February 2017

ISSN 1650-3414

Volume 28 Number 1

Communications and Publications Division (CPD) of the IFCC
Editor-in-chief : Prof. Gábor L. Kovács, MD, PhD, DSc
Department of Laboratory Medicine, Faculty of Medicine, University of Pecs, Hungary
e-mail: ejifcc@ifcc.org

The
Journal of theInternational
Federation ofClinicalChemistry andLaboratory
MedicineIn this issue

Foreword of the editor
Gábor L. Kovács

3

Recent advances in pediatric laboratory medicine
Edgard Delvin

4

Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers
E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin

6

Pediatric Metabolic Syndrome: pathophysiology and laboratory assessment
Victoria Higgins, Khosrow Adeli

25

Pediatric reference intervals for biochemical markers
H. Tahmasebi, V. Higgins, A. Fung, D. Truong, N. White-Al Habeeb, K. Adeli43

The utility of CSF for the diagnosis of primary and secondary monoamine
neurotransmitter deficiencies
A.B. Burlina, A. Celato, G. Polo, C. Edini, A.P. Burlina

64

Pediatric reference intervals for transferrin saturation in the CALIPER cohort
Victoria Higgins, Man Khun Chan, Khosrow Adeli77

Congenital hyperinsulinism caused by a de novo mutationin the ABCC8 gene – a case report
Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh

85

Letter: Hemochromatosis, erythrocytosis and the JAK2 p.V617F mutation
Stephen E. Langabeer92

In this issue: Recent Advances in Pediatric Laboratory Medicine

Foreword of the editor

Editor in Chief: Gábor L. Kovács, M.D., Ph.D., DSc

Professor Edgard Delvin (Canada) was asked
to be the guest-editor of the special issue on“Recent advances in pediatric laboratory medicine”. After obtaining a PhD degree in biochemistry at the University of Montreal, Dr.
Delvin pursued his post-doctoral training inBiochemical Genetics at the Montreal Children’s
Hospital (McGill University). In 1973 he joined
the Shriners Hospital for Children, affiliated to
McGill University, where he became senior investigator and Associate Director of the Genetics
Unit as well as being Associate Professor in the
Department of Experimental Medicine.
In 1992, the University of Montreal recruited him to chair the Department of Clinical
Biochemistry at the University-affiliated SainteJustine Hospital. He also was appointed as fullProfessor of Biochemistry in the Department ofBiochemistry of the Faculty of Medicine. After
his retirement in 2012 he acted as consultant

for the Montreal Children’s Hospital, affiliatedwith McGill University until 2014.
He has served on the Paediatric Endocrinology
Teaching Program of the University of Montreal
until August 2012. After being Editor-in-Chief of
the Journal Clinical Biochemistry from 2006 to
2012, he now is the Special Issues Editor.
In terms of international activities, he has
been Editor-in-chief of the IFCC eNewsletterfrom 2006 till 2012 and now chairs the IFCC
Committee for Public Relations (C-PR) as well
as being Vice-Chair of the CPD-EC.
He has authored or co-authored 245 articles,
published in internationally recognized journals, and 9 book chapters.
In 2003 he received the research ExcellenceAward from the Canadian Society of Clinical
Chemists, and is a Member of the French
Académie Nationale de Médecine.

Page 3eJIFCC2017Vol28No1pp003-003

In this issue: Recent Advances in Pediatric Laboratory Medicine

Recent advances in pediatric laboratory medicine
Guest editor: Edgard Delvin

Department of Biochemistry and CHU Sainte-Justine Research Center, University of Montreal,Montreal, Quebec, Canada

ARTICLE INFOEDITORIAL

Corresponding author:
Edgard Delvin, Ph.D.
CHU Sainte-Justine Research Center
3175 Côte Ste-Catherine
Montréal, Québec, H3T 1C5
CanadaPhone: (514) 345-4931 ext. 6268
E-mail: delvine@sympatico.ca

Pediatric medicine that spans from the perinatal period through adolescence is a field in its own right, as it
requires knowledge about early growth and development that are not present in adult medicine. Indeed,
it is during these crucial years that adult health andwellbeing are being carved. This statement is supported by epidemiological studies and meta-analyses
indicating that many adult diseases are grounded inearly childhood from conception, and thus should
be viewed as developmental disorders (1, 2). Clinicallaboratories are instrumental in the detection of subtle metabolic impairment present often at the onset of ailments. Their efficacy in assisting attending
physicians in their diagnostic and follow-up endeavours is intimately linked to availability of dependable
analytical methods and reliable reference values.
Paediatrics is particularly sensitive to this problem as
rapid developmental changes associated with child
growth, can impose rapid discontinuous changes on
the physiology of individuals (3-5).

Key words:
pediatric obesity, insulin resistance,
metabolic syndrome, reference intervals,
neurotransmitter diseases

This issue of the eJIFCC presents 5 articles focusedon pediatric laboratory medicine. The first 2 review
Page 4eJIFCC2017Vol28No1pp004-005

Edgard Delvin
Recent advances in pediatric laboratory medicine

articles relate to childhood obesity and metabolic syndrome. The first (6) gives an overview
of the relationship of obesity with genetic andenvironmental factors, and raises awareness on
the applicability and limitations of important
potential biomarkers used in preventive andpredictive medicine. The second (7) reviews
the pathophysiology, consensus definitions andlaboratory assessment of pediatric metabolic
syndrome as well as potential novel biomarkers.
The third (8) review is more generic and highlights the achievements and milestones of theCanadian CALIPER project, including the establishment of comprehensive bio-bank and database that have addressed several critical gaps
in age- and sex-specific paediatric reference
intervals impeding improvement of paediatric diagnostics. The fourth (9) article provides
an overview of the diagnosis of primary neurotransmitters diseases (PNDs) through investigations using cerebrospinal fluid (CSF). It also
calls for caution in sample management and analytical methodology as they potentially could
affect diagnostic interpretation. The last short
research article provides novel data on Pediatric
Reference Intervals for Transferrin Saturation
on a large CALIPER Cohort of Healthy Children
and Adolescents (10).
These articles illustrate the effervescence in thefield of paediatric laboratory medicine and invite professionals to continue providing state of
the art data on this ever evolving subject.

REFERENCES
1. Shonkoff JP, Garner AS. The lifelong effects of early childhood adversity and toxic stress. Pediatrics.
2012;129(1):e232-46.
2. Dasinger JH, Alexander BT. Gender differences in developmental programming of cardiovascular diseases. Clinical science (London, England : 1979). 2016;130(5):337-48.
3. Delvin EE, Laxmi Grey V, Vergee Z. Gap analysis of pediatric reference intervals related to thyroid hormones andthe growth hormone-insulin growth factor axis. Clinicalbiochemistry. 2006;39(6):588-94.
4. Mansoub S, Chan MK, Adeli K. Gap analysis of pediatricreference intervals for risk biomarkers of cardiovasculardisease and the metabolic syndrome. Clinical biochemistry. 2006;39(6):569-87.
5. Jung B, Adeli K. Clinical laboratory reference intervals
in pediatrics: the CALIPER initiative. Clinical biochemistry.
2009;42(16-17):1589-95.
6. Levy E, Saenger AK, Steffes M, Delvin E. Paediatric obesity and cardiometabolic disorders: risk factors and biomarkers. eJIFCC. 2017.
7. Higgins V, Adeli K. Pediatric Metabolic Syndrome: Pathophysiology and Laboratory Assessment. eJIFCC. 2017.
8. Tahmasebi H, Higginns V, Fung AWS, Truong G, WhiteAl Habeeb NMA, Adeli K. Pediatric Reference Intervals for
Biochemical Markers: Gaps and Challenges, Recent National Initiatives and Future Perspectives. eJIFCC. 2017.
9. Burlina AB, Celato A, Polo G, Edini C, Burlina AP. Cerebrospinal fluid biomarkers for the diagnosis of monoamine neurotransmitters deficiencies. eJIFCC. 2017.
10. Higgins V, Chan MK, Adeli K. Pediatric Reference Intervals for Transferrin Saturation in the CALIPER Cohort of
Healthy Children and Adolescents. eJIFCC. 2017.

Page 5eJIFCC2017Vol28No1pp004-005

In this issue: Recent Advances in Pediatric Laboratory Medicine

Pediatric obesity and cardiometabolic disorders:risk factors and biomarkers
E. Levy1,2, A.K. Saenger3, M.W. Steffes3, E. Delvin2,4

Department of Nutrition, University of Montreal, Montreal, Quebec, Canada
CHU Sainte-Justine Research Center, Nutrition, Gastroenterology and Hepatology Division,
University of Montreal, Montreal, Quebec, Canada
3
Department of Laboratory Medicine and Pathology, University of Minnesota Health, Minneapolis, MN, USA
4
Department of Biochemistry, University of Montreal, Montreal, Quebec, Canada
1
2

ARTICLE INFOABSTRACT

Corresponding author:
Edgard Delvin, Ph.D.
CHU Sainte-Justine Research Center
3175 Côte Ste-Catherine
Montréal, Québec, H3T 1C5
CanadaPhone: (514) 345-4931 ext. 6268
E-mail: delvine@sympatico.ca

Obesity remains the most prevailing disorder inchildhood males and females worldwide. Its highprevalence markedly predisposes children to insulin
resistance, hypertension, hyperlipidemia and liver
disorders while enhancing the risk of type 2 diabetes
and cardiovascular diseases. In this review, the relationship of obesity with genetic and environmental
factors will be described and the underlined causes
will briefly be reported. As obesity in children constitutes an increasingly health concern, important potential biomarkers have been discussed for the diagnosis, treatment and follow-up of the wide range ofoverweight-related complications. Awareness about
the applicability and limitations of these preventive and predictive biomarkers will intensify the research and medical efforts for new developments inorder to efficiently struggle against childhood obesity.

Key words:
pediatric obesity, insulin resistance,
inflammation, oxidative stress, biomarkers

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

INTRODUCTION
The prevalence of childhood obesity is rapidly
increasing and presents a major public healthconcern in developed and developing countries(1-4), and assessment of obesity is of utmost importance to paediatricians. However, there are
varying definitions of obesity in children and adolescents, along with ethnic-specific variations
in body fat content and distribution, which complicate this undertaking (5). Moreover, these divergences may explain prevalence dissimilarities
associated with cardiometabolic diseases (CMD)(e.g. insulin resistance, hypertension, dyslipidemia and diabetes) in adulthood (6-11). In the
Figure 1

context of epidemiological studies, body massindex (BMI, weight/height2) in adults is currently
considered as a diagnostic test (separator variable)
which is able to identify overweight (25 kg/m2)
and obese (30 kg/m2) individuals and may predispose to increased CMD risk, morbidity andmortality (12, 13). However, no similar definite
values can be used in childhood and adolescencebecause of the substantial changes in BMI, which
occur naturally from birth to adulthood (14, 15),and because of the limited data in youth that relate BMI trajectory to cardiovascular events laterin life. Age- and sex-specific BMI cut-offs weredeveloped to define overweight and obese using different nationally representative age- andInternational age- and sex-specific cut-off points for BMI
for overweight and obesity

BMI kg/m2

30

25

20

15

0

4

8

12
Age (Years)

1620*Adapted with permission from data of Table 4 from Cole TJ et al. (16). Data obtained by averaging the national centiles.
BMI: Body Mass Index. Filled circles: curve for overweight boys; filled square: curve for obese boys;
filled upward triangles: curve for overweight girls; filled downward triangles: curve for obese girls.

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

sex-specific data sets, following recommendations from the International Obesity Task Force(16, 17). International age- and sex-specific BMI
cut-offs for overweight and obese girls and boys
are illustrated in Figure 1. Applying this concept
to BMI trajectory, Attard et al. (18) demonstrated that the odds for diabetes were 2.35 higher
for those with a BMI of 30 kg/m2 relative toyoung male adults who had maintained a BMI
of 23 kg/m2 over an average of 12 years. These
data suggest there is potential for improving
the ability to assess the effect of paediatric obesity on development of diseases at a later time
point. Secular trends demonstrate that the prevalence has plateaued in some countries (19) oreven decreased (20), but has continued to risein others, independent of how overweight andobesity are defined in childhood (1, 21-23). The
Figure 2

apparent contradiction could partially dependon the span of the retrospective studies and on
the years included. Nevertheless, the presenthigh number of young adults with the stigmataof the metabolic syndrome (MetS), and the related non-alcoholic fatty liver disease (NAFLD)
justifies that it be considered a major world public health issue (24). This review briefly describes
the various potential causes of obesity in youth
and underscores the available biomarkers for associated conditions.
Definite BMI thresholds to identify an increased
risk for CMD cannot be used in childhood and adolescence. Age- and sex-specific BMI cut-offs to define overweight and obesity and predict trajectory
into adulthood should be utilized using different
nationally representative age- and sex-specific data.

Factors involved in the development of obesity

Nutrition

Obesity

Page 8eJIFCC2017Vol28No1pp006-024

Environment

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

OBESITY AND LIFESTYLE
Lifestyle is broadly defined as the way or manner by which a person or a group of people lives.
However, lifestyle can be influenced by a complex
set of factors that are intertwined and can affect
the quality of living and health (Figure 2). The
socioeconomic position (SEP) stands out among
these factors because it has a direct impact on
the quality of nutrition and the living environment, including access to adequate physical activity facilities and education. Consequently, acomprehensive view must be adopted whenever addressing this topic but a majority of studies tend to focus in this area in a fragmented
manner.
One such study, based on self-reports, demonstrated that poor children in the United States
have worse health compared to wealthy children. This difference in health status diverged
further as the children aged; thereby suggesting the adult health gradient had its origins inchildhood. However, other than family income
no other factors were considered which could
explain these results (25). SEP may also impact
the quality of nutrition. Darmon et al. (26) reported that higher-quality diets consisting ofwhole grains, lean meats, fish, low-fat dairy
products, fresh vegetables and fruits were associated with greater affluence, whereas energy-dense and nutrient-poor diets (refined
grains, added fats) are preferentially consumed
by persons of lower SEP. Likewise, in a systematic review, Cameron et al. (27) reported thatchildren of lower SEP had a steeper weightgain trajectory initiating at birth and led to agreater prevalence of obesity in children andadults. Pre-pregnancy maternal BMI, diabetes,
pre-pregnancy diet, smoking during pregnancy,
low birth weight, breastfeeding initiation andduration, early introduction of solids, maternal and infant diet quality, and some aspects of
the home food environment were among theearly-life predictors of later obesity and amid
links with SEP. Furthermore, lack of physicalactivity is an additional risk factor for developing obesity. A longitudinal study involving repeated 7-day physical activity recall questionnaires over a 5-year period demonstrated that
greater fluctuations in physical activity led to anincrease in body fat in adolescent girls and boys
(28). An interventional study supported these
conclusions, demonstrating interruption of sedentary time with brief moderate-intensity walking resulted in an improvement of short-term
metabolic function in non-overweight children
without increasing subsequent energy intake(29). Despite the difficulty in directly comparing
studies because of the variety of environmental factors and defined end-points, systematic
reviews consistently highlight that better andsafer access to physical activity resources are
directly related to increased leisure time physical activity in children and adolescents, which
subsequently decreases the risk of developing
obesity (30-34).
Access to physical activity resources is directly
related to higher leisure time physical activity inchildren and adolescents and decreases the riskof developing obesity.				
OBESITY AND GENETIC/EPIGENETIC
FACTORSIn addition to the risk factors previously discussed, genetic background and foetal programming through epigenetic modifications
are equally important in the development ofobesity and related diseases. There is also increasing evidence suggesting synergetic effects between gene variant loci involved inmetabolic traits and dietary or lifestyle factors.
Maes et al. (35) compiled data from more than
25,000 twin pairs and 50,000 biological andadoptive family members and reported thatgenetic components contribute 40-70% to the

Page 9eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

inter-individual variability in common obesity.
Another study showed that parental obesity
doubled the risk of adult obesity among bothobese and non-obese children less than 10
years of age (36). Few studies have investigated the gene-environment interactions related
to sedentary behaviour using large cohorts.
The Identification and prevention of Dietaryand lifestyle-induced health EFfects In Children
and infantS cohort (IDEFICS) used a subsample
of 4406 participants to demonstrate that thefat mass and obesity-related gene (FTO) polymorphism (rs9939609) could explain ~9% of
the obesity variance, thereby suggesting the
FTO gene was sensitive to the social environment (37). To date, genome wide association
studies (GWAS) have provided evidence for anumber of gene variants associated with the
Figure 3

development of obesity in the youth. Willer
et al. (38), based on a cohort of 11 year-old
children, demonstrated significant and consistent association between BMI and variant loci
(SNPs) located in or near the trans-membraneprotein-18 (TMEM18), potassium channel tetramerisation domain containing-15 (KCTD15)
and glucosamine-6-phosphate deaminase-2(GNPDA2) genes. The high brain and hypothalamic expression of these factors, together with
FTO and the melanocortin-4 receptor (MC4R),
independently associated with adiposity andinsulin resistance (39), supports the argument
for a neuronal foundation in obesity. Whether
these loci are modulated under neuronal influence by the environment or lifestyle remains tobe elucidated. Graff et al. (40) provided a partial answer by establishing a dose-dependent

Predicated BMI Z-score from model based coefficients
per 7 hours/week of screen time in the presenceof 0, 1 and 2 risk (T) FLJ35779 (rs2112347) alleles

Page 10eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

Legend: (p for interaction = 0.02) in EA (a), and 0, 1 and 2 risk (A) GNPDA2 (rs10938397) alleles, respectively (p forinteraction = 0.03) in AA (b).
Abbreviations: BMI (body mass index), ST (hours per week of screen time), EA (European American), AA (African
American).
Beta estimates are presented for the interaction model: Multivariable linear models of adolescent BMI Z scores
regressed on SNP and ST (hr/wk), with SNP by ST interaction term, controlling for age, sex, current smoking (at least
one cigarette every day for 30 days), geographic region, and self-reported heights and weights (n=39 EA, n= 12AA), oversampling of highly educated African Americans (n=281; AA stratum only). Random intercepts allowed forindividual, family and school with no sample weighting.
*Reproduced with permission from: Graff et al. Pediatr Obes 2013;8:doi:10.1111/j.2047-6310.2013.00195x.

ta es ima leisures a e screen
res ntetimefo (βt = −0.014,
erainteraction
o0.016,c andt M0.045/7h/week)
Z s ores regr withsse onS P
GNPDA2, ont oll ngage, sex, ur subjects
n smofor(rs10938357)
SNPforin Afro-Americano0, 1 andphi 2 riskeg alleles.
on, andThey
sel -observed
eportedaheghtsimilarersampingforo thehighlyeducated
Afr canAinteraction
FLJ35779(rs2112347)
genepolymorphism(FigureAlthough interactions
ercep
s al owed
for 3).
i dividual
family an
are documented in each study, they are modest, and individually cannot explain the development of obesity or the onset of related disp;eases. Additionalt studies are required
to probe
the relationship between polymorphisms inmde genes
u i involveda ia inl energy
ear mols fmultiplemanagement) w th environmental
P y
i and
e act
n
and(hrthe numerous
lifestyleatfactors.
lea t
we gh
s (n 3 control
E , of geneA expression,
The
epigenetic
ans also
n be81;considered
AA stra um
only
Randommust
in theunderstandingoo
w development
h no samp ofweightin. conceptof theobesity. This

stems from the early work of Barker et al. (41),who proposed that the tendency to store abdominal fat might be a persistent response to
adverse conditions which initiated in the foetal life stage but persisted throughout infancy.

Page 11eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

A myriad of peer-reviewed publications haveconfirmed this initial hypothesis (42-46). Lee etal. (47) suggest there is a gene-foetal environment interaction, one of which occurs throughin utero exposure to maternal cigarette smoking and leads to a preference in adolescence
for moderately enhanced fatty foods by silencing the opioid receptor mu-1 gene (OPRM1) involved in the brain reward system. Small gestational age (SGA) is also well recognized andlinked to an increased risk for rapid postnatal
weight gain and subsequent development ofobesity and chronic metabolic diseases laterin life. The Auckland Birth weight Collaborative
Study demonstrated that smoking, low pregnancy weight, maternal short stature, maternaldiet, ethnic origin of mother and hypertension
are all “environmental” risk factors for SGA(48). A subgroup of the cohort later establishedthat polymorphic FTO (rs9939609, intron),
KCNJ11 (rs5219, missense Lys23Glu), BDNF
(rs925946, 9.2 kb upstream), PFKP (rs6602024,
intron), PTER (rs10508503, 179 kb upstream)and SEC16B (rs10913469, intron) genes, wererelated to obesity, type 2 diabetes, and SGA
which indicates the important interaction between genetic factors and fœtal environment(49). Finally, a prospective singleton normal
pregnancy cohort study demonstrated a direct
Table 1

relationship between the maternal adipokines, leptin (a satiety factor) and adiponectin(an insulin sensitizer). The study included 339
healthy women without pre-existing diabetes
who were evaluated at 24-28 and 32-35 weeks
of gestation and the cord blood (foetal compartment) assessed at birth (50). Foetal insulin
sensitivity was negatively associated with cord
blood leptin and positively with pro-insulin concentrations, suggesting the maternal impact on
foetal adipokines may be an early life pathway
in maternal-foetal transmission of the propensity to develop obesity and insulin resistancelater in life. These examples provide compelling
evidence on the role and impact of the foetal
environment and development of chronic diseases later in life.
Parental obesity more than doubles the riskof adult obesity among obese and non-obese
children.					Gene-environment interactions are modest,
and individually are not able to explain thedevelopment of obesity and the onset of relateddiseases.					There are compelling evidence highlighting therole of foetal environment and development ofchronic diseases later in life.			

Caveats in assessing insulin sensitivity
Variable

Issue

Measurement of insulin

Wide inter-inter method (laboratory) variations
Variation in standardization among methods

Measurement of plasma glucose

Pre-analytical quality of blood specimens
Glycolysis at room temperature
NaF inhibits enolase, a late glycolytic enzyme

Patient preparation

Poor assessment of patient’s nutritional status
Elevation of post-prandial glucose in malnutritionand low carbohydrate diets
Page 12eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

Table 2Cut-off points for defining insulin resistance (IR)Insulin measurement

Population
StudiedAge(years)Gender

HOMA-IR
95th percentile

Ref

Immunoassay(Access, Beckman Coulter)

French Canadian

9
13
16

M/F

1.88/2.07
3.28/3.86
3.31/3.10(76)

Fluoroimmunoassay(AutoDelfia, Pharmacia)

Brazilian

10-19

M/F

>2.93

(77)

Chemiluminescence
Immunoassay(Immulite, Siemens)

American

11-14

M/F

≥2.7

(78)

Chemiluminescence
Immunoassay(Cobas, Roche Diagnostics)

Spanish

8-18

M/F

≥3.6

(79)

OBESITY AND MICROBIOTA
In addition to the above considerations, the gut
microbiota may increasingly be shown to impact
the course of metabolic diseases. This aspect isbriefly reviewed. The synergistic relationshipbetween the human body and the vast microbiotic environment present on all interfaces withthe exterior, particularly the gut lumen, has
become of major interest to the medical community. The microbiome cell number far outnumbers somatic or germ cells and represents a
far more varied gene diversity than the humangenome (51). The advent of high throughput
genome sequencing technologies allowed thefirst meta-sequence of the human gut microbiome to be conducted, utilizing stool collectedfrom 124 individuals, and characterized > 3X106
genes from approximately 1000 different microscopic species (52-54). An excellent review
by Arora et al. (55) discusses the compositionof the gut microbiota and its association withmetabolic diseases. Figure 4, taken from thisreview, shows that 2 phyla, namely Firmicutes
and Bacteriodetes, constitute healthy adult gut

microbiota and their relative proportions differ
among populations.
Neonatal intestinal flora evolves according to its
early environmental exposures, nutrition patterns (maternal or industrial milk), hygiene levels and therapeutic drug usage (56). Differences
in intestinal flora patterns during the first six
months of life may have potential impact anddownstream consequences on the later development of chronic conditions such as type 2
diabetes and allergies (57, 58).
The gut microbiota has emerged as a new important player in the pathogenesis of obesity,
potentially explained by the fact that each microbiotic species transforms the undigested
and partially digested food into metabolites
that may influence the physiological systemsof the host. Therefore, a loss in diversity may
lead to unwanted effects (55). This hypothesis
is supported by the observation that composition of the gut microflora is globally less diverse in obese subjects, with a relative enrichment in Firmicutes and a impoverishment in
Bacteroïdes (59). Moreover, detailed analysis

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

Figure 4

Quantitative comparison of faecal microbiota in two healthy populations
HMP
Bacteroidetes
Firmicutes
Proteobacteria
Verrucomicrobia
Actinobacteria
OthersOthers 0.3%

0.4%
Akkermansia 0.9%
Others 0.2%
Escherichia 0.8%

Sutterella 1.1%
Others 2.6%
Butyrivibrio 1.5%
Roseburia 1.5%
Dialister 2.3%
Faecalibacterium 3.5%

75.7%
20.5%

Ruminococcus 3.7%

Bacteroides 51.1%

Eubacterium 6.9%

Others 6.6%
Alisitpes 12.3%

Prevotella 5.7%

MetaHit
Bacteroides 21.8%

Alisitpes 8.6%

Others 0.5%

2.1%

Akkermansia 2.3%
Others 1.0%
Escherichia 1.0%
Sutterella 0.6%

45.8%
Prevotella 11.8%

Others 6.2%

Butyrivibrio 3.0%

46.8%
Others 3.6%

Roseburia 3.9%
Dialister 4.0%
Eubacterium 14.7%

Faecalibacterium 5.7%
Ruminococcus 6.0%

*Reproduced with permission from: Aurora T et al. J Intern Med 2016 Apr 12. doi: 10.1111/joim.12508.

