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Keywords:

  • Abdominal obesity;
  • cardiometabolic risk;
  • China;
  • physical activity

Summary

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Strong secular declines in physical activity, increased fat and salt intake, and increased obesity, especially abdominal obesity, mark China's recent nutrition transition. The China Health and Nutrition 2009 Survey collected anthropometry, blood pressure and fasting blood samples from more than 9,000 individuals ≥7 years of age. We focus on elevated blood pressure and plasma markers of diabetes, inflammation and dyslipidemia. We used international definitions of cardiometabolic risk and estimated age- and sex-specific prevalence ratios for each outcome for high waist circumference or overweight. We used logistic regression to assess each risk factor's association with diet, physical activity, overweight and abdominal obesity. Cardiometabolic risk prevalence was high in all age groups Prevalence ratios for most risk factors were nearly doubled for overweight or high waist circumference groups. Prevalence ratios were higher in younger than older adults. Low physical activity consistently predicted higher cardiometabolic risk across most outcomes and age-sex groups. The co-occurrence of overweight and high waist circumference was highly predictive of dyslipidemia, elevated glycated haemoglobin and diabetes. High prevalence of cardiometabolic risk factors and their strong association with weight status and abdominal obesity in young adults portend increases in cardiometabolic morbidity and mortality. Early interventions will be required to reverse trends.

Abbreviations
AD

atherogenic dyslipidemia

BMI

body mass index

BP

blood pressure

CHNS

China Health and Nutrition Survey

CM

cardiometabolic

CRP

C-reactive protein; HbA1c, glycated haemoglobin

HDL

high-density lipoprotein cholesterol

HOMA-IR

homeostasis model of assessment–insulin resistance

LDL

low-density lipoprotein cholesterol

MET

metabolic equivalent

TAG

triacylglycerol

WHtR

waist to height ratio

Unprecedented social and economic changes in China have led to patterns of diet and physical activity that are contributing to large increases in the prevalence of overweight and obesity across all ages, regions and levels of socioeconomic status [1-6]. Of particular concern is that waist circumference, an indicator of central fat distribution strongly associated with cardiometabolic (CM) diseases, has increased disproportionately relative to overall body mass (D. Stern, unpublished data). This trend is particularly alarming because Asians are known to have higher amounts of central fat at the same body mass index (BMI) compared to Caucasian populations [7-14] and tend to develop diabetes and related diseases at a younger age and a lower BMI [15].

CM diseases (diabetes, cardiovascular diseases) have increased substantially in China [16, 17], imposing a heavy burden on its healthcare systems. CM diseases are now the leading cause of morbidity and mortality in China and many other Asian countries [18, 19].

Other papers in this issue document important trends in diet, physical activity and overweight or obesity. These changes have important implications for CM disease risk. Much of the urban Chinese population is now characterized by low levels of physical activity at work, as jobs have become less physically demanding [6, 20], and across China diets are higher in fat and sodium compared to just 20 years ago [21, 22].

This paper aims to document the prevalence of CM risk factors among participants in the China Health and Nutrition Survey (CHNS) and to explore factors related to levels of risk with a focus on high blood pressure (BP), indicators of glucose metabolism and insulin resistance, dyslipidemia, and inflammation. In prior publications, we reported the prevalence of selected CM risk factors according to levels of urbanization and income [23] and explored patterns of CM risk in relation to overweight with a focus on the high burden of CM risk even among those who are not overweight [10]. Here we examine differences in CM risk in relation to BMI and waist circumference and relate CM risk to the macronutrient composition of the diet and to levels of physical activity.

Methods

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

The 2009 CHNS collected fasting blood samples from more than 9,000 individuals, ages 7 years and above. We use data on non-pregnant youths (<18 years) and adults (18–98 years) with anthropometry (weight, height and waist circumference) and BP (n = 9,168), glycated haemoglobin (HbA1c) (n = 9,108), C-reactive protein (CRP) (n = 9,175), and fasting glucose and lipids (n = 8,746). Laboratory analysis methods for the CM biomarkers are described in detail elsewhere [23].

