ABSTRACT

Objective.

To evaluate the association between overweight/obesity and multidrug resistance in patients with and without a history of tuberculosis treatment.

Materials and methods.

Cross-sectional study of secondary data from a tuberculosis cohort, which included anthropometric and drug-sensitivity testing data at the baseline visit of patients with and without previous tuberculosis treatment.

Results.

We evaluated 3,734 new cases and 766 with a history of having received treatment for tuberculosis. Overweight/obesity was not associated with multidrug resistance in patients with a history of tuberculosis treatment, with a prevalence ratio of 0.97 and a 95% confidence interval of 0.68-1.38.

Conclusions.

Overweight/obesity is not associated with multidrug resistance in tuberculosis. Overweight/obesity is a dynamic process that may influence the relationship between the immune system and the metabolic system.

Keywords:
Tuberculosis; Overweight; Obesity; Multidrug Resistance; Peru

KEY MESSAGES

1. Motivation for the study. The reason for the increase in cases of multidrug-resistant tuberculosis in Peru compared to the rest of Latin America is unknown. There are factors such as poverty and malnutrition expressed as overweight and obesity that could contribute to the high number of cases.
2. Main findings. No association was found between overweight/obesity and multidrug-resistant tuberculosis. An association was found between having a history of precious treatment for tuberculosis and multidrug-resistant tuberculosis; however, this association was not be modified by overweight/obesity.
3. Implications. More studies are needed to understand the joint behavior of the two epidemics facing Peru.

Keywords:
Tuberculosis; Overweight; Obesity; Multidrug Resistance; Peru

INTRODUCTION

Tuberculosis (TB) affects 8.9 to 11.0 million people and is the cause of death of 1.2 to 1.4 million people globally ¹1. World Health Organization. Tuberculosis control: Global Tuberculosis Report 2021 [Internet]. Geneva; 2021 [citado el 12 de junio de 2022]. Disponible en: https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2021.
https://www.who.int/teams/global-tubercu... . Multidrug-resistant TB (MDR-TB) has a higher rate of treatment failure compared to sensitive TB ²2. Seung KJ, Keshavjee S, Rich ML. Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis. Cold Spring Harb Perspect Med. 2015;5(9):a017863. doi: 10.1101/CSHPERSPECT.A017863.
https://doi.org/10.1101/CSHPERSPECT.A017... . MDR-TB is present in 3.5% of new TB cases and 18% of previously treated cases, which causes 230,000 deaths worldwide ³3. World Health Organization. Global tuberculosis report 2018 [Internet]. Geneva; 2018 [citado el 12 de junio de 2022]. Disponible en: https://www.who.int/publications/i/item/9789241565646.
https://www.who.int/publications/i/item/... .

Peru is one of the countries from the Latin America and Caribbean (LAC) region with the highest rates of MDR-TB, with an incidence of 9.6 per 100,000 inhabitants, and about 1,205 incident cases per year ⁴4. Nathanson E, Nunn P, Uplekar M, Floyd K, Jaramillo E, Lönnroth K, et al. MDR Tuberculosis - Critical Steps for Prevention and Control. N Engl J Med. 2010;363(11):1050-1058. doi: 10.1056/NEJMra0908076.
https://doi.org/10.1056/NEJMra0908076... . The incidence of MDR-TB has been unusually high in Peru for decades and the factors involved in the higher number of cases in Peru (1.5 times more MDR-TB cases than in other LAC countries) are unknown ⁵5. Asencios L, Quispe N, Mendoza-Ticona A, Elena Leo LV, Jave O. Vigilancia Nacional de la Resistencia a Medicamentos Antituberculosos, Perú 2005-2006. Rev Peru Med Exp Salud Pública. 2009;26(3):278-287. doi: 10.17843/rpmesp.2009.263.1376.
https://doi.org/10.17843/rpmesp.2009.263... .

