Research Article
Pharmacogenomics of Alzheimer’s Disease: Genetic Determinants of Phenotypic Variation and Therapeutic Outcome
Ramón Cacabelos, Juan Carlos Carril, Pablo Cacabelos, Oscar Teijido and Dmitry Goldgaber
Corresponding Author: Prof. Ramón Cacabelos, EuroEspes Biomedical Research Center, Institute of Medical Science and Genomic Medicine, 15165 - Bergondo, Corunna, Spain, Tel: +34-981-780505; Fax: +34-981-780511; E-mail: rcacabelos@euroespes.com
Received: May 30, 2016; Accepted: June 10, 2016; Published: November 28, 2016;
Citation: Cacabelos R, Carril J C, Cacabelos P, Teijido O, Goldgaber D. (2016) Pharmacogenomics of Alzheimer’s Disease: Genetic Determinants of Phenotypic Variation and Therapeutic Outcome. J Genomic Med Pharmacogenomics, 1(2): 151-209.
Copyrights: ©2016 Cacabelos R, Carril J C, Cacabelos P, Teijido O, Goldgaber D. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Alzheimer’s disease (AD) is a polygenic/complex disorder in which genomic, epigenomic, cerebrovascular, metabolic, and environmental factors converge to define a progressive neurodegenerative phenotype. Conventional anti-dementia drugs are not cost-effective, and pharmacological breakthroughs have not been achieved for the past 10 years. Major determinants of therapeutic outcome in AD include age- and sex-related factors, pathogenic phenotype, concomitant disorders, treatment modality and polypharmacy, and pharmacogenetics. Different categories of genes are potentially involved in the pharmacogenetic network responsible for drug efficacy and safety. Pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes represent the major genetic determinants of response to treatment in AD. In pharmacogenetic studies, APOE-4 carriers are the worst responders and APOE-3 carriers are the best responders to conventional treatments. Patients harboring a large (L) number of poly T repeats in intron 6 of the TOMM40 gene (L/L or S/L genotypes) in haplotypes associated with APOE-4 are the worst responders whereas patients with short (S) TOMM40 poly T variants (S/S genotype), and to a lesser extent S/VL and VL/VL carriers, in haplotypes with APOE-3 are the best responders to treatment. Only 25% of the Caucasian population are extensive metabolizers for trigenic haplotypes integrating CYP2D6-CYP2C19-CYP2C9 variants. Patients harboring CYP-related poor (PM) and/or ultra-rapid (UM) geno-phenotypes display more irregular profiles in drug metabolism than extensive (EM) or intermediate (IM) metabolizers. Among 111 pentagenic (APOE-APOB-APOC3-CETP-LPL) haplotypes associated with lipid metabolism, carriers of the H26 haplotype (23-TT-CG-AG-CC) exhibit the lowest cholesterol levels and patients with the H104 haplotype (44-CC-CC-AA-CC) are severely hypercholesterolemic. Epigenetic aberrations (DNA methylation, histone modifications, miRNA dysregulation) in genes configuring the pharmacoepigenetic cascade also influence the response/resistance to drugs. Consequently, novel strategies in drug development, either preventive or therapeutic, for AD should take into consideration these pharmacogenetic determinants for treatment optimization.

 

Keywords: Alzheimer’s disease, Anti-dementia drugs, APOE, Atorvastatin, Cholesterol, Epigenetics, CYP haplotypes, LipoEsar, Pharmacogenomics, Pharmacoepigenomics.

 

INTRODUCTION

Alzheimer’s disease (AD) is a major problem of health in developed countries and the most prevalent form of dementia, representing the 6th cause of death in the USA with and age-adjusted death rate of 25.4 per 100,000. Genomic, epigenomic, cerebrovascular, metabolic and environmental factors are potentially involved in the pathogenesis of AD. The age- and sex-related syndromic profile of AD reflects, at least, a tetravalent phenotype: (i) a neuropathological component (classic hallmarks: senile plaques, neurofibrillary tangles, neuritic desarborization, neuronal loss); (ii) a neurobehavioral component: cognitive deterioration, behavioral changes, functional decline; (iii) an age-related biological component (direct-, indirect-, and un-related biochemical, hematological and metabolic phenotypes); and (iv) gender-related phenotypes [1-3]. According to this heterogeneous, complex clinical picture, the therapeutic intervention in dementia is polymodal in order to modify the expression of all these complex phenotypes. AD patients present concomitant disorders including hypertension (20-30%), overweightness or obesity (20-40%), diabetes (20-25%), hypercholesterolemia (>40%), hypertriglyceridemia (20%); excess of urea (>80%), creatinine (6%) and uric acid (5%); alterations in transaminases (ASAT, ALAT, GGT) (>15%), alkaline phosphatase (14%), bilirubin (17%), and ions (>10%); deficits of iron (5%), ferritin (3%), folate (5%), and vitamin B12 (4%); thyroid dysfunction (5-7%), and reduced levels of RBC (3%), HCT (33%), and Hb (35%) [4]. Cardiovascular disorders (>40%), atherosclerosis (>60%), and different modalities of cerebrovascular damage (>60%) are also frequent among patients with AD. Most of these biochemical, hematological and metabolic anomalies exhibit gender differences and may contribute to accelerate the dementia process. The pharmacological treatment of these concomitant pathologies adds complexity and risks to the multifactorial therapeutic intervention in patients with dementia. Of major relevance is the treatment of diabetes, hypertension, dyslipidemia, and cardiovascular, cerebrovascular and neuropsychiatric disorders. The chronic treatment of these illnesses increases the risk of drug interactions and toxicity, aggravating the clinical condition of the demented patient. In this context, the incorporation of pharmacogenetic protocols into clinical practice is fundamental to minimize drug-drug interactions and ADRs, and to optimize the global therapeutic outcome, avoiding deleterious effects on mental function and cognition.

Major determinants of therapeutic outcome in AD include age- and sex-related factors, pathogenic phenotype, concomitant disorders, treatment modality and polypharmacy, and pharmacogenetics. Different categories of genes are potentially involved in the pharmacogenetic network responsible for drug efficacy and safety. Pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes represent the major genetic determinants of response to treatment in AD [5,6]. By-products of these genes are integrated in transcriptomic, proteomic and metabolic networks which are disrupted in AD and represent potential targets for therapeutic intervention [6,7] (Figure 1).

TREATMENTS

AD patients may take 6-12 different drugs/day for the treatment of dementia-related symptoms, including memory deterioration (conventional anti-dementia drugs, neuroprotectants), behavioral changes (antidepressants, neuroleptics, sedatives, hypnotics), and functional decline, or for the treatment of concomitant pathologies (epilepsy, cardiovascular and cerebrovascular disorders, parkinsonism, hypertension, dyslipidemia, anemia, arthrosis, etc). Over 20% of dementia patients are current users of cardiovascular drugs. A high throughput screening study assessed 1600 FDA-approved drugs for their ability to modulate Aβ activity; 559 drugs of the 1600 had no effect on APP processing or were toxic to neurons at the concentration tested, while 800 drugs could reduce Aβ content by over 10% in primary neurons derived from Tg2576 mice, among which, 184 drugs were able to reduce Aβ content by more than 30%; 241 drugs could potentially promote Aβ accumulation, including 26 drugs that could increase the level of Aβ by over 30% [8]. The co-administration of several drugs may cause side-effects and adverse drug reactions in over 60% of AD patients, who in 2-10% of the cases require hospitalization. The prevalence of potentially inappropriate medication (PIM) is around 50% in some European cohorts. Cerebral vasodilators are the most widely used class of PIM, accounting for 24.0% of all prescriptions, followed by atropinic drugs and long half-life benzodiazepines. Atropinic drugs were associated with cholinesterase inhibitors in 16% of patients. In over 20% of the patients, behavioral deterioration and psychomotor function can be severely altered by polypharmacy [9]. The principal causes of these iatrogenic effects are the inappropriate combination of drugs, and the genomic background of the patient, responsible for his/her pharmacogenomic outcome.

During the past 10 years, over 1,000 different compounds have been studied as potential candidate drugs for the treatment of AD [6,7,10,11]. About 50% of these substances are novel molecules obtained from natural sources [6,7]. The candidate compounds can be classified according to their pharmacological properties and/or the AD-related pathogenic cascade to which they are addressed to halt disease progression. In addition to the FDA-approved drugs since 1993 (tacrine, donepezil, rivastigmine, galantamine, memantine) (Table 1), most candidate strategies fall into 6 major categories: (i) novel cholinesterase inhibitors and neurotransmitter regulators, (ii) anti-Aβ treatments (APP regulators, Aβ breakers, active and passive immunotherapy with vaccines and antibodies, β- and γ-secretase inhibitors or modulators), (iii) anti-tau treatments, (iv) pleiotropic products (most of them of natural origin), (v) epigenetic intervention, and (vi) combination therapies [6,7,10,12].

During the 2002-2012 period, 413 AD trials were performed (124 Phase 1 trials, 206 Phase 2 trials, and 83 Phase 3 trials)(78% sponsored by pharmaceutical companies). Registered trials addressed symptomatic agents (36.6%), disease-modifying small molecules (35.1%) and disease-modifying immunotherapies (18%), with a very high attrition rate (overall success rate: 0.4%; failure: 99.6%) [13]. During the past 15 years no new drugs have been approved for the treatment of AD and the available drugs are not cost-effective [14]. Therefore, the pharmacogenetics of AD is very limited, circumscribed to cholinesterase inhibitors and memantine (Table 1), remaining stuck in a primitive stage of underdevelopment due to the lack of novel therapeutic options. Although many studies on the pharmacogenetics of AD have been published since the early 2000’s [15,16], many of them are redundant and contradictory, focusing mainly on the APOE gene and, to a lesser extent, on some CYP family genes and other minor genes [17]. In this context, several considerations are pertinent regarding further steps to be followed in order to achieve a more mature profile of AD pharmacogenomics: (i) a better characterization of the roles played in drug efficacy and safety by genes involved in the pharmacogenomic network is necessary; (ii) since most genes are under the influence of the epigenetic machinery, pharmacoepigenomics is becoming an attractive field which deserves special attention; (iii) drug-drug interactions represent a problematic issue in over 80% of AD patients (most patients require a multifactorial treatment with different drugs); (iv) since the neurodegenerative process underlying AD neuropathology starts 20-30 years before the onset of the disease, novel therapeutics should be addressed to prevent premature neuronal death; (v) specific biomarkers for AD are necessary in 3 different contexts: predictive markers before disease onset, early diagnosis in initial stages, and drug monitoring (in both preventive and/or therapeutic strategies); and (vi) physicians should be aware of the usefulness of pharmacogenomics to prescribe more accurately, avoid adverse reactions and optimize the limited therapeutic resources available for the treatment of dementia [12,18].

PHARMACOGENOMICS

Pharmacogenomics accounts for 60-90% variability in pharmacokinetics and pharmacodynamics. The modest effect (and toxicity) of current AD drugs (Table 1) is in part due to their pharmacogenomic profile, since over 70% of AD patients are deficient metabolizers [3,6,10]. The genes involved in the pharmacogenomic response to drugs in dementia fall into five major categories:

(i) Genes associated with disease pathogenesis: Mendelian mutations affect genes directly linked to AD, including >30 mutations in the amyloid beta precursor protein (APP) gene (21q21)(AD1); >160 mutations in the presenilin 1 (PSEN1) gene (14q24.3)(AD3); and >10 mutations in the presenilin 2 (PSEN2) gene (1q31-q42)(AD4) [19-23]. PSEN1 and PSEN2 are important determinants of γ-secretase activity responsible for proteolytic cleavage of APP and NOTCH receptor proteins. Mendelian mutations are very rare in AD (1:1000). Mutations in exons 16 and 17 of the APP gene appear with a frequency of 0.30% and 0.78%, respectively, in AD patients. Likewise, PSEN1, PSEN2, and microtubule-associated protein Tau (MAPT) (17q21.1) mutations are present in less than 2% of the cases. Mutations in these genes confer specific phenotypic profiles to patients with dementia: amyloidogenic pathology associated with APP, PSEN1 and PSEN2 mutations and tauopathy associated with MAPT mutations representing the two major pathogenic hypotheses for AD [19-25].

Multiple polymorphic risk variants can increase neuronal vulnerability to premature death. There are at least 695 genes potentially associated with AD, of which the top ten are: APOE (19q13.2), BIN1 (2q14), CLU (8p21-p12), ABCA7 (19p13.3), CR1 (1q32), PICALM (11q14), MS4A6A (11q12.1), CD33 (19q13.3), MS4A4E (11q12.2), and CD2AP (6p12) [10,23]. Potentially defective genes associated with AD represent about 1.39% (35,252.69 Kb) of the human genome, which is integrated by 36,505 genes (3,095,677.41 Kb). The highest number of AD-related defective genes concentrate on chromosomes 10 (5.41%; 7,337.83 Kb), 21 (4.76%; 2,289.15 Kb), 7 (1.62%; 2,584.26 Kb), 2 (1.56%; 3,799.67 Kb), 19 (1.45%; 854.54 Kb), 9 (1.42%; 2,010.62 Kb), 15 (1.23%; 1,264.4 Kb), 17 (1.19%; 970.16 Kb), 12 (1.17%; 1,559.9 Kb), and 6 (1.15%; 1,968.22 Kb) [6]. Among susceptibility genes, the apolipoprotein E (APOE) gene (AD2) is the most prevalent as a risk factor for AD, especially in those subjects harboring the APOE-4 allele, whereas carriers of the APOE-2 allele might be protected against dementia [19]. Polymorphic variants in other genes (GRB-associated binding protein 2 (GAB2), TLR9 rs187084 variant homozygote GG, LRRK2 R1628P variant) might also be protective [6]. Ten novel private pathogenic copy number variations (CNVs) in 10 early-onset familial Alzheimer's disease (EO-FAD) families overlapping a set of genes (A2BP1, ABAT, CDH2, CRMP1, DMRT1, EPHA5, EPHA6, ERMP1, EVC, EVC2, FLJ35024 and VLDLR) have also been identified [26].

(ii) Genes associated with the mechanism of action of drugs (enzymes, receptors, transmitters, messengers).

