Type 2 Diabetes Mellitus- and Complication-Related Risk of Nontuberculous Mycobacterial Disease in a South Korean Cohort

ABSTRACT We aimed to investigate whether type 2 diabetes mellitus (T2DM) and diabetes-related complications constitute significant risk factors for nontuberculous mycobacterial (NTM) disease. Data from the National Health Insurance Service-National Sample Cohort (which represents 2.2% of the total South Korean population) recorded between 2007 and 2019 were extracted to establish the NTM-naive T2DM cohort (n = 191,218) and the 1:1 age- and sex-matched NTM-naive matched cohort (n = 191,218). Intergroup comparisons were performed to determine differences in the NTM disease risk of the two cohorts during the follow-up period. During median follow-up of 9.46 and 9.25 years, the incidence of NTM disease was 43.58/100,000 and 32.98/100,000 person-years in the NTM-naive T2DM and NTM-naive matched cohorts, respectively. Multivariable analysis showed that T2DM alone did not confer a significant risk for incident NTM disease, although T2DM with ≥2 diabetes-related complications significantly increased NTM disease risk (adjusted hazard ratio [95% confidence interval], 1.12 [0.99 to 1.27] and 1.33 [1.03 to 1.17], respectively). In conclusion, the presence of T2DM with ≥2 diabetes-related complications significantly increases the risk for NTM disease. IMPORTANCE We assessed whether patients with T2DM are at higher risk for incident NTM disease through analysis of NTM-naive matched cohorts from the data of a national population-based cohort which represents 2.2% of the total South Korean population. Although T2DM alone is not a statistically significant risk factor for NTM disease, T2DM significantly increases the risk of NTM disease in those with ≥2 diabetes-related complications. This finding suggested that patients with T2DM with a larger number of complications should be considered a high-risk group for NTM disease.

DM increases the risk of NTM infection might be explained in terms of immune insufficiency in patients with DM. As already known, this might be associated with the duration of DM. So, merging the patients with DM who were diagnosed before 2007 (who might have longer period of DM and might have more chance of complication) and the patients who were diagnosed with DM after 2007 seems to be unreasonable. What about the risk of NTM infection in patients with DM before 2007 andafter 2007, respectively? 2. The authors adjusted the possible confounding factors. Then, what about HIV or other immune-suppressant use? As described in the manuscript, this showed the incidence of NTM infection (including disseminated NTM disease in HIV and NTM-PD, mainly in non-HIV hosts).
3. The CCI was much higher in patients with DM. So, even though adjusted in multivariate analysis, there might be the possibility of more chance of NTM-detection in patients with DM, as these had more chance of visiting hospitals. 4. How can you explain the exceptionally low rate of receiving treatment for DM? About two third of DM patients in South Korea do not receive DM medications according to the manuscript. 5. Metformin has been known to be used as a host-directed therapy in TB and has been involved in immune regulation in humans. What was the incidence of NTM in patients who received metformin vs other drugs?
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Type 2 diabetes mellitus-and complications-related risk of nontuberculous mycobacterial disease in a South Korean cohort
This paper contains important information in NTM research area. But, there are a few things to point out.

Comment 1)
It would be better to describe the limitations of the study in more detail. Because, articles using diagnostic codes or big data have some unavoidable limitations. Because the authors did not use laboratory results, they cannot confirm whether patients met the ATS diagnostic criteria of NTM infection completely. In addition, NTM diagnostic codes cannot distinguish between pulmonary infection and other disease; so they cannot evaluate the burden of specific disease types. The diagnostic codes for comorbidities other than NTM infection may have been missed if the attending physician did not include the codes in the insurance claim.
So, comorbidity may have been underestimated.

Comment 2)
The number of "NTM naïve T2DM cohort" and "NTM-naïve matched cohort" (n = 191,218) is exactly the same. But I think that, it is not an easy procedure to coordinate like this completely. Were there any errors or difficulties in this process?

Responses to Reviewer #1:
This paper contains important information in NTM research area. But, there are a few things to point out.
 Response: We sincerely thank you for identifying the areas of our manuscript that require revision and have accordingly made changes. We hope that the revised manuscript is now acceptable for publication. Comment 1. It would be better to describe the limitations of the study in more detail.
Because, articles using diagnostic codes or big data have some unavoidable limitations.
Because the authors did not use laboratory results, they cannot confirm whether patients met the ATS diagnostic criteria of NTM infection completely. In addition, NTM diagnostic codes cannot distinguish between pulmonary infection and other disease; so they cannot evaluate the burden of specific disease types. The diagnostic codes for comorbidities other than NTM infection may have been missed if the attending physician did not include the codes in the insurance claim. So, comorbidity may have been underestimated.