The emergence of MDR-TB is associated with the amplification of antibiotic resistance patterns through incomplete treatment and transmission in the most vulnerable populations ⁶6. Keshavjee S, Farmer PE. Tuberculosis, drug resistance, and the history of modern medicine. N Engl J Med. 2012;367(10):931-6. doi: 10.1056/NEJMra1205429.
https://doi.org/10.1056/NEJMra1205429... . The factors that increase the risk of MDR-TB are family contact with MDR-TB diagnosis ⁷7. Becerra MC, Huang CC, Lecca L, Bayona J, Contreras C, Calderon R, et al. Transmissibility and potential for disease progression of drug resistant Mycobacterium tuberculosis: prospective cohort study. BMJ. 2019;367:l5894. doi: 10.1136/bmj.l5894.
https://doi.org/10.1136/bmj.l5894... , co-infection with human immunodeficiency virus (HIV), heavy tobacco smoking, previous incarceration, history of TB ⁸8. Workneh MH, Bjune GA, Yimer SA. Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. PLoS One. 2017;12(4):e0175925. doi: 10.1371/journal.pone.0175925.
https://doi.org/10.1371/journal.pone.017... , and type 2 diabetes mellitus (T2DM) ⁹9. Tegegne BS, Mengesha MM, Teferra AA, Awoke MA, Habtewold TD. Association between diabetes mellitus and multi-drug-resistant tuberculosis: evidence from a systematic review and meta-analysis. Syst Rev. 2018;7(1):161. doi: 10.1186/s13643-018-0828-0.
https://doi.org/10.1186/s13643-018-0828-... .

On the other hand, the epidemic of overweight and obesity has doubled since 1980. In 2018, about 2 to 5 billion adults were overweight, and 30% of this population was obese ¹⁰10. Menon S, Peñalvo JL. Actions Targeting the Double Burden of Malnutrition: A Scoping Review. Nutrients. 2019;12(1):81. doi: 10.3390/nu12010081.
https://doi.org/10.3390/nu12010081... . Like MDR-TB, Peru is one of the LAC countries with the highest overweight rate (38.8%), which is above the regional average (34.4%). In Peru, the prevalence of obesity has increased from 9% in 1975 to 13.8% in men and 23.3% in women in 2010; and up to 23.8% in people aged 30 to 59 years from 2013 to 2014 ¹¹11. Pajuelo Ramírez J. La obesidad en el Perú. An Fac Med. 2017;78(2):179-185 doi: 10.15381/anales.v78i2.13214.
https://doi.org/10.15381/anales.v78i2.13... .

The immune and the metabolic systems work in synergy because they evolved from common structures, with adipose tissue maintaining the same lineage as the immune cells ¹²12. Søndergaard L. Homology between the mammalian liver and the Drosophila fat body. Trends Genet. 1993;9(6):193. doi: 10.1016/0168-9525(93)90113-v.
https://doi.org/10.1016/0168-9525(93)901... . Malnutrition, defined as a body mass index (BMI) under 16, is associated with sputum culture non-conversion within the first four months of MDR-TB treatment ¹³13. Putri FA, Burhan E, Nawas A, Soepandi PZ, Sutoyo DK, Agustin H, et al. Body mass index predictive of sputum culture conversion among MDR-TB patients in Indonesia. Int J Tuberc Lung Dis. 2014;18(5):564-70. doi: 10.5588/ijtld.13.0602.
https://doi.org/10.5588/ijtld.13.0602... . In addition, a BMI under 18 is a risk factor for developing MDR-TB compared to patients with sensitive TB who have a BMI greater than or equal to 18 ¹⁴14. Li XX, Lu W, Zu RQ, Zhu LM, Yang HT, Chen C, et al. Comparing risk factors for primary multidrug-resistant tuberculosis and primary drug-susceptible tuberculosis in Jiangsu province, China: a matched-pairs case-control study. Am J Trop Med Hyg. 2015;92(2):280-285. doi: 10.4269/ajtmh.13-0717.
https://doi.org/10.4269/ajtmh.13-0717... . However, evidence is scarce on whether BMI higher than 25 (overweight/obese) would alter the homeostasis of the metabolic immune axis ¹⁶16. Shim K, Begum R, Yang C, Wang H. Complement activation in obesity, insulin resistance, and type 2 diabetes mellitus. World J Diabetes. 2020;11(1):1-12. doi: 10.4239/wjd.v11.i1.1.
https://doi.org/10.4239/wjd.v11.i1.1... .

