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Potential utility of plasma fatty acid analysis in the diagnosis of cystic fibrosis.

Altered plasma and tissue concentrations of fatty acids in cystic fibrosis (CF) [4] patients have been well described (1-7). Changes in concentrations of selected essential fatty acids have been found in CF patients, notably decreases in the plasma and tissue concentrations of linoleic acid (18:2 n-6) and docosahexaenoic acid (DHA; 22:6 n-3). Increases in the concentration of eicosatrienoic acid (Mead acid; 20:3 n-9) have been identified in several studies (5-7). These alterations in fatty acid concentrations are significantly magnified in patients with severe variations in the CF transmembrane conductance regulator (CFTR) [5] gene, suggesting an association between the basic defect and abnormal essential fatty acid metabolism in CF patients (3). It is unclear how CFTR variation leads to the dysregulation of fatty acid biosynthesis, but these fatty acid abnormalities are clearly not attributable to pancreatic insufficiency and malabsorption in CF patients (5-6). The metabolism of essential fatty acids involves a metabolic sequence of specific enzymes that desaturate, elongate, or shorten the fatty acids. Although the same enzymes are involved in both the n-3 and n-6 pathways, there is no interconversion of products between the 2 pathways. Thus, these essential fatty acids compete for the same enzymes. A study with CF knockout mice demonstrated that arachidonic acid (20:4 n-6) concentrations are increased and DHA concentrations are decreased in tissues and organs most affected in CF (8). Interestingly, oral administration of DHA was responsible for reversing the phenotypic alterations of CF in these mice (8). Alterations in fatty acids similar to those found in CF knockout mice were shown to be present in CFTR-expressing tissues from human subjects with CF (1).

Because certain alterations in plasma fatty acid composition in CF are highly reproducible, fatty acid analysis may be useful as a diagnostic procedure for CF. The altered fatty acid distributions that we observed in our own studies with CF knockout mice and subsequently with CF patients prompted us to reexamine our database of CF patients to determine whether specific plasma fatty acid changes can be used to differentiate between CF patients and persons not suffering from CF.

The sweat chloride test is the gold standard screening test for CF, but this test has limitations. A subset of patients with CF show values for the sweat chloride test that are not diagnostic, and some CF patients demonstrate a sweat chloride value within reference intervals despite confirmed CF status (9-12). Another limitation for the sweat test is that false-positive values may occur in several clinical conditions readily distinguishable from CF (13). In rare cases, some homozygous CF patients have sweat chloride concentrations within reference intervals if a second ameliorating or neutralizing variation in the CFTR gene product, such as R553Q, is also present (13).

Nasal potential difference measurement is also used as a diagnostic procedure in the evaluation for CF (14,15). An increased nasal potential difference is strong evidence for CF, but a value within reference intervals does not exclude the diagnosis (16). False-negative results may arise, especially in the presence of a nasal polyp (17). To confirm a diagnosis of CF, an increased nasal potential difference must be demonstrated twice. The nasal potential difference measurement is a technically challenging procedure (13).

[FIGURE 1 OMITTED]

These limitations of existing methods led us to pursue an alternative screening test involving plasma fatty acid analysis to confirm the diagnosis of CF in patients with questionable sweat test results and positive clinical findings for CF.

