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
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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.
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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.
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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
In spite of some exceptions, the clinical microbiology lab has been a late starter as far as automation is concerned. It has also traditionally been viewed as ‘low tech’, especially when compared to its cousins in clinical chemistry or pathology. A variety of factors, however, have been converging to reverse such a situation.
Automation hampered by process complexity
One of the most important barriers to the automation of a clinical microbiology lab is process complexity. Unlike hematology or chemistry labs, which have little diversity in specimens and generally use standard collection tubes, microbiology laboratories need to work with a vast range of specimen types in a multitude of transport containers. The complex nature of specimen processing and culturing and the ensuing lack of standardization have been major deterrents to automation.
Nevertheless, growth in the presence of automated technologies in clinical microbiology labs is now expected to accelerate as a result of several factors, above all rising demand. This requires agility and high responsiveness, making automation indispensable.
Ageing populations drive demand
Ageing populations with more-complex diseases and conditions require a growing number of tests – for example, to monitor implants and prosthetic devices for infections. The elderly also need greater care in medicating, since they are more prone to adverse drug events.
In the year 2000, an article by Dr. Thomas T. Yoshikawa of the King-Drew Medical Center in Los Angeles noted that though “the major focus in infectious diseases for the past decade has been on young adults”, in the future “the vast majority of serious infectious diseases will be seen in the elderly population.”
Infectious disease, resistant bacteria
The rise in infectious disease outbreaks in recent decades is another factor driving demand for early detection by clinical microbiology labs, to contain their spread.
On their part, multidrug-resistant pathogens pose their own specific challenges. Delays in obtaining lab results leads to over-treatment of many patients – and increased antibiotic resistance. In 2008, a team from Erasmus University Medical Centre in Rotterdam found that quicker microbiological lab turnaround led to “a significant reduction in antibiotic use” in a study of almost 1,500 patients. This finding assumes considerable significance when one takes account of the fact that 5 years later, another study found that just 3% of community-acquired respiratory infections in the UK were guided by laboratory results.
The role of budgets and cutbacks
In an era of budgetary cutbacks, financial considerations too have reinforced demands for the automation of clinical microbiology labs. There is some irony here. Given the nature of a hospital business, it has been easier for administrators to assess the productivity of their clinical laboratories, determine return on investment (RoI) and justify new outlays – via quantifying and benchmark tests and staff numbers. Such an exercise has, in general, already been conducted for other hospital labs. It is now the clinical microbiology laboratory’s turn.
The above considerations are summed up in an article in the December 2013 issue of the journal ‘Clinical Chemistry’ which quotes Gilbert Greub of the Institute of Microbiology at the University Hospital in Lausanne, Switzerland. He says that the key reasons for the Hospital’s decision to move toward a fully automated laboratory consisted of a shortage of financial resources and the concomitant increase in activity of the Hospital’s clinical diagnostic microbiology laboratory “of about 4% to 12% per year.”
Workflow improvement, staff shortages
Indeed, improvements in workflow and quicker test results are also directly related to growing automation. One of the most important collateral effects of this is the freeing up of staff for other work.
In May 2009, the ‘Wall Street Journal’ warned about “the shrinking ranks of skilled lab workers” in the US, which pose “a potential threat to the safety and quality of health care”. Hospitals, it continued, said that “it can take as much as a year to fill some job openings,” while an American Society for Clinical Pathology (ASCP) survey found average job-vacancy rates topping 50% in some states.
The ASCP survey also illustrated another interesting fact. Laboratories which were affected by new technologies found a decreased need for as large a staff. However, 75% of respondents said they were not affected by new technologies. In other words, not only does automation seem to be an answer to staff shortages. There is also a lot of untapped room for growth.
Europe faces staff shortages too. A report by Belgium’s University Hospital at Leuven highlights the challenges of an ageing workforce, alongside major waves of retirement which have started recently and are expected to continue for several years. The problem is exacerbated by a decline in interest in labs as a career and the presence in the workforce of fewer young recruits. As a result, the paper warns, there is a “trend towards employing less-trained technicians.”
Transferring skills to points of need
The benefit of automation in the face of labour shortages is to utilize the skills of medical laboratory professionals where they are most needed and to automate tasks that are repetitive and do not require the comprehensive skill set of a trained professional.
For example, a laboratory could use an automated system for mundane and repetitive tasks such as “planting and streaking of urine samples and other liquid specimens,” while assigning a lab technician “to perform Gram stain review and processing of more-complex specimens, such as tissue.”
At the other end, boredom can also be a problem. In a non-automated environment, lab staff frequently complain of poor turnaround (TAT), referring to the duration or idling time between inoculation of media and microbial growth. By shifting monotonous tasks to automation, while assigning higher-skill tasks to a technologist, the laboratory reduces boredom and increases productivity.
The May 2009 article by the ‘Wall Street Journal’ quotes Dr. Carol Wells, director of the clinical laboratory sciences programme at the University of Minnesota in Minneapolis: “Many tests are automated, but that doesn’t mean a lab monkey can do them.” The machines, she continues, need careful monitoring. Should they “spit out a result” which does not make sense, only a skilled lab technician can catch a possible discrepancy and determine what is wrong.
Liquid-based microbiology, MALDI-TOF mass spectrometry drive demand
Automation of the clinical microbiology lab is also being driven by supply-side factors.
