Diabetes mellitus (DM) is a group of metabolic diseases in which a person has high blood sugar, either because the pancreas does not produce enough insulin or because cells do not respond to the insulin that is produced [
It is important to clarify the predictive value of DM and resting HR in predicting at the general population level, as this information can help clinicians in the prediction, prevention, and treatment of CAN. However, at the population level, the role of DM or resting HR in predicting CAN has not been well defined. The purpose of this study is to evaluate the predictive value of DM and resting HR for CAN in a large sample derived from a Chinese population.
We performed a CAN factor survey on a random sample of the Chinese population. Participants were recruited from rural and urban communities in Shanghai. Survey participants with undiagnosed CAN, aged 30–80 years, were included in this study. A total of 3,012 subjects were invited to a screening visit between 2011 and 2012. Some subjects were excluded from the study to eliminate potential confounding factors that may have influenced their CA function. Briefly, the exclusion criteria were as follows: (1) history or findings of arrhythmia, hyperthyroidism, or hypothyroidism; (2) pregnancy or lactation; and/or (3) serious hepatic or renal dysfunctions (the definition of serious liver or renal disease is that more than 1.5-fold elevation of alanine aminotransferase, aspartate aminotransferase, or direct bilirubin or serum creatinine > 115
All study subjects underwent a complete clinical baseline characteristics evaluation after an eight-hour fast, which included: (1) history and physical examination; (2) heart rate and blood pressure; (3) fasting serum glucose and insulin; and (4) fasting plasma lipids. Body mass index (BMI) was calculated with weight in kilograms divided by the square of height in meters. Physicians measured systolic and diastolic blood pressure (BP) values from the left arm while participants were seated. Fasting plasma glucose (FPG) was quantified by the glucose oxidase procedure; HbA1c was measured by ion-exchange high-performance liquid chromatography (HPLC; Bio-Rad, Hercules, CA, USA). The homeostasis model assessment insulin resistance estimate (HOMA-IR) was calculated as serum glucose (mmol/L) multiplied by plasma insulin (U/mL) and divided by 22.5. Serum total cholesterol (TC), high-density lipoprotein (HDL) cholesterol, triglyceride (TG) levels, serum creatinine (SCr), and uric acid (UA) were measured by an enzymatic method with a chemical analyzer (Hitachi 7600-020, Tokyo, Japan). Low-density lipoprotein (LDL) cholesterol levels were calculated using the Friedewald formula. The day-to-day and interassay coefficients of variation at the central laboratory in our hospital for all analyses were between 1% and 3%.
Short-term HRV has good reproducibility and is more practical in its application. In our large-scale population-based study, this test was used to evaluate CA function. HRV was measured noninvasively by power spectral analysis. Before CA function assessment, participants must avoid alcohol, smoking, and coffee, tea, or other sources of caffeine for 24 hours so as not to influence their resting status. Subjects were studied while awake in the supine position after 20 minutes of rest. Testing times were from 8:00 to 11:00 a.m. A type-I FDP-1 HRV noninvasive detecting system was used with software version 2.0 (Department of Biomedical Engineering of the Fudan University, Shanghai, China). Electrocardiography, respiratory signals, and beat-to-beat blood pressure were continually and simultaneously recorded for 15 minutes through an electrosphygmograph transducer (HMX-3C placed on the radial artery of the dominant arm) and an instrument respiration sensor. Short-term HRV analysis was performed for all subjects using a computer-aided examination and evaluation system for spectral analysis to investigate changes in autonomic regulation. The reproducibility and day-to-day coefficients of variation for above methods were less than 5%.
