Original article | Volume 32, Article 141, 25 Mar 2019 | 10.11604/pamj.2019.32.141.17166

On artherogenic index of plasma in sickle cell anaemia patients

Akinsegun Abduljaleel Akinbami, Ebele Ifeyinwa Uche, Aishatu Maude Suleiman, Ann Abiola Ogbena, Festus Olusola Olowoselu, Benjamin Augustine, Mulikat Adesola Badiru, Rafat Abiodun Bamiro, Omolara Risqat Kamson

Corresponding author: Akinsegun Abduljaleel Akinbami, Department of Haematology and Blood Transfusion, Lagos State University, College of Medicine PMB 21266, Ikeja, Lagos, Lagos State, Nigeria

Received: 22 Sep 2018 - Accepted: 26 Feb 2019 - Published: 25 Mar 2019

Domain: Clinical laboratory sciences

Keywords: Artherogenic index of plasma, lipid profile, sickle cell disease

©Akinsegun Abduljaleel Akinbami et al. Pan African Medical Journal (ISSN: 1937-8688). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article: Akinsegun Abduljaleel Akinbami et al. On artherogenic index of plasma in sickle cell anaemia patients. Pan African Medical Journal. 2019;32:141. [doi: 10.11604/pamj.2019.32.141.17166]

Available online at: https://www.panafrican-med-journal.com/content/article/32/141/full

Home | Volume 32 | Article number 141

Original article

On artherogenic index of plasma in sickle cell anaemia patients

On artherogenic index of plasma in sickle cell anaemia patients

Akinsegun Abduljaleel Akinbami1,&, Ebele Ifeyinwa Uche1, Aishatu Maude Suleiman2, Ann Abiola Ogbena3, Festus Olusola Olowoselu3, Benjamin Augustine2, Mulikat Adesola Badiru4, Rafat Abiodun Bamiro4, Omolara Risqat Kamson4


1Department of Haematology and Blood Transfusion, Lagos State University, College of Medicine PMB 21266, Ikeja, Lagos, Lagos State, Nigeria, 2Department of Haematology and Blood Transfusion, Faculty of Basic Clinical Sciences, College of Health Sciences, Ahmadu Bello University, Zaria, 3Department of Haematology and Blood Transfusion, Faculty of Clinical Sciences, College of Medicine, University of Lagos, Nigeria, 4Department of Hematology and Blood Transfusion, Lagos State University, Teaching Hospital, PMB 21266, Ikeja, Lagos State, Nigeria



&Corresponding author
Akinsegun Abduljaleel Akinbami, Department of Haematology and Blood Transfusion, Lagos State University, College of Medicine PMB 21266, Ikeja, Lagos, Lagos State, Nigeria




Introduction: Sickle cell anaemia (SCA) is an inherited abnormality of haemoglobin associated with reduced life expectancy. Patients' complications include dyslipideamia. This study was aimed at determining the artherogenic index of plasma (AIP) in sickle cell anaemia patients and compares the value to HbAA controls value. A high AIP is strongly predictive of elevated cardiovascular risk.


Methods: a comparative study was conducted among SCA patients attending the haematology clinic, Lagos State University Teaching Hospital (LASUTH) and HbAA Phenotype controls. A total of 304 participants were recruited consisting of equal numbers of SCA and HbAA controls. Single lipid profiles were done; logarithms of triglycerides/high density lipoprotein were calculated to obtain AIP and lipid profile ratios established for all participants.


Results: there were lower mean values of Total Cholesterol (TC), High Density Lipoprotein(HDL) and Low Density Lipoprotein (LDL) amongst SCD participants than controls and higher mean values of triglycerides (TG) and Very Low Density Lipoprotein (VLDL) in SCD p < 0.05. The AIP in SCD ranges from -0.62 to 1.32 while that of controls ranges from -0.56 to 0.61.The mean AIP were 0.14 ± 0.29 and -0.009 ± 0.26 in SCD and controls respectively. P value = 0.002.


Conclusion: AIP value is higher in sickle cell anaemia than controls, the former have lower mean values of TC, HDL and LDL and higher mean values of TG and VLDL.



Introduction    Down

In 1910 Dr James Herrick first described SCA in a Dental student in Chicago, USA [1]. Sickle cell gene is characterized by a point mutation in the 6th codon of the haemoglobin gene in which adenine is replaced by thymine (GAG→GTG).The mutation results in the replacement of glutamic acid by valine on the 6th amino acid in the β globin chain of the haemoglobin molecule. Considerable variation in clinical severity occurs in SCA patients despite possessing the same basic identical genetic mutation (GAG→GTG) [2]. Recognized causes of these variations include co-inheritance of α-thalassaemia [3], expression of adhesion molecules on white blood cells [4], steady state neutrophil counts and function [5], haemoglobin haplotypes and HbF concentrations [6], levels of transferrin/C-reactive protein [7], socio-economic status [8], plasma level of IgG and in particular IgG3 [9], levels of circulating immune complexes [10] and dyslipidaemia [11].


