Clinical Chemistry 48: 499-506, 2002;
(Clinical Chemistry. 2002;48:499-506.)
© 2002 American Association for Clinical Chemistry, Inc.
Reagent-free, Simultaneous Determination of Serum Cholesterol in HDL and LDL by Infrared Spectroscopy
Kan-Zhi Liu1a,
R. Anthony Shaw1,
Angela Man1,
Thomas C. Dembinski2 and
Henry H. Mantsch1
1
Institute for Biodiagnostics, National Research Council Canada, 435 Ellice Ave., Winnipeg, Manitoba, R3B 1Y6 Canada.
2
Department of Clinical Chemistry, Health Sciences Center, Winnipeg, Manitoba, R3A 1R9 Canada.
aAuthor for correspondence. Fax 204-984-5472; e-mail Kan-Zhi.Liu{at}nrc.ca.
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Abstract
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Background: The purpose of this study was to assess the feasibility of infrared (IR) spectroscopy for the simultaneous quantification of serum LDL-cholesterol (LDL-C) and HDL-cholesterol (HDL-C) concentrations.
Methods: Serum samples (n = 90) were obtained. Duplicate aliquots (5 µL) of the serum specimens were dried onto IR-transparent barium fluoride substrates, and transmission IR spectra were measured for the dry films. In parallel, the HDL-C and LDL-C concentrations were determined separately for each specimen by standard methods (the Friedewald formula for LDL-C and an automated homogeneous HDL-C assay). The proposed IR method was then developed with a partial least-squares (PLS) regression analysis to quantitatively correlate IR spectral features with the clinical analytical results for 60 randomly chosen specimens. The resulting quantification methods were then validated with the remaining 30 specimens. The PLS model for LDL-C used two spectral ranges (1700–1800 and 2800–3000 cm-1) and eight PLS factors, whereas the PLS model for HDL-C used three spectral ranges (800–1500, 1700–1800, and 2800–3500 cm-1) with six factors.
Results: For the 60 specimens used to train the IR-based method, the SE between IR-predicted values and the clinical laboratory assays was 0.22 mmol/L for LDL-C and 0.15 mmol/L for HDL-C (r = 0.98 for LDL-C; r = 0.91 for HDL-C). The corresponding SEs for the test spectra were 0.34 mmol/L (r = 0.96) and 0.26 mmol/L (r = 0.82) for LDL-C and HDL-C, respectively. The precision for the IR-based assays was estimated by the SD of duplicate measurements to be 0.11 mmol/L (LDL-C) and 0.09 mmol/L (HDL-C).
Conclusions: IR spectroscopy has the potential to become the clinical method of choice for quick and simultaneous determinations of LDL-C and HDL-C.
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Introduction
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Coronary heart disease (CHD)
1
remains the leading cause of death in North America and Western Europe. The majority of these deaths are attributable to myocardial or cerebral infarction, with atherosclerosis being the principal cause (1). It has long been recognized that cholesterol plays a role in the formation of atherosclerotic plaques; the compound was first noted as a lesion component almost a century ago by Windaus. Cholesterol circulates in the blood in lipoprotein complexes that fall, by definition, into three major classes: LDL, HDL, and VLDL (2). LDL-cholesterol (LDL-C) typically makes up 60–70% of the total serum cholesterol, whereas HDL-cholesterol (HDL-C) makes up 20–33%.
A key risk factor in the development of atherosclerosis is a high concentration of serum LDL-C. The National Cholesterol Education Program (2) recently issued guidelines identifying serum LDL-C concentrations <2.58 mmol/L (1000 mg/L) as optimal, 3.37–4.12 mmol/L as borderline high, and >4.12 mmol/L as high. The dangers associated with high serum LDL-C are supported by several lines of evidence. Epidemiologic investigations of human populations incriminate high concentrations of LDL-C as being atherogenic, with a direct relationship between LDL-C concentrations and the rate of new-onset CHD in men and women who were initially free of CHD (3). Gross pathologic examination of coronary arteries provides direct evidence that the rate of atherogenesis is also proportional to the serum cholesterol concentrations (4). The strongest evidence that LDL is a powerful atherogenic lipoprotein derives from patients with the genetic form of hypercholesterolemia (5); advanced coronary atherosclerosis and premature CHD occur commonly even in the complete absence of other risk factors. On the other hand, HDL is associated with a decreased incidence of atherosclerosis. Produced primarily in the liver and intestine (it can also be derived from chylomicron and VLDL catabolism), the main function of HDL is the transport of cholesterol from peripheral cells to the liver, i.e., "reverse cholesterol transport" (6).
