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Complex reference value distributions and partitioned reference intervals across the pediatric age range for 14 specialized biochemical markers in the CALIPER cohort of healthy community children and adolescents
Clinica Chimica Acta 450 (2015) 196–202
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Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Complex reference value distributions and partitioned reference intervals across the pediatric age range for 14 specialized biochemical markers in the CALIPER cohort of healthy community children and adolescents Jacalyn Kelly a,1, Joshua E. Raizman a,1, Victoria Bevilacqua a, Man Khun Chan a, Yunqi Chen a, Frank Quinn b, Beth Shodin b, David Armbruster b, Khosrow Adeli a,⁎ a b
CALIPER Program, Pediatric Laboratory Medicine, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada Abbott Diagnostics, Abbott Park, IL, USA
a r t i c l e
i n f o
Article history: Received 16 June 2015 Received in revised form 20 August 2015 Accepted 21 August 2015 Available online 24 August 2015 Keywords: CALIPER Reference intervals Pediatric Analyte Biomarker
a b s t r a c t Background: The CALIPER program has previously reported a comprehensive database of pediatric reference intervals for 63 biochemical and immunochemical markers. Here, covariate-stratified reference intervals were determined for a number of special assays not previously reported. Methods: A total of 1917 healthy children and adolescents were recruited and serum concentrations of 14 biochemical markers were measured using the Abbott Architect ci4100 system. Age and gender partitions were statistically determined, outliers removed and reference intervals calculated using CSLI C28-A3 guidelines. Results: Many analytes showed dynamic changes in concentration requiring at least 3 age partitions. Unique intervals were required within the first year of life for: pancreatic amylase, C-peptide, ceruloplasmin, insulin, β-2microglobulin, cystatin C, dehydroepiandrosterone sulfate (DHEA-S), and α-1-glycoprotein. Cholinesterase, cholinesterase–dibucaine number, and immunoglobulin E required only 2 age partitions and α-1-antitrypsin required only one. Anti-CCP and anti-TPO levels were below the detection limit of the assay. Some analytes including insulin and DHEA-S required additional gender partitions for specific age groups. Conclusions: Complex profiles were observed for endocrine and special chemistry markers, requiring establishment of age- and gender-specific reference intervals. These updated reference intervals will allow improved laboratory assessment of pediatric patients but should be validated for each analytical platform and local population as recommended by CLSI. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Reference intervals are health-associated benchmarks that enable clinicians to confidently interpret test results as normal or abnormal, and thus are critical for accurately diagnosing disease. Pediatric reference intervals can be difficult to establish because multiple age and gender partitions are often required to reflect the rapid changes that occur during growth and development. The challenges associated with recruiting newborns and children, coupled with the small blood volumes that can be collected, limit sample size and subsequent robust calculations from Abbreviations: A1AT, α-1-antityrpsin; AGT, α-1-glycoprotein; AmyP, pancreatic amylase; Anti-CCP, anti-cyclic citrullinated peptide antibody; Anti-TPO, antithyroperoxidase antibody; B2M, β-2-microglobulin; CALIPER, Canadian Laboratory Initiative on Pediatric Reference Intervals; Cerul, ceruloplasmin; Che, cholinesterase; CheDi, cholinesterase dibucaine number; CLSI, Clinical Laboratory Standards Institute; CYSC, cystatin C; DHEA-S, dehydroepiandrosterone sulfate. ⁎ Corresponding author at: Clinical Biochemistry, The Hospital for Sick Children, University of Toronto, Toronto, Ontario M5G 1X8, Canada. E-mail address: [email protected] (K. Adeli). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.cca.2015.08.020 0009-8981/© 2015 Elsevier B.V. All rights reserved.
a representative population [1]. In addition, past studies have relied on outdated technology [1], or have used retrospective laboratory data or samples collected from hospitalized patients rather than healthy children [2]. Therefore, a complete, accurate, and up-to-date database of pediatric reference intervals for clinically relevant biomarkers is urgently needed. The CALIPER (Canadian Laboratory Initiative on Pediatric Reference Intervals) project is a multi-center initiative among several pediatric centers across Canada. CALIPER aims to address the challenges of establishing pediatric reference intervals by developing a comprehensive biobank of samples from healthy community children. CALIPER has previously recruited over 8500 children from birth to 19 y to develop statistically valid reference intervals for over 60 blood tests including routine chemistries [3], endocrine and nutritional markers [4], and various sex/fertility hormones [5]. A transference study was also completed to validate key reference intervals on 4 other commonly used analytical platforms in order to expand the utility of the CALIPER database in more clinical laboratories across Canada and worldwide [6]. A major strength of CALIPER is establishment of reference intervals using an a priori approach to collect samples; therefore, they are reflective of the normative
J. Kelly et al. / Clinica Chimica Acta 450 (2015) 196–202
values within a healthy community setting. Furthermore, the statistically rigorous process undertaken to partition reference intervals by age and gender has resulted in a detailed dataset, which underscores the importance of considering the impact that childhood growth and development may have on levels of circulating biomarkers. 2. Materials and methods 2.1. Participant recruitment and sample acquisition Participant recruitment and sample acquisition were conducted as previously described [3]. The study was approved by the Institutional Review Board at the Hospital for Sick Children, Toronto, Ontario, Canada. Briefly, healthy children aged birth to 19 y (n = 1917) were recruited to take part in the CALIPER study in specific CALIPER clinics across the Greater Toronto and Hamilton regions. Participants were asked to complete a questionnaire that collected information on age, gender, ethnicity, diet, medical conditions, medications, and parental health. Ethnic distribution in this sample population is found in Supplemental Table 1. Participants with a history of chronic illness or metabolic disease, who had an acute illness within the previous month, or who used prescribed medication over the previous 2 weeks were excluded from the study. For infants b1 y, samples were collected from apparently healthy/ metabolically stable neonates and children in hospital maternity wards and from select outpatient clinics. Blood draw was performed by trained phlebotomists. Approximately 1–10 ml of blood was collected from each participant in serum separator tubes. All blood samples were centrifuged and serum separated within 5 h of collection. Aliquots were stored at − 80 °C until further testing. 2.2. Sample analysis Twelve special endocrine and chemistry analytes were measured on the integrated Abbott Architect ci4100 system. Analytical methods were controlled according to the manufacturer's instructions using preventive maintenance, function checks, calibration, and quality control material. Analytical performance of the assays for imprecision and linearity is provided in Supplemental Table 2. Samples for this study were tested only when all analytical parameters were acceptable. 2.3. Statistical analysis and reference interval determination Data was analyzed in accordance with the Clinical Laboratory Standards Institute (CLSI) C28-A3 guidelines on defining, establishing, and verifying reference intervals in the clinical laboratory and as described in previous CALIPER reports [3–5]. Briefly, statistical analysis was performed using Microsoft Excel and R software. Scatter and distribution plots were visually inspected for outliers. If the data was not skewed, outliers were removed using the Tukey test [7]. If the data was skewed, outliers were removed using an adjusted Tukey test as previously described [3–5]. Age and/or gender partitions were decided by first visually inspecting the distribution plots, and then by assessment for statistical significance using the Harris and Boyd test [8]. Each reference interval represents the central 95% of the sample distribution. If the sample size within a partition was at least 120, the nonparametric rank method was used to calculate the reference intervals. For partitions with a sample size of less than 120, but containing N 40, the robust statistical method by Horn et al. was used [9,10]. The 90% confidence intervals surrounding the upper and lower limits were then calculated. 3. Results Age- and gender-specific reference intervals for 14 special endocrine and chemistry analytes were calculated using serum samples collected from 1917 boys and girls ranging from 2 days to 19 y. The partitioned
