Non-fasting lipid profile determination in presumably healthy children: Impact on the assessment of lipid abnormalities
Non-fasting lipid profile determination in presumably healthy children: Impact on the assessment of lipid abnormalities
Lukasz Szternel 0 1
Magdalena Krintus 0 1
Katarzyna Bergmann 0 1
Tadeusz Derezinski 1
Grazyna Sypniewska 0 1
0 Department of Laboratory Medicine, Nicolaus Copernicus University , Collegium Medicum in Bydgoszcz, Poland, 2 Outpatient Clinic aEsculapo in Gniewkowo , Poland
1 Editor: Giuseppe Danilo Norata, Universita degli Studi di Milano , ITALY
Despite the common use of non-fasting measurements for lipid profile in children it remains unclear as to the extent non-fasting conditions have on laboratory results of lipids measurements. We aimed to assess the impact of non-fasting lipid profile on the occurrence of dyslipidemia in children.
Data Availability Statement: All relevant data are
found within the paper.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Abbreviations: TC, total cholesterol; HDL-C,
highdensity lipoprotein cholesterol; LDL-C, low-density
lipoprotein cholesterol; TG, triglycerides; sd-LDL-C,
small dense LDL-C; ApoAI, apolipoprotein AI;
Materials and methods
Basic lipid profile including: total cholesterol (TC), high-density lipoprotein cholesterol
(HDLC), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG), as well as small,
dense-LDL-C (sd-LDL-C), apolipoprotein AI (ApoAI), apolipoprotein B (ApoB) and
lipoprotein(a) [Lp(a)], were measured in 289 presumably healthy children aged 9±11 in both fasting
and non-fasting condition. The clinical impact of non-fasting lipid profile was evaluated
individually for each child with estimation of false positive (FP) and false negative (FN) results.
The highest percentage of FP results in non-fasting condition was observed for TG (42.3%)
being significantly higher when compared to FN results (p = 0.003). In contrast, the highest
percentage of FN results in a non-fasting state were shown for LDL-C (14.3%), but the
difference was statistically insignificant when compared to FP results. When comparing fasting
and non-fasting lipid profile a number of significant differences was shown for: TG
(p<0.001), HDL-C (p = 0.002) LDL-C (p<0.001) and ApoAI (p<0.001), respectively. The
occurrence of dyslipidemia, recognized on the basis of non-fasting lipids was significantly
higher (p = 0.010) when compared to fasting lipid profile.
A higher occurrence of dyslipidemia, based on the measurement of non-fasting lipids in
children, is suggestive of possible disorders in lipid metabolism. However, accurate identification
of dyslipidemia by assessment of non-fasting lipids requires the establishment of appropriate
cut-off values for children.
A consensus statement on non-fasting and fasting lipid profile determination proposed by the
European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry
and Laboratory Medicine (EFLM) was recently published [
International guidelines for children advise performing the initial screening of children
with no risk factors for cardiovascular disease (CVD), between the ages of 9±11 [
to these recommendations, it is necessary to measure both TC and HDL-C concentrations in
the non-fasting blood sample, and then calculate the non-HDL (TC-HDL) value. If the initial
screening has not revealed any abnormalities in test results, it is advised to perform a second
screening between the ages of 17 and 21 [
]. The consensus statement of the EAS/ EFLM also
discusses the pros and cons of non-fasting measurement of lipid profile. One of its advantages
is the possibility of performing a comprehensive lipid panel in children, which is crucial for
cardiovascular risk prediction. In this case, a significant simplification of the preanalytical
phase in terms of lipid profile testing in children is very important [
To accurately assess lipid profile abnormalities in children, it is essential to follow the
guidelines presented in 2011 by the National Heart, Lungs and Blood Institute (NHLBI) and
approved by the American Academy of Pediatrics (AAP) [
]. They place emphasis on the
importance of lipid profile screening among children as a proper tool for the early
identification of dyslipidemia which might lead to the development of atherosclerotic lesions and are
still reversible at this stage. It must to be stated that the guidelines concerning the first
screening allow performance of the non-fasting lipid profile tests.