(iii) Genes associated with drug metabolism: (a) phase I reaction enzymes: alcohol dehydrogenases (ADH1-7), aldehyde dehydrogenases (ALDH1-9), aldo-keto reductases (AKR1A-D), amine oxidases (MAOA, MAOB, SMOX), carbonyl reductases (CBR1-4), cytidine deaminase (CDA), cytochrome P450 family (CYP1-51, POR, TBXAS1), cytochrome b5 reductase (CYB5R3), dihydropirimidine dehydrogenase (DPYD), esterases (AADAC, CEL, CES1, CES1P1, CES2, CES3, CES5A, ESD, GZMA, GZMB, PON1, PON2, PON3, UCHL1, UCHL3), epoxidases (EPHX1-2), flavin-containing monooxygenases (FMO1-6), glutathione reductase/peroxidases (GPX1-7, GSR), short-chain dehydrogenases/reductases (DHRS1-13, DHRSX, HSD11B1, HSD17B10, HSD17B11, HSD17B14), superoxide dismutases (SOD1-2), and xanthine dehydrogenase (XDH); and (b): phase II reaction enzymes: amino acid transferases (AGXT, BAAT, CCBL1), dehydrogenases (NQO1-2, XDH), esterases (CES1-5), glucuronosyl transferases (UGT1-8), glutathione transferases (GSTA1-5, GSTK1, GSTM1-5, GSTO1-2, GSTP1, GSTT1-2, GSTZ1, GSTCD, MGST1-3, PTGES), methyl transferases (AS3MT, ASMT, COMT, GNMT, GAMT, HNMT, INMT, NNMT, PNMT, TPMT), N-acetyl transferases (ACSL1-4, ACSM1, ACSM2B, ACSM3, AANAT, GLYAT, NAA20, NAT1-2, SAT1), thioltransferase (GLRX), and sulfotransferases (CHST2-13, GAL3ST1, SULT1A1-3, SULT1B1, SULT1C1-4, SULT1E1, SULT2A1, SULT2B1, SULT4A1, SULT6B1, CHST1).

(iv) Genes associated with drug transporters: In humans there are 49 ABC transporter genes and the multidrug resistance associated proteins (MRP1/ABCC1, MRP2/ABCC2, MRP3/ABCC3, MRP4/ABCC4, MRP5/ABCC5, MRP6/ABCC6, MRP7/ABCC10, MRP8/ABCC11 and MRP9/ABCC12) which belong to the ABCC family integrated by 13 members. Other genes encoding transporter proteins are genes of the solute carrier superfamily (SLC) and solute carrier organic (SLCO) transporter family, responsible for the transport of multiple endogenous and exogenous compounds.

(v) Pleiotropic genes involved in multifaceted cascades and metabolic reactions [5,6,27].

All these genes are under the influence of the epigenetic machinery conditioning their expression and the efficiency of their drug-metabolizing products (enzymes, transporters) [28-30].

Genetic determinants of the pharmacogenetic outcome with conventional neuroprotectants and acetyl cholinesterase inhibitors.

Although the APP, PSEN1, PSEN2 and MAPT genes are considered major pathogenic genes for AD and classic tauopathies [23], mutations in these genes represent less than 5% of the AD population and, consequently, their influence on AD pharmacogenetics associated with conventional anti-dementia drugs is quantitatively negligible; not so in the case of immunotherapy addressing Aβ deposition. Most anti-AD vaccines (active and passive immunization) are based on transgenic models with APP, PSEN1 and PSEN2 mutants [31,32]. In general, most pharmacogenetic studies in AD have been performed with susceptibility genes (APOE) and metabolic genes (CYPs) [6,10].

-APOE-TOMM40

To date, the most influential gene in AD pharmacogenetics is the APOE gene [2,6,10,27,33]. APOE is a pleiotropic gene with multifaceted activities in physiological and pathological conditions, and the presence of the APOE-4 allele is determinant in AD pathogenesis [19]. APOE-4 may influence AD pathology by interacting with APP metabolism and Aβ accumulation, enhancing hyperphosphorylation of tau protein and neurofibrillary tangle formation, reducing choline acetyltransferase activity, increasing oxidative processes, modifying inflammation-related neuroimmunotrophic activity and glial activation, altering lipid metabolism, lipid transport and membrane biosynthesis in sprouting and synaptic remodeling, and inducing neuronal apoptosis and premature neuronal death [5,19]. Multiple studies over the past two decades have demonstrated that APOE variants may affect the therapeutic response to anti-dementia drugs [3,5,6,10,15,16,19,27,33-35]. At least 20 major phenotypic features illustrate the biological disadvantage of APOE-4 homozygotes and the potential consequences that these patients may experience when they receive pharmacological treatment for AD and/or concomitant pathologies [5,6,15,19,33,34,38].

In over 100 clinical trials for dementia, APOE has been used as the only gene of reference for the pharmacogenomics of AD. Several studies indicate that the presence of the APOE-4 allele differentially affects the quality and extent of drug responsiveness in AD patients treated with cholinergic enhancers, neuroprotective compounds, endogenous nucleotides, immunotrophins, neurotrophic factors, combination therapies and other drug categories [1,5,6,35-40]; however, controversial results are frequently found due to methodological problems, study design, and patient recruitment in clinical trials. The major conclusion in most studies is that APOE-4 carriers are the worst responders to conventional treatments [5,6,10]. When APOE and CYP2D6 genotypes are integrated in biogenic clusters and the APOE+CYP2D6-related therapeutic response to a combination therapy is analyzed in AD patients, it becomes clear that the presence of the APOE-4/4 genotype is able to convert pure CYP2D6*1/*1 extensive metabolizers into full poor responders to conventional treatments, indicating the existence of a powerful influence of the APOE-4 homozygous genotype on the drug-metabolizing capacity of pure CYP2D6 extensive metabolizers [3]. In addition, a clear accumulation of APOE-4/4 genotypes is observed among CYP2D6 poor and ultra-rapid metabolizers [3].

Adjacent to the APOE locus (19q13.2) and in linkage disequilibrium with APOE is the TOMM40 gene. A poly T repeat in an intronic polymorphism (rs10524523) (intron 6) in the TOMM40 gene, which encodes an outer mitochondrial membrane translocase involved in the transport of Aβ and other proteins into mitochondria, has been implicated in AD [41-54]. APOE-TOMM40 genotypes have been shown to modify disease risk and age at onset of symptoms [42-47,55]. The rs4420638 at the TOMM40/APOE/APOC1 gene locus is associated with longevity [56,57]. The APOE-TOMM40 genomic region is associated with cognitive aging [58] and with pathological cognitive decline [59]. There are 3 allele groups for rs10524523 ('523'), based on the number of 'T'-residues: 'Short' (S, T ≤ 19), 'Long' (L, 20 ≤ T ≤ 29) and 'Very Long' (VL, T ≥ 30) [49]. Longer lengths of rs10524523 are associated with a higher risk for late-onset AD (LOAD) [43-47]. Intronic poly T (rs10524523) within this region affects expression of the APOE and TOMM40 genes in the brain of patients with LOAD [60]. The 523 VL poly T shows higher expression than the S poly T, indicating that the 523 locus may contribute to LOAD susceptibility by modulating the expression of TOMM40 and/or APOE transcription [60]. S/VL and VL/VL are the only TOMM40 poly T genotypes which interact with all major APOE genotypes; in contrast, the APOE-4/4-TOMM40-L/L association is unique, representing approximately 30% of APOE-4/4 carriers [61] (Figure 2).

The first pharmacogenetic study of  the APOE-TOMM40 region in AD patients receiving a multifactorial treatment revealed that: (i) APOE-4 carriers are the worst responders (Figure 3-4) and APOE-3 carriers are the best responders to conventional treatments (Figure 3-4); (ii) TOMM40 poly T-S/S carriers are the best responders (Figure 3-4), VL/VL and S/VL carriers are intermediate responders, and L/L carriers are the worst responders to treatment (Figure 3-4); (iii) patients harboring a large (L) number of poly T repeats in intron 6 of the TOMM40 gene (L/L or S/L genotypes) in haplotypes associated with APOE-4 are the worst responders to treatment; (iv) patients with short (S) TOMM40 poly T variants (S/S genotype), and to a lesser extent S/VL and VL/VL carriers, in haplotypes with APOE-3 are the best responders to treatment; and (v) in 100% of the cases, the L/L genotype is exclusively associated with the APOE-4/4 genotype (Figure 2), and this haplotype (4/4-L/L) is probably responsible for early onset of the disease, a faster cognitive decline, and a poor response to different treatments [4,61].

Other recent pharmacogenetic studies with pathogenic or mechanistic genes indicate that the response to Acetylcholinesterase inhibitors (AChEIs) is associated with 2 single nucleotide polymorphisms (SNPs) in the intronic region of CHAT rs2177370 and rs3793790 [62]. The CHRNA7 T allele (rs6494223) also associates with a better response to AChEIs and there is further confirmation that APOE-4 carriers are the worst responders to conventional AChEIs [63].

-CYPs

Over 70% of AD patients are deficient metabolizers for the CYP2D6/2C19/2C9 trigenic cluster; and for the CYP2D6/2C19/2C9/3A4 tetragenic cluster, more than 80% of the patients exhibit a deficient metabolizer geno-phenotype [3]. These four CYP genes encode enzymes responsible for the metabolism of 60-80% of drugs of current use, showing ontogenic-, age-, sex-, circadian- and ethnic-related differences [5,6,33,64]. According to the database of the World Guide for Drug Use and Pharmacogenomics [38], 982 drugs are CYP2D6-related: 371 drugs are substrates, over 300 drugs are inhibitors, and 18 drugs are CYP2D6 inducers. Over 600 drugs are CYP2C9-related, 311 acting as substrates (177 are major substrates, 134 are minor substrates), 375 as inhibitors (92 weak, 181 moderate, and 102 strong inhibitors), and 41 as inducers of the CYP2C9 enzyme [38]. Nearly 500 drugs are CYP2C19-related, 281 acting as substrates (151 are major substrates, 130 are minor substrates), 263 as inhibitors (72 weak, 127 moderate, and 64 strong inhibitors), and 23 as inducers of the CYP2C19 enzyme [38]. The CYP3A4/5 enzyme metabolizes over 1900 drugs, 1033 acting as substrates (897 are major substrates, 136 are minor substrates), 696 as inhibitors (118 weak, 437 moderate, and 141 strong inhibitors), and 241 as inducers of the CYP3A4 enzyme [38].

The distribution and frequency of CYP2D6 genotypes are very similar in the general population (GP) (N=3232) and in AD (N=1289), with the exception of the CYP2D6-*3/*4 genotype (p<0.05) which is absent in AD samples (Figure 5). In the GP, CYP2D6 extensive metabolizers (EMs) account for 58.85%, whereas intermediate metabolizers (IMs) account for 31.11%, poor metabolizers (PMs) 4.49%, and ultra-rapid metabolizers (UMs) 5.55% [6,10] (Fig. 5). In AD, EMs, IMs, PMs, and UMs are 57.54%, 31.01%, 5.49%, and 5.96%, respectively. There is an accumulation of AD-related genes of risk in PMs and UMs. EMs and IMs are the best responders, and PMs and UMs are the worst responders to a combination therapy with AChEIs, neuroprotectants, and vasoactive substances. The pharmacogenetic response in AD appears to be dependent upon the networking activity of genes involved in drug metabolism and genes involved in AD pathogenesis [5,6,19,33,34,65-67]. By phenotypes, in the GP, CYP2C9-PMs represent 4.82%, IMs 33.83%, and EMs 61.35%. In AD, PMs, IMs, and EMs are 4.76%, 34.87%, and 60.37%, respectively [6,38] (Figure 6).

The frequencies of the CYP2C19 geno-phenotypes in the GP are: CYP2C19-EMs 74.11%, CYP2C19-IMs 24.43%, and CYP2C19-PMs 1.46% (Figure 7). EMs, IMs, and PMs account for 75.41%, 23.56%, and1.03%, respectively, in AD [6,38] (Figure 7). Concerning CYP3A4/5 polymorphisms in AD, 83.84% of the cases are EMs (CYP3A5*3/*3), 14.62% are IMs (CYP3A5*1/*3), and 1.54% are RMs (CYP3A5*1/*1) [6] (Figure 8), whereas in the GP, EMs, IMs and RMs represent 82.17%, 16.48%, and 1.35%, respectively (Figure 8).

Tetragenic haplotypes integrating CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 variants yield 156 genotypes (Figure 9). The most frequent haplotype is H3 (1/1-1/1-1/1-3/3) (20.87%), representing full extensive metabolizers, and only 17 haplotypes exhibit a frequency higher than 1% in the Spanish population (Figure 10). In addition to H3, the most frequent haplotypes (>2%) are H55 (1/4-1/1-1/1-1/3)(8.41%), H26 (1/1-1/2-1/1-3/3)(8.07%), H4 (1/1-1/1-1/2-3/3)(8.07%), H58 (1/4-1/1-1/2-3/3)(3.99%), H72 (1/4-1/2-1/1-3/3)(3.82%), H2 (1/1-1/1-1/1-1/3)(3.74%), H9 (1/1-1/1-1/3-3/3)(3.57%), and H38 (1xN/1-1/1-1/1-3/3)(2.46%) (Figure 10). This indicates that in the Spanish GP about 80% of the population is deficient for the biotransformation of current drugs which are metabolized via CYP2D6-2C9-2C19-3A4 enzymes.

Most anti-dementia drugs are metabolized via CYP enzymes. Donepezil is a major substrate of CYP2D6, CYP3A4, ACHE, and UGTs, inhibits ACHE and BCHE, and is transported by ABCB1 [2,6,17,33,34,38,65,67-69] (Table 1). CYP2D6 variants affect donepezil efficacy and safety in AD [2,6,17,33,34,38,65,66,69]. The common variant rs1080985 of CYP2D6 is associated with poor response to donepezil [70,71]. A higher frequency of mutated CYP2D6 allele *2A was found in responder than in non-responder patients (75.38 % vs 43.48 %) [72]. In an Italian study, 67% of patients were responders and 33% were non-responders to donepezil treatment, with abnormal enzymes accumulating in responders [73]. Chinese AD patients with the mutant allele CYP2D6*10 may respond better (58% responders) to donepezil than those with wild allele CYP2D6*1 [74]. In contrast, other studies revealed that CYP2D6-PMs and UMs tend to be poor responders to conventional doses of donepezil as compared to EMs and IMs [2,6,17,33,34,38,69,75-77].

