1. Immunology and Inflammation
  2. Microbiology and Infectious Disease
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Persistent inflammation during anti-tuberculosis treatment with diabetes comorbidity

  1. Nathella Pavan Kumar
  2. Kiyoshi F Fukutani
  3. Basavaradhya S Shruthi
  4. Thabata Alves
  5. Paulo S Silveira-Mattos
  6. Michael S Rocha
  7. Kim West
  8. Mohan Natarajan
  9. Vijay Viswanathan
  10. Subash Babu
  11. Bruno B Andrade
  12. Hardy Kornfeld  Is a corresponding author
  1. National Institutes of Health, National Institute for Research in Tuberculosis, International Center for Excellence in Research, India
  2. Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER), Fundação José Silveira, Brazil
  3. Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Brazil
  4. Faculdade de Tecnologia e Ciências, Brazil
  5. Prof. M. Viswanathan Diabetes Research Center, India
  6. Universidade Salvador, Laureate Universities, Brazil
  7. University of Massachusetts Medical School, United States
  8. National Institute for Research in Tuberculosis, India
Research Article
Cite this article as: eLife 2019;8:e46477 doi: 10.7554/eLife.46477
6 figures, 6 tables and 3 additional files

Figures

Mycobacterial burden in sputum smear stratified by glycemic status.

Sputum AFB smear grade in participants with DM comorbidity (TBDM, Orange) or euglycemia (TB, Green). (A) Frequency of India cohort participants (left panel) and Brazil cohort participants (right panel) with different AFB smear grades ranging from 0 to ≥3 + . (B) Frequency of TB and TBDM participants from the combined Indian and Brazil cohorts with different AFB smear grades. Data were analyzed using Pearson’s chi-squared test.

https://doi.org/10.7554/eLife.46477.006
Prospective assessment of plasma biomarkers in pulmonary TB patients with or without concurrent diabetes undergoing anti-TB treatment.

Hierarchical cluster analysis (Ward’s method with 100x bootstrap) of z-score normalized, log-transformed values for each plasma analyte from the Indian and Brazil cohorts (A and C, respectively) at the indicated timepoints of antimicrobial treatment for drug-sensitive pulmonary TB. In the heatmaps, yellow color represents the highest values whereas blue color indicates the lowest values measured for each analyte. Principal component analysis was performed to show the distribution of data from the India cohort and Brazil cohorts (B and D, respectively) on simultaneous assessment of variables shown in the heatmaps.

https://doi.org/10.7554/eLife.46477.010
Figure 3 with 1 supplement
Biomarker profiles during anti-TB treatment in TBDM comorbidity from the India cohort.

(A) Mean log-transformed values for the indicated analytes were calculated and z-score normalized. Heatmaps with values from the India cohort grouped using hierarchical clustering (Ward’s method with 100x bootstrap) was used to illustrate the overall variation in plasma concentrations over time. In addition, one-way ANOVA with linear trans ad hoc test was used to test the direction of variation in each analyte’s concentration between study timepoints. Direction of the arrows highlight statistically significant trends, while “- “denotes differences with P values ≥ 0.05. (B) Mean fold-difference of analyte levels between the TBDM and TB groups in the India cohort. Orange bars indicated statistically significant differences (p<0.05).

https://doi.org/10.7554/eLife.46477.012
Figure 3—figure supplement 1
Biomarker profiles in India cohort TBDM participants with KDM vs NDM at enrollment.

Mean fold-differences at each timepoint are compare participants with known DM diagnosis prior to enrollment (KDM) vs those newly diagnosed with DM at enrollment (NDM). Comparisons were analyzed using the Mann-Whitney U test and adjusted for multiple comparisons. There were no statistically significant differences between the two groups.

https://doi.org/10.7554/eLife.46477.013
Biomarker profiles during anti-TB treatment in TBDM comorbidity from the Brazil cohort.

