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Archive for category: Featured Articles

Featured Articles

p 14

Lipid testing and cardiovascular risk assessment: cut-off points

, 26 August 2020/in Featured Articles /by 3wmedia

Dyslipidemia is one of the major risk factors for the development of cardiovascular disease (CVD). However, which lipoproteins to measure and what cut-off points to use in order to accurately assess this risk remains debatable.

by Mohamed S. Elgendy and Dr Mohamed B. Elshazly

Cardiovascular disease (CVD) mortality in the US in 2011 was estimated at 786 641 deaths representing approximately 33% of total annual deaths [1]. It remains the leading cause of mortality and morbidity in the developed world. Over many years of study, dyslipidemia has been identified as one of the major risk factors for developing CVD that can be modified through behavioral modifications as well as medications.

Lipoproteins
Lipoproteins are small particles formed of lipids and proteins, which play an important role in the transport and metabolism of cholesterol. Based on their relative density, they are divided into five major categories: high-density lipoprotein (HDL), low-density lipoprotein (LDL), intermediate density lipoprotein (IDL), very low-density lipoprotein (VLDL), and chylomicrons. LDL carries 60–70% of total serum cholesterol, HDL carries 20–30%, and VLDL carries 10–15% [2]. The remaining lipoproteins, namely triglyceride-rich lipoproteins such as VLDL, remnants and IDL, in addition to lipoprotein(a), carry a relatively small fraction of total cholesterol. Numerous studies have shown that LDL is the most atherogenic lipoprotein particle and lowering its levels has been the cornerstone of dyslipidemia management and CVD risk reduction in recent years. However, there is emerging evidence indicating that other lipoproteins also play a significant role in the process atherogenesis [23].

Relationship between lipoproteins and CVD risk
Several studies have reported a continuous relationship between LDL reduction and CVD risk reduction [3]. No threshold was identified below which a lower LDL concentration is not associated with lower risk [4]. For example, in the recent IMPROVE-IT trial, the incidence of CVD morbidity and mortality was lower in the ezetimibe/simvastatin group (with a median LDL-C follow-up of 53.7 mg/dL) compared to the simvastatin-alone group (with a median LDL-C follow-up of 69.5 mg/dL) [5]. In another study, individuals with hypobetalipoproteinemia, who have LDL-C levels less than 70 mg/dL, show prolonged longevity and very minimal rates of myocardial infarctions [6]. All of this supports the notion of ‘lower is better’.