Lipid Profile: dyslipidaemia depicts deranged plasma concentration of the lipid profile [12]. Lipid profile consists of total cholesterol (TC), high-density lipoprotein (HDL)-cholesterol, triglycerides (TG), calculated very low density lipoprotein (VLDL) -cholesterol and low density lipoprotein (LDL)-cholesterol. Lipid profiles are useful tests in determining cardiovascular risk and stroke resulting from occlusion of the micro-vasculature and venous thrombo-embolism (VTE) [13] in SCA and general population. These complications are common causes of morbidity in SCA [11]. Pulmonary embolism and hypertension are also common causes of morbidity and mortality in SCA consequent upon thrombo-embolic disease as a result of dyslipidaemia [14].


Dyslipidaemia, cardiovascular diseases and venous thrombo-embolism in SCA: there are several reports on risk factors of cardiovascular diseases (CVD), these are dyslipidaemia, smoking, poor diet, hypertension, sedentary life style and obesity [15-18]. SCA patients are known to be more predisposed to VTE than the general population [19]. Various reported processes involved in the development of VTE in SCA include dyslipidaemia and associated erythrocyte adhesions [20], platelet aggregation [21], coagulation defects [22], free hemoglobin-induced oxidative damage [23], leukocyte activation in the setting of chronic inflammation and erythrocyte-induced endothelial dysfunction [24]. However, amongst these risk factors, dyslipideamia is the most important [25]. In 2002 [26] LDL-C was used to predict first cardiovascular events in a study involving 27,939 apparently healthy American women, however, the prediction failed in 46% of those who had normal LDL-C levels. In 2015, a hospital based study involving 738 coronary heart disease and 157 control participants amongst Chinese Han population [27], it was reported by the authors that serum lipid ratios such as low-density lipoprotein-cholesterol (LDL-C)/HDL-C, triglycerides (TG)/HDL-C and total cholesterol/high-density lipoprotein-cholesterol (TC/HDL-C) were superior predictors of CVD than the single conventional lipid parameters. Logarithm of the ratio of TG and HDL-C known as artherogenic index of plasma (AIP) was first described in 2001 [28], but in 2017, it was considered to be stronger than the serum lipid ratios [29] because it has a stronger sensitivity that reflects the interactions between artherogenic and protective lipoprotein than the serum lipid ratios [30]. AIP categorizes CVD risks into low, intermediate and high risks based on values of <0.11, 0.11-0.21 and >0.21 respectively [31]. This study was aimed at establishing lipid profile reference ranges in SCA patients and HbAA general population and to compare artherogenic index of plasma (AIP) in both groups with a view to determining whether there is a higher AIP in SCA than the non-SCA patients, which could account for their more predisposition to cardiovascular diseases.



Methods Up    Down

Study area: the adult Haematology clinic LASUTH, the general outpatient department and blood donor clinics of the hospital were used for the study. The hospital was established as a cottage hospital in 1955, in July 2001 it became a teaching hospital. It serves the Lagos metropolis with an approximately 20 million population spread unevenly over 20 local government areas.


Study population: adult SCA patients of Haematology clinic and HbAA volunteer participants attending general outpatients and blood donor clinics.


Study design: this was a comparative study of consenting adult SCA patients attending the Haematology clinic, LASUTH and consenting clients of general outpatient department and blood donor clinics who have HbAA Phenotype.


Study period: this study was done over a three months' period from June to August, 2018.


Study site: the laboratory of LASUTH/APIN (APIN Initiative of Nigeria) project located within the haematology clinic was used for the lipid profile assay, while haemoglobin electrophoresis of all participants were done at the haematology main laboratory.


Sampling technique: steady state SCA patients as defined by Ballas SK [32] attending Haematology clinic LASUTH as well as consenting blood donors and general outpatient patients of LASUTH were recruited consecutively into the study. Only consenting participants who have HbAA phenotype and who met the inclusion criteria were used as control population. Heamoglobin phenotype of all cases and controls were performed using alkaline haemoglobin electrophoresis method at pH 8.4 before the lipid profile assays were done.


Subjects/participants: 1) Adults who are HbSS phenotype attending LASUTH Haematology clinic. 2) General outpatient and blood donor clients of LASUTH who have HbAA. 3) Phenotype served as controls.