The importance of accurate measurement of serum LDL-C and HDL-C is reflected by the efficacy of treatment once increased LDL-C is detected. Clinical data show unequivocally that cholesterol-lowering therapy, whether through lifestyle changes or other means, can stabilize plaques and prevent acute coronary syndromes (7). Although a wide variety of methods have been proposed for the determination of serum LDL-C, including electrophoresis, HPLC, sequential and density-gradient ultracentrifugation, precipitation-based methods, and immunoseparation, the standard routine laboratory test is an indirect one. The Friedewald formula has been useful in providing a close approximation to true LDL values, particularly because no practical alternative has emerged for large-scale, rapid routine testing. This approach derives the LDL-C from total cholesterol, HDL-C, and VLDL-cholesterol (VLDL-C) concentrations as follows: [LDL-C] = [total cholesterol] - ([HDL-C] + [VLDL-C]), where the VLDL-C concentration is estimated from the serum triglyceride concentration (in mmol/L) as [VLDL-C] = [triglycerides]/2.22 (8). This relationship assumes the ratio of total triglycerides to VLDL-C to be constant in all samples. However, there are some limitations for this postulation. For example, the formula will overestimate VLDL-C and underestimate LDL-C as a consequence if triglyceride-rich chylomicrons and chylomicron remnants are present in the serum specimen (hence the requirement for a fasting sample) (9). The use of the Friedewald formula is also not recommended for type II diabetes and chronic alcoholic patients because accompanying abnormalities in lipoprotein composition render the underlying assumptions invalid for assessment of cardiovascular risk in these patients (10).
Given the limitations of current methods and the high prevalence of CHD in North America, there is a demand for a more accurate procedure for the determination of LDL-C. Here, we propose an infrared (IR) spectroscopic method for the simultaneous determination of both HDL-C and LDL-C. The pattern of IR absorptions is exquisitely sensitive to both molecular structure and conformation, and these two applications spurred the adoption of IR spectroscopy as an essential part of any chemistry laboratory. In complex organic materials, such as cells and biological fluids, the spectra show distinctive absorptions arising from proteins, lipids, carbohydrates, and nucleic acids (11), with the IR spectrum then reflecting the weighted contributions of each constituent. These spectra have been exploited in recent years to develop an entirely new discipline, namely biomedical IR spectroscopy, that converts spectroscopic information to diagnostic and/or analytical results.
Previous studies from our group have demonstrated that a wide array of serum (12) and urine (13) analytes may be determined by IR spectroscopy of films dried from the fluid of interest. The present study reveals that the spectra of HDL-C and LDL-C complexes are sufficiently distinct to permit the separate quantification of HDL-C and LDL-C according to the IR spectra of dried serum films. These findings offer a simple, reagentless method for the simultaneous determination of HDL-C, LDL-C, and as demonstrated previously (12), total cholesterol and triglycerides.
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Materials and Methods
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materials
Serum specimens were routine laboratory samples from the Winnipeg Health Science Centre. Standard LDL and HDL solutions were obtained from Sigma-Aldrich. The experimental protocol was approved by the Human Ethics Committee of the National Research Council of Canada.
