• Radojčić MR, Pierce M, Hope H, Senior M, Taxiarchi VP, Trefan L, et al. Trends in antipsychotic prescribing to children and adolescents in England: cohort study using 2000–19 primary care data. Lancet Psychiatry. 2023;10:119–28.

    PubMed 

    Google Scholar
     

  • Correll CU, Solmi M, Veronese N, Bortolato B, Rosson S, Santonastaso P, et al. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large‐scale meta‐analysis of 3211,768 patients and 113,383,368 controls. World Psychiatry. 2017;16:163–80.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Papanastasiou E. The prevalence and mechanisms of metabolic syndrome in schizophrenia: a review. Ther Adv Psychopharmacol. 2013;3:33–51.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wong KC-Y, Leung PB-M, Lee BK-W, Sham P-C, Lui SS-Y, So H-C. Long-term metabolic side effects of second-generation antipsychotics in Chinese patients with schizophrenia: a within-subject approach with modelling of dosage effects. Asian J Psychiatry. 2024;100:104172.


    Google Scholar
     

  • Gebhardt S, Theisen F, Haberhausen M, Heinzel‐Gutenbrunner M, Wehmeier P, Krieg JC, et al. Body weight gain induced by atypical antipsychotics: an extension of the monocygotic twin and sib pair study. J Clin Pharm Ther. 2010;35:207–11.

    PubMed 

    Google Scholar
     

  • Ryu S, Cho EY, Park T, Oh S, Jang W-S, Kim S-K, et al. − 759 C/T polymorphism of 5-HT2C receptor gene and early phase weight gain associated with antipsychotic drug treatment. Prog Neuro-Psychopharmacol Biol Psychiatry. 2007;31:673–7.


    Google Scholar
     

  • Balt S, Galloway G, Baggott M, Schwartz Z, Mendelson J. Mechanisms and genetics of antipsychotic‐associated weight gain. Clin Pharmacol Ther. 2011;90:179–83.

    PubMed 

    Google Scholar
     

  • van der Weide K, van der Weide J. The influence of the CYP3A4* 22 polymorphism and CYP2D6 polymorphisms on serum concentrations of aripiprazole, haloperidol, pimozide, and risperidone in psychiatric patients. J Clin Psychopharmacol. 2015;35:228–36.

    PubMed 

    Google Scholar
     

  • Yu H, Yan H, Wang L, Li J, Tan L, Deng W, et al. Five novel loci associated with antipsychotic treatment response in patients with schizophrenia: a genome-wide association study. Lancet Psychiatry. 2018;5:327–38.

    PubMed 

    Google Scholar
     

  • Li Q, Wineinger NE, Fu D-J, Libiger O, Alphs L, Savitz A, et al. Genome-wide association study of paliperidone efficacy. Pharmacogenetics Genomics. 2017;27:7–18.

    PubMed 

    Google Scholar
     

  • Li J, Yoshikawa A, Brennan MD, Ramsey TL, Meltzer HY. Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes. Schizophr Res. 2018;192:194–204.

    PubMed 

    Google Scholar
     

  • Adkins DE, Åberg K, McClay JL, Bukszár J, Zhao Z, Jia P, et al. Genomewide pharmacogenomic study of metabolic side effects to antipsychotic drugs. Mol Psychiatry. 2011;16:321–32.

    PubMed 

    Google Scholar
     

  • Yu H, Wang L, Lv L, Ma C, Du B, Lu T, et al. Genome-Wide Association Study suggested the PTPRD polymorphisms were associated with weight gain effects of atypical antipsychotic medications. Schizophr Bull. 2015;42:814–23.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sjaarda J, Delacrétaz A, Dubath C, Laaboub N, Piras M, Grosu C, et al. Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: a GWAS analysis. Mol Psychiatry. 2023;28:2320–7.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • ter Hark SE, Jamain S, Schijven D, Lin BD, Bakker MK, Boland-Auge A, et al. A new genetic locus for antipsychotic-induced weight gain: a genome-wide study of first-episode psychosis patients using amisulpride (from the OPTiMiSE cohort). J Psychopharmacol. 2020;34:524–31.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liao Y, Yu H, Zhang Y, Lu Z, Sun Y, Guo L, et al. Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation. Mol Psychiatry. 2024;29:1857–68.

    PubMed 

    Google Scholar
     

  • Brandl E, Tiwari A, Zai C, Nurmi E, Chowdhury N, Arenovich T, et al. Genome-wide association study on antipsychotic-induced weight gain in the CATIE sample. Pharmacogenomics J. 2016;16:352–6.

