Medicine
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This repository contains the published and unpublished research of the Faculty of Medicine by the staff members of the faculty
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Item Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci(Frontiers Research Foundation, 2023) de Las, F.L.; Schwande, K.L.; Brown, M.R.; Bentley, A.R.; Winkler, T.W.; Sung, Y.J.; Munroe, P.B.; Miller, C.L.; Aschard, H.; Aslibekyan, S.; Bartz, T.M.; Bielak, L.F.; Chai, J.F.; Cheng, C.Y.; Dorajoo, R.; Feitosa, M.F.; Guo, X.; Hartwig, F.P.; Horimoto, A.; Kolčić, I.; Lim, E.; Liu, Y.; Manning, A.K.; Marten, J.; Musani, S.K.; Noordam, R.; Padmanabhan, S.; Rankinen, T.; Richard, M.A.; Ridker, P.M.; Smith, A.V.; Vojinovic, D.; Zonderman, A.B.; Alver, M.; Boissel, M.; Christensen, K.; Freedman, B.I.; Gao, C.; Giulianini, F.; Harris, S.E.; He, M.; Hsu, F.C.; Kühnel, B.; Laguzzi, F.; Li, X.; Lyytikäinen, L.P.; Nolte, I.M.; Poveda, A.; Rauramaa, R.; Riaz, M.; Robino, A.; Sofer, T.; Takeuchi, F.; Tayo, B.O.; van der, M.P.J.; Verweij, N.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhan, Y.; Amin, N.; Arking, D.E.; Ballantyne, C.; Boerwinkle, E.; Brody, J.A.; Broeckel, U.; Campbell, A.; Canouil, M.; Chai, X.; Chen, Y.I.; Chen, X.; Chitrala, K.N.; Concas, M.P.; de Faire, U.; de Mutsert, R.; de Silva, H.J.; de Vries, P.S.; Do, A.; Faul, J.D.; Fisher, V.; Floyd, J.S.; Forrester, T.; Friedlander, Y.; Girotto, G.; Gu, C.C.; Hallmans, G.; Heikkinen, S.; Heng, C.K.; Homuth, G.; Hunt, S.; Ikram, M.A.; Jacobs, D.R.J.R.; Kavousi, M.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W.P.; Komulainen, P.; Langefeld, C.D.; Liang, J.; Liu, K.; Liu, J.; Lohman, K.; Mägi, R.; Manichaikul, A.W.; McKenzie, C.A.; Meitinger, T.; Milaneschi, Y.; Nauck, M.; Nelson, C.P.; O'Connell, J.R.; Palmer, N.D.; Pereira, A.C.; Perls, T.; Peters, A.; Polašek, O.; Raitakari, O.T.; Rice, K.; Rice, T.K.; Rich, S.S.; Sabanayagam, C.; Schreiner, P.J.; Shu, X.; Sidney, S.; Sims, M.; Smith, J.A.; Starr, J.M.; Strauch, K.; Tai, E.S.; Taylor, K.D.; Tsai, M.Y.; Uitterlinden, A.G.; Heemst, D.V.; Waldenberger, M.; Wang, Y.; Wei, W.; Wilson, G.; Xuan, D.; Yao, J.; Yu, C.; Yuan, J.; Zhao, W.; Becker, D.M.; Bonnefond, A.; Bowden, D.W.; Cooper, R.S.; Deary, I.J.; Divers, J.; Esko, T.; Franks, P.W.; Froguel, P.; Gieger, C.; Jonas, J.B.; Kato, N.; Lakka, T.A.; Leander, K.; Lehtimäki, T.; Magnusson, P.K.E.; North, K.E.; Ntalla, I.; Penninx, B.; Samani, N.J.; Snieder, H.; Spedicati, B.; Harst, P.V.D.; Völzke, H.; Wagenknecht, L.E.; Weir, D.R.; Wojczynski, M.K.; Wu, T.; Zheng, W.; Zhu, X.; Bouchard, C.; Chasman, D.I.; Evans, M.K.; Fox, E.R.; Gudnason, V.; Hayward, C.; Horta, B.L.; Kardia, S.L.R.; Krieger, J.E.; Mook-Kanamori, D.O.; Peyser, P.A.; Province, M.M.; Psaty, B.M.; Rudan, I.; Sim, X.; Smith, B.H.; Dam, R.M.V.; Duijn, C.M.V.; Wong, T.Y.; Arnett, D.K.; Rao, D.C.; Gauderman, J.; Liu, C.; Morrison, A.C.; Rotter, J.I.; Fornage, M.INTRODUCTION: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. METHODS: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). RESULTS: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. DISCUSSION: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.Item The first reported case of co-infection of imported hepatitis E and Plasmodium falciparum malaria in Sri Lanka(Sri Lankan Society for Microbiology, 2023) Senarathne, S.; Rajapakse, S.; de