laboratory clinical chemistry assays
For HDL-C, the lyophilized version of the homogeneous method from Roche Diagnostics was used (14)(15)(16). This assay is derived from the specific detection of cholesterol in HDL particles, achieved by polyethylene glycol-modified cholesterol esterase and cholesterol oxidase and masking of other lipoprotein particles with sulfated cyclodextrin. Total cholesterol and triglycerides were determined by a Hitachi 717 analyzer, with enzymatic, colorimetric detection schemes. For cholesterol, the cholesterol oxidase-4-aminophenazone reaction scheme was used (17)(18); and for triglycerides, the glycerine-3-phosphatase-4-aminophenazone reaction scheme was used. All reagents were from Roche Diagnostics. LDL-C was calculated with the formula developed by Friedewald et al. (8) for volunteers with triglycerides <4.5 mmol/L (<4000 mg/L). Calculated LDL-C values are not valid and hence were not determined for those individuals with triglycerides >4.5 mmol/L.
ir spectra
Duplicate 5-µL serum aliquots were evenly spread on IR- transparent barium fluoride windows (13-mm diameter) and allowed to dry under a reduced pressure (25 mmHg) to produce glassy films. IR spectra of such films were recorded with a Digilab FTS-40A IR spectrometer (Bio-Rad Laboratories, ) at a nominal resolution of 2 cm-1, with a blank barium fluoride window in place for the background measurement. Measurement time was 5 min (on average, 256 scans) for each film (19).
partial least-squares regression analysis
The IR spectrum of serum includes spectral contributions from protein, cholesterol, triglycerides, urea, glucose, and other more dilute compounds. Because each individual component contributes a complex set of several absorptions falling within the mid-IR spectral region, it is impossible to find any single absorption band that can serve as the basis to quantify any single component; coincident absorptions from other species would degrade or completely sabotage the effort. For that reason, IR-based analytical methods are very commonly derived from methods such as partial least squares (PLS), which use spectral information spread across a wider spectral range (12)(20)(21). Within this approach, IR spectroscopy is a secondary analytical method. The IR-based analytical method is derived first by acquiring a set of specimens that spans the concentration range for the analyte of interest and includes any interferents that might appear in the course of routine analysis. IR spectra are then acquired for these specimens, which are analyzed in parallel for the analyte(s) of interest with standard methods, and the PLS technique is then used to derive an algorithm relating the spectra to corresponding analyte concentrations.
Full descriptions of the PLS method have been published elsewhere (20)(21). In essence, the process is to reconstruct all spectra from a limited set of artificial spectra (PLS "factors"), a procedure that in turn provides an algorithm to convert spectra to concentrations for the analyte of interest. From a practical perspective, the procedure uses any of several widely available software packages that take as input the spectra and corresponding analyte concentrations and provide the quantification algorithm, termed the "PLS calibration model" as output. These packages offer the user the flexibility to explore various options, such as preprocessing the spectra (e.g., normalization and derivation), selecting restricted spectral ranges (PLS typically works most effectively if spectral regions containing no signal from the analyte of interest are omitted), and seeking the optimal number of factors to include in the calibration model.
The PLS approach was used for the quantification of LDL-C, HDL-C, cholesterol, and triglycerides from the IR spectra of these serum samples. In each case, the first step was to identify a set of spectra (60 specimens, 120 spectra) to serve as the basis for PLS model calibration. We then developed a PLS calibration model and validated the model by using it to predict the analyte concentrations to predict analyte concentrations for the remaining 30 samples (60 spectra) on the basis of their IR spectra. PLS calibrations and spectral manipulations were carried out with GRAMS/32 software packages (Galactic Industries).
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Results
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ir spectra of hdl and ldl
To characterize the corresponding IR spectral differences, the IR spectra of LDL and HDL are reported in Fig. 1
, together with a representative spectrum of dry serum film. From the IR spectra, one can readily gather general information concerning the molecular constituents and their structures. For example, there are two prominent amide absorptions, one at 1655 cm-1 (arising from C=O stretching, and termed the amide I band), and another at 1546 cm-1, originating from N–H bending (termed the amide II band) vibrations of the peptide groups in proteins. The sharp absorption at 1467 cm-1 is attributed to the bending (scissoring) vibrations of the lipid acyl CH2 groups, whereas the bands at 1446 and 1378 cm-1 correspond to the asymmetric and symmetric bending vibrations of both lipid and protein CH3 groups. The PO2- asymmetric and symmetric stretching vibrations of the phospholipid phosphodiester groups give rise to bands at 1242 and 1088 cm-1. The remaining absorptions originate from ester C–O–C asymmetric and symmetric stretching vibrations (1173 and 1065 cm-1, respectively) of phospholipids, triglycerides, and cholesterol esters. Furthermore, the most distinctive lipid absorption appears at 1735 cm-1 in the IR spectrum of LDL, arising predominantly from the ester C=O groups of cholesterol esters, with the symmetric and asymmetric stretching vibrations of the lipid acyl CH2 groups also contributing dominant features at 2852 and 2926 cm-1.
