Category Archives: Transhuman News

Introduction

Antiretroviral therapy (ART) has significantly reduced HIV-related morbidity and mortality globally. However, the emerging threat of HIV drug resistance may reduce ART efficacy resulting in pronounced negative public health impact, especially in sub-Saharan Africa which account for about 70% of the global HIV epidemic.1 Scale-up of ART availability has been implemented and, in 2019, a global median of 67% of those in need (90% in Botswana)2,3 have had access to ART.4 Among the antiretroviral drugs (ARVs) used as first-line ART regimen, first-generation Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs), such as Efavirenz (EFV) and Nevirapine (NVP), are still largely used for HIV treatment in sub-Saharan Africa,5 although since 2017 the World Health Organization (WHO) has been recommending an EFV/NVP-sparing ART regimen in countries in which resistance to NNRTIs exceeds 10%.6 Despite the introduction of Dolutegravir (DTG)-based first-line ART in Botswana in June 2016, a significant proportion of HIV patients are still on EFV- or NVP-containing ART regimen.7 A recent (2019) WHO HIV drug resistance report encompassing 18 countries (6 from sub-Saharan Africa) revealed that pre-treatment HIV drug resistance rate to EFV/NVP exceeds 10% amongst adults initiating first-line ART (nearly twice as high in women than men).5 The rate was even higher (up to 30%) in those previously exposed to ARVs, including women having taken ARVs for the prevention of mother-to-child transmission.5

Antiretroviral therapy efficacy largely depends on adequate drug exposure to suppress viral replication and allow the immune system to recover. However, occurrence of drug toxicity, suboptimal patients compliance, suboptimal virologic responses, incomplete immune reconstitution and/or emergence of drug resistance limit therapeutic outcomes.8 HIV drug resistance, beside known viral factors, more frequently occurs because of sub-therapeutic ARV drug exposure and/or acquisition of drug-resistant strains. In resource-limited settings such as Botswana, HIV-diagnosed individuals with virologic failure are more likely to stay on virologically failing regimens for prolonged periods, because of lack of adequate virological follow-up. This may result in an ineffective drug exposure potentially causing drug toxicity and a higher risk of selecting and transmitting drug-resistant viruses.9 Moreover, the presence of HIV drug resistance mutations (DRMs) in minor viral populations is associated with an increased risk of virologic failure, in particular for NNRTI-based ART regimens, regardless of adherence, ethnicity, and immuno-virological basal characteristics of patients.8

Efavirenz and NVP are primarily metabolized in the liver by the cytochrome P450 2B6 enzyme (CYP2B6) with a minor contribution from other cytochromes (i.e. CYP2A6, CYP3A4/5).1012 Studies on several populations have shown that Africans display the greatest level of genetic diversity in the CYP2B6 gene.13 Cytochrome P450 2B6 is one of the most polymorphic CYP450 genes in humans with over 100 described single nucleotide polymorphisms (SNPs), numerous complex haplotypes and distinct ethnic frequencies.14 Cytochrome P450 2B6 gene polymorphism has been associated with interindividual differences in drug pharmacokinetics and consequent plasma exposure, with possible consequences on drug efficacy and safety.14 There are different SNPs in the CYP2B6 gene that, according to their combination as haplotypes, may lead to different degrees of slow and/or fast EFV/NVP metabolizer phenotypes.14 Among those SNPs, 516G>T (rs3745274) and 983T>C (rs28399499) are associated with a significant loss of CYP2B6 function, leading to reduced clearance and prolonged half-life both for EFV1518 and NVP.19,20 The 983T>C SNP affects the metabolism of both EFV and NVP; the 516G>T SNP influences mainly the EFV metabolism, while data on its impact on NVP metabolism are less conclusive.21,22 Conversely, two other SNPs, namely 785A>G (rs2279343) and 82T>C (rs34223104) are associated with a gain of CYP2B6 function, leading to lower drug exposure.2325 Indeed, the 785A>G SNP increases EFV26 and NVP27 metabolism, whereas there are no studies assessing the clinical/pharmacological impact in vivo of the 82T>C SNP. Nonetheless, CYP2B6 82T>C has recently been included in the panel of CYP2B6 SNPs that should be considered for the evaluation of therapeutic impact by the Clinical Pharmacogenetics Implementation Consortium.28

Pharmacogenetic studies of EFV and NVP have mostly been based on the CYP2B6 516G>T and 983T>C SNPs, with little or no clear assessment of the impact of the CYP2B6 785A>G and 82T>C SNPs.14 Studies from Botswana on HIV patients taking EFV-based ART showed that the CYP2B6 516T allele was protective against 1-year,29 but not at 6-months,30 virologic breakthrough. However, no HIV DRMs have been assessed in either study. Similar results on the influence of the CYP2B6 516G>T substitution on virologic outcome were observed in studies from the US involving HIV patients of African ancestry31 and HIV-diagnosed women from a multi-ethnic cohort,32 whereas other studies did not find any evidence of protection.3335 Another work, on 66 HIV-diagnosed women from Kenya taking NVP-based ART, showed no associations of CYP2B6 516G>T and 983T>C with virologic response and toxicity at 12 months of follow-up.20

Study results concerning the association of CYP2B6 slow metabolizer profiles (defined by the presence of 516T and/or 983C alleles) and EFV and NVP-related adverse events and/or toxicity are also conflicting, with some showing an association15,3639 and others not.1820,30,31,40,41 Notably, due to the complexity of the CYP2B6 polymorphisms and the highest frequency of slow/intermediate genotypes among individuals of African ancestry, it is likely that haplotypes rather than a single polymorphism are better predictors of EFV/NVP plasma concentrations,14 as well as of toxicity, in which polymorphisms in genes other than the CYP450 system may also play a role.42,43

Efavirenz and NVP have a long half-life (estimated at 40115 and 25164hrs, respectively), a low genetic barrier for HIV drug resistance, and complex pharmacogenetics, which raises the possibility of sub-therapeutic drug concentration in plasma, especially among CYP2B6 fast metabolizers, an aspect that has not been fully studied. The CYP2B6 fast metabolizer profile may allow low EFV/NVP plasma exposure, possibly leading to the selection and spread of HIV mutations and consequent viral drug resistance. On the other hand, EFV/NVP CYP2B6 slow metabolizers are exposed to higher drug plasma concentration, leading to potential higher toxicity and consequently reduced patients adherence and/or loss to care with possibility of sub-therapeutic plasma exposure and higher risk of HIV drug resistance (at least in a long-term perspective). While studies produced conflicting results, a posology adjustment according to the CYP2B6 polymorphism background has been proposed to address these potential issues in the context of personalized medicine.44,45

In summary, different CYP2B6 genotypes may influence immuno-virological response and/or toxicity by affecting EFV and NVP plasma concentration.14 We explored the possible impact of CYP2B6 genetic (and haplotype) variation on the risk of selection, accumulation and spread of HIV DRMs, providing a particular attention to the CYP2B6 fast metabolizer profile. To date, to the best of our knowledge, this aspect has not yet been fully evaluated.

This study was performed in Botswana with the aim to: i) assess CYP2B6 genotypes (for SNPs 82T>C, 516G>T, 785A>G, 983 T>C) in HIV-diagnosed adults taking EFV or NVP containing ART, and to classify corresponding CYP2B6 phenotypes as very slow, slow, extensive, rapid and ultra-rapid metabolizers; ii) construct haplotypes and apply a metabolic score according to the DRMs profile; iii) determine if there is any association of the CYP2B6 genotypes and haplotypes with the presence of EFV/NVP-resistant infections.

This retrospective casecontrol study was part of a larger Tshepo study46 that was conducted at Botswana-Harvard AIDS Institute Partnership (BHP) between 2002 and 2007. The Tshepo study was a 5-years open-label, randomized study with a sample of 650 HIV-1 diagnosed ART nave Botswana citizens (451 females and 199 males) attending the Infectious Disease Care Clinic (IDCC) in Princess Marina Hospital in Gaborone. The aim of Tshepo study was to evaluate the efficacy, tolerability and occurrence of drug resistance of six (6) different first-line ART regimens, all including an NNRTI either EFV or NVP, during a follow-up period of 156 weeks. For the purpose of the present study, being patients adherence assessed and comparable results,46 cases were defined as HIV-diagnosed individuals taking EFV or NVP containing ART and having virological failure (evaluated after at least 4 months of ART) related to DRMs assessed by HIV genotyping, whereas controls were HIV-diagnosed individuals taking EFV or NVP-based ART without virological failure.46

Overall, 242 available residual samples were included in the present study. Of them, 40 were available cases that developed NNRTI resistance mutations, and 202 were controls.

Genomic DNA was extracted using Qiagen kits manual platform according to the manufacturers protocol (Qiagen, Hilden, Germany) from about 200L of peripheral blood mononuclear cells (PMBCs) stored at 80C.

CYP2B6 516G>T (rs3745274) detection was carried out using PCR-RFLP technique according to Lavandera et al47 protocol with minor modifications. CYP2B6 983T>C (rs28399499) detection was carried out using a touchdown PCR-RFLP assay published by Paganotti et al.48 CYP2B6 785A>G (rs2279343) detection was done using an in-house optimized RFLP-PCR protocol.49

For purposes of this study, we also adopted a new in-house assay for analysis of the CYP2B6 82T>C (rs34223104) polymorphism. We designed two (2) primers (forward primer: 5-CAAGCAGGAAGTCTGGGTTC-3; reverse primer: 3-AGTTCCATGGTCCTGGTCT-5). PCR reaction was conducted in a total volume of 20L containing 100ng genomic DNA. PCR protocol with the following conditions was used: 3min of denaturation at 94C, 30s at 94 oC, 30s at 64 oC and 60s at 72 oC for 35 cycles with a final step of 5min at 72 oC. The PCR product was then digested with PsiI restriction enzyme at 37 oC for 90 min. The enzyme cuts the wild-type allele (T) in two fragments of 92 bp and 88bp; while the mutant allele is not cut. The digested fragments were visualized on a 4% metaphor gel stained with ethidium bromide.