Silva, H.J.; Seneviratne, S.; Chulasiri, P.; Fernando, D.Global travel and tourism, especially across tropical countries, may lead to importation of malaria and other infectious diseases into Sri Lanka. This case report describes the first co-infection of imported hepatitis E and Plasmodium falciparum malaria in a tourist diagnosed in Sri Lanka. The patient was initially diagnosed with uncomplicated P. falciparum malaria and was started on treatment with oral Artemisinin-based Combination Therapy (ACT). Deterioration of hepatic enzymes and hyperbilirubinaemia despite the rapid parasitological response to antimalarials led to further investigation and diagnosis of co-infecting hepatitis E in this patient. The importance of clinicians being vigilant on travel associated co-infections is highlighted to ensure early diagnosis and better patient management.Item First co-infection of malaria and hepatitis E diagnosed in Sri Lanka(Sri Lanka Medical Association, 2023) Senarathne, S.; Rajapakse, S.; de Silva, H.J.; Seneviratne, S.; Chulasiri, P.; Fernando, D.INTRODUCTION: Imported malaria cases continue to be reported in Sri Lanka. Similarly, hepatitis E is also considered a travel associated imported disease in Sri Lanka. This is a report of the first co-infection of malaria and hepatitis E in Sri Lanka. OBJECTIVES: A 21-year-old European who visited Sri Lanka after a 2 months stay in India, was admitted to hospital with fever, vomiting, abdominal pain, and dark-coloured urine on the 4th day after his arrival. On examination, he had splenomegaly but no hepatomegaly. He had thrombocytopaenia; 89% neutrophils; 9% lymphocytes; elevated liver enzymes and hyperbilirubinaemia. Urine was positive for bile pigment. METHODS: Considering his travel history to India, he was tested for malaria. The rapid diagnostic test became positive for Plasmodium falciparum while microscopy showed P. falciparum ring stages with a parasite density of 120/μl. He was treated as for uncomplicated P. falciparum malaria with oral Artemisinin-based Combination Therapy. The patient became fever-free and blood smears became negative after 13 hours following 2 doses of antimalarials. RESULTS: However, his liver functions were further deranged with apparent jaundice (ALT: 250 U/L; AST: 175 U/L; ALP: 130 U/L; GGT: 179 U/L; total bilirubin: 10.65 mg/dL; direct bilirubin: 8.08 mg/dL; indirect bilirubin: 2.57 mg/dL). Further blood tests detected hepatitis E-specific IgM antibodies. He was treated with oral ursodiol but no specific antiviral was given. Following the completion of antimalarials, he was discharged from the hospital upon clinical recovery. CONCLUSION: Clinicians should be vigilant on travel-associated co-infections in patients who are diagnosed with imported malaria.Item Identification of type 2 diabetes patients with non-alcoholic fatty liver disease who are at increased risk of significant hepatic fibrosis: a cross-sectional study(Sri Lanka Medical Association, 2023) Mettananda, K.C.D.; Egodage, T.; Dantanarayana, C.; Solangarachchi, M.B.; Fernando, R.; Ranaweera, L.; Siriwardhena, S.; Ranawaka, C.K.; Kottahachchi, D.; Pathmeswaran, A.; Dassanayake, A.S.; de Silva, H.J.INTRODUCTION: Annual screening of patients with diabetes for fatty liver, and identifying those with significant hepatic fibrosis using the FIB-4 score and vibration-controlled transient elastography (VCTE) has been recommended to detect patients who