Convergence of a diabetes mellitus, protein energy malnutrition, and TB epidemic: the neglected elderly population
Bibliographic record
Abstract
BACKGROUND: On a global scale, nearly two billion persons are infected with Mycobacterium tuberculosis. From this vast reservoir of latent tuberculosis (TB) infection, a substantial number will develop active TB during their lifetime, with some being able to transmit TB or Multi-drug- resistant (MDR) TB to others. There is clinical evidence pointing to a higher prevalence of infectious diseases including TB among individuals with Diabetes Mellitus (DM). Furthermore, ageing and diabetes mellitus may further aggravate protein-energy malnutrition (PEM), which in turn impairs T-lymphocyte mediated immunologic defenses, thereby increasing the risk of developing active TB and compromising TB treatment. This article aims to a) highlight synergistic mechanisms associated with immunosenescence, DM and PEM in relation to the development of active TB and b) identify nutritional, clinical and epidemiological research gaps. METHODS: To explore the synergistic relationship between ageing, DM, tuberculosis and PEM, a comprehensive review was undertaken. The MEDLINE and the Google Scholar databases were searched for articles published from 1990 to March 2015, using different MESH keywords in various combinations. RESULTS: Ageing and DM act synergistically to reduce levels of interferon gamma (IFN- γ), thereby increasing susceptibility to TB, for which cell mediated immunity (CMI) plays an instrumental role. These processes can set in motion a vicious nutritional cycle which can predispose to PEM, further impairing the CMI and consequently limiting host defenses. This ultimately transforms the latent TB infection into active disease. A clinical diagnostic algorithm and clinical guidelines need to be established for this population. CONCLUSION: Given the increase in ageing population with DM and PEM, especially in resource-poor settings, these synergistic tripartite interactions must be examined if a burgeoning TB epidemic is to be averted. Implementation of a comprehensive, all-encompassing approach to curb transmission is clearly indicated. To this end, clinical, nutritional and epidemiological research gaps must be addressed without a delay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".