Background
C-reactive protein (CRP) is synthesized by the liver and has been shown to be a sensitive and systemic biomarker of inflammation [
1]. A number of prospective cohort studies and nested case–control studies have reported that CRP is associated with increased risk of developing type 2 diabetes (T2D) [
2]. A recent meta-analysis of 18 prospective studies found that the overall relative risk (RR) of T2D was 1.26 (95% confidence interval [CI] 1.16–1.37) per 1 log mg/L increment in CRP levels [
2]. However, most of the studies have been conducted in US or European populations [
2], and two studies were done in Japanese adults [
3,
4]. To the best of our knowledge, no study has specifically investigated the relation between CRP and risk of incident T2D in a Chinese population.
A few cross-sectional studies have been conducted in Chinese population, and suggest that CRP is positively related to prediabetes (including hyperglycemia and metabolic syndrome) and prevalent diabetes [
5‐
11]. However, the temporal relations cannot be determined in cross-sectional studies and reverse causality is a major concern. The positive relation in cross-sectional studies or case–control studies could be due to CRP being a consequence of hyperglycemia. Therefore, a prospective study is needed to ascertain the elevation of CRP before the onset of hyperglycaemia in the development of T2D. Ye et al. [
12] recently included elevated CRP levels in a prediction model of incident T2D in a 6-year follow-up study of 1912 Chinese adults aged 50–70 years, but the exact association between CRP and diabetes risk was not reported. Studies have consistently shown that circulating CRP levels are generally lower among Asians than Caucasians and Hispanic populations [
5,
13,
14]. Therefore, it is of scientific interest whether CRP could predict the onset of diabetes in Chinese population with relatively lower levels. In this prospective, nested, case–control study, we examined the role of CRP in predicting the development of incident T2D independent of obesity, lifestyle and blood lipid profiles in Chinese adults.
Results
Among T2D cases, the mean (±SD) age at diagnosis was 63.2 ± 6.4 years and the mean (±SD) duration between blood donation and diagnosis of T2D was 4.0 ± 1.7 years. Characteristics of study participants assessed at blood collections (1999–2004) are shown in Table
1. The mean age of the participants was 59.7 (SD 6.2) years, and 41.3% were males. As expected, diabetic participants had high-risk profiles except for the matching factors. They were heavier, were more likely to have history of hypertension than control subjects. No significant differences were found for education level, smoking status, alcohol drinking and physical activity levels. Regarding the plasma biomarkers, diabetic cases had higher levels of HbA1c, random glucose and insulin, and triglycerides, but lower HDL-cholesterol levels. The mean (SD) concentration of hs-CRP was 2.79 (2.65) and 1.86 (2.03) mg/L, respectively, in cases and controls (
P < 0.001). Among the healthy control participants, hs-CRP was inversely correlated with levels of HDL-C (Pearson’s coefficient
r = −0.15), and positively correlated with TG levels and BMI (Pearson’s coefficient
r = 0.11 and 0.26, respectively) (data not shown).
