• dialysis;
  • fatty acids;
  • inflammation;
  • kidney disease;
  • mortality;
  • stearoyl-CoA desaturase-1


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References


Stearoyl-CoA desaturase-1 (SCD-1) converts dietary saturated fatty acids to monounsaturated fatty acids. Elevated SCD-1 activity thus signifies impaired fatty acid metabolism and excess saturated fat intake. In the general population, increased SCD-1 activity is associated with cardiovascular disease and mortality. The determinants and implications of SCD-1 activity in dialysis patients are unknown.


A total of 222 dialysis patients (39% women) with prospective follow-up, median age of 57 years and an average of 12 months of dialysis.


Fatty acid compositions in plasma phospholipids and free fatty acids (FFAs) were assessed by gas–liquid chromatography. SCD-1 activity indices were calculated as the product-to-precursor fatty acid ratio (palmitoleic acid/palmitic acid) in each fraction to reflect SCD-1 activities in the liver and adipose tissue.


Median hepatic and adipose tissue SCD-1 activity indices were 0.016 and 0.150, respectively. In multivariate analyses, SCD-1 was positively associated with age, female sex and serum interleukin-6 level. During 18.4 (interquartile range 5.5–37.3) months of follow-up, there were 61 deaths and 115 kidney transplants. The cut-off level for high SCD-1 indices was determined by receiver operating characteristic curve analyses. In fully adjusted competing risk models, patients with high SCD-1 indices in both phospholipids and FFAs had more than twofold increased mortality risk before kidney transplantation [hazard ratio (HR) 2.29, 95% confidence interval (CI) 1.28–4.11 and HR 2.36, 95% CI 1.38–4.03, respectively], compared with patients with low SCD-1 indices.


Both hepatic and adipose tissue SCD-1 activity indices independently predict mortality in dialysis patients. Further studies are warranted to determine whether reducing SCD-1 activity by dietary intervention (limiting saturated fat) could improve survival in dialysis patients.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Patients with end-stage renal disease (ESRD) are at considerably increased risk of cardiovascular morbidity and mortality, partly owing to a state of persistent inflammation and malnutrition [1, 2]. A suboptimal pattern of dietary fats, characterized by high saturated fat and low unsaturated fat intake, may play an important role in inflammation and cardiovascular risk. It has recently been reported that ESRD patients consume more saturated fatty acids (SFAs) than current recommendations [3, 4] and that low intake of polyun SFA (PUFAs) is correlated with systemic inflammation and poor outcome in dialysis patients [5].

The human body is capable of modulating SFA content by endogenous elongation. This is achieved by the enzyme stearoyl-CoA desaturase-1 (SCD-1, also known as delta-9 desaturase), which synthesizes monounsaturated fatty acids (MUFAs) by introducing a double bond into dietary or de novo synthesized SFA molecules [6, 7]. SCD-1 is expressed in both the liver and adipose tissue. By catalysing this endogenous conversion, SCD-1 maintains membrane fluidity and this may serve as a compensatory mechanism to protect against endogenous accumulation of SFAs [8]. Elevated SCD-1 activity has been associated with multiple aspects of the metabolic syndrome. In fact, mice lacking SCD-1 are largely protected against diet-induced and genetically induced obesity, hepatic steatosis, hypertriglyceridaemia and insulin resistance [9]. In humans, increased SCD-1 activity has been implicated in excess body and liver fat deposition [10, 11], hypertriglyceridaemia [12], insulin resistance [13], diabetes mellitus (DM) [14], inflammation [15, 16], endothelial dysfunction [16] and increased risk of mortality [17].

Of note, SCD-1 is sensitive to dietary factors [8], and its activity increases in response to high intake of SFAs [18] and high-glycaemic carbohydrates [19], and decreases after ingestion of n-6 PUFAs [18]. The activity of SCD-1 could be influenced by dietary and lifestyle interventions [20], and could therefore be an interesting target in both experimental and observational studies. As hepatic and adipose tissue SCD-1 activities may not be regulated in parallel [8], the role of SCD-1 in both tissues needs to be considered.

Both uraemia and the dialysis procedure are associated with altered metabolism of many nutrients [21, 22]. However, the impact on SCD-1 activity and the role of this enzyme in predicting mortality are unknown in dialysis patients. Against this background, we hypothesized that indices of SCD-1 activity in the liver and adipose tissue are elevated in dialysis patients and may be associated with an adverse risk profile and increased mortality. In a well-characterized cohort of dialysis patients, we estimated plasma indices of SCD-1 activity, which indirectly reflect hepatic and adipose tissue enzyme activities, and examined their relationships with risk factors for uraemia and mortality.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Subjects and study design