CYP2B6 genotype and haplotype information was translated into a measure of phenotype using the metabolic score (MS) system,49 already adopted as activity score for CYP2A6,50 CYP2C19,51 CYP2D6,52 and also consistent with Vujkovic et al,41 for CYP2B6. The MS translates composite genotype and/or haplotype information into a qualitative measure of phenotype. The scores are based on the algebraic sum of the individual allele values according to an additive model for CYP2B6.49 The MS was set conferring a 1 value for each slow metabolism alleles (516T, 983C) and +1 for rapid metabolism alleles (82C, 785G), while an extensive metabolism allele was scored 0 (82T, 516G, 785A, 983T) both for composite CYP2B6 genotypes and haplotypes.49

Several methods were applied. We used the Arlequin software (v3.5.2.2)53 to test for HardyWeinberg Equilibrium (HWE) and the genetic fixation index (FST) with default settings, while Linkage Disequilibrium (LD) was tested using the Expectation-Maximization (EM) algorithm with 20,000 permutations and three initial conditions. Binary Logistic Regression (BLR) analysis (run on IBM SPSS statistical package, version 20) was applied to find any association between the dependent variable drug resistance with the independent variables (age, BMI, baseline CD4+ T-cell count and viral load, CYP2B6 genotype). Fishers exact test, chi-square test and z-statistic were applied for statistical significance where needed.

Out of the 242 samples, 15 were excluded (5 due to the lack of complete genotypic information and 10 failed PCR). Thus, 227 samples were retained for analysis, with 40 (18.6%) belonging to the group that developed virological failure with EFV/NVP DRMs. The remaining 187 samples (81.4%) belonged to the group that did not develop virological failure during the follow-up period. Information about gender was available for 223 individuals, 146 (65.5%) being females. The study population characteristics at baseline were available for 225 individuals, being as follows: mean age 33.7 years (range: 20.450.9); mean BMI 21.3 (range: 14.534.6); median baseline CD4+ T-cell count 188 cells/L (IQR: 147221); median baseline viral load 5.30 Log10 copies/mL (IQR: 4.835.71). The ART regimen data were available for 225 individuals, with 117 (50.6%) receiving EFV, and 108 (46.8%) receiving NVP containing ART. Table 1 summarises the baseline characteristics of the study population according to the EFV/NVP resistance status.

Table 1 Baseline Characteristics of the Study Population

This study was based on a subsample of the Tshepo study46 conducted in Botswana on HIV-diagnosed individuals taking EFV/NVP-based ART regimen and followed up to 3-years (156 weeks) after the treatments start. All cases of virologic failure reported in the Tshepo study underwent HIV genotyping46 and EFV/NVP DRMs were detected in all the 40 cases used in the present study (Supplementary Table 1). Thus, in the present study, virologic failure and EFV/NVP DRMs coincide.46 The median interval between start of ART and appearance of virologic failure was 72 weeks (IQR: 45.5104).

The CYP2B6 genotype distribution and allele frequencies of the four SNPs (82T>C, 516G>T, 785A>G, 983T>C) are summarized in Table 2. The comparisons of the three genotypes distribution between the subjects with and those without HIV DRM for each single SNP were all not statistically different but for the CYP2B6 516G>T polymorphism (chi-square = 8.121; P = 0.017) (Figure 1), with the wild-type extensive metaboliser 516G allele at higher frequency among resistant than sensitive infections (70% vs 54%, calculated from Table 2).

Table 2 CYP2B6 Genotype and Allele Frequencies of the Four SNPs

Figure 1 Distribution of CYP2B6-516 genotypes according to NNRTI-resistance status. Chi-square associated P-value is 0.017.

HardyWeinberg Equilibrium analysis showed that CYP2B6 983 locus displayed significant deviations from HWE in EFV/NVP-resistant, EFV/NVP-susceptible and combined (overall) samples (P = 0.001; P < 0.001; P < 0.001, respectively). A reason for this deviation may be due to a defect in heterozygous samples. Furthermore, CYP2B6 82 and CYP2B6 516 did not show a statistically significant deviation from HWE in the EFV/NVP-resistant HIV infections (P = 0.449 and P = 0.230, respectively), whereas a statistically significant deviation from HWE was noted in the EFV/NVP-susceptible (P = 0.039 and P = 0.031, respectively) and all the samples combined (P = 0.030 and P = 0.010, respectively), with an excess of heterozygotes in both groups. CYP2B6 785 genotypes were in equilibrium in all the groups analysed (EFV/NVP-resistant, EFV/NVP-susceptible and both combined).

Linkage disequilibrium was observed between CYP2B6 82 and CYP2B6 516 in all groups (Table 3). Linkage disequilibrium was also observed between CYP2B6 82 and CYP2B6 983, as well as between CYP2B6 516 and CYP2B6 983, in the EFV/NVP-susceptible and overall samples, but not in the EFV/NVP-resistant group (Table 3). Finally, CYP2B6 785 did not show strong association with the other three loci, thus justifying the respect of HWE in all the three groups (EFV/NVP-resistant, EFV/NVP-susceptible, overall samples) (Table 3).

Table 3 Pairwise Linkage Disequilibrium (LD) Analysis for the Four Polymorphic Loci (CYP2B6 82, 516, 785, 983)

The fixation index (FST) between EFV/NVP-resistant and susceptible samples gave a distance of 0.023 (P = 0.008 0.003, 1023 permutations). While significant, this difference is consistent with panmixia between the two groups. Furthermore, an exact test of population differentiation revealed no statistical difference between the two groups (P = 0.060 0.008; 100,000 Markov chain steps).

Binary Logistic Regression analysis was set with the dependent dichotomic variable assuming two values according to the presence of EFV/NVP-susceptible or EFV/NVP-resistant HIV infections. Factors tested were age, BMI, baseline lymphocytes T-CD4+, viral load, genotypes for CYP2B6 82, CYP2B6 516, CYP2B6 785, CYP2B6 983 (see Supplementary Table 2). First step results showed only one significant factor (CYP2B6 516G). After removal of the non-significant factors, the BLR was repeated with only one (significant) factor in the model (CYP2B6 516G>T). This second step analysis revealed a significant statistical association between presence of CYP2B6 516G and DRMs status (OR: 2.26; 95% CI:1.274.01; P = 0.005).

Finally, the CYP2B6 516T allele presence (GT and TT genotypes) was tested for its possible protection against virologic failure (corresponding to EFV/NVP resistance in the present study as stated in the Methods section): no association was found at 6 months, nor beyond six months (up to 3 years) (Fishers exact test, P > 0.05).

Haplotype frequencies for CYP2B6 were estimated using the EM algorithm in Arlequin (Table 4). For all the samples, based on the four SNPs of CYP2B6 analysed (82T>C, 516G>T, 785A>G, 983T>C), the TGAT haplotype was the most common, whereas the CGAC, CTAT and CGGT haplotypes the rarest (Table 4). The haplotype TGAT was also the most abundant haplotype when EFV/NVP-resistant and susceptible samples were analysed separately, whereas no CGGT haplotypes were estimated in the EFV/NVP-susceptible group, and no TGGC, CGAC and CTAT haplotypes were estimated in the EFV/NVP-resistant group (Table 4).

Table 4 Estimated and Maximum-Likelihood (ML) Haplotype Frequencies by Phenotype and for All the Samples Combined. Maximum-Likelihood Haplotype Frequencies are Shown in Parenthesis with Their Standard Deviations (SD). The Order of Nucleotides in the Reconstructed Haplotypes is Made According to the SNP Position in the CYP2B6 Gene (82T>C, 516G>T, 785A>G, 983T>C)

The inferred MS was calculated for the haplotypes and they are shown in Table 4. In order to assess the possible risk of carrying EFV/NVP-resistant HIV infections according to the CYP2B6 fast metabolizer profile, the metabolic phenotypes were classified as follows: MS0, this including extensive, slow and very slow inferred metabolic phenotypes; MS1, this including rapid and ultra-rapid inferred metabolic phenotypes. The breakdown of metabolic phenotypes between groups according to the EFV/NVP resistance status are shown in Table 5. The comparison between EFV/NVP-resistant and susceptible HIV infections by MS was associated to a z-statistic of 1.812 (P = 0.035), therefore showing that the rate of EFV/NV resistance was significantly higher among fast metabolizers haplotypes compared to the other group (30.8% vs 16.8%, respectively).

Table 5 EFV/NVP Resistance by CYP2B6 Metabolic Phenotype

In line with the BLR results, the CYP2B6 516G allele was present in 100% (n=30/30) of rapid (CGAT and TGGT) and ultra-rapid (CGGT) haplotypes, whereas it was only present in 53.8% (n=228/424) of extensive/slow metabolizers haplotypes (Fishers exact test, P < 0.001) (see Table 4, overall column).

Understanding the factors that modulate the selection of DRMs associated with HIV-1 infection is essential to design efficient control strategies. Drug resistance usually emerges rapidly when ARV drugs are administered as monotherapy or in the presence of incomplete viral suppression, suggesting that resistance is caused by the selection of mutant viruses within the host.54 Besides known viral factors (HIV diversity, HIV replication, drug selection pressure and fitness of drug-resistant viral sub-populations) and patient ART adherence, human genetic background is a possible further, not yet fully understood, co-factor affecting HIV drug resistance selection. In this study, we addressed the hypothesis that human pharmacogenetics can drive the selection of HIV drug resistance. We therefore found a statistically significant association between EFV/NVP-resistant HIV infections and CYP2B6 516G allele presence (OR: 2.26; 95% CI:1.274.01; P = 0.005). In fact, EFV/NVP resistant infections had higher 516G allele frequency (Table 2 and Figure 1). Further information comes from the haplotype reconstruction where 100% of rapid (CGAT and TGGT) and ultra-rapid (CGGT) CYP2B6 haplotypes carried the 516G allele (Table 4), whereas only 53.8% of the extensive, slow (TGAC, TTAT and TTGC) and very slow (TTAC) haplotypes carried it. Moreover,

CYP2B6 rapid and ultra-rapid metabolizers showed a significantly higher frequency of EFV/NVP-resistant HIV infections than extensive and slow metabolizers (30.8% vs 16.8%; z-statistic = 1.812; P = 0.035), based on their haplotype reconstruction (Table 5). Nevertheless, it is important to note that the rate of rapid and ultra-rapid haplotypes is less than 6% (26/454) of the total haplotypes.

