Evidence map›Paper›PMID 31955202›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2020

Impact of Diabetes and Low Body Mass Index on Tuberculosis Treatment Outcomes.

Hardy Kornfeld, Shruthi B Sahukar, Elizabeth Procter-Gray, Nathella P Kumar, Kim West, Kevin Kane, Mohan Natarajan, Wenjun Li, Subash Babu, Vijay Viswanathan

Open access · bronzeAbstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 4 pooled it
1.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed, 4 syntheses or guidelines pooled it, 78 citations in OpenAlex.

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  14. Impact of premorbid nutritional status on TB severity.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Hardy KornfeldDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Shruthi B SahukarProf. M. Viswanathan Diabetes Research Center, Chennai, India.
Elizabeth Procter-GrayDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Nathella P KumarNational Institute for Research in Tuberculosis, National Institutes of Health,  International Center for Excellence in Research, Chennai, India.
Kim WestDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Kevin KaneDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Mohan NatarajanNational Institute for Research in Tuberculosis, Chennai, India.
Wenjun LiDepartment of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Subash BabuNational Institute for Research in Tuberculosis, National Institutes of Health,  International Center for Excellence in Research, Chennai, India.
Vijay ViswanathanProf. M. Viswanathan Diabetes Research Center, Chennai, India.
University of Massachusetts Chan Medical School · USNational Institute of Research in Tuberculosis · INIndia Diabetes Research Foundation · IN

Funding

India International Center for Excellence in ResearchZIAAI001065 · NIAID · NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES · PI NUTMAN, THOMAS · 2009 to 2025
$19.0M
6 · The paper itself

Abstract

backgroundDiabetes was identified as a tuberculosis (TB) risk factor mostly in retrospective studies with limited assessments of metabolic variables. The prospective Effects of Diabetes on Tuberculosis Severity study compared adults with pulmonary TB in Chennai, India, who were classified as having either diabetes or a normal glucose tolerance at enrollment.

methodsBaseline TB severity, sputum conversion, and treatment outcomes (cure, failure, death, or loss to follow-up) were compared between groups with respect to glycemic status and body mass index (BMI).

resultsThe cohort of 389 participants included 256 with diabetes and 133 with a normal glucose tolerance. Low BMIs (<18.5 kg/m2) were present in 99 (74.4%) of nondiabetic participants and 85 (33.2%) of those with diabetes. Among participants with normal or high BMIs, rates of cure, treatment failure, or death did not vary by glycemic status. Participants with low BMIs had the highest radiographic severity of disease, the longest time to sputum culture conversion, and the highest rates of treatment failure and death. Among participants with low BMIs, poorly controlled diabetes (glycohemoglobin [HbA1c] ≥8.0%) was unexpectedly associated with better TB treatment outcomes. A high visceral adiposity index was associated with adverse outcomes and, despite an overall correlation with HbA1c, was elevated in some low-BMI individuals with normal glucose tolerance.

conclusionsIn this South Indian cohort, a low BMI was significantly associated with an increased risk for adverse TB treatment outcomes, while comorbid, poorly controlled diabetes lessened that risk. A high visceral adiposity index, either with or without dysglycemia, might reflect a novel TB susceptibility mechanism linked to adipose tissue dysfunction.

Indexed as

Diabetes MellitusTuberculosisAdultBody Mass IndexHumansIndiaProspective StudiesRetrospective StudiesTreatment Outcomebody mass indexdiabetestreatment outcomestuberculosisundernutrition

Identifiers

PMID31955202
PMCPMC7713690
OpenAlexW2999397807

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.