Tuberculosis (TB) remains a health threat to humans. According to the World Health Organization (WHO) 2020 Global tuberculosis report (WHO 2020), there were 10.0 million new cases of TB and 1.5 million deaths from TB in 2019. China has the second largest number of patients with TB, multidrug-resistant
Molecular typing technologies have been used to determine the genotypic diversity of
Jiangxi is facing the health challenge of TB prevention and control (Chen et al. 2019; Luo et al. 2019). However, the association between the prevalence of Beijing genotype strains and the related characterization is still unclear. To understand whether there is an association between Beijing genotype strains and patient characterization, we investigated sociodemographic factors and drug susceptibility tests (DST) between Beijing genotype and non-Beijing genotype strains in Jiangxi province.
where N is the total number of TB isolates, s is the number of distinct patterns discriminated by MIRU-VNTR, and nj is the number of isolates belonging to the jth pattern. Allelic diversity (h) was done using the equation:
where n is the number of isolates and xi is the frequency of the i-th allele at the locus (Selander et al. 1986). As previously reported, the clustering rate was defined as (ncc)/n as (Small et al. 1994). Between patients infected with the Beijing genotype and non-Beijing genotype strains, the distribution of genotype, sex, age, treatment history, region, clinical specimen types, and DST profile was assessed using the chi-square test SPSS17.0 (SPSS Inc., USA).
The MIRU-VNTR genotypes in Jiangxi province, China – the study design. NTM – nontuberculous mycobacteria.
Distribution of isolates. A total of 1,433 strains were acquired from patients diagnosed with tuberculosis during this period in Jiangxi province, China. Of all 1,433 isolates, 376 (26.24%, 376/1,433) were from Nanchang, 246 (17.17%, 246/1,433) from Shangrao, 141 (9.84%, 141/1,433) from Ji’an, 129 (9.00%, 129/1,433) from Yichun, 117 (8.16%, 117/1,433) from Jiujiang, 109 (7.61%, 109/1,433) from Fuzhou, 108 (7.54%, 108/1,433) from Ganzhou and 207 (14.44%, 207/1,433) from other districts. Consequently, for 1,120 strains of the Beijing family, 282 (282/1,120) of them were from Nanchang, 199 (199/1,120) from Shangrao, 118 (118/1,120) from Ji’an, 103 (103/1,120) from Yichun, 84 (84/1,120) from Jiujiang, 90 (90/1,120) from Fuzhou, 91 (91/1,120) from Ganzhou, and 153 (153/1,120) from the rest districts (Fig. 2, Table SI). According to the Jiangxi regional distribution, the whole province can be divided into three regions, including southern regions (Ganzhou and Ji’an districts), central regions (Nanchang, Fuzhou, Yichun, Xinyu, and Pingxiang districts), and northern regions (Jiujiang, Jingdezhen, Shangrao and Yingtan districts). Among 1,433 isolates, the majority come from the central regions (722, 50.38%), followed by the northern regions (462, 32.24%) and the southern regions (249, 17.38%). Fig. 2 indicates the different geographical sources of these isolates.
Map of Jiangxi showing the distribution of 1,433 isolates included in this study (the numbers indicate the absolute number of isolates in every region).
Demographic characteristics and drug resistance patterns. Table I shows the demographic characteristics of these patients. Among 1,433 patients, 860 were men, and 573 were women. Age ranged from 3 to 89 years, with an average age of 43.61. Regarding the history of treatment, 1,174 patients received single treatment, and 257 patients were treated at least twice (including 168 recurrent cases and 91 treatment failure cases). There was a total of 1,120 (1,120/1,433, 78.16%) MTB belonging to the Beijing family. Compared to cases of
Demographic and drug-resistant characteristics of this study’s isolates (n = 1,433).