We conducted a cross-sectional observational study with prospective follow-up in a subset of patients with stored serum samples in an ongoing cohort study described elsewhere in more detail [23]. Initial exclusion criteria included age <18 or >70 years and signs of overt infection. Incident dialysis patients in this cohort were invited to perform a second clinical assessment after approximately 1 year of dialysis, and the current study is based on sampling at this second assessment. A total of 222 dialysis patients participated in the second evaluation between April 1996 and October 2010. Insufficient dialysis vintage, unwillingness to participate and lack of stored plasma for fatty acid analyses were further exclusion criteria. The patient flowchart for the present analysis is shown in Fig. 1. The median age of participants was 57 [interquartile range (IQR) 46–64] years and 61% (= 135) were men. Overall, 114 (51%) patients underwent peritoneal dialysis (PD) at the time of evaluation, with a median dialysis time of 11.8 (IQR 11.2–12.5) months. The remaining 108 (49%) underwent haemodialysis (HD) with a median dialysis time of 12.3 (IQR 11.3–13.2) months. Overall, 61 (28%) patients had DM and 75 (34%) had a history of cardiovascular disease (CVD; defined as cardiac, cerebrovascular or peripheral vascular disease). All patients were prospectively followed up for up to 5 years, or until 30 April 2011, death or kidney transplantation, whichever event occurred first. Causes of death were extracted from medical records by a physician blinded to the study results. The Ethics Committee of Karolinska University Hospital at Huddinge (Stockholm, Sweden) approved the study protocol.


Figure 1. Flowchart showing the inclusion of patients into the study. FFA, free fatty acids; PL, phospholipids.

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Laboratory analyses

Blood samples were obtained after an overnight fast. Plasma and serum were separated and kept frozen at −70 °C, if not analysed immediately. Triglyceride, total cholesterol, HDL, high-sensitivity C-reactive protein (CRP) and albumin concentrations were analysed using standard operating procedures in the Department of Laboratory Medicine at Karolinska University Hospital. The Friedewald equation [24] was used to calculate low-density lipoprotein (LDL) from total cholesterol, HDL and triglyceride levels. Serum concentrations of interleukin-6 (IL-6) were quantified by immunometric assays using an Immulite Analyzer (Siemens Medical Solutions Diagnostics, Los Angeles, CA, USA).

Nutritional status

Subjective global assessment (SGA) was used to evaluate overall nutritional status. SGA relies on clinical judgment and patient reports calculated from a brief history and physical examination [25]. The history focuses on gastrointestinal symptoms (anorexia, nausea, vomiting and diarrhoea) and weight loss in the preceding 6 months. The physical examination includes evaluation of loss of subcutaneous fat over the triceps and mid-axillary line of the lateral chest wall, muscle wasting in the deltoids and quadriceps, and the presence of ankle oedema. These features are classified as 0 = normal, l = mild, 2 = moderate and 3 = severe. On the basis of this information, patients are classified into two groups: those with normal nutritional status (SGA score of 1) and those with protein-energy wasting (PEW; SGA score >1) [26]. Body mass index (BMI) was calculated as body weight divided by the square of body height (kg m−2).

Plasma fatty acid composition and estimation of SCD-1 activities

The plasma fatty acid compositions of phospholipids and free fatty acids (FFAs) were analysed from frozen samples by gas–liquid chromatography (Hewlett Packard 5890, Avondale, PA, USA) on a capillary column (Quadrex, New Haven, CT, USA) at the Unit for Clinical Nutrition Research, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden. As described in detail elsewhere [27], after extraction in chloroform and separation of lipids by thin-layer chromatography and trans-methylation, different fatty acids ranging from myristic acid (14 : 0) to docosahexaenoic acid (22 : 6) were identified using methyl ester standards (GLC-68A; Nu Check Prep, Elysian, MN, USA) and expressed as relative percentages (mol%) of the total. It has been reported that the coefficient of variation (CV) of this method is 1.0–5.5% for the different fatty acids, except α-linolenic acid for which the CV is 8.2% [28]. Two patients were excluded a posteriori because of poor data quality for FFAs.

Direct measurement of SCD-1 activities in humans is complicated and not feasible in large cohort studies. We therefore estimated hepatic and adipose tissue SCD-1 activities by using product-to-precursor fatty acid ratios (palmitoleic acid/palmitic acid). Previous studies have shown a high degree of correlation between serum fatty acid biomarker-derived indices and both liver and adipose tissue-derived indices, with correlation coefficients of 0.86 [29] and 0.63 [30], respectively. The use of the palmitoleic acid/palmitic acid ratio (16 : 1 n-7/16 : 0) is preferable to that of the oleic acid/stearic acid ratio (18 : 1n-9/18 : 0), because the latter may be biased by high dietary intake of oleic acid [31]. Dietary intake of palmitoleic acid, on the other hand, is very low in a typical Swedish diet [32]. Thus, plasma palmitoleic acid is almost exclusively derived from endogenous conversion from palmitic acid by SCD-1 and, in the present study, the ratio of palmitoleic acid/palmitic acid was determined in plasma phospholipids and FFAs to reflect SCD-1 activities in the liver and adipose tissue, respectively [18, 30].