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reference intervals, including the 90th percentile confidence intervals surrounding the lower and upper limits, are shown in Table 1. At least 2 or more unique age partitions were required for all biochemical markers with the exception of α-1-antityrpsin (A1AT). Anti-cyclic citrullinated peptide (CCP) and anti-thyroperoxidase (TPO) antibodies were below the detection limit of the assay. Of the analytes with multiple age-partitions, all but immunoglobulin E (IgE) required unique partitioning within the first year of life, while 8 analytes required partitioning within the first 3 months of life. The dynamic changes observed were not surprising given the rapid physiological and biochemical changes that occur from infancy to childhood to adolescence. Additional gender partitions were required within select age groups for pancreatic amylase, ceruloplasmin, insulin, β2-microglobulin (B2M), cystatin C, and cholinesterase. Gender partitioning for dehydroepiandrosterone sulfate (DHEA-S) was required only in adolescence, from 16–b19 y. Due to small sample size for ethnic groups (Supplemental Table 1), we were unable to perform accurate statistics to calculate ethnicity-specific reference intervals. The dynamic changes in concentration observed for each analyte from birth to adolescence were classified into distinct categories, similar to a previous CALIPER study [4]. The 4 categories include: (1) analytes with lower concentrations in the neonatal period that gradually rose with age; (2) analytes with higher concentrations at birth that declined during infancy, then stabilized with age; (3) analytes with wide variance at birth followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence; and (4) analytes with no gender partitions that displayed little fluctuation across the entire age period. 3.1. Analytes with lower concentrations in the neonatal period that gradually rose with age Pancreatic amylase, C-peptide, insulin and ceruloplasmin all demonstrated the lowest concentrations within the first year of life, followed by a gradual rise in concentration with age (Table 1, Fig. 1). The upper limit of pancreatic amylase doubled after the first 6 months of life, and then gradually increased with age. The lower limit for males in the 1–b2 y age group was significantly less than for females, which necessitated distinct gender partitions in this age group. Insulin and its precursor protein, C-peptide, showed a similar rising pattern that was most pronounced with the onset of puberty. C-peptide required 3 age partitions from 0–b1, 1–b6, and 6–b19 y, but no gender partitions, while insulin demonstrated an additional partition at puberty from 6–b 11 y. Both analytes also displayed a gradual but sustained rise in their lower and upper limits from birth to adolescence, with the widest variance present from 6–b 19 y. Ceruloplasmin required 6 distinct age partitions. For both genders, there was a gradual increase in concentration as early as 2 months, followed by another abrupt increase in levels at 1 y, which continued and peaked in mid childhood (8 y), before declining slightly during puberty. Of note, the decline in concentration between 8 and b19 y was more prominent in males. 3.2. Analytes with higher concentrations at birth that declined during infancy and then stabilized with age β2-Microglobulin (B2M) and cystatin C concentrations peaked within the first year of life and then fell by approximately half after the first year (Table 1, Fig. 2). B2M levels were slightly higher in newborn females than males, and required distinct gender partitions in the 0–3 month age group. B2M concentrations decreased for both genders three months after birth, followed by another decrease at age 2, after which time concentrations remained stable into adolescence. Cystatin C levels declined steadily during infancy, requiring 3 age partitions within the first year of life (0–b 1 month, 1–b5 months, and 5 months–b 1 y). In the 1–b 2 y age group, concentrations decreased for females, but increased slightly for males. Similar to B2M, cystatin C
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Table 1 Age- and gender-specific pediatric reference intervals for 14 special endocrinology and chemistry analytes measured on the Abbott Architect ci4100 system. Female reference intervals
Male reference intervals
Analyte
Age
No. of samples
Lower limit
Upper limit
Lower limit CI
Upper limit CI
No. of samples
Lower limit
Upper limit
Lower limit CI
Upper limit CI
AGP (g/l)
0–b6 months 6 months–b5 y 5–b19 y 0–b19 y 0–b6 months 6 months–b1 y 1–b2 y 2–b19 y 0–b19 y 0–b19 y 0–b3 months 3 months–b2 y 2–b19 y 0–2 months 2–6 months 6 months–b1 y 1–b8 y 8–b14 y 14–b19 y 0–b1 month 1 month–b19 y 0–b1 month 1 month–b19 y
77 259 450 675 67 59 59 517 147 147 60 174 240 49 63 71 229 196 79 51 596 100 611
0.21 0.48 0.48 1.10 b1 0.62 2.63 4.10 b0.5 b1.0 1.89 1.31 1.19 73.5 134.7 137.3 217 205 208.1 3133 7693 797 1523
0.85 2.01 1.14 1.81 11.50 23.16 27.82 31.30 – – 5.81 4.54 2.25 236.5 329.0 388.7 433 402 431.6 12,892 14,856 2478 3280
(0.19, 0.23) (0.46, 0.49) (0.46, 0.49) (1.08, 1.10) – (0.49, 0.81) (1.94, 3.39) (3.00, 6.20) – – (1.41, 2.30) (1.24, 1.35) (1.11, 1.23) (60.9, 86.7) (127.0, 142.5) (109.5, 162.4) (201, 237) (184, 213) (203.9, 214.2) (1394, 4399) (7431, 8073) (682, 911) (1494, 1550)
(0.77, 0.93) (1.74, 2.23) (1.12, 1.22) (1.75, 1.83) (4.36, 15.40) (19.1, 26.9) (24.5, 31.3) (30.4, 33) – – (5.51, 6.10) (3.64, 5.51) (2.17, 2.61) (218.5, 255.6) (308.2, 350.9) (368.2, 409.4) (419, 456) (373, 414) (382.2, 456.1) (12,059, 13,793) (14,646, 14,951) (2361, 2604) (3151, 3331)
77 259 450 675 67 59 62 517 151 151 56 174 240 49 63 71 229 196 82 47 596 100 611
0.21 0.48 0.48 1.10 b1 0.62 0.68 4.10 b0.5 b1.0 1.91 1.31 1.19 73.5 134.7 137.3 217 205 170 4728 7693 797 1523
0.85 2.01 1.14 1.81 11.50 23.16 22.5 31.3 – – 4.70 4.54 2.25 236.5 329.0 388.7 433 402 347.6 11,301 14,856 2478 3280
(0.77, 0.93) (1.74, 2.23) (1.12, 1.22) (1.75, 1.83) (4.36, 15.40) (19.1, 26.9) (19.9, 25.5) (30.4, 33) – – (4.46, 4.90) (3.64, 5.51) (2.17, 2.61) (218.5, 255.6) (308.9, 350.9) (368.5, 409.4) (419, 456) (373, 414) (328.8, 366.4) (10,577, 12,070) (14,646, 14,951) (2361, 2604) (3151, 3331)
0–b1 y 1–b6 y 6–b19 y 0–b1 month 1–b5 months 5 months–b1 y 1–b2 y 2–b19 y 0–b2 months 2–b6 months 6 months–b1 y 1–b6 y 6–b9 y 9–b13 y 13–b16 y 16–b19 y 0–b7 y 7–b19 y 0–b1 y 1–b6 y 6–b11 y 11–b19 y
214 160 407 100 46 98 56 529 115 69 77 78 108 133 98 45 334 365 96 79 72 120
70 116 257 1.49 1.01 0.75 0.60 0.62 28.9 0.65 0.15 0.07 0.14 0.90 1.50 3.96 b25 b25 6.64 9.11 10.47 18.0
1448 1477 2241 2.85 1.92 1.53 1.20 1.11 N40.7 15.6 4.79 3.03 4.14 7.30 12.5 15.5 440.4 449.7 163 279.4 256.6 491.7
(59, 80) (90, 151) (236, 278) (1.43, 1.55) (0.97, 1.04) (0.73, 0.78) (0.56, 0.64) (0.62, 0.64) (15, 33.1) (0.50, 0.85) (0.12, 0.19) (0.06, 0.09) (0.09, 0.21) (0.50, 1) (1.09, 1.98) (3.55, 4.41) – – (4.98, 8.49) (6.4, 12.1) (6.38, 15.73) (14.4, 24.4)
(1086, 1672) (1324, 1986) (2094, 2387) (2.73, 2.98) (1.79, 2.06) (1.45, 1.61) (1.13, 1.27) (1.06, 1.13) – (13.2, 18.1) (3.95, 5.6) (2.38, 3.71) (3.65, 4.66) (6.6, 11.2) (11.4, 13.5) (13.6, 17.3) (321.6, 1000) (379.9, 483.6) (144.2, 182.2) (233.2, 331.7) (223.48, 293.74) (336.5, 589.5)
214 160 407 100 46 98 53 529 115 69 77 75 108 133 98 46 334 365 96 79 72 120
70 116 257 1.49 1.01 0.75 0.77 0.62 28.9 0.65 0.15 0.07 0.14 0.90 1.50 3.36 b25 b25 6.64 9.11 10.47 18.0
1448 1477 2241 2.85 1.92 1.53 1.85 1.11 N40.7 15.6 4.79 0.76 4.14 7.30 12.5 18.2 440.4 449.7 163 279.4 256.60 491.7
(0.19, 0.23) (0.46, 0.49) (0.46, 0.49) (1.08, 1.10) – (0.49, 0.81) (0.18, 1.29) (3.00, 6.20) – – (1.56, 2.20) (1.24, 1.35) (1.11, 1.23) (60.9, 86.7) (127, 142.5) (109.5, 162.4) (201, 237) (184, 213) (161.6, 178.6) (4344, 5156) (7431, 8073) (682, 911) (1494, 1550) (59, 80) (90, 151) (236, 278) (1.43, 1.55) (0.97, 1.04) (0.73, 0.78) (0.75, 0.79) (0.62, 0.64) (15, 33.1) (0.50, 0.85) (0.12, 0.19) (0.06, 0.08) (0.09, 0.21) (0.50, 1) (1.09, 1.98) (1.80, 4.83) – – (4.98, 8.5) (6.37, 12.1) (6.38, 15.73) (14.4, 24.4)
A1AT (g/l) AmyP (U/l)
Anti-CCP (U/ml) Anti-TPO (IU/ml) B2M (mg/l)
Cerul (mg/l)
ChE (U/l) ChEDi (U/l)
C-peptide (pmol/l)
CYSC (mg/l)
DHEA-S (μmol/l)
IgE (IU/ml) ⁎Insulin (pmol/l)
(1086, 1672) (1324, 1986) (2094, 2387) (2.73, 2.98) (1.79, 2.06) (1.45, 1.61) (1.6, 2.15) (1.06, 1.13) – (13.2, 18.1) (3.95, 5.64) (0.65, 0.85) (3.65, 4.66) (6.60, 11.20) (11.4, 13.5) (16.5, 19.7) (321.6, 1000) (379.9, 483.6) (144.2, 182.2) (233.2, 331.7) (223.48, 293.74) (336.50, 589.5)
Bold and underline indicate gender-specific differences. Values below the limit of detection of the assay are denoted with “b”; limits above the linear range are denoted with “N”. ⁎ Additional age partitions were added at 6–b11 y and 11–b19 y to reflect the rising levels at the onset of puberty, consistent with previous reports (see text); however, these partitions were not statistically significant after analysis using the Harris & Boyd test.