Inadequate cut-off values for lipid profile can pose problems in the identification of
children with dyslipidemia. The consensus regarding non-fasting lipid measurements presented
in 2016 by EAS/EFLM highlights the necessity for the proper preparation of lipids laboratory
reports when considering both adequate cut-off values as well as additional information with
consideration of when the subject last ate [
Lack of information on cut-off values for non-fasting lipid profile levels in children may
lead to incorrect interpretation of test results and accordingly to the inappropriate
implementation or withholding of medical treatment [
]. Therefore, we aimed to evaluate the impact of
non-fasting lipid profile on the occurrence of dyslipidemia in children, especially with regard
to obese or overweight subjects within this age group.
Material and methods
Characteristics of the study participants
This cross-sectional study was undertaken with 289 presumably healthy children aged 9±11.
The recruitment of participants was carried out in October and November 2015. The study
involved 152 girls (52.6%) and 137 boys (47.4%) aged 9±11. The clinical and biochemical
characteristics of the study participants are presented in Tables 1 and 2.
The physical activity of study participants is described on the basis of physical education
directives issued by the Minister of National Education for curricula in public schools, classes
I-III. In this three-year period, minimum compulsory physical education is 290 hours, i.e. 3
hours per week, but in subsequent classes, IV-VI this number rises to 4 hours per week [
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Overweight and obese [BMI percentiles 85]
1. p differences between girls and boys.
presented as percentages and absolute values (in brackets) [
BMI: body mass index
All (n = 289)
2. Age, weight, height and waist circumference are presented as median (25±75 percentiles). 3.BMI category (according to currently accepted cut-off values) are
Data from the Central Statistical Office (GUS) regarding extracurricular activities revealed in a
national study that the average child aged 2±14 spends about 2.4 hours in front of a TV screen
and/or computer monitor; this time was slightly longer for boys than girls. On the other hand,
physical activity was reported by almost 85% of children studied aged 6±14 [
1.Results are presented as median (25±75 percentiles); statistical significance at p<0.05.
2.p - differences in measurements fasting vs.non-fasting state.
3. Values in mg/dL were converted to mmol/L by multiplying by 0.0259 for TC-total cholesterol; HDL-C -high density lipoprotein cholesterol; LDL-C-low density
lipoprotein cholesterol; sd-LDL-C-small, dense low density lipoprotein cholesterol; and by 0.0113 for TG-triglycerides.
ApoAI-apolipoprotein AI; non-HDL-C- non high density lipoprotein cholesterol [TC-(HDL-C)]; ApoB- apolipoprotein B; Lp(a)- lipoprotein (a).
Inclusion and exclusion criteria for study participants
The children's age (9±11) was the first inclusion criterion taken into consideration in our
study, in accordance with the AAP and NHLBI guidelines, which recommend performing
(non-fasting) lipid profile screening in children who are not in a high-risk group [
second criterion was the necessity of taking two blood samples for analysis from each
participant. For this, three conditions had to be met: one blood sample from each subject was taken
in fasting state (in accordance with the National Cholesterol Education Program (NCEP),
Adult Treatment Panel III (ATP III) guidelines-at least 8 hours since the subject last ate) [
the second was taken after eating: between the 2nd and 4th hour since their last food intake (in
our study, breakfast was the established meal consumed before the blood sampling). It was
essential to maintain a time interval between the two samplings which could not be longer
than 14 days. The minimum time interval between the first and the second blood sampling in
this study was 2 days [
]. The exclusion criterion was for exceeding the time limit for fasting
(<8 hrs) and non-fasting (> 4 hrs) testing.
Laboratory and anthropometric measurements
Venous blood samples for laboratory analyses were taken by a qualified nursing team with the
use of a vacuum blood collection system (Becton Dickinson, Franklin Lakes, USA) gel
separation tubes. In order to obtain serum, the material was centrifuged for 10 minutes at 3000 rpm.
Serum was then aliquoted and stored at -70ÊC. The following lipid parameters: TC, TG,
LDL-C, HDL-C were measured immediately, whereas an extended lipid profile, i.e. sd-LDL-C,
ApoB, ApoAI, Lp(a) was performed on previously frozen samples.
Anthropometric measurements (weight, height and waist circumference) were performed
on the same day as blood samples were taken. The values of height and weight were used in
order to calculate BMI and BMI percentiles. To calculate BMI an online BMI calculator was
used (based on the ªOLAFº project) [
All laboratory tests were performed at the Department of Laboratory Medicine, Nicolaus
Copernicus University, Collegium Medicum in Bydgoszcz on the Horiba ABX Pentra 400
analyzer (Horiba ABX, Montpellier, France). Reagents for sd-LDL-C (direct automated sdLDL-C
kit) were supplied by Randox Laboratories (Crumlin, UK).