Statistical analyses

After evaluating the distribution of the data variables with the Kolmogorov–Smirnov test, values were expressed as mean ± standard deviation (SD), median (IQR) or percentage of total, as appropriate. Receiver operating characteristic (ROC) curve analyses were performed to estimate SCD-1 cut-off points of maximum sensitivity and highest specificity for prediction of all-cause mortality. These cut-off values were used in subsequent analyses to classify patients with high and low plasma SCD-1 activity indices. Comparisons between the two groups were evaluated by the Student's unpaired t tests for normally distributed continuous variables, the nonparametric Mann–Whitney tests for non-normally distributed continuous variables and chi-square tests for nominal variables. Spearman's rank correlation analysis was used to determine relationships between SCD-1 indices and variables of interest. Multivariate regression models were fitted to study associations between SCD-1 activity indices and variables associated with these indices in univariate analyses. Serum albumin, CRP and IL-6 levels were associated with SCD-1 indices, but because of strong colinearity only the latter was used in the multivariate regression models. Additionally, DM, CVD, triglyceride levels and BMI were forced into the models based on preceding evidence regarding determinants of SCD-1 activity indices [10, 12, 14, 17]. Data are expressed as adjusted coefficients of determination (R2) and standardized regression coefficients (β).

Because kidney transplantation and death before transplantation are mutually exclusive events (i.e. the occurrence of either one prevents the occurrence of the other), traditional Cox regressions may be biased; we therefore calculated the cumulative incidence of death before kidney transplant using the competing risk approach [33]. Crude and multivariable-adjusted (age, sex, comorbidities, dialysis modality, PEW and IL-6 level) models were examined using the ROC-derived dichotomization or using SCD-1 indices as continuous variables (per SD increase). Data are presented as hazard ratios (HRs) and 95% confidence intervals (CIs).

Statistical analyses were performed using statistical software stata version 12 (Stata Corporation, College Station, TX, USA). All tests were two-tailed and < 0.05 was considered significant. P values were not adjusted for multiple testing, and therefore should be considered descriptive.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Estimated SCD-1 activities and fatty acids

Stearoyl-CoA desaturase-1 activity indices, with their precursors (SFAs) and products (MUFAs) in both plasma phospholipids and FFAs, are shown in Table 1. The SCD-1 activity index in phospholipids and the proportion of MUFAs were significantly higher in PD patients, compared with HD patients. In the FFA fraction, no differences between therapies were observed in terms of the SCD-1 index or fatty acid profile.

Table 1. Stearoyl-CoA desaturase-1 (SCD-1) activity indices in plasma phospholipids and free fatty acids (FFAs) and their precursors and products in 222 dialysis patients
 Fatty acids (percentage of all fatty acids, mol%) and SCD-1 indicesP-value
All patientsPD patientsHD patients
  1. HD, haemodialysis; PD, peritoneal dialysis; SCD-1, stearoyl-CoA desaturase-1.

  2. Data are expressed as mean ± standard deviation or median (interquartile range), as appropriate. The numbers of all PD and HD patients were 222, 114 and 108 for phospholipids measurements and 220, 113 and 107 for FFA measurements, respectively. SCD-1 activities were estimated as product-to-precursor ratios of individual fatty acids (palmitoleic acid/palmitic acid) in plasma phospholipids and FFAs . Significant values are given in bold.

Palmitic acid (16 : 0)30.4 ± 1.630.3 ± 1.730.4 ± 1.60.78
Palmitoleic acid (16 : 1 n-7)0.48 (0.38–0.66)0.51 (0.38–0.72)0.45 (0.36–0.58) 0.01
Stearic acid (18 : 0)14.5 ± 1.414.6 ± 1.414.4 ± 1.40.30
Oleic acid (18 : 1 n-9)13.7 ± 1.814.0 ± 2.013.4 ± 1.7 0.03
SCD-1 index0.016 (0.013–0.022)0.017 (0.013–0.024)0.015 (0.012–0.019) 0.008
Free fatty acids
Palmitic acid (16 : 0)26.5 ± 2.926.5 ± 3.026.5 ± 2.80.95
Palmitoleic acid (16 : 1 n-7)3.97 (2.80–5.16)4.09 (3.00–5.21)3.78 (2.52–4.98)0.13
Stearic acid (18 : 0)9.40 ± 2.349.37 ± 2.479.44 ± 2.210.83
Oleic acid (18 : 1 n-9)42.7 ± 4.142.5 ± 4.243.0 ± 4.00.34
SCD-1 index0.15 (0.10–0.20)0.16 (0.11–0.21)0.15 (0.10–0.19)0.14

Estimation of clinical cut-offs according to ROC curves

Owing to the lack of clinically defined cut-off levels for SCD-1 activity indices, we estimated levels on the basis of ROC curve analyses for prediction of all-cause mortality. Both phospholipid and FFA SCD-1 indices were considered good predictors of mortality according to the areas under the ROC curve: 61.3% (95% CI 52.3–70.2%) and 64.7% (95% CI 56.4–73.0%), respectively. The clinical cut-off values with maximum sensitivity and highest specificity were 0.020 for phospholipid and 0.164 for FFA SCD-1 indices. These cut-off values were used in further analyses to define high and low SCD-1 activities in our patient population.