We used internationally accepted definitions of CM risk. For overweight, these include BMI ≥25 kg m−2 or the paediatric equivalent defined by the International Obesity Task Force [24]. We used the waist to height ratio (WHtR) because of its strong association with diabetes in China [25] and common definition for adults and youth, with values >0.5 indicating central adiposity. We used International Diabetes Federation definitions [26] for high BP in adults (systolic ≥130 or diastolic ≥85 or taking anti-hypertension medication) and for youths, a BP >85th percentile of an age-, sex- and height-specific U.S. reference [27]. Lipids-related risk factors include high triacylglycerol (TAG > 150 mg dL−1), high low-density lipoprotein cholesterol (LDL > 130 mg dL−1), low high-density lipoprotein cholesterol (HDL < 40 mg dL−1 for men, <50 mg dL−1 for youths and women) and atherogenic dyslipidemia (AD) (high TAG and low HDL). Markers of glucose control include impaired HbA1c (pre-diabetic or diabetic HbA1c ≥5.6%), diabetes (HbA1c ≥ 6.5%) and insulin resistance (homeostasis model of assessment–insulin resistance, HOMA-IR > 4.65). Inflammation is represented by high CRP (>3 mg dL−1 but <10 mg dL−1).

Diet

Dietary intake was estimated from three consecutive 24-h recalls for each individual, supplemented with a daily inventory of all available foods in the household [28, 29]. We used the 3-day means of energy intake and percentage of energy intake from fat and protein in the analyses.

Physical activity

Physical activity was assessed using questionnaires that probed the time spent in a typical week at work, in leisure time, in travel and in doing household chores. We created a summary variable using minutes spent in each activity multiplied by metabolic equivalents (METs) for that activity [30]. Total MET minutes at work, leisure, chores and travel were summed and represented in models divided into physical activity quintiles. Models that included physical activity were restricted to adults owing to the lack of comparable child data.

Socioeconomic and environmental variables

We categorized level of education for adults as less than primary school, completed primary school, completed secondary school and more than secondary school. We represented urbanicity categorized by quintiles of an urbanization index reflecting population size and density and community infrastructure [31]. We represented household income, estimated from the sum of individual incomes of all earners in the household, categorized by quintiles. We categorized age to represent youths (7 to <18 years) and adults (18 to <35 years, 35 to <50 years, 50 to <65 years, and over 65 or greater years).

Analysis

We estimated, within age and sex strata, crude prevalence ratios for CM risk factors, comparing rates in overweight vs. normal weight and high WHtR vs. normal WHtR participants. We used sex-stratified logistic regression to assess the association of diet and physical activity with each CM risk factor, adjusting for age, education, household income, and level of urbanization and for the clustering of individuals within households. We also estimated, using logistic regression, how being overweight alone, having a high WHtR alone, or having both risk factors related to the likelihood of having each CM risk factor, adjusted for the same factors listed above.

Results

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Prevalence of risk factors

The prevalence of overweight and obesity was higher in middle adulthood than in youth or young adulthood, highest in men after age 30 and women after age 40 (Table 1), but lower in those over 65 years. The prevalence of high WHtR consistently increased with age in men and women. The prevalence of CM risk factors varied substantially across age and sex groups, with consistent and marked increases with age most apparent for high BP and markers of glucose control (Fig. 1).

figure

Figure 1. Prevalence of selected cardiometabolic factors, by age (males (a), females (b)), China 2009.

