A coding variant in SULT1A2 gene: association with obesity and dyslipidemia in southern Chinese adults

doi:10.21203/rs.3.rs-2303063/v2

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A coding variant in SULT1A2 gene: association with obesity and dyslipidemia in southern Chinese adults

https://doi.org/10.21203/rs.3.rs-2303063/v2

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published 04 May, 2023

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Background

A coding variant rs1059491 in the SULT1A2 gene has been reported to be associated with childhood obesity. This study aimed to investigate the association of rs1059491 with risk of obesity and cardiometabolic abnormalities in adults.

Methods

This case-control study included 226 normal weight, 168 overweight and 72 obese adults who underwent a health examination in Taizhou, China. Genotyping of SULT1A2 rs1059491 was performed by Sanger sequencing.

Results

The minor allele frequency of rs1059491 in the overweight combined obesity and control group was 0.0292 and 0.0686, respectively. No differences in weight and body mass index were detected between TT genotype and GT + GG genotype under the dominant model, but levels of serum triglyceride was significantly lower in G-allele carriers than non-G-allele carriers (1.02 (0.74–1.32) vs. 1.35 (0.83–2.13) mmol/L, P = 0.011). The GT + GG genotype of rs1059491 versus TT genotype reduced the risk of overweight and obesity by 54% (OR: 0.46, 95%CI: 0.22–0.96, P = 0.037) after adjusted for sex and age. Similar results were observed for hypertriglyceridemia (OR: 0.25, 95%CI: 0.08–0.74, P = 0.013) and dyslipidemia (OR: 0.37, 95%CI: 0.17–0.83, P = 0.015).

Conclusion

This study revealed that the coding variant rs1059491 is associated with decreased risk of obesity and dyslipidemia in southern Chinese adults. More studies are needed to validate the association in other populations and its underlying mechanisms.

Health sciences/Endocrinology/Endocrine system and metabolic diseases

Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity

Biological sciences/Genetics/Genotype/Genetic predisposition to disease

obesity

triglyceride

SULT1A2

coding variant

genotype

Obesity is a complex and multifactorial chronic metabolic disease, and causes a major public health burden worldwide. According to World Health Organization (WHO), 39% of adults aged 18 years and over were overweight in 2016, and 13% were obese [1]. It has been shown that Asians have higher body fat than Caucasians at the same body mass index (BMI) and that fat is more likely to accumulate in the abdomen [2, 3]. Abdominal obesity, rather than peripheral obesity, is the most common type of obesity in Chinese population. It characterized as visceral adipose tissue distribution and associated with a higher susceptibility to cardiovascular metabolic risk [4]. In an obesogenic environment, excessive dietary energy intake and reduced physical activity are important factors contributing to the development of obesity, while polygenic inheritance and gene-environment interactions inevitably affect the susceptibility to common obesity [5].

The SULT1A2 (sulfotransferase family, cytosolic, 1A, phenoles-preferring, member 2) gene is located at 16p12.1, which is close to 16p11.2, a hot spot in obesity research. SULT1A2 encodes a heat-resistant diphenol sulfate-transferase, a key enzyme in sulphuric acid metabolism that catalyzes a variety of hormones, including protein sulfonate hormones, estrogens, estrogenic alkylphenol, and 17β-estradiol secretory hormones [6]. SULT1A1 and SULT1A2 belong to the same sultyltransferase superfamily, located in the same chromatid, with similar structure and function, and shared substrates. There was also strong linkage between SULT1A1 and SULT1A2 [7]. Animal studies have shown that dietary fat regulates SULT1A1 gene expression in adipose and liver tissues of obese mice [8]. It has been found that a non-synonymous mutation rs141581853 (P. Arg 213 His) in SULT1A1 gene is associated with human obesity, and this locus is linked to SULT1A2 rs1059491 [9]. In addition, exome-wide scan showed SULT1A2 gene is highly expressed in healthy liver and may be involved in the pathogenesis of non-alcoholic fatty liver disease [10]. Furthermore, we have previously demonstrated the coding variant rs1059491 in SULT1A2 was associated with childhood obesity at genome-wide level in Northern China [11].

