Abstract
The correlation between the BDNF rs11030104 single nucleotide polymorphism (SNP) and serum lipid levels has been understudied. The present study was conducted to detect the association of the BDNF rs11030104 SNP and several environmental factors with serum lipid levels in the Jing and Han nationalities. Genotypes of the BDNF rs11030104 SNP in 709 unrelated subjects of Han and 706 unrelated participants of Jing populations were determined by polymerase chain reaction and restriction fragment length polymorphism combined with gel electrophoresis, and further verified by direct sequencing. There was no significant difference in either genotypic or allelic frequencies between the Han and Jing populations. The genotypic and allelic frequencies of the SNP in Jing but not in Han populations were different between male and female subgroups (P<0.05 for each). The levels of serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) in the Jing population were different among the genotypes, the G allele carriers had lower TC and LDL-C levels than the G allele non-carriers. Subgroup analyses showed that the differences in serum TC and LDL-C levels among the genotypes were observed in the Jing males but not in females. Serum lipid profiles were also significantly associated with some environmental factors in the Han and Jing populations, or in male and female subgroups of the two ethnic groups (P<0.05 for all). Our study exhibited a correlation between the BDNF rs11030104 SNP and serum TC and LDL-C levels in the Jing males. These results indicate that there may be a racial/ethnic- and/or sex-specific association of the BDNF rs11030104 SNP and serum lipid parameters.
Keywords: Brain-derived neurotrophic factor, single nucleotide polymorphism, serum lipid levels
Introduction
Cardiovascular disease (CVD) is a worldwide public health problem. It has become the leading cause of death in the world over past decades [1]. Moreover, world-wide CVD mortality rates are still rising. Dyslipidemia is the main cause of atherosclerosis, which is inextricably linked with the development of CVD [2]. Serum lipid level is an important risk factor not only for atherosclerosis, but also for CVD, and is associated with a considerable increase in morbidity and mortality. Unfavorable lipid profiles such as elevated levels of total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), apolipoprotein (Apo) B, and low levels of high-density lipoprotein cholesterol (HDL-C) and ApoA1, play an important role for atherosclerosis and CVD. Dyslipidemia is demonstrated to result from multiple genetic and environmental factors and their interaction [3]. Especially, some studies in families and twins showed that 40-60% of the inter-individual diversity in serum lipid phenotypes was illustrated by genetic polymorphism [4-6]. So, it is necessary to explore the relationship between the single nucleotide polymorphism (SNP) and serum lipid levels.
In recent years, genome-wide association studies (GWASes) have shown more than 95 loci associated with dyslipidemia [7,8]. A study reported the correlation of SNP in the genomic region of Brain-derived neurotrophic factor (BNDF, www.ncbi.nlm.nih.gov/gene, Gene ID: 627; Mim: f113505; Location: Chromosomal 11p14.1) and metabolic disorder. BDNF-encodes a member of the nerve growth factor family of proteins. It can promote neuronal survival in the adult brain. Expression of this gene is decreased in Alzheimer’s, Parkinson’s, and Huntington’s disease patients. BDNF plays an important role in the regulation of nervous disorders, stress response, and mood disorders [9-11]. Recently, several studies have shown that BDNF is associated with body mass index (BMI), obesity, metabolic syndrome, insulin resistance, and diabetes [12-16]. Hyperlipidemia is a main component of metabolic syndrome. But the relationship between the BDNF rs11030104 SNP and serum lipid levels and its effect mechanism are still not clear yet. Moreover, it has never been studied in the Chinese populations so far.
China is a multi-ethnic country with 56 ethnic groups. Han nationality is the largest ethnic group and Jing nationality is one of the other 55 ethnic minorities in south part of China with a small population of 28199 (From the sixth national census statistics of China in 2010). Jing nationality mainly lives in Dongxing city of Guangxi, and is the unique coastal living minority in China. The Jing ancestors immigrated from Vietnam to China in the early stage of 16th century, firstly settled in three islands of Wanwei, Wutou and Sanxin in Dongxing city, where are also the main residence of Jing now. They work in the fishing business and have preserved their custom of ethnic inter-marriage. Seafood is a main part of Jing diet, especially fish and shrimp. There are a lot of differences in geographic culture, dietary habits, lifestyle, genetic background, and custom characteristics between the local Han and Jing nationalities. Some previous studies have showed that associations of variants in a few lipid related genes and serum lipid levels are significantly different between the Jing and Han populations and their gender subgroups [17,18]. However, the association between the BDNF rs11030104 SNP and serum lipid levels has not been previously reported in the Jing population so far. Therefore, the present study evaluated the association between the BDNF rs11030104 SNP and several environmental factors with serum lipid levels in the Guangxi Han and Jing populations.
Materials and methods
Research subjects
In total, 706 unrelated participants of Jing nationality (360 males, 50.99%; 346 females, 49.01%) and 709 unrelated participants of Han nationality (362 males, 51.06%; 347 females, 48.94%) were randomly selected from our previous stratified randomized samples. All subjects were fishery workers (Jing) and/or rural agricultural (Han) living in Jiangping Down, Dongxing City, Guangxi Zhuang Autonomous Region, People’s Republic of China. The age ranged from 15 to 80 years. The mean age of Jing participants was 57.87±13.88 years, while that of Han subjects was 58.71±12.95 years. All participants were essentially healthy and had no evidence of atherosclerosis, CVD, and diabetes. Any participant that had taken medications which affect serum lipid level (lipid-lowering drugs such as statins or fibrates, beta blockers, diuretics, or hormones) was excluded in this study. The study design was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University. Written informed consent was acquired from all participants.
Epidemiological survey
The survey was performed by internationally standardized methods, following a common protocol [19]. Information on demographics, socioeconomic status, healthy history, and lifestyle factors was collected with standardized questionnaires. Alcohol consumption was categorized into groups (<25 g/d and ≥25 g/d). Smoking status was categorized into groups of cigarettes per day (<20 cigarettes/d and ≥20 cigarettes/d). In physical examinations, sitting blood pressure was measured three times using a mercury sphygmomanometer after a 5-10 min rest, and the average of three measurements was recorded. Height, weight and waist circumference were measured in physical examination. BMI (kg/m2) was calculated as weight in kilograms divided by the square of height in meters.
Biochemical measurements
Fasting venous blood samples of 5 ml were obtained from all participants. Serum TC, TG, HDL-C, and LDL-C levels were detected by enzymatic methods with commercially available kits. Serum ApoA1 and ApoB levels were measured by the immunoturbidimetric immunoassay using a commercial kit. All determinations were performed with an auto-analyzer in the Clinical Science Experiment Center of the First Affiliated Hospital of Guangxi Medical University. The remaining sample was transferred into the tubes contained anticoagulants (4.80 g/L citric acid, 14.70 g/L glucose, and 13.20 g/L tri-sodium citrate) and was used to extract deoxyribonucleic acid (DNA).
DNA amplification and genotyping
Genomic DNA was isolated from peripheral blood leukocytes using the phenol-chloroform method [20,21]. The extracted DNA was stored at 4°C until analysis. The genotypes of the BDNF rs11030104 were detected by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP). PCR amplification was performed using 5’-TCAGCCATGACCAACTTCTTGA-3’ as forward and 5’-TGTGATAAAGAGTCATCCGAAGGT-3’ as reverse primer pairs (Sangon, Shanghai, China). Each amplification reaction was performed in a total volume of 25 μl, 13.4 μl of 2 × TaqPCR Master Mix (constituent: 0.1 U Taq polymerase/μL, 500 μM dNTP each and PCR buffer), nuclease-free water 10 μl, 0.3 μl each primer (10 pmol/L) and 1 μl genomic DNA, processing started with 94°C for 5 min and followed by 30 s of denaturing at 94°C, 45 s of annealing at 61°C and 30 s of elongation at 72°C for 34 cycles. The amplification was completed with a final extension at 72°C for 8 min. Following electrophoresis on a 2.0% agarose gel with 0.5 μg/mL ethidium bromide, the amplification products were visualized under ultraviolet light. For the restriction digestion, 10 μL of PCR products and 5 U of MlyI restriction enzyme were added to each reaction mix (constituent: 2 μL buffer solution and nuclease-free water 7.5 μL), and samples were digested at 37°C water-bath for 30 min. After restriction enzyme digestion of the amplified DNA, genotypes were evaluated by electrophoresis on 2% ethidium-bromide stained agarose gels and visualized by ultraviolet illumination. Genotypes were determined by blinding to the epidemiological and lipid results. Six samples (two samples for AA, AG, and GG genotypes, respectively) performed by PCR-RFLP were also confirmed by direct sequencing. The PCR product was purified by low melting point gel electrophoresis and phenol extraction, and then DNA sequences were analyzed using Bio-systems in Shanghai Sangon Biological Engineering Technology & Services Co., Ltd., China.
Diagnostic criteria
The normal ranges of serum TC, TG, HDL-C, LDL-C, ApoA1 and ApoB levels, and the ratio of ApoA1 to ApoB in our Clinical Science Experiment Center were 3.10-5.17, 0.56-1.70, 1.16-1.42, 2.70-3.10 mmol/L, 1.20-1.60, 0.80-1.05 g/L, and 1.00-2.50. The individuals with TC >5.17 mmol/L and/or TG >1.70 mmol/L were defined as hyperlipidemic [22]. Hypertension was diagnosed depend on the criteria of 1999 World Health Organization-International Society of Hypertension Guide-lines for the management of hypertension [23,24]. The diagnostic criteria of overweight and obesity were according to the Cooperative Meta-analysis Group of China Obesity Task Force. Normal weight, overweight and obesity were defined as a BMI <24, 24-28, and >28 kg/m2; respectively [25].
