
==== Front
Eur J Pediatr
Eur J Pediatr
European Journal of Pediatrics
0340-6199
1432-1076
Springer Berlin Heidelberg Berlin/Heidelberg

39098887
5694
10.1007/s00431-024-05694-1
Research
Single nucleotide polymorphism rs7961894, platelet morphological parameters and lipid profile in children with type 1 diabetes: a potential relationship
El-Hawy Mahmoud A. 1
Abdelsattar Shimaa 2
Bedair Hanan M. 3
Elsaady Doaa Z. 4
Hola Ahmed S. Abo ahmed.shawky@med.menofia.edu.eg
dr.ahmadped@yahoo.com

1
1 https://ror.org/05sjrb944 grid.411775.1 0000 0004 0621 4712 Department of Pediatrics, Faculty of Medicine, Menoufia University, Yassin Abdel-Ghafar Street, Shebin El-Kom, Egypt
2 https://ror.org/05sjrb944 grid.411775.1 0000 0004 0621 4712 Clinical Biochemistry and Molecular Diagnostics Department, National Liver Institute, Menoufia University, Shebin El-Kom, Egypt
3 https://ror.org/05sjrb944 grid.411775.1 0000 0004 0621 4712 Clinical Pathology Department, National Liver Institute, Menoufia University, Shebin El-Kom, Egypt
4 grid.415762.3 Egyptian Ministry of Health, Cairo, Egypt
Communicated by Peter de Winter

5 8 2024
5 8 2024
2024
183 10 43854395
29 6 2024
12 7 2024
17 7 2024
© The Author(s) 2024
2024
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Increased cardiovascular risk has been associated with certain platelet morphological parameters, and several single nucleotide polymorphisms (SNPs) have been reported to be linked. Still, little is known about their role among children with type 1 diabetes mellitus (T1DM). So, we aimed to investigate platelet parameters and lipid profile changes in relation to rs7961894 SNP in children with T1DM. Eighty children with T1DM and eighty apparently healthy controls participated in this cross-sectional study. Platelet count, mean platelet volume (MPV), platelet distribution width (PDW), plateletcrit (PCT), HbA1c, triglycerides, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol were measured, and atherogenic indices were calculated. Using a real-time polymerase chain allelic discrimination technique, rs7961894 SNP was genotyped. Children with T1DM had significantly higher MPV, PDW, TC, and LDL-C compared to controls. 25% of patients had rs7961894 CT genotype with significantly higher MPV, PDW, PCT, LDL-C, triglycerides, Castelli’s risk index II (CRI II), and atherogenic index of plasma (AIP) compared to CC genotyped patients. MPV correlated significantly with CRI II and AIP, PDW with CRI II, while PCT correlated substantially with HbA1c, LDL-C, CRI II, and AIP. rs7961894 CT genotype was a significant dependent predictor of the changes in MPV, PDW, and PCT in multivariate regression analysis.

Conclusion: In children with T1DM, rs7961894 CT genotype is significantly linked to MPV, PDW, and PCT changes, which showed a substantial relationship to CRI II and AIP, highlighting the importance of monitoring these patients to identify potential cardiovascular risks early. What is Known:

• Platelets and dyslipidemia are involved in atherosclerosis pathogenesis

• Changes in platelet activity and morphological parameters in diabetes mellitus are contradictory

• rs7961894 single nucleotide polymorphism is associated with significant changes in mean platelet volume (MPV) with no available data in children

	
What is New:

• Children with type 1 diabetes mellitus exhibited significantly higher values of MPV and platelet distribution width (PDW)

• rs7961894 CT genotype was a dependent predictor of the changes in MPV, PDW, and plateletcrit (PCT) values

• Diabetic children with the rs7961894 CT genotype showed substantial alterations in lipid parameters with a strong correlation between MPV, PDW, and PCT and Castelli’s risk index II and the atherogenic index of plasma

	

Keywords

Lipid profile
Mean platelet volume
Platelet activity
Single nucleotide polymorphism
rs7961894
Type 1 diabetes
Minufiya UniversityOpen access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).

issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

Type 1 diabetes mellitus (T1DM) is one of the most common endocrinological disorders in children and adolescents. Its annual incidence in children under 14 ranges from 0.1/100,000 to 36.8/100,000, depending on the country [1]. It has been shown that platelets in diabetic patients are more adhesive and aggregative, both independently and in response to stimulants [2]. Since platelet activation is a primary cause of atherosclerosis, children with T1DM may experience microvascular and macrovascular consequences. Meanwhile, other factors like diabetes duration, glycemic control efficacy, dyslipidemia, obesity, stress, and genetic predisposition may also be at play [3].

Platelet volume and functions can be represented by mean platelet volume (MPV) and platelet distribution width (PDW), and despite PDW being more specific, MPV is regarded as a marker linked to increased platelet activity. Plateletcrit (PCT), a measure of total platelet mass, has also been found to be higher in patients with thrombosis [4].

Hyperglycemia is hypothesized to directly affect platelet membrane glycation, which could be a factor in the increased platelet activity. Nevertheless, it is still unclear how metabolic control affects platelet morphologic characteristics, particularly in T1DM patients, and inconsistent results have been found in multiple investigations [5, 6].

There is compelling evidence that dyslipidemia induces platelet activation based on “in vitro” mechanistic investigations on platelet reactivity and severe adverse cardiac events in patients with coronary artery disease (CAD) [7]. Serum lipid abnormalities have been reported in children with T1DM even with excellent glycemic control, and variations in ethnicity, diet, lifestyle, pubertal influences, or even genetic factors could account for this [8, 9].

Significant diversity in platelet functions between different individuals is influenced by both genetic and environmental variables, and several single nucleotide polymorphisms (SNPs) have been linked to platelet indices, especially MPV, by genome-wide association studies (GWAS) [10, 11]. While there is currently no data in pediatrics, the rs7961894 SNP is associated with significant MPV changes, and a higher risk of stroke in adults [12].

Therefore, in this study, our goal was to evaluate platelet parameters and lipid profile alterations in relation to rs7961894 SNP in children with T1DM.

Subjects and methods

Subjects

Eighty children diagnosed with T1DM from Pediatric Endocrinology Clinic at Menoufia University Hospitals were enrolled in this study, with eighty apparently healthy age and sex matched children as controls. Patients with hypoglycemia, diabetic ketoacidosis, acute or chronic infections, inherited or acquired diseases that may affect platelet counts or functions, associated other chronic diseases, and concomitant drug use such as lipid-lowering or non-steroid anti-inflammatory agents which may interfere with the study were excluded.

Methods

After the consent letter was approved by the patients’ caregivers, all patients were subjected to a detailed history and a thorough clinical examination to exclude any associated criteria that may interfere with the study.

Under aseptic conditions, fasting venous blood samples were withdrawn from all participants in ethylenediaminetetraacetic acid (EDTA) tubes and processed at room temperature within 2 h. Complete blood counts were analyzed through Sysmex XT-1800i Automated Hematology Analyzer, and platelet parameters (platelet count, MPV, PDW, and PCT) were recorded. HbA1c assay was performed through cobas c 311 auto analyzer, Roche diagnostics, Germany. Lipid profile parameters including [total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C)] were measured by using AU 680 Beckmann autoanalyzer, and subsequently, atherogenic indices [non-HDL-C; TC-HDL-C, Castelli’s risk index I (CRI I); TC/HDL-C, Castelli’s risk index II (CRI II); LDL-C/HDL-C, atherogenic index of plasma (AIP); Log TG/ HDL-C, and atherogenic coefficient (AC); non-HDL-C/ HDL-C] were calculated [13].