Response 1.
Thank you for your valuable comments. We agree with your opinion. 1) In our study, we defined NTM disease as the presence of at least 1 claim of the diagnostic code associated with NTM infection (ICD-10 code A31x), as determined in previous studies involving big data analysis in South Korea defined NTM infection as the presence of ICD-10 code A31 and claims data for acid-fast bacilli smears or mycobacterial culture, we did not include claims data for acid-fast bacilli smears or mycobacterial culture in our definition because detailed data on NTM culture results were unavailable in NHIS-NSC dataset. This constraint has been described as a limitation of the present study in the Discussion 4 section of the original manuscript as follows; "First, we determined NTM disease on the basis of ICD-10 codes alone instead of microbiological data. This was because the detailed results of NTM culture data was not available in NHIS-NSC data (Chest 159:1807(Chest 159: -1811." (Page 9, line 214-216 in the original / revised manuscript) As reviewer pointed out, the major weakness of not incluidng laboratory results in the definition of NTM disease was that we were unable to confirm whether patients completely met the ATS diagnostic criteria of NTM infection. Thank you for pointing this out. We had missed including this limitation in the original manuscript and have added the following sentence in the limitation section of the discussion: "Therefore, we were unable to confirm whether patients completely met the diagnostic criteria of NTM disease." (Page 8, line 175-185 in the revised manuscript) 2) As reviewer corrtectly pointed out, we could not evaluate the burden of specific types of NTM disease in our study, because NTM diagnostic codes cannot distinguish between pulmonary infection and other diseases. However, we could cautiously assume that the majority of NTM disease in our study was likely to be NTM pulmonary disease as previous studies of NTM disease indicated that pulmonary NTM is the major component of the overall 3) Concerning the comorbidities, we have added the following sentence to elucidate a limitation of the present study: "Third, the ICD-10 codes for comorbidities other than NTM infection may have been unreported if the attending physician did not include the diagnostic codes in the insurance claim. Thus, it was possible that comorbidity may have been underestimated in our study participants." (Page 9, line 220-223 in the revised manuscript) Comment 2. The number of "NTM naïve T2DM cohort" and "NTM-naïve matched cohort" (n = 191,218) is exactly the same. But I think that, it is not an easy procedure to coordinate like this completely. Were there any errors or difficulties in this process?

Response 2.
As several attempts. However, we believe that we did not make any error in the matching process, as indicated by the distribution of age and sex between the two groups in Table 1.

Responses to Reviewer #2:
The manuscript showed that type 2 DM with two or more complications increased the risk of NTM infection, using health insurance claim data. The topic is timely and the manuscript is well-written. However, there are some issues to be solved.
 Response: We sincerely thank you for identifying the areas of our manuscript that require revision and have accordingly made changes. We hope that the revised manuscript is now acceptable for publication. Thank you for your valuable comments.
As the reviewer correctly pointed out, the patients diagnosed with T2DM before 2007 would evidently have a different duration of diabetes. Therefore, it could be reasonably assumed that patients whose T2DM was diagnosed before 2007 were at higher risk of NTM disease development than those diagnosed with new-onset T2DM between 2008 and 2019. We reanalyzed our data by dividing the matched pair into two groups; (i) 76,151 patients whose   As these Tables show, the association between the higher number of diabetes-related complications and the risk of NTM disease was only evident in those who were diagnosed with DM before 2007. These findings support the theory that patients who had longer duration T2DM were more likely to have an impaired immune system, which conferred increased susceptibility to infectious disease, including NTM disease.
In the Discussion section of the revised manuscript, we have added the following paragraph to describe the association of the duration of T2DM and the risk of NTM disease In addition, we have added Supplementary Tables 2 and 3, which present the detailed crude and adjusted hazard ratios of the two groups.

Response 2.
Thank you for your comments. In the revised manuscript, we have added the following statements on the limitations of the present study. 2) When we extracted data from the raw data of NHIS-NCS, immunosuppressant use was not included among the selected variables. According to the rules of NHIS-NCS data use, only extracted data, but not raw data, are supposed to be archived after the data have been extracted. Therefore, information on immunosuppressant use is unavailable in our current extracted data set. To obtain data on immunosuppressant use, the investigators have to reapply for approval from the NHIS-NCS by submitting an application form and re-submitting a research proposal. Therefore, the raw data, including data on immunosuppressant use, will be available again only if approval is granted. However, the number of researchers who can simultaneously access NHIS-NCS data is limited and, given the current situation, it will take several months (probably a year) after applying for NHIS-NCS access to extract the raw data.
Accordingly, it is extremely difficult for us to receive the data again within the designated revision due date.
Therefore, we could not adjust for immunosuppressant use in our analysis. Considering that a previous study conducted in South Korea (Int J Endocrinol. 2020 Jul 24;2020:9879517) revealed that only approximately 2% of patients with type 2 DM had underlying autoimmune disease, which is the most common disease for prescription of immunosuppressants, we cautiously assumed that the number of patients treated with immunosuppressants were not substantially large enough to affect our main findings.
Comment 3. The CCI was much higher in patients with DM. So, even though adjusted in multivariate analysis, there might be the possibility of more chance of NTM-detection in patients with DM, as these had more chance of visiting hospitals.

Response 3.
15  Thank you for your valuable comments.
As suggested, the high rate of NTM development in our participants could be explained by the fact that patients with type 2 DM with a higher number of comorbid diseases visited the hospital often as compared with those who had fewer comorbidities. This could also explain why our study revealed a significant difference in a higher number of diabetes-related complications. Thank you for pointing this out.
We have accordingly added the following paragraph in the Discussion section of the manuscript.

Response 4.
Thank you for your comments.
We have added the following paragraph to explain the low rate of treatment for DM in our study subjects.  The proportions of patients who received metformin treatment were similar in the case and control groups (60.6% vs 55.7%, p=0.116). In addition, multivariate analysis showed no significant impact of metformin use on the risk of incident NTM disease. Therefore, we concluded that metformin use did not confer an NTM disease-preventive effect in DM patients.
Given the findings of that study, we inferred that prescription of metformin would not affect the main results of our manuscript. Please note, we could present the data only briefly because the other manuscript has not been published yet. Thank you for revising the manuscript and addressing the comments of the reviewers. I am impressed by your detailed reponse and your additional analysis that pertains to diagnosis of diabetes before and after 2007.
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