HT was defined as BP ≥ 140/90 mmHg or a history of hypertension medication. BMI was classified based on the Chinese criteria: normal as BMI < 24.0 kg/m2; overweight as 24.0 kg/m2 ≤ BMI < 28.0 kg/m2; and obese as BMI ≥ 28.0 kg/m2. High FPG was defined as FPG ≥ 5.6 mmol/L. Center obesity was defined using ethnicity-specific values for waist circumference (WC) of ≥90 cm in men and ≥80 cm in women [
The Kolmogorov-Smirnov test was used to determine whether continuous variables followed a normal distribution. Variables that were not normally distributed were log-transformed to approximate normal distribution for analysis. The results are expressed as the mean ± SD or median, unless otherwise stated. The characteristics of the subjects according to CAN groups were assessed using one-way analysis of variance (ANOVA) for continuous variables and the
The baseline clinical characteristics of the 2,902 subjects were grouped according to CAN (Table
Clinical characteristics of subjects.
Variable | Entire sample | Subjects without CAN | Subjects with CAN |
|
---|---|---|---|---|
Demographic information | ||||
|
2096 | 1705 | 387 | |
Age (years) |
|
|
|
<0.001 |
Gender male, (%) | 705 (33.7%) | 562 (32.96%) | 143 (36.95%) | 0.134 |
BMI (kg/m2) |
|
|
|
<0.001 |
WC (cm) |
|
|
|
<0.001 |
SBP (mmHg) |
|
|
|
<0.001 |
DBP (mmHg) |
|
|
|
0.001 |
Medical history | ||||
Smoking yes, (%) | 306 (14.63%) | 244 (14.31%) | 62 (16.02%) | 0.390 |
MetS yes, (%) | 833 (39.82%) | 629 (36.89%) | 204 (52.71%) | <0.001 |
HT yes, (%) | 976 (46.65%) | 735 (43.11%) | 241 (62.27%) | <0.001 |
DM yes, (%) | 446 (21.33%) | 307 (18.02%) | 139 (35.92%) | <0.001 |
Laboratory measurement | ||||
FPG (mmol/L) |
|
|
|
<0.001 |
PBG (mmol/L) |
|
|
|
<0.001 |
HbAlc (%) |
|
|
|
<0.001 |
FINS ( |
|
|
|
<0.001 |
IR (mmol/L) |
|
|
|
<0.001 |
TC (mmol/L) |
|
|
|
0.142 |
TG (mmol/L) |
|
|
|
<0.001 |
HDL (mmol/L) |
|
|
|
0.203 |
LDL (mmol/L) |
|
|
|
0.229 |
SCr ( |
|
|
|
0.561 |
UA ( |
|
|
|
0.216 |
HRV indices | ||||
HR (bpm) |
|
|
|
<0.001 |
TP (ms2) |
|
|
|
<0.001 |
LF (ms2) |
|
|
|
<0.001 |
HF (ms2) |
|
|
|
<0.001 |
LF/HF |
|
|
|
<0.001 |
Note: *presents the difference between subjects with and without cardiovascular autonomic neuropathy (CAN). BMI: body mass index; WC: waist circumference; SBP: systolic blood pressure; DBP: diastolic blood pressure; FPG: fasting plasma glucose; PBG: plasma blood glucose; FINS: fasting blood insulin; IR: insulin resistance; TC: serum total cholesterol; TG: triglyceride; UA: uric acid; HDL: high-density lipoprotein cholesterol; LDL: low density lipoprotein cholesterol; SCr: serum creatinine; HR: heart rate; TP: total power of variance; LF: low frequency; HF: high frequency; MetS: metabolic syndrome; DM: diabetes; HT: hypertension.
The CAN prevalence was 14.54% and 24.49% in subjects without DM and with DM, respectively. The CAN prevalence significantly increased in patients with the DM. CAN prevalence was 5.92%, 12.93%, 23.94%, and 53.67% in the respective groups according to HR. There was an increased CAN prevalence trend in groups with increased HR (
To estimate the potential risk factors of CAN, univariate analysis was performed in the entire sample. These potential risk factors contained the demographic parameters, blood glucose, and insulin function parameters as well as lipid profiles and medical history factors. The results indicate that these potential risk factors, including age, BMI, WC, SBP, DBP, HT, DM, MetS, FPG, PBG, HbAlc, FINS, IR, TG, and HR, were significantly associated with CAN (
Univariate logistic regression analysis for cardiovascular autonomic neuropathy.