A possible interpretation of these results is that EFV/NVP resistance tends to accumulate based on the presence of the CYP2B6 516G allele (GG>GT>TT), and that in rapid and ultra-rapid metabolisers might happen at a higher rate. This could be an important model for drug resistance selection that may be verified on a larger longitudinal cohort and tested on different pathogens, different antimicrobial drugs and epidemiological contexts.

These results are partially in line with similar findings on malaria drug resistance where it has been demonstrated that the CYP2C8 slow metabolizer phenotype is associated with the risk of carrying chloroquine- and amodiaquine-resistant parasites.55,56

We also found that LD into the 1000 bp region analysed (spanning from CYP2B6 82 to 983 nucleotide positions) was strong (P < 0.001) and departures from the HWE were coherent with the physical and genetic linkage between SNPs. Genotype and allele frequencies for the four CYP2B6 SNPs analysed in this study were in line with literature, including most if not all studies from Botswana (Table 6). The only discrepancy was represented by a study41 conducted in the same area as the present research that indicated an allele frequency for CYP2B6 785G of 6%, well outside the accepted range of 35.252.8% for Southern Africa (Table 6). Finally, the two groups (with and without EFV/NVP-resistant HIV infections) into which we subdivided our study population appeared to be in panmixia, therefore offering an ideal situation for the comparison.

Table 6 CYP2B6 SNPs (82T>C, 516G>T, 785A>G, 983T>C) Frequency in Sub-Saharan Africa

In summary, subjects carrying the CYP2B6 516G allele were more likely to carry HIV drug-resistant infections. Coherently with our hypothesis, the 516G allele is always present in the rapid and ultra-rapid haplotypes, confirming the possibility that rug resistance selection is enhanced when drug metabolism is faster. However, the rate of fast metabolisers was not high in this cohort, therefore reducing its possible impact.

The present study has few limitations: i) the sample size was relatively small, therefore haplotypes reconstruction provided higher statistical power in the comparisons, ii) it would have been more powerful to use matched-case control, to avoid any confounding factors, and iii) the lack of data on EFV/NVP plasma exposure hampered the quantitative confirmation of CYP2B6 metabolic phenotypes. Nonetheless, our findings suggest a trend towards a role for the genetic background of patients affecting drug therapy outcomes, and warrants further studies.

In conclusion, this work indicates that the CYP2B6 516G allele, and its combination into rapid and ultra-rapid metabolizer profiles, as defined by the correspondent haplotypes, is directly associated with the risk of development of drug resistance in HIV-diagnosed individuals receiving EFV- or NVP-containing ART in Botswana. However, larger studies will be needed to confirm this association. In general, our findings support the hypothesis that pharmacogenetics may play a significant role in HIV therapy outcomes. Besides the known possible impact of slow EFV/NVP metabolism on ART toxicity and compliance, fast EFV/NVP metabolism may also affect ART outcomes. A deeper knowledge of the genetic background at an individual level could thus be highly beneficial in personalising ART therapies and improving their efficacy, especially in patients who show poor response following initiation of treatment.

This was a retrospective casecontrol study approved by Health Research Division Office (HRDC) of the Botswana Ministry of Health and Wellness. The approval was done in accordance with the amendments made to the initial permit of The host genetics of HIV-1 subtype C infection progression and treatment in Africa/Gwas on determinants of HIV-1 subtype C infection [Reference No: HPDME 13/18/1 X1 (163)]. For the purpose of this study signed informed consent was sought from the participants. In addition, Botswana-Harvard AIDS Institute Partnership, as the database owner authorized by HRDC, gave permission to use its data and samples for the current study. This study was conducted in accordance with the Declaration of Helsinki.

We would like to express our gratitude to SANTHE for funding this research in collaboration with Botswana Harvard AIDS Institute Partnership. We also express gratitude to the University of Botswana, Faculty of Health Sciences, School of Allied Health Professions and Botswana-University of Pennsylvania Partnership laboratory staff for their help, assistance and continuous support to this study. These authors have joint senior authorship: Gianluca Russo, Simani Gaseitsiwe, Giacomo M Paganotti.

This work was supported through the Sub-Saharan African Network for TB/HIV Research Excellence (SANTHE), a DELTAS Africa Initiative [grant # DEL-15-006]. The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS)s Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africas Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust [grant # 107752/Z/15/Z] and the UK government. The views expressed in this publication are those of the author(s) and not necessarily those of AAS, NEPAD Agency, Wellcome Trust or the UK government. The work was also supported by the Penn Center for AIDS Research [grant # P30 AI045008].

The authors report no conflicts of interest in this work.

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26. Wang J, Snnerborg A, Rane A, et al. Identification of a novel specific CYP2B6 allele in Africans causing impaired metabolism of the HIV drug efavirenz. Pharmacogenet Genomics. 2006;16(3):191198. doi:10.1097/01.fpc.0000189797.03845.90

27. Bertrand J, Chou M, Richardson DM, et al. Multiple genetic variants predict steady-state nevirapine clearance in HIV-infected cambodians. Pharmacogenet Genomics. 2012;22(12):868876. doi:10.1097/FPC.0b013e32835a5af2

28. Desta Z, Gammal RS, Gong L, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2B6 and efavirenz-containing antiretroviral therapy. Clin Pharmacol Ther. 2019;106(4):726733. doi:10.1002/cpt.147

29. Vujkovic M, Bellamy SL, Zuppa AF, et al. Brief report: CYP2B6 516G>T minor allele protective of late virologic failure in efavirenz-treated HIV-infected patients in Botswana. J Acquir Immune Defic Syndr. 2017;75(4):488491. doi:10.1097/QAI.0000000000001442

30. Gross R, Bellamy SL, Ratshaa B, et al. CYP2B6 genotypes and early efavirenz-based HIV treatment outcomes in Botswana. AIDS. 2017;31(15):21072113. doi:10.1097/QAD.0000000000001593

31. Ribaudo HJ, Liu H, Schwab M, et al. Effect of CYP2B6, ABCB1, and CYP3A5 polymorphisms on efavirenz pharmacokinetics and treatment response: an AIDS Clinical Trials Group study. J Infect Dis. 2010;202(5):717722. doi:10.1086/655470

32. Frasco MA, Mack WJ, Van Den Berg D, et al. Underlying genetic structure impacts the association between CYP2B6 polymorphisms and response to efavirenz and nevirapine. AIDS. 2012;26(16):20972106. doi:10.1097/QAD.0b013e3283593602

33. Haas DW, Smeaton LM, Shafer RW, et al. Pharmacogenetics of long-term responses to antiretroviral regimens containing efavirenz and/or nelfinavir: an Adult Aids Clinical Trials Group Study. J Infect Dis. 2005;192(11):19311942. doi:10.1086/497610

34. Haas DW, Severe P, Jean Juste MA, Pape JW, Fitzgerald DW. Functional CYP2B6 variants and virologic response to an efavirenz-containing regimen in Port-au-Prince, Haiti. J Antimicrob Chemother. 2014;69(8):21872190. doi:10.1093/jac/dku088

35. Lehmann DS, Ribaudo HJ, Daar ES, et al. Genome-wide association study of virologic response with efavirenz-containing or abacavir-containing regimens in AIDS clinical trials group protocols. Pharmacogenet Genomics. 2015;25(2):5159. doi:10.1097/FPC.0000000000000106

36. Rotger M, Colombo S, Furrer H, et al. Influence of CYP2B6 polymorphism on plasma and intracellular concentrations and toxicity of efavirenz and nevirapine in HIV-infected patients. Pharmacogenet Genomics. 2005;15(1):15. doi:10.1097/01213011-200501000-00001

37. Gounden V, van Niekerk C, Snyman T, George JA. Presence of the CYP2B6 516G> T polymorphism, increased plasma efavirenz concentrations and early neuropsychiatric side effects in South African HIV-infected patients. AIDS Res Ther. 2010;7:32. doi:10.1186/1742-6405-7-32

38. Yuan J, Guo S, Hall D, et al. Toxicogenomics of nevirapine-associated cutaneous and hepatic adverse events among populations of African, Asian, and European descent. AIDS. 2011;25(10):12711280. doi:10.1097/QAD.0b013e32834779df

39. Ciccacci C, Di Fusco D, Marazzi MC, et al. Association between CYP2B6 polymorphisms and nevirapine-induced SJS/TEN: a pharmacogenetics study. Eur J Clin Pharmacol. 2013;69(11):19091916. doi:10.1007/s00228-013-1549-x

40. Mukonzo JK, Okwera A, Nakasujja N, et al. Influence of efavirenz pharmacokinetics and pharmacogenetics on neuropsychological disorders in Ugandan HIV-positive patients with or without tuberculosis: a prospective cohort study. BMC Infect Dis. 2013;13:261. doi:10.1186/1471-2334-13-261

41. Vujkovic M, Bellamy SL, Zuppa AF, et al. Polymorphisms in cytochrome P450 are associated with extensive efavirenz pharmacokinetics and CNS toxicities in an HIV cohort in Botswana. Pharmacogenomics J. 2018;18(5):678688. doi:10.1038/s41397-018-0028-2

42. Kwara A, Lartey M, Sagoe KW, Kenu E, Court MH. CYP2B6, CYP2A6 and UGT2B7 genetic polymorphisms are predictors of efavirenz mid-dose concentration in HIV-infected patients. AIDS. 2009;23(16):21012106. doi:10.1097/QAD.0b013e3283319908

