Diurnal rhythm in clinical chemistry: An underrated source of variation

Time-dependent characteristics of analytical measurands

OBJECTIVES

Biological variation is a relevant component of diagnostic uncertainty. In addition to within-subject and between-subject variation, preanalytical variation also includes components that contribute to biological variability. Among these, daily recurring, i.e., diurnal physiological variation is of particular importance, as it contains both a random and a non-random component if the exact time of blood collection is not known.

METHODS

We introduce four time-dependent characteristics (TDC) of diurnal variations for measurands to assess the relevance and extent of time dependence on the evaluation of laboratory results.

RESULTS

TDC address (i) a threshold for considering diurnality, (ii) the expected relative changes per time unit, (iii) the permissible time interval between two blood collections at different daytimes within which the expected time dependence does not exceed a defined analytical uncertainty, and (iv) a rhythm-expanded reference change value. TDC and their importance will be exemplified by the measurands aspartate aminotransferase, creatine kinase, glucose, thyroid stimulating hormone, and total bilirubin. TDCs are calculated for four time slots that reflect known blood collection schedules, i.e., 07:00-09:00, 08:00-12:00, 06:00-18:00, and 00:00-24:00. The amplitude and the temporal location of the acrophase are major determinates impacting the diagnostic uncertainty and thus the medical interpretation, especially within the typical blood collection time from 07:00 to 09:00.

CONCLUSIONS

We propose to check measurands for the existence of diurnal variations and, if applicable, to specify their time-dependent characteristics as outlined in our concept.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

How kidney clock works: circadian pattern of eGFR based on a population data group

A circadian regulation of renal function it has been described in the last few years. An intradaily variation in glomerular filtration rate (eGFR) has also been discovered at the individual level. The aim of this study was to check if there exists a circadian pattern of eGFR at population data group level and to compare the population results with those described at individual level. We have studied a total of 446,441 samples analysed in the emergency laboratories of two Spanish hospitals between January 2015 and December 2019. We selected all the records of eGFR values between 60 and 140 mL/min/1.73 m² using CKD-EPI formula from patients between 18 and 85 years. The intradaily intrinsic eGFR pattern was computed using the extraction time of day in four nested mixed linear and sinusoidal regression models. All models showed an intradaily eGFR pattern, but the estimated model coefficients differed depending on whether age was included. The inclusion of age improved the performance of the model. In this model, the acrophase occurred at 7:46 h. We describe the distribution of eGFR values depending on the time in two different populations. This distribution is adjusted to a circadian rhythm that behaves similarly to the individual rhythm. This pattern is similar in each of the years studied from each hospital as well as between both hospitals. The results found suggest the incorporation of the concept of "population circadian rhythm" into the scientific world.

Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation

BACKGROUND

Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results.

METHODS

Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error.

RESULTS

ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25.

CONCLUSIONS

AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.

Evaluation of Appetite-Regulating Hormones ın Young Children with Autism Spectrum Disorder

This study aimed to investigate the role of leptin, ghrelin, neuropeptide Y, and nesfatin-1 in young children with autism spectrum disorder (ASD). A total of 44 children with ASD and 44 healthy controls aged 18-60 months were included. Plasma levels of hormones were measured using commercial enzyme-linked immunosorbent assay kits. Plasma leptin and ghrelin levels were significantly higher in the ASD group than in the control group. However, no significant difference for plasma neuropeptide Y and nesfatin-1 levels was detected between the groups. No relation was found between the severity of ASD symptoms, severity of eating problems, and plasma levels of hormones. Leptin and ghrelin may play a potential role in the pathogenesis of ASD.