Results of the baseline scenario expressed as an average ± standard error. Costs per patient (€, 2010)
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). From 2015 he has been appointed chairman of World Conference of Innovative Animal Nutrition and Feeding (WIANF).\r\nHis main research areas are related to pig and poultry nutrition: elimination of harmful effects of heat stress by nutrition tools, energy- amino acid metabolism in livestock, relationship between animal nutrition and quality of animal food products (meat).",institutionString:"University of Debrecen",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Debrecen",institutionURL:null,country:{name:"Hungary"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"25",title:"Veterinary Medicine and Science",slug:"veterinary-medicine-and-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"22877",title:"Cost and Efficacy of Therapies for Advanced Parkinson's Disease",doi:"10.5772/17862",slug:"cost-and-efficacy-of-therapies-for-advanced-parkinson-s-disease",body:'The economic burden of Parkinson’s disease (PD) has become a very important health topic. From the perspective of a general neurologist, health economics can appear as not prioritary; however, it is a growing topic in most modern healthcare systems; consequently health professionals should have at least a minimal understanding of how health economic information is derived. It has become important that payers and providers consider the significant impact the disease has on quality of life and how resource utilisation is directed to improve PD related clinical problems. Such measures are determinant in assessing the value of drug therapy, particularly for chronic conditions such as PD, and in determining the appropriate placement of medications on plan formularies. In the past, a comprehensive examination of a clinical condition would focus almost exclusively on areas considered relevant to patient management, such as diagnosis, etiology, treatment, and prognosis. Now, however, additional demands are being made by health policy decision makers, who can influence medical decisions through coverage and reimbursement policies. Physicians and other professional caregivers increasingly must consider the economic implications of their decisions, which has led to increased demand for disease-specific cost information.
PD is a progressive neurodegenerative disorder of the central nervous system with motor, cognitive, behavioural and autonomic symptoms. PD has a significant economic burden from all perspectives: society, health system, and individual patient and relatives. This is due to the high prevalence of the disease, 6.3 million people around the world (European Parkinson’s disease Association [EPDA], (2008), the nature of the symptoms and the fact that no cure exists and treatments are only aimed at relieving the effects of the disease and to improve patients’ quality of life.
In Spain, taking into account a population of around 47 million people (Spanish National Institute of Statistics, 2010), and considering the different incidence and prevalence rates published (Abasolo-Osionaga et al., 2006; Benito-León et al., 2003; Bergareche et al., 2004; Claveria et al., 2002), the average incidence has been estimated at around 6,400 new cases per year, and the average prevalence at 150,000 people (European Parkinson’s Disease Association [EPDA], (2008) with PD. It is estimated that 30% of these patients are in an advanced stage of the disease (Kulisevsky, 2005). The economic impact of PD is mainly driven by in-patient care and nursing home costs caused by motor and non-motor symptoms that lead patients of PD to progressive disability. In addition, the cost of illness increases dramatically with severity as patients at the advanced stages are bedridden, wheelchair bound or institutionalized.
According to the most recent version of the World Health Organisation (WHO) report on Global Burden of Disease, published in 2008 (Mathers et al., 2008), neuropsychiatric conditions are responsible for 22% of global disability adjusted life years (DALYs) for women aged 15–59 years, the largest cause group in all regions outside Africa. The DALY is used as a measure to quantify the burden of the disease and consists on the years of life lived in less than full health or lost from premature death. It is also important to consider that these costs will increase along with the prevalence of these diseases due to the aging of the population in Europe.
Several studies have been performed to date in different countries to analyze the economic and social burden of PD. According to this literature, the main drivers of the total direct cost of PD are hospitalization and drug costs (Keranen et al., 2003; O’Brien et al., 2009) and the main drivers of total indirect cost are nursing care and productivity loss (Dengler et al., 2006; Hagell et al., 2002; O’Brien et al., 2009).
In the United States, the annual economic impact of PD was estimated in 2007 at $10.78 billion, being 58% of this amount related to direct medical costs ($6.22 billion) (O’Brien et al., 2009). Nursing home care ($2.6 billion) and PD-related medications ($1.47 billion) accounted for 63% of direct medical costs. Regarding indirect costs, annual lost productivity for persons with PD was estimated at $1 billion and caregivers cost at $2.36 billion. When considering the individual economic data, annual cost per patient was $21,626 (direct cost: $12,491).
Another study published in the United States (Huse et al., 2005) in 2005 quantified direct medical care costs for individual patients with PD and compared them with the costs obtained in a control group of persons without PD. Total annual direct cots were $23,101 per patient with PD versus $11,247 for controls. According to the study and based on annual data, PD patients spent approximately 2 more days in the hospital, 43 more days in long-term care institutions, and required more than 20 additional prescriptions than controls. These differences led to an increment of total annual health care costs of approximately $12,000 in PD patients. Around 50% of this excess cost was for long-term care, 24% for outpatient services, 15% for hospitalization and 14% for pharmacological treatment. The study projected the total cost of PD for the United States to be around $23 billion annually.
In an Australian study (Cordato et al., 2006) the total direct health-care cost of PD for patients with Hoehn and Yahr stage 3 was found to be four times higher than that of an age- and sex-matched control group of patients without PD. The estimated annual cost was Australian $7,020 per patient. Medication was the most costly component for both groups, being significantly higher for PD patients. Within the PD group, the health care costs attributable to PD were significantly higher than health care costs not related to PD (for a 3 month period, A$1,202 versus A$553).
As concerns European data, no Pan-European survey of the economic cost of PD has been performed to date. However, several studies from different countries are available in the literature. Some of the most representative ones have been considered for this chapter.
A review of the literature on the economic impact of PD in UK (Findley, 2007) reported an estimated total cost of PD in the UK between £449 million and £3.3 billion annually, depending on the methodology and prevalence rate considered in each individual analysis. In another study in 2003, service use and costs for PD patients in the UK (McCrone et al., 2007) were measured. The annual costs were £13,804 per patient. Formal care costs accounted for 20% of this amount, while informal care was related to the 80% of the burden. Predictors of higher costs were identified, being male gender, level of disability and depression the more significant ones.
A study published in 2003 (Zecchinelli et al., 2003) assessed health care costs associated with PD in Italy. Patients and results were classified using the Hoehn and Yahr scale. Annual direct health costs were €4,320 for mild stage (1-2), €4,748 for moderate stage (2.5-3) and €6,175 for severe stage (4-5). The average was estimated at €4,808. These results were identified as lower than the real cost, as they didn’t consider the societal perspective and neither informal care nor health care costs incurred in the private sector were included.
In Germany a 3-year prospective study of the economic cost of PD (Dengler et al., 2006) was performed in 2006. The average annual cost per patient was estimated at €12,091, of which 55,9% accounted for direct costs. Drugs took up the major share of direct costs, €5,763 per year. Indirect costs accounted for €4,851 per patient per year. Within this, 76% of the costs were related to nursing care and the loss of productivity.
In Sweden resource use and costs in patients with PD were collected from medical records of a cohort of 127 patients (Hagell et al., 2002) in 1996 (year 2000 costs). Direct health care costs averaged approximately €3,200 per patient per year, of which drugs were the most costly component. Non-medical direct costs were higher, €4,800 per patient per year, and costs due to lost productivity were around €5,800 per patient per year. The average total annual cost for PD was therefore estimated at €13,800 per patient.
Costs of PD illness were studied in a Russian Cohort of 100 patients (year 2008 costs) (Winter et al., 2009). From the societal perspective, total annual costs per patient amounted to €5,240 per patient, with direct costs accounting for 67% and indirect costs for 33% of the total. The main drivers of the burden were informal care and drugs. Global costs for the nation were estimated at €1.1 billion per year.
One of the most recent European studies, published in 2010, was performed in a cohort of 100 Czech patients with idiopathic PD to evaluate direct and indirect costs and to identify cost-driving factors (Winter et al., 2010b). Results were assessed for a 6-month period and have been projected to annual costs. Total annual costs for PD were €11,020 per patient. Direct costs accounted for 60% of the total costs and indirect for 40%. Independent cost-driving factors included disease severity, motor complications, psychosis and age.
The degenerative nature of PD leads to an increase of resource consumption in its advance stages. In fact, disease severity has been identified as a strong cost-predictor in several of the studies already mentioned. Motor complications (fluctuations, dyskinesias, dystonia) have been identified as factors increasing PD-related costs (Dowding et al., 2006; McCrone et al., 2007; Winter et al., 2010a; Winter et al., 2010b; Zecchinelli et al., 2003).
In a 6-month observational study of PD in France, Germany and the UK, patients with different degrees of motor complications, measured using the Unified Parkinson Disease Rating Scale (UPDRS), and its effects on health care costs were examined (Péchevis et al., 2005). Dyskinesia (UPDRS part IVa) was associated with significant increases in total health care costs. Each unit increase in dyskinesia score lead to €562 additional costs per patient over a 6-month period.
A 2007 published study developed in the UK (Thanvi et al., 2007) showed that Levodopa induced dyskinesia increased health care costs. Relationship between increasing cost of care and severity of the disease as measured by Hoehn and Yahr stage was proven to be statistically significant. A correlation was also found between the severity of the disease, patient’s age and the use of Social Services.