43. Cummins NW, Neuhaus J, Chu H, et al. Investigation of efavirenz discontinuation in multi-ethnic populations of HIV-positive individuals by genetic analysis. EBioMedicine. 2015;2(7):706712. doi:10.1016/j.ebiom.2015.05.012

44. Mukonzo JK, Owen JS, Ogwal-Okeng J, et al. Pharmacogenetic-based efavirenz dose modification: suggestions for an African population and the different CYP2B6 genotypes. PLoS One. 2014;9(1):e86919. doi:10.1371/journal.pone.0086919

45. Mhandire D, Lacerda M, Castel S, et al. Effects of CYP2B6 and CYP1A2 genetic variation on nevirapine plasma concentration and pharmacodynamics as measured by CD4 cell count in Zimbabwean HIV-infected patients. OMICS. 2015;19(9):553562. doi:10.1089/omi.2015.0104

46. Wester CW, Thomas AM, Bussmann H, et al. Non-nucleoside reverse transcriptase inhibitor outcomes among combination antiretroviral therapy-treated adults in Botswana. AIDS. 2010;24(Suppl 1(Suppl 1)):S27S36. doi:10.1097/01.aids.0000366080.91192.55

47. Lavandera JV, Parera VE, Rossetti MV, Batlle AM, Buzaleh AM. Identification of CYP3A5 and CYP2B6 polymorphisms in porphyria cutanea tarda associated to human immunodeficiency virus. J Clin Exp Dermatol Res. 2011;S2:006. doi:10.4172/2155-9554.S2-006.

48. Paganotti GM, Russo G, Sobze MS, et al. CYP2B6 poor metaboliser alleles involved in efavirenz and nevirapine metabolism: CYP2B6*9 and CYP2B6*18 distribution in HIV-exposed subjects from Dschang, Western Cameroon. Infect Genet Evol. 2015;35:122126. doi:10.1016/j.meegid.2015.08.003

49. Tawe L, Motshoge T, Ramatlho P, et al. Human cytochrome P450 2B6 genetic variability in Botswana: a case of haplotype diversity and convergent phenotypes. Sci Rep. 2018;8(1):4912. doi:10.1038/s41598-018-23350-1

50. Kumondai M, Hosono H, Orikasa K, et al. Genetic polymorphisms of CYP2A6 in a case-control study on bladder cancer in Japanese smokers. Biol Pharm Bull. 2016;39(1):8489. doi:10.1248/bpb.b15-00604

51. Dodgen TM, Drgemller BI, Wright GE, et al. Evaluation of predictive CYP2C19 genotyping assays relative to measured phenotype in a South African cohort. Pharmacogenomics. 2015;16(12):13431354. doi:10.2217/pgs.15.80

52. Gaedigk A, Simon SD, Pearce RE, Bradford LD, Kennedy MJ, Leeder JS. The CYP2D6 activity score: translating genotype information into a qualitative measure of phenotype. Clin Pharmacol Ther. 2008;83(2):234242. doi:10.1038/sj.clpt.6100406

53. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under linux and windows. Mol Ecol Resour. 2010;10(3):564567. doi:10.1111/j.1755-0998.2010.02847.x

54. Mackie N. Resistance to non-nucleoside reverse transcriptase inhibitors. In: Geretti AM, editor. Antiretroviral Resistance in Clinical Practice. London: Mediscript;2006:Chapter 2. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2249/. Accessed October 1, 2020.

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[Full text] CYP2B6 genetic variation with efavirenz and nevirapine | PGPM - Dove Medical Press

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Dickson, Edelson, Musiek, Philips and Stitziel honored

Washington University School of Medicine in St. Louis physician-scientists (clockwise from top left), Jennifer Philips, MD, PhD, Patricia Dickson, MD, Brian T. Edelson, MD, PhD, Erik Musiek, MD, PhD, and Nathan O. Stitziel, MD, PhD, have been elected members of the American Society of Clinical Investigation in recognition of their accomplishments in medical research.

Five physician-scientists at Washington University School of Medicine in St. Louis have been elected members of the American Society for Clinical Investigation in recognition of original, creative and independent investigations in the clinical or allied sciences of medicine. The new members will be inducted April 8.

Patricia Dickson, MD, is the Centennial Professor of Pediatrics, and director of theDivision of Genetics and Genomic Medicine. Her research focuses ongenetic lysosomal storage diseases, which are rare metabolic disorders caused by the bodys inability to produce specific enzymes. The condition can affect various parts of the body such as the brain, heart, skeleton and central nervous system.

The work of immunologistBrian T. Edelson, MD, PhD,an associate professor of pathology & immunology, encompasses autoimmune and infectious diseases. He studies how cytokine production by T cells is regulated, with a focus on how autoreactive T cells mediate the autoimmune disease multiple sclerosis (MS).

Erik Musiek, MD, PhD, an associate professor of neurology, studies the role of circadian rhythm in neurodegenerative diseases such as Alzheimers disease. He has discovered that body clock disturbances are an early sign of Alzheimers and raise the levels of damaging Alzheimers proteins in the brain.

Tuberculosis (TB) expertJennifer Philips, MD, PhD, is an associate professor of medicine and co-director of theDivision of Infectious Diseases. Her work focuses on how TB bacteria evade the bodys immune defenses and cause disease, a key step in developing better drugs and vaccines to combat the lethal infection.

Cardiologist Nathan O.Stitziel, MD, PhD, an associate professor of medicine, uses human genetic studies to understand the inherited basis of cardiovascular diseases caused by either a single gene or by complex interactions of multiple genes. His work aims to leverage insights from these studies to identify and validate novel therapeutic targets for patients with heart disease.

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Researchers elected to American Society for Clinical Investigation - Washington University School of Medicine in St. Louis

Introduction

Although many pathways lead to hyperglycemia in diabetes the so-called Egregious Eleven (Listed in Table 1) - -cell dysfunction is the core defect.1,2 Four basic pathophysiologic mechanisms damage the -cell, namely, genes and epigenetic changes, inflammation, an abnormal environment [especially fuel excess], and insulin resistance (IR).1,2 Further, the same factors that damage the -cell may also lead to cell and tissue damage and elevate the risk of developing typical diabetes-related complications, such as retinopathy, neuropathy, and nephropathy.13 The interplay between these pathophysiologic mechanisms influences the specific risk of development and progression of complications in an individual patient. Importantly, we these mechanisms can also predispose patients to other common conditions including diabetes mellitus (DM), cancer, dementia, psoriasis, atherosclerotic cardiovascular disease (ASCVD), and nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH).415

Table 1 Targets and Potential Utility of Diabetes Therapies in Conditions of the Diabetes Syndrome

In clinical practice we often encounter these common diseases, frequently within one individual patient and they are treated as independent conditions. However, we believe their epidemiologic associations is, in part, due to the same underlying pathophysiologies driving -cell damage and diabetic complications. That is, the same pathophysiologic mechanisms that damage the -cell and promote diabetes-specific complications also have key roles in the pathogenesis of these diseases. The overlapping epidemiology of these conditions highlights this connection and the significant associations that have been found between DM and all these other phenotypes.715 However, we propose these connections go beyond mere epidemiologic links due to overlapping pathophysiology. In fact, these conditions occur together in enough frequency and have common overlapping pathophysiologic drivers that we have created a conceptual framework called The Diabetes Syndrome. The name is inspired by the Greek meaning of syndrom (sun- [together] + dramein [to run]) as the conditions, indeed, run together (Figure 1).

Figure 1 Overlapping pathophysiologic mechanisms that lead to the diabetes syndrome.

Abbreviations: AGE, advanced glycosylation end products; ASCVD, atherosclerotic cardiovascular disease; DM, diabetes mellitus; DNA, deoxynucleic acid; HDL, high density lipoprotein; IR, insulin resistance; MS, metabolic syndrome; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PCOS, polycystic ovary disease.

Notes: Insulin resistance, its complications, and other conditions arise from common pathophysiologies. Metabolic syndrome, obesity, and PCOS, etc, can be viewed as phenotypes related to genes and epigenetic changes associated with IR. Insulin resistance along with inflammation increase the risk of -cell dysfunction as well as the Diabetes Syndrome. *Traditional DM complications include retinopathy, nephropathy, neuropathy-tissues that do not require insulin to get glucose into cells.

This article will describe the shared pathophysiologic and etiologic factors across these prevalent and related diseases within the Diabetes Syndrome conceptual framework discussed within the context of the 4 basic pathophysiologic mechanisms genes and epigenetic changes, abnormal environment, inflammation, and IR with a focus on commonalities between these diseases and DM. In brief, genetics can mediate susceptibility to damage from abnormal external and internal environmental factors, including inflammation and IR. All these mechanisms can promote epigenetic changes. In total, these 4 pathophysiologic mechanisms cross-react with each other to cause damage to cells and tissues ultimately leading to disease.2 If we as physicians begin to recognize and understand the inter-relatedness of these conditions, it may result in better screening, therapy, and cross-purposing of therapies rather than treating these diseases as independent conditions. Furthermore, as more is learned through continued research and clinical experience, this framework of Diabetes Syndrome may grow to include other related conditions.

The common, frequently concomitant chronic conditions of DM, cancer, dementia, psoriasis, ASCVD, NAFLD, and NASH are related beyond shared epidemiologic associations. We propose these conditions are linked by shared pathophysiologies namely, genes and epigenetic changes, abnormal environment, inflammation, and IR. Because these conditions occur together in reasonable regularity and have common overlapping pathophysiologic drivers we have created a conceptual framework called The Diabetes Syndrome. Of note, this new term is not proposed to be used for diagnosis of the Diabetes Syndrome, rather to heighten clinicians awareness of the inter-relationships between these conditions to enhance to quality of care to our patients. This paper will discuss the common links based on current understanding, but it is expected as new findings and data are revealed and viewed within this conceptual framework, that the conditions and diseases of the Diabetes Syndrome framework may grow and evolve.

