Dietary Factors of blaNDM Carriage in Health Community Population: A Cross-Sectional Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Participants
2.2. Data Collection
2.3. Direct Sample Testing with PCR and NDM Genes Quantification with qPCR
2.4. Statistical Analysis
3. Results
3.1. Laboratory Investigations
3.2. Demographic Characters
3.3. Dietary Factor
3.4. Multivariable Logistic Regression Analysis
4. Discussion
4.1. Strengths and Limitation
4.2. Future Research
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Bush, K.; Bradford, P. Epidemiology of β-Lactamase-Producing Pathogens. Clin. Microbiol. Rev. 2020, 33, e00047-19. [Google Scholar] [CrossRef]
- Potter, R.F.; D’souza, A.W.; Dantas, G. The rapid spread of carbapenem-resistant Enterobacteriaceae. Drug Resist. Updates 2016, 29, 30–46. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lili, F.; Xiaohui, L.; Heping, X.; Xiaobo, M.; Yilan, C.; Yue, L.; Guolin, H.; Xianming, L. Epidemiology and risk factors for carbapenem-resistant Enterobacteriaceae colonisation and infections: Case-controlled study from an academic medical center in a southern area of China. Pathog. Dis. 2019, 77, ftz034. [Google Scholar]
- Shah, D.H.; Board, M.M.; Crespo, R.; Guard, J.; Paul, N.C.; Faux, C. The occurrence of Salmonella, extended-spectrum β-lactamase producing Escherichia coli and carbapenem resistant non-fermenting Gram-negative bacteria in a backyard poultry flock environment. Zoonoses Public Health 2020, 67, 742–753. [Google Scholar] [CrossRef] [PubMed]
- Jaja, I.F.; Oguttu, J.; Jaja, C.J.; Green, E. Prevalence and distribution of antimicrobial resistance determinants of Escherichia coli isolates obtained from meat in South Africa. PLoS ONE 2020, 15, e0216914. [Google Scholar] [CrossRef] [PubMed]
- Gomi, R.; Matsuda, T.; Yamamoto, M.; Chou, P.H.; Matsumura, Y. Characteristics of Carbapenemase-Producing Enterobacteriaceae in Wastewater Revealed by Genomic Analysis. Antimicrob. Agents Chemother. 2018, 62, e02501-17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sadek, M.; Soliman, A.M.; Nariya, H.; Shimamoto, T.; Shimamoto, T. Genetic Characterization of Carbapenemase-Producing Enterobacter cloacae Complex and Pseudomonas aeruginosa of Food of Animal Origin from Egypt. Microb. Drug Resist. 2020. [Google Scholar] [CrossRef]
- Yang, W.; Wu, C.; Zhang, Q.; Jing, Q.; Liu, H.; Yu, W.; Tao, H.; Ma, L.; Jing, L.; Shen, Z. Identification of New Delhi Metallo-β-lactamase 1 in Acinetobacter lwoffii of Food Animal Origin. PLoS ONE 2012, 7, e37152. [Google Scholar]
