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Ji Cheng Zhang, Jin Hua Wang, Jia Lin, Guo Ming Su, Yi Lin Shen, Qi Wei Guo, Jia Jing Cai, Jun Yi Liu, Ke Xin Jia, Ding Zhi Fang Department of Biochemistry and Molecular Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, People’s Republic of ChinaCorrespondence: Ding Zhi Fang, Department of Biochemistry and Molecular Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People’s Republic of China, Email dzfang@scu.edu.cnAim: To explore anthropometric, metabolic and dietary factors associated with and their interplays with the Val66Met polymorphism at brain-derived neurotrophic factor (BDNF) gene (Bdnf) on serum BDNF levels in adolescents.Methods: Serum BDNF levels were quantified using an enzyme-linked immunosorbent assay in 644 high school students (278 males/366 females). A polymerase chain reaction and restriction fragment length polymorphism assay were utilized for Bdnf Val66Met genotyping followed by verification using DNA sequencing. Serum levels of metabolic characteristics were assayed by routine methods. The intake of macro and micronutrients was collected by a three-day food record.Results: Serum BDNF levels were found to be significantly different in the subjects with different genotypes of Bdnf Val66Met (Val/Val homozygotes, 60.05 ± 28.07 ng/mL vs Val/Met heterozygotes, 56.37 ± 29.34 ng/mL vs Met/Met homozygotes, 51.32 ± 24.54 ng/mL, p = 0.022). Among the 36 tested variables, waist–hip ratio (WHR) (β = − 0.163, p < 0.001), iodine intake (β = 0.132, p = 0.001), heart rate (β = 0.108, p = 0.005), high-density lipoprotein cholesterol (HDL-C) (β = 0.098, p = 0.011) and dietary fiber intake (β = 0.082, p = 0.084) were the predictor of serum BDNF levels, while SBP (β = 0.097, p = 0.013) and WHR (β = 0.091, p = 0.021) were related with Bdnf Val66Met. Moreover, WHR was observed to play a partial mediating role in the relationship between Bdnf Val66Met and serum BDNF levels (95% CI [− 1.161, − 0.087]) and contribute 13.05% of its total effect on serum BDNF levels.Conclusion: There are interplays between WHR and Bdnf Val66Met on serum BDNF levels, which may be among the explanations for the previous heterogeneous reports and provide novel insights into the regulation of serum BDNF levels.Keywords: serum, brain-derived neurotrophic factor, Val66Met, waist–hip ratio, mediator, adolescent

Shun Yu Sun,1 Wei Yi Yang,2 Zhuo Tan,2 Xing Yu Zhang,1 Yi Lin Shen,1 Qi Wei Guo,1 Guo Ming Su,1 Xu Chen,1 Jia Lin,1 Ding Zhi Fang1 1Department of Biochemistry and Molecular Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People’s Republic of China; 2Department of Preventive Medicine, West China School of Public health, Sichuan University, Chengdu, 610041, People’s Republic of ChinaCorrespondence: Ding Zhi FangDepartment of Biochemistry and Molecular Biology, West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People’s Republic of China, Tel +86 28 85503410, Fax +86 28 85503204, Email dzfang@scu.edu.cnPurpose: To investigate serum levels of free fatty acids (FFAs) and their associations with routine serum lipids in diet-induced obese mice, which have been scantily reported before.Methods: Male C57BL/6 J mice were fed high-fat diets for 12 weeks to induce obesity. Levels of serum FFAs were measured by ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry.Results: Obese mice had higher serum levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), but lower triglycerides (TG) than control mice. A total of 30 FFAs were found, and 3 saturated fatty acids (SFAs), all 8 monounsaturated fatty acids (MUFAs) and 7 polyunsaturated fatty acids (PUFAs) decreased in obese mice, but one SFA (C4:0) increased. Differences in the relative levels of individual FFAs to total FFAs, SFAs, MUFAs or PUFAs between obese and control mice were different from each other and from those evaluated by concrete levels except C4:0, C16:1, C19:1 and C18:4. Only the concrete levels of C4:0, C22:3 and C18:4 were associated with routine serum lipids, including C22:3 negatively with TG in control mice, and C4:0 and C18:4 positively with LDL-C in obese mice, although the relative levels of C4:0 to total MUFAs negatively with TC, and C23:3 to total SFAs or MUFAs negatively with TG in control mice. Different relative levels of the remaining FFAs were differently associated with different routine serum lipids in obese and/or control mice.Conclusion: Obesity may influence serum FFAs profiles. The relationship of individual FFAs and their relative levels to other FFAs with routine serum lipids in obese and control mice suggests that individual FFAs may interact with others and obesity on levels of routine serum lipids. Once confirmed, the interactions may be novel perspectives when fatty acids are used to improve hyperlipidemia in the subjects with obesity.Keywords: free fatty acid, obesity, triglyceride, cholesterol, high-density lipoprotein, low-density lipoprotein