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Figure 1. IR absorption spectra for dried LDL, HDL, and serum films.
Amide I and amide II bands (protein bands) arise from C=O stretching and N–H bending vibrations of the peptide groups. C–O–C/PO2- (lipid bands) are stretching vibrations originating from phospholipids, triglycerides, and cholesterol esters; C=O lipid ester arise from cholesterol esters; CH2 are stretching absorptions originating primarily from lipid acyl chains. The concentrations of the HDL and LDL solutions from which the dry films were prepared were 1.3 and 5.2 mmol/L, respectively. To facilitate qualitative comparison, the spectra have been normalized over the spectral range illustrated and offset on the vertical axis.
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It is the lipid absorptions that give rise to most dramatic differences among the IR spectra in Fig. 1
, with the spectrum of LDL standing out in particular as a consequence of its high lipid content. The lipid-to-protein ratio is much higher in LDL (80:20) than it is for HDL (50:50); this compositional difference is reflected by the very intense lipid C=O stretching absorption at 1735 cm-1 and also by the correspondingly intense acyl CH2 stretching absorptions at 2852 and 2926 cm-1. The two fractions are further distinguished by the nature of the lipid constituents (primarily cholesterol esters for LDL and phospholipids for HDL), which give rise to a cholesterol ester C=O stretching absorption at a lower frequency (1735 cm-1) for LDL than the counterpart phospholipid C=O stretching mode for HDL (1739 cm-1). Furthermore, the spectrum of LDL reveals a clear shoulder at 1620 cm-1. Amide I absorptions in this vicinity have been strongly linked to the formation of a ß-sheet structure, and the observation here is in accord with a previous IR study assigning 
40% ß-sheet structure to the dominant apolipoprotein (apo) B-100 protein of LDL (22). The spectrum of HDL shows no such absorption, consistent with the fact that there are differences in the constituent proteins (apo B-100 for LDL; apo A-I and apo A-II for HDL) and with the fact that apo A-I has been found previously to be nearly devoid of ß-structure (22). The high percentage of ß-sheet structure in apo B-100 was suggested to substantiate the importance of such segments in maintaining the lipid-protein assembly in LDL.
The fact that the spectra of LDL and HDL are clearly different from one another lends support to the notion that HDL and LDL might be quantified separately with IR spectroscopy of serum. As the next step toward exploring this possibility, a series of experiments was carried out to establish whether the absorption intensity of serum samples supplemented with LDL (or HDL) was proportional to the LDL (or HDL) concentration in serum. To that end, a random serum specimen was mixed with an equal volume of stock LDL solution (5.2 mmol/L LDL-C; Sigma). Additional solutions were prepared by serial dilution of this mixture, leading to specimens with 50%, 25%, and 12.5% of the original LDL enrichment. The spectra for these LDL/serum mixtures are shown in Fig. 2A
. As expected, the intensities for the three major lipid bands (1736, 2852, and 2926 cm-1) gradually decreased as dilution of LDL increased. More subtle changes were also observed for the protein amide bands, with both the band positions and relative intensities altered with the gradual dilution of LDL in serum. Most importantly, there was a good correlation between the integrated absorbance (band area) and the LDL-C concentration in the mixture (Fig. 2B
).
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The analogous experiment was carried out for HDL/serum mixtures, and the spectra are shown in Fig. 3A
. Although the naked eye reveals few clear differences among these spectra (the HDL stock solution was 1.3 mmol/L HDL-C; Sigma), the integrated intensity within the same band areas showed a very good correlation with HDL-C concentration (Fig. 3B
).
