The goal of this study was to determine the prevalence and associated risk factors of impaired glucose regulation (IGR) in the population of Tongzhou, China, and to provide scientific basis for preventive interventions. In the study, the overall age-standardized prevalence of IGR (16.0%) in Tongzhou residents was higher than that in the national population (15.0%). There was no significant geographic difference in prevalence of IGR between urban and rural males. Older age, elevated blood pressure, high serum lipids, overweight, and central obesity were significantly associated with increased risk of IGR.
People with diabetes (DM) are at high risk of heart disease, stroke, and kidney failure [
Impaired glucose regulation (IGR), including impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), refers to a metabolic state intermediate between normal glucose tolerance and type 2 diabetes [
As a typical urban fringe district of Beijing, Tongzhou has undergone a dramatic urbanization in the past decade. One result of the transition is the growth of urban residents and the decrease of rural population. To the best of our knowledge, there is only one study that estimated the prevalence of IGR in suburban communities, which was conducted in Guangzhou, Guangdong Province [
A multistage, stratified, simple random sampling design was used to select participants from 4 neighborhood communities in Tongzhou. The communities were chosen to represent the variety of economic development and geographical distribution. A total of 1105 subjects aged 18–40 years were recruited in the study. Data of 1069 participants were successfully collected. The overall response rate was 96.74%.
A questionnaire was designed according to the Method of Chinese Chronic Disease Surveillance (2010) and administrated by trained interviewers. Written informed consents/oral consents were obtained from all participants. The questionnaire included information on social-demographic characteristics (age, sex, occupation, education level, etc.), manual labor, family history of diabetes, medical history, and diet habits. Physical examination was also done and height, weight, waist and hip circumference, and blood pressure were measured using a standard protocol. WHR was calculated through dividing waist circumference by hip circumference. BMI was calculated via dividing weight in kilograms by height in meters squared.
For all participants, blood samples were collected to determine FPG, FINS, TC, TG, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) after an overnight fast of at least 8 hours. Participants without known diabetes were given an oral glucose tolerance test (OGTT) to measure the 2-hour plasma glucose (2hPG). They were given a standard 75-g glucose solution to drink and other blood samples were collected 2 hours later. The blood samples were collected using vacuum tubes containing sodium fluoride and were sent to local laboratories immediately. Qualities of onsite data collection and laboratory test were stringently controlled to ensure the validity of the data.
According to the World Health Organization (WHO) 1999 criteria [