LDL-C levels in the range of 25–60 mg/dL are considered physiologically adequate [7]. Even levels below 25 mg/dL have failed to show any adverse effects in a couple of recent trials [8, 9]. Although adverse effects of very low LDL, like hemorrhagic stroke and neurocognitive deficits, have been reported in some studies, they were neither significant nor consistent [10, 11]. Therefore, the benefits of achieving very low levels of LDL outweigh the risks. On the one hand, the lack of randomized clinical trials comparing the outcome of different LDL goals has made it difficult to reach a consensus among different guidelines on the optimal goals for high-risk patients or those with coronary disease equivalents with the commonly used target still being <70 mg/dL [12–14]. On the other hand, in the most recent American College of Cardiology (ACC)/American Heart Association (AHA) guidelines, targets were abandoned because of the notion that the benefit of statin is independent of LDL level [15]. Despite these differences, we believe that the conglomerate of evidence suggests that your LDL can never be too low although data examining patients with extremely low levels <25 mg/dL is still limited. The potential re-establishment of new even lower LDL targets in upcoming guidelines will require careful examination of data from proprotein convertase subtilisin kexin-9 (PCSK-9) trials to identify specific LDL levels below which risk outweighs benefit. Other factors contribute to total atherogenic risk
Despite the established recognition of LDL as the most atherogenic lipoprotein, it is not representative of total atherogenic risk. Elevated triglycerides were found to be associated with increased risk for CVD and this suggests that triglyceride-rich lipoproteins (TGRLs), especially the remnants, are atherogenic. These lipoproteins include VLDL, IDL, and chylomicrons (only in the non-fasting state). As LDL standard measurement by the Friedewald formula [Total cholesterol – HDL – triglycerides/5] [1] only includes LDL-C and lipoprotein(a), non-HDL has been proposed as a more inclusive parameter of atherogenic risk because it also incorporates VLDL-C, IDL-C and remnants in addition to LDL-C. In fact, several studies have demonstrated that non-HDL-C is more strongly associated with CVD than LDL-C and is a more powerful risk predictor [16–21]. Moreover, non-HDL measurement comes at no extra cost, as it is calculated from the standard lipid profile by subtracting HDL from total cholesterol, and does not require prior fasting. Nevertheless, due to the smaller number of studies examining non-HDL as a target of therapy, compared to that examining LDL, most of the current guidelines recommend non-HDL as a secondary target of therapy [2, 12, 14, 22]. Only the National Lipid Association recommends non-HDL as a primary target of therapy as well as LDL [22]. We believe this current situation represents a transitional phase toward using non-HDL as a primary target of therapy, just like the past transition from total cholesterol to LDL-C. This is most important when discordance exists between LDL and non-HDL levels within individuals, a relatively common finding particularly in patients with low LDL and high triglyceride levels [23]. The currently recommended non-HDL treatment goal is 30 mg/dL higher than that of LDL-C based on the rationale that ‘normal’ VLDL exists when triglycerides level is <150 mg/dL, which is <30 mg/dL [2]. However, in a recent study of 1.3 million US adults, non-HDL level of 93 mg/dL was percentile equivalent to LDL of 70 mg/dL [23] suggesting that a lower non-HDL goal should be targeted. Particle-based measures such as apolipoprotein-B (Apo-B) and LDL particle concentration (LDL-P) also have the potential to replace cholesterol-based measures such as LDL or non-HDL as predictors of risk and targets of therapy. Apo-B constitutes the protein component of almost all the known atherogenic lipoproteins: VLDL, IDL, and LDL,;therefore, Apo-B measurement has been suggested to better estimate particle concentration, a more accurate reflection of subendothelial atherogenesis. Apo-B has been shown to be a better risk marker than LDL in multiple studies [17, 21, 24–29]. Many guidelines currently recommend Apo-B as an optional risk marker and target of therapy [12, 14, 22, 30]. Similarly, almost all the studies comparing LDL-P to LDL-C have shown superiority of particle concentration in terms of CVD risk assessment [31–34]. In the LUNAR trial and Framingham Offspring Study, there was a strong correlation between Apo-B and LDL-P with non-HDL, respectively, suggesting that non-HDL, available from the standard lipid profile, can be used satisfactorily for risk assessment [31, 35] keeping in mind that Apo-B may be superior in instances when discordance exists [36]. Whereas individual lipid parameters are important in risk prediction, summary estimates that assess the ratio of pro-atherogenic to anti-atherogenic lipoproteins also add important prognostic information regarding CVD risk. Out of the ratios that have been considered, total cholesterol to HDL cholesterol ratio (TC/HDL) and Apo-B/A1 are the most propitious. Despite TC/HDLs strong association with CVD risk [37–43], some have argued against any additional benefit this ratio might have, given that its two variables are included in estimating LDL by the Friedewald formula, in calculating non-HDL-C and in CVD risk estimation scores in addition to the contentiousness of HDL raising therapeutic strategies. However, in a recent 1.3 million population study, it has been documented that there is significant TC/HDL patient-level discordance in relation to LDL and non-HDL [44, 45]. This implies that TC/HDL may carry additional information reflecting atherogenic particle size and concentration [44, 45]. Notably, a TC/HDL ratio of 2.6 was percentile equivalent to an LDL level of 70 mg/dL (Table 1). Outcome data examining the clinical impact of TC/HDL discordance is still in progress and thus current guidelines do not currently recommend using TC/HDL. Summary
There is no doubt that the field of dyslipidemia management has been one of the most dynamic fields in cardiology over the last 3 decades. With the recent advent of PCSK-9 inhibitors, we need to re-evaluate our understanding of lipoprotein reduction and ask ourselves important questions: Should guidelines re-establish treatment targets? What is the best lipoprotein parameter for predicting risk? Is it one parameter that is superior or is it the input of multiple parameters? What do we do when discordance between lipid parameters within individuals exists? Although a lot of data necessary to answer these questions is still a work in progress, recent data may be able to provide some insightful answers. First, LDL-C is not the optimal marker for total atherogenic risk. Second, instead of evaluating the performance of individual lipid parameters at a population level, we should evaluate their performance at an individual level where identifying discordance within individuals is key to understanding which marker may be superior. Third, particle-based measures such as Apo-B and LDL-P may be superior to cholesterol-based measures; however, summary estimates such as TC/HDL or Apo-B/A1 ratios also add significant information to individual parameters. Fourth, identifying new lipoprotein treatment goals is dependent on identifying certain lipoprotein levels below which risk may outweigh benefit. Therefore, it seems likely that a future where very low percentile-equivalent cut-off points of several lipoprotein parameters and ratios may be set as simultaneous goals for treatment.