Calculation of the following values were performed: 1) Non-HDL-C = TC-(HDL-C); 2)
remnant cholesterol = TC-(LDL-C)-HDL-C.
Dyslipidemia was defined by at least one abnormal level of serum lipid parameters: total
cholesterol (TC) 4.4 mmol/L ( 170 mg/dL), TG for children aged 0±9 yrs. 0.85 mmol/L
( 75 mg/dL); 10±19 yrs 1.02 mmol/L ( 90 mg/dL), LDL-C 2.85 mmol/L ( 110 mg/
dL) and HDL-C 1.17 mmol/L ( 45 mg/dL) according to currently accepted cut-off values
for lipid parameters in children and adolescents (Table A S1 File) [
The study was approved by the Collegium Medicum Bioethics Committee (at the Nicolaus
Copernicus University, KB 338/2015). Parental written consents forms were obtained from all
participants before inclusion to the study.
The number of patients in our study allows us to estimate a test power of 0.89 for fasting and
postprandial lipid analysis. Therefore, we can assume that there are no statistical differences in
fasting and postprandial parameters due to an actual interrelationships and is not resultant
from inadequate sample size. Agreement between the distribution of investigated variables
and normal distribution was evaluated by means of the W Shapiro-Wilk's test. Parameters
with normal distribution were presented as the mean ± standard deviation (SD), whereas,
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parameters with non-normal distribution were presented as medians and interquartile ranges
(IQR). The comparison of values in the two independent groups was performed by means of
the Student's t-test (normal distribution) or the Mann±Whitney's U test (for non-normal
distribution). In order to compare the values of two related variables the Wilcoxon signed-rank
test was used. Multivariate regression analysis was performed for lipid parameters (fasting and
non-fasting) after adjustment for gender and anthropometric parameters. The clinical
usefulness of lipid profile was assessed both fasting and non-fasting on the basis of percentage
calculation of false positive or false negative test results. In all analyses, the p-value< 0.05 was
considered statistically significant. Statistical analysis was performed using Statistica 12.0 PL
(StatSoft Inc., Tulsa, USA) and PQStat (PQStat 1.6.2, Poznan, Poland).
Baseline characteristics of the study participants are presented in Table 1, while detailed
biochemical characteristics are shown in Table 2.
Significant differences between girls and boys were noted with regard to weight, height and
waist circumference. Differences in the concentration of lipid parameters between boys and
girls, both in the fasting and non-fasting state were non-significant. Following BMI percentile
categories, there was no statistical significance in the occurrence of being overweight and
obese between girls and boys. However, the percentage of those overweight and obese in boys
was higher when compared to girls (14.6 vs 9.3%, respectively). Table 2 presents a comparison
of fasting and non-fasting lipid profile parameters in the whole study group of 289 children
with comparisons in subgroups by gender. Amongst 137 boys statistically significant
differences between fasting and non-fasting states were noted for TG, LDL-C, Lp(a) and ApoAI. In
this group no statistical significance was observed for TC, HDL-C, sd-LDL and ApoB. These
parameters which were considered statistically significant for the whole group, were
analogously noted as statistically significant in girls. Significant differences for lipid parameters
concentration in fasting and non-fasting state were noted for: TG (p<0.001), HDL-C (p = 0.002),
LDL-C (p<0.001) and apoAI (p<0.001), whereas TC, sd-LDL-C and Lp(a) did not differ
significantly in either fasting or non-fasting state, in the group as a whole.
Table 3 presents the comparison of median lipids and apolipoproteins concentrations in
fasting and non-fasting state among children with a normal body mass and those who are
overweight and obese, assessed on the basis of BMI percentiles.
In contrast to the group as a whole and children with a normal BMI (where the
concentration of TC was slightly higher in non-fasting state), in overweight and obese children TC
concentrations were slightly lower (by 0.5% fasting vs. non-fasting). A similar tendency for lower
TC concentrations (by 0.9%) in non-fasting state was observed in girls. Significantly higher
concentrations of TG in non-fasting state were found among children with a BMI 85 (of
28.1%) compared to those with optimal BMI (24.3%). In overweight and obese children, a
slight but non-significant elevation of HDL-C and a lower concentration of apoB was
The occurrence of abnormal concentrations of lipids measured in fasting vs. non-fasting
state in children in relation to the currently recommended values for lipids and
apolipoproteins in fasting condition is presented in Fig 1.
