The Rise of Non-Tuberculosis Mycobacterial Lung Disease

Abstract

The incidence and number of deaths from non-tuberculous mycobacterial (NTM) disease have been steadily increasing globally. These lesser known "cousins" of Mycobacterium tuberculosis (TB) were once thought to be harmless environmental saprophytics and only dangerous to individuals with defective lung structure or the immunosuppressed. However, NTM are now commonly infecting seemingly immune competent children and adults at increasing rates through pulmonary infection. This is of concern as the pathology of NTM is difficult to treat. Indeed, NTM have become extremely antibiotic resistant, and now have been found to be internationally dispersed through person-to-person contact. The reasons behind this NTM increase are only beginning to be elucidated. Solutions to the problem are needed given NTM disease is more common in the tropics. Importantly, 40% of the world's population live in the tropics and due to climate change, the Tropics are expanding which will increase NTM infection regions. This review catalogs the global and economic disease burden, at risk populations, treatment options, host-bacterial interaction, immune dynamics, recent developments and research priorities for NTM disease.

Keywords: Non-tuberculous mycobacteria; immunology; mycobacteria; mycobacteria pathology; pulmonary infection.

Figure 1

The combined host, environmental and organism risk factors that contribute to developing NTM disease. NTM disease can manifest as pulmonary infection or the more severe disseminated form of the disease which is seen in patients with some severe systemic immune compromise. Pulmonary infection is seen in patients who have structural or functional lung defects that lead to innate immune compromise as well as other groups of patients in whom the precise nature of immune compromise is not clearly defined. Some degree of overlap exists in these risk groups with some patients with systemic immune compromise presenting with pulmonary disease well (15). Environmental risk factors include the natural and man-made habitats where these organisms survive and thrive. Increasing overlap between human habitation and NTM habitats is postulated as a reason for the increasing trend in infection. Organism biology also contributes to infection. NTM are a diverse group of organisms, tolerant to a wide range of physical conditions. Their lipid rich cell wall facilitates biofilm formation and aerosolization of bacteria while simultaneously mediated inherent resistance to many antibiotics and disinfectants. This makes both removing organisms from the man made habitats like water pipes as well as treating patients with active infection, difficult. The specific requirements needed to isolate these organisms in laboratory cultures has meant that NTM are often missed in routine sampling. Though not directly a risk factor for developing infection, this is one of the reasons infections are often missed at early stages. ¹Autoantibodies to IFNγ are commonly seen in in adults and have been extensively described in East Asian populations. A genetic component to auto antibody formation is likely with specific HLA types being associated with the disease. Both DNTM and PNTM disease manifestations are observed. ²Pulmonary alveolar proteinosis has a genetic-based form and acquired form. The genetic-based form is due to gene mutation in GM-SCF subunits and the acquired form is due to auto-antibodies against GM-CSF. This results in impaired surfactant disposal which accumulates in the lung and macrophages leading to dysfunction. ³Patients on anti-TNF therapy and cytotoxic therapy are predisposed to both PNTM and DNTM though lung disease is more common. COPD, Chronic Obstructive Pulmonary disease; ABPA, Allergic Broncho Pulmonary Aspergillosis.

Figure 2

Projected NTM cases in Queensland, Australia from 2020 to 2040. NTM cases from 2012 to 2019 were reported by the Epidemiology and Research Unit, QLD Department of Health and analyzed using R v3.5.2. The existing data was converted to a time series object using data from 2012 to 2019. The R package forecast (65) was used to generate the predictions from 2020 to 2040. The order for the model was estimated using the auto. arima() function which takes in a time series and returns the best AutoRegressive Integrated Moving Average (ARIMA) model according to either AIC, AICc, or BIC value. Each model was input to the forecast function with levels average, 5, 10, and 25 plotted.