- Zhang, W.J.; Lu, Z.; Schwarz, S.; Zhang, R.M.; Wang, X.M.; Si, W.; Yu, S.; Chen, L.; Liu, S. Complete sequence of the blaNDM-1-carrying plasmid pNDM-AB from Acinetobacter baumannii of food animal origin. J. Antimicrob. Chemother. 2013, 68, 1681–1682. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, C.; Qiu, S.; Wang, Y.; Qi, L.; Hao, R.; Liu, X.; Shi, Y.; Hu, X.; An, D.; Li, Z. Higher Isolation of NDM-1 Producing Acinetobacter baumannii from the Sewage of the Hospitals in Beijing. PLoS ONE 2013, 8, e64857. [Google Scholar] [CrossRef] [PubMed]
- Zhu, Y.; Liu, W.; Schwarz, S.; Wang, C.; Zhang, W. Characterization of a blaNDM-1-carrying IncHI5 plasmid from Enterobacter cloacae complex of food-producing animal origin. J. Antimicrob. Chemother. 2020, 75, 1140–1145. [Google Scholar] [CrossRef]
- Ma, Z.; Liu, J.; Yang, J.; Zhang, X.; Zeng, Z. Emergence of blaNDM-carrying IncX3 Plasmid in Klebsiella pneumoniae and Klebsiella quasipneumoniae from duck farms in Guangdong Province, China. J. Glob. Antimicrob. Resist. 2020, 22, 703–705. [Google Scholar] [CrossRef] [PubMed]
- He, T.; Wang, Y.; Sun, L.; Pang, M.; Zhang, L.; Wang, R. Occurrence and characterization ofblaNDM-5-positiveKlebsiella pneumoniaeisolates from dairy cows in Jiangsu, China. J. Antimicrob. Chemother. 2016, 72, 90–94. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, B.T.; Zhang, X.Y.; Wan, S.W.; Hao, J.J.; Jiang, R.D.; Song, F.J. Characteristics of Carbapenem-Resistant Enterobacteriaceae in Ready-to-Eat Vegetables in China. Front. Microbiol. 2018, 9, 1147. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Yao, X.; Luo, J.; Lv, L.; Zeng, Z.; Liu, J.H. Emergence of Escherichia coli co-producing NDM-1 and KPC-2 carbapenemases from a retail vegetable, China. J. Antimicrob. Chemother. 2018, 73, 252–254. [Google Scholar] [CrossRef]
- Mulder, M.; Kiefte-De Jong, J.C.; Goessens, W.H.; De Visser, H.; Ikram, M.A.; Verbon, A.; Stricker, B.H. Diet as a risk factor for antimicrobial resistance in community-acquired urinary tract infections in a middle-aged and elderly population: A case-control study. Clin. Microbiol. Infect. 2018, 25, 613–619. [Google Scholar] [CrossRef] [PubMed]
- Fahim, Q.; Mirza, I.; Imtiaz, A.; Khalid, A.; Ahmad, A.; Imtiaz, A. Antimicrobial susceptibility patterns among community and health care acquired carbapenem resistant Enterobacteriaceae, in a tertiary care hospital of Lahore. JPMA J. Pak. Med. Assoc. 2020, 70, 1130–1135. [Google Scholar] [CrossRef] [PubMed]
- Ruth, B.; Liana, D.; Omar, F.; Yair, M.; Rivka, O.; Chen, D.; Tzilia, L.; Ronit, Z.; Jacob, M.G.; Dror, M. The Clinical and Molecular Epidemiology of Noncarbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae: A Case-Case-Control Matched Analysis. Open Forum Infect. Dis. 2020, 7, ofaa299. [Google Scholar]