Materials and Methods

ENROLLMENT OF PATIENTS

The nonblinded study was conducted from October 2000 to September 2003, and the blinded study was conducted from September 2004 to August 2005. CF patients attending the University of Massachusetts Medical Center and Beth Israel Deaconess Medical Center were included in the studies. Reference values were obtained from healthy controls recruited at both sites. Exclusion criteria for the controls included findings consistent with the presence of CF, the use of drugs that affect fatty acid metabolism, or a family history of CF. All study patients had pancreatic insufficiency and met the criteria for CF diagnosis (18). All study participants provided written informed consent before enrollment. Participants were randomly selected for part 2 (blinded) of the study. In the nonblinded and blinded studies, the CF patients, but not the controls, were encouraged to eat high-fat, high-energy diets and to visit a nutritionist regularly, in accordance with the guidelines of the Cystic Fibrosis Foundation (19). The nonblinded study included 13 patients with CF [mean (SD) age, 22.1 (7.5) years; range, 15-41 years] and 11 healthy controls [26.5 (7.2) years; range, 19-33 years]. The blinded study included 10 patients with CF [16.2 (5.6) years; range, 7-22 years] and 9 controls [46 (11.2) years; range, 30-62 years]; patients and controls were selected after we reviewed medical records.

[FIGURE 2 OMITTED]

PLASMA FATTY ACID ISOLATION AND METHYLATION

We obtained peripheral venous blood from nonfasting participants. Samples were collected in heparin-containing vacuum tubes and centrifuged at 45g for 15 min at room temperature. Thereafter, the plasma was removed. Fatty acids from plasma were isolated and methylated according to Moser and Moser (20). The fatty acid methyl ester (FAME) mixture was analyzed by gas chromatography-mass spectrometry (GC-MS).

GC-MS FAME IDENTIFICATION AND QUANTIFICATION

GC-MS analysis was performed on a Hewlett-Packard Series 115890 gas chromatograph coupled to an HP-5971 mass spectrometer (Agilent Technologies) equipped with a Supelcowax SP-10 capillary column (Supelco). The oven temperature was maintained at 150 [degrees]C for 2 min, ramped at 10 [degrees]C /min to 200 [degrees]C and held for 4 min, ramped again at 5 [degrees]C /min to 240 [degrees]C and held for 3 min, and then finally ramped to 270'C at 10 [degrees]C/min and maintained for 5 min. The injector and detector were maintained at 260 [degrees]C and 280 [degrees]C, respectively. Carrier gas flow rate was maintained at a constant 0.8 mL/min throughout. Total ion monitoring was performed, encompassing mass ranges from 50 to 550 atomic mass units. Peak identification was based on comparison of both retention time and mass spectra of the unknown peak to those of known standards within the GC-MS database library. A commercially available standard mixture of FAMEs (Nucheck) was used to calculate masses of fatty acids based on the response factor of 17:0 FAME (21).

Three samples from the control group were excluded from part 1 of the study because the fatty acid profile indicated significant loss of fatty acids in the processing of the samples. In the excluded samples, the normally prominent fatty acids had areas that were 3-10-fold below the area of the internal standard. Accepted samples were those in which the most prominent fatty acids were 3-6-fold greater in peak area than the internal standard. In part 2 of the study, all specimens were analyzed in a blinded fashion.

STATISTICAL ANALYSIS

Plasma fatty acid concentrations of CF patients and non-CF controls were compared. The unpaired Student t-test was used to evaluate differences between the means of the 2 groups. Differences were considered statistically significant at P [less than or equal to] 0.05.

Results

The demographic and genotypic characteristics of the CF patients who were evaluated with plasma fatty acid analysis are shown in Table 1. A genotype consistent with the diagnosis of CF obviated the need for sweat testing in some patients. One of the CF patients from the blinded trial had a genotype that was consistent with a negative sweat-chloride test value.

Eleven different plasma fatty acid markers for differentiating CF patients from controls, including (18:2 n-6) x (22:6 n-3), were tested in the nonblinded trial (Table 2). There were significant differences between CF patients and controls for the multiplication product of (18:2 n-6) x (22:6 n-3; P = 0.0003), the ratio of (22:5 n-6):(22:6 n-3; P = 0.001), and 22:6 n-3 alone (P = 0.007). The multiplication product of (18:2 n-6) x (22:6 n-3; each as percentage of total plasma fatty acid) most effectively distinguished patients with CF from controls (Fig. 1).