References
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3. Boekholdt SM, Hovingh GK, et al. Very low levels of atherogenic lipoproteins and the risk for cardiovascular events: a meta-analysis of statin trials. J Am Coll Cardiol. 2014; 64(5): 485–94.
4. Law MR, Wald NJ, et al. By how much and how quickly does reduction in serum cholesterol concentration lower risk of ischaemic heart disease? BMJ 1994; 308(6925): 367–72.
5. Giugliano RP, Blazing MA. IMProved Reduction of Outcomes:  Vytorin Efficacy International Trial. American College of Cardiology 2015;http://www.acc.org/latest-in-cardiology/clinical-trials/2014/11/18/16/25/improve-it
6. Glueck CJ, Gartside P, et al. Longevity syndromes: familial hypobeta and familial hyperalpha lipoproteinemia. J Lab Clin Med. 1976; 88(6): 941–957.
7. Brown MS, Goldstein JL. A receptor-mediated pathway for cholesterol homeostasis. Science 1986; 232(4746): 34–47.
8. Robinson JG, Farnier M, et al. Efficacy and safety of alirocumab in reducing lipids and cardiovascular events. N Engl J Med. 2015; 372(16): 1489–1499.
9. Horton JD, Cohen JC, et al. PCSK9: a convertase that coordinates LDL catabolism. J Lipid Res. 2009; 50(Supplement): S172–177.
10. Law MR, Thompson SG, et al. Assessing possible hazards of reducing serum cholesterol. BMJ 1994; 308(6925): 373–379.
11. Hsia J, MacFadyen JG, et al. Cardiovascular event reduction and adverse events among subjects attaining low-density lipoprotein cholesterol <50 mg/dl with rosuvastatin: The JUPITER Trial (Justification for the use of statins in prevention: an intervention trial evaluating rosuvastatin). J Am Coll Cardiol. 2011; 57(16): 1666–16675.
12. Genest J, McPherson R, et al. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult – 2009 recommendations. Can J Cardiol. 2009; 25(10): 567–579.
13. Grundy SM, Cleeman JI, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation 2004; 110(2): 227–239.
14. European Association for Cardiovascular Prevention & Rehabilitation, Reiner Ž, Catapano AL, et al. ESC/EAS Guidelines for the management of dyslipidaemias. Eur Heart J. 2011; 32(14): 1769–1818.
15. Stone NJ, Robinson JG, et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 129(25 Suppl 2): S1–45.
16. Bittner V, Hardison R, et al. Non-high-density lipoprotein cholesterol levels predict five-year outcome in the Bypass Angioplasty Revascularization Investigation (BARI). Circulation 2002; 106(20): 2537–2542.
17. Boekholdt S, Arsenault BJ, et al. Association of LDL cholesterol, non–HDL cholesterol, and apolipoprotein B levels with risk of cardiovascular events among patients treated with statins: a meta-analysis. JAMA 2012; 307(12): 1302–1309.
18. Li C, Ford ES, et al. Serum non-high-density lipoprotein cholesterol concentration and risk of death from cardiovascular diseases among U.S. adults with diagnosed diabetes: the Third National Health and Nutrition Examination Survey linked mortality study. Cardiovasc Diabetol. 2011; 10: 46.
19. Liu J, Sempos CT, et al. Non–high-density lipoprotein and very-low-density lipoprotein cholesterol and their risk predictive values in coronary heart disease. Am J Cardiol. 2006; 98(10): 1363–1368.
20. Robinson JG, Wang S, et al. Meta-analysis of the relationship between non–high-density lipoprotein cholesterol reduction and coronary heart disease risk. J Am Coll Cardiol. 2009; 53(4): 316–322.
21. Sniderman AD, Williams K, et al. A meta-analysis of low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, and apolipoprotein B as markers of cardiovascular risk. Circ Cardiovasc Qual Outcomes 2011; 4(3): 337–345.
22. Jacobson TA, Maki KC, et al. National Lipid Association recommendations for patient-centered management of dyslipidemia: part 2. J Clin Lipidol. 2015;http://linkinghub.elsevier.com/retrieve/pii/S1933287415003803
23. Elshazly MB, Martin SS, et al. Non–high-density lipoprotein cholesterol, guideline targets, and population percentiles for secondary prevention in 1.3 million adults: The VLDL-2 Study (very large database of lipids). J Am Coll Cardiol. 2013; 62(21): 1960–1965.
24. Jiang R, Schulze MB, et al. Non-HDL cholesterol and apolipoprotein B predict cardiovascular disease events among men with type 2 diabetes. Diabetes Care 2004; 27(8): 1991–1997.
25. Shai I, Rimm EB,  et al. Multivariate assessment of lipid parameters as predictors of coronary heart disease among postmenopausal women: potential implications for clinical guidelines. Circulation 2004; 110(18): 2824–2830.
26. Sniderman A, Williams K, et al. Non-HDL C equals apolipoprotein B: except when it does not! Curr Opin Lipidol. 2010; 21(6): 518–524.
27. Talmud PJ, Hawe E, et al. Nonfasting apolipoprotein B and triglyceride levels as a useful predictor of coronary heart disease risk in middle-aged UK men. Arterioscler Thromb Vasc Biol. 2002; 22(11): 1918–1923.
28. Walldius G, Jungner I. Apolipoprotein B and apolipoprotein A-I: risk indicators of coronary heart disease and targets for lipid-modifying therapy. J Intern Med. 2004; 255(2): 188–205.
29. Walldius G, Jungner I, et al. High apolipoprotein B, low apolipoprotein A-I, and improvement in the prediction of fatal myocardial infarction (AMORIS study): a prospective study. The Lancet 2001; 358(9298): 2026–2033.
30. Grundy SM, Arai H, et al. An International Atherosclerosis Society position paper: global recommendations for the management of dyslipidemia – full report. J Clin Lipidol. 2014; 8(1): 29–60.
31. Cromwell WC, Otvos JD, et al. LDL particle number and risk of future cardiovascular disease in the Framingham Offspring Study – implications for LDL management. J Clin Lipidol. 2007; 1(6): 583–592.
32. El Harchaoui K, van der Steeg WA, et al. Value of low-density lipoprotein particle number and size as predictors of coronary artery disease in apparently healthy men and women: the EPIC-Norfolk Prospective Population Study. J Am Coll Cardiol. 2007; 49(5): 547–553.
33. Mora S, Otvos JD, et al. Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women. Circulation 2009; 119(7): 931–939.
34. Otvos JD, Mora S, et al. Clinical implications of discordance between LDL cholesterol and LDL particle number. J Clin Lipidol. 2011; 5(2): 105–113.
35. Ballantyne CM, Pitt B, et al. Alteration of relation of atherogenic lipoprotein cholesterol to apolipoprotein B by intensive statin therapy in patients with acute coronary syndrome (from the Limiting UNdertreatment of lipids in ACS With Rosuvastatin [LUNAR] trial). Am J Cardiol. 2013; 111(4): 506–509.
36. Mora S. Advanced lipoprotein testing and subfractionation are not (yet) ready for routine clinical use. Circulation 2009; 119(17): 2396–404.
37. Prospective Studies Collaboration, Lewington S, Whitlock G, et al. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths. Lancet 2007; 370(9602): 1829–1839.
38. Ingelsson E, Schaefer EJ, et al. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA 2007; 298(7): 776–785.
39. Manickam P, Rathod A, et al. Comparative prognostic utility of conventional and novel lipid parameters for cardiovascular disease risk prediction: do novel lipid parameters offer an advantage? J Clin Lipidol. 2011; 5(2): 82–90.
40. Kastelein JJP, Steeg WA van der, et al. Lipids, apolipoproteins, and their ratios in relation to cardiovascular events with statin treatment. Circulation 2008; 117(23): 3002–3009.
41. McQueen MJ, Hawken S, et al. Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTERHEART study): a case-control study. Lancet 2008; 372(9634): 224–233.
42. Mora S, Otvos JD, et al. Lipoprotein particle profiles by nuclear magnetic resonance compared with standard lipids and apolipoproteins in predicting incident cardiovascular disease in women. Circulation 2009; 119(7): 931–939.
43. Ridker PM, Rifai N, et al. Non-HDL cholesterol, apolipoproteins A-I and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women. JAMA 2005; 294(3): 326–333.
44. Elshazly MB, Quispe R, et al. Patient-level discordance in population percentiles of the total cholesterol to high-density lipoprotein cholesterol ratio in comparison with low-density lipoprotein cholesterol and non–high-density lipoprotein cholesterol: The Very Large Database of Lipids Study (VLDL-2B). Circulation.2015; 132(8): 667–676.
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The authors
Mohamed S. Elgendy1 and Mohamed B. Elshazly*2 MD
1Kasr Al Ainy School of Medicine, Cairo University, Cairo, Egypt
2Cleveland Clinic, Heart and Vascular Institute, Cleveland, OH 44195, USA