The main factors affecting the tissue distribution of drugs are BMI, regional blood flow and the affinity of the drug for plasma proteins ¹⁷17. Cheymol G. Effects of obesity on pharmacokinetics implications for drug therapy. Clin Pharmacokinet. 2000;39(3):215-31. doi: 10.2165/00003088-200039030-00004.
https://doi.org/10.2165/00003088-2000390... . The biomedical literature states that tissue blood flow decreases in obese individuals, and cytochrome P450-2E1 activity increases; however, the effect of overweight/obesity on glomerular filtration is unknown, which motivates the application of weight-standardized maintenance doses to correct for differences in drug clearance ¹⁸18. Hanley MJ, Abernethy DR, Greenblatt DJ. Effect of obesity on the pharmacokinetics of drugs in humans. Clin Pharmacokinet. 2010;49(2):71-87. doi: 10.2165/11318100-000000000-00000.
https://doi.org/10.2165/11318100-0000000... . This is relevant in drugs with a low threshold of pharmacokinetic variation such as rifampicin, one of the main anti-TB drugs where, as a rule of thumb, the dose increases in overweight/obese patients ¹⁹19. Chang MJ, Chae JW, Yun HY, Lee JI, Choi HD, Kim J, et al. Effects of type 2 diabetes mellitus on the population pharmacokinetics of rifampin in tuberculosis patients. Tuberculosis (Edinb). 2015;95(1):54-9. doi: 10.1016/j.tube.2014.10.013.
https://doi.org/10.1016/j.tube.2014.10.0... . Early findings showed that T2DM affected the pharmacokinetics of anti-TB drugs, that blood concentrations of isoniazid and rifampicin were lower in patients with TB and T2DM and that T2DM had an effect on the absorption rate and volume of distribution of rifampicin in patients with TB and T2DM ¹⁹19. Chang MJ, Chae JW, Yun HY, Lee JI, Choi HD, Kim J, et al. Effects of type 2 diabetes mellitus on the population pharmacokinetics of rifampin in tuberculosis patients. Tuberculosis (Edinb). 2015;95(1):54-9. doi: 10.1016/j.tube.2014.10.013.
https://doi.org/10.1016/j.tube.2014.10.0... ; however, these statements were later questioned, as the real cause of all these effects was the increased weight of patients with T2DM and TB ²⁰20. Ruslami R, Nijland HM, Alisjahbana B, Parwati I, van Crevel R, Aarnoutse RE. Pharmacokinetics and tolerability of a higher rifampin dose versus the standard dose in pulmonary tuberculosis patients. Antimicrob Agents Chemother. 2007;51(7):2546-51. doi: 10.1128/AAC.01550-06.
https://doi.org/10.1128/AAC.01550-06... .

Low plasma concentrations of isoniazid and rifampicin are associated with increased therapeutic failure and relapse; overweight/obesity affects the concentration-time curve for isoniazid ²¹21. Huerta-García AP, Medellín-Garibay SE, Salazar-González RA, Ortiz-Álvarez A, Magaña-Aquino M, Rodríguez-Pinal CJ, et al. Anthropometric and Genetic Factors Associated With the Exposure of Rifampicin and Isoniazid in Mexican Patients With Tuberculosis. Ther Drug Monit. 2019 Oct;41(5):648-656. doi: 10.1097/FTD.0000000000000631.
https://doi.org/10.1097/FTD.000000000000... . Evidence suggests that low plasma concentrations of drugs with high pharmacokinetic variability, such as rifampicin, are involved in the development of MDR-TB ²²22. Srivastava S, Pasipanodya JG, Meek C, Leff R, Gumbo T. Multidrug-resistant tuberculosis not due to noncompliance but to between-patient pharmacokinetic variability. J Infect Dis. 2011;204(12):1951-9. doi: 10.1093/infdis/jir658.
https://doi.org/10.1093/infdis/jir658... . Weight gain could alter metabolic rates, as occurs in the systemic clearance of other antimicrobials ²³23. Hall RG 2nd, Swancutt MA, Meek C, Leff RD, Gumbo T. Ethambutol pharmacokinetic variability is linked to body mass in overweight, obese, and extremely obese people. Antimicrob Agents Chemother. 2012;56(3):1502-7. doi: 10.1128/AAC.05623-11.
https://doi.org/10.1128/AAC.05623-11... , and could play a role in modifying plasma concentrations of low pharmacokinetic threshold drugs and be a key determinant for the development of MDR-TB²⁴24. Pasipanodya JG, McIlleron H, Burger A, Wash PA, Smith P, Gumbo T. Serum drug concentrations predictive of pulmonary tuberculosis outcomes. J Infect Dis. 2013;208(9):1464-73. doi: 10.1093/infdis/jit352.
https://doi.org/10.1093/infdis/jit352... . Therefore, this study aims to evaluate the association between overweight/obesity and multidrug resistance in patients with and without a history of TB treatment.

