Abstract
Objective
Cardiac autonomic neuropathy (CAN) is an independent risk factor for cardiovascular mortality and also is associated with a high risk of lethal arrhythmias and sudden death in people with type 1 or 2 diabetes. Heart rate variability (HRV) is an index of cardiac autonomic function. To investigate the relationship between HRV and arterial stiffness evaluated by the cardio-ankle vascular index (CAVI), a relatively new marker for arterial stiffness and a predictor of cardiovascular disease, in patients with type 2 diabetes.
Materials and methods
We studied consecutive 313 patients with type 2 diabetes in a cross-sectional design. HRV was estimated by the coefficient of variation of 100 R-R intervals (CVR-R) at rest and during deep breathing (DB). The difference in CVR-R was defined as CVR-R during DB minus CVR-R at rest. Arterial stiffness was evaluated by CAVI, which is independent of blood pressure (BP). A CAVI greater than or equal to 9.0 was defined as significant arterial stiffening.
Results
Linear regression analysis showed that CAVI correlated positively with age, duration of diabetes, urinary albumin creatinine ratio (UACR), CVR-R during DB, and the difference in CVR-R and negatively with body mass index (BMI), estimated glomerular filtration rate, and sensory nerve conduction velocity and action potential of the sural nerve. Multivariate analysis found that age, BMI, systolic blood pressure, UACR, and CVR-R during DB were independently associated with arterial stiffness determined by CAVI. The CVR-R at rest and during deep breathing was significantly lower in the patients with arterial stiffness than in those without it.
Conclusion
Low HRV estimated by CVR-R during DB is closely associated with arterial stiffness measured by CAVI in people with type 2 diabetes, suggesting that arterial stiffness associated with CAN may be an independent risk factor for cardiovascular disease in people with type 2 diabetes.
Supplementary Information
The online version contains supplementary material available at 10.1007/s13340-022-00604-y.
Keywords: Cardiac autonomic neuropathy, Heart rate variation, Arterial stiffness, Cardio-ankle vascular index, Pulse wave velocity, Cardiovascular disease
Introduction
Cardiac autonomic neuropathy (CAN) is an independent risk factor for cardiovascular morbidity and mortality and the progression of diabetic nephropathy and also is associated with a high risk of lethal arrhythmias and sudden death in people with type 1 or 2 diabetes [1–3]. CAN manifests as a range of symptoms, including resting tachycardia, fixed heart rate (HR), orthostatic hypotension, and silent myocardial infarction [1–3].
Heart rate variability (HRV) is a measure of the beat-to-beat fluctuation of the heart rate over time and provides information about cardiac parasympathetic (vagal) and sympathetic activity. Reduced HRV is an early indicator for CAN in people with diabetes [1–3]. In diabetes, low HRV reflects a relative sympathetic predominance via vagal dysfunction or sympathetic overactivity [4]. HRV can be assessed by statistical analysis of the RR interval (time-domain analysis) and spectral analysis (frequency domain analysis) of an array [5]. Some studies indicated that the coefficient of variation of the R-R interval (CVR-R) during deep breathing, a time-domain analysis, may be the most sensitive and valuable variable for detecting CAN [6, 7].
Arterial stiffness—a marker of subclinical vascular disease that can be measured noninvasively by pulse wave velocity (PWV)—is an independent predictor for mortality due to cardiovascular disease in people with type 2 diabetes [5]. Several studies have reported that reduced HRV is associated with increased arterial stiffness evaluated by PWV in patients with type 1 and 2 diabetes [8–15]. Carotid-femoral PWV (cf PWV) has been used to estimate arterial stiffness, but it is greatly influenced by blood pressure at the time of the assessment [16, 17]. High blood pressure due to sympathetic overactivity in CAN can have a large influence on the relationship between reduced HRV and arterial stiffness evaluated by cfPWV [16, 17]. Recently, a novel method was developed for detecting arterial stiffness on the basis of vascular elasticity, the cardio-ankle vascular index (CAVI). The CAVI is calculated on the basis of the β stiffness index and is independent of current blood pressure [18–21]. A systemic review demonstrated an association between CAVI and incident cardiovascular disease (CVD) in high-risk populations, suggesting high CAVI may be a predictor of CVD [22]. Therefore, we investigated the relationship between reduced HRV and arterial stiffness measured by CAVI in patients with type 2 diabetes.
Materials and methods
Subjects
We recruited consecutive 332 patients with type 2 diabetes referred to the diabetes outpatient clinic at Dokkyo Medical University Hospital, Tochigi, Japan, and admitted to the hospital for optimization of glycemic control. We excluded patients taking α-blockers or antiarrhythmic drugs, all of which affect the cardiac autonomic nervous system.