Statistically significant differences in both fasting and non-fasting states were only observed
for TG (33.2% vs 59.1%) and ApoAI (47.0% vs 50.2%). In the remaining lipid parameters there
were no statistically significant differences identified in the percentages of lipid abnormalities.
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2.p significance of differences in measurements fasting vs.non-fasting state.
3.Lack of significant trend.
BMI-Body Mass Index; TC-total cholesterol; TG-triglycerides; LDL-C-low density lipoprotein cholesterol; HDL-C-high density lipoprotein cholesterol;
sd-LDL-Csmall, dense low density lipoprotein cholesterol; Lp(a)- lipoprotein (a); ApoB- apolipoprotein B; ApoAI-apolipoprotein AI; non-HDL-C- non high density lipoprotein
The identification of dyslipidemias based on two lipid indices was greater for non-HDL-C
when compared to the TG/HDL-C ratio, in both fasting (45.2 vs 9.6) and non-fasting (39.2 vs
19.2) states (Fig 2).
Fig 1. The percentage of abnormal lipid parameters results in fasting vs. non-fasting state [N = 289]. Cut-off
point for lipids in accordance with The National Heart, Lung and Blood Institute (NHLBI) guideline [
cholesterol; TG-triglycerides; LDL-C-low density lipoprotein cholesterol; HDL-C-high density lipoprotein cholesterol;
sd-LDL-small, dense low density lipoprotein cholesterol; Apo B- apolipoprotein B; ApoAI-apolipoprotein A1;
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In employing non-HDL-C for the recognition of lipids abnormalities, we obtained similar
percentages in both fasting and non-fasting states. Though there were no significant
differences regarding identification of dyslipidemia for non-HDL-C, when using Pearson's analysis,
we demonstrated a stronger correlation between most of the lipids measured in fasting and
non-fasting states (Table B S1 File).
When comparing the differences between percentages values for particular lipid parameters
exceeding their cut-off values, we noticed that the highest percentage of abnormal lipid and
apolipoproteins concentrations occurs in LDL-C (with 10.7% more abnormal results in
children in fasting state) and in TG and apoAI (respectively 25.9% and 16.7% more abnormal
results in children in non-fasting state). Similar changes were observed after categorization
according to BMI percentiles (Table 4).
Only TG and ApoAI showed significant differences between fasting and non-fasting states
when compared to groups with optimal body mass and with BMI 85. Interestingly,
sdLDL-C was characterized by the highest change in the overweight/obese group in fasting vs.
non-fasting state (7.3% higher), although statistically non important.
A higher occurrence of dyslipidemias, recognized on the basis of abnormal concentrations
of TC, TG, LDL-C or HDL-C in a non-fasting state, was identified in both overweight/obese
children and those with optimal BMI, although the differences in the former group were
nonsignificant. The occurrence of dyslipidemia recognized on the basis of non-fasting lipids
(80.1%) was significantly higher (p = 0.01) than those based on fasting values (69.2%) (Fig 3).
Multiple regression analysis revealed that non-fasting TG concentration was significantly
affected by sd-LDL-C concentrations in a non-fasting state (β = 3.58, p<0.01). This model was
statistically significant and explained 60% of TG variability.
The clinical usefulness of the results of lipid parameters measured in a non-fasting state in
comparison to fasting concentrations is presented in Fig 4.
In order to individually analyze the results of lipid profiles each of the study participants
was assessed in parallel, both in fasting and non-fasting states. The concentration of each
measured lipid parameter in both fasting and non-fasting state was evaluated and described as
normal, elevated or decreased, applying the accepted criteria. Analysis was based on the
identification of false-positive (FP) results if non-fasting values were elevated relative to the
fasting value, and false-negative (FN) results if non-fasting values were decreased relative to
Fig 2. The percentage of diagnosis of dyslipidemias with the use of two lipid indices: Non-HDL-C and TG/HDL-C
in fasting/non-fasting state. non-HDL-C- non high density lipoprotein cholesterol [TC-(HDL-C)]; TG/HDL-C±
triglycerides to high density lipoprotein cholesterol ratio.
