Meta-Analysis
Copyright ©2013 Baishideng.
World J Meta-Anal. May 26, 2013; 1(1): 47-56
Published online May 26, 2013. doi: 10.13105/wjma.v1.i1.47
Table 1 Characteristics and methodological quality of included 38 case-control studies in the meta-analysis
Ref.LocationSample sizeAge (case/control, yr)Source of controlAssessment
End pointsOR (95%CI)NOS
PDCHD
Li et al[13]China88/128> 60Hospital-basedPICCHD1.85 (1.07-3.20)4
López et al[14]Chile35/5142.5 ± 5.7/40.5 ± 6.3Hospital-basedPPDECGCHD3.17 (1.31-7.65)16
Huang et al[15]China146/13658.7 ± 8.9Hospital-basedQCAGCHD2.27 (1.40-3.68)5
Liu et al[16]China216/21659.4 ± 15.3/57.9 ± 13.7Population-basedPICAGCHD5.42 (3.32-8.86)4
Rutger Persson et al[17]Sweden80/8063.4 ± 8.9/61.9 ± 9.1Population-basedABLECGMI14.1 (5.8-34.4)17
Geerts et al[18]Belgium108/6259.2 ± 10.9/57.7 ± 8.7Hospital-basedPPDCCHD6.50 (1.80-23)17
Montebugnoli et al[19]Italy63/5052.3 ± 4.9/54.5 ± 6.1Population-basedPPDCAGCHD4.61 (1.00-23.20)18
Renvert et al[20]Sweden88/8062.7 ± 9.1/NAHospital-basedPPDCMI7.67 (1.13-51.92)16
Tang et al[21]China250/250≥ 45Hospital-basedCPICCHD1.95 (1.36-2.78)4
Buhlin et al[22]Sweden143/5065.9 ± 8.6/64.5 ± 8.3Population-basedPPDCAGCHD3.80 (1.68-8.74)18
Liu et al[23]China45/4054.9 ± 8.1/51.2 ± 6.5Hospital-basedPICAGCHD18.70 (6.25-55.93)4
Wang et al[24]China216/21659 ± 15/58 ± 14Population-basedABLCAGCHD1.76 (1.31-2.36)17
Andriankaja et al[25]United States537/80054.6 ± 8.5/55.0 ± 0.0Population-basedCALCMI2.24 (1.60-3.13)18
Barilli et al[26]Brazil40/5949.2 (30-79)Hospital-basedCPICCHD61 (17.26-214.86)5
Briggs et al[27]United Kingdom92/7956.7 ± 6.3/58.2 ± 6.7Population-basedPPDCAGCHD3.06 (1.02-9.17)18
Geismar et al[28]Denmark110/14065/62.6Hospital-basedABLECGCHD2.0 (0.77-5.08)17
Li et al[29]China357/30572.5 ± 8.9Population-basedPICAGCHD1.16 (0.91-1.48)6
Spahr et al[30]Germany263/52661.0 ± 7.1/61.0 ± 7.1Population-basedCPICAGCHD1.67 (1.08-2.58)18
Zhang et al[31]China77/7450.2 ± 9.6/50.8 ± 9.5Population-basedPPDCAGCHD2.13 (1.08-4.22)6
Zhang et al[32]China277/23857 ± 11.3/55 ± 10.8Hospital-basedCALCAGCHD2.70 (1.52-4.80)17
Latronico et al[33]Italy15/1957.7/55.1Population-basedABLCAGCHD5.85 (1.03-33.12)6
Nonnenmacher et al[34]Germany45/4563.5 ± 7.4/63.6 ± 7.4Hospital-basedCALCAGCHD3.2 (1.2-9.0)17
Rech et al[35]Brazil58/5759.3/70Hospital-basedPPDECGMI1.8 (0.7-4.7)16
Ge et al[36]China13/3055.1 ± 4.8/51.2 ± 4.7Hospital-basedCALCAGCHD2.53 (1.01-6.32)16
Meng et al[37]China150/15071.2 ± 4.6/71.9 ± 4.7Population-basedABLCCHD2.95 (1.74-5.02)5
Wu et al[38]China77/7553.81 ± 8.25/ 51.14 ± 6.44Hospital-basedCALCAGCHD2.18 (1.52-3.13)5
Zamirian et al[39]Iran80/8054.0 ± 8.7/51.9 ± 9.4Hospital-basedCALECGMI3.18 (1.37-7.42)16
Zhu et al[40]China98/10461.34 ± 9.63Population-basedCALCMI11.43 (2.59-50.34)5
Dong et al[41]China161/16233-66/30-70Population-basedABLCAGCHD5.74 (2.07-15.90)17
Ma et al[42]China146/25745-72Hospital-basedCALCAGCHD2.36 (1.49-3.73)5
Oikarinen et al[43]Kuwait88/8848.8 ± 10.0/47.0 ± 11.6Hospital-basedABLCCHD19.69 (19.36-20.02)6
Sun et al[44]China167/24268.28 ± 10.53/ 50.18 ± 10.56Hospital-basedCALCAGCHD9.10 (0.87-95.07)17