Table 1
Characteristics of the diabetes cases and matched controls: The Singapore Chinese Health Study
Number of participants | 571 | 571 | - |
Age, years | 59.6 ± 6.1 | 59.7 ± 6.2 | 0.78 |
Gender (Male) | 236 (41.3) | 236 (41.3) | - |
Dialect (%) | | | - |
Cantonese | 287 (50.3) | 287 (50.3) | |
Hokkien | 284 (49.7) | 284 (49.7) | |
Body mass index, kg/m2
| 24.8 ± 3.6 | 22.8 ± 3.3 | <0.001 |
Level of education (%) | | | 0.15 |
No formal education | 104 (18.2) | 99 (17.3) | |
Primary school | 255 (44.7) | 233 (40.8) | |
Secondary school or higher | 212 (37.1) | 239 (41.9) | |
Cigarette smoking (%) | | | 0.08 |
Never smokers | 410 (71.8) | 425 (74.4) | |
Ever smokers | 161 (28.2) | 146 (25.6) | |
History of hypertension (%) | 265 (46.1) | 148 (25.9) | <0.001 |
Weekly moderate-to-vigorous activity (%) | | | 0.11 |
< 0.5 hours/week | 456 (79.9) | 454 (79.5) | |
0.5–3.9 hours/week | 82 (14.4) | 68 (11.9) | |
≥ 4 hours/week | 33 (5.8) | 49 (8.6) | |
Alcohol intake (%) | | | 0.81 |
Abstainers | 498 (87.2) | 497 (87.0) | |
Weekly drinkers | 55 (9.6) | 59 (10.3) | |
Daily drinkers | 18 (3.2) | 15 (2.6) | |
C-reactive protein, mg/L | 2.79 ± 2.65 | 1.86 ± 2.03 | <0.001 |
HbA1c, % | 6.83 ± 1.44 | 5.55 ± 0.27 | <0.001 |
Random glucose, mmol/L | 7.27 ± 3.65 | 4.84 ± 1.22 | <0.001 |
Random insulin, mIU/L | 27.8 ± 33.8 | 18.0 ± 22.5 | <0.001 |
Total cholesterol, mmol/L | 5.31 ± 0.95 | 5.20 ± 0.85 | 0.049 |
HDL cholesterol, mmol/L | 1.08 ± 0.24 | 1.23 ± 0.32 | <0.001 |
Triglyceride, mmol/L | 2.44 ± 1.53 | 1.80 ± 1.04 | <0.001 |
Table 2
Risk of diabetes according to quartiles of hs-CRP: The Singapore Chinese Health Study
Median (range) | 0.4 (0.1–0.6) | 0.9 (0.7–1.2) | 1.6 (1.3–2.3) | 3.6 (2.4–11.0) | | |
Total diabetes |
Cases/controls | 78/154 | 107/139 | 150/138 | 236/140 | | |
Model 1 | 1.00 | 1.51 (1.03–2.20) | 2.14 (1.47–3.11) | 3.25 (2.28–4.64) | <0.001 | 1.59 (1.40–1.81) |
Model 2 | 1.00 | 1.42 (0.96–2.11) | 1.89 (1.27–2.80) | 2.70 (1.85–3.95) | <0.001 | 1.50 (1.31–1.71) |
Model 3 | 1.00 | 1.23 (0.81–1.86) | 1.50 (0.99–2.27) | 1.97 (1.32–2.94) | <0.001 | 1.34 (1.16–1.54) |
Model 4 | 1.00 | 1.24 (0.79–1.96) | 1.22 (0.77–1.92) | 1.74 (1.12–2.70) | 0.016 | 1.27 (1.09–1.48) |
Undiagnosed diabetes |
Cases/controls | 32/81 | 43/67 | 73/54 | 131/77 | | |
Model 1 | 1.00 | 1.48 (0.84–2.62) | 3.63 (2.02–6.53) | 4.39 (2.59–7.45) | <0.001 | 1.84 (1.52–2.24) |
Model 2 | 1.00 | 1.29 (0.71–2.37) | 3.17 (1.72–5.82) | 3.63 (2.08–6.36) | <0.001 | 1.75 (1.43–2.14) |
Model 3 | 1.00 | 1.21 (0.64–2.28) | 2.41 (1.26–4.59) | 2.55 (1.42–4.58) | <0.001 | 1.53 (1.24–1.89) |
Model 4 | 1.00 | 1.20 (0.59–2.44) | 2.17 (1.07–4.40) | 2.43 (1.25–4.71) | 0.003 | 1.53 (1.20–1.94) |
Incident diabetes |
Cases/controls | 46/73 | 64/72 | 77/84 | 105/63 | | |
Model 1 | 1.00 | 1.45 (0.87–2.43) | 1.45 (0.88–2.39) | 2.54 (1.54–4.20) | <0.001 | 1.38 (1.16–1.64) |
Model 2 | 1.00 | 1.50 (0.85–2.65) | 1.24 (0.70–2.18) | 2.20 (1.26–3.84) | 0.008 | 1.31 (1.08–1.59) |
Model 3 | 1.00 | 1.31 (0.73–2.36) | 1.07 (0.60–1.93) | 1.70 (0.95–3.07) | 0.14 | 1.20 (0.98–1.47) |
Model 4 | 1.00 | 1.24 (0.65–2.39) | 0.75 (0.39–1.45) | 1.24 (0.64–2.39) | 0.93 | 1.06 (0.85–1.33) |
