also consistent with previous studies reporting similar associations in \nyounger cohorts\n15,16, healthy adults17, and in patients with hypertension18 and people \nwith type 2 diabetes mellitus19. \nCarotid vascular stiffness indices are significantly associated with endothelial \ndysfunction as measured by flow-mediated dilatation 20. Given that endothelial \nchanges precede atherosclerosis and correlate with disease severity in both early \nand late stages, carotid CAS may be a more sensitive atherosclerosis biomarker \nthan cIMT. There is little evidence to support the role of lipid-lowering interventions \non CAS, however, a ketogenic diet that increases LDL, has been shown to associate \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nwith a decrease in carotid distensibility (though not in cIMT22) in patients with difficult \nto treat epilepsy. Therefore, CAS may better reflect short-term changes in \natherosclerotic risk factors compared to cIMT. \nThe majority of HRV parameters we appraised in this study showed association with \nboth left and right CAS, with the sole exception of normalised HF power. The \nasymmetrical cardiac reflex response has long been recognized but data is \nconflicting with some studies that focused on the RR interval, reporting greater \ndependence on right carotid sinus stimulation\n23, 24, while another study found no left-\nright differences in the carotid-cardiac reflex responses 25. The fairly consistent \nasymmetry identified in our study, could be related to differences in right/left-sided \ncardiac innervation and to different projections of baroreceptor afferents to the \nsolitary tract nucleus\n23.  Because stimulation of the right carotid sinus in various \nclinical scenarios may have a larger influence on RR interval variability, this may \nconfound the association with HRV biomarkers for a given value of right CAS \ncompared to the left, resulting in a stronger association for the left than the right, as \nseen in our study. \nWe found that although LF and HF power were associated with CAS, the LF/HF ratio \nwas not. This is in agreement with another study assessing HRV and carotid \nvascular stiffness indices in hypertensive patients\n18. While LF and HF power \nincrease as CAS increases, the rate of increase is such that the ratio between the \ntwo remains unchanged. LF and HF power were previously thought to reflect \nsympathetic and parasympathetic tone respectively\n26 but this view has been fairly \nstrongly criticised. Our results would suggest that the reduced CAS affects both \nsystems equally, so sympathovagal balance is maintained. This is contentious \nhowever, and it is likely that there is not such a well-defined boundary between \nrepresentation of sympathetic and parasympathetic tone in HRV analysis. Recent \nstudies suggest that LF power may be reflecting cardiac autonomic outflow by \nbaroreflexes rather than true sympathetic tone\n27,28, in which case LF and HF power \nmay be capturing non-distinct determinants of HRV, detracting from our ability to \ninfer the sympathovagal balance. \nWe found a significant association between CAS and HRV but not between cIMT \nand HRV, despite both CAS and cIMT being putative biomarkers of carotid \natherosclerotic severity. The published literature is similarly divided with some \nprevious studies describing a significant inverse relationship between cIMT and \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nHRV29, 30 and others finding no significant association 31,32. Discrepant results could \nbe attributed to study design differences with some factors such as participants’ age, \nphysical activity levels and other clinically-relevant covariates not being adjusted for \nwhere associations were reported. The baroreflex is initiated in response to \nbaroreceptor stretch in the carotid sinus. cIMT does not closely relate to arterial \nstiffness until an advanced pathological degree of thickening is reached\n33,34. Early \ncIMT thickening may not alter stretch of the baroreceptor thus weaking the observed \nassociation with HRV explaining our study findings. \nA previous study found no association between carotid vascular stiffness indices and \ntotal power, LF or HF power in patients with type 2 diabetes\n35 but this could be \nexplained by the failure to account for cardiac autonomic neuropathy (CAN). Others \nwho adjusted for neuropathy in patients with type 2 diabetes did find a significant \nassociation with HRV\n36. \nIt is also possible that, particularly in those with hyperglycaemia, autonomic \ndysfunction can itself induce arterial stiffness. Parasympathetic dysfunction precedes \nsympathetic dysfunction, resulting in sympathetic innervation dominating\n37. Indeed, \nwe found that HbA1c–a summary measure of blood glucose levels over the \npreceding 3 months–was significantly asso ciated with SDNN, HR V triangular index \nand LF power at univariate analysis and tended towards significance along with HRV \ntriangular index at multivariable analysis, suggesting a potentially significant \nbiological association between HbA1c and HRV. It is also plausible that any link \nbetween HRV and elastic arteries reflects changes in their stress/strain relationship \n(elastance) due to elevated blood sugar and resultant alterations in baroreceptor \nactivation. \nOur study did not find an association between previous stroke and HRV in contrast \nto others\n39, 40 and this is likely due to the small stroke numbers in our cohort. It has \nbeen shown that HRV alterations correlate with infarct site, indicating lateralisation of \nautonomic control. Increased sympathetic discharge may result from injury to the \nright insular cortex, while parasympathetic increase may be a consequence of left \ninsular cortex damage\n41. Brainstem lesions involving the spinal trigeminal nucleus or \nrostral ventrolateral medulla may also impact HRV.  \nOur results demonstrated a significant association between HRV and plasma \ntriglyceride levels at univariate analysis but not with LDL or HDL, replicating findings \nfrom another study on non-diabetic individuals\n42 . Authors suggested that the \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nobserved association between triglycerides and HRV could be wholly explained by \ncarotid atherosclerotic vascular disease. However, it is also possible that the \nalteration in autonomic tone could itself also induce hypertriglyceridaemia. This \ntheory is supported by the recognized effect that β blockade with some β -blockers \nhas on serum triglyceride levels43-46, likely due to altered sympathetic tone. However, \nthe effect of autonomic tone on lipid metabolism more generally is complex47.  \nWe did not find a significant association between physical activity levels and HRV, in \ncontrast to a recent meta-analysis 48. This discrepancy could be down to the age of \nour cohort with generally low levels of physical activity being reported. It could be \nexplained by the subjective self-reported physical activity measures used in our \nstudy, compared to more objective approaches used by others. Another explanation \nrelates to differences in study design, as some of the interventional studies included \nin the meta-analysis had prescribed weeks of moderate intensity exercise and \nmeasured HRV changes before and after.  \n \nLimitations \n \nGiven the cross-sectional study design, it is not possible to imply causality from the \nobserved associations. The inclusion of British people born during the same week in \n1946, leads to issues with external validity as the data cannot be easily generalized \nto non-British populations. Arterial stiffness is highly dependent on blood pressure \nand we did not pursue recently derived formulae to calculate an arterial stiffness \nindex independent of blood pressure\n49. As already noted, our method for measuring \nphysical activity was subjective.  \n \nConclusion  \nRegardless of the presence of carotid atherosclerotic vascular disease (indicated by \ncIMT), hypertension or stroke, carotid arterial function in older age associates with a \ndampened HRV response, potentially through an impaired baroreceptor response.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nData Availability  \n \nNSHD data is available from https://www.nshd.mrc.ac.uk/data. Data spreadsheets \nand statistical codes used for this analysis are provided online in GitHub \nhttps://github.com/MaxFornasiero/HRVxCarotidDistensibility/blob/main/Main \n \nAcknowledgements  \n \nThe authors would like to thank the NSHD participants for their ongoing engagement \nwith the study and attendance at follow-up for data collection. The authors are also \ngrateful to Imran Shah and Andrew Wong at the MRC Unit for Lifelong Health and \nAgeing, UCL for facilitating access to the data. \n \nFunding  \n \nGC has received support in the form of a special project grant from the British Heart \nFoundation with reference SP/20/2/34841 and by the NIHR UCL Hospitals \nBiomedical Research Centre. The NSHD cohort is funded by the UK MRC (program \ncodes MC_UU_12019/1; MC_UU_12019/4; MC_UU_12019/5). J.C.M. is directly and \nindirectly supported by the UCL Hospitals NIHR BRC and Biomedical Research Unit \nat Barts Hospital respectively. AH receives support from the British Heart Foundation, \nthe Economic and Social Research Council (ESRC), the Horizon 2020 Framework \nProgramme of the European Union, the National Institute on Aging, the National \nInstitute for Health Research University College London Hospitals Biomedical \nResearch Centre and the UK MRC.  \n \nConflict of Interest \n \nThe authors declare that there is no conflict of interest. \n \nAuthor Contributions  \n \nAll authors contributed significantly to the design, implementation, analysis, \ninterpretation and manuscript writing. The corresponding author attests that all listed \nauthors meet the authorship criteria and that no others meeting the criteria have \nbeen omitted.  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n \n \n \n \n \n \nFigures  \n \n \n \nFigure 1. Flowchart summarising participant inclusion.  Loss to follow-up over \ntime was a concern in NSHD. Those with lower educational attainment, lower \nchildhood cognition and lifelong smokers were less likely to attend the 60-64 year \nassessment but the sample remained representative of the general population as per \nthe 2001 UK census50.   \nNSHD = National Survey of Health and Development; SDNN = standard deviation of \nnormal-to-normal beats. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n \nTables  \n \nTable 1. Clinicodemographic characteristics of the cohort according to SDNN quartiles. \nC h ar a ct e r i s ti c s   A l l p ar t i ci p an t s N =  89 6 Q u a r t i l e s o f S D N N p- va lu e  \nQ1 , n =  224  Q2,  n  =  2 2 4 Q3, n  = 2 2 4 Q4,  n  =  22 4 \nHRV Para mete rs  \nS D NN 30.01 (23.08-39.59) 19.692 (17.10-21.323) 26.47 (24.91-28.31) 34.16 (31.71-36.88) 48.44 (42.88-57.05) <0.001 \nRM S D D  (ms ) 18.5 5 (13. 39-26.19 )  1 1 . 6 1 (9.2 0 -1 4 . 48) 16.75  (13.7 6 -2 0 . 28 )  21.4 2 (17. 04- 2 6.75 )  30.60  ( 2 3.5 7 -4 1 . 49 )  <0.001 \nH R V  t r i a n g u la r  in d e x   7. 82  ( 6 . 32 - 9. 8 0)  5 . 4 9  ( 4. 8 9- 6 . 1 2 )  7 . 27  ( 6 . 61 - 7. 81 )  8. 86  ( 7 . 93 - 9. 5 3)  1 1 . 4 7  (1 0 . 1 3- 1 3. 0 0)  <0.001 \nNorma lize d HF  powe r  35.2 0 (22. 95-49.88 )  3 2 .80  (2 1 . 21-45. 8 8)  34.65  (23.0 5 -4 8 . 70 )  37.4 1 (25. 16- 5 2.83 )  35.62  ( 2 3.9 8 -5 3 . 30 )  0 . 562  \nHF p o we r (ms 2 ) 1 0 9 . 6 0  ( 56. 0 6- 2 1 8 . 90) 46 . 8 95 (2 5 . 017-78.4 10 )  89.95 0  ( 5 6 . 7 6 0 -1 43. 9 0 0 )  1 4 0 . 8 0  ( 92. 6 1-27 5 . 75) 2 8 7 . 1 0  ( 160.3 5 -4 89.35 )  <0.001 \nP ow e r  s pe c t r a l d e n s i t y  s q u a r e d 0. 23  ( 0 . 18 - 0. 2 9)  0 . 2 2  ( 0. 1 8- 0 . 2 8 )  0 . 23  ( 0 . 18 - 0. 29 )  0. 22  ( 0 . 17 - 0. 2 8)  0 . 24  ( 0 . 18 - 0 . 31 )  0. 5 4 5  \nNorma lize d LF  p o w er 64.8 3 (50. 25-77.16 )  6 7 .28  (5 4 . 16-78. 8 6)  65.56  (51.4 5 -7 7 . 11 )  62.6 2 (47. 21- 7 5.37 )  63.97  ( 4 6.7 9 -7 6 . 03 )  0 . 569  \nLF  power (ms 2 ) 195.6 5  ( 109 . 8 8 -3 55. 7 3)  90.13  ( 6 4.3 1 -1 2 1 .23) 1 6 8 . 6 5  ( 117.8 5 -2 31.62 )  247.6 0  ( 167 . 5 0 -3 47. 0 0)  4 7 7 . 6 0  ( 351.1 0 -7 23.00 )  <0.001 \nLF /HF 1 . 8 4  (1. 0 1- 3 . 37 )  2.05 ( 1 . 18 - 3.72) 1 . 8 9  ( 1 . 0 5 3 -3 .34)  1 . 6 7  (0. 8 9- 3 . 05 )  1. 7 7  ( 0. 8 8-3. 1 6)  0 . 565  \nT o ta l po we r sp e ctra l  densi ty  (m s 2 ) 697. 70 ( 4 2 7 .23-11 9 6 . 5 0 )  30 5 . 35 (2 2 7 .13-38 9 . 85 )  5 7 6 . 6 0  ( 498.4 0 -6 77.00 )  898. 00 ( 7 6 4 .50-10 8 9 . 0 0 )  17 72.00 ( 1 4 01. 0 0- 2 4 04. 0 0)  <0.001 \nCarotid  Varia b l e s  \nCro s s-s ect i on a l CAS  l e ft (% )  13.0 9 (10. 75-15.81 )  1 2 .68  (1 0 . 38-15. 1 9)  13.04  (10.6 5 -1 5 . 65 )  13.1 5 (10. 64- 1 5.73 )  13.97  ( 1 1.6 1 -1 7 . 03 ) 0 .00 1 \nCro s s-s ect i on a l CAS  ri g ht ( %) 13.8 4 (11. 16-16.56 )  1 3 .19  (1 0 . 57-16. 0 8)  13.47  (11.0 5 -1 5 . 72 )  1 4 . 0 5(11.3 0 -1 6 . 96) 14.41  ( 1 1.8 4 -1 7 . 31 ) 0 .00 1 \nAverage C AS  (%)  13.4 9 (11. 28-16.16 )  1 2 . 7 3 (10. 6 -1 5 . 73) 1 3 .15 0  ( 11. 0 7 6 -1 5 . 605 )  1 3 .72  (1 1 . 2 4 4-16 . 2 39) 14.36  ( 1 1.9 7 -1 7 . 17 )  0 .01 1 \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nC h ar a ct e r i s ti c s   A l l p ar t i ci p an t s N =  89 6 Q u a r t i l e s o f S D N N p- va lu e  \nQ1 , n =  224  Q2,  n  =  2 2 4 Q3, n  = 2 2 4 Q4,  n  =  22 4 \nAverage cIM T  ( m m) 0 . 67 ( 0 . 60 - 0. 76)  0.66 ( 0 . 59 - 0.75) 0. 6 7  ( 0. 6 0- 0 . 7 6)  0 . 6 9  (0. 6 1- 0 . 77 )  0. 6 7  ( 0. 6 1-0. 7 6)  0 . 841  \ncIMT ma xim um (mm ) 0 . 7 3  (0. 6 5- 0 . 82 )  0.72 ( 0 . 63 - 0.81) 0. 7 3  ( 0. 6 5- 0 . 8 2)  0. 7 5  ( 0. 6 -0.8 4 )  0. 7 3  ( 0. 6 5-0. 8 3)  0 . 848  \nDemogra p hics  \nA g e ( y e a r s )  6 2. 8 ( 6 2. 0 -6 3. 7 ) 6 2 . 92  ( 6 2 .5 - 6 3 .6 ) 6 2. 87  ( 62 .1 - 63 .5 ) 6 2. 8 4 ( 62 .2 - 6 3 . 5 ) 6 2. 56  ( 6 1 .7 - 63 .4 )  0.008 \nMale, n (%)  417  (4 6 . 5) 1 0 1 (11.3)  9 8  ( 10.9) 101  (1 1 . 3) 1 1 7 (13. 1 )  0 . 262  \nS EP a t 4 3 ye ar s  (m anu a l)        \n               Ma nua l, n ( %) 161  (1 8 . 0) 48 ( 5 . 36 )  3 3  ( 3. 6 8) 4 3  (4. 8 0)  3 7  ( 4.13) 0 . 271  \nAnth ropo m e trics  \nBM I  (kg/ m2)  26.4 7 (24. 09-29.60 )  2 6 .94  (2 4 . 77-30. 0 4)  26.44  (24.0 8 -2 9 . 62 )  26.4 3 (23. 72- 2 9.54 )  26.05  ( 2 3.6 4 -2 8 . 92 )  0 . 584  \nWaist-to- h i p ra tio n  0 . 90 ± 0.080  0.91  ±  0. 0 8 6 0 . 9 0  ± 0 . 075  0 . 9 1  ± 0 . 07 0. 9 0  ± 0 . 0 7 9 0 . 440  \nCardiac \nDBP (mm H g ) 77.5 0 (71. 00-84.00 )  7 8 .00  (7 1 . 00-83. 7 5)  78.00  (70.8 8 -8 2 . 50 )  77.2 5 (71. 00- 8 4.00 )  76.50  ( 7 0.5 0 -8 4 . 50 )  0 . 900  \nS B P  (m mHg)  135.5 0  ( 124 . 0 0 -1 47. 0 0)  13 7 . 00 (1 2 5 .20-14 7 . 50 )  1 3 5 . 0 0  ( 125.0 0 -1 47.50 )  135.0 0  ( 123 . 9 0 -1 46. 5 0)  1 3 4 . 8 0  ( 123.0 0 -1 46.50 )  0 . 896  \nHea rt rate  (m i n -1 )        \nLV ma ss,  g  199.5 0  ( 162 . 1 4 -2 48. 9 8)  20 3 . 75 (1 6 5 .36-24 8 . 60 )  1 9 7 . 7 0  ( 155.5 0 -2 44.60 )  198.7 0  ( 164 . 4 4 -2 67. 6 7)  1 9 7 . 7 6  ( 161.8 4 -2 41.77 )  0 . 876  \nLV ejec ti o n fra cti on bi-plane (%)  65.0 4 (60. 00-69.32 )  6 4 .28  (5 9 . 60-69. 9 6)  64.01  (59.5 7 -6 8 . 66 )  65.2 3 (60. 50- 6 9.25 )  65.05  ( 6 0.3 2 - 69 .5 4 ) 0 .9 5 2 \nBlo od Mark e rs  \nT ot a l  c h ole s t e r o l  ( m m o l/ L )  5. 69  ( 5 . 03 - 6. 4 0)  5 . 5 7  ( 4. 9 3- 6 . 3 0 )  5 . 74  ( 5 . 10 - 6. 40 )  5. 78  ( 5 . 10 - 6. 4 8)  5 . 67  ( 5 . 03 - 6 . 43 )  0. 7 2 5  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nC h ar a ct e r i s ti c s   A l l p ar t i ci p an t s N =  89 6 Q u a r t i l e s o f S D N N p- va lu e  \nQ1 , n =  224  Q2,  n  =  2 2 4 Q3, n  = 2 2 4 Q4,  n  =  22 4 \nHDL rati o  3 . 