(A) Mean log-transformed values for the indicated analytes were calculated and z-score normalized. Heatmaps with values from the Brazil cohort grouped using hierarchical clustering (Ward’s method with 100x bootstrap) was used to illustrate the overall variation in plasma concentrations over time. In addition, one-way ANOVA with linear trans ad hoc test was used to test the direction of variation in each analyte’s concentration between study timepoints. Direction of the arrows highlight statistically significant trends, while “- “denotes differences with P values ≥ 0.05. (B) Mean fold-difference of analyte levels between the TBDM and TB groups in the Brazil cohort. Orange bars indicated statistically significant differences (p<0.05).

https://doi.org/10.7554/eLife.46477.014
Identification of biomarkers showing the strongest associations with TBDM comorbidity.

Decision tree analysis shows the analytes (or combination) that exhibited the highest accuracy in discriminating TBDM from TB in the India and Brazil cohorts (A and C, respectively). Receiver operator characteristics curves were employed to quantify the accuracy of single or combined biomarkers int the India and Brazil cohorts (B and D, respectively).

https://doi.org/10.7554/eLife.46477.015
Associations between radiographic scores, HbA1c, and systemic inflammatory profiles in the India cohort.

(A) Change in radiographic score values before and at the indicated timepoints after the initiation of antimicrobial treatment for pulmonary TB. Dots represent individual participant values and horizontal lines indicate median values. Values were compared between TBDM and normoglycemic TB groups using the Mann-Whitney U test. (B–D). Spearman correlation matrices were built to examine associations between absolute radiographic score values or difference in score values between the indicated timepoints (fold-variation) and the indicated plasma analyte value at each study timepoint. The Spearman rank values are shown in a heatmap scale. Statistically significant correlations (p<0.05) are highlighted in bold squares. Gray squares indicate significant correlations after adjusting for false discovery rate (FDR 1%). Red dots represent positive correlations while blue dots represent negative correlations.

https://doi.org/10.7554/eLife.46477.016

Tables

Table 1
Clinical and demographic characteristics of participants in the India cohort.
https://doi.org/10.7554/eLife.46477.003
CharacteristicTBDMTBP value
n = 43n = 44
Age, median years (IQR)46 (38–52)39.5 (30–47)0.0146
Male sex, no. (%)32 (74.4)37 (84)0.0408
Smoking status, no. (%)0.0584
Current smoker5 (11.6)12 (27.3)
Former smoker9 (21)13 (29.5)
Never smoked29 (67.4)19 (43.2)
Alcohol use, no. (%)0.7410
Current use11 (25.6)21 (47.7)
Former user16 (37.2)10 (22.7)
Never used16 (37.2)13 (29.6)
BMI kg/m2, median (IQR)20.3 (163–23)16·4 (15.2–18.3)<0.0001
HbA1c %, median (IQR)10.0 (7.3–11.8)5.6 (5.4–5.8)<0.0001
Vitamin D ng/dL, median (IQR)15 (9.3–24)17 (13–27)0.1502
Table 2
Characteristics of India cohort participants with KDM vs NDM at enrollment.
https://doi.org/10.7554/eLife.46477.004
CharacteristicKDMNDMP value
Male, n (%)20 (71.4)12 (42.9)0.7190
Age, Median (IQR)45.5 (38.0–52.0)48.0 (38.0–48.0)0.5072
BMI, Median (IQR)21.7 (18.9–23.6)18.9 (16.1–20.8)0.0079
Smoking history, n (%)0.7354
Yes10 (35.7)4 (14.3)
No18 (64.3)11 (39.3)
Current drinker, n (%)0.9999
Yes7 (25.0)4 (14.3)
No21 (75.0)11 (39.3)
Cavitation, n (%)0.133
Yes5 (17.9)6 (21.4)
No24 (85.7)8 (28.6)
Bilateral lung lesion, n (%)0.3319
Yes16 (57.1)5 (17.9)
No13 (46.4)9 (32.1)
  1. KDM, known DM prior to enrollment; NDM, newly diagnosed DM at enrollment screening.

    Data were compared using the chi-squared test except for age, which was compared using the Mann-Whitney U test.