Furthermore, 1 ml of whole blood was added to EDTA tubes and stored in -20 ºC for subsequent genomic DNA extraction, which was performed using Gene JET TM whole blood Genomic DNA purification Mini kit [Thermo Scientific EU/Lithuania] according to the manufacturer’s instructions. Genotyping of the rs7961894 SNP was performed by real-time polymerase chain allelic discrimination technology using TaqMan SNP genotyping assay kit [Thermo Fisher Scientific, Waltham, MA, United States]. Allelic discrimination was manifested by TaqMan fluorogenic minor groove binder probes. Polymerase chain reaction data analysis was conducted on Applied Biosystem 7500 RT-PCR ABI PRISM (Applied Biosystems, United States) software v.2.0.1. For the examined polymorphism, the genotype distribution complied with the Hardy–Weinberg equilibrium.

Statistical analysis

Utilizing IBM SPSS Statistics Version 20.0. (IBM Corp, Armonk, NY), results were statistically analyzed. Descriptive data were presented using the mean ± standard deviation, median, interquartile range (IQR), and percent. Mann–Whitney U test and t test were used to compare the means of non-normally and normally distributed quantitative data, respectively, and Chi-square test analyzed qualitative variables. Spearman’s coefficient was used to evaluate the associations between studied parameters. Regression analysis was used to assess variables that may affect platelet parameters. Statistics were deemed significant with a p-value ≤ 0.05.

With 0.8 statistical power and a 95% confidence level, and according to a previous study [14], the sample size was determined to be a minimum of 67 participants using power analysis techniques to identify group differences larger than 0.2 standard deviation.

Results

Children with T1DM reported a mean age of 13.39 ± 2.81 years, with a non-statistical difference to that of controls (13.43 ± 2.97 years). They exhibited a statistically lower weight and height, and despite being within the normal range, their systolic and diastolic blood measurements were significantly higher than controls (Table 1). Table 1 Comparison of clinical and laboratory data between patients and controls

Variables	Patients
(n = 80)	Control (n = 80)	P value	
Sex	No	%	No	%	0.079	
Male	29	36.3	40	50.0	
Female	51	63.8	40	50.0	
Age (years) Mean ± SD	13.39 ± 2.81	13.43 ± 2.97	0.943	
Weight Z–Score Mean ± SD	-0.45 ± 0.74	-0.17 ± 0.54	0.009*	
Height z score Mean ± SD	-0.68 ± 0.71	-0.43 ± 0.49	0.041*	
BMI z score Mean ± SD	-0.32 ± 1.02	-0.02 ± 0.74	0.128	
Systolic blood pressure (mmHg)

Mean ± SD

	120.3 ± 5.57	110.0 ± 5.88	 < 0.001**	
Diastolic blood pressure (mmHg) Mean ± SD	76.80 ± 3.08	63.25 ± 2.38	 < 0.001**	
Hemoglobin (g/dl)

Mean ± SD

	13.71 ± 1.41	13.41 ± 1.28	0.154	
Red blood cell count (106/mm3)

Mean ± SD

	5.24 ± 0.63	5.12 ± 0.70	0.271	
White blood cell count (103/mm3)

Mean ± SD

	9.05 ± 2.28	8.92 ± 2.15	0.530	
Platelet count (103/mm3)

Mean ± SD

	309.6 ± 69.26	313.4 ± 73.21	0.844	
Plateletcrit %

Mean ± SD

	0.32 ± 0.12	0.30 ± 0.13	0.083	
Mean platelet volume (fL)

Mean ± SD

	9.87 ± 2.30	8.15 ± 0.88	 < 0.001**	
Platelet distribution width %

Mean ± SD

	15.91 ± 4.02	9.89 ± 1.73	 < 0.001**	
HbA1c %

Mean ± SD

	11.07 ± 1.89	4.85 ± 0.32	 < 0.001**	
Total cholesterol (TC) mg/dl

Mean ± SD

	127.9 ± 4.53	121.7 ± 17.67	0.003*	
High-density lipoprotein cholesterol (LDL-C) mg/dl

Mean ± SD

	61.64 ± 2.52	60.69 ± 4.54	0.104	
Low-density lipoprotein cholesterol (HDL-C) mg/dl

Mean ± SD

	88.80 ± 4.37	85.25 ± 8.55	0.001*	
Triglycerides (TG) mg/dl

Mean ± SD

	79.75 ± 13.31	78.09 ± 7.98	0.340	
non-HDL-C (mg/dl)

Mean ± SD

	66.23 ± 5.13	61.00 ± 18.06	0.015*	
Castell’s risk index I (TC/HDL-C) Mean ± SD	2.08 ± 0.11	2.01 ± 0.32	0.095	
Castell’s risk index II (LDL-C/HDL-C) Mean ± SD	1.44 ± 0.10	1.41 ± 0.17	0.130	
Atherogenic index of plasma (log TG/HDL-C) Mean ± SD	0.11 ± 0.08	0.11 ± 0.05	0.806	
Atherogenic coefficient (non-HDL-C/HDL-C) Mean ± SD	1.08 ± 0.11	1.01 ± 0.32	0.095	
SD: standard deviation, *: Statistically significant at p ≤ 0.05, **: highly Statistically significant at p value < 0.001

Regarding hematological parameters, patients had significantly higher MPV and PDW compared to controls, with no significant differences detected in hemoglobin levels, red blood cell counts (RBCs), white blood cell counts (WBCs), platelet counts, and PCT between the studied groups. HbA1c, TC, LDL-C, and non-HDL-C were statistically higher in patients, with no significant differences regarding other lipid parameters (Table 1).

Genotyping of rs7961894 documented a CT genotype frequency of 25% in patients compared to 12.5% in controls, with a significant statistical difference. In addition, odd ratio analysis determined that individuals with the CT genotype were 2.333 times more risky to have T1DM than those with the wild CC genotype (Table 2). Table 2 rs7961894 genotypes and allele distribution among patients and controls

	Patients	Controls	p value	OR (LL – UL 95%C.I)	
No	%	No	%	
rs7961894 genotypes	(n = 80)	(n = 80)			
CC	60	75.0	70	87.5		1.000	
CT	20	25.0	10	12.5	0.046*	2.333(1.014 – 5.371)	
TT	0	0.0	0	0.0	–	–	
rs7961894 allele distribution	(n = 160)	(n = 160)			
C	140	87.50	150	93.8		1.000	
T	20	12.5	10	6.3	0.060	2.143(0.969 – 4.737)	
C.I: Confidence interval, LL: Lower limit, UL: Upper Limit, OR: odd ratio, *: Statistically significant at p ≤ 0.05

Patients with the CT genotype had a statistically higher MPV, PDW, PCT, HbA1c, LDL-C, TG, CRI II, and AIP compared to those with the CC genotype, with no significant differences detected between them regarding other parameters (Table 3). Table 3 Comparison of clinical and laboratory data in patients according to rs7961894 genotyping

	CC genotyped patients (n = 60)	CT genotyped patients (n = 20)	p value	
Sex	No	%	No	%		
Male	23	38.3	6	30.0	0.502	
Female	37	61.7	14	70.0	
Age (years) Mean ± SD	13.53 ± 2.74	12.95 ± 3.05	0.438	
Family history						
Negative	54	90.0	20	100.0	0.328	
Positive	6	10.0	0	0.0	
Age of onset of DM

