Skip to main content
NCBI home page
Journal of Lipid Research logoLink to Journal of Lipid Research
. 2013 May;54(5):1477–1483. doi: 10.1194/jlr.P027516

Interaction between a common variant in FADS1 and erythrocyte polyunsaturated fatty acids on lipid profile in Chinese Hans[S]

Jingwen Zhu *, Qi Sun †,§, Geng Zong *, Yuan Si **, Chen Liu *, Qibin Qi †, Xingwang Ye *, Liang Sun *, Hongguang Sheng ††, Huaixing Li *,1, Xu Lin *,1
PMCID: PMC3622339  PMID: 23396965

Abstract

Little is known about the associations of FADS1 genetic variants with circulating levels of PUFA and lipids in Asian populations who have a different dietary pattern and dyslipidemia prevalence compared with Western populations. In a population-based sample of 3,210 unrelated Han Chinese living in Beijing and Shanghai, we examined a FADS1 genetic variant, rs174550, in relation to blood PUFA and lipid levels. C-allele of rs174550 was significantly associated with levels of erythrocyte PUFAs in upstream and downstream pathways of delta-5 desaturase (D5D) (P ≤ 0.003). Moreover, rs174550 C-allele was associated with a lower HDL cholesterol level (P = 0.02) in total population and a higher triglyceride level (P = 0.0002) in Beijing residents. Interestingly, erythrocyte levels of 18:2n-6 and 18:3n-3 modified the effect of rs174550 on HDL cholesterol level: stronger associations between rs174550 C-allele and lower HDL cholesterol levels were exhibited when erythrocyte 18:2n-6 or 18:3n-3 level was low (P for interaction = 0.02 and 0.03, respectively). These data suggested that FADS1 genetic variant was associated with circulating PUFA and lipid levels and that its effect on HDL cholesterol might depend on PUFA status in the Han Chinese population.

Keywords: fatty acid desaturase 1, delta-5 desaturase, high density lipoprotein cholesterol, triglycerides

The delta-5 desaturase (D5D), encoded by fatty acid desaturase 1 (FADS1) gene, plays a crucial role in the biosynthesis of long-chain polyunsaturated fatty acids (LC-PUFA) through introducing a double bond at the Δ5 position to 20:3n-6 and 20:4n-3, producing 20:4n-6 and 20:5n-3 (eicosapentaenoic acid, EPA), respectively (1, 2). Several studies, including genome-wide association studies (GWAS), have suggested that a cluster of single nucleotide polymorphisms (SNPs) in strong linkage disequilibrium (LD) within the FADS1 region, such as rs174537, rs174546, and rs174547, was significantly associated with PUFA levels in serum, plasma, erythrocytes, or human milk (3–10). In addition, SNPs in FADS1, including rs174546, rs174547, and rs174550, were associated with levels of high-density lipoprotein (HDL) cholesterol (11–16), low-density lipoprotein (LDL) cholesterol (12, 14–16), or triglycerides (11–16). LC-PUFAs are natural ligands for peroxisome proliferator activating receptor α (PPARα) (17–19) which plays an important role in modulating blood lipid levels, particularly HDL cholesterol and triglycerides, through regulating expression of lipoprotein lipase, apolipoprotein (Apo)A-I, A-II, and C-III (20–22). Therefore, effects of FADS1 genetic variants on blood lipids are likely mediated by LC-PUFAs. Up to now, the majority of evidence was generated from studies among Western populations; it remains unclear whether these common variants are also associated with blood lipids and PUFAs in the Chinese population.

In recent decades, China has been experiencing rapid nutrition transition, characterized mainly by increased consumption of fat and animal foods (23). Based upon 1992–2002 China health and nutrition surveys, energy intake from fat and animal foods increased from 22.0% to 29.8% and from 9.3% to 13.7%, respectively (24). Meanwhile, unlike in Western countries where prevalence of hypercholesterolemia is higher than hypertriglyceridemia (54.9% versus 31.2%), hypertriglyceridemia is more prevalent than hypercholesterolemia (11.9% versus 2.9%) in the Chinese population (25, 26). Previously, findings from three studies conducted among peoples with European ancestry suggested that dietary PUFA intake may modify the associations between FADS1 genetic variant and blood lipids (27–29). One of these studies found that rs174547 was associated with lower LDL cholesterol only if dietary long-chain n-3 PUFA was low (29). Thus, it is of interest to examine whether such an interaction may also exist in the Chinese population.

In the current investigation, we used rs174550 as a tag SNP of FADS1 variants to investigate i) the associations of this variant with plasma lipid profiles and erythrocyte PUFAs, objective markers of dietary intakes (30–32), and ii) interactions between rs174550 and erythrocyte PUFA contents on circulating lipid profiles in a Han Chinese population.

MATERIALS AND METHODS

Study population

The study sample consisted of 3,210 unrelated Han Chinese from the Nutrition and Health of Aging Population in China Study. The total population and study design and protocol have been described in detail previously (33). Briefly, a home interview was conducted to collect information, including sociodemographic data, medical history, and physical activity, using a standardized questionnaire. All participants underwent a physical examination, including standardized anthropometric measurements and overnight fasting blood sample collection. Body mass index (BMI) was calculated as [weight/height2 (kg/m2)]. All blood samples collected from different study sites in Beijing and Shanghai were shipped in dry ice to the laboratory at the Institute for Nutritional Sciences and analyzed in the same batch with a random sequence. Total, LDL, and HDL cholesterol and triglyceride concentrations were measured enzymatically on an automatic analyzer (Hitachi 7080, Japan) with reagents purchased from Wako Pure Chemical Industries (Osaka, Japan). Study protocol was approved by the Institutional Review Board of the Institute for Nutritional Sciences, and written informed consent was obtained from all participants.

Genotyping

FADS1 rs174550 was selected as tag SNP of FADS1 variants according to the LD structure among HapMap Han Chinese in Beijing, China (CHB) sample (HapMap Data Rel 24/phase II Nov08, on NCBI B36 assembly, dbSNP b126) and was genotyped by the GenomeLab SNPstream Genotyping System (Beckman Coulter) (34). The concordance rate of the SNP was greater than 99% based on 12% duplicate samples (n = 384). Within each subpopulation, rs174550 was in Hardy-Weinberg Equilibrium (P > 0.05).

Erythrocyte fatty acid measurement

The method for erythrocyte fatty acid measurement has been previously described (35). After being extracted by hexane and isopropanol, erythrocyte fatty acids were incubated with mixture of methanol and sulfuric acid for fatty acid methyl esters (FAME). FAMEs were then separated by gas chromatography (Agilent 6890 GC). Individual FAME was identified by positive chemical ionization using methane as reagent gas (Agilent 5975B). The relative amount of each fatty acid was calculated as a percentage of total fatty acids.