levels declined a final time at 2 y and then remained stable into adolescence. 3.3. Analytes with wide variance in infancy followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence α-1-Glycoprotein (AGP), cholinesterase (ChE), cholinesterase– dibucaine number (ChEDi), and DHEA-S all demonstrated wide variance in infancy, and then, depending on the analyte, showed either a rise or fall in concentration through the remainder of childhood and adolescence (Table 1, Fig. 3). These analytes also demonstrated reduced variance with age, with the widest intervals earlier in childhood and narrower intervals later in life. AGP demonstrated higher variance and increased levels during infancy and early childhood that required age partitions from 0 to 6 months, and 6 months to 5 y. Levels dropped
by approximately one half, from an upper limit of 2.01 g/l in the 6 month–b5 y group, to 1.14 g/l in the 5–b19 y group. No gender partitions were observed. ChE and ChEDi had the widest variance from birth to 1 month of age, followed by a slight fall in concentration that remained stable through to adolescence, necessitating a broad partition from 1 month to b19 y. Unlike ChEDi, ChE also exhibited gender-specific differences within the first month of life. DHEA-S followed a similar U-shaped trend observed previously for testosterone [5]. Eight age partitions were required: 3 within the first year of life, 2 in early childhood, and 3 during puberty and adolescence. Additionally, gender partitioning was required in the 16–b19 y age group. The highest concentrations were observed within first 2 months of life with a cutoff at the upper limit of the assay (40.7 μmol/l), followed by an abrupt drop between 2 months and b 1 y, and a subsequent slow rise into adolescence. The rise in DHEA-S levels observed during adolescence occurred faster in males than females.
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Fig. 1. Age-dependent scatter plots for boys (blue triangles) and girls (pink circles) for analytes with low concentrations in the neonatal period that gradually rose with age. Trend lines are shown for males (black) and females (gray). (A) AmyP, (B) c-peptide, (C) insulin, and (D) ceruloplasmin.
3.4. Analytes with no gender partitions that displayed little fluctuation across the entire age period
below the detection limit of the assay necessitating cutoff values at the lower limit (scatterplots are not shown).
Unlike other analytes, neither A1AT nor IgE required any gender partitioning (Table 1, Fig. 4). Both displayed relatively constant variance from birth to 19 y. A1AT showed a narrow interval (1.10–1.81 g/l) that did not require any age-specific partitions. Although IgE demonstrated a wide variance, a minor increase in concentration was observed with age, requiring only 2 age partitions. Anti-CCP and anti-TPO levels were
4. Discussion
Fig. 2. Age-dependent scatter plots for boys (blue triangles) and girls (pink circles) for analytes with highest concentrations at birth that declined during infancy then stabilized with age. Trend lines are shown for males (black) and females (gray). (A) B2M and (B) CYSC.
The current study is an important addition to the expanding CALIPER database of pediatric reference intervals. Here, we show reference value distributions for 14 special chemistry and endocrine markers and report age- and gender-specific reference intervals for children and adolescents from birth to b19 y. Analytes were categorized according to trends observed across the pediatric age range. The first group included pancreatic amylase, C-peptide, insulin and ceruloplasmin, all of which had lower concentrations in the neonatal period that gradually rose with age. Pancreatic amylase displayed similar values and trends as other reports; however, unlike our study which found gender differences in the 1–b2 y age group, others that used the Roche Modular P analyzer did not observe this trend [11], or required gender partitions only in adolescence [12]. Given that pancreatic amylase secretion is regulated by starch intake, differences in dietary habits, particularly between boys and girls as well as analytical platforms may account for some of the disparities between these studies. The lower limit for newborns 0–b6 months of age was below the detection limit of the assay at 1 U/l, which may reflect the immature digestive system early in life. The rise in concentration with age demonstrates the adaptation of the body to dietary requirements, underscoring the importance of age-specific partitions in monitoring changes in pancreatic function associated with disease, such as cystic fibrosis. This assay will provide improved specificity for pancreatic disorders compared to a general amylase assay that measures both salivary and pancreatic amylase. C-peptide and insulin are secreted in equimolar amounts because they are both products of proinsulin, so it is not surprising that both showed similar profiles and covariate partitions. The steady increase in the levels of these analytes from birth to adolescence coincides with the development of the endocrine system as the pancreas adapts to increased glucose demands during metabolic growth. Soldin et al. measured C-peptide and insulin on the Siemens Immulite 2000 system and also observed increased concentrations from birth to adolescence that required multiple age partitions and was most pronounced at the onset of puberty [2]; however, the lower limits of C-peptide were higher in all age groups by about 2-fold compared to what was observed in our
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Fig. 3. Age-dependent scatter plots of boys (blue triangles) and girls (pink circles) for analytes with wide variance in infancy followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence. Trend lines are shown for males (black) and females (gray). (A) AGP, (B) ChE, (C) ChEDi, and (D) DHEA-S. Trend lines are shown for male (black) and female (gray).
study. In addition, the insulin intervals developed by Soldin et al. were narrower than the data presented here, and required gender-specific partitions within the first 3 y of life. These discordances are likely attributed in part to the differences in study populations. In the Soldin et al. study, reference intervals were calculated from hospitalized patient samples using the Hoffman approach, while the current study used healthy community children from the CALIPER cohort. The differences in observed C-peptide concentrations may also be due to the analytical methods used by the 2 assays. Given the importance of copper in oxygen processing and oxidative phosphorylation, the rising ceruloplasmin levels observed with age reflect its key role in growth and development. Values and trends from other studies generally agree with our data [11,13], although Clifford
Fig. 4. Age-dependent scatter plots of boys (blue triangles) and girls (pink circles) for analytes with no gender partitions that displayed little fluctuation across the entire age period. Trend lines are shown for males (black) and females (gray). (A) A1AT and (B) IgE.
