October 2017

ISSN 1650-3414

Volume 28 Number 3

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

Postprandial dyslipidemia: pathophysiology
and cardiovascular disease risk assessment
Victoria Higgins, Khosrow Adeli

168

e-Learning: a model to support ongoing education
Ronda F. Greaves

185

About the “Compendium of Terminology and Nomenclatureof Properties in Clinical Laboratory Sciences”
Georges Férard, René Dybkaer, Xavier Fuentes-Arderiu

193

Correlation of HbA1C levels with body mass index
in newly diagnosed polycystic ovary syndrome
Manju Bala, Meenakshi, Menaka K., Anjali Gupta

196

Factors affecting quality of laboratory services
in public and private health facilities in Addis Ababa, Ethiopia
Eyob Abera Mesfin, Binyam Taye, Getachew Belay, Aytenew Ashenafi, Veronica Girma

205

Retrospective approach to evaluate interferences in immunoassay
Jayesh Warade

224

Case report: biliary pancreatitis with acute cholangitis
in a patient under anticoagulant treatment with dabigatranFranziska Baumann-Durchschein, Thomas Gary, R.B. Raggam

233

Postprandial dyslipidemia: pathophysiology
and cardiovascular disease risk assessment
Victoria Higgins1,2, Khosrow Adeli1,2

1
2

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

INFO

ABSTRACT

Corresponding author:
Khosrow Adeli
Clinical Biochemistry, DPLM
The Hospital for Sick Children
555 University Avenue
Toronto, ON, M5G 1X8
CanadaE-mail: khosrow.adeli@sickkids.ca

Although the fed state predominates over the courseof a day, the fasting lipid profile has traditionally beenused to assess cardiovascular disease (CVD) risk. The
nonfasting lipid profile may be more reflective ofthe daily circulating plasma lipids and simplifies lipid
monitoring for patients, laboratories, and clinicians.
Nonfasting triglyceride levels are also independently associated with cardiovascular events, leading toseveral clinical guidelines (e.g. in Denmark, the UK,
Europe, and Canada) now recommending nonfasting
lipid testing in the primary prevention setting.

Key words:
postprandial dyslipidemia,
insulin resistance, oral fat tolerance test,
cardiovascular disease,glucagon-like peptide 1

Obese and insulin resistant states are associatedwith intestinal chylomicron overproduction and subsequent remnant lipoprotein accumulation, leading
to development of postprandial dyslipidemia in the
fed state. Postprandial dyslipidemia is thought to bea major contributor of atherogenesis and shown tobe an important CVD risk factor. As intestinal peptides (e.g. glucagon-like-peptide 1; GLP-1) have beenshown to regulate chylomicron output, alterations inthese signaling pathways in insulin resistant states
may play a role in the development and/or progression of postprandial dyslipidemia.
Page 168eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

Although several advances have been made inunderstanding postprandial dyslipidemia in insulin resistance and its association with CVD,
several limitations remain. Although nonfasting
lipid measurements (i.e. random blood sampling) are now recommended in some countries, a more functional assessment of postprandial lipemia involves ingestion of a high-fat meal
with subsequent blood collection over a specified time period (i.e. oral fat tolerance test).
However, oral fat tolerance test methodology
remains largely unstandardized and reference
values to interpret postprandial values remain
to be accurately established. Development ofstandardized methodologies and biomarker
profiles for assessment of postprandial dyslipidemia in clinical practice will enable early andaccurate identification of those at risk for CVD.


Abbreviations
apoB-48: apolipoprotein B-48
apoB-100: apolipoprotein B-100
CM: chylomicron CVD: cardiovascular disease
DGAT: diacylglycerol acyltransferase
DPP-4: dipeptidyl peptidase-4
FA: fatty acid
HDL-C: high-density lipoprotein cholesterol
GLP-1: glucagon-like peptide-1
GLP-2: glucagon-like peptide-2
LDL-C: low-density lipoprotein cholesterol
LPL: lipoprotein lipase
MCP-1: monocyte chemoattractant protein-1
MTP: microsomal triglyceride transfer protein
OFTT: oral fat tolerance test
PAI-1: plasminogen activator inhibitor-1 (PAI-1)
PCOS: polycystic ovary syndrome
RLP: remnant lipoprotein
T2D: type 2 diabetes
TG: triglyceride

TRL: triglyceride-rich lipoprotein
VLDL: very low-density lipoprotein


POSTPRANDIAL LIPID METABOLISM
With the current eating patterns in Western
societies, the fed state predominates over thecourse of a day, with the typical individual onlyin the fasted state for a few hours in the earlymorning (1). Nevertheless, the fasting lipid profile has been a standard assessment of cardiovascular disease (CVD) risk. There are two primary reasons for traditionally measuring fasting
triglycerides (TG): to reduce the variability in
TG concentration following meal ingestion andto accurately calculate low-density lipoproteincholesterol (LDL-C) using the Friedewald equation. However, nonfasting (i.e. random blood
sample measurement irrespective of timesince last meal) TG levels have been reported
to fluctuate only modestly within the same individual (2). Additionally, calculated LDL-C has
been shown to change minimally after foodintake (3) and measured and calculated LDL-C
are highly correlated between fasting and nonfasting states (4,5). As nonfasting TG levels are
independently associated with cardiovascular
events (6), a paradigm shift towards assessing
lipid parameters in the nonfasting or postprandial (i.e. blood sample measurement at specified time points following a standardized meal)state is occurring (7). In fact, postprandial TG
levels obtained after consuming a standardized
high-fat meal, better predict coronary artery disease compared to fasting TG levels (8). Several
clinical guidelines have included nonfasting
lipid testing in the primary prevention setting,including Denmark in 2009 (3), UK in 2014 (9),as well as the European Atherosclerosis Society(EAS) and European Federation of Clinical
Chemistry and Laboratory Medicine (EFLM) (7)and the Canadian Cardiovascular Guidelines inPage 169

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Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

2016 (10). The nonfasting lipid panel has become the clinical standard in Denmark, offering
physicians the option to measure fasting lipids
when TG > 4 mmol/L (3), while in Canada it isrecommended to obtain a fasting measurement
when TG > 4.5 mmol/L (10). Furthermore, theEAS/EFLM guidelines state that nonfasting andfasting measurements should be complementary and not mutually exclusive (7). The option of
nonfasting lipid testing has also been includedin The 2011 National Heart, Lung, and Blood
Institute (NHLBI) Guidelines specific for the pediatric population (11). Assessing the postprandial lipid profile can provide a better indicationof an individual’s capacity to metabolize lipids
following a meal, reflecting their metabolic
efficiency.
Following ingestion of a fat-containing meal,cholesterol, monoacylglycerol, and fatty acids(FAs) are absorbed by the small intestine andre-esterified in the enterocyte. Triglyceriderich lipoproteins, termed chylomicrons (CMs)are formed within the enterocyte, comprised ofcholesterol, TG, phospholipids, and apolipoproteins (12). These TG-rich lipoproteins (TRLs),
containing apolipoprotein B-48 (apoB-48),
are subsequently secreted into lymphatic vessels, entering the blood via the thoracic duct.
As CMs travel through the circulation, TGs arehydrolyzed by lipoprotein lipase (LPL), releasing FAs for subsequent uptake by peripheral tissues. As CMs become TG-depleted, they form
smaller, cholesteryl ester- and apoE-enriched
particles, termed CM remnants. CM remnants
then compete with hepatic-derived TRL (i.e.
apoB-100-containing very-low density lipoprotein (VLDL)) remnants for clearance by the liver
through apoE binding to the LDL receptor or
LDL-receptor-related protein. Accumulation ofremnant lipoproteins (RLPs), often present inobese and insulin resistant states, is a major CVD
risk factor. In fact, T2D patients have a three- tofourfold increased risk of death due to CVD (13),accounting for the majority of deaths in thesepatients (14). Patients with type III hyperlipoproteinemia, a genetic disorder characterized
by RLP accumulation as a result of impaired RLP
removal, are also predisposed to prematureatherosclerosis and CVD risk (15).
This state-of-the-art review discusses the current scientific knowledge of postprandial dyslipidemia. The dysregulation of postprandial lipid
metabolism in insulin resistant states and subsequent CVD risk, regulation of postprandial lipids by the brain-gut neuroendocrine axis, clinical assessment of postprandial dyslipidemia, as
well as current limitations and future research
directions will be discussed.
POSTPRANDIAL LIPIDS AS MARKERSOF CARDIOVASCULAR DISEASE RISK
The traditional fasting lipid profile used toassess CVD risk includes TG, total cholesterol (TC), high-density lipoprotein cholesterol(HDL-C) and LDL-C. However, evidence is lacking that fasting measurements of lipid parameters are superior to nonfasting measurements.
Rather, several notable advantages are recognized for nonfasting measurements, including
a more reflective measure of the daily averageof plasma lipids, simplification of blood sampling for patients, laboratories, and clinicians,
as well as improvement of patient compliancewith lipid testing (7). Although fasting dyslipidemia is strongly associated with CVD risk,
only 47.5% of patients with acute coronary
syndrome present with fasting dyslipidemia(16). Additionally, it has been suggested that
nonfasting and postprandial lipid parameters
may better predict CVD risk compared to fasting measurements (6,17).
The potential atherogenicity of TGs andTRLs in the postprandial state was first proposed by Zilversmit in 1979 (18), statingthat LPL‑mediated lipolysis of CMs results inPage 170eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

cholesteryl ester-enriched CM remnants, which
are subsequently retained by arterial smoothmuscle cells (18). This widely accepted hypothesis regarding the contribution of nonfasting lipids to CVD risk has been further supported bynumerous prospective studies further evaluating
this link. An 11.4 year follow-up study in womenfound that postprandial TG levels, but not fasting
TG levels, are independently associated with incident cardiovascular events (6). Additional prospective studies have reported an association
between nonfasting TG levels and increased riskof coronary heart disease (19), ischemic stroke(2), as well as myocardial infarction, ischemic
heart disease, and death (20). Furthermore,
Langsted et al reported a significant association
between nonfasting levels of TG and cholesterol
with myocardial infarction risk, as well as a significant association between nonfasting TG andtotal mortality (21). Additional postprandial lipid
parameters, including markers of intestinallyderived lipoproteins (i.e. apoB-48) and RLPs,
have also been reported to be significantly associated with CVD risk. Karpe et al reported postprandial levels of CM remnants (apoB-48 in the
Svedberg flotation (Sf) 20-60 subfraction) following ingestion of a mixed-meal correlate with
5-year progression of coronary atherosclerosis
in postinfarction patients (22). Postprandial RLP
cholesterol (RLP-C) (i.e. total remnant cholesterol, including intestinal and hepatic derived) was
more strongly associated with common carotid
artery intima-media thickness (i.e. CIMT, a surrogate marker for atherosclerosis) compared to
fasting RLP-C in healthy middle-aged men (17).
Similarly, Higashi and colleagues found significantly higher RLP-C in patients with coronary
artery disease compared to those without (23).
Accumulation of RLPs is thought to be a main
contributor to the link between postprandial lipid measures and CVD risk. RLPs less than 70 nm
in diameter are able to contribute to atherosclerosis due to their ability to deliver cholesterol

to the arterial wall (24). It has been widely reported that CM remnants are able to penetrate
the arterial wall and are subsequently retainedin the subendothelial space (25–28). Fully hydrolyzed CM remnants contain approximately
forty times more cholesterol than LDL particles(29,30) and may be preferentially retained relative to other lipoproteins (26,31). Thus, it is not
surprisingly that CM remnants substantiallycontribute to the cholesterol deposition within
the intima. Lipolysis products of TRLs (i.e. RLPs)
have been shown to increase endothelial permeability through rearrangement of tight andadherens junctions of endothelial cells (32).
Increased permeability may in turn contributeto increased diffusion of RLPs and LDL into thesubendothelial space via paracellular transport(32). Upon entry to the subendothelial space ofarterial walls, CM remnants induce the development of atherosclerotic lesions (31,33). RLPs
may augment atherosclerosis via several mechanisms including stimulation of the inflammatory state through increased expression of leukocyte activation markers and proinflammatory
genes, as well as complement system activation(34). Inflammation is a key characteristic of atherosclerotic progression and requires monocytes to adhere to endothelial cells and enterthe vascular wall (35). CM remnants in rats havebeen shown to induce mRNA expression andprotein secretion of monocyte chemoattractant
protein-1 (MCP-1), which stimulates monocyte
migration, and thus plays a critical role in atherosclerosis development (36). Endothelial cell
apoptosis (37), as well as increased productionof plasminogen activator inhibitor-1 (PAI-1)
in endothelial cells, an important regulator ofthrombus formation (38), have also been reported in response to CM remnants. Therefore,
understanding the regulation of CM metabolism and how CM remnant accumulation associates with CVD risk in insulin resistant states iscritical to slow atherosclerotic CVD progression.

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Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

THE ROLE OF THE COMPLEX
BRAIN-GUT AXIS IN REGULATINGPOSTPRANDIAL LIPID METABOLISM
The intestine was once thought to be a relatively passive organ in regards to lipid handling,
however intestinal lipid absorption and lipoprotein secretion are now recognized as complex,
regulated processes. The significant changes
in gut hormone secretion and reversion of
T2D following gastric bypass surgery (39,40)
further highlights the critical role of the intestine in metabolic regulation. Two hormones
secreted in equimolar amounts from enteroendocrine L-cells following nutrient ingestion,
glucagon-like peptide-1 (GLP-1) and glucagonlike peptide-2 (GLP-2), paradoxically pose opposite effects on intestinal lipoprotein output(41). While GLP-1R agonists have been shown
to blunt CM output in animal (42) and humanstudies (43,44), administering GLP-2 has beenshown to stimulate secretion of stored TG and
preformed CMs, in a process requiring nitricoxide signaling (45,46).
GLP-1, a potent incretin, mediates several effects involved in regulating glycemia, including glucose-dependent insulin secretion (47).
Pharmaceutical agents have thus been developed to exploit these beneficial effects by either preventing endogenous GLP-1 degradation through inhibiting dipeptidyl peptidase-4(DPP‑4), or stimulating GLP-1 receptors(GLP‑1R). In the scientific community, theseagents have been used as tools to elucidate
functions and subsequent mechanisms of
GLP-1. Animal and human studies using theseagents have reported profound effects, notonly on glycemic regulation, but on lipid metabolism. Hsieh et al reported that injecting a
GLP-1R agonist intraperitoneally reduced postprandial TRL-TG and apoB-48 concentrationcompared to vehicle-treated controls in both
healthy mice and hamsters (42). Furthermore,

3 weeks of DPP-4 inhibitor treatment in an insulin-resistant hamster model reduced TG excursions following a fat-load (42), supporting theinhibitory effects of GLP-1 on postprandial lipemia in both healthy and insulin resistant states.
GLP-1-mediated lipid regulation has also been
confirmed in healthy and T2D humans. A studyof normolipidemic, normoglycemic men foundthat a GLP-1R agonist significantly reduced
TRL‑apoB-48 accumulation in the postprandial
state (44). A single oral dose of the DPP-4 inhibitor sitagliptin decreased chylomicron production in healthy men, independent of changes
in insulin, glucagon, glucose, and free FAs (48).
Furthermore, a double-blind cross-over studyof T2D patients treated with a DPP-4 inhibitor
had reduced postprandial TG and apoB-48 after
an oral lipid tolerance test compared to placebo-treated controls (49).
The mechanisms by which GLP-1 regulates lipid metabolism are incompletely understood.
Improvements in postprandial lipemia with
GLP-1R agonists and DPP-4 inhibitors could result from a number of mechanisms, including
reduced lipid absorption, decreased CM secretion, and/or enhanced clearance from the circulation. Although GLP-1 is a potent stimulatorof insulin secretion and insulin decreases CM
secretion (50,51), the effects of the GLP-1R agonist exenatide on postprandial CM production
are maintained under pancreatic clamp conditions (44). Regulation of intestinal lipoprotein
production by exenatide or a GLP-1 infusion
can also occur independent of changes in gastric emptying as demonstrated by bypassing
the stomach (44,52). Additionally, fractional
catabolic rates of intestinal lipoproteins in response to exenatide and DPP-4 inhibitors do notappear to be significantly affected, supporting
the view that reduced CM accumulation is notdue to impaired clearance (44,48,53). GLP-1R
stimulation may regulate lipid metabolism bydecreasing the rate of intestinal lipid absorption

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Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

and subsequently limiting lipid availability within the enterocyte for CM production. Reduced
absorption of radiolabeled dietary TG has been
observed in hamsters (41) and rats (52) that received a GLP-1 infusion. This may result from a
reduction in pancreatic exocrine secretions including lipases (54).
More recently, a brain-gut axis has been proposed to explain the effects of GLP-1R agonists
on postprandial lipoprotein production. GLP-1producing neurons have been identified in the
nucleus of the solitary tract of the brain stem,
which project to hypothalamic nuclei that express the GLP-1R, including the arcuate (ARC),
paraventricular (PVN), and dorsomedial (DMH)
nuclei (55). A recent study demonstrated that
injecting the GLP-1R agonist exendin-4 into thethird ventricle of the brain reduced plasma and
TRL-TG and TRL-apoB48 levels in fat-loaded
hamsters (56). These effects could be prevented
by adrenergic receptor blockers as well as central delivery of an antagonist to melanocortin-4
receptors (MC4R), which are known to activate
the intermediolateral nuclei (IML) of the spinal
cord to mediate sympathetic outflow to theperiphery (57). These findings support the involvement of MC4R signaling and sympathetic
pathways in the brain-gut axis that GLP-1R agonists may use to regulate postprandial lipid output (56). This study further demonstrated that
GLP‑1R agonists may work by reducing jejunal
FA absorption as well as decreasing microsomal triglyceride transfer protein (MTP) activity,
which is required for apoB-48 lipidation (56).
Despite the potential for a brain-gut axis for intestinal lipid regulation, peripheral exendin-4
was found to sufficiently reduce CM production,
independent of central GLP-1R signaling (56).
Postprandial lipid regulation by GLP-1 has
primarily been studied using pharmaceutical agents which either mimic GLP-1 action or
pharmacologically increase its concentration byblocking its inhibition. However, it is importantto understand if physiological levels of endogenous GLP-1 are also able to regulate intestinal
lipoprotein production. GLP-1R antagonism inchow-fed hamsters augmented TRL-apoB-48
120min after a fat load (42), indicating that endogenous GLP-1R signaling is able to modulate
postprandial lipid metabolism. Furthermore,
GLP-1R-/- mice had enhanced plasma and
TRL‑TG compared to GLP-1R+/+ littermate controls, despite similar gastric emptying rates(42). However, current evidence for the ability of physiological levels of GLP-1 to signal
through the proposed central pathway is limited. Central treatment with the DPP-4 inhibitor MK-0626 reduced TRL-TG and this effectwas negated by central GLP-1R antagonism,
implicating endogenously produced central
GLP-1(56). However, there is a lack of evidence
for the presence of DPP-4 in the brain, and central GLP-1R antagonism alone has no effect of
CM secretion compared to vehicle-treated controls (56). There is some evidence that DPP-4
inhibitors may have additional mechanisms ofaction beyond inhibiting endogenous GLP-1
degradation, such as the stimulatory effects ofsitagliptin on L-cell GLP‑1 secretion (58). Similar
DPP-4-independent mechanisms may accountfor the effects of central MK-0626 treatment
on postprandial lipemia – however, further
studies are needed to delineate the role of endogenous central GLP-1 in regulating chylomicron secretion.
DYSREGULATION OF POSTPRANDIAL
LIPID METABOLISM IN INSULINRESISTANT STATES
Dyslipidemia is commonly present in insulin resistant and T2D patients, which as previously
discussed, leads to an increased risk of CVD due
to the presence of a pro-atherogenic environment (59). It is therefore not surprising that CVD
accounts for the majority of deaths in T2D patients (60). In fact, increased CVD risk is alreadyPage 173eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

present in non-diabetic subjects with impaired
glucose tolerance or impaired fasting glucose(61). While dyslipidemia associated with T2D
has traditionally been described as a combination of elevated TG, reduced HDL-C, and a shift
towards small, dense LDL particles, more recent
studies describe increased intestinally-derived
lipoproteins in T2D patients (62). As nonfasting dyslipidemia independently predicts CVD
events (6), it is critical to understand the link
between insulin resistance and dyslipidemia in
the postprandial state.
Postprandial dyslipidemia in insulin resistantstates has been examined in both human andanimal studies. Postprandial elevations of hepatic and intestinal lipoproteins are evident in T2D
patients, despite normal TG levels in the fastingstate (63). Duez et al reported an increased production rate of intestinal apoB-48-containing lipoproteins in hyperinsulinemic men comparedto those with normal insulin levels, althoughno significant difference in CM clearance wasevident (64). An increased CM production rate
was similarly seen in T2D patients, however,decreased catabolism of CMs was also evident(65). Furthermore, a recent study by Wang et
al reported elevated postprandial TG concentration in insulin resistant, abdominally obese
adults, compared to abdominally obese adults
without insulin resistance and non-abdominally obese controls (66), further highlighting
the association between insulin resistance andpostprandial lipid abnormalities. In addition toelevated postprandial TG concentrations, a prolonged elevation of TG concentration has also
been seen in T2D patients. For example, after
an oral fat load, TG levels peaked at 2 hours inhealthy controls, but were still elevated at 4hours in T2D patients (67).
Several animal studies provide further insight
into potential mechanisms associated with
postprandial dyslipidemia in insulin resistance.
Dysregulation of insulin signaling at the level ofthe enterocyte may contribute to postprandial
dyslipidemia, as CM secretion is reduced fromfetal jejunal explants in response to insulin (68).
Indeed, Federico et al observed cellular changes in the insulin receptor signaling pathway, as
well as a lack of response to insulin-induced
downregulation of CM secretion in a fructosefed hamster model of insulin resistance (69).
Several additional changes accompanying CM
over secretion may occur at the level of the enterocyte through altered CM assembly. CM assembly involves lipidation of apoB-48, mediatedby MTP, which subsequently prevents apoB-48
degradation. Insulin resistant hamsters exhibited increased intracellular apoB-48 stability,
enhanced intestinal de novo lipogenesis, and increased MTP mass, compared to chow-fed controls (70). Diacylglycerol acyltransferase (DGAT)
is another enzyme involved in TG synthesis in
the enterocyte, and thus CM assembly. Activity
and expression of DGAT has also been shown
to be elevated in insulin resistant hamster models (71). Enhanced dietary lipid absorption may
also be upregulated in insulin resistant states
through upregulation of various FA transporters
to provide increased substrates for CM assembly (72). Taken together, intestinal lipoprotein
production is a highly regulated process that
becomes significantly altered in insulin resistantstates.
CLINICAL ASSESSMENT
OF POSTPRANDIAL LIPIDS IN HUMANS
Non-fasting lipid measurement is a simple approach to assess postprandial lipids, however itdoes not allow for a complete functional assessment of postprandial lipid excursion and potential abnormalities in insulin resistant states. An
oral glucose tolerance test (OGTT) is a well-established method used clinically to assess glucose intolerance in pre-diabetic and diabetic
states (73). A similar method to assess lipid parameters at fixed time points following ingestion