To date, whether SULT1A2 rs1059491 associated with adulthood obesity has not ever been reported. In this study, we aimed to evaluate the relationship of rs1059491 and risk of obesity and related metabolic disorder in adults.

2.1 Study Population

We consecutively enrolled adults who underwent weight management from August 10, 2018 to November 30, 2019, and staff health examination from January 1 to March 10, 2020 in Health Management Center of our hospital. Those who were in pregnancy or lactation, were taking medication known to affect body weight or energy metabolism, were mental illness, bulimia, anorexia, gastrointestinal disorders or morbid obesity were excluded. We designed a case-control study of 466 adults including 226 normal-weight, 168 overweight and 72 obese, who provided data on anthropometry and venous blood samples. All participants were independent and unrelated individuals. We obtained the informed consent of each participant. This study was approved by the Ethics Committee of Taizhou Hospital of Zhejiang Province in China. All participants’ identity information was anonymous. All detailed protocols followed the principles of our institutional research ethics committee and were in accordance with the Declaration of Helsinki.

2.2 Measurements

Anthropometric data were collected by trained health technicians using a standard protocol. All instruments were validated following the standard methods of the manufacturers. Weight was measured to the nearest 0.1 kg on a balance beam scale, and height was measured to the nearest 0.1 cm with a wall-mounted stadiometer. BMI was calculated as the weight in kilograms divided by the square of the height in meters. Obesity was defined by a BMI ≥ 30 kg/m², and overweight was a 25 kg/m² ≤ BMI < 30 kg/m² for adults according to WHO.

Blood pressure was measured on the right upper arm using an electronic sphygmomanometer (Omron, HBP-9021) with the cuff maintained at heart level after 5 min of rest in sitting position. Elevated blood pressure was considered with systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg or reported use of antihypertensive medication.

2.3 Blood specimen collection and biochemical index detection

Approximately 3 mL of peripheral venous blood was collected from subjects with fasting for 12 hours in the morning in a tube containing ethylenediaminetetraacetic acid (EDTA)-K2. In addition, serum samples were collected for biochemical tests. Fasting plasma glucose (FPG) was measured by hexokinase method. Serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) were detected by enzyme-coupled colorimetry. All reagents were purchased and blood biochemical indexes were determined by AU5800 automatic biochemical analyzer (BECKMAN COULTER). All laboratory equipment was calibrated.

According to 2016 Chinese guidelines for the management of dyslipidemia in Adults [12], dyslipidemia was defined by the presence of one or more of the following components conditions: TC ≥ 5.20 mmol/L (200 mg/dL), LDL-C ≥ 3.40 mmol/L (130 mg/dL), HDL-C < 1.00 mmol/L (40 mg/dL), TG ≥ 1.70 mmol/L (150 mg/dL), or if they were taking anti-dyslipidemia medication. Impaired fasting glucose (IFG) was diagnosed with fasting plasma glucose (FPG) ≥ 5.60 mmol/L. Abnormalities in either blood pressure or lipid or FPG were considered as cardiometabolic abnormality.

2.4 DNA extraction, PCR and sequencing for SULT1A2 rs1059491

Genomic DNA was extracted from leukocytes by using Magnetic Bead Method Genomic DNA Extraction Kit according to the manufacturer’s instructions (NanoMagBio, Wuhan, China) and then stored at − 20°C for PCR analysis. DNA was amplified in the conditions of preheating at 95°C for 5 min for initial denaturation, followed by 35 cycles of 95°C for 30 s, 60°C for 30 s, 72°C for 45 s, and a final extension at 72°C for 5 min. The sequences for the primers are presented in Supplementary Table 1. The amplified products were performed on ABI 3730xl DNA Analyzer, and the sequencing results were analyzed by using DNAman ( Lynnon Biosoft, America) and Chromas (Technelysium, Australia) software. Genotyping of all samples was performed simultaneously in the same laboratory.

2.5 Statistical analysis

We calculated the sample size and power for a case-control study using Quanto software http://hydra.usc.edu/gxe/. Assuming a dominant genetic model, an allele frequency of 0.06, and overweight and obesity prevalence of 50% in the adults of China, we estimated that an enrollment target of 466 participants (233 per group) would provide the study with greater than 80% statistical power to detect a significant odds ratio of 2.3 for risk of overweight and obesity between the different genotype groups at a significance level of 0.05 using a two-tailed test. The anticipated effect size for risk of obesity was determined on the basis of previous data obtained from northern Chinese children and adolescents [11].