Inclusion criteria


Adult HbSS phenotype patients: 1) Alkaline Haemoglobin electrophoresis showing HbSS phenotype. 2) Age 18 years and above.


General outpatient and donor clinic clients: 1) Alkaline Haemoglobin electrophoresis showing HbAA phenotype. 2) Age 18 years and above.


Exclusion criteria


Adult HbSS phenotype patients: 1) Non-consenting HbSS patients. 2) Other Hb phenotypes (e.g. HbSC, SD, Etc). 3) Non-fasting participants. 4) HbSS patients on lipid lowering medications. 5) HbSS patients who are hypertensive or diabetics.


General outpatient and donor clinic clients: 1) Non-consenting participants. 2) Other Hb phenotypes (e.g. AS, SC, AC etc). 3) Non fasting participants. 4) HbAA controls on lipid lowering medications. 5) HbAA controls who are hypertensive or diabetics.


Sample size determination: sample size was determined using the statistical formula that applies to comparative studies [33].


Sample Size


d2 Where: n = Sample Size; Z = 1.96 (at 95% confidence level); P2 = Reported Prevalence in general population = 1.3% [34]. P1 = Reported Prevalence in high risk population = 8.4% [35]. d = 5% (precision): n = 1.962 [1.3(100 - 1.3) + 8.4(100 - 8.4)] / 52 n = 3.8416 [1.3 x 98.7 + 8.4 x 91.6] / 25; n = 3.8416 [ 128.31 + 769.44] / 25; n = 3.8416 × 897.75 = 3488.79 = 137.95.9 25 25 n ~ 138 for each group of participants With an estimated non-response rate of 10%, attrition factor [36] = 100/100 - x. Attrition factor = 100 / 100 - 10 = 100 / 90 = 1.11. Total sample size for each group of participants with consideration for non response = 138 x 1.11 = 152. ~ 152.


Ethical considerations and clearance: ethical approval was obtained from the Health Research Ethics committee of LASUTH Reference Number: LREC./06/10/1016. Ethical standards and procedures of the committee for human experimentation were adequately followed.


Participant's informed consent: the participants were informed about the study, as well as their rights and benefits. A written informed consent was obtained by means of voluntarily signed consent form. No participant was coerced in any way to participate in this study, which was at no cost to them.


Confidentiality: the names and initials of all participants were not used to guarantee confidentiality. Participants were assigned unique identification numbers. Paper records were stored in a cabinet in a secured room. Electronic data were password protected.


Questionnaire administration and history taking: with the use of an interviewer- administered questionnaire, each participant was interviewed to obtain relevant demographic and clinical data. Some of the questions asked in the questionnaire included age of diagnosis of sickle cell anaemia, history of blood transfusion, frequency of crisis per year, time of last acute painful crisis, history of last hospital admission, drug history and most frequent type of crisis to determine steady state status of the HbSS participants and history of lipid lowering drugs in both HbSS and controls to exclude those on this drug.


Specimen collection: from an intravenous access, under aseptic conditions using a vacutainer needle, 5mls of blood was collected into a lithium heparin vacutainer bottle for lipid profile of all participants. Another 2mls of blood sample was collected into a vacutainer EDTA bottle from all participants and properly mixed with the anticoagulant for alkaline Hb electrophoretic analysis in all participants to ensure the participants are either sickle cell anaemia patients or HbAA participants before the lipid profile assay.


Lipid profile assay and calculation of atherogenic index of plasma and other ratios: TC, TG and HDL assays were done with the cobas C111 chemistry auto-analyzer (Roche Diagnostic, Germany) which uses enzymatic, colorimetric method. VLDL and LDL-C were calculated from Friedewald formula [37]. VLDL as TG/2.2 (mmols/L) while LDL was calculated using LDL-C (mmols/L) = TC-VLDL-HDL. AIP was calculated in all participants [28] as log (TG/HDL-C) from the data generated, the lipid ratios such as low-density lipoprotein-cholesterol (LDL-C)/HDL-C, triglycerides (TG)/HDL-C and total cholesterol/high-density lipoprotein-cholesterol (TC/HDL-C) were also calculated from single lipid profile parameters assayed.


Statistical analysis: data were analyzed by IBM SPSS (Statistical Package for Social Sciences, Inc.) statistics for windows version 20.0 Armonk, New York, USA. The continuous variables were presented as means ± standard deviation (SD). The Pearson chi squared tested for association between discrete variables. Independent t-test and analysis of variance (ANOVA) were used between the two groups. P value was considered to be statistically significant when ≤ 0.05.