After multivariate adjustment for demographic and lifestyle factors, the odds ratio (OR) comparing the extreme quartiles of hs-CRP was 2.70 (95% CI 1.85–3.95;
P for trend <0.001; Table
2). Further adjustment for BMI, plasma levels of TG and HDL-C attenuated the association but it remained significant (OR = 1.74 comparing the extreme quartiles of hs-CRP; 95% CI 1.12–2.70;
P for trend = 0.016). Among the cases, 279 subjects had HbA1c ≥6.5% at the time of blood collection and the OR comparing the extreme quartiles of hs-CRP was 2.43 (95% CI 1.25–4.71;
P for trend = 0.003). The other 292 subjects had HbA1c <6.5% at blood collection and the corresponding OR was 1.24 (95% CI 0.64–2.39;
P for trend = 0.93).
The OR (95% CI) for T2D of each 1 log mg/L increment in hs-CRP levels was 1.27 (1.09–1.48) in the total study samples, 1.53 (1.20–1.94) in those with HbA1c ≥6.5% at blood collection, and 1.06 (0.85–1.33) in those with HbA1c <6.5% at blood collection (Table
2).
We further stratified the analysis by sex (Table
3) and baseline BMI status (Table
4). The association was slightly stronger in women compared to men, but the interaction was not statistically significant (
P for interaction = 0.27). The association was similar in normal weight individuals (BMI <23 kg/m
2) and overweight/obese participants (BMI ≥23 kg/m
2), and the interaction test was not significant (
P for interaction = 0.72).
Table 3
Risk of diabetes according to sex-specific quartiles of hs-CRP: stratified by sex
Men |
Median (range) | 0.3 (0.12–0.4) | 0.8 (0.5–1.1) | 1.5 (1.2–2.0) | 3.4 (2.1–11.0) | | |
Cases/controls | 22/60 | 70/64 | 59/53 | 85/59 | | |
Model 1 | 1.00 | 2.79 (1.54–5.05) | 3.00 (1.60–5.63) | 3.73 (2.04–6.79) | 0.002 | 1.46 (1.21–1.75) |
Model 2 | 1.00 | 3.42 (1.77–6.61) | 3.06 (1.53–6.13) | 3.57 (1.80–7.11) | 0.039 | 1.38 (1.12–1.70) |
Model 3 | 1.00 | 2.76 (1.40–5.47) | 2.45 (1.20–5.03) | 2.80 (1.38–5.69) | 0.029 | 1.30 (1.05–1.61) |
Model 4 | 1.00 | 2.86 (1.36–6.01) | 1.90 (0.86–4.19) | 2.25 (1.06–4.79) | 0.24 | 1.19 (0.94–1.49) |
Women |
Median (range) | 0.5 (0.12–0.7) | 1.1 (0.8–1.3) | 1.8 (1.4–2.5) | 4.3 (2.6–11.0) | | |
Cases/controls | 44/94 | 56/79 | 87/82 | 148/80 | | |
Model 1 | 1.00 | 1.53 (0.91–2.57) | 2.29 (1.38–3.80) | 3.86 (2.39–6.21) | <0.001 | 1.72 (1.44–2.04) |
Model 2 | 1.00 | 1.37 (0.79–2.37) | 2.00 (1.16–3.46) | 3.32 (1.99–5.53) | <0.001 | 1.63 (1.36–1.96) |
Model 3 | 1.00 | 1.10 (0.61–2.00) | 1.45 (0.80–2.66) | 2.19 (1.24–3.85) | 0.002 | 1.42 (1.16–1.73) |
Model 4 | 1.00 | 1.09 (0.54–2.20) | 1.25 (0.63–2.47) | 2.07 (1.07–3.99) | 0.012 | 1.41 (1.12–1.78) |
Table 4
Risk of diabetes according to quartiles of hs-CRP: stratified by baseline BMI
Median (range) | 0.4 (0.1–0.6) | 0.9 (0.7–1.2) | 1.6 (1.3–2.3) | 3.6 (2.4–11.0) | | |
BMI <23 kg/m2
|
Cases/controls | 37/109 | 42/84 | 48/68 | 59/57 | | |
Model 1 | 1.00 | 1.49 (0.88–2.53) | 2.08 (1.23–3.51) | 2.95 (1.75–4.99) | <0.001 | 1.47 (1.22–1.77) |
Model 2 | 1.00 | 1.41 (0.82–2.43) | 1.93 (1.12–3.32) | 2.67 (1.54–4.60) | <0.001 | 1.42 (1.17–1.72) |
Model 3 | 1.00 | 1.27 (0.71–2.25) | 1.38 (0.77–2.47) | 2.07 (1.16–3.70) | 0.02 | 1.29 (1.05–1.58) |