Mean ± SD

	6.68 ± 1.90	5.95 ± 1.96	0.097	
Duration of onset of DM Mean ± SD	6.83 ± 1.65	7.0 ± 2.41	0.829	
Weight Z–Score Mean ± SD	-0.50 ± 0.78	-0.30 ± 0.63	0.333	
Height Z–Score Mean ± SD	-0.74 ± 0.70	-0.50 ± 0.73	0.053	
BMI Z–Score Mean ± SD	-0.35 ± 1.06	-0.26 ± 0.94	0.982	
Systolic blood pressure (mmHg)

Mean ± SD

	120.8 ± 5.53	118.8 ± 5.57	0.177	
Diastolic blood pressure (mmHg) Mean ± SD	76.93 ± 3.04	76.40 ± 3.23	0.506	
Hemoglobin (g/dl)

Mean ± SD

	13.68 ± 1.44	13.81 ± 1.33	0.730	
Red blood cell count (106/mm3)

Mean ± SD

	5.26 ± 0.58	5.17 ± 0.78	0.580	
White blood cell count (103/mm3)

Mean ± SD

	9.07 ± 2.28	8.99 ± 2.34	0.938	
Platelet count (103/mm3)

Mean ± SD

	302.4 ± 64.37	331.5 ± 80.04	0.276	
Plateletcrit %

Mean ± SD

	8.93 ± 1.65	12.72 ± 1.48	 < 0.001**	
Mean platelet volume (fL)

Mean ± SD

	14.40 ± 3.18	20.43 ± 2.68	 < 0.001**	
Platelet distribution width %

Mean ± SD

	0.28 ± 0.10	0.43 ± 0.11	 < 0.001**	
HbA1c %

Mean ± SD

	10.41 ± 1.51	13.06 ± 1.48	 < 0.001**	
Total cholesterol (TC) mg/dl

Mean ± SD

	127.8 ± 4.96	128.1 ± 2.98	0.720	
High-density lipoprotein cholesterol (LDL-C) mg/dl Mean ± SD	61.74 ± 2.40	61.34 ± 2.89	0.543	
Low-density lipoprotein cholesterol (HDL-C) mg/dl Mean ± SD	86.99 ± 2.52	94.22 ± 4.27	 < 0.001**	
Triglycerides (TG) mg/dl

Mean ± SD

	77.22 ± 12.82	87.35 ± 12.04	0.003*	
non-HDL-C (mg/dl)

Mean ± SD

	66.04 ± 5.55	66.77 ± 3.66	0.584	
Castell’s risk index I (TC/HDL-C) Mean ± SD	2.07 ± 0.12	2.09 ± 0.10	0.497	
Castell’s risk index II (LDL-C/HDL-C) Mean ± SD	1.41 ± 0.07	1.54 ± 0.10	 < 0.001**	
Atherogenic index of plasma (log TG/HDL-C) Mean ± SD	0.09 ± 0.08	0.15 ± 0.06	0.001*	
Atherogenic coefficient (non-HDL-C/HDL-C) Mean ± SD	1.07 ± 0.12	1.09 ± 0.10	0.497	
SD: standard deviation, *: Statistically significant at p ≤ 0.05, **: highly Statistically significant at p value < 0.001

Platelet count was significantly correlated with Hb level, RBCs, WBCs, and PCT. Also, PCT was significantly correlated with HbA1c and LDL-C. MPV, PDW, and PCT correlated well with each other, and with CRI II as well. AIP correlated significantly only to MPV and PCT (Table 4, Fig. 1). Table 4 Correlations between platelet parameters and other laboratory data in patients

Variables	Platelet count (109/L)	Plateletcrit %	Mean platelet volume (fL)	Platelet distribution width %	
	rs	p value	rs	p value	rs	p value	rs	p value	
Platelet count (103/mm3)	–	–							
Plateletcrit %	0.614	 < 0.001**	–	–					
Mean platelet volume (fL)	0.130	0.249	0.547	 < 0.001**	–	–			
Platelet distribution width %	0.088	0.440	0.564	 < 0.001**	0.845	 < 0.001**	–	–	
Hemoglobin (g/dl)	0.251	0.024*	0.353	0.001*	-0.034	0.762	0.119	0.292	
Red blood cell count (106/mm3)	0.277	0.013*	0.032	0.776	-0.071	0.530	-0.119	0.293	
White blood cell count (103/mm3)	0.269	0.016*	0.361	0.001*	0.214	0.057	0.163	0.148	
HbA1c %	0.080	0.482	0.263*	0.018*	0.460	 < 0.001*	0.286*	0.010*	
Total cholesterol (TC) mg/dl	-0.170	0.131	-0.048	0.674	-0.056	0.619	0.125	0.270	
High-density lipoprotein cholesterol (HDL-C) mg/dl	-0.093	0.414	-0.171	0.129	-0.118	0.299	-0.082	0.469	
Low-density lipoprotein cholesterol (LDL-C) mg/dl	-0.009	0.936	0.418	 < 0.001**	0.446	0.000	0.504	0.000	
Triglycerides (TG) mg/dl	-0.061	0.589	0.197	0.080	0.401	0.000	0.185	0.100	
non-HDL-C (mg/dl)	-0.076	0.504	0.050	0.658	0.024	0.834	0.154	0.171	
Castell’s risk index I (TC/HDL-C)	-0.010	0.928	0.102	0.368	0.040	0.724	0.144	0.204	
Castell’s risk index II (LDL-C/HDL-C)	0.059	0.600	0.479	 < 0.001**	0.487	 < 0.001**	0.514	 < 0.001**	
Atherogenic index of plasma (log TG/HDL-C)	-0.084	0.456	0.222	0.047*	0.379	0.001*	0.170	0.131	
Atherogenic coefficient (non-HDL-C/HDL-C)	-0.010	0.928	0.102	0.368	0.040	0.724	0.144	0.204	
* Statistically significant at p ≤ 0.05, ** highly Statistically significant at p value < 0.001

Fig. 1 Correlations between platelet morphological parameters and lipid parameters

In multivariate regression analysis, WBCs and the rs7961894 CT genotype were dependent predictors of MPV, PDW, and PCT changes, while Hb was a dependent marker for platelet count and PCT changes (Table 5). Table 5 Univariate and multivariate regression analysis for variables affecting changes in platelet parameters in patients