As shown in the flow diagram of patients’ selection (Supple Figure), 10 patients (9 with atrial fibrillation and 1 with wearing a pacemaker) were excluded from the study. And then after measuring ABI, 9 patients with ABI < 0.90 were excluded. We finally studied 313 patients aged 17–87 years with type 2 diabetes (122 women, 191 men).
Coronary artery disease (CAD) was defined as a history of myocardial infarction, coronary artery bypass grafting, or an abnormal coronary angiography. Stroke was defined as a history of ischemic stroke confirmed by cerebral computed tomography or nuclear magnetic resonance imaging. Peripheral artery disease (PAD) was defined as a history of peripheral artery reconstruction or amputation of foot. Seventy-two patients had CVD (Table 1).
Table 1.
Baseline demographic, clinical, and laboratory data for 313 patients with type 2 diabetes
| Variables | |
|---|---|
| N (m/f) | 313 (191/122) |
| Age (years) | 58.4 ± 13.5 |
| Duration of diabetes (years) | 10 (3, 20) |
| Body weight (kg) | 67.0 ± 16.4 |
| BMI | 26.0 ± 5.4 |
| SBP (mmHg) | 131.3 ± 20.8 |
| DBP (mmHg) | 78.8 ± 12.9 |
| FPG (mg/dl) | 149.8 ± 49.6 |
| HbA1c (%) | 10.5 ± 2.4 |
| Fasting C peptide (ng/ml) | 2.1 (1.4, 3.0) |
| LDL-C (mg/dl) | 108.9 ± 37.6 |
| HDL-C (mg/dl) | 46.2 ± 15.7 |
| Triglyceride (mg/dl) | 127 (95, 179.5) |
| eGFR (ml/min/1.73m2) | 78.1 ± 32.6 |
| UACR (mg/g) | 17 (7, 94.5) |
| Urinary β2 microglobulin (μg/gCr) | 111.5 (72, 279) |
| Hematocrit (%) | 41.1 ± 5.5 |
| Heart rate (bpm) | |
| At test | 76.3 ± 14.1 |
| With deep breathing | 74.9 ± 13.7 |
| CVR-R (%) | |
| At rest | 2.4 ± 1.3 |
| With deep breathing | 4.3 ± 2.5 |
| QTc (s) | 0.427 ± 0.026 |
| CAVI | 9.0 ± 1.5 |
| Sural nerve | |
| NCV (m/s) | 44.9 ± 5.2 |
| SNAP (μV) | 7.5 (5.0, 11) |
| Peroneal nerve | |
| NCV (m/s) | 41.0 ± 6.2 |
| CMAP (mV) | 2.1 (0.9, 3.6) |
| Median nerve MCV (m/s) | 50.3 ± 4.8 |
| Diabetic polyneuropathy, n (%) | 120 (38.3) |
| Diabetic retinopathy (NDR/SDR/PDR) | 174/59/71 |
| Hypertension, n (%) | 188 (60) |
| CVD, n (%) | 72 (23.0) |
| Current smoker, n (%) | 83 (26.5) |
Variables are presented as mean ± SD or median (interquartile range)
BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, Hb hemoglobin, HDL high-density lipoprotein, eGFR estimated glomerular filtration rate, UACR urinary albumin creatinine ratio, CVR-R coefficient of variation of R-R intervals, QTc corrected QT, CAVI cardio-ankle vascular index, MCV motor nerve conduction velocity, CMAP compound muscle action potential, SCV sensory nerve conduction velocity, SNAP sensory nerve action potential, NDR no diabetic retinopathy, SDR non-proliferative simple diabetic retinopathy, PDR proliferative diabetic retinopathy, CVD cardiovascular disease
Participants were divided into the following three groups accounting the worst eye: no diabetic retinopathy (NDR); non-proliferative simple diabetic retinopathy (SDR) and proliferative diabetic retinopathy (PDR), as shown in Table 1.
In all participants, we assessed the body mass index (BMI), duration of diabetes, systolic (SBP) and diastolic (DBP) blood pressure, heart rate, QTc, hematocrit level, estimated glomerular filtration rate (eGFR), urinary albumin creatinine ratio (UACR), and urinary β2 microglobulin.
The study was approved by the Ethics Committee of Dokkyo Medical University (approval number R-4–2; date of approval, November 24, 2017). The study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000040631). All participants gave informed written consent to participate in the study.
Heart rate variability (HRV)
Participants were examined in a supine position after acclimatizing for 10–20 min (rest). Heart rate variability (HRV), i.e., the beat-to-beat variation in the R-R intervals, was measured with an electrocardiogram (ECG) at rest and during deep breathing. The ECG recording was obtained for 2–3 min at rest and for 2–3 min during deep breathing at a frequency of 6 breaths per minute (5 s in and 5 s out). The coefficient of variation of 100 R-R intervals (CVR-R) was then calculated as follows: SD of the mean R-R interval of 100 samples/mean R-R × 100 (%).