If we focus on advanced PD (APD) treatment costs, a systematic review of the available economic evidence of deep brain stimulation (DBS) for APD was performed in 2009 (Puig-Junoy & Puig, 2009). Ten studies were identified, five of them being simple cost analyses and the other five, full economic evaluations.
The cost studies included were: one description of costs of the treatment with DBS (McIntosh et al., 2003) and four comparative analyses (Charles et al., 2004; D’Ausilio et al., 2003; Fraix et al., 2006; Gerzeli et al., 2002) of DBS versus conservative pharmacological treatment. In the comparative analyses, a significant difference was observed when annual average pharmacological cost was compared between conservative treatment and DBS. The independent average cost of the DBS intervention was specified in four of the five cost studies. These results showed some variability from €20.033 (Gerzeli et al., 2002) to -€33.220 (McIntosh et al., 2003), explained mainly by differences in resources utilization, which was highly driven by surgeons’ level of experience. The intervention duration and costs decreased along with the increase of professional experience on DBS.
It was also observed that pharmacological treatment costs were lower in patients after DBS intervention than in patients that remained in conservative pharmacological treatment. Regarding costs distribution, the higher resource consumption for DBS was experienced during the first year of the therapy. The comparison of DBS costs to conventional pharmacological alternative is sensitive to the inclusion of non-health related costs. When productivity loss and informal care costs were considered (Gerzeli et al., 2002), the cost of the DBS alternative was lower to the cost of the conventional pharmacological treatment.
Levodopa combined with adjunct medical therapy is the standard medical treatment for individuals with PD. However, prolonged use of levodopa can cause disabling motor fluctuations and dyskinesias. When medication is no longer effective or produces unacceptable side effects, surgical treatments may be a possible alternative. Ablative surgery and DBS are the main surgical treatments for advanced refractory PD. Ablative surgery includes pallidotomy, thalamotomy and subthalamotomy, which destroy the globus pallidus (GPi), thalamic nucleus and subthalamic nucleus (STN), respectively. Once the suitable target tissue has been located, it is destroyed by a radio frequency or thermocoagulation method. Expert opinion suggests nowadays that ablative procedures are rarely performed in Western countries although such procedures are still available as a treatment option for individuals in developing countries. Ablative surgery has largely been replaced by DBS, in part because DBS is potentially reversible and is perceived to be associated with improved safety and effectiveness and, in part, because ablative surgery is irreversible and regarded as having limited effectiveness and significant safety concerns. In patients with inadequate control of parkinsonian symptoms by medical treatments, bilateral subthalamic nucleus deep brain stimulation (STN-DBS) has emerged as a surgical choice for APD and has been shown to improve motor function, motor fluctuations, and health related quality of life (HRQoL) and to reduce medication usage and drug induced dyskinesia (Deuschl et al., 2006; Krack et al., 2003; Martínez-Martín et al., 2002; Rodríguez-Oroz et al., 2005; Siderowf et al., 2006; Schupbach et al., 2005; Valldeoriola et al., 2002). DBS was approved by the Food and Drug Administration (FDA) and the European Agency for the Evaluation of Medical Products (EMEA) for the treatment of APD, but it is still considered a relatively expensive therapy. Authorities are concerned because of the increased health expenses and are taking containment measures based on the principle that the distribution of resources must be supported by the efficiency and not exclusively by the direct clinical benefit (Greenberg et al., 1999; Weinstein et al., 2001).
The DBS procedure is generally performed in two separate steps, implantation of leads followed by implantation of the neurostimulator to which the leads are connected. Patients need to be tested initially for their responsiveness to therapy. This is accomplished by implanting a lead at the relevant site using a combination of stereotactic techniques such as image-guided stereotactic localisation and physiological techniques such as microelectrode mapping or macrostimulation. The implantation procedure is generally performed under local anaesthetic. The placement of the electrode at a particular site is determined by the patient’s response to stimulation, involving physical evaluation of the lower limbs and face muscles, and interpretation of the microelectrode recording data. Once the target that elicits the best response has been localised, the testing electrodes are removed and replaced with permanent leads.
Although DBS is non-ablative, the procedure may give rise to complications and side effects, some of which are neither reversible nor adaptable. The complications from DBS can arise before surgery, during surgery, in the immediate post-operative period, and after surgery. Data available on adverse events are derived from case series. Findings from these studies indicated the risk associated with DBS but did not allow quantisation of those risks compared with the Best Medical Treatment (BMT). A systematic review of case series to assess the safety and effectiveness of bilateral STN-DBS for the symptoms of PD in a total of 537 individuals has been performed (Amani et al., 2005). The authors reported the mortality rate, adverse events related to stimulation, general neurological and surgical complications and hardware-related complications. Mortality occurred at a rate of 0.4 per cent.
The adverse events related to stimulation (and rates of occurrence) were: hypophonia (5.8%), eyelid apraxia (4.6%), increased libido (0.8%), sialorrhea (0.9%) and decreased memory (1.1%). Other stimulation-related adverse events included dystonia, paraesthesias, diplopia, dyskinesias and dysarthria; however these events were not reported in the studies or were underestimated. The adverse events related to general neurological and surgical complications (and rates of occurrence) were: depression (4.7%), mania/hypomania (2.0%), peri-operative confusion (13.7%), cerebrospinal fluid leak (0.1%), meningitis (0.1%), venous phlebitis (0.7%), pneumonia (0.4%), urinary tract infections (0.3%), pulmonary embolism (0.5%), seizures (0.9%), haemorrhage (2.8%). Weight gain was also considered to fall into this category, but was reported to be under-quantified in the studies. The adverse events from hardware-related complications (and rates of occurrence) were: lead problems including lead migration, breakage and repositioning (4.5%), and infections of the hardware (3.4%).
The results shown n the literature review indicated that DBS allowed the maintenance of abilities to perform activities and increased motor function in the absence of effective medical treatment. In the absence of a comparator group, it is not possible to quantify the effect attributable to DBS; however, the worsening of akinesia, speech, postural stability, freezing of gait and cognitive function is consistent with the natural history of PD over time (Krack et al., 2003).
The effectiveness of DBS for the treatment of symptoms of PD has been assessed from one double-blind crossover and three case-control studies. DBS appears to be effective for the treatment of PD symptoms, with statistically significant changes observed between case and control participants in UPDRS and PDQ-39SI scores. These results therefore show that DBS can ameliorate the symptoms of PD (as measured by the UPDRS ADL and Motor sections) and reduce the antiparkinsonian medication required to maintain control of the symptoms of PD. Patients experienced up to a 90 per cent reduction at 24 months following surgery in the daily OFF rate. However, the assessment of the effectiveness of DBS for the treatment of symptoms is generally limited by the number of individuals analysed; significant losses to follow-up in some studies; and follow-up of the participants to a maximum of only 48 months.
The reduction in antiparkinsonian medication after DBS may also significantly reduce some of the side effects of high-dose levodopa treatment over a long time (Charles et al., 2004; Mínguez-Castellanos et al., 2005; Molinuevo et al., 2000).
Most of the studies on cost-effectiveness in STN-DBS have been designed in the absence of a control group of similar characteristics receiving conventional oral medication supposed to be the BMT possible. In some of the studies, the costs of expensive therapies such as apomorphine pump infusions were also considered within the concept of BMT.
A cost-effectiveness study has been published with the aim of assessing the long term cost-effectiveness of DBS versus BMT (Tomaszewski & Holloway, 2001) using modelling techniques to predict long term clinical and economical consequences of using DBS and BMT in advanced PD patients, showing that DBS may be an efficient therapy to treat PD patients. Such mathematical models can be useful to predict long-term cost-effectiveness, considering that long-term studies are difficult to perform in clinical practice. It is likely that such positive cost-effectiveness results would extend at least to the next two or three years after the intervention. This possibility was suggested in a survey which showed a 32% increase in total costs during the first year after surgery but a reduction of 54% for the second year, when compared to preoperative values (Charles et al., 2004). Other studies have shown similar results: a retrospective cost-effectiveness study of DBS in Germany in 46 parkinsonian patients (Meissner et al., 2005) showed an estimated Incremental Cost Effectiveness Ratio (ICER) of €979 for one point improvement of the UPDRS-III in one year; in this survey the annual pharmacological expenses were of €11,230 one year before the implantation while of only €4,449 two years after surgery. A multicentric French survey (Fraix et al., 2006) observed similar improvement of UPDRS scores and decrement of PD costs. In another survey, a prospective analysis, the incremental cost per total UPDRS unit improvement turned out to be €920 (Spottke et al., 2002). Some studies have included the cost of the battery replacement after five years of use (D’Ausilio et al., 2003; McIntosh et al., 2003; Tomaszewski & Holloway, 2001).
Only a few studies considered a social perspective by showing costs derived from the losses and gains of productivity of both the patient and the caregiver before and after the intervention. Most of studies, however, are based in a retrospective design, with a short number of patients and follow-up and the absence of a control group of patients of similar clinical characteristics.