6 3  (3. 0 5- 4 . 28 )  3.63 ( 3 . 11 - 4.26) 3. 6 4  ( 3. 0 3- 4 . 3 4)  3 . 5 6  (2. 9 8- 4 . 21 )  3. 6 6  ( 3. 1 4-4. 2 7)  0 . 966  \nL D L  ( m m o l/ L )  3. 56  ( 2 . 87 - 4. 1 3)  3 . 4 6  ( 2. 8 3- 3 . 9 8 )  3 . 57  ( 2 . 85 - 4. 26 )  3. 59  ( 2 . 97 - 4. 1 2)  3 . 59  ( 2 . 89 - 4 . 20 )  0. 7 0 1  \nT r ig ly ce r id e  ( m m ol / L )  1. 05  ( 0 . 75 - 1. 4 9)  1 . 1 2  ( 0. 7 3- 1 . 5 5 )  1 . 06  ( 0 . 77 - 1. 56 )  1. 06  ( 0 . 76 - 1. 4 7)  0 . 99  ( 0 . 74 - 1 . 44 )  0. 7 1 7  \nHbA 1c  (mm ol/L)  39.0 0 (37. 00-41.00 )  4 0 .00  (3 8 . 00-42. 0 0)  39.00  (37.0 0 -4 1 . 00 )  38.5 0 (36. 00- 4 1.00 )  39.00  ( 3 6.0 0 -4 1 . 00 )  0 .00 3 \nOthe r  C lin ical Fac to rs  \nSm o k i n g:        \n                 Ex-sm ok er, n  (%)  314  (3 5 . 0) 77 ( 8 . 59 )  8 4  ( 9. 3 8) 8 5  (9. 4 9)  6 8  ( 7.59) 0 . 305  \n                 Current smoke r,  n  (% ) 6 5  (7. 2 5)  15 ( 1 . 67 )  1 4  ( 1. 5 6) 1 8  (2. 0 1)  1 8  ( 2.01)  0 . 838  \nE x er c i s e lev e ls  (m i n utes/day ) 190.9 0  ( 120 . 1 0 -2 84. 6 0)  19 8 . 40 (1 1 6 .00-29 6 . 20 )  1 8 8 . 6 0  ( 120.4 0 -2 74.40 )  201.4 0  ( 117 . 9 0 -2 87. 1 0)  1 8 0 . 8 2  ( 122.6 8 -2 81.25 )  0 . 920  \nMI or a ngi n a , n ( %) 4 4  (4. 9 1)  17 ( 1 . 90 )  1 2  ( 1. 3 4) 1 0  (1. 1 2)  5 (0. 56) 0 . 070  \nS tro k e, n  (%) 8  ( 0 . 8 9 ) 2 ( 0.2 2 ) 4 ( 0 . 45) 0  ( 0 . 00) 2 (0. 22) 0 . 440  \nDia bete s mellit u s,  n (%)  3 8  (4. 2 4)  13 ( 1 . 45 )  9 ( 1 . 00) 8  ( 0 . 89) 8 (0. 89) 0 . 600  \nHypert e nsi on ,  n  (%)  364  (40.6 0 ) 1 0 4 (11. 60) 9 1  ( 1 0 . 2 0 )  8 6  (9. 6 0)  8 3  ( 9.26) 0 . 189  \nhyperchole sterol ae mia, n  ( %) 7 4  (8. 2 6)  11 ( 1 . 23 )  2 3  ( 2. 5 7) 2 3  (2. 5 7)  1 7  ( 1.90) 0 . 120  \nHistory o f ca rdi o v asc u lar ev ent, n  (%) 5 0  (5. 5 8)  18 ( 2 . 01 )  1 5  ( 1. 6 7) 1 0  (1. 1 2)  7 (0. 78) 0 . 103  \nSignificant p values are highlighted in bold (p<0.05).  \nResults are reported as counts (%) for categorical variables, mean ± 1 standard deviation for normally distributed variables (n) or median (interquartile range) \nfor non-normal variables.  \nSDNN = standard deviation of normal-to-normal beats. Q1/2/3/4 = Quartile 1/2/3/4. cIMT = carotid intima-media thickness. SEP = socioeconomic position and \noccupation type. BMI = body mass index. DBP = diastolic blood pressure. SBP = systolic blood pressure. HDL = high-density lipoprotein. LDL = low-density \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nlipoprotein. MI = myocardial infarction. LV = left ventricular. RMSDD = root mean square of successive differences. HRV = heart rate variability. HF = high-\nfrequency. LF = low-frequency. \n \nTable 2. Univariate regression analysis for each exposure or covariate with HRV indices.  \nVari a b l e SDN N  RM SDD  HRV tria ng ul ar i nd e x  H F P o wer  LF Powe r  Nor m ali se d  HF p o w er \nβ Co effici e nt  \n(9 5% CI )  \np -va l ue   Β C o effi ci ent  \n( 95% CI )  \np -v a l ue β  C o e ff ici e nt  \n(9 5% CI )  \np -valu e β  C oeff ici e n t   \n(9 5 %  CI )  \np -valu e  β  C oeffi ci e n t  \n(9 5 % C I )  \np -\nva l ue  \nΒ  C oe f fic ie n t   \n(9 5% CI )  \np -va l ue  \nar oti d  V aria bles  \nr oss-s ecti o na l CA S l eft 0.4 8   \n( 0 . 3,0 .7 )  \n<0.0 01 0.4 5   \n(0 . 2,0 .7 ) \n<0 .00 1 0.1 0   \n(0. 1,0 .1 )  \n< 0 .001  5.64  \n(0. 7,11. 1)  \n0.03 0 9 . 99  \n(4 . 4 , 16. 2)  \n0.002  0.26 \n ( - 0 . 0 6, 0. 5 8 )  \n0.1 0 4  \nro s s - se c t i o n al  C AS  r ig h t   0 . 38  \n (0. 2,0 .6 )  \n<0.0 01 0.4 0   \n(0 . 2,0 .6 ) \n<0 .00 1 0.0 8   \n(0. 0,0 .1 )  \n< 0 .001  5.16  \n(0. 6,9 .8 )  \n0.03 1 4 . 84  \n(0 . 2, 10. 0)  \n0.110  0.3 0   \n(0 . 0 0, 0. 61 )  \n0.046  \nA ve r a g e CA S  0.5 3   \n( 0 . 3,0 .8 )  \n<0.0 01 0.5 3   \n(0 . 3 ,0 .8 ) \n<0 .00 1 0.1 1   \n(0. 1,0 .2)  \n< 0 .001  6.97  \n ( 1. 5, 1 2. 8 )  \n0.01 2 8. 5 0  \n (2. 8,1 4. 8)  \n0.014  0.3 5 \n(0 . 0 1, 0. 71 )  \n0.040  \nA v e rage  c IM T   2. 7   \n( - 4. 7, 10 . 3 )  \n0 . 47 4 3 . 44   \n( - 3. 5, 10 . 7 )  \n0 . 327  0.1 0   \n( -1.4, 1. 6 )  \n0.8 98 39. 18   \n(-11 9 . 8 , 214. 5)  \n0.6 27 17. 12   \n(-17 8 . 6 , 227. 4)  \n0.869  0.7 6   \n(-9.0 5 , 11. 04 )  \n0 . 88 0 \nI MT ma xim um  2.3 9   \n(-3.6, 8. 6)  \n0 . 43 9 2. 83   \n( - 2. 8 ,8 .7 ) \n0 . 324  0.0 9   \n( -1.1, 1. 3 )  \n0.8 84 47. 00 \n (- 8 3. 7, 190.4 )  \n0.4 83 10. 66   \n(-14 9 . 2, 185. 2 )  \n0.900  0.3 7   \n(-7.6 3,8 .8 0 )  \n0 . 929 \nD e m og ra ph ics \nA g e  - 1. 56  \n (-2.3,- 0.8 )  \n<0.0 01 -0 . 3 7  \n(-1.1, 0. 3)  \n0 . 314  -0.37  \n(-0.5,- 0.2)  \n< 0 .001  -10 . 80  \n(-29. 2,5 .3 )  \n0.22 8  -23 . 13  \n( -4 4. 7,-3. 6)  \n0.029  0.4 8                \n ( - 0 . 5 8, 1. 5 0 )  \n0.3 5 7  \nM al e  - 1 . 8 3  \n(-3.6, 0. 0)  \n0.047  0.5 7   \n(-1.1, 2. 2)  \n0 . 499  -0.18  \n( -0.6, 0. 2)  \n0.3 48 34. 56   \n(-3.2, 72 .7 )  \n0.0 71 -47 . 51  \n(-98. 6,1 .4 )  \n0.061  6.7 9 \n( 4 . 4 2,9 .1 8) \n<0.0 01  \nE P at 43 y e ar s ( ma n u al )  0.24   \n( - 2. 0 ,2.7 ) \n0 . 84 1 0 . 34   \n(-1.8, 2. 7)  \n0 . 761  -0.21  \n( -0.7, 0. 3 )  \n0.3 91 2.12  \n(-43 . 8 , 59. 0)  \n0.9 34 -34 . 92  \n(-92. 8 , 33. 3)  \n0.270  2.7 0   \n(-0.5 1,6 .1 6 )  \n0 . 11 1 \nA n th rop ome t r i c s \nMI  - 0 . 