In Italian patients, no association was found between CYP3A4 or CYP3A5 genotypes and plasma donepezil concentrations, or between genotypes and clinical response. The most common ABCB1 haplotypes were 1236C/2677G/3435C (46%) and 1236T/2677T/3435T (41%), and patients homozygous for the T/T/T haplotype had lower plasma donepezil concentration-to-dose ratios and better clinical response than patients with other genotypes [78]. In Brazilian patients treated with AChEIs the response rate was 27.8%, with no apparent effect of APOE and/or CYP2D6 polymorphic variants [79].

The effects of galantamine are potentially influenced by APOE, APP, ACHE, BCHE, CHRNA4, CHRNA7, CHRNB2 variants. This drug is a major substrate of CYP2D6, CYP3A4, and UGT1A1, and an inhibitor of ACHE and BCHE [38,68,69,80-82] (Table 1). Major metabolic pathways are glucuronidation, O-demethylation, N-demethylation, N-oxidation, and epimerization [83]. Galantamine is extensively metabolized by the enzymes CYP2D6 and CYP3A and is a substrate of the P-gp. CYP2D6 variants are determinant for galantamine pharmacokinetics. CYP2D6-PMs exhibit higher dose-adjusted galantamine plasma concentrations than heterozygous and homozygous CYP2D6-EMs [84]; however, these pharmacokinetic changes might not substantially affect pharmacodynamics [85]. The co-administration of galantamine with paroxetine (a CYP2D6 strong inhibitor), ketoconazole (a CYP3A4 strong inhibitor) and erythromycin increases its bioavailability [86,87]. Interaction with foods and nutritional components may alter galantamine bioavailability and therapeutic effects [88].

APOE, APP, CHAT, ACHE, BCHE, CHRNA4, CHRNB2 and MAPT variants may affect rivastigmine pharmacokinetics and pharmacodynamics, but CYP enzymes are not involved in the metabolism of rivastigmine [38,68,69,86,89]. UGT2B7-PMs show higher rivastigmine levels with a poor response to treatment [90].

ACHE, ABCB4, BCHE, CHRNA4, CHRNB2, APOE, MTHFR, CES1, LEPR, GSTM1 and GSTT1 variants may affect the therapeutic and toxic effects of tacrine (the first AChEI introduced in 1993 and withdraw years later due to hepatotoxicity). Tacrine is a major substrate of CYP1A2 and CYP3A4, a minor substrate of CYP2D6, and is transported via SCN1A and ABCB4. Tacrine is an inhibitor of ACHE, BCHE, and CYP1A2 [38]. Both tacrine and some tacrine-hybrids may cause an induction of CYP1A1, 2B1 and 3A2 expression [91]. Tacrine is associated with transaminase elevation in up to 50% of patients. The mechanism of tacrine-induced liver damage is influenced by genetic factors. The strongest association was found between alanine aminotransferase levels and three ABCB4 SNPs [92].

Memantine is an N-Methyl-D-Aspartate (NMDA) receptor antagonist which binds preferentially to NMDA receptor-operated cation channels; it may act by blocking actions of glutamate, mediated in part by NMDA receptors, and is also an antagonist of GRIN2A, GRIN2B, GRIN3A, HTR3A and CHRFAM7A. Several pathogenic (APOE, PSEN1, MAPT) and mechanistic gene variants (GRIN2A, GRIN2B, GRIN3A, HTR3A, CHRFAM7A, c-Fos, Homer1b and PSD-95) may influence its therapeutic effects. Memantine is a strong inhibitor of CYP2B6 and CYP2D6, and a weak inhibitor of CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2E1, and CYP3A4 [38,69,93]. In human liver microsomes (HLM), memantine inhibits CYP2B6 and CYP2D6 activities, decreases CYP2A6 and CYP2C19 activities, and has no effect on CYP1A2, CYP2E1, CYP2C9, or CYP3A4 activities [94]. The co-administration of memantine with CYP2B6 substrates elicits a 65% decrease in its metabolism. In clinical studies, NR1I2 rs1523130 was identified as the unique significant genetic covariate for memantine clearance, with carriers of the NR1I2 rs1523130 CT/TT genotypes presenting a 16% slower memantine elimination than carriers of the CC genotype [95].

Transporters

Polymorphic variants in genes encoding transporter proteins may affect drug metabolism, brain penetrance and accessibility to neuronal/glial targets, and drug resistance [96-98]. Of special importance in AD are the ABC and SLC family genes [98]. ABC genes (ABCB1, ABCC1, ABCG2), and other genes of this family encode proteins which are essential for drug metabolism and transport. Mutations in ABC transporters influence pathogenesis and therapeutics of brain disorders [98,99]. The multidrug efflux transporters (P-gp1/MDR1, multidrug-resistance associated protein 4 (MRP4), breast cancer resistance protein (BCRP)), are located on endothelial cells lining brain vasculature and play important roles in limiting movement of substances into and enhancing their efflux from the brain.

ABCB1 is one of the most important drug transporters in the brain. Over 1270 drugs have been reported to be associated with the ABCB1 transporter protein (P-gp), of which 490 are substrates, 618 are inhibitors, 182 are inducers, and 269 additional compounds which belong to different pharmacological categories of products with potential Abcb1 interaction [38]. The ABCB1 gene has 116 polymorphic sites in Caucasians and 127 in African-Americans, with a minor allele frequency greater than 5%. Common variants are 1236C>T, 2677G>A/T and 3435C>T, and the ABCB1*13 haplotype involves the 1236, 2677 and 3435 (TTT) SNPs and 3 intronic SNPs (in intron 9, 13, and 14) [38]. The ABCB1 C1236T, G2677T/A and C3435T SNPs influence blood-brain barrier (BBB) P-glycoprotein function. AD patients with one or more T in C1236T, G2677T and C3435T have significantly higher binding potential values than patients without a T. Genetic variations in ABCB1 might contribute to the progression of Aβ deposition in the brain [100] and some ABCB1 SNPs (C1236T in exon 12, G2677T/A in exon 21 and C3435T in exon 26) and inferred haplotypes might represent novel biomarkers of AD [101]. ABCB1 directly transports Aβ from the brain into the blood circulation, whereas the cholesterol transporter ABCA1 neutralizes Aβ aggregation capacity in an APOE-dependent manner, facilitating subsequent Aβ elimination from the brain [102]. Some ABCB1 variants are frequent in AD cases over 65 years of age and among females. This association of ABCB1 2677G>T (rs2032582) is more pronounced in APOE4-negative cases [100].

Some other ABCs have shown potential association with AD [98,103]. The G allele of the ABCA7 rs115550680 SNP is associated with AD in Europeans. The effect size for the SNP in ABCA7 was comparable with that of the APOE ε4-determining SNP rs429358 [104]. ABCG2 is involved in Aβ transport and is up-regulated in AD brains. The ABCG2 gene (C421A; rs2231142) (ABCG2 C/C genotype) is associated with AD and the ABCG2 C/C genotype and the APOE ε4 allele may exert an interactive effect on AD risk [105]. Also of importance for AD pharmacogenomics are transporters encoded by genes of the solute carrier superfamily (SLC) and solute carrier organic (SLCO) transporter family, responsible for the transport of multiple endogenous and exogenous compounds, including folate (SLC19A1), urea (SLC14A1-2), monoamines (SLC29A4, SLC22A3), aminoacids (SLC1A5, SLC3A1, SLC7A3, SLC7A9, SLC38A1, 4-5, 7, SLC43A2, SLC45A1), nucleotides (SLC29A2-3), fatty acids (SLC27A1-6), neurotransmitters (SLC6A2 (noradrenaline transporter), SLC6A3 (dopamine transporter), SLC6A4 (serotonin transporter, SERT), SLC6A5-6, 9, 11, 12, 14-19), glutamate (SLC1A6-7), and others [98,106]. Some organic anion transporters (OAT), which belong to the solute carrier (SLC) 22A family, are also expressed at the BBB, and regulate the excretion of endogenous and exogenous organic anions and cations [107]. The transport of amino acids and di- and tri-peptides is mediated by a number of different transporter families, and the bulk of oligopeptide transport is attributable to the activity of members of the SLC15A superfamily (SLC15A1-2, SLC15A2, SLC15A3-4). ABC and SLC transporters expressed at the BBB may cooperate to regulate the passage of different molecules into the brain [6,10,27,108].

Genetic determinants associated with lipid metabolism and cholesterol response to hypolipemic drugs in hypercholesterolemic patients with AD.

Among hundreds of genes potentially involved in AD pathogenesis and concomitant disorders (cardiovascular and cerebrovascular disorders, hypercholesterolemia), at least 4 categories of genes deserve special attention: (i) genes associated with lipid metabolism: APOB (OMIM 107730; rs693 [7545C>T]; risk SNP 7545T)(participates in the atherogenic process in cooperation with VLDL, IDL and LDL); APOC3 (OMIM 107720; rs5128 [3175G>C, S1/S2]; risk SNP 3175G (S2)) (associated with triglyceride levels; inhibits the activity of lipoprotein lipase and hepatic lipase); APOE (OMIM 107741; rs429358/rs7412 [112T>C/158T>C, E2, E3, E4]; risk SNP 112C/158C (E4)) (encodes apolipoprotein E, involved in the catabolism of triglyceride-rich lipoproteins and cholesterol homeostasis); CETP (OMIM 118470; rs708272 [+279G>A, B1/B2]; risk SNP +279G (B1))(contributes to eliminate cholesterol from tissues via reverse cholesterol transport); and LPL (OMIM 609708; rs328 [1421C>G, S474X]; protective SNP 1421G)(hydrolyzes triglycerides which are part of VLDL and chylomicrons and removes lipoproteins from circulation)[28,109-112]; (ii) genes associated with endothelial function and hypertension: NOS3 (OMIM 163729; rs1799983 [894G>T]; risk SNP 894T) (encodes nitric oxide synthase 3 which synthesizes nitric oxide (NO) from the amino acid arginine); ACE (OMIM 106189; rs4332 [547C>T]; risk SNP 547T) (hydrolyzes angiotensin I to angiotensin II, a potent vasopressor and aldosterone-stimulating peptide, and inactivates bradykinin, a potent vasodilator); and AGT (OMIM 1906150; rs699 [9543A>G, T174M]; risk SNP 174M; rs4762 [9360G>A, M235T]; risk SNP 235T) (encodes angiotensinogen, which is converted into angiotensin I by renin) [38,113-116]; (iii) genes associated with immune function and inflammation: IL1B (OMIM 147720; rs1143634 [3954C>T]; risk SNP 3954T) (encodes interleukin-1β, which is involved in the modulation of the inflammatory reaction in thrombus formation); IL6 (OMIM 147620; rs1800795 [-174G>C]; risk SNP -174C; rs1800796 [-573G>C]; risk SNP -573C) (encodes interleukin-6, a pleiotropic cytokine involved in the regulation of the acute phase reaction, immune response, hematopoiesis, and platelet production); IL6R (OMIM 147880; rs8192284 [1510A>G]; risk SNP 1510C) (encodes a subunit of the IL6 receptor complex); and TNFA (OMIM 191160; rs1800629 [-308G>A]; risk SNP -308A) (encodes tumor necrosis factor, a proinflammatory cytokine that influences lipid metabolism, coagulation, insulin resistance and endothelial function) [38,117-127]; and (iv) genes associated with thrombosis and coagulation: F2 (OMIM 17693; rs1799983 [20210G>A]; risk SNP 2021A) (encodes Coagulation Factor 2 (Prothrombin), involved in blood clotting); F5 (OMIM 227400; rs6025 [1691G>A]; risk SNP 1691A) (encodes Factor V Leiden, an important factor involved in blood coagulation); and MTHFR (OMIM 607093; rs1801133 [677C>T]; risk factor 677T; rs1801131 [1298A>C]; risk SNP 1298A) (encodes methylenetetrahydrofolate reductase, an enzyme that catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, a co-substrate for the re-methylation of homocysteine to methionine) [38,128-132].

Although differences in genotype distribution and frequencies of all these genes between patients with AD and control subjects are negligible, except in the case of APOE [133] (Figure 11) some of them may influence the pharmacogenetic outcome in the treatment of major risk factors for dementia, such as hypercholesterolemia, cardiovascular disorders and hypertension [133-137].  Furthermore, many of these genes interact in pathogenic cascades contributing to alter brain cholesterol and Aβ metabolism, subsequently accelerating neuronal death in AD.

PHARMACOGENETICS OF HYPERCHOLESTEROLEMIA IN ALZHEIMER’S DISEASE

Alterations in cholesterol (CHO) metabolism are involved in AD pathogenesis and over 40% of AD patients are hypercholesterolemic. Cognitive deterioration shows a clear age-dependent profile (Figure 12), with an average decline on 3-5 points/year (MMSE score); however, total CHO levels do not appear to affect mental deterioration in AD (Figure 13). Blood lipid levels also show a moderate age-dependent profile (Figure 14). In the GP, CHO levels tend to increase with age reaching a plateau at 60-70 years of age, declining thereafter; however, CHO levels in AD tend to diminish in an age-related fashion (Figure 15).