Statistical analysis
All statistical analyses were performed using SPSS, version 17.0 (SPSS, Chicago, IL, USA). For measurement variables, results were presented as mean ± standard deviation (serum TG levels were presented as medians and interquartile ranges), whereas the qualitative variables were presented as percentages. Allele frequency was confirmed via direct counting, and the standard goodness-of-fit test verified the Hardy-Weinberg equilibrium. The genotype distribution between the two groups was analyzed using a Chi-square test. The difference in general characteristics between Jing and Han was compared by the Student’s unpaired t-test. The association of genotypes and serum lipid parameters was evaluated by analysis of covariance (ANCOVA). Sex, age, BMI, blood pressure, alcohol consumption, and cigarette smoking were adjusted for the statistical analyses. Multivariate linear regression analysis with step-wise modeling was performed to determine the association of serum lipid levels with genotypes (AA=1, AG=2 and GG=3) and several environment factors in the combined population of Jing and Han, Jing, Han, males and females, respectively. P value <0.05 is considered statistical significance.
Results
Demographic, clinical data and serum lipid levels
The demographic, clinical data, and serum lipid levels of the study populations are summarized in Table 1. There was no significant difference in age and gender between the Han and Jing populations. There was significant difference in height, weight, BMI, waist circumference, intake of alcohol, diastolic blood pressure, TC, TG and ApoA1 between the two ethnic groups (P<0.05). The levels of height, weight, BMI, waist circumference, TC and TG were higher in Jing than in Han populations, whereas the intake of alcohol, diastolic blood pressure and ApoA1 were lower in Jing than in Han populations (P<0.05).
Table 1.
The general characteristics and serum lipid levels between Jing and Han Chinese populations
| Parameter | Han | Jing | t (χ2) | P |
|---|---|---|---|---|
| Number | 709 | 706 | ||
| Male/female | 362/347 | 360/346 | 0.001 | 0.980 |
| Age (year) | 58.71±12.95 | 57.87±13.88 | 1.17 | 0.244 |
| Height (cm) | 1.57±7.86 | 1.58±7.81 | -2.184 | 0.029 |
| Weight (kg) | 56.55±9.43 | 58.69±9.91 | -4.134 | 0.000 |
| Body mass index (kg/m2) | 22.87±3.12 | 23.45±3.17 | -3.419 | 0.001 |
| Waist circumference (cm) | 77.85±8.86 | 80.31±9.22 | -5.075 | 0.000 |
| Smoking status [n (%)] | ||||
| Non-smoker | 536 (76.1) | 569 (80.7) | ||
| <20 cigarettes/day | 66 (9.4) | 47 (6.7) | ||
| ≥20 cigarettes/day | 102 (14.5) | 89 (12.6) | 5.064 | 0.079 |
| Alcohol consumption [n (%)] | ||||
| Non-drinker | 535 (76.0) | 601 (80.6) | ||
| <25 g/day | 116 (16.5) | 80 (13.9) | ||
| ≥25 g/day | 53 (7.5) | 24 (5.5) | 21.368 | 0.000 |
| Systolic BP (mmHg) | 132.77±19.15 | 130.71±21.20 | 1.913 | 0.56 |
| Diastolic BP (mmHg) | 81.48±10.37 | 79.98±10.20 | 2.728 | 0.006 |
| Pulse pressure (mmHg) | 50.66±15.46 | 51.30±17.86 | -0.664 | 0.507 |
| Glucose (mmol/L) | 6.71±1.15 | 6.82±1.77 | -1.333 | 0.183 |
| Total cholesterol (mmol/L) | 4.92±0.87 | 5.18±0.90 | -5.599 | 0.000 |
| Triglyceride (mmol/L) | 1.56±0.91 | 1.66±0.89 | -2.105 | 0.035 |
| HDL-C (mmol/L) | 1.78±0.52 | 1.78±0.46 | -0.176 | 0.860 |
| LDL-C (mmol/L) | 2.86±0.43 | 2.82±0.42 | 1.744 | 0.081 |
| ApoA1 (g/L) | 1.32±0.20 | 1.29±0.23 | 2.342 | 0.019 |
| ApoB (g/L) | 1.04±0.24 | 1.05±0.24 | -1.185 | 0.236 |
| ApoA1/ApoB | 1.33±0.38 | 1.28±0.38 | 2.461 | 0.014 |
| UA (μmol/L) | 304.14±101.95 | 325.47±96.09 | -4.044 | 0.000 |
| Scr (μmol/L) | 89.74±34.26 | 84.64±74.01 | 1.660 | 0.097 |
BP, Blood pressure; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; UA, Uric Acid; Scr, Serum Creatinine.
Electrophoresis and genotyping
After the genomic DNA of the samples was amplified by PCR and imaged by agarose gel electrophoresis for the BDNF rs11030104 SNP, the electrophoresis of PCR product of 321 bp nucleotide sequences was presented in the samples (Figure 1). According to the presence (A allele) or absence (G allele) of the ApaI enzyme restriction sites, the genotypes were determined as AA (band at 321-bp), AG (bands at 321-, 239-, and 82-bp) and GG (bands at 239- and 82-bp, Figure 2); respectively.
Figure 1.
Electrophoresis of PCR products of the BDNF rs11030104 polymorphism. Lane M, 25 bp marker ladder; Lanes 1-10, the samples. The 321 bp bands are the PCR products.
Figure 2.
Genotypes of the BDNF rs11030104 SNP. Lane M, 25 bp Marker ladder; Lanes 1 and 4, AA genotypes (321 bp); Lanes 2, 3, 7 and 8, AG genotypes (321-, 239- and 82-bp); Lanes 5, 6, 9 and 10, GG genotypes (239- and 82-bp).
Results of direct sequencing
The results shown as AA, AG and GG genotypes by PCR-RFLP, the AA, AG, and GG genotypes were also verified by direct sequencing (Figure 3); respectively.
Figure 3.
A part of the nucleotide sequences of the BDNF rs11030104 SNP by direct sequencing. AA: AA genotype; AG: AG genotypes; GG: GG genotypes.
Genotypic and allelic frequencies
Table 2 shows the genotypic and allelic frequencies of the BDNF rs11030104 SNP in the two ethnic groups. The genotypes of the BDNF rs11040104 SNP were followed by the Hardy-Weinberg equilibrium (P>0.05). The frequencies of A and G alleles were 61.9% and 38.1% in Han, and 59.4% and 40.6% in Jing (P>0.05) populations respectively. The frequencies of AA, AG and GG genotypes were 40.5%, 42.9% and 16.6% in Han, and 38.0%, 42.9% and 19.1% in Jing (P>0.05) population respectively. The genotypic and allelic frequencies of the SNP in Jing but not in the Han population were different between male and female subgroups (Table 2, P<0.05, respectively).
Table 2.
Comparison of the genotype and allele frequencies of the BDNF rs11030104 SNP in the Han and Jing populations [n (%)]
| Group | N | Genotype | Allele | |||
|---|---|---|---|---|---|---|
|
|
||||||
| AA | AG | GG | A | G | ||
| Han | 709 | 287 (40.5) | 304 (42.9) | 118 (16.6) | 878 (61.9) | 540 (38.1) |
| Jing | 706 | 268 (38.0) | 303 (42.9) | 135 (19.1) | 839 (59.4) | 573 (40.6) |
| χ2 | 1.788 | 1.852 | ||||
| P | 0.409 | 0.174 | ||||
| Han | ||||||
| Male | 362 | 148 (40.9) | 152 (42.0) | 62 (17.1) | 448 (61.9) | 276 (38.1) |
| Female | 347 | 139 (40.1) | 152 (43.8) | 56 (16.1) | 430 (62.0) | 264 (38.0) |
| χ2 | 0.270 | 0.001 | ||||
| P | 0.874 | 0.975 | ||||
| Jing | ||||||
| Male | 360 | 156 (43.3) | 136 (37.8) | 68 (18.9) | 448 (62.2) | 272 (37.8) |
| Female | 346 | 112 (32.4) | 167 (48.3) | 67 (19.4) | 391 (56.5) | 301 (43.5) |
| χ2 | 10.129 | 4.787 | ||||
| P | 0.006 | 0.029 | ||||
Genotypes and serum lipid levels
The association between genotypes and serum lipid levels in the two ethnic groups is shown in Table 3. The levels of TC and LDL-C in Jing but not in Han subjects were different among the genotypes (P<0.05 for each), the G allele carriers had lower TC and LDL-C levels than the G allele non-carriers. Sex-subgroup analysis showed that these results were found in Jing males but not in females (Table 4). There were no differences in the remaining serum lipid parameters among the genotypes in both ethnic groups.
Table 3.
Comparison of the genotypes and serum lipid levels in the Han and Jing populations
| Genotype | N | TC (mmol/L) | TG (mmol/L) | HDL-C (mmol/L) | LDL-C (mmol/L) | ApoA1 (g/L) | ApoB (g/L) | ApoA1/ApoB |
|---|---|---|---|---|---|---|---|---|
| Han | ||||||||
| AA | 285 | 4.94±0.87 | 1.31(0.63) | 1.77±0.57 | 2.80±0.43 | 1.31±0.20 | 1.03±0.24 | 1.33±0.37 |
| AG | 302 | 4.89±0.88 | 1.35(0.69) | 1.81±0.48 | 2.88±0.45 | 1.33±0.19 | 1.05±0.26 | 1.34±0.40 |
| GG | 118 | 4.92±0.83 | 1.28(0.66) | 1.74±0.52 | 2.83±0.39 | 1.31±0.21 | 1.04±0.21 | 1.31±0.35 |
| F | 0.253 | 0.271 | 0.861 | 0.497 | 0.907 | 0.331 | 0.331 | |
| P | 0.776 | 0.873 | 0.423 | 0.608 | 0.404 | 0.719 | 0.719 | |
| Jing | ||||||||
| AA | 268 | 5.28±0.96 | 1.45(0.84) | 1.78±0.52 | 2.87±0.43 | 1.29±0.28 | 1.07±0.26 | 1.28±0.39 |
| AG | 303 | 5.20±0.83 | 1.43(0.64) | 1.81±0.43 | 2.83±0.42 | 1.29±0.21 | 1.06±0.23 | 1.28±0.38 |
| GG | 135 | 4.95±0.89 | 1.40(0.83) | 1.72±0.42 | 2.75±0.39 | 1.29±0.19 | 1.04±0.22 | 1.30±0.38 |
| F | 6.292 | 0.229 | 1.940 | 3.747 | 0.063 | 0.743 | 0.373 | |
| P | 0.002 | 0.892 | 0.144 | 0.024 | 0.939 | 0.476 | 0.689 | |
TC, Total cholesterol; TG, Triglyceride; HLD-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B.