*Corresponding author
E-mail: elshazm@ccf.org

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C236 Everett ED AS fig1

Pharmacometabonomics: a new methodology for personalized medicine

, 26 August 2020/in Featured Articles /by 3wmedia

The application of metabolic profiling of human biofluids to the prediction of drug efficacy, pharmacokinetics, metabolism and/or toxicity forms a paradigm known as pharmacometabonomics. Pharmacometabonomics holds out promise for the improved delivery of personalized medicine in the future, as it takes into account both genetic and environmental factors, including diet, drug intake and most notably, the status of the gut microbiome, in deriving predictions. Pharmacometabonomics is thus complementary to pharmacogenomics and in some instances the two technologies can be synergistically used together. This article introduces pharmacometabonomics and covers current important application areas.

by Dr Dorsa Varshavi, Dorna Varshavi and Prof. Jeremy Everett

Introduction
In 21st century medicine, a major goal is to develop personalized medicine for selected groups of patients in order to reduce the likelihood of adverse drug reactions and to maximize the desired therapeutic effect [1]. Until recently, personalized drug therapy was delivered almost exclusively by pharmacogenomics (PG), where an individual’s genetic makeup is used to predict the outcome of drug treatment [2]. The best-recognized examples of PG involve drug effect predictions made by analysis of genetic polymorphisms in drug-metabolizing enzymes such as the cytochrome P450 isoenzymes [3].

Although genetic variation is an important determinant of individual variability in drug response, it is now well recognized that personalized drug therapy cannot always be attained using genetic knowledge alone. This is because inter-individual variation in drug response is a consequence of multiple factors, including genetic and epigenetic factors and in addition, environmental factors such as nutritional and health status, the condition of the microbiome, exposure to environmental toxins, and co- or pre-administration of other drugs, including alcohol. These environmental factors can strongly affect drug absorption, distribution, metabolism and excretion and thereby cause inter-individual variation in drug efficacy and safety.

Metabonomics is defined as: ‘The study of the metabolic response of organisms to disease, environmental change, or genetic modification’ [4]. In a metabonomics experiment, changes in the levels of biofluid or tissue metabolites, before and after an intervention, such as drug administration, are measured using analytical technologies such as nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS). The alternative term metabolomics is also used and although its definition is observational, rather than interventional, the two terms are now used interchangeably.

Pharmacometabonomics is a recent development from metabonomics and is defined as ‘the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures’ [5–7]. In contrast to a metabonomics experiment, where the effect of an intervention is assessed based on changes in metabolite profiles post-intervention, in a pharmacometabonomics experiment the effect of the intervention is predicted based on the pre-intervention metabolite profiles.

Although first demonstrated in animals [5], pharmacometabonomics was quickly also demonstrated in humans, in a study where an individual’s pre-dose urinary endogenous metabolite profile was used to predict the metabolism of the analgesic paracetamol [6]. NMR-based analyses showed that individuals excreting relatively high levels of the microbial co-metabolite para-cresol sulfate in their pre-dose urine, excreted less paracetamol sulfate and more paracetamol glucuronide post-dose than individuals excreting low pre-dose amounts of para-cresol sulfate (Figs 1 & 2). Para-cresol sulfate is a metabolite produced from the hepatic sulfation of para-cresol, which is itself generated by gut bacteria, particularly Clostridium species. Paracetamol and para-cresol have similar molecular structures and both compete for limited human sulfation capacity via the same sulfotransferase enzymes, particularly SULT1A1. Thus individuals with a microbiome producing large amounts of para-cresol use up a large degree of their sulfonation capacity in metabolizing this toxin to para-cresol sulfate, and a subsequent dose of paracetamol will be metabolized to a greater degree by glucuronidation. This study was important for two key reasons: (1) it was the first demonstration of pharmacometabonomics in humans and (2) it was the first demonstration of the influence of the gut microbiome on human drug metabolism; the fact that the key biomarker in this study of human drug metabolism was a bacterially-derived molecule was a shock. Furthermore, the findings of this study will have implications for other drugs for which sulfonation is important, as well as certain diseases such as autism where abnormal paracetamol metabolism has been observed.