In a group of AD patients (N=920) recruited for pharmacogenomic studies treated with a multifactorial therapy for one year [61], we evaluated the effects of Sardilipin (E-SAR-94010; LipoEsar®) (500 mg/day) (nutraceutical with lipid-lowering effects and anti-atherosclerotic and neuroprotective properties, Patent ID: P9602566) [6,38,138] (whole group), and atorvastatin (10 mg/day)(patients with hypercholesterolemia >220 mg/dL) (43.48%) (first month of treatment) on lipid metabolism (total-cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides) according to the APOE and CYP genotypes of the patients. From these studies we obtained interesting results which enable us to infer some conclusions with important repercussions on the pharmacogenetics of AD: (i) Body Mass Index (BMI) is not affected by total cholesterol (T-CHO) or LDL-CHO; however, there is a clear positive correlation between BMI and triglyceride (TG) levels and an inverse correlation between BMI and HDL-CHO. (ii) Liver transaminase activity is important for lipid metabolism. ASAT, ALAT and GGT exhibit different correlation patterns in relation to lipid levels. ASAT shows an inverse correlation with T-CHO and LDL-CHO; ALAT and GGT activities increase in parallel with TG levels, and tend to show an inverse correlation with HDL-CHO. (iii) Hypercholesterolemic females and males with AD show a similar response to the combination of Atorvastatin + LipoEsar, but more females (60%) are hypercholesterolemic than males (<20%). (iv) CHO levels are APOE-dependent. APOE-4/4 carriers exhibit the highest CHO levels. APOE-2/3, APOE-3/4 and APOE-4/4 carriers experience a gradual age-dependent decrease in CHO levels. (v) The therapeutic response of CHO to Atorvastatin + LipoEsar is APOE-dependent. APOE-3/3 and APOE-3/4 carriers are the best responders and APOE-2/4 and APOE-4/4 carriers are the worst responders. (vi) Basal CHO levels are similar in CYP2D6-EMs, IMs, PMs and UMs. CYP2D6-EMs and IMs show a significant decrease in CHO levels in response to Atorvastatin + LipoEsar, whereas PMs and UMs exhibit a poorer CHO-lowering effect. (vii) Basal CHO levels are higher in CYP2C9-IMs than in EMs. CYP2C9-EMs and IMs effectively respond to Atorvastatin + LipoEsar, with a significant reduction in CHO levels, and CYP2C9-PMs do not respond. (viii) Basal CHO levels are non-significantly higher in CYP2C19-EMs and IMs than in PMs. CYP2C19-EMs and IMs significantly respond to Atorvastatin + LipoEsar, and PMs do not show any effect. (ix) CYP3A4/5-EMs show a significant decrease in CHO levels after one month of treatment with Atorvastatin + LipoEsar. This response is similar for LDL-CHO in EMs and IMs. In hypercholesterolemic patients, over 80% of EMs respond to 20 mg of Atorvastatin + 500 mg of LipoEsar, with an almost complete normalization of CHO levels. The effect in IMs is spectacular, with over 90% of the patients experiencing a drastic reduction in CHO levels, 50% of them entering into a condition of iatrogenic hypocholesterolemia; and 60% of RMs do not respond at all.

In a larger study with 1345 hypercholesterolemic AD patients (CHO>220 mg/dL) (Figure 16) we investigated the pharmacogenetics of cholesterol response to the hypolipemic compounds Atorvastatin + LipoEsar for one month. In the whole sample, the response rate (RR) was 78.95% responders (CHOFigure 17); however, females and males responded similarly to the hypolipemic treatment (Figure 18). The stratification of patients according to their APOE, APOB, APOC3, CETP and LPL genotypes showed no genotype-related differences at basal CHO levels, except in the case of APOE carriers where the highest baseline levels of CHO were found in APOE-4/4 carriers and the lowest levels in APOE-2/2 carriers (Figure 19), in addition to a clear age-related profile (Figure 19).

In a selected group of 933 AD patients we constructed a pentagenic haplotype integrating all possible variants of the APOE+APOB+EPOC3+CETP+LPL genes and identified 111 haplotypes (H) (Figure 20) with differential basal CHO levels (Figure 21). About 75% of these haplotypes in the AD population have a frequency below 1%, 10% have a frequency between 1% and 2%, 8% have a frequency between 2% and 5%, and only 4% of the haplotypes are present in more than 5% of AD patients (Fig. 20). The haplotypes most frequently found are H55 (33-CT-CC-AG-CC)(8.79%), H58 (33-CT-CC-GG-CC) and H37 (33-CC-CC-AG-CC) (7.07%). Haplotypes H104 (44-CC-CC-AA-CC)(0.11%), H110 (44-TT-CC-AG-CG)(0.11%) and H98 (34-TT-CC-AA-CG) (0.11%) showed the highest CHO levels, and the lowest levels corresponded to haplotypes H26 (23-TT-CG-AG-CC) (0.11%), H8 (23-CC-CG-AG-CC)(0.21%), H50 (33-CC-GG-AG-CC)(0.21%), and H63 (33-CT-CG-AA-GG)(0.11%) (Figure 21).

Basal CHO levels tend to be higher in AD patients as compared to GP levels (Figure 22-23). APOE-related blood total CHO profiles are qualitatively distinct among carriers of different APOE genotypes (Figure 24).  The results of APOE-related cholesterol response to hypolipemic treatment in hypercholesterolemic AD patients revealed that in absolute terms all APOE variants respond similarly (RR>70%) to treatment with a significant reductionin CHO levels (p<0.001) (Figure 25-26); however, genotype-related correlation analysis case-by-case (Figure 27) and comparative correlation analyses of APOE variants (Figure 28) show a clear differential APOE-related pattern of CHO response to treatment.

Carriers of APOB-C/C, APOB-C/T and APOB-T/T variants exhibit a similar response (RR>80%), with a significant decrease in CHO levels after treatment (Figure. 29) and almost identical efficiency in comparative analyses (Figure. 30). APOC3-C/C, APOC3-C/G and APOC3-G/G carriers also respond similarly (p<0.001) (RR>80%) (Figure. 31), with a differential comparative profile (Figure 32). CETP-A/A, CETP-A/G and CETP-G/G carriers show an identical response (p<0.001; RR>80%) (Figure 33), with insignificant variability in comparative studies (Figure 34). The same therapeutic response is observed in LPL-C/C, LPL-C/G and LPL-G/G carriers (p<0.001; RR>80%) (Figure 35), though in this case LPL-C/C are the best responders, LPL-C/G are intermediate responders, and LPL-G/G are the most heterogeneous responders (Figure 36).

CYP haplotype-related blood total CHO levels are very heterogeneous (Figure 37), but absolute values of total CHO among the most frequent haplotypes are almost identical (Figure 38). The histograms of frequency associated with CHO levels are qualitatively different among carriers of different CYP variants (Figure 39-40). Basal CHO levels are higher in AD patients harboring the CYP2D6-*1/*1 (p<0.05) and *1xN/*1 genotypes (p<0.003) than in the corresponding GP genotypes (Figure 41), but no differences have been found according to their EM, IM, PM or UM condition (Figure 42). The therapeutic response according to SNPs of metabolic genes (CYP2D6, CYP2C9, CYP2C19, CYP3A4/4) in hypercholesterolemic patients is variable and geno-phenotype-dependent. Although all CYP2D6 variants exhibit a positive response to treatment, significant differences have only been detected in 2D6-*1/*1 (p<0.001), 2D6-*1/*4 (p<0.001) and 2D6-*1/*6 carriers (p<0.05) (Figure 43). In absolute values, CYP2D6 extensive (EM), intermediate (IM), poor (PM) and ultra-rapid (UM) metabolizers behave in a similar manner with a significant reduction in CHO levels (p<0.001) (Figure 44); however, the RR is different in EMs (81%), IMs (78%), PMs (84%), and UMs (90%) (Figure 44), indicating a variable efficiency of CYP2D6 enzymes (Figure 45-46). The comparative analysis indicates that carriers of mutant enzymes (PMs>UMs), with limitations in drug metabolism, display a more efficient response to hypolipemic treatment (Figure 45,46).

No differences are present in basal CHO levels between the GP and AD patients (Figure 47,48). CYP2C9-EMs, IMs and PMs (Figure 49) show a similar response (p<0.001), with lower RR (75%) in PMs as compared with EMs (81%) and IMs (82%), and a clear differential comparative profile (Figure 50).

AD cases harboring the CYP2C19-*1/*2 genotype (Figure 51), corresponding to CYP2C19-IMs (Figure 52), exhibit higher basal CHO levels (p<0.05) than their homologous in the GP (Figure 51,52).

The CHO response among CYP2C19-EMs, IMs, PMs and UMs is more variable, with PMs showing a deficient response (RR=84%) in comparison to EMs (p<0.001; RR=81%), IMs (p<0.001; RR=78%), and UMs (p<0.001; RR=90%), and a clearly different behavioral profile, especially in PMs and UMs (Figure 53-55).

CYP3A4/5 geno-phenotypes in AD and GP show similar basal CHO levels (Figure 56). CYP3A4/5-RMs respond poorly to hypolipemic treatment, with the worst RR (66%), whereas CYP3A4/5-EMs and IMs exhibit an excellent response (p<0.001; RR>80%) (Figure 57,58).

Most of these effects can, in part, be explained on a pharmacogenetic basis. It is obvious that a simple stratification of patients according to single genotypes is of poor value for a fine interpretation of pharmacogenetic results; however, the integration of gene clusters associated with specific phenotypes yields informative haplotypes with potential utility in pharmacogenetic studies. It is likely that thousands of genes are involved in CHO metabolism, and probably not a single gene plays an absolute dominant role over the others; however, some genes exert a powerful effect on other congeners associated with a specific pathogenic cascade (e.g. APOE in AD) or a pharmacogenetic pathway (e.g. APOE vs. CYPs in AD treatment with donepezil) [3,6,17,33,66,67]. Several pathogenic (ACE, APOA1, APOA5, APOB, APOC3, APOE, CETP, FGB, GNB3, LIPC, MMP3, MTTP, NOS3, PON) and mechanistic genes (ABCB1, ABCC1, APOA1, APOA5, APOB, APOC3,

APOE, CRP, CYP11B2, HMGCR, IL10, IL6, LDLR, MMP3, PON1, TNF) are potentially influenced by atorvastatin. This statin is a major substrate of CYP2C8 and CYP3A4/5; it is a strong inhibitor of CYP2C19, a moderate inhibitor of ABCB1, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4 and HMGCR, and an inducer of CYP2B6 and CYP7A1. Atorvastatin is transported by ABCA1, ABCB1, ABCB11, ABCC1, ABCC2, ABCC3, ABCG2, SLCO1B1 and SLCO1B3 proteins, and interacts with the products of various pleiotropic genes (APOA1, APOE, CRP, CYP11B2, ESR1, GNB3, HTR3B, IL6, IL10, ITGB3, MMP3, TNF, USP5) [38] (Table 2). The lipid-lowering effects and the anti-atherosclerotic properties of LipoEsar are APOE-dependent, with APOE-3 carriers acting as the best responders and APOE-4 carriers behaving as the worst responders [5,6]. Sex-related changes in CHO response to statins have been reported in carriers of the HMGCR-AA genotype at rs3846662, who have higher levels of total and LDL-CHO. The percentage reduction in LDL-CHO upon statin treatment is decreased in women with the AA genotype compared with women without it. In hypercholesterolemic patients, HMGCR alternative splicing may explain 22-55% of the variance in statin response [139]. The powerful effect of Atorvastatin in CYP3A4/5-IMs is the result of a poor metabolization of Atorvastatin by mutant CYP3A4/5 enzymes, since Atorvastatin is a major substrate of CYP3A4/5. In contrast, the lack of effect in CYP3A4/5-RMs results from a rapid destruction of the drug in the liver mediated by excessive CYP3A4/5 enzymatic activity. Therefore, the dose of statins should be adjusted to the metabolizing condition of each patient to optimize the lipid-lowering effects of statins and to avoid toxicity [38]. Furthermore, the co-administration of the nutraceutical LipoEsar enhances the hypolipemic effect of Atorvastatin and facilitates a dose reduction of the statin by 50%, minimizing potential ADRs in susceptible patients.

PHARMACOEPIGENOMICS

Pharmacogenetics alone does not predict all phenotypic variation in drug response [27]. The genes involved in the pharmacogenomic network are under the regulatory control of the epigenetic machinery (DNA methylation, histone modifications, miRNA regulation), this configuring the novel pharmacoepigenomic apparatus [27]. Epigenetics involves heritable alterations of gene expression, chromatin organization, and microRNA (miRNA) regulation without changes in DNA sequence. Classical epigenetic mechanisms, including DNA methylation and histone modifications, and regulation by microRNAs (miRNAs), are among the major regulatory elements that control metabolic pathways at the molecular level, with epigenetic modifications regulating gene expression transcriptionally and miRNAs suppressing gene expression post-transcriptionally [140]. Methylation varies spatially across the genome with a majority of the methylated sites mapping to intragenic regions [141]. About 70% of CpG dinucleotides within the human genome are methylated. Not only nuclear DNA (nDNA), but also mitochondrial DNA (mtDNA) may be subjected to epigenetic modifications related to disease development, environmental exposure, drug treatment and aging.

Several pathogenic genes (Table 3) and many other AD-related susceptibility genes with direct or indirect influence on the AD phenotype (i.e. genes associated with vascular risk factors and lipid metabolism)(Table 4) contain methylated CpG sites which exhibit alterations in DNA methylation [142,143]. Different modalities of histone aberrations are present in AD [27-29,142,144,145]. Alterations in epigenetically-regulated miRNAs may contribute to the abnormal expression of pathogenic genes in AD [146,147]. Several lncRNAs are dysregulated in AD (Sox2OT, 1810014B01Rik, BC200, BACE1-AS, NAT-Rad18, 17A, GDNFOS) [147]. Examples of miRNAs directly linked to AD pathogenesis include miR-34a (1p36.22), miR-34b/c (11q23.1), miR-107 (10q23.31), miR-124 (8p23.1/8p12.3/20q13.33), miR-125b (11q24.1/21q21.1), and miR-137 (1p21.3); and examples of epigenetically-regulated miRNAs with targets linked to AD pathogenesis are let-7b (22q13.1), miR-9 (1q22/5q14.3/15q26.1), miR-132/212 (17p13.3), miR-146a (5q34), miR-148a (7p15.2), miR-184 (15q25.1), and miR-200 (miR-200b/200a/429, 1p36.33; miR-200c/141, 12p13.31) [146]. AD-related SNPs interfere with miRNA gene regulation and affect AD susceptibility. The significant interactions include target SNPs present in seven genes related to AD prognosis with the miRNAs- miR-214, -23a & -23b, -486-3p, -30e*, -143, -128, -27a &-27b, -324-5p and -422a. The dysregulated miRNA network contributes to the aberrant gene expression in AD [148-150].