Page
firs 14eJIFCC2017Vol28No1pp006-024
th r d ing rth A m

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkersof the flora in obese subjects reveals a bimodal distribution: those with a low gene count(LGC) characterised by the predominance of
5 pro-inflammatory bacteria and a less diversified metagenome, and those with a highgene count (HGC) with a high percentage of
4 anti-inflammatory bacteria genii (60). The
LGC group presents with insulin-resistance,
dyslipidemia and low-level infiltration of adipose tissue with pro-inflammatory cytokine
secreting immunity cells. It has recently been
established that levels of butyrate-producing
bacteria are reduced in patients with type-2
diabetes, whereas levels of Lactobacillus sp.
are increased, thus the reduction of butyrateproducing bacteria may be causally linked totype 2 diabetes. The causal relationship forthese differences in humans remains to be
Figure 5

elucidated but opens the way to possible treatment of obesity via dietary manipulation. For
example, a low calorie regiment composed ofplant fibres, proteins and low carbohydrates
potentially increases the microbiota diversity(61). Interestingly, bariatric surgery also increases the gut microbiota diversity (62, 63).
As each microbiotic species transforms theundigested and partially digested food into
metabolites that may influence the physiological systems of the host, a loss in diversity may
lead to unwanted effects.
The gut microbiota, a new player in the worldof obesity and cardiometabolic diseases, isincreasingly called upon to elucidate findings
related to these diseases and may eventually
impact their course and treatment.		

Impact of obesity on health status

Insulin resistancesyndrome (IRS)
Metabolic syndrome

NAFLD
Cardiovascular diseases
Type 2 diabetes

Obesity

Dyslipidemia
Hypertension
Impaired coagulation
Hyperinsulinemia
Impaired glucose metabolism

Page 15

eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

BIOMARKERS
The status of metabolically healthy obese (MHO)
individuals has been reported (64, 65) but obesity,
particularly abdominal, remains a major risk
factor for developing a series of complications(Figure 5) such as the metabolic syndrome,
type 2 diabetes, early atherosclerosis and nonalcoholic fatty liver disease (NAFLD), the latter
considered the hepatic manifestation of insulin
resistance (66-68). Cellular redox potential
imbalance, inflammatory processes and insulin
Figure 6

resistance are central in the development of thecomplex chronic metabolic disturbances (Figure6); hence measurement of related biomarkers todetect minor disturbances could help distinguish
MHO from metabolically non-MHO individuals,
and may result in establishing early primordial
prevention programs. However, at the present
time there is no international consensus as tothe specific pathways that should preferentiallybe targeted in order to define the prevalenceand severity of the conditions during childhood
and adolescence.

Cellular redox potential imbalance, inflammatory processes
and insulin resistance in the development
of diabetes and non-alcoholic liver disease (NAFLD)

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

IMAGING TECHNIQUESIn the last decade, utilization of ultrasonography, transient elastography and magnetic resonance imaging (MRI) has increased significantly.
In the context of the present review these techniques, except for MRI, are not suitable for thedetection of metabolic disturbances and areprimarily used to evaluate the extent of liver
damage. Although widely available, ultrasonography is unable to accurately detect or quantify
early liver fatty acid infiltrations. Furthermore,this technique is prone to observer- and operator-dependent variability and its use in obese
patients is subject of debate (69, 70). Transient
elastography, based on the assessment of liver
stiffness, has also been shown to be useful inpresence of significant fibrosis and cirrhosis(71). Liver magnetic resonance imaging–estimated proton density fat fraction (PDFF) is
more sensitive and favourably comparable tohistopathology scores (72). This technology iscurrently restricted to tertiary care institutions,is expensive, and demands experienced staff. In
summary, these imaging techniques are useful
in detecting steatosis, but they are relatively
inefficient in determining early stage liver damage. Biomarkers easily measured in central laboratories are therefore of utmost importanceand should center on insulin resistance, inflammation and oxidative stress, as this triad is thesignature of NAFLD.
INSULIN RESISTANCE
The term insulin resistance (IR) frequently refers to a physiological state characterized by adiminished biological response to insulin. Moreprecisely, IR refers to a holistic reduction of glucose uptake in response to physiological insulin
concentrations, primarily in muscle tissue. The
optimal assessment of IR in children and adolescents remains controversial. Following theConsensus Conference on Childhood IR in 2010,

experts highlighted: 1) the paucity of data regarding cut-offs to define insulin resistance;2) poor performance of surrogate measures
such as fasting plasma insulin; and 3) lack ofjustification for screening children, even obese
children, because there are no accepted treatments for euglycemic IR (73). However, thedevelopment of robust methods for assessing
insulin sensitivity (IS) in paediatric populations
remains of great interest, particularly for epidemiological studies to monitor metabolic trajectory into adulthood.
The hyperinsulinemic-euglycemic clamp is thegold standard for determining total-body IS (73).
However, it is not applicable in the context of
population screening or routine clinical workup. In 2014 Brown and Yanovski (74) publishedan excellent review on this technique as well assurrogate measures and their pitfalls. The hyperinsulinemic-euglycemic clamp, as its name indicates, depends on repeated measures of bothinsulin and blood glucose, each having theirown potential analytical pitfalls that may hinderinter-laboratory comparison (Table 1).
Reliable interpretation of hyperinsulinemiceuglycemic clamp studies is also dependent
upon normal inter-individual biological differences such as insulin clearance rates and timerequired to reach a steady state. Alternative
methods include the insulin tolerance test (ITT),
the hyperglycemic clamp, the insulin-modified
or frequently sampled intravenous glucose tolerance test (FSIGT) and the more frequently
used oral glucose tolerance test (OGTT) (74).
FASTING INSULIN AND THE HOMA-IR
Assessment of IR or IS is frequently conducted using single measurements due to easeof availability and simplicity. Measurementof fasting insulin concentrations are considered representative of insulin hepatic sensitivity (low concentrations) or resistance (highPage 17eJIFCC2017Vol28No1pp006-024

E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

concentrations). In theory, this information
is valuable and may alert clinicians to eventual liver function impairment but there areissues around defining an abnormal elevatedfasting insulin concentration because the dataon reference values in fasting insulinemia arescarce. In addition, the lack of standardizationor harmonization between different insulin
assays hampers direct comparison between
laboratories, peer-reviewed publications, andimpedes coherent measures for treatment
guidelines. This was highlighted in 2007 by
the IFCC Working Group on Standardization of
Insulin Assays, in an evaluation of 12 commercial insulin methods (75). The within-assay CVs
ranged from 3.7% to 39.0% and between assay
CVs from 12% to 66% (75). In 2009 the working
group reported that 4 out of 10 insulin assays,
when re-calibrated with a purified recombinant
insulin preparation, had ≥ 95% of the 39 individual donor sera results within 32% of the target value assigned by an isotope dilution-mass
spectrometry assay. In addition, 7 of 10 assays
had a bias >15% in 36 to 100% of individual
samples. The consensus group concluded thatagreement between assays would improve using an international reference material and ahigher order mass spectrometry method (76).
Subsequent high-throughput mass spectrometry immunoassays have been developed toquantitate human intact insulin as well as insulin analogs, which may allow an accurate
definition of insulinemia to be determined (77,78). Accurate measurement of plasma insulin
is of paramount importance for establishingcomparable Homeostasis Model Assessmentof IR (HOMA-IR) reference values across laboratories, although variation between ethnic
populations may be a confounding factor thatshould be taken into consideration. At the present time HOMA-IR cut-offs are still highly method dependent. Table 2 illustrates the distribution of published cut-off points for defining IR,

and confirm the warning of Wallace et al. (79):“The HOMA model has become a widely usedclinical and epidemiological tool and, whenused appropriately, it can yield valuable data.
However, as with all models, the primary input
data need to be robust, and the data need to
be interpreted carefully.” To address this issue,
the IFCC (http://www.ifcc.org/ifcc-scientific-division/sd-working-groups/wg-sia/), in collaboration with the American Diabetes Association(ADA) and the European Association for the
Study of Diabetes (EASD), has created theWorking group on Standardisation of Insulin
Assays (WG-SIA) with the mandate of improving the standardization of assays for insulin
by the development of a candidate reference
method based on liquid chromatography-tandem mass spectrometry, and of a lyophilizedrecombinant human insulin preparation as primary reference material.
Although insulin resistance is a well-recognizedclinical entity, there are currently no internationally accepted definition of its expression inchildren and adolescents. One well-characterized definition requires the presence of three ormore factors which can be age-adjusted to define hyperinsulinaemia: Overweight, high systolic blood pressure, hypertriglyceridemia, low
HDL-cholesterol and impaired fasting plasma
glucose (84).
Data on normal reference intervals for fasting
insulinemia are scarce.			
Lack of standardized or harmonized insulin assays hampers comparison between laboratories
and impedes coherent measures for treatment
guidelines.					Distinguishing MHO young patients from thoseunhealthy bears a major clinical importance as
they are, for reasons that are yet to be defined,
resistant to develop CMD; hence follow-up andtreatment differ (64). Low-grade inflammation

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

and cellular redox potential imbalance are, together with insulin resistance, key-role players
in the development of the non-healthy state inobese subjects.
INFLAMMATION
Inflammation is the second cause in the development of CMD and NAFLD related to paediatric obesity. A number of biomarkers have been
identified but primarily in the context of clinical trials, thus their specificity, sensitivity andpredictive values have yet to be defined forscreening and diagnostic purposes. C-Reactive
Protein (CRP), a member of the pantraxin family involved in plaque instability, is the mostcommonly utilized inflammatory biomarker.
Although the sensitivity of CRP is generally
high, the specificity is low, particularly in thesetting of potential low-grade inflammation.
Nevertheless, discrete elevation in circulating
CRP concentrations has been associated in thedefinition of the metabolic syndrome (84, 85).
Its advantage resides in its wide accessibility
by central laboratories. However, as for any
other biomarkers, well-defined age-, sex- andethnicity-adjusted reference values or thresholds have to be defined if they are to be used
for clinical purposes. The analytical sensitivity, even for the high-sensitivity CRP (hsCRP)
test, however, limits the definition of reference ranges. One European population-based
study reported that 44% of the 9855 children
tested exhibited serum CRP concentrations
below the detection limit (0.2 mg/l) and confirmed our observation (85) to the effect thatobesity influenced serum CRP concentrations(86).
C-Reactive Protein (CRP) is the most commonly
utilized biomarker of inflammation. The specificity of CRP is questionable, particularly in thesetting of low-level inflammation.		

Well-defined age-, sex- and ethnicity-adjusted
reference values or thresholds have to be defined if they are to be used for clinical purposes.
Visceral adipose tissue per se and its resident
macrophages contribute importantly to systemic inflammation by secreting adipokines andpro- and anti-inflammatory cytokines. Indeed,clinical studies have consistently shown elevated blood concentrations of pro-inflammatory
cytokines such as IL-6, IL-8, TNFα, PAI-1, resistin and amylin in overweight and obese insulinresistant youth (87-90). However, this relationship does not imply unanimity. A recent report
has noted that the relationship between pro-inflammatory and metabolic markers commonly
observed in adults and pubertal adolescentsis reversed in healthy black and white children
before puberty, which warrants questions as towhether these inverse relationships modify the
trajectory later in life (91). Population-based
studies focused on evaluating pro-inflammatory
and metabolic markers to determine which biomarkers constitute sensitive and specific tools
in the context of a diagnosis of insulin resistance would be valuable.
OXIDATIVE STRESS
Oxidative stress is often a neglected cause of
paediatric obesity-related morbidities, and nobiomarkers have been successfully validated
yet for routine clinical use. To our knowledge
there are no clinical research studies demonstrating that circulating concentrations of malonyldialdehyde (MDA), Hydroxynonenal (HNE),
advanced glycation end-products (AGEs) and
8-hydroxy-2-deoxyguanosine (8-OH-dG), which
are surrogate markers for lipids, proteins anddeoxyribonucleic acid damages respectively,
are effective diagnostic tools for CMD in childhood and adolescence.
In an observational study performed on 35
children between the ages of 12 and 18 years,

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E. Levy, A.K. Saenger, M.W. Steffes, E. Delvin
Pediatric obesity and cardiometabolic disorders: risk factors and biomarkers

Khelishadi et al. (92) reported that the age- andsex-adjusted changes in ox-LDL, waist circumference, CRP, MDA and body fat mass had thehighest correlations with changes in coronary
intima media thickness. More recently, in a
population-based study, Galan-Chilet et al. (93)demonstrated a positive association of selenium at plasma concentrations above ~110 μg/L
for 8-oxo-dG, but an inverse association with
GSSG/GSH and MDA. They further identified
potential risk genotypes associated with increased levels of oxidative stress markers withhigh selenium levels.

6. Dulloo AG, Jacquet J, Solinas G, Montani JP, Schutz Y.
Body composition phenotypes in pathways to obesity
and the metabolic syndrome. International journal ofobesity (2005). 2010;34 Suppl 2:S4-17.

CONCLUSIONS

10. Liska D, Dufour S, Zern TL, Taksali S, Cali AM, Dziura
J, et al. Interethnic differences in muscle, liver and abdominal fat partitioning in obese adolescents. PloS one.
2007;2(6):e569.

There is currently no single biomarker which
can adequately define obesity-related CMD
risk in paediatrics or adults. Prospective clinical
trials should focus on devising a score based onwell-characterized and appropriately validated
biomarkers.
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86. Schlenz H, Intemann T, Wolters M, Gonzalez-Gil EM,
Nappo A, Fraterman A, et al. C-reactive protein reference
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78. Van Der Gugten JG, Wong S, Holmes DT. Quantitation of Insulin Analogues in Serum Using Immunoaffinity Extraction, Liquid Chromatography, and Tandem Mass
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serum inflammatory markers with metabolic parameters in healthy, Black and White prepubertal youth.
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In this issue: Recent Advances in Pediatric Laboratory Medicine

Pediatric Metabolic Syndrome: pathophysiology
and laboratory assessment
Victoria Higgins1,2, Khosrow Adeli1,2
12

Clinical Biochemistry, Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
Department of Laboratory Medicine & Pathobiology, Faculty of Medicine, University of Toronto,
Toronto, ON, Canada

ARTICLE INFOABSTRACT

Corresponding author:
Khosrow Adeli, Ph.D., FCACB, DABCC
Clinical Biochemistry
Pediatric Laboratory Medicine
The Hospital for Sick Children
555 University Avenue
Toronto, ON, M5G 1X8
CanadaE-mail: khosrow.adeli@sickkids.ca

Pediatric overweight and obesity is an emerging
public health priority as rates have rapidly increased
worldwide. Obesity is often clustered with other metabolic abnormalities including hypertension, dyslipidemia, and insulin resistance, leading to increased riskof cardiovascular disease. This cluster of risk factors,
termed the metabolic syndrome, has traditionallybeen reported in adults. However, with the increased
prevalence of pediatric obesity, the metabolic syndrome is now evident in children and adolescents.
This complex cluster of risk factors is the result of thepathological interplay between several organs including adipose tissue, muscle, liver, and intestine with acommon antecedent – insulin resistance. The association of the metabolic syndrome with several systemic
alterations that involve numerous organs and tissues
adds to the complexity and challenge of diagnosing
the metabolic syndrome and identifying useful clinical indicators of the disease. The complex physiology
of growing and developing children and adolescentsfurther adds to the difficulties in standardizing laboratory assessment, diagnosis, and prognosis for thediverse pediatric population. However, establishinga consensus definition is critical to identifying andmanaging children and adolescents at high risk of

Key words:
pediatric, obesity, metabolic syndrome,insulin resistance, cardiovascular disease

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developing the metabolic syndrome. As a result,the examination of novel metabolic syndrome
biomarkers which can detect these metabolic
abnormalities early with high specificity andsensitivity in the pediatric population has beenof interest. Understanding this complex clusterof risk factors in the pediatric population is critical to ensure that this is not the first generation
where children have a shorter life expectancy
than their parents. This review will discuss thepathophysiology, consensus definitions andlaboratory assessment of pediatric metabolic
syndrome as well as potential novel biomarkers.


INTRODUCTION
The worldwide prevalence of pediatric overweight and obesity combined has risen by
47.1% between 1980 and 2013 (1). This alarming increase in pediatric obesity has become
a global public health burden, evident by theWorld Health Organization (WHO) Health
Assembly endorsement for the Comprehensive
Implementation Plan on Maternal, Infant, andYoung Child, Nutrition, which consisted of six
global nutrition targets to be achieved by 2025,
including “Target 4: no increase in childhood
overweight” (2). Obesity is the most importantrisk factor for cardiovascular disease (CVD) andis often clustered with additional metabolic
abnormalities including hypertension, dyslipidemia, and insulin resistance (3). These CVD
risk factors tend to cluster, not only in adults,
but more recently in children (4). This common cluster of major determinants of CVD led
to the definition of what is known as the metabolic syndrome (MetS). The current paradigmof MetS was established by Reaven and colleagues (5) in 1988, originally termed Syndrome
X. Reaven described MetS as the interrelation
between insulin resistance, hypertension,
type 2 diabetes (T2D), and CVD. Although thissyndrome was not defined until the late 1980s,
the relationship between obesity, hypertriglyceridemia, and hypertension was first recognizedin the early 1980s (6). This was followed by thedescription of the central roles of insulin resistance and abdominal obesity in MetS in the late1980s to early 1990s (7). Clinical definitions of
MetS have been extremely variable, howeveralmost all definitions require a partial combination of the following five elements: elevatedtriglycerides (TGs), reduced high-density lipoprotein cholesterol (HDL-C), increased bloodpressure, elevated fasting plasma glucose, andincreased waist circumference (3). Although
MetS was once thought to be an adult-onset
disease, this clustering of metabolic disorders is
becoming increasingly prevalent in children andadolescents, making it a public health priority
in the pediatric population as well. This review
will discuss what is currently known about theunderlying pathophysiology of pediatric MetS,
particularly in regards to the major organs involved. Additionally, the difficulty in defining
pediatric MetS, current definitions and laboratory assessment to define and monitor pediatric MetS, and potential novel biomarkers will be
discussed.
In the Third National Health and Nutrition
Examination Survey (NHANES III), conducted
between 1988 and 1994 in the US, the prevalence of MetS in adolescents aged 12-19 years
was 4%, increasing to 28.7% among strictlyobese adolescents (8). A more recent analysis
of NHANES data from 1999-2002, demonstrates
that MetS prevalence in obese adolescents has
since increased to 44% (9). If current trends
continue, the World Health Organization (WHO)predicts that 70 million infants and young children will be overweight or obese by 2025. The
prevalence of MetS directly increases with thedegree of obesity and each component of the
syndrome worsens with increasing obesity, independent of age, sex, and pubertal status (3).

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Childhood obesity is also an early risk factor for
adult morbidity and mortality (10,11) and 85%
of obese children become obese adults (10,12).
It is important to detect MetS early in childhood
and adolescence to prevent further healthcomplications in adulthood and minimize theglobal socio-economic burden of CVD and T2D.
Unless action is taken, diabetes experts agree
that this is the first generation where children
may have a shorter life expectancy than theirparents (13).
PATHOPHYSIOLOGY: UNDERSTANDING
THE COMPLEX CLUSTER
The etiology of MetS is incompletely understood; however, insulin resistance is thought
to be central to the development of MetS andplay a role in the pathogenesis of its individual metabolic components. The World HealthOrganization (WHO) hypothesizes that the association and clustering of T2D, hypertension,
dyslipidemia, and CVD arises from a common
antecedent - insulin resistance (14). Insulin resistance is the decreased tissue response toinsulin-mediated cellular actions.
Although hyperglycemia, the primary complication of insulin resistance, can result in substantial morbidity in T2D, CVD is the leading causeof death in T2D patients, mainly due to lipid
abnormalities (15). This phenomenon is wellsupported by results of the Action to Control
Cardiovascular Risk in Diabetes (ACCORD)
study, in which attempts to tightly control glucose did not lead to an improvement in mortality (16,17). Insulin elicits peripheral effects on
several organ systems, including adipose tissue, muscle, liver, and intestine. Therefore, ininsulin resistant states, metabolic dysfunction
across several organs occurs, together creating
this observed interplay of several concurrent metabolic abnormalities.