Table 4.
Comparison of the genotypes and serum lipid levels between males and females in the Han and Jing populations
| Genotype | N | TC (mmol/L) | TG (mmol/L) | HDL-C (mmol/L) | LDL-C (mmol/L) | ApoA1 (g/L) | ApoB (g/L) | ApoA1/ApoB |
|---|---|---|---|---|---|---|---|---|
| Han/male | ||||||||
| AA | 146 | 4.95±0.88 | 1.29 (0.57) | 1.70±0.56 | 2.89±0.46 | 1.33±0.22 | 1.05±0.25 | 1.34±0.42 |
| AG | 151 | 4.76±0.87 | 1.33 (0.71) | 1.74±0.51 | 2.84±0.45 | 1.32±0.19 | 1.05±0.25 | 1.34±0.43 |
| GG | 62 | 4.76±0.77 | 1.24 (0.92) | 1.66±0.55 | 2.80±0.36 | 1.30±0.17 | 1.04±0.18 | 1.30±0.31 |
| F | 2.000 | 3.426 | 0.498 | 1.002 | 0.395 | 0.146 | 0.251 | |
| P | 0.137 | 0.180 | 0.608 | 0.368 | 0.674 | 0.864 | 0.778 | |
| Han/female | ||||||||
| AA | 139 | 4.94±0.85 | 1.41 (0.67) | 1.84±0.57 | 2.83±0.41 | 1.28±0.17 | 1.01±0.23 | 1.33±0.32 |
| AG | 151 | 5.02±0.88 | 1.35 (0.67) | 1.88±0.44 | 2.91±0.45 | 1.34±0.19 | 1.05±0.26 | 1.34±0.36 |
| GG | 56 | 5.09±0.87 | 1.31 (0.41) | 1.83±0.47 | 2.87±0.41 | 1.32±0.26 | 1.05±0.24 | 1.32±0.40 |
| F | 0.680 | 1.986 | 0.331 | 1.312 | 2.545 | 1.147 | 0.116 | |
| P | 0.507 | 0.370 | 0.718 | 0.271 | 0.080 | 0.319 | 0.891 | |
| Jing/male | ||||||||
| AA | 156 | 5.25±0.91 | 1.61 (0.99) | 1.75±0.56 | 2.84±0.39 | 1.27±0.24 | 1.05±0.24 | 1.27±0.39 |
| AG | 136 | 5.12±0.74 | 1.44 (0.61) | 1.74±0.38 | 2.81±0.34 | 1.27±0.20 | 1.06±0.23 | 1.28±0.44 |
| GG | 68 | 4.89±0.86 | 1.33 (0.94) | 1.66±0.42 | 2.72±0.37 | 1.27±0.20 | 1.06±0.20 | 1.26±0.39 |
| F | 4.382 | 2.560 | 0.084 | 2.880 | 0.042 | 0.096 | 0.050 | |
| P | 0.013 | 0.278 | 0.429 | 0.047 | 0.959 | 0.908 | 0.951 | |
| Jing/female | ||||||||
| AA | 112 | 5.31±1.02 | 1.34 (0.63) | 1.83±0.47 | 2.90±0.47 | 1.32±0.32 | 1.09±0.28 | 1.27±0.37 |
| AG | 167 | 5.27±0.89 | 1.40 (0.67) | 1.87±0.45 | 2.83±0.47 | 1.31±0.21 | 1.06±0.23 | 1.29±0.33 |
| GG | 67 | 5.00±0.92 | 1.46 (0.62) | 1.78±0.41 | 2.78±0.40 | 1.30±0.19 | 1.02±0.24 | 1.35±0.37 |
| F | 2.489 | 2.488 | 1.117 | 1.470 | 0.112 | 1.817 | 1.159 | |
| P | 0.084 | 0.288 | 0.328 | 0.231 | 0.894 | 0.164 | 0.315 | |
TC, Total cholesterol; TG, Triglyceride; HLD-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B.
Multivariate linear regression analysis showed that the levels of LDL-C were significantly associated with BDNF rs11030104 genotypes in both Jing and Han populations (P<0.05, Table 5). Multivariate regression analysis according to sex showed that serum LDL-C in Jing males, TG in Jing females, and TC and ApoA1 in Han females were associated with the BDNF rs11030104 genotypes (P<0.05, respectively, Table 6). In addition, multiple risk factors such as age, gender, BMI, height, weight, waist circumference, intake of alcohol, smoking, serum creatinine, and blood pressure were also associated with serum lipid levels in both ethnic groups or sex subgroups (P<0.05 for all); respectively.
Table 5.
Relationship between serum lipid parameters and relative factors in the Han and Jing populations
| Lipid | Risk factor | B | Std.error | Beta | t | P |
|---|---|---|---|---|---|---|
| Han and Jing | ||||||
| TC | Ethnic group | 0.255 | 0.023 | 0.146 | 10.867 | 0.000 |
| GLU | 0.044 | 0.008 | 0.077 | 5.703 | 0.000 | |
| Height | -0.004 | 0.002 | -0.034 | -1.851 | 0.064 | |
| UA | 0.000 | 0.000 | 0.048 | 3.096 | 0.0002 | |
| Pulse pressure | -0.003 | 0.001 | -0.066 | -4.529 | 0.000 | |
| BMI | 0.107 | 0.029 | 0.062 | 3.731 | 0.000 | |
| Age | 0.003 | 0.001 | 0.043 | 2.617 | 0.009 | |
| Gender | 0.73 | 0.034 | 0.042 | 2.149 | 0.032 | |
| TG | UA | 0.002 | 0.000 | 0.265 | 8.186 | 0.000 |
| Waist circumference | 0.019 | 0.003 | 0.188 | 6.696 | 0.000 | |
| BMI | 0.108 | 0.053 | 0.103 | 2.03 | 0.043 | |
| Gender | 0.229 | 0.072 | 0.126 | 3.202 | 0.001 | |
| Height | -0.016 | 0.004 | -0.14 | -3.956 | 0.000 | |
| Age | -0.007 | 0.002 | -0.105 | -3.692 | 0.000 | |
| GLU | 0.061 | 0.015 | 0.102 | 3.956 | 0.000 | |
| Scr | 0.001 | 0.000 | 0.079 | 3.009 | 0.003 | |
| Cigarette smoking | 0.16 | 0.037 | 0.126 | 4.314 | 0.000 | |
| HDL-C | Height | 0.007 | 0.002 | 0.114 | 3.783 | 0.000 |
| Cigarette smoking | -0.062 | 0.019 | -0.087 | -3.235 | 0.001 | |
| Waist circumference | -0.007 | 0.001 | -0.13 | -5.08 | 0.000 | |
| GLU | -0.015 | 0.008 | -0.044 | -1.891 | 0.059 | |
| Gender | 0.181 | 0.032 | 0.178 | 5.604 | 0.000 | |
| Alcohol consumption | -0.049 | 0.024 | 0.053 | 2.009 | 0.045 | |
| Scr | 0.000 | 0.000 | 0.046 | 1.928 | 0.054 | |
| Ethnic group | 0.044 | 0.025 | 0.043 | 1.785 | 0.075 | |
| LDL-C | Ethnic group | -0.111 | 0.01 | -0.132 | -10.815 | 0.000 |
| Scr | 0.000 | 0.000 | -0.034 | -2.672 | 0.005 | |
| Pulse pressure | 0.001 | 0.000 | 0.026 | 2.138 | 0.033 | |
| Gender | -0.031 | 0.011 | -0.037 | -2.933 | 0.003 | |
| ApoA1 | BMI | -0.002 | 0.001 | -0.026 | -1.868 | 0.062 |
| UA | 8.38E-05 | 0.000 | -0.04 | -2.625 | 0.002 | |
| Pulse pressure | 0.000 | 0.000 | 0.026 | 1.945 | 0.052 | |
| Alcohol consumption | 0.016 | 0.005 | 0.042 | 3.043 | 0.002 | |
| ApoB | Scr | 0.000 | 0.000 | 0.024 | 2.441 | 0.015 |
| GLU | -0.005 | 0.002 | -0.031 | -3.121 | 0.002 | |
| BMI | 0.002 | 0.001 | 0.023 | 2.284 | 0.023 | |
| Age | 0.000 | 0.000 | 0.023 | 2.305 | 0.021 | |
| ApoA1/ApoB | Pulse pressure | 0.000 | 0.000 | -0.015 | -1.656 | 0.098 |
| Han | ||||||
| TC | GLU | 0.058 | 0.013 | 0.079 | 4.396 | 0.000 |
| Gender | 0.068 | 0.034 | 0.04 | 1.99 | 0.047 | |
| Pulse pressure | -0.002 | 0.001 | -0.038 | -2.137 | 0.033 | |
| Alcohol consumption | -0.071 | 0.028 | -0.051 | -2.555 | 0.011 | |
| TG | UA | 0.003 | 0.000 | 0.302 | 6.255 | 0.000 |
| BMI | 0.314 | 0.051 | 0.286 | 6.186 | 0.000 | |
| Gender | 0.417 | 0.100 | 0.221 | 4.178 | 0.000 | |
| Height | -0.012 | 0.006 | -0.098 | -2.1 | 0.036 | |
| Age | -0.01 | 0.003 | -0.142 | -3.669 | 0.000 | |
| Scr | 0.004 | 0.001 | 0.164 | 4.019 | 0.000 | |
| Waist circumference | 0.016 | 0.004 | 0.157 | 4.265 | 0.000 | |
| SBP | 0.018 | 0.004 | 0.177 | 4.853 | 0.000 | |
| GLU | 0.063 | 0.029 | 0.078 | 2.191 | 0.029 | |
| Cigarette smoking | 0.112 | 0.054 | 0.088 | 2.074 | 0.036 | |
| Alcohol consumption | 0.143 | 0.065 | 0.093 | 2.203 | 0.026 | |
| HDL-C | GLU | -0.042 | 0.016 | -0.089 | -2.65 | 0.008 |
| Waist circumference | -0.005 | 0.002 | -0.083 | 2.316 | 0.021 | |
| Gender | 0.235 | 0.054 | 0.212 | 4.331 | 0.000 | |
| Cigarette smoking | -0.089 | 0.029 | -0.118 | -3.096 | 0.002 | |
| Height | 0.008 | 0.003 | 0.12 | 2.731 | 0.007 | |
| UA | 0.001 | 0.000 | 0.097 | 2.282 | 0.023 | |
| LDL-C | BMI | 0.311 | 0.011 | 0.617 | 28.864 | 0.000 |
| Genotype | -0.015 | 0.008 | -0.026 | -1.805 | 0.036 | |
| Cigarette smoking | 0.031 | 0.009 | 0.053 | 3.592 | 0.000 | |
| ApoA1 | Alcohol consumption | 0.018 | 0.006 | 0.053 | 2.84 | 0.005 |
| ApoB | SBP | 0.000 | 0.000 | 0.023 | 1.82 | 0.069 |
| Glu | -0.006 | 0.003 | -0.029 | -2.265 | 0.024 | |
| Scr | 0.000 | 0.000 | 0.047 | 3.529 | 0.000 | |
| ApoA1/ApoB | Scr | 0.000 | 0.000 | 0.026 | 2.151 | 0.032 |
| Jing | ||||||
| TC | GLU | 0.021 | 0.009 | 0.043 | 2.4 | 0.017 |
| Height | -0.008 | 0.002 | -0.072 | -3.563 | 0.000 | |
| Pulse pressure | -0.003 | 0.001 | -0.053 | -2.753 | 0.006 | |
| Cigarette smoking | 0.067 | 0.024 | 0.054 | 2.4 | 0.017 | |
| Age | 0.003 | 0.001 | 0.46 | 2.264 | 0.024 | |
| TG | Waist circumference | 0.018 | 0.004 | 0.188 | 4.679 | 0.000 |
| UA | 0.001 | 0.000 | 0.147 | 3.651 | 0.000 | |
| BMI | 0.634 | 0.084 | 0.29 | 7.534 | 0.000 | |