BMI ≥23 kg/m2
|
Cases/controls | 41/45 | 65/55 | 102/70 | 177/83 | | |
Model 1 | 1.00 | 1.39 (0.79–2.44) | 1.74 (1.02–2.96) | 2.62 (1.56–4.41) | <0.001 | 1.55 (1.29–1.86) |
Model 2 | 1.00 | 1.30 (0.73–2.33) | 1.43 (0.82–2.50) | 2.19 (1.28–3.78) | 0.002 | 1.46 (1.21–1.77) |
Model 3 | 1.00 | 1.11 (0.60–2.05) | 1.20 (0.67–2.15) | 1.68 (0.95–2.97) | 0.041 | 1.33 (1.09–1.63) |
Discussion
In this prospective nested case–control study of Chinese men and women, elevated baseline plasma CRP levels were associated with an increased risk of T2D. However, when stratified by baseline HbA1c levels, we found that CRP was only positively associated with T2D among those already with high HbA1c levels (undiagnosed diabetes), but not in those with low HbA1c levels (incident diabetes). Therefore, elevated CRP levels might be by-products of hyperglycemia, rather than directly contributing to the development of incident T2D.
HbA1c was adapted as a diagnosis criterion of diabetes in 2010 by the American Diabetes Association [
19]; therefore, at the time of blood collection and follow-up in our cohort, HbA1c level was not used in the diagnosis of diabetes in Singapore. In the total study samples, irrespective of HbA1c levels in the cases, we observed a strong positive association between CRP and T2D. The estimate (OR = 1.27 [95% CI 1.09–1.48] per 1 log mg/L increment in CRP levels) was consistent with the pooled relative risk reported from a recent meta-analysis [1.26 (95% CI 1.16–1.37); 18 studies] [
2]. None of the previous studies included HbA1c in their diagnosis criteria, and two prior studies in Caucasian populations have observed positive CRP-T2D associations among subgroup subjects with HbA1c <5.8% [
20] and HbA1c <6.0% [
21], respectively, which were contrary to the findings of the current study. Both studies have also adjusted for HbA1c in the statistical models, and the positive association between CRP and incident diabetes did not change materially [
20,
21]. We did not adjust for HbA1c levels in our model, because we had purposely excluded controls with baseline HbA1c ≥6.0% to reduce the possibility of undiagnosed diabetes among the controls. Therefore, the cases had much higher HbA1c levels compared with the controls at the time of blood collection (Table
1), and adjustment for the Hb1Ac levels would be problematic due to its marked difference between cases and controls. Since no other studies have specifically evaluated the effect of high HbA1c levels at baseline among the incident diabetes cases, it is unclear to what extent the positive association in previous prospective reports could be explained by the effect of undiagnosed diabetes. Some studies also found no significant associations between CRP and incident diabetes in Pima Indians [
22], UK adults [
18], Aboriginal Canadians [
23], Germany men [
24], Mexican men [
25], and US adults [
26]. Several studies have suggested that CRP-diabetes association could be largely explained by obesity [
23,
24,
26,
27], insulin resistance [
26,
27], deranged liver function and lower adiponectin levels [
18].