Lipid partitioning and inflammation
It is widely accepted that obesity and the concomitant development of inflammation are
the major components of insulin resistance(18). In obesity, adipose tissue storage capacity becomes saturated and insulin suppressionof adipose tissue lipolysis is diminished (19).
As a result, plasma free fatty acid (FFA) levels
increase and this excess lipid can be storedin sites other than conventional subcutaneous adipose depots, including intraabdominal(visceral) adipose compartments and insulinresponsive tissues (i.e. muscle and liver). This
altered lipid partitioning can shift the balance
between adipocytokines, producing more inflammatory cytokines (i.e. TNF-α and IL-6) andfewer anti-inflammatory peptides (i.e. adiponectin). In addition to inflammatory effects ofobesity, the increased FFA flux results in several metabolic dysfunctions. When the subcutaneous fat depot reaches its storage capacity
and lipid is shunted to ectopic tissues (i.e. liver
and muscle), peripheral insulin resistance occurs (20).
Derivatives of fatty acids (e.g. long chain fattyacyl-CoA and DAG) in hepatocytes and myocytes may alter the insulin signal transduction
pathway, leading to this observed decrease
in insulin sensitivity. Several studies support
this theory, as lipid content in liver and muscle is increased in obese and T2D subjects
and is a strong predictor of insulin resistance(21). Furthermore, obese adolescents with ahigh visceral to subcutaneous fat ratio demonstrate a markedly adverse metabolic phenotype of severe insulin resistance and alterations in glucose and lipid metabolism (22).
Taken together, obesity results in increased
inflammatory markers and FFA flux, subsequently reducing the insulin sensitivity of several organs (i.e. adipose tissue, muscle, liver,intestine). Insulin resistance across several
organs results in the MetS phenotype which

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includes dyslipidemia, subsequently increasing CVD risk by affecting endothelial function
and the vascular system (23).
Adipose tissue insulin resistance and FFA flux
Adipose tissue enlargement (i.e. obesity) leads
to a proinflammatory state in the cells, with reduced secretion of adiponectin and increased
secretion of several inflammatory cytokines andchemokines (24). One of these chemokines,
monocyte chemoattractant protein-1 (MCP-1),
plays an important role in recruiting macrophages into adipose tissue (24). Macrophages
infiltrate adipose tissue and contribute to adipocyte hypertrophy and further cytokine release (24,25). These cytokines can affect insulin
action in other tissues, such as liver and muscle, but can also lead to local insulin resistance.
Insulin inhibits lipolysis in adipose tissue, andtherefore in insulin resistance, lipolysis is accelerated, leading to increased FFA release into
the circulation (3). Therefore, insulin resistancefurther supports the proinflammatory state ofobesity because its anti-lipolytic and anti-inflammatory effects are negated.
Muscle insulin resistanceand glucose intolerance
Increased plasma FFAs, due to reduced insulin
suppression of adipose tissue lipolysis, disrupt
insulin-mediated glucose uptake by skeletal
muscle, facilitating development of hyperglycemia (26). Insulin resistance in skeletal muscle
may promote atherogenic dyslipidemia by diverting ingested carbohydrate towards hepatic
de novo lipogenesis (DNL), rather than muscle
glycogen storage (23). Young, lean, insulin-sensitive subjects store most of their ingested energy in liver and muscle glycogen, while young,lean insulin-resistant subjects have dysfunctional muscle glycogen synthesis and divert
more of their ingested energy into hepatic DNL(27). This results in increased plasma TGs, lower

HDL-C, and increased hepatic TG synthesis (27).
Mouse studies further support these findings
as muscle-specific inactivation of the insulin
receptor gene results in increased plasma TGs
and increased adiposity as a result of musclespecific insulin resistance (28).
Hepatic insulin resistanceand fasting dyslipidemia
The liver is a main target of insulin action andplays a major role in both carbohydrate andlipid metabolism. Two key hepatic insulin actions are reducing hepatic glucose output andinhibiting secretion of very low-density lipoproteins (VLDLs). To reduce hepatic glucose output, insulin phosphorylates FoxO1, preventing
it from entering the nucleus, and consequently
reducing the expression of genes required for
gluconeogenesis (29). Postprandial insulin release enhances hepatic VLDL production by
upregulating lipogenesis via activation of thetranscription factor sterol regulatory elementbinding protein (SREBP-1c) (30). SREBP-1c increases transcription of genes required for FA
and TG biosynthesis, resulting in increased DNL.
TGs synthesized by DNL and dietary lipids arepackaged with apolipoprotein B100 (apoB100)
into VLDLs. Although insulin increases substrateavailability for VLDL production, it also acutely
reduces VLDL secretion (31). This inhibitory
action is thought to be due to an increase in
apoB100 degradation, the main structural protein of VLDL (31).
Insulin has key metabolic regulatory roles in
the liver, thus several metabolic abnormalities
can clinically manifest with hepatic insulin resistance. Diabetic dyslipidemia is one such abnormality which is characterized by hypertriglyceridemia, increased small dense LDL (sdLDL) anddecreased HDL-C (32). This phenomenon is the
direct result of hepatic insulin resistance which
results in impaired glucose homeostasis due toreduced FoxO1-mediated phosphorylation, andPage 28

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enhanced hepatic DNL due to reduced SREBP-1
activation (33). Therefore, both hyperglycemia
and hypertriglyceridemia are seen in hepatic insulin resistance. In addition to enhanced DNL,
substrates for VLDL synthesis are increased due
to elevated FFA flux from adipose tissue andincreased hepatic uptake of chylomicron remnants (CM; lipoproteins secreted from the intestine) and VLDL remnants (34,35). Increased
substrate availability for VLDL production andreduced apoB degradation can lead to VLDL
overproduction and hypertriglyceridemia. As aresult of hypertriglyceridemia, highly atherogenic sdLDL are also produced in insulin resistant states. sdLDL are produced from the action of cholesteryl ester transfer protein (CETP),
which exchanges VLDL TG for LDL cholesteryl ester (CE), creating CE-depleted, TG-enriched, LDL
particles (36). These particles become sdLDL
after they are lipolyzed by lipoprotein lipase(LPL) or hepatic lipase (HL) (36). CETP action isthought to also contribute to reduced HDL-C
levels in insulin-resistant subjects (36).
Hepatic steatosis, one of the main detriments
to the liver in response to hepatic insulin resistance, is characterized by the accumulation ofexcess lipid in the liver, which can progress to
inflammatory steatohepatitis, fibrosis, and evencirrhosis. This spectrum of diseases is collectively termed non-alcoholic fatty liver disease(NAFLD). Progression of NAFLD can cause liver
failure, leading to the need for a liver transplant,even in adolescents (37). As the prevalence ofpediatric obesity increases, NAFLD has also increased in prevalence, rapidly becoming themost common cause of pediatric liver disease(37). Furthermore, a pediatric study showedthat every 1 cm increase in waist circumference is associated with a 1.97 and 2.08 foldincreased risk of NAFLD in males and females,
respectively (34). Although the pathological link
between MetS and NAFLD is incompletely understood, the theory of the “two-hit model” isthe most widely accepted (38). The first hit isinsulin resistance which promotes the accumulation of hepatocyte lipid due to increased hepatic FFAs available for TG synthesis in an insulin resistant state (36). This results from insulin
failing to block adipose tissue lipolysis, resulting
in increased FFA release from adipose tissue.
Increased circulating FFAs leads to increased
FFA uptake by hepatocytes, increased TG synthesis and impaired FFA oxidation, producing
excess lipid in hepatocytes (38,39). The second
hit is injury from reactive oxygen species (ROS).
Lipid accumulation in hepatocytes impairs theoxidative capacity of the mitochondria and canalso lead directly to further ROS production(40). Increased susceptibility of hepatocytes tooxidative stress and subsequent lipid peroxidation by ROS promotes progression to nonalcoholic steatohepatitis (NASH). This is due tochemoattractants (i.e. by-products of oxidative
stress and lipid peroxidation), which lead to fibrosis and the production of inflammatory cytokines (37).
Intestinal insulin resistanceand postprandial dyslipidemia
In contrast to the numerous studies on insulin
signaling in well-known insulin-sensitive tissues
such as liver, muscle, and adipose, relatively little is known regarding intestinal insulin signaling and potential perturbations with insulin resistance (41). The intestine packages absorbed
dietary fat into apoB-48-containing TG-rich lipoproteins, called chylomicrons (CMs), whichtransport TGs and fat-soluble vitamins to peripheral tissues (42). Similar to its actions in theliver, insulin has a key regulatory role in the production and clearance of TRLs produced fromthe intestine (43). Therefore, another defining feature of diabetic dyslipidemia is elevatedpostprandial levels of CM particles (44). The accumulation of CM particles in insulin resistancehas been attributed to decreased clearance asPage 29

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well as increased intestinal synthesis and secretion (45). Decreased clearance of CM and CM
remnants in insulin resistance has largely been
attributed to increased hepatic VLDL secretion (46), as intestinal and hepatic TRLs sharecommon, saturable, removal mechanisms (47).
Secondly, LPL activity is decreased due to diminished regulation by insulin (48), contributing toslow removal of CM and CM remnants in insulin
resistance.
Although the intestine was conventionally regarded as a passive organ with respect to CM
secretion, it is now evident that CM production
can be actively increased in insulin resistantstates (43). Insulin has been shown to directly
decrease CM secretion from cultured human
fetal jejunal explants (49) and to reduce CM
production in healthy men following an insulin
infusion (50). Mechanisms for CM overproduction in insulin resistance are unclear, yet may include increased apoB stability, increased mass
and activity of microsomal triglyceride transfer
protein (MTP; required for assembly of VLDLs
and CMs), and enhanced DNL in the enterocyte(41,51). The inhibitory effect of insulin on CM
secretion may also partly be due to its suppression of circulating FFAs (46,50,52), an effectthat is blunted by insulin resistance (52) andT2D (53). Overall, human studies suggest that
intestinal CM production is dysregulated in insulin resistance states, with diminished sensitivity to insulin’s inhibitory effects, contributing
to increased plasma CM levels. Intestinal lipoprotein production is particularly important as
postprandial TG levels independently predict
CVD (54). In addition, CM remnants are risk factors for atherosclerosis (55) and apoB-48 can bedetected in atherosclerotic plaques (56).
The intestine is also involved in the pathogenesis of MetS through its important role as an
endocrine organ. The intestine secretes several
gut peptides with glucagon-like peptide 1 (GLP1) playing a significant role in insulin secretionand signaling. GLP-1 is secreted by ileal enteroendocrine L-cells in response to a variety of nutrient, neural, and endocrine factors (57). Thishormone has several biological actions on thepancreas, nervous system, gastrointestinal system, skeletal muscle, adipose tissue, and liver.
As a result of the important roles GLP-1 plays inmetabolism, agonists of GLP-1, as well as inhibitors of dipeptidyl peptidase-4 (DPP-4), the main
protease in GLP-1 degradation, have been successful therapeutics for T2D (58). In the pancreas, GLP-1 stimulates glucose-dependent insulin
secretion, improves the capacity of β-cells tosense and respond to glucose, increases β-cellmass, and inhibits glucagon and stimulates
somatostatin secretion (57). The GLP-1 receptor (GLP-1R) and nerve fibers containing GLP-1
are located in the central nervous system andtherefore several studies have examined central
and peripheral actions of GLP-1. Central actionsof GLP-1 include satiety promotion, reduced energy intake, and consequently decreased bodyweight (59). Additionally, the effects of GLP-1
on the pancreas may be mediated in part by aneural mechanism (60). In the intestine GLP-1
has inhibitory effects on lipoprotein secretion,
gastric acid secretion and gastric emptying,
which slows the transit of nutrients from thestomach to the small intestine, contributing to
the normalization of blood glucose levels (61).
The effect of GLP-1 on muscle, adipose tissue,
and the liver, including stimulation of glucose
uptake and inhibition of hepatic glucose production, remain controversial as to whether
they are independent of changes in insulin or
glucagon (57).
LABORATORY ASSESSMENT
OF PEDIATRIC METABOLIC SYNDROME
An adult definition of MetS cannot simply beapplied for use in the pediatric population because drastic changes in blood pressure, lipid
levels, as well as body size and proportion occur

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Victoria Higgins, Khosrow Adeli
Pediatric Metabolic Syndrome: pathophysiology and laboratory assessmentwith age and development. Puberty also impacts fat distribution, insulin sensitivity, and insulin secretion (62). Children develop transientphysiologic insulin resistance during puberty(63), with a 25-50% decline in insulin sensitivity which recovers upon completion of pubertal
development (64). The dynamic physiologicalchanges that occur in children and adolescentshas led to the lack of standardized measures inpediatrics, including measurements of central
obesity (3), which is a defining feature of adult
MetS. Establishing a consensus definition of
MetS in the pediatric population has therefore
traditionally been a challenge. However, it is important to note that the MetS is not a disease,
but a cluster of metabolic disorders. Therefore,
applying any set of criteria to “define” the MetS
truly reduces the complex reality of this clusterof components. Each component of the MetS is
a continuous variable which gradually changes.
This results in a continuum between a healthy
and unhealthy metabolic profile, rather thana dichotomy of healthy and unhealthy states.
However, an accepted definition of pediatric
MetS is important as a diagnostic and monitoring tool to ensure standardization in clinicalpractice as well as in research to standardize
clinical trials.
Rapid rises in obesity trends sparked the needto understand how to distinguish between children and adolescents at high risk of health complications and those with “simple” uncomplicated obesity. Traditionally, researchers have used
several different definitions (65), resulting in
the prevalence of metabolic syndrome varying
between 0% and 60% in the same group of children, depending on the diagnostic criteria applied (66). This drove the International DiabetesFederation (IDF) to develop a universally accepted and easy to use definition for MetS inchildren and adolescents in 2007 (13). This definition was created with the intention to allow
preventative measures to be taken before thechild or adolescent develops T2D and/or CVD(13). The main component of the definition iswaist circumference because it is an independent predictor of insulin resistance, lipid levels,and blood pressure (67,68). However, percentiles, rather than single cut-off points, must be
used for this measure due to the dynamic metabolic changes that occur throughout the pediatric age range. A cut-off of the 90th percentile
was chosen, as children and adolescents with awaist circumference ≥ 90th percentile are more
likely to have multiple CVD risk factors (13).
The IDF consensus definition of MetS in children and adolescents is shown in Table 1. The
definition excludes children who are younger
than 6 years because of insufficient data for thisage-group (13). For children aged 6-10 years,
MetS should not be diagnosed, but those withabdominal obesity should be strongly advised
to reduce their weight. For children age 10-<16
years, MetS should be diagnosed for those withabdominal obesity and two or more other clinical features including elevated triglycerides, decreased HDL-C, increased blood pressure, andincreased fasting plasma glucose. For adolescents older than 16 years of age, it is recommended to use the IDF adult criteria. This IDF
pediatric definition provides a standard that facilitated comparisons of study results, including
prevalence estimates across studies.
However, the IDF definition of pediatric MetS
is not without limitations. First, this definition
does not provide criteria to diagnose children
under the age of 10 years. Additionally, theblood pressure cut-off used in this definition is
the same as that defined for adults and is thus
too high for the pediatric population. This results in blood pressure contributing to a negligible proportion of children being classified ashaving the MetS using this definition (69). Lastly,rather than being based on evidence from thepediatric population, the IDF consensus definition is modified from a definition created forPage 31

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Table 1Age (years)

6-<10

IDF consensus definition of the Metabolic Syndromein children and adolescents
Obesity (WC)

≥ 90th percentile

Triglycerides

HDL-C

Blood
pressure

GlucoseMetabolic syndrome cannot be diagnosed,
but further measurements should be made if there isa family history of metabolic syndrome, T2DM, dyslipidemia,
cardiovascular disease, hypertension and/or obesity≥5.6 mmol/L(100 mg/dL)<1.03 mmol/L(<40 mg/dL)

Systolic ≥130/
diastolic ≥85
mm Hg<1.03 mmol/L(<40 mg/dL)
Central obesity
in males and(defined as waist<1.29 mmol/L
circumference≥ 16≥1.7 mmol/L(<50 mg/dL)≥ 94cm for(≥150
mg/dL)in females,(adult criteria)
Europid menor specificand ≥ 80cm fortreatment for
Europid women)
these lipid
measurements

Systolic ≥130/
diastolic ≥85
mm Hg, ortreatment ofpreviously
diagnosed
hypertension

10-<16≥ 90th percentile≥1.7 mmol/Lor adult cut-off if(≥150 mg/dL)lower(If ≥5.6mmol/L [orknown T2DM]
recommendan OGTT)
Fasting
plasmaglucose ≥5.6
mmol/L (100mg/dL), or
previously
diagnosedtype 2diabetes*Table adapted from (13).
WC: waist circumference; HDL-C: high-density lipoprotein cholesterol; T2DM: type 2 diabetes mellitus;
OGTT: oral glucose tolerance test.

the adult population. A more recent MetS definition for European pre-pubertal children was
proposed by the Identification and Preventionof Dietary- and Lifestyle-Induced Health Effects
in Children and Infants (IDEFICS) Study which addresses these limitations.
The main factor contributing to the absence of aconsensus MetS definition in children is the lackof reference values for MetS components in thepediatric population (70). Therefore, the IDEFICS
study used reference values provided by theirstudy of European children to classify children
according to the different components of the
MetS (69). They propose a definition with different cut-offs to classify children requiring either
close monitoring (monitoring level) or an intervention (action level) (69). Using age-, sex-, andheight- (in the case of blood pressure) specificPage 32

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percentiles established from the IDEFICS cohort, percentile cut-offs are defined for the
MetS components (shown in Table 2 for themonitoring level). Children are classified as requiring close monitoring of the MetS if threeor more of these risk factors exceed the 90th
percentile defined in the IDEFICS studies (69).
If three or more of these risk factors exceed the95th percentile, defined in the IDEFICS studies, an intervention is appropriate in affectedchildren (69). They also created a simple web
application (www.ideficsstudy.eu) to more easily classify an individual by entering individual
measurement values and obtaining the appropriate percentiles. As a result of using percentile cut-offs established from a pediatric population rather than arbitrary cutoffs for MetS
components, the IDEFICS definition provides amore equal weight to components of the definition, allowing a more equal contribution to theoverall prevalence of the MetS. However, thisdefinition is also not without limitations. In addition to only being applicable to children andnot adolescents, the percentile cut-offs for eachparameter is population-specific and therefore
may differ for smaller, local populations. Also,
Table 2Age (years)

2-<11 years

clinically relevant, prospective outcomes related to the percentile cut-offs which would allow
the assessment of disease risk in relation to defining the MetS are currently lacking.
In addition to proposing definitions to classify
children as requiring monitoring or intervention for the MetS, the IDEFICS study also developed a quantitative CVD risk score. This wasestablished using a z-score standardization to
calculate a continuous score combining the
MetS components, with a higher score indicating a less-favorable metabolic profile. A study
by Pandit et al. supports the use a quantitativerisk score, as this study suggested that a continuous MetS score was a better tool to assess
atherosclerotic risk in children than cut-offs ofindividual MetS components (71). Rather thandichotomizing the population into children witha healthy and unhealthy metabolic profile basedon cut-offs of each MetS component, the scoreprovides a variable that accounts for gradualchanges in these components. The continuousscore better reflects the complex concepts of
the MetS, where risk predictors lie on continuous scale and have complex interactions. The

IDEFICS definition of the Metabolic Syndrome in children–monitoring level
Obesity (WC)

≥ 90th percentile

Triglycerides≥ 90thpercentile

HDL-C≤ 10thpercentile

Bloodpressure

Glucose

HOMA-insulin
resistance
Systolic ≥90th≥ 90thpercentile orpercentile
diastolic ≥ 90th
or fasting
percentile
glucose≥ 90thpercentile*Table adapted from (69).
WC: waist circumference; HDL-C: high-density lipoprotein cholesterol; HOMA: homeostatic model assessment.

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continuous MetS score can be a useful tool inpediatric research and for evaluating interventions (69).
In addition to the parameters included in theconsensus definitions of pediatric metabolic
syndrome, the standard lipid profile aids in CVD
risk assessment. A standard lipid profile includes
fasting measurements of plasma or serum concentrations of total cholesterol, LDL-C, HDL-C,
and triglycerides. Additional markers that havebeen added to the lipid profile in some clinicallaboratories include non-HDL cholesterol, apolipoprotein B (apoB), apolipoprotein A1 (apoA1),
and lipoprotein(a) (Lp(a)) (72). Non-HDL cholesterol, calculated as total cholesterol minus
HDL-C, gives an indicator of the total cholesterol
content of atherogenic lipoproteins. ApoB andapoA1 can also be used as alternatives to nonHDL and HDL cholesterol, respectively, where
they indicate the particle number, rather thancholesterol content. Lastly, Lp(a) should only be
determined in the same patient once as its concentration varies little over time.
POTENTIAL NOVEL BIOMARKERSIN LABORATORY ASSESSMENT
OF PEDIATRIC METABOLIC SYNDROME
With the increasing public health burden of
MetS, the identification and examination ofnovel biomarkers able to detect MetS and subsequently CVD risk early, with high specificity
and sensitivity, is a clinical priority (73). Effective
MetS biomarkers maximize the effectiveness oftreatment in subjects who would benefit themost. The association of MetS with several systemic alterations that involve numerous organs
and tissues adds to the complexity and challenge of identifying MetS biomarkers. A few
categories of potential MetS biomarkers andnontraditional pre-analytical considerations
that have recently been gaining interest will be
discussed.

Adipocytokines
Recent literature has shifted the notion of adipose tissue as a nonfunctional energy storagesite to an important secretory organ. Adipose
tissue secretes low-molecular weight peptides,
called adipocytokines, which have numerous
functions including food intake regulation, glucose and lipid metabolism, and inflammation(74). More recently, studies have shown adipocytokines mediate obesity-associated metabolic disorders independently of other risk
factors (75). One adipocytokine, adiponectin,
is secreted primarily by the adipocyte and isactually decreased in plasma upon an increasein fat mass (76). Adiponectin has several functions including anti-inflammatory and anti-atherogenic effects, as well as insulin sensitizationand lipid regulation (77). Pediatric studies haveshown that plasma adiponectin concentration
is inversely correlated with BMI, waist circumference (WC), fasting insulin concentration, andinsulin resistance (78,79) and is 25% higher inhealthy overweight youth compared to thosewith MetS (80). Additionally, a study of 5,088
adolescents showed that a decreased adiponectin concentration was associated with an increased risk of MetS, independent of age, BMI,
WC, and total cholesterol (81).
Leptin, the first identified adipocytokine, is a
product of the obesity gene and is known as the“satiety hormone” because it decreases food intake and increases energy expenditure. Leptin
concentration has been shown to reflect bodyfat mass and, as a result, can be considered a
reliable marker of fat mass and energy homeostasis in non-insulin resistant individuals (82).
Not only do obese individuals tend to have elevated plasma leptin concentrations, but they
are also leptin-resistant, negating the beneficial effects of leptin (83). Several studies havealso shown this positive association between
fat mass and leptin concentration in the pediatric population (84,85). Furthermore, leptin isPage 34

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Pediatric Metabolic Syndrome: pathophysiology and laboratory assessment

positively associated with insulin resistance in
pre-pubertal children after adjusting for sex,age, and BMI, and for every 1 ng/dL increasein leptin levels, the odds of MetS increase by
3%, suggesting an important role for leptin as amarker of CVD risk (86).
As a result of several studies supporting thepotential roles of both adiponectin and leptin
as MetS biomarkers, studies to develop normative values for adiponectin were warranted. A
study in 2012 established sex-specific reference
intervals (2.5th and 97.5th percentiles of concentration distribution in healthy subjects) for total
adiponectin in cord blood and for each one year
interval from 0-14 years of age (87). Anotherstudy of 111 healthy children aged 0-10 years
provided median, 25th and 75th percentile values for leptin (88). A more recent study established age- and sex-specific reference intervals
for both serum adiponectin and leptin in prepubertal European children (ages 3-9 years)(89). Furthermore, studies have assessed the
diagnostic potential of these biomarkers in thepediatric population. One study determined an
adiponectin concentration of 6.65 µg/mL as acutoff point to identify MetS with 64% and 67%
sensitivity and specificity, respectively (75).
Likewise, a recent study determined a leptin
level of 13.4 ng/mL as a cutoff point to identify
MetS with a sensitivity and specificity of 68%
and 69%, respectively (86). Although further
examination of these biomarkers is needed todetermine their suitability in MetS detection,
extensive progress has been made in the understanding of these adipocytokines in pediatric MetS.
Microalbumin
Microalbuminuria, an increased level of urine
albumin, is thought to be the renal expressionof vascular endothelial damage, particularly
increased vascular permeability, as evidence
suggests that glomerular leaking of albumin

reflects general vascular damage (90–92).
Therefore, microalbuminuria denotes preclinical atherosclerosis and can be used as an early
atherosclerosis indicator (90–92). Obesity isstrongly associated with the two most common
causes of end-stage renal disease: diabetes
and hypertension (93). Additionally, the MetS
is suggested to be an independent risk factor
for both chronic kidney disease and end-stage
renal disease (94). Initially introduced into thecriteria to define the MetS by the WHO in 1988
(14), microalbuminuria screening is now recommended to be added to the assessment of the
CVD risk profile in adults (92). This is the resultof well-established evidence of the relation
between microalbuminuria and hypertension,
central adiposity, the MetS, and CVD mortality (95). More recent studies have examined
the association between microalbuminuria andobesity as well as other CVD risk factors in thepediatric population (93,96,97). A study of 150
obese children by Sanad M et al. found thatobese children with microalbuminuria had asignificantly higher blood pressure, triglyceride
levels, LDL levels, as well as a higher prevalenceof MetS, insulin resistance, and impaired fasting
glucose levels, than those without microalbuminuria (93). Another study by Burgert T et al.
found that 10.1% of an obese, non-diabetic pediatric cohort had a urine albumin to creatinine
ratio in the microalbuminuric range (i.e. 2-20
mg/mmol), which is similar to the expectedprevalence in an obese adult population (96).
Even slight abnormalities in glucose metabolism
may promote early vascular damage in pediatricobesity (96). Microalbuminuria has been suggested as a treatment target in adults (98,99),
and now may also become an approachable
treatment target in pediatric metabolic syndrome, potentially responsive to treatment (i.e.
lifestyle intervention or pharmacotherapy) directed at improving insulin sensitivity and glucose tolerance (96).

