Figures (1)  Tables (5)

    • Figure 1. 

      Flowchart.

    • Study Country Publication type Study type NOS score
      Fan et al., 2018[26] USA Journal article Prospective cohort 8
      Torres et al., 2015[31] USA Journal article Controlled 8
      Olson et al., 2017[29] USA Journal article Pilot 7
      Lin et al., 2013[34] USA Conference proceedings Pilot 7
      Lu et al., 2019[28] China Journal article Controlled 8
      Vogtmann et al., 2019[32] Iran Journal article Controlled 8
      Ren et al., 2017[30] China Journal article Controlled 8
      Half et al., 2019[27] Israel Journal article Controlled 8
      Half et al., 2015[33] Israel Conference proceedings Pilot 6
      Kartal et al., 2022[35] Germany Journal article Case–control 6
      Chen et al., 2023[36] China Journal article Controlled 7
      Hashimoto et al., 2022[38] Japan Journal article Controlled 8
      Sono et al., 2024[39] Japan Journal article Controlled 7
      Zhao et al., 2024[37] China Journal article Controlled 8
      Yang et al., 2023[40] China Journal article Controlled 6
      NOS, Newcastle-Ottawa Scale. USA, United States of America.

      Table 1. 

      Study characteristics.

    • Study Sample size Age % male BMI % smoking
      PC Control PC Control PC Control PC Control PC Control
      Fan et al., 2018[26] 361 371 68.5 68.3 57.1 57.1 − − 57.3 49.9
      Torres et al., 2015[31] 8 100
      (other disease 78 HC 22)      		 71.1 60.7
      (other cancers)      		 75.0 50.0 − − − −
      Olson et al., 2017[29] 40 97
      (IPMN 39; HC 58)      		 < 70, 64.0%; ≥ 70, 35.0% < 70, 42.0%; ≥ 70, 59.0% (IPMN); < 70, 81.0%;
      ≥ 70, 19.0% (HC)      		 53.0 56.0 (IPMN); 40.0 (HC) Normal 38.0%; abnormal 61.0% Normal 36.0%;
      abnormal 64.0% (IPMN); normal 43.0%;
      abnormal 57.0% (HC)      		 44.0 46.0 (IPMN); 31.0 (HC)
      Lin et al., 2013[34] 13 15
      (pancreatitis 3 HC 12)      		 − − − − − − − −
      Lu et al., 2019[28] 30 25 50.8 ± 5.3 48.2 ± 6.0 70.0 80.0 22.5 ± 1.2 22.6 ± 1.6 − −
      Vogtmann et al., 2019[32] 273 285 < 70, 63.7%;
      ≥ 70, 36.3%      		 < 70, 67.4%; ≥ 70, 32.6% 60.4 46.0 Normal 57.5%; abnormal 42.5% Normal 46.3%;
      abnormal 53.7%      		 30.5 25.6
      Ren et al., 2017[30] 85 57 56.0 (33.0–78.0) 52.0 (43.0–67.0) 55.3 63.2 22.7 (19.5–26.0) 23.2 (18.5–27.1) − −
      Half et al., 2019[27] 30 35
      (NAFLD 16; PCL 6 HC 13)      		 68.9 ± 6.2 51.0 ± 10.8 (NAFLD);
      66.0 ± 15.3 (PCL);
      59.0 ± 8.7 (HC)      		 53.3 75.0 (NAFLD);
      83.3 (PCL); 46.2 (HC)      		 − − − −
      Half et al., 2015[33] 15 15 − − − − − − − −
      Kartal et al., 2022[35] 57 79 (HC 50 CP 29) − − − − − − − −
      Chen et al., 2023[36] 40 54 (HC 50 CP 15) − − − − − − − −
      Hashimoto et al., 2022[38] 5 68 70.0−89.0 54.0 40.0 42.6 − − − −
      Sono et al., 2024[39] 30 18 63.7 63.0 53.3 66.7 22.0 24.5 36.7 88.9
      Zhao et al., 2024[37] 29 9 67.6 ± 10.8 30.5 ± 6.8 58.6 33.3 22.4 ± 2.9 20.8 ± 1.5 − −
      Yang et al., 2023[40] 44 50 − − 47.7 − 22.4 ± 2.8 − − −
      /, no related information; BMI, body-mass index; HC, healthy control; IPMN, intraductal papillary mucinous neoplasms; NAFLD, non-alcoholic fatty liver disease; PCL, pre-cancerous lesions. Notes: * It was a prospective study including two large population-based cohorts whose BMI was described in Median or mean.