Epigenetic regulation is also responsible for the tissue-specific expression of genes involved in pharmacogenetic processes, and epigenetics plays a key role in the development of drug efficacy, safety and resistance. Epigenetic changes affect CYP expression, major transporter function, and nuclear receptor interactions [151-154]. Variable methylation patterns have been detected in genes encoding phase I-III enzymes (Table 5). Although this is a still poorly explored field, epigenetic regulation of genes encoding drug-metabolizing enzymes (CYP1A1, 1A2, 1B1, 1A6, 2A13, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 2J2, 2F1, 2R1, 2S1, 2W1, 3A4, 3A5, 3A7, 3A43, UGT1, GSTP1), drug transporters (ABCB1/MDR1/P-gp, ABCC1/MRP1, ABCC11/MRP8, ABCG2/BCRP, SLC19A1, SLC22A8), and nuclear receptors (RARB2, ESR1, NR1I2, HNF41) has been documented in pioneering studies of pharmacoepigenetics [27,151-154].

Epigenetic modifications are also associated with drug resistance [27,153,155]. The acquisition of drug resistance is tightly regulated by post-transcriptional regulators such as RNA-binding proteins (RBPs) and miRNAs, which change the stability and translation of mRNA-encoding factors involved in cell survival, proliferation, epithelial-mesenchymal transition, and drug metabolism [153]. In the complex cascade of pharmacoepigenetic events, the epigenetic factory may act as a promiscuous, redundant security system in which several miRNAs target genes encoding epigenetic regulators. For example, miR-29, -29c, -370, and -450A target DNMT3A, and miR-29, -148, and -29b target DNMT3B, inducing hypomethylation and expression of tumor suppressor genes; let-7a, miR-26a, -101, -138, and -124 target EZH2, decreasing histone methylation and increasing expression of tumor suppressor genes; miR-449 and -874 target HDAC1, inducing growth arrest by decreasing histone acetylation; miR-1 and -155 target HDAC4, promoting myogenesis and impairing transcriptional activity of B-cell lymphoma 6 (BCL6); miR-627 and -155 target JMJD1A, decreasing histone demethylation and hypoxic gene expression; miR-132 and -483-5p target MECP2, promoting demethylation and cell differentiation [156]. Furthermore, epigenetic drugs reverse epigenetic changes in gene expression and might open new avenues in AD therapeutics [29,30,145,157].

 

CONCLUSIONS

1.             AD is a complex disorder with a tetravalent phenotype (neuropathological, neurobehavioral, age-related, and gender-related components).

2.             Major determinants of therapeutic outcome in AD include age- and sex-related factors, pathogenic phenotype, concomitant disorders, treatment modality and polypharmacy, and pharmacogenetics. 

3.             Different categories of genes are potentially involved in the pharmacogenetic network responsible for drug efficacy and safety.

4.             Pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes represent the major genetic determinants of response to treatment in AD.

5.             The genes involved in the pharmacogenomic network are under the regulatory control of the epigenetic machinery (DNA methylation, histone modifications, miRNA regulation), this configuring the novel pharmacoepigenomic apparatus and constituting a novel source of potential therapeutic targets.

6.             By-products of these genes are integrated in transcriptomic, proteomic and metabolic networks which are disrupted in AD and represent potential targets for therapeutic intervention.

7.             In pharmacogenetic studies with conventional anti-dementia drugs and combination treatments, APOE-4 carriers are the worst responders and APOE-3 carriers are the best responders; patients harboring a large (L) number of poly T repeats in intron 6 of the TOMM40 gene (L/L or S/L genotypes) in haplotypes associated with APOE-4 are the worst responders to treatment; patients with short (S) TOMM40 poly T variants (S/S genotype) in haplotypes with APOE-3 are the best responders to treatment; and CYP2D6 and ABCB1 variants may influence the therapeutic response to conventional treatments.

8.             Over 80% of AD patients are daily consumers of different treatments for concomitant disorders. Only 20% of the Caucasian population are extensive metabolizers for the tetragenic haplotype integrated by CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 variants.

9.             Tetragenic haplotypes integrating CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 variants yield 156 genotypes. The most frequent haplotype is H3 (1/1-1/1-1/1-3/3)(20.87%), representing full extensive metabolizers, and only 17 haplotypes exhibit a frequency higher than 1% in the Spanish population.

10.          AD patients exhibit at least 111 pentagenic (APOE-APOB-APOC3-CETP-LPL) haplotypes associated with cholesterol levels. The highest levels of cholesterol are present in carriers of the haplotype H104 (44-CC-CC-AA-CC) and the lowest levels of cholesterol are detected in carriers of the haplotype H26 (23-TT-CG-AG-CC).

11.          The response of cholesterol to specific hypolipemic treatments in hypercholesterolemic AD patients is highly efficient in over 70% of the cases and associates with APOE variants, CHO-related haplotypes and drug-specific CYP metabolizer geno-phenotypes.

12.          The implementation of pharmacogenomic protocols is essential to prescribe more accurately, avoid adverse reactions and optimize the limited therapeutic resources available for the treatment of dementia.

 

