Age and sex variation in serum albumin concentration: an observational study

Weaving, Gary; Batstone, G.F.; Jones, Richard G.

doi:10.1177/0004563215593561

Cited by 171 publications

(130 citation statements)

References 10 publications

Supporting

Mentioning

119

Contrasting

Unclassified

Order By: Relevance

“…Subsequently, a noticeable decrease was observed from >65 years age group (51-89 g/L). This is in support of a study conducted in the United Kingdom by Weaving et al [18] on the evaluation of age and gender variations for albumin; the major constituent of TSP, using the indirect and non-parametric testing methods to determine the reference ranges. The age group of 1-5 years had an albumin level of 43 g/L and there was an increase in the albumin values until the 36-40 years age group.…”

Section: Discussionsupporting

confidence: 81%

Analysis of Reference Ranges of Total Serum Protein in Namibia: Clinical Implications

et al. 2020

View full text Add to dashboard Cite

A reference range is an essential part of clinical laboratory test interpretation and patient care. The levels of total serum protein (TSP) are measured in sera to assess nutritional, liver, and kidney disorders. This study determined the TSP reference range with respect to gender, age, and region in Namibia. A retrospective cross-sectional study was conducted to determine the TSP reference range among 78,477 healthy participants within the ages of less than one year to more than 65 years in 14 regions of Namibia. The reference range of TSP was 51-91 g/L for females and 51-92 g/L for males. A reduced TSP range of 48.00-85.55 g/L (2.5-97.5 percentiles) was established at <1-5 years and increased towards adolescence. An uttermost range of 54-93 g/L was observed from 36-65 years of age. At the age >65 years; a steady decline in the reference range (51.00-89 g/L) was recorded. An upper TSP range of 53-92 g/L (2.5-97.5 percentiles) was detected in Erongo, Zambezi, Hardap, Kavango East, and a comparable trend was also seen in Omusati with a 54-91 g/L range. Meanwhile; a reduced TSP range of 50-89 g/L was identified in Ohangwena. This study showed that gender, age, and geographical location can impact TSP levels with a significant clinical difference (p < 0.05) between each category.Proteomes 2020, 8, 7 2 of 10 also measured as part of the kidney function tests. Disorders such as glomerulonephritis are associated with decreased levels of TSP as a result of damage to the glomeruli; therefore, affecting the filtration that leads to excretion of protein into the urine. The loss of proteins in the urine then leads to decreased levels of proteins in the blood. An example of a kidney disorder that is associated with high TSP levels is kidney inflammation [4].Protein levels in the human body have different ranges depending on age, ethnicity, and gender. This was revealed in a study conducted in Hawaii among four different races, which indicated a wider reference range variation among the different population groups [5]. Scientific reports have shown the effects of dietary protein intake, gender, and age on TSP, and that it decreases as the person ages and the rate of decrease is more rapid in women than men, with a suggestion that an appropriate range of TSP based on age and gender differences should be used for clinical diagnostic applications [6,7].Reference ranges are sets of values used by health professionals to interpret patient results obtained from the laboratory. Reference ranges are considered as the authoritative tools for the interpretation of data and decision making in laboratory science. It is established by collecting samples from a population that is considered to be normal or healthy [8]. The collected samples are tested for the concentration of the different analytes, e.g., sodium, glucose, proteins, enzymes, and many other laboratory analytes. The results obtained are then used to determine a range that lies within the 95% confidence interval on the Gaussian distribution curve. Proteins are important in the...

show abstract

Section: Discussionsupporting

confidence: 81%

Analysis of Reference Ranges of Total Serum Protein in Namibia: Clinical Implications

et al. 2020

View full text Add to dashboard Cite

show abstract

“…14,41 Quantifying free concentration might be relevant in some patients, for example, in cases of suspicion of toxicity, polytherapy, hypoalbuminemia, or renal failure. 12 Because serum albumin concentration increases with age in children 42 and unbound concentration of valproate correlates with albumin concentration, 12 we could expect valproate free concentration to vary according to age. Nevertheless, no relationship between age and unbound valproate concentration has been reported in the literature yet, 43 and the decrease of clearance with increasing age seems to be mostly because of maturational changes in hepatic metabolism.…”

Section: Discussionmentioning

confidence: 99%

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Tauzin

Tréluyer

Nabbout

et al. 2018

The Journal of Clinical Pharma

View full text Add to dashboard Cite

Valproate is an old-generation antiepileptic drug often used in children. The pharmacokinetics of valproate are noteworthy for a large and difficult to predict interindividual variability in measured serum concentrations and for saturable protein binding. A model-based approach to personalize valproate treatment could be relevant in pediatric patients. The aims of this study were to review all published valproate population pharmacokinetic models in children and assess them by external validation to determine their predictive performance. Through simulations with the best model, we evaluated dosing regimen. A validation data set included valproate serum concentrations assayed during routine therapeutic drug monitoring of epileptic children. We applied to our population 11 published pediatric population pharmacokinetic models. For each model, predictive performance was assessed by external validation, using bias and precision calculations as well as goodness-of-fit plots. Dose simulations were conducted with the best predictive model to evaluate dosing regimen. The validation data set contained 178 valproate concentrations ranging from 13.4 to 128 mg/L from 114 patients. The best model exhibited a mean prediction error of 6.6 mg/L and a root mean squared error of 25.1 mg/L, with no model misspecification evidenced by visual predictive check. In our cohort, half the patients had a trough concentration <50 mg/L. Simulations suggested increasing doses, especially for children ࣘ40 kg. External evaluation of published valproate pharmacokinetic models enabled us to identify a suitable model for simulations and Bayesian forecasting. Dosing regimen should be adjusted to weight, with decreasing doses with increasing weight.

show abstract

“…The interaction analysis informed of higher values of IgM and Albumin in males than in females, however when we considered age, it showed a different pattern for each biomarker (IgM increasing with age and Albumin decreasing with age). If we consider those markers representative of the two-axis observed in the hierarchical clustering (acute and humoral responses), it will indicate an increase of the acute inflammatory response and a decrease of the immune responses with age as seen in previous studies (13,(58)(59)(60).…”

Section: Discussionmentioning

confidence: 86%

Association between serum markers of the humoral immune system and inflammation in the Swedish AMORIS study

Santaolalla

Sollie

Rislan

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors influence these interactions.Methods: From the AMORIS cohort, 5,513 individuals were identified with baseline measurements of serum humoral immune (immunoglobulin G, A & M) and inflammation (CRP, albumin, haptoglobin, white blood cells, iron & total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age.Results: Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Markers of the humoral immune system, immunoglobulin A and G, also correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation.Conclusions: These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Correlation and clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers combined, when assessing risk of clinical outcomes such as cancer.

show abstract

Age and sex variation in serum albumin concentration: an observational study

Cited by 171 publications

References 10 publications

Analysis of Reference Ranges of Total Serum Protein in Namibia: Clinical Implications

Analysis of Reference Ranges of Total Serum Protein in Namibia: Clinical Implications

Simulations of Valproate Doses Based on an External Evaluation of Pediatric Population Pharmacokinetic Models

Association between serum markers of the humoral immune system and inflammation in the Swedish AMORIS study

Contact Info

Product

Resources

About