Overweight and obesity as emerging risk factors for multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis

Overweight and obesity as emerging risk factors for multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Overweight and obesity as emerging risk factors for multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis Edinson Dante Meregildo Rodriguez, Gabriela Campos-Silva, Martha Genara Asmat-Rubio, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6491503/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Obesity and tuberculosis (TB) are two escalating global public health challenges. Emerging evidence suggests that overweight and obesity may be associated with an increased risk of multidrug-resistant tuberculosis (MDR-TB). We conducted a systematic review and meta-analysis to assess whether overweight and obesity influence the risk and clinical outcomes of MDR-TB. Methods: We systematically searched five databases for studies published from inception through December 31, 2024. Results: Eight observational studies comprising 6,743 TB cases and 5,339 MDR-TB cases met the inclusion criteria. Our analysis revealed that overweight and obesity were associated with a 38% increased risk of MDR-TB (OR 1.38; 95% CI 1.14-1.67), with moderate heterogeneity ( I 2 = 78.7%, p < 0.0001). Notably, this association was significant only in studies conducted in Asia (OR 1.75; 95% CI 1.49–2.06), suggesting potential racial or regional differences in susceptibility. Due to limited data, we were unable to perform a meta-analysis on other outcomes such as adverse effects of anti-TB treatment, prolonged treatment regimens, or MDR-TB-related mortality. Conclusion: Overweight and obesity may be emerging risk factors for MDR-TB, particularly in Asian populations. These findings highlight the need to consider metabolic and nutritional status in TB control strategies. However, due to study heterogeneity and limited data on clinical outcomes, further high-quality research is essential to confirm these associations and elucidate underlying mechanisms. Endocrinology & Metabolism Infectious Diseases Preventive Medicine Internal Medicine Other Public Policy Tuberculosis Multidrug-resistant tuberculosis Overweight Obesity Body Mass Index Risk Factors Systematic Review Meta-Analysis Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Tuberculosis (TB) and obesity represent two of the most formidable and rapidly growing challenges to global public health [ 1 , 2 ]. TB remains the world's deadliest infectious disease—briefly overshadowed by COVID-19—causing more deaths annually than HIV/AIDS and malaria combined [ 3 , 4 ]. In 2022 alone, an estimated 10.6 million people developed TB, and 1.3 million succumbed to the disease [ 5 ]. Multidrug-resistant TB (MDR-TB) now accounts for 3.5% of new TB cases and 18% of previously treated cases [ 6 ], with approximately 470,000 people falling ill and 230,000 dying from MDR-TB each year [ 7 ]. Extensively drug-resistant TB (XDR-TB), a more severe form of MDR-TB, requires longer and more costly treatment and is associated with significantly higher rates of treatment failure and mortality [ 5 , 7 , 8 ]. Meanwhile, the global epidemic of overweight and obesity has surged, more than doubling since 1990 and quadrupling among children and adolescents [ 9 ]. In 2022, over 1 billion individuals were living with obesity, and roughly 43% of the world’s adult population was classified as overweight or obese [ 10 ]. Nutritional status plays a pivotal role in determining TB risk and treatment outcomes [ 2 , 11 ]. Much of the recent literature has focused on body mass index (BMI)—a common surrogate for nutritional status—and its association with TB incidence, mortality, and sputum conversion rates [ 12 – 14 ]. Being underweight (BMI < 18 kg/m²) is a well-established risk factor for active TB, MDR-TB, and delayed sputum conversion among MDR-TB patients [ 15 – 18 ]. However, the implications of overweight and obesity in the context of TB are less clear. Although obesity is known to increase susceptibility to various infections, including postoperative and nosocomial infections [ 19 ], its role in TB remains paradoxical. Emerging evidence suggests that obesity may alter immune-metabolic homeostasis, yet studies remain limited [ 20 ]. While one study identified a BMI > 28.0 kg/m² as an independent risk factor for latent TB infection (LTBI) [ 21 ], most research indicates an inverse relationship between obesity and the risk of active TB [ 2 , 22 ]. Indeed, a recent systematic review found that overweight and obese individuals had a lower incidence of TB compared to those of normal weight [ 23 ]. This counterintuitive association mirrors the complex relationship between obesity and diabetes mellitus (DM)—a major risk factor for TB [ 2 ]. Obesity significantly increases the risk of developing diabetes, and diabetes in turn raises the risk and severity of TB. One would expect, therefore, that obesity indirectly heightens TB risk via diabetes. Yet epidemiological data do not consistently support this hypothesis [ 2 , 23 ]. Whether overweight and obesity influence the risk or outcomes of MDR-TB remains an open question. It is well established that undernutrition worsens the prognosis of both drug-susceptible and drug-resistant TB [ 2 , 12 , 21 , 24 ]. However, no prior systematic review has explored the paradoxical association between overweight/obesity and MDR-TB. Addressing this gap, the present study aims to clarify whether overweight and obesity are associated with the risk and clinical outcomes of MDR-TB. Identifying modifiable risk factors for drug-resistant TB is critical for optimizing clinical management and strengthening global TB control strategies [ 25 , 26 ]. In particular, gaining a deeper understanding of the intersection between obesity and MDR-TB has far-reaching implications—especially as the global prevalence of diabetes, one of obesity’s major sequelae [ 27 ], continues to rise, particularly in regions with a high TB burden. Notably, diabetes is consistently linked to increased TB incidence and worse outcomes in both drug-susceptible and resistant forms [ 28 – 30 ]. 2. MATERIALS AND METHODS We conducted this systematic review and meta-analysis adhering to the recommendations of the Cochrane Handbook for Systematic Reviews [ 31 ], PRISMA [ 32 ], and AMSTAR 2[ 33 ] guidelines. The protocol was registered in PROSPERO (CRD42023446650). Search strategy. We comprehensively searched five databases: MEDLINE (PubMed), Scopus, EMBASE, Web of Science, and Google Scholar. We screened each database using controlled language terms (MeSH, Emtree, etc.), free terms, and their synonyms, combined with Boolean operators, following a PECO strategy. Keywords primarily focused on exposure, such as "obesity," OR "overweight," and outcome-related terms like "tuberculosis," OR "resistant tuberculosis," OR "MDR-TB," OR "XDR-TB." In addition, we conducted manual secondary searches of references in primary studies and review papers. Searches were not restricted by language or publication year. The search strategy is detailed in the Supplementary Materials, Table S1 . Inclusion and exclusion criteria. Our search included observational studies published from inception until December 31, 2024. We excluded case reports, case series, duplicated publications, conference reports, letters to the editor, and editorials. All articles retrieved from the primary and secondary searches were compiled using Mendeley ® 2.109.0. After removing duplicates, these articles were imported into Rayyan ® , screened, and individually examined by four blinded and independent researchers (GCS, MGAR, GAVT, and KERR). The studies were selected by consensus, and a fifth researcher was the arbitrator (EDMR) in case of discordance. All articles that were collected were examined using the terms of the PECO strategy and the inclusion and exclusion criteria. Study selection and data extraction . The selected articles were exported to a spreadsheet for a second full-text screening. The study selection process is detailed in Fig. 1 . The same researchers that performed the selection process conducted data extraction by examining articles and collecting the relevant details of the study, including the authors, country and year of publication, clinical and epidemiological characteristics of the population, number of patients and cases (events), measures of association, confounding factors, and the most relevant outcomes. For dichotomous and time-to-event variables, we compiled prevalence rates (PRs), odds ratios (ORs), relative risks (RRs), and hazard ratios (HRs) with 95% confidence intervals (95% CI). If important information was missing, at least two emails were sent to the corresponding authors. Data from each paper were extracted and recorded in a spreadsheet. The lead researcher (EDMR) served as the final arbitrator if there was any disagreement. Data synthesis, meta-analysis, and meta-regression. We conducted this meta-analysis using R ® 4.3.2 software and the generic inverse variance method (GIVM) with Restricted Maximum-Likelihood (REML) for tau 2 . As some studies do not report the number of cases and events, we meta-analyzed data using the GIVM. This technique needs only the input of the effect measure (PR, OR, RR, HR) and the 95% CI [ 31 ]. We applied the Hartung and Knapp correction, accounting for the uncertainty in estimating the variance between studies, which is more significant when there are a few studies [ 34 – 36 ]. We considered equivalence between PRs to ORs, and ORs to RRs, if the frequency of the event of interest was < 10% [ 37 , 38 ]. For meta-analysis, we considered each subgroup analysis a separate study for studies reporting effect measures stratified into different subgroups. We predefined in our protocol that we would use a fixed effects model in our meta-analysis if heterogeneity were not statistically significant according to Cochran's Q test and Higgins I 2 statistic ( p > 0.10, I 2 statistics < 40%). Otherwise, we would use a random effects model [ 31 ]. The potential subgroups to be analyzed were the study design, the continent of origin of the study, the classification of nutritional status according to BMI, and having previously received or not received treatment for tuberculosis, diabetes mellitus, and HIV infection status. Furthermore, we conducted sensitivity and influence analysis and meta-regression to assess heterogeneity. In addition to a funnel plot, we performed a correlation test for funnel plot asymmetry, Egger's test, trim-and-fill funnel plot, and a classic fail-safe N analysis to assess the risk of publication bias. Quality assessment. We assessed the risk of bias using the Newcastle–Ottawa scale (NOS) [ 39 ]. GRADE assessment. Three researchers (GCS, MGAR, GAVT, and KERR) independently assessed the certainty of the evidence (CoE) of each study outcome based on GRADE criteria [ 40 , 41 ]. Any reviewer discrepancies were resolved through discussion with the lead researcher (EDMR). Definitions MDR-TB: resistance of M. tuberculosis (MTB) strains to at least isoniazid and rifampicin [ 4 , 5 ]. XDR-TB: MDR-TB that is also resistant to any fluoroquinolone and at least one additional Group A drug (levofloxacin, moxifloxacin, bedaquiline, or linezolid) [ 4 , 5 ]. BMI classification: according to the World Health Organization (WHO) and most clinical guidelines on the diagnosis and treatment of overweight and obesity in adults, we used the following definitions according to the body mass index (BMI) in kg/m 2 : underweight ≤ 18.5, normal weight 18.5–24.9, overweight 25.0–29.9, and obesity ≥ 30.0 [ 42 – 46 ]. 