Age-related changes in laboratory test results. Clinical implications

Inclusive Laboratory Reference Intervals and Clinical Studies to Reduce Health Disparities

Health inequities are common and in part a consequence of non-inclusive health research. The underrepresentation of specific populations in research, including women, children, youth and the elderly, racialized and ethnic groups, and sexual and gender minorities, results in limited ability to develop appropriate reference intervals, determine effective treatments, and establish health guidelines to optimize health outcomes for all. By working together toward greater inclusivity in all aspects of health research and care, health care providers and professional medical societies, including laboratory medicine practitioners, have the ability to achieve health equity.

Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine

The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

Natural Progression of Routine Laboratory Markers after Spinal Trauma: A Longitudinal, Multi-Cohort Study

Our objective was to track and quantify the natural course of serological markers over the 1st year following spinal cord injury. For that purpose, data on serological markers, demographics, and injury characteristics were extracted from medical records of a clinical trial (Sygen) and an ongoing observational cohort study (Murnau study). The primary outcomes were concentration/levels/amount of commonly collected serological markers at multiple time points. Two-way analysis of variance (ANOVA) and mixed-effects regression techniques were used to account for the longitudinal data and adjust for potential confounders. Trajectories of serological markers contained in both data sources were compared using the slope of progression. Our results show that, at baseline (≤ 2 weeks post-injury), most serological markers were at pathological levels, but returned to normal values over the course of 6-12 months post-injury. The baseline levels and longitudinal trajectories were dependent on injury severity. More complete injuries were associated with more pathological values (e.g., hematocrit, ANOVA test; χ2 = 68.93, df = 3, adjusted p value <0.001, and χ2 = 73.80, df = 3, adjusted p value <0.001, in the Sygen and Murnau studies, respectively). Comparing the two databases revealed some differences in the serological markers, which are likely attributable to differences in study design, sample size, and standard of care. We conclude that because of trauma-induced physiological perturbations, serological markers undergo marked changes over the course of recovery, from initial pathological levels that normalize within a year. The findings from this study are important, as they provide a benchmark for clinical decision making and prospective clinical trials. All results can be interactively explored on the Haemosurveillance web site (https://jutzelec.shinyapps.io/Haemosurveillance/) and GitHub repository (https://github.com/jutzca/Systemic-effects-of-Spinal-Cord-Injury).

Establishment of reference intervals for serum thyroid-stimulating hormone, free and total thyroxine, and free and total triiodothyronine for the Beckman Coulter DxI-800 analyzers by indirect method using data obtained from Chinese population in Zhejiang Province, China

BACKGROUND

In order to establish suitable reference intervals of thyroid-stimulating hormone (TSH), free (unbound) T4 (FT4), free triiodothyronine (FT3), total thyroxine (T4), and total triiodothyronine (T3) for the patients collected in Zhejiang, China, an indirect method was developed using the data from the people presented for routine health check-up.

METHODS

Fifteen thousand nine hundred and fifty-six person's results were reviewed. Box-Cox or Case Rank was used to transform the data to normal distribution. Tukey and Box-Plot methods were used to exclude the outliers. Nonparametric method was used to establish the reference intervals following the EP28-A3c guideline. Pearson correlation was used to evaluate the correlation between hormone levels and age, while Mann-Whitney U test was employed for quantification of concentration differences on the people who are younger and older than 50 years old.

RESULTS

Reference intervals were 0.66-4.95 mIU/L (TSH), 8.97-14.71 pmol/L (FT4), 3.75-5.81 pmol/L (FT3), 73.45-138.93 nmol/L (total T4), and 1.24-2.18 nmol/L (total T3) in male; conversely, reference intervals for female were 0.72-5.84 mIU/L (TSH), 8.62-14.35 pmol/L (FT4), 3.59-5.56 pmol/L (FT3), 73.45-138.93 nmol/L (total T4), and 1.20-2.10 nmol/L (total T3). FT4, FT3, and total T3 levels in male and FT4 level in female had an inverse correlation with age. Total T4 and TSH levels in female were directly correlated. Significant differences in these hormones were also found between younger and older than 50 years old except FT3 in female.

CONCLUSIONS

Indirect method can be applied for establishment of reference intervals for TSH, FT4, FT3, total T4, and total T3. The reference intervals are narrower than those previously established. Age factor should also be considered.