Cardio-ankle vascular index
The cardio-ankle vascular index (CAVI) was measured and automatically calculated with the VaSera system (Fukuda Denshi Co, Japan), according to the manufacturer’s recommendations [16]. To assess CAVI, ECG electrodes were placed on both wrists, a microphone (for phonocardiography) was placed on the sternum, and 4 BP cuffs were wrapped around the 4 limbs. The values of CAVI were automatically calculated with the following equation: CAVI = a [2ρ/ΔP × ln(Ps/Pd) × PWV2] + b, where ρ is the blood density, Ps and Pd are SBP and DBP, respectively, in mmHg, and PWV is the pulse wave velocity from the origin of the aorta to the tibial artery at the ankle level. The CAVI is originally derived from the stiffness parameter β [18–21]. The CAVI was not adopted if the ankle-brachial index was less than 0.9 [18–21].
The cutoff values for the CAVI were determined by the Japan Society for Vascular Failure, as follows: normal, less than 8.0; borderline, 8.0 to 8.9; and abnormal, greater than or equal to 9.0, and the Society uses a CAVI greater than or equal to 9.0 as the cutoff point for the presence of arteriosclerotic vascular disease [24].
Neurophysiological measurements
Electroneurography was performed with surface electrodes, and digital equipment was used for stimulation and recordings. We measured the motor conduction velocity (MCV) and compound muscle action potential (CMAP) of the median and peroneal nerves and the sensory conduction velocity (SCV) and sensory nerve action potential (SNAP) of the sural nerve. The amplitudes of CMAP and SNAP were measured from peak to peak, and the sural sensory nerve was evaluated by an orthodromic technique.
Diagnosis of diabetic polyneuropathy
Participants were screened for diabetic polyneuropathy with the simple diagnostic criteria proposed by the Diabetic Neuropathy Study Group in Japan [25]. In brief, the criteria consist of 2 prerequisite items (having diabetes mellitus and not having any neuropathies other than diabetic polyneuropathy) and 3 items from a neurological examination (the presence of symptoms of diabetic polyneuropathy, decreased vibration–perception threshold vibration in the bilateral inner malleoli with a 128-Hz tuning fork, and decreased or absent bilateral ankle tendon reflexes). Diabetic polyneuropathy was diagnosed in patients who fulfilled the 2 prerequisite criteria and 2 or 3 of the neurological criteria.
Statistical analysis
Data are presented as the mean ± SD or the median and interquartile range unless otherwise indicated. Differences in normally distributed data were assessed by a 1-way analysis of variance (ANOVA) with the Newman–Keuls multiple comparison test. For non-normally distributed data, differences between groups were analyzed by the Kruskal–Wallis test with Dunn’s multiple comparison tests. Correlation was determined by linear regression analysis (Pearson’s correlation) or multivariate analysis. The significance of differences in the prevalence between groups was analyzed by a chi-squared test. Logarithmic transformation of UAE and urinary β2 microglobulin was used to render the distribution normal for parametric tests. A P value below 0.05 was considered statistically significant.
We calculated that a sample of 194 was required for 80% power (β = 0.2) at a significance level of 0.05 (α = 0.05) to detect an achievable r of 0.2 between CVR-R and CAVI.
Results
The baseline characteristics of the 313 patients with type 2 diabetes evaluated in this study are shown in Table 1. The mean age was 58.4 ± 13.5 years; mean BMI, 26.0 ± 5.4; mean CVR-R at rest, 2.4 ± 1.3%; mean CVR-R during deep breathing, 4.3 ± 2.5%; and mean CAVI, 9.0 ± 1.5. CAVI was significantly higher in male subjects than female subjects (9.2 ± 1.5 vs. 8.8 ± 1.5, P = 0.0230; data not shown). One hundred five patients (33%) were under insulin treatment in our study.
Linear regression analysis revealed that the CAVI correlated positively with age, duration of diabetes, SBP, UACR, and urinary β2 microglobulin and negatively with BMI, heart rate, median MCV, sural SCV and SNAP, CVR-R at rest and during deep breathing, and the difference in CVR-R (Table 2). To determine factors associated with CAVI, we performed multivariate analysis with forward selection of significant variables. In a model that explained 54.8% of the variation of CAVI, age (β = 0.467, P < 0.001), BMI (β = −0.245, P = 0.001), SBP (β = 0.267, P = 0.003), UACR (β = 0.186, P = 0.012), and CVR-R during deep breathing (β = −0.208, P = 0.005) were independently associated with CAVI (Table 2). We reanalyzed multivariate analysis of the relationship between CAVI and clinical variables separately in patients with SBP ≥ 130 mmHg or < 130 mmHg. We found that age, BMI, and CVR-R during deep breathing were independent factors for CAVI in patients with SBP ≥ 130 mmHg, while age was an independent factor for CAVI in those with SBP < 130 mmHg.