Continuous variables are expressed as mean ± standard deviation (SD). Comparison of variables between groups was performed using one way ANOVA for continuous variables and chi-square tests for categorical variables. Serum TG level without normal distribution was described as geometric mean (interquartile range) and transformed by natural logarithm prior to one-way ANOVA analysis. Hardy-Weinberg equilibrium was performed using Pearson chi-squared test. Assuming a dominant inheritance mode, multivariable logistic regression models were applied to estimate the association between genotype of rs1059491 and overweight combined obesity and related cardiometabolic abnormalities. Sex, age and weight status were adjusted as covariables. All statistical analyses were performed using SPSS Statistics software version 26.0 (IBM Corp., Armonk, New York). Statistical significance was inferred at a two-tailed P value less than 0.05.

3.1 Clinical characteristics of participants

Individuals inclusion and exclusion criteria at each stage of the study are presented in the flowchart (Fig. 1), and a total of 466 participants who provided complete data on anthropometry and venous blood samples were classified as 226 normal weight, 168 overweight and 72 obesity. Figure 2 shows the distribution of genotype of rs1059491 among normal weight, overweight and obese groups. The frequency of GT + GG genotype in overweight and obesity groups was lower than that in normal weight group (χ² = 6.818, P = 0.033). The general characteristics of participants by genotype of rs1059491 are shown in Table 1. Levels of serum triglyceride (1.02 (0.74–1.32) vs. 1.35 (0.83–2.13) mmol/L, P = 0.011) and fasting plasma glucose (5.07 ± 0.40 vs. 5.46 ± 1.31 mmol/L, P = 0.011) were significantly lower in GT + GG genotype of rs1059491 than TT genotype. Similarly, BMI levels were nominally reduced in G-allele carriers compared to their counterpart (24.3 ± 4.5 vs. 25.7 ± 4.6 kg/m², P = 0.063). No statistical differences were observed in other indices between the different genotype groups. The prevalence of overweight (23.3% vs.37.4%) and obesity (9.3% vs. 16.1%) was significantly low in GT + GG genotype versus TT genotype (P = 0.033).

Table 1

Clinical characteristics of subjects
Variables	All	Genotype of rs1059491		P
Variables	(n = 466)	TT (n = 423)	GT + GG (n = 43)	P
Male/female	161/305	150/273	11/32	0.194
Age (years)	38.2 ± 11.8	38.5 ± 11.9	35.8 ± 9.9	0.152
Height (cm)	162.3 ± 14.1	163.2 ± 8.2	162.4 ± 8.5	0.580
Weight (kg)	68.2 ± 16.3	68.6 ± 16.3	64.4 ± 15.5	0.105
BMI (kg/m²)	25.5 ± 4.6	25.7 ± 4.6	24.3 ± 4.5	0.063
SBP (mmHg)	125 ± 16	125 ± 16	126 ± 12	0.610
DBP (mmHg)	75 ± 12	75 ± 12	74 ± 10	0.638
TG (mmol/L)	1.31 (0.82 ‒ 2.06)	1.35 (0.83 ‒ 2.13)	1.02 (0.74 ‒ 1.32)	0.011
TC (mmol/L)	4.85 ± 0.92	4.85 ± 0.93	4.76 ± 0.92	0.556
HDL-C (mmol/L)	1.41 ± 0.34	1.40 ± 0.34	1.47 ± 0.32	0.229
LDL-C (mmol/L)	2.79 ± 0.65	2.57 ± 0.70	2.51 ± 0.76	0.638
FPG (mmol/L)	5.43 ± 1.26	5.46 ± 1.31	5.07 ± 0.40	< 0.001
BMI category				0.033
Normal weight	226 (48.5)	197 (46.6)	29 (67.4)
Overweight	168 (36.1)	158 (37.4)	10 (23.3)
Obesity	72 (15.5)	68 (16.1)	4 (9.3)
Values are expressed as the mean ± standard deviation (SD).
Triglycerides were skewed distribution, expressed as a geometric mean (interquartile range) and one way ANOVA was performed after logarithmic transformation.
Overweight was defined as 25 kg/m² ≤ BMI < 30 kg/m² and obesity as BMI ≥ 30 kg/m².
Bold indicates P value < 0.05.
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FPG, fasting plasma glucose.