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Table 1. Crude prevalence ratios for CM risk factors, comparing high to normal waist circumference to height ratio (WHtR > 0.5) and overweight (OW = BMI ≥ 25 kg m−2) to non-overweight groups, China Health and Nutrition Survey, 2009
 High BPImpaired HbA1c (pre-diabetic or diabetic, HbA1c ≥5.7%)Diabetic (HbA1c ≥ 6.5%)
High BP (prop)High WHtROverweightImpaired HbA1c (prop)High WHtROverweightDiabetic (prop)High WHtROverweight
High WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CI
Males                           
<180.160.122.031.213.400.121.430.802.560.100.120.900.372.180.120.740.281.970.010.12na  0.12nanana
18–<350.220.332.161.622.880.232.061.552.750.130.331.511.002.280.231.240.791.940.020.332.390.817.010.232.150.716.46
35–<500.380.531.861.592.170.341.841.612.110.250.532.141.732.650.341.941.612.340.060.533.031.815.070.342.721.774.18
50–<650.540.571.311.181.460.291.331.211.460.370.571.711.462.000.291.601.401.830.090.572.791.834.240.292.541.823.53
65+0.660.601.161.051.290.261.361.241.480.460.601.261.071.500.261.371.171.600.120.603.021.805.090.262.751.874.03
Females                           
<180.180.071.230.552.771.231.690.953.010.080.071.080.274.270.101.020.323.180.010.07na  0.100.00nana
18–<350.060.264.802.549.064.805.823.2710.370.100.262.031.283.220.121.981.153.410.010.26na  0.120.00nana
35–<500.240.532.131.752.612.132.151.812.550.210.532.201.762.750.302.131.762.580.030.533.101.606.010.302.961.715.11
50–<650.490.731.531.331.761.531.411.281.560.380.731.681.412.000.361.691.491.910.090.732.821.694.690.362.041.482.81
65+0.690.771.281.131.451.281.161.071.260.490.771.301.071.580.311.611.411.830.170.771.931.213.080.312.051.522.75
All  1.981.872.09 1.651.571.73  2.172.012.33 1.841.731.96  3.793.134.60 2.672.303.09
M-H P-value  P < 0.001   P < 0.001    P < 0.001   P = 0.012    P = 0.976   P = 0.935  
 High HOMA-IR (>4.65)High LDL (>130 mg L−1)Low HDL (≤40 mg dL−1 in males and youth, ≤50 mg dL−1 in females)
High HOMA (prop)High WHtROverweightHigh LDL (prop)High WHtROverweightLow HDL (prop)High WHtROverweight
High WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CI
Males                           
<180.160.122.651.664.240.122.971.904.650.030.121.180.275.140.120.540.074.040.090.122.071.054.100.121.810.883.70
18–<350.160.333.122.144.550.233.122.194.430.190.331.901.382.630.231.721.242.400.550.331.571.371.790.231.571.391.79
35–<500.170.532.661.983.560.342.612.043.340.290.531.231.031.470.341.130.951.360.540.531.521.361.690.341.631.481.79
50–<650.140.572.251.643.080.292.662.043.460.320.571.461.241.730.291.321.131.540.440.571.561.371.780.291.581.411.77
65+0.190.602.771.854.130.262.982.234.000.280.601.621.252.100.261.581.261.990.440.601.721.422.090.261.701.461.98
Females                           
<180.160.072.521.364.680.102.391.374.180.050.070.00..0.100.540.073.930.380.072.340.886.190.100.670.172.74
18–<350.090.261.210.702.070.122.351.354.110.120.261.591.052.430.121.380.802.370.290.261.731.362.190.121.931.492.50
35–<500.130.532.501.823.420.302.291.752.990.230.531.311.081.590.301.451.211.750.340.531.661.431.930.301.651.441.89
50–<650.160.732.431.703.460.362.271.802.870.440.731.020.901.160.361.040.921.170.360.731.541.291.840.361.491.301.69
65+0.200.771.721.142.600.312.581.973.390.480.771.311.071.590.311.231.071.420.320.771.561.172.070.311.661.372.01
All  2.211.982.47 2.552.322.80  1.761.641.88 1.421.331.52  1.561.481.64 1.651.581.74
M-H P-value  P = 0.216   P = 0.866    P = 0.004   P = 0.007    P = 0.963   P = 0.785  
 High TAG (>150 mg L−1)(AD = high TAG and low HDL)High CRP (>3 mg L−1, <10 mg L–1)
High TAG (prop)High WHtROverweightAD (prop)High WHtROverweightHigh CRP (prop)High WHtROverweight
High WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CIHigh WHtR (prop)PR95% CIOW (prop)PR95% CI
  1. AD, atherogenic dyslipidemia; BMI, body mass index; BP, blood pressure; CM, cardiometabolic; CRP, C-reactive protein; HDL, high-density lipoprotein; HOMA-IR, homeostasis model of assessment–insulin resistance; LDL, low-density lipoprotein; PR, prevalence ratio; TAG, triacylglycerol; WHtR, waist to height ratio.

