Overweight, Obesity, and Older Age Favor Latent Tuberculosis Infection among Household Contacts in Low Tuberculosis-Incidence Settings within Panama

Cubilla-Batista, Idalina; Ruiz, Nadia; Sambrano, Dilcia; Castillo, Juan; Quinzada, Markela O. de; Gasteluiturri, Begoña; Goodridge, Amador

doi:10.4269/ajtmh.18-0927

Cited by 12 publications

(16 citation statements)

References 15 publications

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“…found an association between a persistently TST negative result and a lower prevalence of lean mass wasting in the Uganda cohort. This is also in line with what some other studies have found ( 73 , 86 ). Verrall et al.…”

Section: Discussionsupporting

confidence: 94%

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Phenotype Definition for “Resisters” to Mycobacterium tuberculosis Infection in the Literature—A Review and Recommendations

et al. 2021

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Tuberculosis (TB) remains a worldwide problem. Despite the high disease rate, not all who are infected with Mycobacterium Tuberculosis (Mtb) develop disease. Interferon-γ (IFN-γ) specific T cell immune assays such as Quantiferon and Elispot, as well as a skin hypersensitivity test, known as a tuberculin skin test, are widely used to infer infection. These assays measure immune conversion in response to Mtb. Some individuals measure persistently negative to immune conversion, despite high and prolonged exposure to Mtb. Increasing interest into this phenotype has led to multiple publications describing various aspects of these responses. However, there is a lack of a unified “resister” definition. A universal definition will improve cross study data comparisons and assist with future study design and planning. We review the current literature describing this phenotype and make recommendations for future studies.

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Section: Discussionsupporting

confidence: 94%

“…Similarly, Igo et al found an association between a persistently TST negative result and a lower prevalence of lean mass wasting in the Uganda cohort. This is also in line with what some other studies have found(73,86). Verrall et al highlighted important evidence that BCG-vaccination provides dose dependent protection against IGRA conversion in HHC…”

supporting

confidence: 87%

Phenotype Definition for “Resisters” to Mycobacterium tuberculosis Infection in the Literature—A Review and Recommendations

et al. 2021

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“…Several lines of evidence support an association between BMI and the development of LTBI. 12 - 20 Cardiometabolic risk markers associated with obesity—such as fasting insulin, total cholesterol, LDL-cholesterol, HDL-cholesterol, and fasting triglycerides—are all linked to increased prevalence of diabetes mellitus. 16 Diabetes increases the risk of both LTBI and TB and has been associated with adverse treatment outcomes, including death.…”

Section: Introductionmentioning

confidence: 99%

Obesity and Prevalence of Latent Tuberculosis: A Population-Based Survey

Badawi

Liu

2021

Infect Dis (Auckl)

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Objective: Diminution in body weight is a known risk factor that increases the burden of active tuberculosis (TB). However, conflicting evidence exists for the effect of body weight on the risk of latent tuberculosis infection (LTBI). The objective of the present study is to examine the prevalence of LTBI at different body weights, evaluate the extent of association between body mass index (BMI) and LTBI and identify factors mediating this relationship in an adult population. Methods: We conducted a cross-sectional study to estimate the relationship between BMI and LTBI in participants from the US-National Health and Nutrition Examination Survey (NHANES; 2012, n = 5156; 514 with LTBI and 4642 controls). Results: The association between BMI and levels of cardiometabolic risk markers in both LTBI and control groups had a similar profile. When adjusted for age and sex, BMI was significantly inversely correlated with the prevalence of LTBI ( r = −0.147, P < .001). Effect of BMI on the risk of LTBI was evaluated using multivariate logistic regression models adjusted for age, sex, diabetes, and level of education. In this model, increasing BMI was significantly associated with lower risk of LTBI (OR = 0.85; 95%CI: 0.77-0.96, P < .01). Conclusion: This study further establishes an inverse relationship between BMI and prevalence of LTBI. Decreased BMI can be considered as a risk factor in LTBI, the reservoir for active TB cases.

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“… 14-16 In contrast, some evidence has emerged demonstrating that individuals who are overweight, in close contact with active TB patients and over 50 years of age are all at higher risk of LTBI compared to their counterparts of normal weight. 17 Furthermore, in a population-based study from rural China, overweight and obese subjects were shown to have higher rates of LTBI positivity compared to individuals with normal body weight. 18 …”

Section: Introductionmentioning

confidence: 98%

Artificial Neural Network to Predict the Effect of Obesity on the Risk of Tuberculosis Infection

Badawi

Liu

Rehim

et al. 2021

Journal of Public Health Research

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Background: Body weight has been implicated as a risk factor for latent tuberculosis infection (LTBI) and the active disease.Design and Methods: This study aimed to develop artificial neural network (ANN) models for predicting LTBI from body weight and other host-related disease risk factors. We used datasets from participants of the US-National Health and Nutrition Examination Survey (NHANES; 2012; n=5,156; 514 with LTBI and 4,642 controls) to develop three ANNs employing body mass index (BMI, Network I), BMI and HbA1C (as a proxy for diabetes; Network II) and BMI, HbA1C and education (as a proxy for socioeconomic status; Network III). The models were trained on n=1018 age- and sex-matched subjects equally distributed between the control and LTBI groups. The endpoint was the prediction of LTBI.Results: When data was adjusted for age, sex, diabetes and level of education, odds ratio (OR) and 95% confidence intervals (CI) for risk of LTBI with increased BMI was 0.85 (95%CI: 0.77 – 0.96, p=0.01). The three ANNs had a predictive accuracy varied from 75 to 80% with sensitivities ranged from 85% to 94% and specificities of approximately 70%. Areas under the receiver operating characteristic curve (AUC) were between 0.82 and 0.87. Optimal ANN performance was noted using BMI as a risk indicator.Conclusion: Body weight can be employed in developing artificial intelligence-based tool to predict LTBI. This can be useful in precise decision making in clinical and public health practices aiming to curb the burden of tuberculosis, e.g., in the management and monitoring of the tuberculosis prevention programs and to evaluate the impact of healthy weight on tuberculosis risk and burden.

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Overweight, Obesity, and Older Age Favor Latent Tuberculosis Infection among Household Contacts in Low Tuberculosis-Incidence Settings within Panama

Cited by 12 publications

References 15 publications

Phenotype Definition for “Resisters” to Mycobacterium tuberculosis Infection in the Literature—A Review and Recommendations

Phenotype Definition for “Resisters” to Mycobacterium tuberculosis Infection in the Literature—A Review and Recommendations

Obesity and Prevalence of Latent Tuberculosis: A Population-Based Survey

Artificial Neural Network to Predict the Effect of Obesity on the Risk of Tuberculosis Infection

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