Assessment of dietary intakes

At home interviews, the information of dietary intake was collected by trained local medical professionals using a 74-item food frequency questionnaire (FFQ), which was a modified version of that used in the China health and nutrition surveys (36). Food composition values were obtained from the Chinese food composition table (37). All nutrient intakes were energy-adjusted utilizing the residual method (38).

Statistical analysis

Hardy-Weinberg equilibrium and differences of genotypic distributions between genders and geographical regions were tested using a likelihood ratio test. Because of an observed difference in the minor allele frequency (MAF) of rs174550 between Shanghai and Beijing participants (P < 0.0001), analyses were performed separately according to the geographic locations. Generalized linear regression (GLM) was applied to examine associations of rs174550 with erythrocyte PUFAs and plasma lipid concentrations. The gene-PUFA interaction on blood lipid parameters was examined by using GLM, where erythrocyte PUFAs, genotypes (homozygous for the major T-allele, and heterozygous and homozygous for the minor C-allele, respectively), and interaction terms (erythrocyte PUFAs × genotype) were entered as independent variables; plasma HDL cholesterol, triglycerides, HDL cholesterol-to-triglycerides ratio, and PUFAs were used as dependent variables. To preserve statistical power, the PUFAs were classified into low and high groups according to the region-specific medians of erythrocyte concentrations [i.e., 14.3% for 18:2n-6, 0.21% for 18:3n-3, 0.32% for EPA, and 3.93% for 22:6n-3 (docosahexaenoic acid, DHA) in Beijing participants; and 13.4% for 18:2n-6, ≤ 0.29% for 18:3n-3, 0.54% for EPA, and 4.84% for DHA in Shanghai participants, respectively]. Multivariate analyses were adjusted for age, sex, BMI, and residence (urban/rural). Participants were considered having dyslipidemia if they had one of following conditions: i) triglycerides greater than 1.70 mmol/l; ii) total cholesterol greater than 5.72 mmol/l; or iii) HDL cholesterol less than 0.91 mmol/l. We excluded participants who were self-reported having hyperlipidemia or using antihyperlipidemia medications (n = 479) prior to the lipid-related analyses.

Results from Beijing and Shanghai participants were meta-analyzed using a random-effects model, and Cochran's Q test was performed to assess the heterogeneity of effect sizes between Beijing and Shanghai participants. Due to the nonnormality of distribution, plasma triglycerides and erythrocyte 18:3n-3 levels were log-transformed before analyses and presented as geometric means whenever necessary. Data analyses were performed with SAS version 9.1 (SAS Institute, Cary, NC) and Stata version 9.2 (Stata, College Station, TX). All P values were two-sided, and P < 0.05 was considered to be statistically significant.

RESULTS

Characteristics of the study population were presented in Table 1. In general, Beijing residents had higher BMI, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides than their Shanghai counterparts (P ≤ 0.006). Levels of total and individual n-6 PUFAs (except 22:2n-6) were higher, whereas total and individual n-3 PUFAs were lower comparing Beijing with Shanghai residents (P < 0.0001). The MAF of rs174550 was 28.6% for Beijing residents and 41.8% for Shanghai residents (supplementary Table I). Analysis of pairwise LD among the SNPs within the FADS1 gene showed that LD is perfect (r2 >0.9) in the HapMap CHB sample and in the GWAS data of our population (supplementary Fig. I).

TABLE 1.

Characteristics of the study population

Characteristic Beijing Shanghai Total P
n (% male) 1574 (45.2%) 1636 (43.5%) 3210 (44.3%) 0.35
Urban (%) 770 (48.9%) 799 (48.8%) 1569 (48.9%) 0.96
Age (years) 58.3 ± 5.9 58.9 ± 6.0 58 ± 6.0 0.01
BMI (kg/m2) 25.2 ± 3.7 23.6 ± 3.3 24.4 ± 3.6 <0.0001
Lipid-lowering medication (%) 141 (9.0%) 85 (5.2%) 226 (7.0%) <0.0001
Hyperlipidemia (%) 249 (15.8%) 172 (10.5%) 421 (13.1%) <0.0001
Total cholesterol (mmol/l) 4.92 ± 0.97 4.46 ± 0.92 4.69 ± 0.97 <0.0001
HDL cholesterol (mmol/l) 1.29 ± 0.34 1.26 ± 0.33 1.28 ± 0.33 0.006
LDL cholesterol (mmol/l) 3.49 ± 0.98 3.03 ± 0.90 3.26 ± 0.97 <0.0001
Triglyceride (mmol/l) 1.13 (0.78–1.77) 1.03 (0.73–1.59) 1.09 (0.75–1.67) 0.0002
Fatty acids (% weight of total FA)
 PUFA 41.4 ± 3.0 38.8 ± 4.3 40.1 ± 3.9 <0.0001
 18:2 n-6 14.6 ± 2.3 13.6 ± 3.1 14.1 ± 2.8 <0.0001
 20:2 n-6 0.40 ± 0.06 0.38 ± 0.06 0.39 ± 0.06 <0.0001
 22:2 n-6 0.077 ± 0.019 0.079 ± 0.019 0.078 ± 0.019 0.02
 18:3 n-6 0.12 ± 0.07 0.11 ± 0.07 0.12 ± 0.07 <0.0001
 20:3 n-6 1.40 ± 0.31 1.27 ± 0.27 1.33 ± 0.30 <0.0001
 20:4 n-6 13.8 ± 1.7 12.0 ± 1.7 12.9 ± 1.9 <0.0001
 22:4 n-6 3.04 ± 0.55 2.24 ± 0.48 2.63 ± 0.65 <0.0001
 22:5 n-6 1.71 ± 0.34 1.33 ± 0.36 1.52 ± 0.40 <0.0001
 Total n-6 PUFA 35.1 ± 2.7 31.1 ± 3.9 33.1 ± 3.9 <0.0001
 18:3 n-3 0.21 (0.17–0.27) 0.29 (0.23–0.37) 0.25 (0.19–0.33) <0.0001
 EPA 0.34 ± 0.13 0.57 ± 0.22 0.46 ± 0.21 <0.0001
 22:5 n-3 1.67 ± 0.28 1.80 ± 0.30 1.73 ± 0.30 <0.0001
 DHA 3.97 ± 0.85 4.85 ± 0.98 4.41 ± 1.02 <0.0001
 Total n-3 PUFA 6.29 ± 1.39 7.71 ± 2.05 7.01 ± 1.89 <0.0001
Open in a new tab

Data are mean ± SD, median (inter-quartile range), or n (%), unless otherwise indicated. P values represent significance of the differences between individuals from Beijing and Shanghai. Fatty acids are erythrocyte concentration (% wt total fatty acids). PUFA is calculated by summing up the percentages of n-3 and n-6 PUFAs.