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Gut peptides
In contrast to the extensively studied adipocytokines, gut peptides, including GLP-1 and GLP2, are more novel potential biomarkers thatare gaining interest in parallel with the recently
accepted metabolic role of the intestine. In addition to its well-known incretin action, GLP-1
also promotes satiety, inhibits gastric emptying,
and regulates lipid metabolism (57). Studies
have shown decreased GLP-1 secretion andblunted postprandial increase in GLP-1 in morbidly obese (83) and T2D individuals (100). Thismay be due to the decreased responsivenessof L-cells to nutrient intake in insulin resistantconditions (101). With the important incretin
effect of GLP-1, it is evident that decreased
GLP-1 secretion in an obese state would have
implications on insulin action. Recent pediatricstudies have shown that fasting total GLP-1 isreduced, but fasting active GLP-1 is elevated inobese compared to normal weight adolescent
girls (102). Overall, GLP-1 secretion and plasma
concentration in obesity remains controversial
and pediatric studies of this phenomenon are
extremely limited. GLP-2, encoded on the samegene and co-secreted in an equimolar amountwith GLP-1, enhances intestinal lipoprotein
production and nutrient absorption, as well as
reduces inflammation (86). Recent studies inobese adults have shown an inverse relationship between GLP-2 secretion and insulin sensitivity, although the underlying mechanisms
are still unknown (103). Studies on GLP-2 areeven more scarce, particularly on obese pediatric subjects. Future studies examining the potential of GLP-1 and GLP-2 as MetS biomarkers
in pediatric subjects are critical to understandtheir potential in laboratory assessment of pediatric MetS.
Lipoproteins and apolipoproteins
Although the standard lipid profile consists oflipids and lipoproteins, with some newly added

apolipoproteins, there are additional lipoprotein subfractions recently receiving attention for
CVD risk assessment. The first parameter, remnant lipoproteins (RLPs) are metabolic productsof TG-rich lipoproteins (i.e. CMs and VLDLs). A
study of 1,567 women from the Framingham
Heart Study showed RLP-C was an independentrisk factor for CVD in women, independent of
TG (55). Postprandial RLP-C was shown to be
an independent predictor of insulin resistanceafter adjusting for age, BMI, and other lipid
profiles in a study of 78 adults (104). Pediatricstudies have shown that RLP-C is significantly
higher in obese subjects and strongly related
to insulin resistance (91). Long-term prospective studies are needed to evaluate whether
children and adolescents with high RLP-C are
at greater risk of developing MetS. The second
parameter is apoB-48 which is a specific marker
of intestinal lipoproteins (i.e. CMs). As CMs are
secreted in the postprandial state, apoB-48 cansubsequently be used to examine postprandial
lipoprotein metabolism (91). Adult studies haveshown fasting apoB-48 is elevated in subjects
with MetS (105) and T2D and is significantly
associated with endothelial dysfunction (106).
Recent studies in pediatrics determined thatfasting plasma apoB-48 concentration is 2-foldhigher in obese versus normal weight subjects(107). However, pediatric data on apoB-48, particularly in the postprandial state, is needed tounderstand the potential of apoB-48 as a MetS
biomarker.
Assessment in the postprandial state
In addition to the recent exploration of novel
MetS biomarkers, emerging pre-analytical conditions that may improve both the simplicityof laboratory testing and the relevance of thelaboratory test results have been examined. In
clinical practice, the lipid profile is traditionallymeasured in a fasting state even though the
postprandial state predominates over a typicalPage 36

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Pediatric Metabolic Syndrome: pathophysiology and laboratory assessment

24 hour day. Therefore, the lipid and lipoprotein
content of a fasting sample does not accuratelyreflect the daily average concentration of theseparameters. Additionally, evidence is lacking
that a fasting sample is superior to a postprandial sample when evaluating for CVD risk assessment, and in fact, postprandial samples seem to
be more advantageous (72). Some advantages
include simplification of blood sampling for patients, particularly pediatrics, improving patientcompliance with lipid testing, and decreasing
the volume burden on laboratories in the morning. Several studies have found that postprandial lipid and lipoprotein measurements sufficefor CVD risk screening, and in some cases areeven better predictors (72). As MetS is a cluster of CVD risk factors, postprandial measurements may be more relevant for clinical guidelines. For example, a meta-analysis including
over 300,000 individuals found that postprandial non-HDL cholesterol and calculated LDL-C
were superior to fasting measurements for predicting CVD risk (108). Furthermore, the novel
MetS biomarkers discussed here are more relevant following nutrient ingestion. For example,
GLP-1 and GLP-2 concentrations are much more
relevant in the postprandial state, as their concentrations in the fasting state are very low andtheir secretion is stimulated upon nutrient ingestion (95). Additionally, approximately 80%of the postprandial increase of TG is due to theincrease in TG of RLPs (109) and apoB-48 is amarker of CMs (i.e. lipoproteins secreted fromthe intestine following a meal). Therefore, if
MetS components lead to an alteration in thesebiomarkers, this change would be apparent in
the postprandial, rather than fasting state.
CONCLUDING REMARKSThe clustering of CVD risk factors, termed themetabolic syndrome, is present in both adults
and children. MetS is primarily driven by excess adipose tissue and subsequent insulin

resistance. Insulin resistance manifests in several organs, including the muscle, liver, and intestine, and as a result is associated with several
systemic complications including hypertension,
dyslipidemia, and impaired glucose tolerance.
The interplay of metabolic dysfunction in several organ systems leads to the development of
atherosclerosis and consequent CVD complications. Defining MetS in the pediatric population
has been controversial due to the difficulties ofgeneralizing both a diverse syndrome and a diverse population. However, establishing a consensus definition is critical for identification andmanagement of youth at a higher risk of developing CVD. As a result, the examination of novel
MetS biomarkers in the pediatric population
has been of interest to identify pediatric subjects with obesity-related metabolic complications early before CVD complications manifest.
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108. Emerging Risk Factors Collaboration, Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, et al. Major lipids, apolipoproteins, and risk of vascular disease.
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In this issue: Recent Advances in Pediatric Laboratory Medicine

Pediatric reference intervals for biochemicalmarkers: gaps and challenges, recent national
initiatives and future perspectives

Houman Tahmasebi1,2, Victoria Higgins1,2, Angela W.S. Fung2, Dorothy
Truong2, Nicole M.A. White-Al Habeeb2, Khosrow Adeli1,2
1
2

CALIPER Program, Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada
Department of Laboratory Medicine & Pathobiology, Faculty of Medicine, University of Toronto, ON, Canada

ARTICLE INFOABSTRACT

Corresponding author:
Khosrow Adeli, Ph.D., FCACB, DABCC
Clinical Biochemistry
Pediatric Laboratory Medicine
The Hospital for Sick Children
555 University Avenue
Toronto, ON, M5G 1X8
CanadaE-mail: khosrow.adeli@sickkids.ca

Reference intervals provide valuable information
to medical practitioners in their interpretation ofquantitative laboratory test results, and are criticalin the assessment of patient health and in clinicaldecision-making. The reference interval serves as ahealth-associated benchmark with which to compare an individual test result. While the concept ofreference intervals and their utility appear straightforward, the process of establishing accurate andreliable reference intervals is considerably complex
and involved. Currently, many pediatric laboratory
tests are inappropriately interpreted using reference intervals derived from either adult populations,
hospitalized pediatric populations, or from outdated
and/or inaccurate technology. Thus, many pediatricreference intervals used in diagnostic laboratories
are incomplete and may be inappropriate for clinicaluse. The use of inappropriate reference intervals impacts clinical decision-making and has potential detrimental effects on the quality of patient healthcare
including misdiagnosis, delayed diagnosis, inappropriate treatments, and patient risk. These are critical gaps in pediatric healthcare and it is imperative to
update and establish appropriate reference intervals
for pediatric populations based on specific age- andKey words:
pediatric, children, adolescent,reference interval, CALIPER

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sex-stratifications. In the present review, specific issues, challenges and deficiencies in pediatric reference intervals for biochemical markers
will be discussed. Early studies using hospitalized patients will be examined, followed by areview of recent national and global initiatives
on establishing reference intervals from healthy
pediatric population. We will highlight theachievements and milestones of the Canadian
CALIPER project, including the establishment of
a comprehensive biobank and database which
has addressed several of these critical gaps.
CALIPER’s mandate is to establish and provide
comprehensive, up-to-date pediatric reference
intervals to all biochemical markers of pediatric disease. CALIPER has also begun knowledgetranslation initiatives to disseminate its data via
peer-reviewed publication, an online database,and a smartphone application to allow greater
access to CALIPER pediatric reference interval
data. Finally, limitations, future perspectives
and harmonization of pediatric reference intervals to improve pediatric diagnostics in Canada
and worldwide will be discussed.


INTRODUCTION
The measurement of disease biomarkers in clinical laboratories are used to screen, diagnose,and monitor a wide range of medical conditions.
To interpret these laboratory test results, physicians compare patient test results with a reference interval, defined as the typical values derived from a healthy population [1]. Statistically,reference intervals are defined as the limiting
values denoting a specified percentage (typically central 95%) of values from an apparently
healthy reference population with 90% confidence. In the central 95% distribution model,
the reference limits are determined by calculating the 2.5th and 97.5th percentiles of test results[2]. In this case, a total of 5% of values may beinterpreted as abnormal or higher risk of disease and require further follow-up and investigation. In other cases, the reference interval
definition may be modified, where a different
percentile may be used, or either the upper orlower limit may be used if only a one-sided distribution is clinically significant [3]. Reference
intervals are therefore a fundamental tool intest result interpretation and serve as a benchmark for health status [4].
Accurately established reference intervals are
critical to clinical decision-making as a lack or
inappropriate use of reference intervals maylead to adverse consequences including misdiagnosis, patient risk, inappropriate treatment,
and/or higher healthcare costs, all of which impact the overall quality of patient healthcare.
Many current reference intervals were determined decades ago with older and less accurate
laboratory instruments and testing methodologies. Furthermore, as instrumentation andreagents are upgraded, reference intervals are
not always appropriately updated [4]. With rapid advances in technology, there is also a lackof data on novel, emerging disease biomarkers. The lack of standardization and harmonization of assay methodology further contributes
to variations of established reference intervals
for both adults and pediatrics. These variations
create confusion in the interpretation of test results for the same patient whose specimen may
be tested in different laboratories.
Additionally, there are specific challenges related to the determination of pediatric reference intervals. The same reference intervals
are sometimes used to interpret test results
for both adults and children. However, children
are not small adults; children have significant
differences in physiology and metabolic state,physical size, organ maturity, bodily fluid compartments, and immune and hormone responsiveness when compared to adults [4]. Most
notably, dynamic physiological changes, growthPage 44eJIFCC2017Vol28No1pp043-063

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Pediatric reference intervals for biochemical markers

and development profoundly influence biomarker concentrations. For example, it is wellknown that sex hormones, growth hormones,
and bone alkaline phosphatase vary with achild’s age and development [4]. Establishingappropriate pediatric reference intervals involves overcoming specific challenges mostly
related to volume and quantity of healthy pediatric samples. Complex physiological factors
may also necessitate the separation of reference interval (called partitions) to be age- andsex-specific, requiring a greater number of reference samples. Moreover, children also suffer from, or are more susceptible to, diseases
that differ from adults, requiring unique or new
biomarker reference interval determinations.
To complicate this further, some of these diseases may be genetically inherited and occur at
a lower frequency, posing challenges in acquiring adequate sample size for statistical calculations. Gap analyses of pediatric reference intervals have identified four major critical areas inpediatrics including bone markers [5], cardiovascular disease and metabolic syndrome risk
markers [6,7], hormones of thyroid and growth
hormone axes [8], and inborn errors of metabolism [9]. Data from published reference interval
studies often suffer from limitations in design,
small sample sizes, and the use of hospitalized
patients [3]. Currently pediatric clinicians andlaboratorians depend on scattered informationand incomplete data from published (scientificjournals and textbooks) and unpublished (hospital, private, reference laboratories) sources inlaboratory test result interpretation. There is anurgent need to establish and update reference
intervals for all populations and particularly pediatric populations.
While the concept and utility of reference intervals appear straightforward, the process toaccurately establish or verify reference intervals is quite complex. The Clinical Laboratory
Standards Institute (CLSI) and International

Federation of Clinical Chemistry and Laboratory
Medicine (IFCC) provide a guideline (C28-A3) on
how to define, establish, and verify reference intervals [10]. When judging the validity of reference intervals, several integral factors should beconsidered including the use of a large healthy
population, appropriate inclusion/exclusion criteria, awareness of physiological, pre-analyticaland analytical factors that affect analyte concentrations, careful outlier exclusion, and appropriate statistical analysis.
The quality of reference intervals depends on
the recruitment of a large number of healthyreference individuals within the age group(s)of interest and well-defined inclusion/exclusion criteria. Laboratories commonly verify andadopt a reference interval provided by a manufacturer or transfer a pre-existing reference interval with 20 healthy samples. One problem
with verification and transference of pre-existing reference intervals is the quality of the original reference interval. Reference intervals supplied by manufacturers often lack information
regarding the study’s sample size, age, sex, andethnic distribution, and often do not include pediatric populations. The success of transference
also depends on comparability of the reference
populations and analytical methods between
the donor and receiving laboratories. To establish robust reference intervals, a de novo reference interval study with at least 120 healthy
subjects per partition is needed. This is typicallydone for novel biomarkers. For a pediatric reference interval study that is divided into 5 age
groups, it would require 600 healthy subjects
or 1200 healthy subjects if also sex stratified[4]. This undoubtedly requires an enormous
amount of effort, resources, time and cost.
There are many inter-related variables that contribute to the validity of reference intervals including ethnic composition, geographic factors(climate), diet and food preferences, and lifestyle factors. Laboratories that serve ethnically

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Pediatric reference intervals for biochemical markers

diverse population should evaluate and determine whether a single reference interval is valid
or whether ethnic-specific reference intervals
are necessary [4]. Inclusion and exclusion criteria require documentation of covariates (e.g.
age, sex, and ethnicity) and assessment of general health using surveys. Pre-analytical factors
include standardization of subject preparation(fasting, diurnal variation), specimen collection(posture, sample volume, tourniquet time), andspecimen-handling, transport, and storage conditions [3]. Analytical factors include a detailed
description of methodology including calibration traceability, imprecision, limit of detection,
linearity, analytical measuring range, interferences, and variability factors. Outliers should be
excluded through robust statistical methods including Dixon’s [11] or Tukey’s method [12] foroutlier exclusion, parametric or non-parametricanalysis, and covariate analysis. Overall, major
challenges in the determination of pediatricreference intervals include the recruitment ofa sufficiently large healthy pediatric population,
the need for parental consent, and difficulties
with pre-analytical variables (i.e. sample collection, sample volume, and a specialized phlebotomist with pediatric experience) [3].
In this review, the important milestones in addressing these gaps and challenges in pediatricreference interval determination will be highlighted. The early approaches to establishmentof pediatric reference intervals derived from
hospitalized patients as well as several recent
global initiatives to close the gaps in pediatric reference intervals based on recruitmentof healthy children will be discussed. The significant progress that the Canadian Laboratory
Initiative on Paediatric Reference Intervals(CALIPER) project has achieved through pilot
studies, the establishment of age- and sexspecific pediatric reference intervals for morethan 100 biomarkers, and various sub-studies
will be reviewed. Lastly, limitations, future

perspectives, and goals for pediatric reference
interval distribution and harmonization will also
be discussed.
INDIRECT APPROACHES TO REFERENCE
INTERVAL ESTABLISHMENT
Ideally, reference methods are determinedbased on a healthy population using a direct apriori approach where individuals are specifically selected for the study. However, the recruitment of many healthy individuals can bequite challenging, particularly in the pediatricpopulation, as it is very costly and time consuming. Additionally, it is difficult to define an individual as “normal” or “healthy” as the potential
for existing subclinical issues is unknown. Thus,
indirect methods, also known as data mining,can be quite useful. The indirect approach uses
existing data to establish reference intervals byidentifying an acceptable reference population
retroactively. One example of using the indirect approach is to identify a group of healthy
individuals from hospital in- and/or out-patient
populations to calculate reference intervals. In
addition to having data readily available throughthe laboratory information system (LIS), indirect
methods remove the need to recruit healthy individuals. This method represents a strong alternative to cases where laboratory markers aremeasured at high volume in community outpatient clinics and therefore should include a relatively healthy population.
The Hoffmann method proposed by Robert G.
Hoffmann in 1963 [13], uses an indirect a posteriori method to determine reference intervals
using available test results from hospital-based
data from in- and out-patients. This approachmakes two assumptions: 1) values obtained fora specific analyte follows a Gaussian distribution; and 2) majority of measurements madein the hospital represent normal individuals.
Reference intervals are determined by plotting

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the cumulative frequency of a result against theanalyte value. The linear portion of the resulting graph, centered on the 50th percentile, ischosen thereby giving these values the greatest
weight. By extrapolating the linear portion of
the graph, the 2.5th and 97.5th centiles are calculated, representing the normal range of values, if the assumptions are held true. Hoffmannused this approach with a relatively small number of patients (n=60) for glucose as a proof-ofconcept. The application of this method using
computer programs has eliminated much of thesubjectivity of the Hoffmann approach and allows for the analysis of a very large number ofsamples.
Although the Hoffmann approach is highly cited,
few authors have applied the Hoffmann methodin their calculations. One notable exception is
the study led by Steven Soldin at the Children’s
National Medical Center in Washington, DC,
USA. Reference intervals were calculated using hospital-based data collected from patientsfrom 1 day to 18 years old. This group reported
reference intervals for several analytes including fertility hormones, thyroid hormones, adrenal hormones, and growth hormones [14]. As
expected, age and sex-specific differences wereidentified for FSH, LH, estradiol, 17-hydroxyprogesterone, DHEA and testosterone. Sexspecific differences were also found for serum
iron, homocysteine, IGF-1 and IgE levels. Lastly,
age-related differences were found for free T4,
TSH and 25-hydroxy vitamin D levels. Levels of
25-hydroxy vitamin D showed seasonal variation, with higher levels found in the summer
months. This work, however, had some limitations. Much of the data was collected on a small
number of Caucasian hospitalized patients andmay not accurately reflect levels in a healthy
multicultural population. Also, semimanual application of the Hoffman analysis of data, added
subjectivity to the calculations.

To determine the accuracy of the Hoffmann
method, Katayev et al. applied a computerizedindirect Hoffmann approach to retrospectively
determine reference intervals for hemoglobin,creatinine, calcium and thyroid stimulating hormone (TSH) and compared to previously published reference intervals [15]. Their method
calculated cumulative frequencies of each test
result and performed least squares analysis, applying a best-fit equation to the linear portionof the data. The statistical difference between
the two methods was determined by calculating the reference change value (RCV), a factor
representing within individual variation andanalytical variation. They found that there wasno statistically significant difference between
the reference intervals calculated using the
Hoffmann method and published reference
intervals.
More recently, as part of the CALIPER initiative,
Shaw et al. compared pediatric reference intervals calculated for 13 analytes (calcium, phosphate, iron, ALP, cholesterol, triglycerides, creatinine, direct bilirubin, total bilirubin, ALT, AST,
albumin and magnesium) from hospital-based
data using the Hoffmann approach (Figure 1) toreference intervals calculated by CALIPER [16].
They sought to determine; 1) whether the hospital-based reference intervals fell within the
90% confidence intervals calculated by CALIPER;
2) whether the hospital-based reference intervals fell within the RCV for each analyte; 3) bycompleting a reference interval validation study
using reference samples from healthy children.
None of the reference intervals calculated using
the Hoffmann approach fell within the 90% confidence intervals calculated by CALIPER. When
the RCV was used for comparison, only some of
the Hoffmann calculated reference intervals fellwithin the RCV range calculated (creatinine 15days–<1 year old and all phosphate partitions
with the exception of 0–<14 days old). Given
the wide biological variation in some analytes, it

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Pediatric reference intervals for biochemical markers

Figure 1

Schematic of statistical approach to calculate reference intervals
from hospital-based data based on the Hoffmann approach
used in Shaw et. al. [16]Group data into age/sex partitions
Remove duplicate patients
Remove outliers using Tukey method
Calculate the cumulative frequency of each measured analyte value
Calculate the inverse cdf of a standard Gaussian distribution
Graph the inverse cdf against each measured analyte value
Identify the linear portion of the distribution using piece-wiselinear regression
Determine the equation representing the linear portion of the
distribution using linear regressionthth

Extrapolate the 2.5 and 97.5 centiles using the linear
equation to define the lower and upper reference limits

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is not surprising that more of the Hoffmanncalculated reference intervals fell within the
RCV than fell within the 90% confidence intervals calculated by CALIPER. Finally, all reference intervals were validated according to
CLSI guidelines except for ALP (13–<15 year old
male; 15–<17 year old male and female), creatinine (15–19 year old male), and iron (14–<19
year old female). Validation data for some age
and/or sex partitions was not performed due toinsufficient numbers. In this study, reference intervals calculated using the modified Hoffmann
approach, were much wider than those calculated by CALIPER, suggesting that the use of
hospital-based data may be limited in pediatrics, especially from a tertiary care center.
The main limitations of the Hoffmann approach
are based on the assumptions made for the
analysis. The method assumes that majority ofmeasurements made in the hospital represent
normal individuals, yet when using hospitalbased data, the true proportion of unhealthy
individuals included in the data set is unknown[17]. This is especially problematic when determining reference intervals for low volume tests
that include a selective patient population, as
the fewer available samples would likely skew
the data due to the inclusion of unhealthy subjects [18]. Additionally, this approach assumes
that the values obtained for a specific analytefollows a Gaussian distribution of healthy results which may not be true in all cases [13].
NATIONAL AND GLOBAL PEDIATRICREFERENCE INTERVAL INITIATIVESBASED ON HEALTHY COHORTS
In contrast to a hospitalized patient population,
the ideal reference population to establish population-based reference intervals is a group ofwell-defined individuals that are similar to thetarget patient in all respects other than the disease condition under investigation. Ultimately,

the quality of a laboratory measurement depends on the quality of the reference intervalthat the value is compared with. In turn, thequality of reference intervals heavily depends
on the selection and recruitment of a large
number of appropriate reference individuals[2,19]. Although this is a challenging task, several national and international initiatives haverecognized the critical gaps in pediatric reference intervals and the need to establish ranges
that are robust and appropriate for the pediatric population (Table 1) [20,21].
The German Health Interview and Examination
Survey for Children and Adolescents (KiGGS),
performed by the Robert Koch Institute (RKI),
is a national initiative aimed at providing information on several health aspects of German
children and adolescents [22,23]. KiGGS has
successfully determined reference intervals fornumerous serum and urine laboratory biomarkers using healthy pediatric samples [20,23]. The
baseline KiGGS study collected whole blood, serum and urine samples in addition to personal
information (i.e. age, sex, socioeconomic status,
geographical location, community population,and immigration status) from 17,641 pediatricsubjects (aged 0-17 years) recruited from May
2003 to May 2006 [24]. Three main categories
of analytes were examined depending on theirrelation to one of the following public healthinterests: nutrition, risk of non-communicabledisease and immunization. 43 analytes wereanalyzed, for some of which median and 90%
reference intervals were calculated, such as total, LDL and HDL cholesterol, triglycerides, andcalcidiol, based on age, sex and other subjective
information [24]. In an additional KiGGS study[25], the median, 25th and 75th percentile values
for serum thyroid hormones and serum lipids
were determined in 12,756 subjects ≥3 yearsof age. This study also examined biomarker
relationships and identified a positive relationship between TSH and serum lipid biomarker

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concentrations, except for HDL, even after adjusting for smoking status, age and sex [25].

expected to increase or decrease by age (fromadolescence to adulthood).