In the blinded trial, we tested 11 different plasma fatty acid markers (Table 2). Again, the multiplication product of (18:2 n-6) x (22:6 n-3) was the most statistically significant parameter in distinguishing CF patients from controls (P = 0.0008; Fig. 1).

The multiplication product of (18:2 n-6) x (22:6 n-3) in the nonblinded trial of our study showed sensitivity, specificity, positive predictive value, and negative predictive value of 92%, 91%, 92%, and 91%, respectively, for a cutoff of 40 arbitrary units compared with 100%, 56%, 71%, and 100% for the blinded trial with the same cutoff of 40 arbitrary units. Combined data from the blinded and nonblinded trials showed values for sensitivity, specificity, positive predictive value, and negative predictive value of 96%, 75%, 81%, and 94%, respectively, for the same cutoff of 40 arbitrary units. In addition, there were no technical limitations associated with sample collection. The sensitivity of the assay for detection of CF was very high, and false positives were definitively identified as CF or non-CF by genetic testing.

Discussion

Our data demonstrate that a multiplication product of plasma (18:2 n-6) x (22:6 n-3) can be used to differentiate CF patients and non-CF controls in the majority of cases. This multiplication product was the most effective parameter in measuring plasma fatty acid status between CF patients and controls, and as a diagnostic marker, it provided a higher level of statistical significance than any other mathematical operations or clinical markers tested.

In a study by Benabdeslam et al. (22), plasma phospholipid fatty acid analysis was performed with fasting blood samples collected from 65 CF patients and 39 controls, whereas in our study, both the blinded and nonblinded trials were performed with samples collected from nonfasting CF patients and controls, a procedural difference that may slightly alter plasma fatty acid composition. Other investigators have shown, however, that it is unlikely that total plasma fatty acid composition is significantly altered by a fasting period (23). The data in Fig. 2 show 3 paired comparisons between a CF group and a control group, including comparison of data from total plasma fatty acid analysis in CF patients and controls using our data and data from the study by Benabdeslam et al. (22). The (18:2 n-6) x (22:6 n-3) value separates CF patients from non-CF controls in all 3 paired comparisons. The absolute values for (18:2 n-6) x (22:6 n-3) are very different, however, especially for the control groups. In the current study, we used total plasma fatty acids because samples do not require additional processing to isolate phospholipids from total fatty acids. Thus, our method simplifies sample preparation for clinical use. In the 2 studies involving total fatty acids, although the control groups were markedly different, the CF groups were very similar. The control group for the blinded trial showed a lower DHA concentration than the control group for the nonblinded trial (Fig. 2), the major difference between the 2 control groups. This finding may be attributable to lower fish consumption or fish oil supplementation in the blinded trial than the nonblinded trial control groups, both of which were randomly selected. Because total fatty acid values were very similar in both CF populations, a value [less than or equal to] 40 could be used as a clinical cutoff for CF; therefore, patients with a value [less than or equal to] 40 should undergo follow-up genetic studies. In the blinded and nonblinded trials, use of a cutoff of 40 would have resulted in a genetic study for 4 controls and 1 control, respectively. The rationale for this approach is analogous to the protocol for the HIV ELISA screening test, which is followed up by a Western blot test for confirmation, with the goal of 100% sensitivity in the screening study.

[FIGURE 3 OMITTED]

To determine whether plasma fatty acid analysis can help in the evaluation of CF patients, we compared the sensitivity, specificity, positive predictive value, and negative predictive value of the sweat chloride test reported in 2 different published studies (24,25) with the most favorable diagnostic fatty acid marker in our study (Table 3). The technical failure rate of plasma fatty acid analysis as a diagnostic test for CF is negligible because blood samples are readily collected.