3. RESULTS We identified 152 records, all retrieved from databases. After removing 13 duplicates, 139 records remained. We then excluded 75 records for various reasons, leaving 22 reports to be assessed for eligibility. Fourteen of these studies were excluded—mainly because they were available only in abstract or did not meet our PECO question ( Supplementary Materials, Table S2 ). Finally, eight papers [ 24 , 47 – 53 ]were included in our systematic review and meta-analysis (Fig. 1 and Table 1 ). Of these eight studies, five were retrospective cohorts, two were cross-sectional, and one was a nested case-control study (Table 1 ). This review encompasses 16,743 tuberculosis cases, of whom 5,339 had MDR tuberculosis (Table 1 ). Table 1 General characteristics of included studies Study, year, country Design Patients and events (cases) Outcome aPR/aOR / aRR / aHR (95% CI) Peinado J et al. [ 47 ], 2022, Perú. Secondary analysis of the database of the "Epidemiology of multidrug-resistant Tuberculosis in Peru" (EPI) study. CSS. N = 4,500 cases of TB, of whom 3,734 were new TB cases, and 766 had received treatment for TB. Three thousand three hundred fifty-one cases had positive cultures, and 1,149 cases had negative cultures for MTB. Patients were recruited in health facilities of 24 districts of Lima, Peru. Inclusion period: June 2008 to June 2014. The results of the anti-TB drug susceptibility test were classified into monoresistance to RIF and resistance to INH and RIF (MDR). Dependent variable: MDR-TB. Median age 28 years (IQR 15–94 years), 62.3% were male. Of the 3,351 cases, 500 (15.1%) had a BMI ≥ 25, 570 (17.1%) had a history of TB treatment, 116 (3.5%) had HIV coinfection, 185 (5.6%) reported having DM2, and 406 (15.9%) of cases were MDR-TB cases. PR 0.96; 95% CI 0.68–1.38 for MDR-TB in patients with overweight/obesity and history of treatment. PR 0.88; 95% CI 0.57–1.38 for MDR-TB in patients with overweight/obesity without a history of treatment. aPR 1.05; 95% CI 0.74–1.49 for MDR-TB in patients with low weight and history of treatment. PRa 0.87; 95% CI 0.59–1.28 for MDR-TB in patients with low weight without a history of treatment. Adjustment factors: Age, gender, History of anti-TB treatment, HIV, DM2, smoking, alcoholism, history of imprisonment, and poverty score. Larico M et al. [ 48 ], 2022, Perú. Data were collected from the database extracted from the care registry of patients with MDR-TB. RCS. N = 1,156 patients with MDR-TB treated in the Sergio Bernales Hospital pulmonology service from January 2010 to December 2018. MDR-TB: resistance to at least one injectable agent (amikacin, kanamycin, or capreomycin) and/or FQ. Male 63.1%, mean age: 27 years, normal nutritional status: 48.6%, HIV coinfection 3.9, and no medical history 72.6%. Resistance expanded in 697/1,156 (60.30%) cases. RR 1.21; 95% CI 1.03–1.41 for MDR-TB in overweight/obese patients compared to normal weight. RR 0.73; 95% CI 0.59–0.91 for MDR-TB in underweight patients. RR 1.18; 95% CI 0.85-1-64 for MDR-TB in overweight/obese patients who previously received anti-TB treatment compared to normal weight. Adjustment factors: Age, sex and academic degree, medical history, HIV, and compliance with the treatment regimen. Kamara R, et al. [ 49 ], 2022, Sierra Leone. A national, retrospective cohort study recruited all people notified with MDR-TB to the Sierra Leone National TB Program admitted to Lakka Hospital between April 2017 and September 2019. Follow-up: until May 2021. RCS. N = 365 people diagnosed with MDR-TB who started anti-TB treatment (317 received the short regimen, 24 received the long regimen, and 24 received no treatment). Relapse: people previously treated for TB, declared cured or treatment completed at the end of their most recent course of treatment, and are now diagnosed with a recurrent episode of TB (either a true relapse or a new episode of tuberculosis caused by reinfection). Treatment after loss to follow-up: previously treated for TB and whose most recent treatment was interrupted for two or more consecutive months. New: no previous history of TB treatment. Age: 35 years (IQR 26–45), 263 were male, 71 were HIV-positive, and 127 were severely underweight (BMI < 16.5 kg/m²). Overall, 267 participants had treatment success, 95 had an adverse outcome, and three were still on treatment in May 2021. OR 6.0; 95% CI 2.3–15 for adverse TB treatment outcome in severely underweight participants with a DST result indicating MDR-TB compared to normal weight. OR 1.7; 95% CI 0.6–4.9 for adverse TB treatment outcome in underweight participants with a DST result available compared to normal weight. OR 6.7; 95% CI 0.4–102.0 for adverse TB treatment outcome in overweight or obese participants with a DST result indicating MDR-TB compared to normal weight. Adjustment factors: Age, gender, HIV, DM, CKD, CLD, etc. Podewils LJ et al. [ 50 ], 2011, Latvia. Patients registered in the National MDR-TB database in Latvia between January 2000 and June 2004. RCS. N = 995 adults (≥ 18 years) who were diagnosed with pulmonary MDR-TB and who initiated individualized MDR-TB treatment. Of the 995 patients, 401 (40%) were classified as having primary drug resistance and 594 (60%) were considered to have acquired drug resistance. Thirty-seven percent had no previous history of TB treatment, 51% had been previously treated for drug-susceptible TB, and 12% had a history of MDR-TB treatment. In total, 199 (20%) patients were considered underweight (BMI < 18.5) at the time of MDR-TB diagnosis. OR 1.7; 95% CI 1.1–2.9 for underweight and culture positivity. HR 1.1; 95% CI 0.9–1.3 for underweight and culture conversion. HR 1.2; 95% CI 0.9–1.5 for underweight and overall treatment failure (default, failed treatment, or died during the treatment course). HR 1.9; 95% CI 1.1–3.5 for underweight and the risk of death. HR 3.2; 95% CI 1.1–9.05 for underweight and risk of death among treatment-naive patients at the time of the current MDR-TB diagnosis. HR 1.6, 95% CI 0.7–3.7 for underweight and risk of death among previously treated for drug-susceptible TB. HR 2.1, 95% CI 0.5–8.1 for underweight and risk of death among patients with previous MDR-TB treatment exposure. All these outcomes refer to underweight patients compared to patients who were normal or overweight at the time of MDR-TB diagnosis. Adjustment factors: Age, sex, unemployment, homelessness, History of imprisonment, heavy alcohol use, History of drug use, HIV, comorbidities, etc. Soeroto AY et al. [ 51 ], 2021, Indonesia. Data was collected at PMDT clinic from December 2019 to January 2020. Follow-up until treatment was complete, with a minimum duration of 18 months. RCS. N = 492 MDR-TB patients. A cohort of MDR-TB patients (≥ 18 years old) treated with a longer regimen at Hasan Sadikin General Hospital, Bandung, from January 2015 to December 2017. Successful treatment: a patient who fulfill recovery criteria or completed treatment. Unfavorable outcomes: a combination of dropout rates, failure of therapy, and death. Success criteria and unfavorable outcomes was defined according to WHO criteria. Longer regimen definition: a combination of FQ (levofloxacin or moxifloxacin), AGs injection (kanamycin or capreomycin), ethionamide, cycloserine, and pyrazinamide. Age > 45 years 157 (32%); male 268 (54.5%); current smokers 271 (55%); BMI: underweight 321 (65.3%), normal 143 (29%), overweight 16 (3.3%), obese 12 (2.4); HIV 15 (3%); DM 76 (15.4%), CKD 59 (12%), History of TB medication: no previous treatment 25 (5%), relapse 167 (34%), failed 216 (43.9%), and loss to follow-up 84 (17.1). Successful treatment 246. Unfavorable outcomes 246 (death 116, loss of follow-up 110, failed therapy 20). aRR 1.21; 95% CI 1.05–1.39 for normal weight and outcome of longer regimen MDR-TB treatment compared to underweight. RR 1.54 (1.15–2.07) for overweight and outcome of longer regimen MDR-TB treatment compared to underweight. RR 1.45 (1.02–2.05) for obese and outcome of longer regimen MDR-TB treatment compared to underweight. RR 2.20 (1.40–3.47) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who received no previous treatment. RR 2.20 (1.50–3.22) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who relapsed treatment. RR 1.77 (1.21–2.58) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who failed treatment. Adjustment factors: Age, sex, smoking, BMI, HIV, DM, CKD, anemia, history of anti-TB medication. Song, W, et al. [ 24 ], 2021, China. Data was collected at 34 monitoring sites of DR-TB in at Shandong Provincial Hospital (SPH) and Shandong Provincial Chest Hospital (SPCH). RCS. N = 8,957 newly diagnosed PTB patients with BMI status and DST results were retrospectively collected from January 1, 2004, to December 31, 2019. Mono-resistance (MR): resistance to only one first-line anti-TB drug. MDR: resistance to at least both INH and RIF. PDR: resistance to at least two first-line anti-TB drugs, except resistance to both INH and RIF. MR-TB (INH): resistance only to INH and susceptibility to other drugs. N = 89,57, of which 6,417 (71.64%) were normal weight, 2,121 (23.68%) were underweight, 373 (4.16%) were overweight, and 46 (0.51%) were obese. Of them, 15.62% aged between 15–24, 25.31% aged between 25–44, 33.16% aged between 45–64, 25.63% aged > 65 and 83.01% were males. The proportion of MDR-TB 219/6417 among normal weight patients, 65/2121 among underweight patients, 20/373 among overweight patients, and 0/46 among obese patients. OR 1.639; 95% CI 1.02–2.633 for overweight and MDR‑TB compared with normal weight group. OR 2.113; 95% CI 1.141–3.912 for overweight and INH + RIF + SPM resistance compared with normal weight group. OR 1.31; 95% CI 1.017–1.686 for underweight and MR‑TB (INH) compared with normal weight group. No previous history of treatment is described. Adjustment factors: Age, sex, alcohol consumption, smoking, pulmonary cavitations, and comorbidities. Avalos A, et al. [ 52 ], 2014, Perú. Healthcare facilities from Callao, Lima, Peru. Patients with primary MDR-TB diagnosed between January 2009 and December 2010. Records of the PCT of the Regional Health Directorate of Callao. CCS. N = 29 primary MDR-TB patients and 37 drug-sensitive TB patients were studied through assessment of the PCT registries and healthcare facilities' clinical records. A case was defined as patients diagnosed with primary MDR-TB according to the WHO/IUATLD definition. Drug sensitive TB group: age ≥ 40 years 18/37; males 22/37; previous imprisonment 2/37; HIV 1/37; DM 1/37; nutritional status: malnutrition 4/37, normal weight 22/37, overweight 7/37, mild obesity 1/37, morbid obesity 0/37. Drug resistant TB group: age ≥ 40 years 8/29; males 17/29; previous imprisonment 2/29; HIV 1/29; DM 1/29; nutritional status: malnutrition 3/29, normal weight 17/29, overweight 4/29, mild obesity 3/29, morbid obesity 0/29. OR 0.97; 95% CI 0.19–4.93 for malnutrition and risk of having primary MDR-TB compared to normal weight. OR 0.73; 95% CI 0.18–2.94 for overweight and risk of having primary MDR-TB compared to normal weight. OR 3.88; 95% CI 0.37–40.70 for obesity and risk of having primary MDR-TB compared to normal weight. Adjustment factors: Age > 40 years, sex, level of education, contact and crowding variables, HIV, DM, use of corticosteroids, drugs or alcohol, BMI. Baluku JB et al. [ 53 ], 2022, Uganda. Four TB treatment sites in Uganda between July to December 2021. CSS. N = 212 adults (age ≥ 18 years) with microbiologically confirmed DR-TB, of whom 118 (55.7%) had HIV. Any form of drug resistance who were receiving treatment at these sites during the period of data collection. Of 212 participants, 156 (73.6%) were male and 118 (55.7%) had HIV coinfection. The median (IQR) age was 37 (30–46) years. The median (IQR) BMI was 19.7 (17.7–22.2) Kg/m 2 . PR 1.02; 95% CI 1.00–1.05 for central obesity and DR-TB. No previous history of treatment is described. Adjustment factors: History of cigarette smoking, DM, HT, high BMI, central obesity, and dyslipidemia. CC:S case-control study, RCS: retrospective cohort study, CSS: cross-sectional study, BMI: body mass index, MTB: Mycobacterium tuberculosis, PTB: pulmonary TB, RIF: rifampicin, INH: isoniazid, DST: Drug susceptibility testing, MDR: Multidrug resistance, PDR: Polydrug resistance, STM: streptomycin, DM: Diabetes mellitus, DM2: Diabetes mellitus type 2, CKD: chronic kidney disease, CLD: chronic lung disease, HT: hypertension, BMI: body mass index, AFB: acid-fast bacilli, FQ: fluoroquinolone, AG: aminoglycosides, PMDT: Programmatic Management of Drug-resistant TB, PCT: the Tuberculosis Control Program, DR-TB drug-resistant tuberculosis, aPR / aOR / aRR / aHR: adjusted PR, OR, RR, HR. Risk of developing MDR