et al. observed ceruloplasmin levels that peaked around late childhood and then declined slightly in subsequent age partitions [11]. Consistent with our data, gender differences during puberty and adolescence were also observed where females had higher levels than males. These gender-specific changes in adolescence may be due to hormonal effects as previously described [14]. The second category, comprising the renal markers, B2M and cystatin C, demonstrated the highest concentrations at birth, which declined during infancy and then stabilized with age. Both analytes decreased within the first 2 y of life, requiring multiple partitions during this time, before levels stabilized into one broad partition for the remaining age group. Unlike creatinine, which increases with age due to its dependence on body mass [3], the declining B2M and cystatin C levels during infancy likely reflect development of the kidneys as filtration function matures at an early age. The overall trends agree with recent studies [15,16]; however, in contrast to these reports, we also observed gender differences within the first year of life for B2M, and within the 1–b2 y age group for cystatin C. In the third category, AGP, ChE, ChEDi, and DHEA-S were grouped based on their wide variance during infancy, and requirement for age partitions within the first 6 months of life. The first analyte examined in this category was AGP, a marker for inflammation and acute bacterial infection. Older studies that analyzed samples using the Behring nephelometer generally agree with our reference intervals for neonates [17], and children [18], but required many more gender partitions than observed here. Furthermore, we expand on the available data by reporting intervals in newborns as early as 2 days. ChE and ChEDi use the same reagent, except the ChEDi test includes the anesthetic drug dibucaine to identify patients who have an abnormal genetic variant of pseudocholinesterase before its administration. As expected, ChEDi interval values were approximately 80% lower than the ChE interval values, which is consistent with the expected inhibitory response of dibucaine on the wild-type ChE enzyme in the assay [19,20]. In patients with abnormal ChE levels, the expected response would be a 20% inhibition of the enzyme during dibucaine treatment [19,20]. Therefore, these updated reference intervals are crucial to properly identify pediatric patients who are dibucaine-resistant in order to adjust dosing appropriately and prevent drug toxicity. Our reference intervals agreed with an older publication that used a stand-alone kit and analyzed using a Cobas Bio centrifugal analyzer, though children below 4 y were
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not included in this study [21]. Here, we also extend the intervals in newborns as early as 2 days old. DHEA-S showed a dynamic pattern requiring a total of 8 age partitions, where similar results were observed for total testosterone in a previous CALIPER study that used the Abbott Architect i2000 analyzer [5]. Given that DHEA-S is a crucial androgen precursor produced in the adrenal cortex, the requirement for multiple age partitions demonstrates the complex role of androgens during development of secondary sex characteristics. The dramatic U-shaped trend parallels that observed previously for total testosterone and reflects the expected pattern of sexual development [5]. Not surprisingly, male adolescents had higher levels than females, demonstrating the role of DHEA-S in male reproductive maturity. DHEA-S intervals agreed well with previous studies using an immunoassay method on the Immulite system [22], but showed a negative bias to recent studies using LC–MS/MS in some age groups, likely due to antibody cross-reactivity with androgen metabolites [23]. In the final category, which included A1AT and IgE, only minor fluctuations across the entire age period were observed and no gender differences were exhibited. As a serine protease inhibitor, A1AT levels did not show any notable changes demonstrating its highly regulated status during liver growth and development. The lower limit of A1AT, used to diagnose deficiency in the workup for liver and lung disorders, was 1.10 g/l, which agrees with previous population studies using healthy children [24] and hospital data [25]. For IgE, only two age partitions were required, and levels increased slightly with age. The stability of this analyte throughout childhood also reflects its highly regulated role as an important mediator of allergic response and immunological defense. The reference intervals are comparable to previous studies, although additional age and gender partitioning was required [2,26]. While the assays in our study and others have used similar IgE calibrators traceable to WHO standards, differences in reference values may be attributable to the study population and the use of samples from healthy community children versus patients [2]. Lastly, anti-CCP and anti-TPO levels were low in pediatric samples requiring cutoff values at the assay limit of detection. This is unsurprising, given that healthy children are at low risk for autoimmune diseases such as rheumatoid arthritis and thyroid abnormalities. The data presented in the present study adds to the growing CALIPER database of pediatric reference intervals established from a large cohort of healthy community children. We have demonstrated a complex pattern of change throughout childhood and adolescence for 14 specialty endocrine and chemistry tests. These dynamic changes highlight the importance of establishing detailed age and gender specific reference intervals that are capable of fully capturing the rapid developmental and physiological changes that occur during childhood. The reference intervals established here will aid in accurate diagnosis of pediatric medical conditions. However, the population included in this study is from the region of Southern Ontario, which is home to many ethnic groups, reflective of the multicultural Canadian society and more specifically the Greater Toronto Area. Although we were unable to partition our dataset into ethnic-specific reference intervals due to small sample size in some ethnic groups, these reference intervals are representative of various genetic backgrounds and serve well the needs for normative data in other populations. Our review of the literature as discussed above has for the most part demonstrated comparable data to those from other populations such as the United States, Japan, Sweden, Germany, and France. Although not extreme, any differences likely reflect variations across different analytical platforms, and methods of analysis such as the use of laboratory data as well as differences in age stratification decisions. We have previously performed some preliminary investigations on the effect of ethnicity on various biomarkers, and have found that most markers indeed are not affected [3]. Yet, it is still possible that homogeneous populations in other geographical regions may demonstrate more pronounced age and gender differences. To the best of our knowledge, this is the most
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comprehensive study for all 14 of the specialized analytes presented here. Although we would caution against fully adapting our reference intervals without validation, we do believe that our data can be implemented in other populations. Validation and/or transference of these reference intervals to other analytical platforms will allow for broader application of this dataset to local populations. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.cca.2015.08.020. Acknowledgments We thank all study participants and their families. Without their participation, this study would not have been possible. We also thank the numerous CALIPER volunteers for their countless hours of hard work and dedication to this project. A special thank you to Michelle Nieuwesteeg and Victoria Higgins for her thoughtful comments and advice during preparation of this manuscript. References [1] K. Adeli, Closing the gaps in pediatric reference intervals: the CALIPER initiative, Clin. Biochem. 44 (2011) 480–482. [2] O.P. Soldin, J.R. Dahlin, E.G. Gresham, J. King, S.J. Soldin, IMMULITE 2000 age and sexspecific reference intervals for alpha fetoprotein, homocysteine, insulin, insulin-like growth factor-1, insulin-like growth factor binding protein-3, C-peptide, immunoglobulin E and intact parathyroid hormone, Clin. Biochem. 41 (2008) 937–942. [3] D.A. Colantonio, L. Kyriakopoulou, M.K. Chan, et al., Closing the gaps in pediatric laboratory reference intervals: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children, Clin. Chem. 58 (2012) 854–868. [4] D. Bailey, D. Colantonio, L. Kyriakopoulou, et al., Marked biological variance in endocrine and biochemical markers in childhood: establishment of pediatric reference intervals using healthy community children from the CALIPER cohort, Clin. Chem. 59 (2013) 1393–1405. [5] D. Konforte, J.L. Shea, L. Kyriakopoulou, et al., Complex biological pattern of fertility hormones in children and adolescents: a study of healthy children from the CALIPER cohort and establishment of pediatric reference intervals, Clin. Chem. 59 (2013) 1215–1227. [6] M.P. Estey, A.H. Cohen, D.A. Colantonio, et al., CLSI-based transference of the CALIPER database of pediatric reference intervals from Abbott to Beckman, Ortho, Roche and Siemens Clinical Chemistry Assays: direct validation using reference samples from the CALIPER cohort, Clin. Biochem. 46 (2013) 1197–1219. [7] J.W. Tukey, Exploratory Data Analysis, Addison-Wesley, Boston, 1977. [8] E.K. Harris, J.C. Boyd, On dividing reference data into subgroups to produce separate reference ranges, Clin. Chem. 36 (1990) 265–270. [9] P.S. Horn, A.J. Pesce, B.E. Copeland, A robust approach to reference interval estimation and evaluation, Clin. Chem. 44 (1998) 622–631. [10] P.S. Horn, A.J. Pesce, Reference Intervals: A User's GuideWashington, DC 2005. [11] S.M. Clifford, A.M. Bunker, J.R. Jacobsen, W.L. Roberts, Age and gender specific pediatric reference intervals for aldolase, amylase, ceruloplasmin, creatine kinase, pancreatic amylase, prealbumin, and uric acid, Clin. Chim. Acta 412 (2011) 788–790. [12] P. Rodoo, P. Ridefelt, M. Aldrimer, F. Niklasson, J. Gustafsson, D. Hellberg, Populationbased pediatric reference intervals for HbA1c, bilirubin, albumin, CRP, myoglobin and serum enzymes, Scand. J. Clin. Lab. Invest. 73 (2013) 361–367. [13] G. Lockitch, A.C. Halstead, G. Quigley, C. MacCallum, Age- and sex-specific pediatric reference intervals: study design and methods illustrated by measurement of serum proteins with the Behring LN nephelometer, Clin. Chem. 34 (1988) 1618–1621. [14] S. Song, J.K. Chen, M.L. He, K. Fotherby, Effect of some oral contraceptives on serum concentrations of sex hormone binding globulin and ceruloplasmin, Contraception 39 (1989) 385–399. [15] Y. Ikezumi, O. Uemura, T. Nagai, et al., Beta-2 microglobulin-based equation for estimating glomerular filtration rates in Japanese children and adolescents, Clin. Exp. Nephrol. 19 (2014) 450–457. [16] P. Ridefelt, M. Aldrimer, P.O. Rodoo, et al., Population-based pediatric reference intervals for general clinical chemistry analytes on the Abbott Architect ci8200 instrument, Clin. Chem. Lab. Med. 50 (2012) 845–851. [17] J. Bienvenu, L. Sann, F. Bienvenu, et al., Laser nephelometry of orosomucoid in serum of newborns: reference intervals and relation to bacterial infections, Clin. Chem. 27 (1981) 721–726. [18] D.J. Malvy, J.D. Poveda, M. Debruyne, et al., Laser immunonephelometry reference intervals for eight serum proteins in healthy children, Clin. Chem. 38 (1992) 394–399. [19] W. KALOW, K. GENEST, A method for the detection of atypical forms of human serum cholinesterase; determination of dibucaine numbers, Can. J. Biochem. Physiol. 35 (1957) 339–346. [20] W. KALOW, N. STARON, On distribution and inheritance of atypical forms of human serum cholinesterase, as indicated by dibucaine numbers, Can. J. Biochem. Physiol. 35 (1957) 1305–1320. [21] L. Lepage, F. Schiele, R. Gueguen, G. Siest, Total cholinesterase in plasma: biological variations and reference limits, Clin. Chem. 31 (1985) 546–550.