	Univariate	Multivariate	
	p value	B (LL – UL 95%C.I)	p value	B(LL – UL 95%C.I)	
Variables for platelet count	
Hemoglobin	0.005*	14.989(4.428 – 25.549)	0.003*	16.905(0.739 – 1.353)	
Red blood cell count	0.020*	28.369(4.595 – 52.143)	0.412	9.813(0.797 – 1.254)	
White blood cell count	0.023*	7.730(1.111 – 14.349)	0.254	3.780(0.805 – 1.242)	
Variables for mean platelet volume	
White blood cell count	0.045*	0.227(0.006 – 0.449)	0.002*	0.237(0.087 – 0.388)	
HbA1c	 < 0.001**	0.594(0.354 – 0.835)	0.637	0.059(-0.190 – 0.308)	
Low-density lipoprotein cholesterol (LDL-C)	 < 0.001**	0.320(0.226 – 0.415)	0.334	0.074(-0.078 – 0.226)	
Triglycerides (TG)	0.008*	0.051(0.013 – 0.088)	0.815	0.003(-0.026 – 0.033)	
Castell’s risk Index II (LDL-C/HDL-C)	 < 0.001**	12.443(7.865 – 17.021)	0.490	1.996(-3.738 – 7.729)	
rs7961894 CT genotype	 < 0.001*	3.788(2.960 – 4.617)	 < 0.001**	2.826(1.505 – 4.147)	
Variables for platelet distribution width	
White blood cell count	0.045*	0.227(0.006 – 0.449)	0.002*	0.240(0.090 – 0.390)	
HbA1c	0.007*	0.633(0.174 – 1.092)	0.662	0.054(-0.192 – 0.300)	
Low-density lipoprotein cholesterol (LDL-C)	 < 0.001**	0.320(0.226 – 0.415)	0.067	0.107(-0.008 – 0.222)	
Atherogenic index of plasma (log TG/HDL-C)	0.009*	8.156(2.075 – 14.237)	0.749	0.766(-3.979 – 5.511)	
rs7961894 CT genotype	 < 0.001**	6.027(4.452 – 7.601)	 < 0.001**	2.844(1.528 – 4.160)	
Variables for plateletcrit	
Hemoglobin	0.001*	0.029(0.012 – 0.047)	0.014*	0.018(0.004 – 0.032)	
White blood cell count	 < 0.001**	0.020(0.009 – 0.031)	 < 0.001**	0.018(0.010 – 0.027)	
HbA1c	0.014*	0.017(0.004 – 0.031)	0.423	-0.005(-0.018 – 0.008)	
Total cholesterol (TC)	 < 0.001**	0.005(0.003 – 0.008)	0.764	-0.001(-0.005 – 0.003)	
High-density lipoprotein cholesterol (HDL-C)	 < 0.001**	-0.022(-0.029 – -0.015)	0.549	-0.002(-0.010 – 0.006)	
Low-density lipoprotein cholesterol (LDL-C)	 < 0.001**	0.010(0.006 – 0.014)	0.052	0.007(0.0 – 0.013)	
rs7961894 CT genotype	 < 0.001**	0.145(0.094 – 0.197)	0.011*	0.102(0.025 – 0.180)	
B: Unstandardized Coefficients, C.I: Confidence interval, LL: Lower limit, UL: Upper Limit, * Statistically significant at p ≤ 0.05, ** highly Statistically significant at p value < 0.001

Discussion

T1DM is a chronic condition with rising prevalence and variable incidence rates among societies, and despite newly developed therapeutic options, chronic T1DM complications are becoming more common with the rising atherosclerotic risk [15, 16]. It has been demonstrated that diabetes is a prothrombotic state with increased platelet activation due to altered platelet membrane glycation and an associated increase in proinflammatory markers [17]. Furthermore, recent research has shown that platelet indices directly impact platelet activity and represent an emerging risk factor for vascular thrombotic complications, which are poorly studied in T1DM [1, 18].

Studies evaluating platelet counts in diabetic individuals have yielded inconsistent findings, possibly due to variations in platelet production and turnover. Raised platelet counts could be a result of stress or reactive thrombocytosis, which may be linked to poor glycemic control [19, 20]. Reduced platelet counts may be the consequence of either ineffective thrombopoiesis or an overreaction of platelets to endogenous agonists, depleting them in the circulation [21]. Others found no significant changes in platelet counts in diabetic individuals [1, 18, 22, 23]. Similarly, our findings documented no discernible difference between children with T1DM and controls regarding platelet counts.

Larger platelets adhere more easily, have denser granules, and produce more thrombotic agents than smaller ones, which is proved by “in vitro” aggregation. The parameters that reflect platelet volume are MPV and PDW [4, 23]. MPV is more frequently used to evaluate platelet size, and several studies reported significantly higher MPV values in children with T1DM than in healthy controls, whether or not this difference was related to glycemic control [3, 21, 24]. Even Brown et al. pointed out that elevated MPV in individuals with T1DM may be more suggestive of atherosclerosis than of diabetes [25]. Meanwhile, other research found no connection between diabetes and MPV [1, 17, 26]. Regarding PDW, Sharma et al. observed significantly elevated MPV and PDW values in patients with T2DM with a significant correlation to diabetes duration and glycemic control status [27]. In T1DM, Malachowska et al. determined an increase in PDW values in patients compared to a control group [28], but others disagreed [1, 29]. In this instance, our study showed that children with T1DM had a statistically higher MPV and PDW when compared to healthy controls.

PCT is assumed to represent platelet heterogeneity and to display the number of platelets in circulation in a single unit volume. Few studies have examined PCT values in diabetes; Korkmaz and Venkatesh et al. observed no discernible variation in PCT values between children with T1DM and controls. However, higher PCT values were seen in another investigation with T2DM patients, especially when associated with chronic complications [30]. Regarding our findings, no significant difference in PCT values was detected between patients and controls.

The non-enzymatic glycosylation of proteins on the platelet surface caused by hyperglycemia has a negative impact on membrane fluidity and raises platelet reactivity as a result. A little research has looked at the relationships between platelet morphological parameters and metabolic regulation in children with type 1 diabetes, still the reported data is contentious, whether it is supportive or not [31–33]. In this context, our results detected no significant correlations between HbA1c levels and platelet parameters except for PCT, which was consistent with Korkmaz’s findings [1].

This controversy could possibly be clarified by considering that factors other than a metabolic imbalance may also contribute to variations in platelet parameters in children with diabetes. Peripheral platelets have been reported to be activated in patients with a new diabetes diagnosis or are even pre-diabetic [28], which may be attributed to a possible genetic basis or an increase in proinflammatory markers that may impact progenitor cells.

Genes play a major role in determining MPV, and several SNPs have been linked to MPV by GWAS [10, 11]. In CAD, a higher MPV has been linked to greater short- and long-term mortality, and there is growing interest in using MPV-associated SNPs as CAD prognostic markers [4, 34]. Meisinger et al. observed a noteworthy correlation between MPV and the transcript level of the WDR 66 gene, which is implicated in the production of platelets [35]. In this instance, various GWAS conducted in the European population have indicated that rs7961894 SNP, which is located in intron 3 of the WDR66 gene, has a powerful association with MPV [36–38]. Furthermore, Miller et al. reported that MPV is a measure of the severity of a stroke, and the T > C variant of rs7961894 is linked to lower 1-year mortality after a stroke and is independently associated with higher MPV during the acute phase of an ischemic stroke [12].

In this context, our study showed that 25% of children with T1DM had a CT genotype for rs7961894 with a significant statistical difference when compared to healthy controls. Also, the odd ratio documented a significantly 2.333 times higher risk for T1DM in patients carrying rs7961894 CT genotyping. Those patients had a significantly higher MPV, PDW, and PCT when compared to those carrying CC genotype. Furthermore, rs7961894 CT genotype was a dependent predictor of changes in MPV, PDW, and PCT in multivariate regression analysis. This emphasizes how crucial it is to determine the genetic components associated with platelet characteristics because variations in these characteristics have been associated with an increased risk of microvascular and macrovascular complications.

Dyslipidemia is a major risk factor in the development of atherosclerosis through chronic accumulation of lipid-rich plaque in vascular walls, and platelets have been demonstrated to play a part in this through the initiation and propagation of atherosclerotic plaques [39, 40]. A substantial relationship between MPV and TC, TG, LDL-C, and HDL-C was found in an adult type 2 diabetes study [41]. In T1DM patients, subtle lipid abnormalities may be encountered despite adequate glycemic control, which may hasten the development of vascular lesions [8, 9]. Despite this, data on lipid profile alterations and their connection to platelet parameters in children with T1DM is very lacking.