All the continuous variables were presented as mean ± standard deviation and categorical variables as percentage. Age-adjusted prevalence was estimated from age-specific prevalence and the national age distribution in 2010 census using standardization method. The differences in proportions were examined using the chi-square test. A multiple unconditional logistic regression model was applied to explore the association between the potential risk factors and IGR, as well as the odds ratios adjusted for other risk factors. Two-sided
Of the 1069 study subjects, 323 (30.2%) were male and 746 (69.8%) were female. A total of 51.5% (551) participants were urban residents, and 48.5% (518) were rural residents. The sex difference in IGR prevalence is not significant (
General characteristics of study subjects.
Age (years) | Mean ± SD | |||||
---|---|---|---|---|---|---|
Waist circumstance |
TG |
SBP |
DBP |
FPG |
HDL | |
20–29 | ||||||
Male | ||||||
Urban | 83.94 ± 9.86 | 1.49 ± 1.93 | 114.46 ± 12.80 | 73.24 ± 7.94 | 4.91 ± 0.76 | 1.07 ± 0.24 |
Rural | 79.38 ± 11.35 | 0.86 ± 0.34 | 112.54 ± 10.80 | 72.83 ± 9.22 | 5.33 ± 0.31 | 1.15 ± 0.164 |
Female | ||||||
Urban | 72.47 ± 11.40 | 0.81 ± 0.46 | 105.78 ± 12.20 | 69.55 ± 8.09 | 4.83 ± 0.43 | 1.32 ± 0.29 |
Rural | 75.23 ± 11.41 | 0.67 ± 0.28 | 110.59 ± 9.10 | 71.54 ± 7.18 | 4.99 ± 0.39 | 1.38 ± 0.24 |
30–39 | ||||||
Male | ||||||
Urban | 90.06 ± 11.18 | 2.96 ± 4.2 | 120.71 ± 18.17 | 78.93 ± 13.03 | 5.31 ± 1.05 | 2.58 ± 1.20 |
Rural | 90.89 ± 11.37 | 1.75 ± 1.28 | 122.21 ± 16.78 | 80.91 ± 12.43 | 5.74 ± 1.21 | 1.12 ± 0.25 |
Female | ||||||
Urban | 76.62 ± 9.50 | 0.92 ± 0.08 | 108.03 ± 11.98 | 72.17 ± 9.70 | 5.14 ± 0.65 | 2.19 ± 0.48 |
Rural | 79.23 ± 9.18 | 1.01 ± 0.73 | 113.84 ± 15.98 | 75.17 ± 11.86 | 5.36 ± 0.70 | 1.31 ± 0.31 |
40–49 | ||||||
Male | ||||||
Urban | 90.21 ± 9.14 | 2.89 ± 3.67 | 120.15 ± 15.39 | 81.87 ± 11.5 | 6.36 ± 2.07 | 0.99 ± 0.33 |
Rural | 90.30 ± 9.67 | 1.36 ± 0.99 | 133.92 ± 17.63 | 84.35 ± 10.49 | 5.95 ± 1.05 | 1.22 ± 0.32 |
Female | ||||||
Urban | 81.19 ± 10.2 | 1.28 ± 0.94 | 120.08 ± 17.30 | 79.44 ± 10.70 | 5.61 ± 1.88 | 1.28 ± 0.30 |
Rural | 85.57 ± 9.51 | 1.18 ± 1.24 | 125.56 ± 18.12 | 82.83 ± 11.82 | 5.59 ± 1.66 | 1.27 ± 0.31 |
50–59 | ||||||
Male | ||||||
Urban | 91.75 ± 12.10 | 1.58 ± 0.71 | 123.39 ± 10.54 | 81.43 ± 9.20 | 5.80 ± 0.97 | 1.08 ± 0.22 |
Rural | 91.69 ± 10.10 | 1.24 ± 0.34 | 137.35 ± 17.18 | 87.82 ± 10.92 | 6.16 ± 2.04 | 1.24 ± 0.34 |
Female | ||||||
Urban | 84.12 ± 9.34 | 1.52 ± 1.12 | 126.42 ± 18.80 | 79.91 ± 10.20 | 5.70 ± 1.74 | 1.26 ± 0.28 |
Rural | 88.22 ± 9.40 | 1.27 ± 0.28 | 131.55 ± 18.06 | 84.18 ± 10.01 | 5.86 ± 1.23 | 1.27 ± 0.28 |
60–69 | ||||||
Male | ||||||
Urban | 85.91 ± 6.32 | 1.10 ± 0.47 | 130 ± 13.54 | 80.23 ± 7.15 | 6.46 ± 1.42 | 1.20 ± 0.28 |
Rural | 90.24 ± 7.97 | 1.28 ± 0.66 | 140.84 ± 18.36 | 87.88 ± 12.87 | 5.65 ± 1.08 | 1.33 ± 0.33 |
Female | ||||||
Urban | 97.23 ± 5.41 | 1.56 ± 0.90 | 132.49 ± 15.18 | 86.85 ± 47.73 | 6.79 ± 2.77 | 1.28 ± 0.35 |
Rural | 91.63 ± 12.05 | 2.29 ± 2.41 | 138.79 ± 15.18 | 83.57 ± 11.34 | 6.46 ± 2.12 | 1.14 ± 0.30 |
70–74 | ||||||
Male | ||||||
Urban | 91.35 ± 10.90 | 1.20 ± 0.71 | 144.12 ± 23.68 | 80.29 ± 11.52 | 6.76 ± 2.08 | 1.17 ± 0.33 |
Rural | 96.66 ± 9.66 | 1.08 ± 0.38 | 141.11 ± 21.03 | 81.11 ± 10.24 | 5.30 ± 0.63 | 1.27 ± 0.15 |
Female | ||||||
Urban | 90.39 ± 10.97 | 1.59 ± 0.71 | 131.62 ± 17.40 | 76.52 ± 8.18 | 6.37 ± 1.65 | 1.38 ± 0.29 |
Rural | 91.60 ± 8.32 | 1.41 ± 0.49 | 142.00 ± 10.95 | 93.00 ± 14.83 | 5.48 ± 0.38 | 1.37 ± 0.22 |
Table
Age-specific and age-standardized prevalence of diabetes and impaired glucose regulation in study subjects, by sex and residence.