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Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessmentof a high-fat meal (i.e. oral fat tolerance test
(OFTT)) to examine the efficiency of lipid metabolism is not currently performed routinely inthe clinic. This is mainly due to a lack of standardized methodology and reference values forresult interpretation. Nevertheless, postprandial lipid responses to fat-containing meals havebeen examined in research settings in humansubjects for the past 30 years (74). Assessing
postprandial lipid metabolism provides indications of an individual’s capacity to process dietary lipids from digestion and absorption oflipids through secretion and clearance of lipoproteins. A wide range of methodologies havebeen used, varying in pre-test meal conditions,the size, ingredients, nutrient composition,and frequency of the meal, time of blood collection, and selection of circulating markers tomeasure (74). Additionally methodology will
differ depending on the research question. For
example, OFTT studies can be used to compare
postprandial lipid responses between different
nutrient mixtures, between subjects with different habitual diets, between subjects with andwithout disease, or even to assess the relationship between postprandial response and othermarkers of disease risk (e.g. fasting lipids, inflammatory markers). Several factors affect TG
response to a fat-containing meal, including theamount of fat consumed, consumption of alcohol before or during the meal, fiber content,
contents of other macronutrients, and physicalactivity (reviewed in (74) and (75)).
Postprandial lipemia studies
in adults using OFTTPrevious studies have analyzed the healthy
postprandial lipid profile following ingestion ofa fat-containing meal. For example, Tanaka et al
performed an OFTT study in 19 healthy adults toobserve the normal postprandial profile of RLPs,
which were found to remain elevated even after
8 hours of OFTT and display a similar profile tototal serum TG (76). Others have assessed differences in the postprandial profile between
healthy and diseased populations. For example,
OFTT cream (Jomo Food Industry) was provided
to T2D patients (normoinsulinemic and hyperinsulinemic) and healthy volunteers at a doseof 17g fat/m2 body surface area and blood samples were collected at fasting, 2 and 4 hours following the oral fat load (66). Although TG levels
did not differ between the three groups, RLP-TG
and RLP-C were higher in hyperinsulinemic T2D
patients compared to the other two groups,
with no difference between normoinsulinemic
T2D and healthy subjects. This suggests that
hyperinsulinemia, rather than the presence ofT2D, may be the causal link between postprandial dyslipidemia and CVD risk. More recently,
Larsen et al found high peak and delayed clearance of serum and CM-TG in obese comparedto healthy adults 6 hours after consumption ofa weight-adjusted (1g fat per kg body weight)
high-fat meal (70% calories from fat) (77).
Postprandial lipemia studies
in pediatrics using OFTT
Although limited, studies have assessed postprandial lipemia in the pediatric population(78–82) (Table 1). The rate of pediatric obesity is increasing significantly worldwide andone in five children with a BMI above the 95th
percentile is hypertriglyceridemic, which is a
7-fold higher rate than for nonobese children(83). Furthermore, vascular pathophysiologicalchanges begin soon after birth and accelerate in adolescence (84). Thus, examining abnormalities in postprandial lipid metabolism inchildren may allow for early identification of
those at risk for additional metabolic co-morbidities. Recent studies found no significant difference in postprandial TG between overweightand normal weight adolescents at fasting, 2
and 4 hours following a high-fat meal (78,79).
However, when Sahade et al examined central

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Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

Table 1

Studies assessing postprandial lipemia after ingestion of a fat-containing
meal in adolescents with obesity and/or associated co-morbidities
Fat-containing
meal

Time
pointsBloodparameters
measured

Reference

Population

Couch et al
Am J Clin
Nutr 2000(82)

60 adolescents(M/F: 27/33,
mean age:
14.0y) fromfamilies withor without
history ofpremature CHD

52.5g fat,24g
carbohydrates,
16g protein
per m2 bodysurface area

0 (fasting),
3, 6, 8hours

Lipids/
Lipoproteins:TC, TG,
HDL-C,LDL-C,
retinylpalmitate,
apoE
genotyping

Delayedpostprandial TG
was associatedwith thecombination ofhigh fasting TG
and low HDL-C.

Moreno etal J Pediatr
Endocrinol
Metab 2001
(80)

12 obeseadolescents(M/F: 5/7, mean
age: 12.8y)

39.0g fat,
47.7g
carbohydrates,
18.5g protein

0 (fasting),
2, 4, 6hours

Lipids/
Lipoproteins:TC, TG,
HDL-C,LDL-C,
apoAI,
apoB

Postprandial
TG positively
correlated withcentral obesity.

Umpaichitra
et al J Pediatr
Endocrinol
Metab 2004
(81)

12 normal
weightadolescents(M/F: 5/7, mean
age: 12.7y)
12 T2Dobese
adolescents(M/F: 5/7, mean
age: 14.0y)

Key findings

Other:
glucose,insulin
117g fat,
41.5g
carbohydrates,
0.5g protein

0 (fasting),
2, 4, 6hours

Lipids/
Lipoproteins:TC, TG,
HDL-C,LDL-C
Other:
glucose,insulin,C-peptide,
HbA1c

15 non-diabetic
obese
adolescents(M/F: 9/6, mean
age: 13.2y)
12 healthyadolescents(M/F: 5/7, mean
age: 14.9y)
Page 176eJIFCC2017Vol28No3pp168-184

Postprandial TG
in T2D obese
adolescents was
associated withthe presenceof insulin
resistance.

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

Sahade et al 49 overweight
Lipids Health adolescents
Dis 2013 (78) (M/F: 20/29,
med(IQR) age:
12.0 (11.0-14.0)y)

25g fat,
25g
carbohydrates,
0g protein

0 (fasting),
2, 4 hours

Other:
glucose,insulin

34 normal
weightadolescents(M/F: 17/17,
med age: 13.0
(11.0-15.3)y)Schauren etal J Dev Orig
Health Dis
2014 (79)Vine etal J Clin
Endocrinol
Metab 2017(85)

38 overweightadolescents(M/F:18/20,
mean age:
14.2y)
24 normal
weightadolescents(M/F: 9/15,
mean age:
14.2y)
12 obesePCOS female
adolescents(mean age:
15.3y)
18 obese female
adolescents(mean age:
14.9y)
10 normal
weight female
adolescents(mean age:
15.6)

Lipids/
Lipoproteins:TC, TG,
HDL-C,
LDL-C

64g fat,69g
carbohydrates,
37g protein

0.61g fat,
0.66g
carbohydrates,
0.16g protein
per kg of bodyweight0 (fasting),
4, 6 hours

0 (fasting),
2, 4, 6, 8
hours

Lipids/
Lipoproteins:TC, TG,
HDL-C,LDL-C

Only overweightadolescentswith insulin
resistance andfasting hypertriglyceridemia
had higher postprandial TG.
Waist
circumference
positively
correlated with
4h TG.
BMI z-scoresignificantly
correlated with
postprandial TG.

Other:
glucose,insulin,fibrinogen,leukocyte count,
hsCRP
Lipids/Obese female
Lipoproteins:adolescents
TC, TG,
with andapoB48,
without
apoB100
PCOS haveexacerbated
Other:
postprandial TG
hormoneand apoB-48
profilecompared tonormal weightcontrols.

*apoA-I: apolipoprotein A-I; apoB-48: apolipoprotein B-48; apoB-100: apolipoprotein B-100;
apoE: apolipoprotein E; BMI: body mass index; CHD: coronary heart disease; HbA1c: haemoglobin A1c;
HDL-C: high-density lipoprotein cholesterol; hsCRP: high-sensitivity C-reactive protein; LDL-C: low-density
lipoprotein cholesterol; PCOS: polycystic ovary syndrome; TC: total cholesterol; TG: triglycerides

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Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

obesity specifically, 4 hour TG levels were significantly higher in subjects with central obesity(78). Similarly, Moreno et al reported that adolescents with central obesity had higher postprandial TG levels compared with subjects
with a peripheral pattern of fat distribution(80). Umpaichitra et al examined postprandial
TG (fasting, 2, 4, and 6 hours after a high-fatmeal) in obese subjects with and without T2D,
as well as non-diabetic, non-obese controls (81).
They found that the degree of insulin resistance, rather than the presence of T2D, determines the degree of postprandial lipemia.
However, additional studies in adolescentsare warranted which measure markers moreindicative of intestinal lipoprotein metabolism (e.g. apoB-48, RLP-C), as well as potential relationships with regulators of lipid metabolism (e.g. GLP-1, GLP‑2). One recent study
measured postprandial apoB-48 in adolescentsfollowing an OFTT (0.61g fat/kg body weight) inobese females with and without polycystic ovary syndrome (i.e. PCOS; a metabolic-endocrine
disorder associated with obesity and insulin resistance), as well as normal weight controls (85).
Obese adolescent females with and without
PCOS were found to have elevated postprandial
TG and apoB-48 compared to controls, and theselevels were associated with indices of adiposity
and insulin resistance (85). These data suggest
apoB-48 may be a good marker of metabolic
disturbances associated with insulin resistancein adolescents, necessitating further studies toconfirm these results, as well as investigate relationships with fasting parameters and hormones
involved in intestinal lipoprotein regulation.
Postprandial biomarkers to measure in OFTTIn addition to the traditional lipid profile (i.e.
TC, LDL-C, HDL-C, TG), it is important for OFTT
studies to measure laboratory markers that are
more reflective of postprandial dyslipidemia
and intestinal lipid metabolism. For example,

measuring apoB-48 (i.e. a measure of CM particle
number) and TRL remnants can provide a more
complete indication of pro-atherogenic remnant accumulation (34). ApoB-48 measurement
allows a direct measure of CMs, as this protein
is specific to intestinally-derived lipoproteins inhumans. ApoB-48 is indicative of CM particle
number, with each CM or CM remnant particle
containing one apoB-48 molecule that does nottransfer to other lipoproteins (86). ApoB-48 canbe determined through commercially available
sandwich ELISA kits (87). Remnant-like particles
can also be measured by commercially available
kits. Japan Immunoresearch Laboratories produce a kit to measure remnant-like particles thatuses an immunoaffinity gel with immobilized
anti-apoAI monoclonal antibodies and a uniqueanti-apoB-100 monoclonal antibody that does
not bind apoE-rich apoB-100 lipoproteins (88).
Lipoproteins that do not bind to the immunoaffinity gel resemble apoE and cholesteryl esterenriched CM and VLDL remnants, called remnant-like particles, from which cholesterol canbe quantified (88). A more recent assay for serum remnant lipoprotein cholesterol (RemL-C)
directly solubilizes and degrades remnants using surfactant and phospholipase-D. This assay
can be performed on an automated analyzer in
only 10 minutes (89). Although RLP-C measurements detect remnants of both intestinal andhepatic origin, examining postprandial RLP-C in
populations with various cardiometabolic conditions can provide an indication of the magnitude and duration of RLP-C in the circulation
following a meal.
Furthermore, measuring markers secreted following meal ingestion, which are involved inregulation of intestinal lipoprotein metabolism,
may aid in early detection of postprandial dyslipidemia and CVD risk. For example, it has beensuggested that postprandial GLP-1 may be an
early biomarker of obesity-associated metabolic dysfunction. After consuming a mixed-meal

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Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment(34% energy from fat), T2D patients had significantly reduced active GLP-1 at 75, 90, and 120
minutes compared to healthy controls (90).
Furthermore, following ingestion of a mixed
meal (42% energy from fat), fasting and postprandial (60-150min) GLP-1 concentration was
significantly lower in T2D compared with healthy
controls (91). However, others found no difference in postprandial GLP-1 between subjects
with and without the metabolic syndrome (i.e. acluster of CVD risk factors including central obesity, fasting dyslipidemia, impaired glucose tolerance/impaired fasting glucose, and hypertension) (92). Overall, pathophysiological changes
in endogenous GLP-1 concentration in obese/insulin resistant states remains controversial, withsome studies indicating an increase (93,94),
decrease (95,96), or unaltered (97) GLP-1 concentration in obese/insulin resistant subjects.
Although studies have measured postprandial
GLP-1, they are often in the context of abnormal glucose metabolism, rather than in relation
to postprandial dyslipidemia. Further studies
are warranted to assess changes in endogenous
GLP-1 concentration in obese and insulin resistant subjects, and examine associations with
postprandial dyslipidemia.
FUTURE DIRECTIONS
A paradigm shift towards measuring postprandial, as opposed to fasting lipids has occurredin recent decades. Some countries have alreadyadopted nonfasting lipid testing (i.e. measured
on a random blood sample irrespective of timesince last meal) in routine practice, including Denmark in 2009 (3), the UK in 2014 (9),as well as Europe (7) and Canada (10) in 2016.
Although several advances have been made inunderstanding dysregulation in intestinal lipid
metabolism in insulin resistant states and its association with CVD, several limitations remain.
Assessment of the functional postprandial lipid profile (i.e. lipid measurements at specified

time points following a standardized meal) is
the preferred methodology to ensure optimalcomparability between test subjects. However,
OFTT methodology remains largely unstandardized, and thus more studies are required to develop standard procedures which are able todistinguish between healthy and at-risk populations, including population-specific meal sizes, nutrient composition, blood sampling time
points, and markers to measure. Additionally,
robust reference values, which are critical to interpret postprandial parameters, remain to beaccurately established. However, these must be
specific to the methodology of the OFTT used.
These limitations become exacerbated in thepediatric population even though the genesis ofthese metabolic disturbances often occur earlyin life. As quantifiable outcome measures areoften harder to detect early in life, it is also difficult to assess predictive ability of biomarkers inchildren and adolescents at risk early enough toprevent disease development later in life.
With the obesity and diabetes epidemics uponus, it is becoming imperative to develop more
effective approaches to assess metabolic abnormalities that increase CVD risk. Recent studies have clearly highlighted the importance ofintestinal lipid dysfunction in pathogenesis ofinsulin resistant and diabetic states. Translating
important new findings from basic researchstudies into the clinic is essential to improve
clinical assessment of postprandial dyslipidemia, increasingly recognized as a major contributor to development of atherosclerosis andfuture CVD. Further studies are also warranted to elucidate mechanisms of postprandial
dyslipidemia associated with insulin resistantstates. A more complete understanding of theunderlying pathobiology will allow subsequentdevelopment of standardized methodologies
and biomarker profiles to be used in clinicalpractice for early and accurate identification of
those at risk for CVD.

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Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

REFERENCES
1. Maillot F, Garrigue MA, Pinault M, Objois M, Théret V,
Lamisse F, et al. Changes in plasma triacylglycerol concentrations after sequential lunch and dinner in healthy subjects. Diabetes Metab. 2005 Feb;31(1):69-77.
2. Freiberg JJ, Tybjaerg-Hansen A, Jensen JS, Nordestgaard BG. Nonfasting triglycerides and risk of ischemic stroke in the general population. JAMA. 2008 Nov
12;300(18):2142–52.
3. Langsted A, Nordestgaard BG. Nonfasting lipids, lipoproteins, and apolipoproteins in individuals withand without diabetes: 58 434 individuals from the Copenhagen General Population Study. Clin Chem. 2011
Mar;57(3):482–9.
4. Mora S, Rifai N, Buring JE, Ridker PM. Comparison of LDL
cholesterol concentrations by Friedewald calculation anddirect measurement in relation to cardiovascular eventsin 27,331 women. Clin Chem. 2009 May;55(5):888–94.
5. Nordestgaard BG, Benn M. Fasting and nonfasting LDLcholesterol: to measure or calculate? Clin Chem. 2009
May;55(5):845–7.
6. Bansal S, Buring JE, Rifai N, Mora S, Sacks FM, Ridker
PM. Fasting compared with nonfasting triglycerides andrisk of cardiovascular events in women. JAMA. 2007 Jul
18;298(3):309–16.
7. Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum
H, Bruckert E, et al. Fasting Is Not Routinely Required for
Determination of a Lipid Profile: Clinical and Laboratory
Implications Including Flagging at Desirable Concentration Cutpoints-A Joint Consensus Statement from theEuropean Atherosclerosis Society and European Federation of Clinical Chemistry and Laboratory Medicine. Clin
Chem. 2016 Jul;62(7):930–46.
8. Patsch JR, Miesenböck G, Hopferwieser T, Mühlberger
V, Knapp E, Dunn JK, et al. Relation of triglyceride metabolism and coronary artery disease. Studies in the
postprandial state. Arterioscler Thromb J Vasc Biol. 1992
Nov;12(11):1336-45.
9. Cardiovascular disease: risk assessment and reduction,
including lipid modifcation. Clinical guideline [CG181] [Internet]. The National Institute for Health and Care Excellence (NICE) UK; 2014 [cited 2017 Jul 28]. Available from:https://www.nice.org.uk/guidance/cg181.
10. Anderson TJ, Grégoire J, Pearson GJ, Barry AR, Couture P, Dawes M, et al. 2016 Canadian CardiovascularSociety Guidelines for the Management of Dyslipidemia
for the Prevention of Cardiovascular Disease in the Adult.
Can J Cardiol. 2016 Nov;32(11):1263-82.
11. Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children andAdolescents, National Heart, Lung, and Blood Institute.
Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics. 2011 Dec;128 Suppl
5:S213-256.
12. Hussain MM. A proposed model for the assembly of
chylomicrons. Atherosclerosis. 2000 Jan;148(1):1–15.
13. Laakso M, Kuusisto J. Insulin resistance and hyperglycaemia in cardiovascular disease development. Nat Rev
Endocrinol. 2014 May;10(5):293–302.
14. Abi Khalil C, Roussel R, Mohammedi K, Danchin N,
Marre M. Cause-specific mortality in diabetes: recentchanges in trend mortality. Eur J Prev Cardiol. 2012
Jun;19(3):374–81.
15. Koopal C, Retterstøl K, Sjouke B, Hovingh GK, Ros E, deGraaf J, et al. Vascular risk factors, vascular disease, lipids
and lipid targets in patients with familial dysbetalipoproteinemia: a European cross-sectional study. Atherosclerosis. 2015 May;240(1):90-7.
16. Gonzalez-Pacheco H, Vallejo M, Altamirano-Castillo
A, Vargas-Barron J, Pina-Reyna Y, Sanchez-Tapia P, et al.
Prevalence of conventional risk factors and lipid profiles
in patients with acute coronary syndrome and significant
coronary disease. Ther Clin Risk Manag. 2014 Oct;815.
17. Karpe F, Boquist S, Tang R, Bond GM, de Faire U, Hamsten A. Remnant lipoproteins are related to intima-media thickness of the carotid artery independently of LDL
cholesterol and plasma triglycerides. J Lipid Res. 2001
Jan;42(1):17–21.
18. Zilversmit DB. Atherogenesis: a postprandial phenomenon. Circulation. 1979 Sep;60(3):473–85.
19. Iso H, Naito Y, Sato S, Kitamura A, Okamura T, Sankai
T, et al. Serum triglycerides and risk of coronary heart disease among Japanese men and women. Am J Epidemiol.
2001 Mar 1;153(5):490–9.
20. Nordestgaard BG, Benn M, Schnohr P, Tybjaerg-Hansen A. Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men andwomen. JAMA. 2007 Jul 18;298(3):299–308.
21. Langsted A, Freiberg JJ, Tybjaerg-Hansen A, Schnohr
P, Jensen GB, Nordestgaard BG. Nonfasting cholesterol
and triglycerides and association with risk of myocardial
infarction and total mortality: the Copenhagen City Heart
Study with 31 years of follow-up. J Intern Med. 2011
Jul;270(1):65–75.
22. Karpe F, Steiner G, Uffelman K, Olivecrona T, Hamsten
A. Postprandial lipoproteins and progression of coronary
atherosclerosis. Atherosclerosis. 1994 Mar;106(1):83–97.
23. Higashi K, Ito T, Nakajima K, Yonemura A, Nakamura
H, Ohsuzu F. Remnant-like particles cholesterol is higher

Page 180

eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

in diabetic patients with coronary artery disease. Metabolism. 2001 Dec;50(12):1462–5.

and the impact for early detection of cardiovascular disease risk. Eur J Clin Invest. 2009 Sep;39(9):741–54.