tuberculosis . We found that overweight and obesity increased the likelihood of MDR-TB by 38% (OR 1.3825; 95% CI 1.1445–1.6701). However, heterogeneity was significant ( I 2 = 78.7%, p < 0.0001) (Fig. 2 a). Heterogeneity analysis . The sensitivity analysis and the leave-one-out test identified the study conducted by Baluku J 2022 [ 38 ] as an outlier that significantly impacted the overall estimate ( Supplementary Materials, Figure S1-S4 ). Subgroup analysis showed that the risk of MDR-TB was not statistically different among the weight categories (normal weight, overweight, or obesity) ( p = 0.65) (Fig. 2 b), history of previous TB treatment (yes or not) ( p = 0.57) (Fig. 2 c), or the type of the study (case-control study, retrospective cohort study, or cross-sectional study) ( p = 0.29) (Fig. 2 d). Conversely, subgroup analysis showed that the risk of developing MDR tuberculosis was statistically different according to the continent of origin of the study (America, Africa, or Asia) ( p < 0.0001) (Fig. 2 e). We performed meta-regression analyses to explore potential sources of between-study heterogeneity. The moderators' test was not statistically significant according to the weight category ( p = 0.8077), history of previous TB treatment ( p = 0.6413), or the type of study design ( p = 0.2758). However, it reached statistical significance according to the continent of origin of the study ( p < 0.0001). That is, only the continent of origin accounted for variability between studies. The test for residual heterogeneity was statistically significant for all of the moderator variables ( p = 0.0011; p = 0.0001; and, p = 0.0012, respectively), except for the continent of origin of the study ( p = 0.6272). This suggests that our model was not well-specified and that other moderating variables should be considered ( Supplementary materials , Table S3 ). Publication bias. Even though our funnel plot (Fig. 3 a) did not suggest any publication bias, we conducted other analyses to explore publication bias. The rank correlation test for funnel plot asymmetry and the Egger test did not suggest a publication bias risk ( p = 0.1412). Considering a reference or threat criterion (5 * k + 10 = 80), the Rosenberg approach (observed significance level p < 0.0001, target significance level p = 0.05, Fail-safe N = 216) suggested that publication bias was not a threat to the existence of a significant effect size in this meta-analysis. Likewise, the trim-and-fill method added one hypothetical missing study on the left side of the funnel plot (Fig. 3 b). However, the effect size remained similar (OR 1.3785; 95% CI 1.1401–1.6679), and the heterogeneity remained significant ( I 2 = 78.16%, p < 0.0001). A meta-analysis could not be performed for adverse treatment outcomes among individuals with multidrug-resistant tuberculosis , mortality in MDR-TB patients , or the risk associated with prolonged MDR-TB treatment regimens , as each of these outcomes was assessed in only a single study ([ 49 ], [ 50 ], [ 51 ], respectively). Risk of bias assessment . All included studies had a low or moderate risk of bias (Table 2 ). Table 2 Risk of bias of the included studies according to NOS tool. Author, study, country Study design Selection Comparability Outcome Total Conclusion Peinado J et al. [ 48 ], 2022, Perú. CSS *** ** *** 8 Low risk Larico M et al. [ 48 ], 2022, Perú. RCS ** ** *** 7 Low risk Kamara R et al. [ 49 ], 2022, Sierra Leone. RCS ** ** ** 6 Intermediate risk Podewils LJ et al. [ 50 ], 2011, Latvia. RCS *** ** *** 8 Low risk. Soeroto AY et al. [ 51 ], 2021, Indonesia. RCS ** *** *** 8 Low risk Song W et al. [ 24 ], 2021, China. RCS ** ** ** 6 Intermediate risk Avalos A et al. [ 52 ], 2014, Perú. CCS *** ** *** 8 Low risk Baluku JB et al. [ 53 ], 2022, Uganda. CSS ** ** ** 6 Intermediate risk. CCS: case-control study, RCS: Retrospective cohort study, CSS: Cross-sectional study. Note: An asterisk (*) represents a star in each domain of the Newcastle–Ottawa scale (NOS) tool. GRADE assessment. We upgraded the level of CoE as all the studies included exhibited a low risk of bias. Indirectness (the included studies compared similar interventions, similar populations, and similar outcomes), imprecision (this systematic review included 16,743 tuberculosis cases, of whom 5,339 had any form of drug-resistant tuberculosis), publication bias, and inconsistency (I 2 = 56.6%) impact not significatively the CoE. Consequently, we judged the CoE using the GRADE criteria as low. 4. DISCUSSION Risk of developing MDR tuberculosis. According to our findings, overweight and obesity may increase the risk of developing MDR-TB by 38% (OR 1.3825; 95% CI 1.1445–1.6701). Our analysis indicated that the continent in which the studies were conducted contributed significantly to the observed heterogeneity. Nevertheless, meta-regression suggested that additional moderator variables may be necessary to fully elucidate the sources of heterogeneity. Importantly, our investigation represents a pioneering effort—the first systematic review and meta-analysis to examine the influence of overweight and obesity on the risk of MDR-TB. This novelty, however, presents a challenge in terms of direct comparison with previously published research. The lack of prior studies in this specific context limits our ability to benchmark our findings. Nonetheless, our results are consistent with several previously published primary studies that explored MDR-TB risk. Three cohort studies have reported an association between overweight/obesity and the risk of drug-resistant TB [ 24 , 48 , 51 ]. In contrast, three other observational studies (two cross-sectional and one case-control) found no such association [ 47 , 52 , 53 ]. Similarly, a systematic review and meta-analysis by Badawi et al. [ 54 ] examined the relationship between obesity and tuberculosis. The authors found that underweight status was associated with a threefold higher prevalence of TB compared to controls (p = 0.001), whereas the prevalence of overweight and obesity was twofold lower (p = 0.001). The adjusted ORs for tuberculosis were 4.96 (95% CI 4.87–5.05) in underweight individuals and 0.26 (95% CI 0.24–0.27) in those with obesity. They concluded that low body weight is a risk factor for TB, while overweight and obesity may confer a protective effect. Notably, the study excluded diabetic patients and included only one of 30 studies reporting MDR-TB cases. Therefore, their results are not directly comparable with ours, which focused specifically on MDR-TB. Still, their findings reinforce the paradoxical association between weight and TB risk. Our data, however, suggest that being overweight may increase the likelihood of developing drug resistance once infected. Interestingly, in our analysis, the only factor accounting for between-study heterogeneity was the continent of origin (i.e., America, Africa, or Asia)—not the weight categories (normal weight, overweight, or obese), previous treatment status, or study design. Subgroup analysis revealed that overweight and obesity were associated with MDR-TB only in studies conducted in Asia (OR 1.75; 95% CI 1.49–2.06), but not in those from America (OR 1.13; 95% CI 0.96–1.33) or Africa (OR 1.02; 95% CI 1.00–1.04). We hypothesize that racial and genetic differences may partly explain these findings. Epidemiological studies suggest that host genetic factors influence susceptibility and resistance to TB [ 55 , 56 ]. Although TB can affect anyone, certain groups are at elevated risk [ 57 ]. I In 2021, 7,882 TB cases were reported in the United States. Compared to non-Hispanic White individuals, the TB case rate was 32 times higher among Asians [ 58 ], 9 times higher among Hispanic or Latino individuals [ 59 ], and 8 times higher among non-Hispanic Black or African American individuals [ 60 ]. Drug resistance in TB primarily results from mutations in genes encoding drug targets, which reduce or eliminate the therapeutic efficacy of anti-TB drugs [ 61 ]. However, susceptibility to MDR-TB is not solely determined by race or ethnicity; rather, it is influenced by a combination of socioeconomic, genetic, and environmental factors. Disparities in healthcare access, living conditions, and disease exposure among different groups contribute to variations in TB risk, including MDR-TB [ 16 , 62 – 64 ]. While certain ethnic groups may exhibit higher TB incidence in specific regions [ 57 – 60 ], this does not necessarily reflect an inherent genetic predisposition to drug resistance. Instead, MDR-TB susceptibility arises from a complex interplay of genetic and environmental factors [ 63 – 65 ]. Therefore, it is crucial to approach the understanding of TB on a global scale, considering not only genetic factors but also socioeconomic and public health influences that contribute to the spread and susceptibility to the disease. Therefore, a comprehensive understanding of MDR-TB requires a global perspective that incorporates genetic, socioeconomic, and public health determinants. Adverse treatment outcomes among individuals with MDR-TB. A meta-analysis for this outcome was not feasible due to the availability of only one study. Kamara RF et al. [ 49 ] assessed treatment outcomes among individuals with MDR-TB and reported a significant association between severe underweight (BMI < 16.5 kg/m²) and adverse treatment outcomes (OR 6.0; 95% CI 2.3–15) compared to those with normal weight (BMI 18.5–24.99 kg/m²). They also found a weaker association between underweight (BMI 16.5–18.49 kg/m²) and adverse outcomes (OR 1.7; 95% CI 0.6–4.9). Additionally, overweight or obesity (BMI ≥ 25 kg/m²) was associated with an increased risk of adverse outcomes (OR 6.7; 95% CI 0.4–102.0), although the confidence interval was wide, indicating uncertainty. Mortality in MDR-TB patients. Only one study evaluated mortality in patients with MDR-TB, precluding meta-analysis. Podewils LJ et al. [ 50 ] investigated the relationship between nutritional status and clinical outcomes in adults undergoing treatment for MDR-TB. They found a significant association between underweight status (BMI < 18.5 kg/m²) and mortality (HR 1.9; 95% CI 1.1–3.5). Among treatment-naive patients, the risk was even higher (HR 3.2; 95% CI 1.1–9.05). However, no significant associations were observed in patients previously treated for drug-susceptible or MDR-TB. Notably, the study did not assess the association between overweight or obesity and mortality. Risk associated with longer MDR-TB treatment regimens.. Only the study conducted by Soeroto AY et al. [ 40 ] assessed this outcome, which prevented meta-analysis. Their study evaluated factors influencing MDR-TB treatment success using extended regimens. Compared to underweight individuals, normal weight (RR 1.21; 95% CI 1.05–1.39), overweight (RR 1.54; 95% CI 1.15–2.07), and obesity (RR 1.45; 95% CI 1.02–2.05) were positively associated with treatment success. Furthermore, overweight/obesity was significantly associated with treatment success in previously untreated patients (RR 2.20; 95% CI 1.40–3.47), those with relapsed TB (RR 2.20; 95% CI 1.50–3.22), and those who had previously failed treatment (RR 1.77; 95% CI 1.21–2.58). The authors concluded that BMI, age, sex, treatment history, sputum conversion time, and HIV status are potential predictors of outcomes in longer MDR-TB regimens. Possible mechanisms linking overweight and obesity to increased MDR-TB risk and poor outcomes. Malnutrition impairs immune function by reducing levels of T-cell subsets (including cytotoxic T cells, helper T cells, and natural killer cells), immunoglobulins, and IL-2 receptors, thereby increasing vulnerability to infections such as TB and HIV [ 66 ]. However, the mechanisms by which overweight and obesity might increase the risk of MDR-TB remain unclear. It is known that obesity complicates TB management by affecting the distribution, pharmacokinetics, and efficacy of anti-tuberculosis drugs [ 67 , 68 ]. Adipose tissue may serve as a long-term reservoir for Mycobacterium tuberculosis, shielding the bacteria from antimicrobial activity and immune detection [ 69 ]. Obese individuals also exhibit increased drug-protein binding, reduced tissue perfusion, and elevated cytochrome P450 2E1 activity [ 69 , 70 ], which impair drug distribution—particularly relevant for drugs with narrow therapeutic windows, such as rifampicin rifampicin [ 67 , 69 – 71 ]. Notably, low plasma levels of rifampicin and other high-variability drugs have been linked to the emergence of MDR-TB [ 72 , 73 ]. Thus, the relationship between obesity and MDR-TB is complex and multifactorial, warranting further investigation. Strengths and Limitations. This study offers several strengths: (1) it is the first systematic review and meta-analysis assessing the impact of overweight and obesity on MDR-TB risk and outcomes; (2) we employed a comprehensive and rigorous search strategy; (3) we included only studies that reported adjusted effect sizes; and (4) we performed analyses of heterogeneity, publication bias, and risk of bias. Therefore, our findings are robust and align with most existing primary studies. A known limitation of pooling unadjusted effect sizes in meta-analyses of observational studies is that it may provide no more information than a univariate analysis [ 74 , 75 ]. The Cochrane Handbook recommends using adjusted estimates with the greatest number of confounders [ 31 ], as unadjusted data may yield misleading conclusions [ 76 ]. Nonetheless, this study has some important limitations: (1) the number of eligible studies was small; (2) heterogeneity remained moderate despite outlier exclusion; (3) due to limited data, subgroup analyses for critical variables such as diabetes mellitus and HIV status were not possible; and (4) we did not address distinctions between metabolically healthy and unhealthy obesity [ 77 ]. 