may progress to advanced hepatic fibrosis/cirrhosis. However, VCTE is not freely available in resource-limited settings. OBJECTIVES: To identify clinical and biochemical predictors of significant liver fibrosis in diabetics with fatty liver. METHODS: We conducted a cross-sectional study among all consenting adults with T2DM and non-alcoholic fatty liver disease (NAFLD) attending the Colombo North Teaching Hospital, Ragama, Sri Lanka from November 2021 to November 2022. FIB-4 scores were calculated and patients with a score ≥1.3 underwent VCTE. Risk associations for liver fibrosis were identified by comparing patients with significant fibrosis (LSM ≥8 kPa) with those without significant fibrosis (FIB-4<1.3). RESULTS: A total of 363 persons were investigated. Of these, 243 had a score of FIB-4 <1.3. Of the 120 with a FIB-4 ≥1.3, 76 had LSM ≥8 kPa. Significant fibrosis was individually associated with age (OR 1.01, p<0.0001), duration of diabetes (OR 1.02, p=0.006), family history of liver disease (OR 1.42, p=0.035), waist (OR 1.04, p=0.035), and FIB-4 (OR 2.08, p<0.0001). However, on adjusted analysis, significant fibrosis was only associated with a family history of liver disease (OR 2.69, p=0.044) and FIB-4 (OR 1.43, p<0.001). CONCLUSION: In patients with T2DM and fatty liver, advancing age, increased duration of diabetes, a family history of liver disease, waist circumference and a high FIB-4 score increase the risk of significant hepatic fibrosis. Targeted interventions in this group may help prevent progression to advanced hepatic fibrosis/cirrhosis.Item Is splenic stiffness measurement(SSM) better than Baveno VII criteria to predict oesophageal and cardio- fundal varices in patients with compensated advanced liver cell disease (cACLD)?(Sri Lanka Medical Association, 2023) de Silva, A.P.; Niriella, M.A.; Nishad, A.A.N.; Samarawickrama, V.T.; Jayasundara, H.; Ranawaka, C.K.; de Silva, S.T.; Withanage, M.; Ediriweera, D.; de Silva, H.J.INTRODUCTION: Liver and splenic stiffness measurements (LSM and SSM) using transient elastography (TE) are being increasingly used as a screening tool to predict varices. OBJECTIVES: We aimed to test the utility of Baveno-VII criteria (LSM>25kPa, LSM>20kPa with platelet count <130,000 and LSM>15kPa with platelet count <110,000) and SSM to predict oesophageal and cardio-fundal varices in a cohort of Sri Lankan patients with aALCD. METHODS: Consecutive patients with newly diagnosed Child’s class A cALCD (non-viral, BMI<30) were recruited prospectively. They underwent upper gastrointestinal endoscopy by an endoscopist followed by a Fibroscan by an operator who is unaware of endoscopy findings using ECHOSENS-Fibroscan-502 to measure LSM and SSM. Validity measurements of three Baveno-VII criteria and SSM values to predict oesophageal and cardio-fundal varices were calculated. RESULTS: One hundred and seventy-four individuals were recruited [Mean (95%CI) age 61.4 (59.7-62.8) years, 110 males], and 106 had varices. Our results indicate that the three Baveno VII criteria had sensitivities of 61%, 63% and 42%, and specificities of 79%, 77% and 87%. SSM>30kPa alone or in combination with LSM>15kPa had sensitivity of 81&75%, specificity of 72&83%, PPV of 82&87%, NPV of 71&67% and accuracy of 78&78% consecutively to predict oesophageal and cardio-fundal varices. CONCLUSION: Baveno VII criteria had low sensitivity but high specificity to predict oesophageal