Table 3
Clinical and demographic characteristics of participants in the Brazil cohort.
https://doi.org/10.7554/eLife.46477.005
CharacteristicTBDMTBP value
n = 25n = 26
Age, median years (IQR)45 (30.5–49.5)46 (37–56)0.131
Male sex, no. (%)13 (52)13 (50)>0.999
Smoking status, no. (%)0.162
Current smoker10 (40)8 (30.7)
Former smoker3 (12)9 (34.6)
Never smoked12 (48)9 (34.6)
Alcoholism, no. (%)10 (40)13 (50)0.473
BMI kg/m2, median (IQR)19.5 (18.3–49.5)20.2 (18.7–22.6)0.114
HbA1c %, median (IQR)8.8 (7.3–10.2)5.2 (4.7–5.5)<0.0001
  1. Alcoholism defined by CAGE questionnaire.

Table 4
Comparison of Brazil and India cohort characteristics.
https://doi.org/10.7554/eLife.46477.008
BrazilIndiaP value
Age, Median (IQR)46.0 (34.0–50.0)43.0 (32.0–54.0)0.5400
Male, n (%)26 (51.0)69 (79.3)0.0011
BMI, Median (IQR)17.8 (15.7–21.0)19.8 (18.6–22.2)0.0001
Smoking history, n (%)0.2871
Yes18 (35.3)17 (26.2)
No33 (64.7)48 (73.9)
Alcohol, n (%)0.1409
Yes23 (45.1)32 (36.8)
No23 (45.1)55 (63.2)
Lung lesions, n (%)0.6336
Unilateral26 (51.0)48 (55.2)
Bilateral25 (49.0)39 (44.8)
Cavitation, n (%)0.2718
Yes33 (64.7)64 (73.6)
No18 (35.3)23 (26.4)
AFB smear grade, n (%)<0.0001
09 (17.6)0 (0)
1+13 (25.4)43 (52.4)
2+14 (27.4)35 (42.7)
≥3 + 15 (29.4)4 (4.9)
  1. Data were compared using the chi-squared test except for age and BMI, which were compared using the Mann-Whitney U test.

Table 5
Characteristics of normoglycemic TB participants and TBDM participants in the India and Brazil cohorts.
https://doi.org/10.7554/eLife.46477.009
CharacteristicsTBTBDM
IndiaBrazilP valueIndiaBrazilP value
Age, Median (IQR)39.5 (30.0–47.2)45.0 (30.5–49.5)0.639047.0 (38.0–51.0)46.0 (37.0–56.0)0.5565
Male, n(%)32 (72.7%)13 (50%)0.300137 (84,09%)13 (52%)0.0058
BMI, Median (IQR)16.4 (15.3–18.3)19.5 (18.3–20.6)0.257920.32 (16.6–23.0)20.20 (18.7–22.6)<0.0001
Smoking, n (%)0.61480.1309
Yes25 (56.8%)17 (65.4%)14 (32.6%)13 (52%)
No19 (43.2%)9 (346%)29 (67.4%)12 (48%)
Alcohol, n (%)>0.99990.2786
Yes21 (47.7%)13 (50%)11 (25.6%)10 (40%)
No23 (52.3%)13 (50%)32 (74.4%)15 (60%)
Lung lesion, n (%)>0.99990.4535
Unilateral26 (591%)16 (61.5%)22 (51,16%)10 (40%)
Bilateral18 (40.9%)10 (38.5%)21 (48,84%)15 (60%)
Cavitation, n (%)0.02120.0003
Yes12 (27.3%)15 (57.7%)11 (25.6%)18 (72%)
No32 (72.7%)11 (42.3%)32 (74.4%)7 (28%)
AFB smear, n (%)0.0024<0.0001
00 (0%)6 (23.1%)0 (0%)3 (12%)
1+26 (61.9%)9 (34.6%)17 (42,5%)4 (16%)
2+13 (30.9%)6 (23.1%)22 (55%)8 (32%)
≥3 + 3 (7.1%)5 (19.2%)1 (2,5%)10 (40%)
  1. Data were compared using the chi-square test except foe age as BMI, which were compared using the Mann-Whitney U test.

Key resources table
Reagent type
(species) or resource
DesignationSource
or reference
IdentifiersAdditional information
Commercial
assay or kit
Multiplex ELISA, Bio-Plex
Pro Human Cytokine
17-plex Assay
Bio-Rad#m5000031yv

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1 through 5.

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