Males                           
<180.100.122.681.474.860.122.491.344.610.030.123.571.2710.030.123.741.3210.490.06 4.902.3310.290.122.320.975.54
18–<350.320.331.921.542.400.231.851.492.310.250.332.291.752.990.232.381.843.080.090.331.681.012.820.232.011.203.36
35–<500.420.532.061.772.380.341.911.692.160.300.532.381.962.890.342.482.102.920.120.532.041.462.840.341.851.372.50
50–<650.350.572.011.692.380.291.791.562.060.230.572.261.792.860.292.181.802.640.130.571.441.081.930.291.110.831.49
65+0.270.602.021.522.680.261.731.372.190.200.602.401.673.450.262.071.562.750.190.601.150.831.590.261.501.102.06
Females                           
<180.090.070.940.243.690.101.270.473.390.060.072.010.2515.660.101.150.158.890.030.070.00..0.102.050.469.11
18–<350.120.262.011.333.030.122.731.774.200.060.262.671.484.800.123.601.986.540.080.262.741.634.610.123.522.085.94
35–<500.240.532.181.782.670.302.061.722.450.150.532.521.893.350.302.201.732.800.100.532.481.733.570.302.291.683.12
50–<650.380.731.711.432.050.361.691.501.920.220.732.161.642.860.361.871.552.240.170.731.971.432.710.362.021.622.53
65+0.360.771.801.362.390.311.811.522.160.200.771.891.242.870.312.391.833.120.240.771.521.072.160.311.841.442.35
All  2.242.082.40 2.041.932.17  2.542.312.80 2.462.272.66  2.191.952.47 1.961.762.17
M-H P-value  P = 0.716   P = 0.010    P = 0.914   P = 0.276    P = 0.0043   P = 0.0096  

Prevalence ratios for cardiometabolic risk factors in relation to waist to height ratio and overweight (Table 1)

Prevalence ratios for high BP and impaired HbA1c (pre-diabetic or diabetic) were heterogeneous across age and sex, and were higher in young to middle-aged adults (<50 years) compared to the older age groups. On average, prevalence ratios for high BP were doubled for those with high vs. normal WHtR, and 1.65 times greater for overweight compared to normal weight individuals. For pre-diabetes and diabetes, prevalence ratios were more than doubled for those with high WHtR, and 1.8 times higher for overweight compared to normal weight, with significant heterogeneity across age and sex groups. The prevalence ratio for diabetes was 3.8 for high WHtR and 2.7 for overweight. Insulin resistance, indicated by high HOMA-IR, was two to three times more prevalent in those with overweight or high WHtR with no significant heterogeneity across age and sex groups.

Markers of dyslipidemia showed a similar pattern. High LDL was nearly 1.8 times more prevalent in those with high WHtR and 1.4 times more prevalent in overweight individuals. These prevalence ratios for high LDL were heterogeneous across age and sex groups with no consistent pattern. In contrast, low HDL was about 1.6 times more prevalent with high WHtR or overweight, and prevalence ratios were similar across age and sex groups. High TAG was about twice as prevalent with high WHtR or overweight in males and females, with higher prevalence ratios in those <50 years vs. ≥50 years. Atherogenic dyslipidemia was about 2.5 times more prevalent with high WHtR or high overweight and was similar across all age and sex groups. Prevalence ratios for CRP varied substantially among age and sex groups, but the overall prevalence ratio was doubled for high WHtR and overweight.

Correlates of adverse cardiometabolic risk factors

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

Associations with dietary fat and physical activity (Table 2).