Due to the absence of prospective studies comparing cost-efficacy of STN-DBS with control patients, we designed an open, prospective longitudinal study comparative of the cost, effectiveness and HRQoL between the treatment with STN-DBS and BMT in patients with APD (Valldeoriola et al., 2007). Twenty-nine patients were enrolled in the study. All were included in a waiting list for STN-DBS at our centre. All participating patients signed an informed consent form. Among the patients in the waiting list for STN-DBS, the first consecutive fourteen patients were enrolled in the treatment group and the last consecutive fifteen patients were enrolled in the control group. The treatment group was assigned to STN-DBS; the control group was assigned to BMT. Both groups (STN-DBS and BMT) were largely comparable in their clinical and demographic variables including mean levodopa equivalent doses. The study estimated only direct costs. We divided them into two categories: direct medical costs, related to costs for goods and services used in the prevention, diagnosis, treatment and rehabilitation of the illness (for example costs for medical visits, hospitalization and pharmaceuticals); and direct non-medical costs, generally assumed by the patient, including expenses related to the disease (for example transportation, social services, adaptation of accommodation and any kind of special equipment, facilities or orthopaedic material). Our results showed that the ICER needed to obtain an additional improvement of one unit in the total UPDRS score was €239.8. Current management of APD includes combinations of expensive drugs and therefore savings after STN-DBS were mainly attributable to the reduction of pharmacological expenses. The incremental cost-effectiveness was of €34,389 per QALY. The quality-adjusted life year (QALY) is a measure of disease burden, including both the quality and the quantity of life lived. It is used in assessing the value for money of a medical intervention. The QALY model requires utility independent, risk neutral, and constant proportional trade-off behaviour. The QALY is based on the number of years of life that would be added by the intervention. Each year in perfect health is assigned the value of 1.0 down to a value of 0.0 for death.
We also performed sensitivity analyses under different situations. When we excluded the BMT patient group patient who had a prolonged hospitalisation from the analysis, the incremental cost per QALY was of €44,078 (X1.3). In this study, two patients in the BMT group were treated with Continuous Subcutaneous Infusion of Apomorphine (CSIA), a therapy that is also considered to be expensive. Consequently we also calculated the cost-effectiveness of STN-DBS when excluding these patients, obtaining a result of €62,148 per QALY (X1.8). This study showed STN-DBS anti-parkinsonian clinical efficacy and that cost-effectiveness is directly related to clinical improvement in parkinsonism and to the reduction of pharmacological expenses after the intervention. As shown by others (Charles et al., 2004; Fraix et al., 2006; Meissner et al., 2005; Spottke et al., 2002), this survey in the Spanish setting showed that STN-DBS is within the adequate limits to be considered as an efficient therapy. Considering the published data available in the literature assessing DBS for APD, it can be considered safe, effective and cost-effective compared with BMT. There is sufficient evidence of safety and effectiveness, and robust information on cost-effectiveness is unlikely to emerge but the total cost is acceptable for patients in whom other therapies are insufficient.
The insertion of a DBS system incurs upfront costs but may result in cost savings from its effect of controlling the motor symptoms of PD as disease progresses, allowing patients to live in more functional health states for longer periods of time with improved QoL. To date, there appears to be no evidence that DBS delays the progression of PD or affects the mortality rate, although it may be argued that mortality due to falling, for example, may decrease with improvements in motor skills. These savings could be realised through a reduced demand for services or a lower expenditure on certain examinations.
For physicians in general and for neurologists in special, it becomes quite challenging to achieve an antiparkinsonian medication regimen that keeps the patient mobile while at the same time does not create side effects that outweigh the benefits of treatment, impacting patient’s quality of life (Adler, 2002). In these APD patients, refractory to pharmacological treatment, three treatments may be recommended such as DBS, Continuous Duodenal Levodopa–Carbidopa Infusion (CDLCI), and CSIA. These therapies are not recommended for all patients with APD and an adequate patient selection allows the optimization of results obtained with the three therapeutic options.
Additionally, the three treatments have no clear positioning in the treatment pathway of APD patients, which leaves the decision about the eligibility of APD patients for one therapy or another to neurologists. Such decision can therefore be subjective and largely depends on the patient’s clinical status, patients’ and physicians’ preferences, as well as previous hospital experience, availability of these treatments or economic constraints.
To date, as it has been described, no economic evaluation has been published comparing the costs-effectiveness profile of these three therapeutic options that exist for APD. The main reason could be the lack of published direct clinical evidence comparing at least two of the three therapies. In these cases, a modelling method can be done as a simulation of the cost-effectiveness results (Stahl, 2008).
A healthcare costs compqarison considering all the costs in the medium and long term (not only the acquisition costs) associated with the treatments and their consequences of chronic diseases management like PD is a valuable tool both for payers and physicians, offering useful information to support their decision making in the treatment of this patient population (Mycka et al., 2010).
In order to address this evidence gap, the first cost study worldwide has already been submitted (Valldeoriola et al., 2011). The SCOPE study[1] - is a descriptive, quantitative and economic analysis that tried to compare the healthcare costs associated with these three alternative treatments for a 5-years, period in patients with APD in the perspective of the Spanish Healthcare System.
One DBS battery replacement was included in the analysis, as the average battery life of a non-rechargeable neurostimulator has seem to oscillate between four to five years (Bin-Mahfoodh et al., 2003; Krack et al., 2003). The costs of the devices and components of CDLCI and CSIA (infusion pump, catheters, etc.) were not included; thus it was assumed that they were provided free of charge by the supplier, and therefore their cost to the healthcare system was zero. Once all the health resources associated with the therapies were identified and quantified, its cost per unit or ‘price` was obtained from the Spanish health resources database (Spanish Cost Database, e-salud (Spanish Cost Database, e-salud, 2010). Finally, the average total cost for each of the three therapies per patient was then obtained for the 5-years, period. In order to test the statistical differences of mean costs, the data analysis was carried out with the SPSS 15.0 software package for Windows and comparisons among the three therapies (non-parametric Kruskal–Wallis one-way analysis of variance by ranks multiple comparisons) and between the two therapies with the lower average total costs, DBS vs CSIA (Hollander & Wolf, 1999).
Due to the higher APD costs in the first six months, when patients consume more health resources (extra visits, hospitalization, dose adjustments, etc.) (D’Ausilio at al., 2003; McIntosh et al., 2003; Tomaszewski & Holloway, 2001), it was decided to divide the HQR in four sections, or phases.
In Table 1, the average cost per patient associated with the three alternative therapies in the baseline scenario for different phases are shown.
Phases | Average Costs | ||
DBS | CDLCI | CSIA | |
1. Pre-treatment period to hospitalization | 1,141 ± 411 | 1,705 ± 357 | 1,231 ± 267 |
2. Hospitalization period to discharge (includes Procedure or Treatment Administration) | 27,236 ± 3,105 * | 3,165 ± 501 * | 785 ± 197 * |
3. Discharge from hospital to 6 months post-op | 4,768 ± 918 * # | 25,521 ± 2,381 * | 12,094 ± 2,158 * # |
4. From month 6 to year 5 | 54,869 ± 4,190 * # | 203,596 ± 8,737 * | 127,284 ± 9,184 # |
Total cost per 5 years | 88,014 ± 2,580 * # | 233,986 ± 10,552 * | 141,393 ± 9,945 * # |
Average Cost per year | 17,603 ± 516 * # | 46,797 ± 2,110 * | 28,279 ± 1,989 * # |
(*) Comparison of the three therapies (p<0.05). (#) DBS vs. CSIA comparison (p<0.05). |
Results of the baseline scenario expressed as an average ± standard error. Costs per patient (€, 2010)
As observed, during the pre-treatment phase, costs are similar for the three alternative therapies. In phase 2, DBS is associated with higher costs due to the therapy acquisition cost. However, starting from discharge to 5 years follow-up, DBS is the least costly therapy compared to CDLCI and CSIA. Differences are statistically significant in the comparison of the three therapies (p<0.0001) and for the DBS vs. CSIA comparison (p=0.023 and p<0.0001, respectively for phases 3, discharge from hospital to 6 months and 4, from month 6 to year 5). This suggests that in the long term DBS is a cost-minimizing therapy versus CDLCI and CSIA. The yearly average cost of DBS was €17,603 compared to €46,797 (p=0.001) for CDLCI and €28,279 for CSIA (p=0.008), indicating that for every patient treated during one year with CDLCI, two patients could be treated with DBS (or €29,194 could be saved) and for every patient treated during one year with CSIA, €10,676 could be saved if DBS would be chosen. Figure 1 describes Cumulative annual costs for the three therapies for the 5-year period. Results show that starting from year two, DBS is the therapy associated with the lowest cumulative costs compared with CDLCI and CSIA. All the differences were statistically significant (p<0.0001) for multiple comparisons (among the three therapies) and starting from year two, the difference in costs between DBS and CSIA was statistically significant for the DBS vs. CSIA comparison (p=0.008).
Cumulative annual costs for DBS, CDLCI and CSIA (€, 2010)
At year 5, mean cumulative costs per patient associated with DBS amount to €88,014±2,580, €141,393±9,945 for CSIA and €233,986±10,552 with CDLCI (p<0.0001) (see Figure 1). For DBS, the high initial investment required during the first two phases (pre-treatment period to discharge; 32.2% of the total 5-year cost) is offset by decreases in antiparkinsonian pharmacological treatment and follow-up costs. The majority of the DBS costs are incurred before the initial sixth month. As explained, these results were obtained considering one battery replacement in the 5-years period and without including acquisition costs for CDLCI and CSIA devices and components (infusion pump, catheters, etc; only drug costs): consequently, if these costs were included, results would probably have been more favourable for DBS.