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BMI-Body Mass Index; TC-total cholesterol; TG-triglycerides; LDL-C-low density lipoprotein cholesterol; HDL-C-high density lipoprotein cholesterol;
sd-LDL-Csmall, dense low density lipoprotein cholesterol; ApoB-apolipoprotein B; ApoAI-apolipoprotein AI; Lp(a)- lipoprotein (a); non-HDL-C- non high density lipoprotein
the fasting value. The largest correspondence for fasting and non-fasting measurements was
noted for Lp(a) (NC = 95.8%), whereas the largest percentage of non-comparable results was
identified for TG (∑ = 48.6%). In the analysis of the standard lipid profile, i.e. TC, TG, LDL-C
and HDL-C, the NC (No Change) between fasting and non-fasting measurements was
identified as: 84%; 51.4%; 81.5% and 91.3%, respectively. The highest figures of FP and FN results
was noted for TG (48.6%), whereas the lowest was for Lp(a) (4.2%). When comparing two
calculated lipid indices such as non-HDL-C and TG/HDL-C we obtained closely comparable
percentages for FP and FN results (16% vs 18%). The TG/HDL-C ratio was characterized by a
better correlation only with ApoAI in a non-fasting state. In non-fasting children
dyslipidemias were recognized almost twice as often when using non-HDL-C (39.2%) compared to TG/
Youden's analysis showed that the area under the ROC curve (AUC) was significantly
greater in non-fasting condition compared to fasting for: TG (AUC 0.887 vs 0.778), ApoB
Fig 3. The occurrence of dyslipidemia according to BMI category. BMI: body mass index.
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Fig 4. The impact of non-fasting measurements on clinical significance. NC (No Change)- measurements in fasting
and non-fasting state showed the same lipid classification; FP (False Positive);FN (False Negative);∑ (the sum of FP
and FN results); TC-total cholesterol; TG-triglycerides; LDL-C-low density lipoprotein cholesterol; HDL-C-high
density lipoprotein cholesterol; sd-LDL-C-small, dense low density lipoprotein cholesterol; ApoB- apolipoprotein B;
ApoAI-apolipoprotein AI; Lp(a)- lipoprotein (a); non-HDL-C- non high density lipoprotein cholesterol
[TC(HDL-C)]; TG/HDL-C±triglycerides to high density lipoprotein cholesterol ratio.
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(AUC 0.824 vs 0.785) and TG/HDL-C ratio (AUC: 0.855 vs 0.723). Remaining lipid
parameters had significantly greater AUC in fasting state, though LDL-C had no statistical difference
in AUC in fasting and non-fasting conditions. Our study showed that among all analyzed lipid
parameters and indices AUC were highest for non-HDL-C (AUC = 0.907) in fasting
condition, although in non-fasting state non-HDL-C and TG had an equal diagnostic power as
AUC = 0.887 (Table C S1 File).
The current NHLBI and AAP guidelines recommend that the first screening for lipid profile
should be carried out in children aged 9±11, with no necessity for fasting prior to testing, with
the proviso that under this conditions it is advisable to measure TC concentrations and
]. The question which arises when we consider measuring non-fasting lipid
parameters, is how much does the non-fasting state actually influence lipid metabolism?
Our study confirmed minor, insignificant differences, between concentrations of most lipid
parameters, excluding TG, LDL-C and ApoAI, measured in non- fasting versus fasting states.
The study of Steiner et al, together with other similar studies, suggests that changes in
non-fasting lipid profile are essentially minor and even if statistically significant, their clinical value
remains negligible [
]. In our study, we aimed to verify the clinical significance of an
extended lipid panel in a non-fasting state in comparison to measurements taken in fasting
conditions. Due to the parallel analysis of lipids in the same children, our assessment showed a
high percentage of false-positive results for non-fasting TG (42.3%), which may incorrectly
imply hypertriglyceridemia. The most significant impact of false negative results measured in
non-fasting conditions was observed for LDL-C (14.3%), which might result in the omission
of highly atherogenic LDL-C cholesterol fraction. The relatively small impact of non-fasting
state on sd-LDL-C concentration makes it an important marker of atherosclerotic processes.
