Browsing by Author "Florez, Jose C."

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    Circulating natriuretic peptide concentrations reflect changes in insulin sensitivity over time in the Diabetes Prevention Program
    (Springer, 2014-05) Walford, Geoffrey A.; Ma, Yong; Christophi, Costas A.; Goldberg, Ronald B.; Jarolim, Petr; Horton, Edward; Mather, Kieren J.; Barrett-Connor, Elizabeth; Davis, Jaclyn; Florez, Jose C.; Wang, Thomas J.; Department of Medicine, IU School of Medicine
    AIMS/HYPOTHESIS: We aimed to study the relationship between measures of adiposity, insulin sensitivity and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in the Diabetes Prevention Program (DPP). METHODS: The DPP is a completed clinical trial. Using stored samples from this resource, we measured BMI, waist circumference (WC), an insulin sensitivity index (ISI; [1/HOMA-IR]) and NT-proBNP at baseline and at 2 years of follow-up in participants randomised to placebo (n = 692), intensive lifestyle intervention (n = 832) or metformin (n = 887). RESULTS: At baseline, log NT-proBNP did not differ between treatment arms and was correlated with baseline log ISI (p < 0.0001) and WC (p = 0.0003) but not with BMI (p = 0.39). After 2 years of treatment, BMI decreased in the lifestyle and metformin groups (both p < 0.0001); WC decreased in all three groups (p < 0.05 for all); and log ISI increased in the lifestyle and metformin groups (both p < 0.001). The change in log NT-proBNP did not differ in the lifestyle or metformin group vs the placebo group (p > 0.05 for both). In regression models, the change in log NT-proBNP was positively associated with the change in log ISI (p < 0.005) in all three study groups after adjusting for changes in BMI and WC, but was not associated with the change in BMI or WC after adjusting for changes in log ISI. CONCLUSION/INTERPRETATION: Circulating NT-proBNP was associated with a measure of insulin sensitivity before and during preventive interventions for type 2 diabetes in the DPP. This relationship persisted after adjustment for measures of adiposity and was consistent regardless of whether a participant was treated with placebo, intensive lifestyle intervention or metformin.
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    Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
    (American Diabetes Association, 2023) Westerman, Kenneth E.; Walker, Maura E.; Gaynor, Sheila M.; Wessel, Jennifer; DiCorpo, Daniel; Ma, Jiantao; Alonso, Alvaro; Aslibekyan, Stella; Baldridge, Abigail S.; Bertoni, Alain G.; Biggs, Mary L.; Brody, Jennifer A.; Chen, Yii-Der Ida; Dupuis, Joseé; Goodarzi, Mark O.; Guo, Xiuqing; Hasbani, Natalie R.; Heath, Adam; Hidalgo, Bertha; Irvin, Marguerite R.; Johnson, W. Craig; Kalyani, Rita R.; Lange, Leslie; Lemaitre, Rozenn N.; Liu, Ching-Ti; Liu, Simin; Moon, Jee-Young; Nassir, Rami; Pankow, James S.; Pettinger, Mary; Raffield, Laura M.; Rasmussen-Torvik, Laura J.; Selvin, Elizabeth; Senn, Mackenzie K.; Shadyab, Aladdin H.; Smith, Albert V.; Smith, Nicholas L.; Steffen, Lyn; Talegakwar, Sameera; Taylor, Kent D.; de Vries, Paul S.; Wilson, James G.; Wood, Alexis C.; Yanek, Lisa R.; Yao, Jie; Zheng, Yinan; Boerwinkle, Eric; Morrison, Alanna C.; Fornage, Miriam; Russell, Tracy P.; Psaty, Bruce M.; Levy, Daniel; Heard-Costa, Nancy L.; Ramachandran, Vasan S.; Mathias, Rasika A.; Arnett, Donna K.; Kaplan, Robert; North, Kari E.; Correa, Adolfo; Carson, April; Rotter, Jerome I.; Rich, Stephen S.; Manson, JoAnn E.; Reiner, Alexander P.; Kooperberg, Charles; Florez, Jose C.; Meigs, James B.; Merino, Jordi; Tobias, Deirdre K.; Chen, Han; Manning, Alisa K.; Epidemiology, Richard M. Fairbanks School of Public Health
    Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry. Article highlights: We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.