  1. Cacabelos R (2003) The application of functional genomics to Alzheimer’s disease. Pharmacogenomics 4: 597-621.
  2. Cacabelos R (2007) Molecular pathology and pharmacogenomics in Alzheimer’s disease: Polygenic-related effects of multifactorial treatments on cognition, anxiety, and depression. Meth Find Exper Clin Pharmacol 29: 1-91.
  3. Cacabelos R (2009) Pharmacogenomics and therapeutic strategies for dementia. Expert Rev Mol Diag 9: 567-611.
  4. Cacabelos R, Goldgaber D, Roses AD,Vostrov A, Matsuki H, et al. (2015) Gene interactions in the Pharmacogenomics of Alzheimer’s Disease. Int J Mol Genet Gene Ther 1: 22.
  5. Cacabelos R (2008) Pharmacogenomics in Alzheimer's disease. Methods Mol Biol 448: 213-357.
  6. Cacabelos R, Cacabelos P, Torrellas C, Tellado I, Carril JC (2014) Pharmacogenomics of Alzheimer's disease: novel therapeutic strategies for drug development. Methods Mol Biol 1175: 323-556.
  7. Cacabelos R, Torrellas C, Carrera I, Cacabelos P, Corzo L, et al. (2016) Novel Therapeutic Strategies for Dementia. CNS Neurol Disord Drug Targets 15: 141-241.
  8. Wang J, Zhao Z, Lin E, Zhao W, Qian X, et al. (2013) Unintended effects of cardiovascular drugs on the pathogenesis of Alzheimer's disease. PLoS One 8: e65232.
  9. Montastruc F, Gardette V, Cantet C, Piau A, Lapeyre-Mestre M, et al. (2013) Potentially inappropriate medication use among patients with Alzheimer disease in the REAL.FR cohort: be aware of atropinic and benzodiazepine drugs. Eur J Clin Pharmacol 69: 1589-1597.
  10. Cacabelos R, Torrellas C, Carrera I (2015) Opportunities in Pharmacogenomics for the treatment of Alzheimer’s Disease. Future Neurol 10: 229-252.
  11. Haibin L, Lirong W, Mingliang L, Rongrong P, Peibo L, et al. (2014) AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. J Chem Inf Model 54: 1050-1060.
  12. Cacabelos R, Teijido O, Carril JC (2016) Can cloud-based tools accelerate Alzheimer's disease drug discovery? Expert Opin. Drug Discov 11: 215-223.
  13. Jeffrey L, Cummings JL, Morstorf T, Zhong K (2014) Alzheimer’s disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther 6: 37.
  14. Bond M, Rogers G, Peters J, Anderson R, Hoyle M, et al. (2012) The effectiveness and cost-effectiveness of donepezil, galantamine, rivastigmine and memantine for the treatment of Alzheimer's disease (review of Technology Appraisal No. 111): a systematic review and economic model. Health Technol Assess 16: 1-470.
  15. Cacabelos R, Alvarez A, Lombardi V, Fernández-Novoa L, Corzo L, Pérez P, et al. (2000) Pharmacological treatment of Alzheimer disease: From psychotropic drugs and cholinesterase inhibitors to pharmacogenomics. Drugs Today 36: 415-499.
  16. Cacabelos R (2002) Pharmacogenomics in Alzheimer’s disease. Min Rev Medic Chem 2: 59-84.
  17. Cacabelos R, Llovo R, Fraile C, Fernández-Novoa L (2007) Pharmacogenetic aspects of therapy with cholinesterase inhibitors: The role of CYP2D6 in Alzheimer’s disease pharmacogenetics. Cur. Alzheimer Res 4: 479-500.
  18. Otsubo Y (2015) Use of Pharmacogenomics and Biomarkers in the Development of New Drugs for Alzheimer Disease in Japan. Clin Ther 37: 1627-1631.
  19. Cacabelos R, Fernández-Novoa L, Lombardi V, Kubota Y, Takeda M (2005) Molecular genetics of Alzheimer’s disease and aging. Meth Find Exp Clin Pharmacol 27: 1-573.
  20. Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: The AlzGene database. Nat Genet 39: 17-23.
  21. Selkoe DJ, Podlisny MB (2002) Deciphering the genetic basis of Alzheimer’s disease. Annu Rev Genomics Hum Genet: 67-99.
  22. Suh YH, Checler F (2002) Amyloid precursor protein, presenilins, and α-synuclein: Molecular pathogenesis and pharmacological applications in Alzheimer’s disease. Pharmacol Rev 54: 469-525.
  23. Karch CM, Cruchaga C, Goate A (2014) Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83: 11-26.
  24. Larner AJ (2013) Presenilin-1 mutations in Alzheimer's disease: An update on genotype-phenotype relationships. J Alzheimers Dis 37: 653-659.
  25. Hardy J, Selkoe DJ (2002) The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science 297: 353-356.
  26. Hooli BV, Kovacs-Vajna ZM, Mullin K, Blumenthal MA, Mattheisen M, et al. (2014) Rare autosomal copy number variations in early-onset familial Alzheimer's disease. Mol Psychiatry 19: 676-681.
  27. Cacabelos R, Torrellas C (2015) Epigenetics of aging and Alzheimer’s disease: implications for pharmacogenomics and drug response. Int J Mol Sci 16: 30486-30543.
  28. Cacabelos R, Torrellas C, López-Muñoz F (2014) Epigenomics of Alzheimer’s disease. J Exp Clin Med 6: 75-82.
  29. Cacabelos R, Torrellas C (2014) Epigenetic drug discovery for Alzheimer’s disease. Exper. Opin. Drug Discov 9: 1059-1086.
  30. Cacabelos R (2014) Epigenomic networking in drug development: From pathogenic mechanisms to pharmacogenomics. Drug Dev Res 75: 348-365.
  31. Schenk D, Barbour R, Dunn W, Gordon G, Grajeda H, et al. (1999) Immunization with amyloid-beta attenuates Alzheimer-disease-like pathology in the PDAPP mouse. Nature 400: 173-177.
  32. Carrera I, Etcheverría I, Fernández-Novoa L, Lombardi V, Cacabelos R, Vigo C (2012) Vaccine Development to Treat Alzheimer’s Disease Neuropathology in APP/PS1 Transgenic Mice. Int J Alzheimers Dis 376138.
  33. Cacabelos R, Fernández-Novoa L, Martínez-Bouza R, McKay A, Carril JC, et al. (2010) Future trends in the pharmacogenomics of brain disorders and dementia: Influence of APOE and CYP2D6 variants. Pharmaceuticals 3: 3040-3100.
  34. Cacabelos R (2012) Pharmacogenomics of central nervous system (CNS) drugs. Drug Dev Res 73: 461-476.
  35. Roses AD (2004) Pharmacogenetics and drug development: the path to safer and more effective drugs. Nat Rev Genet 5: 645-656.
  36. Roses AD (2008) Pharmacogenetics in drug discovery and development: a translational perspective. Nat Rev Drug Discov 7: 807-817.
  37. Roses AD (2009) The medical and economic roles of pipeline pharmacogenetics: Alzheimer’s disease as a model of efficacy and HLA-B(*)5701 as a model of safety. Neuropsychopharmacol 34: 6-17.
  38. Cacabelos R (2012) World Guide for Drug Use and Pharmacogenomics. (1st edn), EuroEspes Publishing, Corunna, Spain.
  39. Roses AD, Saunders AM, Huang Y, Strum J, Weisgraber KH, et al. (2007) Complex disease-associated pharmacogenetics: Drug efficacy, drug safety, and confirmation of a pathogenic hypothesis (Alzheimer’s disease). Pharmacogenomics J 7: 10-28.
  40. Risner M, Saunders A, Altman J, Ormandy G, Craft S, et al. (2006) Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer’s disease. Pharmacogenomics J 6: 246-254.
  41. Takei N, Miyashita A, Tsukie T, Arai H, Asada T, et al. (2009) Genetic association study on in and around the APOE in late-onset Alzheimer disease in Japanese. Genomics 93: 441-448.
  42. 42Potkin SG, Guffanti G, Lakatos A, Turner JA, Kruggel F,  et al. (2009) Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS One 4: e6501.
  43. Roses AD (2010) An inherited variable poly-T repeat genotype in TOMM40 in Alzheimer disease. Arch Neurol 67: 536-541.
  44. Roses AD, Lutz MW, Amrine-Madsen H, Saunders AM, Crenshaw DG, et al. (2010) A TOMM40 variable-length polymorphism predicts the age of late-onset Alzheimer’s disease. Pharmacogenomics J 10: 375-384.
  45. Roses AD, Saunders AM, Lutz MW, Zhang N, Hariri AR, et al. (2014) New applications of disease genetics and pharmacogenetics to drug development. Curr Opin Pharmacol 14: 81-89.
  46. Roses AD, Lutz MW, Crenshaw DG, Grossman I, Saunders AM, et al. (2013) TOMM40 and APOE: Requirements for replication studies of association with age of disease onset and enrichment of a clinical trial. Alzheimers Dement 9: 132-136.
  47. Lutz MW, Crenshaw DG, Saunders AM, Roses AD (2010) Genetic variation at a single locus and age of onset for Alzheimer’s disease. Alzheimers Dement 6: 125-131.
  48. Shen L, Kim S, Risacher SL, Nho K, Swaminathan S, et al. (2010) Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage 53: 1051-1063.
  49. Linnertz C, Saunders AM, Lutz MW, Crenshaw DM, Grossman I, et al. (2012) Characterization of the poly-T variant in the TOMM40 gene in diverse populations. PLoS One 7: e30994.
  50. Omoumi A, Fok A, Greenwood T, Sadovnick AD, Feldman HH, et al. (2014) Evaluation of late-onset Alzheimer disease genetic susceptibility risks in a Canadian population. Neurobiol Aging 35: 5-12.
  51. Bagnoli S, Piaceri I, Tedde A, Bessi V, Bracco L, et al. (2013) TOMM40 polymorphisms in Italian Alzheimer’s disease and frontotemporal dementia patients. Neurol Sci 34: 995-998.
  52. Ma XY, Yu JT, Wang W, Wang HF, Liu QY, et al. (2013) Association of TOMM40 polymorphisms with late-onset Alzheimer’s disease in a Northern Han Chinese population. Neuromolecular Med 15: 279-287.
  53. Valant V, Keenan BT, Anderson CD, Shulman JM, Devan WJ, et al. (2012) TOMM40 in Cerebral Amyloid Angiopathy related intracerebral hemorrhage: Comparative genetic analysis with Alzheimer’s Disease. Transl Stroke Res 3: 102-112.
  54. Vounou M, Janousova E, Wolz R, Stein JL, Thompson PM, et al. (2012) Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer’s disease. Neuroimage 60: 700-716.
  55. Bernardi L, Gallo M, Anfossi M, Conidi ME, Colao R, et al. (2013) Role of TOMM40 rs10524523 polymorphism in onset of alzheimer’s disease caused by the PSEN1 M146L mutation. J Alzheimers Dis 37: 285-289.
  56. Sebastiani P, Solovieff N, Dewan AT, Walsh KM, Puca A, et al. (2012) Genetic signatures of exceptional longevity in humans. PLoS One 7: e29848.
  57. Beekman M, Blanché H, Perola M, Hervonen A, Bezrukov V, et al. (2013) Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study. Aging Cell 12: 184-193.
  58. Davies G, Harris SE, Reynolds CA, Payton A, Knight HM et al. (2014) A genome-wide association study implicates the APOE locus in nonpathological cognitive ageing. Mol Psychiatry 19: 76-87.
  59. Hayden KM, McEvoy JM, Linnertz C, Attix D, Kuchibhatla M, et al. (2012) A homopolymer polymorphism in the TOMM40 gene contributes to cognitive performance in aging. Alzheimers Dement 8: 381-388.
  60. Linnertz C, Anderson L, Gottschalk W, Crenshaw D, Lutz MW, et al. (2014) The cis-regulatory effect of an Alzheimer’s disease-associated poly-T locus on expression of TOMM40 and apolipoprotein E genes. Alzheimers Dement 10: 541-551.
  61. Cacabelos R, Goldgaber D, Vostrov A, Matsuki H, Torrellas C, et al. (2014) APOE-TOMM40 in the Pharmacogenomics of Dementia. J. Pharmacogenomics Pharmacoproteomics 5: 135.
  62. Yoon H, Myung W, Lim SW, Kang HS, Kim S, et al. (2015) Association of the choline acetyltransferase gene with responsiveness to acetylcholinesterase inhibitors in Alzheimer's disease. Pharmacopsychiatry 48: 111-117.
  63. Braga IL, Silva PN, Furuya TK, Santos LC, Pires BC, et al. (2015) Effect of APOE and CHRNA7 genotypes on the cognitive response to cholinesterase inhibitor treatment at different stages of Alzheimer's disease. Am J Alzheimers Dis Other Demen 30: 139-144.
  64. Xie HG, Kim RB, Wood AJ, Stein CM (2001) Molecular basis of ethnic differences in drug disposition and response. Annu Rev Pharm Toxicol 41: 815-850.
  65. Cacabelos R (2011) The path to personalized medicine in mental disorders. The handbook of neuropsychiatric biomarkers, endophenotypes and genes. (4th edn), Springer, Netherlands.
  66. Cacabelos R (2005) Pharmacogenomics and therapeutic prospects in Alzheimer's disease. Expert Opin Pharmacother 6: 1967-1987.
  67. Cacabelos R (2007) Donepezil in Alzheimer’s disease: From conventional trials to pharmacogenetics. Neuropsychiat Dis Treat 3: 303-333.
  68. Jann MW, Shirley KL, Small GW (2002) Clinical pharmacokinetics and pharmacodynamics of cholinesterase inhibitors. Clin Pharmacokinet 41: 719-739.
  69. Noetzli M, Eap CB (2013) Pharmacodynamic, pharmacokinetic and pharmacogenetic aspects of drugs used in the treatment of Alzheimer's Disease. Clin Pharmacokinet 52: 225-241.
  70. Pilotto A, Franceschi M, D'Onofrio G, Bizzarro A, Mangialasche F, et al. (2009) Effect of a CYP2D6 polymorphism on the efficacy of donepezil in patients with Alzheimer disease. Neurology 73: 761-767.
  71. Albani D, Martinelli Boneschi F, Biella G, Giacalone G, Lupoli S, et al. (2012) Replication study to confirm the role of CYP2D6 polymorphism rs1080985 on donepezil efficacy in Alzheimer's disease patients. J Alzheimers Dis 30: 745-749.
  72. Savino M, Seripa D, Gallo AP, Garrubba M, D'Onofrio G, et al. (2011) Effectiveness of a high-throughput genetic analysis in the identification of responders/non-responders to CYP2D6-metabolized drugs. Clin Lab 57: 887-893.
  73. Seripa D, Bizzarro A, Pilotto A, D'onofrio G, Vecchione G, et al. (2011) Role of cytochrome P4502D6 functional polymorphisms in the efficacy of donepezil in patients with Alzheimer's disease. Pharmacogenet. Genomics 21: 225-230.
  74. Zhong Y, Zheng X, Miao Y, Wan L, Yan H, et al. (2013) Effect of CYP2D6*10 and APOE polymorphisms on the efficacy of donepezil in patients with Alzheimer's disease. Am J Med Sci 345: 222-226.
  75. Varsaldi F, Miglio G, Scordo MG, Dahl ML, Villa LM, et al. (2006) Impact of the CYP2D6 polymorphism on steady-state plasma concentrations and clinical outcome of donepezil in Alzheimer's disease patients. Eur J Clin Pharmacol 62: 721-726.
  76. Chianella C, Gragnaniello D, Maisano Delser P, Visentini MF, Sette E, et al. (2011) BCHE and CYP2D6 genetic variation in Alzheimer's disease patients treated with cholinesterase inhibitors. Eur J Clin Pharmacol 67: 1147-1157.
  77. Mannheimer B, Wettermark B, Lundberg M, Pettersson H, von Bahr C, et al. (2010) Nationwide drug-dispensing data reveal important differences in adherence to drug label recommendations on CYP2D6-dependent drug interactions. Br J Clin Pharmacol 69: 411-417.
  78. Magliulo L, Dahl ML, Lombardi G, Fallarini S, Villa LM, et al. (2011) Do CYP3A and ABCB1 genotypes influence the plasma concentration and clinical outcome of donepezil treatment?. Eur J Clin Pharmacol 67: 47-54.
  79. Miranda LF, Gomes KB, Silveira JN, Pianetti GA, Byrro RM, et al. (2015) Predictive factors of clinical response to cholinesterase inhibitors in mild and moderate Alzheimer's disease and mixed dementia: a one-year naturalistic study. J Alzheimers Dis 45: 609-620.
  80. Lilienfeld S (2002) Galantamine-a novel cholinergic drug with a unique dual mode of action for the treatment of patients with Alzheimer's disease. CNS Drug Rev 8: 159-176.
  81. Farlow MR (2003) Clinical pharmacokinetics of galantamine. Clin Pharmacokinet 42: 1383-1392.