and cardio-fundal varices. SSM>30kPa alone or in combination with LSM>15kPa seemed to predict oesophageal and cardio-fundal varices better.Item Prevalence and associated factors for non-alcoholic fatty liver disease among adults in the South Asian Region: a meta-analysis(Elsevier, 2023) Niriella, M.A.; Ediriweera, D.S.; Withanage, M.Y.; Darshika, S.; de Silva, S.T.; de Silva, H.J.BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is the commonest chronic liver disease worldwide. We estimated the prevalence and predefined associated factors for NAFLD among South-Asian adults. METHODS: We searched PubMed and included descriptive, epidemiological studies with satisfactory methodology, reporting the prevalence of NAFLD with ultrasound. Two authors screened and extracted data independently. Gender, urban/rural settings, general population and individuals with metabolic diseases (MetD) stratified the analysis. In addition, a random-effects meta-analysis of the prevalence and effect sizes of associations of NAFLD was performed. FINDINGS: Twenty-two publications were included after the quality assurance process. The difference in the NAFLD prevalence between the general population and people with MetD was found to be statistically significant (Q = 15.8, DF = 1, P < 0.001). The pooled overall prevalence of NAFLD in the general population was 26.9% (95% CI: 18.9-35.8%) with high heterogeneity. The prevalence was similar among men and women (Q = 0.06, DF = 1, P = 0.806). The NAFLD prevalence in the rural communities was 22.6% (95% CI: 13.6-33.1%), and the prevalence in urban communities was 32.9% (95% CI: 22.8-43.8%) and the difference was not statistically significant (Q = 1.92, DF = 1, P = 0.166). The pooled overall prevalence of NAFLD in patients with MetD was 54.1% (95% CI: 44.1-63.9%) with high heterogeneity. The pooled overall prevalence of NAFLD in the non-obese population was 11.7% (95% CI: 7.0-17.3%). The pooled prevalence of non-obese NAFLD in the NAFLD population was 43.4% (95% CI: 28.1-59.4%). Meta-analysis of binary variables showed that NAFLD in the South Asian population was associated with diabetes mellitus, hypertension, dyslipidaemia, general obesity, central obesity and metabolic syndrome. Gender was not associated with NAFLD. INTERPRETATION: The overall prevalence of NAFLD among adults in South Asia is high, especially in those with MetD, and a considerable proportion is non-obese. In the South Asian population, NAFLD was associated with diabetes mellitus, hypertension, dyslipidaemia, general obesity, central obesity, and metabolic syndrome.Item Coagulopathy and fibrinolysis following the bite of a hump-nosed viper (Hypnale hypnale)(Oxford University Press, 1996) Premawardena, A.P.; Seneviratne, S.L.; Jayanthi, S.; Gunatilake, S.B.; de Silva, H.J.No abstract availableItem Climate change maladaptation for health: Agricultural practice against shifting seasonal rainfall affects snakebite risk for farmers in the tropics(Cell Press, 2023) Goldstein, E.; Erinjery, J.J.; Martin, G.; Kasturiratne, A.; Ediriweera, D.S.; Somaweera, R.; de Silva, H.J.; Diggle, P.; Lalloo, D.G.; Murray, K.A.; Iwamura, T.Snakebite affects more than 1.8 million people annually. Factors explaining snakebite variability include farmers' behaviors, snake ecology and climate. One unstudied issue is how farmers' adaptation to novel climates affect their health. Here we examined potential