24. Dallinga-Thie GM, Kroon J, Borén J, Chapman MJ.
Triglyceride-Rich Lipoproteins and Remnants: Targets for
Therapy? Curr Cardiol Rep. 2016 Jul;18(7):67.

35. Fujioka Y, Ishikawa Y. Remnant lipoproteins as strong
key particles to atherogenesis. J Atheroscler Thromb.
2009 Jun;16(3):145–54.

25. Proctor SD, Mamo JCL. Intimal retention of cholesterol derived from apolipoprotein B100- and apolipoprotein B48-containing lipoproteins in carotid arteries of
Watanabe heritable hyperlipidemic rabbits. Arterioscler
Thromb Vasc Biol. 2003 Sep 1;23(9):1595–600.

36. Domoto K, Taniguchi T, Takaishi H, Takahashi T, Fujioka
Y, Takahashi A, et al. Chylomicron remnants induce monocyte chemoattractant protein-1 expression via p38 MAPK
activation in vascular smooth muscle cells. Atherosclerosis. 2003 Dec;171(2):193–200.

26. Proctor SD, Mamo JC. Retention of fluorescent-labelled chylomicron remnants within the intima of thearterial wall--evidence that plaque cholesterol may bederived from post-prandial lipoproteins. Eur J Clin Invest.
1998 Jun;28(6):497–503.

37. Kawasaki S, Taniguchi T, Fujioka Y, Takahashi A, Takahashi T, Domoto K, et al. Chylomicron remnant induces
apoptosis in vascular endothelial cells. Ann N Y Acad Sci.
2000 May;902:336–41.

27. Shaikh M, Wootton R, Nordestgaard BG, Baskerville P,
Lumley JS, La Ville AE, et al. Quantitative studies of transfer in vivo of low density, Sf 12-60, and Sf 60-400 lipoproteins between plasma and arterial intima in humans.
Arterioscler Thromb J Vasc Biol. 1991 Jun;11(3):569–77.
28. Mamo JC, Wheeler JR. Chylomicrons or their remnants penetrate rabbit thoracic aorta as efficiently as do
smaller macromolecules, including low-density lipoprotein, high-density lipoprotein, and albumin. Coron Artery
Dis. 1994 Aug;5(8):695–705.
29. Fielding CJ. Lipoprotein receptors, plasma cholesterol
metabolism, and the regulation of cellular free cholesterol concentration. FASEB J Off Publ Fed Am Soc Exp Biol.
1992 Oct;6(13):3162–8.
30. Proctor SD, Vine DF, Mamo JCL. Arterial permeability
and efflux of apolipoprotein B-containing lipoproteins assessed by in situ perfusion and three-dimensional quantitative confocal microscopy. Arterioscler Thromb Vasc
Biol. 2004 Nov;24(11):2162–7.
31. Proctor SD, Vine DF, Mamo JCL. Arterial retention of apolipoprotein B(48)- and B(100)-containing lipoproteins in atherogenesis. Curr Opin Lipidol. 2002
Oct;13(5):461–70.
32. Eiselein L, Wilson DW, Lame MW, Rutledge JC. Lipolysis products from triglyceride-rich lipoproteins increase
endothelial permeability, perturb zonula occludens-1
and F-actin, and induce apoptosis. AJP Heart Circ Physiol.
2007 Mar 9;292(6):H2745–53.
33. Vine DF, Takechi R, Russell JC, Proctor SD. Impaired
postprandial apolipoprotein-B48 metabolism in theobese, insulin-resistant JCR:LA-cp rat: increased atherogenicity for the metabolic syndrome. Atherosclerosis.
2007 Feb;190(2):282–90.
34. Su JW, Nzekwu M-MU, Cabezas MC, Redgrave T, Proctor
SD. Methods to assess impaired post-prandial metabolism

38. Morimoto S, Fujioka Y, Hosoai H, Okumura T, Masai M,
Sakoda T, et al. The renin-angiotensin system is involvedin the production of plasminogen activator inhibitor type
1 by cultured endothelial cells in response to chylomicron
remnants. Hypertens Res Off J Jpn Soc Hypertens. 2003
Apr;26(4):315–23.
39. Beckman LM, Beckman TR, Earthman CP. Changes ingastrointestinal hormones and leptin after Roux-en-Y gastric bypass procedure: a review. J Am Diet Assoc. 2010
Apr;110(4):571–84.
40. Nguyen NT, Varela E, Sabio A, Tran C-L, Stamos M,
Wilson SE. Resolution of hyperlipidemia after laparoscopic Roux-en-Y gastric bypass. J Am Coll Surg. 2006
Jul;203(1):24–9.
41. Hein GJ, Baker C, Hsieh J, Farr S, Adeli K. GLP-1 and
GLP-2 as yin and yang of intestinal lipoprotein production: evidence for predominance of GLP-2-stimulated
postprandial lipemia in normal and insulin-resistantstates. Diabetes. 2013 Feb;62(2):373–81.
42. Hsieh J, Longuet C, Baker CL, Qin B, Federico LM,
Drucker DJ, et al. The glucagon-like peptide 1 receptor is essential for postprandial lipoprotein synthesis
and secretion in hamsters and mice. Diabetologia. 2010
Mar;53(3):552–61.
43. Schwartz EA, Koska J, Mullin MP, Syoufi I, Schwenke
DC, Reaven PD. Exenatide suppresses postprandial elevations in lipids and lipoproteins in individuals with impaired glucose tolerance and recent onset type 2 diabetes mellitus. Atherosclerosis. 2010 Sep;212(1):217–22.
44. Xiao C, Bandsma RHJ, Dash S, Szeto L, Lewis GF. Exenatide, a glucagon-like peptide-1 receptor agonist,
acutely inhibits intestinal lipoprotein production in
healthy humans. Arterioscler Thromb Vasc Biol. 2012
Jun;32(6):1513–9.
45. Hsieh J, Trajcevski KE, Farr SL, Baker CL, Lake EJ, Taher J, et al. Glucagon-Like Peptide 2 (GLP-2) Stimulates
Postprandial Chylomicron Production and Postabsorptive

Page 181

eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

Release of Intestinal Triglyceride Storage Pools via Induction of Nitric Oxide Signaling in Male Hamsters and Mice.
Endocrinology. 2015 Oct;156(10):3538–47.
46. Dash S, Xiao C, Morgantini C, Connelly PW, Patterson
BW, Lewis GF. Glucagon-like peptide-2 regulates releaseof chylomicrons from the intestine. Gastroenterology.
2014 Dec;147(6):1275–1284.e4.
47. Baggio LL, Drucker DJ. Biology of incretins: GLP-1 and
GIP. Gastroenterology. 2007 May;132(6):2131–57.
48. Xiao C, Dash S, Morgantini C, Patterson BW, Lewis GF.
Sitagliptin, a DPP-4 inhibitor, acutely inhibits intestinal lipoprotein particle secretion in healthy humans. Diabetes.
2014 Jul;63(7):2394–401.
49. Tremblay AJ, Lamarche B, Deacon CF, Weisnagel SJ,
Couture P. Effect of sitagliptin therapy on postprandial
lipoprotein levels in patients with type 2 diabetes. Diabetes Obes Metab. 2011 Apr;13(4):366–73.
50. Loirdighi N, Ménard D, Levy E. Insulin decreases chylomicron production in human fetal small intestine. Biochim Biophys Acta. 1992 Dec 15;1175(1):100-6.
51. Pavlic M, Xiao C, Szeto L, Patterson BW, Lewis GF. Insulin acutely inhibits intestinal lipoprotein secretion inhumans in part by suppressing plasma free fatty acids.
Diabetes. 2010 Mar;59(3):580–7.
52. Qin X, Shen H, Liu M, Yang Q, Zheng S, Sabo M, et al.
GLP-1 reduces intestinal lymph flow, triglyceride absorption, and apolipoprotein production in rats. Am J Physiol
Gastrointest Liver Physiol. 2005 May;288(5):G943-949.
53. Tremblay AJ, Lamarche B, Kelly I, Charest A, Lépine
M-C, Droit A, et al. Effect of sitagliptin therapy on triglyceride-rich lipoprotein kinetics in patients with type 2 diabetes. Diabetes Obes Metab. 2014 Dec;16(12):1223-9.
54. Wettergren A, Schjoldager B, Mortensen PE, Myhre
J, Christiansen J, Holst JJ. Truncated GLP-1 (proglucagon
78-107-amide) inhibits gastric and pancreatic functions inman. Dig Dis Sci. 1993 Apr;38(4):665–73.

58. Sangle GV, Lauffer LM, Grieco A, Trivedi S, Iakoubov R,
Brubaker PL. Novel biological action of the dipeptidylpeptidase-IV inhibitor, sitagliptin, as a glucagon-like peptide-1
secretagogue. Endocrinology. 2012 Feb;153(2):564–73.
59. Kumar V, Madhu SV, Singh G, Gambhir JK. Post-prandial hypertriglyceridemia in patients with type 2 diabetes
mellitus with and without macrovascular disease. J Assoc
Physicians India. 2010 Oct;58:603–7.
60. Miettinen H, Lehto S, Salomaa V, Mähönen M,
Niemelä M, Haffner SM, et al. Impact of diabetes on mortality after the first myocardial infarction. The FINMONICA Myocardial Infarction Register Study Group. Diabetes
Care. 1998 Jan;21(1):69-75.
61. Coutinho M, Gerstein HC, Wang Y, Yusuf S. The relationship between glucose and incident cardiovascular
events. A metaregression analysis of published data from20 studies of 95,783 individuals followed for 12.4 years.
Diabetes Care. 1999 Feb;22(2):233–40.
62. Duez H, Pavlic M, Lewis GF. Mechanism of intestinal
lipoprotein overproduction in insulin resistant humans.
Atheroscler Suppl. 2008 Sep;9(2):33–8.
63. Rivellese AA, De Natale C, Di Marino L, Patti L, Iovine C,
Coppola S, et al. Exogenous and endogenous postprandial lipid abnormalities in type 2 diabetic patients with optimal blood glucose control and optimal fasting triglyceride
levels. J Clin Endocrinol Metab. 2004 May;89(5):2153–9.
64. Duez H, Lamarche B, Uffelman KD, Valero R, Cohn JS,
Lewis GF. Hyperinsulinemia is associated with increased
production rate of intestinal apolipoprotein B-48-containing lipoproteins in humans. Arterioscler Thromb Vasc
Biol. 2006 Jun;26(6):1357–63.
65. Hogue J-C, Lamarche B, Tremblay AJ, Bergeron J, Gagné C, Couture P. Evidence of increased secretion of apolipoprotein B-48-containing lipoproteins in subjects with
type 2 diabetes. J Lipid Res. 2007 Jun;48(6):1336-42.

55. Larsen PJ, Tang-Christensen M, Holst JJ, Orskov C.
Distribution of glucagon-like peptide-1 and other preproglucagon-derived peptides in the rat hypothalamus andbrainstem. Neuroscience. 1997 Mar;77(1):257–70.

66. Wang F, Lu H, Liu F, Cai H, Xia H, Guo F, et al. Consumption of a liquid high-fat meal increases triglycerides
but decreases high-density lipoprotein cholesterol in abdominally obese subjects with high postprandial insulin
resistance. Nutr Res N Y N. 2017 Jul;43:82–8.

56. Farr S, Baker C, Naples M, Taher J, Iqbal J, Hussain
M, et al. Central Nervous System Regulation of Intestinal Lipoprotein Metabolism by Glucagon-Like Peptide-1
via a Brain-Gut Axis. Arterioscler Thromb Vasc Biol. 2015
May;35(5):1092–100.

67. Ai M, Tanaka A, Ogita K, Sekinc M, Numano F, Numano F, et al. Relationship between plasma insulin concentration and plasma remnant lipoprotein response to anoral fat load in patients with type 2 diabetes. J Am Coll
Cardiol. 2001 Nov 15;38(6):1628–32.

57. Yamamoto H, Lee CE, Marcus JN, Williams TD, Overton JM, Lopez ME, et al. Glucagon-like peptide-1 receptor stimulation increases blood pressure and heart rate
and activates autonomic regulatory neurons. J Clin Invest.
2002 Jul;110(1):43–52.

68. Levy E, Sinnett D, Thibault L, Nguyen TD, Delvin E, Ménard D. Insulin modulation of newly synthesized apolipoproteins B-100 and B-48 in human fetal intestine: gene
expression and mRNA editing are not involved. FEBS Lett.
1996 Sep 16;393(2-3):253-8.

Page 182

eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

69. Federico LM, Naples M, Taylor D, Adeli K. Intestinal
insulin resistance and aberrant production of apolipoprotein B48 lipoproteins in an animal model of insulin resistance and metabolic dyslipidemia: evidence for activationof protein tyrosine phosphatase-1B, extracellular signalrelated kinase, and sterol regulatory element-binding
protein-1c in the fructose-fed hamster intestine. Diabetes. 2006 May;55(5):1316–26.
70. Haidari M, Leung N, Mahbub F, Uffelman KD, KohenAvramoglu R, Lewis GF, et al. Fasting and postprandial
overproduction of intestinally derived lipoproteins in an
animal model of insulin resistance. Evidence that chronic
fructose feeding in the hamster is accompanied by enhanced intestinal de novo lipogenesis and ApoB48-containing lipoprotein overproduction. J Biol Chem. 2002
Aug 30;277(35):31646–55.
71. Casaschi A, Maiyoh GK, Adeli K, Theriault AG. Increased diacylglycerol acyltransferase activity is associated with triglyceride accumulation in tissues ofdiet-induced insulin-resistant hyperlipidemic hamsters.
Metabolism. 2005 Mar;54(3):403–9.
72. Hsieh J, Hayashi AA, Webb J, Adeli K. Postprandial
dyslipidemia in insulin resistance: mechanisms and roleof intestinal insulin sensitivity. Atheroscler Suppl. 2008
Sep;9(2):7–13.
73. American Diabetes Association. (2) Classificationand diagnosis of diabetes. Diabetes Care. 2015 Jan;38
Suppl:S8–16.
74. Lairon D, Lopez-Miranda J, Williams C. Methodology for studying postprandial lipid metabolism. Eur J Clin
Nutr. 2007 Oct;61(10):1145–61.
75. Sahade V, França S, Badaró R, Fernando Adán L. Obesity and postprandial lipemia in adolescents: risk factors
for cardiovascular disease. Endocrinol Nutr Organo Soc
Espanola Endocrinol Nutr. 2012 Feb;59(2):131-9.

80. Moreno LA, Quintela I, Fleta J, Sarría A, Roda L, GinerA, et al. Postprandial triglyceridemia in obese and nonobese adolescents. Importance of body composition andfat distribution. J Pediatr Endocrinol Metab JPEM. 2001
Feb;14(2):193-202.
81. Umpaichitra V, Banerji MA, Castells S. Postprandial
hyperlipidemia after a fat loading test in minority adolescents with type 2 diabetes mellitus and obesity. J Pediatr
Endocrinol Metab JPEM. 2004 Jun;17(6):853–64.
82. Couch SC, Isasi CR, Karmally W, Blaner WS, Starc TJ,
Kaluski D, et al. Predictors of postprandial triacylglycerol
response in children: the Columbia University Biomarkers
Study. Am J Clin Nutr. 2000 Nov;72(5):1119–27.
83. Miller M, Stone NJ, Ballantyne C, Bittner V, Criqui MH,
Ginsberg HN, et al. Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2011 May 24;123(20):2292–333.
84. McGill HC, McMahan CA, Herderick EE, Tracy RE, Malcom GT, Zieske AW, et al. Effects of coronary heart diseaserisk factors on atherosclerosis of selected regions of theaorta and right coronary artery. PDAY Research Group.
Pathobiological Determinants of Atherosclerosis in Youth.
Arterioscler Thromb Vasc Biol. 2000 Mar;20(3):836–45.
85. Vine DF, Wang Y, Jetha MM, Ball GD, Proctor SD. Impaired ApoB-Lipoprotein and Triglyceride Metabolism inObese Adolescents With Polycystic Ovary Syndrome. J
Clin Endocrinol Metab. 2017 Mar 1;102(3):970–82.
86. Phillips ML, Pullinger C, Kroes I, Kroes J, Hardman DA,
Chen G, et al. A single copy of apolipoprotein B-48 is present on the human chylomicron remnant. J Lipid Res. 1997
Jun;38(6):1170–7.
87. Kinoshita M, Kojima M, Matsushima T, Teramoto T.
Determination of apolipoprotein B-48 in serum by asandwich ELISA. Clin Chim Acta Int J Clin Chem. 2005
Jan;351(1–2):115–20.

76. Tanaka A, Tomie N, Nakano T, Nakajima K, Yui K,
Tamura M, et al. Measurement of postprandial remnantlike particles (RLPs) following a fat-loading test. Clin Chim
Acta Int J Clin Chem. 1998 Jul 6;275(1):43–52.

88. Nakajima K, Saito T, Tamura A, Suzuki M, Nakano T,
Adachi M, et al. Cholesterol in remnant-like lipoproteinsin human serum using monoclonal anti apo B-100 andanti apo A-I immunoaffinity mixed gels. Clin Chim Acta Int
J Clin Chem. 1993 Dec 31;223(1–2):53–71.

77. Larsen MA, Goll R, Lekahl S, Moen OS, Florholmen J. Delayed clearance of triglyceride-rich lipoproteins in young, healthy obese subjects. Clin Obes. 2015
Dec;5(6):349–57.

89. Miyauchi K, Kayahara N, Ishigami M, Kuwata H, Mori
H, Sugiuchi H, et al. Development of a homogeneous
assay to measure remnant lipoprotein cholesterol. Clin
Chem. 2007 Dec;53(12):2128–35.

78. Sahade V, França S, Adan LF. The influence of weightexcess on the postprandial lipemia in adolescents. Lipids
Health Dis. 2013 Feb 13;12:17.

90. Vilsbøll T, Krarup T, Deacon CF, Madsbad S, Holst JJ.
Reduced postprandial concentrations of intact biologically active glucagon-like peptide 1 in type 2 diabetic patients. Diabetes. 2001 Mar;50(3):609-13.

79. Schauren BC, Portal VL, Beltrami FG, dos Santos TJ,
Pellanda LC. Postprandial metabolism and inflammatory
markers in overweight adolescents. J Dev Orig Health Dis.
2014 Aug;5(4):299–306.

91. Toft-Nielsen MB, Damholt MB, Madsbad S, Hilsted
LM, Hughes TE, Michelsen BK, et al. Determinants of
the impaired secretion of glucagon-like peptide-1 inPage 183

eJIFCC2017Vol28No3pp168-184

Victoria Higgins, Khosrow Adeli
Postprandial dyslipidemia: pathophysiology and cardiovascular disease risk assessment

type 2 diabetic patients. J Clin Endocrinol Metab. 2001
Aug;86(8):3717–23.

oral glucose and intravenous arginine. J Clin Invest. 1991
Feb;87(2):415–23.

92. Kiec-Klimczak M, Malczewska-Malec M, Razny U,
Zdzienicka A, Gruca A, Goralska J, et al. Assessment of
incretins in oral glucose and lipid tolerance tests may be
indicative in the diagnosis of metabolic syndrome aggravation. J Physiol Pharmacol Off J Pol Physiol Soc. 2016
Apr;67(2):217–26.

95. Ranganath LR, Beety JM, Morgan LM, Wright JW,
Howland R, Marks V. Attenuated GLP-1 secretion in obesity: cause or consequence? Gut. 1996 Jun;38(6):916–9.