During the total 5-year period, with the amount necessary to treat one patient with CDLCI, two patients could be treated if DBS is chosen (or €145,972 could be saved) and €53,379 could be saved with CSIA.
Around 95% of the total 5-years cost of CDLCI and CSIA is related to constant pharmacological costs, mainly driven by Levodopa–Carbidopa intestinal gel cartridges and Apomorphine ampoules acquisition costs (84.4% for CDLCI and 65.7% for CSIA), while for DBS, antiparkinsonian drugs represent only 43% of the total (see Figure 2).
Costs Distribution of each therapy in the 5 years (€, 2010)
Several alternative scenarios were tested in the sensitivity analysis: e.g., a second replacement in DBS therapy and a reduction of CDLCI and Apomorphine pharmacological costs. In all different the scenarios DBS remained the lowest costly therapy, even with the most unfavourable assumptions (p<0.05).
Due to the complexity and variability in the treatment of APD, the decision was taken to apply the Panel approach. This may be the main limitation of the study described. However, a Panel of Experts can contribute to mapping complex treatment processes, and to provide estimates of health care resources (Simoens, 2006). It is worth mentioning that the eleven experts of the Panel can be considered representative of the clinical practice of APD in Spain, as both neurologists and neurosurgeons from nine centres located in five different Spanish regions were involved, with experience on at least two of the three alternative therapies evaluated in this analysis.
Other published studies that estimated healthcare costs at least with one of the therapies to check if similar results are obtained. Only one Swedish study from 2008 estimated the annual cost of CDLCI (Sydow, 2008). Cost estimates oscillated between €40,000 and €80,000 according to the dosage used. These results corroborated the results of the SCOPE study, as it was observed an average drug cost of €44,839 per patient per year and the intestinal gel cartridges of CDLCI represented an 84.4% of this total drug costs.
For DBS, as it was described, several published international economic evaluations were conducted in other countries, as presented in the review by Puig-Junoy et al (Puig-Junoy & Puig, 2009). Alike our study, five cost studies and five complete economic evaluations of DBS showed a reduction in medication costs associated with DBS.
From this survey, it can be concluded that despite DBS is perceived as a costly therapy, the initial investment for the implant is offset by a reduction in the consumption of other healthcare resources by patients over the years. For every patient yearly treated with CDLCI, two patients could be treated with DBS (€29,194 saved) and for every patient treated with CSIA; €10,676 could be saved if DBS would be chosen. On the other hand, CDLCI and CSIA are shown to require a constant use of relatively similar health resources, with pharmacological costs being the main source. Based on the results of this study, DBS seems to be the less costly therapy under the Spanish NHS perspective, compared to CDLCI and CSIA when applied to the adequate candidates.
Parkinson’s disease is a complex illness, encompassing many different symptoms and costs, which makes undertaking an economic evaluation challenging. Numerous cost-effectiveness analyses have been published over the last 10-15 years, with significantly different approaches used to represent the progression of the disease, the effect of treatment upon symptoms and other outcomes, and the duration of treatment effect. Furthermore, there is currently an absence of an analysis which directly compares all relevant treatment options for patients with advanced PD. Outlined below are some of the key aspects for consideration in future economic evaluations of interventions for advanced PD.
There are multiple interventions available for the management of patients with APD. For example, DBS, CDLCI, CSIA and BMT are all options for patients at this stage of thedisease, and although there may be specific reasons for choosing one therapy over another, it is appropriate to consider the full range of interventions available. To date, economic evaluations have focused on comparing a limited number of interventions, rather than addressing the relative cost-effectiveness of each of the interventions listed above.
An analysis which considers a wide range of interventions would be beneficial, both in terms of helping payers to understand the value of each intervention, and in helping clinicians to make informed treatment decisions for their patients.
PD is a complex disease which has many different aspects affecting management costs and health outcomes. When attempting to model the disease and the impact of different interventions, it is important to capture both the natural progression of the disease and the changes in disease indicators which occur over time. Previous models have dealt with disease progression in different ways: some have used UPDRS scores as the basis for measuring disease progression, whilst others have used the Hoehn and Yahr scale.
The majority of cost-effectiveness models (across all therapeutic areas) use a Markovian approach, in which patients are categorised into one of a finite number of health states. Data are then used to estimate the movement of patients between these states over time, to reflect changes in disease status (this could include events such as disease progression and Mortality). In PD, the Hoehn and Yahr scale offers a logical way of splitting patients up into a relatively small number of categories. Trial data could then be used to determine how quickly patients move between these states e.g. from Hoehn and Yahr 2 to Hoehn and Yahr 3, and also to inform how mortality differs between the various stages. The UPDRS scale is less amenable to this approach, since there is no natural way of partitioning patients into categories according to their score. Since the Hoehn and Yahr class in the ‘OFF’ periods represents the status of the underlying disease, this is the most appropriate way of defining which Hoehn and Yahr class a patient is in at a given point in time.
There are other aspects of advanced PD which are relevant to both costs and health outcomes. For example, the amount of ‘OFF’ time is a key outcome, whilst outcomes such as non-motor symptoms and dyskinesias are also relevant endpoints to consider within a cost-effectiveness model. Each intervention impacts upon these aspects of the disease in different ways, and representing these differences is key to a comparative assessment of cost-effectiveness.
PD is a debilitating condition which can have a significant impact upon patients’ quality of life (QoL). Some of the aspects of the disease which influence QoL include:
The proportion of time the patient spends in the ‘OFF’ state;
The severity of the ‘OFF’ periods;
The predictability of the ‘OFF’ stages;
Disease progression (e.g. in terms of Hoehn and Yahr stage).
Previous cost-effectiveness analyses have focused solely on the impact of motor fluctuations; however, there is a growing view that non-motor symptoms are also important determinants of QoL. Such symptoms may include depression, pain and sleep problems, and whilst the effect of these outcomes may be captured inherently within QoL assessments made routinely during trials, it is important that the effect of interventions upon these outcomes is addressed.
A standard approach to accounting for quality of life is to assign a health state utility to each health state within a model (e.g. one utility for Hoehn and Yahr 3, and a separate utility for Hoehn and Yahr 4). However, given that interventions for PD are focused on symptoms rather than underlying disease progression, such an approach may not be sufficiently sensitive to detect important treatment differences e.g. to represent the impact of an intervention which reduces the amount of ‘OFF’ time. In order to capture effects such as these, one approach would be to separate patients within each Hoehn and Yahr class into different levels of ‘OFF’ time, or to evaluate the change in their level of ‘OFF’ time compared with the baseline level.
Numerous tools have been used to assess patients’ QoL during treatment, including generic and disease-specific questionnaires. Typical examples of generic tools include the EQ-5D and the SF-36, which can be readily converted into estimates of utility for the purposes of carrying out cost-utility analysis. The key disease-specific instrument for the measurement of QoL is the PDQ-39. However, to date, no mapping algorithm has been developed to allow the results of this tool to be mapped to health state utilities and so its use in cost-effectiveness modelling is currently limited.
A final issue relating to QoL is the impact of the disease upon patients’ carers and families. Whilst this effect has been little-studied, it is a relevant outcome to consider owing to the burden which advanced PD can have upon these individuals. It is, however, a difficult endpoint to quantify, and this probably explains its omission from previous economic evaluations.
Advanced PD is associated with a wide range of costs for health systems, patients and their carers. Accurately representing the costs associated with the disease, and the impact of each intervention upon these costs, has a large bearing upon the cost-effectiveness outputs of a model. In general, a disease model which closely reflects the true natural history of the disease and incorporates its key aspects makes the process of assigning costs much simpler. Some examples of costs which should be included in a cost-effectiveness analysis include:
Device acquisition and implantation;
Management of device-related adverse events e.g. infections;
Pump infections (for patients on CDLCI);
Device explantation;
Battery replacement;
Ongoing drug costs;
Nursing home care for patients with very severe disease;
Inpatient care;
Routine follow-up appointments.
This list focuses on costs falling on the health services; however, there are also wider costs associated with PD which are relevant. For example, some patients are forced to take early retirement due to the debilitating nature of the disease, and this has an impact upon society in terms of lost productivity of the workforce. This societal cost impact also extends to patients’ families, who sometimes have to give up work.
The majority of existing economic evaluations in the field of advanced PD have used a relatively short time horizon for assessing cost-effectiveness (between five and ten years), primarily due to the absence of robust trial data to populate long-term outcomes. This is a sensible approach, since any extrapolation of the trial data to predict long-term outcomes is inherently subject to considerable uncertainty. However, given that the impact of interventions may be expected to continue in the long-term, assessing the relative cost-effectiveness of different interventions over a longer time horizon may be beneficial. Many economic models in other fields (e.g. oncology) use a lifetime horizon in which patients are followed until death; such an approach would allow scenarios to be explored under assumptions of different levels of long-term treatment effect.