The stronger association between CRP and glycemia in Chinese women compared to men in some studies [
7,
8,
11,
28] may be explained by the greater accumulation of subcutaneous fat in women than in men [
29]. Two prospective cohort studies in Mexican [
25] and German [
30] populations observed positive CRP-T2D associations in women but not in men, while two cohort studies in Japanese populations [
3,
4] found no significant gender differences in the association. In our study, although no significant interaction was observed between sex and CRP (
P = 0.27), the association with T2D risk was stronger in women compared to men when CRP was examined as a continuous variable, and this finding is generally consistent with previous prospective studies [
18].
Our finding of a positive association between CRP and increased risk of undiagnosed diabetes but not incident diabetes suggests that CRP might not be a causal factor for diabetes, but is a marker of hyerglycaemia in the pathway. Although the meta-analysis revealed a statistically significant increased diabetes risk associated with CRP [
2], the results are not entirely consistent and a number of studies did not report any significant association either in the whole population [
18,
22,
23,
26] or in men [
24,
25]. The current evidence remains controversial whether CRP is a causal risk factor or just a downstream intermediate for T2D [
31]. Hence, the clinical potential of targeting CRP in the prevention of diabetes remains uncertain. A recent Mendelian randomization analysis in the Whitehall II Study found that CRP haplotypes were not associated with incident diabetes despite the association with baseline serum CRP [
32]. Other Mendelian randomization studies also found no causal relation between CRP and metabolic syndrome [
33], as well as coronary heart disease [
34‐
36]. Therefore, the lack of concordance between the effect of CRP genotypes and CRP levels on T2D and coronary heart disease risks argues against a causal role of CRP in the etiologies of these two diseases.
The strength of the present study was its prospective design and hence the presumed lack of recall bias in exposure data (questionnaires, collected biospecimens) prior to T2D diagnosis. However, there are some limitations to the present study as well. First, we measured CRP only once at baseline and this may not represent the long-term lipid profile. However, this would lead to non-differential misclassification and may underestimate the association. In addition, the BMI was calculated from self-reported height and weight, and residual confounding is possible. Second, incident diabetes was obtained from self-reported information, thus undiagnosed diabetes may exist. However, we have measured HbA1c levels, which was updated as a diagnosis criterion of diabetes in 2010 by the American Diabetes Association [
19], and further performed stratified analysis among subgroups with HbA1c <6.5% and ≥6.5%. Furthermore, we have used HbA1c as a selection criterion for controls to minimize bias due to undiagnosed diabetes; therefore, we could not include it in our model adjustment. Last but not least, the present study was conducted in a middle-aged and elderly population with a higher diabetes incidence, therefore, the findings may not be generalizable to younger people.
Conclusion
In conclusion, we found that elevated plasma levels of hs-CRP were only positively associated with T2D among those already with high HbA1c levels, but not in those with low HbA1c levels in this Chinese population. Therefore, elevated CRP levels may be a consequence of hyperglycemia, instead of being an etiological biomarker in T2D development. Current evidence remains controversial whether CRP is a causal risk factor or just a downstream intermediate for T2D. Therefore, more carefully constructed prospective studies in different populations are warranted to validate this finding, and investigate the biochemical and genetic basis for the relationship between hs-CRP and T2D risk.
Acknowledgements
We thank Siew-Hong Low of the National University of Singapore for supervising the fieldwork of the Singapore Chinese Health Study, and Renwei Wang for the maintenance of the cohort study database. We also thank the founding principal investigator of the Singapore Chinese Health Study, Mimi C. Yu.