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Figure 2. IR spectra for a series of LDL/serum mixtures (A) and the correlation between band intensity in the two shaded areas and the concentration of the LDL/serum mixtures (B).
The mixtures are enriched in LDL by 2.6, 1.3, 0.65, and 0.32 mmol/L relative to the original serum concentration. The spectra have been offset on the vertical axis for clarity of presentation.
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Figure 3. IR spectra for a series of HDL/serum mixtures (A) and correlation between band intensity in the two shaded areas and the concentration of the HDL/serum mixtures (B).
The mixtures are enriched in LDL by 0.65, 0.32, 0.16, and 0.08 mmol/L relative to the original serum concentration. The spectra have been offset on the vertical axis for clarity of presentation.
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These experiments confirmed that HDL and LDL have characteristic absorption bands and that the absorbance intensity can be related to the analyte concentration (at least for a simple series of dilutions in serum) through the Beer-Lambert relationship (23). These findings provided a sound basis to expect that both LDL-C and HDL-C may be quantified with IR spectrum of serum.
quantification of ldl-c, hdl-c, cholesterol, and triglycerides
Optimal PLS models were derived by taking second derivatives (9-point Savitzky-Golay derivatives) of the original absorption spectra. For total cholesterol and triglyceride quantification, the PLS calibration models made use of spectral regions identified previously (12) (see Table 1
). Quantification of LDL-C was derived from two spectral regions encompassing strong lipid absorptions, namely 1700–1800 cm-1, which contains the C=O stretching vibration, and 2800–3000 cm-1, which includes the acyl CH2 stretching modes. The HDL-C quantification method required additional spectral information, namely the 900–1500 cm-1 range, for optimal performance.
The IR-based analytical methods are summarized in Table 1
. The IR assays yielded analytical results with SEs of 0.31 mmol/L (HDL-C) to 0.38 mmol/L (total cholesterol) with PLS models including those with 6–10 factors. The precision for the IR-based assays was estimated by the SD of duplicate measurements to be 0.11 mmol/L for LDL-C, 0.09 mmol/L for HDL-C, 0.11 mmol/L for triglycerides, and 0.19 mmol/L for total cholesterol. The IR-predicted HDL-C and LDL-C concentrations are compared with the clinical laboratory results for the test samples in Fig. 4
, whereas the scatterplots for cholesterol and triglycerides are shown in Fig. 5
.
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Figure 4. Comparison of IR-derived LDL-C (A) and HDL-C (B) concentrations with values provided by accepted clinical laboratory methods (see Materials and Methods).
The line of identity is included for reference. Best-fitting regression lines y = ax + b are a = 0.90 and b = 0.30 (r = 0.96) for LDL-C and a = 1.0 and b = 0.01 (r = 0.82) for HDL-C .
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Figure 5. Comparison of IR-derived total cholesterol (A) and triglyceride (B) concentrations with values provided by accepted clinical laboratory methods (see Materials and Methods).
The line of identity is included for reference. Best-fitting regression lines y = ax + b are a = 0.85 and b = 0.30 (r = 0.93) for total cholesterol and a = 0.91 and b = 0.17 (r = 0.95) for triglycerides.
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Discussion
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CHD remains the leading cause of mortality in North America, and LDL is the major lipoprotein fraction responsible for the development of atherosclerotic lesions. Diagnosis and treatment of hypercholesterolemia is critically dependent on an accurate assay for LDL-C. This report demonstrates that IR spectroscopy can provide a simple, rapid, reagent-free measure of LDL-C and that the same physical measurement (the IR spectrum) also yields HDL-C, total cholesterol, and triglyceride concentrations.
The IR-based approach includes several features that make it attractive for routine use. The ATP III guidelines recommend that the initial screening test include a complete lipoprotein profile (total cholesterol, HDL-C, LDL-C, and triglycerides) rather than screening for total cholesterol and HDL alone. The IR-based approach yields all four of these analytes from a single physical measurement (the IR spectrum). The method requires only a very small amount of serum (5 µL), is linear through the physiologic range for all four analytes (no dilution is required), and requires no reagents. As a consequence of the reagent-free nature of this technique, it has the potential to provide the complete lipoprotein profile at a fraction of the cost of present methods.