Results Up    Down

A total of 304 participants were finally recruited after excluding non- consenting SCD patients and controls including 25 controls that have sickle cell trait and are double heterozygotes like HbSC, participants consisted of equal numbers of sickle cell patients and controls. The mean age of SCD was lower compared with controls and expectedly the mean BMI of SCD was also lower than that of controls. There are more males than females in both groups. The mean number of crisis/year in SCD was 1.6 ± 0.6 and the mean number of blood transfusion per year was 1.79 ± 1.28 (Table 1). The reference ranges of total cholesterol, triglyceride, HDL, VLDL and LDL in SCD and controls are presented in Table 2. The means of total cholesterol, triglyceride, HDL, VLDL and LDL in SCD and that of controls are shown in Table 3. Analysis of variance (ANOVA) of these means produced F ratio of 581.70 and 685.90 for SCD and controls respectively with p values of both groups being < 0.05. The ratio of LDL/HDL, TG/HDL and TC/HDL for both the SCD and controls are also presented in Table 3. ANOVA of the ratios also produced F ratio of 74.41 and 265.50 of both the SCD and controls respectively and p values < 0.05 for both groups. The artherogenic index of plasma (AIP) in SCD ranges from -0.62 to 1.32 while that of controls ranges from -0.56 to 0.61. The mean AIP were 0.14 ± 0.29 and -0.009 ± 0.26 in SCD and controls respectively (Table III). Using independent t-test for comparisons of SCD and controls, the AIP, MI, age, total cholesterol, high density lipoprotein and low density lipoprotein were all statistically significant with p values of < 0.05,while triglyceride and very low density lipoprotein were not statistically significant, p values were 0.76 and 0.64 respectively (Table 4). Similarly, the ratio of TG/HDL and TC/HDL were also statistically significant with a p value < 0.05, while the ratio of LDL/HDL did not reach a significant value.



Discussion Up    Down

This study reported lower mean values of TC, HDL and LDL amongst Nigerian SCA adult participants than HbAA controls and higher mean values of TG and VLDL (p < 0.05). Similar finding was also reported among Nigerian SCA Children and adolescent by Vanderjagt et al. [38]. These results are in keeping with values in adult African -American SCA patients reported in 2003 in which there was a higher than normal reference in plasma triglyceride but lower values of total cholesterol, high density lipoprotein (HDL) and low density lipoprotein (LDL) [39]. The findings are also in keeping with data of Indians in 2016 [40] and Senegalese in 2014 [41]. By implication, hypocholesterolemia, low LDL and HDL and higher values of TG and VLDL reported severally in SCA patients may not be related to age, race, socio-economic status or diet but may be genetic and related to the pathophysiology of the disease. Our study also reported statistically significant higher mean values of the ratios of LDL/HDL, TG/HDL, TC/HDL and AIP in SCA than controls (p < 0.05). TG/HDL ratio was reported higher among the Senegalese SCA patients while AIP was higher in the Indians Sickle cell anaemia patients than controls. The consequence of this is that, SCA patients are more predisposed to developing atherosclerosis and cardiovascular diseases than HbAA controls based on the reported AIP's predictive role to developing coronary heart disease [29].


A study on lipid homeostasis in SCA is necessary because red blood cells membrane is made up approximately 50% of lipid and plasma phospholipids contributes significantly to the synthesis of erythrocytes membranes [42]. Plasma non esterified fatty acids (NEFA) are also building blocks of erythrocyte membranes and its alteration could impact on the structure and function of red blood cells [43]. Therefore, abnormal lipid homeostasis could alter red blood cell membrane fluidity and functions leading to a significant worsening in sickle cell anaemia [44]. Abnormalities in total cholesterol either an increased or decreased level is associated with increased mortality from all causes [45]. Several authors have reported lower than reference value of cholesterol in SCA than general population [46-48]. Though, in our study the mean values of these parameters were generally lower and statistically significant in SCA than in controls, they were within normal reference ranges (Table 2). Similar findings were reported amongst Nigerians in 2017 [49].


It is well established that various haemolytic anaemias with high erythropoietic activity including sickle cell anaemia have been described to be associated with hypocholesterolemia [50]. The pathogenesis of sickle cell anaemia-lipid associated abnormalities have been linked to high erythropoietic activity because of increased cholesterol use, defective liver function secondary to iron overload and malfunctioning post absorptive plasma homeostasis of fatty acids in sickle cell anaemia [51]. The clinical relevance of hypocholesterolemia in SCA include a risk factor to developing depression and suicidal tendencies as reported in 1994 [52]. It may also increase probability of mortality in SCA than in controls [53, 54]. Weather impacts on serum cholesterol and triglycerides. The latter is reported normally lower in winter than in summer, while cholesterol is higher in winter than summer [55, 56]. This study was carried out in rainy season in Nigeria which could have impacted on the results. Prolonged tourniquet application between 2-5 minutes before sample collection which was avoided during samples collection in this study is known to increase cholesterol level from 5 to 15% [57]. Disease conditions such as hypothyroidism and nephrotic syndrome also impact on LDL- cholesterol, VLDL-cholesterol and total cholesterol by increasing their levels [58], while infection and inflammation may decrease total cholesterol and HDL cholesterol and increase triglycerides [59] Dilutional effect due to a postural change from an upright to a supine position could reduce the cholesterol levels by 10% and triglycerides by 12% [60]. All these could have influenced the AIP results obtained for SCA and controls and are all possible limitations of the study.