23  \n(-0.4, 0. 0)  \n0.025  -0 . 0 2  \n( - 0 . 2,0 .2) \n0 . 803  -0.04  \n( -0.1, 0. 0 )  \n0.03 9 2.01  \n( - 2. 2,6 .5 ) \n0.3 77 -4.09  \n( - 8. 7, 1. 3)  \n0.133  0.27 \n (0. 01, 0. 55)  \n0 . 05 9 \nW aist-t o -hi p r ati o - 4 . 7 0  \n( -1 6. 0,6 .7 )  \n0 . 420 - 6 .9 2   \n( -1 7. 4,3 .5 )  \n0 . 194  -1.54  \n( -3.9, 0. 8 )  \n0.1 94 -1 90. 30 \n (- 4 28.3, 47.7 )  \n0.1 17 - 1 31. 50 \n (-443.0 ,1 79 . 9 )  \n0.407  -16.5 6   \n(-31. 06 , - 1 . 91)  \n0.032  \nar d i ac  \nV  m ass  0.0 0   \n( 0 . 0,0 .0 )  \n0 . 96 4 0 . 01   \n(0 . 0 ,0 .0 ) \n0 . 335  0.0  \n(0. 0,0 .0 )  \n0.22 5 -0.96  \n( - 2. 7 ,0 .9 ) \n0.3 28 0 . 42  \n( - 2. 1 ,3 .0 )  \n0.747  - 0 . 1 8  \n (-0.3 0,-0. 06)  \n0.004  \nV  ej e ction f ra ction b i -pl ane  - 0 . 0 4  \n( - 0 . 2,0 .1 ) \n0 . 59 8 - 0 .0 1   \n(-0.1, 0. 1)  \n0 . 892 0.0  \n(0. 0,0 .0 )  \n0.8 47 -0.39  \n ( - 1 . 3 ,0 .7 ) \n0.4 54 0 . 30  \n( - 1. 0, 1. 6)  \n0.659  -0.06  \n(-0.1 2,0 .0 0 )  \n0 . 05 6 \nM e a n D B P   - 0. 03   \n(-0.1, 0. 1)  \n0 . 57 8 - 0 .0 8   \n( - 0 . 2,0 .0 ) \n0 . 074  -0.01  \n (0. 0,0 .0 )  \n0.3 92 0.09  \n( - 0 . 2,0 .4 ) \n0.5 98 0 . 19  \n( - 0 . 2,0 .6 )  \n0.321  0.0 0 \n ( - 0 . 02, 0 .0 2 ) \n0 . 727 \nM e a n S B P   - 0. 01   \n(-0.1, 0. 0)  \n0 . 66 1 - 0 .0 3   \n(-0.1, 0. 0)  \n0 . 200  0.0 0 \n (0. 0,0 .0 )  \n0.5 75 0.64  \n( - 2. 2,3 .2) \n0.6 41 -0.22  \n( - 3. 6, 2. 9)  \n0.906  0.1 1 \n ( - 0 . 0 7, 0. 29 )  \n0.21 6  \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nVari a b l e SDN N  RM SDD  HRV tria ng ul ar i nd e x  H F P o wer  LF Powe r  Nor m ali se d  HF p o w er \nβ Co effici e nt  \n(9 5% CI )  \np -va l ue   Β C o effi ci ent  \n( 95% CI )  \np -v a l ue β  C o e ff ici e nt  \n(9 5% CI )  \np -valu e β  C oeff ici e n t   \n(9 5 %  CI )  \np -valu e  β  C oeffi ci e n t  \n(9 5 % C I )  \np -\nva l ue  \nΒ  C oe f fic ie n t   \n(9 5% CI )  \np -va l ue  \nloo d  Ma r ke r s \notal  c ho l est er ol  0.1 6   \n( 0 . 0,1 .0 )  \n0 . 69 5 - 0 .26   \n(-1.0, 0. 5)  \n0. 4 9 7  0 . 0 6  \n( -0.1, 0. 2)  \n0.5 07 -6.96  \n ( - 21 . 8, 8. 9 )  \n0.4 10 6 . 71  \n ( -1 6. 7, 30. 5)  \n0.558  - 0 . 3 3  \n ( - 1 . 3 5, 0. 7 1 )  \n0 . 54 4 \nH DL r ati o 0.0 6   \n(-1.0, 1. 1)  \n0 . 90 0 - 0 .3 9   \n(-1.3, 0. 5)  \n0. 4 0 0  0 . 0 0  \n(- 0 . 2,0 .2 )  \n0.9 79 -11 . 47  \n ( - 27 . 1, 9. 3 )  \n0.248 -5.99  \n(-33 . 0 , 23. 9)  \n0.663  - 0 . 6 4  \n ( - 1 . 8 7, 0. 6 7 )  \n0 . 34 4 \nD L  0.5 6   \n(-0.4, 1. 5)  \n0.24 9  -0.0  \n ( - 0 . 9 ,0 .8 ) \n0 . 962 0.1 3 \n ( - 0 . 1 ,0 .3 )  \n0.1 84 -4 . 1   \n(-21 . 9 , 14. 8)  \n0.6 83 11. 89   \n(-16 . 5 , 40. 6)  \n0.383  - 0 . 3 5  \n ( - 1 . 5 5, 0. 8 7 )  \n0 . 58 5 \nr i gl y c e r i d e   - 1. 44   \n(-2.3,- 0 . 4)  \n0.008  -1 . 1 2 \n (-1.8,- 0.2)  \n0.01 9  -0.30  \n(-0.5,- 0.1 )  \n0.00 9 -15 . 52  \n(- 3 4 .6 ,- 5 . 6 ) \n0.00 2 -30 . 76  \n( -5 9. 0,-1. 6)  \n0.039  -0 .7 5 \n ( - 2. 1 6, 0. 9 0 )  \n0.3 3 8  \nH bA 1c   - 0. 20  \n (-0.3,- 0.1 )  \n<0.0 01 -0 . 1 0  \n( - 0 . 2,0 .0 ) \n0 . 060  -0.05  \n ( - 0 . 1 ,0 .0 )  \n< 0 .001  -1.40  \n ( - 2. 5 ,1 .7 )  \n0.1 96 -1.61  \n(-2.1,- 1.2 )  \n<0.0 01  0 . 0 9  \n ( - 0 . 1 0, 0. 3 1 )  \n0.3 5 8  \nO ther Cli n i cal  Fact or s  \nm o ki n g  - 0. 47  \n ( - 1 . 9 ,1 .0 ) \n0 . 53 1 - 0 .6 1 \n ( - 2. 0 ,0 .7 ) \n0. 3 8 6  0 . 0 7  \n(- 0 . 2,0 .4 )  \n0.6 40 -16 . 27  \n ( -4 8. 4, 12. 2)  \n0.3 29 -20 . 09  \n ( -6 7. 8, 17. 6)  \n0.357  -0.91  \n(-2.9 2,1 .0 3 )  \n0 . 37 3 \nx er ci se  le vel s   0 . 00  \n (0. 0,0 .0 )  \n0 . 73 6 0 . 00  \n (0. 0,0 .0 )  \n0 . 624  0.0 0   \n(0. 0,0 .0 )  \n0.6 86 0.08  \n( - 0. 1, 0. 2)  \n0.3 24 -0.08  \n ( - 0 . 3 ,0 .1 ) \n0.412 0.0 1   \n(0 . 0 1, 0. 02)  \n0.003  \nM I  o r  a ngin a  - 4 . 1 0  \n(-7.5,- 0 . 1)  \n0.029  1.7 6 \n ( - 2. 1 ,6 .6 ) \n0 . 422  -0.76  \n ( - 1 . 5 ,0 .1 )  \n0.0 66 42. 93   \n(- 4 1 .1 ,1 91 .6 )  \n0.4 36 -78 . 84  \n ( - 1 56 .7 ,4 3 . 0) \n0.102 3.8 2  \n ( -1 . 87, 10. 65)  \n0 . 22 9 \nt rok e  - 2. 7 1  \n( -1 0. 0,8 .0 )  \n0 . 54 3 - 1 .0 7 \n (-1.6,- 0.1 )  \n0.00 2  -0.27  \n ( - 0 . 4 ,0 .0 )  \n0.00 9 -16 . 07  \n (- 23. 4,-8. 7)  \n< 0 .001  -22 . 84  \n ( - 3 5 . 2,- 10 .5 )  \n<0.0 01 -0 .4 7 \n ( - 1 . 7 1, 1. 7 7 )  \n0.5 5 2  \nD iabet e s m e ll itu s  - 2. 9 1  \n ( - 6 . 7 ,1 .5 ) \n0 . 16 3 - 0 .7 5 \n ( - 4 . 3 ,3 .7 ) \n0 . 712 -0.56  \n( -1.4, 0. 4 )  \n0.200 11. 88 \n (- 6 6. 4, 159.3 )  \n0.8 21 - 1 06. 20 \n ( -1 7 5.6 ,6. 5)  \n0.015  1 . 5 5   \n(-4.3 6,8 .8 1 )  \n0.6 4 2  \nH yp e rt en s i o n  - 1 . 4 6  \n ( - 3 . 3 ,0 .4 ) \n0 . 11 5 - 1 .6 7 \n ( - 3 . 3 ,0 .0 ) \n0 . 051  -0.31  \n( -0.7, 0. 1 )  \n0.1 01 -22 . 43  \n(-60 . 0 , 16. 2)  \n0.243 -6.34  \n ( -5 6. 1, 45. 5)  \n0.805  -2.35  \n(-4.7 8,0 .1 1 )  \n0 . 05 9 \nH y p erc h ole s te ro lae m ia 1 . 29  \n ( - 1 . 9 ,4 .9 ) \n0 . 45 4 1 . 19  \n ( - 1 . 7 ,4 .6 ) \n0 . 457  0.3 1 \n ( - 0 . 4 ,1 .0 )  \n0.3 77 34. 68   \n(- 3 0 .3 ,1 32.0 )  \n0.3 83 -23 . 15  \n ( -9 4. 7, 75. 3)  \n0.582 2 .3 7 \n ( - 1 . 9 1, 7. 24 )  \n0 . 30 9 \nH istory of ca rdi ov a sc u l a r event  - 3 . 4 8  \n ( - 6 . 8 ,0 .3 )  \n0.0 5 4  1.4 2 \n ( - 2. 1 ,5 .7 )  \n0 . 465  -0.54  \n( -1.3, 0. 3 )  \n0.1 64 60. 49   \n( -24 . 1, 20 5 . 5)  \n0.270 -62 . 80  \n(- 1 3 8.0, 5 2. 6)  \n0.175  4.4 1 \n ( -1 . 