Table 2. Logistic regression models of CM risk factors as outcomes: odds ratios and 95% confidence intervals for associations with diet and physical activity variables, adjusted for age, household income, urbanicity, and education level, and clustering of multiple individuals within households and adults, CHNS 2009
 OverweightHigh WHtRHigh blood pressureHbA1c ≥5.7%DiabeticHigh HOMA-IR
MalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemales
% Fat11.041.011.041.020.971.030.970.93*1.111.110.931.07
[0.96,1.12][0.94,1.08][0.97,1.11][0.95,1.09][0.90,1.04][0.97,1.11][0.90,1.04][0.87,1.00][0.98,1.26][0.99,1.25][0.85,1.03][0.98,1.17]
% Protein11.65***1.081.58***1.131.031.071.261.42**1.301.191.311.64**
[1.26,2.17][0.85,1.39][1.21,2.06][0.88,1.46][0.80,1.34][0.82,1.38][0.96,1.65][1.10,1.82][0.85,1.98][0.77,1.84][0.95,1.80][1.22,2.22]
PA Q20.931.040.820.970.960.910.860.880.791.141.020.76*
[0.73,1.18][0.85,1.28][0.65,1.04][0.78,1.20][0.76,1.20][0.74,1.12][0.68,1.09][0.72,1.08][0.54,1.15][0.84,1.54][0.77,1.36][0.59,0.98]
PA Q30.960.75*0.900.73**0.90.71**0.960.68**0.730.61*0.790.68**
[0.76,1.21][0.59,0.96][0.72,1.13][0.57,0.92][0.72,1.13][0.56,0.90][0.75,1.22][0.54,0.87][0.48,1.10][0.40,0.95][0.58,1.06][0.51,0.91]
PA Q40.860.780.73**0.76*0.78*0.790.71**0.63***0.59*0.61*0.880.65**
[0.68,1.09][0.61,1.01][0.59,0.91][0.59,0.97][0.62,0.98][0.61,1.02][0.56,0.90][0.49,0.81][0.38,0.91][0.38,0.98][0.66,1.18][0.48,0.89]
PA Q50.69**0.76*0.61***0.77*0.65***0.840.780.790.640.770.840.56***
[0.52,0.90][0.59,0.98][0.48,0.78][0.60,0.99][0.51,0.83][0.66,1.08][0.60,1.01][0.61,1.01][0.41,1.01][0.50,1.19][0.60,1.18][0.41,0.77]
 High LDLLow HDLHigh TAGAtherogenic dyslipidemiaHigh CRP
MalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemales
  1. 1Percentage of energy from fat or percentage of energy from protein, scaled so that coefficients represent a change of 10 percentage points in the exposure. AD, atherogenic dyslipidemia; CM, cardiometabolic; CRP, C-reactive protein; HDL, high-density lipoprotein; HOMA-IR, homeostasis model of assessment–insulin resistance; LDL, low-density lipoprotein; TAG, triacylglycerol; WHtR, waist to height ratio. *P < 0.05, **P < 0.01, ***P < 0.001. Text in boldface signifies results that are statistically significant at P < 0.05.

% Fat11.041.10**1.07*0.961.011.07*1.061.041.081.10*
[0.96,1.12][1.03,1.18][1.00,1.14][0.90,1.02][0.94,1.09][1.00,1.15][0.98,1.14][0.96,1.13][0.98,1.19][1.01,1.19]
% Protein11.131.41**1.140.881.271.131.40*1.051.211.03
[0.86,1.49][1.10,1.80][0.89,1.47][0.69,1.13][0.98,1.65][0.88,1.46][1.05,1.86][0.78,1.42][0.84,1.74][0.76,1.41]
PA Q21.040.971.001.060.941.050.891.101.020.99
[0.82,1.32][0.79,1.19][0.80,1.25][0.87,1.30][0.75,1.19][0.86,1.29][0.69,1.14][0.86,1.41][0.75,1.39][0.77,1.28]
PA Q30.941.050.880.930.970.77*0.870.760.940.83
[0.74,1.19][0.84,1.33][0.71,1.10][0.74,1.16][0.77,1.22][0.60,0.97][0.68,1.12][0.57,1.03][0.69,1.29][0.61,1.12]
PA Q40.790.940.850.880.800.67**0.80.70*0.800.71*
[0.62,1.01][0.73,1.21][0.68,1.06][0.69,1.11][0.63,1.01][0.52,0.86][0.62,1.03][0.52,0.95][0.58,1.10][0.51,0.98]
PA Q50.870.870.56***0.71**0.72*0.73*0.57***0.64**0.70*0.55***
[0.67,1.14][0.68,1.12][0.44,0.72][0.56,0.91][0.56,0.94][0.57,0.94][0.43,0.77][0.47,0.86][0.49,0.99][0.39,0.76]

The main dietary exposures were macronutrient densities. Alternate models examined the percentage of calories from fat alone or with the percentage of calories from protein, adjusting for total energy intake. Consuming a higher percentage of calories from fat was positively associated with an increased likelihood of having high LDL, high TAG and high CRP in females only and with having low HDL in males. A higher percentage of energy intake from protein, adjusted for the percentage of calories from fat, was associated with increased risk of overweight, high WHtR and AD in males, and impaired HbA1c, high HOMA-IR and high LDL in females. For example, an increase of 10 percentage points in energy intake from fat (consistent with the increase between 1991 and 2011 from 22% to 32% of energy intake from fat reported by Popkin in this issue) is related to an 8% increase in the likelihood of having high LDL among females. A 1 percentage point increase in the percentage of calories from protein (consistent with the increase between 1991 and 2011 from 12% to 13%) was associated with a 4% increase in risk of high LDL in females or of having AD in males.

Physical activity was inversely associated with all adverse outcomes and showed a clear dose response with most outcomes, with risk being reduced in the highest compared to the lowest quintile by 30–45%. Higher physical activity was similarly associated with lower CM risk in males and females.