5. CONCLUSIONS Overweight and obesity may increase the risk of MDR-TB by approximately 38%. Therefore, they should be recognized as potential novel risk factors for MDR-TB. Our findings highlight the public health importance of addressing overweight and obesity in the context of MDR-TB prevention and control. However, due to the moderate heterogeneity of the included studies, these findings should be interpreted with caution. Further high-quality research is needed to confirm and expand upon our results. Declarations Funding: No external funding. Institutional Review Board Statement : It was not necessary to request approval from any ethics committee since this is a secondary study (systematic review and meta-analysis). Informed Consent Statement : Patient consent was waived were waived since this is a secondary study. Data Availability Statement : The protocol is available at https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=446650 (accessed on January 25, 2024) Conflicts of Interest : The authors declare no conflict of interest. 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Supplementary Files Supplementarymaterials.docx Table S1: Search strategy; Table S2: Excluded studies; Table S3: Meta-regression analysis of the studies included in the metanalysis; Figure S1: Influence analysis of the studies included in the metanalysis; Figure S2: Sensitivity analysis of the studies included in the metanalysis; Figure S3: Leave-one-out analysis sorted by effect size of the studies included in the metanalysis; Figure S4: Leave-one-out analysis sorted by I2 of the studies included in the metanalysis Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Rodriguez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYPACCRmGA8wHIOwDRGrhYTjAlkCSFgagFh4D4rTwix0+9uHDLwsevuM93yR//GGQ47uRwPbgAx4tkrPTkmfO7JPgkTxzdps0bxuDseSNBHbDGXi0GNzOMWbm7ZHgMbiRu02asYEhcQPQFmkePFrsb+d/Zv4L1pLzDOSwerCWP/hskc5hZmb4AdbCJsHDxpBgANKCz/sSt9OMGXsbQH45ZmzN2yZhOPPMw3bDHjxa+GcnP2b48adOju9488ObP/7YyPMdTz724Ac+a0CAsQ1hKyoXN0DzLRsRWkbBKBgFo2AEAQAMAU2/30tHhgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1814-5593","institution":"Escuela de Posgrado, Universidad Señor de Sipán, Chiclayo, Lambayeque, Perú","correspondingAuthor":true,"prefix":"","firstName":"Edinson","middleName":"Dante Meregildo","lastName":"Rodriguez","suffix":""},{"id":445472509,"identity":"92d60de2-c85e-48a7-9a07-327f1b321007","order_by":1,"name":"Gabriela Campos-Silva","email":"","orcid":"https://orcid.org/0000-0001-9906-3092","institution":"Escuela de Medicina, Universidad César Vallejo, Trujillo, Perú","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Campos-Silva","suffix":""},{"id":445472510,"identity":"9f7df6d5-7970-4273-99a4-223794438f41","order_by":2,"name":"Martha Genara Asmat-Rubio","email":"","orcid":"https://orcid.org/0000-0001-6235-2173","institution":"Escuela de Farmacia y Bioquímica, Universidad Nacional de Trujillo, Trujillo, Perú","correspondingAuthor":false,"prefix":"","firstName":"Martha","middleName":"Genara","lastName":"Asmat-Rubio","suffix":""},{"id":445472511,"identity":"cc5035af-95af-44a6-a5fa-3d21fa0f1ad0","order_by":3,"name":"Gustavo Adolfo Vásquez-Tirado","email":"","orcid":"https://orcid.org/0000-0002-2109-6430","institution":"Escuela de Medicina, Universidad Privada Antenor Orrego, Trujillo, Perú","correspondingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"Adolfo","lastName":"Vásquez-Tirado","suffix":""},{"id":445472512,"identity":"927f0260-9bf9-40f7-a696-9fd579e2b014","order_by":4,"name":"Karen Evelyn Ramos-Rodríguez","email":"","orcid":"https://orcid.org/0009-0002-3086-3408","institution":"Escuela de Posgrado, Universidad Privada Antenor Orrego, Trujillo, Perú","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"Evelyn","lastName":"Ramos-Rodríguez","suffix":""}],"badges":[],"createdAt":"2025-04-21 01:30:22","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6491503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6491503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81198121,"identity":"5d5ae5dd-f7ba-4297-bbdd-4612a20c9b29","added_by":"auto","created_at":"2025-04-23 10:45:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111986,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 flow diagram.\u003c/p\u003e","description":"","filename":"image.png","url":"https://assets-eu.researchsquare.com/files/rs-6491503/v1/74fe2b8bd43411b6c5c8987a.png"},{"id":81198127,"identity":"78162574-200b-4e97-980a-f30f4f0f8c68","added_by":"auto","created_at":"2025-04-23 10:45:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1425949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2a.\u003c/strong\u003e Forest plot of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2b. \u003c/strong\u003eForest plot of the combined effect of overweight and obesity on the risk of developing multidrug-resistant tuberculosis (MDR-TB) in all patients, according to subgroup of weight category (overweight, obesity, or both).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2c. \u003c/strong\u003eForest plot of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients, according to the subgroup of anti-tuberculosis treatment (previous treatment, no previous treatment, or not reported).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2d. \u003c/strong\u003eForest plot of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients, according to the subgroup of the type (CCS, CSS, or RCS) of study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2e. \u003c/strong\u003eForest plot of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients, according to the study's subgroup of the continent of origin (America, Africa, or Asia).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6491503/v1/ad7c50da608ba729e6bd6e41.png"},{"id":81198129,"identity":"49da43a5-ae05-4273-a74f-4042d52c0cd1","added_by":"auto","created_at":"2025-04-23 10:45:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75068,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3a.\u003c/strong\u003e Funnel plot of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3b.\u003c/strong\u003e Trim-and-fill analysis of the combined effect of overweight and obesity on the risk of developing MDR tuberculosis in all patients.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6491503/v1/455b672d29c4eec454f98248.png"},{"id":81199501,"identity":"60cf6d0a-9501-4c16-8c04-4cfaff3273ba","added_by":"auto","created_at":"2025-04-23 11:01:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9171312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6491503/v1/0f534d87-c8c1-47d1-bad6-2a5780fe11af.pdf"},{"id":81198118,"identity":"dcf6e891-2867-4f5b-beac-375faaddd8c9","added_by":"auto","created_at":"2025-04-23 10:45:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":127539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e: Search strategy; \u003cstrong\u003eTable S2\u003c/strong\u003e: Excluded studies; \u003cstrong\u003eTable S3\u003c/strong\u003e: Meta-regression analysis of the studies included in the metanalysis; \u003cstrong\u003eFigure S1\u003c/strong\u003e: Influence analysis of the studies included in the metanalysis; \u003cstrong\u003eFigure S2\u003c/strong\u003e: Sensitivity analysis of the studies included in the metanalysis; \u003cstrong\u003eFigure S3\u003c/strong\u003e: Leave-one-out analysis sorted by effect size of the studies included in the metanalysis; \u003cstrong\u003eFigure S4\u003c/strong\u003e: Leave-one-out analysis sorted by I2 of the studies included in the metanalysis\u003c/p\u003e","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6491503/v1/1c15f7d5098c82c1a03b92fd.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eOverweight and obesity as emerging risk factors for multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTuberculosis (TB) and obesity represent two of the most formidable and rapidly growing challenges to global public health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. TB remains the world's deadliest infectious disease\u0026mdash;briefly overshadowed by COVID-19\u0026mdash;causing more deaths annually than HIV/AIDS and malaria combined [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2022 alone, an estimated 10.6\u0026nbsp;million people developed TB, and 1.3\u0026nbsp;million succumbed to the disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multidrug-resistant TB (MDR-TB) now accounts for 3.5% of new TB cases and 18% of previously treated cases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with approximately 470,000 people falling ill and 230,000 dying from MDR-TB each year [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Extensively drug-resistant TB (XDR-TB), a more severe form of MDR-TB, requires longer and more costly treatment and is associated with significantly higher rates of treatment failure and mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMeanwhile, the global epidemic of overweight and obesity has surged, more than doubling since 1990 and quadrupling among children and adolescents [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In 2022, over 1\u0026nbsp;billion individuals were living with obesity, and roughly 43% of the world\u0026rsquo;s adult population was classified as overweight or obese [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nutritional status plays a pivotal role in determining TB risk and treatment outcomes [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Much of the recent literature has focused on body mass index (BMI)\u0026mdash;a common surrogate for nutritional status\u0026mdash;and its association with TB incidence, mortality, and sputum conversion rates [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeing underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18 kg/m\u0026sup2;) is a well-established risk factor for active TB, MDR-TB, and delayed sputum conversion among MDR-TB patients [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, the implications of overweight and obesity in the context of TB are less clear. Although obesity is known to increase susceptibility to various infections, including postoperative and nosocomial infections [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], its role in TB remains paradoxical. Emerging evidence suggests that obesity may alter immune-metabolic homeostasis, yet studies remain limited [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While one study identified a BMI\u0026thinsp;\u0026gt;\u0026thinsp;28.0 kg/m\u0026sup2; as an independent risk factor for latent TB infection (LTBI) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], most research indicates an inverse relationship between obesity and the risk of active TB [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Indeed, a recent systematic review found that overweight and obese individuals had a lower incidence of TB compared to those of normal weight [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis counterintuitive association mirrors the complex relationship between obesity and diabetes mellitus (DM)\u0026mdash;a major risk factor for TB [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Obesity significantly increases the risk of developing diabetes, and diabetes in turn raises the risk and severity of TB. One would expect, therefore, that obesity indirectly heightens TB risk via diabetes. Yet epidemiological data do not consistently support this hypothesis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhether overweight and obesity influence the risk or outcomes of MDR-TB remains an open question. It is well established that undernutrition worsens the prognosis of both drug-susceptible and drug-resistant TB [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, no prior systematic review has explored the paradoxical association between overweight/obesity and MDR-TB. Addressing this gap, the present study aims to clarify whether overweight and obesity are associated with the risk and clinical outcomes of MDR-TB.