In our study, children with T1DM had significantly elevated TC, LDL-C, and non-HDL-C levels when compared to healthy controls. Moreover, patients with the rs7961894 CT genotype had significantly elevated LDL-C and TG when compared to those with the CC genotype. Additionally, only PCT correlated significantly with LDL-C levels.

Some studies suggested that calculated lipid ratios are more closely related to atherosclerosis progression than any other single lipid parameter [42, 43]. In angiographically CAD verified cases, Bhardwaj et al. found that AIP, CRI I, CRI II, and AC were highly elevated and greatly increased the risk of CAD, with AIP contributing 31%, CRI I contributing 20%, AC contributing 17%, and CRI II contributing 20% [44]. Additionally, AIP was found to be substantially higher in T2D patients and to be connected with cardiovascular risk factors, according to Lumu et al. [45].

In this concern, we observed that diabetic children with the rs7961894 CT genotype had significantly elevated CRI II and AIP when compared to those with the CC genotype. Furthermore, MPV and PCT correlated significantly with CRI II and AIP, while PDW correlated significantly only with CRI II. These results suggest the necessity for additional extensive research regarding the involvement of the rs7961894 SNP in lipid profile alterations and their connection to platelet morphological parameters.

Study limitations and strengths

Our study’s primary limitation was the small sample size, particularly with no funding support. Moreover, there is a dearth of data in this field to compare with. Nevertheless, to the best of our knowledge, this study is the first to analyze the rs7961894 SNP in children with T1DM and correlate it with changes in lipid profiles and platelet parameters in these patients.

Conclusion

The current study highlights that the rs7961894 SNP CT genotype is linked to higher values of MPV, PDW, PCT, LDL-C, TG, CRI II, and AIP. These parameters are all strongly correlated with an increased risk of atherosclerosis according to different studies. Therefore, they should be closely monitored for the early identification of any potential microvascular and macrovascular consequences in children with T1DM as atherosclerotic diseases are conditions that can be prevented.

Abbreviations

AIP Atherogenic index of plasma

CRI II Castelli’s risk index II

LDL-C Low-density lipoprotein cholesterol

MPV Mean platelet volume

PDW Platelet distribution width

PCT Plateletcrit

SNPs Single nucleotide polymorphisms

T1DM Type 1 diabetes mellitus

TC Total cholesterol

Author contributions

M.A.E. and A.S.A.: idea and design. M.A.E., S.A., H.M.B., D.Z.E., and A.S.A.: participant enrolment, data collection, and statistical analysis. M.A.E., S.A., and A.S.A.: manuscript writing. M.A.E., S.A., H.M.B., and A.S.A.: manuscript revision. All authors contributed to the article and approved the submitted version.

Funding

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Authors confirmed that no fund received from any organization and the research done by their own.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval

The Institutional Review Board (IRB) of the Menoufia Faculty of Medicine approved the study (ID number: 6/2021PEDI5). Research work was performed in accordance with the Declaration of Helsinki.