      Table 2. 

      Population characteristics.

    • Study Sample Temperature
      for storage      		 Measurement
      method      		 Diversity assessment Antibiotics Probiotic or
      prebiotic
      α-diversity β-diversity
      Fan et al., 2018[26] Oral wash −80 °C 16S V3–V4 Shannon, Simpson PCoA − −
      Torres et al., 2015[31] Saliva −80 °C 16S Chao1 ANOSIM Not in 2 wk −
      Olson et al., 2017[29] Saliva − 16S V4–V5 NP Shannon, Inverse Simpson NA Not in 30 d −
      Lin et al., 2013[34] Oral wash − 16S − − − −
      Lu et al., 2019[28] Tongue coat − 16S V3–V4 Shannon, Simpson, inverse Simpson, Obs, Chao 1, ACE PCoA Not in 8 wk Not in 8 wk
      Vogtmann et al., 2019[32] Saliva −70 °C 16S V4 Observed SVs, Shannon, Faith's PD PCoA − −
      Ren et al., 2017[30] Stool −80 °C 16S V3–V5 Shannon, Simpson, Chao 1 PCoA Not in 8 wk Not in 8 wk
      Half et al., 2019[27] Stool −80 °C 16S Shannon PCoA Not in 8 wk −
      Half et al., 2015[33] Stool − 16S − ANOSIM − −
      Kartal et al., 2022[35] Stool and saliva −80 °C 16S V4 Shannon, Simpson Unweighted TINA index − −
      Chen et al., 2023[36] Fecal and saliva −80 °C 16S V3–V4 Chao1, Shannon observed species, and PD whole tree PCoA Not in 4 wk Not in 4 wk
      Hashimoto et al., 2022[38] Stool and saliva −80 °C 16S Shannon PCoA Not in 6 months Not in 6 months
      Sono et al., 2024[39] Stool and saliva − 16S V3–V4 Observed features, Shannon BrayeCurtis dissimilarity QIIME 2 − −
      Zhao et al., 2024[37] Stool −80 °C 16S V3–V4 Chao 1 Acex, Shannon Simpson Sobs i Coverage Mothur software Qiime Not in 8 wk Not in 8 wk
      Yang et al., 2023[40] Stool −80 °C 16S Chao 1 Shannon PCoA − −
      −, no related information. d, day; wk, week.

      Table 3. 

      Methodologic characteristics.