| GLU | 0.074 | 0.017 | 0.152 | -4.333 | 0.000 | |
| Height | -0.025 | 0.005 | -0.225 | -5.455 | 0.000 | |
| Age | -0.006 | 0.002 | -0.088 | -2.296 | 0.004 | |
| Cigarette smoking | 0.207 | 0.049 | 0.163 | 4.197 | 0.000 | |
| HDL-C | Waist circumference | -0.009 | 0.002 | -0.174 | -5.375 | 0.000 |
| Height | 0.004 | 0.002 | 0.075 | 1.921 | 0.055 | |
| Alcohol consumption | 0.112 | 0.03 | 0.115 | 3.69 | 0.000 | |
| Gender | 0.138 | 0.034 | 0.15 | 4.06 | 0.000 | |
| LDL-C | Alcohol consumption | -0.033 | 0.015 | -0.038 | -2.199 | 0.026 |
| Genotype | -0.016 | 0.01 | -0.031 | -1.852 | 0.065 | |
| Scr | 0.000 | 0.000 | -0.03 | -1.761 | 0.079 | |
| ApoA1 | BMI | -0.003 | 0.001 | -0.046 | -2.345 | 0.019 |
| Pulse pressure | 0.001 | 0.000 | 0.058 | 2.905 | 0.004 | |
| Age | 0.000 | 0.000 | -0.038 | -1.87 | 0.062 | |
| Glu | -0.004 | 0.002 | -0.032 | -1.67 | 0.095 | |
| ApoB | Waist circumference | -0.042 | 0.01 | -0.082 | -4.387 | 0.000 |
| ApoA1/ApoB | Alcohol consumption | 0.111 | 0.033 | 0.135 | 30.366 | 0.001 |
| Gender | 0.064 | 0.031 | 0.084 | 20.098 | 0.036 |
TC, Total cholesterol; TG, Triglyceride; LDL, Low-density lipoprotein; HDL, High-density lipoprotein; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; UA, Uric Acid; Scr, Serum creatinine; GLU, Glucose; BMI, Body mass index; SBP, Systolic pressure.
Table 6.
Relationship between serum lipid parameters and relative factors in the males and females of the Han and Jing populations
| Lipid | Risk factor | B | Std.error | Beta | t | P |
|---|---|---|---|---|---|---|
| Han/male | ||||||
| TC | DBP | 0.003 | 0.002 | 0.037 | 1.700 | 0.090 |
| Age | 0.006 | 0.001 | 0.087 | 3.869 | 0.000 | |
| Scr | -0.002 | 0.001 | -0.083 | -3.546 | 0.000 | |
| Alcohol consumption | -0.062 | 0.025 | -0.054 | -2.485 | 0.013 | |
| TG | UA | 0.002 | 0.001 | 0.204 | 3.401 | 0.001 |
| Age | -0.020 | 0.004 | -0.265 | -5.057 | 0.000 | |
| Alcohol consumption | 0.187 | 0.063 | 0.135 | 2.952 | 0.003 | |
| Scr | 0.003 | 0.001 | 0.112 | 2.194 | 0.029 | |
| Waist circumference | 0.013 | 0.006 | 0.106 | 2.264 | 0.024 | |
| HDL-C | DBP | 0.005 | 0.003 | 0.089 | 1.893 | 0.059 |
| Cigarette smoking | -0.056 | 0.031 | -0.086 | -1.807 | 0.072 | |
| LDL-C | DBP | -0.002 | 0.001 | -0.047 | -2.351 | 0.019 |
| Age | -0.002 | 0.001 | -0.057 | -2.979 | 0.003 | |
| Scr | 0.000 | 0.000 | 0.036 | 1.615 | 0.107 | |
| UA | 0.000 | 0.000 | 0.038 | 1.681 | 0.094 | |
| ApoA1 | Height | -0.003 | 0.001 | -0.090 | -3.306 | 0.001 |
| Pulse pressure | 0.001 | 0.000 | 0.05 | 1.937 | 0.054 | |
| Alcohol consumption | 0.022 | 0.008 | 0.077 | 2.856 | 0.005 | |
| ApoB | Scr | 0.000 | 0.000 | 0.057 | 2.809 | 0.005 |
| Waist circumference | 0.001 | 0.001 | 0.033 | 1.762 | 0.079 | |
| ApoA1/ApoB | Pulse pressure | -0.001 | 0.000 | -0.041 | -2.427 | 0.000 |
| Han/female | ||||||
| TC | BMI | 0.736 | 0.054 | 0.875 | 3.144 | 0.000 |
| GLU | 0.093 | 0.021 | 0.12 | 4.337 | 0.000 | |
| UA | 0.001 | 0.000 | 0.093 | 3.438 | 0.001 | |
| SBP | -0.004 | 0.001 | -0.091 | -3.285 | 0.001 | |
| Genotype | 0.062 | 0.033 | 0.05 | 1.887 | 0.06 | |
| TG | UA | 0.005 | 0.001 | 0.434 | 8.494 | 0.000 |
| Waist circumference | 0.009 | 0.004 | 0.101 | 2.081 | 0.038 | |
| HDL-C | Scr | 0.003 | 0.001 | 0.202 | 4.964 | 0.000 |
| Pulse pressure | -0.002 | 0.001 | -0.075 | -1.947 | 0.053 | |
| LDL-C | UA | 0.000 | 0.000 | -0.068 | -3.377 | 0.001 |
| SBP | 0.001 | 0.000 | 0.059 | 2.921 | 0.047 | |
| ApoA1 | Scr | 0.000 | 0.000 | -0.051 | -1.743 | 0.08 |
| UA | 0.000 | 0.000 | -0.088 | -3.036 | 0.003 | |
| Genotype | 0.027 | 0.011 | 0.129 | 2.407 | 0.017 | |
| ApoB | Scr | 0.000 | 0.000 | 0.055 | 3.112 | 0.002 |
| GLU | -0.008 | 0.004 | -0.037 | -2.214 | 0.026 | |
| ApoA1/ApoB | UA | 0.000 | 0.000 | 0.052 | 20.932 | 0.004 |
| BMI | -0.004 | 0.002 | -0.043 | -20.408 | 0.017 | |
| GLU | -0.011 | 0.005 | -0.033 | -10.98 | 0.049 | |
| Jing/male | ||||||
| TC | BMI | 0.013 | 0.007 | 0.047 | 1.785 | 0.075 |
| Age | 0.006 | 0.002 | 0.115 | 3.782 | 0.000 | |
| Cigarette smoking | 0.111 | 0.026 | 0.116 | 4.234 | 0.000 | |
| Alcohol consumption | 0.082 | 0.035 | 0.061 | 2.35 | 0.019 | |
| TG | Waist circumference | 0.025 | 0.006 | 0.241 | 4.138 | 0.000 |
| Cigarette smoking | 0.188 | 0.061 | 0.163 | 3.096 | 0.002 | |
| GLU | 0.097 | 0.025 | 0.192 | 3.875 | 0.000 | |
| Height | -0.026 | 0.008 | -0.182 | -3.122 | 0.002 | |
| Age | -0.007 | 0.004 | -0.11 | -1.793 | 0.740 | |
| UA | 0.001 | 0.001 | 0.114 | 2.242 | 0.026 | |
| HDL-C | Alcohol consumption | 0.131 | 0.03 | 0.172 | 4.396 | 0.000 |
| Waist circumference | -0.16 | 0.004 | -0.328 | -3.626 | 0.000 | |
| Height | 0.054 | 0.018 | 0.792 | 3.025 | 0.003 | |
| Age | 0.004 | 0.001 | 0.128 | 2.974 | 0.003 | |
| Scr | 0.000 | 0.000 | 0.078 | 2.239 | 0.026 | |
| BMI | 0.172 | 0.063 | 1.105 | 2.726 | 0.007 | |
| Weight | -0.055 | 0.023 | -1.171 | -2.346 | 0.020 | |
| LDL-C | Alcohol consumption | -0.035 | 0.016 | -0.058 | -2.188 | 0.029 |
| Genotype | 0.132 | 0.024 | 0.168 | 5.467 | 0.000 | |
| ApoA1 | BMI | -0.004 | 0.002 | -0.059 | -2.231 | 0.026 |
| Pulse pressure | 0.001 | 0.000 | 0.062 | 2.57 | 0.011 | |
| ApoB | BMI | 0.012 | 0.003 | 0.153 | 3.574 | 0.000 |
| Pulse pressure | -0.001 | 0.000 | -0.09 | -4.101 | 0.000 | |
| Age | 0.001 | 0.000 | 0.059 | 2.637 | 0.009 | |
| Alcohol consumption | 0.026 | 0.008 | 0.069 | 3.05 | 0.002 | |
| Waist circumference | -0.003 | 0.001 | -0.118 | -2.68 | 0.008 | |
| Scr | 0.000 | 0.000 | 0.041 | 2.06 | 0.040 | |
| ApoA1/ApoB | Pulse pressure | -0.001 | 0.000 | -0.042 | -2.467 | 0.014 |
| Jing/female | ||||||
| TC | Scr | 0.003 | 0.001 | 0.093 | 3.751 | 0.000 |
| GLU | 0.038 | 0.001 | 0.067 | 2.723 | 0.007 | |
| Pulse pressure | -0.003 | 0.001 | -0.054 | -2.227 | 0.027 | |
| Waist circumference | -0.005 | 0.003 | -0.042 | -1.726 | 0.085 | |
| TG | UA | 0.002 | 0.001 | 0.18 | 3.358 | 0.001 |
| Cigarette smoking | 0.856 | 0.279 | 0.151 | 3.067 | 0.002 | |
| Genotype | 0.117 | 0.055 | 0.105 | 2.124 | 0.035 | |
| Height | -0.023 | 0.007 | -0.178 | -3.235 | 0.001 | |
| Waist circumference | 0.009 | 0.005 | 0.101 | 1.818 | 0.070 | |
| HDL-C | Waist circumference | -0.01 | 0.002 | -0.18 | -4.031 | 0.000 |
| LDL-C | Waist circumference | 0.002 | 0.001 | 0.044 | 1.822 | 0.070 |
| Scr | -0.001 | 0.000 | -0.078 | -3.149 | 0.002 | |
| Age | 0.002 | 0.001 | 0.052 | 2.036 | 0.043 | |
| ApoA1 | BMI | 0.725 | 0.025 | 0.248 | 2.633 | 0.000 |
| Waist circumference | 0.886 | 0.037 | 0.024 | 4.062 | 0.000 | |
| ApoB | Pulse pressure | -0.681 | 0.020 | -0.015 | -3.296 | 0.000 |
| ApoA1/ApoB | Waist circumference | -0.202 | 0.027 | -0.807 | -4.284 | 0.000 |
| Scr | 0.038 | 0.031 | 0.602 | 3.063 | 0.000 | |
TC, Total cholesterol; TG, Triglyceride; LDL, Low-density lipoprotein; HDL, High-density lipoprotein; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; UA, Uric Acid; Scr, Serum creatinine; GLU, Glucose; BMI, Body mass index; SBP, Systolic pressure; DBP, Diastolic pressure.
Discussion