The Nordic Reference Interval Project (NORIP)
was established in 1998 to determine Nordicspecific reference intervals for common blood
analytes [26,27]. Some of the general inclusion criteria included feeling healthy, aged ≥ 18
years, and individuals who are not pregnant,ill or hospitalized. Personal information obtained from subjects included age, sex, bodymass index (BMI), cigarette and alcohol usage,
ethnic origin and physical activity [28]. Serum,plasma and whole blood samples were obtained from 3,036 healthy adults (aged ≥ 18
years) in 102 Nordic laboratories in Denmark,Finland, Iceland, Norway and Sweden [21,26].
Reference intervals (central 95%) for 25 common serum biomarkers, including enzymes,
were calculated based on IFCC recommendations, with 90% confidence intervals aroundupper and lower limits. Data were also partitioned based on age, sex and blood sample(plasma or serum). Interestingly, a 2013 study
analyzed 21 blood biomarkers based on 1421
(596 males and 825 females) healthy Danish
pediatric subjects between ages 5-20 who participated in The COPENHAGEN Puberty Study(from 2006-2008) [29], and the results werecompared to the NORIP (adult) findings. This
study used nonparametric statistics to calculate
the central 95% RIs, and confidence intervals of
upper and lower limits; the RIs were calculated for both sexes and 6 age groups. This paper
compared the results from the oldest pediatricage group to the youngest NORIP adult results[29]. Although many of the results were similar,
there were some differences between the twostudies, some values being higher and others
being lower in the pediatric study compared to
the NORIP adult study. However, discrepancy insome of these analytes, such as alkaline phosphatase, lactate dehydrogenase and creatinine,
was explained to be normal as these values areChildren’s Health Improvement throughLaboratory Diagnostics (CHILDx) program has
been establishing pediatric RIs based on ahealthy cohort (6 months to 17 years) in Utah,
United States since 2002 [30-32]. This group hasdetermined RIs for a variety of analytes, suchas vitamins [33], enzymes [32], hormones [34],coagulation tests [30] and bone markers [35].
A 2005 CHILDx paper describes reference intervals for seven common coagulation tests thatwere determined based on 902 healthy pediatric participants with ages ranging from 7 to
17 years [30]. Median, 95% reference intervals
and 90% confidence intervals around upper andlower limits were determined for each parameter. The results were reported based on 3-year
age groups. Another CHILDx study in 2011 focused on analyzing the serum levels of sevenanalytes, consisting of enzymes, prealbumin
and uric acid, based on a healthy cohort of 1765
children and adolescents [32]. Participants weredivided into 3-year age groups (except for the
6m-2y age group), and mean, median, and statistical difference between age and sex groups
were determined. Subsequently, central 95%
age-specific and sex-specific (sex differences
for about one-third of analytes) RIs were determined using nonparametric statistics.
United States National Health and Nutrition
Examination Survey (NHANES) evaluates thehealth status of pediatrics and adults in the U.S.
population by receiving laboratory and interview details from their cohort, while recruiting
thousands of additional participants each year
[36-41]. The NHANES study examined the effects of age, sex, BMI, socioeconomic status andethnicity on various health parameters, including biomarkers. For example, a study publishedin 2000, looked at the upper 95th percentile
limit of C-reactive protein (CRP) concentrations
in a sample size of more than 22,000 healthy

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pediatric and adult individuals (from NHANESIII) based on age, sex and ethnicity [42]. Females
generally had a higher concentration comparedto male counterparts. They also demonstrated
that Caucasians and Hispanics have similar upper limit CRP values, compared to black adults.
CRP levels were also higher in older adults
compared to children. Furthermore, in 2004,
NHANES III data were used to determine reference intervals for whole blood count based on
approximately healthy 25,000 subjects ranging from pediatric to geriatric age groups (aged
10 to >75 years), partitioned based on age,sex and ethnicity (Mexican, white and black)[40]. Age-, sex- and/or ethnic-related differences were observed in some of the analytes.
In 2012, NHANES data from 6062 healthy pediatric (ages 2-19 years) individuals were used
to establish RIs for the same 3 ethnic groups(Mexican, white and black) [43]. In addition,
95% RIs, geometric means and statistical difference between the ethnic groups were determined for vitamins and lipids (total, LDL- andHDL-cholesterol). RIs were partitioned based onsex, age and other factors; this is interesting as
it allowed for both genetic and environmental
comparisons between the three ethnic groups.
Overall, NHANES studies, along with other studies, highlight the importance of partitioning reference intervals based on sex, age and ethnicity
since reference values are highly influenced bythese factors [40,43,44].
The Lifestyle of Our Kids (LOOK) program is alongitudinal study that was initiated in Australia
in order to study healthy children and adolescents, and the effects of physical activity on
their health outcomes [45]. In one of the mostsignificant studies done by this group, central
95% RIs and medians of 37 blood analytes were
calculated from a sample of 852 healthy individuals [45]. Sex-specific as well as age-specific
RIs were calculated for ages 8, 10 and 12 basedon measurements in 2005, 2007 and 2009,

respectively. Interestingly, they compared theirresults with those from other groups, including
CALIPER RIs for ferritin, CRP, cholesterol, TSH andmagnesium. In a 2012 paper, LOOK data from854 pediatric individuals was used to calculate
blood NT-proBNP RIs [46]. Median, 95% RIs and
90% confidence intervals were calculated basedon sex (male, female and combined) and for thesame 3 age groups (ages 8, 10 and 12). In addition, pairwise comparisons in concentrations
between the sex groups and the 3 age groups
were conducted with the use of Mann–Whitney
U test. While no significant difference was observed between the sex groups of the sameage, concentration differences were shown to
be statistically significant between certain age
groups.
Australasian Association of Clinical Biochemists(AACB) Committee for Common Reference
Intervals and AACB Harmonisation Committee
have been promoting the adaptation of common reference intervals, based on the values
used in hospitals in Australia and New Zealand[47,48]. In a 2014 preliminary publication, reference intervals used by each laboratory, alongwith results of freshly obtained serum for various analytes, were reported by each of the 123
laboratories, partitioning analytes by sex whenapplicable [48]. This was used to identify differences in reference intervals used by eachlaboratory and analytical methods between
the laboratories. Interestingly, it was demonstrated that for majority of analytes, reference
interval variation was greater than analytical(i.e., measured sample) variation between thelaboratories. Linear regression was also used
to compare measured results against reported
upper and lower reference limits reported byeach laboratory [48]. Finally, the AACB group
determined the location of the measured
sample’s value with regards to each laboratory’s reference range, determining its relative
location from upper and lower limits [48]; thePage 51

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Pediatric reference intervals for biochemical markers

results were then compared between the laboratories. In another study published within thesame year, harmonized 95% reference intervals
were determined for 11 analytes in adults and
9 in pediatrics based on healthy subjects, while
partitioning further based on age in pediatrics
and sex in both pediatrics and adults (aged ≥ 18
years), where applicable [47]. Throughout thecommon reference interval selection process,many workshops and validation processes were
held to identify analytical accuracy and biases, consider clinical importance, and compare
the results to other studies (e.g. NORIP and
Aussie Normals). Similar to other studies, thisstudy highlights the importance of considering
between-instrument/analytical method differences and the significance of local validation[44,49-51].
THE CANADIAN LABORATORY INITIATIVEON PEDIATRIC REFERENCE
INTERVALS (CALIPER) PROJECTThe Canadian Laboratory Initiative on Pediatric
Reference Intervals (CALIPER), is aimed at creating a comprehensive database of pediatricreference intervals to disseminate for use byother pediatric centres nationally and globally. This initiative was launched through thePediatric Focus Group of the Canadian Societyof Clinical Chemists (CSCC) and is a collaborative effort amongst pediatric institutions acrossCanada. CALIPER is an a priori prospective study
that has been recruiting thousands of healthy
community children and adolescents and establishing age- and sex-specific reference intervals
for many routine and specialized biochemicalmarkers.
The early stages of CALIPER involved extensiveplanning to establish standardized proceduresfor pre-analytical, analytical and post-analytical aspects of the project. For example, bloodcollection, sample and statistical analysis werestandardized to ensure consistency among different collection sites. A CALIPER team consisting of experienced research coordinators, project coordinators and volunteers was formed tohelp with promotion of the CALIPER campaign
and recruitment of participants. Trained phlebotomists with expertise in pediatric sampling
were also recruited to ensure ease and efficiency of collection.
Initially, preliminary pilot studies were conducted by CALIPER to refine project logistics andgain experience in sample collection, sample
analysis, as well as statistical analysis and establishment of reference intervals. The initial
CALIPER pilot studies included 2,809 serum andplasma specimens from apparently healthy andmetabolically stable children from outpatient
clinics. Overall, these initial studies analyzed
over 50 chemistry and immunoassay biomarkers on the Abbott ARCHITECT ci8200 analyzer[52]. Using these data, CALIPER generated
preliminary reference intervals according to
CLSI and IFCC C28-A3 guidelines. Age and sexspecific reference intervals were established
for five age groups. This was an important firststep for CALIPER and formed the basis for more
projects to come. However, as outlined by CLSI/IFCC C28-A3 guidelines, establishment of reference intervals should include recruitment ofat least 120 healthy individuals per partition.
Initial CALIPER pilot studies recruited apparently healthy children from outpatient clinics.
According to CLSI guidelines, this was less thanideal since underlying disease in children fromoutpatient clinics could confound interpretation and establishment of reference intervals.
Furthermore, a sufficient sample size of 120 patients per partition was not feasible in the initial
CALIPER preliminary studies.
To address this limitation, subsequent CALIPER
reference interval studies recruited healthy
children and adolescents from Toronto and theGreater Toronto Area to establish a biobank

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H. Tahmasebi, V. Higgins, A. Fung, D. Truong, N. White-Al Habeeb, K. Adeli
Pediatric reference intervals for biochemical markersof healthy serum samples. With the help of the
CALIPER team, over 9000 serum samples werecollected from healthy children and adolescents
allowing for appropriate sample size of 120 participants per partition, as per CLSI guidelines
Figure 2(Figure 2). Furthermore, to ensure adherence to
CLSI guidelines and only inclusion of healthy participants, recruitment was limited to those without a history of chronic illness or acute illness
within the previous month and without current

Schematic of general statistical approach to calculate reference intervals
in CALIPER study based on a healthy reference population [53,55]

Inspect data for age and sex partitions using box plots
Remove outliers for estimate partition using Tukey or adjusted Tukey
method twice for normally distributed and skewed data, respectively
Determine statistically significant partitions
Using Harris & Boydmethod for normally
distributed partitionsth

Log transform data forskewed partitionsth

Calculate 2.5 and 97.5 centiles as lower and upper
reference interval limits, respectively

Partitions ≥ 120 sample size:
Calculate using nonparametricrank method

Partitions > 40 and < 120
sample size:
Calculate using Horn andPesce robust method

Calculate 90% confidence intervals around lower and upper limits
pprochPage 53n a heal hy e ere ce popu
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Pediatric reference intervals for biochemical markers

use of prescribed medication. Diverse collection
sites, including community centres, day cares,
churches and schools, further strengthened
the CALIPER study and ensured that recruitedparticipants accurately reflected the Canadian
diversity. The first of these a priori studies, included the establishment of age-specific reference intervals for over 40 routine chemistry
markers analyzed on the Abbott ARCHITECT
c8000 system [53]. This hallmark study was
the first of many CALIPER studies to begin tofill the gap in pediatric reference intervals forbiomarkers routinely assessed in the pediatricpopulation, including bone markers, markers ofcardiovascular disease risk and metabolic syndrome. Findings from this study indicated thatmany analytes required multiple age partitions
and the number of age partitions varied foreach analyte. Oftentimes, developmental milestones are used to arbitrarily set age partitions.
However, this study demonstrated that age partitions did not necessarily correlate with age developmental milestones, which was a paradigm
shift from what was currently practiced. For
example, alkaline phosphatase (ALP) required
seven age partitions (0-14 days, 15 days - <1 yr,
1 - <10 yr, 10 - <13 yr, 13 - <15 yr, 15 - <17 yr,
and 17 - <19 yr), as shown in Figure 3A, whereas
alanine aminotransferase (ALT) required three
age partitions (0 - < 1 yr, 1 - <13 yr, 13 - <19yr), as shown in Figure 3B [53]. This study also
performed preliminary analysis of differences
between the major Canadian ethnic groups (i.e.
Caucasian, South Asian, and East Asian). This
analysis demonstrated biomarker concentration differences between ethnic groups for ALT,
amylase, IgG, IgM, magnesium, total protein
and transferrin [53]. While these three ethnic
groups comprise a large proportion of Canada,
future CALIPER studies will focus on expanding
the analysis to other ethnicities.
As children grow and undergo pubertal development, there are significant changes in fertility

hormone concentration. In 2013, CALIPER recruited healthy children and adolescents andmeasured 7 fertility hormones [54]. Age-specificreference intervals were required for all fertility
hormones, and aside from prolactin, sex partitions were also required. This study also determined Tanner stage-specific reference intervals.
Tanner staging is used to monitor progress ofpuberty in children. It is especially important tohave Tanner stage-specific reference intervals
for fertility hormones since every child enters
puberty at various ages. Tanner staging is basedon a 5-stage scale, with stage I correlating withpre-pubertal development and stage V correlating with adult development. Tanner staging
was determined by providing participants withimages of Tanner stages I to V and participants
self-assessed their development relative to thediagram. Shortly following the examination offertility hormones, pediatric reference intervals
were established for additional endocrine andbiochemical markers on the Abbott analyzer[55].
With the success of the initial CALIPER studies, the CALIPER initiative extended their studies to more specialized testing and moved beyond general automated analyzers. Age-specificsteroid hormone reference intervals werecompleted on the AB SCIEX 4000 QTRAP mass
spectrometer [56], HPLC analysis aided in establishment of vitamin A and vitamin E pediatricreference intervals [57], and 25-hydroxyvitaminD reference intervals were determined using
LC-MS/MS analysis [58]. The 25-hydroxyvitamin
D study also demonstrated elevated concentrations of 25-hydroxyvitamin D C3 epimer (C3epi-25-OH-D2 and C3-epi-25-OH-D3) in neonatesless than 1 year of age, which could interferewith 25-OH vitamin D measurements in this pediatric population [58,59]. Thus, caution should
be exercised in measurement of 25-hydroxyvitamin D in neonates.

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H. Tahmasebi, V. Higgins, A. Fung, D. Truong, N. White-Al Habeeb, K. Adeli
Pediatric reference intervals for biochemical markers

Figure 3

Concentration (U/L)

A

Scatter plot demonstrating the serum concentrationsof (A) alkaline phosphatase and (B) alanine aminotransferase
over the pediatric age range

Alkaline Phosphatase, 4-Nitrophenyl Phosphate (ALP)

600
500
400
300

Male
Female

200
100
0

0

5

10

15

20

Age (Yrs)

B

45

Alanine Aminotransferase – Without Piridoxal Phosphate (ALT)

Con ent (U/L)
a io
Concentration

40
35
30
25
20

Male

15

Female

10
5
0

0

5

10
Age (Yrs)*Adapted from: Colantonio et. al. [53].

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Pediatric reference intervals for biochemical markers

In addition to these large milestones, CALIPER
allotted resources to smaller substudies. They
have analyzed the effect of freezing conditions
on samples and analyte stability [60], biologicalvariation [61], and fasting on biomarker concentrations [62]. The stability of serum chemistry, protein and hormones were analyzed on
three analyzers (Ortho Vitros Chemistry System,
Roche Cobas Integra 400 Plus and Siemens
Immulite 2500) using specimens that werefrozen at -80°C [60]. The results of this study
demonstrated that -80°C is a suitable storage
method for serum samples since no significant
deviations in analyte concentrations were observed. An understanding of biological variation is important for accurate laboratory test
interpretation and for determining whether achange in biomarker concentration is clinicallysignificant. This data is often lacking for many
biomarkers, especially for the pediatric population, since it is often difficult to obtain sequential samples from one child. CALIPER analyzed
the within and between-individual biologicalvariation for the pediatric population. Four
samples were obtained from 29 healthy participants, and over 30 analytes were analyzed todetermine their biological variation [61].
To further expand the CALIPER database, recent
studies have established age- and sex-specificreference intervals on the Abbott ARCHITECT
immunoassays for cancer biomarkers [63],metabolic disease biomarkers [64], testosterone indices [65], and specialized biochemicalmarkers [66]. To expand the CALIPER databaseto additional analyzers, a series of transference
and verification studies have been performed
to transfer reference intervals for chemistry
analytes established on Abbott assays to assays
on other analyzers including Beckman, Ortho,
Roche, and Siemens [44,49-51]. The statisticalalgorithm CALIPER uses to transfer and verify
reference intervals is summarized in Figure 4.
To date, CALIPER has created a comprehensive

and robust age- and sex-specific pediatric reference interval database for over 100 biomarkers. Through knowledge translation initiatives,
CALIPER has also disseminated reference intervals through peer-reviewed publications, development of a free online database (www.caliperdatabase.com), and a smartphone application
easily accessible for physicians.
LIMITATIONS OF PEDIATRIC REFERENCE
INTERVALS AND FUTURE DIRECTIONS
Despite several advances in addressing the critical gaps in pediatric reference intervals, limitations of the completed and ongoing studies
leave additional gaps to be addressed. Current
initiatives have extensively examined how
analyte concentrations vary with age and sex.
However, the influence of additional covariates,
such as ethnicity and body mass index (BMI), on
analyte concentrations remain to be comprehensively analyzed. Several studies suggest thatanalyte concentrations may vary by ethnicity.
For example, Gupta et al. discussed significant
differences in serum prostate-specific antigen(PSA) concentration between various ethnicities, with relatively higher concentrations in
African American subjects and lower concentrations in Asian subjects [67]. Therefore, ethnic specific PSA reference intervals should beconsidered in accurate interpretation of test
results in cancer diagnosis. Another study by
Troy et al. measured hematologic and immunologic reference intervals in healthy Zimbabwean
infants and compared them to those established using mainly Caucasian subjects [68].
Interestingly, majority of Zimbabwean subjects
were considered to have adverse events andimmunodeficiency based on hemoglobin andCD4% reference intervals established using
Caucasian subjects, respectively. Thus, ethnicspecific and locally-validated reference intervals are required for accurate laboratory test
interpretation. The influence of ethnicity on the

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Pediatric reference intervals for biochemical markers

Figure 4

Schematic of general statistical approach to transfer original reference
intervals to other clinical chemistry assays

Create scatterplot of method comparison data
Remove isolated extreme high and low data points

2

2

R < 0.70

0.70 < R < 0.95

2

R ≥ 0.95

Do not transfer

Determine theequation of theline of best fit
using Deming
Regression

Determine equationof the line of best fit
by linear regression
using the leastsquares approach

Assess appropriateness of linear model with normally distributed data
points (Review Bland-Altman, standardized residual and Q-Q plots)
Linear model not appropriate
and/or assumptions not met

Linear model appropriate andassumptions met

Do not transfer

Use equation to transfer originalreference interval to other assays

Determine 95% confidence intervals around upper and lower limits using
the root of mean square error

Verify the transferred reference intervals by calculating the percentage ofhealthy sample results that fall within the transferred reference interval*Adapted from: Estey et. al. and Araujo et. al. [44,49].

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Pediatric reference intervals for biochemical markers

concentration of several analytes may be due
to several factors including genetic differences,environmental factors, and dietary patterns.
Several national pediatric reference interval
initiatives have established reference intervals
based on a reference population comprised of asingle ethnicity [69-72]. As Canada is comprisedof a multi-ethnic population, the reference population used in the CALIPER studies proportionally represents the main ethnic groups of thenation (i.e. Caucasian, South Asian, East Asian).
CALIPER performed analysis on these ethnic
groups, however, the relatively small sample
size of ethnicities, other than Caucasian, limited
the scope of this study to strictly preliminary
analysis [53-55]. Large, comprehensive studies
are warranted for a thorough understanding of
how analyte concentrations differ between ethnic groups and where warranted the establishment of ethnic-specific reference intervals.
With pediatric obesity becoming an important
public health concern, it is also important tounderstand how analyte concentrations changewith BMI [73,74]. A reference population should
be comprised of subjects who are representative of the local population, and therefore thisdefinition becomes challenging when covariates such as BMI is constantly changing in thegeneral population. As the average BMI in thegeneral population increases, reference intervals for analytes that change with BMI may also
be shifted, or a substantial subset of the local
population must be excluded from the reference population. Therefore, it is important tounderstand which analytes are significantly influenced by BMI and if these changes are physiological and not of clinical significance, or if thischange is clinically significant and may be indicative of subclinical progression of a metabolic
disease. Understanding how analyte concentrations change with BMI are critical for laboratory
specialists and physicians to interpret blood
tests from overweight and obese pediatricpatients. Some studies have been performed
to examine the effect of BMI on analyte levels
in a healthy population, however these studies
only included adults [75] or were performed fora very limited number of analytes [76]. Further
studies are needed to comprehensively examine the influence of BMI on analyte concentrations in the pediatric population.
The ultimate end goal of pediatric laboratory medicine is to achieve harmonization.
Laboratory test interpretation, based on reference intervals and decision limits, remain
highly variable and poorly harmonized acrosslaboratories. This leads to great potential forinappropriate patient care when laboratory
test results on the same sample can be interpreted differently depending on the reference
interval reported by the laboratory. Several
groups have launched initiatives to harmonize
reference intervals including the NORIP [26],the UK Pathology Harmony project [77], theAustralasian Harmonised Reference Intervals
for Adults (AHRIA) and Australasian Reference
Interval for Paediatrics (AHRIP) [47]. In Canada,a Working Group on reference interval harmonization has also been initiated to identify
the variation in reference intervals being usedin clinical practice and establish/recommend
practice guidelines on the use of harmonizedreference intervals in clinical laboratories acrossCanada.
Major challenges have been overcome andsignificant advances have been made in theestablishment and wide-spread dissemination of accurately established pediatric reference intervals. However, with the continuously
evolving technological and clinical advances,
national and international research initiatives
need to ensure pediatric reference intervals
continuously improve and adapt to the changing environment.

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Pediatric reference intervals for biochemical markers

Table 1

Major pediatric reference interval studies
based on healthy children and adolescent populations

Study

AACB

Age
range
(years)Sex

StatisticalmethodExamples of groupsof biomarkers studied

References

Australia and All age
New Zealand groups

Both

Central95%

Common blood analytes(mostly ions and enzymes)[47,48]Country

Common biochemicalmarkers
Endocrine markers
CALIPER

Canada0-18

BothTumor markers

Central95%

Vitamins
Metabolic diseasebiomarkers[4,6,8,18,44,
49-52,
54-57,
60-62,
66]

Testosterone indices
Enzymes
CHILDxUnitedStates0.5-17

Both

Median,mean andcentral95%

Coagulation tests
Hormones[30-35]

Vitamins
Bone markers

COPENHAGEN

KiGGS

LOOK

Denmark

Germany

Australia

5-20

0-18

8, 10
and 12

Both

Both

Both

Central95%
Medianandcentral90%

Common blood analytes
Nutrient deficiency markers

Medianandcentral95%

Page 59[29]

eJIFCC2017Vol28No1pp043-063

Non-communicablediseases and lipids
Immunology markers[20,
22-25]

Thyroid hormones
Cardiac Biomarker
Common blood analytes[45,46]

H. Tahmasebi, V. Higgins, A. Fung, D. Truong, N. White-Al Habeeb, K. Adeli
Pediatric reference intervals for biochemical markers

2.5 , 25 ,median,75th, or
97.5thth

NHANESNORIP

UnitedStatesNordicCountries(Denmark,
Finland,
Iceland,
Norway andSweden)All agegroups

≥ 18

Both

Both

th

97.5
percentile
or central
95%

Lipid profile
Immunology andhematologic markers[36-43]

Vitamins
Inflammatory markers

Tumor markers
Common blood analytes[21,
26-28]AACB = Australasian Association of Clinical Biochemists
CALIPER = Canadian Laboratory Initiative on Paediatric Reference Intervals
CHILDx = Children’s Health Improvement through Laboratory Diagnostics
COPENHAGEN = The Copenhagen Puberty Study
KiGGS = German Health Interview and Examination Survey for Children and Adolescents
LOOK = Lifestyle of Our Kids
NHANES = National Health and Nutrition Examination Survey
NORIP = Nordic Reference Interval Project

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In this issue: Recent Advances in Pediatric Laboratory Medicine

The utility of CSF for the diagnosis
of primary and secondary monoamine
neurotransmitter deficiencies
A.B. Burlina1, A. Celato1, G. Polo1, C. Edini1, A.P. Burlina2

Division of Inherited Metabolic Diseases, Reference Centre Expanded Newborn Screening,
Department of Women’s and Children’s Health, University Hospital, Padova, Italy
2
Neurological Unit, St. Bassiano Hospital, Bassano del Grappa, Italy
1

ARTICLE INFOABSTRACT

Corresponding author:
Alberto Burlina, M.D.
Division of Inherited Metabolic Diseases
Reference Centre Expanded Newborn Screening
Department of Women’s and Children’s Health
University Hospital
Via Orus 2/B 35129
Padova, Italy
Phone: +39 049 821 3569 ext.7462
Fax: +39 049 8217474
E-mail: alberto.burlina@unipd.it

Biogenic amine defects constitute a complex and expanding group of neurotransmitter disorders affecting
cognitive, motor and autonomic system development,
mostly in the pediatric age. In recent years different
enzymatic defects have been identified impairing thetetrahydrobiopterin cofactor pathway and/or biogenic
amine synthesis, catabolism and transport, with subsequent new disease entities described. The lumbar
puncture, with subsequent withdrawal of cerebrospinal fluid (CSF), remains a key step in the diagnostic
procedure. Due to the specific nature of CSF, timingof analysis, sample collection and storage, technicalissues of the analytic process are still crucial for the
diagnosis and follow-up of patients. A progressive
approach to the diagnosis of biogenic amine defects
is presented, pointing out criticalities and difficulties
concerning sample collection and results interpretation, especially due to the increasing reports of secondary neurotransmitter alterations that, at present,
constitute a challenge.

Key words:
monoamine neurotransmitter deficiencies,
cerebrospinal fluid,
tetrahydrobiopterin defects

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

major neurotransmitters within the central nervous system (CNS). Their regulation is tuned andgoverned by the rate of neurotransmitter (NT)synthesis, packaging, release, re-uptake, degradation, and by receptor status. Clinically they aremainly characterized by a range of extrapyramidal
manifestations including dystonia, hyperkinesia,
chorea and oculogyric crisis (1,2). These defects
are characterized by an elevation of phenylalanine, the presence of abnormal monoamines andderanged monoamine synthesis, degradationand transport (3) (Figure 1). Recently, abnormal
neurotransmitter profiles have been reported inassociation with other non-metabolic and genetic diseases, and defined as secondary neurotransmitter abnormalities (4,5) (Table 1). Biochemical
distinction between the two groups is difficult
due to a considerable overlap in the concentrations of HVA and 5HIAA. Hence, a careful analysisof the pattern of all metabolites may be necessary
to reach the correct diagnosis. Recent discoveries
emphasizing the role of NTs in brain development
have led to the possibility of treating these defects. Therefore an early and accurate diagnosisof biogenic amine disorder is paramount to an efficient therapeutic intervention (7).

