Evidence map›Paper›PMID 39177079›Full record

SynthesisThe Cochrane database of systematic reviews2024

Diabetes as a risk factor for tuberculosis disease.

Juan Va Franco, Brenda Bongaerts, Maria-Inti Metzendorf, Agostina Risso, Yang Guo, Laura Peña Silva, Melanie Boeckmann, Sabrina Schlesinger, Johanna Aag Damen, Bernd Richter and 8 more

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in The Cochrane database of systematic reviews, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

33 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Delayed-Type Hypersensitivity toInternational journal of molecular sciences · 2026
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  4. Diabetes as a risk factor for tuberculosis disease.The Cochrane database of systematic reviews · 2024
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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

18 authors.

Juan Va FrancoInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Brenda BongaertsInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Maria-Inti MetzendorfInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Agostina RissoFamily and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Yang GuoInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Laura Peña SilvaInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Melanie BoeckmannFaculty of Human and Health Sciences, University of Bremen, Bremen, Germany.
Sabrina SchlesingerInstitute for Biometrics and Epidemiology, German Diabetes Center (Deutsches Diabetes-Zentrum/DDZ), Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Johanna Aag DamenCochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Bernd RichterInstitute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
Annabel BaddeleyGlobal Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
Mathieu BastardGlobal Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
Anna CarlqvistGlobal Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
Maria Nieves Garcia-CasalDepartment of Nutrition and Food Safety, World Health Organization, Geneva, Switzerland.
Bianca HemmingsenDepartment of Noncommunicable Diseases, World Health Organization, Geneva, Switzerland.
Farai MavhungaGlobal Tuberculosis Programme, World Health Organization, Geneva, Switzerland.
Jennifer Manne-GoehlerBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Kerri VineyGlobal Tuberculosis Programme, World Health Organization, Geneva, Switzerland.

Funding

World Health Organization 001
6 · The paper itself

Abstract

backgroundTuberculosis (TB) is amongst the leading causes of death from an infectious disease, with an estimated 1.3 million deaths from TB in 2022. Approximately 25% of the global population is estimated to be infected with the TB bacterium, giving rise to 10.6 million episodes of TB disease in 2022. The prevalence of diabetes influences TB incidence and TB mortality. It is associated not only with an increased risk of TB disease but also death during TB treatment, TB relapse after treatment completion and multidrug-resistant TB. Since 2011, the World Health Organization (WHO) has recommended collaborative TB and diabetes activities as outlined in the Collaborative Framework for Care and Control of TB and Diabetes.

objectivesTo determine the prognostic value of diabetes mellitus (DM) in the general population of adults, adolescents and children for predicting tuberculosis disease. SEARCH

methodsWe searched the literature databases MEDLINE (via PubMed) and WHO Global Index Medicus, and the WHO International Clinical Trials Registry Platform (ICTRP) on 3 May 2023 (date of last search for all databases); we placed no restrictions on the language of publication. SELECTION CRITERIA: We included retrospective and prospective cohort studies, irrespective of publication status or language. The target population comprised adults, adolescents and children from diverse settings, encompassing outpatient and inpatient cohorts, with varying comorbidities and risk of exposure to tuberculosis. DATA COLLECTION AND ANALYSIS: We used standard Cochrane methodology and the Quality In Prognosis Studies (QUIPS) tool. Prognostic factors assessed at enrolment/baseline included diabetes, as defined by the individual studies, encompassing patient-reported status, abstracted from medical records or claims data, or diagnosed by plasma glucose/glycosylated haemoglobin. The primary outcome was the incidence of tuberculosis disease. The secondary outcome was recurrent TB disease. We performed a random-effects meta-analysis for the adjusted hazard ratios, risk ratios, or odds ratios, employing the restricted maximum likelihood estimation. We rated the certainty of the evidence using the GRADE approach. MAIN

resultsWe included 48 cohort studies with over 61 million participants from the six WHO regions. However, the representation was variable as eight population-based studies were from South Korea and 19 from China, with overlapping study periods, and only one from the African region (Ethiopia). All studies included adults, and nine studies also included children and adolescents. Most studies diagnosed DM based on clinical records, including fasting blood glucose levels or glucose-lowering treatments. The studies did not distinguish between type 1 and type 2 DM; only one study focused on type 1 DM. Diagnosis and exclusion of TB were performed using culture or molecular WHO-recommended rapid diagnostic tests (mWRD) in only 12 studies, which could have biassed the effect estimate. The median follow-up time was five years (interquartile range 1.5 to 10, range 1 to 16.9), and the studies primarily reported an adjusted hazard ratio from a multivariable Cox-proportional hazard model. Hazard Ratios (HR) The HR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. We present 95% confidence intervals (CI) and prediction intervals, which show between-study heterogeneity represented in measuring the variability of effect sizes (i.e. the interval within which the effect size of a new study would fall considering the same population of studies included in the meta-analysis). DM may increase the risk of tuberculosis disease (HR 1.90, 95% CI 1.51 to 2.40; prediction interval 0.83 to 4.39; 10 studies; 11,713,023 participants). The certainty of the evidence is low, due to a moderate risk of bias across studies and inconsistency. Considering a risk without diabetes of 129 cases per 100,000 population, this represents 102 more (59 to 153 more) cases per 100,000. When stratified by follow-up time, the results are more consistent across < 10 years follow-up (HR 1.52, 95% CI 1.47 to 1.57; prediction interval 1.45 to 1.59; 7 studies; 10,380,872 participants). This results in a moderate certainty of the evidence due to a moderate risk of bias across studies. However, at 10 or more years of follow-up, the estimates yield a wider CI and a higher HR (HR 2.44, 95% CI 1.22 to 4.88; prediction interval 0.09 to 69.12; 3 studies; 1,332,151 participants). The certainty of the evidence is low due to the moderate risk of bias and inconsistency. Odds Ratio (OR) DM may increase the odds of tuberculosis disease (OR 1.61, 95% CI 1.27 to 2.04; prediction interval 0.96 to 2.70; 4 studies; 167,564 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is low due to a moderate risk of bias and inconsistency. Risk Ratio (RR) The RR estimates represent the highest certainty of the evidence, explored through sensitivity analyses and excluding studies at high risk of bias. DM probably increases the risk of tuberculosis disease (RR 1.60, 95% CI 1.42 to 1.80; prediction interval 1.38 to 1.85; 6 studies; 44,058,675 participants). Stratification by follow-up time was not possible as all studies had a follow-up < 10 years. The certainty of the evidence is moderate due to a moderate risk of bias. AUTHORS'

conclusionsDiabetes probably increases the risk of developing TB disease in the short term (< 10 years) and may also increase the risk in the long term (≥ 10 years). As glycaemic control and access to care may be potential effect modifiers of the association between diabetes and the risk of TB disease, the overall estimates should be interpreted with caution when applied locally. Policies targeted at reducing the burden of diabetes are needed to contribute to the aims of ending TB. Large population-based cohorts, including those derived from high-quality national registries of exposures (diabetes) and outcomes (TB disease), are needed to provide estimates with a high certainty of evidence of this risk across different settings and populations, including low- and middle-income countries from different WHO regions. Moreover, studies including children and adolescents and currently recommended methods for diagnosing TB would provide more up-to-date information relevant to practice and policy.

fundingWorld Health Organization (203256442) REGISTRATION: PROSPERO registration: CRD42023408807.

Indexed as

Diabetes MellitusTuberculosisAdolescentAdultChildHumansIncidencePrognosisRisk Factors

Identifiers

PMID39177079
PMCPMC11342417

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Registered trials

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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.