Disease management requires constant evaluation in regard of the quality of treatment as well as of cost-effectiveness. We have reviewed the issue of cost-effectiveness in PD and we think it is demonstrated that there is a need for formal and informal care of patients suffering from chronic progressive diseases which is major challenge for health and social care systems in the years to come. However, more research is necessary to evaluate the full burden of PD and to explore efficacy and effectiveness of the disease management. At the present moment, giving the scarce evidence that we have found and the lack of direct prospective comparisons, it seems that DBS is the most cost-effective therapy for APD.
I thank Dr. Pablo Martínez-Martín for his valuable contribution to this chapter.
The emerging high-throughput omics such as genomics, transcriptomics, proteomics, and metabolomics have been used in the search of new biomarkers in several diseases. Proteomic analyses or metabolomics and lipidomics as well as complementary technologies such as mass spectrometry (MS) (i.e., LC-MS-MS and MALDI-TOF/TOF), nuclear magnetic resonance spectroscopy, and other omics technologies, are being widely used in the search of new sources of markers, candidates for vaccine, and alteration of expression patterns in response to environmental changes and signaling pathways in different diseases. The search for proteins in the dynamic system of a proteome requires various proteomics approaches and the use of proteomics is crucial for the early disease diagnosis, prognosis, and to monitor the disease development. The proteomics is essential for the understanding of complex biochemical processes, and the high-throughput proteomics increases the depth of proteome coverage.
\nLipoproteins are macromolecular complex particles of lipids and proteins, which are related to the extracellular transport of lipids in many organisms [1]. Besides, lipoproteins have been also implicated as important host defense mediators and in the initiation of immune responses [2, 3, 4]. Lipid content has long been recognized as a critical factor in lipoprotein metabolism that acts as an important determinant of human health [5]. However, in last years, apolipoproteins and lipoprotein-associated proteins have been taken on relevance regarding lipoprotein metabolism since they serve as a frame for their assembly, maintain their structure, and interact with the membrane receptors and enzymes. Therefore, these proteins could be crucial in the identification of biomarkers related to diseases due to the fact that they are being studied under a global approach, denominated lipoproteome [6, 7].
\nLipoproteins are complex protein particles of an amphipathic nature, structurally are formed by an outer layer of phospholipids, free cholesterol, and apolipoproteins, and inside contain a nucleus of cholesterol esters and triglycerides [8]. When the lipoprotein complex is formed, the orientation of the hydrophilic proportions is toward the outside and the lipophilic proportions toward the interior; this structural characteristic allows the complex to have the ability to emulsify fats in extracellular fluids [8]. Based on their density defined by the protein to lipids ratios, lipoproteins are grouped into six classes: chylomicrons, very low density lipoproteins (VLDL), intermediate density lipoproteins (IDL), low density lipoproteins (LDL), high density lipoproteins (HDL), and lipoprotein (a) [Lp (a)] [8, 9].
\nTriglycerides, derived from dietary fat absorption by the small intestine, are carried by the chylomicrons into blood. After triglyceride digestion to free fatty acids (FFA) by lipases in the peripheral tissues, the size of these particles is reduced, which leads to the formation of chylomicron remnants. These latter are cleared by liver uptake via LDL receptor-related protein (LRP). To synthesize VLDL, newly synthesized triglyceride and cholesterol by the liver are incorporated into chylomicron remnants [8, 10]. These large triglyceride-rich VLDLs are released to the circulation and travel to peripheral tissues, where the lipase digestion of triglycerides occurs resulting in the IDL formation (VLDL remnants). These IDL particles can be either cleared from the circulation by the liver in a similar manner to that described for chylomicrons remnants or can be digested by hepatic lipase to generate cholesterol-rich LDL particles, which is taken up by the peripheral tissues via LDL receptor to supply their cholesterol requirements [10].
\nThe HDL lipoproteins have the highest relative density as compared to other lipoproteins despite being the smallest in size and heterogeneous in terms of composition. These HDL particles play an important role in the transport of reverse cholesterol as a carrier in the movement of cholesterol from peripheral tissues back into the liver [11]. The liver and intestine, practically in response to the lipolysis of triglyceride-rich lipoproteins, synthesize and secrete the nascent discoid HDLs that consist primarily of phospholipids and free cholesterol. Then, these particles reach the plasma where additional exchangeable apolipoproteins are picked up and excess free cholesterol is removed from both extrahepatic cells and other circulating lipoproteins, forming mature spherical HDL particles. Finally, these cholesterol-rich HDL particles can be delivered back to the liver [10, 12].
\nThe protein cargo related to lipoproteins consists of apolipoproteins and lipoprotein-associated proteins. These proteins play key roles in lipoprotein metabolism such as structural component, enzyme interaction, and receptors recognition, among other functions [7]. Changes in the quantity and type of these proteins could be involved in the outcome of diseases related to lipid metabolism. Therefore, the proteomic analysis of lipoproteins is an important notion for the present revision.
\nThe apolipoproteins can be classified as integral or peripheral, either they act as constituent components of the plasma membrane of the lipoproteins or they bind to the membrane and can be exchanged between one complex and another, respectively. Apolipoproteins are distributed throughout all lipoproteins and subfractions, with the proportion of them varying in each family. These variations in the amount of proteins present in the lipoprotein families in their membrane gives them their capacity to interact with different tissues [7, 10].
\nWithin the apolipoprotein A (ApoA) group is the apolipoprotein A-I (ApoA-I), the main protein component of HDL, which plays multiple roles in the transport of cholesterol, in addition to having been linked to the regulation of some functions of the inflammatory and immune response [13]. ApoA-II, also found in HDL, acts as an enzymatic inhibitor of lipoproteins and liver lipases. The apolipoproteins are capable of binding to each other to modify the interaction of the lipoprotein complex, as is the case with apoA-I and apoA-II in LDL [10].
\nApoA-IV is mainly synthesized in the small intestine where it is attached by enterocytes to the chylomicrons and secreted during a high-fat meal intake. ApoA-IV is associated with HDL and chylomicron remnants in circulation, but a significant portion is free [14]. On the other hand, ApoA-V participates in the regulation of triglyceride levels in plasma [15]. ApoA-V is expressed only in the liver and circulates at low concentrations. Despite this, apoA-V can be recovered in association with plasma lipoproteins [16].
\nThe apolipoprotein B (ApoB), the main protein component of chylomicrons, LDL, VLDL, IDL, and Lp (a), is encoded by a single gene that gives rises to two isoforms: ApoB-48 and ApoB-100. ApoB-48 is produced exclusively in the intestine and is the major structural protein of chylomicrons and chylomicron remnants. ApoB-100 is expressed in the liver and is found only in VLDL, IDL, LDL, and Lp (a). ApoB-containing lipoproteins are characterized by a spherical shape and contain one single apoB-48 or apoB-100 molecule per lipoprotein [17].
\nApolipoprotein C (ApoC) works as an inhibitor of certain processes and modulators of catabolism. ApoC is mainly synthesized in the liver and easily transferred between lipoprotein particles and therefore are found associated with chylomicrons, VLDL, and HDL [10]. ApoC-I is responsible for inhibiting the binding of lipoprotein to its receptor as well as the function of the esterified cholesterol transfer protein (CETP). ApoC-II is an essential cofactor of the lipoprotein lipase involved in triglyceride hydrolysis. Both apoC-III and apoC-IV inhibit triglyceride hydrolysis. In addition, apoC-III decreases the clearance of VLDL [10, 18].
\nSeveral tissues synthesize apolipoprotein E (ApoE), an exchangeable apolipoprotein associated with chylomicrons, chylomicron remnants, VLDL, IDL, and a subgroup of HDL particles, but the liver and intestine are the principal producers of circulating ApoE [10]. ApoE functions as a lipoprotein ligator with hepatocytes (clearance of apoE-containing lipoproteins) and peripheral cells related to the LDL receptor. ApoCs and apoEs are interchangeable between complexes during the conversion of VLDL to LDL. ApoEs may function as a lecithin-cholesterol acyltransferase (LCAT) activator and influence the activity of hepatic lipase and CETP [19, 20].
\nProteins belonging to the lipocalins family such as apoD, apoM, the orosomucoid protein, and retinol-binding protein (RBP) interact with the surface receptors of the cells, intervening in the formation of macromolecular complexes. It has been described that apoD binds to apoA-II and apoB-100 by means of disulfide bridges; it is associated with LCAT, so it is involved in the transport of lipids in the blood system. ApoJ is involved in a wide variety of processes, acting as a chaperone protein and its main role is the inhibition of lipid transfer and is related to cell death. There are apoproteins such as apoF whose function is unknown; however, there is a theory of their role in abnormal lipid composition inhibiting CETP in the small and dense LDL particles (sdLDL) [21, 22].
\nIn addition to apolipoproteins, lipoproteins require other proteins with specific activities to interact with the environment, which are denominated as lipoprotein-associated proteins. Between these proteins are included phospholipase A2, whose activity indicates the presence of sdLDL in plasma [23]. Serum amyloid A protein (SAA) is associated with several lipoproteins, especially in LDL [24]. Some proteins such as albumin and prenyl cysteine oxidase (PCYOX1) are responsible for protecting against oxidation by lipoproteins such as LDL and splitting the thioether bond of the prenyl cysteine generating H2O2, respectively. Also, proteomic studies have identified the apoL-I, PON1, and PAF-AH in small amounts linked to the complexes [25, 26].