Variable |
Age- (years) specific prevalence |
Age- (years) standardized prevalence | ||||||
---|---|---|---|---|---|---|---|---|
20–29 | 30–39 | 40–49 | 50–59 | 60–69 | 70–74 | Total | 20–74 | |
DM | ||||||||
Male | ||||||||
Urban | 1 (3.2) | 2 (14.3) | 10 (29.4) | 7 (25.0) | 5 (22.7) | 5 (29.4) | 30 (20.5) | 22.1 |
Rural | 0 (0) | 4 (11.1) | 9 (18.0) | 7 (14.6) | 4 (10.5) | 3 (33.3) | 27 (14.9) | 10.3 |
Total | 1 (3.2) | 6 (12.0) | 19 (22.6) | 14 (18.4) | 9 (15.0) | 8 (30.8) | 57 (17.4) | 14.4 |
Female | ||||||||
Urban | 0 (0) | 2 (3.3) | 11 (11.7) | 15 (14.7) | 13 (27.1) | 7 (30.4) | 48 (14.6) | 14.3 |
Rural | 0 (0) | 5 (5.5) | 10 (8.8) | 17 (17.3) | 9 (25.7) | 1 (20.0) | 42 (12.3) | 9.4 |
Total | 0 (0) | 7 (4.6) | 21 (10.1) | 32 (16.0) | 22 (26.5) | 8 (28.6) | 90 (13.4) | 11.9 |
IGR | ||||||||
Male | ||||||||
Urban | 2 (6.3) | 1 (7.1) | 4 (11.8) | 7 (25.0) | 6 (27.3) | 8 (47.1) | 28 (19.0) | 18.4 |
Rural | 0 (0) | 6 (16.7) | 4 (8.0) | 12 (25.0) | 8 (24.2) | 2 (22.2) | 32 (18.2) | 14.0 |
Total | 2 (6.3) | 7 (14.0) | 8 (9.5) | 19 (25.0) | 14 (25.5) | 10 (38.5) | 60 (18.6) | 16.4 |
Female | ||||||||
Urban | 5 (6.6) | 7 (11.5) | 10 (10.6) | 15 (14.7) | 14 (29.2) | 5 (21.7) | 56 (13.9) | 15.8 |
Rural | 0 (0) | 5 (5.5) | 21 (18.6) | 26 (26.5) | 11 (31.4) | 2 (40.0) | 65 (19.0) | 14.0 |
Total | 5 (6.6) | 12 (7.9) | 31 (15.0) | 41 (20.5) | 25 (30.1) | 7 (25.0) | 121 (16.2) | 16.1 |
Generally, IGR prevalence increased with age in both men and women except that it decreased after hitting a peak of 25.0% at the age of 50 to 59 years in rural men and after reaching a maximum of 29.2% at the age between 60 and 69 years in urban women. The difference of IGR rates across age groups was significant for both males and females (
IGR was slightly more prevalent in men than in women (18.6% in men and 16.2% in women) without significant difference (
Age, blood pressure, TG, TC, BMI, and waist circumstance were significantly positively associated with IGR after adjusting for multiple risk factors (
Adjusted OR and 95% CI of IGR by associated risk factors.
Variable |
|
|
OR | 95% CI |
---|---|---|---|---|
Age | 0.262 | 0.000 | 2.307 | 1.713–2.411 |
BMI | 0.372 | 0.000 | 1.447 | 1.357–1.511 |
Waist circumstance | 0.441 | 0.019 | 1.703 | 1.700–1.782 |
TG | 0.661 | 0.000 | 1.933 | 1.899–1.980 |
TC | 0.277 | 0.015 | 1.333 | 1.577–2.315 |
Hypertension | 0.413 | 0.017 | 1.516 | 1.412–1.693 |
Heavy manual labor | 0.222 | 0.510 | 0.071 | 0.692–1.020 |
Family history of DM | 0.011 | 0.600 | 0.780 | 0.531–1.021 |
Through the multivariate logistic model, we estimated that IGR risk in overweight and obese people was 1.4 times higher than that in normal-weight population. Central obesity and hypertension increased the risk of IGR by 1.7 and 1.5 times, respectively. Every one-mmol/L-increase of TG and TC corresponded to 1.9- and 1.3-time increase in the risk of IGR, respectively (Table
This cross-sectional study demonstrated an IGR prevalence of 16.9% in Tongzhou population. IGR was slightly more prevalent in men than in women, both before and after age standardization. In addition, IGR prevalence increased with age in both men and women regardless of residence. The prevalence of IGR turned out to be significantly higher over 50 years in men, which could be explained by the increasing blood pressure, serum lipids, and some other risk factors of IGR in this population. Similarly, the increase of IGR prevalence was dramatic after 60 years in women, consistent with a previous study [
Another main issue explored in this study was the residential difference in IGR in a typical urban-rural fringe. The crude prevalence of IGR was significantly higher in rural women compared to urban women. But such significant residential difference was not seen in men. After age standardization, IGR prevalence in urban residents was shown to be higher than that in rural population, for both men and women. However, the difference was not significant. The rapid economic growth and urbanization of rural area in Tongzhou may explain a similar prevalence of IGR in urban and rural population. On the one hand, the lifestyle of rural population has changed. Intake of high-fat and high-calorie food has increased while physical activities have decreased in rural residents. On the other hand, the gap between urban and rural areas on access and quality of medical system exacerbates the elevation of IGR rate among rural population. This finding suggests that preventive intervention of diabetes should be applied regardless of economic development, especially in urban fringe district where the border between urban and rural areas has been blurring.
Furthermore, we found that age, blood pressure, TG, TC, BMI, and waist circumstance were significantly positively associated with IGR after adjusting for multiple risk factors, in agreement with previous studies [
In this study, we used the 1999 WHO criteria. We also noticed that the cutoff of IFG was set up as 5.6 mmol/L instead of 6.1 mmol/L in the 2003 ADA criteria. Given its better sensitivity in predicting future diabetes, it can be reasonably expected that the prevalence of IGR and diabetes will be even higher than the numbers presented in our study.
The strength of this study was that it was the first population-based study to determine the epidemic characteristic of IGR in urban fringe district of Beijing. In addition, this study conducted a comprehensive exploration on more key risk indicators of diabetes compared to similar studies in china.
The limitation of this study was the imbalanced gender among our sample population, which may be explained by the time and site of subject selection. Since sex difference in IGR may exist, the overall prevalence of IGR and diabetes in this study may be different from the true prevalence in Tongzhou.
The authors declare that they have no conflict of interests.