  82. Zhao Q, Brett M, Van Osselaer N, Huang F, Raoult A, et al. (2002) Galantamine pharmacokinetics, safety, and tolerability profiles are similar in healthy Caucasian and Japanese subjects. J Clin Pharmacol 42: 1002-1010.
  83. Mannens GS, Snel CA, Hendrickx J, Verhaeghe T, Le Jeune L, et al. (2002) The metabolism and excretion of galantamine in rats, dogs, and humans. Drug Metab Dispos 30: 553-563.
  84. Noetzli M, Guidi M, Ebbing K, Eyer S, Zumbach S, et al. (2013) Relationship of CYP2D6, CYP3A, POR, and ABCB1 genotypes with galantamine plasma concentrations. Ther Drug Monit 35: 270-275.
  85. Clarke JA, Cutler M, Gong I, Schwarz UI, Freeman D, et al. (2011) Cytochrome P450 2D6 phenotyping in an elderly population with dementia and response to galantamine in dementia: A pilot study. Am J Geriatr Pharmacother 9: 224-233.
  86. Bentué-Ferrer D, Tribut O, Polard E, Allain H (2003) Clinically significant drug interactions with cholinesterase inhibitors: A guide for neurologists. CNS Drugs 17: 947-963.
  87. Huang F, Fu Y (2010) A review of clinical pharmacokinetics and pharmacodynamics of galantamine, a reversible acetylcholinesterase inhibitor for the treatment of Alzheimer's disease, in healthy subjects and patients. Curr Clin Pharmacol 5: 115-124.
  88. Zhai XJ, Lu YN (2012) Food-drug interactions: Effect of capsaicin on the pharmacokinetics of galantamine in rats. Xenobiotica 42: 1151-1155.
  89. Polinsky RJ (1998) Clinical pharmacology of rivastigmine: a new-generation acetylcholinesterase inhibitor for the treatment of Alzheimer's disease. Clin Ther 20: 634-647.
  90. Sonali N, Tripathi M, Sagar R, Velpandian T, Subbiah V (2013) Clinical effectiveness of rivastigmine monotherapy and combination therapy in Alzheimer's patients. CNS Neurosci Ther 19: 91-97.
  91. Lupp A, Appenroth D, Fang L, Decker M, Lehmann J, et al. (2010) Tacrine-NO donor and tacrine-ferulic acid hybrid molecules as new anti-Alzheimer agents: Hepatotoxicity and influence on the cytochrome P450 system in comparison to tacrine. Arzneimittelforschung 60: 229-237.
  92. Alfirevic A, Mills T, Carr D, Barratt BJ, Jawaid A, et al. (2007) Tacrine-induced liver damage: An analysis of 19 candidate genes. Pharmacogenet Genomics 17: 1091-1100.
  93. Yang Z, Zhou X, Zhang Q (2013) Effectiveness and safety of memantine treatment for Alzheimer's disease. J Alzheimers Dis 36: 445-458.
  94. Micuda S, Mundlova L, Anzenbacherova E, Anzenbacher P, Chladek J, et al. (2004) Inhibitory effects of memantine on human cytochrome P450 activities: Prediction of in vivo drug interactions. Eur J Clin Pharmacol 60: 583-589.
  95. Noetzli M, Guidi M, Ebbing K, Eyer S, Wilhelm L, et al. (2013) Population pharmacokinetic study of memantine: Effects of clinical and genetic factors. Clin. Pharmacokinet 52: 211-223.
  96. Marquez B, Van Bambeke F (2011) ABC multidrug transporters: Target for modulation of drug pharmacokinetics and drug-drug interactions. Curr. Drug Targets 12: 600-620.
  97. Haufroid V (2011) Genetic polymorphisms of ATP-binding cassette transporters ABCB1 and ABCC2 and their impact on drug disposition. Curr. Drug Targets 12: 631-646.
  98. Cacabelos R, López-Muñoz F (2014) The ABCB1 transporter in Alzheimer’s disease. Clin Exp Pharmacol 4: e128.
  99. Li G, Gu HM, Zhang DW (2013) ATP-binding cassette transporters and cholesterol translocation. IUBMB Life 65: 505-512.
  100. van Assema DM, Lubberink M, Rizzu P, van Swieten JC, Schuit RC, et al. (2012) Blood-brain barrier P-glycoprotein function in healthy subjects and Alzheimer's disease patients: Effect of polymorphisms in the ABCB1 gene. EJNMMI Res 2: 57.
  101. Chen KD, Chang PT, Ping YH, Lee HC, Yeh CW, et al. (2011) Gene expression profiling of peripheral blood leukocytes identifies and validates ABCB1 as a novel biomarker for Alzheimer's disease. Neurobiol Dis 43: 698-705.
  102. Elali A, Rivest S (2013) The role of ABCB1 and ABCA1 in beta-amyloid clearance at the neurovascular unit in Alzheimer's disease. Front Physiol 4: 45.
  103. Cascorbi I, Flüh C, Remmler C, Haenisch S, Faltraco F, et al. (2013) Association of ATP-binding cassette transporter variants with the risk of Alzheimer's disease. Pharmacogenomics 14: 485-494.
  104. Reitz C, Jun G, Naj A, Rajbhandary R, Vardarajan BN, et al. (2013) Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E ϵ4, and the risk of late-onset Alzheimer disease in African Americans. JAMA 309: 1483-1492.
  105. Fehér Á, Juhász A, László A, Pákáski M, Kálmán J, et al.(2013) Association between the ABCG2 C421A polymorphism and Alzheimer's disease. Neurosci Lett 550: 51-54.
  106. Cacabelos R (2012) The metabolomic paradigm of pharmacogenomics in complex disorders. Metabolomics 2: e119.
  107. Hosoya K, Tachikawa M (2011) Roles of organic anion/cation transporters at the blood-brain and blood-cerebrospinal fluid barriers involving uremic toxins. Clin Exp Nephrol 15: 478-485.
  108. Carl SM, Lindley DJ, Das D, Couraud PO, Weksler BB, et al. (2010) ABC and SLC transporter expression and Pot substrate characterization across the human CMEC/D3 blood-brain barrier cell line. Mol Pharm 7: 1057-1068.
  109. Eckel RH (1989) Lipoprotein lipase. A multifunctional enzyme relevant to common metabolic diseases. N Engl J Med 320: 1060-1068.
  110. Pare G, Serre D, Brisson D, Anand SS, Montpetit A, et al. (2007) Genetic analysis of 103 candidate genes for coronary artery disease and associated phenotypes in a founder population reveals a new association between endothelin-1 and high-density lipoprotein cholesterol. Am J Hum Genet 80: 673-682.
  111. Pedro-Botet J, Sentí M, Nogués X, Rubiés-Prat J, Roquer J, et al. (1992) Lipoprotein and apolipoprotein profile in men with ischemic stroke. Role of lipoprotein (a), triglyceride-rich lipoproteins, and apolipoprotein E polymorphism. Stroke 23: 1556-1562.
  112. Stocks J, Thorn JA, Galton DJ. (1992) Lipoprotein lipase genotypes for a common premature termination codon mutation detected by PCR-mediated site-directed mutagenesis and restriction digestion. J Lipid Res 33: 853-857.
  113. Jeunemaitre X, Soubrier F, Kotelevtsev YV, Lifton RP, Williams CS, et al. (1992) Molecular basis of human hypertension: role of angiotensinogen. Cell 71: 7-20.
  114. Katsuya T, Koike G, Yee TW, Sharpe N, Jackson R, et al. (1995) Association of angiotensinogen gene T235 variant with increased risk of coronary heart disease. Lancet 345: 1600-1603.
  115. Rankinen T, Rice T, Pérusse L, Chagnon YC, Gagnon J, et al. (2000) NOS3 Glu298Asp genotype and blood pressure response to endurance training: the HERITAGE family study. Hypertension 36: 885-889.
  116. Slowik A, Turaj W, Dziedzic T, Haefele A, Pera J, et al. (2004) DD genotype of ACE gene is a risk factor for intracerebral hemorrhage. Neurology 63: 359-361.
  117. Danesh J, Appleby P (1998) Persistent infection and vascular disease: a systematic review. Expert Opin Investig Drugs 7: 691-713.
  118. Fernández-Real JM, Broch M, Vendrell J, Richart C, Ricart W (2000) Interleukin-6 gene polymorphism and lipid abnormalities in healthy subjects. J Clin Endocrinol Metab 85: 1334-1339.
  119. Iacoviello L, Di Castelnuovo A, Gattone M, Pezzini A, Assanelli D, et al. (2005) Polymorphisms of the Interleukin-1ß Gene Affect the Risk of Myocardial Infarction and Ischemic Stroke at Young Age and the Response of Mononuclear Cells to Stimulation In Vitro. Arterioscler Thromb Vasc Biol 25: 222-227.
  120. Libby P (2002) Inflammation in atherosclerosis. Nature 420: 868-874.
  121. Luther T, Mackman N (2001) Tissue factor in the heart. Multiple roles in hemostasis, thrombosis, and inflammation. Trends Cardiovasc Med 11: 307-312.
  122. Rainero I, Grimaldi LM, Salani G et al. (2004) Association between the tumor necrosis factor-alpha -308 G/A gene polymorphism and migraine. Neurology 62: 141-143.
  123. Reich D, Patterson N, Ramesh V, De Jager PL, McDonald GJ, et al. (2007) Admixture mapping of an allele affecting interleukin 6 soluble receptor and interleukin 6 levels. Am J Hum Genet 80: 716-26.
  124. Revilla M, Obach V, Cervera A, Dávalos A, Castillo J, et al. (2002) A -174G/C polymorphism of the interleukine-6 gene in patients with lacunar infarction. Neurosci Lett 324: 29-32.
  125. 125. Rosmond R, Chagnon M, Bouchard C, Björntorp P (2001) G-308A polymorphism of the tumor necrosis factor alpha gene promoter and salivary cortisol secretion. J Clin Endocrinol Metab 86: 2178-2180.
  126. Rost NS, Wolf PA, Kase CS, Kelly-Hayes M, Silbershatz H, et al. (2001) Plasma concentration of C-reactive protein and risk of ischemic stroke and transient ischemic attack: the Framingham study. Stroke 32: 2575-2579.
  127. Yamada Y, Metoki N, Yoshida H, Satoh K, Ichihara S, et al. (2006) Genetic risk for ischemic and hemorrhagic stroke. Arterioscler Thromb Vasc Biol 26: 1920-1925.
  128. Bethke L, Webb E, Murray A, Schoemaker M, Feychting M, et al. (2008) Functional polymorphisms in folate metabolism genes influence the risk of meningioma and glioma. Cancer Epidemiol Biomarkers Prev 17: 1195-1202.
  129. Casas JP, Hingorani AD, Bautista LE, Sharma P (2004) Meta-analysis of genetic studies in ischemic stroke: thirty-two genes involving approximately 18,000 cases and 58,000 controls. Arch Neurol 61: 1652-1661.
  130. De Stefano V, Martinelli I, Mannucci PM, Paciaroni K, Rossi E, et al. (1999) The risk of recurrent deep venous thrombosis among heterozygous carriers of both factor V Leiden and the G20210A prothrombin mutation. N Engl J Med 341: 801-806.
  131. Gerhardt A, Goecke TW, Beckmann MW, Wagner KJ, Tutschek B, et al. (2000) Prothrombin and factor V mutations in women with a history of thrombosis during pregnancy and the puerperium. N Engl J Med 342: 374-380.
  132. Wald DS, Law M, Morris JK (2002) Homocysteine and cardiovascular disease: evidence on causality from a meta-analysis. Brit Med J 325: 1202-1207.
  133. 133. Carril JC, Cacabelos (2016) Genetic risk factors in cerebrovascular disorders and cognitive deterioration. Current Genomics.
  134. Xue-Shan Z, Juan P, Qi W, Zhong R, Li-Hong P, et al. (2016) Imbalanced cholesterol metabolism in Alzheimer's disease.Clin Chim Acta 456: 107-114.
  135. Chen YL, Wang LM, Chen Y, Gao JY, Marshall C, et al. (2016) Changes in astrocyte functional markers and β-amyloid metabolism-related proteins in the early stages of hypercholesterolemia.Neuroscience 316: 178-191.
  136. Dias HK, Brown CL, Polidori MC, Lip GY, Griffiths HR (2015) LDL-lipids from patients with hypercholesterolaemia and Alzheimer's disease are inflammatory to microvascular endothelial cells: mitigation by statin intervention. Clin Sci 129: 1195-1206.
  137. Chakrabarti S, Khemka VK, Banerjee A, Chatterjee G, Ganguly A, et al. (2015) Metabolic Risk Factors of Sporadic Alzheimer's Disease: Implications in the Pathology, Pathogenesis and Treatment. Aging Dis 6: 282-299.
  138. Cacabelos R, Vallejo AI, Lombardi V, Fernández-Novoa L, Pichel V (2004) E-SAR-94010 (LipoEsar®): A pleiotropic lipoprotein compound with powerful anti-atheromatous and lipid lowering effects. CNS Drug Rev 10: 200-201.
  139. Leduc V, Bourque L, Poirier J, Dufour R (2016) Role of rs3846662 and HMGCR alternative splicing in statin efficacy and baseline lipid levels in familial hypercholesterolemia. Pharmacogenet Genomics 26: 1-11.
  140. Szulwach KE, Jin P (2014) Integrating DNA methylation dynamics into a framework for understanding epigenetic codes. Bioessays 36: 107-117.
  141. Gavery MR, Roberts SB (2013) Predominant intragenic methylation is associated with gene expression characteristics in a bivalve mollusc. Peer J 1: e215.
  142. Wang J, Yu JT, Tan MS, Jiang T, Tan L (2013) Epigenetic mechanisms in Alzheimer's disease: Implications for pathogenesis and therapy. Ageing Res Rev 12: 1024-1041.
  143. Mastroeni D, Grover A, Delvaux E, Whiteside C, Coleman PD (2011) Epigenetic mechanisms in Alzheimer’s disease. Neurobiol Aging 32: 1161-1180.
  144. Gräff J, Tsai LH (2013) The potential of HDAC inhibitors as cognitive enhancers. Annu Rev Pharmacol Toxicol 53: 311-330.
  145. Adwan L, Zawia NH (2013) Epigenetics: a novel therapeutic approach for the treatment of Alzheimer's disease. Pharmacol Ther 139: 41-50.
  146. Van den Hove DL, Kompotis K, Lardenoije R, Kenis G, Mill J, et al. (2014) Epigenetically regulated microRNAs in Alzheimer's disease. Neurobiol Aging 35: 731-745.
  147. Wu P, Zuo X, Deng H, Liu X, Liu L, Ji A (2013) Roles of long noncoding RNAs in brain development, functional diversification and neurodegenerative diseases. Brain Res Bull 97: 69-80.
  148. Qureshi IA, Mehler MF (2011) Advances in epigenetics and epigenomics for neurodegenerative diseases. Curr. Neurol. Neurosci Rep 11: 464-473.
  149. Enciu AM, Popescu BO, Gheorghisan-Galateanu A (2012) MicroRNAs in brain development and degeneration. Mol Biol Rep 39: 2243-2252.
  150. Mallick B, Ghosh Z (2011) A complex crosstalk between polymorphic microRNA target sites and AD prognosis. RNA Biol 8: 665-673.
  151. Cacabelos R (2015) Pharmacoepigenomics and the metabolomics of drug efficacy and safety. Metabolomics 5: 3.
  152. Tang X, Chen S (2015) Epigenetic regulation of cytochrome P450 enzymes and clinical implication. Curr Drug Metab 16: 86-96.
  153. Kang H, Kim C, Lee H, Kim W, Lee EK (2013) Post-transcriptional controls by ribonucleoprotein complexes in the acquisition of drug resistance. Int J Mol Sci 14: 17204-17220.
  154. Kim IW, Han N, Burckart GJ, Oh JM (2014) Epigenetic changes in gene expression for drug-metabolizing enzymes and transporters. Pharmacotherapy 34: 140-150.
  155. Cacabelos R (2015) Metabolomics of drug resistance in cancer: The Epigenetic Component. Metabolomics 5: e141.
  156. Varela MA, Roberts TC, Wood MJ (2013) Epigenetics and ncRNAs in brain function and disease: mechanisms and prospects for therapy. Neurotherapeutics 10: 621-631.
  157. Caraci F, Leggio GM, Drago F, Salomone S (2013) Epigenetic drugs for Alzheimer's disease: hopes and challenges. Br J Clin Pharmacol 75: 1154-1155.
  158. Yu CE, Cudaback E, Foraker J, Thomson Z, Leong L, et al. (2013) Epigenetic signature and enhancer activity of the human APOE gene. Hum Mol Genet 22: 5036-5047.
  159. Soares HD, Potter WZ, Pickering E, Kuhn M, Immermann FW, et al. (2012) Plasma biomarkers associated with the apolipoprotein E genotype and Alzheimer disease. Arch Neurol 69: 1310-1317.
  160. Chouliaras L, Rutten BP, Kenis G, Peerbooms O, Visser PJ, et al. (2010) Epigenetic regulation in the pathophysiology of Alzheimer's disease. Prog Neurobiol 90: 498-510.
  161. Wang SC, Oelze B, Schumacher A (2008) Age-specific epigenetic drift in late-onset Alzheimer’s disease. PLoS One 3: e2698.