- Richelsen, R.; Smit, J.; Laxsen Anru, P.; Schønheyder, H.C.; Nielsen, H. Risk factors of community-onset extended-spectrum β-lactamase Escherichia coli and Klebsiella pneumoniae bacteraemia: An 11-year population-based case–control–control study in Denmark. Clin. Microbiol. Infect. 2020. [Google Scholar] [CrossRef] [PubMed]
- Lei, L.; Wang, Y.; He, J.; Cai, C.; Liu, Q.; Yang, D.; Zou, Z.; Shi, L.; Jia, J.; Wang, Y.; et al. Prevalence and risk analysis of mobile colistin resistance and extended-spectrum β-lactamase genes carriage in pet dogs and their owners: A population based cross-sectional study. Emerg. Microbes Infect. 2021, 10, 242–251. [Google Scholar] [CrossRef] [PubMed]
- Neter, J.; Wasserman, W.; Kutner, M.H. Applied Linear Regression Models, 2nd ed.; Richard, D., Ed.; Irwin, Inc.: Homewood, CA, USA, 1989. [Google Scholar]
- Lu, J.; Guo, J. Disinfection spreads antimicrobial resistance. Science 2021, 371, 474. [Google Scholar] [CrossRef] [PubMed]
- Wang, Z.; Qin, R.R.; Huang, L.; Sun, L.Y. Risk Factors for Carbapenem-resistant Klebsiella pneumoniae Infection and Mortality of Klebsiella pneumoniae Infection. Chin. Med. J. 2018, 131, 56–62. [Google Scholar] [CrossRef] [PubMed]
- Li, J.; Li, Y.; Song, N.; Chen, Y. Risk factors for carbapenem-resistant Klebsiella pneumoniae infection: A meta-analysis. J. Glob. Antimicrob. Resist. 2020, 21, 306–313. [Google Scholar] [CrossRef] [PubMed]
- Zhu, W.M.; Yuan, Z.; Zhou, H.Y. Risk factors for carbapenem-resistant Klebsiella pneumoniae infection relative to two types of control patients: A systematic review and meta-analysis. Antimicrob. Resist. Infect. Control 2020, 9, 23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liu, P.; Li, X.; Luo, M.; Xu, X.; Su, K.; Chen, S.; Qing, Y.; Li, Y.; Qiu, J. Risk Factors for Carbapenem-Resistant Klebsiella pneumoniae Infection: A Meta-Analysis. Microb. Drug Resist. 2017, 24, 190–198. [Google Scholar] [CrossRef]
- Jiao, Y.; Qin, Y.; Liu, J.; Li, Q.; Dong, Y.; Shang, Y.; Huang, Y.; Liu, R. Risk factors for carbapenem-resistant Klebsiella pneumoniae infection/colonization and predictors of mortality: A retrospective study. Pathog. Glob. Health 2015, 109, 68–74. [Google Scholar] [CrossRef] [Green Version]
- Zhai, R.; Fu, B.; Shi, X.; Sun, C.; Liu, Z.; Wang, S.; Shen, Z.; Walsh, T.R.; Cai, C.; Wang, Y.; et al. Contaminated in-house environment contributes to the persistence and transmission of NDM-producing bacteria in a Chinese poultry farm. Environ. Int. 2020, 139, 105715. [Google Scholar] [CrossRef] [PubMed]
- Wu, Y.B.; Wan, J.W.; Choe, U.; Pham, Q.; Schoene, N.W.; He, Q.; Li, B.; Yu, L.L.; Wang, T.T.Y. Interactions Between Food and Gut Microbiota: Impact on Human Health. Annu. Rev. Food. Sci. Technol. 2019, 10, 389–408. [Google Scholar] [CrossRef]