The rs174550 C-allele was significantly associated with higher levels (%) of erythrocyte 18:2n-6, 20:2n-6, 22:2n-6, 20:3n-6, and 18:3n-3, and lower levels of 18:3n-6, 20:4n-6, 22:4n-6, EPA, and 22:5n-3 in total population and in participants living in either geographic location (all P ≤ 0.003), whereas no significant association was detected between this SNP and 22:5n-6 or DHA levels (Table 2). Additionally, rs174550 C-allele was strongly associated with lower 20:4n-6/20:3n-6 ratio (β = −2.18, P = 1.89 × 10−26), which reflects the activity of D5D. However, due to undetectable level of 20:4n-3, a substrate of D5D, we could not evaluate the association with EPA/20:4n-3 ratio. The meta-analyses showed significant heterogeneities for the associations of rs174550 with 18:3n-6, 20:3n-6, 22:4n-6, EPA, and 20:4n-6/20:3n-6 between Beijing and Shanghai residents (Table 2).

TABLE 2.

Associations of FADS1 rs174550 with erythrocyte PUFAs

Beijing
 Shanghai
 Total
Fatty Acid (% wt of total FA) β (SE) P β (SE) P β (SE) P P for Heterogeneity
18:2n-6 0.81 (0.08) 2.01 × 10−25 0.77 (0.08) 2.70 × 10−24 0.79 (0.06) 5.11 × 10−48 0.73
20:2n-6 0.028 (0.002) 1.91 × 10−32 0.026 (0.002) 3.05 × 10−38 0.027 (0.001) 7.91 × 10−69 0.54
22:2n-6 0.004 (0.0007) 4.36 × 10−9 0.005 (0.0006) 4.66 × 10−16 0.005 (0.0005) 4.77 × 10−23 0.31
18:3n-6 −0.022 (0.003) 5.06 × 10−16 −0.035 (0.002) 1.70 × 10−49 −0.029 (0.006) 2.98 × 10−6 0.0007
20:3n-6 0.25 (0.010) 1.17 × 10−145 0.17 (0.009) 2.08 × 10−82 0.21 (0.04) 8.30 × 10−7 8.70 × 10−11
20:4n-6 −0.97 (0.06) 9.66 × 10−59 −0.87 (0.06) 8.82 × 10−52 −0.92 (0.05) 1.01 × 10−73 0.22
22:4n-6 −0.21 (0.02) 2.77 × 10−27 −0.16 (0.02) 7.25 × 10−21 −0.19 (0.03) 8.97 × 10−11 0.03
22:5n-6 −0.002 (0.012) 0.87 0.021 (0.012) 0.07 0.010 (0.008) 0.26 0.18
18:3n-3 0.07 (0.02) 0.0008 0.10 (0.02) 6.04 × 10−7 0.08 (0.02) 3.02 × 10−7 0.25
EPA −0.025 (0.005) 9.11 × 10−7 −0.050 (0.007) 6.58 × 10−11 −0.04 (0.01) 0.003 0.006
22:5n-3 −0.06 (0.01) 2.70 × 10−8 −0.07 (0.01) 1.98 × 10−13 −0.07 (0.01) 1.86 × 10−13 0.21
DHA −0.023 (0.03) 0.45 0.002 (0.03) 0.96 −0.01 (0.02) 0.60 0.58
20:4n-6/20:3n-6 −2.39 (0.08) 7.73 × 10−189 −1.98 (0.07) 2.51 × 10−175 −2.18 (0.20) 1.89 × 10−26 0.0001
Open in a new tab

18:3n-3 was log-transformed before analyses. βs (SE) are expressed as changes in the levels of PUFA or their ratio for increasing copy of FADS1 rs174550 C-allele in the total population. P values are adjusted for age, sex, BMI, and residence (urban/rural) using an additive model. Random-effect model was used in the meta-analysis.

In an additive model, C-allele of rs174550 was significantly associated with lower HDL cholesterol concentrations in Shanghai residents (β = −0.030, P = 0.009) and total population (β = −0.021, P = 0.02) (Table 3). This allele was also significantly associated with higher plasma triglycerides in Beijing participants (β = 0.092, P = 0.0002) but not in their Shanghai counterparts. In contrast, C-allele of rs174550 was not associated with total or LDL cholesterol (P ≥ 0.14).

TABLE 3.

Associations of FADS1-rs174550 with lipids

Beijing
 Shanghai
 Total
β (SE) P β (SE) P β (SE) P P for Heterogeneity
Total cholesterol (mmol/l)
 Model 1a 0.003 (0.041) 0.95 −0.061 (0.032) 0.06 −0.037 (0.025) 0.14 0.22
 Model 2b 0.021 (0.041) 0.62 −0.048 (0.033) 0.15 −0.021 (0.026) 0.42 0.19
 Model 3c −0.018 (0.044) 0.68 −0.078 (0.035) 0.02 −0.055 (0.027) 0.05 0.29
HDL cholesterol (mmol/l)
 Model 1a −0.009 (0.014) 0.50 −0.030 (0.012) 0.009 −0.021 (0.009) 0.02 0.25
 Model 2b −0.006 (0.014) 0.69 −0.023 (0.012) 0.05 −0.016 (0.009) 0.08 0.36
 Model 3c 0.020 (0.015) 0.17 −0.019 (0.013) 0.13 −0.002 (0.010) 0.82 0.05
LDL cholesterol (mmol/l)
 Model 1a 0.008 (0.041) 0.84 −0.029 (0.031) 0.36 −0.016 (0.025) 0.53 0.47
 Model 2b 0.023 (0.041) 0.58 −0.020 (0.032) 0.53 −0.004 (0.025) 0.88 0.41
 Model 3c 0.015 (0.044) 0.73 −0.032 (0.034) 0.34 −0.014 (0.027) 0.59 0.40
Triglycerides (mmol/l)
 Model 1a 0.092 (0.025) 0.0002 0.024 (0.020) 0.22 0.056 (0.034) 0.10 0.03
 Model 2b 0.096 (0.025) 0.0001 0.025 (0.020) 0.21 0.059 (0.035) 0.10 0.03
 Model 3c −0.004 (0.025) 0.88 −0.022 (0.021) 0.29 −0.015 (0.016) 0.37 0.58
Open in a new tab

βs (SE) were expressed as changes in blood lipids for increasing copy of FADS1 rs174550 C-allele. Triglyceride was log-transformed before analyses. βs (SE) and corresponding P values were adjusted for covariates using an additive model. Random-effect model was used in the meta-analysis.

a

Adjusted for age, sex, BMI, and residence (urban/rural).

b

Model 1 further adjusted for EPA.

c

Model 1 further adjusted for 20:4n-6.

