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Apolipoproteins – new perspectives for lipid profiling, but other challenges remain
Atherosclerotic cardiovascular diseases (CVD) are the leading cause of death in the West, and dyslipidemia is considered to be one of their key risk factors. The majority of CVD cases could be prevented by effective management of dyslipidemia. The use of new biomarkers like apolipoproteins as part of extended lipid profiles may be among the most significant new tools for such a task.
Dyslipidemias
Dyslipidemias cover a broad spectrum of lipid abnormalities. Clinicians have so far paid maximum attention to elevated levels of total cholesterol (TC) and low-density lipoprotein-cholesterol (LDL-C). Many other types of dyslipidemias, however, also appear to enhance the risk of CVD.
Lipid metabolism can become imbalanced or disturbed in several ways, resulting in changes to plasma lipoprotein function and thereafter to the development of atherosclerosis. Many patients who have high cardiovascular risk still have unfavourable lipid profiles.
Given the fast-growing interest in lipidology, clinicians have sought ways to apply evidence-based medicine daily in dyslipidemia management. There are several lipid guidelines from professional societies in different parts of the world to diagnose and make assessments of dyslipidemia.
The role of apolipoproteins
In recent years, both Europe and the US have witnessed revisions in CVD guidelines and in the approach to lipid profiling. One major new area of attention is the role of apolipoproteins.
Apolipoproteins serve to bind lipids (fat and cholesterol) to form lipoproteins. Lipids are insoluble in water. However, apolipoproteins have amphiphilic (detergent-like) properties, which make them both fat- and water-soluble. As a result, the lipoprotein particle effectively becomes water-soluble, allowing for the transport of lipids through the lymph and circulatory systems. Apolipoproteins also regulate lipoprotein metabolism.
So far, most efforts have been focused on two apolipoproteins, apolipoprotein B (apo B) and apo A-I.
From a technical viewpoint, there are numerous advantages in determining concentrations of apo B and apo A-I. Robust immunochemical methodologies are possible to attain with conventional assays using appropriate reagents. These methodologies have also been shown to provide required levels of analytical performance. Moreover, apo assays do not require fasting conditions and are not sensitive to moderately high levels of triglycerides (TG).
Apo B
Apo B is the main protein in LDL-C and directly associated with cholesterol uptake. Elevated apo B indicates an increased risk of CVD even when total cholesterol and LDL-C levels are otherwise normal.
In the laboratory, apo B concentration provides a good indicator of the number of particles in plasma of VLDL (very low-density lipoprotein), IDL (intermediate-density lipoprotein) and LDL (low-density lipoprotein).
Apo B has aroused especially high interest given its presence in high concentrations of small dense LDL. The latter is seen to be “an important predictor of cardiovascular events and progression of coronary artery disease (CAD)” and it has been endorsed as an emerging cardiovascular risk factor by the US National Cholesterol Education Program Adult Treatment Panel III in 2007.
Some contradictory evidence indicates need for more study
A host of prospective studies have shown apo B to be equal to LDL-C in risk prediction. Post-hoc analyses of numerous statin trials suggest that apo B may be not only a good biomarker but also a better treatment target than LDL-C.
However, verifying this is likely to take some more years. Apo B is yet to be included in risk calculation algorithms. Meanwhile, data about its utility remains contradictory.
For example, a meta-analysis by the Emerging Risk Factor Collaboration in 2009 indicates that apo B does not provide any benefit beyond non-high-density lipoprotein cholesterol (non-HDL-C) or traditional lipid ratios. A year later, apo B showed no benefit compared to traditional lipid markers in diabetics in the so-called FIELD study (Fenofibrate Intervention and Event Lowering in Diabetes). On the other hand, in 2011, another meta-analysis of LDL-C, non-HDL-C and apo B conducted by Canadian researchers found the apolipoprotein to be a superior marker of CV risk. Indeed, the authors, from the Royal Victoria Hospital at McGill University concluded that apo B would prevent more than one-and-a-half times the number of CV events compared to a non-HDL-C strategy alone.
