Community based reference interval of selected clinical chemistry parameters among apparently healthy Adolescents in Mekelle City, Tigrai, Northern Ethiopia

BACKGROUND

Locally established clinical laboratory reference intervals (RIs) are required to interpret laboratory test results for screening, diagnosis and prognosis. The objective of this study was establishing reference interval of clinical chemistry parameters among apparently healthy adolescents aged between 12 and 17 years in Mekelle, Tigrai, northern part of Ethiopia.

METHODS

Community based cross sectional study was employed from December 2018 to March 2019 in Mekelle city among 172 males and 172 females based on Multi stage sampling technique. Blood samples were tested for Fasting blood sugar (FBS), alanine aminino transferase (ALT), aspartate amino transferase (AST), alkaline phosphatase (ALP), Creatinine, urea, total protein, albumin (ALB), direct and indirect bilirubin (BIL.D and BIL.T) using 25 Bio system clinical chemistry analyzer. Results were analyzed using SPSS version 23 software and based on the Clinical Laboratory Standard Institute (CLSI)/ International Federation of Clinical Chemistry (IFCC) C 28-A3 Guideline which defines the reference interval as the 95% central range of 2.5th and 97.5th percentiles. Mann Whitney U test, descriptive statistics and box and whisker were statistical tools used for analysis.

RESULTS

This study observed statistically significant differences between males and females in ALP, ALT, AST, Urea and Creatinine Reference intervals. The established reference intervals for males and females, respectively, were: ALP (U/L) 79.48-492.12 versus 63.56-253.34, ALT (U/L) 4.54-23.69 versus 5.1-20.03, AST 15.7-39.1 versus 13.3-28.5, Urea (mg/dL) 9.33-24.99 versus 7.43-23.11, and Creatinine (mg/dL) 0.393-0.957 versus 0.301-0.846. The combined RIs for Total Protein (g/dL) was 6.08-7.85, ALB (g/dL) 4.42-5.46, FBS(mg/dL) 65-110, BIL.D (mg/dL) 0.033-0.532, and BIL.T (mg/dL) 0.106-0.812.

CONCLUSIONS

The result showed marked difference among sex and with the company derived values for selected clinical chemistry parameters. Thus, use of age and sex specific locally established reference intervals for clinical chemistry parameters is recommended.

The theory of reference values: an unfinished symphony

The history of the theory of reference values can be written as an unfinished symphony. The first movement, allegro con fuoco, played from 1960 to 1980: a mix of themes devoted to the study of biological variability (intra-, inter-individual, short- and long-term), preanalytical conditions, standardization of analytical methods, quality control, statistical tools for deriving reference limits, all of them complex variations developed on a central melody: the new concept of reference values that would replace the notion of normality whose definition was unclear. Additional contributions (multivariate reference values, use of reference limits from broad sets of patient data, drug interferences) conclude the movement on the variability of laboratory tests. The second movement, adagio, from 1980 to 2000, slowly develops and implements initial works. International and national recommendations were published by the IFCC-LM (International Federation of Clinical Chemistry and Laboratory Medicine) and scientific societies [French (SFBC), Spanish (SEQC), Scandinavian societies…]. Reference values are now topics of many textbooks and of several congresses, workshops, and round tables that are organized all over the world. Nowadays, reference values are part of current practice in all clinical laboratories, but not without difficulties, particularly for some laboratories to produce their own reference values and the unsuitability of the concept with respect to new technologies such as HPLC, GCMS, and PCR assays. Clinicians through consensus groups and practice guidelines have introduced their own tools, the decision limits, likelihood ratios and Reference Change Value (RCV), creating confusion among laboratorians and clinicians in substituting reference values and decision limits in laboratory reports. The rapid development of personalized medicine will eventually call for the use of individual reference values. The beginning of the second millennium is played allegro ma non-troppo from 2000 to 2012: the theory of reference values is back into fashion. The need to revise the concept is emerging. The manufacturers make a friendly pressure to facilitate the integration of Reference Intervals (RIs) in their technical documentation. Laboratorians are anxiously awaiting the solutions for what to do. The IFCC-LM creates Reference Intervals and Decision Limits Committee (C-RIDL) in 2005. Simultaneously, a joint working group IFCC-CLSI is created on the same topic. In 2008 the initial recommendations of IFCC-LM are revised and new guidelines are published by the Clinical and Laboratory Standards Institute (CLSI C28-A3). Fundamentals of the theory of reference values are not changed, but new avenues are explored: RIs transference, multicenter reference intervals, and a robust method for deriving RIs from small number of subjects. Concomitantly, other statistical methods are published such as bootstraps calculation and partitioning procedures. An alternative to recruiting healthy subjects proposes the use of biobanks conditional to the availability of controlled preanalytical conditions and of bioclinical data. The scope is also widening to include veterinary biology! During the early 2000s, several groups proposed the concept of 'Universal RIs' or 'Global RIs'. Still controversial, their applications await further investigations. The fourth movement, finale: beyond the methodological issues (statistical and analytical essentially), important questions remain unanswered. Do RIs intervene appropriately in medical decision-making? Are RIs really useful to the clinicians? Are evidence-based decision limits more appropriate? It should be appreciated that many laboratory tests represent a continuum that weakens the relevance of RIs. In addition, the boundaries between healthy and pathological states are shady areas influenced by many biological factors. In such a case the use of a single threshold is questionable. Wherever it will apply, individual reference values and reference change values have their place. A variation on an old theme! It is strange that in the period of personalized medicine (that is more stratified medicine), the concept of reference values which is based on stratification of homogeneous subgroups of healthy people could not be discussed and developed in conjunction with the stratification of sick patients. That is our message for the celebration of the 50th anniversary of Clinical Chemistry and Laboratory Medicine. Prospects are broad, enthusiasm is not lacking: much remains to be done, good luck for the new generations!