Our results show that sd-LDL-C is a significant marker of atherogenic triad (elevated
sdLDL-C, TG and lowered HDLC-C concentrations) in accord with Nishikura et al., who also
underline the usefulness of sd-LDL particles especially in predicting and monitoring CVD
]. The paucity of sd-LDL-C measurements in a routine lipid panel necessitates of its
supplementation with an equally useful lipid marker. The strong relationship between sd-LDL-C and
non-HDL-C supports the use of the latter as a surrogate for non-fasting lipid indices without
incurring any additional cost [
The problem which is brought to the fore in implementing lipid measurements in a
nonfasting state in children, is the lack of adequate cut-off values for particular lipid components.
The issue of cut-offs has been widely studied but only in an adult population, resulting in the
proposed EAS/EFLM reference ranges for lipids and apolipoproteins measured in a
non-fasting state [
]. Khendi et al. (2015) and the EAS/ EFLM have proposed cuf-off values for lipid
profile, which are optimal concentrations and are meant to be a precise tool for risk prediction
of CVD whilst minimizing the danger of obtaining innacurate laboratory results [
In overweight and obese children there were no statistical differences in percentages for the
occurrence of dyslipidemias between fasting and non-fasting states. On the other hand, in
children with optimal body mass, higher occurrences of dyslipidemias were identified in
non-fasting children; though this tendency was identified for all participants with optimal BMI.
In analyzing standard lipid profile which include (TC, LDL-C, HDL-C,TG) significant
differences were shown for most parameters except for TC. In accord with previous studies
concerning the analysis of lipid levels in a non-fasting state, our study confirmed significantly
higher TG concentrations measured in non-fasting conditions (by 22.6%). We also identified a
stronger association when compared to a fasting state between non-fasting TG and sd-LDL-C
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(β = 2.28 vs 3.58; p<0.01). This relationship highlighted the underestimated diagnostic value
of non-fasting TG concentration as a useful tool in CVD assessment. According to
Nordestgaard et al, non-fasting TG concentrations are characterized by a significant usefulness in the
assessment of the risk of atherogenic changes development [
]. In addition other studies,
suggest that non-fasting TG concentrations are characterized by a stronger predictive ability for
CVD relative to their counterpart measured in fasting conditions [
]. It is noteworthy that
our study highlights a higher sensitivity in the identification of hypertriglyceridemia,
evidenced in our results-findings in non-fasting condition. In contrast to TG, the concentrations
of LDL-C determined in a non-fasting state were significantly lower. Similarly, lower LDL-C
concentrations were also observed by Steiner et al . Non-fasting lower concentrations of
TC were identified in girls and overweight and obese children similar to the study by Steiner
et al. which confirmed lower concentrations of TC in non-fasting conditions, but without
gender and BMI categorization [
]. Our study is consistent with Sidhu and Naugler's which
indicated no statistically significant changes in TC concentrations comparing fasting and
nonfasting measurements [
]. The statistically significant differences between HDL-C
concentration in a fasting and non-fasting state, were shown in the study group overall, and the
subgroups of girls together with those of optimal weight. However, among the boys and children
with BMI 85, HDL-C in a non-fasting state was insignificantly higher. Lower HDL-C
concentrations in a non-fasting state were identified only by Langsted et al, whereas in other studies
no significant changes were observed [
]. Significant differences in non-fasting
concentrations of extended lipid profile (which in our study include sd-LDL-C, ApoAI, ApoB, Lp
[a]), were found for Lp(a) (only in boys and children with BMI 85) and for ApoAI in the
study group overall.