Abbreviations
CSF: cerebrospinal fluid
PNDs: primary neurotransmitters diseases
Phe: phenylalanine
AD-GTPCH1: autosomal dominantguanosin triphosphate cyclohydrolase 1
AR-GTPCH1: autosomal recessive guanosin
triphosphate cyclohydrolase 1
PTPS: 6-pyruvoyltetrahydropterin synthase
SR: sepiapterin reductase
PCD: pterin-4a-carbinolamin dehydratase
DHPR: dihydropteridin reductase
TH: tyrosine hydroxylase
TPH: Tryptophan hydroxylase
AADC: aromatic L-aminoacid decarboxylase
MAO: monoamine oxidase
DβH: dopamine beta hydroxylase
VMAT2: vesicular monoamine transporter
DAT: dopamine transporter
HVA: homovanillic acid
5-HIAA: 5-hydroxyindolacetic acid
3-OMD: 3 orthomethyldihydroxyphenylalanine
5-HTP: 5-hydroxytryptophan
MHPG: 3-methoxy-4-hydroxyphenylglycol
BH4: tetrahydrobiopterin
BH2: dihydrobiopterin
Neo: neopterin
Sep: sepiapterin
Prim: primapterin

In this review, we provide an overview of primaryneurotransmitter diseases (PNDs) by cerebrospinal fluid (CSF) investigations. Sample management, analytical methodology, and diagnostic
interpretation are described.

INTRODUCTION

CLINICAL AND BIOCHEMICALFEATURES OF MONOAMINENEUROTRANSMITTER DISEASES

Monoamine neurotransmitter defects are included in the group of neurometabolic syndromes
attributable to disturbances of neurotransmitter
metabolism/transport and cofactors (i.e. tetrahydrobiopterin) synthesis/regeneration. Biogenic
amines, serotonin and all catecholamines (dopamine, epinephrine and norepinephrine) are

In all monoamine metabolic disorders, the clinical symptoms are strictly related to the effects of
dopamine and serotonin deficiency. Signs of dopamine deficiency include Parkinsonism, dystonia, chorea, oculogyric crisis, ptosis, hypersalivation, and myoclonic epilepsy. The manifestationsof serotonin deficiency are less well defined, and

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

include temperature instability, sweating, aggressive behaviour, irritability and sleeping disturbance. Non-specific symptoms include epileptic
encephalopathy, mental retardation, microcephaly, swallowing difficulties, and pyramidal tract
features mimicking cerebral palsy. A potential
defect in biogenic amine should be considered in
an infant presenting with any of the above symptoms, which may appear in isolation or together(8). Furthermore clinical monitoring of thesepatients could be challenging. Firstly, symptoms
can fluctuate according to last medication withon-off effects. Secondly, high doses of L-Dopa
inhibit postsynaptic receptors, resulting in symptoms (e.g., involuntary movements, dyskinesia,
irritability, insomnia and opisthotonus) that are
indistinguishable from the one resulting fromunder-treatment and/or dopamine deficiency.
Figure 1

Defect of tetrahydrobiopterin (BH4) with consequent dopamine and serotonin deficiency are associated with an increased plasma phenylalanine(Phe) concentration (8). BH4 acts as a cofactor or
co-substrate in a range of biochemical reactions
including the hydroxylation of aromatic aminoacids (Phe, tyrosine and tryptophan) by the corresponding hydroxylases. De novo biosynthesisof BH4 from GTP requires 3 enzymes (GTPCH,PTPS and SR), while dihydropteridine reductase(DHPR) regenerates BH4 from q-dihydrobiopterin (qBH2). Overall BH4 synthesis, as such, is thus
complex, and not completely understood as yet.
In autosomal recessive and autosomal dominant
GTP cyclohydrolase 1 (GTPCH1), 6-pyruvoyltetrahydropterin synthase (PTPS), and DHPR, themeasurement of blood and CSF pterins facilitates the diagnosis. Noteworthy, all disorders

Biochemical pathways involving dopamine, serotonin, epinephrine,
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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

LegendFigure 1: Biochemical pathways (p. 66 above)

The monoamines consist of catecholamines (for instance dopamine, norepinephrine and epinephrine)and serotonin. The amines are synthesized throughout a complex multienzymatic pathway which converts, tryptophan and tyrosine into serotonin and dopamine respectively, through reactions catalysedby tryptophan hydroxylase (TPH, EC 1.14.16.4), tyrosine hydroxylase (TH, EC 1.14.16.2) and aromaticL-aminoacid decarboxylase (AADC, EC 4.1.1.28). This latter enzyme acts as a common converging decarboxylating system for active neurotransmitter biosynthesis. In the case of AADC deficiency, thedopamine precursor (L-dihydroxyphenylalanine, DOPA) is metabolized into 3-Orthomethyldihydroxyphenylalanine (3-OMD) and vanillactic acid (VLA). Both TPH and TH require tetrahydrobiopterin (BH 4)
as cofactor, while AADC needs vitamin B6 (pyridoxine). In noradrenergic neurons, dopamine is further
converted by dopamine beta hydroxylase (DBH) into norepinephrine and epinephrine by phenylethanolamine N-methyltransferase (PNMT). Since BH4 is crucial in serotonin and dopamine biosynthesis, alarge subset of monoamine defects is to be referred to pterins build up and regeneration mostly presenting hyperphenylalaninemia as a characterizing hallmark. The biosynthesis and regeneration of BH4
is carried out by a complex system of enzymes starting from guanosine triphosphate cyclohydrolase 1(GTPCH1), the rate limiting enzyme for BH4 biosynthesis, which is responsible for the hydrolysis of guanosine triphosphate into 7,8-dihydroneopterin triphosphate (H2NP3), thus releasing neopterin. H2NP3
is further metabolized into 6-pyruvoyltetrahydropterin (6-PTP) by 6-pyruvoyltetrahydropterin synthase(PTPS), the second critical enzyme for BH4 build up. 6-PTP is used to form BH4 through sepiapterin(SPT) by a two step enzymatic pathway of aldose reductase (AR) and sepiapterin reductase (SR). BH4
is the basic cofactor of tyrosine hydroxylase (TH) and tryptophan hydroxylase (TPH) and once it links
with this enzyme it is then released as tetrahydrobiopterin-4a-carbinolamine. Tetrahydrobiopterin-4acarbinolamine is used to recycle BH4 through the biosynthesis of quinonoid-dihydrobiopterin (qBH2) bypterin-4a-carbinolamin dehydratase (PCD) and dihydropteridin reductase (DHPR), which is also linked
to folate metabolism via methylenetetrahydrofolate reductase (MTHFR) through an incompletely defined mechanism. This two step enzymatic regeneration (through PCD and DHPR) can be partly enzymatic partly not. BH4 is also related to nitric oxide (NO) metabolism through arginine, cytrulline andnicotinamide adenine dinucleotide phosphate (NADPH). This finding may explain the effect of BH4 in
vasogenic control. The catabolism of the monoamine is then carried out by a two step pathway involving monoamine oxidase (MAO) and cathecol-O-methyltransferase (COMT) and major metabolites are
represented by homovanillic acid (HVA), 5-hydroxyindolacetic acid (5-HIAA) and 3-methoxy-4-hydroxyphenylglycol (MHPG) and also vanillylmandelic acid (VMA). The three major end products are measured in the CSF reflecting overall dopaminergic, serotoninergic and noradrenergic activity. The process
of monoamine neurotransmission requires, in summary, the biosynthesis of monoamines in the nerve
terminal, their upload in the synaptic vesicles through the vesicular monoamine transporter (VMAT2)with the subsequent excytotic release, action at specific receptors in the postsynaptic interface andthe termination of the effect either by degradation or by reuptake by dopamine transporter (DAT).

that present hyperphenylalaninemia are diagnosed by neonatal screening.
If all metabolic investigations are normal in plasma
and urine and the patient is suggestive for a possible PNDs, a CSF puncture should be performedfor determination of HVA and 5HIAA (9). These
metabolites allow the diagnosis of the following defects: tyrosine hydroxylase (TH), aromaticL-aminoacid decarboxylase(AADC), sepiapterine reductase (SR), dopamine transport deficiency including β-hydroxylase deficiencies(DβH)
and vesicular monoamine transporters defects(VMAT2).

MONOAMINENEUROTRANSMITTERANALYSIS: KEY FEATURES
Analysis of the CSF neurotransmitter metabolites
includes the measurement of homovanillic acid
(HVA), 5-hydroxyindolacetic acid (5-HIAA) andtheir ratio. The concentrations of 3-O-methyldopa(3-OMD), L-3,4-dihydroxyphenylalanine (DOPA),
3,4-dihydroxyphenylacetic acid (DOPA-C), 5-hydroxytryptophan (5-HTP), and 3-methoxy-4-hydroxyphenylglycol (MHPG) may be highly variable due to possible drug interactions.

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

Table 1

Acquired and genetic neurological diseases with abnormal biogenic
amine values (data from literature and cases presentation)
Low HVA and low 5-HIAA

epileptic encephalopathies (SCN2A, SCN8A), Rett syndrome (FOXG1), organic acidurias (ACSF3),
disorders of cholesterol synthesis (Smith-Lemli-Opitz Syndrome), brain tumours, leukemia(del6q21), perinatal hypoxia/ischaemia, preterm haemorrhagic injuries, thiamine metabolism
disorders (SLC19A3), oligosaccharidoses, diseases of copper metabolism (Occipital Horn
Syndrome), lysosomal disorders (Niemann-Pick type C), Lesch-Nyhan syndrome, pontocerebellar
hypoplasia type 2, Steinert disease, stroke, dysautonomia, leukodystrophies, eye disorders (PITX3),
acute necrotizing encephalopathy (RanBP2), intellectual disability (CASK), mitochondrial diseases(SDH), lysinuric protein intolerance (SLC7A2)
Low or normal HVA and normal 5-HIAA

Aicardi-Goutiéres syndrome (ADAR1, RNASEH2A, RNASEH2B), pontocerebellar hypoplasia (i.e.
EXOSC3), seizure/epileptic encephalopathies (KCNQ2), chromosomal abnormalities (dup17p13.3),
organic acidurias (ACSF3), alternating hemiplegia (ATP1A3), folate metabolism disorders (FOLR1,
MTHFR, MTHFD1), mitochondrial diseases (NFU1, POLG, KSS, congenital myopathies (MTM1), ,meningitis/encephalitis, malformative syndromes, astrocytoma
Normal HVA and low 5-HIAA

Hartnup disease (SLC6A19), chromosomal abnormalities (del8p23, tris12p23), post vaccine
High HVA high 5-HIAA

mitochondrial diseases (POLG)SAMPLE COLLECTION
AND STORAGE OF CSF
The measurement of biogenic amines can bemisleading and hard to interpret if sample
collection and handling do not follow strict
procedures (10). There are several factorsthat can affect metabolite concentration,some playing a minor effect that are difficult to monitor, while others require careful
and detailed instructions to minimize theirimpact. In should be mentioned that there
is a rostrocaudal concentration gradient of
monoamines in CSF. Therefore, CSF withdrawal should always be performed at thesame spinal level (usually from a lumbar
spinal tap). The first 0.5 mL aliquot is used forbiogenic amine metabolite; the second can be
stored at -70°C for further future analysis; thethird 1 mL is either immediately frozen with dry
ice at the bedside or filled with an antioxidant mixture ensuring BH 4 stability to measure pterins. The fourth 1 mL aliquot is used asa back-up sample. Finally, as blood contamination of CSF can cause a rapid metabolite
oxidation (degradation of HVA and 5HIAA),
the contaminated samples must be rapidly
centrifuged and the supernatant transferredin new tubes before freezing. Therefore red
blood cell count and proteins should always
be measured, to exclude blood-brain barrier damage, and found normal for properinterpretation.

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

ANALYTICAL METHODS
The biogenic amine metabolites are tested using
several different techniques including capillaryelectrophoresis (CE), GC-MS, HPLC with electrochemical (EC) detection or fluorescent detection(FD) and liquid chromatography coupled withtandem mass spectrometry (LC-MS/MS) (11-17).
HPLC with Electrochemical detection (HPLC-EC)
HPLC-EC has been considered the gold standard
for biogenic amine metabolite analysis becauseof its relatively low costs, and technical feasibility and simplicity, demonstrated in many procedures in which CSF was directly injected into the
HPLC system without prior derivatization of thesample.
Chromatographic separation of analytes is in
general performed by reverse-phase column in
an isocratic condition. The elution order of biogenic amines is in order of increasing hydrophobicity. Acidic pH and ion pairing modifiers arecommonly used in the mobile phase in orderto suppress the positive charge of the catecholring and optimize interaction with the stationary
phase. Adapting the percentage of organic solvent (acetonitrile or methanol) retention times
can be changed to obtain an accurate separation of analytes from other compound contents
in CSF that can affect an accurate measurement.
This is important especially for MPHG, DOPAC
and DOPA that, as compare to HVA or 5-HIAA,
are present in small amounts.
Since then, HPLC with electrochemical and/or
fluorescence analysis has represented the gold
standard in biogenic amine metabolite analysis and in defining biogenic amine metabolites
age-related reference values. The fluorimetric method allows rapid sample preparationand the simultaneous determination of up to
7 biogenic amine metabolites in CSF. HPLC has
also been largely tested in series of biologicalsamples proving to be highly sensitive despite

different concentration levels of biogenic amine
metabolites in CSF. Nevertheless some limitations occur concerning the inability to pinpoint
the interference of closely eluting metabolites
regardless of the use of ion-pairing reagents toimprove retention.
Liquid chromatography tandem
mass spectrometry (LC-MS/MS)
In the past ten years the application of LC-MS/MS
has increased in routine clinical chemistry andinborn error of metabolism diagnostic laboratories. Detection by LC-MS/MS is based on structural characterization of small molecules. The
application of this technique to biogenic amines
overcomes the requirement of a highly resolved
column separation necessary for other types ofdetectors (i.e. EC). Several LC-MS/MS methods
have been recently introduced and standardized for the analysis of biogenic amine metabolites in bodily fluids (11-17). The high sensitivity
acquired through positive ESI LC-MS/MS overcome the need of derivatization while improved
separation and chromatographic resolution
permit unequivocal identification of closely related compounds in a short time run, which isstill a limit in HPLC analysis. The possibility toquantitate BH4, BH2, sepiapterin and neopterin
simultaneously and the ability to detect conjugated neurotransmitters (such as glucuronides
and sulfates) represents on one hand a clear advantage in differentiating pterin defects (mostof all for DHPR and SR deficiency) and on theother hand the ability to speculate the role ofconjugates in monoamine metabolism and brainneurotransmission.
QUALITY CONTROL
Since 2016, an established External Quality Control(EQA) scheme by ERNDIM is available that includes HVA, 5-HIAA, 3OMD and 5-HTP (http://
www.erndim.org/home/start.asp). The scheme
evaluates the quantitative performance andPage 69

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

assesses the ability of laboratories to diagnose
inborn errors of neurotransmitter metabolism.
The scheme consists of 8 spiked samples, paired
two by two, of lyophilised pooled CSF (4 samples) and lyophilised artificial CSF (4 samples).
Accuracy, recovery, precision, linearity and interlaboratory CV are evaluated by ERNDIM. A highdegree of harmonization between laboratories
is important for the use of common reference
values.
During the two-year pilot study period (20142015), a general agreement was observed in
5HIAA and HVA concentrations while a significant
discrepancy was detected concerning 3OMD and
5HTP between different laboratories. Overall
there was improvement of laboratory performance during these two years.
For internal quality control, commercial quality
control samples are not available, but they canbe easily prepared in-house. Aliquots of pooled“positive controls” CSF or spiked CSF with biogenic amine standards can be stored at -70°C
and used to monitor the performance of themethod (19).
Table 2

AGE-RELATED REFERENCE RANGE
Despite precise internal collecting protocols,
the recent increase in CSF biogenic amines measurement for the diagnosis of neurologicallycompromised children has resulted in challenging monoamine profiles difficult to interpret.
As such, a critical role in the reliability of CSF
biogenic amines interpretation is represented
by the issue of reference values that can help
to recognize misleading alterations or milder/moderate forms of deviations outside primarybiogenic amine defects. Age-related reference
ranges are important for differential diagnosis,
management and monitoring (see Table 2) (20).
Biogenic amines have been tested, to build
up reference range values, both in CSF ofhealthy neonates and infants, and in mixed
samples of healthy infants and infants withmedical complications, and, most recently,
in series of patients with several neurologic
diseases. Metabolite concentration shows adecrement with age over the first few years,
with peak values found during the first threemonths and with concentrations reaching a

CSF HVA and 5HIAA age specific reference ranges. Reference ranges are
established by our laboratory based on data from 100 individuals from ourgeographical area
Age

HVA (nmol/L)

5HIAA (nmol/L)

0 - 30 d

601 - 1397

382 - 949

1m–5m

345 - 1111

206 - 922

6 m – 1 yr

302 - 797

120 - 345

2 yrs – 4 yrs

242 - 684

95 - 329

5 yrs – 10 yrs

130 - 573

80 - 183

11 yrs – 16 yrs

122 - 515

68 - 187> 16 yrs

111 - 371

55 - 163

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

sort of plateau around five years of age. No
correlation has been found between lengthof the child and age, i.e., for craniocaudal
gradient (20). In adults a seasonal variation
has been anecdotally described, but neverconfirmed in children. Reference intervals
are critical. The available reference intervals in the literature are quite similar, but
the differences may be related to the age ofpatients. This reinforces the concept that establishment of reference intervals specific tolaboratories that perform neurotransmitter
analysis is mandatory (18).
Table 3aDIAGNOSTIC PATTERNSOF CSF MONOAMINENEUROTRANSMITTER DISEASES
Table 3a summarizes the biochemical features ofeach monoamine neurotransmitter disorder withand without plasma hyperPhe. The biochemicalclassification of the disease is based on plasma
phenylalanine as reported in the introduction section. In GTPCH1, DHPR and PTPS the determination of HVA and 5HIAA are reduced and pterin isabnormal. In SR, CSF analysis reveals decreased
levels of HVA and 5-HIAA, normal to slightly

Disorders of pterin metabolism with and without hyperphenylalaninemia
HVA

5-HIAA 3-OMD 5-HTP

MHPG

MTHF

BH4

BH2

Neo Sep

Prim(U)

with hyperphenylalaninemia
AR-GTPCH1

↓

↓

PTPS

↓

↓

↓
↓

↓

PTPS mild
DHPR

↓
n/↓

↓
↓

↓

↓

PCD

n↓

↑
↑

↑

↓

↑

↑

without hyperphenylalaninemia
AD-GTPCH1*

n/↓

n/↓

↓

↓

compoundheterozygotes
AR-GTPCH1

↓

↓

↓

↓

SR

↓

↓

n/↓

↑

↑

AD-GTPCH1: autosomal dominant guanosin triphosphate cyclohydrolase 1; AR-GTPCH1: autosomal recessive
guanosin triphosphate cyclohydrolase 1; PTPS: 6-pyruvoyltetrahydropterin synthase; SR: sepiapterin reductase;
PCD: pterin-4a-carbinolamin dehydratase; DHPR: dihydropteridin reductase; HVA: homovanillic acid;
5-HIAA: 5-hydroxyindolacetic acid; 3-OMD: 3 orthomethyldihydroxyphenylalanine; 5-http: 5-hydroxytryptophan;
MHPG: 3-methoxy-4-hydroxyphenylglycol; BH4: tetrahydrobiopterin; BH2: dihydrobiopterin; Neo: neopterin;
Sep: sepiapterin; Prim: primapterin; n: normal.
Note: Empty cells should be considered as normal values.
*AD-GTPCH1 present compromised Phe catabolism at Phe oral loading test.

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

increased neopterin, and elevated total biopterin,
dihydrobiopterin (BH2) and sepiapterin. Minimal
abnormalities in random collections of plasma or
urinary pterins are occasionally found but are not
Figure 2

consistent. Oral phenylalanine-loading test (100mg/kg) demonstrates abnormal and prolonged increase of phenylalanine levels due to impaired phenylalanine hydroxylation under loading conditions.

Example chromatograms(a) Patient with GTPCH1 deficiency; note the decrease in concentration of HVA and 5HIAA(b) Patient with AADC deficiency; note the decrease in concentration of HVA and 5HIAA and the elevations of DOPA,
3OMD and 5HTP(c) Patient with TH deficiency; note the decrease in concentration of HVA and the normal 5HIAA(d) Healthy control with normal concentrations of 5HIAA and HVA. Chromatographic conditions: 50 µl CSF diluted
with 50 µl of mobile phase was injected onto a 250 x 4.6 mm, 5 µm Spherisorb ODS1 column (Waters). Compounds
were eluted under isocratic conditions at flow rate of 1.0 mL/min and mobile phase consisting of Acetate buffer (pH3.5) containing sodium 1-eptasuphonate, EDTA and 10% methanol. Compounds were detected using a ESA Coularray
5600 electrochemical detector.

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

Table 3b

Disorders of biogenic amine biosynthesis and transport
HVA

TH

HVA/5-HIAA

↓

TPH
AADC

5-HIAA

5-HTP

↓
↓

↓

3-OMD

↓

↑

↓

MHPG

↓
↑

↑

↓

VMAT2
DAT

↑

↑

TH: tyrosine hydroxylase; TPH: Tryptophan hydroxylase; AADC: aromatic L-aminoacid decarboxylase;
VMAT2: vesicular monoamine transporter; DAT: dopamine transporter; HVA: homovanillic acid;
5-HIAA: 5-hydroxyindolacetic acid; 3-OMD: 3 orthomethyldihydroxyphenylalanine; 5-http: 5-hydroxytryptophan;
MHPG: 3-methoxy-4-hydroxyphenylglycol; BH4: tetrahydrobiopterin; BH2: dihydrobiopterin; Neo: neopterin;
Sep: sepiapterin; Prim: primapterin; n: normal.
Note: Empty cells should be considered as normal values

Figure 2 shows the CSF neurotransmitter chroma­
tograms.
Table 3b includes monoamine neurotransmitter
disorder due to enzyme deficiencies (TH, AADC)
and defects of monoamine transport recently
reported (Dopamine transporter deficiency
syndrome, DTDS, and vesicular monoamine transporter 2 deficiency, VMAT2).
DTDS is associated with an increase of HVA while
5HIAA is normal leading to a raised HVA:5HIAA ratio. In VMAT2 both HVA and 5HIAA are normal.
TH affected patients have very low concentrations of HVA in CSF, and the HVA/5HIAA ratio isthe most sensitive marker. In AADC deficiency,
levels of HVA and 5HIAA are both low, but 3-OMD
is highly increased.
CONCLUSIONSThe utility of CSF analyses for the investigationof monoamine metabolism abnormalities has
been proposed in patients with progressive

extrapyramidal movement disorders, especiallyparkinsonism-dystonia, chorea or mixed movement disorders combined with truncal hypotonia or in patients presenting epileptic encephalopathies of unknown origin. With the analysis
of CSF, it is possible to appreciate the global
functioning of monoaminergic neurotransmission. Homovanillic acid is a stable end productof dopamine and can be used as a marker of
dopamine metabolism, whereas 5-hydroxyindoleacetic acid is a stable end product of serotonin and is a marker of serotonin turnover.
Therefore, their quantification allows for identification of either defects of neurotransmitter
biosynthesis (e.g. TH or AADC deficiencies) or
defects of tetrahydrobiopterin biosythesis andregeneration (e.g. GTPCH1, PTPS, SR and DHPR
deficiencies). However, abnormalities of biogenic amines are not only observed in PNDs
but were recently reported in several acquired
and genetic neurological diseases (see Table1). Furthermore, HVA and 5-HIAA measurement can also be influenced by drugs that are

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

Table 4

Drugs that may interfere with monoamine measurement
Metabolites precursors or monoamines

3,4-dihydroxyphanylalanine (L-DOPA)/carbidopa, 5-hydroxytryptophan (5-HTP), dopamine
Monoamine oxydase (MAO) inhibitors

hydrazines, nialamide, isocarboxazid, bifemelane, pirlindole, toloxatone, rasagiline, selegiline,
moclobemide, tranylcypromine, phenelzine, safinamide
Cathecol-O-methyltransferase (COMT) inhibitors

entacapone, tolcapone
Reuptake inhibitors

fluoxetine, citalopram, fluvoxamine, paroxetine, sertraline, venlafaxine, amantadine
Othersdiazepam, chlorpromazine, pyridoxine, pyridoxal-5-phosphate, sapropterin, physostigmine,
propranolol, phenotiazine, valproate, vigabatrin, 4-hydroxybutirrate, anticholinergic drugs
In some cases, drug interference is based on experimental models or mechanism of drug action.
No data are available on dopamine agonist drugs.

frequently used in neurological patients presenting hypokinetic movement disorders andimpaired mood associated with dopaminergic/
serotoninergic dysfunction. Therefore a complete documentation is mandatory. The clinicalhistory, including neurological examination,
should enclose all special features such as any
deviation from the protocol, sample color, all
medications and current clinical conditions
such as fever. In fact, monoamine profile canbe incorrectly interpreted due to the lackof information about pharmacological concomitant therapy that is not fulfilled in patient
information schedules and the referring center
is not completely aware about its effect on metabolite measurements. It is to be considered
that, besides common drugs that acts upon dopamine and serotonin pathways, several others

have been reported to possibly present biasing
effects (see Table 4 for a detailed list of drugs
that can impair monoamine CSF values). In theview of this, a drug-free wash out period is recommended for at least 1 week and up to 4 weeks
before executing the lumbar puncture (21).
NT levels are age dependent with particular attention to the neonatal period. Preterm neurologically compromised neonates represent,in this sense, the most striking example of the
difficulties of results interpretation. In this case,samples refer more frequently to severely affected patients. Patients sometimes present hypoxic/ischaemic encephalopathies or anoxia thatcan be a presenting symptom of several primaryneurotransmitter disorders, but monoamine
measurement in CSF can be impaired by brain
atrophy itself. Furthermore, preterm neonates

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The utility of CSF for the diagnosis of primary and secondary monoamine neurotransmitter deficiencies

can be hemodynamically unstable thus requiring inotrope and vasopressor support, which isfrequently obtained using dopamine administration. The diagnostic process is further complicated by the fact that a marked and non-linear
age dependency as well as a craniocaudal gradient of the CSF concentrations must be taken into
account.
Nevertheless, laboratories have different agerelated reference ranges requiring variations in
the technique of CSF sampling and the precise
aliquots used for analysis. As it is for other biochemical parameters, internal age-related reference ranges are thus recommended in laboratories that perform CSF monoamine metabolite
measurements.
There may also be diurnal or catamenial changes, which are probably subtle (except in patients with Segawa syndrome) and not totallyinvestigated.
Regardless of the growing interest in PNDs, theincidence of this cluster of disorders remains uncertain, with controversial results even among
patients presenting with the most typical features of the disorders (i.e. movement disorders
and/or epilepsy). This finding raises the question
about the possibility of unrecognized patients
presenting parkinsonian-like features even in
adulthood. CSF neurotransmitter analysis, oneof the few available direct measurements ofbrain neurotransmission, can help us to better
understand brain pathophysiology and its potential connection with functional neuroimaging
techniques.
In conclusion, a close collaboration between laboratory experts and clinicians is mandatory for theinterpretation of CSF neurotransmitters levels.