Pharmacometabonomics has been also used to predict individual responses to therapy, including the prediction of patient responses to treatment with the statin simvastatin. Statins can reduce low-density lipoprotein cholesterol (LDL-C) and, therefore, are used for treatment of cardiovascular disease. Kaddurah-Daouk et al. demonstrated that pre-dose plasma levels of the phosphatidylcholine metabolite PC18:2n6, the cholesterol ester CE18:1n7 and the free fatty acid FA18:3n3, were positively correlated with the magnitude of simvastatin-induced reduction in LDL-C, in 36 good responders and 36 poor responders [8]. A targeted pre-dose plasma analysis by MS then demonstrated (amongst other results) a strong correlation between the degree of reduction in LDL-C and higher pre-dose concentrations of three secondary, bacteria-derived, bile acids: lithocholic acid (LCA), taurolithocholic acid (TLCA) and glycolithocholic acid (GLCA), as well as coprostanol (COPR) [9]. This study further supported the contribution of the microbiome in influencing drug responses.

Pharmacometabonomics studies can be pursued jointly with, or followed up by pharmacogenetics studies. A good exemplification of the so-called ‘pharmacometabonomics informed pharmacogenomics approach’ is an MS-based study which demonstrated that pre-dose plasma levels of glycine, a central nervous system inhibitory neurotransmitter, were associated with rates of response or remission during citalopram/escitalopram treatment in patients with major depressive disorder (MDD) in the Mayo Clinic–NIH Pharmacogenetics Research Network (PGRN) Citalopram/Escitalopram Pharmacogenomics (Mayo–PGRN SSRI) study [10]. Tag single-nucleotide polymorphism (SNP) genotyping of the genes encoding enzymes in the glycine pathway from 529 patients enrolled in the Mayo-PGRN SSRI study was then completed. A series of SNPs in the gene encoding glycine dehydrogenase (GLDC) were found to be significantly associated with disease remission, with rs10975641 SNP showing the strongest association. This study demonstrated that pharmacometabonomics data can inform and complement pharmacogenomics data and, when combined, they can provide improved insights into the mechanisms influencing variability in drug response.

There are now many examples of the use of pharmacometabonomics for the prediction of human drug efficacy, toxicity, metabolism and pharmacokinetics and recent reviews are available [7, 11].

Conclusion and future prospect
Since its initial discovery [5], pharmacometabonomics has been increasingly applied in both preclinical and clinical studies to predict drug safety, efficacy, metabolism and pharmacokinetics. Pharmacometabonomics has an important advantage over pharmacogenomics in that it takes into account both genetic and environmental influences on drug administration. Pharmacometabonomics is itself just one specific example of a broader class of approaches known as predictive metabonomics, where the analysis of pre-intervention metabolite profiles can be used to predict clinical responses to other types of intervention, including diet, exercise, or even just the passage of time. A good example of predictive metabonomics can be seen in the recent study by Wang-Sattler et al. [12], who demonstrated that low baseline levels of glycine and lysophosphatidylcholine were predictive of the development of impaired glucose tolerance and/or type-2 diabetes in hundreds of subjects from the population-based, Cooperative Health Research in the Region of Augsburg (KORA) cohort. Another emerging area for which pharmacometabonomics holds promise is in the monitoring of patients over time as they progress through therapies such as cancer chemotherapy or surgery, a paradigm called longitudinal pharmacometabonomics [13]. This approach involves the metabolic profiling of patients before, during and after clinical therapy, in order to predict responses to future treatments and thus choose the optimal treatment regime. PG is now over 50 years old and is still limited in its impact on the practice of medicine. Pharmacometabonomics is much younger and it will take time for it to impact in the clinical arena. We predict that in the near future, personalized medicine will be conducted with assistance from both PG and pharmacometabonomics.

References
1. Pokorska-Bocci A, Stewart A, Sagoo GS, Hall A, Kroese M, Burton H. ‘Personalized medicine’: what’s in a name? Personalized Medicine 2014; 11(2): 197–210.
2. Jorgensen JT. A challenging drug development process in the era of personalized medicine. Drug Discov Today 2011; 16(19–20): 891–897.
3. Pirmohamed M. Personalized pharmacogenomics: predicting efficacy and adverse drug reactions. Ann Rev Genomics Hum Genet. 2014; 15: 349–370.
4. Lindon J, Nicholson J, Holmes E, Everett J. Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson. 2000; 12(5): 289–320.
5. Clayton T, Lindon J, Cloarec O, Antti H, Charuel C, Hanton G, Provost JP, Le Net JL, Baker D, Walley RJ, Everett JR, Nicholson JK. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006; 440(7087): 1073–1077.
6. Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc Natl Acad Sci U S A 2009; 106(34): 14728–14733.
7. Everett JR. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 2015; 16(7): 737–754.
8. Kaddurah-Daouk R, Baillie RA, Zhu HJ, Zeng ZB, Wiest MM, Nguyen UT, Watkins SM, Krauss RM. Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study. Metabolomics 2010; 6(2): 191–201.
9. Kaddurah-Daouk R, Baillie RA, Zhu H, Zeng ZB, Wiest MM, Nguyen UT, Wojnoonski K, Watkins SM, Trupp M, Krauss RM. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One 2011; 6(10): e25482.
10. Ji Y, Hebbring S, Zhu H, Jenkins GD, Biernacka J, Snyder K, Drews M, Fiehn O, Zeng Z, Schaid D, Mrazek DA, Kaddurah-Daouk R, Weinshilboum RM. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin Pharmacol Ther. 2011; 89(1): 97–104.
11. Everett JR. NMR-based pharmacometabonomics: a new approach to personalized medicine. In: Everett JR, Harris RK, Lindon JC, Wilson ID. (eds) NMR in Pharmaceutical Sciences, pp 359–372. Wiley 2015.
12. Wang-Sattler R, Yu Z, Herder C, Messias AC, Floegel A, He Y, Heim K, Campillos M, Holzapfel C, Thorand B, Grallert H, Xu T, et al. Novel biomarkers for pre-diabetes identified by metabolomics. Mol Syst Biol. 2012; 8: 615.
13. Nicholson JK, Everett JR, Lindon JC. Longitudinal pharmacometabonomics for predicting patient responses to therapy: drug metabolism, toxicity and efficacy. Expert Opin Drug Metab Toxicol. 2012; 8(2): 135–139.