\u003c/p\u003e \u003cp\u003eIdentifying modifiable risk factors for drug-resistant TB is critical for optimizing clinical management and strengthening global TB control strategies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In particular, gaining a deeper understanding of the intersection between obesity and MDR-TB has far-reaching implications\u0026mdash;especially as the global prevalence of diabetes, one of obesity\u0026rsquo;s major sequelae [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], continues to rise, particularly in regions with a high TB burden. Notably, diabetes is consistently linked to increased TB incidence and worse outcomes in both drug-susceptible and resistant forms [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe conducted this systematic review and meta-analysis adhering to the recommendations of the Cochrane Handbook for Systematic Reviews [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], PRISMA [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and AMSTAR 2[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] guidelines. The protocol was registered in PROSPERO (CRD42023446650).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSearch strategy.\u003c/b\u003e We comprehensively searched five databases: MEDLINE (PubMed), Scopus, EMBASE, Web of Science, and Google Scholar. We screened each database using controlled language terms (MeSH, Emtree, etc.), free terms, and their synonyms, combined with Boolean operators, following a PECO strategy. Keywords primarily focused on exposure, such as \"obesity,\" OR \"overweight,\" and outcome-related terms like \"tuberculosis,\" OR \"resistant tuberculosis,\" OR \"MDR-TB,\" OR \"XDR-TB.\" In addition, we conducted manual secondary searches of references in primary studies and review papers. Searches were not restricted by language or publication year. The search strategy is detailed in the \u003cb\u003eSupplementary Materials, Table S1\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion and exclusion criteria.\u003c/b\u003e Our search included observational studies published from inception until December 31, 2024. We excluded case reports, case series, duplicated publications, conference reports, letters to the editor, and editorials. All articles retrieved from the primary and secondary searches were compiled using Mendeley\u003csup\u003e\u0026reg;\u003c/sup\u003e 2.109.0. After removing duplicates, these articles were imported into Rayyan\u003csup\u003e\u0026reg;\u003c/sup\u003e, screened, and individually examined by four blinded and independent researchers (GCS, MGAR, GAVT, and KERR). The studies were selected by consensus, and a fifth researcher was the arbitrator (EDMR) in case of discordance. All articles that were collected were examined using the terms of the PECO strategy and the inclusion and exclusion criteria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy selection and data extraction\u003c/b\u003e. The selected articles were exported to a spreadsheet for a second full-text screening. The study selection process is detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The same researchers that performed the selection process conducted data extraction by examining articles and collecting the relevant details of the study, including the authors, country and year of publication, clinical and epidemiological characteristics of the population, number of patients and cases (events), measures of association, confounding factors, and the most relevant outcomes. For dichotomous and time-to-event variables, we compiled prevalence rates (PRs), odds ratios (ORs), relative risks (RRs), and hazard ratios (HRs) with 95% confidence intervals (95% CI). If important information was missing, at least two emails were sent to the corresponding authors. Data from each paper were extracted and recorded in a spreadsheet. The lead researcher (EDMR) served as the final arbitrator if there was any disagreement.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData synthesis, meta-analysis, and meta-regression.\u003c/b\u003e We conducted this meta-analysis using R\u003csup\u003e\u0026reg;\u003c/sup\u003e 4.3.2 software and the generic inverse variance method (GIVM) with \u003cem\u003eRestricted Maximum-Likelihood\u003c/em\u003e (REML) for tau\u003csup\u003e2\u003c/sup\u003e. As some studies do not report the number of cases and events, we meta-analyzed data using the GIVM. This technique needs only the input of the effect measure (PR, OR, RR, HR) and the 95% CI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We applied the Hartung and Knapp correction, accounting for the uncertainty in estimating the variance between studies, which is more significant when there are a few studies [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We considered equivalence between PRs to ORs, and ORs to RRs, if the frequency of the event of interest was \u0026lt;\u0026thinsp;10% [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For meta-analysis, we considered each subgroup analysis a separate study for studies reporting effect measures stratified into different subgroups.\u003c/p\u003e \u003cp\u003eWe predefined in our protocol that we would use a fixed effects model in our meta-analysis if heterogeneity were not statistically significant according to Cochran's Q test and Higgins I\u003csup\u003e2\u003c/sup\u003e statistic (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.10, I\u003csup\u003e2\u003c/sup\u003e statistics\u0026thinsp;\u0026lt;\u0026thinsp;40%). Otherwise, we would use a random effects model [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The potential subgroups to be analyzed were the study design, the continent of origin of the study, the classification of nutritional status according to BMI, and having previously received or not received treatment for tuberculosis, diabetes mellitus, and HIV infection status. Furthermore, we conducted sensitivity and influence analysis and meta-regression to assess heterogeneity. In addition to a funnel plot, we performed a correlation test for funnel plot asymmetry, Egger's test, trim-and-fill funnel plot, and a classic fail-safe N analysis to assess the risk of publication bias.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality assessment.\u003c/b\u003e We assessed the risk of bias using the Newcastle\u0026ndash;Ottawa scale (NOS) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eGRADE assessment.\u003c/b\u003e Three researchers (GCS, MGAR, GAVT, and KERR) independently assessed the certainty of the evidence (CoE) of each study outcome based on GRADE criteria [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Any reviewer discrepancies were resolved through discussion with the lead researcher (EDMR).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinitions\u003c/strong\u003e \u003cp\u003eMDR-TB: resistance of \u003cem\u003eM. tuberculosis\u003c/em\u003e (MTB) strains to at least isoniazid and rifampicin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. XDR-TB: MDR-TB that is also resistant to any fluoroquinolone and at least one additional Group A drug (levofloxacin, moxifloxacin, bedaquiline, or linezolid) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. BMI classification: according to the World Health Organization (WHO) and most clinical guidelines on the diagnosis and treatment of overweight and obesity in adults, we used the following definitions according to the body mass index (BMI) in kg/m\u003csup\u003e2\u003c/sup\u003e: underweight\u0026thinsp;\u0026le;\u0026thinsp;18.5, normal weight 18.5\u0026ndash;24.9, overweight 25.0\u0026ndash;29.9, and obesity\u0026thinsp;\u0026ge;\u0026thinsp;30.0 [\u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe identified 152 records, all retrieved from databases. After removing 13 duplicates, 139 records remained. We then excluded 75 records for various reasons, leaving 22 reports to be assessed for eligibility. Fourteen of these studies were excluded\u0026mdash;mainly because they were available only in abstract or did not meet our PECO question (\u003cb\u003eSupplementary Materials, Table S2\u003c/b\u003e). Finally, eight papers [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48 CR49 CR50 CR51 CR52\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]were included in our systematic review and meta-analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these eight studies, five were retrospective cohorts, two were cross-sectional, and one was a nested case-control study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This review encompasses 16,743 tuberculosis cases, of whom 5,339 had MDR tuberculosis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy, year, country\u003c/p\u003e \u003cp\u003eDesign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients and events (cases)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaPR/aOR / aRR / aHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeinado J et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], 2022, Per\u0026uacute;. Secondary analysis of the database of the \"Epidemiology of multidrug-resistant Tuberculosis in Peru\" (EPI) study. CSS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,500 cases of TB, of whom 3,734 were new TB cases, and 766 had received treatment for TB. Three thousand three hundred fifty-one cases had positive cultures, and 1,149 cases had negative cultures for MTB. Patients were recruited in health facilities of 24 districts of Lima, Peru. Inclusion period: June 2008 to June 2014.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe results of the anti-TB drug susceptibility test were classified into monoresistance to RIF and resistance to INH and RIF (MDR).\u003c/p\u003e \u003cp\u003eDependent variable: MDR-TB. Median age 28 years (IQR 15\u0026ndash;94 years), 62.3% were male. Of the 3,351 cases, 500 (15.1%) had a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, 570 (17.1%) had a history of TB treatment, 116 (3.5%) had HIV coinfection, 185 (5.6%) reported having DM2, and 406 (15.9%) of cases were MDR-TB cases.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR 0.96; 95% CI 0.68\u0026ndash;1.38 for MDR-TB in patients with overweight/obesity and history of treatment. PR 0.88; 95% CI 0.57\u0026ndash;1.38 for MDR-TB in patients with overweight/obesity without a history of treatment. aPR 1.05; 95% CI 0.74\u0026ndash;1.49 for MDR-TB in patients with low weight and history of treatment. PRa 0.87; 95% CI 0.59\u0026ndash;1.28 for MDR-TB in patients with low weight without a history of treatment. Adjustment factors: Age, gender, History of anti-TB treatment, HIV, DM2, smoking, alcoholism, history of imprisonment, and poverty score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarico M et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], 2022, Per\u0026uacute;. Data were collected from the database extracted from the care registry of patients with MDR-TB. RCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,156 patients with MDR-TB treated in the Sergio Bernales Hospital pulmonology service from January 2010 to December 2018.