General characteristics

Baseline clinical characteristics of the 222 dialysis patients included in the study are summarized in Table 2, according to high or low SCD-1 activity indices. Overall, patients with high SCD-1 indices were more often women, were older and had higher levels of inflammation (evidenced by lower serum albumin and higher IL-6 concentrations). Individuals with a high phospholipid SCD-1 index were more likely to be undergoing PD and presented with increased CRP concentration, whereas those with a high FFA SCD-1 index had a higher HDL concentration and were more often malnourished (shown by a larger proportion of patients with PEW and lower BMI and triglyceride concentrations).

Table 2. General characteristics in the dialysis patients grouped according to stearoyl-CoA desaturase-1 indices in plasma phospholipids and free fatty acids (FFAs)
VariablePhospholipid SCD-1 indexFree fatty acid SCD-1 index
  1. BMI, body mass index; CRP, C-reactive protein; HDL, high-density lipoprotein; IL-6, interleukin-6; LDL, low-density lipoprotein; PEW, protein–energy wasting; SCD-1, stearoyl-CoA desaturase-1; SGA, subjective global assessment.

  2. Data are expressed as mean ± standard deviation, median (interquartile range), or number of subjects (percentages), as appropriate. SCD-1 activities were estimated by the ratios of palmitoleic acid/palmitic acid in plasma phospholipids and FFAs. In each lipid fraction, patients were classified as having a high or low SCD-1 index according to cut-off values (0.020 in phospholipids and 0.164 in FFAs), accessed by receiver operating characteristic curve analyses. Significant values are given in bold.

n 15864 13189 
Women, n (%)54 (34)33 (52) 0.02 37 (28)49 (55)<0.001
Age, years55 (44–64)61 (53–67) 0.002 56 (43–64)59 (51–66) 0.02
Diabetes, n (%)40 (25)21 (33)0.2737 (28)24 (27)0.81
Cardiovascular disease, n (%)50 (32)25 (40)0.2740 (31)34 (38)0.27
Peritoneal dialysis, n (%)72 (46)42 (66) 0.006 63 (48)50 (56)0.24
Triglycerides, mmol L−11.8 (1.3–2.6)1.9 (1.3–3.1)0.402.0 (1.4–2.9)1.7 (1.2–2.6) 0.03
Total cholesterol, mmol L−15.4 (4.3–6.5)5.1 (4.4–6.2)0.765.4 (4.3–6.7)5.3 (4.3–6.2)0.37
HDL, mmol L−11.3 ± 0.41.5 ± ± 0.41.5 ± 0.5<0.001
LDL, mmol L−13.2 ± 1.33.0 ± 1.50.333.2 ± 1.43.0 ± 1.40.19
BMI, kg m−225.4 ± 4.424.3 ± 4.10.1025.7 ± 4.024.3 ± 4.60.43
PEW (SGA > 1), n (%)29 (18)15 (25)0.3020 (15)23 (26) 0.02
Albumin, g L−137 ± 533 ± 6<0.00138 ± 534 ± 6<0.001
CRP, mg L−13.0 (1.0–6.7)6.2 (2.3–22)<0.0014.3 (1.1–7.9)3.9 (1.7–18)0.29
IL-6, pg mL−14.7 (2.9–7.3)6.5 (4.9–13)<0.0014.8 (3.1–7.4)5.8 (4.0–11) 0.01

Univariate and multivariate correlations

Using Spearman's rank correlation tests (Table 3), the phospholipid SCD-1 index was positively associated with female sex, age, PD therapy, presence of PEW and the inflammatory biomarkers CRP and IL-6 (Fig. 2a), but negatively associated with LDL and albumin levels. Similar correlations were observed for the FFA SCD-1 index (Fig. 2b), except with regard to dialysis modality and LDL and CRP levels. Moreover, obesity and triglyceride levels were negatively but HDL was positively associated with FFA SCD-1 activity.


Figure 2. Correlation between stearoyl-CoA desaturase-1 (SCD-1) activity indices in plasma phospholipids (PLs; (a) or free fatty acids (FFAs; (b) and serum interleukin-6 concentrations in dialysis patients.

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Table 3. Univariate correlation and multivariate regression between stearoyl-CoA desaturase-1 (SCD-1) activity indices and other variables in dialysis patients
VariablePhospholipid SCD-1 indexFree fatty acid SCD-1 index
Univariate rho (P)Multivariatea β (P)Univariate rho (P)Multivariateb β (P)
  1. BMI, body mass index; CRP, C-reactive protein; LDL, low-density lipoprotein; SGA, subjective global assessment.

  2. a

    R2 = 0.19 (= 208).

  3. b

    R2 = 0.25 (= 207). SCD-1 activities were estimated by the ratios of palmitoleic acid/palmitic acid in plasma phospholipids and free fatty acids. –, not included in the multivariate regression models. Significant values are given in bold.

