Abstract P3156: Cardiovascular Kidney Metabolic Syndrome Stage Prevalence varies by Asian Subgroup
Bibliographic record
Abstract
Introduction: Cardiovascular Kidney Metabolic (CKM) Syndrome characterizes the interplay of cardiovascular, renal, and metabolic disorders with five progressive stages. Prior data have shown that CKM syndrome is lower among aggregated Asian people when compared to Non-Hispanic White (NHW) people. However, when disaggregated, some Asian groups have a greater burden of CKM-related conditions (e.g., diabetes) compared to NHW people. We examined CKM prevalence among Asians compared to NHW people using the National Health Interview Survey (NHIS). Hypothesis: We hypothesize that CKM syndrome prevalence will be higher in Asian subgroups compared to NHWs. Methods: Using 2015-2018 NHIS data, we estimated the prevalence of CKM Stages among Asian Indians, Chinese, and Filipinos compared to NHW people. Variables included obesity, using Asian-specific Body Mass Index (BMI) values, diabetes status, hypertension, cholesterol, and the presence of cardiovascular symptoms to distinguish CKM stages. Kidney function was unavailable in NHIS. Stages were defined as: 0 (BMI <23), 1 (BMI ≥23 or prediabetes), 2/3 (BMI ≥23, prediabetes, hypertension, diabetes, or high cholesterol), or 4 (established cardiovascular disease). Associations between level of physical activity, access to care, socioeconomic status, and CKM stages were tested using bivariate (ANOVA, Pearson’s chi-squared) and regression analysis. Results: We examined 86,762 adults aged 20-80 (mean = 45.8). Prevalence of stages varied by race, with CKM being highest in Chinese (45.7%) for Stage 0, Asian Indians (53.8%) for Stage 1, and Filipinos (29.6%) for Stage 2/3 (Figure 1). The prevalence of CKM in the aforementioned groups was significantly higher compared to NHW, with Stage 0 prevalence being 15.2% higher in Chinese, Stage 1 prevalence being 19.0% among Asian Indians, and Stage 2/3 prevalence being 3.7% higher among Filipinos (Figure 1). Conclusions: When disaggregated, Asian Americans experience a different burden of CKM syndrome when compared to NHW people. Factors such as the prevalence of prediabetes, diabetes, high cholesterol, and higher BMI values undoubtedly contribute to the disparities that are noted. The different burden of CKM stage by race highlights the need for culturally appropriate interventions to reduce racial disparities in CKM syndrome. Further research is warranted on the impact of demographic, socioeconomic, and behavioral factors on CKM in Asian Americans.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".