Table 2.
Univariate and multivariate analyses of relationships between CAVI and clinical variables in 313 patients with type 2 diabetes
| Variable | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| r | P value | β | P value | |
| Age (years) | 0.6135 | < 0.0001 | 0.467 | < 0.001 |
| Duration of diabetes (ys) | 0.2517 | < 0.0001 | −0.072 | 0.332 |
| BMI | −0.3332 | < 0.0001 | −0.245 | 0.001 |
| SBP (mmHg) | 0.1595 | 0.0053 | 0.221 | 0.003 |
| DBP (mmHg) | 0.0364 | 0.5263 | NE | |
| FPG (mg/dl) | −0.0238 | 0.6826 | NE | |
| HbA1c (%) | −0.0508 | 0.3789 | NE | |
| Serum C peptide (ng/ml) | −0.0946 | 0.1032 | NE | |
| LDL cholesterol (mg/dl) | −0.0418 | 0.4706 | NE | |
| HDL cholesterol (mg/dl) | 0.0661 | 0.2526 | NE | |
| Triglyceride (mg/dl) | −0.0946 | 0.1009 | NE | |
| Hematocrit (%) | −0.1026 | 0.0736 | NE | |
| eGFR (ml/min/1.73m2) | −0.3101 | < 0.0001 | −0.059 | 0.433 |
| UACR (log10 mg/g) | 0.2303 | < 0.0001 | 0.181 | 0.012 |
| U-β2mg (log10μg/gCr) | 0.2259 | < 0.0001 | NE | |
| Heart rate at rest (bpm) | −0.1454 | 0.0113 | −0.053 | 0.475 |
| Heart rate with DB (bpm) | −0.0896 | 0.1201 | NE | |
| Median N MCV (m/s) | −0.1905 | 0.0019 | −0.033 | 0.660 |
| Sural N SCV (m/s) | −0.1439 | 0.0359 | −0.025 | 0.735 |
| Sural N SNAP (μV) | −0.1434 | 0.0369 | NE | |
| Peroneal N MCV (m/s) | −0.0650 | 0.3150 | NE | |
| Peroneal N CMAP (mV) | −0.1012 | 0.1117 | NE | |
| CVR-R at rest (%) | −0.1481 | 0.0100 | NE | |
| CVR-R with DB (%) | −0.291 | < 0.0001 | −0.208 | 0.005 |
| QTc (s) | −0.002 | 0.9713 | NE | |
| The difference in CVR-R (%) | −0.253 | < 0.0001 | NE | |
| R2 | 0.548 (P < 0.001) | |||
β indicates partial coefficient; NE, does not enter the final model
BMI body mass index, FPG fasting plasma glucose, LDL low-density lipoprotein, HDL high-density lipoprotein, eGFR estimated glomerular filtration rate, UACR urinary albumin creatinine ratio, U-β2 mg urinary β2 microglobumin, CAVI cardio-ankle vascular index, MCV motor nerve conduction velocity, CMAP compound muscle action potential, SCV sensory nerve conduction velocity, SNAP sensory nerve action potential, DB deep breathing, QTc corrected QT
We found that age, BMI, and CVR-R during deep breathing were independent factors for CAVI in patients with SBP ≥ 130 mmHg, while age was an independent factor for CAVI in those with SBP < 130 mmHg.
The CVR-R at rest correlated positively with eGFR, hematocrit, median MCV, peroneal CMAP, and sural SNAP and negatively with age, duration of diabetes, UACR, urinary β2 microglobulin, CAVI, heart rate, and QTc (Table 3). CVR-R during deep breathing also correlated positively with eGFR, hematocrit, median MCV, sural SCV, and sural SNAP and negatively with age, duration of diabetes, UAE, urinary β2 microglobulin, CAVI, heart rate, and QTc (Table 3). The difference in CVR-R correlated positively with eGFR, median MCV, and sural SCV and negatively with age, duration of diabetes, UACR, urinary β2 microglobulin, and CAVI (Table 3).
Table 3.