    PubMed 

    Google Scholar
     

  • Malhotra AK, Correll CU, Chowdhury NI, Müller DJ, Gregersen PK, Lee AT, et al. Association between common variants near the melanocortin 4 receptor gene and severe antipsychotic drug–induced weight gain. Arch Gen Psychiatry. 2012;69:904–12.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li L, Huang P, Sun X, Wang S, Xu M, Liu S, et al. The ChinaMAP reference panel for the accurate genotype imputation in Chinese populations. Cell Res. 2021;31:1308–10.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lui SS, Sham P, Chan RC, Cheung EF. A family study of endophenotypes for psychosis within an early intervention programme in Hong Kong: rationale and preliminary findings. Chin Sci Bull. 2011;56:3394–7.


    Google Scholar
     

  • Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48:1284–7.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Loh P-R, Danecek P, Palamara PF, Fuchsberger C, A Reshef Y, K Finucane H, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48:1443–8.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • VandenBerg AM. An update on recently approved long-acting injectable second-generation antipsychotics: knowns and unknowns regarding their use. Ment Health Clin. 2022;12:270–81.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chávez-Castillo M, Ortega Á, Nava M, Fuenmayor J, Lameda V, Velasco M, et al. Metabolic risk in depression and treatment with selective serotonin reuptake inhibitors: are the metabolic syndrome and an increase in cardiovascular risk unavoidable. Vessel Plus. 2018;2:2574–1209.


    Google Scholar
     

  • Fiorentino N, Soddu A, Solomita B, Rosato G, Franza F, Tavormina G. Metabolic alterations and drug interactions: the role of the association between antipsychotics/mood stabilizers and cognitive deficits. Psychiatr Danub. 2022;34(Suppl 8):100–4.

    PubMed 

    Google Scholar
     

  • Pardiñas AF, Nalmpanti M, Pocklington AJ, Legge SE, Medway C, King A, et al. Pharmacogenomic variants and drug interactions identified through the genetic analysis of clozapine metabolism. Am J Psychiatry. 2019;176:477–86.

    PubMed 

    Google Scholar
     

  • Smith RL, O’Connell K, Athanasiu L, Djurovic S, Kringen MK, Andreassen OA, et al. Identification of a novel polymorphism associated with reduced clozapine concentration in schizophrenia patients—a genome-wide association study adjusting for smoking habits. Transl Psychiatry. 2020;10:198.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Allen E, Knopp K, Rhoades G, Stanley S, Markman H. Between- and within-subject associations of PTSD symptom clusters and marital functioning in military couples. J Family Psychol. 2018;32:134–44.


    Google Scholar
     

  • Schirmbeck F, Konijn M, Hoetjes V, Vermeulen J, Zink M, Dekker J, et al. Stressful experiences affect the course of co-occurring obsessive-compulsive and psychotic symptoms: a focus on within-subject processes. Schizophr Res. 2020;216:69–76.

    PubMed 

    Google Scholar
     

  • Schirmbeck F, Konijn M, Hoetjes V, Zink M, de Haan L, For Genetic R, et al. Obsessive–compulsive symptoms in psychotic disorders: longitudinal associations of symptom clusters on between- and within-subject levels. Eur Arch Psychiatry Clin Neurosci. 2019;269:245–55.

    PubMed 

    Google Scholar
     

  • Hamaker EL Why researchers should think “within-person”: a paradigmatic rationale. Handbook of research methods for studying daily life. New York, NY, US: The Guilford Press; 2012, pp. 43–61.

  • McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics. 2020;76:1262–72.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guindo-Martínez M, Amela R, Bonàs-Guarch S, Puiggròs M, Salvoro C, Miguel-Escalada I, et al. The impact of non-additive genetic associations on age-related complex diseases. Nat Commun. 2021;12:2436.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The ensembl variant effect predictor. Genome Biol. 2016;17:1–14.


    Google Scholar
     

  • Ochoa D, Hercules A, Carmona M, Suveges D, Baker J, Malangone C, et al. The next-generation open targets platform: reimagined, redesigned, rebuilt. Nucleic Acids Res. 2023;51:D1353–D1359.

    PubMed 

    Google Scholar
     

  • Sollis E, Mosaku A, Abid A, Buniello A, Cerezo M, Gil L, et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023;51:D977–D985.

    PubMed 

    Google Scholar
     

  • Evangelista JE, Xie Z, Marino GB, Nguyen N, Clarke DJ, Ma’ayan A. Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic Acids Res. 2023;51:W168–W179.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25–29.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48:D845–D855.