Consent to participate

Informed consent was obtained from the parents of all children included in the study.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Korkmaz O Assessment of the platelet parameters in children with type 1 diabetes mellitus J Endocrinol Metab 2018 8 6 144 148 10.14740/jem537
Korkmaz O (2018) Assessment of the platelet parameters in children with type 1 diabetes mellitus. J Endocrinol Metab 8(6):144–148
2. Watala C Blood platelet reactivity and its pharmacological modulation in (people with) diabetes mellitus Curr Pharm Des 2005 11 18 2331 2365 10.2174/1381612054367337 16022671
Watala C (2005) Blood platelet reactivity and its pharmacological modulation in (people with) diabetes mellitus. Curr Pharm Des 11(18):2331–2365. 10.2174/138161205436733716022671
3. Tihić-Kapidžić S Čaušević A Fočo-Solak J Malenica M Dujić T Hasanbegović S Babić N Begović E Assessment of hematologic indices and their correlation to hemoglobin A1c among Bosnian children with type 1 diabetes mellitus and their healthy peers J Med Biochem 2021 40 2 181 192 10.5937/jomb0-25315 33776568
Tihić-Kapidžić S, Čaušević A, Fočo-Solak J, Malenica M, Dujić T, Hasanbegović S, Babić N, Begović E (2021) Assessment of hematologic indices and their correlation to hemoglobin A1c among Bosnian children with type 1 diabetes mellitus and their healthy peers. J Med Biochem 40(2):181–192. 10.5937/jomb0-2531533776568
4. Chu SG Becker RC Berger PB Bhatt DL Eikelboom JW Konkle B Mohler ER Reilly MP Berger JS Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis J Thrombosis Haemostasis : JTH 2010 8 1 148 156 10.1111/j.1538-7836.2009.03584.x
Chu SG, Becker RC, Berger PB, Bhatt DL, Eikelboom JW, Konkle B, Mohler ER, Reilly MP, Berger JS (2010) Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis. J Thrombosis Haemostasis : JTH 8(1):148–156. 10.1111/j.1538-7836.2009.03584.x
5. Lattanzio S Santilli F Liani R Vazzana N Ueland T Di Fulvio P Formoso G Consoli A Aukrust P Davì G Circulating dickkopf-1 in diabetes mellitus: association with platelet activation and effects of improved metabolic control and low-dose aspirin J Am Heart Assoc 2014 3 4 e001000 10.1161/JAHA.114.001000 25037197
Lattanzio S, Santilli F, Liani R, Vazzana N, Ueland T, Di Fulvio P, Formoso G, Consoli A, Aukrust P, Davì G (2014) Circulating dickkopf-1 in diabetes mellitus: association with platelet activation and effects of improved metabolic control and low-dose aspirin. J Am Heart Assoc 3(4):e001000. 10.1161/JAHA.114.00100025037197
6. Singer J Weissler Snir A Leshem-Lev D Rigler M Kornowski R Lev EI Effect of intensive glycemic control on platelet reactivity in patients with long-standing uncontrolled diabetes Thromb Res 2014 134 1 121 124 10.1016/j.thromres.2014.05.010 24857190
Singer J, Weissler Snir A, Leshem-Lev D, Rigler M, Kornowski R, Lev EI (2014) Effect of intensive glycemic control on platelet reactivity in patients with long-standing uncontrolled diabetes. Thromb Res 134(1):121–124. 10.1016/j.thromres.2014.05.01024857190
7. Yang M Kholmukhamedov A Platelet reactivity in dyslipidemia: atherothrombotic signaling and therapeutic implications Rev Cardiovasc Med 2021 22 1 67 81 10.31083/j.rcm.2021.01.256 33792249
Yang M, Kholmukhamedov A (2021) Platelet reactivity in dyslipidemia: atherothrombotic signaling and therapeutic implications. Rev Cardiovasc Med 22(1):67–81. 10.31083/j.rcm.2021.01.25633792249
8. Guy J Ogden L Wadwa RP Hamman RF Mayer-Davis EJ Liese AD D’Agostino R Jr Marcovina S Dabelea D Lipid and lipoprotein profiles in youth with and without type 1 diabetes: the SEARCH for Diabetes in Youth case-control study Diabetes Care 2009 32 3 416 420 10.2337/dc08-1775 19092167
Guy J, Ogden L, Wadwa RP, Hamman RF, Mayer-Davis EJ, Liese AD, D’Agostino R Jr, Marcovina S, Dabelea D (2009) Lipid and lipoprotein profiles in youth with and without type 1 diabetes: the SEARCH for Diabetes in Youth case-control study. Diabetes Care 32(3):416–420. 10.2337/dc08-177519092167
9. Machnica L Deja G Polanska J Czupryniak L Szymanska-Garbacz E Loba J Jarosz-Chobot P Blood pressure disturbances and endothelial dysfunction markers in children and adolescents with type 1 diabetes Atherosclerosis 2014 237 1 129 134 10.1016/j.atherosclerosis.2014.09.006 25238220
Machnica L, Deja G, Polanska J, Czupryniak L, Szymanska-Garbacz E, Loba J, Jarosz-Chobot P (2014) Blood pressure disturbances and endothelial dysfunction markers in children and adolescents with type 1 diabetes. Atherosclerosis 237(1):129–134. 10.1016/j.atherosclerosis.2014.09.00625238220
10. Pujol-Moix N Vázquez-Santiago M Morera A Ziyatdinov A Remacha A Nomdedeu JF Fontcuberta J Soria JM Souto JC Genetic determinants of platelet large-cell ratio, immature platelet fraction, and other platelet-related phenotypes Thromb Res 2015 136 2 361 366 10.1016/j.thromres.2015.06.016 26148565
Pujol-Moix N, Vázquez-Santiago M, Morera A, Ziyatdinov A, Remacha A, Nomdedeu JF, Fontcuberta J, Soria JM, Souto JC (2015) Genetic determinants of platelet large-cell ratio, immature platelet fraction, and other platelet-related phenotypes. Thromb Res 136(2):361–366. 10.1016/j.thromres.2015.06.01626148565
11. Johnson AD The genetics of common variation affecting platelet development, function and pharmaceutical targeting J Thrombosis Haemostasis : JTH 2011 9 Suppl 1 11 246 257 10.1111/j.1538-7836.2011.04359.x
Johnson AD (2011) The genetics of common variation affecting platelet development, function and pharmaceutical targeting. J Thrombosis Haemostasis : JTH 9 Suppl 1(11):246–257. 10.1111/j.1538-7836.2011.04359.x
12. Miller MM Henninger N Słowik A Mean platelet volume and its genetic variants relate to stroke severity and 1-year mortality Neurology 2020 95 9 e1153 e1162 10.1212/WNL.0000000000010105 32576634
Miller MM, Henninger N, Słowik A (2020) Mean platelet volume and its genetic variants relate to stroke severity and 1-year mortality. Neurology 95(9):e1153–e1162. 10.1212/WNL.000000000001010532576634
13. Sujatha R Kavitha S Atherogenic indices in stroke patients: A retrospective study Iran J Neurol 2017 16 2 78 82 28761629
Sujatha R, Kavitha S (2017) Atherogenic indices in stroke patients: A retrospective study. Iran J Neurol 16(2):78–8228761629
14. Panova-Noeva M Schulz A Hermanns MI Grossmann V Pefani E Spronk HM Laubert-Reh D Binder H Beutel M Pfeiffer N Blankenberg S Zeller T Münzel T Lackner KJ Ten Cate H Wild PS Sex-specific differences in genetic and nongenetic determinants of mean platelet volume: results from the Gutenberg Health Study Blood 2016 127 2 251 259 10.1182/blood-2015-07-660308 26518434
Panova-Noeva M, Schulz A, Hermanns MI, Grossmann V, Pefani E, Spronk HM, Laubert-Reh D, Binder H, Beutel M, Pfeiffer N, Blankenberg S, Zeller T, Münzel T, Lackner KJ, Ten Cate H, Wild PS (2016) Sex-specific differences in genetic and nongenetic determinants of mean platelet volume: results from the Gutenberg Health Study. Blood 127(2):251–259. 10.1182/blood-2015-07-66030826518434
15. Patterson C Guariguata L Dahlquist G Soltész G Ogle G Silink M Diabetes in the young – a global view and worldwide estimates of numbers of children with type 1 diabetes Diabetes Res Clin Pract 2014 103 2 161 175 10.1016/j.diabres.2013.11.005 24331235
