Salivary Biomarkers of Nutritional Status: a Systematic Review
Bibliographic record
Abstract
Abstract Introduction: Full nutritional assessments are currently complex and invasive. There is a need for a non-invasive, timely and cost-effective method to assess nutritional status. Evidence indicates the usefulness of saliva in diagnosing oral or systemic disorders. Saliva is suggested to be a reliable and non-invasive matrix in which to measure nutritional biomarkers. The aim of this work was to systematically review the evidence for salivary biomarkers as indicators of nutritional status. Materials and Methods: Studies identifying salivary biomarkers in relation to nutritional status or dietary intake outcomes were included. A search strategy combined terms “saliva” AND “biomarkers” AND “nutrition”. Four databases were searched, MEDLINE, EMBASE, Web of Science and Scopus. All study designs conducted in humans of all ages, from all countries and settings were included. Non-English and animal studies were excluded. Risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias tool where applicable. (PROSPERO Registration Number:CRD42018107667) Results: 6585 papers were identified, 4836 papers remained after removing duplicates, 4715 were irrelevant, 134 full-texts were assessed for eligibility and 64 papers included in the final analysis. A number of potential salivary biomarkers related to nutritional status were identified including: total protein, albumin, prealbumin, transferrin, ferritin and iron. Total protein levels in saliva in malnourished individuals were significantly different to controls in 7/10 studies (70%). In one study conducted in individuals with iron deficiency anaemia (IDA), total protein was significantly different to controls. Albumin levels in malnourished individuals were significantly different to controls in 5/8 studies (62.5%). Prealbumin and transferrin levels in malnourished individuals were significantly different to controls in 3/3 studies (100%). In one study conducted in malnourished individuals, salivary ferritin levels was significantly different to controls. Ferritin levels in individuals with IDA were significantly different to controls in 3/3 studies (100%). Iron levels in individuals with IDA were significantly different in 2/2 studies (100%). However, even within the studies above where significant differences existed, the direction of salivary biomarker differences was sometimes inconsistent. For example, total protein in malnourished individuals was significantly lower than controls in three studies, higher in three studies and one showed mixed findings. In addition, overall the quality of evidence available was very poor. Discussion: Despite conflicting evidence in salivary nutritional biomarkers in individuals with malnutrition or IDA, saliva may be a useful non-invasive matrix to assess nutritional status. Further high quality research exploring the utility of these biomarkers is required.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".