Apo A-I and HDL
Unlike apo B (and LDL cholesterol), apo A-I is the major protein of HDL and provides a good estimate of HDL concentration. One HDL particle could carry several apo A-I molecules. So far, plasma apo A-I correspondences have been established (with levels of <120 mg/dL for men and <140 mg/dL for women) correlating to ‘low’ HDL-C.
Apo A-I is sometimes tested alongside apo B. The ratio between apo B and apo A-I can be used as an alternative to the total cholesterol/HDL cholesterol ratio or non-HDL-C/HDL-C ratio for indicating risk. However, as with the latter, for diagnosis and treatment, the components of the ratio have to be considered separately.
Other apolipoproteins
Research is also under way into the other apolipoproteins. Indeed, several clinical labs already offer analysis of their concentrations. These include apo A-II, apo C-II and C-III and apo E and have also provoked interest in clinical researchers.
Like apo A-I, apo A-II is also a major constituent of HDL-C, and the distribution of the former in HDL is primarily determined by the rate of production of apo A-II. Apo A-II has an important role in reverse cholesterol transport and lipid metabolism. Its increased production promotes atherosclerosis by decreasing the proportion of anti-atherogenic HDL containing Apo A-I.
Apo C-II is a co-factor for lipoprotein lipase, which breaks down lipoproteins and hydrolyses triglycerides and VLDL for absorption into tissue cells. Low concentrations of apo C-II have been linked with hypertriglyceridemia.
Apo C-III modulates uptake of triglyceride-rich lipoproteins by LDL receptor related proteins through inhibition of lipoprotein lipase. Elevated apo C-III levels are associated with both primary and secondary hypertriglyceridemia.
Apo E is found in IDLs and has several functions. These include transporting triglycerides to the liver and distributing cholesterol between cells. Apo B affects the formation of atherosclerotic lesions by inhibiting platelet aggregation and its deficiency provokes high serum cholesterol and triglyceride levels, leading to premature atherosclerosis.
CVD guidelines and apolipoproteins
In spite of the growing interest in other apolipoproteins, the highest level of interest is on apo B and A-1. Both are covered by recent modifications in certian CVD guidelines.
In 2011, the European Atherosclerosis Society (EAS) and the European Society of Cardiology (ESC) updated several CVD guidelines. Changes included doubling the stratification of cardiovascular risk from two to four categories – “very high”, “high”, “moderate” and “low”, along with the tightening of therapeutic targets for each category.
While acceptable LDL-C levels were reduced significantly across risk groups, two new therapeutic targets were recommended for patients in “very high” and “high” risk categories, especially those with combined dyslipidemia. These consisted of non-HDL cholesterol and apolipoprotein (apo) B levels.
In Europe, updated EAS/ESC guidelines recommend baseline lipid evaluation be made either on the basis of TC (total cholesterol), TG, HDL-C, and LDL-C. These are typically calculated by the so-called Friedewald formula. The new guidelines also propose using “apo B and the apo B/apo A1 ratio,” which it acknowledges are “at least as good risk markers compared with traditional lipid parameters.”
Meanwhile, in the US, professional endocrinology bodies have directly enhanced their focus on dyslipidemia since 2012, when the American Association of Clinical Endocrinologists (AACE) released new clinical practice guidelines (CPG) on the ‘Management of Dyslipidemia and Prevention of Atherosclerosis’.
The AACE’s aim was to update its existing CPGs and to complement the Diabetes Mellitus Comprehensive Care Plan CPG. Nevertheless, the AACE emphasizes that the ‘landmark’ National Cholesterol Education Program (NCEP) guidelines of 1993 continued to serve as the ‘backbone’ of its revised recommendations.
Though the new CPGs from the AACE continue to emphasize the importance of LDL-C reduction and support the measurement of inflammatory markers to stratify risk in certain situations, they nevertheless have several noteworthy features. What makes them unique for endocrinologists is their recommendation on the use of apo B or LDL particle number measurements in order to “achieve effective LDL-C lowering, provide screening recommendations for persons of different ages, and identify special issues for pediatric patients.”
Need to harmonize lipid guidelines
In spite of growing enthusiasm about apolipoproteins, some endocrinologists have said there first needs to be more harmony in lipid guidelines. It is no secret that lipid guidelines have critical differences, including recommended lipoprotein levels for risk assessment, CVD risk estimation methodologies and the need for a treatment target or the use of drugs other than statins.