15, 11. 05)  \n0 . 15 4 \nSignificant p values are highlighted in bold (p<0.05).  \nAbbreviations as in Table 1. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n \nTable 3. Multivariable regression analysis for CD with HRV indices.  \n \nVa r iab l e S DNN  RM SDD  H R V  Tr i a n g ul ar  I n de x  HF pow e r  LF p o wer  No rma l is ed  H F  Po w e r \nβ C oe ff i c ien t  ( 95%  CI ) p -va l ue   β Co effici ent ( 95 % C I)  p -valu e β  C oeffi ci e n t (9 5% CI )  p -va l ue  β  Co effici ent ( 95 % \nCI)  \np -va l ue  β  Co effici ent ( 95 % \nCI)  \np -valu e  β  C oeffi ci e n t (9 5% CI )  p -valu e \nA g e  - 1. 11   \n(- 2.0 ,-0 .2 )  \n0.01 6  -0.12 \n ( - 1 . 0 ,0 .7 ) \n0.7 81 \n \n-0.34  \n(-0.5,- 0.1 )  \n<0.0 01 \n \n-6 . 23  \n (-23. 8, 1 1. 3)  \n0 . 48 6 \n \n-8.17   \n(- 3 1. 8,1 5. 4)  \n0.4 97 0.39  \n ( -0 .8 , 1 .5 ) \n0.5 07 \nex  -2. 7 5  \n( - 4. 9, - 0. 6 )  \n0.01 1  0 . 44  \n(-1.6, 2. 5)  \n0.6 70 \n \n-0.34  \n ( -0 .8 , 0 .1 ) \n0 . 14 9 \n \n34 . 98  \n (- 7 . 6 , 77.5 )  \n0 . 10 7 \n \n- 6 2.8 0  \n(-117.6,- 7.9 )  \n0.02 5 7.61  \n(4. 8,10. 4)  \n< 0 .001  \nA ve r a g e CA S  0.5 2 \n (0. 2,0 .8 )  \n0.00 1  0. 5 9  \n ( 0. 3, 0 . 9 )  \n< 0 .001  \n \n0.0 9 \n (0. 0,0 .2)  \n0.005  \n \n8.3 3 \n (2. 2,1 4 . 4)  \n0.007  \n \n8 . 47 \n (1. 0,1 5 . 9 )  \n0.02 6 0.55  \n (0. 2,0 .9)  \n0.00 6 \nE P at 43 y e ar s  -0.10 \n ( - 2. 7 ,2.6 ) \n0 . 942 -1.59 \n ( - 3 . 9 ,1 .0 ) \n0.1 98 \n \n-0.26  \n( -0.8, 0. 3 )  \n0 . 37 5 \n \n-28.5 0 \n (-74. 9, 1 7. 9)  \n0 . 22 8 \n \n-1 8 . 43   \n(- 8 0. 7,4 3. 9)  \n0.5 62 -0.46  \n( - 3. 9, 3. 3)  \n0.8 00 \nMI  -0.06 \n ( - 0 . 3 ,0 .2) \n0 . 646  0 . 05  \n ( - 0 . 2,0 .3 ) \n0.7 30 \n \n-0.02  \n( -0.1, 0. 0 )  \n0 . 48 6 \n \n2.5 6 \n ( - 3 . 1 ,8 .2) \n0 . 37 5 \n \n-4.06 \n (- 1 0. 2, 2.1 )  \n0.1 94 0.11  \n ( -0 .3 , 0 .5 ) \n0 . 577  \nr i g l y c er i d e - 0 .9 4   \n( - 2. 4 ,0 .6 ) \n0.20 7  -1.10   \n( - 2. 4 ,0 .4 ) \n0.1 09 \n \n-0.19  \n( -0.5, 0. 2)  \n0.25 2 \n \n-19.6 9 \n (- 4 3. 1, 3.7 )  \n0 . 09 8 \n \n-6.61 \n (-38. 3, 25 . 0 )  \n0.6 82 -0.72  \n( - 2. 6 ,1 .4 ) \n0.4 66 \nH b A 1 c  -0.10 \n ( - 0 . 2,0 .0 ) \n0 . 14 3 - 0 . 0 3   \n(-0.1, 0. 1)  \n0.6 34 \n \n-0.03  \n ( -0 .1 , 0 .0 ) \n0 . 06 1 \n \n-0 . 1 8  \n ( - 2. 9 ,2.6 ) \n0 . 90 0 \n \n-1.06 \n ( - 3 . 3 ,1 .2) \n0.3 50 0.08  \n( - 0. 1, 0. 3)  \n0.4 61 \nM I /an gin a -2.46 \n ( - 6 . 8 ,2.7 ) \n0.3 0 2 2 . 81  \n( - 2. 0 ,9 .0 ) \n0.298 -0.37  \n( -1.4, 0. 8 )  \n0 . 48 5 \n \n1 0 7.56  \n (- 5 2 .2,267 .4 )  \n0 . 18 7 \n \n26. 62  \n ( - 7 8. 0, 13 1 . 3 )  \n0.6 18 5.92  \n(-1.1, 14 .6 )  \n0.1 34 \nt r o k e  - 0. 89   \n(-1.8, 0. 7)  \n0.1 4 2 -0.68   \n(-1.5, 1. 2)  \n0.230 -0.14  \n ( -0 .4 , 0 .2) \n0.3 3 9  -9.18 \n(-26. 6,8. 2)  \n0 . 30 0 -1 5 . 55   \n( - 3 5 . 8 ,4 .7 ) \n0.1 32 -0.62  \n( - 2. 0 ,2.1 ) \n0.5 24 \nH yp e rt en s i o n  -1.59 \n ( - 3 . 8 ,0 .6 ) \n0 . 15 7 - 1 . 3 3   \n(-3.4, 0. 8)  \n0.217 \n \n-0.34  \n( -0.8, 0. 2)  \n0 . 17 5 \n \n-13.4 3   \n(-57. 2,3 0 . 3 )  \n0 . 54 6 \n \n-3 6 . 02  \n(- 9 1. 1,1 9. 1)  \n0.200 -1.43  \n( - 4. 4, 1. 6)  \n0.3 41 \nOnly fully adjusted data for the final model (Model 4) are shown. Model 1 (centered on age) adjusted for sex; Model 2 additionally adjusted for SEP; Model \n3 additionally adjusted for clinical covariates namely BMI, HBA1c and Triglycerides; Model 4 additionally adjusted for cardiac covariates namely, \nhypertension, prior stroke and previous MI or angina. \nSignificant p values are highlighted in bold (p<0.05).  \nAbbreviations as in Table 1. \n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nReferences \n1. Boesen ME, Singh D, Menon BK, Frayne R. A systematic literature review of \nthe effect of carotid atherosclerosis on local vessel stiffness and elasticity. \nAtherosclerosis 2015;243(1):211-22. \n2. Bonyhay I, Jokkel G, Kollai M. Relation between baroreflex sensitivity and \ncarotid artery elasticity in healthy humans. Am J Physiol 1996;271(3 Pt 2):H1139-44. \n3. La Rovere MT, Pinna GD, Hohnloser SH, Marcus FI, Mortara A, Nohara R, \nBigger JT, Jr., Camm AJ, Schwartz PJ, Tone AIA, Reflexes After Myocardial I. \nBaroreflex sensitivity and heart rate variability in the identification of patients at risk \nfor life-threatening arrhythmias: implications for clinical trials. Circulation \n2001;103(16):2072-7. \n4. La Rovere MT, Pinna GD, Maestri R, Mortara A, Capomolla S, Febo O, \nFerrari R, Franchini M, Gnemmi M, Opasich C, Riccardi PG, Traversi E, Cobelli F. \nShort-term heart rate variability strongly predicts sudden cardiac death in chronic \nheart failure patients. Circulation 2003;107(4):565-70. \n5. Jaiswal M, Fingerlin TE, Urbina EM, Wadwa RP, Talton JW, D'Agostino RB, \nHamman RF, Daniels SR, Marcovina SM, Dolan LM, Dabelea D. Impact of Glycemic \nControl on Heart Rate Variability in Youth with Type 1 Diabetes: The SEARCH CVD \nStudy. Diabetes Technology & Therapeutics 2013;15(12):977-983. \n6. T K, M J, M K, A J, T L, L K-J, J V, I V, OT R. Relations between carotid artery \ndistensibility and heart rate variability The Cardiovascular Risk in Young Finns Study. \nAutonomic neuroscience : basic & clinical 2011;161(1):75-80. \n7. Kuh D, Pierce M, Adams J, Deanfield J, Ekelund U, Friberg P, Ghosh AK, \nHarwood N, Hughes