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMDR-TB: resistance to at least one injectable agent (amikacin, kanamycin, or capreomycin) and/or FQ.\u003c/p\u003e \u003cp\u003eMale 63.1%, mean age: 27 years, normal nutritional status: 48.6%, HIV coinfection 3.9, and no medical history 72.6%. Resistance expanded in 697/1,156 (60.30%) cases.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR 1.21; 95% CI 1.03\u0026ndash;1.41 for MDR-TB in overweight/obese patients compared to normal weight. RR 0.73; 95% CI 0.59\u0026ndash;0.91 for MDR-TB in underweight patients. RR 1.18; 95% CI 0.85-1-64 for MDR-TB in overweight/obese patients who previously received anti-TB treatment compared to normal weight. Adjustment factors: Age, sex and academic degree, medical history, HIV, and compliance with the treatment regimen.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamara R, et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], 2022, Sierra Leone. A national, retrospective cohort study recruited all people notified with MDR-TB to the Sierra Leone National TB Program admitted to Lakka Hospital between April 2017 and September 2019. Follow-up: until May 2021. RCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;365 people diagnosed with MDR-TB who started anti-TB treatment (317 received the short regimen, 24 received the long regimen, and 24 received no treatment).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelapse: people previously treated for TB, declared cured or treatment completed at the end of their most recent course of treatment, and are now diagnosed with a recurrent episode of TB (either a true relapse or a new episode of tuberculosis caused by reinfection). Treatment after loss to follow-up: previously treated for TB and whose most recent treatment was interrupted for two or more consecutive months. New: no previous history of TB treatment. Age: 35 years (IQR 26\u0026ndash;45), 263 were male, 71 were HIV-positive, and 127 were severely underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;16.5 kg/m\u0026sup2;). Overall, 267 participants had treatment success, 95 had an adverse outcome, and three were still on treatment in May 2021.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR 6.0; 95% CI 2.3\u0026ndash;15 for adverse TB treatment outcome in severely underweight participants with a DST result indicating MDR-TB compared to normal weight. OR 1.7; 95% CI 0.6\u0026ndash;4.9 for adverse TB treatment outcome in underweight participants with a DST result available compared to normal weight. OR 6.7; 95% CI 0.4\u0026ndash;102.0 for adverse TB treatment outcome in overweight or obese participants with a DST result indicating MDR-TB compared to normal weight.\u003c/p\u003e \u003cp\u003eAdjustment factors: Age, gender, HIV, DM, CKD, CLD, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePodewils LJ et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], 2011, Latvia. Patients registered in the National MDR-TB database in Latvia between January 2000 and June 2004. RCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;995 adults (\u0026ge;\u0026thinsp;18 years) who were diagnosed with pulmonary MDR-TB and who initiated individualized MDR-TB treatment.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOf the 995 patients, 401 (40%) were classified as having primary drug resistance and 594 (60%) were considered to have acquired drug resistance. Thirty-seven percent had no previous history of TB treatment, 51% had been previously treated for drug-susceptible TB, and 12% had a history of MDR-TB treatment. In total, 199 (20%) patients were considered underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5) at the time of MDR-TB diagnosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR 1.7; 95% CI 1.1\u0026ndash;2.9 for underweight and culture positivity. HR 1.1; 95% CI 0.9\u0026ndash;1.3 for underweight and culture conversion. HR 1.2; 95% CI 0.9\u0026ndash;1.5 for underweight and overall treatment failure (default, failed treatment, or died during the treatment course). HR 1.9; 95% CI 1.1\u0026ndash;3.5 for underweight and the risk of death. HR 3.2; 95% CI 1.1\u0026ndash;9.05 for underweight and risk of death among treatment-naive patients at the time of the current MDR-TB diagnosis. HR 1.6, 95% CI 0.7\u0026ndash;3.7 for underweight and risk of death among previously treated for drug-susceptible TB. HR 2.1, 95% CI 0.5\u0026ndash;8.1 for underweight and risk of death among patients with previous MDR-TB treatment exposure. All these outcomes refer to underweight patients compared to patients who were normal or overweight at the time of MDR-TB diagnosis.\u003c/p\u003e \u003cp\u003eAdjustment factors: Age, sex, unemployment, homelessness, History of imprisonment, heavy alcohol use, History of drug use, HIV, comorbidities, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoeroto AY et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], 2021, Indonesia. Data was collected at PMDT clinic from December 2019 to January 2020.\u003c/p\u003e \u003cp\u003eFollow-up until treatment was complete, with a minimum duration of 18 months. RCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;492 MDR-TB patients. A cohort of MDR-TB patients (\u0026ge;\u0026thinsp;18 years old) treated with a longer regimen at Hasan Sadikin General Hospital, Bandung, from January 2015 to December 2017.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuccessful treatment: a patient who fulfill recovery criteria or completed treatment. Unfavorable outcomes: a combination of dropout rates, failure of therapy, and death. Success criteria and unfavorable outcomes was defined according to WHO criteria. Longer regimen definition: a combination of FQ (levofloxacin or moxifloxacin), AGs injection (kanamycin or capreomycin), ethionamide, cycloserine, and pyrazinamide.\u003c/p\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;45 years 157 (32%); male 268 (54.5%); current smokers 271 (55%); BMI: underweight 321 (65.3%), normal 143 (29%), overweight 16 (3.3%), obese 12 (2.4); HIV 15 (3%); DM 76 (15.4%), CKD 59 (12%), History of TB medication: no previous treatment 25 (5%), relapse 167 (34%), failed 216 (43.9%), and loss to follow-up 84 (17.1). Successful treatment 246. Unfavorable outcomes 246 (death 116, loss of follow-up 110, failed therapy 20).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaRR 1.21; 95% CI 1.05\u0026ndash;1.39 for normal weight and outcome of longer regimen MDR-TB treatment compared to underweight. RR 1.54 (1.15\u0026ndash;2.07) for overweight and outcome of longer regimen MDR-TB treatment compared to underweight. RR 1.45 (1.02\u0026ndash;2.05) for obese and outcome of longer regimen MDR-TB treatment compared to underweight.\u003c/p\u003e \u003cp\u003eRR 2.20 (1.40\u0026ndash;3.47) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who received no previous treatment.\u003c/p\u003e \u003cp\u003eRR 2.20 (1.50\u0026ndash;3.22) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who relapsed treatment.\u003c/p\u003e \u003cp\u003eRR 1.77 (1.21\u0026ndash;2.58) for overweight and obesity and outcome of longer regimen MDR-TB treatment compared to underweight among those who failed treatment.\u003c/p\u003e \u003cp\u003eAdjustment factors: Age, sex, smoking, BMI, HIV, DM, CKD, anemia, history of anti-TB medication.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong, W, et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], 2021, China. Data was collected at 34 monitoring sites of DR-TB in at Shandong Provincial Hospital (SPH) and Shandong Provincial Chest Hospital (SPCH). RCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8,957 newly diagnosed PTB patients with BMI status and DST results were retrospectively collected from January 1, 2004, to December 31, 2019.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMono-resistance (MR): resistance to only one first-line anti-TB drug. MDR: resistance to at least both INH and RIF. PDR: resistance to at least two first-line anti-TB drugs, except resistance to both INH and RIF. MR-TB (INH): resistance only to INH and susceptibility to other drugs. N\u0026thinsp;=\u0026thinsp;89,57, of which 6,417 (71.64%) were normal weight, 2,121 (23.68%) were underweight, 373 (4.16%) were overweight, and 46 (0.51%) were obese. Of them, 15.62% aged between 15\u0026ndash;24, 25.31% aged between 25\u0026ndash;44, 33.16% aged between 45\u0026ndash;64, 25.63% aged\u0026thinsp;\u0026gt;\u0026thinsp;65 and 83.01% were males. The proportion of MDR-TB 219/6417 among normal weight patients, 65/2121 among underweight patients, 20/373 among overweight patients, and 0/46 among obese patients.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR 1.639; 95% CI 1.02\u0026ndash;2.633 for overweight and MDR‑TB compared with normal weight group.\u003c/p\u003e \u003cp\u003eOR 2.113; 95% CI 1.141\u0026ndash;3.912 for overweight and INH\u0026thinsp;+\u0026thinsp;RIF\u0026thinsp;+\u0026thinsp;SPM resistance compared with normal weight group. OR 1.31; 95% CI 1.017\u0026ndash;1.686 for underweight and MR‑TB (INH) compared with normal weight group.\u003c/p\u003e \u003cp\u003eNo previous history of treatment is described.\u003c/p\u003e \u003cp\u003eAdjustment factors: Age, sex, alcohol consumption, smoking, pulmonary cavitations, and comorbidities.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvalos A, et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], 2014, Per\u0026uacute;. Healthcare facilities from Callao, Lima, Peru. Patients with primary MDR-TB diagnosed between January 2009 and December 2010. Records of the PCT of the Regional Health Directorate of Callao. CCS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29 primary MDR-TB patients and 37 drug-sensitive TB patients were studied through assessment of the PCT registries and healthcare facilities' clinical records.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA case was defined as patients diagnosed with primary MDR-TB according to the WHO/IUATLD definition. Drug sensitive TB group: age\u0026thinsp;\u0026ge;\u0026thinsp;40 years 18/37; males 22/37; previous imprisonment 2/37; HIV 1/37; DM 1/37; nutritional status: malnutrition 4/37, normal weight 22/37, overweight 7/37, mild obesity 1/37, morbid obesity 0/37. Drug resistant TB group: age\u0026thinsp;\u0026ge;\u0026thinsp;40 years 8/29; males 17/29; previous imprisonment 2/29; HIV 1/29; DM 1/29; nutritional status: malnutrition 3/29, normal weight 17/29, overweight 4/29, mild obesity 3/29, morbid obesity 0/29.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR 0.97; 95% CI 0.19\u0026ndash;4.93 for malnutrition and risk of having primary MDR-TB compared to normal weight. OR 0.73; 95% CI 0.18\u0026ndash;2.94 for overweight and risk of having primary MDR-TB compared to normal weight.\u003c/p\u003e \u003cp\u003eOR 3.88; 95% CI 0.37\u0026ndash;40.70 for obesity and risk of having primary MDR-TB compared to normal weight.\u003c/p\u003e \u003cp\u003eAdjustment factors: Age\u0026thinsp;\u0026gt;\u0026thinsp;40 years, sex, level of education, contact and crowding variables, HIV, DM, use of corticosteroids, drugs or alcohol, BMI.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaluku JB et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], 2022, Uganda. Four TB treatment sites in Uganda between July to December 2021. CSS.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;212 adults (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) with microbiologically confirmed DR-TB, of whom 118 (55.7%) had HIV.