Though LDL-C remains a primary target in most guidelines, the International Atherosclerosis Society (ISA) favours non-HDL-C for dyslipidemia management, as does the AACE in certain conditions. Non-HDL-C is also considered to have higher predictive capability than LDL-C in a wide variety of clinical situations, and be more practical since it can be performed in a non-fasting state.
Yet another source of much debate concerns differences in approach to CVD risk assessment. Time frames for risk vary from 10-years to life time. On their part, the American College of Cardiologists (ACC) and American Heart Association (AHA) recommend measuring 30-year risk in patients aged 20–59.
CVD risk is defined as the risk of both mortality and morbidity in most guidelines. However, the joint (and revised) EAS/ESC guidelines mentioned above calculate fatal CVD risk only. Many guidelines calculate 10-year risk of CVD. However, ISA recommends measuring the lifetime risk.
Physicians ‘bewildered’
A recent issue of ‘European Endocrinology’ poses some candid questions: Today, “physicians are bewildered by a multitude of guidelines written by different professional societies, which have more diversities than commonalities.” The author calls for “organizing a working group in dyslipidemia management and integrating existing guidelines into a general consensus document.” However, he concludes, “owing to the number of controversial areas, this is not likely to occur soon.”
Healthcare portal Medscape puts the ball back in the clinician’s court – in some senses, restating the obvious. Lipid guidelines do not “override the individual responsibility of health professionals to make appropriate decisions in the circumstances of the individual patients, in consultation with that patient, and, where appropriate and necessary, the patient’s guardian or carer”.
Sepsis diagnosis in the laboratory
Sepsis is one of the major challenges in healthcare today, with some statistics predicting over 1 million cases per year in the United States, with mortality rates of about 10%. In a recently published study, between 36.9% and 55.9% of deaths among hospitalized individuals occurred in septic patients [1]. Two other findings of this study are critical to the laboratory. First, they observed that patients with initially less severe sepsis made up the majority of sepsis deaths. Secondly, most patients were already septic at the time they were admitted to the hospital. Thus laboratory testing during the initial emergency department (ED) encounter could be critical to improve sepsis-related mortality.
A systematic review of the literature [2] looked at nearly 200 proposed biomarkers for sepsis. Quoting the study’s final conclusions: “Our literature review indicates that there are many biomarkers that can be used in sepsis, but none has sufficient specificity or sensitivity to be routinely employed in clinical practice. PCT and CRP have been most widely used, but even these have limited abilities to distinguish sepsis from other inflammatory conditions or to predict outcome.” Here we take a look at the key issues the healthcare industry is facing and why physicians still do not have a reliable marker for sepsis to offer for their patients.
by Fernando Chaves
Key factors which can impact outcome in sepsis
In the last decade since sepsis awareness became more prevalent, many institutions have started implementing sepsis treatment protocols, which have been successful in decreasing mortality [3-4]. These protocols call for the collection of multiple blood cultures plus immediate start of intravenous fluids and antibiotics, and were implemented because studies have clearly demonstrated that the single most important factor in decreasing sepsis mortality was early intervention.
Later, a large prospective study comparing variations of these protocols, including invasive patient monitoring did not show any significant differences in mortality rates [5]. This indicates that there is minimal additional decreases in sepsis mortality that can be attained through improvements in treatment.
In contrast there are still options available to improve sepsis mortality through diagnostic testing. An optimal approach for early detection of sepsis still eludes us. Diagnosis today is still based primarily on the clinical recognition of systemic inflammation — increased heart and respiratory rates, fever, mental confusion, etc. followed by the documentation of a site of infection. When clinicians recognize these signs and symptoms, the window of opportunity to further decrease sepsis mortality by an earlier diagnosis has passed. Therefore, a laboratory test that could allow for earlier recognition of septic patients is a major unmet need for physicians.
What features should a laboratory test have to address this unmet need?
In order to allow for early recognition of septic patients, a laboratory test would need to meet both clinical performance and accessibility criteria. First, it must have sufficient diagnostic performance (measured by area under the curve “AUC” in the receiver operator curve “ROC curve”) to discriminate sepsis not only from healthy individuals, but also from other sick patients with conditions which mimic sepsis, such as systemic inflammatory response syndrome (SIRS). The traditional laboratory tests used during initial evaluation of patients, such as the complete blood count (CBC) fail to achieve this diagnostic performance.