In the present study, we adopted a standard design of epidemiological investigation and ensured a good representation of the study population as well as reasonable research methods. The results show that serum TC and TG levels are higher in Jing than in Han populations, whereas ApoA1 levels were lower in Jing than in Han populations. There were no significant differences in the levels of LDL-C, HDL-C, and ApoB. Dyslipidemia is affected by environmental factors, including demographics, diet, alcohol consumption, cigarette smoking, obesity, exercise, hypertension [26] and genetic factors, including lipid-associated gene variants, and their interactions [27]. The differences in serum lipid parameters between the two ethnic groups may be due to genetic diversity, different environmental factors [28] and their interactions [29,30]. Jing is a special ethnic group in coastal area of China which lives off sea fishing. Seafood is their main business and main diet. Fish solute is the most popular flavoring for each meal, which is made by amarinate small fish. Their marriages are family-arranged but cross-cousin marriage and getting married with someone with the same last name are forbidden. The Jing population preserves their custom of intra-ethnic marriages. So Jing nationality has a special lifestyle, dietary habits, and customs compared with Han and other landlocked nationalities. Moreover, several hereditary characteristics and genotypes of lipid metabolism-related gene in Jing might be different from those in the Han population.
The genotypic and allelic frequencies of the BDNF rs11030104 SNP in different racial/ethnic groups are inconsistent. According to the data from International HAP-Map Project, the frequency of A allele was 60.0% in Han Chinese in Beijing, 62.8% in Japanese, 77.0% in European, 99.67 in African. The frequencies of AA, AG, and GG genotypes were 35%, 50% and 15% in Chinese; 44.2%, 37.2%, and 18.6% in Japanese; 58.4%, 37.2%, and 4.4% in European; 99.1%, 0.88%, and 0% in African, respectively. Our study results demonstrated that the frequencies of AA, AG, and GG genotypes were 40.5%, 42.9%, and 16.6% in Han population, and 38.0%, 42.9%, and 19.1% in Jing population and the frequencies of A and G alleles were 61.9% and 38.1% in Han, and 59.4% and 40.6% in Jing populations respectively. These results were slightly different from the data of Beijing, which may be due to different sizes and regions (Northern China vs. Southern China). We also found that the genotypic and allelic frequencies of the SNP in the Jing but not in the Han populations was different between males and females, which may be caused by racial and/or gender factors.
BDNF is a member of the neurotrophin family, plays an important role in neurological disorders [31]. BDNF and its high-affinity receptor TrkB are highly expressed in the hypothalamus, where this neurotrophic factor has major regulatory roles in the control of appetite and metabolism [32]. In recent years, several studies have showed that BDNF is also positively correlated with the risk factors of metabolic syndrome [33], BMI and obesity [33-35]. Serum BDNF levels in various physiologic states will likely aid in the assessment and management associated cardiovascular risk [36]. Higher serum BDNF is associated with a decreased risk of CVD and mortality. Mendelian randomization suggests a causal protective role of BDNF in the pathogenesis of CVD. Several studies showed that the BDNF rs11030104 SNP was closely associated with BMI level, obesity, and metabolic syndrome [37]. However, there are hardly any studies that exhibit a direct relationship between the BDNF rs11030104 SNP and serum lipid levels. Our study showed that the genotypes of BNDF rs11030104 SNP were significantly associated with serum TC and LDL-C levels. The G allele carriers had lower TC and LDL-C levels than the G allele non-carriers in the Jing population. Moreover, subgroup analysis presented the G allele carriers had lower TC and LDL-C levels in Jing males. These results show that the association of BDNF rs11030104 SNP and serum lipid levels probably has racial/ethnic and/or sex specificity. But, this association needs to be confirmed by further studies with larger sample size.
We also found a gender difference in the association of the BDNF rs11030104 SNP and serum lipid levels in the Jing population. Some unknown genetic factors may be involved in affecting this status. In addition, the sample size was possibly not large enough to measure the association of BDNF rs11030104 SNP and serum lipid levels in the sex subgroup analyses. Further research should be done to confirm these findings.
It is well known that environmental factors (such as dietary pattern, lifestyle, and physical inactivity) are closely related with serum lipid levels [38-40]. In our study, multivariate linear regression analysis also found that age, gender, BMI, waist circumference, alcohol consumption, cigarette smoking, serum creatinine (Scr) and blood pressure affected serum lipid parameters. These data suggest that environmental factors also play an important role in determining serum lipid levels in our study populations. Jing people like to eat seafood, especially fish. Fish are rich in omrga-3 polyunsaturated fatty acids (N-3PUFA) which have been considered to have a positive effect on serum lipid concentrations. But, previous studies presented the influence of N-3PUFA on key metabolism function, including significant increase in TC, TG, and LDL-C levels and decrease in HDL-C [41,42]. Furthermore, several studies show that BMI and alcohol consumption may interact with lipid-related genes to determine serum lipid levels in other populations [43,44]. Therefore, the effects of dietary pattern, lifestyle, customs, and environmental factors may alter the association of genetic diversities and serum lipid levels in our study populations.
Furthermore, the presence of dyslipidemia was significantly associated with increasing age, male sex, higher BMI, higher blood glucose concentration, higher blood pressure, smoking, high cholesterol diet, and sedentary lifestyle [45,46]. Our study also showed that age, male, BMI, waist circumference, blood pressure, smoking, and intake of alcohol were associated with serum lipid levels. Moreover, we also found that BMI, waist circumference were higher in Jing than in Han populations. BMI and obesity were already demonstrated to have closely correlation with individual lipid profiles [47,48]. Waist circumference is also an important index in evaluation of dyslipidemia, waist circumference and BMI are equally useful for monitoring the consequences of obesity [49]. Many studies have shown that smoking and alcohol consumption are major risk factors for atherosclerotic CVD through leading to dyslipidemia [50,51]. For example, TG increased by 0.15 mmol/L and HDL-C decreased by 0.09 mmol/L with every 20 cigarettes smoked [28]. An another study exhibited TG increased by 5.69 mg/dl, HDL-C decreased by 3.99 mg/dl, and ApoA1 decreased by 8.83 mg/dl with every 30 g intake of alcohol per day [52]. Lifestyle modifications can have similar therapeutic effects with statins. It can effectively control serum lipid levels and reduce the prevalence of CVD [53].
In addition, our study indicates that Scr is significantly associated with several lipid parameters. More and more research has demonstrated that renal dysfunction is associated with dyslipidemia [54-57]. Bowe et al. found that low level of HDL-C was significantly correlated with the prevalence and development of chronic kidney disease (CKD). The level of HDL-C was independently associated with eGFR and the prevalence of low HDL-C level was increasing with reduced grading of eGFR [56]. LDL-C/ApoB and HDL-C/ApoA1 ratios may predict incident of CKD [55].