Women0.27 (<0.001)0.28 (<0.001)0.34 (<0.001)0.26 (<0.001)
Age0.21 (0.002)0.19 (0.008)0.22 (0.001)0.19 (0.008)
Diabetes0.05 (0.47)0.06 (0.39)−0.02 (0.75)0.01 (0.92)
Cardiovascular disease0.05 (0.44)−0.01 (0.84)0.04 (0.56)−0.04 (0.61)
Peritoneal dialysis0.18 (0.007)0.14 (0.04)0.10 (0.14)0.01 (0.84)
Triglycerides0.01 (0.85)0.19 (0.004)−0.20 (0.003)0.01 (0.96)
Total cholesterol−0.05 (0.43)0.03 (0.71)
HDL0.12 (0.07)0.27 (<0.001)0.12 (0.11)
LDL−0.16 (0.02)−0.13 (0.04)−0.01 (0.92)
BMI−0.10 (0.16)−0.14 (0.06)−0.24 (<0.001)−0.20 (0.006)
Protein-energy wasting (SGA > 1)0.14 (0.05)0.01 (0.89)0.23 (0.001)0.02 (0.77)
Albumin−0.23 (0.001)−0.27 (<0.001)
CRP0.21 (0.002)0.11 (0.12)
Interleukin-60.27 (<0.001)0.16 (0.02)0.26 (<0.001)0.26 (<0.001)

In multivariate regression models (Table 3), female sex, age and IL-6 independently and positively contributed to SCD-1 variances in both studied SCD-1 activity indices. In addition, PD therapy and triglyceride levels were positively but LDL was negatively associated with the phospholipid SCD-1 index, and BMI was independently and negatively related to the FFA SCD-1 index.

Mortality analyses

Overall, 61 (27%) patients died during a median follow-up period of 18.4 (IQR 5.5–37) months. The main causes of death were CVD related (= 37, 61% of total deaths). Furthermore, 116 (52%) individuals underwent kidney transplantation. Competing risk Cox models are shown in Table 4. In the crude models, patients with high phospholipid and FFA SCD-1 activity indices presented significantly higher mortality risk before kidney transplantation, compared with those with low SCD-1 indices. The results were statistically significant after adjustment for potential confounders. In the sensitivity analysis, replacement of CRP by IL-6 in the multivariable-adjusted models did not affect the results, neither did further adjustment for blood lipoprotein levels (data not shown). When both SCD-1 indices were tested as continuous variables (per SD increase), similar direct associations with outcome were observed. Results were re-analysed, as a sensitivity analysis, according to dialysis modality. In crude competing risk models, a high phospholipid SCD-1 activity index similarly predicted mortality in PD (= 114; 29 events; HR 4.28, 95% CI 2.00–9.15) and HD (= 108; 32 events; HR 2.67, 95% CI 1.32–5.42) patients separately. The same was true for a high FFA SCD-1 index (in PD patients: HR 2.20, 95% CI 1.04–4.65; in HD patients: HR 2.70, 95% CI 1.36–5.37).

Table 4. Competing risk Cox proportional hazard ratios (HRs) for stearoyl-CoA desaturase-1 (SCD-1) activity indices in plasma phospholipids and free fatty acids (FFAs) on all-cause mortality before kidney transplantation
 Crude modelMultivariable-adjusted model
HR (95% CI)P-valueHR (95% CI)P-value
  1. CI, confidence interval; HR, hazard ratio; SCD-1, stearoyl-CoA desaturase-1.

  2. In each lipid fraction, HRs are presented either as categories (high or low SCD-1 according to receiver operating characteristic cut-off values (0.020 in phospholipids and 0.164 in FFAs) or presented as a continuous variable showing risk associated with each SD increase. Covariance in multivariable-adjusted models includes age, sex, comorbidities (composite score of diabetes mellitus and cardiovascular disease), dialysis modality, protein–energy wasting and interleukin-6. Significant values are given in bold.

High SCD-1 index (reference: low)3.32 (2.02–5.44)<0.0012.29 (1.28–4.11) 0.006
SCD-1 index, per standard deviation (SD) increase1.46 (1.21–1.77)<0.0011.28 (1.00–1.64) 0.05
Free fatty acids
High SCD-1 index (reference: low)2.42 (1.46–4.02) 0.001 2.36 (1.38–4.03) 0.002
SCD-1 index, per SD increase1.55 (1.33–1.81)<0.0011.42 (1.19–1.70)<0.001


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

To our knowledge, this is the first study to investigate outcome implications of both hepatic and adipose tissue SCD-1 activity indices in dialysis patients. The main finding of this study is the strong and direct prognostic value of both SCD-1 indices with regard to mortality. This is in line with the finding of a previous community study, in which hepatic SCD-1 index measured in serum cholesterol esters also predicted mortality [17]. This association may involve deleterious effects previously associated with high SCD-1 activity, such as promotion of hepatic lipogenesis and steatosis [34], insulin resistance [9, 13], endothelial dysfunction [16] and atherosclerosis [35].