To examine whether EPA or 20:4n-6, two downstream products of D5D, could explain the associations of FADS1 rs174550 with lipid profiles, we repeated the associations between FADS1 rs174550 and plasma lipids by further adjusting for EPA (Table 3, model 2) and 20:4n-6 (Table 3, model 3), respectively. Notably, adjustment for EPA attenuated the associations of FADS1 rs174550 with HDL cholesterol (P = 0.08), but the association with triglycerides remained significant (P = 0.0001). The associations with plasma HDL cholesterol and triglycerides levels were abolished after controlling for 20:4n-6 (P = 0.82 and 0.88, respectively) (Table 3).

The interactions between rs174550 and erythrocyte levels of 18:2n-6, 18:3n-3, EPA, and DHA on lipid profiles are presented in Table 4. In the total population, the association between rs174550 C-allele and lower HDL cholesterol was significant only when erythrocyte 18:2n-6 was low (P = 0.001) but not when this fatty acid was high (P = 0.98; P for interaction = 0.02). Similarly, rs174550 C-allele was also associated with lower HDL cholesterol (P = 0.01) and HDL/triglycerides ratio (P = 0.03) with low erythrocyte 18:3n-3 status (P for interaction = 0.03 and 0.04, respectively) (Table 4). Moreover, no interaction was found between rs174550 and essential PUFAs on downstream PUFAs, such as 20:4n-6, EPA, or DHA (Table 5). Results for participants in different geographic locations are presented in supplementary Table II.

TABLE 4.

Interactions of erythrocyte PUFAs and FADS1 rs174550 genotypes on lipid profiles

Low (n = 1335)
 High (n = 1,337)
Erythrocyte PUFA (%) TT TC CC P TT TC CC P P for Interaction
18:2n-6 (%) 11.5 ± 0.1 11.9 ± 0.1 12.0 ± 0.1 15.7 ± 0.1 16.1 ± 0.1 16.4 ± 0.1
 Sample size (n) 662 566 107 459 638 240
 HDL cholesterol  (mmol/l) 1.34 ± 0.01 1.29 ± 0.01 1.27 ± 0.03 0.001 1.26 ± 0.02 1.28 ± 0.01 1.25 ± 0.02 0.98 0.02
 Triglycerides  (mmol/l) 0.95 ± 0.02 0.99 ± 0.02 1.03 ± 0.05 0.07 1.18 ± 0.03 1.22 ± 0.03 1.24 ± 0.05 0.26 0.76
 HDL cholesterol/ triglycerides 1.72 ± 0.04 1.54 ± 0.04 1.46 ± 0.09 0.001 1.36 ± 0.04 1.31 ± 0.04 1.29 ± 0.06 0.27 0.18
 LDL cholesterol  (mmol/l) 3.07 ± 0.03 3.04 ± 0.03 2.96 ± 0.08 0.22 3.38 ± 0.04 3.40 ± 0.04 3.30 ± 0.06 0.44 0.51
18:3n-3 (%) 0.18 ± 0.001 0.19 ± 0.002 0.20 ± 0.004 0.34 ± 0.01 0.35 ± 0.01 0.36 ± 0.01
 Sample size (n) 628 582 125 493 622 222
 HDL cholesterol  (mmol/l) 1.36 ± 0.01 1.33 ± 0.01 1.32 ± 0.03 0.01 1.23 ± 0.01 1.24 ± 0.01 1.23 ± 0.02 0.57 0.03
 Triglycerides  (mmol/l) 0.91 ± 0.02 0.94 ± 0.02 0.89 ± 0.04 0.43 1.25 ± 0.03 1.26 ± 0.03 1.31 ± 0.05 0.42 0.20
 HDL cholesterol/ triglycerides 1.79 ± 0.04 1.64 ± 0.04 1.74 ± 0.10 0.03 1.24 ± 0.04 1.23 ± 0.03 1.19 ± 0.05 0.66 0.04
 LDL cholesterol  (mmol/l) 3.16 ± 0.03 3.19 ± 0.04 3.14 ± 0.08 0.92 3.25 ± 0.04 3.25 ± 0.04 3.17 ± 0.06 0.37 0.84
18:3n-3/18:2n-6 (%) 0.015 ± 0.0001 0.015 ± 0.0001 0.015 ± 0.0002 0.038 ± 0.008 0.053 ± 0.007 0.038 ± 0.013
 Sample size (n) 587 601 147 534 603 200
 HDL cholesterol  (mmol/l) 1.37 ± 0.01 1.33 ± 0.01 1.30 ± 0.03 0.008 1.23 ± 0.01 1.24 ± 0.01 1.24 ± 0.02 0.71 0.14
 Triglycerides  (mmol/l) 0.90 ± 0.02 0.92 ± 0.02 0.88 ± 0.03 0.96 1.23 ± 0.03 1.30 ± 0.03 1.37 ± 0.05 0.007 0.05
 HDL cholesterol/ triglycerides 1.80 ± 0.04 1.66 ± 0.04 1.77 ± 0.08 0.21 1.29 ± 0.04 1.19 ± 0.03 1.11 ± 0.06 0.007 0.20
 LDL cholesterol  (mmol/l) 3.28 ± 0.04 3.24 ± 0.04 3.23 ± 0.07 0.45 3.12 ± 0.04 3.18 ± 0.04 3.08 ± 0.06 0.93 0.40
EPA (%) 0.35 ± 0.003 0.34 ± 0.003 0.30 ± 0.005 0.58 ± 0.01 0.58 ± 0.01 0.57 ± 0.02
 Sample size (n) 488 611 236 633 593 111
 HDL cholesterol  (mmol/l) 1.30 ± 0.01 1.29 ± 0.01 1.28 ± 0.02 0.38 1.31 ± 0.01 1.28 ± 0.01 1.26 ± 0.02 0.05 0.72
 Triglycerides  (mmol/l) 1.02 ± 0.02 1.07 ± 0.03 1.19 ± 0.05 0.0002 1.04 ± 0.02 1.11 ± 0.02 1.11 ± 0.05 0.11 0.35
 HDL cholesterol/ triglycerides 1.60 ± 0.04 1.48 ± 0.04 1.34 ± 0.07 0.0004 1.53 ± 0.04 1.39 ± 0.04 1.41 ± 0.07 0.05 0.37
 LDL cholesterol  (mmol/l) 3.13 ± 0.04 3.17 ± 0.03 3.11 ± 0.06 0.95 3.26 ± 0.04 3.26 ± 0.04 3.20 ± 0.08 0.62 0.81
DHA (%) 3.72 ± 0.02 3.67 ± 0.02 3.70 ± 0.04 5.12 ± 0.02 5.13 ± 0.02 5.16 ± 0.04
 Sample size (n) 542 611 182 579 593 165
 HDL cholesterol  (mmol/l) 1.32 ± 0.01 1.29 ± 0.01 1.30 ± 0.02 0.23 1.29 ± 0.01 1.28 ± 0.01 1.24 ± 0.02 0.04 0.69
 Triglycerides  (mmol/l) 1.05 ± 0.02 1.12 ± 0.03 1.20 ± 0.06 0.01 1.03 ± 0.02 1.06 ± 0.02 1.09 ± 0.04 0.05 0.55
 HDL cholesterol/ triglycerides 1.63 ± 0.04 1.44 ± 0.04 1.37 ± 0.08 0.003 1.49 ± 0.04 1.41 ± 0.04 1.39 ± 0.06 0.03 0.96
 LDL cholesterol  (mmol/l) 3.11 ± 0.04 3.17 ± 0.04 3.11 ± 0.06 0.68 3.29 ± 0.04 3.27 ± 0.04 3.19 ± 0.07 0.25 0.69
EPA+DHA (%) 4.13 ± 0.02 4.05 ± 0.02 4.03 ± 0.04 5.65 ± 0.03 5.67 ± 0.03 5.66 ± 0.05
 Sample size (n) 537 610 188 584 594 159
 HDL cholesterol  (mmol/l) 1.32 ± 0.01 1.29 ± 0.01 1.30 ± 0.03 0.21 1.29 ± 0.01 1.28 ± 0.01 1.24 ± 0.02 0.04 0.79
 Triglycerides  (mmol/l) 1.04 ± 0.02 1.12 ± 0.03 1.20 ± 0.06 0.003 1.04 ± 0.02 1.06 ± 0.02 1.09 ± 0.04 0.14 0.41
 HDL cholesterol/ triglycerides 1.64 ± 0.04 1.44 ± 0.04 1.35 ± 0.08 0.001 1.47 ± 0.04 1.41 ± 0.04 1.41 ± 0.06 0.06 0.67
 LDL cholesterol  (mmol/l) 3.11 ± 0.04 3.15 ± 0.04 3.12 ± 0.06 0.68 3.29 ± 0.04 3.29 ± 0.04 3.17 ± 0.07 0.26 0.70
Open in a new tab