- Stecher, B.R.; Maier, L.; Hardt, W.D. ‘Blooming’ in the gut: How dysbiosis might contribute to pathogen evolution. Nat. Rev. Microbiol. 2013, 11, 277–284. [Google Scholar] [CrossRef] [Green Version]
- John, P.; Stobberingh, E.E.; Savelkoul, P.H.; Wolffs, P.F. The human microbiome as a reservoir of antimicrobial resistance. Front. Microbiol. 2013, 4, 87. [Google Scholar]
- Wu, G.; Zhang, C.; Wang, J.; Zhang, F.; Wang, R.; Shen, J.; Wang, L.; Pang, X.; Zhang, X.; Zhao, L. Diminution of the gut resistome after a gut microbiota-targeted dietary intervention in obese children. Sci. Rep. 2016, 6, 24030. [Google Scholar] [CrossRef] [Green Version]
- Singh, R.K.; Chang, H.W.; Yan, D.; Lee, K.M.; Ucmak, D.; Wong, K.; Abrouk, M.; Farahnik, B.; Nakamura, M.; Zhu, T.H. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 2017, 15, 73. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zheng, H.; Yde, C.C.; Clausen, M.R.; Kristensen, M.; Lorenzen, J.; Astrup, A.; Bertram, H.C. Metabolomics Investigation To Shed Light on Cheese as a Possible Piece in the French Paradox Puzzle. J. Agric. Food Chem. 2015, 63, 2830–2839. [Google Scholar] [CrossRef] [PubMed]
- Visvanathan, R.; Jayathilake, C.; Chaminda Jayawardana, B.; Liyanage, R. Health-beneficial properties of potato and compounds of interest. J. Sci. Food Agric. 2016, 96, 4850–4860. [Google Scholar] [CrossRef] [PubMed]
- Maier, T.V.; Lucio, M.; Lee, L.H.; Verberkmoes, N.C.; Brislawn, C.J.; Bernhardt, J.R.; Lamendella, R.; Mcdermott, J.E.; Bergeron, N.; Heinzmann, S.S. Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome. mBio 2017, 8, e01343-17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Variables | Rank | All Participants (n = 515) | blaNDM Carrier (n = 99) | blaNDM Non-Carrier (n = 416) | blaNDM Carrier vs. Non-Carrier, p-Value |
---|---|---|---|---|---|
Age (years) | 57.16 ± 12.157 | 57.19 ± 12.193 | 57.02 ± 12.063 | 0.953 | |
BMI | 24.26 ± 3.146 | 25.51 ± 7.868 | 24.27 ± 3.448 | 0.038 * | |
Gender, n (%) | Male | 180 (35.0) | 39 (39.4) | 141 (33.9) | 0.302 |
Female | 335 (65.0) | 60 (60.6) | 275 (66.1) | ||
Medical contact in recent three months, n (%) | No | 31 (6.0) | 6 (6.1) | 25 (6.0) | 0.985 |
Yes | 484 (94.0) | 93 (93.9) | 391 (94.0) | ||
Antibiotic usage in recent three months, n (%) | No | 466 (91.4) | 94 (95.9) | 372 (90.3) | 0.075 |
Yes | 44 (8.6) | 4 (4.1) | 40 (9.7) | ||
Smoking, n (%) | No | 421 (81.7) | 82 (82.8) | 339 (81.5) | 0.725 |
Yes | 42 (8.2) | 9 (9.1) | 33 (7.9) | ||
Quit | 52 (10.1) | 8 (8.1) | 44 (10.6) | ||
Drinking, n (%) | No | 411 (79.8) | 76 (76.8) | 335 (80.5) | 0.078 |
Yes | 73 (14.2) | 20 (20.2) | 53 (12.7) | ||
Quit | 31 (6.0) | 3 (3.0) | 28 (6.7) | ||
Physical exercise, n (%) | No | 89 (17.3) | 23 (23.2) | 66 (15.9) | 0.081 |
Yes | 426 (82.7) | 76 (76.8) | 350 (84.1) | ||