The authors
Dorsa Varshavi PhD, Dorna Varshavi MSc, Jeremy Everett* PhD
Medway Metabonomics Research Group, University of Greenwich, Chatham, Kent ME4 4TB, UK
*Corresponding author
E-mail: j.r.everett@greenwich.ac.uk

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26885 Insertion CLI TRAPISTV6 09 2015

TRAPIST v6

, 26 August 2020/in Featured Articles /by 3wmedia
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Trends in infectious diseases and clinical microbiology at ECCMID 2016

, 26 August 2020/in Featured Articles /by 3wmedia

Keynote sessions on antimicrobial resistance, the microbiome and systems vaccinology as well as presentations on late-breaking research on refugee health and colistin resistance at ECCMID 2016

The annual meeting of the European Society of Clinical Microbiology and Infectious Diseases is taking place this year from April 9 – 12 in Amsterdam. At the world’s largest congress focused on infectious diseases and medical microbiology researchers will present more than 3,000 abstracts with the latest findings and recommendations, which are set to help improve diagnosis, prevention and the clinical care given to patients. Discussions on this vibrant platform not only help translate the research findings into diagnostic tools, guidelines, best practices, and international policies; they also raise awareness of emerging healthcare challenges.
The congress offers more than 150 oral presentations, including keynote lectures, symposia, oral sessions, educational workshops and meet-the-experts session as well as more than 2,000 poster presentations. The event also provides mini-oral e-poster presentations. Posters are presented as printed posters, but also on e-poster viewing stations, where visitors can scroll through abstracts presented as papers.
The main topics are strategies to detect and tackle antimicrobial resistance in various settings, approaches for prevention involving vaccines and infection control as well as descriptions of novel diagnostic technologies. The most popular sessions include lectures by winners of the ESCMID Award for Excellence and the Young Investigator Awards, as well as oral presentations on ground-breaking research approaches and findings, and the late-breaking abstracts.
The keynote speeches include presentations on innovative approaches to vaccines; the microbiome and tuberculosis therapies; lectures on how non-human antibiotics affect public health; and an economic perspective on antimicrobial resistance.

This year, the ECCMID Programme Committee has decided to offer two special tracks for the late-breaking abstract sessions, focused on two topics, requiring a coordinated response from infection specialists across all disciplines.
The first topic is refugee and migrant health. The thousands of people who are currently migrating challenge public health systems in transition and host countries. Clinicians and public health specialists need to develop strategies for the screening, the prevention, and the treatment of infectious diseases some of which were largely eradicated in Europe are now gradually being reintroduced.
The second focus of the late-breaking abstracts is on emerging colistin resistance. Reports about the emergence of plasmid-borne resistance to this last-resort antibiotic have reached us from China, Canada, the UK and most countries in continental Europe.

Hala Audi, head of the UK government review on antimicrobial resistance (AMR review) will examine not only the long-term consequences of increasing antibiotic resistance in terms of healthcare, but also its economic cost. If the present situation fails to improve, the impact could be as high as ten million lives lost every year and €90 trillion in lost productivity by 2050. Hala Audi will present her findings on how we can address this, and describe new financial models, which may be necessary to start developing newer classes of antibiotics.
Another keynote session by Prof. Lance B. Price of George Washington University will address how the use of antibiotics in animal food production is significantly contributing to antimicrobial resistance. Notably, he is pioneering the use of genomic epidemiology to understand how the misuse of antibiotics in animal feed affects public health. Prof. Price found that by analysing the genomes of bacteria – in human and animals – one is able to trace strains of antibiotic-resistant pathogens to industrial livestock productions. In light of this association,  it is alarming that many companies are still using antibiotics to prevent infection spread – what is not clear, is how endemic this use is and to what extent antibiotic use can be minimized and avoided in livestock production.

In terms of viral infections, experts at the congress will evaluate HIV and hepatitis C treatments in several sessions. At the same time, researchers will present results on emerging infections including those caused by the Zika virus. The problem with the current outbreak of the Zika virus is that we do not yet have any definitive evidence on how it is affecting their hosts – particularly on its potential link to microcephaly and Guillain-Barré syndrome – or on how this outbreak is different from previous outbreaks, and most crucially of all, on how to prevent transmission. Recent reports from the U.S. have indicated that the virus may be transmitted sexually – yet only a few weeks ago the CDC was stating this as ‘only a theoretical risk’. It is important that infectious disease specialists get together and discuss how to best tackle outbreaks of emerging or re-emerging infectious diseases. ECCMID offers an interdisciplinary platform for these debates.

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The role of lipid measurement in the assessment of cardiovascular disease risk: when to screen and what to measure

, 26 August 2020/in Featured Articles /by 3wmedia

Cardiovascular disease (CVD) carries considerable morbidity and mortality and poses a large economic cost on societies. Screening for CVD in order to identify high-risk individuals who may then be treated has been shown to be an effective way of alleviating the associated burden of disease. Numerous risk factors have been identified, of which lipids are a major modifiable factor.

by Dr Ravinder Sodi, Jarlath Eastwood, Dr Ian M. Godber

Cardiovascular disease (CVD), which manifests as coronary artery disease, peripheral vascular disease and cerebrovascular disease, accounts for approximately one-third of deaths worldwide [1], with three-quarters of them occurring in middle- and low-income countries [2]. The assessment of risk factors helps to determine those who are at high risk for CVD, which may then lead to lifestyle and dietary modifications as well as the use of medications in an attempt to mitigate the risk of associated mortality and morbidity. The primary aim of lipid measurements in the clinical setting is to aid in cardiovascular disease risk estimation. In the United Kingdom (UK), the National Institute for Health and Care Excellence (NICE) has issued guidance on the cardiovascular risk assessment and the modification of blood lipids to prevent CVD [3].