Secondly, it must meet accessibility criteria, meaning it must be a test which can be widely used in all patients coming to the ED, without the need for the clinician to have an initial suspicion for sepsis. As discussed above, waiting for the physician to order the test will likely miss the window of opportunity to further improve mortality. Currently antibiotics and IV fluids are already being initiated before testing, as per the sepsis protocols now becoming increasingly prevalent in hospitals worldwide.
With so many proposed biomarkers for sepsis, which ones have met these criteria?
Unfortunately, although there have been many promises and exciting results in initial studies, results to date have been disappointing.
Early studies often yielded promising results because of their smaller size, and typically they were case-control studies comparing septic patients with healthy individuals [2]. The real challenge is discriminating sepsis from the plethora of mimicking conditions physicians encounter in the ED.
Once the biomarkers were evaluated in real life scenarios, their diagnostic performance, measured by AUC curve, did not match the results of earlier smaller studies. A perfect example was procalcitonin, PCT, the best known proposed biomarker for sepsis which initially showed very good discriminatory ability for sepsis. As PCT became more widely used and systematically studied, it became clear that it was far from the silver bullet it was optimistically thought to be. A systematic literature review and meta-analysis [6] showed an AUC of 0.78, with diagnostic performance upwardly biased in smaller studies, but moving towards a null effect in larger studies. Several years after PCT became available as a reportable test worldwide, its adoption among hospital laboratories is still sporadic, and when it is used, the most common clinical objective is the monitoring of antibiotic therapy for safe discontinuation, rather than initial diagnosis of the sepsis.
Even if the performance of these tests had been excellent, the accessibility challenge would still limit their ability to positively impact patient outcomes. As mentioned above, if the test for sepsis is ordered by a physician based on observation, the opportunity to start antibiotics sooner was missed, and the positive outcome reduced. Thus, real improvements in patient mortality will only be seen when tests are ordered routinely during initial patient care in the ED, such as the complete blood count with differential (CBC-diff).
Can we diagnose sepsis sooner using only data from a CBC-diff?
To date, there have been multiple attempts to improve the early detection of sepsis using CBC data, either via new parameters or via the creation of index values combining results from multiple traditional parameters. But so far no significant improvement in performance has been achieved —in great part due to the fact that cell counts are also elevated in inflammatory conditions mimicking sepsis. Thus, what is lacking is a parameter which is less sensitive to the inflammatory process, and more specific for the sepsis infection.
Cellular morphologic changes may be the critical tipping point in this quest. As key players in the fight against infection, white blood cells, such as monocytes and neutrophils, get activated and change their morphology. In fact, such changes have been used for years by pathologists and technologists when making their diagnostic decisions at the microscope.
Certain hematological analysers collect cellular morphologic data in their quest to recognize and count cells. Multiple studies have been published over the last decade discussing these parameters and their potential value for the early diagnosis of sepsis. However, as was true for multiple other proposed sepsis biomarkers, the small sample size and retrospective design of these studies limited their value to reliably assess their potential diagnostic performance for sepsis. This limitation has been addressed in a recent large prospective trial, probing the clinical value of morphologic parameters in the early diagnosis of sepsis in the general ED population, as well as in the discrimination between sepsis and its key mimic, SIRS.
The results of this trial will be presented at the Society for Critical Care Medicine (SCCM) annual meeting, and will not be in the public domain in time for publication in this article. But readers are invited to pay close attention to this data once it becomes available in the literature, as this abstract has been selected as one of the “Star Research Presentations” at the upcoming Critical Care Congress in February 2016.
References:
1. Liu V, Escobar GJ, et al. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014 Jul 2;312(1):90-2.
2. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14(1):R15.
3. Rivers E, Nguyen B, et al. Early Goal-Directed Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. N Engl J Med. 2001 Nov 8;345(19):1368-77.
4. Jones AE, Shapiro NI, et al. Implementing early goal-directed therapy in the emergency setting: the challenges and experiences of translating research innovations into clinical reality in academic and community settings. Acad Emerg Med 2007;14:1072-8.
