Peripheral Inflammation subgroups in Alzheimer’s disease and related dementias
Bibliographic record
Abstract
BACKGROUND: A growing body of research has focused on inflammation as both a potential biomarker and a risk factor for Alzheimer's disease (AD). The cytokine Interleukin-6 (IL-6) is involved in the pathogenesis of inflammatory disorders and in the physiological homeostasis of neural tissue. AD has been associated with increased IL-6 expression in brain, however, increased levels of IL-6 have also been linked to conditions such as diabetes and hypertension. We examined groups of individuals with AD and related disorders (ADRD) as well as healthy controls to check if elevated levels of IL-6 (above 1.9 ng/L) were related to the presence of comorbidities. METHODS: We investigated individuals with AD (n = 58); mild cognitive impairment (MCI; n = 139); subjective cognitive impairment (SCI; n = 28); and cognitively unimpaired age matched controls (CU; n = 53). We considered four possible subgroups: Group 0 (defined as individuals with normal IL-6 levels and without comorbidities that could be linked to elevated IL-6); Group 1 (individuals with normal levels of IL-6, despite having comorbidities); Group 2 (individuals with elevated IL-6 without comorbidities), and Group 3 (individuals with elevated IL-6 and comorbidities). The comorbidities investigated were hypertension, diabetes, peripheral vascular disease, history of cerebrovascular accident, and rheumatoid arthritis. Logistic regression was used to investigate the relationship between the occurrence of elevated IL-6, age, sex, history of smoking, body mass index (BMI), Montreal Cognitive Assessment scores (MoCA), nutrition and sleep. RESULTS: We found individuals with and without elevated IL-6, and with and without comorbidities in all cohorts (Table 1). We found a significant relationship between elevated IL-6 and aging in all cohorts (Figure 1a). A significant relationship was also found between elevated IL-6 and greater BMI (MCI group only; Figure 1b), lower nutrition scores (SCI group only, Figure 1c), history of smoking (AD group only; Figure 2a) and sex, whereby males had more elevated IL-6 (MCI group; Figure 2b). CONCLUSIONS: We found variation in patterns of peripheral inflammation and other common conditions in aging and ADRD. These variations may reflect different mechanistic groups which might help the understanding of variation in clinical features of ADRD, disease progression and response to different therapeutic interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".