Mass spectrometry-based biomarker discovery: toward a global proteome index of individuality

Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry-based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed.

Evaluating sequential values using time-adjusted biological variation

One can compare the difference between two sequential values with the biological variation. Biological variation is a measure of the random disturbances of an analyte's value, measured at different times. When the difference > Z square root 2 SD(BV) then the difference is due to an underlying disease process or physiologic change. A Z value of 1.96 yields a 95% confidence limit. When using multiple sequential values or time periods exceeding that for the empirically derived biological variance, a random walk model allows one to estimate the spread of the variance. For a difference, (delta) to be significant, delta > Z square root 2n SD(BV), where n is the ratio of time reflecting the longer time period divided by the shorter time period. Not all variances grow to this degree over time, because restoring forces diminish the extent of random disturbances. The relationship between a biological variance measured over a longer time period to the one measured over a shorter period can be expressed in terms of this restoring force as SD2 BV,n = SD2 BV,1 sigma(n) j=1 e(-2)(j-1)phi, where n is the ratio of time periods. One can calculate phi using this formula and a spreadsheet. From phi one can calculate the biological variance for any time period, within experimental limits, and compare the difference in sequential values with it. Test intervals can be calculated based on these biological variances.

A method for early detection of abnormal trends in serial clinical examination results over time

In this paper we describe a method for the early detection of changing health conditions which may eventually lead to serious disease. The method makes use of health screening test results accumulated for individuals and can be applied to those who have taken at least four health examinations. The degree of abnormality is calculated using a logistic regression equation. The variables defining the equation are selected by factor analysis and a stepwise variable selection method based on the likelihood ratio criterion. Statistical estimates include linear regression coefficients, isotonic regression probabilities and their standard deviations. These are used to represent trends in health screening results over time. The method is illustrated using a sample of 308 persons with gastric cancer and 3002 healthy persons. Cross-validations were also performed.

Intra-individual variation: significant changes in parameters of lipid and carbohydrate metabolism in the individual and intra-individual variation in different test populations

Fasting serum triglyceride, cholesterol, HDL cholesterol, HDL 2&3 cholesterol, fasting plasma glucose and insulin and haemoglobin A1 were measured under standardised conditions in a group of laboratory volunteers. Intra-individual variation was calculated for each parameter from weekly measurements on at least eleven successive occasions, and the minimum change in each parameter that would be significant at the level P less than 0.05 was calculated. A further study compared intra-individual variation in different test populations. Data from the group of laboratory volunteers were taken to represent intra-individual variation under standard test conditions with an informed test population and data from a group of regularly monitored out-patients undergoing drug therapy for hypercholesterolaemia were taken to represent intra-individual variation under standard conditions in a typical test population. A group of in-patients under strict dietary control provided information on variation under extreme standardisation of test conditions. Intra-individual variation was greatest for all parameters in the outpatient population. Extreme standardisation of test condition reduced intra-individual variation beyond that of the laboratory volunteers only in the case of serum triglyceride.

Discriminant analysis based on multivariate response curves: a descriptive approach to dynamic allocation

We examine the problem of discriminating between two groups in the context of multivariate response curves observed over a specified time interval. We propose a descriptive solution for the case where one can determine the response curves by linear interpolation between successive observations. Unlike most previously reported methods that use only the current multivariate observation, our approach accounts for the history of the process. Moreover the method has the potential advantage of being applicable dynamically, as one observes the multivariate response curve. Finally, the method demonstrates simplicity and flexibility, two important features for successful, routine, clinical application.