The conditions of the Diabetes Syndrome are common and often occur within the same patient. Even just considering DM alone, nearly 1 in 10 people worldwide are living with diabetes, translating to a prevalence of 463 million adults.16 Diabetes and the conditions within the Diabetes Syndrome are often considered co-morbidities and/or risk factors of each other.

Cancer is a broad and diverse disease, however, links have been found between certain types of cancer and DM, with the strongest associations found with pancreatic and hepatic cancer.14 Moreover, multiple studies demonstrate a higher incidence of cancer in patients with DM than without.12 The hypothesized connection between DM and carcinogenesis include hyperglycemia, chronic inflammation, and hyperinsulinemia.12,14

Dementia is estimated to affect 57% of elderly patients worldwide, with Alzheimers disease (AD) representing a majority of cases.17 There is a growing body of research that reveals a link between DM and cognitive decline/dysfunction, and dementia (including AD).9,10,18 Indeed, a phenotypic consequence of IR or deficiency can result in AD, sometimes referred to as type 3 diabetes or diabetes of the brain.19 Patients with DM are at increased risk for dementia and both diseases have similar risk factors and biologic mechanisms.9,10,18,19 Indeed, AD is twice as frequent in diabetic patients compared to patients without DM and shared pathophysiologic features include IR, amyloid aggregations, inflammatory stress, and cognitive disturbances.10,20

Approximately 8 million people in the US are diagnosed with psoriasis, a chronic, systemic inflammatory condition of the skin condition.21,22 It is characterized by underlying inflammatory processes including regulatory immune cells, cytokines, and adipokines.21 Psoriasis is often associated with other chronic conditions including cardiometabolic disease, cancer, NAFLD, NASH, psoriatic arthritis, inflammatory bowel disease, chronic kidney disease, cancer, and mood disorders.2224

Cardiovascular disease is the leading cause of premature mortality and disability worldwide with the vast majority due to underlying atherosclerotic pathogenesis including coronary artery disease, cerebrovascular disease, venous thromboembolism, and peripheral vascular disease.25 These conditions can lead to myocardial infarction, cardiac arrhythmias, and stroke.25 Common risk factors for both DM and ASCVD include hyperlipidemia, IR, inflammation, hypertension, obesity, smoking, and lack of physical activity.4,25 Indeed, CVD is the leading cause of death in patients with Type 2 DM (T2DM), with both impaired micro and macrovascular circulation leading to CVD in patients with DM.13

Nonalcoholic fatty liver disease, the most common form of chronic liver disease in the world, is associated with potentially severe sequela.26,27 It encompasses a spectrum of disorders ranging from simple steatosis to the more aggressive necro-inflammatory form, NASH, which can result in cirrhosis, hepatocellular carcinoma, and end-stage liver disease.26,27 Type 2 DM is considered a risk factor for NAFLD and, conversely, DM appears to accelerate progression of disease in NAFLD.15 The multiple-hit pathogenesis of NAFLD is thought to include nutritional factors, gut microbiota, genetic and epigenetic factors, IR, and hormones secreted from the adipose tissue.6

Clearly, there are many shared comorbidities and associations between the conditions within the proposed Diabetes Syndrome construct. However, it is our contention that this is due to shared overlapping pathophysiologies that link them. This paper will discuss the common links based on current understanding; however, it is anticipated that as new research comes to light and is viewed within this conceptual framework that the conditions and diseases conscribed within the Diabetes Syndrome Framework may be refined.

Perhaps the most powerful and compelling connection of the conditions within the Diabetes Syndrome conceptual framework is genetic. Genes and genetic variation can mediate susceptibility to damage from abnormal external and internal environmental factors, including inflammation and IR. All these mechanisms can promote epigenetic changes.2,28,29 Epigenetics, physical genomic alterations that lead to changes in gene expression in response to environmental triggers are believed to have a generally strong influence on patient phenotype, likely more influential than specific base changes in the genes themselves.2,29 In fact, nutritional state and exposure to stress are hypothesized to have phenotypic consequences extending to children and even grandchildren.29 Furthermore, intriguing research using genetic variation in a Mendelian Randomization (MR) framework is being conducted to assess causality for disease, including epigenetic state.3033

Although observational epidemiological studies have a valuable role in identifying risk factors or comorbid conditions associated with disease, MR studies harness genetic data in population and observational studies to offer new insights regarding causality.30,33 Mendelian randomization studies rely on the random assortment of genetic variants in a population at birth to avoid confounding, similar to randomization to an experimental drug or controlling in randomized controlled trials.30,33 In addition, MR analyses avoid reverse causality as genetic randomization precedes the disease onset and are not modified by progression of a disease.30,33

Many of the connections we propose within the Diabetes Syndrome are testable by leveraging current genetic knowledge from efforts that include powerful MR approaches which, in turn, can inform implementation of precision medicine.30,31 Further, in the absence of MR studies, genetic and epigenetic links can also be elucidated by genetic susceptibility loci, microRNA (short, single-stranded noncoding RNAs that influence and regulate gene expression), chromatin marks, 3D genomic architecture, etc.

Although not all MR studies have identified causative associations between DM and cancer in general, there have been reports of important connections to specific cancer types. In a recent MR study, genetic predisposition to T2DM was associated with higher odds of pancreatic, kidney, uterine, and cervical cancer, lower odds of melanoma and esophageal cancer, and no association with 16 other types of specific cancer types.34 Although there is limited evidence of causal association between fasting glucose and cancer, genetically predicted insulin levels have been reported to be positively associated with uterine, kidney, pancreatic, and lung cancer in a two-sample, MR study.34 This causal association between common DM features and pancreatic cancer has also been shown in another MR study, where genetically predicted increasing BMI and fasting insulin levels were causally associated with increased risk of pancreatic cancer.35 However, interestingly, no evidence of such a causal relationship between T2DM or dyslipidemia and pancreatic cancer has been reported to date.

Furthermore, a variety of converging genetic pathways between DM and cancer have been identified.12,36 For example, various genetic features at the TCF7L2 locus increase risk for colorectal cancer and T2DM pathogenesis, while the T2D THADA, JAZF1, and TCF2 loci have yielded associations for both prostate cancer and T2DM.12 Epigenetic changes and modifications may occur due to sustained excess fuel/nutrition, leading in turn to stress and the formation of reactive oxygen species (ROS) which may activate oncogenes or deactivate tumor suppressor genes.12 Hundreds of epigenetically regulated genes involved in glucose metabolism and adaptive survival have been identified and it is likely that shared pathways between energy metabolism and tumorigenesis exist.12 Indeed, a number of noncoding RNAs have been identified that regulate the insulin growth factor-1 receptor (IGF-1R), a master regulator in DM and cancer.37

Although observational trials have demonstrated that DM is associated with major subtypes of dementia, in particular dementias with obvious vascular pathologies (eg, vascular dementia and unspecified dementia), MR has not shown T2DM to be a direct cause of AD in a recent two-sample study.38 The MR approach has not been viable to date to test the causal effect between T2DM and vascular and unspecified dementia due to the lack of publicly available genetic data.38

Recent genetic association studies have identified 86 loci common to dementia, DM, and metabolic syndrome and 159 common between dementia and DM.36,39 Gene set enrichment analyses have revealed common co-localized genes involved in energy metabolism, metabolic pathways, and immune responses.39 Multiple environmental factors, abnormal metabolic environment, and exogenous fuel excess can engender epigenetic changes. For example, increased expression of histone deacetylases associated with altered expression of synaptic proteins was found in the brains of diabetic patients compared with controls.40 Furthermore, it has been hypothesized that altered expression of DM genes in AD brains may be exacerbated by peripheral IR or DM.20

Multiple genetic susceptibility loci have been identified that are shared between DM and psoriasis.41,42 Examination of DM susceptibility loci in thousands of psoriasis and control patients identified 3 loci significant in psoriasis and DM related to immune signaling regulation, glucose metabolism, and T cell activation.42 Further, shared epigenetic changes have been described. For example, several microRNAs, have been found that are implicated in both DM and psoriasis, including an microRNA that is upregulated in T helper cells in psoriatic skin lesions as well as activated as a promotor of genes in Type 1 DM (T1DM).43

Two MR studies have included T2DM as the exposure in a putative causal relationship with coronary heart disease (CHD) as the outcome.33,44 Using publicly available data from consortia of risk loci, these MR studies demonstrated a strong causal relationship between higher T2DM risk and higher CHD risk.33,44

Shared genetic predisposition for DM and coronary artery disease includes signals on a chromosome 9p21 involving a risk allele for coronary artery disease and variation within the adiponectin gene.45 Moreover, epigenetic changes and signatures have been identified that link DM and CV dysfunction.36,46 For example, histone modifications impacting the expression of NF-kB dependent genes are thought to contribute to vascular dysfunction in patients with DM.46,47 Further, elevated renin and growth differentiation factor 15 and lower adiponectin were shared both T2DM and coronary artery disease in a proteomic study.48

Nonalcoholic fatty liver disease, T2DM and obesity are epidemiologically correlated with each other, but until recently their causal relationship had not been fully elucidated. An MR study demonstrated that NAFLD causally promotes T2DM with a late-onset type 1-like diabetic sub-phenotype and central obesity.49 Conversely, T2DM, obesity, and central obesity all causally increase the risk of NAFLD.49 The authors suggested that these results indicate that closely related diseases should be stratified into subtypes in order to aid prevention, diagnosis, and treatment of these conditions.49

Common genetic susceptibility to NAFLD and NASH are also found in DM36,50 Moreover, genetic commonality between NAFLD and T2DM has been suggested based on gene/protein co-occurrences.50 From an epigenetics perspective, differential DNA methylation in the liver of patients with NASH has been associated with expression of genes linked with insulin metabolism.51