93. Fukase N, Igarashi M, Takahashi H, Manaka H, Yamatani K, Daimon M, et al. Hypersecretion of truncatedglucagon-like peptide-1 and gastric inhibitory polypeptide in obese patients. Diabet Med J Br Diabet Assoc.
1993 Feb;10(1):44–9.
94. Orskov C, Jeppesen J, Madsbad S, Holst JJ. Proglucagon products in plasma of noninsulin-dependent diabetics and nondiabetic controls in the fasting state and after

96. Vaag AA, Holst JJ, Vølund A, Beck-Nielsen HB. Gut
incretin hormones in identical twins discordant for noninsulin-dependent diabetes mellitus (NIDDM)--evidence
for decreased glucagon-like peptide 1 secretion during
oral glucose ingestion in NIDDM twins. Eur J Endocrinol.
1996 Oct;135(4):425-32.
97. Ahrén B, Larsson H, Holst JJ. Reduced gastric inhibitory polypeptide but normal glucagon-like peptide 1
response to oral glucose in postmenopausal womenwith impaired glucose tolerance. Eur J Endocrinol. 1997
Aug;137(2):127-31.

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e-Learning: a model to support ongoing education
Ronda F. Greaves1,212School of Health & Biomedical Sciences, RMIT University, Victoria, Australia
Murdoch Children’s Research Institute, Melbourne, Australia

INFO

OPINION

Corresponding author:
Ronda Greaves
School of Health and Biomedical Sciences
RMIT University
PO Box 71, Bundoora, Victoria
3083 Australia
Phone: +61 (0)3 9925 7080
E-mail: ronda.greaves@rmit.edu.au

INTRODUCTION

Key words:
e-Learning, blended learning,on-going education, continuing
professional development
Funding:
There was no specific grant for this work.
Financial disclosure:
The author has no financial relationships
relevant to this article to disclose.
Conflict of interest:
No conflict of interest exists in relation
to this work.

Education is fundamental for creating opportunities,
empowering individuals and stimulating long-lastingchange in communities. It is internationally recognised that education is the right of every child andmust be equally accessible to girls and boys and be
free and fair [1]. Access to education relates to all
children attending and completing primary school,secondary school and tertiary or vocational education. In this process great teachers have the power toinstil a love of learning, leading students to embraceeducation as a vital tool that provides opportunities
to foster growth and success for themselves and thewider community. Many of these fundamental concepts surrounding education can be equally applied
to adult learners. (Figure 1)
The opportunity for on-going education for professionals in medical science is important for ensuringthat the quality of patient care remains optimal.
International guidelines and standards, such as
ISO15189:2012, include requirements for on-goingeducation in their documents to ensure employers
and employees are held accountable for this activity [2]. However, the reality is that developed urban
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e-Learning: a model to support ongoing education

Figure 1

Article 28 of the Convention on the Rights of the Child

1. States Parties recognize the right of the child to education, and with a view to achieving thisright progressively and on the basis of equal opportunity, they shall, in particular:(a) Make primary education compulsory and available free to all;(b) Encourage the development of different forms of secondary education, including general andvocational education, make them available and accessible to every child, and take appropriate
measures such as the introduction of free education and offering financial assistance in case ofneed;(c) Make higher education accessible to all on the basis of capacity by every appropriate means;(d) Make educational and vocational information and guidance available and accessible to all
children;(e) Take measures to encourage regular attendance at schools and the reduction of drop-out
rates.
2. States Parties shall take all appropriate measures to ensure that school discipline is administered in a manner consistent with the child’s human dignity and in conformity with the present
Convention.
3. “States Parties shall promote and encourage international cooperation in matters relating to
education, in particular with a view to contributing to the elimination of ignorance and illiteracy
throughout the world and facilitating access to scientific and technical knowledge and modern
teaching methods. In this regard, particular account shall be taken of the needs of developing
countries” [1].
Article 28 of the Convention on the Rights of the Child which was adopted and opened for signature, ratification andaccession by General Assembly resolution 44/25 of 20 November 1989 entry into force 2 September 1990, in accordance with article 49 of the United Nationals. Many of these principles, particularly 28.1c and 28.3, can be applied
to the direction of efforts for ongoing education in medical science (Figure 1).

communities have greater access to on-goingeducation compared to rural, remote anddeveloping communities. Now we possess
the information communication technology(ICT) capability to make significant steps inaddressing this disparity. As such, e-Learning
can be implemented to reach out to healthprofessionals who have previously not had
the same access to opportunities for professional development.
This manuscript is an outcome of a presentation which aimed to discuss the development ofan e-Learning strategy for the medical science

community in Myanmar. To achieve this aim,some definitions, examples, recommendations
and personal thoughts related to e-Learning areprovided.
LEARNING STYLES AND STRATEGIES
On-going education for health professionals
is important for optimising the quality of patient care. The preferred method for providing
professional development activities remains
debated, and this is not surprising given that
individuals can have different styles of learning [3].

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The main learning styles are: visual learners (divided further into two sub-channels of linguistic
and spatial); auditory learners; and kinaesthetic
learners (divided further into two sub-channelsof movement and tactile i.e. touch). Ideally ongoing education activities should encompass
aspects that incorporate all of these learning
styles.
There are seven main learning strategies foron-going education; these being lectures, reading, audio-visual, demonstration, discussion,practical doing and teaching others. Lectures,
reading and audio-visual strategies are key activities that can be provided face-to-face anddelivered equally well remotely through ICT.
Demonstrations can also be provided as partof face-to-face training or incorporated into
audio-visual e-Learning. Discussions and practicals are often best delivered face-to-face, although there are some examples of applying
these on-line. Finally, “teaching others” by anyof the other six strategies provides an effective
learning approach; hence learners should be
encouraged to deliver professional development activities. Each of these learning strategies has been applied with varying success to
e-Learning.
A simple and clear definition of e-Learning is“learning conducted via electronic media, typically on the Internet” [4]. E-Learning’s greatest
advantage over many other forms of education delivery is that it can be accessed twenty
four hours a day seven days per week from almost anywhere and is cost effective. As such,
e-Learning activities have increased significantly
in recent years, and many industries worldwide
have actively embraced this approach for compliance management and on-going professional
development. The breadth of activities has led
to a variety of terms used synonymously with
e-Learning (including blended learning, on-line
learning and distance education) and the definition can vary as the result of adapting the term

for an organisation’s specific purpose [5]. Whilst
e-Learning offers many advantages, the computer based approach cannot currently achieve
all goals, especially related to hands on practicalexperience. Hence, universities in particular are
now embracing the concept of “blended learning” which combines e-Learning with face-to-faceworkshops/practicals. Figure 2 demonstrates thepotential approaches for blended learning, demonstrating the main role of e-Learning.
PROFESSIONAL SOCIETIES’ROLE IN e-LEARNING
Significant professional and personal benefits
arise from individuals engaging with their relevant professional society. Engagement provides
a network for individuals to exchange ideas andremain up to date with wider developments inthe field. Often, a primary goal of such societies is to promote on-going education activities
which are often valued by industry for theirperceived relevance and commercial independence. Whilst traditional face-to-face education
may provide greater networking opportunities,
it may also potentially limit the access and participation of professionals working outside ofmajor cities. With the consistent improvements
in ICT there are now options to supplement or
replace these face-to-face activities with online learning i.e. e-Learning. Online learning
activities, such as live streaming and recordingof webinars, enable societies to reach out tocolleagues who otherwise may not be able toparticipate. In addition, an important aspect of
on-line education is its potential to eliminate orminimise the duplication of activities and provide a broader and more comprehensive approach to continuing professional development.
“The International Federation of Clinical Chemistry
and Laboratory Medicine (IFCC) is a worldwide,
non-political organisation for clinical chemistry andlaboratory medicine”. In 2012 the IFCC endorsed

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Figure 2

Seven learning strategies for ongoing educationin the blended learning model

Blended Learning
LectureReading

Audio-visuale-Learning

Demonstration

Discussion

Practice Doing

face to face training

Teaching
Others

Lectures, reading and audio-visual are key activities that can be incorporated into e-Learning.
Demonstrations are categorised as face to face training, however they may also be incorporated into audio-visuale-Learning.
Discussions and practicals are often best delivered face to face, although there are some examples of applying theseto e-Learning.
Finally, teaching by any of these six methods provides an effective learning approach; hence learners should be
encouraged to contribute to professional development activities.

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two committees (one to develop education material and the other to implement the ICT strategy)
to work collaboratively for the new IFCC e-Academy. The “IFCC e-Academy is an open educational
resource created and/or reviewed by IFCC experts
for the continuous professional development ofmembers of IFCC member organisations” globally[6]. This e-Learning resource has a focus to support the national professional societies that do
not already have an established on-going education framework; this equates to focused support
for developing economies.
The timeline and overall development of the IFCC
e-Academy can generally be surmised as the following: 2011 plan by the IFCC and developmentof the terms of reference; 2012 committees wereformed and first meetings conducted; 2013 was
the development, distribution to member societies and review of a questionnaire; 2014 material
was sourced, and website, copyrights and gaps
reviewed; 2015 the pilot e-Academy module waslaunched [7]; 2016 saw the review, improvement
and expansion of e-Academy content; and 2017
has involved finalising the curriculum and mapping content to the curriculum. Although there is
still work to be done, there are already significant
demonstrated advantages of the e-Academy, including that the material is free open access andobtained from resources worldwide. In addition,
this resource is not static and will provide interactive links in the medium term.
RECOMMENDATIONSContinuing professional development is importantfor medical quality. Edwards Deming is a globally
recognised champion of quality who implemented Deming’s management improvement cycle toprovide a mechanism to move forward within aquality framework. Deming’s simple “Plan-DoCheck-Act” (PDCA), also known as “Plan-DoStudy-Act”, approach has been used and updated
since its inception, but the underlying approach isstill applicable to project management today [8].
The PDCA cycle process can be directly related tothe concept of continuous improvement, which
in turn can be related to continuous learning
throughout our careers. As such the PDCA cycle
can be directly applied to learning and to the development of an e-Learning strategy.
Plan-do-check-act is considered a cyclical process that provides a mechanism to efficiently
utilise resources and implement in stages. It isakin to the lean start-up methodology whereby
a minimum viable product (MVP) is developed
then subjected to testing to validate its usefulness. The learning’s from this process arethen utilised to improve its effectiveness anddevelop other products or features of value.
Unwanted features and items not aligned tothe mission statement are to be discarded or atleast “parked” for future review. In applying thismethodology to e-Learning the focus must remain on the establishment of a valued and credible point of learning.
The initial PDCA cycle to apply to an e-Learning
strategy could involve the following:
Plan
Create a committee that engages both local andregional stakeholders, ensuring the people nominated comprise a variety of skill sets. Establish
a communication system for the committee todiscuss activities between official meetings. The
committee and/or people forming the committee should develop a terms of reference (TOR) to
guide the committee’s activities. It is important tounderstand and work within any ICT limitations –these may be different for various parts of thecountry. Once these are understood, then proceed to investigate what is already available; i.e.
there is no need to “reinvent the wheel”. From
this a vision can be synthesized and framed intoa mission statement as an established point ofreference for ongoing planning and decisions.

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2. central office;Find and utilise resources to build the e-Learning platform. A website, establishes the connection to the intended customers of the endeavour. The website should target only the main
objectives and provide only the minimum content consistent with the mission statement yet
allow for expansion or evolution to a new platform at a later date. This activity establishes thefundamental building blocks.

3. regular management meetings; andCheck
All stakeholders engaged in the development of
the terms of reference should be encouraged tointeract with the website as a process of validating the result of the initial endeavour against
the mission statement. This engagement process should stimulate great contribution anddiscussion as to the content and priority whichshould be established.
In addition, the development and distributionof a questionnaire of needs should be a priority; later review questionnaires can also be
used to see what people think about the success of the activity. The de-identified results of
the questionnaire/s should be reviewed by thecommittee to:
1. look for opportunities for improvement; and2. summarise and share back to stakeholders.
This primary information can be used to develop
a pilot education strategy, e.g. one topic, should
be developed and trialled. Recording (i.e. documenting) and analysis of this feedback should
guide the next steps.

Laying out the curriculum, prioritising the content development and establishing key roles
for the ongoing maintenance and updating the
web page should suffice for the initial iterationof the PDCA cycle for establishing e-learning.
Individual learning styles and strategies should
be considered throughout the cycles to ensure
the developed e-Learning model for on-goingeducation is effective.
After the launch of your e-Learning initiative it
is still necessary to perform an on-going review
and have or develop a plan to expand to otherregions. In these later cycles activities can be reviewed by:
1. asking people what worked welland what needs improvements;
2. drilling down to the root causeof any problems;
3. reviewing and refining the plan;
4. identifying gaps; and5. deciding if more pilots are needed.
Finally, the success of the e-Learning projectwill also depend on the self-motivation of individuals to study effectively and therefore thecreation of a reward system should be considered to support participant motivation.
There are some important general points to avoid
in the development of the-Learning strategy:
1. Do not make the plan overly complicated;

Act
The “act” will be to roll-out the e-Learning strategy. In preparation for the “role out” of the eLearning approach, ideally the following should
also be in place:
1. web page that is low tech;

4. on-going planning.

2. Don’t be afraid to receive feedback;3. Do not limit it to one type of activity;
4. Don’t make the committee too large andensure members are activity participating- this can usually be achieved through thePage 190

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e-Learning: a model to support ongoing education

development of committee rules related
to structure, expectations and length ofmembership;

In these respects, the Myanmar Academy ofMedical Science is well placed to lead the development and implementation of e-Learning.

5. The final don’t is – do not reinvent thewheel i.e. if the relevant e-Learning information is already freely available make useof it e.g. the IFCC e-Academy.
Relationship to WHO eHealthIn relation to the World Health Organization’s eHealth plan there are many synergies that could
be adapted across to e-Learning [9]. An outlineof the WHO strategy adapted to e-Learning is
generally concordant with the suggested PDCA
approach as it includes:
1. identifying and engaging with the key stakeholders who will need to be involved in thedevelopment of the e-Learning vision andplan and its subsequent implementation;
2. establishing a governance mechanism
to provide improved visibility, coordinationand control;
3. establishing the strategic context to enable
the Ministry of Health and Sport to assess
and make informed decisions on whether
to pursue opportunities that present themselves from the ICT industry and otherstakeholders; and4. assessing the current e-Learning environment in terms of the components that already exist as well as existing programmes or
projects that will deliver on-going education.
Summary
In summary, the development and implementation of an e-Learning program will take planningand courage: clear communication, observation
via pilot studies, working within the uncertainty
of ICT, reviewing strategies, asking questionsof the intended students, and development ofguidelines or terms of reference and evaluationof the success against the criteria developed.

FINAL THOUGHTS
Education is about creating opportunities and asignificant driver for on-going education is passion. In the opening address for the Myanmar
National Education Strategic Plan 2016 – 2021,
Aung Sun Suu Kyi stated “all the leading implementers who set up policies and teachers mustencourage the desire to learn mindset, and they
themselves need to have the desire to learn inorder to be able to spread the sentiment widely” [10]. Supporting the implementation andoverall success of e-Learning for ongoing professional development in Medical Science requires this commitment and desire for life-longlearning at all levels.


Acknowledgements
This manuscript is the outcome of the presentation delivered as part of the “Symposium on
Development of eHealth in Myanmar”. This symposium was organised by the Myanmar Academyof Medical Science (MAMS) and presented on
Saturday 25th February 2017 at the Departmentof Medical Research (DMR), Ministry of Healthand Sport (MOHS), Yangon. I wish to express mysincere thanks for the invitation to present on
on-going education and the warm hospitality
afforded by Prof. Col. Myo Nyunt (Vice Presidentof MAMS) and his wife and also Prof. Dr. U Ne
Win (President of MAMS). I also would like to acknowledge all the other speakers, delegates andsupport staff for the enjoyment and friendshipexpressed and felt during this visit to Yangon. I
hope you have found the information provided auseful base to further your discussions, as I havecertainly learnt also from this experience. Thankyou.

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REFERENCES

6. http://eacademy.ifcc.org/. Accessed 26th March 2017.

1. Article 28, Convention on the Rights of the Child, 1989.
http://www.ohchr.org/EN/ProfessionalInterest/Pages/
CRC.aspx. Accessed 26th March 2017.

7. Greaves RF, Grant J, Smith JM, Vervaart PP. Developingof online content: the mass spectrometry module. Symposium Abstract – IFCC EuroMedLab Paris 2015, 21-25
June 2015 • Clin Chem Lab Med 2015; 53, Special Suppl,
p S42. DOI 10.1515/cclm-2015-5001.

2. International Standard. ISO 15189:2012. Medical laboratories — Requirements for quality and competence.
3. http://www.nwlink.com/~donclark/hrd/styles/vakt.html.
Accessed 5th April 2017.
4. https://www.google.com.au/webhp?sourceid=chromeinstant&ion=1&espv=2&ie=UTF-8#q=what+is+e+learning&*.
Accessed 20th February 2017.
5. Google search (www.google.com.au) “ what is e Learning ?” returned 44,800,000 in 35 seconds on 14th February 2017 and 60,900,000 in 60 seconds on 26th March2017.

8. https://blog.deming.org/2015/05/the-history-and-evolution-of-the-pdsa-cycle/. Accessed 20th February 2017.
9. World Health Organization National eHealth Strategy
Toolkit. http://www.who.int/ehealth/publications/overview.pdf. Accessed 26th March 2017.
10. Aung Sun Suu Kyi. Speech to the Opening ceremony of
Myanmar National Education Strategic Plan 2016 – 2021.
Held 23rd Feb 2017, Myanmar International Convention
Centre - Nay Pyi Taw. Report - The Global New Light ofMyanmar 24th Feb 2017.

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About the “Compendium of Terminology
and Nomenclature of Properties in ClinicalLaboratory Sciences”
Georges Férard1,*, René Dybkaer2,*, Xavier Fuentes-Arderiu3,*

University of Strasbourg, France (retired)Department of Standardization in Laboratory Medicine, Frederiksberg Hospital, Denmark
3
Clinical Laboratory Sciences C, Barcelona, Spain (retired)* On behalf of the joint IFCC and IUPAC Committee-Subcommittee
on Nomenclature for Properties and Units (C-SC-NPU)
1
2

INFO

BOOK REVIEWCorresponding author:
Georges Férard
Phone: 00 33 3 31 28 60
E-mail: georges.ferard@noos.fr

Since the publication of the ‘‘Compendium of Terminology
and Nomenclature of Properties in Clinical Laboratory
Sciences’’ (Silver Book) in 1995, significant development in metrological concepts and terminology has occurred. The second edition of the Silver Book [1], published in 2017 with the joint support of IFCC and IUPAC
updates recommendations and technical reports forthe clinical laboratory sciences. The book describes
a common structure and language for a reliable exchange of person examination data. This format, developed by the IFCC-IUPAC Committee-Subcommittee on
Nomenclature for Properties and Units (NPU), applies
to multiple disciplines, including clinical allergology,
clinical chemistry, clinical haematology, clinical immunology and blood banking, clinical microbiology, clinicalpharmacology, molecular biology and genetics, reproduction and fertility, thrombosis and haemostasis, andtoxicology. It is adapted both to quantitative and qualitative information (where no magnitude is involved).
Concepts and rules in the communication of clinicallaboratory information are explained and numerous
examples are given to ensure interoperability between
the various participants in clinical laboratory sciences.

Key words:
Silver Book, nomenclature for properties
and units, transmission of clinicallaboratory information

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Georges Férard, René Dybkaer, Xavier Fuentes-Arderiu
About the “Compendium of Terminology and Nomenclature of Properties in Clinical Laboratory Sciences”

CONTENTS OF THE REVISED SILVER BOOK
Section 1 - History of recommendations on
properties in clinical laboratory sciencesof people. It is based on three main items structured as follows:
System—Component; kind-of-property

Over the period 1954-2016 section 1 describes
the steps to implement in clinical chemistry therecommendations of the IUPAC Commission on
Symbols and Physicochemical Terminology andof ISO Technical Committee 12 on Quantities andUnits. Preferences were affirmed for amount-ofsubstance with the acceptance of the mole as abase unit, for the liter in the expression of concentration, for the katal in expressing catalytic
activity, for the adoption of the concept ‘kindof-property’. A unified format (NPU format) toexpress a property of a system was developed
under the auspices of IFCC and IUPAC. The listingof dedicated kinds-of-property in an IFCC-IUPAC
data bank was another step.
Section 2 - Definitions of some disciplines
applied in the clinical laboratory
Forty-five disciplines related to clinical laboratory sciences are defined. They are hierarchicallyrelated in a generic concept diagram.
Section 3 - Conventions and instructions for use
Details are given to understand the structure
and wording of the text. The primary purposeof this Silver Book is to be a guide towards a
structured and uniform way of reporting on examinations from clinical laboratories ensuring
interoperability, i.e. the ability of information
systems to communicate successfully across organisational and systems boundaries [2].
Section 4 - Fundamental concepts
in communication of clinicallaboratory information
A set of rules based on the IFCC-IUPAC NPU format is given to obtain and communicate information on the chemical, biochemical, physiological,
pathological, and sometimes physical properties

The concept of ‘property’ comprises several
types, i.e.: nominal property, ordinal property,
linear differential property, logarithmic differential property, and rational property. The conceptof ‘kind-of-property’ relates to both quantities
and properties of a classificatory nature that are
devoid of magnitude. Thus, a NPU format is not
limited to measurements, but is generalized to
dedicated kinds-of-property without dimensionsof the International System of Quantities (ISQ).
Section 5 - Principles and practiceof kinds-of-quantity and units
The International System of Units or SI, basedon the ISQ, is described with their terms andsymbols, including a series of prefixes and theirterms and symbols, together with rules for theiruse. Corresponding examples taken from clinical laboratory practice are given.
Section 6 - Requesting, generating, andtransmitting clinical laboratory information
This part is mainly devoted to the recommendations regarding the clinical laboratory report. It is based on the recommendations elaborated by several international organizations.
Proposed English-language abbreviations are
given for systems in the human body and forkinds-of-property.
Section 7 - Choice and use of kinds-of-property
for different examination purposes
This part presents recommendations for the expression of compositional and material kindsof-quantity. Terminology for kinds-of-quantity
related to different measurement principles(optical spectroscopy, centrifugation, electrophoresis and enzymology) are listed with
examples.