3.2 Effect of rs1059491 genotype on obesity-related quantitative traits

Table 2 shows the effects of genotype (GT + GG vs. TT) of rs1059491 on phenotypic traits under a dominant genetic model. Carrying the G allele of rs1059491 decreased serum TG levels even after adjustment for age and sex (β = -1.23 mmol/L, P = 0.031). However, there are no significant influence of the genotype of SULT1A2 rs1059491 on other obesity-related phenotypic traits.

Table 2

Effect of GT + GG genotype versus TT genotype of rs1059491 under dominant model on obesity-related phenotypic traits
Variables	Model 1			Model 2
Variables	β	95%CI	P	β	95%CI	P
Height (cm)	-0.80	-3.66 ‒ 2.05	0.58	0.54	-1.39 ‒ 2.47	0.585
Weight (kg)	-4.22	-9.33 ‒ 0.89	0.105	-1.99	-5.86 ‒ 1.88	0.313
BMI (kg/m²)	-1.36	-2.80 ‒ 0.08	0.063	-0.83	-2.11 ‒ 0.46	0.205
SBP (mmHg)	1.29	-3.66 ‒ 6.23	0.61	3.75	-0.64 ‒ 8.13	0.094
DBP (mmHg)	-0.89	-4.62 ‒ 2.83	0.638	0.71	-2.66 ‒ 4.07	0.681
TG (mmol/L)	-1.32	-1.64 ‒ (-1.07)	0.011	-1.23	-1.49 ‒ (-1.02)	0.031
TC (mmol/L)	-0.09	-0.41 ‒ 0.22	0.556	-0.04	-0.35 ‒ 0.27	0.807
HDL-C (mmol/L)	0.07	-0.04 ‒ 0.19	0.229	0.04	-0.06 ‒ 0.15	0.425
LDL-C (mmol/L)	-0.06	-0.30 ‒ 0.18	0.638	-0.01	-0.24 ‒ 0.22	0.941
FPG (mmol/L)	-0.39	-0.82 ‒ 0.04	0.073	-0.31	-0.72 ‒ 0.10	0.137
Model 1: Unadjusted
Model 2: Adjusted for sex and age.
Bold indicates P value < 0.05.
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; FPG, fasting plasma glucose.

3.3 The association of rs1059491 genotype with cardiometabolic abnormalities

Allele G was the minor allele of rs1059491, and overall minor allele frequency (MAF) was 0.048. The MAF in overweight and obesity group was lower than that of control group (0.0292 vs. 0.0686). Hardy-Weinberg equilibrium test showed that the rs1059491 genotype distribution was in accordance with the genetic balance except for the two subgroups (IFG and high LDL-C). Accordingly, further analyses of rs1059491 associations with obesity-related cardiometabolic abnormalities did not include IFG and high LDL-C. Table 3 and supplementary Fig. 1 display the associations of the rs1059491 genotype with the risk of being overweight combined with obesity and other cardiometabolic abnormalities after adjusting for age and sex assuming a dominant genetic model. Individuals carried the minor allele G of rs1059491 had lower risk of overweight and obesity than the non-G-allele carriers (OR: 0.46, 95%CI: 0.22–0.96, P = 0.037). Similar results were also observed in hypertriglyceridemia (OR: 0.28, 95%CI: 0.09–0.88, P = 0.030) and dyslipidemia (OR: 0.41, 95%CI: 0.18–0.93, P = 0.032) even after adjusting for sex, age and weight status. However, no associations were observed between rs1059491 genotype and risk of elevated blood pressure, elevated TC or decreased HDL-C.