Practical implementation would require two modifications to the technique used here. The present study made use of IR-transparent barium fluoride windows that are too expensive to be considered for routine use. We have previously shown that ordinary glass can serve as an inexpensive substitute for certain analyses (24)(25), despite its limited transmission range in the IR region (glass is opaque <2000 cm-1). This option cannot be used for the present purpose, however, because the HDL-C assay relies heavily on spectral information in the 800–1800 cm-1 spectral range that is inaccessible through glass. However, other inexpensive options exist, and we are exploring their use in this application. A second, related development that is underway in our laboratory is the automation of sample preparation for use in high- throughput laboratories. Progress in that area will be reported separately.
Previous related IR spectroscopic measurements have been restricted to conformational studies, primarily to delineate the secondary structures of apolipoproteins. For example, IR spectroscopy was used to explore the conformational stability of apo B (26). More recently, the secondary structure of apo B was reexamined, first qualitatively, with resolution-enhancement techniques (27), and then quantitatively, with curve-fitting of deconvolved spectra (22). IR spectroscopy has also been exploited to characterize the lipid-attached proteins that remain after proteolytic digestion of solvent-exposed regions (28).
The quantification approach we have used here has been applied previously for the determination of a wide array of serum (12) and urine (13) analytes and amniotic fluid fetal-lung maturity assays (19)(23). The common theme uniting all IR-based assays, including those presented here, is that the analytes of interest must provide IR spectral signatures that allow them to be distinguished from the other component species. Although it was not clear at the outset of this study that the spectra of HDL and LDL would differ sufficiently from one another to permit their separate quantification, the spectral differences highlighted in Fig. 1
were adequate. Nevertheless, it would be important before adopting this approach into routine use to fully explore the possible influence of various contaminants on the spectra. That stated, the two most common interferents in clinical testing (hemolysis and lipemia) are unlikely to present major problems. The key to ensuring that these or any other possible interferents do not sabotage the IR-based method is to ensure that several hemolytic and lipemic specimens are included in the set of PLS calibration samples. This is standard practice in the development of PLS methods; by ensuring that the calibration specimens span the full range of concentrations expected not only for the analyte(s) of interest, but for any potential interferents, the method is rendered immune to possible errors that might otherwise be caused by those interferents (20).
It is important to be clear about the nature of problems that lipemic specimens might or might not present to the IR-based LDL-C analysis. If the IR-based PLS method is calibrated against LDL-C values provided by the Friedewald approximation, as is the case here, then the IR-based method is unlikely to surmount the deficiencies inherent to that approximation. That does not mean that the IR-based method cannot be more accurate than the Friedewald method. The possibility that the new technique might be capable of providing more accurate LDL-C values than the Friedewald approximation can be answered only by recalibrating the PLS calibration model to LDL-C values from a true clinical reference method (i.e., ß quantification). If the method is indeed derived from IR spectroscopic features that uniquely characterize the LDL-C serum component, then it is reasonable to expect that it might provide for accurate analyses even for those samples that are inappropriate for use of the Friedewald approximation. If this is the case, the IR-based method would also distinguish LDL-C from intermediate-density lipoprotein-cholesterol and lipoprotein(a) fractions that are typically pooled together with true LDL-C and assumed to be negligible by comparison (29)(30). To properly evaluate these possibilities, further studies are planned wherein the IR-based LDL-C analytical method will be recalibrated with a direct reference method, including samples with high triglyceride values, high lipoprotein(a) values, and high VLDL values (nonfasting samples).
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Footnotes
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1 Nonstandard abbreviations: CHD, coronary heart disease; LDL-C, LDL- cholesterol; HDL-C, HDL-cholesterol; VLDL-C, VLDL-cholesterol; IR, infrared; PLS, partial least squares; and apo, apolipoprotein. 
2 The combined spectral regions 1450–1800 and 2800–3000 cm-1 were chosen because the combined intensity in these regions reflects changes in the major lipid and protein absorptions of lipoproteins.
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Abstract
Introduction