Associations with high waist to height ratio and overweight (Table 3)

Table 3. Likelihood of having CM risk factors in relation to weight status (overweight, OW = BMI ≥ 25 kg m−2) and high waist to height ratio (WHtR>0.50): odds ratios and 95% confidence intervals from logistic regression models adjusted for age, education, urbanicity, income, clustering of multiple individuals within households, CHNS 2009
 High BPImpaired HbA1c (>5.6%)Diabetic HbA1c (>6.5%)High HOMA-IRHigh CRP (>3, <10)
MalesFemalesMalesFemalesMalesFemalesMalesFemalesMalesFemales
High WHtROR1.47***1.79***1.82***1.59***2.17***2.25***1.85***1.72***1.43**1.64***
95% CI[1.24,1.73][1.50,2.13][1.52,2.19][1.32,1.91][1.52,3.10][1.55,3.28][1.43,2.39][1.35,2.21][1.12,1.83][1.27,2.11]
OWOR1.361.342.36***2.21**2.32*4.59***2.23**3.51***1.142.50**
95% CI[0.88,2.09][0.78,2.31][1.52,3.67][1.31,3.74][1.08,5.00][2.07,10.20][1.30,3.84][1.98,6.21][0.60,2.16][1.31,4.78]
OW + high WHtROR3.16***3.16***2.87***3.42***4.28***3.94***4.69***3.76***1.91***3.26***
95% CI[2.66,3.74][2.64,3.79][2.40,3.43][2.86,4.09][3.09,5.93][2.74,5.66][3.74,5.88][2.98,4.74][1.52,2.41][2.55,4.16]
n 3,9364,4063,9014,3853,9034,3883,7334,2313,7764,282
 High LDLLow HDLHigh TAGAtherogenic dyslipidemia
MalesFemalesMalesFemalesMalesFemalesMalesFemales
  1. AD, atherogenic dyslipidemia; BMI, body mass index; BP, blood pressure; CM, cardiometabolic; CRP, C-reactive protein; HDL, high-density lipoprotein; HOMA-IR, homeostasis model of assessment–insulin resistance; LDL, low-density lipoprotein; TAG, triacylglycerol; WHtR, waist to height ratio. *p < 0.05, **p < 0.01, ***p < 0.001. Text in boldface signifies results that are statistically significant at p < 0.05.

High WHtROR1.54***1.24*1.76***1.58***2.46***1.79***2.29***2.03***
95% CI[1.28,1.85][1.04,1.47][1.48,2.08][1.33,1.88][2.05,2.95][1.48,2.15][1.86,2.82][1.59,2.58]
OWOR1.411.532.64***1.97**3.49***2.93***3.88***3.37***
95% CI[0.91,2.18][0.90,2.59][1.73,4.04][1.19,3.26][2.30,5.28][1.74,4.94][2.54,5.92][1.86,6.09]
OW + high WHtROR1.66***1.51***3.52***2.70***3.87***3.61***4.50***3.88***
95% CI[1.38,1.99][1.27,1.80][2.97,4.18][2.27,3.20][3.24,4.63][3.01,4.34][3.69,5.48][3.09,4.87]
n 3,7404,2373,7464,2393,7504,2413,7464,239

In sex-stratified logistic regression models adjusted for age, income, urbanicity and education, we compared the odds of having a CM risk factor in those who had high WHtR only, overweight only, and overweight plus high WHtR to those with neither condition. Overweight alone was not related to high BP or high LDL. In contrast, high WHtR significantly predicted all CM risk factors. The combination of overweight and high WHtR was a particularly powerful predictor of outcomes related to glucose control. The combination of these two risk factors was associated with a three to five times greater likelihood of elevated HbA1c, diabetes and insulin resistance. There was not a marked synergism of WHtR and overweight for LDL compared to measures of glucose control. High WHtR and overweight were each independently associated with low HDL, high TAG and AD, with synergistic effects of weight status and central obesity demonstrated by much higher odds ratios for males and females with both conditions.

Discussion

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

The high prevalence of CM risk factors even among youths and young adults portends high morbidity and mortality from diabetes and cardiovascular disease in China. Estimated prevalence rates from the CHNS are comparable to those from several other large population-based studies in China [17, 32], and the results emphasize the need for early life prevention and intervention. Currently, 80% of diabetes deaths occur in low- and middle-income countries [33], and China has experienced recent, rapid increases in diabetes and other CM diseases [34-36]. There are clear disparities in diabetes [37-39] and other non-communicable diseases by sociodemographic and urbanization-related factors [17, 40] across China.