This study is also the first to investigate determinants of SCD-1 activities in dialysis patients. As in nonrenal populations [10], female sex and old age are factors associated with increased SCD-1 indices. The underlying mechanisms for the effect of sex differences and ageing have not been fully elucidated, yet a putative role of differences in fat deposition within tissues and levels of hormones across genders has been proposed [10]. An interesting finding in our study is that uraemic inflammation was strongly associated with higher SCD-1 indices both in the liver and in adipose tissue. Such a correlation is supported by findings in animals, cell studies [36, 37] and community-based cohorts [15, 16]. Because SCD-1 activity increases in response to SFA intake [18], these observations support the notion that SFAs have pro-inflammatory functions [38, 39]. However, SCD-1 per se may also cause inflammation in liver and adipose tissue [36], a finding supported by observations in SCD-1 knockout mice that are protected from macrophage, endothelial cell and adipose tissue inflammation [37]. Despite this earlier experimental evidence, the association between SCD-1 activity indices and mortality in our study remained after adjustment for IL-6 levels, perhaps suggesting that systemic inflammation is not a key mediator. Various serum lipoproteins and BMI were also associated with SCD-1 indices in our study, which is in agreement with some but not all previous findings [9, 12, 34, 40]. In the context of ESRD, the prevalence of PEW may further complicate the interpretation of the role of lipids [41] as well as of BMI [42].

In agreement with recent findings [43], PD patients in the present study had higher proportions of phospholipid MUFAs than HD patients, and this resulted in a higher hepatic SCD-1 activity index. PD therapy has been associated with alterations in metabolism of other nutrients [21, 22] and PD patients may also have a less favourable lipid profile than HD patients [44]. Based on these results, we hypothesize that endogenous lipid metabolism may be altered in PD patients. It is plausible that de novo lipogenesis after glucose absorption from the dialysate [45] contributes to increase SCD-1 activities.

Because SCD-1 activity increases in humans in response to high SFA intake and low unsaturated fat intake [11, 18], replacing palmitic acid (16 : 0) or refined carbohydrates by MUFAs or PUFAs in the diet might be a useful strategy to reduce SCD-1 activities. However, it is unclear whether such an intervention could be of clinical importance; it remains to be shown in humans whether SCD-1 per se has adverse health effects. On the other hand, there is convincing evidence that replacing dietary palmitic acid by n-6 PUFAs reduces cardiovascular events in humans [46]. In this context, it has been speculated that increased SCD-1 is an adaptive response to excess intake of SFAs and/or sugars and that such a response will prevent the toxic effects of high cellular levels of palmitic acid by conversion to MUFAs [8].

Several strengths and limitations should be considered when interpreting these data. First, our observational design cannot infer causality. Secondly, including patients with similar dialysis vintage eliminates an important potential confounder, but at the same time conveys a selection of individuals that limits the ability to generalize to other populations. Thirdly, we used estimations of SCD-1 indices, which are considered to reflect hepatic and adipose tissue SCD-1 activities accurately [29, 30] and have been widely adopted [30, 47-49]. Finally, detailed phenotypic characterization allows potential confounders of the complex phenotype of uraemic patients to be taken into account, but unmeasured or unknown confounders cannot be excluded.

In conclusion, the results of the present study demonstrate an independent strong association between increased indices of hepatic and adipose tissue SCD-1 activities and mortality in dialysis patients. In addition, SCD-1 indices were found to be closely associated with age, female sex and systemic inflammation. As SCD-1 activities can be modified by increasing PUFA intake and reducing SFAs [18-20], SCD-1 could potentially be targeted through dietary modification. Pharmacological inhibition of SCD-1 activity is currently being considered as a therapeutic target for the metabolic syndrome and CVD [50]. Whether such interventions may reduce the impaired metabolic risk profile associated with both kidney disease and elevated SCD-1 activity warrants further research.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

We would like to thank all patients and personnel involved in this study. We are grateful for the technical assistance of Siv Tengblad and thank our research staff Annika Nilsson, Anki Emmoth, Ulrika Jensen, Björn Anderstam, Monica Ericsson and Ann-Christin Bragfors-Helin.

This work was supported by grants from Abbott Nutrition, the Swedish Research Council, and the Loo and Hans Osterman, the Stig and Gunborg Westman, and the Fredrik and Ingrid Thuring Foundations. Dr Huang received a PhD scholarship from the China Scholarship Council. Baxter Novum is a research division at Karolinska Institutet resulting from an unrestricted grant of Baxter Healthcare Corporation.