2. Hyland K (2008) Clinical utility of monoamine neurotransmitter metabolite analysis in cerebrospinal fluid,
Clin Chem 54(4):633-41.
3. Ng J, Papandreou A, Heales SJ, Kurian MA (2015) Monoamine neurotransmitter disorders – clinical advances andfuture perspectives, Nat Rev Neurol 11(10):567-84
4. Ormazabal A, García-Cazorla A, Fernández Y, Fernández-Álvarez E, Campistol J, Artuch R (2005) HPLC withelectrochemical and fluorescence detection proceduresfor the diagnosis of inborn errors of biogenic amines andpterins, J Neurosci Method 142:153-8.
5. García-Cazorla A, Serrano M, Pérez-Dueñas B, González
V, Ormazábal A, Pineda M, Fernándz-Álvarez E, Campistol JMD, Artuch RMD (2007) Secondary abnormalities ofneurotransmitters in infants with neurological disorders,
Dev Med Child Neurol 49:740-4
6. Marín-Valencia I, Serrano M, Ormazabal A, PérezDueñas B, García-Cazorla A, Campistol J, Artuch R. (2008)
Biochemical diagnosis of dopaminergic disturbances inpaediatric patients: analysis of cerebrospinal fluid homovanillic acid and other biogenic amines.Clin Biochem.
41(16-17):1306-15.
7. Ng J, Heales SJR, Kurian MA (2014) Clinical features andpharmacotherapy of childhood monoamine neurotransmitter disorders, Ped Drugs 16:275-91.
8. Burlina A, Blau N, (2014) Tetrahydrobiopterin disorders
presenting with hyperphenylalaninemia, Congenital Neurotransmitter disorders: a Clinical approach. Hoffman G,
Blau N, eds Nova.
9. Rodan LH, Gibson KM, Pearl PL (2015) Clinical use of
CSF neurotransmitters, Ped Neurol 53(4):277-86.
10. Hyland K (2003) The lumbar puncture for diagnosis ofpediatric neurotransmitter diseases, Ann Neurol 54 Suppl
6):S13-17.
11. Bertilsson L, Asberg M (1984) Amine metabolites
in the cerebrospinal fluid as a measure of central neurotransmitter function: methodological aspects, Adv Biochem Psychopharmacol 39:27-34.
12. Manini P, Andreoli R, Cavazzini S, Bergamaschi E,
Mutti A, Niessen WMA (2000) Liquid chromatographyelectrospray tandem mass spectrometry of acidic monoamine metabolites, J Chromatograph 744(2):423-31.

REFERENCES

13. Bourcier, S, Benoist, J.-F, Clerc, F, Rigal, O, Taghi, M andHoppilliard, Y (2006), Detection of 28 neurotransmittersand related compounds in biological fluids by liquid chromatography/tandem mass spectrometry. Rapid Commun. Mass Spectrom, 20:1405–1421.

1. Hyland K (1999) Presentation, diagnosis, and treatment of the disorders of monoamine neurotransmitter
metabolism, Sem Perinatol 23,2:194-203.

14. Ormazabal, A, García-Cazorla, A, Fernández, Y, Fernández-Álvarez, E, Campistol, J, Artuch, R. (2005) HPLC withelectrochemical and fluorescent detection procedure

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for the diagnosis of inborn erros of biogenic amines andpterins. J Neurosci Methods, 142:153-158.

sulfate conjugates. PLoS One, 8(6):e68007. doi:10.1371/
journal.pone.0068007.

15. Cox JM, Butler JP, Lutzke BS, Jones BA, Buckholz JE,
Raymond Biondolillo R, Jayne A TalbotJA, Eyassu Chernet
E, Svensson KA, Ackermann BL (2015) A validated LC–MS/
MS method for neurotransmitter metabolite analysis in
human cerebrospinal fluid using benzoyl chloride derivatization, Bioanalysis, 7, 19, 2461.

18. Hyland, Surtees AH, Heales SJR, Bowron A, Howells
DW, Smith I (1993) Cerebrospinal fluid concentrations ofpterins and metabolites of serotonin and dopamine in apediatric reference population, Ped Res 34(1):10-4.

16. Arning, E, Bottiglieri, T (2014) LC-MS/MS analysis of
cerebrospinal fluid metabolites in the pterin biosynthetic
pathway. JIMD Rep, 29:1-9.
17. Suominen, T, Uutela, P, Ketola, RA, Bergquist, J, Hillered, L, Finel, M, Zhang, H, Laakso, A, Kostiainen, R (2013)
Determination of serotonin and dopamine metabolites inhuman brain microdialysis and cerebrospinal fluid samples by UPLC-MS/MS: discovery of intact glucuronide and19. Bräutigam C, Weykamp C, Hoffmann GF, Wevers RA(2002) Neurotransmitters metabolites in CSF: an external
quality control scheme, J Inherit Metab Dis 25:287-98.
20. Chotai J, Murphy DL, Costantino JN (2006) Cerebrospinal fluid monoamine metabolite levels in human newborn infants born in winter differ from those born in summer, Psychiatry Res 145:189-97.
21. King, DJ, Cooper, SJ, Liddle, J (1983) The effect of propanolol on CSF amine metabolites in psychiatric patients.
Br J Clin Pharmacol 15:331-337.

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In this issue: Recent Advances in Pediatric Laboratory Medicine

Pediatric reference intervals for transferrin
saturation in the CALIPER cohortof healthy children and adolescents
Victoria Higgins1,2, Man Khun Chan2, Khosrow Adeli1,2

CALIPER Program, Department of Pediatric Laboratory Medicine, The Hospital for Sick Children,
Toronto, ON, Canada
2
Department of Laboratory Medicine & Pathobiology, Faculty of Medicine, University of Toronto, ON, Canada
1

ARTICLE INFOABSTRACT

Corresponding author:
Khosrow Adeli, Ph.D., FCACB, DABCCCALIPER Program
Department of Pediatric Laboratory Medicine
The Hospital for Sick Children
555 University Avenue
Toronto, ON, M5G 1X8
CanadaE-mail: khosrow.adeli@sickkids.ca

BackgroundKey words:
reference interval, biochemical markers,
children, adolescents, CALIPER

Transferrin saturation reference intervals specific forage and sex have not been previously reported forthe pediatric population. The reference values fortransferrin saturation have been previously reported
to be lower in children compared to adults, caused
by a combination of low serum iron and high serumtransferrin levels in children, warranting specific reference intervals.
Here we use the original iron and transferrin datafrom the CALIPER cohort to establish age- and sexspecific pediatric reference intervals for transferrin
saturation.
Methods
Iron and transferrin concentrations were measured
in serum samples from the CALIPER cohort of healthy
children and adolescents on the Abbott Architect
c8000. Transferrin saturation was subsequently calculated and statistically relevant age- and sex-partitions were determined.
After removing outliers, age- and sex-specific reference intervals with corresponding 90% confidence intervals were calculated using CLSI C28-A3 guidelines.
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Pediatric reference intervals for transferrin saturation in the CALIPER cohort

Results
Transferrin saturation required 3 separate age
partitions, with an additional sex partition for
14-<19 year olds. Transferrin saturation was
more variable during the first year of life, evident by a wider reference interval, which subsequently narrowed at one year until adolescence. Upon adolescence, a sex difference wasapparent with females having lower percent
transferrin saturation than males.
Conclusions
Age- and sex-specific pediatric reference intervals for transferrin saturation were establishedbased on a large cohort of healthy pediatricsubjects. Transference studies suggest thatthese intervals established using Abbott assays
are comparable to those on Beckman, Ortho,
Roche, and Siemens assays. Individual laboratories should however verify these reference intervals for their individual instrument and local
population as per CLSI guidelines.


INTRODUCTION
Transferrin is a plasma glycoprotein synthesized by the liver, that controls the level of freeiron in the circulation. Transferrin has several
functions related to iron activity and transport
including rendering iron soluble, preventing
iron-mediated free-radical toxicity, and facilitating iron transport into cells. One transferrin
molecule can bind two Fe3+ ions and, together
with ferritin, binds essentially all circulatingplasma iron (1). The sum of these iron bindingsites on transferrin, defined as the total ironbinding capacity (TIBC), can be easily assessedby measuring serum transferrin concentration.
The percentage of these iron binding sites occupied by iron is defined as the transferrin saturation. Determining levels of iron and TIBC,

and calculating transferrin saturation are usefulparameters in distinguishing between several
conditions including iron deficiency, hemochromatosis, chronic illness, hemolytic anemia, andiron poisoning.
Reference intervals for these parameters are essential to correctly interpret patient laboratory
test results. Traditionally, the normative range
for transferrin saturation in adults has been reported as between 20-50%, with less than 20%
indicating iron deficiency. However, this adultreference interval cannot be generalized tothe pediatric population, as normal transferrin
saturation levels have been shown to be lower
in children than adults, caused by a combination of low serum iron and high serum transferrin levels in children (2). Unfortunately, in manycases pediatric laboratory test results are interpreted based on reference intervals establishedfrom an adult reference population. This is due
to several challenges that are encountered inestablishing pediatric reference intervals, including small sample volumes and collecting
blood from a sufficient number of healthy children and adolescents to cover the extensive periods of growth and development (3).
To address these gaps, the Canadian Laboratory
Initiative on Pediatric Reference Intervals(CALIPER) program has established reference
intervals for several analytes including common
biochemical markers, protein markers, lipids andenzymes (4), specialty endocrine markers (5),fertility hormones (6), cancer biomarkers (7),vitamins (8), metabolic disease biomarkers (9),testosterone indices (10), and specialized biochemical markers (11). In our first publication of
40 routine biochemical markers we establishedage- and sex-specific reference intervals for serum iron and transferrin (4). Here we use theoriginal iron and transferrin data from healthy
children and adolescents to calculate transferrin saturation and establish age- and sex-specificpediatric reference intervals for this parameter.

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Victoria Higgins, Man Khun Chan, Khosrow Adeli
Pediatric reference intervals for transferrin saturation in the CALIPER cohort

METHODSThis study was approved by the Institutional
Review Board at the Hospital for Sick Children,
Toronto, Canada.
Participant recruitment
and sample acquisition
Healthy children and adolescents (age 1 to < 19years) were recruited from schools and community centres in the Greater Toronto Area.
Samples from subjects <1 year were collectedby heel stick from apparently healthy and metabolically stable subjects from outpatient clinics at the Hospital for Sick Children and Mount
Sinai Hospital.
Participation in the study consisted of written informed consent, completion of a healthquestionnaire, anthropometric measurements(height, weight, and waist circumference) anddonation of a blood sample. Prior to statistical analysis, participants were excluded if they
were pregnant, had a history of chronic illness(including chronic low iron status) or metabolic
disease, an acute illness within the previous
month, or use of prescribed medication within
the previous two weeks.
Blood samples were drawn into serum separator
tubes (SST™; Becton, Dickinson and Company,
Franklin Lakes, NJ, USA), centrifuged, separated
and aliquoted within 4 hours of collection, andstored at –80 °C until testing.
Sample analysis
Serum samples were analyzed for iron, transferrin, and C-reactive protein (CRP) on the Abbott
Architect c8000 analyzer.
Analytical methods were controlled according to
the manufacturer’s instructions using preventative maintenance, function checks, calibration,and quality control.

Statistical analysis
and reference interval determination
Data were analyzed in accordance with Clinicaland Laboratory Standards Institute (CLSI) EP28A3c guidelines on defining, establishing, andverifying reference intervals in the clinicallaboratory (12). Statistical analysis was performed using Microsoft Excel and R software.
Transferrin saturation (%) was calculated by((Iron (µmol/L)/Transferrin (g/L) x 25.1) x 100)
(13). To determine if subjects with an elevated
CRP value should be excluded from analysis,
the Spearman’s rank correlation coefficient between transferrin saturation and CRP concentration was calculated. Transferrin saturation and
CRP were significantly negatively correlated,
and therefore all subjects with an elevated CRP
value (≥ 10 mg/L) were removed from the reference population. To determine if subjects taking oral contraceptives should be excluded fromanalysis, the Harris and Boyd method (14) wasused to determine if transferrin saturation was
significantly different between female adolescents using and not using oral contraceptives. If
transferrin saturation was significantly different
between these groups, those using oral contraceptives would be removed from the reference
population. Subjects were chosen to ensure theethnic composition of study participants wasin accordance with the 2006 Canadian census
data for the province of Ontario (15). The statistical approach for calculating reference intervals has been described previously (4). Briefly,
scatter plots were generated to visually inspect
the data and manually exclude extreme outliers. If data were not skewed, outliers were removed using the Tukey test (16). Conversely, if
data were skewed, outliers were removed using the adjusted Tukey test by multiplying theinterquartile range by a factor using the medcouple measure of skewness (17). The Harrisand Boyd method was used to determine statistically relevant age and sex partitions (14).

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Pediatric reference intervals for transferrin saturation in the CALIPER cohort

Reference intervals for partitions with a sample
size ≥ 120 participants were calculated using
the nonparametric rank method. For partitions
containing < 120 and > 40 participants, the robust statistical method of Horn and Pesce (18)
was used to calculate the reference interval. For
each reference limit, corresponding 90% confidence intervals were calculated.
RESULTS
Prior to calculating transferrin saturation pediatric reference intervals, the effects of CRP andoral contraceptive use on transferrin saturation
values were determined. The Spearman’s rankcorrelation coefficient between transferrin
saturation and CRP concentration was -0.280(p < 0.0001). As transferrin saturation and CRP
were shown to be significantly (negatively)
correlated, participants with an elevated CRP
Figure 1

value (≥ 10 mg/L) were excluded. Using theHarris and Boyd method, transferrin saturation was not significantly different between female adolescents using oral contraceptives andthose not using oral contraceptives. Therefore,
females using oral contraceptives were not excluded from the reference population.
A total of 852 subjects had available data on both
serum iron and transferrin concentrations, after
excluding subjects based on the above statedcriteria. Figure 1 shows a scatterplot of percent
transferrin saturation plotted against age, withmales shown in blue and females shown in pink.
Age- and sex-specific pediatric reference intervals for percent transferrin saturation are shownin Table 1.
Transferrin saturation required 3 separate age
partitions (0-<1 year, 1-<14 years, and 14-<19

Age- and sex-specific scatter plot of percent transferrin saturationin the CALIPER pediatric population

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Victoria Higgins, Man Khun Chan, Khosrow Adeli
Pediatric reference intervals for transferrin saturation in the CALIPER cohort

Table 1

Pediatric age- and sex-specific reference intervals
for transferrin saturation
Males

Transferrin
saturation
(%)

Upper
limit

Lower
ConfidenceIntervalUpperconfidence
interval

Age range

n

Lower
limit

0-<1 year

92

4.1

59

(3.0, 5.3)

(51, 67)

1-<14 years

473

6.5

39

(5.3, 7.1)

(35, 42)14-<19 years

136

9.6

58

(8.6, 11.7)(43, 60)

Females

Transferrin
saturation
(%)

Upper
limit

Lower
confidence
intervalUpperconfidence
interval

Age range

n

Lower
limit

0-<1 year

92

4.1

59

(3.0, 5.3)

(51, 67)

1-<14 years

473

6.5

39

(5.3, 7.1)

(35, 42)14-<19 years

135

5.2

44

(4.6, 9.1)

(37, 54)

Underlined partition indicates sex-specific reference intervals.

years), with an additional sex partition for 14-<19
year olds. This parameter was shown to be morevariable during the first year of life, evident by awider reference interval, which subsequently
narrowed at one year until adolescence. Uponadolescence, a sex difference was apparent withfemales having lower transferrin saturation thanmales.
DISCUSSION
Reference intervals for serum iron and transferrin were previously established by the CALIPER
program (4). The present study is an extension
to these previously determined reference intervals, by establishing age- and sex-specific pediatric reference intervals for transferrin saturation using Abbott assays.

Our transferrin saturation reference intervals
demonstrated a slight sex difference, withmales having higher transferrin saturation thanfemales after 14 years of age. Previous studies also showed a higher transferrin saturationlevels in male adolescents (19,20), while others
showed no difference (2,21). As iron has beenshown to also be higher in males, but transferrin has no apparent sex difference, the transferrin saturation sex difference is most likely a result of higher iron levels in males (4). Althoughtransferrin saturation is statistically different
between sexes when comparing the mean andstandard deviation of transferrin saturation between sexes, further examination is needed todetermine if this difference is clinically significant. The upper limits reported for males andfemales differ by 27% and the lower limits differ

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Victoria Higgins, Man Khun Chan, Khosrow Adeli
Pediatric reference intervals for transferrin saturation in the CALIPER cohort

by 59%. To determine if these differences areclinically significant, they must result in different clinical decisions. Unlike statistical significance, wide-spread accepted criteria to judge
clinical significance is lacking and is most often
based on the judgement of the clinician (22).
Additionally, similar to our study, others havedemonstrated a small increase in transferrin
saturation with age (2,19–21). CALIPER reference values were also similar to those publishedmost recently by Aldrimer, et al, who definedreference intervals for 6 months – 11 years and
12 – 18 years as 6.2%-41% and 6.2%-48%, respectively (21). These values aligned closelywith our reference intervals, which span from
4.1%-59% transferrin saturation.
To ensure that the established transferrin saturation reference intervals sufficiently represented the healthy pediatric population, those witha CRP concentration ≥ 10 mg/L were excluded.
The acute phase response down-regulates
the hepatic synthesis of transferrin, therefore
decreasing the TIBC, meaning there are lessavailable transferrin binding sites for iron (19).
Additionally, inflammation is accompanied bylower concentrations of iron in serum (23). As aresult, transferrin saturation may also be affected by inflammation. The calculation Spearman’srank correlation coefficient between transferrin
saturation and CRP concentration was -0.280 (p< 0.0001). Although not very strong correlation,transferrin saturation and CRP were shown to besignificantly (negatively) correlated. Therefore
participants with a CRP concentration ≥ 10 mg/L
were excluded to ensure falsely low transferrin
saturation values were not included.
Estrogen greatly affects both iron and transferrin concentrations (24). Contraceptive medication can increase transferrin levels by as muchas 15% (25). Additionally, contraceptives raise
serum iron concentration and after cessation ofcontraceptive intake, serum iron concentrations
decrease by as much as 30% concurrently withuterine bleeding (26). Another study showed
serum iron, TIBC, and serum transferrin levels
were significantly greater in users of oral contraceptives, while transferrin saturation was notdifferent from the control group’s level (27). The
stimulatory effect of estrogen on the liver’s biosynthesis of protein may explain the increase
in serum transferrin and TIBC levels with oral
contraceptive use (28). Additionally, increased
serum iron concentration as a result of contraceptive use may be due to a decrease in menstrual blood loss. Oral contraceptive use did nothave a significant impact on percent transferrin
saturation in our pediatric reference population. Therefore, female adolescents using oral
contraceptives were not excluded from the reference population prior to calculating reference
intervals for transferrin saturation.
In this study, we establish pediatric reference intervals for transferrin saturation using an Abbott platform. However, transferrin
saturation values obtained using Abbott assays
should also be applicable to values obtainedusing Beckman, Ortho, Roche, and Siemens
assays. Iron and transferrin, the two values
used to compute transferrin saturation, werepreviously successfully transferred and verified from Abbott assays to Beckman, Ortho,
Roche, and Siemens assay through a series of
CALIPER transference studies (29–32). To perform transference, a method comparison was
performed using 200 patient samples spanning
the analytical measuring range. If there was asufficient correlation between the methods andthe residuals were normally distributed, the regression line equation was used to transfer thereference interval established using the Abbott
assay to each of the other manufacturer assays. The transferred reference intervals werethen validated if ≥ 90% of 100 healthy pediatricsamples from the CALIPER cohort fell within thetransferred reference interval. Therefore, thesestudies suggest that these intervals established

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Victoria Higgins, Man Khun Chan, Khosrow Adeli
Pediatric reference intervals for transferrin saturation in the CALIPER cohort

using Abbott assays are comparable to those on
Beckman, Ortho, Roche, and Siemens assays.
This study provides age- and sex-specific reference intervals for percent transferrin saturation
based on a multi-ethnic Canadian population
aged 0-<19 years of age. These data will improve
test result interpretation for pediatric patients.
Prior to implementing these reference intervals
into clinical practice, they should be validated
by individual laboratories for their individual instrument and local population.
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2. Milman N, Cohn J. Serum iron, serum transferrin andtransferrin saturation in healthy children without iron deficiency. Eur J Pediatr. 1984 Dec;143(2):96–8.
3. Jung B, Adeli K. Clinical laboratory reference intervals
in pediatrics: the CALIPER initiative. Clin Biochem. 2009
Nov;42(16–17):1589–95.
4. Colantonio DA, Kyriakopoulou L, Chan MK, Daly CH,
Brinc D, Venner AA, et al. Closing the Gaps in PediatricLaboratory Reference Intervals: A CALIPER Database of 40
Biochemical Markers in a Healthy and Multiethnic Population of Children. Clin Chem. 2012 May 1;58(5):854–68.

cohort and establishment of age-stratified reference intervals. Clin Biochem. 2014 Jun;47(9):812–5.
9. Teodoro-Morrison T, Kyriakopoulou L, Chen YK, Raizman JE, Bevilacqua V, Chan MK, et al. Dynamic biological changes in metabolic disease biomarkers in childhood
and adolescence: A CALIPER study of healthy community
children. Clin Biochem. 2015 Sep;48(13–14):828–36.
10. Raizman JE, Quinn F, Armbruster DA, Adeli K. Pediatric reference intervals for calculated free testosterone, bioavailable testosterone and free androgen index
in the CALIPER cohort. Clin Chem Lab Med. 2015 Sep
1;53(10):e239-243.
11. Kelly J, Raizman JE, Bevilacqua V, Chan MK, Chen
Y, Quinn F, et al. Complex reference value distributions
and partitioned reference intervals across the pediatricage range for 14 specialized biochemical markers in the
CALIPER cohort of healthy community children and adolescents. Clin Chim Acta Int J Clin Chem. 2015 Oct 23;450:
196–202.
12. Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guidelines - third
edition CLSI document C28-A3. Clinical and Laboratory
Standards Institute (CLSI); 2008.
13. Yamanishi H, Iyama S, Yamaguchi Y, Kanakura Y, Iwatani
Y. Total iron-binding capacity calculated from serum transferrin concentration or serum iron concentration andunsaturated iron-binding capacity. Clin Chem. 2003 Jan;49(1):175–8.
14. Harris EK, Boyd JC. On dividing reference data into
subgroups to produce separate reference ranges. Clin
Chem. 1990 Feb;36(2):265–70.

