Abstract
Introduction:
Nocturnal SBP dipping is independently related to CVD risk, but it is unclear if insulin sensitivity associates with SBP dipping in patients with metabolic syndrome (MetS).
Methods:
Eighteen adults with MetS (ATP III criteria 3.3±0.6; 53.2±6.5 y; BMI 35.8±4.5 kg·m2) were categorized as “dippers” (≥10% change in SBP; n=4F/3M) or “non-dippers” (<10%; n=9F/2M). Twenty-four-hour ambulatory blood pressure was recorded to assess SBP dipping. A euglycemic-hyperinsulinemic clamp (40 mU·m2·min, 90 mg·dl−1) was performed to test insulin sensitivity. A graded, incremental exercise test was conducted to estimate sympathetic activity. Heart rate recovery after exercise was then used to determine parasympathetic activity. Metabolic panels and body composition (DXA) were also tested.
Results:
Dippers had greater drops in SBP (16.63±5.2 vs. 1.83±5.6%, P<0.01) and experienced an attenuated rise in both SBPslope (4.7±2.3 vs. 7.2±2.5 mmHg/min, P=0.05) and HRslope to the incremental exercise test compared to non-dippers (6.5±0.9 vs. 8.2±1.7 bpm/min, P=0.03). SBP dipping correlated with higher insulin-stimulated FMD (r=0.51, P=0.02), although the relationship was no longer significant after co-varying for HRslope (P=0.09).
Discussion/Conclusion:
Attenuated rises in blood pressure and heart rate to exercise appear to play a larger role than vascular sensitivity in SBP dipping in adults with MetS.
Keywords: blood pressure, metabolic syndrome, insulin sensitivity, vascular function, autonomic function
Introduction
The World Health Organization identifies elevated blood pressure as the leading risk factor for cardiovascular disease (CVD) [1]. The American College of Cardiology and American Heart Association recently established new guidelines to define hypertension (HTN; ≥130/80 mmHg) that classified ~45% of American adults to have HTN [2,3]. Importantly, 24-h ambulatory blood pressure monitoring (AMBP) better predicts CVD risk and target organ damage than the traditional, single clinic blood pressure assessment [4]. While systolic blood pressure (SBP) typically dips approximately 10-20% in the transition from daytime to nighttime, blunted dips (e.g. <10%) have been linked to both elevated CVD risk [5] as well as all-cause mortality, [6,7] independent of daytime or clinical HTN status based on a single measurement. Thus, identifying key clinical factors that account for blood pressure circadian rhythm is needed to optimize care.
Traditional risk factors for clinic HTN (e.g., high dietary sodium, lack of physical activity, arterial stiffness, and obesity) have been well-described [8,9]. However, factors affecting 24-h blood pressure are less understood. Interestingly, insulin resistance has emerged as a factor promoting HTN [10]. Hyperinsulinemia generally increases sodium reabsorption in the kidneys, in part, due to insulin over-activating sympathetic drive, and this is purported to promote essential HTN in patients with obesity-related type 2 diabetes (T2D) [11,12] as well as metabolic syndrome (MetS) [13]. However, it is also important to acknowledge that the correlation between hyperinsulinemia and hypertension in individuals with metabolic syndrome (MetS) may be somewhat protective in natriuresis, thereby favoring blood pressure homeostasis [14] [15]. In either case, the potential for greater sympathetic drive is considered to promote systemic and renal blood vessel vasoconstriction that raises blood pressure [16,17]. Similarly, a hypertensive response to exercise (>210 mmHg for men; >190 mmHg for women), which is suggestive of elevated sympathetic drive, may also relate to reduced endothelial function given that impaired NO synthesis contributes to a vasoconstrictive state [18-20]. However, a major limitation of prior studies examining the role of insulin sensitivity is that they assessed clinic blood pressure, and none of them used the “gold-standard” euglycemic clamp to directly test insulin action on metabolic and vascular tissue. Thus, there is a knowledge gap about the role of insulin sensitivity in relation to 24-h AMBP. Moreover, few studies [21,22] have has assessed blood pressure dipping in adults with MetS. This is concerning given that MetS is a cluster of risk factors inclusive of HTN and patients with MetS have a higher risk of CVD compared to those without [23]. In fact, it was reported that MetS-Score, a global risk assessment scoring system for MetS based on multivariate analysis [22], was a strong predictor of non-dipping and elevated nighttime SBP (>110/65 mmHg) [5,21]. Whether insulin sensitivity, endothelial function, and/or sympathovagal balance associates with nighttime SBP in MetS adults remains unclear. Therefore, the purpose of this study was to examine factors that relate to SBP dipping and 24-h blood pressure in patients with MetS. We hypothesized that metabolic insulin sensitivity, endothelial function, and sympathovagal balance would significantly associate with AMBP in patients with MetS. We further hypothesized that patients who experienced a ≥10% drop in their SBP at night would have more favorable metabolic and vascular outcomes.
Materials and Methods
Participants.
Eighteen adults (n=18; 13F/5M; 53.2±6.5 y; body mass index (BMI) 35.8±4.5 kg·m2) were recruited via social media and/or newspaper advertisements from the Charlottesville, VA community. Participants were nonsmoking, sedentary (exercise <60 min·wk), weight stable (≤2 kg over the previous 3 months) and screened for MetS based on the National Cholesterol Education Panel Adult Treatment Panel (ATP) III criteria. Participants were excluded if they had chronic disease (e.g., cardiovascular, renal, hepatic, pulmonary, etc.) or were taking antidiabetic (e.g., sulfonylureas, biguanides, α-glucosidase inhibitors, etc.), weight loss (e.g., orlistat, lorcaserin, etc.), lipid-lowering medications (e.g., statins), and/or medications affecting blood pressure as well as heart rate/rhythm (e.g. beta blockers, ACE-inhibitors, etc.) based on self-reported medical history. Blood analyses were performed prior to physical exam (e.g., electrolytes, eGFR, etc.) that also included a resting electrocardiogram (ECG) and peak exercise stress test to rule out heart dysfunction. All participants provided written and verbal informed consent approved by the University of Virginia Institutional Review Board (IRB#19364, NCT03355469).
Body Composition and Aerobic Fitness.
