The Effect of Aging on Circulating Levels of Proinflammatory Cytokines During Septic Shock
Bibliographic record
Abstract
BACKGROUND: As the proportion of the population that is older continues to rise, infection in older people has become an important healthcare problem. Although aging is associated with multiple abnormalities in immune function, the effect of aging on the production of proinflammatory cytokines has not been well studied under conditions of clinical stress. OBJECTIVES: The aim of this study was to examine the effect of aging on circulating levels of the proinflammatory cytokines in a large cohort of septic shock patients. We hypothesized that aging would be associated with a diminished proinflammatory cytokine response to sepsis. DESIGN: Patients with septic shock who were enrolled in the placebo limb of the North American Sepsis Trial (NORASEPT II) study were analyzed. SETTING: The intensive care units of 105 hospitals in the United States and Canada. PARTICIPANTS: Nine hundred and thirty patients presenting to hospital within 12 hours of the onset of septic shock. MEASUREMENTS: Interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), soluble tumor necrosis factor-receptor-55 (sTNF-R55), and soluble tumor necrosis factor-receptor-75 (sTNF-R75) concentrations were measured at enrollment. The study population was broken down into five age groups as follows: less than 50 years (group one), 50 to 64 years (group two), 65 to 74 years (group three), 75 to 84 years (group four), and 85 or older (group five). Clinical, demographic, and cytokine data were extracted to describe each age group. RESULTS: Data were available for 930 patients. The patients' mean age (+/- SD) was 59 +/- 17 years (range, 18 to 102). There were 280 patients in group one, 242 in group two, 210 in group three, 150 in group four, and 48 in group five. The primary diagnoses; clinical characteristics; and IL-6, sTNF-R55, and sTNF-R75 levels were similar among the five age groups. The TNF-alpha levels were significantly higher, however, in the oldest group of patients (group five). The 28-day survival was 49% in patients over the age of 75 and 58% in those under 75 years (P = .03). There was no gender difference in survival or cytokine levels. CONCLUSIONS: Contrary to our expectations, we found that aging was not associated with a decline in the circulating levels of proinflammatory cytokines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".