Among the first is the advent of new technologies, such as liquid-based microbiology and mass spectrometry. Liquid-based microbiology allows specimens of varying viscosities (e.g. stool or sputum) to be homogenized into a liquid phase, in order to enable greater consistency in the inoculation of medium. Specimen elution from recent flocked-style swabs into liquid phase has also resulted in a significant increase in the release of viable organisms from the swab, in other words resulting in greater sensitivity for detection of microorganisms.
The second technology is matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry. This permits the accurate and rapid identification of microorganisms isolated from clinical specimens. MALDI-TOF procedures “are highly amenable to automation because they are relatively simple, do not change based on organism, and are reproducible.” In addition, target plate spotting and extraction of proteins “can be standardized for most organisms, and when combined with automation, automated crude extraction using the on-plate formic acid extraction method can be performed with minimal staffing.”
Specimen processing as entry point
One of the first points of entry by new technology in a clinical microbiology lab consists of front-end instrumentation to automate and standardize initial specimen processing. Automation of the front-end makes it possible for tests to be conducted as soon as specimens arrive, obviating the need for a separate ‘stat’ lab.
Nevertheless, the impact of automation in specimen processing is not necessarily uniform, and depends on the needs of a particular laboratory. One study by researchers at Penn State College of Medicine and Medical College of Wisconsin estimated break-even point of more than 4 years for a barcode-driven, conveyor-connected automated specimen processing system which included plating and streaking, incubation, image acquisition, and digital microbiology.
Full- versus partial automation
As with other types of clinical labs, automation of the clinical microbiology laboratory can be classified into total (or full) laboratory automation or modular automation. Most automation systems have traditionally been designed for the larger, high-volume laboratory with substantial specimen throughput requirements. More recently, automated processing units have been designed for the pre-analytical section of smaller/medium-sized laboratories.
In effect, smaller labs can choose to automate only some of the processing steps. The investment required for automation makes such a choice imperative. The report by the University Hospital at Leuven cited previously notes that its own implementation of ‘full lab automation’ cost €3.2 million with €1.7 million in capital investment and another €1.5 million for refurbishment. Time needs to be also factored in. The Leuven study notes that their automation entailed 1 year in preparatory work and 1 year for adaptation. Against this, the savings achieved were equivalent to 3.8 full time employees (FTE).
Some hospitals begin small and scale up. For example, the University Hospital in Lausanne, Switzerland, started with two stand-alone automated systems for microbial identification. It moved after a few years “to a fully automated laboratory, by adding the missing pieces to the puzzle, i.e., smart incubators, high-quality digital imaging, an automated colony-picking system, and all required transport belts in between.”
A glimpse of the future
Today, state-of-the-art clinical microbiology labs have the potential to automate nearly all areas of testing, including inoculation of primary culture plates, detection of growth on culture media, identification of microorganisms, susceptibility testing, and extraction and detection of nucleic acids in clinical samples. In the future, process standardization is expected to be reinforced by high-resolution digital imaging and robotics, and take automation in the clinical microbiology lab to wholly new frontiers.
The aim of the first annual World Antibiotic Awareness Week, held in November, was to raise recognition of the growing problem of bacterial resistance to antimicrobials and to disseminate information on how these drugs can be used more prudently. Is it still possible, though, to prevent an antibiotic apocalypse?
The development of drug-resistant bacteria is the inevitable result of natural selection, but formerly the discovery of novel compounds kept pace with microbial evolution; this is no longer the case. During the past decade numerous academic articles have reported alarming examples of antibiotic resistance in microorganisms, including multidrug-resistant Staphylococcus aureus and Mycobacterium tuberculosis, as well as extensively drug-resistant tuberculosis, and the mass media has duly disseminated this information to the general public. Around 5 years ago the NDM-1 gene, which confers resistance to the potent carbapenem antibiotics used against multi-resistant strains of Gram-negative bacilli, was found in Enterobacteriaceae including the ubiquitous Escherichia coli. The latest catastrophe is the emergence of the MCR-1 mechanism that allows polymixin-resistance plasmids to be transferred between strains of Enterobacteriaceae. And polymixins are (or should be) the drugs of last resort to treat infections with bacteria that are multidrug resistant, including carbapenem-resistant strains.
As well as over-liberal medical prescription of unnecessary antibiotics without prior diagnostic testing, premature cessation of treatment and unregulated sources of drugs enabling “self-prescription”, the routine use of antimicrobials in industrialized agriculture has greatly exacerbated the resistance problem. The recently reported polymixin resistance was first observed in China during routine testing of commensal E. coli in food animals, prompting a robust study that discovered the MCR-1 mechanism in 15% of E. coli isolates from raw meat, 21% of isolates from livestock and 1% of isolates from infected patients. Although the problem is currently confined to China, this type of mechanism spreads resistance so easily between bacteria that it will soon become a global problem. Should polymixin-resistance plasmids be transferred to Enterobacteriaceae that are already multidrug-resistant, truly untreatable Gram-negative bacterial infections would result.
Initiatives to prevent the further squandering of antibiotics coupled with rigorous infection control procedures are highly unlikely to prevent an antibiotic apocalypse now. But a worldwide ban on the veterinary use of medical antimicrobials might just stem the tide until new drugs (such as teixobactin for multidrug-resistant Gram-positive pathogens) have been approved.
April | May 2025
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