Body mass (kilograms) and height (meters) were measured using a digital scale and stadiometer, respectively, to assess body mass index (BMI). Total body fat and lean body mass (LBM) were measured using dual-energy x-ray absorptiometry (DXA, Lunar Prodigy; GE Healthcare). Participants completed a continuous incremental peak oxygen consumption (VO2peak) test on a treadmill with indirect calorimetry (CareFusion, Vmax CART, Yorba Linda, CA, USA) using routine criteria [e.g., plateau in VO2, >1.1 RER, or within 10% of age-predicted HRmax (220 - age)]. A self-selected speed was chosen by the participant and incline rose by 2.5% every 2 minutes until participants reached VO2peak. Blood pressure was obtained during the last 30 seconds of rest and stages 1 (baseline), 3, 4, and peak. Heart rate (HR) was continuously monitored using a 12-lead ECG during exercise up to peak (HRpeak) to estimate sympathetic activity. HR was also tested 1- and 2-minutes post exercise to assess heart rate recovery (HRR; peak - post-exercise) while participants walked at 1.5 mph at 2.5% grade to estimate parasympathetic withdrawal. In addition, rate pressure product (RPP = HR x SBP) was calculated for each stage of exercise.
Control Period.
Participants were provided a standardized American Heart Association diet that consisted of 55% carbohydrate, 30% fat, and 15% protein for 24-h prior to measurements. Caloric needs were determined using indirect calorimetry and multiplying resting metabolic rate (RMR) by an activity factor of 1.2. Dietary information was analyzed using The Food Nutrition Processor Analysis Software (version 11.1; ESHA Research, Salem, OR). Participants were also instructed to refrain from vigorous exercise 72-h prior to measurements as well as alcohol and caffeine 24-h prior to the clamp procedure and receiving the AMBP monitor.
24-h Blood Pressure.
All participants wore the ABPM (Welch Allyn ABPM-6100, Skaneateles Falls, NY) cuff on their upper left arm following manufacturer’s instructions for a period of 24 hours, including rest/sleep (bedtime and wake times were self-selected). Cuffs were set to record daytime blood pressure every 30 minutes and nighttime blood pressure every hour at night. Unsuccessful recordings, defined as an incomplete inflation, were excluded from the data to ensure measurements were accurate. Participants were then categorized as “dippers” (n=7; 4F) if they experienced a ≥10% decrease in mean daytime to mean nighttime SBP or “non-dippers” (n=11; 9F) if they experienced <10% decrease or rise in SBP from daytime to nighttime [5,24].
Euglycemic-Hyperinsulinemic Clamp.
Participants reported to the Clinical Research Unit after an ~10-h overnight fast. Participants sat semi-supine in a quiet, dimly lit room for approximately 5 minutes before clinical blood pressure was recorded using an automated platform (DINAMAPProcare 400; GE Medical Systems). Two intravenous catheters were then placed in the right arm for blood draws and insulin/glucose infusion, respectively. Insulin was infused at a primed (250 mU·m−2·min−1) constant rate (40 mU·m−2·min−1) for 120 minutes with a variable infusion of dextrose 20% to achieve a blood glucose concentration of 90 mg·dl−1. Blood glucose was analyzed every 5 minutes during the procedure. Insulin concentrations were assessed at 0, 90, 105 and 120 minutes and averaged during the last 30 min to reflect steady state. Metabolic insulin sensitivity was defined as glucose infusion rate (GIR) during the final 30 min of the clamp. Baseline and 120-minute brachial SBP and diastolic blood pressure (DBP) were assessed and standardized to the left arm, and data were averaged across three consecutive measurements at both time points, with a one-minute break between each recording. HR was also recorded at baseline and 120 minutes.
Endothelial Function.
Endothelial function was assessed using flow mediated dilation (FMD) at the brachial artery of the left arm using a 12-3 MHz range linear transducer approximately 5 cm proximal to the antecubital crease using B-mode ultrasound (Epiq 7C Ultrasound Machine; Philips Medical Systems, Andover, MA). A blood pressure cuff was placed around the forearm and baseline images of the brachial artery were taken prior to inflation. The cuff was manually inflated to at least 50 mmHg over the participant’s resting SBP for 5 minutes and then deflated. Thereafter, images were captured every 10 seconds post deflation for 2 minutes to determine post ischemic peak diameter and the time to reach peak diameter (time to peak). Measurements were collected at 0 minutes (baseline) and approximately 120 minutes (steady-state) of the clamp procedure. FMD (unscaled) was defined as the percent change in peak diameter compared to the baseline diameter as previously performed by our group [25,26]. Vascular insulin sensitivity was defined as FMD at 120 min to mimic GIR. In addition, FMD was allometrically scaled to account for potential baseline diameter influence. All images were stored in Digital Imaging and Communication in Medicine (DICOM) format for analysis and images were assessed using commercially available software (Brachial Analyzer for Research v.6, Medical Imaging Applications LLC, Coralville, IA).
Biochemical Analysis.
Plasma glucose was measured immediately after collection using a glucose oxidase assay (YSI 2700, Yellow Springs, OH). Sodium, potassium, chloride, calcium, BUN, and creatine were analyzed via the hospital medical laboratory. Estimated glomerular filtration rate (eGFR) was estimated using CKD-EPI calculations accordingly for renal function from these analytes. Insulin was centrifuged at 4°C for 10 minutes at 3,000 rpm and stored at −80°C until analysis via radioimmunoassay.
Statistics.
Data were analyzed with SPSS Software (version 27, 2020) and graphs were produced via GraphPad Prism (version 9.2.0, 2021). Normality was assessed by Shapiro-Wilk tests. Outliers were assessed using the interquartile range method and sensitivity analyses were used to confirm removal. Independent two sample two-tailed t-tests were performed to assess group differences when appropriate. A Fisher’s exact test was used to assess differences in sex and racial/ethnic distribution between groups. A two-way (group x time) ANOVA with repeated measures was used to assess group differences (dipper vs. non-dipper) during the clamp procedure (0-min/baseline vs. 120-minute) and graded exercise test (stages 1-peak) when appropriate. Pearson correlations were used to examine the strength of associations among variables. Given that dippers had lower concentrations of HDL and LDL cholesterol, correlations were performed as partial correlations controlling for HDL and LDL, when appropriate. Effect sizes were calculated using Cohen D (baseline) and partial η2 (group x time) when appropriate [27,28]. We interpret Cohen D with 0.2, 0.5, or 0.8, and partial η2 with 0.01, 0.06, and 0.14 representing small, medium, and large effect sizes, respectively. Data are reported as mean ± SD and significance was accepted as P<0.05.
Results
Participant Characteristics.
There were no significant differences in ATP III criteria, age, VO2peak, and body composition between dippers and non-dippers (shown in Table 1), although VO2peak effect sizes were large in dippers versus non-dippers. Similarly, there were no significant differences in blood sodium, potassium, chloride, creatine, calcium, BUN, or eGFR (shown in Table 1). There were no statistically significant differences in sex distribution (P=0.33) and racial/ethnic distribution (P=0.31) between groups.
Table 1.