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAny form of drug resistance who were receiving treatment at these sites during the period of data collection. Of 212 participants, 156 (73.6%) were male and 118 (55.7%) had HIV coinfection. The median (IQR) age was 37 (30\u0026ndash;46) years. The median (IQR) BMI was 19.7 (17.7\u0026ndash;22.2) Kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePR 1.02; 95% CI 1.00\u0026ndash;1.05 for central obesity and DR-TB.\u003c/p\u003e \u003cp\u003eNo previous history of treatment is described.\u003c/p\u003e \u003cp\u003eAdjustment factors: History of cigarette smoking, DM, HT, high BMI, central obesity, and dyslipidemia.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCC:S case-control study, RCS: retrospective cohort study, CSS: cross-sectional study, BMI: body mass index, MTB: Mycobacterium tuberculosis, PTB: pulmonary TB, RIF: rifampicin, INH: isoniazid, DST: Drug susceptibility testing, MDR: Multidrug resistance, PDR: Polydrug resistance, STM: streptomycin, DM: Diabetes mellitus, DM2: Diabetes mellitus type 2, CKD: chronic kidney disease, CLD: chronic lung disease, HT: hypertension, BMI: body mass index, AFB: acid-fast bacilli, FQ: fluoroquinolone, AG: aminoglycosides, PMDT: Programmatic Management of Drug-resistant TB, PCT: the Tuberculosis Control Program, DR-TB drug-resistant tuberculosis, aPR / aOR / aRR / aHR: adjusted PR, OR, RR, HR.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk of developing MDR tuberculosis\u003c/b\u003e. We found that overweight and obesity increased the likelihood of MDR-TB by 38% (OR 1.3825; 95% CI 1.1445\u0026ndash;1.6701). However, heterogeneity was significant (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;78.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHeterogeneity analysis\u003c/b\u003e. The sensitivity analysis and the leave-one-out test identified the study conducted by Baluku J 2022 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] as an outlier that significantly impacted the overall estimate (\u003cb\u003eSupplementary Materials, Figure S1-S4\u003c/b\u003e). Subgroup analysis showed that the risk of MDR-TB was not statistically different among the weight categories (normal weight, overweight, or obesity) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), history of previous TB treatment (yes or not) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.57) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), or the type of the study (case-control study, retrospective cohort study, or cross-sectional study) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). Conversely, subgroup analysis showed that the risk of developing MDR tuberculosis was statistically different according to the continent of origin of the study (America, Africa, or Asia) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eWe performed meta-regression analyses to explore potential sources of between-study heterogeneity. The moderators' test was not statistically significant according to the weight category (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8077), history of previous TB treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6413), or the type of study design (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2758). However, it reached statistical significance according to the continent of origin of the study ( \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). That is, only the continent of origin accounted for variability between studies. The test for residual heterogeneity was statistically significant for all of the moderator variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001; and, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012, respectively), except for the continent of origin of the study (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6272). This suggests that our model was not well-specified and that other moderating variables should be considered (\u003cb\u003eSupplementary materials\u003c/b\u003e, \u003cb\u003eTable S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePublication bias.\u003c/b\u003e Even though our funnel plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) did not suggest any publication bias, we conducted other analyses to explore publication bias. The rank correlation test for funnel plot asymmetry and the Egger test did not suggest a publication bias risk (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1412). Considering a reference or threat criterion (5 * \u003cem\u003ek\u003c/em\u003e\u0026thinsp;+\u0026thinsp;10\u0026thinsp;=\u0026thinsp;80), the Rosenberg approach (observed significance level \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, target significance level \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05, Fail-safe N\u0026thinsp;=\u0026thinsp;216) suggested that publication bias was not a threat to the existence of a significant effect size in this meta-analysis. Likewise, the trim-and-fill method added one hypothetical missing study on the left side of the funnel plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). However, the effect size remained similar (OR 1.3785; 95% CI 1.1401\u0026ndash;1.6679), and the heterogeneity remained significant (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;78.16%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA meta-analysis could not be performed for \u003cb\u003eadverse treatment outcomes among individuals with multidrug-resistant tuberculosis\u003c/b\u003e, \u003cb\u003emortality in MDR-TB patients\u003c/b\u003e, or \u003cb\u003ethe risk associated with prolonged MDR-TB treatment regimens\u003c/b\u003e, as each of these outcomes was assessed in only a single study ([\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], respectively).\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk of bias assessment\u003c/b\u003e. All included studies had a low or moderate risk of bias (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk of bias of the included studies according to NOS tool.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor, study, country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComparability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeinado J et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], 2022, Per\u0026uacute;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarico M et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], 2022, Per\u0026uacute;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKamara R et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], 2022, Sierra Leone.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntermediate risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePodewils LJ et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], 2011, Latvia.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoeroto AY et al. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], 2021, Indonesia.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong W et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], 2021, China.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntermediate risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvalos A et al. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], 2014, Per\u0026uacute;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaluku JB et al. [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], 2022, Uganda.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntermediate risk.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCCS: case-control study, RCS: Retrospective cohort study, CSS: Cross-sectional study.\u003c/p\u003e \u003cp\u003eNote: An asterisk (*) represents a star in each domain of the Newcastle\u0026ndash;Ottawa scale (NOS) tool.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGRADE assessment.\u003c/b\u003e We upgraded the level of CoE as all the studies included exhibited a low risk of bias. Indirectness (the included studies compared similar interventions, similar populations, and similar outcomes), imprecision (this systematic review included 16,743 tuberculosis cases, of whom 5,339 had any form of drug-resistant tuberculosis), publication bias, and inconsistency (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;56.6%) impact not significatively the CoE. Consequently, we judged the CoE using the GRADE criteria as low.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eRisk of developing MDR tuberculosis.\u003c/b\u003e According to our findings, overweight and obesity may increase the risk of developing MDR-TB by 38% (OR 1.3825; 95% CI 1.1445\u0026ndash;1.6701). Our analysis indicated that the continent in which the studies were conducted contributed significantly to the observed heterogeneity. Nevertheless, meta-regression suggested that additional moderator variables may be necessary to fully elucidate the sources of heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eImportantly, our investigation represents a pioneering effort\u0026mdash;the first systematic review and meta-analysis to examine the influence of overweight and obesity on the risk of MDR-TB. This novelty, however, presents a challenge in terms of direct comparison with previously published research. The lack of prior studies in this specific context limits our ability to benchmark our findings. Nonetheless, our results are consistent with several previously published primary studies that explored MDR-TB risk.\u003c/p\u003e \u003cp\u003eThree cohort studies have reported an association between overweight/obesity and the risk of drug-resistant TB [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In contrast, three other observational studies (two cross-sectional and one case-control) found no such association [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similarly, a systematic review and meta-analysis by Badawi et al. [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] examined the relationship between obesity and tuberculosis. The authors found that underweight status was associated with a threefold higher prevalence of TB compared to controls (p\u0026thinsp;=\u0026thinsp;0.001), whereas the prevalence of overweight and obesity was twofold lower (p\u0026thinsp;=\u0026thinsp;0.001). The adjusted ORs for tuberculosis were 4.96 (95% CI 4.87\u0026ndash;5.05) in underweight individuals and 0.26 (95% CI 0.24\u0026ndash;0.27) in those with obesity. They concluded that low body weight is a risk factor for TB, while overweight and obesity may confer a protective effect. Notably, the study excluded diabetic patients and included only one of 30 studies reporting MDR-TB cases. Therefore, their results are not directly comparable with ours, which focused specifically on MDR-TB. Still, their findings reinforce the paradoxical association between weight and TB risk. Our data, however, suggest that being overweight may increase the likelihood of developing drug resistance once infected.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eInterestingly, in our analysis, the only factor accounting for between-study heterogeneity was the continent of origin (i.e., America, Africa, or Asia)\u0026mdash;not the weight categories (normal weight, overweight, or obese), previous treatment status, or study design. Subgroup analysis revealed that overweight and obesity were associated with MDR-TB only in studies conducted in Asia (OR 1.75; 95% CI 1.49\u0026ndash;2.06), but not in those from America (OR 1.13; 95% CI 0.96\u0026ndash;1.33) or Africa (OR 1.02; 95% CI 1.00\u0026ndash;1.04).