Data are means ± SE or n. P values were adjusted for age, sex, BMI, residence (urban/rural), and region (Beijing/Shanghai) using an additive model. Low and high groups are stratified by medians of erythrocyte 18:2n-6, 18:3n-3, 18:3n-3/18:2n-6, EPA, DHA, and EPA+DHA, respectively.

TABLE 5.

Interactions of 18:2n-6 and 18:3n-3 with FADS1 rs174550 genotypes on erythrocyte PUFA

Low
 High
Erythrocyte PUFAs (%) TT TC CC P TT TC CC P P for Interaction
18:2 n-6 11.7 ± 0.1 12.1 ± 0.1 12.2 ± 0.1 16.0 ± 0.1 16.3 ± 0.1 16.8 ± 0.1
20:4n-6 13.58 ± 0.06 12.82 ± 0.07 11.91 ± 0.15 7.38 × 10−31 13.41 ± 0.06 12.59 ± 0.05 11.42 ± 0.08 1.14 × 10−79 0.47
18:3 n-3 0.18 ± 0.001 0.19 ± 0.002 0.20 ± 0.003 0.35 ± 0.01 0.36 ± 0.01 0.36 ± 0.01
EPA 0.46 ± 0.01 0.43 ± 0.01 0.38 ± 0.02 5.17 × 10−7 0.45 ± 0.01 0.45 ± 0.01 0.40 ± 0.01 8.07 × 10−13 0.09
DHA 4.54 ± 0.03 4.49 ± 0.04 4.49 ± 0.08 0.38 4.30 ± 0.04 4.29 ± 0.04 4.33 ± 0.06 0.83 0.96
Open in a new tab

Data are means ± SE. P values were adjusted for age, sex, BMI, residence (urban/rural), and region (Beijing/Shanghai) using an additive model. Low and high groups are stratified by medians of erythrocyte 18:2n-6 and 18:3n-3 (in bold), respectively.

Dietary fish intake was moderately correlated with erythrocyte EPA (r = 0.15, P < 0.0001), DHA (r = 0.21, P < 0.0001), and EPA+DHA (r = 0.22, P < 0.0001), while vegetable oil intake was associated with erythrocyte 18:2n-6 (r = 0.12, P < 0.0001) (supplementary Table III). There was only marginally significant interaction between rs174550 and fish intake on HDL cholesterol (P for interaction = 0.08) (supplementary Table IV).

DISCUSSION

In this study, all erythrocyte PUFAs examined were associated with rs174550, with the exception of 22:5n-6 and DHA. This genetic variant was also associated with unfavorable plasma HDL cholesterol and triglycerides profiles, and the associations can be largely ascribed to erythrocyte 20:4n-6 level. Moreover, the association between FADS1 rs174550 and plasma HDL cholesterol was modified by erythrocyte 18:2n-6 and 18:3n-3 status, particularly with low 18:2n-6 or 18:3n-3 level.