Abnormal metabolic environment and fuel excess are hallmarks of DM. A wide-angled MR study intended to produce an atlas of T2DM risk factors found 8 factors associated with T2DM after adjustment for BMI.52 Systolic blood pressure, smoking, insomnia, and alanine aminotransferase levels were positively associated with T2DM, while testosterone, sex hormone binding globulin, high density lipoprotein and total cholesterol levels were inversely associated with T2DM.52 Further, another MR study demonstrated a causal association between childhood adiposity and T1DM risk.53

Additionally, an abnormal metabolic environment and exogenous fuel excess have been associated with increased risk of cancer or increased cancer morality.12 For example, higher BMI, high nutrient intake, hyperglycemia, hyperinsulinemia as well as IR and inflammation have been independently associated with increased cancer risk and decreased cancer survival.12 Increasing BMI (as predicted by genes) was reported as causally associated with increased risk of pancreatic cancer using an MR approach.35 Similarly, in another MR study, genetically predicted post-prandial glucose and BMI were positively associated with breast cancer.54 In addition, dietary advanced glycation end products (AGEs) trigger various downstream changes to gene expression and have been hypothesized to be potential endocrine disruptors.2,55 Manufacture of processed food, increasingly more prevalent, involves extreme temperatures that result in the formation of AGEs which interfere with endogenous hormone production and action.55 With regard to tumorigenesis, dietary AGEs have been found to be pro-tumorigenic on estrogen breast cancer cells, human pancreatic ductal adenocarcinoma cell lines, and a mouse pancreatic cancer model.55

Diabetes and cognitive dysfunction share many shared environmental risk factors including smoking, physical inactivity, pollution/pesticides, etc.10 These environmental toxicants are thought to induce pathogenesis through interaction with genetic factors leading to downstream oxidative stress, mitochondrial dysfunction, inflammation, and IR, all common with pathogenesis of DM.10 Food AGEs, discussed above, accumulate with long-lived structural protein, contribute to complications in both DM and AD, and are thought to have a role in neurodegenerative diseases.55 Furthermore, an altered metabolic environment is thought to drive both conditions with common links of altered energy metabolism, cholesterol modifications, and dysfunctional protein O-GlcNAcylation formation.20 Finally, it has been found that high plasma glucose (as predicted by genes) is causally related to unspecified dementia, but not AD or vascular dementia risk in a recent MR study.56

Environmental risk factors believed to play a key role in psoriasis including UV exposure, medications, smoking, diet and obesity, alcohol intake, infections, and stress, are also common environmental risk factors for DM.2,57 Many of these are associated with downstream dysregulation of the immune system, likely though epigenetic modifications.2,57 The gastrointestinal tract has a large role in mediating glucose homeostasis though stimulation of incretin hormones (eg, glucagon-like protein-1 [GLP-1]) which prompt postprandial insulin release.58 Interestingly, level and function of incretin hormones appear to be reduced in patients with DM as well as psoriasis patients compared with controls.2,58,59 Mendelian Randomization has leveraged such genetic markers to determine causality between obesity/BMI, a risk factor for DM, and psoriasis.60,61

Abnormal metabolic environment is a common pathophysiologic mechanism associated with ASCVD and DM, with elevated lipids and glucose both important drivers.2,4 Intracellular fuel excess leads to oxidative stress and production of ROS that result in inflammation and induction of transcription factors that subsequently lead to changes in gene expression that finally results in cell dysfunction not only the -cell but also other cells including cardiomyocytes and vascular smooth muscle cells.2 Indeed, in a 2-sample MR study, genetic predisposition to childhood obesity was causally associated with an increased risk of both T2DM and coronary artery disease in adults.62 As with other conditions within the Diabetes Syndrome, a relationship between the gut microbiome and ischemic heart disease has also been suggested. For example, an MR approach demonstrated a beneficial association of the gut bacteria Bifidobacterium with ischemic heart disease, adiposity, high-density lipid cholesterol (HDL-C), and insulin resistance.63 Environmental risk factors including life-style choices such as smoking, in addition to air pollutants, pesticides, as well as food AGEs impact DM and CV mortality.45,55 Indeed, restriction of food AGEs in human studies has been associated with beneficial effects on IR and CV disease.55

Nutrition, fuel excess, and abnormal metabolic environment are associated with conditions across the Diabetes Syndrome including DM and NAFLD/NASH, leading to cell and tissue damage and dysfunction.2,6 Further, aberrant gut biome is implicated in the pathogenesis of both DM and NAFLD/NASH.2,6 Changes in the microbiome are thought to cause inflammation, alter intestinal permeability, and modulate metabolism of fatty acids resulting in.6,64 Decreased GLP-1 secretion (incretin effect) from the gut flora has also been associated with DM.2,58 Endocrine disruptors such as food AGEs are implicated in IR, DM, and NAFLD.55,65 Furthermore, a common link of endocrine disruptors to various conditions including DM, NAFLD, PCOS, and obesity may be due to their impact on IR through modification of transcription of various IR-related genes.36,65

Inflammation negatively impacts a wide swath of other organ systems outside of those involved in DM specifically.2 Hyperinsulinemia which can occur as a result of IR or exogenous insulin therapy is associated with inflammation, a common biological process of both DM and cancer.1,37 Moreover, exposure to hyperglycemia and glucolipotoxicity leads to ROS which activate pathways (eg, polyol flux, AGE formation, protein kinase C activation, hexosamine flux) which lead to inflammation and, in turn, can lead to altered gene expression and epigenetic changes.2,3,12 Inflammation within adipose tissue can lead to production of cytokines that interfere with insulin signaling. Overproduction of inflammation cytokines (such as TNF- and IL-6) has been found to be associated with IR and the development and progression of DM and cancer.37

Inflammatory responses are associated with peripheral and central IR.66 Elevated cytokines have been detected in the cerebral spinal fluid of patients with AD and mouse models have revealed inflammation may interact with both processing and deposit of A.20 Chronic inflammation is frequent in both DM and AD and it is commonly known that neuroinflammation occurs in AD.20,66,67

Systemic inflammation plays an important role in both psoriasis and DM pathogenesis.68 Of note, inflammatory mediators involved in the development of IR including TNF-, IL-6, leptin, and adiponectin have been found to be altered in patients with psoriasis.69 Leptin activates induction of proinflammatory cytokines associated with IR and keratinocyte proliferation.69 Adiponectin, an inducer of anti-inflammatory cytokines and enhanced insulin sensitivity, is reduced in patients with DM and psoriasis.69,70

Inflammation and IR are intimately intertwined in all conditions of the Diabetes Syndrome, not the least of which with ASCVD. Insulin resistance and compensatory hyperinsulinemia promote inflammation, vascular smooth muscle growth and proliferation, and atherogenesis.71 Further, excess fat in adipocytes induces inflammation and secretion of IR-provoking and proinflammatory cytokines while inhibiting adiponectin, an insulin sensitizer.71 Formation of atherosclerotic lesions is hypothesized to be driven by local inflammation in the vascular wall triggered by dyslipidemia.72

Increased lipotoxicity in patients with NAFLD leads to mitochondrial dysfunction and oxidative stress.6 Hepatocyte fat content and local liver inflammation lead to a state of chronic inflammation which can lead to fibrosis and NASH.6 In addition, inflammation and NF-k activation can also promote carcinogenesis which may play a role in hepatocellular cancer development, and potential downstream outcome of NASH.6

Finally, although auto-immunity links T1DM with other autoimmune diseases, much remains unknown. To wit, although there is a high degree of overlap in involved variants in autoimmune diseases with similar pathophysiology, in disease with differing pathophysiology (eg, DM and IBD), the same variants are often implicated in opposite roles.73 Further, even in diseases with differing pathophysiology and many non-overlapping variants and oppositely implicated shared variants, variants which are overlapping or shared still exist.73 This is to say, for now, the addition of autoimmune diseases to the Diabetes Syndrome framework is not warranted until we understand more.

Inflammation and IR are intimately connected and play robust roles in the etiology and progression of diseases within the Diabetes Syndrome.2,74 In our view, IR can be expressed in a variety of different phenotypes that result from genetic and epigenetic variation in response to an abnormal environment, exogenous fuel excess, and/or inflammation including metabolic syndrome, obesity, PCOS, etc. Indeed, IR may have a role in the development of prediabetes phenotypes as well.28 Importantly, IR has been suggested as a critical role in both traditionally classified T1 and T2DM in the accelerator hypothesis.75 The postulation is that DM should be viewed as a continuum and the interaction between IR and genetic responses determines that age at which critical B-cell loss occurs.75 In this hypothesis, autoimmunity retains its role, though not necessarily as the primary causative factor in T1DM.75

Insulin resistance and resultant hyperinsulinemia drive increased ROS, overexpression or overactivation of insulin-related receptors, Ras/MEK activation leading to tumorigenesis, and PTEN and mTOR activation leading to cell growth and survival.12 It has been hypothesized that cell growth regulation is reprogrammed due to altered circulating hormones (including insulin), substrate availability, and adipose cell dysfunction.76 Moreover, hyperinsulinemia and increased availability of cellular substrate appear to allow cancer cells to bypass cell growth checkpoints.12 Genetically predicted increasing fasting insulin levels have been causally associated with increased risk of pancreatic cancer estrogen receptor positive breast cancer in MR studies.45,54

Insulin resistance is a common pathophysiologic mechanism between both DM and AD and is a common feature of AD patients even without concomitant DM.20,66 It has been proposed that AD should be considered a degenerative metabolic disease caused by brain IR and insulin deficiency.66 Indeed, it is thought that IR may also occur in the brain, possibly earlier than the development of peripheral IR, potentially triggered by A oligomers and cognitive decline.20 Mendelian randomization analyses suggest that, in fact, insulin sensitivity can impact AD risk more so than overall T2DM.77

Insulin resistance is an important pathophysiologic mechanism implicated in both DM and psoriasis. Patients with psoriasis have IR and the degree of IR is correlated with psoriasis area and severity.68,69,78 Further, hyperinsulinemia increases insulin binding to IGF receptors which lead to keratinocyte and fibroblast proliferation.70

