Establishment of reference intervals of ten commonly used clinical chemistry analytes: a real-world study in China

Appropriate and reliable population-based reference intervals (RIs) serve as an important index for clinical diagnosis and treatment [1]. The ‘defining, establishing and verifying RIs’ in the Clinical Laboratory; Approved Guideline, developed by the Clinical and Laboratory Standards Institute and the International Federation of Clinical Chemistry and Laboratory Medicine, is the most widely recommended and used standard method for defining and determining RIs [2]. The guideline suggested that ideally RIs should be obtained from a healthy population of at least 120 individuals and its 90% CI should be estimated as well [3]. Nevertheless, practically, due to strict inclusion and exclusion criteria, large sample size requirement and difficulty in recruiting healthy volunteers, the establishment of RIs is often time consuming and costly [4]. Rather than establishing their own RIs, many clinical laboratories in China used RIs provided by guidelines or reagent manufacturer’s recommendations [5,6]. However, the RIs recommended by manufactures were often derived from data of another population, while RIs of analytes usually vary from one population to another [5,6]. The use of inappropriate RIs may result in misclassification of healthy and diseased conditions, and inappropriate treatment, which could have harmful effects on patients [7].

By using a large sample size of check-up data from the apparently healthy check-up population of Beijing, China, the present study aimed to establish RIs of ten commonly used clinical chemistry analytes, thereby providing evidence on RIs of population of China. The ten analytes were as follows: total cholesterol (TC), triglycerides (TG), apolipoprotein A1 (Apo A1), apolipoprotein B (Apo B), creatine kinase (CK), creatine kinase isoenzyme MB (CK-MB), glucose, alkaline phosphatase (ALP), γ-glutamyltransferase (GGT) and blood urea nitrogen (BUN).

Materials & methods

Study design & ethics approval

A retrospective cross-sectional study was conducted, using real-world check-up data from apparently healthy population of Beijing, China. The study was approved by the Institutional Review Board of Peking University Health Science Center (ID of the approval: IRB00001052-19077). The requirement for informed consent was waived due to the use of deidentified data obtained as part of routine check-ups.

Study participants

Participants who attended routine check-ups at Beijing MJ Health Check-up Center from 1 January 2010 to 31 December 2018 were selected as potential reference population to be included in this study. Since 2003, the MJ Health Group has recruited participants for paid membership in a standard health check-up program that provides a series of medical examinations with standard protocols including anthropometric measurements and physical and laboratory examinations such as blood and urinary tests, etc. Participants joined the health check-up program were encouraged to visit the center periodically to receive comprehensive medical assessments. All the participants filled in questionnaires before check-up. Demographic characteristics (age and gender), lifestyle (smoking and alcohol drinking) and past medical history (major illnesses, medications and surgery) were collected. For participants who underwent more than one check-up during the study period, the most recent record was included in this study. The following exclusion criteria were applied based on laboratory results, demographic information, lifestyle and medical history of each participant.

• Age <20 or >80 years;

• BMI <18.5 or ≥28 kg/m²;

• Excessive smoking (≥20 cigarettes/day) or alcohol drinking (≥30 g/day);

• Pregnant women or postpartum less than a year before;

• History of cancer, autoimmune disease, hypertension, diabetes, cardiovascular disease, cerebrovascular disease, tuberculosis, hepatitis or liver cirrhosis;

• History of surgery during the past 6 months, blood donation or severe blood loss during the past 4 months, or taking medications within 2 weeks;

• Systolic blood pressure/diastolic blood pressure ≥140/90 mmHg;

• Hemoglobin (Hb) <120 g/l (male) or <110 g/l (female) or white blood cell (WBC) >12.5 × 10⁹/l or <3 × 10⁹/l;

• Glucose ≥7.0 mmol/l, or HbA1c ≥38.8 mmol/mol;

• TG ≥2.26 mmol/l, TC ≥6.22 mmol/l, low-density lipoprotein cholesterol ≥4.1 mmol/l or high-density lipoprotein cholesterol <1.0 mmol/l;

• Creatinine >120 mmol/l, or uric acid >475 mmol/l;

• CK >500 U/l;

• Albumin <35 g/l, alanine aminotransferase >60 U/l (male) or >45 U/l (female) or aspartate aminotransferase >45 U/l (male) or >40 U/l (female);

• Positive hepatitis B surface antigen (HBsAg), anti-HCV or anti-HIV;

• Any abnormal results of protein, glucose, ketone, bilirubin, urobilinogen, nitrite, erythrocytes or WBC abnormal in urine routine test.