Table 3

Effect of rs1059491 genotype on cardiometabolic abnormalities under dominant model (GT + GG versus TT)
Cardiometabolic abnormality	MAF		Model 1			Model 2
Cardiometabolic abnormality	Case	Control	OR	95%CI	P	OR	95%CI	P
Overweight combined obesity (BMI ≥ 25 kg/m²)	0.0292	0.0686	0.42	0.22 ‒ 0.82	0.011	0.46	0.22 ‒ 0.96	0.037
Elevated blood pressure	0.0549	0.0461	1.19	0.63 ‒ 2.27	0.591	1.67	0.83 ‒ 3.36	0.149
(SBP/DBP ≥ 130/85 mmHg)	0.0549	0.0461	1.19	0.63 ‒ 2.27	0.591	1.67	0.83 ‒ 3.36	0.149
Elevated triglycerides	0.0155	0.0655	0.24	0.08 ‒ 0.69	0.008	0.25	0.08 ‒ 0.74	0.013
(fasting triglycerides ≥ 1.7 mmol/L (150 mg/dL))	0.0155	0.0655	0.24	0.08 ‒ 0.69	0.008	0.25	0.08 ‒ 0.74	0.013
Elevated TC	0.0435	0.0526	0.71	0.33 ‒ 1.52	0.379	0.79	0.36 ‒ 1.72	0.552
(fasting TC ≥ 5.2 mmol/L (200 mg/dL))	0.0435	0.0526	0.71	0.33 ‒ 1.52	0.379	0.79	0.36 ‒ 1.72	0.552
Decreased HDL-C	0.0313	0.0511	0.64	0.15 ‒ 2.79	0.552	0.78	0.17 ‒ 3.54	0.742
(HDL-C < 1.0 mmol/L (40 mg/dL))	0.0313	0.0511	0.64	0.15 ‒ 2.79	0.552	0.78	0.17 ‒ 3.54	0.742
Dyslipidemia	0.03	0.0686	0.35	0.17 ‒ 0.75	0.007	0.37	0.17 ‒ 0.83	0.015
Model 1: Unadjusted
Model 2: Adjusted for sex and age.
Dyslipidemia was defined by the presence of one or more of the following components conditions: TC ≥ 5.20 mmol/L (200 mg/dL), LDL-C ≥ 3.40 mmol/L (130 mg/dL), HDL-C < 1.00 mmol/L (40 mg/dL), TG ≥ 1.70 mmol/L (150 mg/dL), or if they were taking anti-dyslipidemia medication.
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

To the best of our knowledge, the present study firstly investigated the genetic association of SULT1A2 rs1059491 variant with overweight and obesity in 466 adults who underwent a physical examination. The genotyping result revealed that the frequency of minor allele G is lower in obesity or cardiometabolic abnormality group than that in their counterpart. The variant rs1059491 G-allele significantly decreased the risk of overweight and obesity, hypertriglyceridemia and dyslipidemia in adults after adjusting for sex and age.

This case-control study identified the genetic association of SULT1A2 rs1059491 variant with obesity in Taizhou adults, and found the G allele over-represented in the normal weight group. However, our previous study indicated that the G allele is a risk factor for obesity in children from northern China [11]. This inconsistency may be due to age and regional differences between the two populations. Genetic contribution on BMI varies with age and may be greater during childhood than adulthood life because of the cumulative influence of environmental factors [13]. Therefore, long-term follow up is required to monitor the occurrence and development of obesity and related metabolic abnormalities to further verify the effect of genes.

SULT1A2 gene could encode 6 isoenzymes, among which the two most common isoenzymes are different due to the substitution of two amino acids (Ile7Thr and Asn235Thr), especially the change of the 235th codon with an important function. The mutation frequency of rs1059491 (c.704T/G, p.Asn235Thr) varies greatly among different populations [14]. Data from the Exon Sequencing Project (ESP6500) in the United States showed that the frequency of G allele was 0.317, and the heterozygous and homozygous mutation frequencies were 0.421 and 0.107, respectively. However, the allele frequency in East Asians based on the 1000 Genomes Project was 0.0744. In our study population, the MAF of rs1059491 was only 0.048, and almost all G allele carriers were heterozygous. Our results are similar to the G allele frequency of the East Asian population in The Genome Aggregation Database (gnomAD) (0.0553) and ALFA (Allele Frequency Aggregator) projects (0.054) [15].