Insulin resistance has been linked to ASCVD via multiple studies.71 Insulin resistance contributes to ASCVD by reduction of nitric oxide production (a vasodilator and antiatherogenic agent) leading to endothelial dysfunction, and, as a result of compensatory hyperinsulinemia, excessive stimulation of the MAPK pathway resulting in inflammation, vascular smooth muscle cell proliferation, and atherogenesis.48 Furthermore, IR is associated with a variety of phenotypes (see Figure 1) and risk factors, all independently associated with ASCVD.71 The fact that some anti-diabetic agents are associated with CV benefits despite modest impact on hyperglycemia highlights that other mechanisms are in place.71 (see also Thiazolidinediones Section below)

Insulin resistance is a key factor in the progression of NAFLD to NASH.6 Increased hepatic lipogenesis and impaired inhibition of adipose tissue lipolysis as a result of IR lead to increased fatty acids in the liver.6 In addition, IR-stimulated adipose tissue dysfunction leads to altered production and secretion of adipokines and inflammatory cytokines.6 Further, cluster analysis of diabetes phenotypes related to age, obesity, glycemia, and insulin deficiency or resistance has linked patients with T2DM with severe IR-related diabetes to increase incidence of hepatic fibrosis.79 A bi-directional MR study demonstrated genetic predisposition to higher fasting insulin, but not T2DM, was related to increased circulating ALT, markers of NAFLD.80 Genetically predicted higher circulating ALT and AST were related to increased risk of T2DM.80 These outcomes provide support for the potential of IR resulting in NAFLD which, in turn, increases T2DM risk.80

In earlier work, we have espoused the logic of combination therapy with the least number of safe and efficacious agents to treat the highest number of overlapping pathophysiologies contributing to DM.1,2,81 This same concept can be further applied to the Diabetes Syndrome to allow us to expand our treatment armamentarium. Indeed, there is promise for many therapies traditionally associated with DM management to provide benefit in the Diabetes Syndrome (Table 1). The potential impact on patient management of using existing and new medicines and classes with this in mind is considerable. Continued investigations and research are warranted.

Thiazolidinediones (TZDs) are PPAR agonists (eg, pioglitazone) extensively used for DM treatment as insulin sensitizers. Potential benefits of TZD use in cancer, dementia, psoriasis, ASCVD, and NASH have also been reported. In general, TZD use is associated with reduced cancer risk including liver, stomach, and colorectal (although increased bladder cancer risk has been reported).82 Furthermore, risk of dementia in pioglitazone users is lower than in non-users.8386 Moreover, recent studies have suggested that TZDs may be antipsoriatic due to PPARs ability to promote keratinocyte differentiation, inhibit epidermal growth, and reduce inflammatory responses.87 A meta-analysis of randomized controlled trials concluded pioglitazone was efficacious for psoriasis treatment.87 In addition, pioglitazone reduced CV events and retarded the atherosclerotic process in high-risk DM patients in multiple large outcome trials.71 Finally, pioglitazone was associated with a trend toward improved histology and a significant reduction in liver enzymes (ALT, AST), hepatic steatosis, and lobular inflammation in patients with NASH compared with placebo.88

Metformin, often used as first-line treatment in DM and PCOS based on its ability to decrease the rate of hepatic gluconeogenesis and potentially decrease IR, is also being evaluated as an agent to treat other conditions including cancer, AD, ASCVD, metabolic syndrome, etc.74 Reduced risk for pancreatic, breast, lung, prostate, colorectal, and liver cancer has been associated with metformin use.82 Further, meta-analysis results prompted the recommendation that metformin should be used as first-line therapy for DM at risk for developing dementia or AD.76,83 Trials in patients without DM, although smaller, show positive results with regard to effect of metformin on dementia symptoms.82 Further, the potential protective effect of metformin in youth and adults with T1DM and in adults with pre- and T2DM has been suggested based on surrogate measures of ASCVD, though based on mixed study results whether metformin improves CV outcomes remains uncertain.71,89 Finally, although increasing AMPK activity (which is reduced by inflammation, obesity, and DM) through metformin use has considered as a potential treatment for NAFLD and NASH, to date results have been mixed.90,91

GLP-1 RAs are approved for the treatment of DM and to reduce risk of MACE events in DM patients and promote weight loss.92 GLP-1 has multiple metabolic functions including delayed gastric emptying, appetite suppression, enhanced liver glucose update, peripheral insulin sensitivity as well as glucose-dependent insulin secretion and inhibition of glucagon release from cells.58 Although there have been reports of potential for cancer with GLP-1 RA use, a recent meta-analysis of GLP-1 RA did not reveal increased cancer risk in T2DM patients and showed a decreased risk with albiglutide.93 Studies using neurodegenerative disease animal models support potential for GLP-1 receptor stimulation to improve cognitive impairment (regardless of DM status).94 Further, reduction of MACE events has been recently added to GLP-1 RA labels on the basis of large CV outcomes trials.71 Multiple RCTs have been conducted in NAFLD/NASH that demonstrated significant reduction in liver steatosis, fibrosis, and fat content.88,95,96

Dipeptidyl peptidase-4 inhibitors inhibit the degradation of GLP-1 and are approved for DM. A DPP-4 inhibitor (sitagliptin) has been associated with improvement in cognitive function in elderly patients with and without AD.97 To date, results of DDP-4 inhibitors have been mixed with regard to liver enzymes, fat content, and fibrosis in patients with NAFLD/NASH.88

SGLT-2 inhibitors block the reabsorption of glucose in the kidney and are approved for DM. Recent studies have shown CV protective effect of SGLT-2 inhibitors, including reduction in MACE, hospitalization for heart failure, and CV mortality in high-risk patients with DM.71 In addition to reduction of hyperglycemia, it is hypothesized that increased levels of ketone bodies, potent anti-inflammatory molecules, may contribute to this effect.98 Moreover, based on a recent systematic review, SGLT-2 inhibitors improved liver enzymes, decreased liver fat and fibrosis, and improved other metabolic parameters (obesity, IR, glycemia, lipid parameters) in T2DM patients with NAFLD.99

Bromocriptine QR, approved for DM, acts centrally and while it is only associated with modest improvement of glycemic control, it confers a clinically meaningful CV benefit.100,101 Indeed, the reduction in CV disease in patients treated with bromocriptine compared with placebo over 12 months in patients with good glycemic control at baseline (HbA1c 7%), in addition to higher odds of remaining in good glycemic control suggests that in patients with T2DM there are important mechanisms beyond glycemic control (eg, reducing vascular sympathetic tone/reducing endothelial dysfunction) that may be contributing to the CV risk reduction observed with this dopamine D2 agonist.100

Abatacept, an immunomodulator that inhibits T cell activation is approved for rheumatoid arthritis, juvenile idiopathic arthritis, and adult psoriatic arthritis.102 It has been used for DM management due to reports of improved insulin sensitivity in patients with rheumatoid arthritis.103 It is currently under evaluation in the T1DM setting for deceleration of -cell damage in newly diagnosed patients, prevention of DM in at risk patients, and prevention of destruction/rejection of transplanted pancreatic islets.103105

The Diabetes Syndrome can be thought of as a supra-structure that can facilitate understanding the inter-relationships of superficially disparate conditions. It allows physicians and researchers to immediately place new ideas and articles into a coherent whole. The structure will allow new science to be added easily and facilitates the evaluation of therapeutics for the management of disease. Further, many of the connections we propose within the Diabetes Syndrome are testable by leveraging current genetic knowledge from efforts that include powerful MR approaches which, in turn, can inform implementation of precision medicine.30,31 The structure should be considered organic and will be refined and modified as we grow in our basic understanding of the interplay between genetics and epigenetics, inflammation, environment, and IR. Furthermore, recognizing the association of the conditions with the Diabetes Syndrome due to interconnection of common driving elements and the differential contribution of the various pathophysiologies for individual patients has the potential to provide both benefit to the patient (eg, prevention, early detection, precision medicine) and to the advancement of medicine (eg, driving education, research, and dynamic decision-based medical practice).

Dr. Rachfal is a clinical drug development consultant. None of her past or current clients has provided compensation related to this manuscript. Dr. Rachfal has been compensated by Dr. Schwartz for assistance in medical editing and preparation of the manuscript. Dr. Rachfal is self-employed at Stage Gate Partners, LLC with no compensation from Stage Gate Partners related to the manuscript. Dr. Grant has active NIH grants R01 DK122586 and UM1 DK126194, which cover the topic of functional mechanisms of Type 1 diabetes and Type 2 diabetes risk variants, respectively, and their target genes using 3D epigenomics and single cell approaches. Dr. Schwartz is on advisory boards for Salix Pharmaceuticals and Arkay Therapeutics and on the Speakers Bureau for Salix Pharmaceuticals, Johnson and Johnson, Arkay, Novo, Janssen Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, and Merck. Dr. Schwartz is employed and receive financial compensation from his clinical practice, Stanley Schwartz, MD, LLC (affiliated with Main Line Health [with no monetary connection]) with no compensation related to the manuscript. He is also an emeritus professor for University of Pennsylvania, Perlman School of Medicine, Philadelphia, PA, USA. With the exception of the listed potential conflict of interests herein, the authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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[Full text] The Diabetes Syndrome A Collection of Conditions with Common, | IJGM - Dove Medical Press

Results of the UK study confirm for clinical laboratory professionals the importance of fully understanding the design and function of SNP chips they may be using in their labs

Here is another example of a long-established clinical laboratory test thatupon new evidenceturns out to be not as accurate as once thought. According to research conducted at the University of Exeter in Devon, UK, Single-nucleotide polymorphism (SNP) chips (aka, SNP microarrays)technology commonly used in commercial genetic testingis inadequate at detecting rare gene variants that can increase breast cancer risk.

A news release announcing the results of the large-scale study states, A technology that is widely used by commercial genetic testing companies is extremely unreliable in detecting very rare variants, meaning results suggesting individuals carry rare disease-causing genetic variants are usually wrong.

