The Association Between Food Insecurity and Hypertension in the Context of CKD: Reviewing the Literature
Bibliographic record
Abstract
Background: Hypertension (HTN) and dietary choices are closely related. HTN is a leading cause of CKD and a poor diet contributes to about 80% of HTN. Food insecurity (FI) is defined as the lack of secure access to sufficient amounts of safe and nutritious food for normal growth and development of an active and healthy life and is prevalent and determines dietary choices. The prevalence of FI for adults has increased by 84% in the last 20 years and the mortality rate from CKD has also increased by over 40% in the same time frame making this a relevant issue to address. We wanted to explore the relationship between the modifiable risk factors of FI and HTN in the context of CKD. Methods: A narrative review was performed using a systematic search in PubMed. Search terms were food insecurity, chronic disease, hypertension, social determinants of health and for years 2004-2023. After manual appraisal of each study, findings were narrowed down to exclude other literature or systematic reviews. Data synthesis was conducted according to a thematic synthesis approach. Results: Out of a total of 147 articles, 26 studies were included in the final review. The thematic synthesis enabled the construction of 4 themes: “Low income negatively affects diet and is associated with food insecurity and HTN”; “Education and health literacy affect diet and the correlation between food insecurity and HTN”; “Access to healthy food feeds into the association between food insecurity and HTN”; and “Poor diet aggravates other adverse health conditions and is correlated to food insecurity and HTN”. Conclusions: FI is correlated to poor nutrition and HTN. Both FI and HTN together are an increasing global public health concern. The literature indicated how diet and health behavior are modifiable by addressing low income, limited access to healthy food and poor education thereby making it relevant to mitigate the negative dietary consequences of food insecurity on HTN and cardiovascular disease at a local and global scale. This is a threat also for the development of CKD. Relevance Statement: Globally, HTN is a leading non-communicable risk factor for cardiovascular morbidity and mortality. Hypertension is one of the leading causes of CKD. FI is associated with HTN, and these global health threats beg for urgent attention to improve health outcomes on a worldwide scale.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".