Association between Taste Disorder and Dietary Quality and Macronutrient Intake in US Adults: A National Health and Nutrition Examination Survey (2011–2014)
Bibliographic record
Abstract
Background: Taste disorders are associated with various health conditions, including COVID-19. However, the prevalence and correlation of taste disorders are not comprehensively described at the population level. This study aimed to (i) estimate the prevalence of taste disorders, (ii) identify modifiable and non-modifiable correlates, and (iii) investigate the association between taste disorders and diet quality and macronutrient intake. Methods: This cross-sectional study analyzed data from 7408 adults aged ≥40 years in the 2011–2014 National Health and Nutrition Examination Survey (NHANES). Taste disorders and sociodemographic factors were identified through self-reported questionnaires and interviews. Dietary quality was assessed using the Healthy Eating Index (HEI)-2015. Weighted logistic regression models were used to identify correlates of taste disorders, while Wilcoxon rank-sum tests and linear regression models were used to analyze dietary quality and macronutrient intake. Multivariable-adjusted models were adjusted for age, sex, race/ethnicity, socioeconomic status (as measured by educational attainment and family income level), smoking status, physical activity, body mass index (BMI), and a history of chronic diseases (hypertension, high cholesterol, cardiovascular disease, diabetes, and cancer). Results: Taste disorders had a prevalence of 5.1% (95% confidence interval (CI): 4.5–5.6). Higher rates were observed in individuals with lower educational attainment, lower income, and a history of diabetes (7.3%; 95% CI: 5.8–8.7), cardiovascular disease (9.7%; 95% CI: 7.7–12.0), and cancer (7.0%; 95% CI: 4.1–10.0). Those with taste disorders had poorer diet quality, lower energy intake, lower protein intake, and higher sugar consumption compared with those without (all p < 0.01). Conclusions: US adults exhibited a high prevalence of taste disorders, particularly among individuals with lower socioeconomic status, chronic conditions, and those who are non-Hispanic White, non-Hispanic Black, and Hispanic. An increased prevalence is associated with poorer diet quality, highlighting the need for targeted interventions to address the nutritional impacts. Due to the limitations of the cross-sectional design, this study could not establish causality between taste disorders and dietary intake or patterns, underscoring the need for longitudinal studies to confirm the observed relationships.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".