MATERIALS AND METHODS

Study type

Analytical, observational, cross-sectional study conducted through a secondary analysis of the database of the cohort “Epidemiology of multidrug-resistant Tuberculosis in Peru” (EPI), which is registered in the ClinicalTrials.gov platform under the code NCT00676754. This cohort was carried out by Socios En Salud Peru branch.

Study population and sample

We included data from all index cases of the EPI study with positive culture for Mycobacterium tuberculosis; data from household contacts were not included. The EPI study population included patients with TB who were recruited in healthcare facilities of the Ministry of Health (MINSA) in 24 districts of the Health Directorates of Lima Centro and Lima Este. We used patient data from June 2008 to June 2014.

Sample size calculation

The statistical program G*Power version 3.1.9.6 (© 2021 Heinrich-Heine-Universität Düsseldorf, Germany) was used to calculate the sample size. A proportion of MDR-TB in persons with normal BMI of 10% ⁵5. Asencios L, Quispe N, Mendoza-Ticona A, Elena Leo LV, Jave O. Vigilancia Nacional de la Resistencia a Medicamentos Antituberculosos, Perú 2005-2006. Rev Peru Med Exp Salud Pública. 2009;26(3):278-287. doi: 10.17843/rpmesp.2009.263.1376.
https://doi.org/10.17843/rpmesp.2009.263... , a proportion of MDR-TB in overweight/obese persons of 17.4% ²⁵25. Song WM, Guo J, Xu TT, Li SJ, Liu JY, Tao NN. Association between body mass index and newly diagnosed drug-resistant pulmonary tuberculosis in Shandong, China from 2004 to 2019. BMC Pulm Med. 2021;21(1):399. doi: 10.1186/s12890-021-01774-2.
https://doi.org/10.1186/s12890-021-01774... , a precision of 0.05, a power of 95% and a sample size ratio of 1 were used as parameters. We obtained a sample size of at least 972 persons.

Procedures

We considered the data of all participants who met the inclusion criteria: those who had a positive smear or culture, agreed to take the survey, provided sputum and blood samples, and had taken a chest X-ray.

Data Collection

Questionnaires

Baseline data (baseline visit) of index cases regarding demographic characteristics, signs and symptoms of active TB, socioeconomic status indicators, height and weight measurements (measured on weekly calibrated scales), comorbidities (including T2DM), harmful habits (smoking tobacco and drinking alcohol), history of TB treatment and previous incarceration were included. Chest X-ray results were interpreted by two TB experts.

Biological samples

Results from bacteriological culture (Petroff method in Löwenstein-Jensen medium), first-line drug sensitivity test (Löwenstein-Jensen method), pyrazinamide susceptibility test (Wayne method), and second-line drug panel (agar plate method) were included.