Characteristics | Number | Beijing family | Non-Beijing family | OR (95% CI) | |
---|---|---|---|---|---|
All | 1,433 | 1,120 | 313 | – | – |
Sex | |||||
Men | 860 | 651 | 209 | – | – |
Women | 57 3 | 41 9 | 154 | 0.910.–114. 46 | 0.29 |
Age | |||||
≥ 50 | 512 | 376 | 136 | – | – |
30–50 | 46 7 | 38 1 | 86 | 1.114.–414. 83 | 0.003 |
≤ 30 | 45 4 | 36 3 | 91 | 1.015.–313. 67 | 0.02 |
Treatment history | |||||
New | 1,174 | 900 | 274 | – | – |
Recurrent | 16 8 | 14 5 | 23 | 0.303.–502. 83 | 0.004 |
Treatment failure | 91 | 7 5 | 16 | 0.400.–710. 22 | 0.24 |
Region | |||||
Southern regions | 249 | 181 | 68 | – | – |
Central regions | 72 2 | 56 7 | 155 | 0.502.–713. 01 | 0.07 |
Northern regions | 46 2 | 37 2 | 90 | 0.405.–604. 92 | 0.02 |
DST profile | |||||
DST profile | 1,433 | 1,120 | 313 | – | – |
Pansusceptible | 99 2 | 79 4 | 198 | 0.703.–819. 09 | 0.27 |
RIF | 2 7 | 1 8 | 9 | 0.810.–749. 02 | 0.16 |
INH | 3 3 | 2 5 | 8 | 0.511.–124. 56 | 0.68 |
SM | 5 2 | 4 4 | 8 | 0.300.–615. 40 | 0.31 |
EMB | 6 | 5 | 1 | 0.008.–762. 15 | 1.00 |
AK | 1 9 | 1 5 | 4 | 0.301.–925. 90 | 1.00 |
CM | 1 8 | 1 3 | 5 | 0.419.–338. 89 | 0.57 |
LEV | 3 8 | 3 0 | 8 | 0.403.–925. 10 | 1.00 |
RIF + INH | 5 6 | 4 7 | 9 | 0.303.–618. 41 | 0.41 |
RIF + SM | 2 0 | 1 5 | 5 | 0.413.–139. 31 | 0.78 |
INH + SM | 4 1 | 3 4 | 7 | 0.302.–714. 68 | 0.57 |
RIF + EMB | 5 | 3 | 2 | 0.402–.3194 .34 | 0.30 |
INH + EMB | 1 | 1 | 0 | – | – |
RIF + INH + EMB | 7 1 | 6 0 | 11 | 0.304.–616. 26 | 0.24 |
RIF + INH + SM | 2 9 | 2 5 | 4 | 0.200.–517. 66 | 0.37 |
INH + SM + EMB | 5 | 4 | 1 | 0.100.–980. 03 | 1.00 |
RIF + INH + SM + EMB | 2 | 1 | 1 | 0.223–.5587 .37 | 0.39 |
MDR | 158 | 133 | 25 | 0.410.64 –1.01 | 0.05 |
To investigate the allelic diversity of these MIRU-VNTR loci, we calculated the Hunter-Gaston discriminatory index (HGDI) for each locus. As previously reported, the MIRU-VNTR loci were considered highly discriminatory (> 0.6), moderately (0.3–0.6), or poorly (< 0.3) discriminatory loci based on HGDI scores (Chen et al. 2016). These loci had a significant discriminatory ability with various HGDI scores (Table SII). According to the situation described above, three loci were considered highly discriminatory, including MIRU26 (HGDI = 0.6580), Qub26 (HGDI = 0.6344), and ETRE (HGDI = 0.6320). Eight loci (Mtub04, MIRU40, MIRU10, Mtub21, Qub11b, Mtub30, Mtub39, and Qub4156) had a moderate discriminatory ability, and the biomarkers of the remains were poorly discriminatory loci. The 15-loci discriminatory power reached 0.9963. At the same time, 1,433 strains were classified into 878 genotypes by adopting MIRU-VNTR cluster analysis, including 103 clusters and 775 unique patterns. The largest cluster was made up of 67 strains, and 11 clusters were made up of two strains. As a result, the clustering rate was 38.7% (555/1,433), and the recent transmission rate was 31.5% (452/1,433).
Prevalent characterization of clustered and non-clustered strains.