Our study participants were fasted before blood samples were drawn for lipid profile, should a lipid profile sample be fasting or non-fasting? European and National Cholesterol Education Program (NCEP) guidelines recommend fasting lipid profile samples for cardiovascular risk assessment [61]. Secondly, postprandial triglycerides may be raised several hours after meal [62] and most reference values for lipid profiles are on fasting samples. However, total cholesterol, HDL-cholesterol and non-HDL cholesterol (total cholesterol-HDL cholesterol) remain unaltered in fasting and non-fasting samples [63]. Similarly, little difference exists between values of lipoproteins and apolipoproteins in fasting and non-fasting states which are all associated with a good cardiovascular risk prediction [62]. Bansal et al. [64] and Nordestgaard et al. [65] independently concluded that non-fasting triglycerides may be a better predictor of cardiovascular risk as compared to fasting triglycerides which is contrary to European and NCEP guidelines. AIP, apart from having stronger sensitivity reflecting interactions between artherogenic and protective lipoproteins thus being a better predictor of coronary artery disease, another advantage it has over single lipid profile is its ability for correction when there is no normal distribution because it is calculated in logarithm, its calculation however, requires no extra cost. Future researches on AIP should focus more on its link with metabolic syndrome, diabetes mellitus [66] and oxidative stress [67].



Conclusion Up    Down

AIP value is higher in sickle cell anaemia patients than controls and the former have lower mean values of TC, HDL and LDL than controls and higher mean values of TG and VLDL.

What is known about this topic

  • Patients with sickle cell anaemia have an increased risk of development of cardiovascular disease and venous thromboembolism;
  • Dyslipidaemia is the most important risk factor for the development of cardiovascular disease;
  • AIP (artherogenic index of plasma) is strongly predictive of cardio.

What this study adds

  • Patients with sickle cell anaemia have higher AIP compared with HbAA phenotype control;
  • They therefore have an increased risk of development of cardiovascular disease.



Competing interests Up    Down

The authors declare no competing interests.



Authors’ contributions Up    Down

Akinsegun Akinbami, Ebele Uche and Aishatu Suleiman did the study conceptualization and design. Ann Ogbena, Festus Olowoselu, Mulikat Badiru and Rafat Bamiro did data acquisition. Akinsegun Akinbami, Ebele Uche and Ann Ogbena did data analysis and interpretation. Manuscript drafting and review was done by all the authors. All the authors have read and agreed to the final manuscript.



Acknowledgments Up    Down

The authors wish to thank Mr Femi Olaosebikan who analyzed the samples and all LASUTH/APIN Project Laboratory personnel.



Tables Up    Down

Table 1: participants’ age, BMI and gender

Table 2: reference ranges of lipid profiles of SCA and controls

Table 3: mean values of lipid profiles in SCA and controls

Table 4: bivariate analysis of the parameters using independent t-test



References Up    Down

  1. Herrick JB. Peculiar elongated and sickled red blood corpuscle in a case of severe anaemia. Arch Intern Med. 1910; 6(5): 517-21. Google Scholar

  2. Serjeant GR. Sickle cell disease. Lancet. 1997; 350(9079): 725-30. PubMed | Google Scholar

  3. Embury SH, Dozy AM, Miller J. Concurrent sickle cell anaemia and alpha-thalassaemia: Effect on severity of anemia. N Engl J Med. 1982; 306(5): 270-4. PubMed | Google Scholar

  4. Okpala IE, Daniel Y, Haynes R, Odoemene D, Goldman JM. Relationship between the clinical manifestations of sickle cell disease and the expression of adhesion molecules on white blood cells. Eur J Haematol. 2002; 69(3): 135-44. PubMed | Google Scholar

  5. Anyaegbu CC, Okpala IE, Aken'Ova AY, Salimonu LS. Peripheral blood neutrophil count and candidacidal activity correlate with the clinical severity of sickle cell anaemia. Eur J Haematol. 1998; 60(4): 267-8. PubMed | Google Scholar