Variables

BMI was categorized into underweight (<18.5), normal (18.5-24.9), overweight (25-29.9) and obese (30 or more). Anti-TB drug sensitivity test results were categorized into rifampicin monoresistance and isoniazid and rifampicin resistance (MDR). The dependent variable was MDR-TB, while the independent variable was overweight/obesity (BMI ≥25.0 at enrollment). The covariates were: age categorized as <18, 18-24, 25-44 and 45 or older, sex, history of having received anti-TB treatment (registered by the physician at enrollment), HIV co-infection determined by ELISA test and a confirmatory test, self-report of T2DM, harmful habits (smoking tobacco and drinking alcohol) and previous incarceration. All variables were registered by the nurse at enrollment during the anamnesis. Likewise, we used the poverty score as a variable, which was defined according to its definition in the original study ²⁶26. Odone A, Calderon R, Becerra MC. Acquired and Transmitted Multidrug Resistant Tuberculosis: The Role of Social Determinants. PLoS One. 2016;11(1):e0146642. doi: 10.1371/journal.pone.0146642.
https://doi.org/10.1371/journal.pone.014... .

Statistical analysis

First, data were extracted from the original EPI study using OpenMRS (open-source electronic medical record system). Stata version 16 (Stata Corporation, College Station, Texas, USA) was used to process and analyze the data. The demographic characteristics of the participants were described using absolute frequencies and percentages for qualitative variables; measures of central tendency (mean, median, range) and dispersion (standard deviation and interquartile range) were used for quantitative variables. On the other hand, the proportions of the dependent and independent variables and the covariates were compared using the two-tailed chi-square test and the Mann-Whitney test. Each test was selected based on the evaluated assumptions (normal distribution, quantitative data or categorical data). A two-tailed significance level of less than 0.05 was used as a reference to reject or accept the null hypothesis. Finally, the association between the dependent and independent variables was estimated by calculating the crude (PR) and adjusted (aPR) prevalence ratios with their respective 95% confidence intervals (CI) using a Poisson regression with log link and robust variances. In order to calculate the aPR, an initial model was constructed with the variables associated with the event of interest; then we added the predictor variables that were independently associated with the event of interest. We used a p-value of less than 0.05 in the Wald test to determine whether the association was statistically significant.

Ethical Aspects

This study was approved by the Ethics Committee of the Universidad Peruana Cayetano Heredia (code 66894). The original study was approved by the bioethics committee of the National Institute of Health and the Harvard School of Public Health, Boston, USA.

RESULTS

We evaluated 4500 cases, of which 1149 had negative solid-media cultures for Mycobacterium tuberculosis at their baseline visit. The final study population consisted of 3351 culture-positive cases. The median age was 28 years with a range of 15 to 94 years, 62.3% were male, 5.2% had previous incarceration, 2.7% reported heavy social smoking, and 44.2% reported heavy social drinking.

Of the 3351 cases, 15.1% had a BMI greater than or equal to 25, 17.1% had a history of TB treatment, 3.5% had HIV coinfection, 5.6% reported having T2DM, and 33.6% had some type of resistance: monoresistance, polyresistance and resistance to isoniazid and rifampicin (MDR) (Table 1).

On the other hand, 15.9% were found to have MDR-TB. When comparing the proportions of covariates and MDR-TB, we found differences in age by category (p=0.007), previous incarceration (p=0.041), and social to heavy tobacco smoking (p=0.010). On the other hand, the difference between MDR-TB and overweight/obesity was not statistically significant (Table 2). We assessed the proportion of MDR-TB in patients with and without a history of TB treatment according to the demographic characteristics and medical history. In this regard, we found differences between not having a history of TB treatment and social/heavy tobacco smoking (p=0.016). We also found differences between having a history of TB treatment and age (p=0.004) and sex (p=0.001) (Table 3).

The multivariate analysis showed that MDR-TB was not associated with being underweight (aPR: 1.05; 95%CI: 0.74-1.49 in patients with treatment history; aPR: 0.87; 95%CI: 0.59-1.28 in patients with no treatment history) nor with overweight/obesity (aPR: 0.96; 95%CI: 0.68-1.38 in patients with treatment history; aPR: 0.88; 95%CI: 0.57-1.38 in patients with no treatment history).

DISCUSSION

Few studies explore the relationship between the TB epidemic and the overweight/obesity epidemic, particularly regarding MDR-TB; with the aim of implementing strategies to help eradicate TB. We assessed the association between overweight/obesity and MDR-TB in patients with and without a history of TB (cases with a history of TB and new TB cases). Our results show that there is no association between overweight/obesity and MDR-TB, i.e., overweight/obesity was not associated with MDR-TB in new patients without a history of TB treatment or in patients with a history of TB treatment.