Subject Characteristics
Data are mean ± SD. BMI: body mass index; LBM: lean body mass; VO2peak: peak oxygen consumption; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; TG: triglycerides; HDL: high density lipoprotein cholesterol; Plasma BUN: plasma blood urinary nitrogen; plasma eGFR: plasma estimated glomerular filtration rate; CHO: carbohydrates; Na:K ratio, sodium:potassium ratio. P-values represent the t-test difference between dippers and non-dippers. Significance was set at P<0.05.
| Whole-Group | Dippers | Non-Dippers | P-value |
Cohen’s D
Effect Size |
|
|---|---|---|---|---|---|
| Subjects (F/M) | 18 (13F/5M) | 7 (4F/3M) | 11 (9F/2M) | ||
| Caucasian | 17 | 6 | 11 | ||
| African American | 0 | 0 | 0 | ||
| Hispanic | 1 | 1 | 0 | ||
| Age (years) | 54.3±7.0 | 52.6±6.3 | 55.5±7.4 | 0.41 | 0.43 |
| Body Composition | |||||
| Weight (kg) | 105.8±12.9 | 107.0±15.8 | 105.0±11.5 | 0.76 | 0.15 |
| BMI (kg·m2) | 36.6±3.9 | 36.6±4.3 | 36.5±4.3 | 0.92 | 0.05 |
| Fat mass (kg) | 45.4±7.7 | 48.2±8.6 | 45.1±9.5 | 0.21 | 0.76 |
| LBM (kg) | 55.1±8.6 | 59.0±10.9 | 51.3±3.00 | 0.13 | 0.27 |
| Aerobic Fitness | |||||
| VO2peak (L·min−1) | 2.5±0.6 | 2.7±0.6 | 2.3±0.4 | 0.11 | 0.82 |
| VO2peak (ml·kg−1·min−1) | 23.4±4.1 | 25.5±4.6 | 22.0±3.3 | 0.07 | 0.92 |
| MetS Criteria | |||||
| WC (cm) | 115.6±7.9 | 118.1±8.4 | 114.0±7.4 | 0.29 | 0.52 |
| Clinic SBP (mmHg) | 131.9±10.0 | 129.9±13.5 | 133.0±7.4 | 0.52 | 0.84 |
| Clinic DBP (mmHg) | 77.9±8.2 | 77.2±11.4 | 78.3±5.9 | 0.78 | 0.52 |
| FPG (mg·dl−1) | 101.1±11.3 | 99.7±11.2 | 101.9±11.8 | 0.70 | 0.19 |
| TG (mg−dl−1) | 117.5±41.4 | 110.4±29.8 | 122.0±48.1 | 0.58 | 0.27 |
| HDL (mg·dl−1) | 46.7±9.5 | 40.5±8.9 | 50.6±7.9 | 0.02 | 1.21 |
| Renal Function | |||||
| Plasma Sodium (mEQ, dl) | 139.6±1.9 | 140.0±1.7 | 139.3±2 | 0.50 | 0.33 |
| Plasma Potassium (mmol· dl−1) | 4.2±0.3 | 4.1±0.3 | 4.2±0.3 | 0.73 | 0.17 |
| Plasma Chloride (mmol· dl−1) | 105.4±2.2 | 106.5±2.1 | 104.7±2 | 0.08 | 0.88 |
| Plasma Creatine (mmol· dl−1) | 0.8±0.2 | 0.8±0.2 | 0.8±0.1 | 0.31 | 0.50 |
| Plasma Calcium (mmol· dl−1) | 9.5±0.4 | 9.3±0.5 | 9.5±0.2 | 0.35 | 0.46 |
| Plasma BUN (mg·dl−1) | 15.7±3.6 | 16.1±1.8 | 15.3±4.4 | 0.67 | 0.21 |
| Plasma eGFR (ml·min·m2) | 85.2±15.2 | 79.8±17.8 | 88.5±12.9 | 0.24 | 0.58 |
| Study Diet | |||||
| Calories (kcal) | 1587.3±265.3 | 1631.1 ±223.5 | 1562.9±289.0 | 0.53 | 0.73 |
| Protein (g) | 69.7±15.6∣ | 65.5 ±18.4 | 72.1±13.7 | 0.29 | 0.36 |
| Protein (%) | 18.7±5.3 | 17.1 ±4.3 | 19.6±5.7 | 0.24 | 0.67 |
| CHO (g) | 231.5±52.0 | 242.3±59.7 | 225±47.9 | 0.42 | 0.44 |
| CHO (%) | 57.7±7.9 | 58.6±8.5 | 57.3±7.8 | 0.68 | 0.83 |
| Fat (g) | 41.0±15.4 | 38.2±9.7 | 42.5±17.9 | 0.49 | 0.73 |
| Fat (%) | 22.8±6.5 | 21.2±5.3 | 23.6±7.1 | 0.36 | 0.59 |
| Saturated Fat (g) | 12.3±4.8 | 11.5±3.9 | 12.8±5.3 | 0.50 | 0.79 |
| Sodium (mg) | 1922.8±364.8 | 1884.3±288.8 | 1945.4±409.7 | 0.68 | 0.17 |
AMBP.
The number of successful measurements recorded during the day (P=0.84), night (P=0.43), and 24-h period (P=0.99) were similar between groups (shown in Table 2). As expected by design, dippers had a significantly greater drop in SBP from daytime to nighttime than non-dippers (16.63±5.2 vs. 1.83±5.6%, P<0.01). Daytime SBP (153.4±21.5 vs. 145.3±21.9 mmHg, P=0.45) and nighttime SBP (127.4±16.3 vs. 142.2±19.5 mmHg, P=0.76) were not statistically different between dippers and non-dippers. Additionally, there were no significant differences in fasting SBP (134.1±8.6 vs. 133.1±7.4 mmHg, P=0.81) or DBP (81.1±5.6 vs. 78.4±5.9 mmHg, P=0.37) measured the morning of the clamp.
Table 2.
24-h Ambulatory Blood Pressure
Data are mean ± SD. SBP: systolic blood pressure; DBP: diastolic blood pressure. P-values represent the t-test difference between dippers and non-dippers. Sleep/wake times were self-selected by participants. Significance was set at P<0.05.
| Whole-Group | Dippers | Non-Dippers | P-value |
Cohen’s D
Effect Size |
|
|---|---|---|---|---|---|
| Daytime Blood Pressure | |||||
| Successful Measurements (n) | 19.9±7.3 | 19.4±7.0 | 20.2±7.8 | 0.84 | 0.05 |
| Mean SBP (mmHg) | 148.4±21.5 | 153.4±21.5 | 145.3±21.9 | 0.45 | 0.37 |
| Mean DBP (mmHg) | 78.6±12.2 | 78.9±12.9 | 78.4±12.4 | 0.94 | 0.04 |
| Nighttime Blood Pressure | |||||
| Successful Measurements (n) | 7.4±1.7 | 7.9±1.7 | 7.2±1.7 | 0.43 | 0.51 |
| Mean SBP (mmHg) | 136.4±19.3 | 127.4±16.3 | 142.2±19.5 | 0.12 | 0.80 |
| Mean DBP (mmHg) | 68.1±11.1 | 63.3±7.7 | 71.1±12.1 | 0.15 | 0.73 |
| 24-h Blood Pressure | |||||
| Successful Measurements (n) | 27.4±8.4 | 27.3±8.5 | 27.4±8 | 0.99 | 0.15 |
| Mean SBP (mmHg) | 144.6±20.3 | 145.6±19.5 | 144±21.7 | 0.88 | 0.07 |
| Mean DBP (mmHg) | 75.7±11.3 | 74.4±11.6 | 76.5±11.6 | 0.72 | 0.17 |
Insulin Sensitivity and Vascular Function.