Willershausen et al[45]Germany125/12561.8 ± 10.4/63.4 ± 10.7Population-basedCALECGMI3.65 (2.02-6.56)17
Bokhari et al[46]Pakistan45/3541.67 ± 5.11/ 40.31 ± 6.97Hospital-basedPPDCAGCHD6.37 (1.26-32.27)6
Chen[47]China46/3438-68/35-66Hospital-basedABLCAGCHD9.87 (3.50-27.82)4
Sikka et al[48]India100/10054.97 ± 7.97/ 55.1 ± 8.08Population-basedCPICAGCHD2.66 (1.50-4.71)7
Ashraf et al[49]Pakistan145/14553.3 ± 12.3/51.7 ± 11.6Hospital-basedCPICCHD1.20 (0.93-1.55)17
Zhang et al[50]China162/16266.7/66.0Hospital-basedQCAGCHD2.16 (1.65-2.83)16
Table 2 Adjustments in case-control studies included in this meta-analysis
Ref.Adjustment
López et al[14]DM, systolic blood pressure, and smoking
Rutger Persson et al[17]Smoking
Geerts et al[18]Age, gender, smoking, DM, hypertension, hyperlipidemia, diet, and alcohol
Montebugnoli et al[19]Age, smoking, DM, hypertension, high/low density lipoprotein, CRP, leukocytes, BMI, social class
Renvert et al[20]Smoking
Buhlin et al[22]Age, gender, smoking, DM, BMI, education, place of birth
Wang et al[24]Gender, age, BMI, smoking, hypertension, DM, blood lipid, CRP, white blood count, and fibrinogen
Andriankaja et al[25]Age, gender, hypertension, cholesterol, DM, and smoking
Briggs et al[27]Smoking, education, alcohol, BMI, exercise, unemployment, hobby, plaque, and CRP
Geismar et al[28]Gender, smoking, DM, and education
Spahr et al[30]Age, sex, BMI, smoking, alcohol, DM, hypertension, hyperlipoproteinemia, education, exercise, and statin intake
Zhang et al[32]Smoking, age, gender, BMI, hypertension, DM, high-density lipoproteincholesterol, total Cholesterol, total glycerin
Nonnenmacher et al[34]Smoking and BMI
Rech et al[35]Age, gender, smoking, DM
Ge et al[36]Blood pressure and BMI
Zamirian et al[39]Smoking and alcohol
Dong et al[41]Smoking, age, and education
Sun et al[44]Age and BMI
Willershausen et al[45]Age, gender, and smoking
Ashraf et al[49]Age, gender, and education
Zhang et al[50]Age, gender, smoking, alcohol, hypertension, and BMI
Table 3 Results of overall and subgroups analyses of pooled odds ratios and 95%CIs
Total and subgroupNo. of trialsHeterogeneity
ModelMeta-analysis
I2 (%)POR95%CIP
Total3898.59< 0.001Random3.792.23-6.43< 0.001
Adjustment for covariates
Yes2166.02< 0.001Random2.722.13-3.46< 0.001
No1798.68< 0.001Random4.622.10-10.18< 0.001
Source of control
HB2298.5< 0.001Random4.042.00-8.12< 0.001
PB1680.38< 0.001Random3.082.23-4.26< 0.001
Methodological quality (NOS)
< 61183.28< 0.001Random3.381.75-6.52< 0.001
≥ 62798.73< 0.001Random4.162.71-6.40< 0.001
End point
CHD3198.75< 0.001Random3.632.00-6.59< 0.001
MI770.15< 0.001Random4.122.35-7.22< 0.001
Assessment of PD
ABL897.93< 0.001Random5.571.90-16.33< 0.001
CAL1000.5Fixed2.542.13-3.04< 0.001
CPI590.24< 0.001Random2.761.45-5.23< 0.001
PI591.95< 0.001Random3.121.38-7.05< 0.001
PPD800.76Fixed3.552.39-5.27< 0.001
Questionnaire200.86Fixed2.191.73-2.77< 0.001
Assessment of CHD
CAG2273.04< 0.001Random2.852.23-3.64< 0.001
Cardiologists1099< 0.001Random5.091.71-15.14< 0.001
ECG660.720.03Random3.552.06-6.12< 0.001
Ethnicity
America488.24< 0.001Random4.751.50-15.020.01
Asia2399.01< 0.001Random3.461.73-6.94< 0.001
Europe1156.820.01Random3.812.46-5.91< 0.001