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    Lifestyle and Metformin Ameliorate Insulin Sensitivity Independently of the Genetic Burden of Established Insulin Resistance Variants in Diabetes Prevention Program Participants
    (American Diabetes Association, 2016-02) Hivert, Marie-France; Christophi, Costas A.; Franks, Paul W.; Jablonski, Kathleen A.; Ehrmann, David A.; Kahn, Steven E.; Horton, Edward S.; Pollin, Toni I.; Mather, Kieren J.; Perreault, Leigh; Barrett-Connor, Elizabeth; Knowler, William C.; Florez, Jose C.; Department of Medicine, IU School of Medicine
    Large genome-wide association studies of glycemic traits have identified genetics variants that are associated with insulin resistance (IR) in the general population. It is unknown whether people with genetic enrichment for these IR variants respond differently to interventions that aim to improve insulin sensitivity. We built a genetic risk score (GRS) based on 17 established IR variants and effect sizes (weighted IR-GRS) in 2,713 participants of the Diabetes Prevention Program (DPP) with genetic consent. We tested associations between the weighted IR-GRS and insulin sensitivity index (ISI) at baseline in all participants, and with change in ISI over 1 year of follow-up in the DPP intervention (metformin and lifestyle) and control (placebo) arms. All models were adjusted for age, sex, ethnicity, and waist circumference at baseline (plus baseline ISI for 1-year ISI change models). A higher IR-GRS was associated with lower baseline ISI (β = -0.754 [SE = 0.229] log-ISI per unit, P = 0.001 in fully adjusted models). There was no differential effect of treatment for the association between the IR-GRS on the change in ISI; higher IR-GRS was associated with an attenuation in ISI improvement over 1 year (β = -0.520 [SE = 0.233], P = 0.03 in fully adjusted models; all treatment arms). Lifestyle intervention and metformin treatment improved the ISI, regardless of the genetic burden of IR variants.
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    Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
    (Nature Publishing Group, 2015-01-29) Wessel, Jennifer; Chu, Audrey Y.; Willems, Sara M.; Wang, Shuai; Yaghootkar, Hanieh; Brody, Jennifer A.; Dauriz, Marco; Hivert, Marie-France; Raghavan, Sridharan; Lipovich, Leonard; Hidalgo, Bertha; Fox, Keolu; Huffman, Jennifer E.; An, Ping; Lu, Yingchang; Rasmussen-Torvik, Laura J.; Grarup, Niels; Ehm, Margaret G.; Li, Li; Baldridge, Abigail S.; Stančáková, Alena; Abrol, Ravinder; Besse, Céline; Boland, Anne; Bork-Jensen, Jette; Fornage, Myriam; Freitag, Daniel F.; Garcia, Melissa E.; Guo, Xiuqing; Hara, Kazuo; Isaacs, Aaron; Jakobsdottir, Johanna; Lange, Leslie A.; Layton, Jill C.; Li, Man; Hua Zhao, Jing; Meidtner, Karina; Morrison, Alanna C.; Nalls, Mike A.; Peters, Marjolein J.; Sabater-Lleal, Maria; Schurmann, Claudia; Silveira, Angela; Smith, Albert V.; Southam, Lorraine; Stoiber, Marcus H.; Strawbridge, Rona J.; Taylor, Kent D.; Varga, Tibor V.; Allin, Kristine H.; Amin, Najaf; Aponte, Jennifer L.; Aung, Tin; Barbieri, Caterina; Bihlmeyer, Nathan A.; Boehnke, Michael; Bombieri, Cristina; Bowden, Donald W.; Burns, Sean M.; Chen, Yuning; Chen, Yii-DerI; Cheng, Ching-Yu; Correa, Adolfo; Czajkowski, Jacek; Dehghan, Abbas; Ehret, Georg B.; Eiriksdottir, Gudny; Escher, Stefan A.; Farmaki, Aliki-Eleni; Frånberg, Mattias; Gambaro, Giovanni; Giulianini, Franco; Goddard, William A.; Goel, Anuj; Gottesman, Omri; Grove, Megan