\nThe function of many of the proteins that interact with lipoproteins is still unknown. The proteomic analyses have shown that the lipoprotein-associated proteins are involved in cardiovascular risk, immune system, inflammatory processes, among others [7, 26]. However, there is no guide for the analysis of lipoproteins at the proteomic level basically because it depends on the biological question to be investigated, and this determines the approach and the methods or tools to be used as well as the technological platform to perform it.
\nBefore describing the methodologies used for lipoproteomic studies, it is important to mention some general characteristics of lipoprotein-associated proteins and the importance of the research question to establish a good methodological flow chart to address this question. We will start by mentioning that must first consider the lipoprotein obtention by using methods for separation, concentration, and protein stability due to the heterogeneity of these particles.
\nDiverse chromatographic techniques—prior to proteomic techniques—have been used for the separation of plasma lipoproteins as well as the protein content, such as capillary electrophoresis, size exclusion, cation exchange, gel filtration, fast protein liquid, among other chromatographic techniques [7, 27, 28, 29, 30, 31, 32].
\nThe two-dimensional electrophoresis (2-DE) is one of the most usual methods that have been applied to separate the protein cargo related to lipoproteins. Although usually, the first-dimensional separation applied for the proteins in the 2-DE is on base of their charge (isoelectrofocusing), some lipoproteomic analyses have substituted this step by either one of the abovementioned methods to separate lipoproteins or electrophoresis on native gels, followed by SDS-PAGE, which could be denominated as gel-based lipoproteomics [27, 33, 34, 35].
\nLipoprotein-associated proteins are not easy to study by proteomic methods, mainly the embedded in the lipoprotein membranes. These latter have rigid transmembrane domains that contain α-helices or β-barrels, which stabilize the protein by strong secondary structural characteristics and these regions can resist proteolytic digestion [6, 36]. Thus, the protein identification for lipoproteomes has been mainly performed by two methods: mass spectrometry and resonance magnetic nuclear.
\nModern mass spectrometry (MS) techniques have allowed for thorough characterizations of the lipoproteins [37]. However, different experimental conditions have been reported to avoid contamination of the biological samples as well as selective and optimized methods to detect lipoproteins, usually liquid-phase separation techniques, prior to spectrometric techniques [36, 37]. Thus, different spectrometric experimentation conditions have been reported for the study of lipoproteins and apoproteins, respectively (Table 1).
\nPre-analysis | \nChromatography | \nMass spectrometry | \nAnalysis | \nPost-analysis | \n\n | |
---|---|---|---|---|---|---|
Source | \nMethods | \nchromatographic technique = Instrumentation (method) | \nMass analyzer = Instrumentation (method) | \nDatabase search | \nNumber of lipoproteins and apoproteins | \nReferences | \n
Mice C57BL/6 J: plasma | \nGel filtration/size exclusion chromatography, phospholipid-containing particles using CSH | \nLC-ESI = C18 reverse phase column (GRACE; 150 × 0.500 mm) | \nQuadrupole/TOF = 4800 scans, mass range: 300–1800 m/z, charge states:2–5, excluded target ions: 300 s | \nSwiss-Prot protein knowledge base for Mus musculus, PeptideProphet algorithm | \nVLDL/LDL: 32, HDL: 104; lipid poor lipoproteins: 55 | \n[37] | \n
Human serum (HDL) | \nTrypsin, delipidation, gold nanoparticles and LDI-MS, EDX, SPE, FT-IR | \n\n | MALDI-MS = nr | \nSwiss-Prot, Lipidmaps database | \nHDL (delipidation): 10 (prior), 6 (after). HDL (anion exchangers): 23 | \n[38] | \n
Mice C57BL/6 J: apoA-I KO and apoA-II KO, apoA-I KO and WT: plasma | \nParticles of CSH. Plasma separation by size exclusion chromatography | \nLC-ESI = column (C18 reverse phase (150 × 0.500 mm)) | \nQuadrupole/TOF = MS/MS. tolerance were set to ±35 PPM, and up to 3 missed tryptic cleavage sites were allowed | \nUniProtKB/Swiss-Prot Protein Knowledgebase, Peptide Prophet algorithm | \nHDL: WT:25 vs. ApoA-I KO: 21; ApoA-II KO: 11 vs. WT: 14; and ApoA-IV: 6 KO vs. WT: 7 | \n[28] | \n
17 Subjects (exposure to organic pollutants): plasma (HDL) | \nUltracentrifugation (290,000 g, 15°C, 4 h) | \nnLC: Column C18, ESI | \nLinear ion trap-Orbitrap: CID. ms/ms: 0.6 Da | \nMaxQuant v1.5.0, human Uniprot/Swiss-prot database | \nLCMS permit identified the pathway of interaction between HDL-proteins | \n[39] | \n
34 Men (2 dietary: weight loss/high car): plasma (LDL, ApoC-III) | \nELISA assay | \n\n | MALDI-TOF: 500 laser shots mass spectra | \n\n | Detection and concentrations values of 12 apoC-III glycoforms | \n[40] | \n
Mice (female, LDLr−/−, 8 week old): plasma (VLDL, LDL, HDL) | \nFPLC, ELISA assay | \nLC | \nOrbitrap: mass tolerance: 0.8 Da | \nSwiss-Prot database | \nHDL: 91 LDL: 49 VLDL: 39 | \n[41] | \n
1000 Children (6–8 years, Nepal rural zone): plasma | \nCation exchange chromatography | \nLC | \nOrbitrap | \nRefseq 40 database | \nApo-AI, Apo-AII, Apo-CIII | \n[42] | \n
458 Children (6–11 years, exposure to environmental tobacco smoke): serum (HDL, LDL) | \n\n | SPE-HPLC-TIS-MS/MS | \n\n | \n | LC detected various polyfluoroalkyl substances in serum A negative association PFOS and non-HDL | \n[43] | \n
57 Males (exposed to arsenicum): plasma, urinary (LDL, HDL, Lp(a), Apo-A1, Apo-B) | \nCentrifuged at 3500 rpm for 10 min | \n\n | ICP-MS: extraction voltage −100 V, Rf power 1400 W, focus voltage 12 V, and nebulizer gas flow rate (using a Burgener Miramist nebulizer) 0.83 L/min. Dwell times were 50 ms for 75As and 10 ms for internal standard (72 Ge) | \n\n | ICP-MS: Potential risk of the arsenic on lipoproteins and apolipoproteins | \n[44] | \n
Human healthy, normolipidemic males: plasma (LDL) | \nGel filtration chromatography, ultracentrifugation. | \nnLC: column (IntePepMap 100, C18, particle size 3 um), flow rate = 300 nL/min ESI: 2.5 kV, 150°C | \nTriple quadrupole TOF: mass tolerance: 50 mDa, 350–1800 m/z window, MSscan type: 0.25 s | \nUniProtKB/Swiss-Prot Protein Knowledgebase, Peptide Prophet algorithm | \nLC-MS permit the abundance protein as well as antioxidant activity | \n[30] | \n
110 Samples (purchased) | \n\n | Phospholipids: UHPLC: column (2.1 × 100 mm, 1.7 uM particle), flow rate: 0.7 mL/min, ESI | \nQtrap: MRM scanning, negative and positive mode | \nMultiquant software functions, JMP (SAS Institute) | \nLC-MS analysis of serum/lipoproteins | \n[45] | \n
23 Healthy volunteers: serum | \nGel filtration chromatography, sequential ultracentrifugation, immunoassay | \nUPLC: column (UPLCR BEH C8), floe rate: 450 uL/min., 60°C, autosampler: 4°C ESI | \nTriple-quadrupole: positive ion mode. Quantification of plasmalogens: Capillary voltage: 3500 V, source temperature 80°C, desolvation: 400°C, cone voltage: 35 V. CE: 20–32 eV | \n\n | LCMS: distribution of each molecular species in plasmalogen and choline plasmalogen | \n[46] | \n
Healthy volunteers: plasma (VLDL, LDL, HDL) | \nUltracentrifugation, SDS-PAGE, size exclusion chromatography, circular dichroism, spectroscopy, spectropolarimeter | \n\n | MALDI-TOF: 337 nm nitrogen laser, positive ion: 20 kV | \n\n | Identification of apolipoproteins released from VLDL by mass spectrometry | \n[47] | \n
20 Patients with lipoproteins (a) (18–70 years): plasma (LDL, HDL) | \nUltracentrifugation, ELISA | \nLC: solid-phase extraction | \nTriple Quadrupole-linear ion tramp: SRM. (energy collision: 34 V, Q1: 786–788; Q2: 1069–1072 m/z)\n | \n\n | LC-MS system: concentrations of lipid, lipoprotein and apolipoprotein | \n[48] | \n
Healthy donors (nonlipidemic, 24–65 years, purchased samples): plasma (HDL, LDL) | \nUltracentrifugation (330,000 g, 6 h), SDS-PAGE and Western blotting, Negative stain electron microscopy | \nUPLC: column (Kinetex EVOC18), ESI | \nTriple-quadrupole: lipid species were analyzed by selected reaction monitoring (from 141 to 369 m/z, from 0 to 50 eV) | \nExtraction and ionization efficacy by calculating analyte/ISTD ratios (AU) and expressed as AU/mg protein | \nLCS permit the separation of a mixture in HDL protein | \n[49] | \n
12 Healthy male (36–67 years): plasma (HDL, LDL) | \nUltracentrifugation (40,000 rpm, 44 h, 15°C), fractioned, apoA-I was detected by Western blotting, internal standars | \nHILIC-UHPLC-FLD: Glycan chromatography column, 150 × 2.1 mm i.d., 1.7 μm BEH particles, flow rate of 0.56 mL/min | \nMALDI-TOF-MS: 25 kV, acceleration voltage: 140 ns extraction delay, mass window: 1000–5000 m/z. For each spectrum: 10,000 laser shots, laser frequency of 2000 Hz | \n\n | LDL: 18 HDL: 22 N-glycome of human plasma lipoproteins | \n[50] | \n
16 Healthy adults: plasma (HDL, apoA-I, apoB) | \nUltracentrifugation. PRM analysis (shotgun proteomics experiments). | \nUPLC: flow rate: 0.6 uL, column (Xbridge BEH C18) ESI | \nOrbitrap: PRM mode, isolation window: 2 Th, HCD: 27%, orbitrap analyzer: 15,000 resolution, AGC: 5 × 104, maximum ion time 30 ms | \nPeptideAtlas mass spectral database | \nMeal macronutrient content HDL composition in the postprandial state | \n[51] | \n
47 Volunteers: serum (HDL, non-HDL). | \nAnti-apoAI magnetic nanoparticles (10 mg) and serum (5 μl) were mixed FTIR, X-ray diffraction | \nID/LC/MS system: LC: column (waters symmetry C18), flow rate: 0.3 mL/min. APCI: corona current: 5 uA, source temperature: 450°C | \nAPI 4000 tandem mass spectrometer (triple-quadrupole): Collision energy: 26 eV, collision exit potential: 6 V | \n\n | ID/LC-MS permit the monitoring serum in clinical settings to dyslipidemia and atherosclerosis | \n[52] | \n
Chromatographic and spectrometric methodologies used recently in lipoproteomic analysis.
