Table 2 The relationship between dietary factors and biliary diseases
First author, Year | No. of included studies | Type of study | Dietary factor (Subgroup or Dose response) | Effects model | MA metric | Estimates | 95%CI | Test for overall effect (p-value) | I2% (p-value) | Egger test (p-value) | Publication bias and small-study effect |
|---|---|---|---|---|---|---|---|---|---|---|---|
Gallbladder cancer | |||||||||||
Chen [29] | 6 | Case–control | All spicy food | random | OR | 1.78 | (0.83–3.83) | NA | 75 (0.001) | 0.714 | No publication bias |
Chen [29] | 6 | Case–control | Chili pepper | random | OR | 1.78 | (0.83–3.83) | NA | 75 (0.001) | 0.714 | No publication bias |
ZHU [22] | 6 | Case–control(4);Cohort(2) | Tea | random | OR | 0.67 | (0.40–1.12) | 0.13 | 82 (< 0.0001) | Only funnel plot (N) | No publication bias |
ZHU [22] | 4 | Case–control(3);Cohort(1) | Tea (highest vs. lowest/none) | random | OR | 0.57 | (0.25–1.29) | 0.18 | 82 (0.001) | Only funnel plot (N) | No publication bias |
ZHU [22] | 4 | Case–control(3);Cohort(1) | Tea (moderate vs. low/none) | random | OR | 0.62 | (0.33–1.14) | 0.12 | 77 (0.004) | Only funnel plot (N) | No publication bias |
Biliary tract cancer | |||||||||||
Godos [13] | 8 | Total Cohort(5);Case–control(3) | Coffee | random | OR | 0.83 | (0.64–1.08) | NA | 0 (0.58) | Only funnel plot (N) | No publication bias |
Godos [13]# | 5 | Cohort(5) | Coffee | random | OR | 0.74 | (0.34–1.63) | NA | 0 (0.82) | Only funnel plot (N) | No publication bias |
Godos [13]# | 3 | Case–control(3) | Coffee | random | OR | 0.84 | (0.61–1.15) | NA | 22 (0.27) | Only funnel plot (N) | No publication bias |
Xiong [36] | 8 | Total Case–control(5);Cohort(3) | Tea | random | RR | 0.66 | (0.48–0.85) | NA | 81.1 (0.000) |  > 0.05 | No publication bias |
Xiong [36]# | 3 | Cohort | Tea | random | RR | 0.62 | (0.44–0.80) | NA | 55.8 (0.009) |  > 0.05 | No publication bias |
Xiong [36]# | 5 | Case–control | Tea | random | RR | 0.84 | (0.77–0.90) | NA | 60 (0.001) |  > 0.05 | No publication bias |
Xiong [36] | 8 | Total Case–control(5);Cohort(3) | Tea (every 1cup/day increment) | – | RR | 0.96 | (0.93–0.98) | 0.001 | NA |  > 0.05 | No publication bias |
Huai [37] | 10 | Total Case–control(8);Cohort(1); Nested case–control(1) | Vegetable | random | RR | 0.48 | (0.22–0.74) | NA | 86.8 (0.000) | 0.84 | No publication bias |
Huai [37]# | 8 | Case–control | Vegetable | random | RR | 0.45 | (0.14–0.75) | NA | 88 (< 0.001) | 0.84 | No publication bias |
Huai [37]# | 1 | Cohort | Vegetable | – | RR | 0.96 | (0.37–1.55) | NA | – | 0.84 | No publication bias |
Huai [37]# | 1 | Nested case–control | Vegetable | – | RR | 0.40 | (0.23–0.76) | NA | – | 0.84 | No publication bias |
Huai [37] | 8 | Case–control(6);Cohort(1); Nested case–control(1) | Vegetable (every 100 g/day increment) | – | RR | 0.31 | (0.20–0.47) |  < 0.001 | NA | 0.84 | No publication bias |