Most of the risk is hidden, because owing to inadequate screening more than half of the high BP and diabetes risk indicated by the measurements included in the CHNS was undiagnosed or unrecognized by participants [41]. This poses particular challenges and high costs for an overburdened healthcare system [42].

Our work has important implications for intervention. Higher levels of physical activity were quite consistently associated with reduced prevalence of CM risk factors. Another paper in this issue shows clearly that occupation and domestic activities are by far the largest contributors to physical activity among adults, and these types of activity decreased significantly between 1991 and 2011 [43]. Moreover, reduction in physical activity has important implications for weight gain, as shown in prior longitudinal analyses of the CHNS [6]. As occupational physical activity declines with increased technological development [44], it becomes even more important to increase leisure time physical activity to promote good health.

Similarly, dietary trends have important implications for CM diseases. The percentage of calories from fat in the diet increased from 22% in 1991 to 32% in 2011. We showed that this level of change was associated with an 8% increase in the likelihood of having high LDL in women. Given that younger, more urban females have higher fat intakes, this finding has important implications. In general, our observed associations with diet were weaker than those reported in another large national study in 2002 in China that collected dietary data using a food frequency questionnaire [45]. For hypertension, sodium intake may be especially important. Recently, Du and colleagues [46] noted high risks associated with sodium and the sodium-potassium ratio, a critical issue, because by age 50 about half of the adults in the CHNS had raised BPs.

The increases in the prevalence of overweight and obesity and in particular the increases in abdominal obesity in China have very strong implications for CM diseases. We showed that the prevalence ratios for many CM risk factors were nearly doubled for overweight individuals and especially for those with abdominal obesity marked by high WHtR. The higher prevalence ratio for younger participants suggests that the CM risks of overweight are even greater for younger individuals, even though the prevalence of risk in this age group is lower than that among older participants. Again, this emphasizes the importance of early obesity prevention. A recent paper by our group has shown dramatic increases in waist circumference over the past two decades in China and also a higher waist circumference–BMI ratio over this period at all BMI levels [47]. The risks associated with high waist circumference [10, 48] and the synergistic effects of overweight and high waist circumference [49] are well known from other large studies in China. We found that the risk of diabetes, insulin resistance and AD was about four times greater among overweight adults with a high WHtR compared to those with neither risk factor. Overweight alone, even using a BMI cutpoint of 25 kg m−2, was not significantly associated with risk of elevated BP or high LDL. Given the small number of youths with adverse CM outcomes in each exposure category, we were not able to estimate comparable models for youths.

The present analysis has several limitations that will be addressed by future work with this important sample. We currently have only one round of biomarker data, precluding analysis of risk incidence. However, a future round of data collection is anticipated. In our analysis of diet, we focused only on macronutrient densities. A high percentage of energy intake from fat can reflect intake of atherogenic as well as healthy fats. Across all levels of percentages of energy intake from fat, about 60% came from plant sources and 40% from animal sources. Future work will examine fat and carbohydrate compositions of the diet to identify specific atherogenic components.

Despite these limitations, our work on the prevalence of CM risk in this population clearly demonstrates the health consequences of China's rapid nutrition transition. At the same time, it identifies possible avenues for prevention in the future.

Conflict of interest statement

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

The authors declare no conflict of interest. None of the individuals acknowledged received compensation for any role in the study.

Acknowledgements

  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References

This work was supported by the National Institutes of Health (NIH): NHLBI (R01-HL108427). The NIH had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; or preparation, review and approval of the manuscript. This research uses data from the CHNS, funded by NIH: NICHD (R01-HD30880), although no direct support was received from the grant for this analysis. We also are grateful to the Carolina Population Center (R24 HD050924) for general support. We thank Ms. Frances Dancy, BS, University of North Carolina at Chapel Hill, Carolina Population Center, for her helpful administrative assistance; and Jim Terry, AB; Guifeng Jin, PhD; and Phil Bardsley, PhD, for programming and technical support.

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  2. Summary
  3. Methods
  4. Results
  5. Correlates of adverse cardiometabolic risk factors
  6. Discussion
  7. Conflict of interest statement
  8. Acknowledgements
  9. References
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