Variable |
|
SE |
|
OR | 95% CI |
---|---|---|---|---|---|
Age | 0.042 | 0.007 | <0.001 | 1.04 | 1.029–1.103 |
Gender | 0.176 | 0.117 | 0.134 | 1.19 | 0.947–1.547 |
BMI | 0.066 | 0.016 | <0.001 | 1.07 | 1.034–1.046 |
WC | 0.034 | 0.006 | <0.001 | 1.03 | 1.023–1.024 |
SBP | 0.018 | 0.003 | <0.001 | 1.02 | 1.012–1.030 |
DBP | 0.019 | 0.006 | 0.001 | 1.02 | 1.007–1.261 |
Smoking | 0.133 | 0.155 | 0.390 | 1.14 | 0.843–2.733 |
MetS | 0.646 | 0.114 | <0.001 | 1.91 | 1.527–1.307 |
HT | 0.779 | 0.116 | <0.001 | 2.18 | 1.736–3.248 |
HR | 0.952 | 0.068 | <0.001 | 2.59 | 2.267–2.565 |
DM | 0.936 | 0.123 | <0.001 | 2.55 | 2.003–3.156 |
DM-HR | 0.487 | 0.033 | <0.001 | 1.63 | 1.525–1.737 |
FPG | 0.178 | 0.027 | <0.001 | 1.19 | 1.133–1.149 |
PBG | 0.111 | 0.014 | <0.001 | 1.12 | 1.087–1.722 |
HbAlc | 0.392 | 0.077 | <0.001 | 1.48 | 1.271–1.026 |
FINS | 0.014 | 0.006 | 0.015 | 1.01 | 1.003–1.159 |
IR | 0.091 | 0.029 | 0.001 | 1.10 | 1.036–1.212 |
TC | 0.082 | 0.056 | 0.142 | 1.09 | 0.973–1.369 |
TG | 0.213 | 0.051 | <0.001 | 1.24 | 1.119–1.129 |
HDL | −0.225 | 0.177 | 0.203 | 0.80 | 0.564–1.259 |
LDL | 0.088 | 0.073 | 0.229 | 1.09 | 0.946–1.005 |
UA | 0.001 | 0.001 | 0.216 | 1.00 | 0.999–1.108 |
Note: CAN: cardiovascular autonomic neuropathy;
Multivariate logistic regression analysis for cardiovascular autonomic neuropathy.
Model | Variable |
|
SE |
|
OR | 95% CI |
---|---|---|---|---|---|---|
Model 1 | DM | 0.573 | 0.144 | <0.001 | 1.77 | 1.337–2.351 |
HR | 0.937 | 0.072 | <0.001 | 2.55 | 2.216–2.938 | |
Model 2 | DM-HR | 0.475 | 0.035 | <0.001 | 1.61 | 1.501–1.721 |
Note: Model 1 and Model 2 adjusted for age, gender, smoking, BMI, IR, TG, UA, HT;
To evaluate the predictive performance of DM, HR, and DM-HR for CAN, the AUC in an ROC curve was calculated. For the DM variable, the AUC was 0.589 (95% CI: 0.556–0.622,
Receiver-operating characteristic curves showed the performance of resting heart rate (HR), diabetes (DM), and categorical variable of DM-HR in predicting cardiovascular autonomic neuropathy (CAN) prevalence in this dataset. The 95% confidence interval (CI) is given in parentheses. AUC represents area under the curve. HR: AUC = 0.719, 95% CI 0.690–0.748,
A large-scale, population-based, cross-sectional study was conducted to evaluate the extent to which DM and resting HR are associated with CAN among 2,092 participants in the Chinese population. This sample was a good representation of the Chinese population, and the findings may work similarly well outside the areas studied in China. Importantly, in general Chinese population, we first performed predictive value analysis for CAN by using resting HR and DM. It is crucial for us to understand the predictive value of the two factors on CAN in the general population. This is partly because the DM prevalence has increased rapidly in China and may also contribute to CAN progression. Clinicians can expect to treat more DM patients having CAN progression. Moreover, a better understanding of the predictive value of the two factors will help clinicians in preventing and treating CAN.