There were no group differences in fasting glucose or insulin concentrations as well as metabolic insulin sensitivity as reflected by glucose infusion rates (2.2±0.6 vs. 2.8±1.3 mg·kg−1·min−1, P=0.22). While there were no group differences in glucose concentrations at 120 minutes of the clamp between dippers and non-dippers (2.2±0.6 vs. 2.8±1.3 mg·dl−1, P=0.22), dippers had greater post-clamp insulin concentrations (n=14 due to technical assay issues, 119.1±14.2 vs. 75.5±17.8 uU·ml−1, P<0.01). There were no group differences in pre-occlusion brachial artery diameter at 0 minutes (4.2±0.9 vs. 3.8±0.8 mm, P=0.35) or 120 minutes of the clamp (4.1±0.8 vs. 3.8±0.8 mm, P=0.21). Similarly, there were no group differences in post-occlusion brachial artery diameter at 0 (4.3±1.0 vs. 3.8±0.8 mm, P=0.21) or 120 minutes of the clamp (4.2±0.9 vs. 3.7±0.7 mm, P=0.25, shown in Table 3). Time to peak was similar between dippers and non-dippers at both 0 minutes (n=17, 52.4±32.9 vs. 31.9±22.5, P=0.15) and 120 minutes (n=15, 71.2±33.2 vs. 61.4±43.0, P=0.63) of the clamp. There were no statistical differences in unsealed FMD at baseline (6.2±2.1 vs 6.0±1.8%, P>0.99) or 120 minutes of the clamp between dippers and non-dippers (7.0±1.1 vs. 5.5±1.6%, P=0.15; shown in Fig. 1). Similarly, there were no differences in scaled FMD at baseline (6.72±1.05 vs. 6.87±0.81%, P>0.99) or 120 minutes of the clamp between groups (7.00±0.81 vs. 6.67±0.61%, P=0.83). However, it is worth noting that there were medium effect sizes for insulin to promote increases in FMD in dippers vs. non-dippers (shown in Fig 1). There were no differences in baseline SBP (129.9±13.5 vs. 133.1±7.4, P=0.53) or DBP (77.2±11.4 vs. 78.4±5.9, P=0.78) between dippers and non-dippers. Similarly, there were no differences in SBP (126.4±7.2 vs. 134.5±11.2, P=0.13) or DBP (77.1±8.3 vs. 82.3±11.0, P=0.33) at 120 minutes of the clamp between dippers and non-dippers. While baseline HR was similar between dippers and non-dippers during the clamp (62.0±8.6 vs. 66.8±9.9, P=0.32), dippers had a significantly lower HR at 120 minutes (60.1±7.0 vs. 72.0±9.9, P=0.02). Greater insulin-stimulated FMD correlated with increased SBP dipping correlated (r=0.52, P=0.03; shown in Fig. 1) but the relationship was no longer significant when controlling for HR slope (r=0.42, P=0.09). Additionally, the relationship was no longer significant when allometrically scaling FMD (r=0.26, P=0.30; shown in Fig. 1)
Table 3.
Insulin Sensitivity, Arterial Diameter, and Cardiovascular Function
Data are mean ± SD. GIR: glucose infusion rate and HOMA-IR: homeostatic model assessment of insulin resistance. All post-clamp measurements, including GIR and insulin sensitivity, were averaged over the last 30 minutes of the clamp procedure. P-values represent the t-test difference between dippers and non-dippers. Significance was set at P<0.05.
| n (dippers/non- dippers) |
Whole- Group |
Dippers | Non-Dippers | P-value |
Cohen’s D
Effect Size |
|
|---|---|---|---|---|---|---|
| Insulin Sensitivity | ||||||
| Glucose 0 min (mg·dl−1) | 18 (7/11) | 101.1±11.3 | 99.7±11.2 | 101.9±11.8 | 0.85 | 0.19 |
| Glucose steady-state (mg·dl−1) | 18 (7/11) | 89.9±4.2 | 90.0±4.3 | 89.8±4.3 | 0.22 | 0.55 |
| Insulin 0 min (uU·ml−1) | 14 (6/8) | 17.5±8.5 | 22.4±8.5 | 13.9±6.9 | 0.06 | 1.11 |
| Insulin steady-state (uU·ml−1) | 14 (6/8) | 94.2±27.4 | 119.1±14.2 | 75.5±17.8 | <0.01 | 2.57 |
| GIR (mg·k−1·min−1) | 18 (7/11) | 2.6±1.1 | 2.2±0.6 | 2.8±1.3 | 0.22 | 0.61 |
| HOMA-IR | 13 (5/8) | 3.9±1.6 | 4.5±1.0 | 3.5±1.8 | 0.26 | 0.38 |
| Brachial Artery Diameter | ||||||
| Diameter Pre-occlusion 0 min (mm) | 18 (7/11) | 4.0±0.9 | 4.2±0.9 | 3.8±0.8 | 0.35 | 0.46 |
| Diameter Post-occlusion 0 min (mm) | 18 (7/11) | 4.0±0.9 | 4.3±1.0 | 3.8±0.8 | 0.21 | 0.64 |
| Diameter Pre-occlusion 120 min (mm) | 18 (7/11) | 3.9±0.8 | 4.1±0.8 | 3.8±0.8 | 0.45 | 0.38 |
| Diameter Post-occlusion 120 min (mm) | 18 (7/11) | 3.9±0.8 | 4.2±0.9 | 3.7±0.7 | 0.25 | 0.63 |
| Time to Peak 0 min (s) | 17 (7/10) | 40.3±28.3 | 52.4±32.9 | 31.9±22.5 | 0.15 | 0.75 |
| Time to Peak 120 min (s) | 15 (5/10) | 67.9±35.5 | 61.4±43.0 | 71.2±33.2 | 0.63 | 0.27 |
| Cardiovascular Function | ||||||
| SBP 0 min (mmHg) | 18 (7/11) | 131.9±10.0 | 129.9±13.5 | 133.1±7.4 | 0.53 | 0.32 |
| SBP 120 min (mmHg) | 15 (7/8) | 130.7±10.2 | 126.4±7.2 | 134.5±11.2 | 0.13 | 0.84 |
| DBP 0 min (mmHg) | 18 (7/11) | 77.9±8.2 | 77.2±11.4 | 78.4±5.9 | 0.78 | 0.14 |
| DBP 120 min (mmHg) | 15 (7/8) | 79.9±9.8 | 77.1±8.3 | 82.3±11.0 | 0.33 | 0.52 |
| HR 0 min (mmHg) | 17 (7/10) | 64.8±9.4 | 62.0±8.6 | 66.8±9.9 | 0.32 | 0.51 |
| HR 120 min (mmHg) | 15 (7/8) | 66.5±10.4 | 60.1±7.0 | 72.0±9.9 | 0.02 | 1.36 |
Fig. 1.