Participants who met any of the criteria above were excluded from the study. If an analyte of interest was involved in a criterion, then when establishing the RIs of this analyte, this criterion was not applied.

Specimen collection & processing

Participants were informed to keep routine diet and normal life habits 3 days before the check-up. Venous blood was collected under fasting conditions (at least 10-h overnight fasting). All blood samples were collected into 5 ml serum gel separator tubes (Becton, Dickinson and Company, NJ, USA), and left at room temperature for 30 min to clot, then centrifuged at 3500 r/min for 20 min (KUBOTA 5310, Kubota Corporation, Osaka, Japan). All the samples were analyzed immediately by the same automatic analyzer.

Instrumentation & analytical methods

A calibrated Roche Cobas c701 automatic analyzer (Roche Diagnostics, Mannheim, Germany) was employed to measure the concentrations of selected chemistry analytes. All testing reagents, including calibrators and controls, were supplied by Roche Diagnostics. The type of kits of the selected chemistry analytes was listed in Supplementary Table 1. All assays were carried out according to the manufacturers’ instructions using routine methods. TC, TG and GGT were measured using enzymatic colorimetric assay; CK, CK-MB, ALP and BUN were tested using colorimetric assay; Apo A1 and Apo B were detected using immunoturbidimetry method; glucose was examined using hexokinase method.

Quality control materials were measured every day before specimen testing, and internal quality control data were used to indicate whether laboratory test results obtained during the study period were accurate and reliable. The instruments were regularly calibrated by Multicalibrator C.f.a.s. calibration (Roche Diagnostics) and maintained according to the manufacturer’s specifications. The laboratory has been accredited by International Organization for Standardization 15,189 and followed the standards of External Quality Assessment and Internal Quality Control developed by National Health Commission of the People’s Republic of China. The intra- and inter-assay coefficients of variation for all concentrations were <2.5 and <5.0%, respectively.

Statistical analyses

The determination of RIs of these ten clinical chemistry analytes was performed according to the EP28-A3c guideline on ‘defining, establishing and verifying RIs’ [2]. To establish age and gender-partitioned RIs, the participants were grouped according to gender and age groups: 20–29, 30–39, 40–49, 50–59 and 60–79. Outliers were identified and excluded using the Dixon test as follows: the ratio D/R, where D is the absolute difference between an extreme observation (large or small) and the next largest (or smallest) observation, and R is the range of all observations, including extremes. If the difference D is equal to or greater than a third of the range R, the extreme observation is deleted. This process was repeated until all outliers were identified and removed. After excluding the outliers, the lower and upper reference limits of RIs were assumed to demarcate the estimated 2.5th and 97.5th percentiles. For gender- and age-specific subgroups with a sample size larger than 120 (inclusive), nonparametric method was used to estimate the lower and upper reference limits as well as the 90% CI for the limits. For gender- and age-specific subgroups with a sample size between 40 and 119 (inclusive), robust method was employed to set the lower and upper reference limits and bootstrap resampling method was conducted to calculate their 90% CI [8]. If the number of participants in a gender- and age-specific subgroup was less than 39 (inclusive), then this subgroup was merged with the adjacent subgroup, and appropriate method was selected for analysis according to the merged sample size [2].

It is generally assumed that as long as the difference between the observed means of two subgroups is statistically significant, then each subgroup warrants its own RI [2]. It was decided in this study that adjacent age subgroups in males or females without statistically significant differences were merged and the RI for the merged subgroup were re-calculated and reported. Kolmogorov–Smirnov test (for analytes with a sample size larger than 2000) or Shapiro–Wilk test (for analytes with a sample size less than 2000) were used to test if selected analytes follow the normal distribution. It was found that all ten selected analytes did not follow Gaussian distribution. Consequently, partitioning of the RIs according to age and gender was evaluated by Kruskal–Wallis test, followed by Dwass–Steel–Critchlow–Fligner test for pairwise comparison. Besides gender- and age-specific results, RIs for males, females and total participants were further estimated, respectively, to enhance the applicability of the findings.

Regarding characteristics of included participants, continuous variables were presented as median (quartile 1–quartile 3 [Q1–Q3]) and were compared using Wilcoxon test. Categorical variables were presented as percentage and were compared using χ² test. All statistical analyses were conducted by SAS 9.2 (SAS Institute, NC, USA). p < 0.05 (two-tailed) was considered statistically significant.