Linear regression analysis of relationships between coefficient variation of R-R intervals (CVR-R) at rest (AR), CVR-R during deep breathing (DB), or the difference between CVR-R AR and DB and clinical variables in a total of 313 patients with type 2 diabetes
| Variable | CVR-R at rest | CVR-R with DB | The difference | |||
|---|---|---|---|---|---|---|
| r | P value | r | P value | r | P value | |
| Age (years) | −0.1411 | 0.0130 | −0.2069 | 0.0003 | −0.1589 | 0.0052 |
| Diabetes duration (years) | −0.2264 | < 0.0001 | −0.2861 | < 0.0001 | −0.2023 | 0.0005 |
| BMI | −0.0358 | 0.5310 | −0.0293 | 0.6082 | −0.0137 | 0.8113 |
| SBP (mmHg) | 0.0683 | 0.2352 | −0.0990 | 0.0855 | −0.0745 | 0.1959 |
| DBP (mmHg) | 0.0232 | 0.6868 | 0.0293 | 0.6115 | 0.0207 | 0.7195 |
| FPG (mg/dL) | −0.0393 | 0.4962 | 0.0565 | 0.3289 | 0.0947 | 0.1010 |
| HbA1c (%) | −0.0116 | 0.8402 | −0.0078 | 0.8922 | −0.0019 | 0.9739 |
| LDL chol (mg/dL) | 0.0516 | 0.3704 | −0.0106 | 0.8540 | −0.0429 | 0.4573 |
| Triglyceride (mg/L) | −0.0351 | 0.5404 | −0.0154 | 0.7886 | 0.0029 | 0.9595 |
| HDL chol (mg/dL) | −0.0995 | 0.0829 | 0.0005 | 0.9929 | 0.0603 | 0.2946 |
| eGFR (mL/min/1.73m2) | 0.181 | 0.0014 | 0.2589 | < 0.0001 | 0.1961 | 0.0005 |
| UACR (log10 mg/gCr) | −0.2206 | 0.0001 | −0.286 | < 0.0001 | −0.2031 | 0.0004 |
| U-β2 mg (log10 ng/ml) | −0.1569 | 0.0068 | −0.2045 | 0.0004 | −0.1464 | 0.0118 |
| Hematocrit (%) | 0.1682 | 0.0030 | 0.1663 | 0.0034 | 0.0939 | 0.1001 |
| CAVI | −0.1481 | 0.0100 | −0.291 | < 0.0001 | −0.253 | < 0.0001 |
| Median MCV (m/s) | 0.2977 | < 0.0001 | 0.2869 | < 0.0001 | 0.1613 | 0.0089 |
| Peroneal MCV (m/s) | 0.1066 | 0.0972 | 0.1179 | 0.0671 | 0.0753 | 0.2432 |
| Peroneal CMAP (mV) | 0.1544 | 0.0160 | 0.1238 | 0.0544 | 0.0560 | 0.3856 |
| Sural SCV (m/s) | 0.0717 | 0.2940 | 0.1654 | 0.0152 | 0.1465 | 0.0317 |
| Sural nerve SNAP (μV) | 0.2705 | < 0.0001 | 0.1693 | 0.0132 | 0.0363 | 0.5973 |
| Heart rate at rest (bpm) | −0.2728 | < 0.0001 | −0.0983 | 0.0850 | 0.0488 | 0.3931 |
| Heart rate with DB (bpm) | −0.2959 | < 0.0001 | −0.1671 | 0.0033 | −0.0182 | 0.7499 |
| QTc (ms) | −0.2739 | < 0.0001 | −0.1858 | 0.0011 | −0.0526 | 0.3580 |
BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, Hb hemoglobin, LDL low-density lipoprotein, chol cholesterol, HDL high-density lipoprotein, eGFR estimated glomerular filtration rate, UACR urinary albumin creatinine rattio, U β2 mg urinary β2 microglobumin, CAVI cardio-ankle vascular index, MCV motor nerve conduction velocity, CMAP compound muscle action potential, SCV sensory nerve conduction velocity, SNAP sensory nerve action potential, DB deep breathing, QTc corrected QT
We next divided the participants into 2 groups according to the presence or absence of arterial stiffening, i.e., a CAVI greater than or equal to 9.0 or less than 9.0, respectively. As shown in Table 4, the patients with arterial stiffening were older and less obese and had higher SBP and worse renal function than those without arterial stiffening. Median MCV and sural SCV were lower in the patients with arterial stiffening than in those without it. The prevalence of hypertension or CVD was significantly higher in the patients with arterial stiffening than in those without it. The classification of diabetic retinopathy was more severe in the patients with arterial stiffening than in those without it. Next, we performed multivariate logistic regression analysis for the CAVI ≥ 9.0 after adjusting all potential confounding factors. As shown in Table 5, age, BMI, SBP, eGFR, and CVR-R during deep breathing were independent factor for the CAVI ≥ 9.0 in people with type 2 diabetes.
Table 4.