Our research also has some limitations. First, the cross-sectional study design limits the ability to confirm causality of the relationships observed. Second, we were not able to eliminate the effect of diet during the statistical analysis since the diet intake was self-reported and difficult to classify. Third, there are still some unmeasured environmental and genetic factors which should be considered. Fourth, we only detected serum TC, TG, HDL-C, LDL-C, ApoA1, ApoB levels, and the ratio of ApoA1 to ApoB and measured their relationships with the BDNF rs11030104 SNP without comprehensive measurement of the subclasses lipoproteins such as HDL2, HDL3, small dense LDL, and large buoyant LDL. TC, TG, HDL-C and LDL-C are the most important indexes for judging dyslipidemia.
The interaction of gene-gene, gene-environment, and environment-environment on serum lipid levels remain to be confirmed later. Therefore, we propose that these interaction mechanisms and the relationship between BDNF rs11030104 SNP and different lipid parameters need to be verified in further depth investigations.
Conclusions
This study shows that BDNF rs11030104 SNP is associated with serum TC and LDL-C levels in the Jing males. These results suggest that there may be a racial/ethnic- and/or sexspecific association of the BDNF rs11030104 SNP and serum lipid levels in our study populations.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (No. 81160111).
Disclosure of conflict of interest
None.
References
- 1.Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM, Carnethon MR, Dai S, de Simone G, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Greenlund KJ, Hailpern SM, Heit JA, Ho PM, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, McDermott MM, Meigs JB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Rosamond WD, Sorlie PD, Stafford RS, Turan TN, Turner MB, Wong ND, Wylie-Rosett J American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2011 update: a report from the american heart association. Circulation. 2011;123:e18–e209. doi: 10.1161/CIR.0b013e3182009701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Expert panel on detection evaluation and treatment of high blood cholesterol in adults. executive summary of The third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) JAMA. 2001;285:2486–2497. doi: 10.1001/jama.285.19.2486. [DOI] [PubMed] [Google Scholar]
- 3.Huang KK, Yin RX, Zeng XN, Huang P, Lin QZ, Wu J, Guo T, Wang W, Yang DZ, Lin WX. Association of the rs7395662 SNP in the MADDFOLH1 and several environmental factors with serum lipid levels in the mulao and han populations. Int J Med Sci. 2013;10:1537–1546. doi: 10.7150/ijms.6421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Surakka I, Whitfield JB, Perola M, Visscher PM, Montgomery GW, Falchi M, Willemsen G, de Geus EJ, Magnusson PK, Christensen K, Sorensen TI, Pietilainen KH, Rantanen T, Silander K, Widen E, Muilu J, Rahman I, Liljedahl U, Syvanen AC, Palotie A, Kaprio J, Kyvik KO, Pedersen NL, Boomsma DI, Spector T, Martin NG, Ripatti S, Peltonen L, Genom EP. A genomewide association study of monozygotic twinpairs suggests a locus related to variability of serum high-density lipoprotein cholesterol. Twin Res Hum Genet. 2012;15:691–699. doi: 10.1017/thg.2012.63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Perusse L, Rice T, Despres JP, Bergeron J, Province MA, Gagnon J, Leon AS, Rao DC, Skinner JS, Wilmore JH, Bouchard C. Familial resemblance of plasma lipids, lipoproteins and postheparin lipoprotein and hepatic lipases in the HERITAGE family study. Arterioscler Thromb Vasc Biol. 1997;17:3263–3269. doi: 10.1161/01.atv.17.11.3263. [DOI] [PubMed] [Google Scholar]
- 6.Sniderman AD, Peterson ED. Genetic studies help clarify the complexities of lipid biology and treatment. JAMA. 2017;318:915–917. doi: 10.1001/jama.2017.11750. [DOI] [PubMed] [Google Scholar]
- 7.Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM, Pramstaller PP, Penninx BW, Janssens AC, Wilson JF, Spector T, Martin NG, Pedersen NL, Kyvik KO, Kaprio J, Hofman A, Freimer NB, Jarvelin MR, Gyllensten U, Campbell H, Rudan I, Johansson A, Marroni F, Hayward C, Vitart V, Jonasson I, Pattaro C, Wright A, Hastie N, Pichler I, Hicks AA, Falchi M, Willemsen G, Hottenga JJ, de Geus EJ, Montgomery GW, Whitfield J, Magnusson P, Saharinen J, Perola M, Silander K, Isaacs A, Sijbrands EJ, Uitterlinden AG, Witteman JC, Oostra BA, Elliott P, Ruokonen A, Sabatti C, Gieger C, Meitinger T, Kronenberg F, Doring A, Wichmann HE, Smit JH, McCarthy MI, van Duijn CM, Peltonen L, Consortium E. Loci influencing lipid levels and coronary heart disease risk in 16 european population cohorts. Nat Genet. 2009;41:47–55. doi: 10.1038/ng.269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, Rieder MJ, Cooper GM, Roos C, Voight BF, Havulinna AS, Wahlstrand B, Hedner T, Corella D, Tai ES, Ordovas JM, Berglund G, Vartiainen E, Jousilahti P, Hedblad B, Taskinen MR, Newton-Cheh C, Salomaa V, Peltonen L, Groop L, Altshuler DM, Orho-Melander M. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet. 2008;40:189–197. doi: 10.1038/ng.75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Zhivolupov SA, Samartsev IN, Marchenko AA, Puliatkina OV. [The prognostic significance of brain-derived neurotrophic factor (BDNF) for phobic anxiety disorders, vegetative and cognitive impairments during conservative treatment including adaptol of some functional and organic diseases of nervous system] . Zh Nevrol Psikhiatr Im S S Korsakova. 2012;112:37–41. [PubMed] [Google Scholar]
- 10.Ihara K, Yoshida H, Jones PB, Hashizume M, Suzuki Y, Ishijima H, Kim HK, Suzuki T, Hachisu M. Serum BDNF levels before and after the development of mood disorders: a casecontrol study in a population cohort. Transl Psychiatry. 2016;6:e782. doi: 10.1038/tp.2016.47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fuchikami M, Yamamoto S, Morinobu S, Takei S, Yamawaki S. Epigenetic regulation of BDNF gene in response to stress. Psychiatry Investig. 2010;7:251–256. doi: 10.4306/pi.2010.7.4.251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sha H, Xu J, Tang J, Ding J, Gong J, Ge X, Kong D, Gao X. Disruption of a novel regulatory locus results in decreased Bdnf expression, obesity, and type 2 diabetes in mice. Physiol Genomics. 2007;31:252–263. doi: 10.1152/physiolgenomics.00093.2007. [DOI] [PubMed] [Google Scholar]
- 13.Schwartz E, Mobbs CV. Hypothalamic BDNF and obesity: found in translation. Nat Med. 2012;18:496–497. doi: 10.1038/nm.2716. [DOI] [PubMed] [Google Scholar]
- 14.Chaldakov GN, Fiore M, Stankulov IS, Hristova M, Antonelli A, Manni L, Ghenev PI, Angelucci F, Aloe L. NGF, BDNF, leptin, and mast cells in human coronary atherosclerosis and metabolic syndrome. Arch Physiol Biochem. 2001;109:357–360. doi: 10.1076/apab.109.4.357.4249. [DOI] [PubMed] [Google Scholar]
- 15.Motamedi S, Karimi I, Jafari F. The interrelationship of metabolic syndrome and neurodegenerative diseases with focus on brain-derived neurotrophic factor (BDNF): kill two birds with one stone. Metab Brain Dis. 2017;32:651–665. doi: 10.1007/s11011-017-9997-0. [DOI] [PubMed] [Google Scholar]
- 16.Bonaccorso S, Sodhi M, Li J, Bobo WV, Chen Y, Tumuklu M, Theleritis C, Jayathilake K, Meltzer HY. The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is associated with increased body mass index and insulin resistance measures in bipolar disorder and schizophrenia. Bipolar Disord. 2015;17:528–535. doi: 10.1111/bdi.12294. [DOI] [PubMed] [Google Scholar]
- 17.Lin JH, Liu ZH, Lv FJ, Fu YG, Fan XL, Li SY, Lu JM, Liu XY, Xu AL. Molecular analyses of HLADRB1, -DPB1, and -DQB1 in Jing ethnic minority of southwest China. Hum Immunol. 2003;64:830–834. doi: 10.1016/s0198-8859(03)00128-9. [DOI] [PubMed] [Google Scholar]
- 18.Guo T, Yin RX, Lin WX, Wang W, Huang F, Pan SL. Association of the variants and haplotypes in the DOCK7, PCSK9 and GALNT2 genes and the risk of hyperlipidaemia. J Cell Mol Med. 2016;20:243–265. doi: 10.1111/jcmm.12713. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.An epidemiological study of cardiovascular and cardiopulmonary disease risk factors in four populations in the people’s republic of China. Baseline report from the P.R.C.-U.S.A. collaborative study. People’s republic of China--United States cardiovascular and cardiopulmonary epidemiology research group. Circulation. 1992;85:1083–1096. doi: 10.1161/01.cir.85.3.1083. [DOI] [PubMed] [Google Scholar]