FADS1 gene plays a crucial role in LC-PUFAs biosynthesis (2). FADS1 genetic variants may lead to lower D5D enzyme activity and subsequently cause accumulation of desaturase substrates and reduced yield of downstream products (39). This notion has been supported by evidence from multiple human studies. For example, Schaeffer et al. reported that haplotypes of 11 SNPs in FADS1 and FADS2 genes were significantly associated with 20:4n-6 (β = −1.48) and EPA (β = −0.18) and that these haplotypes explained 28.5% variability for 20:4n-6 and 6.9% for EPA levels (8). Similarly, our current study and previous investigations in Western populations (3, 5, 8, 10, 40) consistently demonstrated that genetic variants in FADS1 gene were most significantly associated with lower levels of 20:4n-6 (β = −0.92) and EPA (β = −0.04), but not with downstream fatty acids, such as DHA and 22:5n-6, in general populations. On the other hand, FADS1 rs174561 was associated with DHA in plasma phospholipids and human milk among 309 pregnant women (7), whereas significant associations between erythrocyte DHA and FADS1 rs174548, rs174556, and rs174561 were also observed in 4,457 pregnant women (4). The discrepancy between our study and these studies might be explained by higher conversion rate of 22:5n-3 to DHA in pregnant women but not in general populations (10).

The associations between FADS1 rs174550 and lipids in the current study were in line with results from a meta-analysis of 122,743 nondiabetic participants (15), although significant associations were also detected between the genetic variant and lower total and LDL cholesterol in the meta-analysis. It is possible that the limited sample size and different dyslipidemia pattern may explain the null findings for total and LDL cholesterol in the current study. Furthermore, distinctive diet and lifestyle may also modify the effects of FADS1 on lipid metabolism, even in genetically similar populations (12), which might serve as another plausible explanation for the heterogeneity of associations between rs174550 and triglycerides among Beijing and Shanghai participants. It was noteworthy that adjustment of 20:4n-6 abolished the associations between rs174550 and HDL cholesterol and triglycerides, suggesting that 20:4n-6 might mediate the effect of FADS1 genetic variant on lipid metabolism (41). Of note, erythrocyte EPA level (0.46%) in our study population was much lower than that in Western populations (1.15%) (32), which may explain the lack of contribution of EPA to the associations between rs174550 and HDL cholesterol and triglycerides. Apparently, more mechanistic studies are needed to provide insights on whether and how 20:4n-6 mediates the associations between FADS1 variants and blood lipids.

Interestingly, our data showed that rs174550 C-allele carriers appeared to be more vulnerable to insufficient essential fatty acid status than noncarriers, as it was shown that the association between FADS1 rs174550 C-allele and HDL cholesterol was stronger when erythrocyte 18:2n-6 or 18:3n-3 level was lower. So far, only limited studies have evaluated interactions of fatty acids with FADS1 genetic variants on lipid traits even in populations of European ancestry (27–29). Similar to our finding, Hellstrand et al. reported that rs174547 C-allele was associated with lower LDL or HDL cholesterol when dietary long-chain n-3 PUFA or 18:3n-3/18:2n-6 was low (29). Moreover, Lu et al. and Domont et al. observed that dietary fatty acids could interact with FADS1 genetic variants on lipid traits among Western populations (27, 28). Although biological mechanisms of how dietary PUFAs modify the effect of FADS1 rs174550 on HDL remain unclear, it is possible that people with minor allele of FADS1 rs174550 might have impaired capacity to produce long-chain n-6 and n-3 PUFAs, such as 20:4n-6 and EPA, from those of essential fatty acids. Low yields of LC-PUFAs may lead to lower activity of PPARα, which subsequently reduces HDL cholesterol by suppressing expression of Apo A-I, A-II, and lipoprotein lipase, while inducing expression of Apo C-III (41). Indeed, animal studies demonstrated that FADS1 gene expression in liver was regulated by dietary LC-PUFA intake, which might support the interaction between variant of FADS1 and dietary PUFA intake (42).

To the best of our knowledge, this is the first study to investigate the associations of FADS1 rs174550, as well as its interactions, with erythrocyte PUFAs and blood lipids in Han Chinese with a distinct dietary pattern from Western populations. The main strength of our study is that erythrocyte PUFAs were measured as objective markers of dietary essential fatty acid intake, levels of which do not depend on the accuracy of participant recall (43, 44). Owing to the cross-sectional nature of our study, possible reverse causation between dietary fat intake and blood lipids cannot be excluded. However, this scenario is unlikely, as participants with diagnosed hyperlipidemia and using antihyperlipidemia medications were excluded from analyses. Although there were some heterogeneities in terms of genetic variant distribution and erythrocyte fatty acid levels between Beijing and Shanghai residents, Xu et al. also reported that MAF of SNPs in FADS1 and FADS2 regions were significantly different between northern and southern Han Chinese (45). Thus, we performed statistical analyses separately, and the results were largely consistent between the participants from two geographic regions. Finally, we cannot entirely exclude the possibility that the observed associations were partially due to unmeasured confounding. Certainly, more studies are merited to confirm our findings.

In conclusion, we found significant associations of FADS1 rs174550 with blood PUFA, HDL cholesterol, and triglycerides levels in Han Chinese. The association between rs174550 and HDL cholesterol was modified by erythrocyte PUFA levels (18:2n-6 and 18:3n-3), indicating the importance of essential PUFA status in modulating effects of genes on lipid profiles.

Supplementary Material

Supplementary Figure 1
supp_54_5_1477__index.html (1.9KB, html)

Acknowledgments

The authors are grateful to all participants of the Nutrition and Health of Aging Population in China. The authors also thank colleagues at the laboratory and local CDC staffs of Beijing and Shanghai for their assistance with data collection.

Footnotes

Abbreviations:

BMI
body mass index
CHB
Chinese Han in Beijing
D5D
delta-5 desaturase
DHA
docosahexaenoic acid
EPA
eicosapentaenoic acid
FADS1
fatty acid desaturase 1
FFQ
food frequency questionnaire
GLM
general linear regression
GWAS
genome-wide association study
LD
linkage disequilibrium
MAF
minor allele frequency
PPARα
peroxisome proliferator activating receptor alpha
SNP
single nucleotide polymorphism

This work was supported by the National Natural Science Foundation of China (30930081, 81021002, and 81170734), the National Basic Research Program of China (973 Program 2012CB524900), the National High Technology Research and Development Program (863 Program 2009AA022704), the Chinese Academy of Sciences (KSCX2-EW-R-10 and SIBS2008006), the Key Discipline of Shanghai Public Health - Food and Nutritional Sciences (12GWZX0702), the Xuhui hospital project (CRC2011005), and the China Postdoctoral Science Foundation (2011M500827).

[S]

The online version of this article (available at http://www.jlr.org) contains supplementary data in the form of five tables.

