Introduction

Blood pressure variability (BPV) is now thought to be a risk factor for cardiovascular diseases (CVD)1,2. Recent data suggest that BPV persisting across several clinic visits (i.e., long-term BPV) is associated with stroke3,4, coronary heart disease and all-cause mortality5. BPV measured over 24 h by ambulatory blood pressure monitoring (ABPM) (i.e., short-term BPV) is also associated with CVD events6,7. However, few studies have examined the importance of BPV measured during a single clinic visit (i.e., very-short-term BPV). Recent studies have demonstrated that within-visit BPV is associated with metabolic syndrome score8, target organ damage (left ventricular hypertrophy and albuminuria)9 and the risk of stroke3,4, but not with overall CVD or all-cause mortality10,11. If it is associated with specific CVD risk factors, within-visit BPV could be a clinically useful measure because it can be assessed in a single visit. Therefore, we sought to determine the associations between within-visit BPV and common CVD risk factors.

Results

Overall, 312 821 subjects had complete data for CVD risk factors and had one or more BP measurement. We excluded 115 461 subjects who were taking antihypertensive drugs and/or had a history of cerebrovascular or cardiovascular diseases. We also excluded 188 487 subjects in whom only one value of blood pressure (BP) was recorded. After excluding these subjects, 17 795 subjects (6 907 men and 10 888 women) were included in the present analyses. There were no significant differences in the characteristics of subjects in whom BP was measured multiple times or once (Table 1).

Table 1 Characteristics of subjects included and excluded from the study
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The characteristics of subjects according to the BPV categories are shown in Table 2. Compared with subjects with low-BPV, those with high-BPV tended to be older and had higher BP, glycemic levels, lipid levels, waist circumference (WC) and body mass index (BMI). The prevalence of proteinuria was also higher in subjects with high-BPV, which may be explained by the higher prevalence of diabetes. There was no difference in estimated glomerular filtration rate (eGFR) or the proportions of male/female subjects.

Table 2 Characteristics of study subjects according to the level of blood pressure variability (N = 17,795)
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After adjusting for age, sex, mean systolic BP (SBP), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), use of lipid-lowering medication, WC, BMI and current smoking, the prevalence of prediabetes and diabetes were higher in subjects with high-BPV than those with low-BPV (odds ratio [OR] 1.16, 95% confidence interval [CI] 1.00–1.33 for prediabetes; and OR 1.39, 95% CI 1.11–1.75 for diabetes). Other CVD risk factors were not associated with high-BPV after adjusting for age, sex and mean SBP (Table 3).

Table 3 Cardiovascular risk factors according to the level of blood pressure variability (N = 17,795)
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As BPV is closely related to the mean SBP, the prevalence of prediabetes/diabetes was compared in each mean SBP category. The prevalence of prediabetes/diabetes were higher in subjects with high-BPV compared to those with low-BPV in each mean SBP category (34.4% vs. 32.5%, 43.8% vs. 36.1% and 44.0% vs. 40.7% in high-BPV vs. low-BPV for mean SBP of <120, 120–139 and ≥140 mm Hg, respectively). However, the difference was statistically significant only in subjects with mean SBP of 120–140 mm Hg after adjustment; the ORs were 1.00 (0.70–1.41), 1.34 (1.11–1.63) and 1.12 (0.92–1.37) for mean SBP of <120, 120–139 and ≥140 mm Hg, respectively, after adjustment.

Discussion

In the present study, high within-visit BPV was significantly associated with the prevalence of prediabetes and diabetes, independent of mean SBP, in a large general population. Our results suggest that measuring BPV at a single visit may help to identify subjects at increased risk of having impaired glycemic control.

Recent data suggest that BPV is a strong prognostic factor for stroke, coronary heart disease and all-cause mortality1,2,3,4,5,6,7. Several methods are available to measure BPV; visit-to-visit BPV is an index of long-term BPV, while 24-h BPV assessed by ABPM is an index of short-term BPV1,2. Both of these measurements of BPV are useful for assessing the risk of CVD3,12.