    • Study Bacteria taxonomic level
      Phylum Class Order Family Genus
      Fan et al., 2018[26] Bacteroidetes (↑) SR1[C-1] (↑) Fusobacteriales (↓) Leptotrichiaceae (↓) Alloprevotella, Porphyromonas gingivalis, and Aggregatibacter actinomycetemcomitans (↑)
      Fusobacteria (↓) Fusobacteria (↓) Leptotrichia (↓)
      Torres et al., 2015[31] Firmicutes (↑) Leptotrichia Bacteroides (↑)
      Proteobacteria (↓) Porphyromonas Aggregatibacter Neisseria (↓)
      Olson et al., 2017[29] Firmicutes (↑) Bacilli (↑) Lactobacillales (↑) Streptococcaceae (↑) Streptococcus (↑)
      Proteobacteria (↓) Gammaproteobacteria; Betaproteobacteria (↓) Pasteurellales Neisseriales (↓) Pasteurellaceae Neisseriaceae (↓) Haemophilus Neisseria (↓)
      Lin et al., 2013[34] Bacteroides (↑)
      Corynebacterium Aggregatibacter (↓)
      Lu et al., 2019[28] Firmicutes, Fusobacteria and Actinobacteria (↑) Leptotrichiaceae, Fusobacteriaceae, Actinomycetaceae, Lachnospiraceae, Micrococcaceae, Erysipelotrichaceae, and Campylobacteraceae (↑) Leptotrichia, Fusobacterium, Actinomyces, Rothia, Solobacterium, Oribacterium, Campylobacter, Atopobium, and Parvimonas (↑)
      Bacteroidetes (↓) Prevotellaceae, Pasteurellaceae, and Porphyromonadaceae (↓) Porphyromonas, Haemophilus, and Paraprevotella (↓)
      Vogtmann et al., 2019[32] Enterobacteriales (↑) Enterobacteriaceae, Bacteroidaceae, Staphylococcaceae (↑) Lachnospiraceae G7 (↑)
      Haemophilus (↓)
      Ren et al., 2017[30] Bacteroidetes (↑) Prevotella, Veillonella, Klebsiella, Selenomonas, Hallella, Enterobacter, and Cronobacter (↑)
      Firmicutes and Proteobacteria (↓) Gemmiger, Bifidobacterium, Coprococcus, Clostridium IV, Blautia, Flavonifractor, Anaerostipes, Butyricicoccus, and Dorea (↓)
      Half et al., 2019[27] Bacteroidetes (↑) Bacteroidia; Verrucomicrobiae; Clostridia Bacteroidales; Verrucomicrobiales; Clostridiales Porphyromonadaceae; Verrucomicrobiaceae; Clostridiaceae1 Odoribacter, Akkermansia (↑)
      Firmicutes (↓) Clostridiumsensustricto1 (↓)
      Half et al., 2015[33] Bacteroidetes Verrucomicrobia (↑) Sutterella, Veillonella, Bacteroides, Odoribacter, and Akkermansia (↑)
      Firmicutes and Actinobacteria (↓)
      Kartal et al., 2022[35] Veillonella atypica, Fusobacterium, nucleatum/hwasookii, Alloscardovia, omnicolens (↑) Romboutsia timonensis, Faecalibacterium, rausnitzii, Bacteroides, coprocola, Bifidobacterium, and bifidum (↓)
      Chen et al., 2023[36] Bacteroidetes (↑) Veillonella, Peptostreptococcus, Akkermansia, Parvimonas, Solobacterium, Olsenella, and Escherichia-Shigella (↑)
      Firmicutes (↑)
      Proteobacteria (↑)
      Verrucomicrobia (↑)
      Hashimoto et al., 2022[38] Actinomyces, Lactobacillus, Streptococcus, and Veillonella (↑)
      Anaerostipes (↓)
      Sono et al., 2024[39] Firmicutes (↑) Streptococcus (↑)
      Proteobacteria (↓) Neisseria (↓)
      Zhao et al., 2024[37] Moraella, Sphingomonas Oxalobacteriae
      Yang et al., 2023[40] Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria (↑) Streptococcus, Lactobacillus, and Bifidobacterium (↑)

      Table 4. 

      Discriminating taxa.

    • Study Models Sensitivity Specificity AUC
      Lu et al., 2019[28] Fusobacterium, Leptotrichia, and Porphyromonas 0.771 0.786 0.802
      Ren et al., 2017[30] Based on the 40 genera 0.859 0.667 0.842
      Half et al., 2019[27] Based on discriminating taxa 0.769 0.8 0.825
      Chen et al., 2023[36] Random forest − − 0.916
      Yang et al., 2023[40] Random forest − − 0.927
      AUC, area under the receiver operator characteristic curve.

      Table 5. 

      Diagnostic models.