A, Macfarlane PW, Mishra G, Pellerin D, Wong A, Stephen AM, \nRichards M, Hardy R, scientific N, data collection t. Cohort profile: updating the \ncohort profile for the MRC National Survey of Health and Development: a new clinic-\nbased data collection for ageing research. Int J Epidemiol 2011;40(1):e1-9. \n8. Camm AJ, Malik M, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, Coumel P, \nFallen EL, Kennedy HL, Kleiger RE, Lombardi F, Malliani A, Moss AJ, Rottman JN, \nSchmidt G, Schwartz PJ, Singer DH. Heart rate variability. Standards of \nmeasurement, physiological interpretation, and clinical use. European Heart Journal \n1996;17(3):354-381. \n9. Johnson W, Kuh D, Tikhonoff V, Charakida M, Woodside J, Whincup P, \nHughes AD, Deanfield JE, Hardy R, Scientific N, Data Collection T. Body mass index \nand height from infancy to adulthood and carotid intima-media thickness at 60 to 64 \nyears in the 1946 British Birth Cohort Study. Arterioscler Thromb Vasc Biol \n2014;34(3):654-60. \n10. Topriceanu CC, Moon JC, Hardy R, Chaturvedi N, Hughes AD, Captur G. \nLongitudinal birth cohort study finds that life-course frailty associates with later-life \nheart size and function. Sci Rep 2021;11(1):6272. \n11. Murray ET, Jones R, Thomas C, Ghosh AK, Sattar N, Deanfield J, Hardy R, \nKuh D, Hughes AD, Whincup P. Life Course Socioeconomic Position: Associations \nwith Cardiac Structure and Function at Age 60-64 Years in the 1946 British Birth \nCohort. PLoS One 2016;11(3):e0152691. \n12. Jones R, Hardy R, Sattar N, Deanfield JE, Hughes A, Kuh D, Murray ET, \nWhincup PH, Thomas C, Scientific N, Data Collection T. Novel coronary heart \ndisease risk factors at 60-64 years and life course socioeconomic position: the 1946 \nBritish birth cohort. Atherosclerosis 2015;238(1):70-6. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n13. Strand BH, Mishra G, Kuh D, Guralnik JM, Patel KV. Smoking history and \nphysical performance in midlife: results from the British 1946 birth cohort. J Gerontol \nA Biol Sci Med Sci 2011;66(1):142-9. \n14. Ryan A, Heath S, Cook P. Dyslipidaemia and cardiovascular risk. BMJ \n2018;360:k835. \n15. Koskinen T, Juonala M, Kahonen M, Jula A, Laitinen T, Keltikangas-Jarvinen \nL, Viikari J, Valimaki I, Raitakari OT. Relations between carotid artery distensibility \nand heart rate variability The Cardiovascular Risk in Young Finns Study. Auton \nNeurosci 2011;161(1-2):75-80. \n16. M J, EM U, RP W, JW T, Jr DAR, RF H, TE F, SR D, SM M, LM D, D D. \nReduced heart rate variability is associated with increased arterial stiffness in youth \nwith type 1 diabetes: the SEARCH CVD study. Diabetes care 2013;36(8):2351-8. \n17. B G, A S, K C, W K, G G, P B, S L, K N. Relationship between heart rate \nvariability, blood pressure and arterial wall properties during air and oxygen \nbreathing in healthy subjects. Autonomic neuroscience : basic & clinical \n2013;178(1):60-6. \n18. H T, Y K, Y S, K S, A Y, I S, Y I, H Y, S M, N D.  Effects of an ACE inhibitor \nand a calcium channel blocker on cardiovascular autonomic nervous system and \ncarotid distensibility in patients with mild to moderate hypertension. United States; \n1998. \n19. S C, I E, A T, I M, A P, A K, PP S, N T. Pulse wave velocity and cardiac \nautonomic function in type 2 diabetes mellitus. BMC endocrine disorders \n2017;17(1):27. \n20. Frolow M, Drozdz A, Kowalewska A, Nizankowski R, Chlopicki S. \nComprehensive assessment of vascular health in patients; towards endothelium-\nguided therapy. Pharmacol Rep 2015;67(4):786-92. \n21. Wassel CL, Allison MA, Barinas-Mitchell EJ, Ix JH, Jenny NS, Denenberg JO, \nRifkin DE, McQuaide BJ, Marasco A, Criqui MH. Femoral Artery Distensibility and \nIntima Media Thickness (IMT), but Not the Ankle Brachial Index, Are Associated With \nMeasures of Physical Function: The San Diego Population Study (SDPS). \nCirculation 2014;130(suppl_2):A17695-A17695. \n22. Kapetanakis M, Liuba P, Odermarsky M, Lundgren J, Hallbook T. Effects of \nketogenic diet on vascular function. Eur J Paediatr Neurol 2014;18(4):489-94. \n23. Tafil-Klawe M, Raschke F, Hildebrandt G. Functional asymmetry in carotid \nsinus cardiac reflexes in humans. Eur J Appl Physiol Occup Physiol 1990;60(5):402-\n5. \n24. Furlan R, Diedrich A, Rimoldi A, Palazzolo L, Porta C, Diedrich L, Harris PA, \nSleight P, Biagioni I, Robertson D, Bernardi L. Effects of unilateral and bilateral \ncarotid baroreflex stimulation on cardiac and neural sympathetic discharge \noscillatory patterns. Circulation 2003;108(6):717-23. \n25. Williamson JW, Raven PB. Unilateral carotid-cardiac baroreflex responses in \nhumans. Am J Physiol 1993;265(4 Pt 2):H1033-7. \n26. Santaella DF, Devesa CR, Rojo MR, Amato MB, Drager LF, Casali KR, \nMontano N, Lorenzi-Filho G. Yoga respiratory training improves respiratory function \nand cardiac sympathovagal balance in elderly subjects: a randomised controlled trial. \nBMJ Open 2011;1(1):e000085. \n27. Malpas SC. Neural influences on cardiovascular variability: possibilities and \npitfalls. Am J Physiol Heart Circ Physiol 2002;282(1):H6-20. \n28. Goldstein DS, Bentho O, Park MY, Sharabi Y. Low-frequency power of heart \nrate variability is not a measure of cardiac sympathetic tone but may be a measure \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\nof modulation of cardiac autonomic outflows by baroreflexes. Exp Physiol \n2011;96(12):1255-61. \n29. Gautier C, Stine L, Jennings JR, Sutton-Tyrrell K, Muldoon MB, Kamarck TW, \nKaplan GA, Salonen J, Manuck SB. Reduced low-frequency heart rate variability \nrelates to greater intimal-medial thickness of the carotid wall in two samples. Coron \nArtery Dis 2007;18(2):97-104. \n30. Kadoya M, Koyama H, Kurajoh M, Kanzaki A, Kakutani-Hatayama M, Okazaki \nH, Shoji T, Moriwaki Y, Yamamoto T, Emoto M, Inaba M, Namba M. Sleep, cardiac \nautonomic function, and carotid atherosclerosis in patients with cardiovascular risks: \nHSCAA study. Atherosclerosis 2015;238(2):409-14. \n31. Cardoso CR, Moraes RA, Leite NC, Salles GF. Relationships between \nreduced heart rate variability and pre-clinical cardiovascular disease in patients with \ntype 2 diabetes. Diabetes Res Clin Pract 2014;106(1):110-7. \n32. Hoshi RA, Santos IS, Dantas EM, Andreao RV, Mill JG, Goulart AC, Lotufo \nPA, Bensenor I. Relationship between heart rate variability and carotid intima-media \nthickness in the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil. Clin \nPhysiol Funct Imaging 2020;40(2):122-130. \n33. Riley WA, Evans GW, Sharrett AR, Burke GL, Barnes RW. Variation of \ncommon carotid artery elasticity with intimal-medial thickness: the ARIC Study. \nAtherosclerosis Risk in Communities. Ultrasound Med Biol 1997;23(2):157-64. \n34. van Popele NM, Grobbee DE, Bots ML, Asmar R, Topouchian J, Reneman \nRS, Hoeks AP, van der Kuip DA, Hofman A, Witteman JC. Association between \narterial stiffness and atherosclerosis: the Rotterdam Study. Stroke 2001;32(2):454-\n60. \n35. I E, GC D, A T, G K, PP S, AD P, N T. Pulse pressure amplification and \ncardiac autonomic dysfunction in patients with type 2 diabetes mellitus. Journal of \nhuman hypertension 2018;32(8):531-539. \n36. Chorepsima S, Eleftheriadou I, Tentolouris A, Moyssakis I, Protogerou A, \nKokkinos A, Sfikakis PP, Tentolouris N. Pulse wave velocity and cardiac autonomic \nfunction in type 2 diabetes mellitus. BMC Endocr Disord 2017;17(1):27. \n37. Jin HY, Baek HS, Park TS. Morphologic Changes in Autonomic Nerves in \nDiabetic Autonomic Neuropathy. Diabetes Metab J 2015;39(6):461-7. \n38. Liatis S, Alexiadou K, Tsiakou A, Makrilakis K, Katsilambros N, Tentolouris N. \nCardiac autonomic function correlates with arterial stiffness in the early stage of type \n1 diabetes. Exp Diabetes Res 2011;2011:957901. \n39. Colivicchi F, Bassi A, Santini M, Caltagirone C. Cardiac autonomic \nderangement and arrhythmias in right-sided stroke with insular involvement. Stroke \n2004;35(9):2094-8. \n40. Chen CF, Lin HF, Lin RT, Yang YH, Lai CL. Relationship between ischemic \nstroke location and autonomic cardiac function. J Clin Neurosci 2013;20(3):406-9. \n41. Manea MM, Comsa M, Minca A, Dragos D, Popa C. Brain-heart axis--Review \nArticle. J Med Life 2015;8(3):266-71. \n42. Intzilakis T, Hartmann G, Mouridsen MR, Eugen-Olsen J, Kumarathurai P, \nMadsbad S, Almdal TP, Haugaard SB, Sajadieh A. Soluble urokinase plasminogen \nactivator receptor, C-reactive protein and triglyceride are associated with heart rate \nvariability in non-diabetic Danes. Eur J Clin Invest 2013;43(5):457-68. \n43. Shaw J, England JD, Hua AS. Beta-blockers and plasma triglycerides. Br Med \nJ 1978;1(6118):986. \n44. Day JL, Metcalfe J, Simpson CN. Adrenergic mechanisms in control of \nplasma lipid concentrations. Br Med J (Clin Res Ed) 1982;284(6323):1145-8. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n45. Andreasen F, Jakobsen P, Kornerup HJ, Pedersen EB, Pedersen OL, Weeke \nJ. Changes in blood chemistry in hypertensive patients during propranolol therapy. \nBr J Clin Pharmacol 1984;17(3):265-71. \n46. Fogari R, Zoppi A, Pasotti C, Poletti L, Tettamanti F, Malamani G, Corradi L. \nPlasma lipids during chronic antihypertensive therapy with different beta-blockers. J \nCardiovasc Pharmacol 1989;14 Suppl 7:S28-32. \n47. Bruinstroop E, Fliers E, Kalsbeek A. Hypothalamic control of hepatic lipid \nmetabolism via the autonomic nervous system. Best Pract Res Clin Endocrinol \nMetab 2014;28(5):673-84. \n48. Raffin J, Barthelemy JC, Dupre C, Pichot V, Berger M, Feasson L, Busso T, \nDa Costa A, Colvez A, Montuy-Coquard C, Bouvier R, Bongue B, Roche F, Hupin D. \nExercise Frequency Determines Heart Rate Variability Gains in Older People: A \nMeta-Analysis and Meta-Regression. Sports Med 2019;49(5):719-729. \n49. Spronck B, Avolio AP, Tan I, Butlin M, Reesink KD, Delhaas T. Arterial \nstiffness index beta and cardio-ankle vascular index inherently depend on blood \npressure but can be readily corrected. J Hypertens 2017;35(1):98-104. \n50. Stafford M, Black S, Shah I, Hardy R, Pierce M, Richards M, Wong A, Kuh D. \nUsing a birth cohort to study ageing: representativeness and response rates in the \nNational Survey of Health and Development. Eur J Ageing 2013;10(2):145-157. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint \n\n \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not certified by peer review)preprint \nThe copyright holder for thisthis version posted September 25, 2025. ; https://doi.org/10.1101/2025.09.23.25336429doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}