  6. Powars DR, Meiselman HJ, Fisher TC, Hiti A, Johnson C. Beta-S gene cluster haplotypes modulate haematologic and haemorheologic expression in sickle cell anemia. Am J Pediatr Hematol Oncol. 1994; 16(1): 55-61. PubMed | Google Scholar

  7. Hedo CC, Aken'Ova AY, Okpala IE, Durojaiye AO, Salimonu LS. Acute phase reactants and the severity of homozygous sickle cell anaemia. J Intern Med. 1993; 233(6): 467-70. PubMed | Google Scholar

  8. Okpala IE. The management of crisis in sickle cell disease (review). Eur J Haematol. 1998; 60(1): 1-6. PubMed | Google Scholar

  9. Hedo CC, Okpala IE, Aken'Ova AY, Salimonu LS. Correlates of severity in sickle cell anaemia. Blood. 1996; 88(Suppl): 17b.

  10. Anyaegbu CC, Okpala IE, Aken'Ova AY, Salimonu LS. Complement haemolytic activity, circulating immune complexes and the morbidity of sickle cell anaemia. APMIS. 1999; 107(7): 699-702. PubMed | Google Scholar

  11. Ataga KI, Key NS. Hypercoagulability in sickle cell disease: new approaches to an old problem. Hematology Am Soc Hematol Educ Program. 2007; 91-96. PubMed | Google Scholar

  12. Cummings KC. Lipid and Cardiac Risk profiles. Clinical Chemistry. 2003; 47: 407-409.

  13. Nnodim JK, Opara AU, Nwanjo HU. Plasma Lipid Profile in Sickle Cell Disease Patients in Owerri, Nigeria. Pakistan Journal of Nutrition. 2012; 11(1): 64-65. Google Scholar

  14. Minter KR, Gladwin MT. Pulmonary complications of sickle cell anemia. A need for increased recognition, treatment and research. AJ Resp Crit Care Med. 2001; 164(11): 2016-2019. PubMed | Google Scholar

  15. Rang HP, Dale MM, Ritter JM, Moore PK. Pharmacology. 5th ed. Elsevier: India. 2005 ISBN: 81: 8147-917-3.

  16. Martirosyan DM, Miroshnichenko LA, Kulokawa SN, Pogojeva AV, Zoloedov VI. Amaranth oil application for heart disease and hypertension. Lipids Health Dis. 2007; 6: 1. PubMed | Google Scholar

  17. Shen GX. Lipid Disorders in Diabetes Mellitus and Current Management Curr Pharmaceut. Analys. 2007; 3(1): 17-24. Google Scholar

  18. Takasaki Y. Serum Lipid Levels and Factors Affecting Atherogenic Index in Japanese Children. Journal of Physiological Anthropology and Applied Human Science. 2005; 24(4): 511-515. PubMed | Google Scholar

  19. Ataga KI, Orringer EP. Hypercoagulability in sickle cell disease: a curious paradox. Am J Med. 2003 Dec 15; 115(9): 721-8. PubMed | Google Scholar

  20. Setty BN, Rao AK, Stuart MJ. Thrombophilia in sickle cell disease: the red cell connection. Blood. 2001; 98(12): 3228-3233. PubMed | Google Scholar

  21. Kenny MW, George AJ, Stuart J. Platelet hyperactivity in sickle-cell disease: a consequence of hyposplenism. J Clin Pathol. 1980; 33(7): 622-625. PubMed | Google Scholar

  22. Westerman MP, Green D, Gilman-Sachs A. Antiphospholipid antibodies, proteins C and S, coagulation changes in sickle cell disease. J Lab Clin Med. 1999; 134(4): 352-362. PubMed | Google Scholar

  23. Zhou Z, Behymer M, Guchhait P. Role of extracellular hemoglobin in thrombosis and vascular occlusion in patients with sickle cell anemia. Anemia. 2011; 2011: 918916. PubMed | Google Scholar

  24. Hidalgo A, Chang J, Jang JE, Peired AJ, Chiang EY, Frenette PS. Heterotypic interactions enabled by polarized neutrophil microdomains mediate thrombo inflammatory injury. Nat Med. 2009; 15(4): 384-391. PubMed | Google Scholar

  25. Kanthe PS, Patil BS, Bagali SH, Deshpande A, Shaikh G, Aithala M. Atherogenic Index as a Predictor of Cardiovascular Risk among Women with Different Grades of Obesity. IJCRIMPH. 2012; 4(10): 1767-1774. Google Scholar

  26. Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events. N Engl J Med. 2002; 347(20): 1557-1565. PubMed | Google Scholar

