Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.

BACKGROUND: We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. METHODS: We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. FINDINGS: Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. INTERPRETATION: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.

Nutrient partitioning during treatment of tuberculosis: gain in body fat mass but not in protein mass.

BACKGROUND: Tuberculosis is an important cause of wasting. The functional consequences of wasting and recovery may depend on the distribution of lost and gained nutrient stores between protein and fat masses. OBJECTIVE: The goal was to study nutrient partitioning, ie, the proportion of weight change attributable to changes in fat mass (FM) versus protein mass (PM), during antimycobacterial treatment. DESIGN: Body-composition measures were made of 21 men and 9 women with pulmonary tuberculosis at baseline and after 1 and 6 mo of treatment. All subjects underwent dual-energy X-ray absorptiometry and deuterium bromide dilution tests, and a four-compartment model of FM, total body water (TBW), bone minerals (BM), and PM was derived. The ratio of PM to FM at any time was expressed as the energy content (p-ratio). Changes in the p-ratio were related to disease severity as measured by radiologic criteria. RESULTS: Patients gained 10% in body weight (P < 0.001) from baseline to month 6. This was mainly due to a 44% gain in FM (P < 0.001); PM, BM, and TBW did not change significantly. Results were similar in men and women. The p-ratio decreased from baseline to month 1 and then fell further by month 6. Radiologic disease severity was not correlated with changes in the p-ratio. CONCLUSIONS: Microbiological cure of tuberculosis does not restore PM within 6 mo, despite a strong anabolic response. Change in the p-ratio is a suitable parameter for use in studying the effect of disease on body composition because it allows transformation of such effects into a normal distribution across a wide range of baseline proportion between fat and protein mass.

Pulmonary manifestations of systemic autoimmune disease.

Patients with systemic autoimmune disease may present with a number of different pulmonary manifestations. In order to recognise, diagnose and manage these manifestations, it is necessary to have a working knowledge of the anatomy and physiology of the thorax. This chapter will describe the clinical symptoms and clinical examination findings in patients who may have underlying pulmonary disease. It will describe the investigations that can be used to confirm or refute a possible diagnosis and describe approaches to managing these complex clinical cases. The importance of multidisciplinary team working using the skills of clinicians, radiologists and pathologists will be highlighted. The use of high-resolution computed tomography scanning of the thorax to help to delineate the type of interstitial lung disease will be described and some of the newer modalities available for the treatment of pulmonary hypertension introduced. By the end of the chapter, the reader should understand that patients with a single underlying autoimmune disease may present with one or more pulmonary manifestations and that different autoimmune diseases may present with similar pulmonary manifestations. This heterogeneity poses both diagnostic and treatment challenges, and many questions still remain regarding optimal treatment.

Leptin and energy metabolism in pulmonary tuberculosis.

BACKGROUND: Pulmonary tuberculosis is the classic cause of "consumption," but the pathogenesis of such wasting is largely unknown. Animal studies in other conditions suggest that leptin may be a mediator between proinflammatory cytokine activity and wasting. OBJECTIVE: We tested whether the leptin concentration, after control for body fat mass, is higher during active pulmonary tuberculosis than after recovery and whether it correlates with energy metabolism and proinflammatory cytokine activity. DESIGN: Nondiabetic adults with pulmonary tuberculosis (n = 32) were recruited into a prospective observational study. Patients found to be antibody positive for human immunodeficiency virus were excluded from the study. Dual-energy X-ray absorptiometry, indirect calorimetry, and food intake protocols were performed at baseline and after 1 and 6 mo of tuberculosis treatment. Fasting plasma leptin, tumor necrosis factor alpha and its soluble receptor, and interleukin 6 were measured by enzyme-linked immunosorbent assay. RESULTS: Resting energy expenditure was close to Harris-Benedict predictions and did not change significantly during treatment, but energy intake increased. Leptin concentration was correlated in a log-linear fashion with percentage body fat but was independent of cytokines and energy intake. There was no significant difference in leptin, corrected for energy balance and fat mass, at baseline and after 1 and 6 mo of treatment. CONCLUSIONS: These data are compatible with recovery from anorexia or starvation without discernible hyper- or hypometabolism. The close correlation of leptin with body fat mass is similar to observations in healthy subjects. No additional influence of disease state or proinflammatory cytokine activity was found. Leptin does not appear to be a component of the immune response to human pulmonary tuberculosis, and thus it cannot account for the weight loss and anorexia associated with tuberculosis.