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[22] M.W. Elmlinger, W. Kuhnel, M.B. Ranke, Reference ranges for serum concentrations of lutropin (LH), follitropin (FSH), estradiol (E2), prolactin, progesterone, sex hormone-binding globulin (SHBG), dehydroepiandrosterone sulfate (DHEAS), cortisol and ferritin in neonates, children and young adults, Clin. Chem. Lab. Med. 40 (2002) 1151–1160. [23] V. Moal, E. Mathieu, P. Reynier, Y. Malthiery, Y. Gallois, Low serum testosterone assayed by liquid chromatography–tandem mass spectrometry. Comparison with five immunoassay techniques, Clin. Chim. Acta 386 (2007) 12–19.
[24] A.M. Ward, P.A. White, G. Wild, Reference ranges for serum alpha 1 antitrypsin, Arch. Dis. Child. 60 (1985) 261–262. [25] L.J. Donato, S.M. Jenkins, C. Smith, J.A. Katzmann, M.R. Snyder, Reference and interpretive ranges for alpha(1)-antitrypsin quantitation by phenotype in adult and pediatric populations, Am. J. Clin. Pathol. 138 (2012) 398–405. [26] T.B. Martins, M.E. Bandhauer, A.M. Bunker, W.L. Roberts, H.R. Hill, New childhood and adult reference intervals for total IgE, J. Allergy Clin. Immunol. 133 (2014) 589–591.
Contents lists available at ScienceDirect
Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Complex reference value distributions and partitioned reference intervals across the pediatric age range for 14 specialized biochemical markers in the CALIPER cohort of healthy community children and adolescents Jacalyn Kelly a,1, Joshua E. Raizman a,1, Victoria Bevilacqua a, Man Khun Chan a, Yunqi Chen a, Frank Quinn b, Beth Shodin b, David Armbruster b, Khosrow Adeli a,⁎ a b
CALIPER Program, Pediatric Laboratory Medicine, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada Abbott Diagnostics, Abbott Park, IL, USA
a r t i c l e
i n f o
Article history: Received 16 June 2015 Received in revised form 20 August 2015 Accepted 21 August 2015 Available online 24 August 2015 Keywords: CALIPER Reference intervals Pediatric Analyte Biomarker
a b s t r a c t Background: The CALIPER program has previously reported a comprehensive database of pediatric reference intervals for 63 biochemical and immunochemical markers. Here, covariate-stratified reference intervals were determined for a number of special assays not previously reported. Methods: A total of 1917 healthy children and adolescents were recruited and serum concentrations of 14 biochemical markers were measured using the Abbott Architect ci4100 system. Age and gender partitions were statistically determined, outliers removed and reference intervals calculated using CSLI C28-A3 guidelines. Results: Many analytes showed dynamic changes in concentration requiring at least 3 age partitions. Unique intervals were required within the first year of life for: pancreatic amylase, C-peptide, ceruloplasmin, insulin, β-2microglobulin, cystatin C, dehydroepiandrosterone sulfate (DHEA-S), and α-1-glycoprotein. Cholinesterase, cholinesterase–dibucaine number, and immunoglobulin E required only 2 age partitions and α-1-antitrypsin required only one. Anti-CCP and anti-TPO levels were below the detection limit of the assay. Some analytes including insulin and DHEA-S required additional gender partitions for specific age groups. Conclusions: Complex profiles were observed for endocrine and special chemistry markers, requiring establishment of age- and gender-specific reference intervals. These updated reference intervals will allow improved laboratory assessment of pediatric patients but should be validated for each analytical platform and local population as recommended by CLSI. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Reference intervals are health-associated benchmarks that enable clinicians to confidently interpret test results as normal or abnormal, and thus are critical for accurately diagnosing disease. Pediatric reference intervals can be difficult to establish because multiple age and gender partitions are often required to reflect the rapid changes that occur during growth and development. The challenges associated with recruiting newborns and children, coupled with the small blood volumes that can be collected, limit sample size and subsequent robust calculations from Abbreviations: A1AT, α-1-antityrpsin; AGT, α-1-glycoprotein; AmyP, pancreatic amylase; Anti-CCP, anti-cyclic citrullinated peptide antibody; Anti-TPO, antithyroperoxidase antibody; B2M, β-2-microglobulin; CALIPER, Canadian Laboratory Initiative on Pediatric Reference Intervals; Cerul, ceruloplasmin; Che, cholinesterase; CheDi, cholinesterase dibucaine number; CLSI, Clinical Laboratory Standards Institute; CYSC, cystatin C; DHEA-S, dehydroepiandrosterone sulfate. ⁎ Corresponding author at: Clinical Biochemistry, The Hospital for Sick Children, University of Toronto, Toronto, Ontario M5G 1X8, Canada. E-mail address: [email protected] (K. Adeli). 1 These authors contributed equally to this work.
http://dx.doi.org/10.1016/j.cca.2015.08.020 0009-8981/© 2015 Elsevier B.V. All rights reserved.