The Gibson-Cooke Sweat Test (GCST) is the standard technique used in the diagnosis of CF. Two previous published studies (24,25) compared the results of the GCST technique with results obtained using different assays for sweat chloride collection and measurement. The 1st study (24) used a cutoff value of 70 mmol/L to differentiate between intermediate and abnormal sweat chloride test results rather than the cutoff value of 60 mmol/L used by the Cystic Fibrosis Foundation (26). In the Mastella et al. study (24), 3.6% of the samples did not contain enough sweat to perform a sweat chloride analysis. Among the CF patients, sweat chloride concentrations were outside the reference interval in 91.2%, within the reference interval in 1%, and borderline in 7.8%. Among healthy controls, 4% had borderline sweat chloride concentrations (24). In this study (24), the sensitivity and the specificity for the GCST were 91% and 100%, respectively. In the 2nd study (25) the GCST could not be performed on 15% of the CF patients because these patients failed to produce enough sweat for analysis. The sensitivity and the specificity values for the GCST were 93% and 99%, respectively, for this study (25).

Despite the high sensitivity and specificity of the sweat chloride gold standard diagnostic screening test, practical difficulties limit the performance of the sweat test, particularly in infants younger than 4 weeks (27). In cases in which sweat testing is technically not possible or is clinically misleading, plasma fatty acid analysis as a screening test for CF may be useful, as proposed in Fig. 3.

Although the sensitivity values of plasma fatty acid analysis support its utility as a diagnostic test for patients with CF, this analysis requires a relatively sophisticated assay involving gas chromatography to generate the required fatty acid profile. Because the fatty acids of interest are predominant in the plasma, it is likely that the fatty acid analysis can be performed with a gas chromatograph with a standard flame ionization detector and may not require a mass spectrometer, as was used in our study. We reported plasma fatty acid concentrations as percentage of total fatty acids because mole percent or gram percent data are much simpler to obtain than are amounts of the individual fatty acids in micrograms of fatty acid per milliliter of plasma. When actual amounts in mass are required, meticulous attention to fatty acid recovery for the individual fatty acids is necessary. With variable loss of fatty acids between specimens, the mole or gram percentage of total fatty acids stays the same. The simplicity of using mole percentages is important for fatty acid analyses performed in a clinical laboratory.

In summary, fatty acid analysis is not a substitute for sweat testing, but it may be a useful test for CF when the sweat chloride test does not provide a definitive answer at the screening level. Our findings demonstrate that the multiplication product of (18:2 n-6) x (22:6 n-3) can diagnostically differentiate CF from non-CF cases. Larger studies with different CFTR gene variations and more patients with borderline sweat test values will be informative. In addition, further study is necessary to determine the diagnostic accuracy of plasma fatty acid analysis in a CF clinical setting. The data in this initial study, however, indicate that fatty acid analysis is a promising screening test for CF if sweat chloride testing cannot be performed.

Received July 27, 2006; accepted October 26, 2006. Previously published online at DOI: 10.1373/clinchem.2006.077008

References

(1.) Freedman SD, Blanco PG, Zaman MM, Shea JC, Ollero M, Hopper IK, et al. Association of cystic fibrosis with abnormalities in fatty acid metabolism. N Engl J Med 2004;350:560-9.

(2.) Lepage G, Levy E, Ronco N, Smith L, Galeano N, Roy CC. Direct transesterification of plasma fatty acids for the diagnosis of essential fatty acid deficiency in cystic fibrosis. J Lipid Res 1989;30:1483-90.

(3.) Strandvik B, Gronowitz E, Enlund F, Martinsson T, Wahlstrom J. Essential fatty acid deficiency in relation to genotype in patients with cystic fibrosis. J Pediatr 2001;139:650-5.

(4.) Carlstedt-Duke J, Bronnegard M, Strandvik B. Pathological regulation of arachidonic acid release in cystic fibrosis: the putative basic defect. Proc Natl Acad Sci U S A 1986;83:9202-6.

(5.) Farrell PM, Mischler EH, Engle MJ, Brown DJ, Lau SM. Fatty acid abnormalities in cystic fibrosis. Pediatr Res 1985;19:104-9.