impacts of adaptation on snakebite using individual-based simulations, focusing on strategies meant to counteract major crop yield decline because of changing rainfall in Sri Lanka. For rubber cropping, adaptation led to a 33% increase in snakebite incidence per farmer work hour because of work during risky months, but a 17% decrease in total annual snakebites because of decreased labor in plantations overall. Rice farming adaptation decreased snakebites by 16%, because of shifting labor towards safer months, whereas tea adaptation led to a general increase. These results indicate that adaptation could have both a positive and negative effect, potentially intensified by ENSO. Our research highlights the need for assessing adaptation strategies for potential health maladaptations.Item Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis(BioMed Central Ltd, 2022) Kanoni, S.; Graham, S.E.; Wang, Y.; Surakka, I.; Ramdas, S.; Zhu, X.; Clarke, S.L.; Bhatti, K.F.; Vedantam, S.; Winkler, T.W.; Locke, A.E.; Marouli, E.; Zajac, G.J.M.; Wu, K.H.; Ntalla, I.; Hui, Q.; Klarin, D.; Hilliard, A.T.; Wang, Z.; Xue, C.; Thorleifsson, G.; Helgadottir, A.; Gudbjartsson, D.F.; Holm, H.; Olafsson, I.; Hwang, M.Y.; Han, S.; Akiyama, M.; Sakaue, S.; Terao, C.; Kanai, M.; Zhou, W.; Brumpton, B.M.; Rasheed, H.; Havulinna, A.S.; Veturi, Y.; Pacheco, J.A.; Rosenthal, E.A.; Lingren, T.; Feng, Q.; Kullo, I.J.; Narita, A.; Takayama, J.; Martin, H.C.; Hunt, K.A.; Trivedi, B.; Haessler, J.; Giulianini, F.; Bradford, Y.; Miller, J.E.; Campbell, A.; Lin, K.; Lin, K.; Millwood, I.Y.; Rasheed, A.; Hindy, G.; Faul, J.D.; Zhao, W.; Weir, D.R.; Turman, C.; Huang, H.; Graff, M.; Choudhury, A.; Sengupta, D.; Mahajan, A.; Brown, M.R.; Zhang, W.; Yu, K.; Schmidt, E.M.; Pandit, A.; Gustafsson, S.; Yin, X.; Luan, J.; Zhao, J.H.; Matsuda, F.; Jang, H.M.; Yoon, K.; Medina-Gomez, C.; Pitsillides, A.; Hottenga, J.J.; Wood, A.R.; Ji, Y.; Gao, Z.; Haworth, S.; Yousri, N.A.; Mitchell, R.E.; Chai, J.F.; Aadahl, M.; Bjerregaard, A.A.; Yao, J.; Manichaikul, A.; Hwu, C.M.; Hung, Y.J.; Warren, H.R.; Ramirez, J.; Bork-Jensen, J.; Kårhus, L.L.; Goel, A.; Sabater-Lleal, M.; Noordam, R.; Mauro, P.; Matteo, F.; McDaid, A.F.; Marques-Vidal, P.; Wielscher, M.; Trompet, S.; Sattar, N.; Møllehave, L.T.; Munz, M.; Zeng, L.; Huang, J.; Yang, B.; Poveda, A.; Kurbasic, A.; Lamina, C.; Forer, L.; Scholz, M.; Galesloot, T.E.; Bradfield, J.P.; Ruotsalainen, S.E.; Daw, E.; Zmuda, J.M.; Mitchell, J.S.; Fuchsberger, C.; Christensen, H.; Brody, J.A.; Vazquez-Moreno, M.; Feitosa, M.F.; Wojczynski, M.K.; Wang, Z.; Preuss, M.H.; Mangino, M.; Christofidou, P.; Verweij, N.; Benjamins, J.W.; Engmann, J.; Tsao, N.L.; Verma, A.; Slieker, R.C.; Lo, K.S.; Zilhao, N.R.; Le, P.; Kleber, M.E.; Delgado, G.E.; Huo, S.; Ikeda, D.D.; Iha, H.; Yang, J.; Liu, J.; Demirkan, A.; Leonard, H.L.; Marten, J.; Frank, M.; Schmidt, B.; Smyth, L.J.; Cañadas-Garre, M.; Wang, C.; Nakatochi, M.; Wong, A.; Hutri-Kähönen, N.; Lyssenko, V.; Fernandez-Lopez, J.C.; Huerta-Chagoya, A.; Xia, R.; Sim, X.; Nongmaithem, S.S.; Bayyana, S.; Stringham, H.M.; Irvin, M.R.; Oldmeadow, C.; Kim, H.N.; Ryu, S.; Timmers, P,R,H,J,; Arbeeva, L.; Dorajoo, R.; Lange, L.A.; Prasad, G.; Lorés-Motta, L.; Pauper, M.; Long, J.; Li, X.; Theusch, E.; Takeuchi, F.; Spracklen, C.N.; Loukola, A.; Bollepalli, S.; Warner, S.C.; Wang, Y.X.; Wei, W.B.; Nutile, T.; Ruggiero, D.; Sung, Y.J.; Chen, S.; Liu, F.; Yang, J.; Kentistou, K.A.; Banas, B.; Nardone, G.G.; Meidtner, K.; Bielak, L.F.; Smith, J.A.; Hebbar, P.; Farmaki, A.E.; Hofer, E.; Lin, M.; Concas, M.P.; Vaccargiu, S.; van