Demographic, clinical, and laboratory data for type 2 diabetic patients with the absence or presence of arterial stiffness defined as the cardio-ankle vascular index (CAVI) ≥ 9.0
| CAVI | P value | ||
|---|---|---|---|
| < 9.0 | ≥ 9.0 | ||
| N (M/F) | 136 (77/59) | 170 (111/59) | 0.1263 |
| Age (years) | 52.5 ± 13.5 | 65.4 ± 9.7 | < 0.0001 |
| Body weight (kg) | 72.0 ± 17.1 | 64.5 ± 14.1 | < 0.0001 |
| BMI (kg/m2) | 27.4 ± 6.0 | 24.7 ± 4.4 | < 0.0001 |
| FPG (mg/dl) | 152.3 ± 52.2 | 147.2 ± 46.9 | 0.3792 |
| HbA1c (%) | 10.6 ± 2.4 | 10.5 ± 2.3 | 0.6053 |
| SBP (mmHg) | 127.6 ± 17.9 | 134.2 ± 22.5 | 0.0049 |
| DBP (mmHg) | 77.7 ± 11.6 | 78.2 ± 13.9 | 0.7168 |
| LDL cholesterol (mg/dl) | 108.3 ± 37.1 | 107.9 ± 37.1 | 0.8642 |
| Triglyceride (mg/dl) | 131 (97, 186) | 123 (89.25, 170) | 0.3192 |
| HDL cholesterol (mg/dl) | 44.5 ± 12.8 | 47.9 ± 17.8 | 0.0589 |
| Hematocrit (%) | 41.5 ± 5.3 | 40.6 ± 5.87 | 0.1722 |
| eGFR (ml/min/1.73m2) | 87.4 ± 33.4 | 69.2 ± 26.3 | < 0.0001 |
| UACR (mg/g) | 12 (6, 62) | 23 (8.5, 195) | 0.0005 |
| U-β2mg (ng/ml) | 98 (66.25, 157.75) | 129 (76.75, 550.5) | 0.0002 |
| Median MCV (m/s) | 51.3 ± 4.7 | 49.5 ± 4.7 | 0.0024 |
| Peroneal MCV (m/s) | 41.6 ± 6.9 | 40.39 ± 5.4 | 0.0970 |
| Peroneal CMAP (mV) | 2.5 (0.85, 4) | 2 (0.8, 3.4) | 0.1726 |
| Sural SCV (m/s) | 46.0 ± 4.5 | 43.8 ± 5.5 | 0.0014 |
| Sural SNAP (μV) | 8.0 (5, 11.6) | 7.1 (4.6, 10.8) | 0.2077 |
| Heart rate at rest (bpm) | 78.5 ± 13.8 | 74.7 ± 14.3 | 0.0211 |
| Heart rate with DB (bpm) | 75.8 ± 13.3 | 74.4 ± 14.0 | 0.3621 |
| QTc (s) | 0.425 ± 0.030 | 0.427 ± 0.026 | 0.5350 |
| Current smoker, n (%) | 37 (27.2) | 48 (28.2) | 0.8981 |
| Hypertension, n (%) | 72 (52.9) | 114 (67.1) | 0.0135 |
| Diabetic polyneuropathy, n (%) | 46 (33.8) | 74 (43.5) | 0.0991 |
| Diabetic retinopathy (N/S/P), n | 88/19/24 | 79/70/47 | < 0.0001 |
| CVD, n (%) | 23 (16.9) | 49 (28.8) | 0.0151 |
Data are the mean ± SD or the median and inter-quartile ranges
BMI body mass index, FPG fasting plasma glucose, Hb hemoglobin, LDL low-density lipoprotein, HDL high-density lipoprotein, eGFR estimated glomerular filtration rate, UACR urinary albumin creatinine ratio, U-β2mg urinary β2 microglobulin, MCV motor nerve conduction velocity, CMAP compound muscle action potential, SCV sensory nerve conduction velocity, SNAP sensory nerve action potential, DB deep breathing, QTc corrected QT, N no diabetic retinopathy, S non-proliferative simple diabetic retinopathy, P proliferative diabetic retinopathy, CVD cardiovascular disease
Table 5.