Why is this a significant finding for clinical laboratories? Because medical laboratories performing genetic tests that use SNP chips should be aware that rare genetic variantswhich are clinically relevant to a patients casemay not be detected and/or reported by the tests they are running.

UK Researchers Find Shockingly High False Positives

The objective of the Exeter study published in British Medical Journal (BMJ), titled, Use of SNP Chips to Detect Rare Pathogenic Variants: Retrospective, Population Based Diagnostic Evaluation, was To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population.

The conclusion reached by the Exeter researchers, the BMJ study states, is that SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

Leigh Jackson, PhD, Lecturer in Genomic Medicine at University of Exeter and co-author of the BMJ study, said in the news release, The number of false positives on rare genetic variants produced by SNP chips was shockingly high. To be clear: a very rare, disease-causing variant detected using [an] SNP chip is more likely to be wrong than right.

Large-Scale Study Taps UK Biobank Data

The Exeter researchers were concerned about cases of unnecessary invasive medical procedures being scheduled by women after learning of rare genetic variations in BRCA1 (breast cancer type 1) and BRCA2 (breast cancer 2) tests.

The inherent technical limitation of SNP chips for correctly detecting rare genetic variants is further exacerbated when the variants themselves are linked to very rare diseases. As with any diagnostic test, the positive predictive value for low prevalence conditions will necessarily be low in most individuals. For pathogenic BRCA variants in the UK Biobank, the SNP chips had an extremely low positive predictive value (1-17%) when compared with sequencing. Were these results to be fed back to individuals, the clinical implications would be profound. Women with a positive BRCA result face a lifetime of additional screening and potentially prophylactic surgery that is unwarranted in the case of a false positive result, they wrote.

Using UK Biobank data from 49,908 participants (55% were female), the researchers compared next-generation sequencing (NGS) to SNP chip genotyping. They found that SNP chipswhich test genetic variation at hundreds-of-thousands of specific locations across the genomeperformed well when compared to NGS for common variants, such as those related to type 2 diabetes and ancestry assessment, the study noted.

Because SNP chips are such a widely used and high-performing assay for common genetic variants, we were also surprised that the differing performance of SNP chips for detecting rare variants was not well appreciated in the wider research or medical communities. Luckily, we had recently received both SNP chip and genome-wide DNA sequencing data on 50,000 individuals through the UK Biobanka population cohort of adult volunteers from across the UK. This large dataset allowed us to systematically investigate the performance of SNP chips across millions of genetic variants with a wide range of frequencies, down to those present in fewer than 1 in 50,000 individuals, wrote Wright and Associate Professor of Bioinformatics and Human Genetics at Exeter, Michael Weedon, PhD, in a BMJ blog post.

The Exeter researchers also analyzed data from a small group of people in the Personal Genome Project who had both SNP genotyping and sequencing information available. They focused their analysis on rare pathogenic variants in BRCA1 and BRCA2 genes.

The researchers found:

Advantages and Capabilities of SNP Chips

Compared to next-gen genetic sequencing, SNP chips are less costly. The chips use grids of hundreds of thousands of beads that react to specific gene variants by glowing in different colors, New Scientist explained.

Common variants of BRCA1 and BRCA2 can be found using SNP chips with 99% accuracy, New Scientist reported based on study data.

However, when the task is to find thousands of rare variants in BRCA1 and BRCA2 genes, SNP chips do not fare so well.

It is just not the right technology for the job when it comes to rare variants. Theyre excellent for the common variants that are present in lots of people. But the rarer the variant is, the less likely they are to be able to correctly detect it, Wright told CNN.

SNP chips cant detect all variants because they struggle to cluster needed data, the Exeter researchers explained.

SNP chips perform poorly for genotyping rare genetic variants owing to their reliance on data clustering. Clustering data from multiple individuals with similar genotypes works very well when variants are common, the researchers wrote. Clustering becomes more difficult as the number of people with a particular genotype decreases.

Clinical laboratories Using SNP Chips

The researchers at Exeter unveiled important information that pathologists and medical laboratory professionals will want to understand and monitor. Cancer patients with rare genetic variants may not be diagnosed accurately because SNP chips were not designed to identify specific genetic variants. Those patients may need additional testing to validate diagnoses and prevent harm.

Donna Marie Pocius

Related Information:

Large-scale Study Finds Genetic Testing Technology Falsely Detects Very Rare Variants

Use of SNP Chips to Detect Rare Pathogenic Variants: Retrospective, Population-Based Diagnostic Evaluation

The Home DNA Kits Falsely Warning of High Risk of Cancer: DIY Genetic Tests are Extremely Unreliable at Detecting Rare Genetic Variants, Major New Study Warns

SNP Chips Perform Poorly for Detecting Rare Genetic Variants

Chip-based DNA Testing Wrong More than Right for Very Rare Variants

Common Genetic Tests Often Wrong When Identifying Rare Disease-Causing Variants Such as BRCA1and BRCA2, Study Says

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Common DNA Testing Method Using SNP Chips Struggles to Find Rare Variants Associated with BRCA Test, UK Researchers Find - DARKDaily.com - Laboratory...

CAMBRIDGE, Mass., March 15, 2021 (GLOBE NEWSWIRE) -- Beam Therapeutics Inc. (Nasdaq: BEAM), a biotechnology company developing precision genetic medicines through base editing, today announced that drug development expert Amy Simon, M.D., has been appointed as the companys chief medical officer. Dr. Simon brings more than 20 years of clinical experience to Beam, serving in roles as a physician-scientist in academia and the biotechnology industry.

Amy is a dedicated physician and experienced drug developer who has worked across a wide range of disease areas, delivering innovative genetic medicines into the clinic and through approval, said John Evans, chief executive officer of Beam. We have made significant progress advancing our lead base editing programs through research, preclinical and now IND-enabling studies, with our first IND application for our lead candidate, BEAM-101 slated for the second half of this year. Amys translational and clinical development expertise will be invaluable as we prepare for our next phase as a clinical-stage company. We are thrilled to welcome her to the Beam team.

Beam is built on an inspiring vision of bringing life-changing medicines to patients, and I couldnt be more excited to join this talented team, said Dr. Simon. We have a unique opportunity to advance a broad portfolio of potentially one-time, disease-altering programs using base editing technology and a precision medicine approach supported by clear human genetics. I believe 2021 will be a transformative year for Beam as we leverage our strong research organization and further build out our clinical development organization. I look forward to partnering with the entire team to help bring new therapeutic options to patients suffering from serious diseases.

Dr. Simon joins Beam from Alnylam Pharmaceuticals, where she spent over a decade in various roles with increasing responsibility for the clinical development of RNAi-based medicines, most recently serving as vice president, clinical development. During her tenure at Alnylam, she led the successful execution of clinical programs from natural history studies to Phase 1 through Phase 4 studies, regulatory interactions with both U.S. and ex-U.S. authorities, and drug approvals in the U.S. and EU. Dr. Simon was the lead clinician developing GIVLAARI (givosiran) for patients with acute hepatic porphyria, which was approved by the Food and Drug Administration in 2019. Prior to entering the biotech industry, Dr. Simon worked in academia at Tufts University, serving as a professor and a director of the Asthma Center in the Pulmonary and Critical Care Division at Tufts University School of Medicine and as a professor at Tufts Graduate School of Biomedical Science where her laboratory conducted basic science research on asthma. She began her career in clinical practice, training as a resident in internal medicine and as a fellow in pulmonary and critical care medicine at Tufts Medical Center. Dr. Simon holds a B.A. in history and science from Harvard University, and an M.D. from Tufts University School of Medicine.

About Beam Therapeutics

Beam Therapeutics (Nasdaq: BEAM) is a biotechnology company committed to establishing the leading, fully integrated platform for precision genetic medicines. To achieve this vision, Beam has assembled a platform that includes a suite of gene editing and delivery technologies and is in the process of building internal manufacturing capabilities. Beams suite of gene editing technologies is anchored by base editing, a proprietary technology that enables precise, predictable and efficient single base changes, at targeted genomic sequences, without making double-stranded breaks in the DNA. This enables a wide range of potential therapeutic editing strategies that Beam is using to advance a diversified portfolio of base editing programs. Beam is a values-driven organization committed to its people, cutting-edge science, and a vision of providing life-long cures to patients suffering from serious diseases.

Forward-Looking Statements

This press release contains forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Investors are cautioned not to place undue reliance on these forward-looking statements, including, but not limited to, statements related to: the expected timing of filing our first investigational new drug application, our ability to advance programs to the clinic; and the therapeutic applications and potential of our technology, including our ability to develop life-long, curative, precision genetic medicines for patients through base editing. Each forward-looking statement is subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied in such statement, including, without limitation, risks and uncertainties related to: our ability to develop, obtain regulatory approval for, and commercialize our product candidates, which may take longer or cost more than planned; our ability to raise additional funding, which may not be available; our ability to obtain, maintain and enforce patent and other intellectual property protection for our product candidates; the potential impact of the COVID-19 pandemic; that preclinical testing of our product candidates and preliminary or interim data from preclinical and clinical trials may not be predictive of the results or success of ongoing or later clinical trials; that enrollment of our clinical trials may take longer than expected; that our product candidates may experience manufacturing or supply interruptions or failures; risks related to competitive products; and the other risks and uncertainties identified under the heading Risk Factors in our Annual Report on Form 10-K for the year ended December 31, 2020, and in any subsequent filings with the Securities and Exchange Commission. These forward-looking statements (except as otherwise noted) speak only as of the date of this press release. Factors or events that could cause our actual results to differ may emerge from time to time, and it is not possible for us to predict all of them. We undertake no obligation to update any forward-looking statement, whether as a result of new information, future developments or otherwise, except as may be required by applicable law.

Contacts:

Investors:Chelcie ListerTHRUST Strategic Communicationschelcie@thrustsc.com

Media:Dan Budwick1ABdan@1abmedia.com

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Beam Therapeutics Appoints Amy Simon, M.D., Chief Medical Officer - GlobeNewswire