Conflict of interest

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References

Dr Lindholm is employed by Baxter Healthcare Corporation. None of the other authors has any personal or financial conflicts of interest to declare.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Conflict of interest
  9. References
  • 1
    Carrero JJ, Stenvinkel P. Persistent inflammation as a catalyst for other risk factors in chronic kidney disease: a hypothesis proposal. Clin J Am Soc Nephrol 2009; 4(Suppl. 1): S4955.
  • 2
    Carrero JJ, Park SH, Axelsson J, Lindholm B, Stenvinkel P. Cytokines, atherogenesis, and hypercatabolism in chronic kidney disease: a dreadful triad. Semin Dial 2009; 22: 3816.
  • 3
    Khoueiry G, Waked A, Goldman M et al. Dietary intake in hemodialysis patients does not reflect a heart healthy diet. J Ren Nutr 2011; 21: 43847.
  • 4
    Arslan Y, Kiziltan G. Nutrition-related cardiovascular risk factors in hemodialysis patients. J Ren Nutr 2010; 20: 18592.
  • 5
    Huang X, Stenvinkel P, Qureshi AR et al. Essential polyunsaturated fatty acids, inflammation and mortality in dialysis patients. Nephrol Dial Transplant 2012. DOI: 10.1093/ndt/gfs132. PMID: 22565059.
  • 6
    Vessby B. Dietary fat, fatty acid composition in plasma and the metabolic syndrome. Curr Opin Lipidol 2003; 14: 159.
  • 7
    Ntambi JM, Miyazaki M. Regulation of stearoyl-CoA desaturases and role in metabolism. Prog Lipid Res 2004; 43: 91104.
  • 8
    Bjermo H, Riserus U. Role of hepatic desaturases in obesity-related metabolic disorders. Curr Opin Clin Nutr Metab Care 2010; 13: 7038.
  • 9
    Brown JM, Chung S, Sawyer JK et al. Inhibition of stearoyl-coenzyme A desaturase 1 dissociates insulin resistance and obesity from atherosclerosis. Circulation 2008; 118: 146775.
  • 10
    Warensjo E, Ohrvall M, Vessby B. Fatty acid composition and estimated desaturase activities are associated with obesity and lifestyle variables in men and women. Nutr Metab Cardiovasc Dis 2006; 16: 12836.
  • 11
    Bjermo H, Iggman D, Kullberg J et al. Effects of n-6 PUFAs compared with SFAs on liver fat, lipoproteins, and inflammation in abdominal obesity: a randomized controlled trial. Am J Clin Nutr 2012; 95: 100312.
  • 12
    Attie AD, Krauss RM, Gray-Keller MP et al. Relationship between stearoyl-CoA desaturase activity and plasma triglycerides in human and mouse hypertriglyceridemia. J Lipid Res 2002; 43: 1899907.
  • 13
    Riserus U, Arnlov J, Berglund L. Long-term predictors of insulin resistance: role of lifestyle and metabolic factors in middle-aged men. Diabetes Care 2007; 30: 292833.
  • 14
    Vessby B, Aro A, Skarfors E, Berglund L, Salminen I, Lithell H. The risk to develop NIDDM is related to the fatty acid composition of the serum cholesterol esters. Diabetes 1994; 43: 13537.
  • 15
    Petersson H, Basu S, Cederholm T, Riserus U. Serum fatty acid composition and indices of stearoyl-CoA desaturase activity are associated with systemic inflammation: longitudinal analyses in middle-aged men. Br J Nutr 2008; 99: 11869.
  • 16
    Petersson H, Lind L, Hulthe J, Elmgren A, Cederholm T, Riserus U. Relationships between serum fatty acid composition and multiple markers of inflammation and endothelial function in an elderly population. Atherosclerosis 2009; 203: 298303.
  • 17
    Warensjo E, Sundstrom J, Vessby B, Cederholm T, Riserus U. Markers of dietary fat quality and fatty acid desaturation as predictors of total and cardiovascular mortality: a population-based prospective study. Am J Clin Nutr 2008; 88: 2039.
  • 18
    Warensjo E, Riserus U, Gustafsson IB, Mohsen R, Cederholm T, Vessby B. Effects of saturated and unsaturated fatty acids on estimated desaturase activities during a controlled dietary intervention. Nutr Metab Cardiovasc Dis 2008; 18: 68390.
  • 19
    Chong MF, Hodson L, Bickerton AS et al. Parallel activation of de novo lipogenesis and stearoyl-CoA desaturase activity after 3 d of high-carbohydrate feeding. Am J Clin Nutr 2008; 87: 81723.
  • 20
    Corpeleijn E, Feskens EJ, Jansen EH et al. Improvements in glucose tolerance and insulin sensitivity after lifestyle intervention are related to changes in serum fatty acid profile and desaturase activities: the SLIM study. Diabetologia 2006; 49: 2392401.
  • 21
    Choi JY, Yoon YJ, Choi HJ et al. Dialysis modality-dependent changes in serum metabolites: accumulation of inosine and hypoxanthine in patients on haemodialysis. Nephrol Dial Transplant 2011; 26: 130413.
  • 22
    Chuang CK, Lin SP, Chen HH et al. Plasma free amino acids and their metabolites in Taiwanese patients on hemodialysis and continuous ambulatory peritoneal dialysis. Clin Chim Acta 2006; 364: 20916.
  • 23
    Stenvinkel P, Heimburger O, Paultre F et al. Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int 1999; 55: 1899911.
  • 24
    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: 499502.
  • 25
    Detsky AS, McLaughlin JR, Baker JP et al. What is subjective global assessment of nutritional status? JPEN J Parenter Enteral Nutr 1987; 11: 813.