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Section 8 - Kinds-of-quantityof dimension one; SI Unit 1
Ten subsections list numerous types of number,fraction, ratio, relative kinds-of-quantity, derivative kinds-of-quantity, activity, and factors.
Section 9 - Kinds-of-quantity of dimension
other than one
Kinds-of-quantity are classified in 110 subsections
according to their dimension. Recommendations
regarding terms, abbreviation, symbol(s), definition, relevant information, and often example(s)are given.
Section 10 - Kinds-of-property
without dimensions of the ISQ
The text concerns nominal and arbitrary kinds-ofquantity that have no dimension and are outside

the ISQ. It presents nominal and arbitrary kindsof-property with definition, explanatory notes,
and examples.
An extensive list of Symbols, Terms and SI Units
for Kinds-of-quantity is presented. References
to ISO, IEC, CGPM, WHO, and EN standards to
IUPAC and IFCC recommendations and technical reports, as well as to some secondary sources are given at the end of each section.
REFERENCES
1. Férard G, Dybkaer R, Fuentes-Arderiu X, Compendiumof Terminology and Nomenclature of Properties in ClinicalLaboratory Sciences. Recommendations 2016. The Royal
Society of Chemistry Publications, 2017 (xxix + 182 pp.).
2. Flatman R, Férard G, Dybkaer R. Understanding the ‘Silver Book’ – An important reference for standardised nomenclature in clinical laboratory sciences. Clin Chim Acta
2017;467:4-7.

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Correlation of HbA1C levels with body mass index
in newly diagnosed polycystic ovary syndrome
Manju Bala1, Meenakshi1, Menaka K.1, Anjali Gupta2
1
2

Department of Biochemistry, Pt. B. D. Sharma Postgraduate Institute of Medical Sciences (PGIMS), India
Department of Obstetrics and Gynaecology, Pt. B. D. Sharma Postgraduate Institute of Medical Sciences(PGIMS), Haryana, India

INFO

ABSTRACT

Corresponding author:
Manju Bala
Department of Biochemistry
Pt. B. D. Sharma Postgraduate Instituteof Medical Sciences (PGIMS)
Rohtak 124001, Haryana
India
E-mail: manjupahwadoc1@yahoo.co.in

Introduction

Key words:
HbA1c, body mass index, PCOS

Polycystic ovary syndrome (PCOS) is a heterogeneous, multisystem endocrinopathy in women of
reproductive age manifested with various metabolic
disturbances and a wide spectrum of clinical features
such as obesity, menstrual abnormalities and hyperandrogenism. Hyperinsulinemia was noted in 50% to
70% of PCOS patients and plays a central role in thedevelopment of further complications. The prevalence of obesity in PCOS ranges from 38% to 87%. It
has been reported in previous studies that the prevalence of insulin resistance (IR) is higher in obese PCOS
women than obesity without PCOS.
Objective
To correlate HbA1c levels with body mass index (BMI)
in newly diagnosed polycystic ovary syndrom (PCOS)
women.
Material and methods
This case-control study performed at Pt. B. D. Sharma
PGIMS, Rohtak includes 30 newly diagnosed PCOS
patients and 30 age matched healthy controls of any
age. Fasting venous blood samples were obtained forPage 196

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Manju Bala, Meenakshi, Menaka K., Anjali Gupta
Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

analysis of fasting blood glucose, haemoglobinand HbA1c after obtaining written consent.
Results
HbA1c concentration was higher in cases(6.03±0.19%) than in controls (5.35± 0.08%)
with p = 0.002. 33.3% were in pre diabetic range(5.7- 6.4%), 36.7% in diabetic range (≥6.5%) and
30% in non diabetic range (<5.7%). BMI of cases(26.09±4.2 kg/m2) was significantly higher thancontrols (22.9±3.3 kg/m2) with p=0.002. HbA1cshowed positive correlation with BMI (p= 0.001).
Conclusion
In conclusion, BMI is associated with glycemic
control in PCOS.
INTRODUCTION
Polycystic ovary syndrome (PCOS) is a heterogeneous, multisystem endocrinopathy in womenof reproductive age manifested with various
metabolic disturbances and a wide spectrumof clinical features such as obesity, menstrual
abnormalities and hyperandrogenism. Current
incidence of PCOS (5-6%) in women is related
to change in lifestyle and stress [1].
According to the World Health Organization, it
was estimated that 116 million women (3.4%)
have PCOS with prevalence ranging from 2.2%
to 26% globally [2]. Hyperinsulinemia was noted in 50% to 70% of PCOS patients and playeda central role in the development of further
complications. In spite of hyperinsulinemia,
there is an increased prevalence of insulin resistance (IR) in PCOS patients which can lead toincreased glucose tolerance and type 2 diabetic
mellitus (T2DM). Increased insulin leads to increased androgen production from the ovarian
thecal cells and this hyperandrogenemia is responsible for androgenic obesity.

The prevalence of obesity in PCOS ranges from
38% to 87%. It was reported that in obese PCOS
women, the prevalence of IR was higher thanobese women without PCOS in the control
group [3].
MATERIAL AND METHODS
The present case control study was conductedin the Department of Biochemistry in collaboration with the Department of Obstetrics andGynaecology, Pt. B. D. Sharma PGIMS, Rohtak.
After getting written consent from the casesand controls, detailed history were obtained
and recorded in their respective proforma.
They were subjected to physical examination
and anthropometric measurements as per protocol followed by systemic examination, urine
pregnancy test was done to rule out pregnancy,
ultrasonography of abdomen and pelvis were
performed in all subjects of this study.
Inclusion criteria
Newly diagnosed cases of PCOS as per Rotterdam
definition - the existence of the following threecriteria to make the diagnosis of PCOS:
1. oligo-ovulation or anovulation;
2. clinical or biochemical signs of hyper­
androgenism; and3. polycystic ovaries by Ultrasonography [4].
Exclusion criteria
Any history suggestive of other potential causesof hyperandrogenism/oligo/amenorrhea [congenital adrenal hyperplasia, androgen secreting
tumor], hypothyroidism, cushing’s syndrome,
hyperprolactinemia, other pituitary/adrenal disorders, other insulin resistance conditions (acromegaly), history of any drug intake and pregnancy [3].
Thirty age matched healthy females with regular menstrual cycle and not on any treatment
were enrolled as controls.

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Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

Anthropometric measurements
The weight and standing height of all study
subjects were measured twice by using calibrated weighing scale and stadiometer with afixed vertical backboard and an adjustable headpiece respectively by two different examiners to
avoid subjective error. BMI is expressed in theunits of kg/m2. BMI can be calculated by thepresent weight in kg divided by square of heightin metres (Quetelet index). According to theWorld Health Organisation, BMI can be graded
into the categories listed in Table 1.
SAMPLE COLLECTION
Six mL of venous blood sample after fasting(10 - 12 hours) was taken from the antecubital
vein aseptically on the second day of menstruation, out of which 2 mL of blood in EDTA anticoagulant vacutainer, 2 mL in sodium fluoride
vacutainer and 2 mL in plain vacutainer. Serumfrom sodium fluoride and plain vacutainer
were separated by centrifugation at 2000 rpm
for 5 minutes and fasting plasma glucose (FBG)
and hormones were determined. EDTA anticoagulant sample was used for hemoglobin andHbA1c determination.
Table 1The FBG was performed by enzymatic method (Glucose oxidase method) [6], HbA1c by latex agglutination inhibition assay [7] on the
RANDOX autoanalyser. Prolactin, total testosterone, luteinising hormone (LH) & follicular
stimulating hormone (FSH) were estimated on
the Advia Centaur’ CP immunoassay system bychemiluminescence method [8]. Thyroid stimulating hormone (TSH) was quantitated by immunoradiometric assay by IRMA kit (IRMAK - 9)[9]. Hemoglobin estimation was done by acidhaematin method using Sahli’s hemoglobinometer [10].
STATISTICAL ANALYSIS
Unpaired ‘t’ test and two-tailed Pearson’s correlation were done between variables of PCOS
cases and controls using the IBM SPSS version
20 statistical package. Data were considered tobe significant if p < 0.05 and highly significant
with p < 0.001.
RESULTS AND OBSERVATIONSIn the present study, we found that 8 newly diagnosed PCOS cases (26.7%) had history of sudden weight gain. Based on categories of BMI, 16

WHO classification of BMI grading [5]BMI (kg/m2)

Classification< 18.5

Underweight

18.5–24.9

Normal weight

25.0–29.9

Overweight

30.0–34.9

Class I obesity

35.0–39.9

Class II obesity≥ 40.0Class III obesity
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Manju Bala, Meenakshi, Menaka K., Anjali Gupta
Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

Table 2

Descriptive data of the study groups

S.No.

Descriptive data

Cases
(n = 30)
Mean ± SD

Controls(n = 30)
Mean ± SD‘t’ value

p value1.

Age (years)

23.26 ± 5.65

23.6 ± 6.45

-0.2130.832

2.

BMI (kg/m2)

26.1 ± 4.2

22.9 ± 3.3

3.245

0.002** - significant difference, ** - highly significant difference

cases and 7 controls were overweight and obese(≥ 25 kg/m2) with statistically significant difference between cases and controls (p=0.002).
Thirteen cases were within the normal range of
BMI and 1 case was underweight.
We observed that 7 cases (89.2 ± 13.9 mg/dL) and2 controls (88.5 ± 2.7 mg/dL) had FBG ≥ 100 mg/dL
with non significant p value > 0.05. We found that
HbA1c showed statistical significant difference(p=0.002) between cases (mean 6.03 ± 1.03%)
and controls (mean 5.35 ± 0.43%) (Tables 2 & 3).
We noted from our HbA1c data, 10 cases (33.3%)
were in pre diabetic range (5.7 - 6.4%), 11 cases(36.7%) were in diabetic range (≥ 6.5%) and 9cases (30%) were in non diabetic range (< 5.7%)
according to ADA criteria (Figure 1).
Table 3

We observed the prevalence of T2DM and prediabetes were 13.3% (4 cases) and 6.6% (2 cases)
in obese PCOS women, respectively. But theprevalence of T2DM and prediabetes were 10%(3 cases) and 16.6% (5 cases) in overweight PCOS
women, respectively.
We found that 7 cases (23.3%) were prediabetes
with FG 100-126 mg/dL. But among controls, we
noticed only one case (3.3%) had prediabetes(Figure 2).
TWO-TAILED PEARSON’S CORRELATIONBETWEEN PARAMETERS
In the present study, it was found that HbA1c had
positive correlation with BMI (r=0.439, p=.001)(Figure 3).

Analytical data of the study groups

S.No.

Analytical data

Cases
(n = 30)
Mean ± SE

Controls(n = 30)
Mean ± SE‘t’ value

p value1.

FG (mg/dL)

89.23 ± 2.5

88.47 ± 1.4

0.264

0.792

2.

Hb (g/dL)

14.16 ± 3.24

11.5 ± 0.17

0.929

0.357

3.

HbA1c (%)

6.03 ± 0.19

5.35 ± 0.08

3.364

0.002** - significant difference, ** - highly significant difference

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Manju Bala, Meenakshi, Menaka K., Anjali Gupta
Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

Figure 1

The mean value ± SE of HbA1c of the cases and controls

6.4

Mean HbA1c

6.03 ± 0.19

6.2
6
5.8
5.6
%

5.35 ± 0.08

5.4
5.2
5
4.8
4.6

r
m
Figure 2

Cases

Controls

re ti
e
h
d n o
1c
Bar diagram representing various categories
based on HbA1c levelin the cases and controls

76.7

80

Diabetic
Normal

70
60
50
% 40

36.7

33.3

30
23.3

30
20
10
0

0
Cases

Controls
0Page 200eJIFCC2017Vol28No3pp196-204

PrediabeticManju Bala, Meenakshi, Menaka K., Anjali Gupta
Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome‐ 3: Gra 3h
Figurewing showing
c rr la ion
b correlation
een BMI & HbA
1c
Graph
the
between
BMI & HbA1c

HbA1C
9
8.5

r = 0.439
p = 0.001

8

HbA1c %

7.5
7
6.5
6
5.5
5
4.5
4

15

20

25

30

35

40

BMI Kg/m2

DISCUSSION
When the hemoglobin is exposed to plasma
glucose, there is a spontaneous non-enzymatic
glycation of hemoglobin resulting in HbA1c formation. The rate of formation of HbA1c is directly
proportional to the concentration of the glucose
in the blood and represents integrated values forthe glucose over the preceding 8 to 12 weeks.
HbA1c is formed by the condensation of glucosewith the N-end of each beta chain to form an
unstable schiff base. The schiff base may dissociate or may undergo an amadori rearrangement to form a stable ketomine called as HbA1c.
It has been established that HbA1c is an index oflong term blood glucose concentrations and as ameasure of the risk for the development of microvascular complications in patients with diabetes mellitus. The formation of HbA1c is an irreversible process and depends on lifespan of RBC
and glucose concentration in the plasma. Severe
anemia can show false high HbA1c value as theold red blood cell (RBC) population is higher thannon anaemic conditions. In haemolytic anaemia,
HbA1c value is falsely low as life span of RBC isless. Therefore, it is mandatory to rule out anaemia in our study to avoid false results [11].
Lerchbaum et al reported that the prevalenceof obesity was 24.8% and overweight was
21.8% among PCOS women [12]. Gomathi et al
found that 54% of the women with PCOS were
overweight or obese with respect to WHO classification of BMI grading [13]. Randeva et al reviewed that excess triglycerides enter into cells
and activate proteins kinase C-ε and C-θ, ultimately reducing the glucose uptake. This leads
to compensatory hyperinsulinemia which canstimulate excess fat deposition by hypertrophyand hyperplasia of adipose cells in the excess
calorie environment. This is further aggravating
IR by increasing obesity as a vicious cycle. Thisabnormal fat accumulation increases IR causing
glucose intolerance and T2DM [14].

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Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

The prevalence of obesity in the PCOS population ranges from 38% to 87%. It has been reported that in obese PCOS women, the prevalence of IR was higher than obese women withnormal menstrual cycle in the control group [3].
Medeiros et al concluded from their study,
among amazonian PCOS women, that HbA1c
was elevated in nearly 40% of PCOS patientsand had positive correlation with several anthropometric, metabolic factors and androgenlevels [15].
While evaluating PCOS women, along with FBG
estimation, they are needed to have oral glucose tolerance test (OGTT) or HbA1c also to assess their metabolic status and can be categorised as per the American Diabetic Association(ADA) guidelines 2015 by the following any oneof the criteria listed in Table 4.
As OGTT is time consuming and cumbersome
test, HbA 1c and FBG have been suggested as
screening tools for prediabetes and T2DM.
Table 4

They also recommended HbA1c as the superior
screening tool for dysglycemia assessment in
PCOS women [12].
Increased LH leads to increased stromal growthaccounts for increased ovarian volume. Increased
circulating LH stimulates ovarian thecal cells to
produce more androgens leading to hyperandrogenemia in turn leads to increased male pattern(top / apple shaped upper abdomen) of fat accumulation both subcutaneously and aroundviscera which in turn leads to obesity, increased
BMI.
High saturated fat, trans fat diet, less poly unsaturated fatty acid (PUFA) intake, high fructose/sucrose intake, low fat and high carbohydrate diet, sedentary life style, leptin resistancecan contribute high triglycerides and VLDL-C
level in the circulation which can alter plasma
membrane composition resulting in decreased
tyrosine acetylphosphorylation of insulin receptor [17-26].

The ADA guidelines 2015 for diagnosis of diabetes & prediabetes [16]Criteria for diabetes diagnosis

1.

HbA1c ≥ 6.5%

2.

FBG ≥ 126 mg/dL (7.0 mmol/L)

3.

2-hr plasma glucose ≥ 200 mg/dL (11.1 mmol/L) during OGTT (75g)

4.

Random plasma glucose ≥ 200mg/dL (11.1mmol/L)
Criteria for prediabetes diagnosis

1.

HbA1c 5.7%-6.4%

2.

FBG 100-125 mg/dL (5.6-6.9 mmol/L)

3.

2-hr plasma glucose 140-199 mg/dL (7.8-11.0 mmol/L) during OGTT (75g)
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Manju Bala, Meenakshi, Menaka K., Anjali Gupta
Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

Kumar et al also observed higher BMI in womenwith PCOS than in controls [27]. The present
study observations showed that 14 cases (46%)
had increased BMI (≥ 25 Kg/m2) and increased
HbA1c and 16% of cases had normal BMI with increased HbA1c levels. Among controls who had
increased HbA1c, we found that 6.7% of themhad increased BMI and 3.3% had normal BMI.
The prevalence of IR was found to be increasedin obese PCOS women than in normally menstruating obese women. IR plays a central role in
the pathogenesis of PCOS. Obesity is a co-morbid condition which can increase its risk. Thoughobesity is more prevalent among PCOS women,
about 20 - 30% of PCOS women are not obese.
Hyperinsulinemia causes direct hypothalamic
effects which lead to abnormal appetite andgonadotropin secretion resulting in increased
LH secretion in PCOS. This increased level of LH
causes excessive androgen production in theovaries [3].

5. The world health report 2000 - Health systems: improving performance. p. 9.

CONCLUSION

12. Lerchbaum E, Schwetz V, Giuliani A, Pietsch OB. Assessment of glucose metabolism in polycystic ovary syndrome. Hum Reprod 2013;28:2537-44.

In conclusion, BMI is associated with glycemic
control in PCOS.
REFERENCES
1. Disorders of the ovary and benign tumours. In: Padubidri VG, Daftary SN, editors. Howkins & Bourne Shaw’s
textbook of gynaecology. 15thed. New Delhi: Elsevier;2011. p. 369-71.
2. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C,
Ezzati M, et al. Years lived with disability (YLDs) for 1160
sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the global burden of disease study
2010. Lancet 2012;380:2163-96.
3. Attaran M. Disease management project clinical decisions. Polycystic ovary syndrome [Internet]. 2010 [cited
2010 August 1]. Available from: http://www.clevelandclinicmeded.com/medicalpubs/diseasemanagement/
womens-health/polycystic-ovary-syndrome
4. The Rotterdam ESRE/ASRM sponsored PCOS consensus
workshop group revised 2003 consensus on diagnostic
criteria and long term health risks related to polycystic
ovarian syndrome. Fertil Steril 2004;81:19-25.

6. Sacks DB. Carbohydrates. In: Burtis CA, Ashwood ER,
Bruns DE, editors. Tietz textbook of clinical chemistry.
5th ed. New Delhi: Elsevier; 2012. p. 718-21.
7. Khan MAH, Rabeya MR, Saiedullah M. Measurementsof HbA1c by high performance liquid chromatography in
D-10 analyzer and immunological method by Beckman
Coulter AU480 System: A comparative study. J Enam Med
Col 2012;2:62-6.
8. Mohseni MG, Hosseini SR, Alizadeh F, Rangzan N. Serum testosterone and gonadotropins levels in patientswith premature ejaculation: A comparison with normal
men. Adv Biomed Res 2014;3:6.
9. Samad MA, Haque MM, Shah MK, Islam MR, Mia MC.
Evaluation of TSH, T4 and T3 in human serum: standardization on normal individuals. American Journal of Modern Physics 2013;2:202-7.
10. Balasubramiam P, Malathi A. Comparative study ofhemoglobin estimated by Drabkin’s and Sahli’s methods.
J Postgrad Med 1992;38:8-9.
11. Sacks DB. Diabetes Mellitus. In: Burtis CA, Ashwood
ER, Bruns DE, editors. Tietz textbook of clinical chemistry.
5th ed. New Delhi: Elsevier; 2012. p. 1441-3.

13. Gomathi K, Shaafie IA, Mummigatti K, Shahid S, Sreedharan J. Biochemical parameters in women with polycystic ovary syndrome in Ajman, UAE. NJOG 2011;6:7-10.
14. Randeva HS, Tan BK, Weickert MO, Lois K, Nestler JE,
Sattar N, et al. Cardiometabolic aspects of the polycystic
ovary syndrome. Endocr Rev 2012;33:812-41.
15 Medeiros SFD, Yamamoto MMW, Bueno HB, Belizario
D, Barbosa JS. Prevalence of elevated glycated hemoglobin concentrations in the polycystic ovary syndrome: anthropometrical and metabolic relationship in amazonian
women. J clin med res 2014;6:278-86.
16. American Diabetes Association. Standards of medical care in diabetes-2015. Diabetes Care 2015;38(Suppl-1): S93.
17. Schinner S, Scherbaum WA, Bornstein SR, Barthel
A. Molecular mechanisms of insulin resistance. Diabetic
Med 2005;22:674-82.
18. Morino K, Petersen K, Shulman G. Molecular mechanisms of insulin resistance in humans and their potential links with mitochondrial dysfunction. Diabetes
2006;55:S9-15.