Dietary pattern, n (%) | Meat-based | 31 (6.0) | 4 (4.0) | 27 (6.5) | 0.008 ** |
Balanced diet | 372 (72.2) | 66 (66.7) | 306 (73.6) | ||
Vegetarian-based | 110 (21.4) | 27 (27.3) | 83 (20.0) | ||
Not clear | 2 (0.4) | 2 (2.0) | 0 (0.0) | ||
Degree of satiety, n (%) | Full | 45 (8.8) | 7 (7.1) | 38 (9.2) | 0.046 * |
90 percent | 165 (32.1) | 21 (21.2) | 144 (34.7) | ||
80 percent | 218 (42.4) | 56 (56.6) | 162 (39.0) | ||
70 percent | 78 (15.2) | 14 (14.1) | 64 (15.4) | ||
60 percent | 6 (1.2) | 1 (1.0) | 5 (1.2) | ||
<60 percent | 2 (0.4) | 0 (0.0) | 2 (0.5) | ||
Pet | No | 482 (94.0) | 96 (97.0) | 386 (93.2) | 0.161 |
Yes | 31 (6.0) | 3 (3.0) | 28 (6.8) | ||
Hypertension, n (%) | No | 388 (75.3) | 71 (71.7) | 317 (76.2) | 0.352 |
Yes | 127 (24.7) | 28 (28.3) | 99 (23.8) | ||
Hyperlipemia, n (%) | No | 411 (80.0) | 82 (82.8) | 329 (79.3) | 0.428 |
Yes | 103 (20.0) | 17 (17.2) | 86 (20.7) | ||
Obesity, n (%) | No | 467 (90.7) | 91 (91.9) | 376 (90.4) | 0.637 |
Yes | 48 (9.3) | 8 (8.1) | 40 (9.6) | ||
Diabetes, n (%) | No | 472 (91.7) | 95 (96.0) | 377 (90.6) | 0.085 |
Yes | 43 (8.3) | 4 (4.0) | 39 (9.4) | ||
Gastrointestinal disorders, n (%) | No | 435 (84.5) | 80 (80.8) | 355 (85.3) | 0.264 |
Yes | 80 (15.5) | 19 (19.2) | 61 (14.7) | ||
Mental illness, n (%) | No | 511 (99.4) | 97 (98.0) | 414 (99.8) | 0.037 * |
Yes | 3 (0.6) | 2 (2.0) | 1 (0.2) | ||
Blood urine acid | <202 μmol/L (male) <142 μmol/L (female) | 6 (1.2) | 3 (3.0) | 3 (0.7) | 0.150 |
202~416 μmol/L (male) 142~339 μmol/L (female) | 351 (68.2) | 65 (65.7) | 286 (68.8) | ||
>416 μmol/L (male) >339 μmol/L (female) | 158 (30.7) | 31 (31.3) | 127 (30.5) | ||
Triglyceride, n (%) | <1.7 mmol/L | 346 (67.2) | 58 (58.6) | 288 (69.2) | 0.043 * |
≥1.7 mmol/L | 169 (32.8) | 41 (41.4) | 128 (30.8) | ||
Fast blood sugar (FBS), n (%) | <3.9 mmol/L | 6 (1.2) | 3 (3.0) | 3 (0.7) | 0.109 |
3.9~6.1 mmol/L | 443 (86.0) | 81 (81.8) | 362 (87.0) | ||
>6.1 mmol/L | 66 (12.8) | 15 (15.2) | 51 (12.3) |
Figure 515. | Rank | All Participants (n = 515) | blaNDM Carrier (n = 99) | blaNDM Non-Carrier (n = 416) | blaNDM Carrier vs. Non-Carrier, p-Value | Adjust p-Value |
---|---|---|---|---|---|---|
Oil | Lack | 111 (21.6) | 16 (16.2) | 95 (22.8) | 0.149 | 0.145 |
Recommended | 82 (15.9) | 21 (21.2) | 61 (14.7) | |||
Over | 322 (62.5) | 62 (62.6) | 260 (62.5) | |||
Vegetable oil | 49.70 ± 35.66 | 49.39 ± 29.07 | 49.77 ± 37.09 | 0.232 | ||
Animal fats | 2.60 ± 8.84 | 3.02 ± 9.57 | 2.50 ± 8.67 | 0.294 | ||
Livestock and poultry | Lack | 220 (42.7) | 44 (44.4) | 176 (42.3) | 0.892 | 0.897 |
Recommended | 182 (35.3) | 33 (33.3) | 149 (35.8) | |||
Over | 113 (21.9) | 22 (22.2) | 91 (21.9) | |||
Pork | 87.84 ± 99.25 | 89.43 ± 98.27 | 87.46 ± 99.59 | 0.957 | ||