A Cross-Sectional Study of Ageing and Cardiovascular Function over the Baboon Lifespan

Background

Ageing is associated with changes at the molecular and cellular level that can alter cardiovascular function and ultimately lead to disease. The baboon is an ideal model for studying ageing due to the similarities in genetic, anatomical, physiological and biochemical characteristics with humans. The aim of this cross-sectional study was to investigate the changes in cardiovascular profile of baboons over the course of their lifespan.

Methods

Data were collected from 109 healthy baboons (Papio hamadryas) at the Australian National Baboon Colony. A linear regression model, adjusting for sex, was used to analyse the association between age and markers of ageing with P < 0.01 considered significant.

Results

Male (n = 49, 1.5–28.5 years) and female (n = 60, 1.8–24.6 years) baboons were included in the study. Age was significantly correlated with systolic (R² = 0.23, P < 0.001) and diastolic blood pressure (R² = 0.44, P < 0.001), with blood pressure increasing with age. Age was also highly correlated with core augmentation index (R² = 0.17, P < 0.001) and core pulse pressure (R² = 0.30, P < 0.001). Creatinine and urea were significantly higher in older animals compared to young animals (P < 0.001 for both). Older animals (>12 years) had significantly shorter telomeres when compared to younger (<3 years) baboons (P = 0.001).

Conclusion

This study is the first to demonstrate that cardiovascular function alters with age in the baboon. This research identifies similarities within cardiovascular parameters between humans and baboon even though the length of life differs between the two species.

Biological variation and reference change values of common clinical chemistry and haematologic laboratory analytes in the elderly population

BACKGROUND

Biological variation (BV) and reference change values (RCVs) have been widely described for the general population, but the use of these data derived from adults in the elderly population is a controversial issue. We determined the within- and between-subject BV and RCV in both elderly and young people and compared them with previously published analyses.

METHODS

Samples were collected from 135 volunteers over 80 years of age at weekly intervals over 4 weeks. Eighteen biochemical and eight haematological analytes were measured. The Fraser and Harris methods were used to calculate the components of BV and RCV. To perform a comparative analysis, a reference group of 118 young subjects was studied under the same conditions.

RESULTS

The obtained coefficients of BV showed statistical differences in many cases, but in general, both the elderly and young patient data fall within the ranges previously described for the general population. The indexes of individuality for the analytes investigated did not exceed 1.4 in any case and were <0.6 for some analytes. The RCVs derived from elderly subjects were similar to those published in the young population, both in healthy and diseased individuals.

CONCLUSIONS

The strong individuality observed supports the preferential use of RCVs rather than population-based reference intervals in elderly people. For most of the analytes studied, data from the young population can be applied to elderly people, but the specific elderly coefficients of BV and RCVs are a recommended option.

Optimal thyrotropin level: normal ranges and reference intervals are not equivalent

This paper marshals arguments in support of a narrower, optimal or true normal range for thyrotropin (TSH) of 0.4 to 2.5 mIU/L, based on clinical results and recent information on the relatively stable and narrow range of values in patients without thyroid disease. The terminology used for TSH results is clarified in an attempt to help physicians interpret, explain, and respond to TSH test results for their patients.

Clinical chemistry variables in normal elderly and healthy ambulatory populations: comparison with reference values

Laboratory values of the most commonly assayed clinical chemistry variables were determined in selected elderly and healthy ambulatory populations. The upper and lower limits (2.5 and 97.5 fractiles) were compared with the adult reference values in use in university hospitals of Switzerland. The results suggest that conventional adult reference values can be used for most variables in the elderly and that these values are also useful in an ambulatory population.

Epidemiological evidence for the disruption of ionized calcium homeostasis in the elderly

Ionized calcium (Ca2+), phosphate, albumin, total calcium, and pH measurements taken from participants in a large population-based epidemiological study were examined to determine the change in physiological variation with age for persons over 43 years old. Only Ca2+ showed a statistically significant increase in SD with age (p < 0.0001). The Ca2+ coefficients of variation (CV) increased from 2.92% in the youngest age group (43-54 years) to 3.69% in the oldest age group (75-86 years of age). In females, the increase in Ca2+ variability was nearly complete by age 55. Males also showed a significant (p = 0.006) increase in SD between the 43-54 age group and the 55-64 age group, however, Ca2+ variability did not plateau after age 55 in men as it did in women. In the 43-54 (p = 0.04) and 55-64 (p = 0.03) age group men showed significantly better physiological control of Ca2+ than women. Phosphate showed a slight decrease in CV with age. These data suggest that Ca2+ homeostasis is disrupted in the same age groups that are most vulnerable to osteoporosis.