Results of two-way ANOVA test with repeated measures (group x time) comparing differences in unscaled flow-mediated dilation (FMD) (a) and scaled FMD (b). Results of Pearson correlations between SBP dipping and unscaled baseline FMD (c), unscaled 120-minute FMD (d), scaled baseline FMD (e), and scaled 120-minute FMD (f). Data are reported as mean ± SD and/or individual points. Significance was set at P<0.05.
Sympathovagal Balance.
There was no significant difference in resting HR between dippers and non-dippers during the graded exercise test (71.6±3.2 vs. 73.1±7.2 bpm, P=0.67). However nondippers had an elevated HR at both stage 3 (104.7±5.6 vs. 119.9±13.8 bpm, P=0.03) and stage 4 (113.6±5.3 vs. 132.8±13.3 bpm, P<0.01) of exercise (shown in Fig. 2). Non-dippers also had an elevated HR slope during exercise (6.5±0.9 vs. 8.2±1.7 bpm/min, P=0.03). Further, SBPslope was higher in non-dippers compared to dippers (4.7±2.3 vs. 7.2±2.5 mmHg/min, P=0.05; shown in Fig. 2). Non-dippers had an elevated RPP stage 4 (17828.0±3769.9 vs. 23129.2 ± 4495.4 mmHg·bpm, P=0.05) and peak (29254.7 ±5744.4 vs. 31610.2±12241.4 mmHg·bpm, P=0.03; shown in Fig. 2) of exercise as well as a steeper rise in RPP slope (1577.1±298.5 vs. 2277.7±519.0 mmHg·bpm/min, P<0.01). HRR did not differ between dippers and non-dippers at 1 minute (17.9±7.1 vs. 20.0±3.7 bpm, P=0.43) nor 2 minutes after exercise (33.3±8.0 vs. 38.2±4.9 bpm, P=0.13, shown in Fig 2). VO2 rose comparably across stage 1-4, although we observed a significant interaction seemingly from stage 4 to peak (P=0.01; shown in Fig. 2). Interestingly, SBP dipping did not correlate with SBP responses during exercise (r=−0.14, P=0.59) or HR during exercise (r=−0.14, P=0.60)
Fig. 2.
Results of two-way ANOVA test with repeated measures (group x time) comparing differences in systolic blood pressure (SBP) during exercise (a), diastolic blood pressure (DBP) during exercise (b), heart rate (HR) during exercise (c), heart rate recovery post exercise (d), rate pressure product (RPP) during exercise (e), and oxygen consumption (VO2) during exercise Data are reported as mean ± SD and/or individual points. Significance was set at P<0.05.
Discussion
The major finding from this study is that SBP dipping is paralleled by attenuated elevations in blood pressure and heart rate compared with non-dippers. Moreover, there were no statistical group differences in metabolic or vascular insulin sensitivity in patients with MetS despite medium effect sizes for both outcomes. These findings suggest insulin action on endothelial function and/or glucose metabolism is unlikely a primary factor in explaining blood pressure dipping, although additional work confirming these results are warranted based on effect sizes. Indeed, prior work in adolescents with obesity reported that decreased insulin sensitivity as measured via HOMA-IR was associated with blunted dipping when adjusting for daytime SBP, age, and BMI [29]. Interestingly, when examining the role of pre/post-clamp insulin concentrations in the present study though in relation to SBP dipping (n=14), higher post-clamp insulin levels were observed in dippers (shown in Table 3). Moreover, we detected a strong effect size difference between groups in circulating insulin among dippers. Given that insulin levels were higher in dippers during the clamp (due to possibly alterations in insulin clearance and/or synthesis), it would suggest that per unit of insulin our non-dippers were to our surprise more insulin sensitive than dippers. These findings are in contrast to previous work in adults with HTN and T2D that have shown insulin to increase daytime SBP through increased sympathetic stimulation and sodium retention [30,31]. We do not have a readily available explanation for this observation in our study. As such, our collective work highlights, with no clinical differences in sodium, creatine, eGRF or potassium that the elevated metabolic effects of insulin observed in dippers likely has little clinical consequence in this cohort on nocturnal blood pressure as previously noted [11,15,30].
Alternatively, it is reasonable to anticipate that endothelial function may relate to SBP dipping. FMD is a measure of shear rate-induced NO production independent of insulin-activated NO release through Akt/eNOS related pathways [32,33]. Although the findings of this study suggest that neither fasting or insulin-stimulated FMD are statistically different between dippers and nondippers, dippers had medium effect size results favoring time to peak during fasting as well as vascular insulin sensitivity. However, despite observing a significant correlation between insulin-stimulated FMD at 120 minutes of the clamp with SBP dipping, these results were no longer significant after controlling for HRslope. Our work appears partially at odds with recent work in patients with chronic kidney disease (CKD) that reported higher baseline brachial artery FMD in dippers compared to non-dippers, and that baseline/fasting FMD correlated with lower nighttime SBP [18]. Similarly, prior work in patients with HTN reported that carotid-femoral pulse wave velocity (cf-PWV), the “gold standard” for measuring arterial stiffness, was associated with blunted SBP dipping [34]. Furthermore, another study examining brachial artery FMD and cf-PWV in normotensive participants with MetS reported decreased endothelial function and increased arterial stiffness [35]. An important consideration between prior work and data presented here is that our MetS cohort had relatively high baseline FMD (~6%) compared with others (−3-4%) (16). This suggests that our patients had relatively normal fasting endothelial function, and shear rate-induced NO was not likely a primary factor contributing to SBP dipping. Our observation is consistent with recent work examining the role of microvascular function in relation to SBP dipping, whereby adults with stage-1 HTN exhibited normal fasting microvascular endothelial function [36]. While prior work reported elevated endothelin-1 (ET-1) expression, indicative of vasoconstrictive pathways, in patients with T2D compared to lean healthy controls under hyperinsulinemic conditions [37], no study to date has assessed insulin-stimulated FMD responses in relation to dipping status. Given medium effect size in dippers, it is speculative that insulin promoted Akt-related eNOS activation more than non-dippers (40). Thus, our work expands on current literature as this is the first study to examine insulin-stimulated FMD in SBP dipping.