    PubMed 

    Google Scholar
     

  • Gargano MA, Matentzoglu N, Coleman B, Addo-Lartey EB, Anagnostopoulos AV, Anderton J, et al. The human phenotype ontology in 2024: phenotypes around the world. Nucleic Acids Res. 2024;52:d1333–d1346.

    PubMed 

    Google Scholar
     

  • Wang G, Sarkar A, Carbonetto P, Stephens M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J R Stat Soc Ser B: Stat Methodol. 2020;82:1273–1300.


    Google Scholar
     

  • Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67:850–7.

    PubMed 

    Google Scholar
     

  • Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.


    Google Scholar
     

  • Fernando R, Nettleton D, Southey B, Dekkers J, Rothschild M, Soller M. Controlling the proportion of false positives in multiple dependent tests. Genetics. 2004;166:611–9.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Efron B. Simultaneous inference: When should hypothesis testing problems be combined? Ann Appl Stat. 2008;2:197–223.


    Google Scholar
     

  • Katzmarzyk PT, Janssen I, Ross R, Church TS, Blair SN. The importance of waist circumference in the definition of metabolic syndrome: prospective analyses of mortality in men. Diabetes Care. 2006;29:404–9.

    PubMed 

    Google Scholar
     

  • So H-C, Xue X, Ma Z, Sham P-C. SumVg: total heritability explained by all variants in genome-wide association studies based on summary statistics with standard error estimates. Int J Mol Sci. 2024;25:1347.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Purcell S, Cherny SS, Sham PC. Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics. 2003;19:149–50.

    PubMed 

    Google Scholar
     

  • Liu P, Hwang JG. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics. 2007;23:739–46.

    PubMed 

    Google Scholar
     

  • Quillen EE, Chen XD, Almasy L, Yang F, He H, Li X, et al. ALDH2 is associated to alcohol dependence and is the major genetic determinant of “daily maximum drinks” in a GWAS study of an isolated rural Chinese sample. Am J Med Genet B Neuropsychiatr Genet. 2014;165:103–10.


    Google Scholar
     

  • Cai N, Revez JA, Adams MJ, Andlauer TF, Breen G, Byrne EM, et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Nat Genet. 2020;52:437–47.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Comuzzie AG, Cole SA, Laston SL, Voruganti VS, Haack K, Gibbs RA, et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population. PLoS One. 2012;7:e51954.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tian R, Abarientos A, Hong J, Hashemi SH, Yan R, Dräger N, et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat Neurosci. 2021;24:1020–34.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leng K, Rose IV, Kim H, Xia W, Romero-Fernandez W, Rooney B, et al. CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states. Nat Neurosci. 2022;25:1528–42.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhu Z, Guo Y, Shi H, Liu C-L, Panganiban RA, Chung W, et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol. 2020;145:537–49.

    PubMed 

    Google Scholar
     

  • Ligthart S, Vaez A, Hsu Y-H, Consortium IWGotC, PMI-WG-XCP, Study LC. et al. Bivariate genome-wide association study identifies novel pleiotropic loci for lipids and inflammation. BMC Genomics. 2016;17:1–10.


    Google Scholar
     

  • Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Martin S, Cule M, Basty N, Tyrrell J, Beaumont RN, Wood AR, et al. Genetic evidence for different adiposity phenotypes and their opposing influences on ectopic fat and risk of cardiometabolic disease. Diabetes. 2021;70:1843–56.

    PubMed 

    Google Scholar
     

  • Scimè A, Rudnicki MA. Anabolic potential and regulation of the skeletal muscle satellite cell populations. Curr Opin Clin Nutr Metab Care. 2006;9:214–9.

    PubMed 

    Google Scholar
     

  • Meyer JM, Rosenblatt LC, Kim E, Baker RA, Whitehead R. The moderating impact of ethnicity on metabolic outcomes during treatment with olanzapine and aripiprazole in patients with schizophrenia. J Clin Psychiatry. 2009;70:318.

    PubMed 

    Google Scholar
     

  • Ader M, Garvey WT, Phillips LS, Nemeroff CB, Gharabawi G, Mahmoud R, et al. Ethnic heterogeneity in glucoregulatory function during treatment with atypical antipsychotics in patients with schizophrenia. J Psychiatr Res. 2008;42:1076–85.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang S, Yu B, Yu W, Dai S, Feng C, Shao Y, et al. Development and validation of an age-sex-ethnicity-specific metabolic syndrome score in the Chinese adults. Nat Commun. 2023;14:6988.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Heymsfield SB, Peterson CM, Thomas DM, Heo M, Schuna JrJ. Why are there race/ethnic differences in adult body mass index–adiposity relationships? A quantitative critical review. Obes Rev. 2016;17:262–75.