Beef | 16.15 ± 31.27 | 17.18 ± 32.93 | 15.91 ± 30.90 | 0.800 | ||
Mutton | 4.22 ± 17.87 | 5.44 ± 25.34 | 3.93 ± 15.60 | 0.350 | ||
Poultry | 41.49 ± 77.00 | 34.32 ± 58.82 | 43.19 ± 80.69 | 0.043 * | ||
Gamey meat | 0.50 ± 4.71 | 0.25 ± 1.71 | 0.56 ± 5.17 | 0.359 | ||
Animal innards | 2.13 ± 7.30 | 2.36 ± 6.30 | 2.08 ± 7.53 | 0.830 | ||
Aquatic product | Lack | 306 (59.4) | 58 (58.6) | 248 (59.6) | 0.516 | 0.524 |
Recommended | 98 (19.0) | 16 (16.2) | 82 (19.7) | |||
Over | 111 (21.6) | 25 (25.3) | 86 (16.7) | |||
Freshwater fish | 43.48 ± 75.88 | 48.15 ± 76.55 | 42.36 ± 75.77 | 0.771 | ||
Marine fish | 15.92 ± 32.24 | 16.20 ± 28.26 | 15.85 ± 33.15 | 0.731 | ||
Egg | Lack | 202 (39.2) | 41 (41.4) | 161 (38.7) | 0.517 | 0.508 |
Recommended | 273 (53.0) | 53 (53.5) | 220 (52.9) | |||
Over | 40 (7.8) | 5 (5.1) | 35 (8.4) | 0.030 * | 0.030 * | |
Vegetables | Lack | 196 (38.1) | 28 (28.3) | 168 (40.4) | ||
Recommended | 206 (40.0) | 41 (41.4) | 165 (39.7) | |||
Over | 113 (21.9) | 30 (30.3) | 83 (20.0) | |||
Leafy vegetables | 256.62 ± 190.15 | 268.14 ± 148.09 | 253.88 ± 198.91 | 0.293 | ||
Root and tuber crops | 127.18 ± 148.89 | 156.96 ± 207.11 | 120.09 ± 130.63 | 0.002 ** | ||
Fruit | Lack | 438 (85.0) | 92 (92.9) | 346 (83.2) | 0.036 * | 0.033 * |
Recommended | 73 (14.2) | 6 (5.1) | 67 (16.1) | |||
Over | 4 (0.8) | 1 (1.0) | 3 (0.7) | |||
Grains | Lack | 12 (2.3) | 1 (1.0) | 11 (2.6) | 0.135 | 0.155 |
Recommended | 70 (13.6) | 19 (19.2) | 51 (12.3) | |||
Over | 433 (84.1) | 79 (79.8) | 354 (85.1) | |||
Rice | 305.71 ± 255.39 | 294.62 ± 247.09 | 308.35 ± 257.55 | 0.461 | ||
Wheat | 77.21 ± 86.80 | 68.70 ± 93.17 | 79.24 ± 85.21 | 0.311 | ||
Coarse grains | 89.27 ± 114.69 | 119.38 ± 159.80 | 82.11 ± 99.97 | 0.000 * | ||
Dairy product | Lack | 456 (88.5) | 93 (93.6) | 363 (87.3) | 0.061 | 0.057 |
Recommended | 0 (0.0) | 0 (0.0) | 0 (0.0) | |||
Over | 59 (11.5) | 6 (6.1) | 53 (12.7) | |||
Milk or milk-products | 94.38 ± 118.49 | 85.47 ± 104.56 | 96.51 ± 121.59 | 0.129 | ||
Yogurt | 29.66 ± 62.64 | 21.19 ± 41.33 | 31.67 ± 66.60 | 0.010 * | ||
Soybean and its product | Lack | 408 (79.2) | 85 (85.9) | 323 (77.6) | 0.194 | 0.196 |
Recommended | 22 (4.3) | 3 (3.0) | 19 (4.6) | |||
Over | 85 (16.5) | 11 (11.1) | 74 (17.8) |
Food Groups | Crude | Model 1 | Model 2 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
OR | 95% CI | p | OR | 95% CI | p | OR | 95% CI | p | |||||
Drinking | No | 1.000 | 0.085 | 1.000 | 0.006 ** | ||||||||
Yes | 1.663 | 0.939 | 2.946 | 0.081 | 2.348 | 1.241 | 4.442 | 0.009 ** | |||||
Quit | 0.472 | 0.140 | 1.594 | 0.227 | 0.264 | 0.056 | 1.247 | 0.093 | |||||
Antibiotic usagein recent three months | No | 1.000 | 1.000 | ||||||||||