Another possible explanation is that SBP dipping relates to low sympathetic and high parasympathetic activity. In fact, Jeong et al. reported greater muscle sympathetic nerve activity as determined by microneurography in non-dippers compared to dippers in patients with CKD [18]. Similarly, a recent meta-analysis examining autonomic function and dipping patterns found that sustained sympathetic overdrive has been linked to essential HTN [38]. In the present study, we demonstrate that the rise in SBP and heart rate was elevated in non-dippers compared to dippers during exercise. Further, the higher RPP between dippers and non-dippers during the submaximal graded exercise test is also of interest since VO2 appeared similar, albeit peak fitness tended to be higher in dippers than non-dippers. While our work is consistent with views that autonomic function may be an important factor regulating ambulatory blood pressure in patients with MetS, future research should employ more direct measurements of sympathetic activity (i.e. MSNA or plasma catecholamines) to elucidate the relationship between blood pressure responses to exercise and sympathetic activity. The clinical consequence of greater submaximal SBP is that non-dippers may experience greater cardiac workload during exercise compared to dippers. However, it is important to acknowledge that previous work has suggested that an RPP above 30,000 is associated with a heightened risk for cardiovascular disease [39] and both groups have an average peak RPP value at or above 30,000, which may be more reflective of their MetS status. In either case, we observed no statistical difference in HRR between groups, suggesting that parasympathetic activity was comparable between dippers and non-dippers. Collectively, we interpret our the greater rises in HR and RPP during the graded exercise test in non-dippers to reflect heightened sympathetic activity [40-42]. Importantly, however, it should be noted that a graded exercise test is not a direct measure of sympathetic activity and additional work is warranted to directly measure sympathetic activation before and during insulin stimulation to confirm this hypothesis. This is clinically relevant because sympathetic overactivation has been linked to sudden cardiac arrest in a variety of conditions [38,43,44].
There are limitations to this study that warrant discussion. The sample population was relatively small and homogenous (~94% White and ~72% female (all post-menopausal)). This limits the generalizability of our results to the population as a whole. While previous work by Hyman et al reported similar dipping patterns across White, Black, and Hispanic populations [45], more racial/ethnic disaggregated data on AMBP is needed. It is also worth noting that muscle sympathetic nerve activity is a more direct measurement of sympathetic function than using a graded exercise test with HR and SBP measurements [46,47], despite changes in HR being linked to insulin resistance and interpreted to reflect neural activity [48]. Notably, guidelines for recording AMBP suggest that a minimum of 8 measures should be recorded during sleep and 15 during wake time [49]. However, given that sleep measurements were taken once every hour and 10 participants (4 dippers, 6 non-dippers) reported getting less than 8 hours of sleep, these individuals fell below this threshold. Additionally, FMD was not corrected for shear rate due to technical issues with obtainment of blood flow velocity. Nonetheless, FMD unscaled is an independent factor predicting CVD and provides clinical insight [50]. Additionally, since we did not continuously capture arterial diameter post-deflation, it is likely that we captured peak and not maximal post-ischemic diameter. Finally, participants in this study were dichotomized as dippers and non-dippers. We acknowledge that there are two dipping sub-categories known as extreme dippers (>20% drop in SBP) and risers (<0% drop in SBP) [5]. Risers, also known as reverse dippers, are most at risk for CVD and stroke compared to all other dipping profiles in older participants [51]. Extreme dippers also have an increased risk of CVD and are more susceptible to brain and cardiac hypoperfusion than other categories [52]. In the present study, three (2F/1M) non-dippers exhibited a rising pattern and one (M) dipper exhibited extreme dipping patterns. However, removal of these participants did not affect the overall pattern of responses (data not shown). Regardless, future work should consider further characterizing these SBP dipping/non-dipping phenotypes to improve precision medical care.
In conclusion, our findings demonstrate that patients with MetS who experience a ≥10% dip in nocturnal SBP have greater SBP, HR, and RPP responses during exercise. While there were no statistical differences in fasting or insulin-stimulated FMD between groups, it is worth noting dippers had modest effects to raise insulin-stimulated endothelial function versus non-dippers. When taken with comparable heart rate recovery responses post-exercise, these overall findings suggest that sympathetic activity, and to a lesser extent vascular insulin sensitivity, may be an important factor modulating shifts from daytime to nighttime blood pressure in patients with MetS. Future work should consider direct assessments of sympathetic function, with and without insulin, to understand mechanisms explaining blood pressure dipping in MetS. Further work awaits investigation as well in examining how exercise, with or without pharmaceutical or dietary intervention, impacts nocturnal blood pressure to minimize CVD risk.
Acknowledgements
We thank the dedicated research assistants of the Applied Metabolism & Physiology Lab and participants for their effort. We also thank Dr. Eugene J. Barrett, M.D., Ph.D. as well as Ms. Linda Jahn, M.Ed., R.N. for training and assistance with ultrasound measures as well as the nursing staff of the Clinical Research Unit for technical assistance, and Ms. Lisa Farr, M.Ed. from the Exercise Physiology Core Lab for fitness testing.
Funding
This study was supported by the National Institutes of Health R01-HL130296 (SKM)
Footnotes
Ethics
This study protocol was reviewed and approved by the University of Virginia Institutional Review Board (IRB#19364, NCT03355469) and conducted according to the principles of the World Medical Association Declaration of Helsinki. All participants provided written and verbal consent to participate in the study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Data Availability
Availability of data and material is subject to permission by the contributing principal investigators of the study.