a representative population [1]. In addition, past studies have relied on outdated technology [1], or have used retrospective laboratory data or samples collected from hospitalized patients rather than healthy children [2]. Therefore, a complete, accurate, and up-to-date database of pediatric reference intervals for clinically relevant biomarkers is urgently needed. The CALIPER (Canadian Laboratory Initiative on Pediatric Reference Intervals) project is a multi-center initiative among several pediatric centers across Canada. CALIPER aims to address the challenges of establishing pediatric reference intervals by developing a comprehensive biobank of samples from healthy community children. CALIPER has previously recruited over 8500 children from birth to 19 y to develop statistically valid reference intervals for over 60 blood tests including routine chemistries [3], endocrine and nutritional markers [4], and various sex/fertility hormones [5]. A transference study was also completed to validate key reference intervals on 4 other commonly used analytical platforms in order to expand the utility of the CALIPER database in more clinical laboratories across Canada and worldwide [6]. A major strength of CALIPER is establishment of reference intervals using an a priori approach to collect samples; therefore, they are reflective of the normative
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values within a healthy community setting. Furthermore, the statistically rigorous process undertaken to partition reference intervals by age and gender has resulted in a detailed dataset, which underscores the importance of considering the impact that childhood growth and development may have on levels of circulating biomarkers. 2. Materials and methods 2.1. Participant recruitment and sample acquisition Participant recruitment and sample acquisition were conducted as previously described [3]. The study was approved by the Institutional Review Board at the Hospital for Sick Children, Toronto, Ontario, Canada. Briefly, healthy children aged birth to 19 y (n = 1917) were recruited to take part in the CALIPER study in specific CALIPER clinics across the Greater Toronto and Hamilton regions. Participants were asked to complete a questionnaire that collected information on age, gender, ethnicity, diet, medical conditions, medications, and parental health. Ethnic distribution in this sample population is found in Supplemental Table 1. Participants with a history of chronic illness or metabolic disease, who had an acute illness within the previous month, or who used prescribed medication over the previous 2 weeks were excluded from the study. For infants b1 y, samples were collected from apparently healthy/ metabolically stable neonates and children in hospital maternity wards and from select outpatient clinics. Blood draw was performed by trained phlebotomists. Approximately 1–10 ml of blood was collected from each participant in serum separator tubes. All blood samples were centrifuged and serum separated within 5 h of collection. Aliquots were stored at − 80 °C until further testing. 2.2. Sample analysis Twelve special endocrine and chemistry analytes were measured on the integrated Abbott Architect ci4100 system. Analytical methods were controlled according to the manufacturer's instructions using preventive maintenance, function checks, calibration, and quality control material. Analytical performance of the assays for imprecision and linearity is provided in Supplemental Table 2. Samples for this study were tested only when all analytical parameters were acceptable. 2.3. Statistical analysis and reference interval determination Data was analyzed in accordance with the Clinical Laboratory Standards Institute (CLSI) C28-A3 guidelines on defining, establishing, and verifying reference intervals in the clinical laboratory and as described in previous CALIPER reports [3–5]. Briefly, statistical analysis was performed using Microsoft Excel and R software. Scatter and distribution plots were visually inspected for outliers. If the data was not skewed, outliers were removed using the Tukey test [7]. If the data was skewed, outliers were removed using an adjusted Tukey test as previously described [3–5]. Age and/or gender partitions were decided by first visually inspecting the distribution plots, and then by assessment for statistical significance using the Harris and Boyd test [8]. Each reference interval represents the central 95% of the sample distribution. If the sample size within a partition was at least 120, the nonparametric rank method was used to calculate the reference intervals. For partitions with a sample size of less than 120, but containing N 40, the robust statistical method by Horn et al. was used [9,10]. The 90% confidence intervals surrounding the upper and lower limits were then calculated. 3. Results Age- and gender-specific reference intervals for 14 special endocrine and chemistry analytes were calculated using serum samples collected from 1917 boys and girls ranging from 2 days to 19 y. The partitioned
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reference intervals, including the 90th percentile confidence intervals surrounding the lower and upper limits, are shown in Table 1. At least 2 or more unique age partitions were required for all biochemical markers with the exception of α-1-antityrpsin (A1AT). Anti-cyclic citrullinated peptide (CCP) and anti-thyroperoxidase (TPO) antibodies were below the detection limit of the assay. Of the analytes with multiple age-partitions, all but immunoglobulin E (IgE) required unique partitioning within the first year of life, while 8 analytes required partitioning within the first 3 months of life. The dynamic changes observed were not surprising given the rapid physiological and biochemical changes that occur from infancy to childhood to adolescence. Additional gender partitions were required within select age groups for pancreatic amylase, ceruloplasmin, insulin, β2-microglobulin (B2M), cystatin C, and cholinesterase. Gender partitioning for dehydroepiandrosterone sulfate (DHEA-S) was required only in adolescence, from 16–b19 y. Due to small sample size for ethnic groups (Supplemental Table 1), we were unable to perform accurate statistics to calculate ethnicity-specific reference intervals. The dynamic changes in concentration observed for each analyte from birth to adolescence were classified into distinct categories, similar to a previous CALIPER study [4]. The 4 categories include: (1) analytes with lower concentrations in the neonatal period that gradually rose with age; (2) analytes with higher concentrations at birth that declined during infancy, then stabilized with age; (3) analytes with wide variance at birth followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence; and (4) analytes with no gender partitions that displayed little fluctuation across the entire age period. 3.1. Analytes with lower concentrations in the neonatal period that gradually rose with age Pancreatic amylase, C-peptide, insulin and ceruloplasmin all demonstrated the lowest concentrations within the first year of life, followed by a gradual rise in concentration with age (Table 1, Fig. 1). The upper limit of pancreatic amylase doubled after the first 6 months of life, and then gradually increased with age. The lower limit for males in the 1–b2 y age group was significantly less than for females, which necessitated distinct gender partitions in this age group. Insulin and its precursor protein, C-peptide, showed a similar rising pattern that was most pronounced with the onset of puberty. C-peptide required 3 age partitions from 0–b1, 1–b6, and 6–b19 y, but no gender partitions, while insulin demonstrated an additional partition at puberty from 6–b 11 y. Both analytes also displayed a gradual but sustained rise in their lower and upper limits from birth to adolescence, with the widest variance present from 6–b 19 y. Ceruloplasmin required 6 distinct age partitions. For both genders, there was a gradual increase in concentration as early as 2 months, followed by another abrupt increase in levels at 1 y, which continued and peaked in mid childhood (8 y), before declining slightly during puberty. Of note, the decline in concentration between 8 and b19 y was more prominent in males. 3.2. Analytes with higher concentrations at birth that declined during infancy and then stabilized with age β2-Microglobulin (B2M) and cystatin C concentrations peaked within the first year of life and then fell by approximately half after the first year (Table 1, Fig. 2). B2M levels were slightly higher in newborn females than males, and required distinct gender partitions in the 0–3 month age group. B2M concentrations decreased for both genders three months after birth, followed by another decrease at age 2, after which time concentrations remained stable into adolescence. Cystatin C levels declined steadily during infancy, requiring 3 age partitions within the first year of life (0–b 1 month, 1–b5 months, and 5 months–b 1 y). In the 1–b 2 y age group, concentrations decreased for females, but increased slightly for males. Similar to B2M, cystatin C