5. ProCESS Investigators, Yealy DM, et al. A randomized trial of protocol-based care for early septic shock. N Engl J Med. 2014 May 1;370(18):1683-93.
6. Tang BM, Eslick GD, et al. Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis. Lancet Infect Dis. 2007 Mar;7(3):210-7
The author
Fernando Chaves, Director of Global Scientific Affairs, Beckman Coulter Diagnostics.
Amplification-free direct detection of Ebola virus on a hybrid optofluidic platform
Low-complexity detection of infectious diseases with high sensitivity and specificity is urgently needed, especially in resource-limited settings. Optofluidic integration combines clinical sample preparation with optical sensing on a single chip-scale system, enabling the direct, amplification-free detection of single RNA from Ebola viruses. The optofluidic system fulfils all key requirements for chip-based clinical analysis, including a low limit of detection, wide dynamic range, and the ability to detect multiple pathogens simultaneously.
by Dr Hong Cai, Prof. Aaron R. Hawkins and Prof. Holger Schmidt
Introduction
The recent Ebola and Zika outbreaks [1, 2] have made it clear that viral infections continue to pose diverse and widespread threats to humanity. Resource-limited settings, in particular, call for diagnostic devices and technologies that are robust and feature relatively low complexity for easy handling by potentially unskilled personnel. At the same time, such instruments need to fulfil all the technical requirements for accurate and reliable diagnosis. These include a limit of detection and dynamic range that are compatible with clinically observed viral loads as well as the ability to carry out multiplexed differential detection by screening simultaneously for several pathogens with similar clinical symptoms.
The ‘gold standard’ test for hemorrhagic fevers as well as other infectious diseases is real-time polymerase chain reaction (RT-PCR) [3]. PCR fulfils the sensitivity and specificity requirement for clinical testing. However, it is not ideal for resource-limited environments and point-of-care applications because of to its complexity. An alternative economic and portable option is antigen-capture enzyme-linked immunosorbent assay (ELISA) testing. However, ELISA requires more highly concentrated samples and thus its clinical application, especially for early disease detection, is restricted.
For the last two decades, the lab-on-chip approach, which features a small footprint and sample volume, has been considered as a promising candidate for the next generation low-complexity medical diagnostics [4]. Among all the approaches, optofluidics, which integrates optics and microfluidics in the same platform, has received increased attention [5, 6]. Microfluidics is ideal for performing biological sample processing on a chip-scale level and leads to miniaturization and simplification of the current diagnostic system. If it can be integrated with an optical sensing/read-out platform that enables high detection sensitivity down to the single pathogen level, an analytic system for which nucleic acid amplification is no longer needed becomes possible.
In order to detect single molecular biomarkers and bioparticles, an in-flow based detection scheme is preferred. In a typical in-flow detection scheme, bioparticles are transported to the sensing region in a stream of gas or liquid where they are detected in transient fashion as they pass an optical interrogation region [7, 8]. Therefore, fast read-out of the optical signal from single bioparticles in sequence can be achieved, and many concerns associated with traditional surface-based sensing schemes such as unwanted nonspecific binding, probe photobleaching, and diffusion-limited transportation are eliminated.
Anti-resonant reflecting optical waveguides (ARROWs) have been proven to be highly efficient in detecting single bioparticles. By properly designing a Fabry–Perot reflector surrounding a hollow channel, light can propagate inside the ARROWs. Therefore, ARROWs confine both liquid and light in the same microfluidic channel, such that light and matter have near-perfect overlap and the sensing capability is maximized [8, 9]. Figure 1(a) shows a cross-section of a liquid-core ARROW using state-of-the-art fabrication technology [9]. Moreover, a two-dimensional photonic sensing platform can be constructed with lithography patterning. Figure 1(b) shows an ARROW platform with solid-core and liquid-core ARROWs crossing orthogonally. Excitation light from an external laser is confined in the solid-core ARROW, producing a few-micron-wide optical mode in the intersecting region. Liquid flow is generated inside the liquid-core ARROW to transport the bioparticles to the excitation volume which is of the order of femtolitres for typical waveguide and channel dimensions of a few micrometers. Optical read-out is extracted orthogonally through the liquid-core ARROW to achieve a low-noise signal, sufficient for reaching single particle fluorescence detection.