Multiple logistic regression analysis for the CAVI ≥ 9.0
| Variables | B | S.E | Wald | Odds ratio | P values |
|---|---|---|---|---|---|
| Age (years) | 0.078 | 0.018 | 19.225 | 1.081 | < 0.001 |
| BMI | −0.119 | 0.043 | 7.819 | 0.888 | 0.005 |
| SBP (mmHg) | 0.020 | 0.009 | 4.764 | 1.020 | 0.029 |
| eGFR (ml/min/1.73m2) | −0.021 | 0.008 | 7.215 | 0.979 | 0.007 |
| Median N MCV (m/s) | 0.019 | 0.051 | 0.132 | 1.019 | 0.716 |
| Sural N SCV (m/s) | −0.082 | 0.044 | 3.498 | 0.922 | 0.061 |
| CVR-R DB (%) | −0.174 | 0.080 | 4.705 | 0.840 | 0.030 |
Nagelkerke R2 = 0.481 (P < 0.001)
BMI body mass index, eGFR estimated glomerular filtration, N nerve, MCV motor nerve conduction velocity, SCV sensory nerve conduction velocity, CVR-R coefficient of variation of R-R intervals, DB deep breathing
The CVR-R was significantly lower in the patients with arterial stiffening than in those without it both at rest (2.06% [1.33%, 2.72%] vs 2.33% [1.57%, 3.25%], P = 0.0053) and during deep breathing (3.33% [1.94%, 4.64%] vs 4.70% [2.82%, 6.54%], P < 0.0001) (Fig. 1A, B). The difference in CVR-R was lower in the patients with arterial stiffening than in those without it (0.96 [0.23%, 2.44%] vs 1.97 [0.72%, 3.32%], P = 0.0001) (Fig. 1C).
Fig. 1.
Comparison of coefficient variation of the 100 R-R intervals (CVR-R) at rest (A) and with deep breathing (B), and the difference in CVR-R (C) in diabetic patients with and without arterial stiffness, according to the cardio-ankle vascular index (CAVI) ≥ 9.0 or < 9.0
Discussion
Our study confirmed that reduced HRV is associated with increased arterial stiffness in people with type 2 diabetes, a finding that is in agreement with previous studies [10–15]. This finding suggests that arterial stiffness may be associated with cardiovascular disease in people with type 2 diabetes and CAN. Unlike previous studies, which used heart-femoral (hf) PWV [9–11, 13–15], our study evaluated arterial stiffness with the CAVI, a relatively new measure of the overall arterial stiffness from the aorta to the ankle [18]. The CAVI, which is calculated on the basis of the β stiffness index, is not subjective to current blood pressure at the time of the assessment [18–21]. A systemic review demonstrated a modest association between CAVI and incident CVD risk in high-risk populations [22]. A CAVI greater than or equal to 9 is the cutoff point for the presence of arteriosclerotic disease [24]. In fact, the present study also showed that the prevalence of CVD was significantly higher in diabetic patients with CAVI ≥ 9.0 than in those with CAVI < 9.0. We found that the CVR-R both at rest and during deep breathing was significantly lower in patients with arterial stiffness (CAVI ≥ 9) than in those without it. Moreover, the difference in CVR-R was significantly lower in patients with arterial stiffness. Multivariate analysis revealed that CVR-R during deep breathing is an independent factor that affects arterial stiffness in patients with type 2 diabetes. In particular, low HRV estimated by CVR-R during DB is closely associated with arterial stiffness measured by CAVI in patients with type 2 diabetes. Our study demonstrated that CVR-R during deep breathing was a significant independent associated factor for CAVI, while nerve conduction velocity was not. However, a previous study showed that nerve conduction velocity is associated independently with CAVI in diabetic patients [26]. One possible explanation for the discrepancy between our study and a previous study is a difference in variables used in multivariate analysis. Our multivariate analysis for CAVI included a couple of factors regarding HRV, while that of a previous study did not.
Low HRV, which reflects a state of relative sympathetic overdrive, is an early sign of CAN [1–3]. Time and frequency domain analyses of HRV enable cardiac parasympathetic and sympathetic autonomic function to be accurately evaluated. Of these analyses, the CVR-R (time-domain analysis), especially during deep breathing, may be the most valuable and reproducible assessment for detecting CAN [6, 7]. HRV during deep breathing under predominant parasympathetic control is the most frequently impaired heart-related variable in the natural course of CAN [1–3]. One study found that a simple bedside test that measures 1-min HRV during deep breathing was a good predictor of all-cause mortality for diabetic patients after a first myocardial infarction [27]. Moreover, several studies demonstrated that CVR-R during deep breathing can be a sensitive and valuable method for detecting CAN [6, 7]. Therefore, in the present study we used CVR-R during deep breathing as an indicator of CAN.
Hypertension is closely associated with sympathetic nerve activity, and blood pressure is the physiological factor with the greatest effect on PWV [16, 17]. Researchers proposed that concomitant hypertension may affect HRV in people with type 2 diabetes because approximately 75% of patients with diabetes have concomitant hypertension [28]. The finding is worrisome that high blood pressure is a significant confounding factor for arterial stiffness and may transiently affect arterial stiffness during hfPWV assessments [16, 17]. Compared with hfPWV or brachial-ankle (ba) PWV, CAVI is not susceptible to the effects of blood pressure during measurements, so it can provide a better assessment of the intrinsic elastic properties of the arterial walls [18]. Therefore, one strength of our study is that unlike previous studies, which used hfPWV or baPWV [9–15], we evaluated the relationship between reduced HRV and increased arterial stiffness more accurately by excluding the effect of blood pressure. Our multivariate analysis revealed that age, BMI, SBP, UACR, and CVR-R during deep breathing were independently associated with CAVI in patients with type 2 diabetes. These results suggest that reduced HRV may be associate with arterial stiffness in people with type 2 diabetes independent of other cardiovascular risks.