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Correlation of HbA1C levels with body mass index in newly diagnosed polycystic ovary syndrome

19. Storlien LH, Jenkins AB, Chisholm DJ, Pascoe WS,
Khouri S, Kraegen EW. Influence of dietary fat composition on development of insulin resistance in rats. Diabetes 1991;40:280-9.
20. Kazunori K, Chen G, Lee Y, Unger RH. Tissue triglycerides, insulin resistance and insulin production: implications for hyperinsulinemia of obesity. Am J Physiol
1997;273:708-13.
21. Michael R, Price TB, Perseghin G, Petersen KF, Rothman DL, Cline GW, et al. Mechanism of free fatty acidinduced insulin resistance in humans. J Clin Invest
1996;97:2859-65.
22. Heather B, Lisa F, Khosrow A. Fructose, insulin resistance and metabolic dyslipidemia. Nutr Metab (Lond)
2005;2:5.
23. Huang YJ, Fang VS, Juan CC, Chou YC, Kwok CF, Ho LT.
Amelioration of insulin resistance and hypertension ina fructose fed rat model with fish oil supplementation.
Metab Clin Exp 1997;46:1252-8.
24. Wang J, Obici S, Morgan K, Barzilai N, Feng Z, Rossetti
L. Overfeeding rapidly induces leptin and insulin resistance. Diabetes 2001;50:2786-91.
25. Davis MEJ, d’Agostino R, Karter AJ, Haffner SM, Rewers
MJ, Saad M, et al. Intensity and amount of physical activity
in relation to insulin sensitivity. JAMA 1998;279:669-74.
26. Haugaard SB, Sten M, Erik HC, Allan V. Dietary intervention increases n-3 long-chain polyunsaturated fatty
acids in skeletal muscle membrane phospholipids ofobese subjects. Implications for insulin sensitivity. Clin
Endocrinol 2006;64:169-78.
27. Kumar A, Woods K S, Bartolucci A A and Azziz R.
Prevalence of adrenal androgen excess in patients withthe polycystic ovary syndrome (PCOS). Clin Endocrinol
2005;62:644-9.

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Factors affecting quality of laboratory services
in public and private health facilities
in Addis Ababa, Ethiopia

Eyob Abera Mesfin1, Binyam Taye2, Getachew Belay3, Aytenew Ashenafi4,Veronica Girma5

Quality Africa Network (Pty) /GIZ, Addis Ababa, Ethiopia
Addis Ababa University College of Health Science, School of Medicine, Ethiopia
3
Ethiopian Health and nutrition Research Institutes, Addis Ababa, Ethiopia
4African Society for Laboratory Medicine (ASLM), Addis Ababa, Ethiopia
5
Addis Ababa City Administration Health Bureau, Health Research and Laboratory Services, Ethiopia
1
2

INFO

ABSTRACT

Corresponding author:
Eyob Abera Mesfin
Quality Africa Network (Pty) /GIZ
Addis Ababa
Ethiopia
E-mail: eyob2001@gmail.com

BackgroundKey words:
laboratory quality service, quality management
system, quality assurance factors
Competing interests:
All authors declare that they have noconflicts of interest associated withthe publication of this manuscript.

Quality laboratory service is an essential componentof health care system but in Sub-Saharan Africa suchas Ethiopia, laboratories quality system remains weak
due to several factors and it needs more attention tostrengthen its capacity and quality system.
Methodology
A cross sectional study was conducted using a questionnaire to assess factors affecting the quality oflaboratory service at private and public health institutions in Addis Ababa.
Results
A total of 213 laboratory professionals participatedin the study and 131 (61.5%) participants had bachelor degree. Majority, 133 (62.4%), of the professionals did not attend any work related training. Seventy
five (35.2%) respondents believed that their laboratories did not provide quality laboratory services andPage 205

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Eyob Abera Mesfin, Binyam Taye, Getachew Belay, Aytenew Ashenafi, Veronica Girma
Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

the major reported factors affecting provisionof quality services were shortage of resources(64.3%), poor management support (57.3%),
poor equipment quality (53.4%), high workload(41.1%), lack of equipment calibration (38.3%)
and lack of knowledge (23.3%). Moreover logistic regression analysis showed that provisionof quality laboratory service was significantly
associated with result verification (AOR=9.21,
95% CI=2.26, 37.48), internal quality control(AOR= 6.11, 95% CI=2.11, 17.70), turnaroundtime (AOR=5.11, 95% CI=1.94, 13.46), shortageof equipment (AOR=7.76, 95% CI=2.55, 23.66),
communication with clinicians (AOR=3.24,
95% CI=1.25, 8.41) and lack of job description(AOR=3.67, 95% CI=1.319, 10.22).

Poor quality laboratory services lead to unnecessary expenditures, misery in human lives andsuffering, and producing wrong data in diseaseprevalence due to misdiagnosis. The results areover-treatment and overuse of antibiotics for inappropriate clinical circumstances which leads
to the emergence of drug resistant microorganisms include multi-drug resistant TB5. Likewisepatient safety is also influenced by the frequency and seriousness of errors that occur in thehealth care system6.

INTRODUCTION

It is well known that quality of laboratory service is dependent on technical skills, quality
management systems and the motivation ofhuman resources7. However, the first barrier for
quality improvement at health care system ishuman capacity development, which continues
to be a gap in implementing health programs
including laboratory services8, in addition several barrier are identified in laboratory services,
including lack of laboratory supplies, poor quality management system, absence of laboratory
standards and policy9. Besides lack of access toreliable diagnostic services and under-resourced
laboratory infrastructure in developing countries
are another challenges for quality of diagnoses
which lead to inadequate treatment, increased
morbidity, and inaccurate determination of theburden of disease10.

In Sub-Saharan Africa, the major challenge fordelivering quality health service is the lack of reliability of medical laboratory services1. Quality
laboratory service is essential for a wide range ofdiagnosis, treatment and monitoring in healthcare delivery. But due to lack of awareness on
the laboratory service role in many developing
countries, laboratory services have shortage ofresources, poor management system2-3, lack ofquality assurance program, shortages of equipment, shortage of training and poor staff motivation system4.

Moreover, studies from Sub-Saharan Africa revealed that major factors affecting the laboratory services were staff shortages, poor communication system, inadequate equipment, lowmotivation, lack of training11, lack of internal
quality control (IQC), power supply interruption, equipment failure, and poor infrastructure.
Furthermore, another limitation for establishing an efficient laboratory system in developing
countries was a lack of resources and a system
for implementing, managing, and monitoring
laboratory activities9.

Conclusion
In conclusion, the major factors that affecting
the quality of laboratory service were associatedwith poor human resource management, poor
resources provision, poor management commitment, ineffective communication system andlack of well-established quality management
system.


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Eyob Abera Mesfin, Binyam Taye, Getachew Belay, Aytenew Ashenafi, Veronica Girma
Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

To tackle these factors, several efforts are continued in sub-Saharan Africa however there arestill numerous challenges hindering the quality of laboratory services as well as health caresystem. Moreover as Ethiopia is one of SubSaharan Africa country, laboratory infrastructure and quality assurance activities remain
weak12 and there is little information available
on factors affecting quality of medical laboratory services. Therefore, this study was conducted
to assess factors affecting the provision of quality of laboratory services and provide baseline
information to policy and decision makers.

n=[1.962 x 0.5(1-0.5]/0.052=384
n=384

According to Health and Health Related
Indicators there were 394 laboratory professionals in Addis Ababa14, therefore, the correction factor was done using the finite population
formula (nf) from a target population (N) andthe sample size was reduced according to thefollowing formula:nf=n/(1+n/N)

nf=384/(1+384/394)=194

MATERIALS AND METHODS

nf=194

Study design
A descriptive cross sectional study was conducted using questionnaire to assess factorsthat affecting the provision of quality of medicallaboratory services in public (owned by government) and private (owned by private and nongovernment organization) health facilities from
December 2013 and February 2014 in Addis
Ababa, Ethiopia. The estimated population sizeof Addis Ababa was 2.74 million13 and according
to 2011 Health and Health Related Indicators
report the city had 42 public and private hospitals, 37 health centers and 394 laboratory professionals14. Health institution that have functional laboratories and willing to participate
were included in the study.
Sampling procedures
A single population proportion formula wasused for determination of the sample size considering the following assumptions: proportionof 50% taken due to absence of similar previous
study, level of significance = 0.05, Marginal of error (d) = 5%, Z (α/2) = Z-score at 95% confidence
interval = 1.96.
n=[Za/22 x p(1-p)]/d2

Considering 10% of non-response rate, the sample size for laboratory professionals was 213.
Finally sample size was allocated proportionately for 30 voluntary and eligible health facilities
according to number of laboratory professionals
working in their laboratories and laboratory professionals were selected for the interview using
random sampling method.
Data collection procedures
Structured questionnaire was used for data collection. It included different questions, such associo-demography, education background, work
experience, motivation, communication, training, quality assurance activities, and factors affecting quality of laboratory service.
All laboratory professionals having more than ayears experience and willing to participate were
included in the study. Trained and experiencelaboratory technologists interviewed laboratory
professionals using questionnaire for data collection. Principal investigator involved in overall controlling activities of data collections andassisting data collectors during the process ofdata collection.

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Data management and statistical analysis

potential affecting factors and provision of
Quality laboratory services.

All data were coded and fed into SPSS version
16 statistical software and data were checked
for completeness and consistency of variables.
Descriptive statistics were computed to calculate the frequency and percentage, and bivariate analysis were also conducted to assess
the presence of associations between dependent variable and the independent variables.
Dependent variable is provision of quality laboratory services; defined as the ability of a laboratory service to satisfy stated or implied needs ofa specific customer or fulfils requirements, andthe independent variables are socio demography variables, education background, work experience, motivation, communication, training,
workload and quality assurance practices (Job
descriptions, supplies & reagents management,
equipment calibration & maintenance, result
verification, Internal quality control activities
practice and turnaround time customer management and services interruption).

Ethical consideration
Before any attempt to collect data, ethical clearance was obtained from Institutional Review
Board (IRB) of School of Medicine, Addis Abba
University. Each participant was informed about
the purpose of the study, the right to refuse toparticipate in the study, and anonymity andconfidentiality of the information gathered.
RESULTS
Socio-demographic characteristics
A total of 213 laboratory professionals participated in this study from 13 public and 17
private health institutions in Addis Ababa, and
130 (61%) of the respondents were employedin public health institutions and majority 135(63.4%) of participants were male. One hundred and twenty-one (56.8%) of respondents
were between 20-30 years old with an averageage of 32 years. One hundred thirty one (61.5%)
were Medical Laboratory Technologist (Bachelor
Degree) referred to as highly trained laboratory professionals and 82 (38.5%) were Medical
Laboratory Technicians (Diploma) referred to as
mid-level trained laboratory professionals.

Moreover multiple logistic regressions werecarried out to control the confounding factors,
and variables which had a p-value less or equal
to 0.05 in bivariate analysis were included in themultivariate logistic regression model. Odds ratio with 95% confidence interval were used tomeasure the strength of association between
Table 1

In terms of work experience, 179 (84.0%) the respondents had 3 and above years of experience

Socio-demographic characteristics of laboratory professionals working
in public and private health institutions in Addis Ababa Ethiopia (n=213)
Variable

Frequency (%)

Sex
Male

135 (63.4%)

Female78 (36.6%)
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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Age group
20-30 Years

121 (56.8%)

31-40 Years

59 (27.7%)

41-50 Years

18 (8.5%)

51-60 Years

15 (7.0%)
Educational level (profession)

Diploma (Laboratory Technician)82 (38.5%)

Bachelor Degree (Laboratory Technologist)

13 (161.5%)

Working organization
Public130 (61.0%)

Private83 (39.0%)

Working experience in laboratory fields
1-2 Years

34 (16.0%)

3-5 Years

61 (28.6%)

6-10 Years

48 (22.5%)>10 Years

70 (32.9%)
PositionLaboratory head

20 (9.4%)

Supervisor

33 (15.5%)

Expert152 (71.3%)

Quality Officer

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Laboratory discipline
General laboratory

127 (59.6%)

Clinical chemistry28 (13.1%)

Hematology

23 (10.8%)

Parasitology

15 (7.0%)

Microbiology

11 (5.2%)

Immunology/Serology

9 (4.2%)and they worked as head, supervisor, quality officer and expertise and 127 (59.6%) of the professionals worked at general laboratory which
perform basic chemistry, hematology, parasitology, microbiology and serology tests and the remaining professionals work at Clinical Chemistry,
Hematology, Parasitology, Microbiology and
Immunology laboratories (Table 1).
Motivation and communication
Most 187 (87.8%) of the laboratory professionals were not satisfied with their salary and 178(83.6%) respondents indicated that there was nosystem for staff recognition, and 133 (62.4%) of
the laboratory professionals did not attend any
task specific training and 128 (60.1%) continuing education program. A total of 150 (70.4%) of
the laboratory professionals had high workload
while 125 (58.7%) of the respondents indicateda shortage of human resources in their laboratories. In addition 110 (51.6%) of the laboratory
professionals did not have job descriptions forthe task assigned. Concerning communication,
the laboratory professionals indicated that
there were no systems to communicate withlaboratory staff, upper management, and clinicians with respondents 126 (59.2%), 120 (56.3%)
and 75 (35.2%) respectively (Table 2).

Quality assurance practices
In terms of quality assurance practice, findings
from our study revealed that 85 (39.9%) laboratory professionals did not perform equipment
calibration & maintenance as per instruction
and 91 (42.7%) of the respondents also indicated that there was supplies and reagents
interruption and the available supplies and reagents had also poor quality. Likewise, internal
quality control (IQC) was not conducted regularly as stated by more than 33% of the respondents besides 34 (16.0%) and 55 (25.8%) of thelaboratory professionals did not participate on
external quality assessment (EQA) and quality
improvement activities respectively, is shown in
Table 3.
Table 3 shows that 33 (15.5%) laboratory professionals did not verify laboratory results and70 (32.9%) of respondents claimed that laboratory results were not released within predefined turnaround time. Laboratory documentation (documents and records) system were
not practiced as per standard by 45 (21.1%) thelaboratory professionals. One hundred twentyone (56.8%) laboratory professionals also indicated that customer services management system was poor in their respective laboratories.

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Table 2

Knowledge & laboratory management activities
reported by laboratory professionals working in public
and private health institutions in Addis Ababa, Ethiopia (n=213)
Variable

Frequency (%)

Knowledge on laboratory quality system essentials
Yes

197 (92.5%)

No

16 (7.5%)

Laboratory communication with CliniciansYes

138 (64.8%)

No

75 (35.2%)

Laboratory communication with upper managementYes

93 (43.7%)

No

120 (56.3%)

Laboratory communication among laboratory staff
Yes

126 (59.2%)

No

87 (40.8%)

Laboratory staff satisfied with their salaryYes

26 (12.2%)

No

187 (87.8%)
System for employees recognitionYes

35 (16.4%)

No

178 (83.6%)
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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Attending of continuing education programYes

85 (39.9%)

No

128 (60.1%)

Attending of laboratory refreshment trainingYes

80 (37.6%)

No

133 (62.4%)
Job descriptions for assigned task

Yes

103 (48.4%)

No

110 (51.6%)

Availability of quality and adequate equipment in laboratory
Yes

93 (43.7%)

No

120 (56.1%)

Availability of quality and adequate supplies & reagentsYes

122 (57.3%)

No

91 (42.7%)

Adequate number of staff for laboratory services
Yes

88 (41.3%)

No

125 (58.7%)
Laboratory workload

High

150 (70.4%)

Fair

63 (29.6%)
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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Table 3

Quality assurance practices and provision of laboratory services
reported by laboratory professionals working in public and private healthinstitutions in Addis Ababa, Ethiopia (n=213)
Variable

Frequency (%)

Laboratory documentation (documents and records)
Yes

168 (78.9%)

No

45 (21.1%)

Adherence to the standard operating procedures
Yes

143 (67.1%)

No

70 (32.9%)
Customer services managementYes

92 (43.2%)

No

121 (56.8%)
Equipment calibration & maintenanceYes

128 (60.1%)

No

85 (39.9%)

Laboratory quality improvement activities
Yes

158 (74.2%)

No

55 (25.8%)
External quality assessment activities

Yes

179 (84.0%)

No

34 (16.0%)
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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Internal quality control activities
Yes

141 (66.2%)

No

72 (33.8%)

Providing diagnostic services for all requested tests
Yes

139 (65.3%)

No

74 (34.7%)

Providing uninterrupted laboratory services
Yes

92 (43.2%)

No

111 (52.1%)

No information

10 (4.7%)

Laboratory result verificationYes

180 (84.5%)

No

33 (15.5%)

Laboratory results reported within turnaround timeYes

143 (67.1%)

No

70 (32.9%)
Laboratory safety practices

Yes

140 (65.7%)

No

73 (34.3%)

Utilization of personal protective equipment
Yes

164 (77.0%)

No

49 (23.0%)
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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Provision of quality laboratory services
Yes

138 (64.8%)

No

75 (35.2%)

Seventy-three (34.3%) laboratory professionals
did not monitor laboratory safety practices and69 (23%) the professionals did not use personal
protective equipment (PPE) during working inlaboratory.
Factors affecting provisionof quality laboratory services

53.4% respectively. Moreover high workload,lack of equipment calibration, lack of knowledge and skills and poor staff motivation werethe major factors that affecting the provisionof quality of laboratory services in this study, isshown in Figure 1.
Logistic regression analysis result

Regarding to factors that affecting the quality assurance practice, our finding discoveredthat high workload, shortage of resource, poor
management supports, poor staff motivation,lack of knowledge, high workload equipment
failure, shortage of supplies and reagents werethe major factors that affecting the quality assurance practice at the laboratories, is shown in
Table 4.
As regard to laboratory services, 75 (35.2%)
laboratory professionals believed that their laboratories did not provide quality of laboratory
services as per the standards and 74 (34.7%) respondents indicated that their laboratories did
not provide diagnostic services for all requested
tests, in addition to this, 111 (52.1%) of respondents also reported that there were laboratory
services interruption in their respective laboratories due to several reasons.
Concerning to factors that affecting the provision of quality laboratory services, our finding
showed that shortage of resources was rated
highest factor influenced the provision of quality laboratory services to a great extent by
64.3% and the second and third highest ranked
factors were lack of management supports andpoor equipment quality and rated by 57.4% andLogistic regression analysis showed that provision of quality laboratory services was significantly associated with laboratory result verification(adjusted odds ratio (AOR)=9.21, 95% CI=2.26,
37.48), shortage of equipment (AOR= 7.76, 95%
CI= 2.55, 23.66), internal quality control (AOR=
6.11, 95% CI=2.11, 17.70), results turnaroundtime (AOR=5.11, 95% CI=1.94, 13.46), communication with clinicians (AOR= 3.24, 95% CI= 1.25,
8.41) and lack of job description (AOR= 3.67, 95%
CI= 1.319, 10.22).
Laboratory professionals, who did not verify laboratory results were found to be 9.2 times more
likely to provide poor quality laboratory services
when compared with those who verify laboratory results.
Laboratory professionals who did not practiceinternal quality control activities were 6 times
more likely to provide poor quality laboratory
services compared to regular practice. Moreoverlaboratory professionals who did not report results within defined turnaround time were 5
times more likely to provide poor quality laboratory services when compared with those whoreport results within turnaround time, is shownin Table 5.

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Table 4

Factors affecting quality assurance practices reported
by laboratory professionals working in public and private healthinstitutions in Addis Ababa, Ethiopia (n=213)

Variable

Shortage
Lack
Poor
Poor
High
Equipment Factors
of
of
staff
management
workload
failure unknown
resources knowledge motivation
supportDocuments
and records(n=45)

21(46.7 %)

6
(13.3 %)

20
(44.4 %)

24
(53.3 %)

21
(46.7 %)-

5
(11.1 %)

Customer
servicesmanagement(n= 121)39(32.2 %)

22
(18.2 %)

39
(32.2 %)

34
(28.1 %)

39
(32.2 %)-

5
(4.1 %)

Equipment
calibration &preventive
maintenance(n=85)

36(42.4 %)

29
(34.1 %)

33
(38.8 %)

23
(27.1 %)

35
(41.2 %),--

Laboratory
quality
improvement
activities(n=55)

12(21.8 %)

8
(14.5 %)

39
(70.9 %)

24
(43.6 %)

39
(70.9 %)-

5
(9.1 %)

Internal
qualitycontrol
activities(n=72)

35(48.6 %)

12
(16.7 %)

30
(41.7 %)

18
(25%)

-

-

-

External
qualityassessment
activities(n=34)

11(32.4 %)

10
(29.4 %)

4
(11.8 %)-

11
(32.4 %)-

9(26.5 %)

Laboratory
resultverification(n=33)

14(42 %)

3(9.1 %)

11(33.3 %)

7
(21.2 %)--

3
(9.1 %)

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Laboratory
results
reported
withinturnaroundtime(n=70)

43(61.4 %)-

30
(42.9 %)

49
(70 %)-

48(68.6 %)-

Laboratory
safetypractice(n=73)

30(41.1 %)

16
(21.9 %)

18
(24.7 %)

21
(28.8 %)

30
(41.1 %)-

4
(5.5 %)

Utilizationof personal
protective
equipment(n=49)

46(93.8 %)

3
(6.2 %)

-

-

-

-

-

Note: Since respondents provided more than one answer, the total percentage of responses wasmore than 100%.