- 20.Wu DF, Yin RX, Aung LH, Li Q, Yan TT, Zeng XN, Huang KK, Huang P, Wu JZ, Pan SL. Sex-specific association of ACAT-1 rs1044925 SNP and serum lipid levels in the hypercholesterolemic subjects. Lipids Health Dis. 2012;11:9. doi: 10.1186/1476-511X-11-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Li Q, Yin RX, Yan TT, Miao L, Cao XL, Hu XJ, Aung LH, Wu DF, Wu JZ, Lin WX. Association of the GALNT2 gene polymorphisms and several environmental factors with serum lipid levels in the mulao and han populations. Lipids Health Dis. 2011;10:160. doi: 10.1186/1476-511X-10-160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Ruixing Y, Yuming C, Shangling P, Fengping H, Tangwei L, Dezhai Y, Jinzhen W, Limei Y, Weixiong L, Rongshan L, Jiandong H. Effects of demographic, dietary and other lifestyle factors on the prevalence of hyperlipidemia in Guangxi Hei Yi Zhuang and Han populations. Eur J Cardiovasc Prev Rehabil. 2006;13:977–984. doi: 10.1097/01.hjr.0000239476.79428.25. [DOI] [PubMed] [Google Scholar]
- 23.Ishii M. [The sixth report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure, and 1999 world health organization-international society of hypertension guidelines for the management of hypertension] . Nihon Rinsho. 2000;58(Suppl 1):267–275. [PubMed] [Google Scholar]
- 24.Hitworth JA World Health Organization, International Society of Hypertension Writing Group. 2003 World Health Organization (WHO)/international society of hypertension (ISH) statement on management of hypertension. J Hypertens. 2003;21:1983–1992. doi: 10.1097/00004872-200311000-00002. [DOI] [PubMed] [Google Scholar]
- 25.Muller MJ, Lagerpusch M, Enderle J, Schautz B, Heller M, Bosy-Westphal A. Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome. Obes Rev. 2012;13(Suppl 2):6–13. doi: 10.1111/j.1467-789X.2012.01033.x. [DOI] [PubMed] [Google Scholar]
- 26.Robinson D, Kawamura T, Hinohara S, Sakamoto Y, Takahashi T. Levels of cardiovascular risk factors in japanese people living in the UK. J Cardiovasc Risk. 1995;2:449–458. doi: 10.1177/174182679500200510. [DOI] [PubMed] [Google Scholar]
- 27.Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, Guiducci C, Perola M, Jula A, Sinisalo J, Lokki ML, Nieminen MS, Melander O, Salomaa V, Peltonen L, Kathiresan S. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet. 2010;376:1393–1400. doi: 10.1016/S0140-6736(10)61267-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Hata Y, Nakajima K. Life-style and serum lipids and lipoproteins. J Atheroscler Thromb. 2000;7:177–197. doi: 10.5551/jat1994.7.177. [DOI] [PubMed] [Google Scholar]
- 29.Ordovas JM, Shen AH. Genetics, the environment, and lipid abnormalities. Curr Cardiol Rep. 2002;4:508–513. doi: 10.1007/s11886-002-0115-4. [DOI] [PubMed] [Google Scholar]
- 30.Ordovas JM, Robertson R, Cleirigh EN. Gene-gene and gene-environment interactions defining lipid-related traits. Curr Opin Lipidol. 2011;22:129–136. doi: 10.1097/MOL.0b013e32834477a9. [DOI] [PubMed] [Google Scholar]
- 31.Nagahara AH, Tuszynski MH. Potential therapeutic uses of BDNF in neurological and psychiatric disorders. Nat Rev Drug Discov. 2011;10:209–219. doi: 10.1038/nrd3366. [DOI] [PubMed] [Google Scholar]
- 32.Nawa H, Carnahan J, Gall C. BDNF protein measured by a novel enzyme immunoassay in normal brain and after seizure: partial disagreement with mRNA levels. Eur J Neurosci. 1995;7:1527–1535. doi: 10.1111/j.1460-9568.1995.tb01148.x. [DOI] [PubMed] [Google Scholar]
- 33.Golden E, Emiliano A, Maudsley S, Windham BG, Carlson OD, Egan JM, Driscoll I, Ferrucci L, Martin B, Mattson MP. Circulating brainderived neurotrophic factor and indices of metabolic and cardiovascular health: data from the baltimore longitudinal study of aging. PLoS One. 2010;5:e10099. doi: 10.1371/journal.pone.0010099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Suwa M, Kishimoto H, Nofuji Y, Nakano H, Sasaki H, Radak Z, Kumagai S. Serum brainderived neurotrophic factor level is increased and associated with obesity in newly diagnosed female patients with type 2 diabetes mellitus. Metabolism. 2006;55:852–857. doi: 10.1016/j.metabol.2006.02.012. [DOI] [PubMed] [Google Scholar]
- 35.Rios M, Fan G, Fekete C, Kelly J, Bates B, Kuehn R, Lechan RM, Jaenisch R. Conditional deletion of brain-derived neurotrophic factor in the postnatal brain leads to obesity and hyperactivity. Mol Endocrinol. 2001;15:1748–1757. doi: 10.1210/mend.15.10.0706. [DOI] [PubMed] [Google Scholar]
- 36.Kaess BM, Preis SR, Lieb W, Beiser AS, Yang Q, Chen TC, Hengstenberg C, Erdmann J, Schunkert H, Seshadri S, Vasan RS CARDIoGRAM. Assimes TL, Deloukas P, Holm H, Kathiresan S, Konig IR, McPherson R, Reilly MP, Roberts R, Samani NJ, Stewart AF. Circulating brain-derived neurotrophic factor concentrations and the risk of cardiovascular disease in the community. J Am Heart Assoc. 2015;4:e001544. doi: 10.1161/JAHA.114.001544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, Powell C, Vedantam S, Buchkovich ML, Yang J, Croteau-Chonka DC, Esko T, Fall T, Ferreira T, Gustafsson S, Kutalik Z, Luan J, Magi R, Randall JC, Winkler TW, Wood AR, Workalemahu T, Faul JD, Smith JA, Zhao JH, Zhao W, Chen J, Fehrmann R, Hedman AK, Karjalainen J, Schmidt EM, Absher D, Amin N, Anderson D, Beekman M, Bolton JL, Bragg-Gresham JL, Buyske S, Demirkan A, Deng G, Ehret GB, Feenstra B, Feitosa MF, Fischer K, Goel A, Gong J, Jackson AU, Kanoni S, Kleber ME, Kristiansson K, Lim U, Lotay V, Mangino M, Leach IM, Medina-Gomez C, Medland SE, Nalls MA, Palmer CD, Pasko D, Pechlivanis S, Peters MJ, Prokopenko I, Shungin D, Stancakova A, Strawbridge RJ, Sung YJ, Tanaka T, Teumer A, Trompet S, van der Laan SW, van Setten J, Van Vliet-Ostaptchouk JV, Wang Z, Yengo L, Zhang W, Isaacs A, Albrecht E, Arnlov J, Arscott GM, Attwood AP, Bandinelli S, Barrett A, Bas IN, Bellis C, Bennett AJ, Berne C, Blagieva R, Bluher M, Bohringer S, Bonnycastle LL, Bottcher Y, Boyd HA, Bruinenberg M, Caspersen IH, Chen YI, Clarke R, Daw EW, de Craen AJM, Delgado G, Dimitriou M, Doney ASF, Eklund N, Estrada K, Eury E, Folkersen L, Fraser RM, Garcia ME, Geller F, Giedraitis V, Gigante B, Go AS, Golay A, Goodall AH, Gordon SD, Gorski M, Grabe HJ, Grallert H, Grammer TB, Grassler J, Gronberg H, Groves CJ, Gusto G, Haessler J, Hall P, Haller T, Hallmans G, Hartman CA, Hassinen M, Hayward C, Heard-Costa NL, Helmer Q, Hengstenberg C, Holmen O, Hottenga JJ, James AL, Jeff JM, Johansson A, Jolley J, Juliusdottir T, Kinnunen L, Koenig W, Koskenvuo M, Kratzer W, Laitinen J, Lamina C, Leander K, Lee NR, Lichtner P, Lind L, Lindstrom J, Lo KS, Lobbens S, Lorbeer R, Lu Y, Mach F, Magnusson PKE, Mahajan A, McArdle WL, McLachlan S, Menni C, Merger S, Mihailov E, Milani L, Moayyeri A, Monda KL, Morken MA, Mulas A, Muller G, Muller-Nurasyid M, Musk AW, Nagaraja R, Nothen MM, Nolte IM, Pilz S, Rayner NW, Renstrom F, Rettig R, Ried JS, Ripke S, Robertson NR, Rose LM, Sanna S, Scharnagl H, Scholtens S, Schumacher FR, Scott WR, Seufferlein T, Shi J, Smith AV, Smolonska J, Stanton AV, Steinthorsdottir V, Stirrups K, Stringham HM, Sundstrom J, Swertz MA, Swift AJ, Syvanen AC, Tan ST, Tayo BO, Thorand B, Thorleifsson G, Tyrer JP, Uh HW, Vandenput L, Verhulst FC, Vermeulen SH, Verweij N, Vonk JM, Waite LL, Warren HR, Waterworth D, Weedon MN, Wilkens LR, Willenborg C, Wilsgaard T, Wojczynski MK, Wong A, Wright AF, Zhang Q LifeLines Cohort Study; Brennan EP, Choi M, Dastani Z, Drong AW, Eriksson P, Franco-Cereceda A, Gadin JR, Gharavi AG, Goddard ME, Handsaker RE, Huang J, Karpe F, Kathiresan S, Keildson S, Kiryluk K, Kubo M, Lee JY, Liang L, Lifton RP, Ma B, McCarroll SA, McKnight AJ, Min JL, Moffatt MF, Montgomery GW, Murabito JM, Nicholson G, Nyholt DR, Okada Y, Perry JRB, Dorajoo R, Reinmaa E, Salem RM, Sandholm N, Scott RA, Stolk L, Takahashi A, Tanaka T, van’t Hooft FM, Vinkhuyzen AAE, Westra HJ, Zheng W, Zondervan KT ADIPOGen Consortium; AGEN-BMI Working Group; CARDIOGRAMplusC4D Consortium; CKDGen Consortium; GLGC; ICBP; MAGIC Investigators; MuTHER Consortium; MIGen