The mechanisms responsible for the relationship between reduced HRV and increased arterial stiffness in diabetes remain unclear. CAN may affect the elasticity of the arterial wall by changing the smooth muscle tone of arteries [29]. In diabetic patients with CAN, relative sympathetic overactivity due to parasympathetic impairment, evidenced by reduced HRV during deep breathing, may increase vascular tone, leading to increased arterial stiffness [30]. Another possible explanation is that CAN may develop in parallel with arterial stiffness as a complication of angiopathies. Chronic hyperglycemia plays a role in the development of CAN and also promotes accumulation of advanced glycation end products within the vessel wall, resulting in arterial stiffness [31].
The present study demonstrated that CVR-R during deep breathing was associated more strongly with the presence of diabetic kidney disease and arterial stiffness than with CVR-R at rest in patients with type 2 diabetes. HRV with deep breathing is the most widely used test of cardiovagal function and has about 80% specificity [7, 32]. Because measurement of CVR-R at rest alone may be not enough to detect vagal dysfunction in people with type 2 diabetes, CVR-R should also be measured during deep breathing.
The present study has some limitations. One major limitation is the cross-sectional design, which cannot prove a causal relationship between arterial stiffness and CAN. Another limitation is the lack of a control group; therefore, we cannot investigate a potential difference in the association of CAN with arterial stiffness between people with and without type 2 diabetes. The third limitation is a single measurement of CVR-R as assessment of HRV. We believe, however, that among a variety of HRV indices, CVR-R during deep breathing may be the most sensitive and valuable variable for detecting CAN [6, 7]. Fourth limitation is that age may affect the relationship between CVR-R and CAVI in our study, since age has a large effect on both HRV and CAVI [33, 34]. To exclude effects of age on the relationship between CVRR and CAVI, a further study should be conducted in participants with a much narrower age range. However, in the present study, multivariate analysis revealed that CVR-R during deep breathing is an independent factor that affects arterial stiffness in patients with type 2 diabetes.
In conclusion, we confirmed the relationship between CAN (reduced HRV) and increased arterial stiffness evaluated by CAVI, independently of blood pressure, in people with type 2 diabetes, suggesting that arterial stiffness associated with CAN may be an independent risk factor for cardiovascular disease in people with type 2 diabetes. CVR-R during deep breathing was associated strongly arterial stiffness and the presence of diabetic kidney disease in people with type 2 diabetes.
Supplementary Information
Below is the link to the electronic supplementary material.
Author contributions
MK, TN, and TI contributed to the study design, data collection, and drafting of the manuscript; MS, SS, TT, and TJ contributed to the discussion and reviewed the manuscript; IU reviewed and edited the manuscript; and YA researched the data and wrote, reviewed, and edited the manuscript.
Funding
The author(s) received no financial support for the research, authorship and/or publication of this article.
Declarations
Conflict of interest
Masato Kase, Toshie Iijima, Takafumi Niitani, Masaaki Sagara, Shintaro Sakurai, Takuya Tomaru, Teruo Jojima, and Isao Usui declare no conflicts of interests. Yoshimasa Aso received an honorarium from Mitsubishi Tanabe Pharma Co., Ltd. (Osaka, Japan), Sumitomo Pharma Co., Ltd.(Tokyo, Japan), Diichi Sankyo Co., Ltd. (Tokyo, Japan), and Novo Nordisk Pharma Ltd. (Tokyo, Japan), and received research funding from Mitsubishi Tanabe Pharma Co., Ltd. (Osaka, Japan), Ono Pharmaceutical Co., Ltd. (Osaka, Japan), Taisho Pharmaceutical Co., Ltd. (Tokyo, Japan), Daiichi Sankyo Co., Ltd. (Tokyo, Japan), Nippon Boehringer Ingelheim Co., Ltd.(Tokyo, Japan), Terumo Corp. (Tokyo, Japan), Abbott Diagnostics Medical Co., Ltd.(Tokyo, Japan), LifeScan Japan (Tokyo, Japan),and Kowa Company, Ltd. (Aichi, Japan).
Human rights
The study was approved by the Ethics Committee of Dokkyo Medical University (approval number R-4–2; date of approval, November 24, 2017). The study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trials Registry (UMIN000040631).
Informed consent
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and/or with the Helsinki Declaration of 1964 and later versions. Informed consent or substitute for it was obtained from all patients for being included in the study.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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