Patterson C, Guariguata L, Dahlquist G, Soltész G, Ogle G, Silink M (2014) Diabetes in the young – a global view and worldwide estimates of numbers of children with type 1 diabetes. Diabetes Res Clin Pract 103(2):161–175. 10.1016/j.diabres.2013.11.00524331235
16. Gagnum V Stene LC Sandvik L Fagerland MW Njølstad PR Joner G Skrivarhaug T All-cause mortality in a nationwide cohort of childhood-onset diabetes in Norway 1973–2013 Diabetologia 2015 58 8 1779 1786 10.1007/s00125-015-3623-7 25972232
Gagnum V, Stene LC, Sandvik L, Fagerland MW, Njølstad PR, Joner G, Skrivarhaug T (2015) All-cause mortality in a nationwide cohort of childhood-onset diabetes in Norway 1973–2013. Diabetologia 58(8):1779–1786. 10.1007/s00125-015-3623-725972232
17. Söbü E DemirYenigürbüz F Özçora GDK Köle MT Evaluation of the impact of glycemic control on mean platelet volume and platelet activation in children with type 1 diabetes J Trop Pediatr 2022 68 4 fmac063 10.1093/tropej/fmac063 35920158
Söbü E, DemirYenigürbüz F, Özçora GDK, Köle MT (2022) Evaluation of the impact of glycemic control on mean platelet volume and platelet activation in children with type 1 diabetes. J Trop Pediatr 68(4):fmac063. 10.1093/tropej/fmac06335920158
18. Baghersalimi A Koohmanaee S Darbandi B Farzamfard V Hassanzadeh Rad A Zare R Tabrizi M Dalili S Platelet indices alterations in children with type 1 Diabetes Mellitus J Pediatr Hematol Oncol 2019 41 4 e227 e232 10.1097/MPH.0000000000001454 30883461
Baghersalimi A, Koohmanaee S, Darbandi B, Farzamfard V, Hassanzadeh Rad A, Zare R, Tabrizi M, Dalili S (2019) Platelet indices alterations in children with type 1 Diabetes Mellitus. J Pediatr Hematol Oncol 41(4):e227–e232. 10.1097/MPH.000000000000145430883461
19. Kim JH Bae HY Kim SY Clinical marker of platelet hyperreactivity in diabetes mellitus Diabetes Metab J 2013 37 6 423 428 10.4093/dmj.2013.37.6.423 24404513
Kim JH, Bae HY, Kim SY (2013) Clinical marker of platelet hyperreactivity in diabetes mellitus. Diabetes Metab J 37(6):423–428. 10.4093/dmj.2013.37.6.42324404513
20. Hekimsoy Z Payzin B Ornek T Kandoğan G Mean platelet volume in Type 2 diabetic patients J Diabetes Complicat 2004 18 3 173 176 10.1016/S1056-8727(02)00282-9
Hekimsoy Z, Payzin B, Ornek T, Kandoğan G (2004) Mean platelet volume in Type 2 diabetic patients. J Diabetes Complicat 18(3):173–176. 10.1016/S1056-8727(02)00282-9
21. Pirgon O Tanju IA Erikci AA Association of mean platelet volume between glucose regulation in children with type 1 diabetes J Trop Pediatr 2009 55 1 63 64 10.1093/tropej/fmn084 18820316
Pirgon O, Tanju IA, Erikci AA (2009) Association of mean platelet volume between glucose regulation in children with type 1 diabetes. J Trop Pediatr 55(1):63–64. 10.1093/tropej/fmn08418820316
22. Mousa SO Sayed SZ Moussa MM Hassan AH Assessment of platelets morphological changes and serum butyrylcholinesterase activity in children with diabetic ketoacidosis: a case control study BMC Endocr Disord 2017 17 1 23 10.1186/s12902-017-0174-6 28376867
Mousa SO, Sayed SZ, Moussa MM, Hassan AH (2017) Assessment of platelets morphological changes and serum butyrylcholinesterase activity in children with diabetic ketoacidosis: a case control study. BMC Endocr Disord 17(1):23. 10.1186/s12902-017-0174-628376867
23. Elsenberg EH van Werkum JW van de Wal RM Zomer AC Bouman HJ Verheugt FW Berg JM Hackeng CM The influence of clinical characteristics, laboratory and inflammatory markers on 'high on-treatment platelet reactivity' as measured with different platelet function tests Thromb Haemost 2009 102 4 719 727 10.1160/TH09-05-0285 19806258
Elsenberg EH, van Werkum JW, van de Wal RM, Zomer AC, Bouman HJ, Verheugt FW, Berg JM, Hackeng CM (2009) The influence of clinical characteristics, laboratory and inflammatory markers on “high on-treatment platelet reactivity” as measured with different platelet function tests. Thromb Haemost 102(4):719–727. 10.1160/TH09-05-028519806258
24. Venkatesh V Kumar R Varma DK Bhatia P Yadav J Dayal D Changes in platelet morphology indices in relation to duration of disease and glycemic control in children with type 1 diabetes mellitus J Diabetes Complications 2018 32 9 833 838 10.1016/j.jdiacomp.2018.06.008 30099984
Venkatesh V, Kumar R, Varma DK, Bhatia P, Yadav J, Dayal D (2018) Changes in platelet morphology indices in relation to duration of disease and glycemic control in children with type 1 diabetes mellitus. J Diabetes Complications 32(9):833–838. 10.1016/j.jdiacomp.2018.06.00830099984
25. Brown AS Hong Y de Belder A Megakaryocyte ploidy and platelet changes in human diabetes and atherosclerosis Arterioscler Thromb Vasc Biol 1997 17 802 807 10.1161/01.ATV.17.4.802 9108797
Brown AS, Hong Y, de Belder A et al (1997) Megakaryocyte ploidy and platelet changes in human diabetes and atherosclerosis. Arterioscler Thromb Vasc Biol 17:802–8079108797
26. Ersoy M Selcuk Duru HN Elevli M Ersoy O Civilibal M Aortic Intima-Media Thickness and Mean Platelet Volume in Children With Type 1 Diabetes Mellitus Iran J Pediatr 2015 25 2 e368 10.5812/ijp.368 26196002
Ersoy M, Selcuk Duru HN, Elevli M, Ersoy O, Civilibal M (2015) Aortic Intima-Media Thickness and Mean Platelet Volume in Children With Type 1 Diabetes Mellitus. Iran J Pediatr 25(2):e368. 10.5812/ijp.36826196002
27 Sharma M Narang S Nema SK Study of altered platelet morphology with changes in glycaemic status Int J Res Med Sci 2016 4 3 757 761 10.18203/2320-6012.ijrms20160513
Sharma M, Narang S, Nema SK (2016) Study of altered platelet morphology with changes in glycaemic status. Int J Res Med Sci 4(3):757–761. 10.18203/2320-6012.ijrms20160513
28. Malachowska B Tomasik B Szadkowska A Baranowska-Jazwiecka A Wegner O Mlynarski W Fendler W Altered platelets’ morphological parameters in children with type 1 diabetes – a case-control study BMC Endocr Disord 2015 15 17 10.1186/s12902-015-0011-8 25886514
Malachowska B, Tomasik B, Szadkowska A, Baranowska-Jazwiecka A, Wegner O, Mlynarski W, Fendler W (2015) Altered platelets’ morphological parameters in children with type 1 diabetes – a case-control study. BMC Endocr Disord 15:17. 10.1186/s12902-015-0011-825886514
29. Kodiatte TA Manikyam UK Rao SB Jagadish TM Reddy M Lingaiah HK Lakshmaiah V Mean platelet volume in Type 2 diabetes mellitus J Lab Physicians 2012 4 1 5 9 10.4103/0974-2727.98662 22923915
Kodiatte TA, Manikyam UK, Rao SB, Jagadish TM, Reddy M, Lingaiah HK, Lakshmaiah V (2012) Mean platelet volume in Type 2 diabetes mellitus. J Lab Physicians 4(1):5–9. 10.4103/0974-2727.9866222923915
30. Akpinar I Sayin MR Gursoy YC Aktop Z Karabag T Kucuk E Sen N Aydin M Kiran S Buyukuysal MC Haznedaroglu IC Plateletcrit and red cell distribution width are independent predictors of the slow coronary flow phenomenon J Cardiol 2014 63 2 112 118 10.1016/j.jjcc.2013.07.010 24012331
Akpinar I, Sayin MR, Gursoy YC, Aktop Z, Karabag T, Kucuk E, Sen N, Aydin M, Kiran S, Buyukuysal MC, Haznedaroglu IC (2014) Plateletcrit and red cell distribution width are independent predictors of the slow coronary flow phenomenon. J Cardiol 63(2):112–118. 10.1016/j.jjcc.2013.07.01024012331
31. Lippi G Salvagno GL Nouvenne A Meschi T Borghi L Targher G The mean platelet volume is significantly associated with higher glycated hemoglobin in a large population of unselected outpatients Prim Care Diabetes 2015 9 3 226 230 10.1016/j.pcd.2014.08.002 25249479
