Cardiovascular Contributors to Cognitive Impairment Among People Living With HIV Age 40 Years and Older in Kazakhstan
Bibliographic record
Abstract
The Kazakh population has been increasing in age over the last two decades. Life expectancy in Kazakhstan in 2022 was 73.8 years (y). Noncommunicable diseases (NCDs) accounted for ∼84% of deaths, particularly among men, and included cardiovascular disease, diabetes, chronic respiratory disease and cancer. It is anticipated that life expectancy trends will be similar among People Living With HIV (PLWH) who are virally suppressed in Kazakhstan. However, the prevalence and types of aging-related NCDs among Kazakh PLWH are unknown, despite ∼40% of Kazakh PLWH being age >40 years (y). In addition, and limited knowledge exists about the NCD-HIV care continuum. An ongoing cross-sectional study is being conducted among PLWH, >40y at the Almaty AIDS center. Cardiovascular, clinical, sociodemographic, mental health, medical history, health behavior, and HIV measures are collected. The Montreal Cognitive Assessment was included (range: 0-30). 113 PLWH were interviewed over ∼6 months (43.4% females; 54.9% age 40-49y, 30.1%, 11.5% and 3.5% age 50-59, 60-69, >70 years, respectively; gender: 58.3% cis men, 41.7% cis women; 20.4% self-reported Asian (Kazakh) race, 56.6% White (Russian), 23.0% unknown; 55.4% were employed; 25.7% reported education beyond college; 65.5% consumed alcohol; 76.1% were current smokers and 26.5% drug users. 54% had healthy BMI (18.5- <25 kg/m2). Systolic blood pressure range was 90-140mmHg (median 120); diastolic blood pressure range, 60-100mmHg (median 80). Median oxygen saturation was 98%. 76.7% participants had undetectable HIV viral load (<50 copies/ml), and 16.7% exhibited CD4 cell count <200 cells/mm3. However, 36.3% had high NT-pro-BNP (≥125 pg/ml), which was accompanied by a higher mean HIV viral load (p=0.026). Mean plasma glucose (mmol/l) and triglycerides were higher (p<0.10) among those with NT- proBNP ≥125 pg/ml. Among those taking antiretroviral therapies over a longer time period, there was higher NT-proBNP, however p>0.05. The MoCA indicated that 61.9% scored <26 (raw score); average 23.1. Comparing those with MoCA <26 versus ≥26, there were no differences in pro-BNP or lipid levels, HIV viral load or CD4+ count. However, diastolic blood pressure was higher among those with MoCA<26 (p=0.043). Further investigation to understand cardiovascular contributors to cognitive impairment among PLWH is necessary.
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 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 | 0.000 |
| 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) | 0.000 | 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".