  27. Zhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol. 2015; 73(10): 931-938. PubMed | Google Scholar

  28. Dobiá'ová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FERHDL). Clin Biochem. 2001; 34(7): 583-8. PubMed | Google Scholar

  29. Cai G, Shi G, Xue S, Lu W. The artherogenic index of plasma is a strong and independent predictor for coronary artery disease in the Chinese Han Population. Medicine. 2017; 96(37): e8058. PubMed | Google Scholar

  30. Nwagha UI, Igweh JC. Atherogenic Index of Plasma: a significant indicator for the onset of atherosclerosis during menopause in hypertensive females of South East Nigeria. Journal of College of Medicine. 2005; 10(2): 67-71. Google Scholar

  31. Dobiasova M. AIP-atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice. Vnitr Lek. 2006; 52(1): 64-71. PubMed | Google Scholar

  32. Ballas SK. More definitions in sickle cell disease: steady state v base line data. American journal of Haematology. 2012; 87(3): 338. PubMed | Google Scholar

  33. Goyal RC. Research methodology for health professionals including proposal, thesis and article writing. First edition, 2013. London: Jaypee Brothers Medical Publishers. Google Scholar

  34. Hansson PO, Welin L, Tibblin G, Eriksson H. Deep vein Thrombosis and pulmonary embolism in the general population: "The Study of Men Born in 1913". Arch Intern Med. 1997; 157(15): 1665-70. PubMed | Google Scholar

  35. Al-Gahtani FH. Thromboembolic events among patients with sickle Cell disease: risk factors and prevalence in a tertiary hospital in Saudi Arabia. J Hematol Thromb. 2016; Dis 4: 254.

  36. Betty R. Kirkwood and Jonathan AC Sterne. Essential medical statistics. Second edition; 2010: pp 423. Google Scholar

  37. Friedewald, WT, Levi, RI, Fredrickson, DS. Estimation of the concentration of low density lipoproteins cholesterol in plasma without use of the ultracentrifuge. Clin Chem. 1972; 18(6): 499-502. PubMed | Google Scholar

  38. Vanderjadt DJ, Shores J, Okorodudu A, Okolo SN, Glew RH. Hypocholesterolemia in Nigerian Children. J Trop Pediat. 2001; 47: 1-6.

  39. Shores J, Peterson J, VanderJagt D, Glew RH. Reduced cholesterol levels in African-American adults with sickle cell disease. J Natl Med Assoc. 2003; 95(9): 813-7. PubMed | Google Scholar

  40. Benazeer SJ, Bardapurkar JS, Vinod RB, Bardapurkar SJ. Evaluation of lipid profile status in sickle cell disease patients of North Maharasthra. Biomedicine. 2016; 36(2): 50-54.

  41. Diatta A, Cisse F, Gueye TF, Diallo F, Toure FA, Sarr GN et al. Serum lipids and oxidized low density lipoprotein levels in sickle cell disease: assessment and pathobiological significance. African Journal of Biochemistry Research. 2014; 8(2): 39-42. Google Scholar

  42. Sasaki J, Waterman MR, Buchanan GR, Cottan GL. Plasma and erythrocyte lipids in sickle cell. Clin Lab Haematol. 1983; 5(1): 35-44. PubMed | Google Scholar

  43. Glatz J, Soffers A, Katan M. Fatty acid composition of serum cholesteryl esters and erythrocyte membranes as indicators of linoleic acid intake in man. Am J Clin Nutr. 1989; 49(2): 269-276. PubMed | Google Scholar

  44. Zorca S, Freeman L, Hildesheim M, Allen D, Remaley AT, Taylor JG, 6th et al. Lipid levels in sickle-cell disease associated with haemolytic severity, vascular dysfunction and pulmonary hypertension. Br J Haematol. 2010; 149(3): 436-45. PubMed | Google Scholar

  45. Schatz IJ, Masaki K, Yano K, Clen RS, Rodriguez BL, Curb JD. Cholesterol and all-cause mortality in elderly people from Honolulu Heart Program. Lancet. 2001; 358(9279): 351-355. PubMed | Google Scholar

  46. Oforofuo IA, Adedeji MO. Effect of sickle-cell gene expression on plasma cholesterol in a Nigerian population. Clin Biochem. 1994; 27(6): 505-508. PubMed | Google Scholar

  47. Saha N, Samuel APW. Sickle cell gene and liver functions in a Sudanese population. Acta Haenmat. 1982; 68(1): 65-67. PubMed | Google Scholar

  48. El-Hazmi MAF, Jabbar FA, Warsy AS. Cholesterol and triglyceride level in patients with sickle cell anemia. Scand J Clin Lab Inmest. 1987; 47(4): 351-354. PubMed | Google Scholar

