Natural stable isotopes: new tracers in environmental health studies
Bibliographic record
Abstract
An important objective of modern environmental sciences is to elucidate the relationship between environmental pollution and human health. The development of adverse health effects is heavily influenced by extrinsic environmental factors. There is evidence that environmental factors may contribute >70%–90% to the development of most common cancer types, while only ∼10%–30% are attributed to unavoidable intrinsic risks (e.g. random errors in DNA replication) [1]. However, research into environmental health is limited by a lack of proper analytical tools. It is still extremely difficult to screen health risks and trace the sources of pollutants in complex systems by conventional analytical methods that mainly rely on the concentration analysis of environmental samples. The analysis of stable isotope ratios (beyond conventional elements such as C, H, O, N, S) can provide powerful tracers and chronometers for geosciences, archaeology, anthropology and environmental studies [2]. In the past decade, their application in health research has also been explored, but at a relatively slow rate. The isotopic composition of several biologically relevant elements (e.g. Fe, Ca, Cu, S) in the human body has been examined. For example, the Fe isotopic composition of blood was found to differ between individuals and sexes [3] and the Fe in blood of hereditary hemochromatosis patients was isotopically heavier than that of healthy individuals due to isotope-sensitive intestinal Fe absorption [4]. Another study found that the natural stable isotope composition of Ca in urine responded rapidly to changes in bone mineral balance [5]. The onset of bone loss could be detected by monitoring changes in Ca isotope ratios in one week—much quicker than that using desitometry, which often requires wait times in the order of months or years. Thus, the analysis of Ca isotope ratios may provide a more promising approach for early diagnosis and therapy for metabolic bone diseases. A recent paper studied natural Cu and S isotope ratio variations in the blood of hepatocellular carcinoma (HCC) patients [6]. The hypoxic tumor microenvironment may affect the metabolism and the redox state of some bioessential elements, such as Cu and S, consequently resulting in stable isotope fractionation. With regard to liver cancer, the liver is the main Cu reservoir in the human body and a pivotal organ for the metabolism of S-containing amino acids. In the blood of HCC patients, the Cu level was higher than that in control subjects, and Cu and S were enriched in light isotopes (63Cu enriched by ∼0.4‰ and 32S enriched by ∼1.5‰) compared with the control subjects. Notably, the isotopic signatures indicated that the extra Cu burden in the blood of HCC patients was not due to exogenous Cu uptake (dietary origin), but rather reflected the massive reallocation and subsequent immobilization of Cu within cysteine-rich proteins, such as metallothioneins [6]. Therefore, stable isotopes may not only be employed as new biomarkers for cancer diagnosis, but also provide extra information in pathological studies by enabling tracing of elements. Inspired by those early reports, we believe that stable isotope ratio measurements are potentially powerful tools in different branches of environmental health science. They may be used as fast-responding biomarkers in environmental epidemiological studies and provide extra concentration-independent information reflecting the biological processes (e.g. redox reaction, metal-protein binding) on toxicological mechanisms and health risks in exposure studies. Further studies are also encouraged to expand the application to additional elements, including environmental contaminants (e.g. Hg, Pb, Cr), to directly link extrinsic environmental factors with diseases.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".