Song et al. found an association between new MDR-TB cases and being overweight in data collected from 2004 to 2019 ²⁵25. Song WM, Guo J, Xu TT, Li SJ, Liu JY, Tao NN. Association between body mass index and newly diagnosed drug-resistant pulmonary tuberculosis in Shandong, China from 2004 to 2019. BMC Pulm Med. 2021;21(1):399. doi: 10.1186/s12890-021-01774-2.
https://doi.org/10.1186/s12890-021-01774... , which differs from our results. Earlier studies on this subject reported that overweight/obesity was associated with low risk of active TB, although they did not evaluate MDR-TB ²⁷27. Lin HH, Wu CY, Wang CH, Fu H, Lönnroth K, Chang YC, et al. Association of Obesity, Diabetes, and Risk of Tuberculosis: Two Population-Based Cohorts. Clin Infect Dis. 2018 Feb 10;66(5):699-705. doi: 10.1093/cid/cix852.
https://doi.org/10.1093/cid/cix852... ^,2828. Yen YF, Hu HY, Lee YL, Ku PW, Lin IF, Chu D, et al. Obesity/overweight reduces the risk of active tuberculosis: a nationwide population-based cohort study in Taiwan. Int J Obes (Lond). 2017;41(6):971-975. doi: 10.1038/ijo.2017.64.
https://doi.org/10.1038/ijo.2017.64... ; their findings also are different from ours. The difference between our results and those from other studies could be due to genetic variations related to pharmacogenetics, which was proposed by Pasipanodya et al. ²⁴24. Pasipanodya JG, McIlleron H, Burger A, Wash PA, Smith P, Gumbo T. Serum drug concentrations predictive of pulmonary tuberculosis outcomes. J Infect Dis. 2013;208(9):1464-73. doi: 10.1093/infdis/jit352.
https://doi.org/10.1093/infdis/jit352... . This variability could explain the high rates of MDR-TB in the Peruvian territory, being one of the countries with the highest rates in the LAC region for several decades ⁵5. Asencios L, Quispe N, Mendoza-Ticona A, Elena Leo LV, Jave O. Vigilancia Nacional de la Resistencia a Medicamentos Antituberculosos, Perú 2005-2006. Rev Peru Med Exp Salud Pública. 2009;26(3):278-287. doi: 10.17843/rpmesp.2009.263.1376.
https://doi.org/10.17843/rpmesp.2009.263... . Another possible for this difference is the type of overweight/obesity, Peru being the country with the highest percentage of energy derived from carbohydrate consumption (62.9%), unlike other countries in the region, where obesity is due to the high consumption of fats and proteins ²⁹29. Kovalskys I, Fisberg M, Gómez G, Pareja RG, Yépez García MC, Cortés Sanabria LY, et al. Energy intake and food sources of eight Latin American countries: results from the Latin American Study of Nutrition and Health (ELANS). Public Health Nutr. 2018;21(14):2535-2547. doi: 10.1017/S1368980018001222.
https://doi.org/10.1017/S136898001800122... .

Another possible cause may be the fact that overweight/obese women account for 5-6% of the population in the other studies, whereas in our study they represent 19-20% of the total sample. This agrees with the study by Woolcott et al., who conducted a cross-sectional analysis of 31,549 people from the National Center of Food and Nutrition of Peru and reported that the crude prevalence of obesity is 11.3% for men and 20.7% for women and varies according to the altitude of residence, which complicates overweight and obesity ³⁰30. Woolcott OO, Gutierrez C, Castillo OA, Elashoff RM, Stefanovski D, Bergman RN. Inverse association between altitude and obesity: A prevalence study among andean and low-altitude adult individuals of Peru. Obesity (Silver Spring). 2016;24(4):929-37. doi: 10.1002/oby.21401.
https://doi.org/10.1002/oby.21401... . Besides, the aforementioned studies only evaluated new cases, whereas our study included old and new TB cases. Considering these subpopulations, we found that overweight/obesity would not modify the association between previous TB treatment and MDR-TB ⁸8. Workneh MH, Bjune GA, Yimer SA. Prevalence and associated factors of tuberculosis and diabetes mellitus comorbidity: A systematic review. PLoS One. 2017;12(4):e0175925. doi: 10.1371/journal.pone.0175925.
https://doi.org/10.1371/journal.pone.017... ^,3131. Berhan A, Berhan Y, Yizengaw D. A meta-analysis of drug resistant tuberculosis in Sub-Saharan Africa: how strongly associated with previous treatment and HIV co-infection? Ethiop J Health Sci. 2013;23(3):271-82. doi: 10.4314/ejhs.v23i3.10.
https://doi.org/10.4314/ejhs.v23i3.10... .