The rs1059491 is a missense variant on the exon 2 of SULT1A2. Different software predictions show that rs1059491: p.Asn235Thr ( c.704T / G ) is related with gene function (‘probably damaging’ PolyPhen-2, ‘non-neutral’ SNAP and ‘deleterious’ SIFT ). However, our previous findings suggest rs1059491 was an eQTL that may contribute to obesity by altering the expression of obesity-related gene rather than affecting protein structure [11]. Available transcriptome and GETx data revealed that rs1059491 was located at transcription factor binding sites of PPARγ2 and RXRA [16], which could lead to abnormal lipid metabolism and obesity.

For the first time, we found an association between the coding variant rs1059491 in SULT1A2 gene and obesity risk in adults, which laid a foundation for further research on gene function and molecular mechanism of obesity. Previous fine mapping studies of obesity candidate genes in 16p11.2 region indicated that rare mutations in nearby genes APOBR [17] and SH2B1 [18] but not SULT1A2 were strongly associated with the risk of obesity or extreme obesity. However, genome-wide association study reported an important BMI-related SNP rs7359397 located in SH2B1, which was highly linked with nearby non-synonymous variant rs1059491 of SULT1A2 (R² > 0.75) [19]. SH2B1 is the most likely obesity-causing gene in this region [20]. The SH2B1 protein is involved in the regulation of energy balance by increasing leptin and insulin in the downstream signaling pathway.

In addition, a study of 230 breast cancer patients in Taiwan found that SULT1A2 gene polymorphism may be associated with the early-onset of breast cancer patients [21]. Both breast cancer and obesity are associated with increased levels of SULT1A2 substrate 17β-estradiol, estrone and estrone sulfate [22]. Recently, another research showed BMI is the only breast cancer risk factor that affect metabolic fluxes to adducts of estrogens with DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2) [23]. These results suggest that SULT1A2 may be involved in the regulation of body weight and energy balance by regulating steroid metabolism [24]. The indirect evidence supported the association of the coding variant rs1059491 in SULT1A2 with risk of obesity and dyslipidemia.

There are several limitations in our study. First, the power calculation was performed and our study had 89% power (enrollment of 240 overweight and obese subjects) to detect ORs of 0.39 for overweight and obesity under a dominant genetic model. Thus, the statistical power was enough to detect similar effect sizes. However, the sample size was still small for polygenetic association study. Second, this study was a case-control design. The phenotypic data collection was only done once, so we did not observed how the weight status changes over time. Third, our study only included participants from one medical centre in China, all of whom were medical personnel with high work intensity and stress, which may induce to selection bias. Therefore, the results may not be generalized or widely applicable to other populations. Fourth, we did not investigate whether our study subjects came from other regions with different genetic backgrounds. However, our study site was in a county in southern China, where Han Chinese populations are concentrated. The population stratification may not be a concern. Future studies with larger sample sizes should be validated in various populations with different age and geographic regions.

In conclusion, this study revealed that the coding variant rs1059491 in SULT1A2 gene is associated with decreased risk of obesity and dyslipidemia in southern Chinese adults. More studies are required to validate the relationship between SULT1A2 rs1059491 and obesity in other populations, and the underlying mechanisms need to be explored in the future.

Ethical Statement

This study involving human participants was reviewed and approved by the Ethics Committee of Taizhou Hospital of Zhejiang Province in China. The studies involving human participants were reviewed and approved by the Ethics Committee of Taizhou Hospital of Zhejiang Province in China. All procedures were performed in accordance with the guidelines of the institutional ethics committee of the authors and adhered to the tenets of the Declaration of Helsinki. All data, including demographic, biochemical, and genetic information, were anonymized. Personal information is not involved in this article.

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

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published 04 May, 2023

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Editorial decision: Major revision
17 Mar, 2023
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Reviewers agreed at journal
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Reviews received at journal
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First submitted to journal
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You are reading this latest preprint version

A coding variant in SULT1A2 gene: association with obesity and dyslipidemia in southern Chinese adults

Status:

Journal Publication

Version 2

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Study Population

2.2 Measurements

2.3 Blood specimen collection and biochemical index detection

2.4 DNA extraction, PCR and sequencing for SULT1A2 rs1059491

2.5 Statistical analysis

3. Results

3.1 Clinical characteristics of participants

3.2 Effect of rs1059491 genotype on obesity-related quantitative traits

3.3 The association of rs1059491 genotype with cardiometabolic abnormalities

4. Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 2