References
- 1.World Health Organization. Hypertension [Internet]. 2019. [cited 2021 Feb 25]. Available from: https://www.who.int/news-room/fact-sheets/detail/hypertension
- 2.Center for Disease Control and Prevention. Facts About Hypertension ∣ cdc.gov [Internet]. Centers for Disease Control and Prevention. 2020. Sep [cited 2021 Feb 18]. Available from: https://www.cdc.gov/bloodpressure/facts.htm [Google Scholar]
- 3.Whelton Paul K, Carey Robert M, Aronow Wilbert S, Casey Donald E, Collins Karen J, Dennison Himmelfarb Cheryl, et al. 2017. ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. Journal of the American College of Cardiology. 2018 May;71(19):e127–248. [DOI] [PubMed] [Google Scholar]
- 4.Mancia G, Parati G. Ambulatory blood pressure monitoring and organ damage. Hypertension (Dallas, Tex : 1979). 2000;36(5):894–900. [DOI] [PubMed] [Google Scholar]
- 5.Kazuomi Kario. Nocturnal Hypertension. Hypertension. 2018. Jun;71(6):997–1009. [DOI] [PubMed] [Google Scholar]
- 6.Banegas Luis, Ruilope Luis, de la Sierra Alejandro, Vinyoles Ernest, Gorostidi Manuel, de la Cruz Juan J., et al. Clinic Versus Daytime Ambulatory Blood Pressure Difference in Hypertensive Patients [Internet]. 2017. DOI: 10.1161/HYPERTENSIONAHA.116.08567 [DOI] [PubMed] [Google Scholar]
- 7.Hansen TW, Jeppesen J, Rasmussen S, Ibsen H, Torp-Pedersen C. Ambulatory Blood Pressure and Mortality. Hypertension. 2005. Apr;45(4):499–504. [DOI] [PubMed] [Google Scholar]
- 8.Oh SW, Han KH, Han SY, Koo HS, Kim S, Chin HJ. Association of Sodium Excretion With Metabolic Syndrome, Insulin Resistance, and Body Fat. Medicine (Baltimore). 2015. Oct;94(39). DOI: 10.1097/MD.0000000000001650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Smulyan H, Lieber A, Safar ME. Hypertension, Diabetes Type II, and Their Association: Role of Arterial Stiffness. American Journal of Hypertension. 2016. Jan;29(1):5–13. [DOI] [PubMed] [Google Scholar]
- 10.Wang F, Han L, Hu D. Fasting insulin, insulin resistance and risk of hypertension in the general population: A meta-analysis. Clin Chim Acta. 2017. Jan;464:57–63. [DOI] [PubMed] [Google Scholar]
- 11.Tarray R, Saleem S, Afroze D, Yousuf I, Gulnar A, Laway B, et al. Role of insulin resistance in essential hypertension. Cardiovascular Endocrinology & Metabolism. 2014. Dec;3(4):129–33. [Google Scholar]
- 12.Artunc F, Schleicher E, Weigert C, Fritsche A, Stefan N, Häring H-U. The impact of insulin resistance on the kidney and vasculature. Nat Rev Nephrol. 2016. Dec;12(12):721–37. [DOI] [PubMed] [Google Scholar]
- 13.Huggett RJ, Burns J, Mackintosh AF, Mary DASG. Sympathetic neural activation in nondiabetic metabolic syndrome and its further augmentation by hypertension. Hypertension. 2004. Dec;44(6):847–52. [DOI] [PubMed] [Google Scholar]
- 14.DeFronzo RA. The effect of insulin on renal sodium metabolism. A review with clinical implications. Diabetologia. 1981. Sep;21(3):165–71. [DOI] [PubMed] [Google Scholar]
- 15.Brands MW. Role of Insulin-Mediated Antinatriuresis in Sodium Homeostasis and Hypertension. Hypertension. 2018. Dec;72(6):1255–62. [DOI] [PubMed] [Google Scholar]
- 16.Hall JE, Brands MW, Henegar JR. Mechanisms of hypertension and kidney disease in obesity. Ann NY Acad Sci. 1999. Nov;892:91–107. [DOI] [PubMed] [Google Scholar]
- 17.Seravalle G, Grassi G. Obesity and hypertension. Pharmacological Research. 2017. Aug; 122:1–7. [DOI] [PubMed] [Google Scholar]
- 18.Jeong JH, Fonkoue IT, Quyyumi AA, DaCosta D, Park J. Nocturnal blood pressure is associated with sympathetic nerve activity in patients with chronic kidney disease. Physiol Rep. 2020. Oct;8(20):el4602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Schultz MG, Sharman JE. Exercise Hypertension. PLS. 2013; 1(3–4):161–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sartori C, Lepori M, Scherrer U. Interaction between nitric oxide and the cholinergic and sympathetic nervous system in cardiovascular control in humans. Pharmacology & Therapeutics. 2005. May;106(2):209–20. [DOI] [PubMed] [Google Scholar]
- 21.Tartan Z, Uyarel H, Kasikcioglu H, Alper AT, Ozay B, Bilsel T, et al. Metabolic syndrome as a predictor of non-dipping hypertension. Tohoku J Exp Med. 2006. Sep;210(1):57–66. [DOI] [PubMed] [Google Scholar]
- 22.Macchia A, Levantesi G, Borrelli G, Franzosi MG, Maggioni AP, Marfisi R, et al. A clinically practicable diagnostic score for metabolic syndrome improves its predictivity of diabetes mellitus: The Gruppo Italiano per lo Studio della Sopravvivenza nell’Infarto miocardico (GISSI)–Prevenzione scoring. American Heart Journal. 2006. Mar;151(3):754.e7–754.e17. [DOI] [PubMed] [Google Scholar]
- 23.Galassi A, Reynolds K, He J. Metabolic Syndrome and Risk of Cardiovascular Disease: A Meta-Analysis. The American Journal of Medicine. 2006. Oct;119(10):812–9. [DOI] [PubMed] [Google Scholar]
- 24.Verdecchia P, Schillaci G, Guerrieri M, Gatteschi C, Benemio G, Boldrini F, et al. Circadian blood pressure changes and left ventricular hypertrophy in essential hypertension. Circulation. 1990. Feb;81(2):528–36. [DOI] [PubMed] [Google Scholar]
- 25.Gilbertson NM, Miller SL, Eichner NZM, Malin SK. Endothelial function following interval exercise plus low-calorie diet treatment in obese females. Physiol Rep. 2019. Sep;7(18):e14239. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Malin SK, Gilbertson NM, Eichner NZM, Heiston E, Miller S, Weltman A. Impact of Short-Term Continuous and Interval Exercise Training on Endothelial Function and Glucose Metabolism in Prediabetes. J Diabetes Res. 2019;2019:4912174. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Cohen J. Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educ Psychol Meas. 1973;33:107–12. [Google Scholar]
- 28.Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Routledge; 1988. DOI: 10.4324/9780203771587 [DOI] [Google Scholar]
- 29.Westerstahl M, Marcus C. Association between nocturnal blood pressure dipping and insulin metabolism in obese adolescents. Int J Obes (Lond). 2010. Mar;34(3):472–7. [DOI] [PubMed] [Google Scholar]