Results

Characteristics of included participants

In total, 312,065 person-times attended routine check-ups at Beijing MJ Health Check-up Center from 1 January 2010 to 31 December 2018. After exclusion of participants with missing data on analytes of interest, and execution of exclusion criteria, finally 17,356 participants (reference subjects) were included into this study (Supplementary Figure 1 & Table 2). The main characteristics of included participants are shown in Table 1. Among the 17,356 participants, 9297 participants (53.6%) were male and 3/4 of them were under 45 years old. Compared with females, males seemed to be slightly older and have higher BMI, systolic blood pressure and diastolic blood pressure levels. The proportions of current smoking and current drinking in males were markedly higher than in females. Furthermore, males had greater levels of Hb, glucose, HbA1c, TG, TC, low-density lipoprotein cholesterol, WBC, creatinine, CK, UA, albumin, ALT and AST and lower level of high-density lipoprotein cholesterol, compared with females. Characteristics of included participants for each analyte of interest were similar to total reference population (Supplementary Tables 3–12).

RIs of the ten clinical chemistry analytes

Data were grouped according to gender and age groups for each analyte of interest. Extreme observations were used to identify outliers in each gender- and age-specific subgroup. In total, nine outliers were identified and removed from the analysis. One outlier was identified in Apo A1, CK and ALP each, and two outliers were detected in TG, CK-MB and GGT each. Table 2 presents the RIs of the ten clinical chemistry analytes in apparently healthy check-up population of Beijing, China.

The levels of all the ten analytes had significant differences between males and females. Results for Apo A1 appeared lower in males than in females (p < 0.001), whereas males had markedly greater levels of all the other analytes (TC, TG, Apo B, CK, CK-MB, glucose, ALP, GGT and BUN) compared with females (all p < 0.001). In both genders, TC, TG, Apo B, glucose, GGT and BUN demonstrated an increased concentration with increasing age, while CK showed a decreased level with increasing age. The levels of Apo A1 and CK-MB appeared to have no age-related tendency. Notably, the pattern of ALP change with increasing age showed a significant gender difference. In the level of ALP showed an increase with increasing age in females, however, no differences were detected among age groups in males.

Discussion

This real-world study using check-up data of apparently healthy population of Beijing, China estimated RIs of ten commonly used disease biomarkers that indicated different health issues: important body enzymes (CK, CK-MB, ALP, GGT), blood indicators of liver and kidney function (BUN) and biochemical markers of glucose and lipid metabolism, (glucose, TC, TG, Apo A1 and Apo B). The RIs established in our study was similar to those established in other studies conducted in Chinese population, which also used EP28-A3c guideline and had similar means of BMI, age and sex ratio with our study [9–12]. Appropriate population-based RIs are critical in the interpretation of clinical laboratory test results, and are important to support clinical decision making. Although reference populations are all consisted of healthy individuals, due to the diversity in genetics, environment and diet of different populations, studies have found that the levels of some physiology and chemistry analytes varied from one population to another [9,13–15]. Previous studies rarely used large sample of data of Chinese population to establish RIs. Besides, instead of developing RIs for a broad spectrum of clinical chemistry analytes, previous publications usually focused on a single analyte or specific health issue, such as thyroid-associated biomarkers or myocardial enzyme analytes [16,17]. Hence, our study could fill these gaps to some extent.

The results of our study showed gender differences in the levels of all the ten analytes. All the ten analytes appeared higher levels in males compared with females, except Apo A1. The level of Apo A1 in females was significantly higher than in males, and this was consistent with the Chinese guidelines on prevention and treatment of dyslipidemia in adults (2016 revision) [10]. We speculated that this gender difference in Apo A1 might be partly explained by the impact of estrogen on lipoprotein metabolism. Furthermore, age differences were observed in most analytes, except Apo A1, CK-MB and ALP (male only). TC, TG, Apo B, glucose, GGT, BUN and ALP (female) showed upward trends with increasing age, while CK decreased with increasing age.

Generally, the RIs established in our study were parallel to those provided in current national standards and developed in previous studies in China [9,11,12]. It was worth noting that the RIs obtained in our study were more finely partitioned, due to large sample size in each gender and age subgroup, which had more statistical power for detecting significant differences. Taking the ALP female group as an example, the RI of female ALP was divided into two subgroups in the ‘RIs for Common Clinical Biochemistry Tests’ developed by National Health Commission of China [12], namely the 20–49 and 50–79 years subgroups. In our study, the results showed statistically different levels of ALP among various age groups, and thus separated RIs were estimated for 20–39, 40–49, 50–59 and 60–79 years subgroups, respectively. However, different with our findings, a study conducted in Canadian found that the RI of ALP showed no differences for age subgroups [18]. A possible reason for this difference is that the ethical backgrounds of the study populations were entirely different.

