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IRIS
Background: Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology. Methods: Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. Results: 10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions. Conclusions: The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.
Genotype-stratified treatment for monogenic insulin resistance: a systematic review
Semple, Robert K.;Patel, Kashyap A.;Auh, Sungyoung;Tobias, Deirdre K.;Merino, Jordi;Ahmad, Abrar;Aiken, Catherine;Benham, Jamie L.;Bodhini, Dhanasekaran;Clark, Amy L.;Colclough, Kevin;Corcoy, Rosa;Cromer, Sara J.;Duan, Daisy;Felton, Jamie L.;Francis, Ellen C.;Gillard, Pieter;Gingras, Véronique;Gaillard, Romy;Haider, Eram;Hughes, Alice;Ikle, Jennifer M.;Jacobsen, Laura M.;Kahkoska, Anna R.;Kettunen, Jarno L. T.;Kreienkamp, Raymond J.;Lim, Lee-Ling;Männistö, Jonna M. E.;Massey, Robert;Mclennan, Niamh-Maire;Miller, Rachel G.;Morieri, Mario Luca;Most, Jasper;Naylor, Rochelle N.;Ozkan, Bige;Patel, Kashyap Amratlal;Pilla, Scott J.;Prystupa, Katsiaryna;Raghaven, Sridaran;Rooney, Mary R.;Schön, Martin;Semnani-Azad, Zhila;Sevilla-Gonzalez, Magdalena;Svalastoga, Pernille;Takele, Wubet Worku;Tam, Claudia Ha-ting;Thuesen, Anne Cathrine B.;Tosur, Mustafa;Wallace, Amelia S.;Wang, Caroline C.;Wong, Jessie J.;Yamamoto, Jennifer M.;Young, Katherine;Amouyal, Chloé;Andersen, Mette K.;Bonham, Maxine P.;Chen, Mingling;Cheng, Feifei;Chikowore, Tinashe;Chivers, Sian C.;Clemmensen, Christoffer;Dabelea, Dana;Dawed, Adem Y.;Deutsch, Aaron J.;Dickens, Laura T.;DiMeglio, Linda A.;Dudenhöffer-Pfeifer, Monika;Evans-Molina, Carmella;Fernández-Balsells, María Mercè;Fitipaldi, Hugo;Fitzpatrick, Stephanie L.;Gitelman, Stephen E.;Goodarzi, Mark O.;Grieger, Jessica A.;Guasch-Ferré, Marta;Habibi, Nahal;Hansen, Torben;Huang, Chuiguo;Harris-Kawano, Arianna;Ismail, Heba M.;Hoag, Benjamin;Johnson, Randi K.;Jones, Angus G.;Koivula, Robert W.;Leong, Aaron;Leung, Gloria K. W.;Libman, Ingrid M.;Liu, Kai;Long, S. Alice;Lowe, William L.;Morton, Robert W.;Motala, Ayesha A.;Onengut-Gumuscu, Suna;Pankow, James S.;Pathirana, Maleesa;Pazmino, Sofia;Perez, Dianna;Petrie, John R.;Powe, Camille E.;Quinteros, Alejandra;Jain, Rashmi;Ray, Debashree;Ried-Larsen, Mathias;Saeed, Zeb;Santhakumar, Vanessa;Kanbour, Sarah;Sarkar, Sudipa;Monaco, Gabriela S. F.;Scholtens, Denise M.;Selvin, Elizabeth;Sheu, Wayne Huey-Herng;Speake, Cate;Stanislawski, Maggie A.;Steenackers, Nele;Steck, Andrea K.;Stefan, Norbert;Støy, Julie;Taylor, Rachael;Tye, Sok Cin;Ukke, Gebresilasea Gendisha;Urazbayeva, Marzhan;Van der Schueren, Bart;Vatier, Camille;Wentworth, John M.;Hannah, Wesley;White, Sara L.;Yu, Gechang;Zhang, Yingchai;Zhou, Shao J.;Beltrand, Jacques;Polak, Michel;Aukrust, Ingvild;de Franco, Elisa;Flanagan, Sarah E.;Maloney, Kristin A.;McGovern, Andrew;Molnes, Janne;Nakabuye, Mariam;Njølstad, Pål Rasmus;Pomares-Millan, Hugo;Provenzano, Michele;Saint-Martin, Cécile;Zhang, Cuilin;Zhu, Yeyi;Auh, Sungyoung;de Souza, Russell;Fawcett, Andrea J.;Gruber, Chandra;Mekonnen, Eskedar Getie;Mixter, Emily;Sherifali, Diana;Eckel, Robert H.;Nolan, John J.;Philipson, Louis H.;Brown, Rebecca J.;Billings, Liana K.;Boyle, Kristen;Costacou, Tina;Dennis, John M.;Florez, Jose C.;Gloyn, Anna L.;Gomez, Maria F.;Gottlieb, Peter A.;Greeley, Siri Atma W.;Griffin, Kurt;Hattersley, Andrew T.;Hirsch, Irl B.;Hivert, Marie-France;Hood, Korey K.;Josefson, Jami L.;Kwak, Soo Heon;Laffel, Lori M.;Lim, Siew S.;Loos, Ruth J. F.;Ma, Ronald C. W.;Mathieu, Chantal;Mathioudakis, Nestoras;Meigs, James B.;Misra, Shivani;Mohan, Viswanathan;Murphy, Rinki;Oram, Richard;Owen, Katharine R.;Ozanne, Susan E.;Pearson, Ewan R.;Perng, Wei;Pollin, Toni I.;Pop-Busui, Rodica;Pratley, Richard E.;Redman, Leanne M.;Redondo, Maria J.;Reynolds, Rebecca M.;Sherr, Jennifer L.;Sims, Emily K.;Sweeting, Arianne;Tuomi, Tiinamaija;Udler, Miriam S.;Vesco, Kimberly K.;Vilsbøll, Tina;Wagner, Robert;Rich, Stephen S.;Franks, Paul W.;Brown, Rebecca J.;null, null
2023-01-01
Abstract
Background: Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology. Methods: Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. Results: 10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions. Conclusions: The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/369151
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.