Characteristics | Clustered | Non-clustered | ||||||
---|---|---|---|---|---|---|---|---|
Beijing family n = 612 | Non-Beijing family n = 46 | OR (95%CI) | Beijing family n = 508 | Non-Beijing family n = 267 | OR (95%CI) | |||
Sex | ||||||||
Men | 384 | 32 | – | – | 267 | 177 | – | – |
Women | 228 | 14 | 0.308.–714. 41 | 0.43 | 241 | 90 | 0.401.–506. 77 | < 0.001 |
Age | ||||||||
≤ 30 | 187 | 13 | – | – | 176 | 78 | – | – |
30–50 | 232 | 19 | 0.517.–128. 45 | 0.72 | 149 | 67 | 0.618.–012. 50 | 1.00 |
≥ 50 | 193 | 14 | 0.418.–024. 28 | 1.00 | 183 | 122 | 1.016.5–024.1 4 | 0.03 |
Treatment history | ||||||||
New | 507 | 28 | – | – | 443 | 246 | – | – |
Recurrent | 84 | 14 | 1.533.–052. 97 | 0.003 | 61 | 9 | 0.103.2–606.5 4 | 0.00 |
Treatment failure | 21 | 4 | 1.131.–41409. 73 | 0.05 | 4 | 12 | 1.752.–41062. 93 | 0.002 |
HIV status | ||||||||
Negative | 584 | 37 | – | – | 485 | 260 | – | – |
Positive | 28 | 9 | 0.009.–200. 45 | 0.001 | 23 | 7 | 0.715.–746. 16 | 0.24 |
Sputum | ||||||||
Negative | 121 | 12 | – | – | 104 | 83 | – | – |
Positive | 391 | 34 | 0.440.88 –1.75 | 0.72 | 404 | 184 | 0.410.57 –0.80 | 0.001 |
Cavity | ||||||||
Yes | 108 | 7 | – | – | 86 | 42 | – | – |
No | 504 | 39 | 0.512.–129. 74 | 0.84 | 422 | 225 | 0.713.–019. 63 | 0.76 |
Prevalent characterization of Beijing and non-Beijing family strains.
Characteristics | Beijing family | Non-Beijing family | ||||||
---|---|---|---|---|---|---|---|---|
Clustered n = 612 | Non-clustered n = 508 | OR (95% CI) | Clustered n = 46 | Non-clustered n = 267 | OR (95% CI) | |||
Sex | ||||||||
Men | 384 | 267 | – | – | 32 | 177 | – | – |
Women | 228 | 241 | 1.210.–512. 93 | 0 .001 | 14 | 90 | 0.519.–126. 29 | 0.74 |
Age | ||||||||
≤ 30 | 187 | 176 | – | – | 13 | 78 | – | – |
30–50 | 232 | 149 | 0.501.–608. 91 | 0 .01 | 19 | 67 | 0.207.5–818.2 8 | 0.24 |
≥ 50 | 193 | 183 | 0.716.–011. 34 | 1 .00 | 14 | 122 | 0.615.–435. 25 | 0.41 |
Treatment history | ||||||||
New | 530 | 461 | – | – | 32 | 203 | – | – |
Recurrent | 67 | 38 | 0.403.–605. 99 | 0 .05 | 11 | 52 | 0.305.–714. 58 | 0.42 |
Treatment failure | 15 | 9 | 0.300.–619. 59 | 0 .42 | 3 | 14 | 0.200.–724. 70 | 0.71 |
HIV status | ||||||||
Positive | 28 | 23 | – | – | 2 | 14 | – | – |
Negative | 584 | 485 | 0.518.–011. 78 | 1 .00 | 44 | 253 | 0.108.–832. 74 | 1.00 |
Sputum | ||||||||
Negative | 121 | 104 | – | – | 12 | 83 | – | – |
Positive | 391 | 404 | 0.819.–210. 62 | 0 .23 | 34 | 184 | 0.309.–718. 59 | 0.60 |
Cavity | ||||||||
No | 494 | 432 | – | – | 39 | 225 | – | – |
Yes | 118 | 76 | 0.919.–316. 86 | 0 .07 | 7 | 42 | 0.400.–926. 30 | 1.00 |
To better implement preventive measures in Jiangxi province, it is necessary to understand the association of Beijing genotype
In addition to gender, age was another important factor for Beijing genotype strain infection. Two previous studies showed that younger people (less than 25 years old) were prone to be infected with Beijing genotype strains (Pang et al. 2012; Huang et al. 2020). Our analysis also had the same results that young people (less than 30 years old) were more likely to be infected with the Beijing genotype
We found that the Beijing genotype was significantly associated with clustering, suggesting that recent transmission was substantially different from non-Beijing genotype strains, consistent with a study of Shanghai (Zanini et al. 2014).
Evidence has shown that the drug-resistance ability of
Although we have demonstrated important findings, this study has some limitations. Firstly, we use traditional MIRU-VNTR methods to study all TB transmission, while some patients with the same MIRU-VNTR patterns did not have epidemiological links. As a result, recent transmission rates were overestimated (Chen et al. 2016). MIRU-VNTR is more convenient and cost-effective than whole-genome sequencing (WGS); it is also of great value in defining the recent transmission of tuberculosis (Rizvi et al. 2020). Second, concerns about risk factors for defining clusters and distinguishing Beijing genotype were arguable. We had no opportunity to overcome selection bias for incomplete data on tuberculosis in the local population. However, the individuals in our study were completely random. Therefore, our findings had a high level of feasibility. In conclusion, our results had a specific value in controlling tuberculosis spread, especially for the Beijing genotype