The present study adopted a real-world retrospective cross-sectional design by using health check-up data from apparently healthy population of Beijing, China. In recent years, laboratory medicine has become an important application field of real-world data. Clinical laboratory studies leveraging real-world data have been conducted to establish RIs of chemical analytes, implement real-time quality control and investigate sources of variation, etc. [19–21]. Especially, real-world data have been considered to be a valuable data source to establish RIs over the years [22]. A real-world study design is actually a posteriori method, which applies criteria after the collection of samples, and is also good indirect technique practice. In Western countries, increasing studies emerged using real-world data to establish their own laboratories’ RIs in a specific population [4,23–25]. Compared with traditional methods, a real-world study might be more economical and efficient. The retrospective use of real-world data (electronic health records, claims and billing data, mobile devices, check-up records, etc.) usually can avoid the complicated procedures and huge costs involved in recruiting healthy reference individuals and performing comprehensive health examination. Furthermore, it has been argued that since the samples of real-world data are derived from real-world population, the external validity of its conclusions are probably higher [9,26,27]. Nevertheless, it should be noted that some real-world studies used hospital database to establish RIs, which usually resulted in significant bias. Hospital database often lacks of lifestyle information, and taking past medical history, physical and biochemical examination are generally not comprehensive enough, making it difficult to fully exclude unhealthy reference individuals [2,22]. More importantly, patients visiting hospitals often suffer from one or more diseases, they may not truly reflect the levels of physiology and chemistry analytes of healthy people. Our study leveraged check-up data derived from apparently healthy population of Beijing, China, which can effectively avoid the selection bias that may be caused by using hospital patient database.

Our study has two strengths. First, to our best knowledge, this was the first real-world study in China using check-up data of apparently healthy population to establish RIs of commonly used clinical chemical analytes. Our study provided a new strategy for designing studies to establish population-based RIs in countries that has rich second-hand health data like China. Second, the health check-up data provided by Beijing MJ Health Check-up Center were both informative and of high quality with a large sample size [28]. The check-ups in this Center not only covered physical, laboratory and image examination, but also systematically collected demography, lifestyle, past medical history and family history for every participant. This database had the potential to be applied in the future to establish and verify RIs of more physiology and chemistry analytes. There are some limitations in this study. First, participants of our study were from a single health check-up center located in Beijing, China, which was not a nationally representative sample. Nevertheless, attendees to this Center, which adopted membership management, came from all over country with no obvious geographical feature. Second, several gender- and age-specific subgroups had a number of participants fewer than 120. In order to accurately estimate the RIs in these subgroups (40–120 participants), we used robust method and bootstrap method to estimate the reference limits as well as their 90% CIs. The accuracy of these methods was confirmed in previous studies [29]. Third, we identified and removed outliers by using Dixon method in this study. The EP28-A3c guideline suggests the Tukey method as an alternative to the Dixon method since the latter is not a highly sensitive method that can identify multiple outliers in studies with a large sample size. However, the Tukey method is often found to be overly conservative in the sense that it removes too many outliers. Suspected biological outliers, which represent natural biological variability associated with the sample matrix, may be incorrectly excluded as well [30]. Furthermore, the more finely partitioned RIs produced by this study might have limited the applicability of the findings in clinical practice. However, our study, by using large sample size of check-up data from apparently healthy population in Beijing, China, provided a unique and unprecedented opportunity to scrutinize the differences between age groups and gender in the levels and RIs of selected analytes. Clinicians may further evaluate and determine the partitioning choice carefully so as to acquire clinically appropriate and useful RIs for practical application, since gender-and age-specific RIs may not be justified unless they are clinically useful or are well grounded physiologically. Meanwhile, in order to enhance the applicability of our findings, we also provided the RIs for male, female and total participants in addition to separate RIs. Finally, although we have excluded participants with heavy drinking and smoking habits, some factors as diet, physical activity and socio-economic status might also affect the health status of subjects. Studies with more comprehensive covariates and exclusion criteria are needed in the future.

All the chemical analytes were measured on Roche biochemical analyzer (Cobas c701) using Roche reagents by routine methods in this study. The RIs of the ten commonly used analytes are applicable to users of the same instrumentation and reagents, and may be applicable to users of different instrumentation or reagents, but with similar analytic methods after verification.

Conclusion

The RIs established in this study by using check-up data of apparently healthy population were similar to previous RIS established in Chinese population and would be appropriate and reliable reference for clinical diagnosis and evaluations. Real-world studies may play an important and practical role in the determination of RIs of clinical chemistry analytes in the future.