The interesting finding was that resting HR alone or combined with DM both had a high value in predicting CAN in the general population. First of all, univariate and association analysis for CAN show that DM and resting HR are strongly and independently associated with CAN in the general population. Moreover, after adjustment for potential confounds, MLR analysis demonstrated that DM and resting HR very significantly and independently remain associated with CAN (
Our previous study [
In addition, the predictive performance of DM and resting HR for CAN was evaluated by using AUC in receiver operative characteristic curve to show resting HR and DM-HR having high predictive value for CAN in general population. The AUC was calculated to show that resting HR strongly predicts CAN (AUC = 0.719, 95% CI: 0.690–0.748). For the analysis of the predictive value of DM alone on CAN, although association analysis showed that DM was very significantly and independently associated with CAN, the AUC was calculated to indicate that DM moderately predicts CAN (AUC = 0.589, 95% CI: 0.556–0.622). However, we used a categorical variable of DM-HR, which combined information between resting HR and DM, to signify a high value in predicting CAN in the general population (AUC = 0.738, 95% CI: 0.710–0.766). The sensitivity and specificity of CAN were 73.90% and 61.50% when the optimal cutoff point of DM-HR was set to 3 of 7. In particular, the CAN prevalence was 66.67% in subjects with resting HR > 85 bpm and DM (DM-HR = 8), while its prevalence decreased to 5.33% in subjects with resting HR < 65 bpm without DM (DM-HR = 1). These results supported that resting HR and DM-HR have a high value in predicting CAN in the general Chinese population. The DM-HR and resting HR cannot obtain a sensitivity of 100%. A false negative is mainly attributed to the fact that other risk factors contribute to the outcome. The natural history of CAN was sign of resting tachycardia due to impaired parasympathetic nervous systems in its early stage, and a normal resting HR would be detected due to both impaired sympathetic and parasympathetic nervous systems in the end of stage. In this study, some of the patients with CAN were not obese or had a normal resting HR due to the end of stage of CAN. Little is known about the CAN prevalence in subjects with a normal resting HR in the population. In addition, false-negative individuals had a lower resting HR, indicating that those people had long-term CAN. To our knowledge, this is the first study to have reported resting HR combined with DM having such a high predictive value for CAN in a Chinese population. This finding is of importance to the clinical practice of preventing and treating CAN in the general population.
Several limitations of this study deserve comment. First, the study design was cross-sectional, and thus the temporal sequence between risk factors and outcome was questionable. In addition, it is important to mention that our study was performed on Chinese individuals, and our findings may not be relevant to people of other ethnicities.
In conclusion, there was a tendency toward increased CAN prevalence with increased resting HR. Our findings signify that resting HR and DM are independently associated with CAN; and resting HR alone or combined with DM both have a high predictive value in predicting CAN in the general population. These observations provide novel insights into biological functions in the future.
Area under curve
Body mass index
Body surface area
Confidence intervals
Cardiovascular autonomic neuropathy
Serum creatinine
Diastolic blood pressure
Diabetes
Fasting plasma glucose
Glycosylated hemoglobin
High-density lipoprotein cholesterol
Homeostasis model assessment insulin resistance estimate
Heart rate variability
Hypertension
International Diabetes Federation
Low-density lipoprotein cholesterol
Metabolic syndrome
Multivariable logistic linear regression
Oral glucose tolerance test
Odds ratios
Postprandial blood glucose
Receiver operative curve
Hypertension
Blood pressure
Serum total cholesterol
Triglyceride
Waist circumference
Uric acid.
The authors declare that they have no conflict of interests regarding the publication of this paper.
Zi-Hui Tang designed study, analyzed data, and wrote paper, Zhongtao Li contributed to collecting reagents, materials, and analysis tools, Fangfang Zeng contributed to collecting reagents, materials, and analysis tools, and Linuo Zhou conceived and designed study.
The authors thank the grant from China National Grant on Science and Technology to support the study (Grant no. 30570740).