SRM: selected reaction monitoring. HPLC: high performance liquid chromatography. HILIC-UHPLC-FLD: hydrophilic-interaction ultra-high-performance liquid chromatography with fluorescence detection. LC: liquid Chromatography. UPLC: ultra-performance liquid chromatography. MALDI: matrix-assisted laser desorption/ionization. TOF: time of flight. Qtrap or LTQ linear trap: triple quadrupole-linear ion trap. CE: collision energy. MS/MS: tandem mass spectrometry. PRM: parallel reaction monitoring. AGC: automatic gain control. ID/LC/MS: isotope dilution liquid chromatography mass spectrometry. ESI: electrospray. nESI: nano-electrospray. FPLC: fast protein liquid chromatography. MRM: multiple reaction monitoring. HILIC: silica-based and solid-core reverse phase after hydrophilic interaction. ICP-MS: Inductively coupled plasma-mass spectrometer. SPE: solid phase extraction. SPE-HPLC-TIS-MS/MS: solid phase extraction coupled to high performance liquid chromatography-turbo ion spray ionization-tandem mass spectrometry. SEC-FPLC: size exclusion chromatography by fast protein liquid chromatography. APCI: atmospheric pressure chemical ionization. CID: collision-induced dissociation. LDI-MS: laser desorption/ionization mass spectrometry, EDX: dispersive X-Ray Spectroscopy. CSH: calcium silica hydrate. WT: wild-type. KO: knockout. FTIR: Fourier transformation infrared.
There are several challenges in the lipoproteomic analyses performed with the most advanced mass spectrometry methods. Some of them are the abundance of proteins from the biological source and the lipoprotein(s) purification steps, which conditioned the protein content and constitution. However, if this obstacle can be overcome, the mass spectrometry analysis has been demonstrated to be a useful tool to identify a diverse array of proteins related to lipoproteins, avoiding aberrant integration of unexpected proteins by reducing the suppression of ionization at high peptide resolution [53].
\nOn the other hand, the nuclear magnetic resonance (NMR) technique permit, besides protein identification, can provide information of the lipoproteins at both molecule and atomic levels under physiological or ‘near-physiological’ conditions. The signal most used to quantify lipoproteins is the methyl signal because give a specific response in the lipids that travel inside the lipoproteins [54, 55]. In this way, proton spectroscopy has been the most used nuclear magnetic resonance technique to quantify lipoproteins, but it is not the only one as will be seen later (Table 2).
\nPre-analysis | | \nSpectroscopy experiment NMR type | \nPost-analysis | \nReferences | \n|
---|---|---|---|---|
Source | \nisolation method | \n|||
133 Caucasian participants (T2DM, >18 years): serum (VLDL, LDL, and HDL) | \n\n | 1H: FT, 400 MHz | \nDeterminate the lipoprotein subtraction characteristics | \n[56] | \n
98 people (T2DM/ nor T2DM): plasma (GlycA vs. Lp-PLA2) | \nCentrifugation (1400 g, 15 min) | \n\n1H: 400 MHz, 47°C, CaEDTA resonance at 2.519 ppm was used as the internal chemical | \nGlycA is correlated with LP-PLA2 in plasma (person without T2DM/MetS) | \n[57] | \n
23 patients with primary aldosteronism: plasma (HDL, VLDL, LDL, ApoB, and ApoA-I) | \nImmunoturbidimetric assay | \n\n1H: 400 MHz, 47°C. (LipoProfile-3 algorithm) | \nCirculating LDL may contribute to adrenal steroidogenesis in humans | \n[58] | \n
115 nondiabetic women (35–55 years, mediterranean diet, physical exercise, 2 years): plasma (HDL. LDL) | \n\n | \n1H | \nLipoprotein size, particle and subclass concentrations | \n[54] | \n
Human serum (purchased) spiked into phlebotomy tubes (LDL, HDL) | \nCentrifugation (3000 g, 5 min, 4 h) | \n\n1H: 600 MHz | \nLipoprotein subclass analysis standardized by tube type and tube size to prevent risk of analytical interference. | \n[59] | \n
Patients with HFrEF (782), HFpEF (1004), and no HF (4742): plasma (HDL) | \n\n | NMR LipoProfile-3 algorithm | \nQuantify concentrations of HDL. Phenotyped cohorts of HFrEF, HFpEF, and patients without HF | \n[60] | \n
4897 subjects: plasma (LDL) | \n\n | \n1H: 400 MHz | \nDifferentiate in the size of particle in LDL profile | \n[61] | \n
309 patients (MACE): plasma (HDL, LDL, and VLDL). Control:902 | \n\n | \n1H | \nNeither baseline HDL nor the change in HDL on treatment with dalcetrapib or placebo was associated with risk of MACE after ACS | \n[62] | \n
Normal volunteer: plasma (HDL, LDL, andVLDL) | \nSequential ultracentrifuge | \n1H, 13C, 15N: 600.55 MHz, 47°C, different pressures | \nShow the spatial arrangement, phase behavior and molecular dynamics in the particle core | \n[55] | \n
3446 participants (HDL, LDL, and VLDL) | \n\n | \n1H: LipoProfile-3 algorithm | \nAssociation between FGF21 and NAFLD | \n[63] | \n
NMR methodology applied recently in lipoprotein-based analyses.
MHz: megahertz (106). MetS or MS: metabolic syndrome. T2DM: type 2 Diabetes mellitus. FT: Fourier transforms. CaEDTA: EDTA mono calcium. GlycA: glycoprotein acetylation. Lp-PLA2: lipoprotein-associated phospholipase A2. HFrEF: reduced ejection fractions. HFpEF: preserved ejection fraction. HF: heart failure. MACE: major adverse cardiovascular events. FGF21: fibroplast growth factor21. NAFLD: nonalcoholic fatty liver disease.
Furthermore, not only 1H NMR has been the technique used for the study of lipoproteomics but also the two-dimensional NMR techniques have been used. The two-dimensional heteronuclear 13C▬1H chemical-shift made it possible to analyze macromolecular complexes like HDL, but with a limited resolution in reduced peaks above 50 ppm and the limited resolution in the 29–33 ppm region, inclusive with artifacts that would later be discarded by the spectra of one dimension (1H and 13C) [64].
\nIn addition, the material to be used for the spectroscopic analysis should be the optimal one to avoid contamination, as in the case of a tube used for the collection of biological material and used in clinical research, which should be specifically for the analysis of lipoproteins [59].
\nAlso, the experimental conditions do not always favor the use of NMR for the analysis of lipoproteins. Thus, mass spectrometry permits the particle identification via LC-MS system in contrast to NMR spectroscopy, which failed. Due to that, the NMR spectroscopy makes HDL particle quantification only in a physiological setting: full serum or plasma but not in HDL-containing suspensions [49]. Therefore, the technician must consider the biological and technical variables for an assertive lipoproteomic analysis.
\nThe lipoproteomic analyses have been focused on understanding the functional mechanisms underlying apolipoproteins and lipoprotein-associated proteins that can be used to develop new diagnostic and/or prognostic biomarkers for many lipoproteins’ metabolism-related illness.