Yes | 0.396 | 0.138 | 1134 | 0.084 | 0.302 | 0.097 | 0.937 | 0.038 * | |||||
Pet | No | 1.000 | 1.000 | ||||||||||
Yes | 0.431 | 0.128 | 1.447 | 0.173 | 0.353 | 0.099 | 1.263 | 0.109 | |||||
Physical exercise | No | 1.000 | 1.000 | ||||||||||
Yes | 0.623 | 0.365 | 1.064 | 0.083 | 0.503 | 0.277 | 0.916 | 0.025 * | |||||
Degree of satiety, n (%) | Full | 1.000 | 0.062 | 1.000 | 0.059 | ||||||||
90 percent | 0.792 | 0.313 | 2.001 | 0.621 | 0.749 | 0.269 | 2.085 | 0.580 | |||||
80 percent | 1.877 | 0.793 | 4.441 | 0.152 | 1.895 | 0.725 | 4.955 | 0.192 | |||||
70 percent | 1.188 | 0.440 | 3.203 | 0.734 | 1.058 | 0.351 | 3.188 | 0.920 | |||||
60 percent | 1.086 | 0.110 | 10.758 | 0.944 | 2.468 | 0.217 | 28.097 | 0.467 | |||||
<60 percent | <0.001 | 0.000 | 0.999 | <0.001 | <0.001 | 1.000 | |||||||
Dietary pattern, n (%) | Meat-based | 1.000 | 0.349 | 1.000 | 0.451 | ||||||||
Balanced diet | 1.456 | 0.493 | 4.301 | 0.497 | 1.504 | 0.466 | 4.852 | 0.494 | |||||
Vegetarian-based | 2.196 | 0.705 | 6.840 | 0.175 | 2.238 | 0.647 | 7.735 | 0.203 | |||||
Not clear | <0.001 | 0.000 | 0.999 | >10 | <0.001 | 0.999 | |||||||
Poultry | 0.998 | 0.995 | 1.002 | 0.307 | 0.997 | 0.993 | 1.001 | 0.128 | 0.996 | 0.992 | 1.000 | 0.037 * | |
Root and tuber crops | 1.001 | 1.000 | 1.003 | 0.031 * | 1.002 | 1.000 | 1.003 | 0.051 | 1.003 | 1.001 | 1.004 | 0.002 ** | |
Fruit | Lack | 1.000 | 0.047 * | 1.000 | 0.048 * | 1.000 | |||||||
Recommended | 0.337 | 0.142 | 0.801 | 0.014 * | 0.310 | 0.121 | 0.795 | 0.015 ** | 0.208 | 0.081 | 0.539 | 0.001 ** | |
Over | 1.254 | 0.129 | 12.193 | 0.846 | 1.483 | 0.129 | 17.113 | 0.752 | 0.892 | 0.000 | - | 0.988 | |
Coarse grains | 1.002 | 1.001 | 1.004 | 0.005 ** | 1.003 | 1.001 | 1.005 | 0.001 ** | 1.003 | 1.001 | 1.005 | 0.005 ** | |
Yogurt | 0.996 | 0.992 | 1.001 | 0.139 | 0.996 | 0.991 | 1.001 | 0.139 | 0.995 | 0.989 | 1.000 | 0.060 |
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Hu, S.; Lv, Z.; Xiang, Q.; Wang, Y.; Shen, J.; Ke, Y. Dietary Factors of blaNDM Carriage in Health Community Population: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2021, 18, 5959. https://doi.org/10.3390/ijerph18115959
Hu S, Lv Z, Xiang Q, Wang Y, Shen J, Ke Y. Dietary Factors of blaNDM Carriage in Health Community Population: A Cross-Sectional Study. International Journal of Environmental Research and Public Health. 2021; 18(11):5959. https://doi.org/10.3390/ijerph18115959
Chicago/Turabian StyleHu, Shuangfang, Ziquan Lv, Qiumei Xiang, Yang Wang, Jianzhong Shen, and Yuebin Ke. 2021. "Dietary Factors of blaNDM Carriage in Health Community Population: A Cross-Sectional Study" International Journal of Environmental Research and Public Health 18, no. 11: 5959. https://doi.org/10.3390/ijerph18115959