Figure 1

Factors affecting the provision of quality laboratory services
in public and private health institutions in Addis Ababa, Ethiopia

Note: Since respondents provided more than one answer, the total percentage of responses wasmore than 100%.

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Table 5

Logistic regression analysis showing the association between provisionof quality laboratory services and covariates in public and private
health institution laboratories in Addis Ababa, Ethiopia

Variable

Provisionof quality laboratory
services
Yes

Crude
odds ratio(95 % CI)

Adjustedodd ratio(95 % CI)

No

Profession
Lab Technician

63

20

1

1

Lab Technologist

75

55

2.310 (1.253, 4.258)

0.701 (0.255, 1.925)

Work Experience
1-2 Years

27

7

1

1

3-5 Years

39

22

0.611 (0.195, 1.911)

1.114 (0.242, 5.122)

6-10 Years

24

24

0.636 (0.248, 1.631)

1.558 (0.325, 7.481)>10 Years

48

220.733 (0.137, 3.938)

0.947 (0.204, 4.409)

Satisfaction with salary
Yes

24

2

1

1

No

114

73

7.684 (1.763, 33.491)

5.926 (0.719, 48.81)

Providing uninterrupted services
Yes

76

15

1

1

No

62

60

4.903 (2.540, 9.465)

1.938 (0.677, 5.549)

Communication with clients
Yes

105

33

1

1

No

33

42

4.050 (2.221, 7.384)

3.238 (1.246, 8.414)*

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Communication with upper management
Yes

69

24

1

1

No

69

51

4.050 (2.221, 7.384)

0.320 (0.106, 0.961)

Job descriptions
Yes

85

18

1

1

No

53

57

5.079 (2.701, 9.548)

3.672 (1.319, 10.22)*

Enough equipment
Yes

82

11

1

1

No

56

64

8.519 (4.129, 17.579)

7.76 (2.548, 23.659)*

Enough supplies & reagents
Yes

88

34

1

1

No

50

41

2.122 (1.198, 3.760)

1.113 (0.458, 2.701)

Adherence to SOP
Yes

107

36

1

1

No

31

39

3.739 (2.044, 6.842)

1.028 (0.364, 2.905)

Clients’ satisfactions assessment
Yes

81

11

1

1

No

57

64

8.268 (4.009, 17.053)

2.261 (0.851, 6.007)

Equipment calibration & maintenance
Yes

99

29

1

1

No

39

46

4.027 (2.222, 7.296)

0.605 (0.207, 1.767)

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

Laboratory results verification
Yes

131

49

1

1

No

7

26

9.930 (4.050, 24.346)

9.21, (2.263, 37.482)*

Internal quality control activities practice
Yes

111

30

1

1

No

27

45

6.167 (3.302, 11.518

6.105, (2.105, 17.70)*

Results reported within turnaround time
Yes

114

29

1

1

No

24

46

7.534 (3.972, 14.290)

5.111 (1.94, 13.464)** The odds ratio indicated that there was significant association between provision of quality laboratory
service and independent variables.

DISCUSSION
The majority of laboratory professionals work
under high workload without job descriptions,continuing education and training. However
Baidoun and Zairi pointed out that education,
training and motivation are major factors for
implementation of quality system15 and nontrained professionals can be costly to the laboratory system due to inaccurate test results. In
addition to this, more than 83% of the professionals were not satisfied with their salary andstaff recognition system, as well as poor communication system, this is in agreement withstudies done by Lyons et al16 and Al-Enezi et al17.
However it is well understood that motivation
could be brought with simple letter of recognition18 and effective communication also contributes to quality of services19.
Moreover it is well documented that implementation of laboratory standards helps laboratories to demonstrate a well-functioning quality

management system, technical competence,and customer-focused services that contributeto health care services20. But this study foundout that documentation system, result verification & reporting system, equipment calibration& maintenance, quality control activities, customer management and laboratory safety were
not implemented as per the standards. So poor
quality management system directly affects theprovision of quality laboratory services as wellas patients and health care services at large.
Besides this, our study discovered that the major factors that affecting provision of quality
laboratory services were high workload, shortage of resource, poor management supports,
poor staff motivation, lack of knowledge andskills, high workload, equipment failure andlack of calibration, shortage of supplies and reagents. It was comparable with previous studies
done by Wanjau et al., Alash’le et al., Birx D etal., and Bates et al. who found that major factors

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

affecting laboratory services were lack of lack ofresource9,19, equipment failure, poor management system9, shortage of staff, low staff motivation, lack of knowledge19,21 and lack of training11.
Furthermore, another study done in Ethiopia
revealed that lack of equipment maintenance,
shortage of reagents and supplies, poor laboratory management and lack of follow-up wereidentified as factors22.
Additionally, the study also revealed that provision of quality laboratory services had statistically significant association with internal quality
control, result verification, result report time,communication, equipment and job description. This clearly indicates that there existed anegative relationship between factors affecting
quality laboratory service and provision of quality laboratory service and it implied that thevariables affect delivery of quality laboratory
service to health sector programs and patientsatisfaction.
In general the major findings from this study
were factors associated with human resource,
resources, infrastructure, quality management
system, and polices, and these challenges facing laboratory systems in resource-poor settings like Ethiopia. Together, these factors
compromise the provision of quality laboratory services and quality health care services
delivery as well as public health provision. So
improving laboratory quality systems requires
political commitment, financial support, andstrong support and follow up from the healthsystem leaders, and competent and motivatedlaboratory staff.

laboratory services were shortage of resources,
lack of management supports, poor equipment
management system, high workload, lack ofcompetent staff, low staff motivation, ineffective communication system and lack of wellestablished quality management system. In
addition poor internal quality control practice,absence of result verification system, delay ofresult reporting time, and lack of job description
were affected the provision of quality laboratory
services.
As laboratory services is an essential component of the health care system, laboratory capacity building and quality management system
implementation will enable to provide quality
and reliable services for disease treatment andprevention. Therefore, government and stakeholders should understand and address thefactors affecting the provision quality laboratory service and they should work together forstrengthening laboratory quality assurance andaccreditation program.


Authors’ contributions
Eyob Abera Mesfin conceived and designed
the study and collected data, performed analysis, interpretation of data and draft the manuscript. Binyam Taye, Getachew Belay, Aytenew
Ashenafi and Veronica Girma assisted with thedesign, performed analysis, interpretation ofdata and the critical review of the manuscript.
All authors read and approved the final manuscript. All authors participated in critical appraisal and revision of the manuscript.

CONCLUSION

Acknowledgements

In conclusion, most of the laboratories did nothave well established laboratory quality management system, system for staff recognition,and continuing education/training program. The
major factors affecting the provision of quality

The study was funded by Addis Ababa University,
School of Medical Laboratory science. We would
like to acknowledge study subject and participating laboratory staffs of each hospital for their administrative support and cooperation.

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Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

What is known about this topic?
•••

Laboratory service is an essential componentof health care system with a wide range of
diseases diagnosis, treatment and monitoring services.
There are many factors affecting provisionof quality laboratory services in developing
countries especially in sub-Sahara African.
Efforts to improve laboratory capacity andquality system in resource-limited countries
are very limited and access to reliable laboratory testing remains limited in many countries. This results in delayed diagnosis, misdiagnosis, & inappropriate treatment andleads to increase morbidity and mortality.

What this study adds•••

There are several factors affecting provisionof quality laboratory services in public andprivate health institution in Addis Ababa
Ethiopia.
There is need for a government and stakeholders to be supporting and strengthening
laboratory quality system since it is an essential component of health care system.
Additionally this study adds knowledge andinformation on factors affecting the provision of quality of laboratory services inhealth care system to health policy and decision makers and provides useful baseline
information for all efforts that will be madeto improve quality laboratory services infutures.

REFERENCES

3. Koplan JP, Puska P, Jousilahti P, Cahill K, Huttunen J;
National Public Health Institute partners. Improving
the world’s health through national public health institutes. Bulletin of the World Health Organization. 2005;
83(2):154-157.
4. Ndongmo CB. Clinical laboratory diagnostics in Africa.
African Technology Development Forum Journal. 2005;
2(3):21-22.
5. World Health Organization. Joint WHO – CDC Conference on Health Laboratory Quality Systems. Lyon France:
WHO; 2008.
6. Howanitz PJ. Errors in laboratory medicine: practicallessons to improve patient safety. Archives of Pathology
and Laboratory Medicine. 2005; 129(10):1252-1261.
7. Simundic AM, Bilic-Zulle L, Nikolac N, Supak-Smolcic
V, Honovic L, Avram S, et al. The quality of the extraanalytical phase of laboratory practice in some developing European countries and Mexico – a multicentric
study. Clinical Chemistry and Laboratory Medicine. 2011;49(2):215–228.
8. Manafa O, McAuliffe E, Maseko F, Bowie C, MacLachlan
M, Normand C. Retention of health workers in Malawi:
perspectives of health workers and district management.
Human resources for health. 2009; 7(1):65.
9. Alash’le G A, Croxton T, Akintunde E, Okelade B, Jugu
J, Peters S, et al. Experiences in establishing a PEPFARsupported laboratory quality system in Nigeria. American
journal of clinical pathology. 2010; 134(4):541-549
10. Abimiku AG; Institute of Human Virology, Universityof Maryland School of Medicine PEPFAR Program (AIDS
Care Treatment in Nigeria [ACTION]). Building laboratory
infrastructure to support scale-up of HIV/AIDS treatment,
care, and prevention. American journal of clinical pathology. 2009; 131(6):875-886.
11. Bates I, Maitland K. Are laboratory services comingof age in Sub-Saharan Africa? Clinical Infectious Diseases.
2006; 42(3):383-384.
12. Ethiopia Health Nutrition Research Institute. National Laboratory Accreditation Assessment for Clinical andPublic Health Laboratories. 2010. Addis Ababa Ethiopia.
Ethiopia Health Nutrition Research Institute.

1. Petti CA, Polage CR, Quinn TC, Ronald AR, Sande MA.
Laboratory medicine in Africa: a barrier to effective healthcare. Clin Infect Dis. 2006; 42(3):377–382.

13. Ethiopia Central Statistics Agency. Summary and statistical report of the 2007 population and housing census. Addis Ababa, Ethiopia: Federal democratic republicof Ethiopia population census commission. 2008:1-0.

2. Dacombe R J, Squire S B, Ramsay A R, Banda H T, Bates
I. Essential medical laboratory services: their role in delivering equitable health care in Malawi. Malawi Medical
Journal. 2006; 18(2):77-9.

14. Federal Democratic Republic of Ethiopia Ministry of
Health. Health and Health Related Indicators 2003 E.C.
2010/11G.C. Addis Ababa Ethiopia: Federal democratic
republic of Ethiopia ministry of health. 2011.

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eJIFCC2017Vol28No3pp205-223

Eyob Abera Mesfin, Binyam Taye, Getachew Belay, Aytenew Ashenafi, Veronica Girma
Factors affecting quality of laboratory services in health facilities in Addis Ababa, Ethiopia

15. Baidoun S, Zairi M. A proposed model of TQM implementation in the Palestinian context. Total Quality Management and Business Excellence. 2003; 14(10):1193-1211.
16. Lyons KJ., Lapin J, Young B. A study of job satisfactionof nursing and allied health graduates from a mid-Atlantic
university. Journal of Allied Health. 2003; 32(1):10-7.
17. Al-Enezi N, Shah MA, Chowdhury RI, Ahmad A. Medical laboratory sciences graduates: are they satisfied atwork? Education for Health. 2008; 21(2):100.
18. Nocochea E, Bossenemeyer D. Standards-Based Management and recognition: A practical approach for improving the performance of health services. A Field guide. Baltimore Maryland USA: JHPIEGO; 2005.
19. Wanjau KN, Muiruri BW, Ayodo E. Factors affecting
provision of service quality in the public health sector: Acase of Kenyatta national hospital. International Journalof Humanities and Social Science 2012: 2(13):114-125
20. The Clinical & Laboratory Standards Institute (CLSI).
GP26 - A3. Application of a Quality Management System
Model for Laboratory Services; Approved Guideline—
Third Edition. Pennsylvania USA: CLSI; 2004.
21. Birx D, de Souza M, Nkengasong JN. Laboratory Challenges in the Scaling Up of HIV, TB, and Malaria Programs
The Interaction of Health and Laboratory Systems, Clinical Research, and Service Delivery. American Journal of
Clinical Pathology. 2009; 131(6):849-851.
22. Belete T, Hailu M, Wegene T, Negussie G, Zinet A, Hiwot B, et al. The status of HIV screening laboratories in
Ethiopia: achievements, problems encountered and possible solutions. Ethiopian Journal of Health Development.
2002; 16(2):209-215.

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Retrospective approach to evaluate
interferences in immunoassay
Jayesh Warade

Laboratory Services, Meenakshi Mission Hospital and Research Center, Madurai, Tamilnadu, India

INFO

ABSTRACT

Corresponding author:
Jayesh Warade
Laboratory Services
Meenakshi Mission Hospitaland Research Center
Madurai, Tamilnadu
IndiaE-mail: jdyajdo@gmail.com

BackgroundKey words:
heterophilic - antibody, immunoassay,interferences, cross reactive

Despite the increase in sensitivity and specificity of
immunoassay technique over years, analytical interference remains to be major area of concern.
The interfering substances are endogenous substancesthat are natural, polyreactive antibodies as heterophilic or auto antibodies, or human anti-animal antibodies
together with other unsuspected binding proteins thatare unique to the individual. Interfering substances
can interfere with the reaction between analyte andreagent antibodies in immunoassay resulting in false
positive or negative values. This ultimately results in
misinterpretation of patients reports and finally towrong course of treatment.
Objective
In our study, we used a retrospective approach to findout the extent of interferences and type of interferences in some cases during our routine practice.
MethodThe immunoassay reports which were clinically notcorrelating were retrospectively evaluated after discussion with the clinician. Over a period of six month atotal of 42 samples were evaluated for interference forPage 224

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Retrospective approach to evaluate interferences in immunoassay

different immunoassay parameters such as Beta
HCG, Estradiol, CA 125, AFP, prolactin, Hepatitis
B Surface antigen (HbSAg) and troponin I. The
samples were treated with commercially available antibody blocking agents and were reanalyzed. Commercially available diluents were usedin some cases to evaluate high dose hook effect.
Different platform, methodology and reagentswere used for re -analysis.
Results
Out of 42 samples, 19 were found to be affectedby interferences The data obtained for interferences was as follows beta HCG - 6 samples (2positive and 4 negative interference); estradiol- 3 samples (2 positive and 1 negative interference); CA-125 - 3 samples (2 positive and 1 negative interference), Alfa Feto Protein - 2 samples(2 positive interference); prolactin - 1 sample(positive interference); Hepatitis B Surface antigen - 1 samples (negative interference); troponin I - 2 samples (positive interference).
Conclusion
Despite the use of state of the art laboratory
equipments, chances of interference in immunoassay analysis resulting from endogenous
substances could not be ruled out. In conclusion, thorough evaluation of all immunoassay reports should be carried out in cases of suspectedinterference.


INTRODUCTION
All laboratory assays are subject to interferences. The effects of hemolysis, lipemia, and bilirubinemia (i.e., icterus) on laboratory methods
are well documented. Each of these may affectthe analytical measurement. Despite the analytical sensitivity of immunoassays and measurements often being made without the needfor prior extraction, immunoassays may lack adequate specificity and accuracy (1).
Developing immunoassays for the quantification of an analyte in a buffer solution has its
own challenges, nonetheless quantification of
the same analyte in a biological matrix (usually
serum or plasma) bears additional complexities. The challenges include background assay
signal changes, biological variability (between
matrix samples) exceeding analytical imprecision and recovery of the spiked reference standard can be challenging. Despite the increase insensitivity and specificity of the immunoassay
techniques over years, analytical interference
remains to be a major area of concern.
The interfering substances change the measurable concentration of the analyte or the alteredantibody binding can potentially result in immunoassay interference. These are endogenous
substances that are natural, polyreactive antibodies with other unsuspected binding proteinsthat are unique to the individual. These substances can interfere with the reaction between
analytes and reagent antibodies in immunoassay resulting in false positive or false negativevalues (2,3,4,5,6). This ultimately results in misinterpretation of patients reports and finally towrong course of treatment.
Heterophile antibodies accounts for large
amount of interference in immunoassay. The
presence of a heterophile antibody is characterized by broad reactivity with antibodies
of other animal species (which are often thesource of the assay antibodies). Such antibodies are commonly referred to as human antianimal antibodies (HAAA). Human anti-mouse
antibodies (HAMA) belong to this category.
These can result in both false positive and false
negative results (7). These are endogenous antibodies produced against poorly defined antigens. Both IgG and IgM heterophilic antibodies
have been reported (8). These antibodies react

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with various antigens and the variable region ofother antibodies (anti-idiotypic antibodies). In
most of the cases there is no history of medical treatment with animal immunoglobulin
or other well-defined immunogens, these arecharacteristically multi-specific (reacts with immunoglobulin from two or more species) or exhibit rheumatoid activity. So-called ‘sandwich’immunoassays are particularly susceptible tothis interference.
High dose hook effect is one of the cause of analytical interference in immunoassay. The hookeffect or the prozone effect is a type of interference which plagues certain immunoassays andnephelometric assays, resulting in false negatives or inaccurately low results (9). The effectcan also occur because of antigen excess, whenboth the capture and detection antibodies become saturated by the high analyte concentration. In most case no sandwich can be formed
Table 1by the capturing antibody, the antigen and thedetection antibody (10).
Analytical interference may also be due to crossreactivity. It is the most common interference- mostly in competitive assay. Cross reacting
substance compete for binding site of antibody,resulting in over - or underestimation of analyte
concentration (11). Cross reactivity is a major
problem in diagnostic immunoassays. Cross reactivity can occur where endogenous moleculeswith a similar epitopes to the measured analyte
exist in the sample, these may be metabolitesof the analyte, or structurally similar pharmaceutical agents. (12)Considering these interferences in immunoassay we have done follow up of non clinicallycorrelating results. After excluding the pre analytical and post analytical factors root cause
analysis of analytical interferences is done andpresented here.

Details of various platforms, methodologies, reagents used in study

Sr.
No.

Equipment model & makeMethodology

Reagent used1.

Access 2 Beckman Coulter

Chemiluminescence
Immunoassay

Dedicated reagent

2.

DxI600 Beckman Coulter

Chemiluminescence
Immunoassay

Dedicated reagent

3.

Vitros ECi Ortho Clinical
Diagnostic

Particle Enhanced
Chemiluminescence
Immunoassay

Dedicated reagent

4.

Vitros 3600 Ortho Clinical
Diagnostic

Particle Enhanced
Chemiluminescence
Immunoassay

Dedicated reagent

5.

Mindray ELISA Reader

Enzyme Linked Immuno
absorbent Assay

DRG Diagnostics ELISA kits
for beta HCG, estradiol,
Ca 125, AFP, Prolactin

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Table 2

Details of antibody blocking agent used in study

Sr.
No.

Test parameter

Antibody
blocking agent

Specification

1.

Estradiol

True Block

Active HA/HAMA blocker - goat anti-human IgG(GAH IgG) Fc fragment

2.

CA 125

True Block

Active HA/HAMA blocker - goat anti-human IgG(GAH IgG) Fc fragment

3.

AFP

True Block

Active HA/HAMA blocker - goat anti-human IgG(GAH IgG) Fc fragment

4.

Prolactin

True Block

Active HA/HAMA blocker - goat anti-human IgG(GAH IgG) Fc fragment

5.

Troponin I

True Block

Active HA/HAMA blocker - goat anti-human IgG(GAH IgG) Fc fragment

OBJECTIVE
In our study, we have used a retrospective approach to evaluate the extent and type of interferences in immunoassays during our routine
practice laboratory.
METHOD
All the immunoassay reports which were clinically non correlating were retrospectively evaluated following consultation with the clinicians.
Over a period of six month nearly 87,780 immunoassays were performed of which 42 samples
were evaluated for interference - Beta HCG,
Estradiol, CA 125, AFP, prolactin, HbSAg andtroponin I results were scrutinized. Thoroughpatient history was collected to evaluate exposure to animal antibody and recent immunoglobulin inoculation. The samples were treatedwith commercially available antibody blocking agents and were reanalyzed (Table No. 2).
Commercially available diluents were used toevaluate high dose hook effect (Table No. 3).