Our study seems to confirm the results of Varbo et al, which highlights non-HDL-C
indicator as a good, independent from the fasting state marker, reflecting proatherogenic particles
which are the main cause in the development of atherosclerotic changes [
]. In our study
both non-HDL-C were the best predictors in the diagnosis of dyslipidemias in children in
non-fasting state having an equal diagnostic power. Taking Youden's statistics into
consideration, we might assert that in our study, non-HDL-C was superior to other lipid parameters
and better identified highly atherogenic particles, especially in non-fasting state. Both TG/
HDL-C and non-HDL-C correlate well with the increased number of sd-LDL-C particles,
however based on AUC, non-HDL-C appears to be slightly better when compared to the TG/
HDL-C ratio in the diagnosis of dyslipidemias among children in non-fasting condition [
Yoo et al. showed TG/HDL-C to be a good predictor of cardiovascular disease in obese
children and adolescents, as well as highlighting on association with insulin resistance, though
recent lipid screening guidelines for children recommend non-HDL-C as a universal screening
test in non-fasting condition [
]. In agreement with the Bogalusa Heart Study and the
National Heart, Lung, and Blood Institute, non-HDL-C is considered a reliable marker in the
screening of dyslipidemias in children [
]. The impact of non-fasting state on clinical
decision making in the identification of dyslipidemias is similar for both non-HDL-C and TG/
HDL-C, although non-HDL-C better reveals abnormalities in lipid metabolism, covering all
atherogenic particles containing ApoB [
]. Moriyama et al. endorse the use of non-HDL-C in
patients with hypertriglyceridemia (> 400 mg/dL; > 4.52 mmol/L) in the diagnosis of
dyslipidemias, with strong evidence for the relationship between non-HDL-C and sd-LDL-C [
Existing AAP recommendations suggest the special usefulness of non-fasting non-HDL-C
indicator. Among non-fasting children who are obese and overweight the only indicator
which showed statistical importance was non-HDL-C. Non-HDL-C as well as remnant
cholesterol assess the presence of strongly proatherogenic apolipoproteins that include ApoB
11 / 14
]. Our study showed that in contrast to non-HDL-C of better diagnostic value
but only in fasting conditions was observed for remnant cholesterol .
The indisputable utility of our study is the particular age of children involved and the nature
of its parallel lipid and apolipoprotein determination in both fasting and non-fasting
conditions which were not previously employed in similar comparative studies. The age range
which was set between the physiological period of increasing lipid parameter concentrations
before the age of 9, and the physiological decrease of lipid parameter concentrations in
adolescence can be called as a ªlipid diagnostic gapº [
]. The innovative nature of this study is related
to individual assessment of lipid metabolism in the same individual children tested in both
fasting and non-fasting states, which allowed a direct insight and comparison of the dynamics
of lipid changes in non-fasting state relative to fasting conditions.
A potential limitation of our study is the lack of a standardized meal before blood sampling
in the determination of lipid parameters. The only attempt at standardizing the meal was
establishing its type, which in our case was breakfast. Another limitation of this study was the
lack of appropriate non-fasting cut-off values for children at this age. For the purpose of this
study we used cut-off values for lipids and apolipoproteins established in fasting conditions
appropriate for children in this age [
]. Due to the lack of an adequate questionnaire
specifically referring to alimentary lifestyles and physical activity we are compelled to include this
field as a limitation of our study, although we would like to highlight that the parents of
participating children did not receive laboratory results before the second blood collection, to avoid
any bias resulting from changes to their normal activities.
The higher occurrence of FP TG results in non-fasting condition as well as FN results for
nonfasting LDL-C, could potentially lead to diagnostic misclassification. Due to the potentially
small impact of non-fasting TC and HDL-C and their slight changes in concentration compared
to a fasting state, we have underlined the usefulness of non-HDL-C in assessing proatherogenic
cholesterol fractions. More frequent diagnosis of dyslipidemia among non-fasting children may
be due to the lack of appropriate cut-off points. To interpret non-fasting lipid and
apolipoprotein test results correctly, it is necessary to implement adequate cut-off values according to
nonfasting condition, age and sex, in order to minimize false-positive or false-negative results.
S1 File. Table A: Currently accepted cut-off values for lipids in children (according to NCEP)
]. Table B: Pearson analysis of lipid indices in non-fasting condition. Table C: ROC curve
for lipid parameters in dyslipidemia diagnosis.
We would like to thank all of the children and their parents for participating in our study.
Conceptualization: Lukasz Szternel, Tadeusz Derezinski, Grazyna Sypniewska.
Data curation: Lukasz Szternel.
Formal analysis: Katarzyna Bergmann.
Funding acquisition: Lukasz Szternel, Magdalena Krintus.
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Methodology: Lukasz Szternel, Katarzyna Bergmann.
Resources: Magdalena Krintus.
Supervision: Magdalena Krintus, Tadeusz Derezinski, Grazyna Sypniewska.
Validation: Tadeusz Derezinski, Grazyna Sypniewska.
Writing ± original draft: Lukasz Szternel, Grazyna Sypniewska.
Writing ± review & editing: Lukasz Szternel, Magdalena Krintus, Grazyna Sypniewska.
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