der Most, P.J.; Pitkänen, N.; Cade, B.E.; van der Laan, S.W.; Chitrala, K.N.; Weiss, S.; Bentley, A.R.; Doumatey, A.P.; Adeyemo, A.A.; Lee, J.Y.; Petersen, E.R.B.; Nielsen, A.A.; Choi, H.S.; Nethander, M.; Freitag-Wolf, S.; Southam, L.; Rayner, N.W.; Wang, C.A.; Lin, S.Y.; Wang, J.S.; Couture, C.; Lyytikäinen, L.P.; Nikus, K.; Cuellar-Partida, G.; Vestergaard, H.; Hidalgo, B.; Giannakopoulou, O.; Cai, Q.; Obura, M.O.; van Setten, J.; Li, X.; Liang, J.; Tang, H.; Terzikhan, N.; Shin, J.H.; Jackson, R.D.; Reiner, A.P.; Martin, L.W.; Chen, Z.; Li, L.; Kawaguchi, T.; Thiery, J.; Bis, J.C.; Launer, L.J.; Li, H.; Nalls, M.A.; Raitakari, O.T.; Ichihara, S.; Wild, S.H.; Nelson, C.P.; Campbell, H.; Jäger, S.; Nabika, T.; Al-Mulla, F.; Niinikoski, H.; Braund, P.S.; Kolcic, I.; Kovacs, P.; Giardoglou, T.; Katsuya, T.; de Kleijn, D.; de Borst, G.J.; Kim, E.K.; Adams, H.H.H.; Ikram, M.A.; Zhu, X.; Asselbergs, F.W.; Kraaijeveld, A.O.; Beulens, J.W.J.; Shu, X.O.; Rallidis, L.S.; Pedersen, O.; Hansen, T.; Mitchell, P.; Hewitt, A.W.; Kähönen, M.; Pérusse, L.; Bouchard, C.; Tönjes, A.; Chen, Y.I.; Pennell, C.E.; Mori, T.A.; Lieb, W.; Franke, A.; Ohlsson, C.; Mellström, D.; Cho, Y.S.; Lee, H.; Yuan, J.M.; Koh, W.P.; Rhee, S.Y.; Woo, J.T.; Heid, I.M.; Stark, K.J.; Zimmermann, M.E.; Völzke, H.; Homuth, G.; Evans, M.K.; Zonderman, A.B.; Polasek, O.; Pasterkamp, G.; Hoefer, I.E.; Redline, S.; Pahkala, K.; Oldehinkel, A.J.; Snieder, H.; Biino, G.; Schmidt, R.; Schmidt, H.; Bandinelli, S.; Dedoussis, G.; Thanaraj, T.A.; Kardia, S.L.R.; Peyser, P.A.; Kato, N.; Schulze, M.B.; Girotto, G.; Böger, C.A.; Jung, B.; Joshi, P.K.; Bennett, D.A.; de Jager, P.L.; Lu, X.; Mamakou, V.; Brown, M.; Caulfield, M.J.; Munroe, P.B.; Guo, X.; Ciullo, M.; Jonas, J.B.; Samani, N.J.; Kaprio, J.; Pajukanta, P.; Tusié-Luna, T.; Aguilar-Salinas, C.A.; Adair, L.S.; Bechayda, S.A.; de Silva, H.J.; Wickremasinghe, A.R.; Krauss, R.M.; Wu, J.Y.; Zheng, W.; Hollander, A.I.; Bharadwaj, D.; Correa, A.; Wilson, J.G.; Lind, L.; Heng, C.K.; Nelson, A.E.; Golightly, Y.M.; Wilson, J.F.; Penninx, B.; Kim, H.L.; Attia, J.; Scott, R.J.; Rao, D.C.; Arnett, D.K.; Hunt, S.C.; Walker, M.; Koistinen, H.A.; Chandak, G.R.; Mercader, J.M.; Costanzo, M.C.; Jang, D.; Burtt, N.P.; Villalpando, C.G.; Orozco, L.; Fornage, M.; Tai, E.; van Dam, R.M.; Lehtimäki, T.; Chaturvedi, N.; Yokota, M.; Liu, J.; Reilly, D.F.; McKnight, A.J.; Kee, F.; Jöckel, K.H.; McCarthy, M.I.; Palmer, C.N.A.; Vitart, V.; Hayward, C.; Simonsick, E.; van Duijn, C.M.; Jin, Z.B.; Qu, J.; Hishigaki, H.; Lin, X.; März, W.; Gudnason, V.; Tardif, J.C.; Lettre, G.; Hart, L.M.; Elders, P.J.M.; Damrauer, S.M.; Kumari, M.; Kivimaki, M.; van der Harst, P.; Spector, T.D.; Loos, R.J.F.; Province, M.A.; Parra, E.J.; Cruz, M.; Psaty, B.M.; Brandslund, I.; Pramstaller, P.P.; Rotimi, C.N.; Christensen, K.; Ripatti, S.; Widén, E.; Hakonarson, H.; Grant, S.F.A.; Kiemeney, L.A.L.M.; de Graaf, J.; Loeffler, M.; Kronenberg, F.; Gu, D.; Erdmann, J.; Schunkert, H.; Franks, P.W.; Linneberg, A.; Jukema, J.W.; Khera, A.V.; Männikkö, M.; Jarvelin, M.R.; Kutalik, Z.; Francesco, C.; Mook-Kanamori, D.O.; van