Lippi G, Salvagno GL, Nouvenne A, Meschi T, Borghi L, Targher G (2015) The mean platelet volume is significantly associated with higher glycated hemoglobin in a large population of unselected outpatients. Prim Care Diabetes 9(3):226–230. 10.1016/j.pcd.2014.08.00225249479
32. Zaccardi F Rocca B Rizzi A Ciminello A Teofili L Ghirlanda G De Stefano V Pitocco D Platelet indices and glucose control in type 1 and type 2 diabetes mellitus: A case-control study Nutr Metab Cardiovasc Dis 2017 27 10 902 909 10.1016/j.numecd.2017.06.016 28838851
Zaccardi F, Rocca B, Rizzi A, Ciminello A, Teofili L, Ghirlanda G, De Stefano V, Pitocco D (2017) Platelet indices and glucose control in type 1 and type 2 diabetes mellitus: A case-control study. Nutr Metab Cardiovasc Dis 27(10):902–909. 10.1016/j.numecd.2017.06.01628838851
33. Shah B Sha D Xie D Mohler ER 3rd Berger JS The relationship between diabetes, metabolic syndrome, and platelet activity as measured by mean platelet volume: the National Health And Nutrition Examination Survey, 1999–2004 Diabetes Care 2012 35 5 1074 1078 10.2337/dc11-1724 22410814
Shah B, Sha D, Xie D, Mohler ER 3rd, Berger JS (2012) The relationship between diabetes, metabolic syndrome, and platelet activity as measured by mean platelet volume: the National Health And Nutrition Examination Survey, 1999–2004. Diabetes Care 35(5):1074–1078. 10.2337/dc11-172422410814
34 Sansanayudh N Numthavaj P Muntham D Yamwong S McEvoy M Attia J Sritara P Thakkinstian A Prognostic effect of mean platelet volume in patients with coronary artery disease. A systematic review and meta-analysis Thrombosis and Haemostasis 2015 114 6 1299 1309 10.1160/TH15-04-0280 26245769
Sansanayudh N, Numthavaj P, Muntham D, Yamwong S, McEvoy M, Attia J, Sritara P, Thakkinstian A (2015) Prognostic effect of mean platelet volume in patients with coronary artery disease. A systematic review and meta-analysis. Thrombosis and Haemostasis 114(6):1299–1309. 10.1160/TH15-04-028026245769
35. Meisinger C Prokisch H Gieger C Soranzo N Mehta D Rosskopf D Lichtner P Klopp N Stephens J Watkins NA Deloukas P Greinacher A Koenig W Nauck M Rimmbach C Völzke H Peters A Illig T Ouwehand WH Meitinger T Wichmann HE Döring A A genome-wide association study identifies three loci associated with mean platelet volume Am J Hum Genet 2009 84 1 66 71 10.1016/j.ajhg.2008.11.015 19110211
Meisinger C, Prokisch H, Gieger C, Soranzo N, Mehta D, Rosskopf D, Lichtner P, Klopp N, Stephens J, Watkins NA, Deloukas P, Greinacher A, Koenig W, Nauck M, Rimmbach C, Völzke H, Peters A, Illig T, Ouwehand WH, Meitinger T, Wichmann HE, Döring A (2009) A genome-wide association study identifies three loci associated with mean platelet volume. Am J Hum Genet 84(1):66–71. 10.1016/j.ajhg.2008.11.01519110211
36. Gieger C Radhakrishnan A Cvejic A New gene functions in megakaryopoiesis and platelet formation Nature 2011 480 7376 201 208 10.1038/nature10659 22139419
Gieger C, Radhakrishnan A, Cvejic A et al (2011) New gene functions in megakaryopoiesis and platelet formation. Nature 480(7376):201–208. 10.1038/nature1065922139419
37. Shameer K Denny JC Ding K Jouni H Crosslin DR de Andrade M Chute CG Peissig P Pacheco JA Li R Bastarache L Kho AN Ritchie MD Masys DR Chisholm RL Larson EB McCarty CA Roden DM Jarvik GP Kullo IJ A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects Hum Genet 2014 133 1 95 109 10.1007/s00439-013-1355-7 24026423
Shameer K, Denny JC, Ding K, Jouni H, Crosslin DR, de Andrade M, Chute CG, Peissig P, Pacheco JA, Li R, Bastarache L, Kho AN, Ritchie MD, Masys DR, Chisholm RL, Larson EB, McCarty CA, Roden DM, Jarvik GP, Kullo IJ (2014) A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet 133(1):95–109. 10.1007/s00439-013-1355-724026423
38. Soranzo N Spector TD Mangino M A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium Nat Genet 2009 41 11 1182 1190 10.1038/ng.467 19820697
Soranzo N, Spector TD, Mangino M et al (2009) A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium. Nat Genet 41(11):1182–1190. 10.1038/ng.46719820697
39. Hasheminasabgorji E Jha JC Dyslipidemia, diabetes and atherosclerosis: role of inflammation and ros-redox-sensitive factors Biomedicines 2021 9 11 1602 10.3390/biomedicines9111602 34829831
Hasheminasabgorji E, Jha JC (2021) Dyslipidemia, diabetes and atherosclerosis: role of inflammation and ros-redox-sensitive factors. Biomedicines 9(11):1602. 10.3390/biomedicines911160234829831
40. Singh A Singh A Kushwaha R Yadav G Tripathi T Chaudhary SC Verma SP Singh US Hyperlipidemia and Platelet Parameters: Two Sides of the Same Coin Cureus 2022 14 6 e25884 10.7759/cureus.25884 35734024
Singh A, Singh A, Kushwaha R, Yadav G, Tripathi T, Chaudhary SC, Verma SP, Singh US (2022) Hyperlipidemia and Platelet Parameters: Two Sides of the Same Coin. Cureus 14(6):e25884. 10.7759/cureus.2588435734024
41. Ansari J Co-relation of platelet volume indices with lipid profile in diabetic and non-diabetic patients: a case control study EJPMR 2017 4 565 569
Ansari J (2017) Co-relation of platelet volume indices with lipid profile in diabetic and non-diabetic patients: a case control study. EJPMR 4:565–569
42. El Zayat RS Hassan FM Aboelkhair NT Abdelhakeem WF Abo Hola AS Serum endocan, asymmetric dimethylarginine and lipid profile in children with familial Mediterranean fever Pediatr Res 2024 10.1038/s41390-024-03093-8.Advanceonlinepublication.10.1038/s41390-024-03093-8 38396131
El Zayat RS, Hassan FM, Aboelkhair NT, Abdelhakeem WF, Abo Hola AS (2024) Serum endocan, asymmetric dimethylarginine and lipid profile in children with familial Mediterranean fever. Pediatr Res. 10.1038/s41390-024-03093-8.Advanceonlinepublication.10.1038/s41390-024-03093-838396131
43. Enomoto M Adachi H Hirai Y Fukami A Satoh A Otsuka M Kumagae S Nanjo Y Yoshikawa K Esaki E Kumagai E Ogata K Kasahara A Tsukagawa E Yokoi K Ohbu-Murayama K Imaizumi T LDL-C/HDL-C Ratio predicts carotid intima-media thickness progression better than HDL-C or LDL-C Alone J Lipids 2011 2011 549137 10.1155/2011/549137 21773051
Enomoto M, Adachi H, Hirai Y, Fukami A, Satoh A, Otsuka M, Kumagae S, Nanjo Y, Yoshikawa K, Esaki E, Kumagai E, Ogata K, Kasahara A, Tsukagawa E, Yokoi K, Ohbu-Murayama K, Imaizumi T (2011) LDL-C/HDL-C Ratio predicts carotid intima-media thickness progression better than HDL-C or LDL-C Alone. J Lipids 2011:549137. 10.1155/2011/54913721773051
44. Bhardwaj S Bhattacharjee J Bhatnagar MK Tyag S Atherogenic index of plasma, Castelli risk index and atherogenic coefficient- new parameters in assessing cardiovascular risk Int J Pharm Bio Sci 2013 3 354 364
Bhardwaj S, Bhattacharjee J, Bhatnagar MK, Tyag S (2013) Atherogenic index of plasma, Castelli risk index and atherogenic coefficient- new parameters in assessing cardiovascular risk. Int J Pharm Bio Sci 3:354–364
45. Lumu W Bahendeka S Wesonga R Kibirige D Kasoma RM Ssendikwanawa E Atherogenic index of plasma and its cardiovascular risk factor correlates among patients with type 2 diabetes in Uganda Afr Health Sci 2023 23 1 515 527 10.4314/ahs.v23i1.54 37545918
Lumu W, Bahendeka S, Wesonga R, Kibirige D, Kasoma RM, Ssendikwanawa E (2023) Atherogenic index of plasma and its cardiovascular risk factor correlates among patients with type 2 diabetes in Uganda. Afr Health Sci 23(1):515–527. 10.4314/ahs.v23i1.5437545918