\nThe HDL lipoprotein fraction has been the most studied according to lipoproteomic relationships with different diseases. Among the HDL-associated proteins, ApoC-III levels have been seen increased in the patients with either a lupus nephritis (lupus erythematosus) or with a cerebral lacunar infarction, which could be related with a reduced anti-inflammatory activity of HDL particles [65, 66].
\nHowever, HDL-carried ApoC-III has been more implicated in cardiovascular disease (CVD) risk. One of the first evaluations was performed with coronary artery disease patients, which exhibit increased levels of ApoC-III [67]. Recently, in a cohort study, it was demonstrated that HDL-carried ApoC-III is related to a higher risk for coronary heart disease [68].
\nIn addition to ApoC-III and ApoC-II, other HDL-associated apolipoproteins, were proposed as biomarkers for CVD risk in patients with chronic hemodialysis [69]. The ApoC-III/ApoC-II/ApoE levels in VLDL lipoproteins, independent of HDL, were associated with incident CVD, which supports the concept of targeting triacylglycerol-rich lipoproteins to reduce the CVD risk [70].
\nOther HDL-associated apolipoproteins have been associated with cardiac pathologies. For example, ApoA-I, ApoA-IV, ApoE, and ApoL1 levels have been seen enriched in HDL3 fraction from patients with acute coronary syndrome (ACV), with a concomitant reduction of these apolipoproteins in the HDL2 fraction [71]. Furthermore, ApoC-I was significantly decreased in the HDL particles of coronary heart disease (CAD) patients in comparison to normal individuals [72]. The existence of CAD has recently been correlated with an HDL apolipoproteomic score, independent of circulating ApoA-I and ApoB rates and other typical cardiovascular risk factors [73].
\nRegarding HDL-associated proteins, many of them have been related to heart illness. For example, serotransferrin, haptoglobin, hemopexin, complement factor B, ras-related protein Rab-7b, and paraoxonase-3 (PON3) levels have been seen reduced in HDL from patients with some cardiac pathology. Meanwhile, PON1, alpha-1B-glycoprotein, vitamin D-binding protein, alpha-1-antitrypsin (A1AT), acid ceramidase, serum amyloid A and P proteins, sphingosine-1-phosphate, filamin A, and pulmonary surfactant-associated protein B are increased in HDL fractions from patients with cardiometabolic disorders [69, 71, 72, 74, 75].
\nDiabetes is, perhaps, the major controllable risk factor for CVD. In particular, related to the HDL fraction, ApoA-I, ApoA-II, ApoA-IV, ApoE, ApoJ, as well as PON1, transthyretin, complement C3, and vitamin D-binding protein have shown changes in patients with type 2 diabetes (T2D) [76, 77]. In contrast, individuals with type 1 diabetes (T1D) had proteomic alterations of their HDL particles. For example, the complement factor H-related protein 2 was elevated, independent of glucose control, in T1D patients in comparison to healthy controls. Also, the optimal glucose control corrected the elevated levels of the alpha-1-beta glycoprotein and inter alpha trypsin inhibitor 4. Furthermore, the HDL particles in T1DM individuals, independent of glucose control, exhibit a higher abundance of irreversible post-translational modifications of HDL-associated apolipoproteins [78, 79].
\nAlso, in psoriatic patients, the levels of HDL-associated ApoA-I exhibit a significant reduction, whereas levels of apoA-II, serum amyloid A, C3, and A1AT, among other proteins were increased [80]. Besides C3 and C9, complement factor B, as well as ApoJ, fibrinogen, haptoglobin, and serum amyloid A have been also increased in HDL fraction from patients with rheumatoid arthritis [81]. Taken together, these results suggest that HDL-associated proteins could be involved in anti-inflammatory properties in chronic illness.
\nConcerning other diseases, the proteome of HDL particles has been used to identify protein markers. In nonalcoholic fatty liver disease, including individuals with nonalcoholic steatohepatitis, changes in the abundance of HDL-associated proteins such as antithrombin III and plasminogen have been identified [82]. Although not directly to HDL particles, proteomic analysis of some HDL-related apolipoproteins has been associated with viral diseases. For example, a change in the expression level of ApoA-I was suggested as a specific and appropriate alternative to conventional HIV diagnosis and progression measurements in clinical research settings [83]. Increased concentration of ApoM in sera patients with HBV infection have been detected [84]. Recently, a downregulation of ApoA-I and ApoM levels have been associated with the severity of COVID-19 infection [85].
\nIn addition to HDL, only other few lipoproteomic studies have been developed to discover changes in lipoproteins-associated proteins in diverse pathologies. Proteomic studies of LDL have reported that carry apolipoproteins AI, A-II, CI, C-II, C-III, C-IV, D, E, and F, in addition to apoB, as well as clusterin, C3, C4a, and C4b, and PON1 that are also associated to this particles [86]. Serum amyloid A levels were found to increase in all lipoprotein fractions, especially in LDL from atherosclerotic patients [24]. An enriched content of all the apoC-III isoforms and a lower content of apoA-I, apoC-I, and apoE were detected in the sdLDL from subjects with metabolic syndrome and subclinical peripheral atherosclerosis [87]. VLDL, IDL, and LDL fractions from Alzheimer patients exhibit low levels of complement C3 [88].
\nTarget discovery, which is an important step in the drug development process, includes discovering and validating targets associated with the diseases. It is increasingly recognized that, in many instances, metabolomics is used to identify novel biomarkers, which can help in the discovery of therapeutic strategies for many diseases [89].
\nUntil now, lipoproteomics has proved to be an effective method for identifying candidate cardiovascular disease markers. Studying the profiles of protein expression in drug-treated patients contributes to the discovery of multiple drug-specific targets. Traditionally, statin therapy has proven efficacy in reducing cardiovascular events, but as aforementioned, the identification of HDL-associated proteins is variable. The statin therapy has a notorious effect on the increment of A1AT associated to the large fraction of HDL particles enhancing its anti-inflammatory functionality, which may interfere with the statins outcome on reducing cholesterol levels [37]. In addition, a relationship between the CAD treatment and the HDL proteome was demonstrated using statin and niacin therapy, observing that ApoE and ApoC-II levels are enriched in the HDL3 fraction, which contains less ApoJ levels [90].
\nThe increment in the HDL levels by CETP inhibitors is another biomarker that has continued to be disappointing in clinical research. In fact, in patients with deficiency of CETP (CETP-D), the HDL particles are enriched with ApoE, angiopoietin-like3 protein, and complement regulatory proteins such as C3, C4a, C4b, and C9 that could be associated to the increased atherogenic profile in CETP-D patients [91].
\nThus, it is important to consider that among the diverse HDL populations not all possess a cardioprotective effect [92]. Therefore, we need a better knowledge of the protein cargo of the HDL populations with anti-atherogenic actions. A comprehensive understanding of the HDL proteomics can lead to the design of more effective anti-atherogenic drugs based on the activity of HDL-associated proteins that will provide new therapeutic strategies at the molecular level.
\nIn the dynamic drug discovery process, the different proteomic methods, which include MS-proteomics, expand beyond the general aim of target drug discovery. It must consider the drug-protein interactions as well as elucidate the mode of action of candidate drug molecules [93]. Thus, novel HDL-based therapeutic agents, besides the traditional statins, will need consider to the functional HDL lipoproteomics to characterize the interaction of the drug with the HDL-associated proteins. This is necessary as an attempt either to elucidate the mechanism of action by direct drug-protein interaction or as a biomarker for drug validation by monitoring the pharmacological effect through an increase of HDL populations with an anti-atherogenic protein cargo.
\nThe lipoproteins can be involved in different pathologies related to lipid metabolism such as atherosclerosis, cardiovascular risk, obesity, metabolic síndrome, and diabetes, among others. However, the protein cargo of these particles has been associated with several functions, which differ from the amply recognized as structural composition and receptors recognition during their function as lipid transporters. For this reason, the identification of variants of apolipoproteins and lipoprotein-associated proteins has been an important referent to detect new biomarkers. In fact, as we described here, several methodologies have been developed to improve the lipoproteomic profile.
\nDespite these studies, the majority concord that both biological source and lipoprotein purification are important steps to avoid protein contamination, principally from samples that are used directly as serum or plasma. Also, the most used method to identify the lipoprotein-associated proteins is the mass spectrometry, but some limits are presented that depend on the platforms to apply this methodology. Also, it is important to highlight that not everyone has the facility to use this methodology and it is necessary to develop new methodologies to apply in clinical fields to ensure discoveries about these proteins both in new lipoproteins’ fractions and new diseases.
\nAlso, the lipoproteomic analyses could be a new clinical area to evaluate the therapy of the pathologies described here as prognostic analytes. In this sense, the HDL lipoproteomics is, perhaps, the more advanced field considering the evaluations of populations with statins’ treatment. However, novel HDL therapeutic agents must consider the functional lipoproteomics of these particles. Finally, these lipoproteomes can help us to describe the molecular mechanism to understand the interaction of apolipoproteins as well as the lipoprotein-carried proteins to support the other omics such as lipidomics and metabolomics.
\nThis work was supported by the Autonomous University of Mexico City (UACM) awarded to MEAS.
\nThe authors declare no conflict of interest.
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