REFERENCES

  • 1.Marquardt A., Stohr H., White K., Weber B. H. 2000. cDNA cloning, genomic structure, and chromosomal localization of three members of the human fatty acid desaturase family. Genomics. 66: 175–183 [DOI] [PubMed] [Google Scholar]
  • 2.Lattka E., Illig T., Koletzko B., Heinrich J. 2010. Genetic variants of the FADS1 FADS2 gene cluster as related to essential fatty acid metabolism. Curr. Opin. Lipidol. 21: 64–69 [DOI] [PubMed] [Google Scholar]
  • 3.Bokor S., Dumont J., Spinneker A., Gonzalez-Gross M., Nova E., Widhalm K., Moschonis G., Stehle P., Amouyel P., De Henauw S., et al. 2010. Single nucleotide polymorphisms in the FADS gene cluster are associated with delta-5 and delta-6 desaturase activities estimated by serum fatty acid ratios. J. Lipid Res. 51: 2325–2333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Koletzko B., Lattka E., Zeilinger S., Illig T., Steer C. 2011. Genetic variants of the fatty acid desaturase gene cluster predict amounts of red blood cell docosahexaenoic and other polyunsaturated fatty acids in pregnant women: findings from the Avon Longitudinal Study of Parents and Children. Am. J. Clin. Nutr. 93: 211–219 [DOI] [PubMed] [Google Scholar]
  • 5.Malerba G., Schaeffer L., Xumerle L., Klopp N., Trabetti E., Biscuola M., Cavallari U., Galavotti R., Martinelli N., Guarini P., et al. 2008. SNPs of the FADS gene cluster are associated with polyunsaturated fatty acids in a cohort of patients with cardiovascular disease. Lipids. 43: 289–299 [DOI] [PubMed] [Google Scholar]
  • 6.Mathias R. A., Sergeant S., Ruczinski I., Torgerson D. G., Hugenschmidt C. E., Kubala M., Vaidya D., Suktitipat B., Ziegler J. T., Ivester P., et al. 2011. The impact of FADS genetic variants on omega6 polyunsaturated fatty acid metabolism in African Americans. BMC Genet. 12: 50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Moltó-Puigmartí C., Plat J., Mensink R. P., Muller A., Jansen E., Zeegers M. P., Thijs C. 2010. FADS1 FADS2 gene variants modify the association between fish intake and the docosahexaenoic acid proportions in human milk. Am. J. Clin. Nutr. 91: 1368–1376 [DOI] [PubMed] [Google Scholar]
  • 8.Schaeffer L., Gohlke H., Muller M., Heid I. M., Palmer L. J., Kompauer I., Demmelmair H., Illig T., Koletzko B., Heinrich J. 2006. Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Hum. Mol. Genet. 15: 1745–1756 [DOI] [PubMed] [Google Scholar]
  • 9.Tanaka T., Shen J., Abecasis G. R., Kisialiou A., Ordovas J. M., Guralnik J. M., Singleton A., Bandinelli S., Cherubini A., Arnett D., et al. 2009. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS Genet. 5: e1000338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Xie L., Innis S. M. 2008. Genetic variants of the FADS1 FADS2 gene cluster are associated with altered (n-6) and (n-3) essential fatty acids in plasma and erythrocyte phospholipids in women during pregnancy and in breast milk during lactation. J. Nutr. 138: 2222–2228 [DOI] [PubMed] [Google Scholar]
  • 11.Kathiresan S., Willer C. J., Peloso G. M., Demissie S., Musunuru K., Schadt E. E., Kaplan L., Bennett D., Li Y., Tanaka T., et al. 2009. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41: 56–65 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Nakayama K., Bayasgalan T., Tazoe F., Yanagisawa Y., Gotoh T., Yamanaka K., Ogawa A., Munkhtulga L., Chimedregze U., Kagawa Y., et al. 2010. A single nucleotide polymorphism in the FADS1/FADS2 gene is associated with plasma lipid profiles in two genetically similar Asian ethnic groups with distinctive differences in lifestyle. Hum. Genet. 127: 685–690 [DOI] [PubMed] [Google Scholar]
  • 13.Zietemann V., Kroger J., Enzenbach C., Jansen E., Fritsche A., Weikert C., Boeing H., Schulze M. B. 2010. Genetic variation of the FADS1 FADS2 gene cluster and n-6 PUFA composition in erythrocyte membranes in the European Prospective Investigation into Cancer and Nutrition-Potsdam study. Br. J. Nutr. 104: 1748–1759 [DOI] [PubMed] [Google Scholar]
  • 14.Park M. H., Kim N., Lee J. Y., Park H. Y. 2011. Genetic loci associated with lipid concentrations and cardiovascular risk factors in the Korean population. J. Med. Genet. 48: 10–15 [DOI] [PubMed] [Google Scholar]
  • 15.Dupuis J., Langenberg C., Prokopenko I., Saxena R., Soranzo N., Jackson A. U., Wheeler E., Glazer N. L., Bouatia-Naji N., Gloyn A. L., et al. 2010. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42: 105–116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Teslovich T. M., Musunuru K., Smith A. V., Edmondson A. C., Stylianou I. M., Koseki M., Pirruccello J. P., Ripatti S., Chasman D. I., Willer C. J., et al. 2010. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 466: 707–713 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Fruchart J. C., Duriez P., Staels B. 1999. Peroxisome proliferator-activated receptor-alpha activators regulate genes governing lipoprotein metabolism, vascular inflammation and atherosclerosis. Curr. Opin. Lipidol. 10: 245–257 [DOI] [PubMed] [Google Scholar]
  • 18.Sampath H., Ntambi J. M. 2004. Polyunsaturated fatty acid regulation of gene expression. Nutr. Rev. 62: 333–339 [DOI] [PubMed] [Google Scholar]
  • 19.Berger J., Moller D. E. 2002. The mechanisms of action of PPARs. Annu. Rev. Med. 53: 409–435 [DOI] [PubMed] [Google Scholar]
  • 20.Vu-Dac N., Schoonjans K., Laine B., Fruchart J. C., Auwerx J., Staels B. 1994. Negative regulation of the human apolipoprotein A-I promoter by fibrates can be attenuated by the interaction of the peroxisome proliferator-activated receptor with its response element. J. Biol. Chem. 269: 31012–31018 [PubMed] [Google Scholar]
  • 21.Hertz R., Bishara-Shieban J., Bar-Tana J. 1995. Mode of action of peroxisome proliferators as hypolipidemic drugs. Suppression of apolipoprotein C-III. J. Biol. Chem. 270: 13470–13475 [DOI] [PubMed] [Google Scholar]