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Table 1 Age- and gender-specific pediatric reference intervals for 14 special endocrinology and chemistry analytes measured on the Abbott Architect ci4100 system. Female reference intervals
Male reference intervals
Analyte
Age
No. of samples
Lower limit
Upper limit
Lower limit CI
Upper limit CI
No. of samples
Lower limit
Upper limit
Lower limit CI
Upper limit CI
AGP (g/l)
0–b6 months 6 months–b5 y 5–b19 y 0–b19 y 0–b6 months 6 months–b1 y 1–b2 y 2–b19 y 0–b19 y 0–b19 y 0–b3 months 3 months–b2 y 2–b19 y 0–2 months 2–6 months 6 months–b1 y 1–b8 y 8–b14 y 14–b19 y 0–b1 month 1 month–b19 y 0–b1 month 1 month–b19 y
77 259 450 675 67 59 59 517 147 147 60 174 240 49 63 71 229 196 79 51 596 100 611
0.21 0.48 0.48 1.10 b1 0.62 2.63 4.10 b0.5 b1.0 1.89 1.31 1.19 73.5 134.7 137.3 217 205 208.1 3133 7693 797 1523
0.85 2.01 1.14 1.81 11.50 23.16 27.82 31.30 – – 5.81 4.54 2.25 236.5 329.0 388.7 433 402 431.6 12,892 14,856 2478 3280
(0.19, 0.23) (0.46, 0.49) (0.46, 0.49) (1.08, 1.10) – (0.49, 0.81) (1.94, 3.39) (3.00, 6.20) – – (1.41, 2.30) (1.24, 1.35) (1.11, 1.23) (60.9, 86.7) (127.0, 142.5) (109.5, 162.4) (201, 237) (184, 213) (203.9, 214.2) (1394, 4399) (7431, 8073) (682, 911) (1494, 1550)
(0.77, 0.93) (1.74, 2.23) (1.12, 1.22) (1.75, 1.83) (4.36, 15.40) (19.1, 26.9) (24.5, 31.3) (30.4, 33) – – (5.51, 6.10) (3.64, 5.51) (2.17, 2.61) (218.5, 255.6) (308.2, 350.9) (368.2, 409.4) (419, 456) (373, 414) (382.2, 456.1) (12,059, 13,793) (14,646, 14,951) (2361, 2604) (3151, 3331)
77 259 450 675 67 59 62 517 151 151 56 174 240 49 63 71 229 196 82 47 596 100 611
0.21 0.48 0.48 1.10 b1 0.62 0.68 4.10 b0.5 b1.0 1.91 1.31 1.19 73.5 134.7 137.3 217 205 170 4728 7693 797 1523
0.85 2.01 1.14 1.81 11.50 23.16 22.5 31.3 – – 4.70 4.54 2.25 236.5 329.0 388.7 433 402 347.6 11,301 14,856 2478 3280
(0.77, 0.93) (1.74, 2.23) (1.12, 1.22) (1.75, 1.83) (4.36, 15.40) (19.1, 26.9) (19.9, 25.5) (30.4, 33) – – (4.46, 4.90) (3.64, 5.51) (2.17, 2.61) (218.5, 255.6) (308.9, 350.9) (368.5, 409.4) (419, 456) (373, 414) (328.8, 366.4) (10,577, 12,070) (14,646, 14,951) (2361, 2604) (3151, 3331)
0–b1 y 1–b6 y 6–b19 y 0–b1 month 1–b5 months 5 months–b1 y 1–b2 y 2–b19 y 0–b2 months 2–b6 months 6 months–b1 y 1–b6 y 6–b9 y 9–b13 y 13–b16 y 16–b19 y 0–b7 y 7–b19 y 0–b1 y 1–b6 y 6–b11 y 11–b19 y
214 160 407 100 46 98 56 529 115 69 77 78 108 133 98 45 334 365 96 79 72 120
70 116 257 1.49 1.01 0.75 0.60 0.62 28.9 0.65 0.15 0.07 0.14 0.90 1.50 3.96 b25 b25 6.64 9.11 10.47 18.0
1448 1477 2241 2.85 1.92 1.53 1.20 1.11 N40.7 15.6 4.79 3.03 4.14 7.30 12.5 15.5 440.4 449.7 163 279.4 256.6 491.7
(59, 80) (90, 151) (236, 278) (1.43, 1.55) (0.97, 1.04) (0.73, 0.78) (0.56, 0.64) (0.62, 0.64) (15, 33.1) (0.50, 0.85) (0.12, 0.19) (0.06, 0.09) (0.09, 0.21) (0.50, 1) (1.09, 1.98) (3.55, 4.41) – – (4.98, 8.49) (6.4, 12.1) (6.38, 15.73) (14.4, 24.4)
(1086, 1672) (1324, 1986) (2094, 2387) (2.73, 2.98) (1.79, 2.06) (1.45, 1.61) (1.13, 1.27) (1.06, 1.13) – (13.2, 18.1) (3.95, 5.6) (2.38, 3.71) (3.65, 4.66) (6.6, 11.2) (11.4, 13.5) (13.6, 17.3) (321.6, 1000) (379.9, 483.6) (144.2, 182.2) (233.2, 331.7) (223.48, 293.74) (336.5, 589.5)
214 160 407 100 46 98 53 529 115 69 77 75 108 133 98 46 334 365 96 79 72 120
70 116 257 1.49 1.01 0.75 0.77 0.62 28.9 0.65 0.15 0.07 0.14 0.90 1.50 3.36 b25 b25 6.64 9.11 10.47 18.0
1448 1477 2241 2.85 1.92 1.53 1.85 1.11 N40.7 15.6 4.79 0.76 4.14 7.30 12.5 18.2 440.4 449.7 163 279.4 256.60 491.7
(0.19, 0.23) (0.46, 0.49) (0.46, 0.49) (1.08, 1.10) – (0.49, 0.81) (0.18, 1.29) (3.00, 6.20) – – (1.56, 2.20) (1.24, 1.35) (1.11, 1.23) (60.9, 86.7) (127, 142.5) (109.5, 162.4) (201, 237) (184, 213) (161.6, 178.6) (4344, 5156) (7431, 8073) (682, 911) (1494, 1550) (59, 80) (90, 151) (236, 278) (1.43, 1.55) (0.97, 1.04) (0.73, 0.78) (0.75, 0.79) (0.62, 0.64) (15, 33.1) (0.50, 0.85) (0.12, 0.19) (0.06, 0.08) (0.09, 0.21) (0.50, 1) (1.09, 1.98) (1.80, 4.83) – – (4.98, 8.5) (6.37, 12.1) (6.38, 15.73) (14.4, 24.4)
A1AT (g/l) AmyP (U/l)
Anti-CCP (U/ml) Anti-TPO (IU/ml) B2M (mg/l)
Cerul (mg/l)
ChE (U/l) ChEDi (U/l)
C-peptide (pmol/l)
CYSC (mg/l)
DHEA-S (μmol/l)
IgE (IU/ml) ⁎Insulin (pmol/l)
(1086, 1672) (1324, 1986) (2094, 2387) (2.73, 2.98) (1.79, 2.06) (1.45, 1.61) (1.6, 2.15) (1.06, 1.13) – (13.2, 18.1) (3.95, 5.64) (0.65, 0.85) (3.65, 4.66) (6.60, 11.20) (11.4, 13.5) (16.5, 19.7) (321.6, 1000) (379.9, 483.6) (144.2, 182.2) (233.2, 331.7) (223.48, 293.74) (336.50, 589.5)
Bold and underline indicate gender-specific differences. Values below the limit of detection of the assay are denoted with “b”; limits above the linear range are denoted with “N”. ⁎ Additional age partitions were added at 6–b11 y and 11–b19 y to reflect the rising levels at the onset of puberty, consistent with previous reports (see text); however, these partitions were not statistically significant after analysis using the Harris & Boyd test.
levels declined a final time at 2 y and then remained stable into adolescence. 3.3. Analytes with wide variance in infancy followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence α-1-Glycoprotein (AGP), cholinesterase (ChE), cholinesterase– dibucaine number (ChEDi), and DHEA-S all demonstrated wide variance in infancy, and then, depending on the analyte, showed either a rise or fall in concentration through the remainder of childhood and adolescence (Table 1, Fig. 3). These analytes also demonstrated reduced variance with age, with the widest intervals earlier in childhood and narrower intervals later in life. AGP demonstrated higher variance and increased levels during infancy and early childhood that required age partitions from 0 to 6 months, and 6 months to 5 y. Levels dropped
by approximately one half, from an upper limit of 2.01 g/l in the 6 month–b5 y group, to 1.14 g/l in the 5–b19 y group. No gender partitions were observed. ChE and ChEDi had the widest variance from birth to 1 month of age, followed by a slight fall in concentration that remained stable through to adolescence, necessitating a broad partition from 1 month to b19 y. Unlike ChEDi, ChE also exhibited gender-specific differences within the first month of life. DHEA-S followed a similar U-shaped trend observed previously for testosterone [5]. Eight age partitions were required: 3 within the first year of life, 2 in early childhood, and 3 during puberty and adolescence. Additionally, gender partitioning was required in the 16–b19 y age group. The highest concentrations were observed within first 2 months of life with a cutoff at the upper limit of the assay (40.7 μmol/l), followed by an abrupt drop between 2 months and b 1 y, and a subsequent slow rise into adolescence. The rise in DHEA-S levels observed during adolescence occurred faster in males than females.
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Fig. 1. Age-dependent scatter plots for boys (blue triangles) and girls (pink circles) for analytes with low concentrations in the neonatal period that gradually rose with age. Trend lines are shown for males (black) and females (gray). (A) AmyP, (B) c-peptide, (C) insulin, and (D) ceruloplasmin.
3.4. Analytes with no gender partitions that displayed little fluctuation across the entire age period
below the detection limit of the assay necessitating cutoff values at the lower limit (scatterplots are not shown).
Unlike other analytes, neither A1AT nor IgE required any gender partitioning (Table 1, Fig. 4). Both displayed relatively constant variance from birth to 19 y. A1AT showed a narrow interval (1.10–1.81 g/l) that did not require any age-specific partitions. Although IgE demonstrated a wide variance, a minor increase in concentration was observed with age, requiring only 2 age partitions. Anti-CCP and anti-TPO levels were
4. Discussion
Fig. 2. Age-dependent scatter plots for boys (blue triangles) and girls (pink circles) for analytes with highest concentrations at birth that declined during infancy then stabilized with age. Trend lines are shown for males (black) and females (gray). (A) B2M and (B) CYSC.