(6.) Hubbard VS, Dunn GD, di Sant'Agnese PA. Abnormal fatty-acid composition of plasma lipids in cystic fibrosis. A primary or secondary defect? Lancet 1977;2:1302-4.

(7.) Lloyd-Still JD, Johnson SB, Holman RT. Essential fatty acid status in cystic fibrosis and the effect of safflower oil supplementation. Am J Clin Nutr 1981;34:1-7.

(8.) Freedman SD, Katz MH, Parker EM, Laposata M, Urman MY, Alvarez JG. A membrane lipid imbalance plays a role in the phenotypic expression of cystic fibrosis in cftr (-/-) mice. Proc Natl Acad Sci U S A 1999;96:13995-14000.

(9.) Stewart B, Zabner J, Shuber AP, Welsh MJ, McCray PB Jr. Normal sweat chloride values do not exclude the diagnosis of cystic fibrosis. Am J Respir Crit Care Med 1995;151:899-903.

(10.) Leoni GB, Pitzalis S, Podda R, Zanda M, Silvetti M, Caocci L, et al. A specific cystic fibrosis mutation (T3381) associated with the phenotype of isolated hypotonic dehydration. J Pediatr 1995;127:281-3.

(11.) Highsmith WE, Burch LH, Zhou Z, Olsen JC, Boat TE, Spock A, et al. A novel mutation in the cystic fibrosis gene in patients with pulmonary disease but normal sweat chloride concentrations. N Engl J Med 1994;331:974-80.

(12.) Fitzgerald D, Van Asperen P, Henry R, Walters D, Freelander M, Wilson M, et al. Delayed diagnosis of cystic fibrosis in children with a rare genotype (delta F508/R117H). J Paediatr Child Health 1995;31:168-71.

(13.) Stern RC. The diagnosis of cystic fibrosis. N Engl J Med 1997; 336:487-91.

(14.) Alton EW, Currie D, Logan-Sinclair R, Warren JO, Hodson ME, Geddes DM. Nasal potential difference: a clinical diagnostic test for cystic fibrosis. Eur Respir J 1990;3:922-6.

(15.) Sauder RA, Chesrown SE, Loughlin GM. Clinical application of transepithelial potential difference measurements in cystic fibrosis. J Pediatr 1987;111:353-8.

(16.) Rosenstein BJ, Cutting GR. The diagnosis of cystic fibrosis: a consensus statement. Cystic Fibrosis Foundation Consensus Panel. J Pediatr 1998;132:589-95.

(17.) Knowles MR, Paradiso AM, Boucher RC. In vivo nasal potential difference: techniques and protocols for assessing efficacy of gene transfer in cystic fibrosis. Hum Gene Ther 1995;6:445-55.

(18.) Wilmott RW. Making the diagnosis of cystic fibrosis. J Pediatr 1998;132:563-5.

(19.) Borowitz D, Baker RD, Stallings V. Consensus report on nutrition for pediatric patients with cystic fibrosis. J Pediatr Gastroenterol Nutr 2002;35:246-59.

(20.) Moser HW, Moser AB. Measurement of saturated very long chain fatty acids in plasma. In: Hommes FA, ed. Techniques in Diagnostic Human Biochemical Genetics. New York: Wiley-Liss, 1991: 177-91.

(21.) Dodds ED, McCoy MR, Rea LID, Kennish JM. Gas chromatographic quantification of fatty acid methyl esters: flame ionization detection vs. electron impact mass spectrometry. Lipids 2005;40: 419-28.

(22.) Benabdeslam H, Garcia I, Bellon G, Gilly R, Revol A. Biochemical assessment of the nutritional status of cystic fibrosis patients treated with pancreatic enzyme extracts. Am J Clin Nutr 1998;67: 912-8.

(23.) Gopaul NK, Zacharowski K, Halliwell B, Anggard EE. Evaluation of the postprandial effects of a fast-food meal on human plasma FZ isoprostane levels. Free Radic Biol Med 2000;28:806-14.