5. Bailey D, Colantonio D, Kyriakopoulou L, Cohen AH,
Chan MK, Armbruster D, et al. Marked biological variancein endocrine and biochemical markers in childhood: establishment of pediatric reference intervals using healthy
community children from the CALIPER cohort. Clin Chem.
2013 Sep;59(9):1393–405.

15. Ethnic origins, 2006 counts, for Canada, provinces
and territories - 20% sample data [Internet]. Statistics
Canada; 2010 [cited 2016 Oct 18]. Available from: http://
www12.statcan.ca/census-recensement/2006/dp-pd/
hlt/97-562/pages/page.cfm?Lang=E&Geo=PR&Code=35&Data=Count&Table=2&StartRec=1&Sort=3&Display=All

6. Konforte D, Shea JL, Kyriakopoulou L, Colantonio D,
Cohen AH, Shaw J, et al. Complex biological pattern of
fertility hormones in children and adolescents: a study ofhealthy children from the CALIPER cohort and establishment of pediatric reference intervals. Clin Chem. 2013
Aug;59(8):1215–27.

16. Tukey J. Exploratory data analysis. Boston: AddisonWesley; 1977.

7. Bevilacqua V, Chan MK, Chen Y, Armbruster D, Schodin
B, Adeli K. Pediatric population reference value distributions for cancer biomarkers and covariate-stratified reference intervals in the CALIPER cohort. Clin Chem. 2014
Dec;60(12):1532–42.
8. Raizman JE, Cohen AH, Teodoro-Morrison T, Wan B,
Khun-Chen M, Wilkenson C, et al. Pediatric reference
value distributions for vitamins A and E in the CALIPER

17. Hubert M, Van der Veeken S. Outlier detection for
skewed data. J Chemom. 2008 Mar;22(3–4):235–46.
18. Horn P, Pesce A. Reference Intervals: a user’s guide.
Washington, DC: AACC Press; 2005.
19. Ritchie RF, Palomaki GE, Neveux LM, Navolotskaia O,
Ledue TB, Craig WY. Reference distributions for serumiron and transferrin saturation: a comparison of a large cohort to the world’s literature. J Clin Lab Anal. 2002;16(5):
246–52.
20. Yip R, Johnson C, Dallman PR. Age-related changes
in laboratory values used in the diagnosis of anemia

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Pediatric reference intervals for transferrin saturation in the CALIPER cohortand iron deficiency. Am J Clin Nutr. 1984 Mar;39(3):427–36.
21. Aldrimer M, Ridefelt P, Rödöö P, Niklasson F, Gustafsson J, Hellberg D. Population-based pediatric reference
intervals for hematology, iron and transferrin. Scand J Clin
Lab Invest. 2013 Apr;73(3):253–61.
22. Ranganathan P, Pramesh C, Buyse M. Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspect Clin Res. 2015;6(3):169.
23. Konijn AM. Iron metabolism in inflammation. Baillieres Clin Haematol. 1994 Dec;7(4):829–49.
24. Bick RL, Frenkel E, Baker W, Sarode R. HematologicalComplications in Obstetrics, Pregnancy, and Gynecology.
Cambridge University Press; 2006.
25. Song CS, Merkatz IR, Rifkind AB, Gillette PN, Kappas
A. The influence of pregnancy and oral contraceptive steroids on the concentration of plasma proteins. Studies
with a quantitative immunodiffusion method. Am J Obstet Gynecol. 1970 Sep 15;108(2):227–31.
26. Burtis CA, Ashwood E, Bruns D. Chapter 32: Hemoglobin, Iron, and Bilirubin. In: Tietz Textbook of Clinical
Chemistry and Molecular Diagnostics. Fifth. St. Louis,
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27. Frassinelli-Gunderson EP, Margen S, Brown JR. Iron
stores in users of oral contraceptive agents. Am J Clin
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28. McKnight GS, Lee DC, Palmiter RD. Transferrin gene
expression. Regulation of mRNA transcription in chick liver by steroid hormones and iron deficiency. J Biol Chem.
1980 Jan 10;255(1):148–53.
29. Estey MP, Cohen AH, Colantonio DA, Chan MK, Marvasti TB, Randell E, 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. 2013
Sep;46(13–14):1197–219.
30. Higgins V, Chan MK, Nieuwesteeg M, Hoffman BR,
Bromberg IL, Gornall D, et al. Transference of CALIPER
pediatric reference intervals to biochemical assays on
the Roche cobas 6000 and the Roche Modular P. Clin Biochem. 2016 Jan;49(1–2):139–49.
31. Araújo PAT, Thomas D, Sadeghieh T, Bevilacqua V,
Chan MK, Chen Y, et al. CLSI-based transference of the
CALIPER database of pediatric reference intervals to
Beckman Coulter DxC biochemical assays. Clin Biochem.
2015 Sep;48(13–14):870–80.
32. Abou El Hassan M, Stoianov A, Araújo PAT, Sadeghieh T, Chan MK, Chen Y, et al. CLSI-based transference of CALIPER pediatric reference intervals to Beckman Coulter AU biochemical assays. Clin Biochem. 2015
Nov;48(16–17):1151–9.

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Congenital hyperinsulinism caused by a de novo
mutation in the ABCC8 gene – a case report
Zsuzsanna Molnár1, Lídia Balogh2, János Kappelmayer1, László Madar1,
Éva Gombos1, István Balogh1
12Department of Laboratory Medicine, University of Debrecen, Debrecen, HungaryFirst Department of Pediatrics, Semmelweis University, Budapest, Hungary

ARTICLE INFOABSTRACT

Corresponding author:
István Balogh, Ph.D.
Division of Clinical Genetics
Department of Laboratory Medicine
Faculty of Medicine
University of Debrecen
Hungary
Phone: +36 52-340-006
Fax: +36 52-417-631
E-mail: balogh@med.unideb.hu

Congenital hyperinsulinism (CHI) is a rare genetic disorder characterized by inappropriate insulin secretion and severe hypoglycaemia. There are two histological subtypes: diffuse and focal form. Diffuse form
is most common in autosomal recessive mutations in
ABCC8/KCNJ11 gene, while focal CHI is caused a paternally inherited mutation and a somatic maternalallele loss.

Key words:
congenital hyperinsulinism, hypoglycaemia
with hyperinsulinism, ABCC8 genetic testing

Here we report a case of a term male infant presented with severe hyperinsulinaemic hypoglycaemia.
Gene panel testing was performed to give rapid genetic diagnosis. We detected the c.4415-13G>A heterozygous mutation in the ABCC8 gene. Targeted genetic testing of the parents proved the de novo originof the mutation. The mutation has been previously
described. The infant received octreotide treatment
and is prepared for 18-fluoro-dopa PET-CT examination in order to localize the lesion.

Author contributions:
ZsM - molecular diagnosis of hyperinsulinism,preparing manuscript;
LB - clinical diagnosis, collecting data,revising the manuscript;
LM, ÉB - molecular diagnosis;
JK - revising the manuscript;
IB - molecular diagnosis of hyperinsulinism,
revising the manuscript
Funding: None
Conflicts of interest:
None declared

Rapid genetic testing might be crucial in the clinicalmanagement strategy, with decision algorithms taking into account of the genetic status of the patient.

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case report

INTRODUCTION
Congenital hyperinsulinism (CHI) is the mostcommon cause of nonketotic persistent severe
hypoglycaemia in neonates, and is caused by aninappropriate insulin secretion from pancreaticß cells. The estimated incidence of CHI is 1 in
50,000 live births (1). A number of different genetic causes has been associated with CHI. KATP
channel subunits (ABCC8, KCNJ11) alterations
are considered to be channelopathies, while
metabolopathies are enzyme defects, transcription factor defects or metabolic defects(GLUD1, SLC16A1, HNF4A, GCK, HNF1A, HADH,
UCP2) (2).
CHI shows considerable genetic heterogeneity,
it can be inherited in a recessive or dominantmanner (3). Of the several genes implicated,the most common ones are ABCC8 (ATP binding cassette subfamily C member 8) and KCNJ11(potassium voltage-gated channel subfamily J
member 11) being responsible for 40-50% of
the cases. ATP sensitive K+ (KATP) channels arecomposed of eight subunits from two different gene products, ABCC8 encoding SUR1 andKCNJ11 encoding Kir6.2 subunit (2). The KATP
channel is a key component in the regulation of
insulin secretion in response to elevating glucose level. The channels close as a consequenceof glucose metabolism (increased ATP level)leading to membrane depolarization which ultimately results in the opening of a voltage-gated
Ca2+ channel. The rise of the intracellular Ca2+
concentration stimulates exocytosis of insulincontaining granules from the pancreatic ß cell
(Figure 1). The KATP channel is a Janus-faced protein as gain-of-function mutations will lead toneonatal or maturity-onset diabetes while lossof-function mutations will cause CHI (4).
Histologically 2 different types of congenital hyperinsulinism can be distinguished. In diffuse
forms all ß cells are affected, which is a resultof an autosomal recessive CHI. In focal forms a

localized hyperplasia is present in the otherwise
normal pancreatic tissue. These focal lesions
might be the results of two different events.
One possible event is a paternally inherited mutation in the ABCC8 or KCNJ11 gene that would
remain silent, but if there is an acquired somaticdeletion of the maternal allele, focal CHI will develop because of the loss of heterozygosity. The
ABCC8 and KCNJ11 containing chromosome
region also contains several imprinted genes,
which are responsible for cell replication. The
paternally expressed genes are involved in cell
replication, proliferation. Normally they arecompensated by the maternally expressed geneproducts, but in case of maternal allele loss, ßcell hyperplasia will develop (8).
Diffuse CHI is suspected when the detected alterations are homozygous, compound heterozygous or in the case when only maternal heterozygous mutation is found.
Focal CHI is most commonly associated withpaternal heterozygosity, but focal CHI has also
been reported with mutations arising either denovo or of unknown parental origin. Althoughpaternal heterozygosity has a higher probability for focal CHI, additional investigation suchas 18-fluoro-dopa PET-CT scanning is necessary
to localize the lesion in focal CHI (Figure 2) (9).
According to some reports a proportion with paternal heterozygous mutations might have diffuse CHI which could be explained by dominant
inheritance or inability to identify a maternalmutation in a recessively-inherited disease (2).
Rapid, high-throughput techniques for analysis
of candidate genes have helped the care of patients with CHI. Genotyping guides further investigation of the patient and, because of thestrong genotype-phenotype correlation, also
the therapy (10,11).
The main goal of CHI treatment is to prevent
brain damage. The nutritional approach includes glucose infusion and enteral feeding.

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case report

Figure 1

Mechanism of the KATP channel regulated insulin secretionin pancreatic β-cell. Model of the KATP channel protein subunits

Medical treatment includes administration ofdiazoxide, octreotide, nifedipine and glucagon.
Diazoxide acts on the SUR1 subunit of the KATP
channel. It keeps the channel open, halting insulin secretion. Octreotide is a somatostatin analogue that inhibits insulin secretion. Glucagon
stimulates glycogenolysis and gluconeogenesis.
Growing evidence of published cases suggest anovel treatment option, sirolimus therapy (12).
When CHI patient fail to respond to medical
treatment, surgery should be considered. For
patients diagnosed with focal form, surgicalresection of the lesion is the appropriate cure.
In case of diffuse CHI subtotal pancreatectomy

may be necessary in severe, medically unresponsive cases (1).
CASE REPORT
A term male infant was born after uneventful
pregnancy. His birth weight was 4050 g (95-97
percentile), Apgar scores at birth were 9 at 1 min
and 10 at 5 min. The infant did not have any
dysmorphic features or congenital anomalies.
The mother was primipara and had no diabetes
during pregnancy. Due to being large for gestational age blood glucose monitoring begun, andhypoglycaemia was detected at one hour of life.

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case report

Table 1

Glucose, insulin and C-peptide levels during treatment
Upon
admission

Day 1

Day 2

Reference range

Blood glucose (mmol/L)

1.6

2.1

5.6

3.6-6.0

Insulin (mU/L)

16.65

4.46

N.D.

2.6-24 (innormoglycaemia)

C-peptide (ng/mL)

2.43

0.76

N.D.

0.48-5 (innormoglycaemia)

glucose infusion

diazoxideoctreotide-

Therapy
N.D.: not determined

Figure 2

Clinical diagnostic algorithm for managementof congenital hyperinsulinism integrating molecular genetic testing

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case report

Figure 3

Genetic analysis revealed a heterozygous c.4415-13G>A mutationof the ABCC8 genePart A: Next-generation sequencing result
Left Y axis shows the percentage of the mutant allele
Right Y axis and the blue line show the coverage
Region of the ABCC8 gene containing the mutation site is shown
Part B: Sanger sequencing electropherogramHe was treated with i.v. glucose infusion and hydrocortisone. Adrenal insufficiency was excluded (cortisol level: 398 nmol/L, reference range:
140-690 nmol/L). After the sixth day of life theblood glucose normalized, the infant did not require any medication. A diagnosis of transienthypoglycaemia was made. At the age of twomonths, he was admitted to hospital becauseof frequent myoclonic movements of the lower
and later the upper limbs, sweat and pallor wasobserved with episodes of hypoglycaemia (glucose: 1.1-1.9 mmol/L). In time of hypoglycaemia (glucose: 1.6 mmol/L) the insulin level was
16.65 mU/L, and C-peptide level was 2.43 ng/mL.
Normoglycaemia could only be maintained bycontinuous i.v. glucose load up to 20mg/kg/min.
Insulin and C-peptide levels were elevated during hypoglycaemia.
Oral diazoxide was administered because of thehyperinsulinism at 13 mg/kg/day in divided doses. He had poor response to diazoxide, the glucose infusion was further administered. With thistherapy the blood glucose could be maintaned at
2.1-2.3 mmol/l. Insulin was 4.5 mU/L, C-peptide
was 0.76 ng/mL (Table 1). Genetic testing was initiated. After starting octreotide therapy his blood
glucose begun to improve. Subcucatenously introduced octreotide was successfully replacedby lancreotide every four weeks.

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case report

Genomic DNA was extracted from peripheral
blood leukocytes. Next-generation panel genesequencing was performed on a Roche GS Junior
DNA sequencer using a Multiplicom MODY
MASTR Kit which contains the following genes:
ABCC8, GCK, HNF1A, HNF4A, HNF1B, INS andKCNJ11. The detected mutation was confirmed
by Sanger sequencing.
We detected the c.4415-13G>A heterozygous
mutation in the ABCC8 gene (Figure 3). Targeted
genetic testing of the parents proved the denovo origin of the mutation. The c.4415-13G>A
mutation has been previously described, andwas associated with octreotide responsive, focal
histological forms (4). Although there is no functional study analyzing the consequence of themutation, the c.4415-13G>A has been shown tobe present in many CHI patients (5,6,7).
The infant received octreotide treatment andcould be prepared for 18-fluoro-dopa PET-CT
examination.
DISCUSSION
Early diagnosis and treatment is crucial for theappropriate management of CHI (1). The primary goal of treatment is to maintain normoglycaemia in order to prevent neurological damage.
Rapid gene mutation analysis with short turnaround time is a key part of the clinical management strategy, as it is shown in the decisionalgorithm (Figure 2). Importantly, the finding ofa paternal heterozygous mutation or a de novo
mutation is a key step before 18-fluoro-dopa
PET-CT scanning and the subsequent possible
finding of a solitary focal lesion in the pancreas that can be cured by lesionectomy (8,9,10).
Genotype-phenotype correlations is also helpful in the management of the patient (3,11).
The treatment algorithm recommends the KATP
agonist, diazoxide as first line therapy. Our patient was also octreotide responsive, similarly
to the patient in the first literature report (4).

TAKE-HOME MESSAGES/LEARNING POINTS
1. CHI is a cause of severe hypoglycaemia in
neonates.
2. The main pathophysiological characteristicof CHI is that insulin secretion is independent from the glucose concentration, showing elevated insulin and C-peptide levels
even in the presence of hypoglycaemia.
3. Loss of function mutations in the ABCC8 orin the KCNJ11 gene can lead to CHI.
4. Rapid genetic testing in patients with CHI is
crucial and guides further investigations.
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Vaury C, Verkarre V, Arnoux JB, Valayannopoulos V,
Gobrecht S, Sempoux C, Rahier J, Fournet JC, Jaubert F, Aigrain Y, Nihoul-Fékété C, de Lonlay P. ABCC8and KCNJ11 molecular spectrum of 109 patients with

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Z. Molnár, L. Balogh, J. Kappelmayer, L. Madar, É. Gombos, I. Balogh
Congenital hyperinsulinism caused by a de novo mutation in the ABCC8 gene – a case reportdiazoxide-unresponsive congenital hyperinsulinism. J
Med Genet. 2010;47(11):752-9.
8. Saint-Martin C, Arnoux JB, de Lonlay P, Bellanné-Chantelot C. KATP channel mutations in congenital hyperinsulinism. Semin Pediatr Surg. 2011;20(1):18-22.
9. Banerjee I, Avatapalle B, Padidela R, Stevens A, Cosgrove K, Clayton P, Dunne M. Integrating genetic and imaging investigations into the clinical management of congenital hyperinsulinism. Clin Endocrinol (Oxf) 2013;78(6):803–813.

10. Stanley CA. Perspective on the Genetics and Diagnosis
of Congenital Hyperinsulinism Disorders. J Clin Endocrinol Metab. 2016;101(3):815-26.
11. Gonera A, Buraczewska M, Borowiec M, Barg E. Congenital hyperinsulinism: course and consequenes - casereport. Pediatr Endocrinol Diabetes Metab. 2015;21(1):46-50.
12. Méder Ü, Bokodi G, Balogh L, Körner A, Szabó M,
Pruhova S, Szabó AJ. Severe Hyperinsulinemic Hypoglycemia in a Neonate: Response to Sirolimus Therapy. Pediatrics. 2015;136(5):e1369-72.

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Hemochromatosis, erythrocytosis
and the JAK2 p.V617F mutation
Stephen E. Langabeer

St. James’s Hospital, Dublin, Ireland

INFO

LET TER TO THE EDITOR

Corresponding author:
Stephen E. Langabeer, Ph.D., FRCPath
Cancer Molecular Diagnostics
Central Pathology Laboratory
St. James’s Hospital
Dublin 8, Ireland
Phone: +35 314103576
Fax: +35 314103513
E-mail: slangabeer@stjames.ie

Hereditary hemochromatosis (HH) is an inheritediron overload disorder, particularly prevalent in Irish
and Scandinavian populations (1), characterised byabnormal iron metabolism, leading to excess iron deposition in the parenchymal cells of the liver, heart,and endocrine organs. HH is commonly associatedwith mutations in the HFE gene and in other genes
such as HJV, that regulate the biology of hepcidin, a
key regulator of iron homeostasis, and TRF2 that is
responsible for uptake of transferrin bound iron (2,3). A rare but recurrent hematological manifestation of HH is a raised hematocrit due to an excess of
red cells (erythrocytosis) possibly an important clue
to an underlying hepatoma (4). Therapy is based on
the removal of excess iron by phlebotomy or erythrocytapharesis, with ferritin levels used to monitor treatment effectiveness (5). The most common
cause of acquired primary erythrocytosis is the myeloproliferative neoplasm polycythemia vera (PV).
Approximately 95% of PV patients harbour the JAK2
p.V617F mutation. In hematopoietic stem cells, thismutation leads to constitutively activated, intracellular JAK-STAT signalling resulting in increased production of red and white blood cells and platelets.
For many years the mainstay of PV therapy has been

Key words:
hemochromatosis, erythrocytosis,
molecular diagnostics
Conflicts of interest:
None declared

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Stephen E. Langabeer
Letter: Hemochromatosis, erythrocytosis and the JAK2 p.V617F mutation

phlebotomy and cytoreductive agents to prevent thrombotic events and transformation toacute leukemia, however recently developed
targeted therapies have shown considerable efficacy (6).
A sporadically observed trigger for requesting
JAK2 p.V617F analysis is for investigation of anerythrocytosis or raised hematocrit in patientswith known HH. In order to address the clinicalvalue and laboratory impact of such requests, aretrospective audit was performed on all JAK2
p.V617F requests received at a molecular diagnostics centre for hematological malignancies.
From January 2006 to December 2016 inclusive,
14617 diagnostic requests for JAK2 p.V617F
mutation analysis were received. Of these, 48
requests (0.3%) were received from known HH
patients with accompanying clinical details of araised hemoglobin and/or hematocrit or erythrocytosis. Using an allele-specific PCR screening
assay capable of detecting 2% mutant alleles,
the JAK2 p.V617F mutation was not detected inany of these 48 patients.
The role of HFE genotypes as risk factors for development of a myeloproliferative disorder remains somewhat confounding (7, 8). However,considering the above data and, while the number of requests for JAK2 p.V617F mutation in patients with HH does not considerably impact onoverall laboratory workload, routine screening
for the PV-associated JAK2 p.V617F mutation inpatients with HH with raised red cell counts appears inappropriate.
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G, Marra M, De Sousa M, Vatcher G. Transferrin receptor
2 (TfR2) and HFE mutational analysis in non-C282Y ironoverload: identification of a novel TfR2 mutation. Blood
2002;100:1075-1077.
3. Gehrke SG, Pietrangelo A, Kascak M, Eisold M, Kulaksiz
H, Herrmann T, Hebling U, Bents K, Gugler R, Stremmel
W. HJV gene mutations in European patients with juvenile
hemochromatosis. Clin Genet 2005;67:425-428.
4. Raphael B, Cooperberg AA, Niloff P. The triad of hemochromatosis, hepatoma and erythrocytosis. Cancer
1979;43:690-694.
5. Rombout-Sestrienkova E, van Kraaij MG, Koek GH. How
we manage patients with hereditary haemochromatosis.
Br J Haematol 2016;175:759-770.
6. Vannucchi AM. From leeches to personalized medicine:
evolving concepts in the management of polycythemia
vera. Haematologica 2017;102:18-29.
7. Barton JC, Bertoli LF, Acton RT. HFE C282Y and H63D in
adults with malignancies in a community medical oncology practice. BMC Cancer 2004;4:6.
8. Andrikovics H, Meggyesi N, Szilvasi A, Tamaska A, Halm
G, Lueff S, Nahajevszky S, Egyed M, Varkonyi J, Mikala G,
Sipos A, Kalasz L, Masszi T, Tordai A. HFE C282Y mutation
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Editor-in-chief
Gábor L. Kovács
Institute of Laboratory Medicine, Faculty of Medicine, University of Pécs, Hungary

Assistant Editor 				Case Editor			Harjit Pal Bhattoa				Department of Laboratory Medicine		
University of Debrecen, Hungary		Reinhard B. Raggam
Department of Internal Medicine
Division of Angiology, University of Graz, Austria

Editorial Board
Khosrow Adeli, The Hospital for Sick Children, University of Toronto, Canada
Borut Božič, University Medical Center, Lubljana, Slovenia
Nilda E. Fink, Universidad Nacional de La Plata, Argentina
Ronda Greaves, School of Health and Biomedical Sciences, RMIT University, Victoria, Australia
Mike Hallworth, Shrewsbury, United Kingdom
Andrea R. Horvath, Prince of Wales Hospital and School of Medical Sciences, University of New
South Wales, Sydney, Australia
Ellis Jacobs, Alere Inc., New York, USA
Allan S. Jaffe, Mayo Clinic, Rochester, USA
Bruce Jordan, Roche Diagnostics, Rotkreuz, Switzerland
János Kappelmayer, Faculty of Medicine, University of Debrecen, Hungary
Evelyn Koay, National University, Singapore
Gary Myers, Joint Committee for Traceability in Laboratory Medicine, USA
Maria D. Pasic, Laboratory Medicine and Pathobiology, University of Toronto, Canada
Maria del C. Pasquel Carrera, College of Chemists, Biochemists and Pharmacists, Pichincha, Ecuador
Oliver Racz, University of Kosice, Slovakia
Rosa Sierra Amor, Laboratorio Laquims, Veracruz, Mexico
Sanja Stankovic, Institute of Medical Biochemistry, Clinical Center of Serbia, Belgrade, Serbia
Danyal Syed, Ryancenter, New York, USA
Grazyna Sypniewska, Collegium Medicum, NC University, Bydgoszcz, Poland
Jillian R. Tate, Queensland Royal Brisbane and Women’s Hospital, Herston, Australia
Peter Vervaart, LabMed Consulting, Australia
Stacy E. Walz, Arkansas State University, USAPublisher: IFCC Communications and Publications Division (IFCC-CPD)
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