Different platform, methodology, reagents wereused for re-analysis (Table No. 1). Strict quality
control measures were followed throughout
the analysis process.
RESULTSOut of 42 samples 19 were found to be affectedby analytical interferences. The pre-analyticalinterferences was found in 20 cases and were
attributed to wrong time of collection, wrong
patient identity and wrong dilution protocols.
Post analytical errors were found in 3 cases
and were mainly due to wrong transcription in
manual entry and wrong calculation in case ofmanual dilutions.
The present study focused on analytical interference. The data obtained for analytical interferences was as follows: beta HCG - 6 samples (2positive and 4 negative interference); estradiol- 3 samples (2 positive and 1 negative interference); CA-125 - 3 samples (2 positive and 1 negative interference), Alfa Feto Protein - 2 samples

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Table 3

Details of diluents used in study

Sr.
No.

Test parameter

Antibody
blocking agent

1.

Beta HCG

2.

Ca 125

Access Sample Diluent A

On board sample diluents available
ready to use from

3.

AFP

Access AFP sample Diluent

Ready to use

4.

Troponin I

Access Sample Diluent A

On board sample diluent available
ready to use from

5.

Estradiol

VITROS High Sample Diluent
A Reagent

On board sample diluent available
ready to use fromSpecification

Access Total β hCG

S0 calibrator with beta HCG
concentration 0 mIU/mL.

Calibrator S0 (zero)

Table 4

Result before and after evaluation with probable interference

SamplenumberFirst report

Report after evaluation

Interference reason

Beta HCG
1.

12586 mIU/mL

6875 mIU/mL(Alternate Platform)

Antibody Interference/
Cross Reactivity

2.

1856 mIU/mL

15 mIU/mL (Antibody Blocking)Antibody Interference3.

23 mIU/mL

7584 mIU/mL(1:10 Dilution)

High Dose Hook Effect4.

115 mIU/mL

23584mIU/mL (1:20 Dilution)

High Dose Hook Effect5.

85 mIU/mL

2846 mIU/mL (1:5 Dilution)

High Dose Hook Effect6.

8 mIU/mL3589 mIU/mL (1:5 Dilution)

High Dose Hook EffectEstradiol
1.

4285 pg/mL

2865 pg/mL (Alternate Platform)

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Antibody Interference/
Cross Reactivity

Jayesh Warade
Retrospective approach to evaluate interferences in immunoassay

2.

58 pg/mL

3421 pg/mL (Antibody Blocking)Antibody Interference

3.

1884 pg/mL

758 pg/mL (Alternate Platform)

Antibody Interference/
Cross Reactivity

CA 125
1.

58 U/ml

87564 U/ml (1:100 Dilution)

High Dose Hook Effect2.

13 U/ml

158964 U/ml (1:500 Dilution)

High Dose Hook Effect3.

1852 U/ml

58 U/ml (Antibody Blocking)Antibody Interference

AFP1.

86 ng/mL

86945 ng/mL (1:50 Dilution)

High Dose Hook Effect2.

25 ng/mL

148697 ng/mL (Antibody Blocking)Antibody Interference

Prolactin
1.

984 ng/mL

28 ng/mL (Antibody Blocking)Antibody Interference

Hbs Ag1.

Negative

Positive (Alternate Platform)

Antibody Interference/
Cross Reactivity

Troponin I
1.

0.02 ng/ml>150 ng/ml (1:3 Dilution)

High Dose Hook Effect2.

0.02 ng/ml

87 ng/ml (Antibody Blocking)Antibody Interference(2 positive interference); prolactin - 1 sample(positive interference); HBsAg - 1 samples (negative interference); troponin I - 2 samples (positive interference).

different chemiluminescence (CLIA and CMIA)
platforms were used. Cases where high dose
hook effect was suspected serial dilutions were
performed with commercially available diluents.

In our study, the cases wherever cross reactivity
or antibody interference were suspected an alternate platform was used, alternately commercial antibody blocking agents were used. Two

The serial dilutions were performed in the following order; 1:1, 1:2, 1:4, 1;10, 1:20, 1:50, 1:100,
1:500. The final results obtained in specific dilutions are shown in Table No. 4.

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DISCUSSION

can potentially result in assay interference (14,15,16). These interfering substances may beunique to individuals and their concentration
may changes over a period of time. The interfering substances may have low or high affinity
and its concentration determines the extent ofinterference and may affect one or more analytes. Antibody blocking agents may not be sufficient to overcome all types of interferences(14, 15,16).

Analytical errors arising from the presence ofantibodies to mouse (monoclonal) immunoglobulins in the patient’s plasma or serum havereceived the most attention but are just one of
the many causes of interference in immunoassays. Reproducibility within a laboratory or
among laboratories using the same or different
analytical systems is no guarantee of the validity or correctness of the results (13). Possibly themost important of the idiosyncratic interfering
substances found in patient samples are thosethat are either autoantibodies against the analyte itself (e.g., insulin autoantibodies) or heterophilic (including antianimal) antibodies thatreact with one or more of the assay reagents.
Both types of antibodied can produce false highor false low results (13).

In immunoassays the heterophile antibody (orany other cross reacting substance) bridges
the capture and detection antibodies to mimic
analyte binding as such resulting in false highvalues. In contrast even in the presence of theanalyte, heterophile antibody (or any othercross reacting substance) bind to the capture
antibody preventing the analyte binding withthe capture antibody resulting in falsely low
values (negative interference). This is the basic
mechanism for false high or low values resulting from the presence of heterophile antibody(Figure 1).

Specificity of an immunoassay depend on thebinding property of the antibody, composition of the antigen and the matrix. Substancesthat alter the measurable concentration of ananalyte in the sample or alter antibody binding
Figure 1

Showing mechanism for:(A) Intended Antigen - Antibody Bridge falsely; (B) high;and (C) low values in presence of heterophile(or any other cross reacting substance) antibody

A

B

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CJayesh Warade
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Heterophile antibodies may be present in all
patients (17). The frequency of immunoassay
interferences resulting from these antibodies
depends on the magnitude of bias in the analytical method that contributes to significant
interference (13). The prevalence of potentiallyinterfering antibodies has been reported to beas high as 40%, the incidence of immunoassay
interference is estimated to be less than 2%.
They probably arise from mundane activities
such as keeping pets, ingesting animal antigens,vaccination, infection, or even blood transfusion. High concentration of interfering proteins,which may be measurable in grams per litre, or
proteins with high binding affinity can, however, overwhelm the analytical system, leading toassay interference and erroneous results (17).
The interference from heterophile antibodies
can be avoided by combining different assay antibodies from different species with little crossFigure 2

reactivity (18) or by using antibody blocking
agent which contains IgM and IgG antibodies
with high affinity for the anti-animal antibody.
In high dose hook effect, all the available binding site from capture as well as detection antibodies are occupied by the analyte creatinga stage of hyper saturation preventing formation of true antigen-antibody bridge. This canbe prevented by diluting the sample as suchdecreasing analyte concentration and allowing
improved antigen - antibody complex formation, eventually and final concentration is obtained by multiplying with the dilution fraction(Figure 2).
Limitation of our study is our retrospective approach, where evaluation was indicated only
following feedback from clinicians. The screening for interfering antibody was done only after suspecting interference and was done only
for the related subjects. The screening was not

Analyte is binding to both capture as well as detection antibody
leading to a reduction in formation of antibody-antigen-antibody
complexes and a decrease in signal at higher concentrations of analyte

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Jayesh Warade
Retrospective approach to evaluate interferences in immunoassay

done actively for all the patients visiting the laboratory. As such, some of the interferences may
have been missed during this period.
CONCLUSION
Despite use of state of the art laboratory equipment, chances of interference in immunoassay
analysis resulting from endogenous substances
cannot be ruled out. In conclusion, thoroughevaluation of all non clinically correlating immunoassay results is advised.
REFERENCES
1. Eleftherios P. Diamandis. Immunoassay interference:a relatively rare but still important problem. Clinical Biochemistry 37 (2004) 331– 332
2. Jane F. Emerson, MD, PhD, Keane K.Y. Lai, MD. Endogenous Antibody Interferences in Immunoassays. Winter2013 | Volume 44, Number 1 Lab Medicine 69 - 74
3. M P Brugts, J G L M Luermans, E G W M Lentjes, N J
van Trooyen-van Vrouwerff, F A L van der Horst3, P H Th
J Slee1, S W J Lamberts and J A M L Janssen Heterophilic
antibodies may be a cause of falsely low total IGF1 levels
European Journal of Endocrinology (2009) 161 561–565.
4. Selby C. Interference in immunoassay. Ann Clin Biochem 1999;36:704–21.
5. Kricka LJ. Human anti-animal antibody interference inimmunological assays. Clin Chem 1999;45:942–56.
6. Preiser W, Brink NS, Hayman A, Waite J, Balfe P, Tedder
RS. False-negative HIV antibody test results. J Med Virol
2000;600:43–7.

9. Dasgupta A, Wells A, Datta P. Effect of digoxin Fab antibody on the measurement of total and free digitoxin byfluorescence polarization and a new chemiluminescentimmunoassay. Ther Drug Monit 1999;21:251–5.
10. CLSI. Interference Testing in Clinical Chemistry; Approved Guideline - Second Edition. CLSI document EP07A2. Wayne, PA, Clinical and Laboratory Standard Institiute; 2005.
11. Matthew D Krasowski, Denny Drees, Cory S Morris,
Jon Maakestad, John L Blau and Sean Ekins Cross-reactivity of steroid hormone immunoassays: clinical significanceand two-dimensional molecular similarity prediction Krasowski et al. BMC Clinical Pathology 2014, 14:33
12. Miller JJ, Valdes R Jr. Approaches to minimizing interference by cross-reacting
molecules in immunoassays. Clin Chem 1991;37:144–53.
13. Adel AA Ismail Interference in immunoassays is insidious and could adversely affect patient care BMJ
2001;323:705–6
14. Hennig C, Rink L, Fagin U, Jabs WJ, Kirchner H. The
influence of naturally occurring heterophilic anti-immunoglobulin antibodies on direct measurement of serum
proteins using sandwich ELISAs. J Immunol Methods
2000;235:71–80.
15. Revelen R, Bordron A, Dueymes M, Youinou P, Arvieux
J. False positivity in a cyto-ELISA for anti-endothelial cell
antibodies caused by heterophile antibodies to bovine
serum proteins. Clin Chem 2000;46:273–8.
16. Lichtenwalner MR, Mencken T, Tully R, Petosa M.
False-positive immunochemical screen for methadone
attributable to metabolites of verapamil. Clin Chem
1998;44:1039–41.

7. Bjerner, J. – Bormer, O.P, Nustad, K: The war on heterophilic interference. Clin Chem 2005; 51: 9–11

17. Hennig C, Rink L, Fagin U, Jabs WJ, Kirchner H. The
influence of naturally occurring heterophilic anti-immunoglobulin antibodies on direct measurement of serum
proteins using sandwich ELISAs. J Immunol Methods
2000; 235:71–80.

8.The Ghost in the assay tube: heterophil antibody interferences in immunoassays – an ever-recurring but often
forgotten problem. Communiqué: A Mayo Reference Services Publication. 2003; 28(3):1–4.

18. Revelen R, Bordron A, Dueymes M, Youinou P, Arvieux
J. False positivity in a cyto-ELISA for anti-endothelial cell
antibodies caused by heterophile antibodies to bovine
serum proteins. Clin Chem 2000; 46:273–8.

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Case report: biliary pancreatitis with acutecholangitis in a patient under anticoagulant
treatment with dabigatranFranziska Baumann-Durchschein1, Thomas Gary2, R.B. Raggam2
1
2

Department of Internal Medicine, Division of Gastroenterology, Medical University of Graz, Austria
Department of Internal Medicine, Division of Angiology, Medical University of Graz, Austria

INFO

ABSTRACT

Corresponding author:
Franziska Baumann-Durchschein
Department of Internal Medicine
Division of Gastroenterology
Medical University of Graz
AustriaE-mail: franziska.durchschein@medunigraz.at

We report a case of acute onset of a biliary pancreatitis with cholangitis presented in our emergency
department. The patient was under anticoagulant
therapy with dabigatran due to persistent atrial fibrillation. Pancreatic enzymes including lipase were elevated above the linear measuring range and bilirubin together with cholestasis enzymes was also highly
elevated. An ERCP with papillotomy was urgently
indicated because postponing could lead to further
deterioration of the patient’s condition. Coagulation
testing showed a prolonged thrombin time above
160sec which was followed by a diluted thrombin time
(Haemoclot Test) resulted in a peak-level of dabigatran thus confirming full anticoagulation. Therefore,
idarucizumab (Praxbind®) was administered pre-procedural of ERCP, the patient underwent uneventful
ERCP without any bleeding complications, a full recovery was achieved and the patient was scheduled
for elective cholecystectomy.

Key words:
biliary pancreatitis, cholangitis,
anticoagulant therapy, bleeding

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Franziska Baumann-Durchschein, Thomas Gary, R.B. Raggam
Biliary pancreatitis with acute cholangitis in a patient under anticoagulant treatment with dabigatran

Table 1

Laboratory testing of coagulation parameters

Prothrombin time (PT)

10 sec[9-12 sec]

Activated partial thromboplastin time (aPTT)

54 sec[26-36 sec]

Thrombin time (TT)150 sec[-22 sec]

Drug level dabigatran (diluted TT; Haemoclot test)

54 ng/ml[50-300 ng/ml] Peak level

INTRODUCTION
Non‐VKA oral anticoagulants (NOACs) havebeen approved for various thromboembolic indications. In the past, the biggest disadvantageof these drugs has been the lack of specific antidotes to reverse the anticoagulant effect inemergency situations. Idarucizumab represents
the first novel antidote against the direct thrombin inhibitor dabigatran and was approved by
US FDA in October 2015 (1).
In our case report we used idarucizumab for
dabigatran reversal in a patient with mechanical cholestasis and cholangitis needing urgent
endoscopic retrograde cholangiopancreatography (ERCP).
CLINICAL-DIAGNOSTIC CASE
We report the case of a 75 year old man who
was admitted to the emergency department because of right sided upper abdominal pain with
Figure 1

a duration of a few hours. Besides he describeda clay-coloured stool and brown urine in the lastdays. He denied vomiting, fever or chills.
About 6 weeks before, he was hospitalized inanother clinic because of an acute cholecystitis
with multiple gallstones. He was taking dabigatran 150mg twice a day for non-valvular atrial
fibrillation (A-fib).
He had a CHA2DS2‐VASc score of 3 and no history of bleeding. He complained about pressure
pain in the epigastrium and right upper quadrant, with no peritoneal signs on examination.
Laboratory testing showed a white cell count of
17.5 × 106/L, with 88% neutrophils, 6.2 U/L bilirubin, (normal range: 0.1-1.2), 131 U/L ALT (035), 230 U/L AST (0-45), 164 U/L ALP (40-130),410 U/L GGT (0-55), 22.000 U/L lipase(0-60) and
37 CRP (0-5). Calculated creatinine clearance(Cockcroft and Gault) was 61 mL/min.

Sphincterotomy during the ERCP showing an oozing bleeding.

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Biliary pancreatitis with acute cholangitis in a patient under anticoagulant treatment with dabigatran

The abdominal ultrasound revealed dilated intrahepatic bile ducts in both liver lobes. The
common bile duct (CBD) and pancreas were notvisible due to intestinal gas overlay. Indication ofurgent ERCP due to acute cholangitis was made.
As the patient has taken dagibatran only a few
hours before, laboratory testing of coagulation
parameters were ordered including thrombin
time (TT) and dabigatran drug level (Table 1).
During the ERCP a small sphincterotomy was
performed with an oozing bleeding (Figure 1).
Consequently, 2.5 g idarucizumab (Praxbind®)
was injected intravenously and the bleeding
stopped immediately. Afterwards one impacted
concrement was removed from the distal CBDwith the aid of a dormia basket. After the intervention the patient received the second dose of
idarucizumab (2.5g) and no further bleeding occurred throughout hospital stay.
DISCUSSION
Bleeding is a rare complication of Non‐VKA oral
anticoagulants potentially associated with highmortality rates (2). The humanized monoclonal antibody fragment idarucizumab is the firstavailable specific antidote for the NOAC dabigatran. It binds dabigatran with 350-fold higher
affinity compared to dabigatran for thrombin(3). The application of idarucizumab completely
reversed the anticoagulant effect of dabigatran
within minutes and this effect is maintained for
up to 24 hours (4).
The recently published ISTH (International
Society on Thrombosis and Haemostasis) guideline (3) recommends to use this antidote in thecase of life-threatening bleeding, bleeding into a
critical organ or closed space, prolonged bleeding despite local hemostatic measures, high riskof recurrent bleeding because of overdose ordelayed clearance of NOACs, and need for an
urgent intervention associated with a high riskof bleeding (3).

In our case report, the mechanical cholestasiswith cholangitis represents an urgent intervention. ERCP with sphincterotomy is considered as
higher risk procedure for bleeding, and according to the European Society of Gastrointestinal
Endoscopy (ESGE) guidelines the last dose of
NOACs should be taken at least 48 hours before
the intervention (5).
The thrombin time (TT) and dabigatran drug
level represents useful parameters to determine the anticoagulation effect and bleedingrisk of dabigatran (3). The TT determines theconversion of fibrinogen to fibrin, which is the
final step in the coagulation cascade. It is prolonged in the presence of thrombin inhibitors
such as dabigatran and this prolongation is directly proportional to the dabigatran concentration (6).
When analysing dabigatran drug concentration, it is important to consider when the lastdose of the NOAC was taken to determine
whether the levels are likely to raise or decline
over time (3).
According to the ISTH recommendations, antidote administration should be considered if the
drug concentration exceeds 30 ng/mL and thepatient requires an urgent intervention associated with a high risk of bleeding (3).
In conclusion, idarucizumab can safely normalize clotting times within a few minutes.
According to the ISTH guidelines, this antidote
is indicated in patients with life-threatening
bleeding and/or urgent interventions with ahigh bleeding risk (3).
At the moment, several antidotes for NOACs areunder various stages of development: for example andexanet alfa, an antidote for the oral
factor Xa inhibitors or ciraparantag, an antidoteof all NOACs (3). Further studies are howeverneeded to refine the use of antidotes for in different situations and patient groups.

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Franziska Baumann-Durchschein, Thomas Gary, R.B. Raggam
Biliary pancreatitis with acute cholangitis in a patient under anticoagulant treatment with dabigatran

TAKE HOME MESSAGES/LEARNING POINTS••With the use of idarucizumab urgent, vitalinterventions can be performed safely andquickly despite patients are on fully effective
therapy with dabigatran.
To determine a substantial bleeding risk inpatients with urgent interventions in patientson dabigatran, determination of thrombin
time (TT) and dabigatran drug level using the
Haemoclot® test are useful to verify dabigatran effectiveness. This is even more indicated, if clear history of medication intake cannot be obtained.

REFERENCES
1. Tummala R, Kavtaradze A, Gupta A, Ghosh RK. Specificantidotes against direct oral anticoagulants: A comprehensive review of clinical trials data. International journalof cardiology. 2016;214:292-8.

2. Heidbuchel H, Verhamme P, Alings M, Antz M, Diener
HC, Hacke W, et al. Updated European Heart Rhythm
Association practical guide on the use of non-vitamin-K
antagonist anticoagulants in patients with non-valvularatrial fibrillation: Executive summary. European heart
journal. 2017;38(27):2137-2149.
3. Levy JH, Ageno W, Chan NC, Crowther M, Verhamme
P, Weitz JI, et al. When and how to use antidotes for thereversal of direct oral anticoagulants: guidance from the
SSC of the ISTH. Journal of thrombosis and haemostasis :JTH. 2016;14(3):623-7.
4. Pollack CV, Jr., Reilly PA, Eikelboom J, Glund S, Verhamme P, Bernstein RA, et al. Idarucizumab for Dabigatran Reversal. The New England journal of medicine.
2015;373(6):511-20.
5. Veitch AM, Vanbiervliet G, Gershlick AH, Boustiere C,
Baglin TP, Smith LA, et al. Endoscopy in patients on antiplatelet or anticoagulant therapy, including direct oral
anticoagulants: British Society of Gastroenterology (BSG)
and European Society of Gastrointestinal Endoscopy(ESGE) guidelines. Endoscopy. 2016;48(4):c1.
6. Kyrle PA, Binder K, Eichinger S, Fugger R, Gollackner B,
Hiesmayr JM, et al. Dabigatran: patient management inspecific clinical settings. Wiener klinische Wochenschrift.
2014;126(17-18):503-8.

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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
Edgard Delvin, CHU Sainte-Justine Research Center, Montréal, Québec, Canada
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
Tomris Ozben, Akdeniz University, Antalya, Turkey
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

				

			

				
		
		

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