(24.) Mastella G, Di Cesare G, Borruso A, Menin L, Zanolla L. Reliability of sweat-testing by the Macroduct collection method combined with conductivity analysis in comparison with the Gibson and Cooke technique. Acta Paediatr 2000;89:933-7.

(25.) Warwick WJ, Hansen LG, Brown IV, Laine WC, Hansen KL. Sweat chloride: quantitative patch for collection and measurement. Clin Lab Sci 2001;14:155-9.

(26.) Cystic Fibrosis Foundation. The Sweat Test. http://www.cff.org/AboutCF/Testing/SweatTest/(accessed February 2006).

(27.) Parad RB. Buccal cell DNA mutation analysis for diagnosis of cystic fibrosis in newborns and infants inaccessible to sweat chloride measurement. Pediatrics 1998;101:851-5.

(28.) Farrell PM, Koscik RE. Sweat chloride concentrations in infants homozygous or heterozygous for F508 cystic fibrosis. Pediatrics 1996;97:524-8.

[4] Nonstandard abbreviations: CF, cystic fibrosis; DHA, docosahexaenoic acid; CFTR, cystic fibrosis transmembrane conductance regulator; FAME, fatty acid methyl ester; GC-MS, chromatography-mass spectrometry; GCST, Gibson-Cooke Sweat Test.

[5] Human gene: CFTR, cystic fibrosis transmembrane conductance regulator (ATP-binding cassette subfamily C, member 7).

IBRAHIM BATAL, [1] MHD-BASSEL ERICSOUSSI, [1] JOANNE E. CLUETTE-BROWN, [1] BRIAN P. O'SULLIVAN, [2] STEVEN D. FREEDMAN, [3] JUANITO E. SAVAILLE, [1] and MICHAEL LAPOSATA [1]

[1] Division of Laboratory Medicine, Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.

[2] Department of Pediatrics, University of Massachusetts Memorial Health Care and University of Massachusetts Medical School, Worcester, MA.

[3] Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.

* Address correspondence to this author at: Director of Clinical Laboratories, Massachusetts General Hospital, 235 Gray Bldg., 55 Fruit St., Boston, MA 02114. Fax 617-726-3256; e-mail mlaposata@partners.org.
Table 1. Demographic and genotypic characteristics
of the CF patients.

Nonblinded Trial

No. Sex Age, years Sweat Genotype
 (a) chloride,
 mEq/L

1 M 18 87 dF508/N1303K
2 M 18 91 dF508/dF508
3 F 28 117 dF508/dF508
4 F 17 88 dF508/dF508
5 M 18 84 dF508/G551D
6 F 41 150 dF508/dI507
7 M 15 100 dF508/W1282X
8 F 23 116 dF508/1717-1G[right arrow]A
9 F 16 NA dF508/-- (e)
10 F 23 NA dF508/dF508
11 M 19 129 dF508/G551D
12 F 19 115 dF508/G542X
13 M 32 NA dF508/--

Blinded Trial

No. Sex Age, years Sweat Genotype
 chloride,
 mEq/L

1 F 18 114 dF508/dF508
2 M 21 115 dF508/dI507
3 M 22 Unknown (b) dF508/dF508
4 F 17 101 dF508/dF508
5 F 12 35 dF508/3849 + 10kb C-T (c)
6 F 7 101 dF508/dF508
7 M 7 NA (d) dF508/Y1092X
8 M 19 78 dF508/dF508
9 M 21 122 dF508/W1282X
10 F 18 104 dF508/dF508

(a) M, male; F, female.
(b) The patient had been seen at another medical center
and was found to have a genotype consistent with CF;
therefore, the sweat chloride test was not repeated
in our study.

(c) This genotype is consistent with negative sweat
chloride test values in CF.

(d) NA, not analyzed

(e)-, Unidentified genotype.

Table 2. Mean (SD) plasma fatty acidsaof CF patients
and healthy controls.