  • 26
    Qureshi AR, Alvestrand A, Danielsson A et al. Factors predicting malnutrition in hemodialysis patients: a cross-sectional study. Kidney Int 1998; 53: 77382.
  • 27
    Boberg M, Croon LB, Gustafsson IB, Vessby B. Platelet fatty acid composition in relation to fatty acid composition in plasma and to serum lipoprotein lipids in healthy subjects with special reference to the linoleic acid pathway. Clin Sci (Lond) 1985; 68: 5817.
  • 28
    Smedman AE, Gustafsson IB, Berglund LG, Vessby BO. Pentadecanoic acid in serum as a marker for intake of milk fat: relations between intake of milk fat and metabolic risk factors. Am J Clin Nutr 1999; 69: 229.
  • 29
    Kotronen A, Seppanen-Laakso T, Westerbacka J et al. Comparison of lipid and fatty acid composition of the liver, subcutaneous and intra-abdominal adipose tissue, and serum. Obesity (Silver Spring) 2010; 18: 93744.
  • 30
    Warensjo E, Rosell M, Hellenius ML, Vessby B, De Faire U, Riserus U. Associations between estimated fatty acid desaturase activities in serum lipids and adipose tissue in humans: links to obesity and insulin resistance. Lipids Health Dis 2009; 8: 37.
  • 31
    Vessby B, Uusitupa M, Hermansen K et al. Substituting dietary saturated for monounsaturated fat impairs insulin sensitivity in healthy men and women: The KANWU Study. Diabetologia 2001; 44: 3129.
  • 32
    Becher W, Pearson M. Riksmaten. Kostvanor och näringsintag i Sverige. (National Survey of Dietary Habits and Nutrient Intake in Sweden). Uppsala: Livsmedelsverket (National Food Administration), 1997–1998.
  • 33
    Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94: 496509.
  • 34
    Miyazaki M, Flowers MT, Sampath H et al. Hepatic stearoyl-CoA desaturase-1 deficiency protects mice from carbohydrate-induced adiposity and hepatic steatosis. Cell Metab 2007; 6: 48496.
  • 35
    Savransky V, Jun J, Li J et al. Dyslipidemia and atherosclerosis induced by chronic intermittent hypoxia are attenuated by deficiency of stearoyl coenzyme A desaturase. Circ Res 2008; 103: 117380.
  • 36
    Menghini R, Menini S, Amoruso R et al. Tissue inhibitor of metalloproteinase 3 deficiency causes hepatic steatosis and adipose tissue inflammation in mice. Gastroenterology 2009; 136: 66372.
  • 37
    Liu X, Miyazaki M, Flowers MT et al. Loss of Stearoyl-CoA desaturase-1 attenuates adipocyte inflammation: effects of adipocyte-derived oleate. Arterioscler Thromb Vasc Biol 2010; 30: 318.
  • 38
    Shi H, Kokoeva MV, Inouye K, Tzameli I, Yin H, Flier JS. TLR4 links innate immunity and fatty acid-induced insulin resistance. J Clin Invest 2006; 116: 301525.
  • 39
    Nguyen MT, Favelyukis S, Nguyen AK et al. A subpopulation of macrophages infiltrates hypertrophic adipose tissue and is activated by free fatty acids via Toll-like receptors 2 and 4 and JNK-dependent pathways. J Biol Chem 2007; 282: 3527992.
  • 40
    Brown JM, Chung S, Sawyer JK et al. Combined therapy of dietary fish oil and stearoyl-CoA desaturase 1 inhibition prevents the metabolic syndrome and atherosclerosis. Arterioscler Thromb Vasc Biol 2010; 30: 2430.
  • 41
    Chmielewski M, Carrero JJ, Qureshi AR et al. Temporal discrepancies in the association between the apoB/apoA-I ratio and mortality in incident dialysis patients. J Intern Med 2009; 265: 70816.
  • 42
    Kalantar-Zadeh K, Block G, Humphreys MH, Kopple JD. Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 2003; 63: 793808.
  • 43
    An WS, Kim SE, Kim KH et al. Comparison of fatty acid contents of erythrocyte membrane in hemodialysis and peritoneal dialysis patients. J Ren Nutr 2009; 19: 26774.
  • 44
    Heimburger O. Statins and lipid-lowering strategies in PD. Contrib Nephrol 2012; 178: 10610.
  • 45
    Andersson G, Bergquist-Poppen M, Bergstrom J, Collste LG, Hultman E. Glucose absorption from the dialysis fluid during peritoneal dialysis. Scand J Urol Nephrol 1971; 5: 779.
  • 46
    Astrup A, Dyerberg J, Elwood P et al. The role of reducing intakes of saturated fat in the prevention of cardiovascular disease: where does the evidence stand in 2010? Am J Clin Nutr 2011; 93: 6848.
  • 47
    Chajes V, Jenab M, Romieu I et al. Plasma phospholipid fatty acid concentrations and risk of gastric adenocarcinomas in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Am J Clin Nutr 2011; 94: 130413.
  • 48
    Sjogren P, Sierra-Johnson J, Gertow K et al. Fatty acid desaturases in human adipose tissue: relationships between gene expression, desaturation indexes and insulin resistance. Diabetologia 2008; 51: 32835.
  • 49
    Saadatian-Elahi M, Slimani N, Chajes V et al. Plasma phospholipid fatty acid profiles and their association with food intakes: results from a cross-sectional study within the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr 2009; 89: 33146.
  • 50
    Brown JM, Rudel LL. Stearoyl-coenzyme A desaturase 1 inhibition and the metabolic syndrome: considerations for future drug discovery. Curr Opin Lipidol 2010; 21: 1927.