Consortium; PAGE Consortium; ReproGen Consortium; GENIE Consortium; International Endogene Consortium. Heath AC, Arveiler D, Bakker SJL, Beilby J, Bergman RN, Blangero J, Bovet P, Campbell H, Caulfield MJ, Cesana G, Chakravarti A, Chasman DI, Chines PS, Collins FS, Crawford DC, Cupples LA, Cusi D, Danesh J, de Faire U, den Ruijter HM, Dominiczak AF, Erbel R, Erdmann J, Eriksson JG, Farrall M, Felix SB, Ferrannini E, Ferrieres J, Ford I, Forouhi NG, Forrester T, Franco OH, Gansevoort RT, Gejman PV, Gieger C, Gottesman O, Gudnason V, Gyllensten U, Hall AS, Harris TB, Hattersley AT, Hicks AA, Hindorff LA, Hingorani AD, Hofman A, Homuth G, Hovingh GK, Humphries SE, Hunt SC, Hypponen E, Illig T, Jacobs KB, Jarvelin MR, Jockel KH, Johansen B, Jousilahti P, Jukema JW, Jula AM, Kaprio J, Kastelein JJP, Keinanen-Kiukaanniemi SM, Kiemeney LA, Knekt P, Kooner JS, Kooperberg C, Kovacs P, Kraja AT, Kumari M, Kuusisto J, Lakka TA, Langenberg C, Marchand LL, Lehtimaki T, Lyssenko V, Mannisto S, Marette A, Matise TC, McKenzie CA, McKnight B, Moll FL, Morris AD, Morris AP, Murray JC, Nelis M, Ohlsson C, Oldehinkel AJ, Ong KK, Madden PAF, Pasterkamp G, Peden JF, Peters A, Postma DS, Pramstaller PP, Price JF, Qi L, Raitakari OT, Rankinen T, Rao DC, Rice TK, Ridker PM, Rioux JD, Ritchie MD, Rudan I, Salomaa V, Samani NJ, Saramies J, Sarzynski MA, Schunkert H, Schwarz PEH, Sever P, Shuldiner AR, Sinisalo J, Stolk RP, Strauch K, Tonjes A, Tregouet DA, Tremblay A, Tremoli E, Virtamo J, Vohl MC, Volker U, Waeber G, Willemsen G, Witteman JC, Zillikens MC, Adair LS, Amouyel P, Asselbergs FW, Assimes TL, Bochud M, Boehm BO, Boerwinkle E, Bornstein SR, Bottinger EP, Bouchard C, Cauchi S, Chambers JC, Chanock SJ, Cooper RS, de Bakker PIW, Dedoussis G, Ferrucci L, Franks PW, Froguel P, Groop LC, Haiman CA, Hamsten A, Hui J, Hunter DJ, Hveem K, Kaplan RC, Kivimaki M, Kuh D, Laakso M, Liu Y, Martin NG, Marz W, Melbye M, Metspalu A, Moebus S, Munroe PB, Njolstad I, Oostra BA, Palmer CNA, Pedersen NL, Perola M, Perusse L, Peters U, Power C, Quertermous T, Rauramaa R, Rivadeneira F, Saaristo TE, Saleheen D, Sattar N, Schadt EE, Schlessinger D, Slagboom PE, Snieder H, Spector TD, Thorsteinsdottir U, Stumvoll M, Tuomilehto J, Uitterlinden AG, Uusitupa M, van der Harst P, Walker M, Wallaschofski H, Wareham NJ, Watkins H, Weir DR, Wichmann HE, Wilson JF, Zanen P, Borecki IB, Deloukas P, Fox CS, Heid IM, O’Connell JR, Strachan DP, Stefansson K, van Duijn CM, Abecasis GR, Franke L, Frayling TM, McCarthy MI, Visscher PM, Scherag A, Willer CJ, Boehnke M, Mohlke KL, Lindgren CM, Beckmann JS, Barroso I, North KE, Ingelsson E, Hirschhorn JN, Loos RJF, Speliotes EK. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206. doi: 10.1038/nature14177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Ruixing Y, Qiming F, Dezhai Y, Shuquan L, Weixiong L, Shangling P, Hai W, Yongzhong Y, Feng H, Shuming Q. Comparison of demography, diet, lifestyle, and serum lipid levels between the Guangxi Bai Ku Yao and Han populations. J Lipid Res. 2007;48:2673–2681. doi: 10.1194/jlr.M700335-JLR200. [DOI] [PubMed] [Google Scholar]
- 39.Barnard RJ. Effects of life-style modification on serum lipids. Arch Intern Med. 1991;151:1389–1394. [PubMed] [Google Scholar]
- 40.Erkkila AT, Sarkkinen ES, Lehto S, Pyorala K, Uusitupa MI. Dietary associates of serum total, LDL, and HDL cholesterol and triglycerides in patients with coronary heart disease. Prev Med. 1999;28:558–565. doi: 10.1006/pmed.1998.0478. [DOI] [PubMed] [Google Scholar]
- 41.Sala-Vila A, Guasch-Ferre M, Hu FB, Sanchez-Tainta A, Bullo M, Serra-Mir M, Lopez-Sabater C, Sorli JV, Aros F, Fiol M, Munoz MA, Serra-Majem L, Martinez JA, Corella D, Fito M, Salas-Salvado J, Martinez-Gonzalez MA, Estruch R, Ros E PREDIMED Investigators, B. Dietary alpha-linolenic acid, marine omega-3 fatty acids, and mortality in a population with high fish consumption: findings from the PREvencion con DIeta MEDiterranea (PREDIMED) Study. J Am Heart Assoc. 2016;5:e002543. doi: 10.1161/JAHA.115.002543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Dias CB, Wood LG, Garg ML. Effects of dietary saturated and n-6 polyunsaturated fatty acids on the incorporation of long-chain n-3 polyunsaturated fatty acids into blood lipids. Eur J Clin Nutr. 2016;70:812–818. doi: 10.1038/ejcn.2015.213. [DOI] [PubMed] [Google Scholar]
- 43.Aung LH, Yin RX, Miao L, Hu XJ, Yan TT, Cao XL, Wu DF, Li Q, Pan SL, Wu JZ. The proprotein convertase subtilisin/kexin type 9 gene E670G polymorphism and serum lipid levels in the Guangxi Bai Ku Yao and Han populations. Lipids Health Dis. 2011;10:5. doi: 10.1186/1476-511X-10-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Guo T, Yin RX, Li H, Wang YM, Wu JZ, Yang DZ. Association of the Trp316Ser variant (rs1801690) near the apolipoprotein H (beta2-glycoprotein-I) gene and serum lipid levels. Int J Clin Exp Pathol. 2015;8:7291–7304. [PMC free article] [PubMed] [Google Scholar]
- 45.Toth PP, Potter D, Ming EE. Prevalence of lipid abnormalities in the United States: the National health and nutrition examination survey 2003-2006. J Clin Lipidol. 2012;6:325–330. doi: 10.1016/j.jacl.2012.05.002. [DOI] [PubMed] [Google Scholar]
- 46.Wang S, Xu L, Jonas JB, You QS, Wang YX, Yang H. Prevalence and associated factors of dyslipidemia in the adult Chinese population. PLoS One. 2011;6:e17326. doi: 10.1371/journal.pone.0017326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, Curran JE, Blangero J, Meikle PJ. Plasma lipid profiling in a large population-based cohort. J Lipid Res. 2013;54:2898–2908. doi: 10.1194/jlr.P035808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Olusi SO. Obesity is an independent risk factor for plasma lipid peroxidation and depletion of erythrocyte cytoprotectic enzymes in humans. Int J Obes Relat Metab Disord. 2002;26:1159–1164. doi: 10.1038/sj.ijo.0802066. [DOI] [PubMed] [Google Scholar]
- 49.Lara M, Bustos P, Amigo H, Silva C, Rona RJ. Is waist circumference a better predictor of blood pressure, insulin resistance and blood lipids than body mass index in young Chilean adults? BMC Public Health. 2012;12:638. doi: 10.1186/1471-2458-12-638. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Berlin I, Luquiens A, Aubin HJ. Smoking as a confounder of the association of suicidality with serum lipid levels. J Psychiatry Neurosci. 2016;41:E24. doi: 10.1503/jpn.150361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Attard R, Dingli P, Doggen CJM, Cassar K, Farrugia R, Wettinger SB. The impact of passive and active smoking on inflammation, lipid profile and the risk of myocardial infarction. Open Heart. 2017;4:e000620. doi: 10.1136/openhrt-2017-000620. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Rimm EB, Williams P, Fosher K, Criqui M, Stampfer MJ. Moderate alcohol intake and lower risk of coronary heart disease: metaanalysis of effects on lipids and haemostatic factors. BMJ. 1999;319:1523–1528. doi: 10.1136/bmj.319.7224.1523. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Krumholz HM. Treatment of cholesterol in 2017. JAMA. 2017;318:417–418. doi: 10.1001/jama.2017.6753. [DOI] [PubMed] [Google Scholar]
- 54.Hertlova M, Sobotova D, Mocek J, Breinek P. [Disorders of lipid metabolism as a risk factor for atherosclerosis in patients with chronic kidney insufficiency] . Vnitr Lek. 1986;32:127–131. [PubMed] [Google Scholar]
- 55.Bae JC, Han JM, Kwon S, Jee JH, Yu TY, Lee MK, Kim JH. LDL-C/apoB and HDL-C/apoA-1 ratios predict incident chronic kidney disease in a large apparently healthy cohort. Atherosclerosis. 2016;251:170–176. doi: 10.1016/j.atherosclerosis.2016.06.029. [DOI] [PubMed] [Google Scholar]
- 56.Bowe B, Xie Y, Xian H, Balasubramanian S, Al-Aly Z. Low levels of high-density lipoprotein cholesterol increase the risk of incident kidney disease and its progression. Kidney Int. 2016;89:886–896. doi: 10.1016/j.kint.2015.12.034. [DOI] [PubMed] [Google Scholar]
- 57.Acuna L, Sanchez P, Soler L, Alvis LF. Total cholesterol (Tc), low-density lipoprotein cholesterol (Ldl-C) and high-density lipoprotein cholesterol (Hdl-C) levels in patients with hypertension (Ht), diabetes (Dm), both (Ht And Dm) and chronic kidney disease (Ckd) Value Health. 2015;18:A405–406. [Google Scholar]