Clinical indications for screening
The clinical indication for screening is to identify high-risk individuals for the primary (first event) prevention of CVD. The most recent NICE guidance [3] recommends screening all individuals aged 40–74 years and/or with type 2 diabetes mellitus (DM) using the QRISK2 screening tool [3]. If the 10-year risk is ≥20%, a full, formal risk assessment should be undertaken; otherwise reviewing on an ongoing basis is suggested. The guidelines state ‘offer atorvastatin 20 mg for the primary prevention of cardiovascular disease (CVD) to people who  have a 10% or greater 10-year risk of developing CVD’ [3]. This recommendation has not yet been widely adopted and has been critiqued as it would mean that a considerable proportion of the general population would require lipid-lowering therapy [4]. If the 10-year risk is ≥20%, lifestyle changes as well as lipid-lowering medications should be considered. The use of clinical judgement and pragmatism is advised. The QRISK2 is not applicable in those ≥85 years; with type 1 DM; an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2, albuminuria or both; pre-existing CVD; and, family history of dyslipidemia. These patients are already at high risk and as such screening is of no further benefit. It is important to bear in mind that CVD risk may be underestimated in people treated for HIV, those with serious mental health issues, medications causing dyslipidemias, systemic inflammatory disorders, those on anti-hypertensives or lipid-lowering drugs, those who are severely obese (body mass index, BMI >40 kg/m2) and those who have recently stopped smoking. In general, for both primary and secondary prevention of CVD, the cardio-protective diet, physical activity, weight management, reduced alcohol consumption and smoking cessation are recommended together with lipid modification therapy, as applicable.

Risk factors for cardiovascular disease
There are numerous modifiable, partly modifiable and unmodifiable risk factors for CVD as shown in Table 1 [5]. The major modifiable risk factors for CVD are cigarette smoking, hypertension, dyslipidemia and depending on the presence of chronic complications of hyperglycemia, DM may be a partly modifiable factor. CVD risk increases with age and is higher in males than females, with the exception of postmenopausal women who smoke [6]. CVD is more common in those with a family history of the same and appears to be more common in Indians compared to Caucasians [1, 7]. Partly modifiable risk factors may be difficult to modulate as these may be beyond the control of both patient and clinician.

Lipid variables and cardiovascular disease risk
An elevated plasma concentration of low-density lipoprotein (LDL)-cholesterol has been shown to be a strong independent predictor of CVD [8]. Lipid-lowering guidelines have recommended LDL-cholesterol as the main target of treatment with lipid-lowering drugs. Astonishingly, the Friedewald formula (Table 2) used to determine the LDL-cholesterol in most clinical laboratories [9] has never been validated for use in patients treated with lipid-lowering medications. This information is not always available to laboratories when reporting lipid results. Moreover, this formula is only valid in individuals with triglycerides <4.5 mmol/L, necessitating an overnight fast before its determination and is inaccurate in those with low LDL-concentrations. As there are three variables required to determine LDL-cholesterol using the Friedewald formula, it is subject to three sources of inherent bias and imprecision. It also assumes that the cholesterol content of the very-low density lipoprotein (VLDL)-cholesterol is constant and does not account for other atherogenic lipoproteins and, therefore, is not valid in those with familial dysbetalipoproteinemia (type III hyperlipoproteinemia, broad-beta disease or remnant removal disease) where the LDL-cholesterol is overestimated [10]. Finally, it must be pointed out that the recommended QRISK2 tool does not require LDL-cholesterol as a variable to determine CVD risk but requires total-cholesterol and high-density lipoprotein (HDL)-cholesterol as separate variables [11]. HDL-cholesterol is a powerful independent cardiovascular risk factor with an inverse relationship with atherosclerotic disease (with risk rising sharply when levels are <1.04 mmol/L) [12]. However, Total-cholesterol/HDL-cholesterol ratio has been shown to be a better measure of CVD risk than individual components [13]. Total-cholesterol and high-density lipoprotein (HDL)-cholesterol are included in the QRISK2 tool as separate entities in determining CVD risk [3, 11]. Non-HDL-cholesterol (Table 2) has long been known to be a better predictor of CVD risk than LDL-cholesterol, but is as good as apolipoprotein-B [14]. It is known that many patients who achieve their LDL-cholesterol targets still develop CVD due in part to the residual risk not identified by LDL-cholesterol. Non-HDL-cholesterol serves as an index of all atherogenic, apolipoprotein-B containing lipoproteins: LDL, VLDL, intermediate-density-lipoprotein (IDL), lipoprotein(a). Most important, from a pragmatic stance, it does not require patients to be fasted overnight and can be used in those with high triglycerides. In addition, the recent NICE guidelines endorse the use of non-HDL-cholesterol recommending specialist referral if it is >7.5 mmol/L. Non-HDL cholesterol is particularly of importance in DM, where LDL-cholesterol may not be raised but the risk of CVD is considerable. Moreover, it has been shown that in DM, non-HDL cholesterol is a stronger predictor of mortality from CVD than LDL-cholesterol [15]. The prediction of CVD in those on lipid-lowering therapy remains an important goal and non-HDL-cholesterol may help address this. One disadvantage of non-HDL-cholesterol is that the positive bias in HDL-cholesterol measurement seen in cases of hypertriglyceridemia may mitigate any benefits [16]. However, taken together, non-HDL cholesterol provides an accurate alternative to LDL-cholesterol and there is a compelling case to include it in the laboratory test repertoire especially given that no additional reagent is required other than a simple calculation.