  162. Caesar I, Gandy S (2012) Evidence that an APOE 4 ‘double whammy’ increases risk for Alzheimer’s disease. BMC Med 10: 36.
  163. Hartley I, Elkhoury FF, Heon Shin J, Xie B, Gu X, et al. (2013) Long-lasting changes in DNA methylation following short-term hypoxic exposure in primary hippocampal neuronal cultures. PLoS One 8: e77859.
  164. Somaio Neto F, Ikejiri AT, Bertoletto PR, Chaves JC, Teruya R, et al. (2014) Gene Expression Related to Oxidative Stress in the Heart of Mice after Intestinal Ischemia. Arg Bras Cardiol 102: 165-173.
  165. Akram A, Schmeidler J, Katsel P, Hof PR, Haroutunian V (2010) Increased expression of RXRα in dementia: an early harbinger for the cholesterol dyshomeostasis?. Mol Neurodegener 15: 5-36.
  166. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, et al. (2014) Alzheimer's disease pathology is associated with early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 17: 1156-1163.
  167. Chapuis J, Hansmannel F, Gistelinck M, Mounier A, Van Cauwenberghe C, et al. (2013) GERAD consortium. Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology. Mol Psychiatry.18(11):1225-1234.
  168. Rauhala HE, Porkka KP, Saramäki OR, Tammela TL, Visakorpi T (2008) Clusterin is epigenetically regulated in prostate cancer. Int J Cancer 123: 1601-1609.
  169. Yu L, Chibnik LB, Srivastava GP, Pochet N, Yang J, et al. (2015) Association of brain DNA methylation in SORL1, ABCA7, HLADRB5, SLC24A4, and BIN1 with pathological diagnosis of Alzheimer’s disease. JAMA Neurol 72: 15-24.
  170. Karch CM, Jeng AT, Nowotny P, Cady J, Cruchaga C, Goate AM (2012) Expression of novel Alzheimer’s disease risk genes in control and Alzheimer’s disease brains. PLoS One 7: e50976.
  171. Taher N, McKenzie C, Garrett R, Baker M, Fox N, et al. (2014) Amyloid-β alters the DNA methylation status of cell-fate genes in an Alzheimer's disease model. J Alzheimers Dis 38: 831-844.
  172. Chen KL, Wang SS, Yang YY, Yuan RY, Chen RM, et al. (2009) The epigenetic effects of amyloid-beta(1-40) on global DNA and neprilysin genes in murine cerebral endothelial cells. Biochem Biophys Res Commun 378: 57-61.
  173. Lu H, Liu X, Deng Y, Qing H (2013) DNA methylation, a hand behind neurodegenerative diseases. Front Aging Neurosci 5: 85.
  174. Marques SC, Lemos R, Ferreiro E, Martins M, de Mendonça A, et al. (2012) Epigenetic regulation of BACE1 in Alzheimer's disease patients and in transgenic mice. Neuroscience 220: 256-66.
  175. Soh J, Iqbal J, Queiroz J, Fernandez-Hernando C, Mahmood Hussain M (2013) MicroRNA-30c reduces hyperlipidemia and atherosclerosis in mice by decreasing lipid synthesis and lipoprotein secretionNature Medicine 19: 892-900.
  176. Lee TH, Matta B, King BD, Hodges MR, Tillmann HL, et al. (2015) MicroRNA-122 associates with serum apolipoprotein B but not liver fibrosis markers in CHC genotype 1 infection. J Med Virol 87: 1722-1726.
  177. Haworth KE, Farrell WE, Emes RD, Ismail KM, Carroll WD, et al. (2013) Combined influence of gene-specific cord blood methylation and maternal smoking habit on birth weight. Epigenomics 5: 37-49.
  178. Soares HD, Potter WZ, Pickering E, Kuhn M, Immermann FW, et al. (2012) Plasma Biomarkers Associated With the Apolipoprotein E Genotype and Alzheimer Disease. Arch Neurol 69: 1310-1317.
  179. Shih YH, Tsai KJ, Lee CW, Shiesh SC, Chen WT, et al. (2014) Apolipoprotein C-III is an Amyloid-β-Binding Protein and an Early Marker for Alzheimer’s Disease. J Alzheimer’s Disease 41: 855-865.
  180. Guay SP, Brisson D, Lamarche B, Marceau P, Vohl MC, et al. (2013) DNA methylation variations at CETP and LPL gene promoter loci: new molecular biomarkers associated with blood lipid profile variability. Atherosclerosis 228: 413-420.
  181. Postberg J, Kanders M, Forcob S, Willems R, Orth V, et al. (2015) CpG signalling, H2A.Z/H3 acetylation and microRNA-mediated deferred self attenuation orchestrate foetal NOS3 expression. Clin Epigenetics 7: 9.
  182. Fish JE, Marsden PA (2006) Endothelial nitric oxide synthase: insight into cell-specific gene regulation in the vascular endothelium. Cell Mol Life Sci 63: 144-162.
  183. Fish JE, Matouk CC, Rachlis A, Lin S, Tai SC, et al. (2005) The expression of endothelial nitric-oxide synthase is controlled by a cell-specific histone code. J Biol Chem 280: 24824-24838.
  184. Fish JE, Matouk CC, Yeboah E, Bevan SC, Khan M, et al. (2007) Hypoxia-inducible expression of a natural cis-antisense transcript inhibits endothelial nitric-oxide synthase. J Biol  Chem 282: 15652-15666.
  185. Fish JE, Yan MS, Matouk CC, St Bernard R, Ho JJ, et al. (2010) Hypoxic repression of endothelial nitric-oxide synthase transcription is coupled with eviction of promoter histones. J Biol Chem 285: 810-826.
  186. Zill P, Baghai TC, Schüle C, Born C, Früstück C, et al. (2012) DNA methylation analysis of the angiotensin converting enzyme (ACE) gene in major depression. PLoS One 7: e40479.
  187. Rangel M, dos Santos JC, Ortiz PH, Hirata M, Jasiulionis MG, et al. (2014) Modification of Epigenetic Patterns in Low Birth Weight Children: Importance of Hypomethylation of the ACE Gene Promoter. PLoS One 9: e106138.
  188. Ryosuke S (2016) Histone deacetylase 9 is an epigenetic suppressor of intrarenal angiotensinogen, serving as a key mechanism in angiotensinogen augmentation in hypertension. Tulane University, New Orleans, LA, United States.
  189. Lovrečić L, Maver A, Zadel M, Peterlin B (2016)The role of epigenetics in neurodegenerative diseases: Neurodegenerative Diseases. 
  190. Mi XB, Zeng FQ. (2008) Hypomethylation of interleukin-4 and -6 promoters in T cells from systemic lupus erythematosus patients. Acta Pharmacol Sin 29: 105-112.
  191. Nile CJ, Read RC, Akil M, Duff GW, Wilson A (2008) Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis Rheum 58: 2686-2693.
  192. Liu, B, Whisler, RL (1998) Transcriptional activation and redox regulation of the tumor necrosis factor-alpha promoter in human T cells: role of the CRE/kappa3 promoter region. J. Interferon Cytokine Res 18: 999-1007.
  193. Pieper HC, Evert BO, Kaut O, Riederer PF, Waha A, et al. (2008) Different methylation of the TNF-alpha promoter in cortex and substantia nigra: Implications for selective neuronal vulnerability. Neurobiol Dis 32: 521-527.
  194. Pike B, Greiner TC, Wang X, Weisenburger DD, Hsu YH, et al. (2008) DNA methylation profiles in diffuse large B-cell lymphoma and their relationship to gene expression status. Leukemia 22: 1035-1043.
  195. Shaknovich R, Melnick A (2011) Epigenetics and B-cell lymphoma. Curr Opin Hematol 18: 293-299.
  196. Rotondo JC, Bosi S, Bazzan E, Di Domenico M, De Mattei M, et al. (2012) Methylenetetrahydrofolate reductase gene promoter hypermethylation in semen samples of infertile couples correlates with recurrent spontaneous abortion. Hum Reprod 27: 3632-3638.
  197. Peng L, Xhong X. (2015) Epigenetic regulation of drug metabolism and transport. Acta Pharm Sin B 5: 106-112.
  198. Kim H, Lapointe J, Kaygusuz G, Ong DE, Li C, et al. (2005) The retinoic acid synthesis gene ALDH1a2 is a candidate tumor suppressor in prostate cancer. Cancer Res 65: 8118-8124.
  199. Sharma R, Panda NK, Khullar M (2010) Hypermethylation of carcinogen metabolism genes, CYP1A1, CYP2A13 and GSTM1 genes in head and neck cancer. Oral Dis 16: 668-673.
  200. Gomez A, Ingelman-Sundberg M (2009) Pharmacoepigenetics: its role in interindividual differences in drug response. Clin Pharmacol Ther 85: 426-430.
  201. Ingelman-Sundberg M, Sim SC, Gomez A, Rodriguez-Antona C (2007) Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol Ther 116: 496-526.
  202. Okino ST, Pookot D, Li LC, Zhao H, Urakami S, et al. (2006) Epigenetic inactivation of thedioxin-responsive cytochrome P4501A1 gene in human prostate cancer. Cancer Res 66: 7420-7428.
  203. 203. Suter M, Abramovici A, Showalter L, Hu M, Shope CD, et al. (2010) In utero tobacco exposure epigenetically modifies placental CYP1A1 expression. Metabolism 59: 1481-1490.
  204. 204. Anttila S, Hakkola J, Tuominen P, Elovaara E, Husgafvel-Pursiainen K, et al. (2003) Methylation of cytochrome P4501A1 promoter in the lung is associated with tobacco smoking. Cancer Res 63: 8623-8628.
  205. Habano W, Gamo T, Sugai T, Otsuka K, Wakabayashi G, et al. (2009) CYP1B1, but not CYP1A1, is downregulated by promoter methylation in colorectal cancers. Int J Oncol 34: 1085-1091.
  206. Cacabelos R (2015) Impact of genomic medicine on the future of neuropsychopharmacology. J Neuropsychopharmacol Mental Health 1: 1.
  207. Tokizane T, Shiina H, Igawa M, Enokida H, Urakami S, et al. (2005) Cytochrome P450 1B1 is overexpressed and regulated by hypomethylation in prostate cancer. Clin. Cancer Res.11(16):5793-5801.
  208. Ingelman-Sundberg M, Zhong XB, Hankinson O, Beedanagari S, Yu AM, et al. (2013) Potential role of epigenetic mechanisms in the regulations of drug metabolism and transport. Drug. Metab. Dispos.41(10):1725-1731.
  209. Habano W, Kawamura K, Iizuka N, Terashima J, Sugai T, et al. (2015) Analysis of DNA methylation landscape reveals the roles of DNA methylation in the regulation of drug metabolizing enzymes. Clin Epigenetics 7: 105.
  210. Widschwendter M, Siegmund KD, Müller HM, Fiegl H, Marth C, et al. (2004) Association of breast cancer DNA methylation profiles with hormone receptor status and response to tamoxifen. Cancer Res 64: 3807-3813.
  211. Chung I, Karpf AR, Muindi JR, Conroy JM, Nowak NJ, et al. (2007) Epigenetic silencing of CYP24 in tumor-derived endothelial cells contributes to selective growth inhibition by calcitriol. J Biol Chem 282: 8704-8714.
  212. Zhu H, Wang X, Shi H, Su S, Harshfield GA, et al. (2013) A genome wide methylation study of severe vitamin D deficiency in African American adolescents. J Pediatr 162: 1004-1009.
  213. Shi H, Yan PS, Chen CM, Rahmatpanah F, Lofton-Day C, et al. (2002) Expressed CpG island sequence tag microarray for dual screening for DNA hypermethylation and gene silencing in cancer cells. Cancer Res 62: 3314-3320.
  214. Khorchide M, Lechner D, Cross HS (2005) Epigenetic regulation of vitamin D hydroxylase expression and activity in normal and malignant prostate cells. J Steroid Biochem Mol Biol 93: 167-172.
  215. Novakovic B, Sibson M, Ng HK, Manuelpillai U, Rakyan V, et al. (2009) Placenta-specific methylation of the vitamin D 24-hydroxylase gene: implications for feedback autoregulation of active vitamin D levels at the fetomaternal interface. J Biol Chem 284: 14838-14848.
  216. Wjst M, Heimbeck I, Kutschke D, Pukelsheim K (2010) Epigenetic regulation of vitamin D converting enzymes. J Steroid Biochem Mol Biol 121: 80-83.
  217. Kaut O, Schmitt I, Wüllner U (2012) Genome-scale methylation analysis of Parkinson's disease patients' brains reveals DNA hypomethylation and increased mRNA expression of cytochrome P4502E1. Neurogenetics 13: 87-91.
  218. Jiménez-Garza O, Baccarelli AA, Byun HM, Márquez-Gamiño S, Barrón-Vivanco BS, Albores A (2015) CYP2E1 epigenetic regulation in chronic, low-level toluene exposure: Relationship with oxidative stress and smoking habit. Toxicol Appl Pharmacol 286: 207-215.
  219. Gomez A, Karlgren M, Edler D, Bernal ML, Mkrtchian S, et al. (2007) Expression of CYP2W1 in colon tumors: regulation by gene methylation. Pharmacogenomics 8: 1315-1325.
  220. Karlgren M, Gomez A, Stark K, Svärd J, Rodriguez-Antona C, et al. (2006) Tumor-specific expression of the novel cytochrome P450 enzyme, CYP2W1. Biochem Biophys Res Commun 341: 451-458.
  221. Edler D, Stenstedt K, Ohrling K, Hallström M, Karlgren M, et al. (2009) The expression of the novel CYP2W1 enzyme is an independent prognostic factor in colorectal cancer. A pilot study. Eur J Cancer 45: 705-712.
  222. Olsson M, Gustafsson O, Skogastierna C, Tolf A, Rietz BD, et al.(2007) Regulation and expression of human CYP7B1 in prostate: overexpression of CYP7B1 during progression of prostatic adenocarcinoma. Prostate 67: 1439-1446.
  223. Rønneberg JA, Tost J, Solvang HK, Alnaes GI, Johansen FE, et al. (2008) GSTP1 promoter haplotypes affect DNA methylation levels and promoter activity in breast carcinomas. Cancer Res 68: 5562-5571.
  224. Woodson K, O'Reilly KJ, Hanson JC, Nelson D, Walk EL, et al. (2008) The usefulness of the detection of GSTP1 methylation in urine as a biomarker in the diagnosis of prostate cancer. J Urol 179: 508-511.
  225. Kalinina EV, Chernov NN, Saprin AN, Kotova YN, Remizov VI, et al. (2007) Expression of genes for redox-dependent glutathione S-transferase isoforms GSTP1-1 and GSTA4-4 in tumor cell during the development doxorubicin resistance. Bull Exp Biol Med 43: 328-330.
  226. Kim SJ, Kang HS, Chang HL, Jung YC, Sim HB, et al. (2008) Promoter hypomethylation of the N-acetyltransferase 1 gene in breast cancer. Oncol Rep 19: 663-668.
  227. Kwon MS, Kim SJ, Lee SY, Jeong JH, Lee ES, et al. (2006) Epigenetic silencing of the sulfotransferase 1A1 gene by hypermethylation in breast tissue. Oncol Rep 15: 27-32.
  228. David GL, Yegnasubramanian S, Kumar A, Marchi VL, De Marzo AM, et al. (2004) MDR1 promoter hypermethylation in MCF-7 human breast cancer cells: changes in chromatin structure induced by treatment with 5-Aza-cytidine. Cancer Biol Ther 3: 540-548.
  229. Kusaba H, Nakayama M, Harada T, Nomoto M, Kohno K, et al. (1999) Association of 5′ CpG demethylation and altered chromatin structure in the promoter region with transcriptional activation of the multidrug resistance 1 gene in human cancer cells. Eur J Biochem 262: 924-932.
  230. Reed K, Hembruff SL, Sprowl JA, Parissenti AM (2010) The temporal relationship between ABCB1 promoter hypomethylation, ABCB1 expression and acquisition of drug resistance. Pharmacogenom J 10: 489-504.
  231. Yu J, Zhu T, Wang Z, Zhang H, Qian Z, et al. (2007) A novel set of DNA methylation markers in urine sediments for sensitive/specific detection of bladder cancer. Clin Cancer Res 13: 7296-7304.
  232. Bram EE, Stark M, Raz S, Assaraf YG. (2009) Chemotherapeutic drug- induced ABCG2 promoter demethylation as a novel mechanism of acquired multidrug resistance. Neoplasia 11: 1359-1370.
  233. Ferreri AJ, Dell'Oro S, Capello D, Ponzoni M, Iuzzolino P, et al. (2004) Aberrant methylation in the promoter region of the reduced folate carrier gene is a potential mechanism of resistance to methotrexate in primary central nervous system lymphomas. Br J Haematol 126: 657-664.
  234. Chen L, Hong C, Chen EC, Yee SW, Xu L, et al. (2013) Genetic and epigenetic regulation of the organic cation transporter 3, SLC22A3. Pharmacogenomics J 13: 110-120.