    PubMed 

    Google Scholar
     

  • Twisk JWR, de Vente W. Hybrid models were found to be very elegant to disentangle longitudinal within- and between-subject relationships. J Clin Epidemiol. 2019;107:66–70.

    PubMed 

    Google Scholar
     

  • Nasyrova RF, Shnayder NA, Osipova SM, Khasanova AK, Efremov IS, Al-Zamil M, et al. Genetic predictors of antipsychotic efflux impairment via blood-brain barrier: role of transport proteins. Genes. 2023;14:1085.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bohn K, Lange A, Chmielewski J, Hrycyna CA. Dual modulation of human P-glycoprotein and ABCG2 with prodrug dimers of the atypical antipsychotic agent paliperidone in a model of the blood–brain barrier. Mol Pharmaceutics. 2017;14:1107–19.


    Google Scholar
     

  • Chasman DI, Giulianini F, MacFadyen J, Barratt BJ, Nyberg F, Ridker PM. Genetic determinants of statin-induced low-density lipoprotein cholesterol reduction: the Justification for the use of Statins in prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial. Circ Cardiovasc Genet. 2012;5:257–64.

    PubMed 

    Google Scholar
     

  • Alrajeh K, Roman YM. The frequency of rs2231142 in ABCG2 among Asian subgroups: implications for personalized rosuvastatin dosing. Pharmacogenomics. 2023;24:15–26.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang Y, Iwasaki H, Wang H, Kudo T, Kalka TB, Hennet T, et al. Cloning and characterization of a new human UDP-N-Acetyl-α-d-galactosamine: Polypeptiden-Acetylgalactosaminyltransferase, designated pp-GalNAc-T13, that is specifically expressed in neurons and synthesizes GalNAc α-Serine/Threonine antigen. J Biol Chem. 2003;278:573–84.

    PubMed 

    Google Scholar
     

  • Clee SM, Yandell BS, Schueler KM, Rabaglia ME, Richards OC, Raines SM, et al. Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus. Nat Genet. 2006;38:688–93.

    PubMed 

    Google Scholar
     

  • Goodarzi MO, Lehman DM, Taylor KD, Guo X, Cui J, Quinones MJ, et al. SORCS1: a novel human type 2 diabetes susceptibility gene suggested by the mouse. Diabetes. 2007;56:1922–9.

    PubMed 

    Google Scholar
     

  • Florez JC, Manning AK, Dupuis J, McAteer J, Irenze K, Gianniny L, et al. A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets. Diabetes. 2007;56:3063–74.

    PubMed 

    Google Scholar
     

  • Lin SX, Berlin I, Younge R, Jin Z, Sibley CT, Schreiner P, et al. Does elevated plasma triglyceride level independently predict impaired fasting glucose? The Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2013;36:342–7.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Beshara A, Cohen E, Goldberg E, Lilos P, Garty M, Krause I. Triglyceride levels and risk of type 2 diabetes mellitus: a longitudinal large study. J Investig Med. 2016;64:383–7.

    PubMed 

    Google Scholar
     

  • Dotevall A, Johansson S, Wilhelmsen L, Rosengren A. Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women. A prospective 18‐year follow‐up of the BEDA* study. Diabet Med. 2004;21:615–22.

    PubMed 

    Google Scholar
     

  • Subkhangulova A, Malik AR, Hermey G, Popp O, Dittmar G, Rathjen T, et al. SORCS 1 and SORCS 3 control energy balance and orexigenic peptide production. EMBO Rep. 2018;19:e44810.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Le TT, Ziemba A, Urasaki Y, Hayes E, Brotman S, Pizzorno G. Disruption of uridine homeostasis links liver pyrimidine metabolism to lipid accumulation. J Lipid Res. 2013;54:1044–57.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Le TT, Urasaki Y, Pizzorno G. Uridine prevents fenofibrate-induced fatty liver. PLoS One. 2014;9:e87179.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Urasaki Y, Pizzorno G, Le TT. Chronic uridine administration induces fatty liver and pre-diabetic conditions in mice. PLoS One. 2016;11:e0146994.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang Y, Repa JJ, Inoue Y, Hayhurst GP, Gonzalez FJ, Mangelsdorf DJ. Identification of a liver-specific uridine phosphorylase that is regulated by multiple lipid-sensing nuclear receptors. Mol Endocrinol. 2004;18:851–62.