 Nonblinded Analysis

Fatty acid parameter CF patients Controls P value
 (n = 13) (n = 11) (b)

(18:2 n-6) x (22:6 n-3) 23.6 (2.7) 79.7 (10.5) 0.0003
22:6 n-3 1.04 (0.14) 2.92 (0.56) 0.007
Total n-6/Total n-3 14.6 (1.13) 9.94 (1.23) 0.01
18:2 n-6 23.9 (1.7) 29.2 (1.9) 0.05
(22:5 n-6)/(22:6 n-3) 0.23 (0.03) 0.10 (0.02) 0.001
(22:5 n-6)/(22:5 n-3) 0.47 (0.12) 0.38 (0.09) NS
(18:2 n-6) x (22:6 n-3)/ 97.6 (16.1) 321.9 (92.6) 0.04
 (22:5 n-6)
(18:2 n-6)/(16:0) 1.08 (0.10) 1.41 (0.10) 0.03
(20:3 n-9)/(18:2 n-6) 0.02 (0.005) 0.002 (0.001) 0.01
(20:3 n-9) x (22:5 n-6) 0.005 (0.002) 0.0004 (0.0002) 0.01
(18:2 n-6) x (22:6 n-3)
20:3 n-9 0.34 (0.1) 0.05 (0.03) 0.01

 Blinded Analysis

Fatty acid parameter CF patients Controls P value
 (n = 10) (n = 9) (b)

(18:2 n-6) x (22:6 n-3) 20.2 (2.2) 42.4 (4.5) 0.0008
22:6 n-3 0.89 (0.06) 1.59 (0.2) 0.006
Total n-6/Total n-3 16.6 (0.5) 12.2 (1.3) 0.008
18:2 n-6 22.3 (1.2) 27.2 (1.1) 0.01
(22:5 n-6)/(22:6 n-3) 0.37 (0.03) 0.19 (0.03) 0.0008
(22:5 n-6)/(22:5 n-3) 1.05 (0.2) 0.65 (0.07) 0.04
(18:2 n-6) x (22:6 n-3)/ 67.4 (8.6) 194.7 (52.6) 0.04
 (22:5 n-6)
(18:2 n-6)/(16:0) 0.93 (0.07) 1.11 (0.05) 0.05
(20:3 n-9)/(18:2 n-6) 0.006 (0.002) 0.001 (0.0006) NS
(20:3 n-9) x (22:5 n-6) 0.003 (0.001) 0.0002 (0.9) NS
(18:2 n-6) x (22:6 n-3)
20:3 n-9 0.12 (0.04) 0.03 (0.01) NS

(a) Values mean (SE) for individual fatty acids are expressed
as mole percentages, and calculations are derived from mole
percentages of individual fatty acids.

(a) Derived from the Student t-test. NS, not significant.

Table 3. Diagnostic sensitivity and specificity of sweat
chloride test and of (18:2 n-6) x (22:6 n-3). (a)

Diagnostic Tests Technical Diagnostic Diagnostic
 Failure Rate Sensitivity Specificity

Sweat test, % Mastella 3.6 91 100
 et al. (24)
Sweat test, % Warwick 14 93 99
 et al. (25)
Nonblinded analysis, % Negligible 92 91
 (18:2 n-6) x (22:6 n-3)
Blinded analysis, % Negligible 100 56
 (18:2 n-6) x (22:6 n-3)
Blinded + nonblinded Negligible 96 75
 analysis, % (18:2 n-6)
 x (22:6 n-3)

(a) Cutoff value for fatty acid analyses is [less than or
equal to]40 arbitrary units
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Title Annotation:Lipids, Lipoproteins, and Cardiovascular Risk Factors
Author:Batal, Ibrahim; Ericsoussi, Mhd-Bassel; Cluette-Brown, Joanne E.; O'Sullivan, Brian P.; Freedman, St
Publication:Clinical Chemistry
Date:Jan 1, 2007
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