Besides the optical sensing aspect, miniaturizing and optimizing sample preparation is equally important in order to achieve a complete bioanalysis detection system. The ARROW-based optofluidic system is particularly well suited for such hybrid integration strategies. The planar optical layout based on intersecting solid-core and target-carrying liquid-core waveguides leaves the third dimension open for vertical integration of other functionalities. A separate microfluidic sample processing layer can be made and optimized and then connected to the ARROW platform (Fig. 1c) [10, 11]. Through this approach, we can perform multiple sample preparation steps, such as mixing, distributing, sorting and pre-concentrating on the microfluidic layer and transfer the sample to the ARROW chip for sensing without compromising each of the layers’ performance [10].
Amplification-free detection of Ebola nucleic acids on an opto-
fluidic system
In our recent work, Zaire Ebola virus RNA detection from clinical samples has been demonstrated in a hybrid optofluidic ARROW system [12]. Through a strain-specific solid-phase extraction method, we extracted and labelled target RNA from Ebola infected Vero cells and put them through the optofluidic chip for detection. The ARROW chip provided a sequence of optical signals when individual fluorescent virus RNAs passed through the small excitation volume. Figure 1(d) shows the recorded digital RNA counts at low concentration levels of from 2.1×102 to ~2.1×104 pfu/mL within one second. We were able to detect six orders of magnitude of the clinical concentration range using the ARROW chip only. The lower concentration limit is determined by the detection time, which was set to be 10 min maximum. As a negative control, we used the same method to test for Sudan Ebola virus and Marburg virus. Our results showed no detectable signals and thus our method is target specific.
In order to incorporate critical sample processing steps and detect RNA at even lower concentrations within 10 min, we adapted a programmable microfluidic chip – an automaton – to handle processing of larger sample volumes (Fig. 1b). The polydimethylsiloxane (PDMS) based automaton chip consists of a two-layer microvalve array. Each valve’s state is controlled individually by the top pneumatic layer through a reprogrammable software program. We used the automaton chip to perform an extra pre-concentration step by processing a large amount of clinical sample. We washed, released and labelled the RNA on the same automaton chip after pre-concentration. Through ~460× concentration, the virus detection limit was improved down to 0.2 pfu/mL, with seven orders of magnitude of concentration range (Fig. 1e). This demonstration exhibits an amplification-free chip-based virus and nucleic acid analysis technique with high sensitivity and wide dynamic range, whose performance is comparable with the gold standard, more complex PCR technique.
Wavelength division multiplexing detection
ARROWs also enable simultaneous detection of multiple pathogens through the wavelength division multiplexing (WDM) technique [13]. WDM is generated using a multi-mode interferometer waveguide (MMI). When an MMI is excited by a single optical mode, all of the modes inside the MMI propagate at different phase velocities. When a constructive interference condition is satisfied, various numbers of self-imaging spots which resemble the excitation mode are formed along the MMI. This allows us to design an MMI section that intersects the fluidic channel, where multiple excitation spots are generated (Fig. 2a). As a fluorescent target flows past this excitation region, multi-peak signals are recorded in the time domain. For a single wavelength excitation, the fixed pattern multi-peak detection enables a signal-to-noise improvement compared to single-mode detection [14].
For a given MMI, the number of the self-imaging spots is wavelength dependent. We can generate various spot patterns at various laser wavelengths. For example, 9, 8 and 7 excitation spots are generated using 488nm, 553nm and 633nm lasers (Fig. 2b). With this approach, multiple targets labelled with different dye can be distinguished by the number and spacing of the peaks in the detected signal. Figure 2(c) shows influenza virus H1N1 and H3N2, which were labelled with different dye, generating 9 and 6 peaks in the time domain, respectively. We also labelled H2N2 virus with a combination of these two dyes which resulted in a superposition of the 9-spot and 6-spot fluorescence signals (Fig. 2c, bottom). A signal-processing algorithm checks for the presence of signals at the two characteristic time delays and can easily identify the mixed-labelled virus particle. This technique was shown to discriminate between three influenza subtypes, again with single virus sensitivity, using only two excitation colours. Thus, the ARROW-based platform has now met all the fundamental requirements for clinical virus detection using single particle sensing.