Within-visit BPV, an index of very-short-term BPV, is related to autonomic cardiovascular modulation13, mental stress14 and arterial stiffness15. However, few studies have evaluated the importance of BPV measured in a single clinic visit. One study revealed that within-visit BPV is associated with the risk of stroke3, but other studies found no association between within-visit BPV and the risks of all-cause or CVD mortality10,11. Different definitions of high-BPV might explain this discrepancy. Of note, BPV was not associated with CVD risk in studies where high-BPV was defined as the highest quintile or quartile of BPV10,11. By contrast, Rothwell et al. defined high-BPV as the highest decile of BPV3 and our cutoff point (20 mm Hg) is close to the 95th percentile of BPV. Thus, the highest level of BPV might be associated with the risk of CVD, which should be examined in future studies.

The prevalence of prediabetes was associated with within-visit BPV in this study. Previous studies have shown that subjects with diabetes had greater 24-h BPV compared with non-diabetic subjects16 and glycosylated hemoglobin A1c (HbA1c) was associated with 24-h BPV in subjects with diabetic neuropathy17. A recent study revealed that fasting plasma glucose was correlated with within-visit BPV8, but the data were not adjusted for mean SBP. To the best of our knowledge, our study was the first to show an association between prediabetes and within-visit BPV and that the association was independent of mean SBP in a large general population.

The mechanism underlying the association between prediabetes and BPV is somewhat unclear and needs to be considered. One possible mechanism is endothelial injury caused by impaired glucose tolerance18,19. BPV is related to abnormalities in endothelial and vascular smooth muscle function, which suggests BPV may influence the pathogenesis of CVD20. Another mechanism could be sympathetic overactivity and autonomic imbalance initiated by impaired glucose tolerance18,21 and these changes may lead to impaired ability to control BPV22,23, as circadian BP abnormality24. Further studies are needed to confirm these findings and to better understand the underlying mechanisms.

Several limitations of this study should be mentioned. First, we defined prediabetes and diabetes according to HbA1c rather than fasting plasma glucose. HbA1c is an index of long-term glycemic control and can be used to define prediabetes and diabetes25. Future studies should assess whether prediabetes defined according to fasting plasma glucose is also associated with high-BPV. Second, BPV was based on first and second measurements of BP. Although the first BP measurement is often considered unreliable26, a recent study suggested that the first BP measurement is related to CVD risk8. Additionally, it can be an easy and useful measure if difference of only two measurements of SBP reflects the risk of diabetes. Third, although mean SBP was adjusted in this study, the strong relationship between the mean SBP and BPV should be considered. The association between BPV and impaired glycemic status was observed strongly in subjects with prehypertensive range, but the association was weak in subjects with normotension or hypertension, which should be confirmed in another study. Fourth, selection bias should be considered because BP was measured twice in just 9% of the subjects who underwent health check-ups. However, there were no significant differences in the first BP measurement or other clinical factors between subjects who underwent one or multiple BP measurements. Although the guideline of this program recommends measurement of BP twice, single measurement of BP is also accepted and it depends on each facility. Fifth, considering the cross-sectional design of this study, we could not determine the cause-effect relationship between BPV and prediabetes. Unfortunately, we lack the data on heart rate variability which reflects antonomic dysfunction and there is no information about detailed glycemic status in the past. Whether treating subjects with prediabetes and high-BPV can prevent the progression to diabetes should be examined in longitudinal studies.

In conclusion, we have demonstrated that high within-visit BPV was significantly associated with the prevalence of prediabetes and diabetes, independent of mean SBP, in a large general population. Therefore, we consider that glycemic parameters should be monitored in subjects with high-BPV. Because BPV can be assessed in a single visit, it may be an easy and useful measure to identify subjects at increased risk of impaired glycemic control.

Methods

Study population

The subjects were people who underwent specific health check-ups and health guidance between April 2008 and March 2009 as part of the Tokutei-Kenshin (Special Health Check-up) programme in Aichi Prefecture, Japan. This programme was started by the Japanese government in 2008 to facilitate the early diagnosis and interventions for metabolic syndrome27. The target population comprises all Japanese citizens aged 40–74 years. The present study included subjects with complete data for at least two SBP and diastolic BP (DBP) measurements in one visit and for the following CVD risk factors: HbA1c, HDL-C, LDL-C, TG, WC and BMI. The guideline recommends taking a rest of ≥5 min before taking the first BP measurement and a rest of ≥1 min before taking the second BP measurement using automated machines. The first and second measurement of BP were used for the analysis because very few subjects had data on the third measurement of BP (n = 269). Serum creatinine was measured in most of the subjects. According to the program's guidelines proposed by the Japanese government, the clinician could measure either fasting plasma glucose or HbA1c as an index of glycemic control. Therefore, fasting plasma glucose was unknown in most of the study subjects with complete data for the other variables listed above.