L.; Gustafsson, Stefan; Hai, Yang; Hallmans, Göran; Heo, Jiyoung; Hoffmann, Per; Ikram, Mohammad K.; Jensen, Richard A.; Jørgensen, Marit E.; Jørgensen, Torben; Karaleftheri, Maria; Khor, Chiea C.; Kirkpatrick, Andrea; Kraja, Aldi T.; Kuusisto, Johanna; Lange, Ethan M.; Lee, I. T.; Lee, Wen-Jane; Leong, Aaron; Liao, Jiemin; Liu, Chunyu; Liu, Yongmei; Lindgren, Cecilia M.; Linneberg, Allan; Malerba, Giovanni; Mamakou, Vasiliki; Marouli, Eirini; Maruthur, Nisa M.; Matchan, Angela; McKean-Cowdin, Roberta; McLeod, Olga; Metcalf, Ginger A.; Mohlke, Karen L.; Muzny, Donna M.; Ntalla, Ioanna; Palmer, Nicholette D.; Pasko, Dorota; Peter, Andreas; Rayner, Nigel W.; Renström, Frida; Rice, Ken; Sala, Cinzia F.; Sennblad, Bengt; Serafetinidis, Ioannis; Smith, Jennifer A.; Soranzo, Nicole; Speliotes, Elizabeth K.; Stahl, Eli A.; Stirrups, Kathleen; Tentolouris, Nikos; Thanopoulou, Anastasia; Torres, Mina; Traglia, Michela; Tsafantakis, Emmanouil; Javad, Sundas; Yanek, Lisa R.; Zengini, Eleni; Becker, Diane M.; Bis, Joshua C.; Brown, James B.; Adrienne Cupples, L.; Hansen, Torben; Ingelsson, Erik; Karter, Andrew J.; Lorenzo, Carlos; Mathias, Rasika A.; Norris, Jill M.; Peloso, Gina M.; Sheu, Wayne H.-H.; Toniolo, Daniela; Vaidya, Dhananjay; Varma, Rohit; Wagenknecht, Lynne E.; Boeing, Heiner; Bottinger, Erwin P.; Dedoussis, George; Deloukas, Panos; Ferrannini, Ele; Franco, Oscar H.; Franks, Paul W.; Gibbs, Richard A.; Gudnason, Vilmundur; Hamsten, Anders; Harris, Tamara B.; Hattersley, Andrew T.; Hayward, Caroline; Hofman, Albert; Jansson, Jan-Håkan; Langenberg, Claudia; Launer, Lenore J.; Levy, Daniel; Oostra, Ben A.; O'Donnell, Christopher J.; O'Rahilly, Stephen; Padmanabhan, Sandosh; Pankow, James S.; Polasek, Ozren; Province, Michael A.; Rich, Stephen S.; Ridker, Paul M.; Rudan, Igor; Schulze, Matthias B.; Smith, Blair H.; Uitterlinden, André G.; Walker, Mark; Watkins, Hugh; Wong, Tien Y.; Zeggini, Eleftheria; Laakso, Markku; Borecki, Ingrid B.; Chasman, Daniel I.; Pedersen, Oluf; Psaty, Bruce M.; Shyong Tai, E.; van Duijn, Cornelia M.; Wareham, Nicholas J.; Waterworth, Dawn M.; Boerwinkle, Eric; Linda Kao, W. H.; Florez, Jose C.; Loos, Ruth J. F.; Wilson, James G.; Frayling, Timothy M.; Siscovick, David S.; Dupuis, Josée; Rotter, Jerome I.; Meigs, James B.; Scott, Robert A.; Goodarzi, Mark O.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Fasting ​glucose and ​insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in ​GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l−1, P=3.4 × 10−12), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early ​insulin secretion (β=−0.07±0.035 pmolinsulin mmolglucose−1, P=0.048), but higher 2-h ​glucose (β=0.16±0.05 mmol l−1, P=4.3 × 10−4). We identify a gene-based association with FG at ​G6PC2 (pSKAT=6.8 × 10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ​ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l−1, P=1.3 × 10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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    Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program
    (American Diabetes Association, 2019-12) Chen, Zsu-Zsu; Liu, Jinxi; Morningstar, Jordan; Heckman-Stoddard, Brandy M.; Lee, Christine G.; Dagogo-Jack, Samuel; Ferguson, Jane F.; Hamman, Richard F.; Knowler, William C.; Mather, Kieren J.; Perreault, Leigh; Florez, Jose C.; Wang, Thomas J.; Clish, Clary; Temprosa, Marinella; Gerszten, Robert E.; Medicine, School of Medicine
    Novel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.