- 30.Shimamoto K, Hirata A, Fukuoka M, Higashiura K, Miyazaki Y, Shiiki M, et al. Insulin sensitivity and the effects of insulin on renal sodium handling and pressor systems in essential hypertensive patients. Hypertension. 1994. Jan;23(1 Suppl):I29–33. [DOI] [PubMed] [Google Scholar]
- 31.Trevisan R, Fioretto P, Semplicini A, Opocher G, Mantero F, Rocco S, et al. Role of insulin and atrial natriuretic peptide in sodium retention in insulin-treated IDDM patients during isotonic volume expansion. Diabetes. 1990. Mar;39(3):289–98. [DOI] [PubMed] [Google Scholar]
- 32.Barrett EJ, Eggleston EM, Inyard AC, Wang H, Li G, Chai W, et al. The vascular actions of insulin control its delivery to muscle and regulate the rate-limiting step in skeletal muscle insulin action. Diabetologia. 2009. May;52(5):752–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Maruhashi T, Kajikawa M, Kishimoto S, Hashimoto H, Takaeko Y, Yamaji T, et al. Diagnostic Criteria of Flow-Mediated Vasodilation for Normal Endothelial Function and Nitroglycerin-Induced Vasodilation for Normal Vascular Smooth Muscle Function of the Brachial Artery. J Am Heart Assoc. 2020. Jan;9(2):e013915. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Cicek Y, Durakoglugil ME, Kocaman SA, Cetin M, Erdogan T, Dogan S, et al. Non-dipping pattern in untreated hypertensive patients is related to increased pulse wave velocity independent of raised nocturnal blood pressure. Blood Press. 2013. Feb;22(1):34–8. [DOI] [PubMed] [Google Scholar]
- 35.Ghiadoni Lorenzo, Penno Giuseppe, Giannarelli Chiara, Plantinga Yvonne, Bernardini Melania, Pucci Laura, et al. Metabolic Syndrome and Vascular Alterations in Normotensive Subjects at Risk of Diabetes Mellitus. Hypertension. 2008. Feb;51(2):440–5. [DOI] [PubMed] [Google Scholar]
- 36.Dillon GA, Greaney JL, Shank S, Leuenberger UA, Alexander LM. AHA/ACC-defined stage 1 hypertensive adults do not display cutaneous microvascular endothelial dysfunction. Am J Physiol Heart Circ Physiol. 2020. Sep;319(3):H539–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Mahmoud AM, Szczurek MR, Blackburn BK, Mey JT, Chen Z, Robinson AT, et al. Hyperinsulinemia augments endothelin-1 protein expression and impairs vasodilation of human skeletal muscle arterioles. Physiol Rep. 2016. Aug;4(16):e12895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Grassi G, Pisano A, Bolignano D, Seravalle G, D’Arrigo G, Quarti-Trevano F, et al. Sympathetic Nerve Traffic Activation in Essential Hypertension and Its Correlates: Systematic Reviews and Meta-Analyses. Hypertension. 2018. Aug;72(2):483–91. [DOI] [PubMed] [Google Scholar]
- 39.Fomitano LD, de Godoy MF. [Increased rate-pressure product as predictor for the absence of significant obstructive coronary artery disease in patients with positive exercise test]. Arq Bras Cardiol. 2006. Feb;86(2):138–44. [DOI] [PubMed] [Google Scholar]
- 40.Koskinen T, Kähönen M, Jula A, Mattsson N, Laitinen T, Keltikangas-Järvinen L, et al. Metabolic syndrome and short-term heart rate variability in young adults. The cardiovascular risk in young Finns study. Diabet Med. 2009. Apr;26(4):354–61. [DOI] [PubMed] [Google Scholar]
- 41.Licht CMM, Vreeburg SA, van Reedt Dortland AKB, Giltay EJ, Hoogendijk WJG, DeRijk RH, et al. Increased sympathetic and decreased parasympathetic activity rather than changes in hypothalamic-pituitary-adrenal axis activity is associated with metabolic abnormalities. J Clin Endocrinol Metab. 2010. May;95(5):2458–66. [DOI] [PubMed] [Google Scholar]
- 42.Min K-B, Min J-Y, Paek D, Cho S-I. The impact of the components of metabolic syndrome on heart rate variability: using the NCEP-ATP III and IDF definitions. Pacing Clin Electrophysiol. 2008. May;31(5):584–91. [DOI] [PubMed] [Google Scholar]
- 43.Grassi G. Role of the sympathetic nervous system in human hypertension. J Hypertens. 1998. Dec; 16(12 Pt 2):1979–87. [DOI] [PubMed] [Google Scholar]
- 44.Nishimura M, Tokoro T, Nishida M, Hashimoto T, Kobayashi H, Yamazaki S, et al. Sympathetic overactivity and sudden cardiac death among hemodialysis patients with left ventricular hypertrophy. International Journal of Cardiology. 2010. Jun;142(1):80–6. [DOI] [PubMed] [Google Scholar]
- 45.Hyman DJ, Ogbonnaya K, Taylor AA, Ho K, Pavlik VN. Ethnic differences in nocturnal blood pressure decline in treated hypertensives. Am J Hypertens. 2000. Aug;13(8):884–91. [DOI] [PubMed] [Google Scholar]
- 46.Wallin BG, Esler M, Dorward P, Eisenhofer G, Ferrier C, Westerman R, et al. Simultaneous measurements of cardiac noradrenaline spillover and sympathetic outflow to skeletal muscle in humans. J Physiol. 1992;453:45–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Wallin BG, Thompson JM, Jennings GL, Esler MD. Renal noradrenaline spillover correlates with muscle sympathetic activity in humans. J Physiol. 1996. Mar;491(Pt 3):881–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Yeckel CW, Gulanski B, Zgorski ML, Dziura J, Parish R, Sherwin RS. Simple exercise recovery index for sympathetic overactivity is linked to insulin resistance. Med Sci Sports Exerc. 2009. Mar;41(3):505–15. [DOI] [PubMed] [Google Scholar]
- 49.O’Brien E, Coats A, Owens P, Petrie J, Padfield PL, Littler WA, et al. Use and interpretation of ambulatory blood pressure monitoring: recommendations of the British Hypertension Society. BMJ. 2000. Apr;320(7242):1128–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Yeboah J, Crouse JR, Hsu F-C, Burke GL, Herrington DM. Brachial flow-mediated dilation predicts incident cardiovascular events in older adults: the Cardiovascular Health Study. Circulation. 2007. May; 115(18):2390–7. [DOI] [PubMed] [Google Scholar]
- 51.Kario K, Pickering TG, Matsuo T, Hoshide S, Schwartz JE, Shimada K. Stroke prognosis and abnormal nocturnal blood pressure falls in older hypertensives. Hypertension. 2001. Oct;38(4):852–7. [DOI] [PubMed] [Google Scholar]
- 52.Yano Y, Kario K. Nocturnal blood pressure and cardiovascular disease: a review of recent advances. Hypertension Research. 2012. Jul;35(7):695–701. [DOI] [PubMed] [Google Scholar]
Associated Data
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Data Availability Statement
Availability of data and material is subject to permission by the contributing principal investigators of the study.