    PubMed 

    Google Scholar
     

  • Döring A, Gieger C, Mehta D, Gohlke H, Prokisch H, Coassin S, et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects. Nat Genet. 2008;40:430–6.

    PubMed 

    Google Scholar
     

  • Wallace C, Newhouse SJ, Braund P, Zhang F, Tobin M, Falchi M, et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am J Hum Genet. 2008;82:139–49.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tin A, Marten J, Halperin Kuhns VL, Li Y, Wuttke M, Kirsten H, et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels. Nat Genet. 2019;51:1459–74.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vitart V, Rudan I, Hayward C, Gray NK, Floyd J, Palmer CN, et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout. Nat Genet. 2008;40:437–42.

    PubMed 

    Google Scholar
     

  • Zhang Y, Wei F, Chen C, Cai C, Zhang K, Sun N, et al. Higher triglyceride level predicts hyperuricemia: a prospective study of 6-year follow-up. J Clin Lipidol. 2018;12:185–92.

    PubMed 

    Google Scholar
     

  • Giacomello A, Di Sciascio N, Quaratino CP. Relation between serum triglyceride level, serum urate concentration, and fractional urate excretion. Metabolism. 1997;46:1085–9.

    PubMed 

    Google Scholar
     

  • Zheng R, Ren P, Chen Q, Yang T, Chen C, Mao Y. Serum uric acid levels and risk of incident hypertriglyceridemia: a longitudinal population-based epidemiological study. Ann Clin Lab Sci. 2017;47:586–91.

    PubMed 

    Google Scholar
     

  • Umlai U-KI, Toor SM, Al-Sarraj YA, Mohammed S, Al Hail MS, Ullah E et al. Identification of a novel SLC2A9 gene association with LDL-C levels and evaluation of polygenic scores in a Multi-Ancestry Genome Wide Association Study. medRxiv: 2024.2007.2004.24309936. [Preprint]. 2024. Available from: https://www.medrxiv.org/content/10.1101/2024.07.04.24309936v1.

  • Garelnabi M, Lor K, Jin J, Chai F, Santanam N. The paradox of ApoA5 modulation of triglycerides: evidence from clinical and basic research. Clin Biochem. 2013;46:12–19.

    PubMed 

    Google Scholar
     

  • Pennacchio LA, Rubin EM. Apolipoprotein A5, a newly identified gene that affects plasma triglyceride levels in humans and mice. Arterioscler Thromb Vasc Biol. 2003;23:529–34.

    PubMed 

    Google Scholar
     

  • Martin S, Nicaud V, Humphries SE, Talmud PJ. Contribution of APOA5 gene variants to plasma triglyceride determination and to the response to both fat and glucose tolerance challenges. Biochimi Biophys Acta Mol Basis Dis. 2003;1637:217–25.


    Google Scholar
     

  • Park YJ, Moon S, Choi J, Kim J, Kim H-J, Son H-Y, et al. Genome-wide association study for metabolic syndrome reveals APOA5 single nucleotide polymorphisms with multilayered effects in Koreans. Lipids Health Dis. 2024;23:272.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ueyama C, Horibe H, Yamase Y, Fujimaki T, Oguri M, Kato K, et al. Association of FURIN and ZPR1 polymorphisms with metabolic syndrome. Biomed Rep. 2015;3:641–7.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paquette M, Fantino M, Bernard S, Baass A. The ZPR1 genotype predicts myocardial infarction in patients with familial hypercholesterolemia. J Clin Lipidol. 2020;14:660–6.

    PubMed 

    Google Scholar
     

  • Guan F, Niu Y, Zhang T, Liu S, Ma L, Qi T, et al. Two-stage association study to identify the genetic susceptibility of a novel common variant of rs2075290 in ZPR1 to type 2 diabetes. Sci Rep. 2016;6:29586.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Esteve-Luque V, Padró-Miquel A, Fanlo-Maresma M, Corbella E, Corbella X, Pintó X, et al. Implication between genetic variants from APOA5 and ZPR1 and NAFLD severity in patients with hypertriglyceridemia. Nutrients. 2021;13:552.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang J-P, Lencz T, Zhang RX, Nitta M, Maayan L, John M, et al. Pharmacogenetic associations of antipsychotic drug-related weight gain: a systematic review and meta-analysis. Schizophr Bull. 2016;42:1418–37.

    PubMed 
    PubMed Central 

    Google Scholar