Yun-Liang Shen,1,* Si-Yu Long,2,* Wen-Ming Kong,1 Li-Mei Wu,1 Li-Juan Fei,1 Qiang Yao,1 Hong-Sheng Wang2 1Department of Leprosy Control, Zhejiang Provincial Institute of Dermatology, Huzhou, People’s Republic of China; 2Laboratory of Leprosy and Other Mycobacterial Infections, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hong-Sheng WangLaboratory of Leprosy and Other Mycobacterial Infections, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, St 12 Jiangwangmiao, Nanjing, Jiangsu 210042, People’s Republic of ChinaEmail whs33@vip.sina.comQiang YaoDepartment of Leprosy Control, Zhejiang Provincial Institute of Dermatology, St 61, Wuyuan, Huzhou, Zhejiang 313200, People’s Republic of ChinaEmail 7793908@qq.comPurpose: Genome-wide association studies (GWAS) have identified multiple genetic variants associated with leprosy. To investigate the single and combined associations between single-nucleotide polymorphisms (SNPs) and the development of leprosy, we therefore performed generalized multi-analytical (GMDR) analysis in Chinese leprosy household contacts and constructed a risk prediction model.Patients and Methods: This case–control study included 229 leprosy cases and 233 healthy household contacts in Zhejiang province, China. Participants were genotyped for 17 polymorphisms selected from GWAS. The Pearson χ2 test, logistic regression and GMDR analysis were performed to investigate gene–gene interactions and construct a risk prediction model for leprosy.Results: The genotype and the allele distributions of rs142179458, rs2275606, rs663743 and rs73058713 were significantly different between patients and controls. rs2275606, rs6478108, rs663743 and rs73058713 showed an association after adjusting for sex and age in the logistic regression. A five-way interaction model consisting of rs2058660, rs2275606, rs4720118, rs6478108 and rs780668 was chosen as the optimal model for determining leprosy susceptibility. The model classified 237 (51.3%) into the low-risk group and 225 (48.7%) individuals into the high-risk group. The area under the curve (AUC) of this model was 0.757 (95% CI: 0.712– 0.803), and the odds ratio for leprosy between the high- and low-risk groups was 9.733 (95% CI: 6.384– 14.960; P< 0.001). The sensitivity and specificity of the model were observed to be 74.7% and 76.8%, respectively.Conclusion: Our results suggest that rs2058660, rs2275606, rs4720118, rs6478108 and rs780668, five SNPs with a significant sole effect on leprosy, interact to confer a higher risk for the disease in leprosy household contacts (HHCs).Keywords: leprosy, gene–gene interactions, generalized multi-analytical, risk stratification

Munirah Alsaleh,1 Thomas A Barbera,1 Helen L Reeves,2 Matthew E Cramp,3 Stephen Ryder,4,5 Hani Gabra,1,6 Kathryn Nash,7 Yi-Liang Shen,1,8 Elaine Holmes,1 Roger Williams,9 Simon D Taylor-Robinson11Division of Surgery and Cancer, Imperial College London, London; 2Northern Institute for Cancer Research, Medical School, University of Newcastle, Newcastle upon Tyne, UK; 3Liver Unit, Derriford Hospital, Plymouth, Devon , UK; 4Nottingham Digestive Diseases Centre, University of Nottingham, Nottingham, UK; 5NIHR Biomedical Research Unit, Nottingham University Hospitals NHS Trust, Queen’s Medical Centre, Nottingham, UK; 6Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK; 7Liver Unit, Southampton General Hospital, Southampton, Hampshire, UK; 8Department of Radiation Oncology, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan; 9Institute of Hepatology, UKBackground: Outside South-East Asia, most cases of cholangiocarcinoma (CCA) have an obscure etiology. There is often diagnostic uncertainty. Metabolomics using ultraperformance liquid chromatography mass spectrometry (UPLC-MS) offers the portent to distinguish disease-specific metabolic signatures. We aimed to define such a urinary metabolic signature in a patient cohort with sporadic CCA and investigate whether there were characteristic differences from those in patients with hepatocellular carcinoma (HCC), metastatic secondary liver cancer, pancreatic cancer and ovarian cancer (OCA).Methods: Spot urine specimens were obtained from 211 subjects in seven participating centers across the UK. Samples were collected from healthy controls and from patients with benign hepatic disease (gallstone, biliary strictures, sphincter of Oddi dysfunction and viral hepatitis) and patients with malignant conditions (HCC, pancreatic cancer, OCA and metastatic cancer in the liver). The spectral metabolite profiles were generated using a UPLC-MS detector and data were analyzed using multivariate and univariate statistical analyses.Results: The greatest class differences were seen between the metabolic profiles of disease-free controls compared to individuals with CCA with altered acylcarnitine, bile acid and purine levels. Individuals with benign strictures showed comparable urine profiles to patients with malignant bile duct lesions. The metabolic signatures of patients with bile duct tumors were distinguishable from patients with hepatocellular and ovarian tumors, but no difference was observed between CCA cases and patients with pancreatic cancer or hepatic secondary metastases.Conclusion: CCA causes subtle but detectable changes in the urine metabolic profiles. The findings point toward potential applications of metabonomics in early tumor detection. However, it is key to utilize both global and targeted metabonomics in a larger cohort for in-depth characterization of the urine metabolome in hepato-pancreato-biliary disease.Keywords: cholangiocarcinoma, metabolomics, diagnostic biomarkers