Huai [37] | 13 | Total Case–control(11);Cohort(1); Nested case–control(1) | Fruit | random | RR | 0.47 | (0.32–0.61) | NA | 63.3 (0.001) | 0.64 | No publication bias |
Huai [37]# | 11 | Case–control | Fruit | random | RR | 0.41 | (0.26–0.56) | NA | 61.6 (0.004) | 0.64 | No publication bias |
Huai [37]# | 1 | Cohort | Fruit | – | RR | 0.87 | (0.47–1.27) | NA | – | 0.64 | No publication bias |
Huai [37]# | 1 | Nested case–control | Fruit | – | RR | 0.60 | (0.33–0.98) | NA | – | 0.64 | No publication bias |
Huai [37] | 8 | Case–control(6);Cohort(1); Nested case–control(1) | Fruit (every 100 g/day increment) | – | RR | 0.89 | (0.66–1.18) | 0.35 | NA | 0.64 | No publication bias |
Kamsa-ard [30] | 3 | Case–control | Raw Fish | fixed | OR | 2.54 | (1.94–3.35) |  < 0.00001 | 0 (0.80) | NA | NA |
Kamsa-ard [30] | 2 | Case–control | Fermented Fish | fixed | OR | 1.61 | (0.76–3.41) | 0.21 | 45 (0.18) | NA | NA |
Kamsa-ard [30] | 3 | Case–control | Glutinous Rice | fixed | OR | 1.30 | (0.85–2.01) | 0.23 | 62 (0.07) | NA | NA |
Kamsa-ard [30] | 2 | Case–control | Meat | random | OR | 1.03 | (0.57–1.85) | 0.92 | 59 (0.06) | NA | Na |
Kamsa-ard [30] | 3 | Case–control | Betel nut | fixed | OR | 1.45 | (0.69–3.02) | 0.33 | 60 (0.06) | NA | NA |
Steele [23] | 3 | Total Case–control(2) Nested case–control(1) | Fermented Meats | random | OR | 1.81 | (0.96–3.39) | 0.066 | 17 (0.28) | NA | NA |
Steele [23] | 5 | Total Case–control(3) Nested case–control(2) | High Nitrate Foods | random | OR | 1.41 | (1.05–1.91) | 0.024 | 46 (0.01) | NA | NA |
Steele [23] | 2 | Case–control | Rice | random | OR | 0.88 | (0.48–1.63) | 0.688 | 34 (0.22) | NA | NA |
Cholecystolithiasis/gallbladder diease | |||||||||||
Zhang [39] | 7 | Cohort | Coffee | random | RR | 0.83 | (0.76–0.89) | NA | 35.9 (0.154) | 0.39 | No publication bias |
Zhang [39] | 4 | Cohort | Coffee (every 1Cup/Day increment) | – | RR | 0.95 | (0.91–1.00) | 0.049 | 54.4 (0.019) | 0.39 | No publication bias |
Zhang [40] | 14 | Total Case–control(4);Cohort(9);Cross sectional(1) | Vegetables | random | RR | 0.83 | (0.74–0.94) | NA | 82.5 (0.000) | 0.682 | No publication bias |
Zhang [40]# | 9 | Cohort | Vegetables | random | RR | 0.92 | (0.82–1.02) | NA | 80.2 (0.001) | 0.682 | No publication bias |
Zhang [40]# | 4 | Case–control | Vegetables | random | RR | 0.39 | (0.24–0.62) | NA | 59.8 (0.058) | 0.682 | No publication bias |
Zhang [40]# | 1 | Cross sectional | Vegetables | random | RR | 0.92 | (0.80–1.07) | NA | – | 0.682 | No publication bias |
Zhang [40] | 6 | Cohort | Vegetables(every 200 g/Day increment) | – | RR | 0.96 | (0.93–0.98) | 0.001 | NA | 0.682 | No publication bias |
Zhang [40] | 5 | Cohort | Fruits | random | RR | 0.88 | (0.83–0.92) | NA | 0 (0.456) | 0.735 | No publication bias |
Zhang [40] | 4 | Cohort | Fruits (every 200 g/Day increment) | – | RR | 0.97 | (0.96–0.98) |  | NA | 0.735 | No publication bias |