The current study is an important addition to the expanding CALIPER database of pediatric reference intervals. Here, we show reference value distributions for 14 special chemistry and endocrine markers and report age- and gender-specific reference intervals for children and adolescents from birth to b19 y. Analytes were categorized according to trends observed across the pediatric age range. The first group included pancreatic amylase, C-peptide, insulin and ceruloplasmin, all of which had lower concentrations in the neonatal period that gradually rose with age. Pancreatic amylase displayed similar values and trends as other reports; however, unlike our study which found gender differences in the 1–b2 y age group, others that used the Roche Modular P analyzer did not observe this trend [11], or required gender partitions only in adolescence [12]. Given that pancreatic amylase secretion is regulated by starch intake, differences in dietary habits, particularly between boys and girls as well as analytical platforms may account for some of the disparities between these studies. The lower limit for newborns 0–b6 months of age was below the detection limit of the assay at 1 U/l, which may reflect the immature digestive system early in life. The rise in concentration with age demonstrates the adaptation of the body to dietary requirements, underscoring the importance of age-specific partitions in monitoring changes in pancreatic function associated with disease, such as cystic fibrosis. This assay will provide improved specificity for pancreatic disorders compared to a general amylase assay that measures both salivary and pancreatic amylase. C-peptide and insulin are secreted in equimolar amounts because they are both products of proinsulin, so it is not surprising that both showed similar profiles and covariate partitions. The steady increase in the levels of these analytes from birth to adolescence coincides with the development of the endocrine system as the pancreas adapts to increased glucose demands during metabolic growth. Soldin et al. measured C-peptide and insulin on the Siemens Immulite 2000 system and also observed increased concentrations from birth to adolescence that required multiple age partitions and was most pronounced at the onset of puberty [2]; however, the lower limits of C-peptide were higher in all age groups by about 2-fold compared to what was observed in our
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Fig. 3. Age-dependent scatter plots of boys (blue triangles) and girls (pink circles) for analytes with wide variance in infancy followed by a rise or fall in concentration that remained relatively stable for the remainder of childhood and adolescence. Trend lines are shown for males (black) and females (gray). (A) AGP, (B) ChE, (C) ChEDi, and (D) DHEA-S. Trend lines are shown for male (black) and female (gray).
study. In addition, the insulin intervals developed by Soldin et al. were narrower than the data presented here, and required gender-specific partitions within the first 3 y of life. These discordances are likely attributed in part to the differences in study populations. In the Soldin et al. study, reference intervals were calculated from hospitalized patient samples using the Hoffman approach, while the current study used healthy community children from the CALIPER cohort. The differences in observed C-peptide concentrations may also be due to the analytical methods used by the 2 assays. Given the importance of copper in oxygen processing and oxidative phosphorylation, the rising ceruloplasmin levels observed with age reflect its key role in growth and development. Values and trends from other studies generally agree with our data [11,13], although Clifford
Fig. 4. Age-dependent scatter plots of boys (blue triangles) and girls (pink circles) for analytes with no gender partitions that displayed little fluctuation across the entire age period. Trend lines are shown for males (black) and females (gray). (A) A1AT and (B) IgE.
et al. observed ceruloplasmin levels that peaked around late childhood and then declined slightly in subsequent age partitions [11]. Consistent with our data, gender differences during puberty and adolescence were also observed where females had higher levels than males. These gender-specific changes in adolescence may be due to hormonal effects as previously described [14]. The second category, comprising the renal markers, B2M and cystatin C, demonstrated the highest concentrations at birth, which declined during infancy and then stabilized with age. Both analytes decreased within the first 2 y of life, requiring multiple partitions during this time, before levels stabilized into one broad partition for the remaining age group. Unlike creatinine, which increases with age due to its dependence on body mass [3], the declining B2M and cystatin C levels during infancy likely reflect development of the kidneys as filtration function matures at an early age. The overall trends agree with recent studies [15,16]; however, in contrast to these reports, we also observed gender differences within the first year of life for B2M, and within the 1–b2 y age group for cystatin C. In the third category, AGP, ChE, ChEDi, and DHEA-S were grouped based on their wide variance during infancy, and requirement for age partitions within the first 6 months of life. The first analyte examined in this category was AGP, a marker for inflammation and acute bacterial infection. Older studies that analyzed samples using the Behring nephelometer generally agree with our reference intervals for neonates [17], and children [18], but required many more gender partitions than observed here. Furthermore, we expand on the available data by reporting intervals in newborns as early as 2 days. ChE and ChEDi use the same reagent, except the ChEDi test includes the anesthetic drug dibucaine to identify patients who have an abnormal genetic variant of pseudocholinesterase before its administration. As expected, ChEDi interval values were approximately 80% lower than the ChE interval values, which is consistent with the expected inhibitory response of dibucaine on the wild-type ChE enzyme in the assay [19,20]. In patients with abnormal ChE levels, the expected response would be a 20% inhibition of the enzyme during dibucaine treatment [19,20]. Therefore, these updated reference intervals are crucial to properly identify pediatric patients who are dibucaine-resistant in order to adjust dosing appropriately and prevent drug toxicity. Our reference intervals agreed with an older publication that used a stand-alone kit and analyzed using a Cobas Bio centrifugal analyzer, though children below 4 y were
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not included in this study [21]. Here, we also extend the intervals in newborns as early as 2 days old. DHEA-S showed a dynamic pattern requiring a total of 8 age partitions, where similar results were observed for total testosterone in a previous CALIPER study that used the Abbott Architect i2000 analyzer [5]. Given that DHEA-S is a crucial androgen precursor produced in the adrenal cortex, the requirement for multiple age partitions demonstrates the complex role of androgens during development of secondary sex characteristics. The dramatic U-shaped trend parallels that observed previously for total testosterone and reflects the expected pattern of sexual development [5]. Not surprisingly, male adolescents had higher levels than females, demonstrating the role of DHEA-S in male reproductive maturity. DHEA-S intervals agreed well with previous studies using an immunoassay method on the Immulite system [22], but showed a negative bias to recent studies using LC–MS/MS in some age groups, likely due to antibody cross-reactivity with androgen metabolites [23]. In the final category, which included A1AT and IgE, only minor fluctuations across the entire age period were observed and no gender differences were exhibited. As a serine protease inhibitor, A1AT levels did not show any notable changes demonstrating its highly regulated status during liver growth and development. The lower limit of A1AT, used to diagnose deficiency in the workup for liver and lung disorders, was 1.10 g/l, which agrees with previous population studies using healthy children [24] and hospital data [25]. For IgE, only two age partitions were required, and levels increased slightly with age. The stability of this analyte throughout childhood also reflects its highly regulated role as an important mediator of allergic response and immunological defense. The reference intervals are comparable to previous studies, although additional age and gender partitioning was required [2,26]. While the assays in our study and others have used similar IgE calibrators traceable to WHO standards, differences in reference values may be attributable to the study population and the use of samples from healthy community children versus patients [2]. Lastly, anti-CCP and anti-TPO levels were low in pediatric samples requiring cutoff values at the assay limit of detection. This is unsurprising, given that healthy children are at low risk for autoimmune diseases such as rheumatoid arthritis and thyroid abnormalities. The data presented in the present study adds to the growing CALIPER database of pediatric reference intervals established from a large cohort of healthy community children. We have demonstrated a complex pattern of change throughout childhood and adolescence for 14 specialty endocrine and chemistry tests. These dynamic changes highlight the importance of establishing detailed age and gender specific reference intervals that are capable of fully capturing the rapid developmental and physiological changes that occur during childhood. The reference intervals established here will aid in accurate diagnosis of pediatric medical conditions. However, the population included in this study is from the region of Southern Ontario, which is home to many ethnic groups, reflective of the multicultural Canadian society and more specifically the Greater Toronto Area. Although we were unable to partition our dataset into ethnic-specific reference intervals due to small sample size in some ethnic groups, these reference intervals are representative of various genetic backgrounds and serve well the needs for normative data in other populations. Our review of the literature as discussed above has for the most part demonstrated comparable data to those from other populations such as the United States, Japan, Sweden, Germany, and France. Although not extreme, any differences likely reflect variations across different analytical platforms, and methods of analysis such as the use of laboratory data as well as differences in age stratification decisions. We have previously performed some preliminary investigations on the effect of ethnicity on various biomarkers, and have found that most markers indeed are not affected [3]. Yet, it is still possible that homogeneous populations in other geographical regions may demonstrate more pronounced age and gender differences. To the best of our knowledge, this is the most
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comprehensive study for all 14 of the specialized analytes presented here. Although we would caution against fully adapting our reference intervals without validation, we do believe that our data can be implemented in other populations. Validation and/or transference of these reference intervals to other analytical platforms will allow for broader application of this dataset to local populations. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.cca.2015.08.020. Acknowledgments We thank all study participants and their families. Without their participation, this study would not have been possible. We also thank the numerous CALIPER volunteers for their countless hours of hard work and dedication to this project. A special thank you to Michelle Nieuwesteeg and Victoria Higgins for her thoughtful comments and advice during preparation of this manuscript. References [1] K. 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