Dijk, K.W.; Watkins, H.; Strachan, D.P.; Grarup, N.; Sever, P.; Poulter, N.; Chuang, L.M.; Rotter, J.I.; Dantoft, T.M.; Karpe, F.; Neville, M.J.; Timpson, N.J.; Cheng, C.Y.; Wong, T.Y.; Khor, C.C.; Li, H.; Sabanayagam, C.; Sabanayagam, C.; Peters, A.; Gieger, C.; Hattersley, A.T.; Pedersen, N.L.; Magnusson, P.K.E.; Boomsma, D.I.; Willemsen, A.H.M.; Cupples, L.; van Meurs, J.B.J.; Ghanbari, M.; Gordon-Larsen, P.; Huang, W.; Kim, Y.J.; Tabara, Y.; Wareham, N.J.; Langenberg, C.; Zeggini, E.; Kuusisto, J.; Laakso, M.; Ingelsson, E.; Abecasis, G.; Chambers, J.C.; Kooner, J.S.; de Vries, P.S.; Morrison, A.C.; Hazelhurst, S.; Ramsay, M.; North, K.E.; Daviglus, M.; Kraft, P.; Martin, N.G.; Whitfield, J.B.; Abbas, S.; Saleheen, D.; Walters, R.G.; Holmes, M.V.; Black, C.; Smith, B.H.; Baras, A.; Justice, A.E.; Buring, J.E.; Ridker, P.M.; Chasman, D.I.; Kooperberg, C.; Tamiya, G.; Yamamoto, M.; van Heel, D.A.; Trembath, R.C.; Wei, W.Q.; Jarvik, G.P.; Namjou, B.; Hayes, M.G.; Ritchie, M.D.; Jousilahti, P.; Salomaa, V.; Hveem, K.; Åsvold, B.O.; Kubo, M.; Kamatani, Y.; Okada, Y.; Murakami, Y.; Kim, B.J.; Thorsteinsdottir, U.; Stefansson, K.; Zhang, J.; Chen, Y.; Ho, Y.L.; Lynch, J.A.; Rader, D.J.; Tsao, P.S.; Chang, K.M.; Cho, K.; O'Donnell, C.J.; Gaziano, J.M.; Wilson P.W.F.; Frayling, T.M.; Hirschhorn, J.N.; Kathiresan, S.; Mohlke, K.L.; Sun, Y.V.; Morris, A.P.; Boehnke, M.; Brown, C.D.; Natarajan, P.; Deloukas, P.; Willer, C.J.; Assimes, T.L.; Peloso, G.M.BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Item Identification of patients with type 2 diabetes with non-alcoholic fatty liver disease who are at increased risk of progressing to advanced fibrosis: a cross-sectional study(BMJ Publishing Group Ltd, 2023) Mettananda, C.; Egodage, T.; Dantanarayana, C.; Fernando, R.; Ranaweera, L.; Luke, N.; Ranawaka, C.; Kottahachchi, D.; Pathmeswaran, A.; de Silva, H.J.; Dassanayake, A.S.INTRODUCTION: Identification of advanced hepatic fibrosis in non-alcoholic fatty liver disease (NAFLD) is important as this may progress to cirrhosis and hepatocellular carcinoma. The risk of hepatic fibrosis is especially high among patients with diabetes with NAFLD. Annual screening of patients with diabetes for fatty liver and calculation of Fibrosis-4 (FIB-4) score and exclusion of significant fibrosis with vibration-controlled transient elastography (VCTE) have been recommended. However, VCTE is expensive and may not be freely available in resource-limited settings. We aim to identify predictors of significant liver fibrosis who are at increased risk of progression to advanced liver fibrosis and to develop a prediction model to prioritise referral of patients with diabetes and NAFLD for VCTE. METHODS AND ANALYSIS: This cross-sectional study is conducted among all consenting adults with type 2 diabetes mellitus with NAFLD at the Colombo North Teaching Hospital, Ragama, Sri Lanka. All patients get the FIB-4 score calculated. Those with FIB-4 ≥1.3 undergo VCTE (with FibroScan by Echosens). Risk associations for progression to advanced liver fibrosis/cirrhosis will be identified by comparing patients with significant fibrosis (liver stiffness measure (LSM) ≥8 kPa) and without significant fibrosis (LSM <8 kPa). A model to predict significant liver fibrosis will be developed using logistic regression. ETHICS AND DISSEMINATION: Ethical approval has been obtained from the Ethics Committee of the Faculty of Medicine, University of Kelaniya (P/66/07/2021). Results of the study will be disseminated as scientific publications in reputable journals.