All of the subjects completed a self-administered questionnaire to document their current medications for hypertension, diabetes and hyperlipidemia, history of cardiovascular and cerebrovascular diseases and smoking habits (current smoker or not). BP measurement and blood and urine sampling were performed at the local medical institutions. HbA1c values are presented as National Glycohemoglobin Standardization Program (NGSP) values, which were calculated with the following equation: HbA1c (NGSP, %) = 1.02 × HbA1c (Japan Diabetes Society, %) + 0.25%. Subjects currently taking antihypertensive drugs and those with a history of cerebrovascular or cardiovascular diseases reported in the self-administered questionnaire were excluded from the present analyses, because antihypertensive medication influences BPV and as the purpose of this study is to find the non-invasive methods (e.g. BPV in a single visit) to identify subjects at increased risk for cerebrovascular or cardiovascular diseases.

This study was conducted by the Aichi Chronic Kidney Disease Epidemiology Conference with support from the Aichi Kidney Foundation. Written informed consent was not required as only the existing information was used. This study was conducted in accordance with the Ethical Guidelines for Epidemiological Research by Japanese government. This study was approved by the Ethics Committee of Nagoya University School of Medicine (approval number 679).

Definitions of BPV categories and CVD risk factors

As previous described8,9, subjects were classified into 3 groups according to the maximum minus minimum systolic BP (ΔSBP) value as low-BPV (ΔSBP ≤ 10 mmHg) moderate-BPV (ΔSBP 11–20 mmHg) and high-BPV (ΔSBP > 20 mmHg). HbA1c was used as a marker of glycemic status. Subjects were divided into those without prediabetes (HbA1c < 5.7%) and those with prediabetes (HbA1c 5.7–6.4%) or diabetes (HbA1c ≥ 6.5% or under treatment for diabetes)25,28. Low HDL-C was defined as HDL-C < 40 mg/dL in men and <50 mg/dL in women, or the use of lipid-lowering drugs. High LDL-C was defined as LDL-C ≥ 160 mg/dL or the use of lipid-lowering drugs. Borderline-high TG was defined as TG 150–199 mg/dL and high TG was defined as TG ≥ 200 mg/dL or the use of lipid-lowering drugs29. Elevated WC was defined based on criteria for Asians30 as WC ≥ 90 cm in men and ≥80 cm in women. Using criteria for Asians31, overweight was defined as BMI 23.0–27.4 kg/m2 and obesity was defined as BMI ≥ 27.5 kg/m2. The eGFR was calculated using the Japanese GFR equation32, as follows: eGFR (mL/min/1.73 m2) = 194 × serum creatinine−1.094 (mg/dL) × age−0.287 (years) (×0.739 if female). Reduced eGFR was defined as eGFR < 60 mL/min/1.73 m2.

Statistical analyses

We compared the characteristics of subjects between those who were included and those who were excluded from the study. We also compared the characteristics of subjects among the groups of high-BPV, moderate-BPV and low-BPV. The prevalences of CVD risk factors in the high-BPV and moderate-BPV groups were compared with the low-BPV group. ORs and 95% CI were calculated for each CVD risk factor using unconditional logistic regression analysis adjusted for age (as a continuous variable), sex, mean SBP, HDL-C, LDL-C, TG, use of lipid-lowering medication, WC, BMI and current smoking. HDL-C, LDL-C, TG and use of lipid-lowering medication were not included in the analyses of HDL-C, LDL-C and TG. WC and BMI were not included in the analyses of WC and BMI. Values of p < 0.05 in adjusted models were considered statistically significant. All analyses were carried out using STATA software version 9 (Stata Corp, College Station, TX, USA).