Conclusion
For the next generation of medical diagnostic devices, low-complexity detection with high sensitivity and specificity is required on the detection side, along with small footprint and multi-functional analyte handling on the sample processing side. In-flow based optofluidic devices in which both analyte handling and optical sensing are carried out on the chip scale are promising candidates. Using our ARROW-based optofluidic system, we demonstrated multi-stage sample processing and detection of clinical Zaire Ebola virus samples using hybrid integration. We also demonstrated wavelength multiplex detection of multiple analytes at the same time. This fulfils all quantitative requirements for clinical virus detection. Therefore, a fully integrated microsystem for front-to-back amplification-free virus analysis is within reach.
References
1. Fact sheet. The top 10 causes of death. World Health Organization 2014. (http://www.who.int/mediacentre/factsheets/fs310/en/).
2. Fact sheet. Zika virus. World Health Organization 2016. (http://www.who.int/mediacentre/factsheets/zika/en/).
3. Kuypers J, Wright N, Morrow R. Evaluation of quantitative and type-specific real-time RT-PCR assays for detection of respiratory syncytial virus in respiratory specimens from children. J Clin Virol. 2004; 31: 123–129.
4. Craighead H. Future lab-on-a-chip technologies for interrogating individual molecules. Nature 2006; 442: 387–393.
5. Fan X, White IM. Optofluidic microsystems for chemical and biological analysis. Nature Photon. 2011; 5: 591–607.
6. Schmidt H, Hawkins AR. The photonic integration of non-solid media using optofluidics. Nature Photon. 2011; 5: 598–604.
7. Zhu H, White IM, Suter JD, Zourob M, Fan X. Opto-fluidic micro-ring resonator for sensitive label-free viral detection. Analyst 2008; 133: 356–360.
8. Bernini R, Campopiano S, Zeni L, Sarro PM. ARROW optical waveguides based sensors. Sensors and Actuators B 2004; 100: 143–146.
9. Yin D, Barber JP, Hawkins AR, Deamer DW, Schmidt H. Integrated optical waveguides with liquid cores. Appl Phys Lett. 2004; 85: 3477–3479.
10. Parks JW, Cai H, Zempoaltecatl L, Yuzvinsky TD, Leake K, Hawkins AR, Schmidt H. Hybrid optofluidic integration. Lab Chip 2013; 13: 4118–4123.
11. Testa G, Persichetti G, Sarro, PM, Bernini R. A hybrid silicon-PDMS optofluidic platform for sensing applications. Biomed Opt Express 2014; 5: 417–426.
12. Cai H, Parks JW, Wall TA, Stott MA, Stambaugh A, Alfson K, Griffiths A, Mathies RA, Carrion R, Patterson JL, Hawkins AR, Schmidt H. Optofluidic analysis system for amplification-free, direct detection of Ebola infection. Scientific Reports 2015; 5: 14494.
13. Ozcelik D, Parks JW, Wall TA, Stott MA, Cai H, Parks JW, Hawkins AR, Schmidt H. Optofluidic wavelength division multiplexing for single-virus detection. Proc Nat Acad Sci U S A 2015; 112: 12933–12937.
14. Ozcelik D, Stott MA, Parks JW, Black JA, Wall TA, Hawkins AR, Schmidt H. Signal-to-noise enhancement in optical detection of single viruses with multi-spot excitation, IEEE J Sel Top Quant Elec. 2016; DOI: 10.1109/JSTQE.2015.2503321.
The authors
Hong Cai1 PhD, Aaron R. Hawkins2 PhD, Holger Schmidt*1 PhD
1School of Engineering, University of California Santa Cruz, Street, Santa Cruz, CA 95064 USA
2ECEn Department, 459 Clyde Building, Brigham Young University, Provo, UT 84602 USA
*Corresponding author
E-mail: hschmidt@soe.ucsc.edu
Lipid testing and cardiovascular risk assessment: cut-off points
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.
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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
Pharmacometabonomics: a new methodology for personalized medicine
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
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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