Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection.

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).

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

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.