  • 22.Vu-Dac N., Schoonjans K., Kosykh V., Dallongeville J., Fruchart J. C., Staels B., Auwerx J. 1995. Fibrates increase human apolipoprotein A-II expression through activation of the peroxisome proliferator-activated receptor. J. Clin. Invest. 96: 741–750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Zhai F., Wang H., Du S., He Y., Wang Z., Ge K., Popkin B. M. 2007. Lifespan nutrition and changing socio-economic conditions in China. Asia Pac. J. Clin. Nutr. 16(Suppl. 1): 374–382 [PubMed] [Google Scholar]
  • 24.Jin S. 2008. Chinese National Nutrition and Health Survey in 2002: Diet and Nutrients Intake. People's Medical Publishing House, Beijing. [Google Scholar]
  • 25.Ford E. S., Li C., Pearson W. S., Zhao G., Mokdad A. H. 2010. Trends in hypercholesterolemia, treatment and control among United States adults. Int. J. Cardiol. 140: 226–235 [DOI] [PubMed] [Google Scholar]
  • 26.Ervin R. B. 2009. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006. Natl. Health Stat. Report. 5: 1–7 [PubMed] [Google Scholar]
  • 27.Dumont J., Huybrechts I., Spinneker A., Gottrand F., Grammatikaki E., Bevilacqua N., Vyncke K., Widhalm K., Kafatos A., Molnar D., et al. 2011. FADS1 genetic variability interacts with dietary {alpha}-linolenic acid intake to affect serum non-HDL-cholesterol concentrations in European adolescents. J. Nutr. 141: 1247–1253 [DOI] [PubMed] [Google Scholar]
  • 28.Lu Y., Feskens E. J., Dolle M. E., Imholz S., Verschuren W. M., Muller M., Boer J. M. 2010. Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and HDL-cholesterol concentrations in the Doetinchem Cohort Study. Am. J. Clin. Nutr. 92: 258–265 [DOI] [PubMed] [Google Scholar]
  • 29.Hellstrand S., Sonestedt E., Ericson U., Gullberg B., Wirfalt E., Hedblad B., Orho-Melander M. 2012. Intake levels of dietary long-chain PUFAs modify the association between genetic variation in FADS and LDL-C. J. Lipid Res. 53: 1183–1189 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Arab L.2003. Biomarkers of fat and fatty acid intake. J. Nutr. 133(Suppl. 3): 925S–932S [DOI] [PubMed] [Google Scholar]
  • 31.Hodson L., Skeaff C. M., Fielding B. A. 2008. Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Prog. Lipid Res. 47: 348–380 [DOI] [PubMed] [Google Scholar]
  • 32.Sun Q., Ma J., Campos H., Hankinson S. E., Hu F. B. 2007. Comparison between plasma and erythrocyte fatty acid content as biomarkers of fatty acid intake in US women. Am. J. Clin. Nutr. 86: 74–81 [DOI] [PubMed] [Google Scholar]
  • 33.Ye X., Yu Z., Li H., Franco O. H., Liu Y., Lin X. 2007. Distributions of C-reactive protein and its association with metabolic syndrome in middle-aged and older Chinese people. J. Am. Coll. Cardiol. 49: 1798–1805 [DOI] [PubMed] [Google Scholar]
  • 34.Liu C., Li H., Qi L., Loos R. J., Qi Q., Lu L., Gan W., Lin X. 2011. Variants in GLIS3 and CRY2 are associated with type 2 diabetes and impaired fasting glucose in Chinese Hans. PLoS ONE. 6: e21464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Baylin A., Kabagambe E. K., Ascherio A., Spiegelman D., Campos H. 2003. High 18:2 trans-fatty acids in adipose tissue are associated with increased risk of nonfatal acute myocardial infarction in Costa Rican adults. J. Nutr. 133: 1186–1191 [DOI] [PubMed] [Google Scholar]
  • 36.Zhai F., Yang X. 2005. The Nutrition and Health Status of the Chinese People 2002: Diet and Nutrients Intake. People's Medical Publishing House, Beijing. [Google Scholar]
  • 37.Yang Y., editor 2005. Chinese Food Composition Table 2004 (Book 2). Peking University Medical Press, Beijing [Google Scholar]
  • 38.Willett W., Stampfer M. J. 1986. Total energy intake: implications for epidemiologic analyses. Am. J. Epidemiol. 124: 17–27 [DOI] [PubMed] [Google Scholar]
  • 39.Merino D. M., Ma D. W., Mutch D. M. 2010. Genetic variation in lipid desaturases and its impact on the development of human disease. Lipids Health Dis. 9: 63. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Martinelli N., Girelli D., Malerba G., Guarini P., Illig T., Trabetti E., Sandri M., Friso S., Pizzolo F., Schaeffer L., et al. 2008. FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to linoleic acid are associated with inflammation and coronary artery disease. Am. J. Clin. Nutr. 88: 941–949 [DOI] [PubMed] [Google Scholar]
  • 41.Lattka E., Illig T., Heinrich J., Koletzko B. 2010. Do FADS genotypes enhance our knowledge about fatty acid related phenotypes? Clin. Nutr. 29: 277–287 [DOI] [PubMed] [Google Scholar]
  • 42.Reardon H. T., Hsieh A. T., Jung Park W., Kothapalli K. S., Anthony J. C., Nathanielsz P. W., Thomas Brenna J. 2013. Dietary long-chain polyunsaturated fatty acids upregulate expression of FADS3 transcripts. Prostaglandins Leukot. Essent. Fatty Acids. 88: 15–19 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Hu F. B., Stampfer M. J., Rimm E., Ascherio A., Rosner B. A., Spiegelman D., Willett W. C. 1999. Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. Am. J. Epidemiol. 149: 531–540 [DOI] [PubMed] [Google Scholar]
  • 44.Cantwell M. M. 2000. Assessment of individual fatty acid intake. Proc. Nutr. Soc. 59: 187–191 [DOI] [PubMed] [Google Scholar]
  • 45.Xu S., Yin X., Li S., Jin W., Lou H., Yang L., Gong X., Wang H., Shen Y., Pan X., et al. 2009. Genomic dissection of population substructure of Han Chinese and its implication in association studies. Am. J. Hum. Genet. 85: 762–774 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure 1
supp_54_5_1477__index.html (1.9KB, html)
supp_P027516_jlr.P027516-1.doc (36.5KB, doc)
supp_P027516_jlr.P027516-2.doc (30.5KB, doc)
supp_P027516_jlr.P027516-3.doc (208.5KB, doc)
supp_P027516_jlr.P027516-4.doc (26KB, doc)
supp_P027516_jlr.P027516-5.doc (48KB, doc)

Articles from Journal of Lipid Research are provided here courtesy of American Society for Biochemistry and Molecular Biology

RESOURCES