α‐1 acid glycoprotein but not C‐reactive protein is associated with significantly lower serum zinc concentrations among Congolese children aged 6–59 months and has a substantial impact on prevalence estimates of zinc deficiency
Bibliographic record
Abstract
Background Serum zinc (Zn) is a negative acute phase reactant; hence, concentrations decrease in the presence of inflammation and infection. There is no current consensus on how to adjust serum Zn concentrations for inflammation. Objectives The aim was to determine associations between serum Zn concentration and inflammation biomarkers (C‐reactive protein [CRP] and α‐1 acid glycoprotein [AGP]) and to compare means and the prevalence of Zn deficiency using unadjusted serum Zn concentrations and serum Zn concentrations adjusted for inflammation using study generated correction factors (CFs) among children in the Democratic Republic of the Congo. Methods Non‐fasting blood was collected in trace‐element free vacutainers from 744 children (6–59 mo) recruited from South Kivu and Bas Congo provinces in 2014 using a probability proportionate to size sampling method. Serum was analyzed for Zn (n=691), CRP and AGP concentrations (n=687). Linear regression was used to estimate associations between serum Zn and AGP and CRP concentrations and to generate CFs (calculated as 1 divided by the geometric mean ratio) for Zn based on three stages of inflammation: (incubation [CRP >5 mg/L], early convalescence [CRP >5 mg/L and AGP >1 g/L] and late convalescence [AGP >1 g/L]), relative to children with no inflammation. Results Overall, the prevalence of acute (CRP >5 mg/L) and chronic (AGP >1 g/L) inflammation was 29% (n=197) and 66% (n=455), respectively. Unadjusted mean (95% CI) serum Zn concentration was 9.4 (9.3, 9.6) μmol/L. Unadjusted mean ± SD serum Zn concentration was 10.0 ± 1.9 μmol/L among children with no inflammation (n=213; 35%), 9.8 ± 1.1 μmol/L among children in the incubation stage (n=11; 2%), 8.7 ± 2.1 μmol/L among children in early convalescence (n=117; 19%), and 9.4 ± 2.1 μmol/L among children in late convalescence (n=260; 43%). A 1 g/L increase in AGP was associated with a 0.5 (95% CI: 0.3, 0.7) μmol/L lower serum Zn concentration (P<0.001). CRP was not significantly associated with serum Zn concentration (P=0.11). Study generated CFs (95% CI) for Zn were 1.01 (0.88, 1.15), 1.16 (1.11, 1.21) and 1.07 (1.03, 1.11) for incubation, early and late convalescence stages, respectively. After applying the CFs, adjusted mean (95% CI) serum Zn concentration was 10.1 (9.8, 10.2) μmol/L. Overall, the prevalence of Zn deficiency (<8.7 μmol/L) decreased from 35% (n=244) using unadjusted serum Zn concentrations to 24% (n=160) using serum Zn concentrations adjusted for inflammation. Conclusion AGP is associated with significantly lower serum Zn concentrations and the application of study generated CFs had a substantial impact on Zn deficiency prevalence rates. AGP appears more useful than CRP to measure the effects of inflammation on serum Zn in our study population; however, we acknowledge that the use of more than one inflammation biomarker is ideal. Adjustment for inflammation appears to be warranted for accurate estimates of population‐level Zn status. Support or Funding Information Funding for this research was provided by HarvestPlus. C.D.K. received a doctoral research award from the International Development Research Centre (Canada) and a Vanier scholarship from the Canadian Institutes of Health Research (CIHR).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".