Variables classically associated with MDR-TB such as HIV coinfection, T2DM and poverty did not have statistically significant differences in this study. It is possible that we did not find differences between HIV infection and MDR-TB because HIV is more frequent in specific populations that may have not been represented in our study. Other studies reported an association between HIV coinfection and MDR-TB, particularly primary MDR-TB, in populations with higher HIV rates, such as Africa or Asia ³²32. Sultana ZZ, Hoque FU, Beyene J, Akhlak-Ul-Islam M, Khan MHR, Ahmed S, et al. HIV infection and multidrug resistant tuberculosis: a systematic review and meta-analysis. BMC Infect Dis. 2021;21(1):51. doi: 10.1186/s12879-020-05749-2.
https://doi.org/10.1186/s12879-020-05749... . In our study, data regarding T2DM diagnosis was based on self-reports, and given that T2DM is an almost asymptomatic disease in its early stages, it is likely that it was not accurately diagnosed ³³33. Mordarska K, Godziejewska-Zawada M. Diabetes in the elderly. Prz Menopauzalny. 2017 Jun;16(2):38-43. doi: 10.5114/pm.2017.68589.
https://doi.org/10.5114/pm.2017.68589... , which may explain why we did not find differences between T2DM and MDR-TB. Additionally, no differences were found between poverty and MDR-TB, which may be due to the type of instrument we used ³⁴34. Chen X, Zhou T, Wang D. The Impact of Multidimensional Health Levels on Rural Poverty: Evidence from Rural China. Int J Environ Res Public Health. 2022;19(7):4065. doi: 10.3390/ijerph19074065.
https://doi.org/10.3390/ijerph19074065... ^,3535. Instituto Nacional de Estadística e Informática. Informe técnico, evolución de la pobreza 2007-2011 [Internet]. INEI: Perú; 2012 [citado el 12 de junio de 2022]. Disponible en: https://www.inei.gob.pe/media/cifras_de_pobreza/pobreza_informetecnico.pdf.
https://www.inei.gob.pe/media/cifras_de_... , given that the original study recruited participants from poor districts of Lima, it is possible that the number of non-poor people is too small to determine a statistically significant difference.

Our study has several important limitations. The cross-sectional design prevents us from establishing causality between the variables, and we could only determine their association. The BMI categories were based on single cut-off points for all age groups, without considering that for people aged 60 years or older, who made up 9.5% of the sample, the cut-off points could be different. The history of having received treatment for TB was self-reported during the anamnesis. This, together with the fact that there is no information available to confirm whether the patient was cured or not after the treatment, could cause a recall bias. In addition, T2DM diagnosis was also self-reported, which does not allow diagnostic confirmation and prevents establishing absolute glycemic values. Overweight/obesity changes over time, this condition may have changed between diagnosis and treatment. We did not collect information regarding weight before enrollment, nor during follow-up, this is an important limitation, since, this variable could increase with age. On the other hand, we found that only 2.3% of the participants were obese, which is why we added the overweight population (12.8%). This lack of cases with nutritional disorders could limit the statistical power to evaluate their association with MDR-TB. The most appropriate design would have been a large cohort during several years of observation and data collection, not only for BMI but also for other nutritional parameters such as waist-to-hip ratio.

We conclude that there is no association between overweight/obesity and anti-TB drug resistance in patients with a history of TB treatment.

References

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History