Elevated triglycerides concentrations are also an independent risk factor for CVD although it is weaker than LDL-cholesterol [17]. A high triglyceride level is a component of the metabolic syndrome, which is associated with high risk of CVD. Severe hypertriglyceridemia also increases the risk of pancreatitis [18]. Secondary causes of hypertriglyceridemia include: alcohol excess, medication-related (thiazides, beta-blockers, estrogens, corticosteroids, antiretroviral protease inhibitors, immunosuppressants, antipsychotics), untreated DM, renal disease, liver disease, pregnancy and some autoimmune disorders [18].

Apolipoproteins-B, -A1, low-density lipoprotein particle number and size have all been advocated as markers of CVD risk but offer no advantage over routine lipid parameters discussed above, are expensive requiring additional reagents but may be useful in identifying patients with dysbetalipoproteinemia [19]. At present in the UK, they are only measured in specialist laboratories.

Conclusion
Lipid testing offers a simple and cost-effective mode of determining CVD risk. Clinical laboratories should make every effort to start reporting non-HDL-cholesterol as part of their lipid profiles. It requires no additional reagent other than software configurations for calculation, obviates the need for fasting and requires no knowledge of lipid-lowering medications.

References
1. Moran AE, Roth GA, Narula J, Mensah GA. 1990–2010 global cardiovascular disease atlas. Glob Heart. 2014; 9: 3–16.
2. World Health Organization. Cardiovascular diseases (CVDs). WHO 2015; http://www.who.int/mediacentre/factsheets/fs317/en/
3. National Institute for Health and Care Excellence. Lipid modification: cardiovascular risk assessment and the modification of blood lipids for the primary and secondary prevention of cardiovascular disease. Clinical Guideline 181. 2014; https://www.nice.org.uk/guidance/cg181.
4. Wise J. Open letter raises concerns about NICE guidance on statins. BMJ 2014; 348: g3937.
5. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364: 937–952.
6. Billups KL, Miner MM, Wierzbicki AS, Jackson G. Gender-based cardiometabolic risk evaluation in minority and non-minority men grading the evidence of non-traditional determinants of cardiovascular risk. Int J Clin Pract. 2011; 65: 134–147.
7. Lovegrove JA. CVD risk in South Asians: the importance of defining adiposity and influence of dietary polyunsaturated fat. Proc Nutr Soc. 2007; 66: 286–298.
8. McQueen MJ, Hawken S, Wang X, Ounpuu S, Sniderman A, Probstfield J, et al. Lipids, lipoproteins, and apolipoproteins as risk markers of myocardial infarction in 52 countries (the INTERHEART study): a case-control study. Lancet. 2008; 372: 224–233.
9. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18: 499–502.
10. Zhao SP, Smelt AH, Leuven JA, van den Maagdenberg AM, van der Laarse A, van ‘t Hooft FM. Lipoproteins in familial dysbetalipoproteinemia. Variation of serum cholesterol level associated with VLDL concentration. Arterioscler Thromb. 1993; 13: 316–323.
11. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336: 1475–1482.
12. Cooney MT, Dudina A, De Bacquer D, Wilhelmsen L, Sans S, Menotti A, et al. HDL cholesterol protects against cardiovascular disease in both genders, at all ages and at all levels of risk. Atherosclerosis 2009; 206: 611–616.
13. Lemieux I, Lamarche B, Couillard C, Pascot A, Cantin B, Bergeron J, et al. Total cholesterol/HDL cholesterol ratio vs LDL cholesterol/HDL cholesterol ratio as indices of ischemic heart disease risk in men: the Quebec Cardiovascular Study. Arch Intern Med. 2001; 161: 2685–2692.
14. Hirsch G, Vaid N, Blumenthal RS. Perspectives: The significance of measuring non-HDL-cholesterol. Prev Cardiol. 2002; 5: 156–159.
15. Liu J, Sempos C, Donahue RP, Dorn J, Trevisan M, Grundy SM. Joint distribution of non-HDL and LDL cholesterol and coronary heart disease risk prediction among individuals with and without diabetes. Diabetes Care 2005; 28: 1916–1921.
16. Cramb R, French J, Mackness M, Neely RD, Caslake M, MacKenzie F. Lipid external quality assessment: commutability between external quality assessment and clinical specimens. Ann Clin Biochem. 2008; 45: 260–265.
17. Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet 2014; 384: 626–635.
18. Berglund L, Brunzell JD, Goldberg AC, Goldberg IJ, Sacks F, Murad MH, et al. Evaluation and treatment of hypertriglyceridemia: an Endocrine Society clinical practice guideline. J Clin Endocrinol Metab. 2012; 97: 2969–8299.
19. Dominiczak MH, Caslake MJ. Apolipoproteins: metabolic role and clinical biochemistry applications. Ann Clin Biochem. 2011; 48: 498–515.

The authors
Ravinder Sodi* 1,2 PhD, CSci, FRCPath; Jarlath Eastwood1 BSc, Ian M. Godber2 PhD, CSci, FRCPath
1Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
2Department of Clinical Biochemistry, NHS Lanarkshire, Wishaw General
Hospital, Wishaw, UK

*Corresponding author
E-mail: ravsodi@yahoo.com

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