\u003c/p\u003e\u003cp\u003eWe hypothesize that racial and genetic differences may partly explain these findings. Epidemiological studies suggest that host genetic factors influence susceptibility and resistance to TB [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Although TB can affect anyone, certain groups are at elevated risk [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. I In 2021, 7,882 TB cases were reported in the United States. Compared to non-Hispanic White individuals, the TB case rate was 32 times higher among Asians [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], 9 times higher among Hispanic or Latino individuals [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and 8 times higher among non-Hispanic Black or African American individuals [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDrug resistance in TB primarily results from mutations in genes encoding drug targets, which reduce or eliminate the therapeutic efficacy of anti-TB drugs [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. However, susceptibility to MDR-TB is not solely determined by race or ethnicity; rather, it is influenced by a combination of socioeconomic, genetic, and environmental factors. Disparities in healthcare access, living conditions, and disease exposure among different groups contribute to variations in TB risk, including MDR-TB [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. While certain ethnic groups may exhibit higher TB incidence in specific regions [\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], this does not necessarily reflect an inherent genetic predisposition to drug resistance. Instead, MDR-TB susceptibility arises from a complex interplay of genetic and environmental factors [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Therefore, it is crucial to approach the understanding of TB on a global scale, considering not only genetic factors but also socioeconomic and public health influences that contribute to the spread and susceptibility to the disease. Therefore, a comprehensive understanding of MDR-TB requires a global perspective that incorporates genetic, socioeconomic, and public health determinants.\u003c/p\u003e\u003cp\u003e \u003cb\u003eAdverse treatment outcomes among individuals with MDR-TB.\u003c/b\u003e A meta-analysis for this outcome was not feasible due to the availability of only one study. Kamara RF et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] assessed treatment outcomes among individuals with MDR-TB and reported a significant association between severe underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;16.5 kg/m\u0026sup2;) and adverse treatment outcomes (OR 6.0; 95% CI 2.3\u0026ndash;15) compared to those with normal weight (BMI 18.5\u0026ndash;24.99 kg/m\u0026sup2;). They also found a weaker association between underweight (BMI 16.5\u0026ndash;18.49 kg/m\u0026sup2;) and adverse outcomes (OR 1.7; 95% CI 0.6\u0026ndash;4.9). Additionally, overweight or obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;) was associated with an increased risk of adverse outcomes (OR 6.7; 95% CI 0.4\u0026ndash;102.0), although the confidence interval was wide, indicating uncertainty.\u003c/p\u003e\u003cp\u003e \u003cb\u003eMortality in MDR-TB patients.\u003c/b\u003e Only one study evaluated mortality in patients with MDR-TB, precluding meta-analysis. Podewils LJ et al. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] investigated the relationship between nutritional status and clinical outcomes in adults undergoing treatment for MDR-TB. They found a significant association between underweight status (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;) and mortality (HR 1.9; 95% CI 1.1\u0026ndash;3.5). Among treatment-naive patients, the risk was even higher (HR 3.2; 95% CI 1.1\u0026ndash;9.05). However, no significant associations were observed in patients previously treated for drug-susceptible or MDR-TB. Notably, the study did not assess the association between overweight or obesity and mortality.\u003c/p\u003e\u003cp\u003e \u003cb\u003eRisk associated with longer MDR-TB treatment regimens..\u003c/b\u003e Only the study conducted by Soeroto AY et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] assessed this outcome, which prevented meta-analysis. Their study evaluated factors influencing MDR-TB treatment success using extended regimens. Compared to underweight individuals, normal weight (RR 1.21; 95% CI 1.05\u0026ndash;1.39), overweight (RR 1.54; 95% CI 1.15\u0026ndash;2.07), and obesity (RR 1.45; 95% CI 1.02\u0026ndash;2.05) were positively associated with treatment success. Furthermore, overweight/obesity was significantly associated with treatment success in previously untreated patients (RR 2.20; 95% CI 1.40\u0026ndash;3.47), those with relapsed TB (RR 2.20; 95% CI 1.50\u0026ndash;3.22), and those who had previously failed treatment (RR 1.77; 95% CI 1.21\u0026ndash;2.58). The authors concluded that BMI, age, sex, treatment history, sputum conversion time, and HIV status are potential predictors of outcomes in longer MDR-TB regimens.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePossible mechanisms linking overweight and obesity to increased MDR-TB risk and poor outcomes.\u003c/b\u003e Malnutrition impairs immune function by reducing levels of T-cell subsets (including cytotoxic T cells, helper T cells, and natural killer cells), immunoglobulins, and IL-2 receptors, thereby increasing vulnerability to infections such as TB and HIV [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, the mechanisms by which overweight and obesity might increase the risk of MDR-TB remain unclear.\u003c/p\u003e\u003cp\u003eIt is known that obesity complicates TB management by affecting the distribution, pharmacokinetics, and efficacy of anti-tuberculosis drugs [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Adipose tissue may serve as a long-term reservoir for Mycobacterium tuberculosis, shielding the bacteria from antimicrobial activity and immune detection [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Obese individuals also exhibit increased drug-protein binding, reduced tissue perfusion, and elevated cytochrome P450 2E1 activity [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], which impair drug distribution\u0026mdash;particularly relevant for drugs with narrow therapeutic windows, such as rifampicin rifampicin [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Notably, low plasma levels of rifampicin and other high-variability drugs have been linked to the emergence of MDR-TB [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Thus, the relationship between obesity and MDR-TB is complex and multifactorial, warranting further investigation.\u003c/p\u003e\u003cp\u003e \u003cb\u003eStrengths and Limitations.\u003c/b\u003e This study offers several strengths: (1) it is the first systematic review and meta-analysis assessing the impact of overweight and obesity on MDR-TB risk and outcomes; (2) we employed a comprehensive and rigorous search strategy; (3) we included only studies that reported adjusted effect sizes; and (4) we performed analyses of heterogeneity, publication bias, and risk of bias. Therefore, our findings are robust and align with most existing primary studies.\u003c/p\u003e\u003cp\u003eA known limitation of pooling unadjusted effect sizes in meta-analyses of observational studies is that it may provide no more information than a univariate analysis [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. The Cochrane Handbook recommends using adjusted estimates with the greatest number of confounders [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], as unadjusted data may yield misleading conclusions [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Nonetheless, this study has some important limitations: (1) the number of eligible studies was small; (2) heterogeneity remained moderate despite outlier exclusion; (3) due to limited data, subgroup analyses for critical variables such as diabetes mellitus and HIV status were not possible; and (4) we did not address distinctions between metabolically healthy and unhealthy obesity [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverweight and obesity may increase the risk of MDR-TB by approximately 38%. Therefore, they should be recognized as potential novel risk factors for MDR-TB. Our findings highlight the public health importance of addressing overweight and obesity in the context of MDR-TB prevention and control. However, due to the moderate heterogeneity of the included studies, these findings should be interpreted with caution. Further high-quality research is needed to confirm and expand upon our results.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eFunding:\u003c/p\u003e \u003cp\u003eNo external funding.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInstitutional Review Board Statement\u003c/b\u003e: It was not necessary to request approval from any ethics committee since this is a secondary study (systematic review and meta-analysis).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInformed Consent Statement\u003c/b\u003e: Patient consent was waived were waived since this is a secondary study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Availability Statement\u003c/b\u003e: The protocol is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=446650\u003c/span\u003e\u003cspan address=\"https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=446650\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on January 25, 2024)\u003c/p\u003e \u003cp\u003e \u003cb\u003eConflicts of Interest\u003c/b\u003e: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M et al (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. 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Metabolism 92:51\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.metabol.2018.11.009\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2018.11.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"None. ","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Multidrug-resistant tuberculosis, Overweight, Obesity, Body Mass Index, Risk Factors, Systematic Review, Meta-Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6491503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6491503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Obesity and tuberculosis (TB) are two escalating global public health challenges. Emerging evidence suggests that overweight and obesity may be associated with an increased risk of multidrug-resistant tuberculosis (MDR-TB). We conducted a systematic review and meta-analysis to assess whether overweight and obesity influence the risk and clinical outcomes of MDR-TB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We systematically searched five databases for studies published from inception through December 31, 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eEight observational studies comprising 6,743 TB cases and 5,339 MDR-TB cases met the inclusion criteria. Our analysis revealed that overweight and obesity were associated with a 38% increased risk of MDR-TB (OR 1.38; 95% CI 1.14-1.67), with moderate heterogeneity (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = 78.7%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). Notably, this association was significant only in studies conducted in Asia (OR 1.75; 95% CI 1.49–2.06), suggesting potential racial or regional differences in susceptibility. Due to limited data, we were unable to perform a meta-analysis on other outcomes such as adverse effects of anti-TB treatment, prolonged treatment regimens, or MDR-TB-related mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Overweight and obesity may be emerging risk factors for MDR-TB, particularly in Asian populations. These findings highlight the need to consider metabolic and nutritional status in TB control strategies. However, due to study heterogeneity and limited data on clinical outcomes, further high-quality research is essential to confirm these associations and elucidate underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Overweight and obesity as emerging risk factors for multidrug-resistant tuberculosis (MDR-TB): a systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 10:45:11","doi":"10.21203/rs.3.rs-6491503/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43d4c3d4-7a19-461c-b301-72944f38cdac","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47421068,"name":"Endocrinology \u0026 Metabolism"},{"id":47421069,"name":"Infectious Diseases"},{"id":47421070,"name":"Preventive Medicine"},{"id":47421071,"name":"Internal Medicine"},{"id":47421072,"name":"Other Public Policy"}],"tags":[],"updatedAt":"2025-04-23T10:45:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 10:45:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6491503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6491503","identity":"rs-6491503","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}