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    The prevention of type 2 diabetes
    (2008-07) Crandall, Jill P.; Knowler, William C.; Kahn, Steven E.; Marrero, David G.; Florez, Jose C.; Bray, George A.; Haffner, Steven M.; Hoskin, Mary; Nathan, David M.; Diabetes Prevention Program Research Group
    Type 2 diabetes mellitus (T2DM) affects more than 7% of adults in the US and leads to substantial personal and economic burden. In prediabetic states insulin secretion and action—potential targets of preventive interventions—are impaired. In trials lifestyle modification (i.e. weight loss and exercise) has proven effective in preventing incident T2DM in high-risk groups, although weight loss has the greatest effect. Various medications (e.g. metformin, thiazolidinediones and acarbose) can also prevent or delay T2DM. Whether diabetes-prevention strategies also ultimately prevent the development of diabetic vascular complications is unknown, but cardiovascular risk factors are favorably affected. Preventive strategies that can be implemented in routine clinical settings have been developed and evaluated. Widespread application has, however, been limited by local financial considerations, even though cost-effectiveness might be achieved at the population level.
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    Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP)
    (Springer, 2022) Maxwell, Taylor J.; Franks, Paul W.; Kahn, Steven E.; Knowler, William C.; Mather, Kieren J.; Florez, Jose C.; Jablonski, Kathleen A.; Medicine, School of Medicine
    The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10-7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10-9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, is associated with ulcerative colitis, sclerosing cholangitis, Crohn's disease, BMI and coronary artery disease. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.
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    Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine
    (Springer Nature, 2023) 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; Raghavan, Sridharan; 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., Jr.; 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.; Semple, Robert K.; 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.; Pediatrics, School of Medicine
    Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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    Whole Genome Sequence Association Analysis of Fasting Glucose and Fasting Insulin Levels in Diverse Cohorts from the NHLBI TOPMed Program
    (Springer Nature, 2022-07-28) DiCorpo, Daniel; Gaynor, Sheila M.; Russell, Emily M.; Westerman, Kenneth E.; Raffield, Laura M.; Majarian, Timothy D.; Wu, Peitao; Sarnowski, Chloé; Highland, Heather M.; Jackson, Anne; Hasbani, Natalie R.; de Vries, Paul S.; Brody, Jennifer A.; Hidalgo, Bertha; Guo, Xiuqing; Perry, James A.; O’Connell, Jeffrey R.; Lent, Samantha; Montasser, May E.; Cade, Brian E.; Jain, Deepti; Wang, Heming; D’Oliveira Albanus, Ricardo; Varshney, Arushi; Yanek, Lisa R.; Lange, Leslie; Palmer, Nicholette D.; Almeida, Marcio; Peralta, Juan M.; Aslibekyan, Stella; Baldridge, Abigail S.; Bertoni, Alain G.; Bielak, Lawrence F.; Chen, Chung-Shiuan; Chen, Yii-Der Ida; Choi, Won Jung; Goodarzi, Mark O.; Floyd, James S.; Irvin, Marguerite R.; Kalyani, Rita R.; Kelly, Tanika N.; Lee, Seonwook; Liu, Ching-Ti; Loesch, Douglas; Manson, JoAnn E.; Minster, Ryan L.; Naseri, Take; Pankow, James S.; Rasmussen-Torvik, Laura J.; Reiner, Alexander P.; Reupena, Muagututi’a Sefuiva; Selvin, Elizabeth; Smith, Jennifer A.; Weeks, Daniel E.; Xu, Huichun; Yao, Jie; Zhao, Wei; Parker, Stephen; Alonso, Alvaro; Arnett, Donna K.; Blangero, John; Boerwinkle, Eric; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; Duggirala, Ravindranath; He, Jiang; Heckbert, Susan R.; Kardia, Sharon L.R.; Kim, Ryan W.; Kooperberg, Charles; Liu, Simin; Mathias, Rasika A.; McGarvey, Stephen T.; Mitchell, Braxton D.; Morrison, Alanna C.; Peyser, Patricia A.; Psaty, Bruce M.; Redline, Susan; Shuldiner, Alan R.; Taylor, Kent D.; Vasan, Ramachandran S.; Viaud-Martinez, Karine A.; Florez, Jose C.; Wilson, James G.; Sladek, Robert; Rich, Stephen S.; Rotter, Jerome I.; Lin, Xihong; Dupuis, Josée; Meigs, James B.; Wessel, Jennifer; Manning, Alisa K.; Epidemiology, School of Public Health
    The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.