  49. Uche CI, Akinola NO. Correlates of Steady State Lipid Profile and Anthropometric Parameters in Adult Sickle Cell Anaemia Patients in South-West Nigeria. Journal of Dental and Medical Sciences. 2017; 16(2): 76-81. Google Scholar

  50. Rahimia Z, Abmad M, Mansour H. Plasma lipids in Iranians with sickle cell disease: hypocholesterolemia in sickle cell anemia and increase of HDL-cholesterol in sickle cell trait. Clinica Chimica Acta. 2006 Mar; 365(1-2): 217-20. Epub 2005 Sep 26. PubMed | Google Scholar

  51. Shalev H, Kapelushnik J, Asher M. Hypocholesterolemia in Chronic Anaemias with Increased Erythropoietic Activity. Am J of Hematol. 2007; 82(3): 199-202. PubMed | Google Scholar

  52. Glueck CJ, Kuller FE, Hamer T, Rodriguez R, Sosa F, Sieve-Smith L. Hypocholesterolemia, hypertriglyceridemia, suicide, and suicide ideation in children hospitalized for psychiatric diseases. Pediaitr Res. 1994 May; 35(5): 602-10. PubMed | Google Scholar

  53. Song Y-M, Sung J, Kim JS. Which cholesterol level is related to the lowest mortality in a population with low mean cholesterol level: a 64-year follow-up study of 482,472 Korean men. Ame J Epidermiol. 2000; 151(8): 739-747. Google Scholar

  54. Schatz IJ, Masaki K, Yano K, Chen RS, Rodriguez BL, Curb JD. Cholesterol and all-cause mortality in elderly people from the Honolulu Heart Program. Lancet. 2001; 358(9279): 351-355. PubMed | Google Scholar

  55. Cooper GR, Myers GL, Smith J, Schlant RC. Blood lipid measurements: variations and practical utility. JAMA. 1992; 267(12): 1652-1660. PubMed | Google Scholar

  56. Narayanan S. Physiological variables in blood sampling. Mitt Klin Chem. 1993; 24: 130-134.

  57. Young DS. Biological variability. In: Brown SS, Mitchell FL, Young DS, editors. Chemical Diagnosis of Disease. New York: Elsevier; 1979; pp. 1-113.

  58. Joven J, Villabona C, Vilella E. Abnormalities of lipoprotein metabolism in patients with nephrotic syndrome. N Engl J Med. 1990; 323(9): 579-584. PubMed | Google Scholar

  59. Alvarez C, Ramos A. Lipids, lipoproteins and apolipoproteins in serum during infection. Clin Chem. 1986; 32(1 pt 1): 142-145. PubMed | Google Scholar

  60. Narayana S. Pre and post analytical errors in lipid determination. Indian J Clin Biochem. 1996; 11: 12. Google Scholar

  61. Backer G, Ambrosioni E, Borch-Johnson K, Brotons C. European guidelines on cardiovascular disease and prevention in clinical practice. Atherosclerosis. 2003; 171(1): 145-155. PubMed

  62. Campose H, Khoo C, Sacks FM. Diurnal and acute pattern of postprandial apolipoprotein B-48 in VLDL, IDL and LDL from normo-lipidemic human. Atherosclerosis. 2005; 181(2): 345-351. PubMed | Google Scholar

  63. Third report of the National Cholesterol Education Program (NCEP) Expert panel on detection, evaluation and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002; 106(25): 3143-3421. Google Scholar

  64. Bansal S, Buring JE, Rifai N, Mora S, Sacks FM, Ridker PM. Fasting compared with nonfasting triglycerides and risk of cardiovascular events in women. JAMA. 2007; 298(3): 309-316. PubMed | Google Scholar

  65. Nordestgaard BG, Benn M, Schnohr P, Tybjaerg-Hansen A. Non fasting triglycerides and risk of myocardial infarction, ischemic heart disease and death in men and women. JAMA. 2007; 298(3): 299-308. PubMed | Google Scholar

  66. Zhu XW, Deng FY, Lei SF. Meta-analysis of artherogenic index of plasma and other lipid parameters in relation to risk of type 2 diabetes mellitus. Prim Care Diabetes. 2014; 9(1): 60-67. PubMed | Google Scholar

  67. Amrita J, Mahajan M, Bhanwer AJ, Mohan G. Oxidative stress: an effective prognostic tool for an early detection of cardiovascular disease in menopausal women. Biochem Res Int. 2016; 2016: 6157605. PubMed | Google Scholar






























Original article

On artherogenic index of plasma in sickle cell anaemia patients

Original article

On artherogenic index of plasma in sickle cell anaemia patients

Original article

On artherogenic index of plasma in sickle cell anaemia patients