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Johnson KC, Anderson A, Beavers KM, Crandall CJ, Hazuda HP, Lewis CE, Lipkin E, Schwartz AV, Pi-Sunyer FX, Zhao Q. The long-term effect of intentional weight loss on changes in bone mineral density in persons with type 2 diabetes: results from the Look AHEAD randomized trial. Arch Osteoporos 2023; 18:97. [PMID: 37452151 PMCID: PMC10348976 DOI: 10.1007/s11657-023-01303-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/03/2023] [Indexed: 07/18/2023]
Abstract
Intentional weight loss has been shown to increase bone loss short term but the long-term effects are not known. Data from the Look AHEAD clinical trial shows that a long term intentional weight loss intervention was associated with greater bone loss at the hip in men. PURPOSE Intentional weight loss has been shown to increase bone loss short term and increase frailty fracture risk, but the long-term effects on bone mineral density (BMD) are not known. METHODS Data from a subgroup from the Look AHEAD (LA) multicenter, randomized clinical trial was used to evaluate whether a long term intentional weight loss intervention would increase bone loss. In a preplanned substudy, BMD was assessed at 5 of the 16 LA clinical centers using dual-energy X-ray absorptiometry at baseline, year 8, and the observational visit 12.6-16.3 years after randomization (year 12-16). RESULTS At year 8, bone density loss (%) was greater in the Intensive Lifestyle Intervention (ILI) group compared with the control group (DSE) for the femoral neck (p = 0.0122) but this finding was not observed at the year 12-16 visit. In analyses stratified by gender, bone density loss (%) was greater at the total hip for men in the ILI group than the DSE group at both the year 8 and year 12-16 visits (year 8 p = 0.0263 and year 12-16 p = 0.0062). This finding was not observed among women. CONCLUSION Long term intentional weight loss was associated with greater bone loss at the hip in men. These results taken with the previously published Look AHEAD data from the entire clinical trial showing increased frailty fracture risk with weight loss in the ILI group suggest that when intentional weight loss is planned, consideration of bone density preservation and fracture prevention strategies is warranted. TRIAL REGISTRATION Clinicaltrials.gov Identifier: NCT00017953. June 21, 2001.
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Affiliation(s)
- Karen C Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA.
| | | | - Kristen M Beavers
- Department of Health and Exercise Science, Wake Forest Univesity, Winston-Salem, NC, USA
| | - Carolyn J Crandall
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Helen P Hazuda
- Univesity of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Cora E Lewis
- Depatment of Epidemiology, Univeristy of Alabama at Birmingham, Birmingham, AL, USA
| | - Edward Lipkin
- Division of Metabolism, Endocrinology and Nutrition, University of Washington, Seattle, WA, USA
| | - Ann V Schwartz
- Deparment of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - F X Pi-Sunyer
- Department of Medicine, Columbia University, New York, NY, USA
| | - Qi Zhao
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
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Domalpally A, Whittier SA, Pan Q, Dabelea DM, Darwin CH, Knowler WC, Lee CG, Luchsinger JA, White NH, Chew EY, Gadde KM, Culbert IW, Arceneaux J, Chatellier A, Dragg A, Champagne CM, Duncan C, Eberhardt B, Greenway F, Guillory FG, Herbert AA, Jeffirs ML, Kennedy BM, Levy E, Lockett M, Lovejoy JC, Morris LH, Melancon LE, Ryan DH, Sanford DA, Smith KG, Smith LL, St.Amant JA, Tulley RT, Vicknair PC, Williamson D, Zachwieja JJ, Polonsky KS, Tobian J, Ehrmann DA, Matulik MJ, Temple KA, Clark B, Czech K, DeSandre C, Dotson B, Hilbrich R, McNabb W, Semenske AR, Caro JF, Furlong K, Goldstein BJ, Watson PG, Smith KA, Mendoza J, Simmons M, Wildman W, Liberoni R, Spandorfer J, Pepe C, Donahue RP, Goldberg RB, Prineas R, Calles J, Giannella A, Rowe P, Sanguily J, Cassanova-Romero P, Castillo-Florez S, Florez HJ, Garg R, Kirby L, Lara O, Larreal C, McLymont V, Mendez J, Perry A, Saab P, Veciana B, Haffner SM, Hazuda HP, Montez MG, Hattaway K, Isaac J, Lorenzo C, Martinez A, Salazar M, Walker T, Hamman RF, Nash PV, Steinke SC, Testaverde L, Truong J, Anderson DR, Ballonoff LB, Bouffard A, Bucca B, Calonge BN, Delve L, Farago M, Hill JO, Hoyer SR, Jenkins T, Jortberg BT, Lenz D, Miller M, Nilan T, Perreault L, Price DW, Regensteiner JG, Schroeder EB, Seagle H, Smith CM, VanDorsten B, Horton ES, Munshi M, Lawton KE, Jackson SD, Poirier CS, Swift K, Arky RA, Bryant M, Burke JP, Caballero E, Callaphan KM, Fargnoli B, Franklin T, Ganda OP, Guidi A, Guido M, Jacobsen AM, Kula LM, Kocal M, Lambert L, Ledbury S, Malloy MA, Middelbeek RJ, Nicosia M, Oldmixon CF, Pan J, Quitingon M, Rainville R, Rubtchinsky S, Seely EW, Sansoucy J, Schweizer D, Simonson D, Smith F, Solomon CG, Spellman J, Warram J, Kahn SE, Fattaleh B, Montgomery BK, Colegrove C, Fujimoto W, Knopp RH, Lipkin EW, Marr M, Morgan-Taggart I, Murillo A, O’Neal K, Trence D, Taylor L, Thomas A, Tsai EC, Dagogo-Jack S, Kitabchi AE, Murphy ME, Taylor L, Dolgoff J, Applegate WB, Bryer-Ash M, Clark D, Frieson SL, Ibebuogu U, Imseis R, Lambeth H, Lichtermann LC, Oktaei H, Ricks H, Rutledge LM, Sherman AR, Smith CM, Soberman JE, Williams-Cleaves B, Patel A, Nyenwe EA, Hampton EF, Metzger BE, Molitch ME, Johnson MK, Adelman DT, Behrends C, Cook M, Fitzgibbon M, Giles MM, Heard D, Johnson CK, Larsen D, Lowe A, Lyman M, McPherson D, Penn SC, Pitts T, Reinhart R, Roston S, Schinleber PA, Wallia A, Nathan DM, McKitrick C, Turgeon H, Larkin M, Mugford M, Abbott K, Anderson E, Bissett L, Bondi K, Cagliero E, Florez JC, Delahanty L, Goldman V, Grassa E, Gurry L, D’Anna K, Leandre F, Lou P, Poulos A, Raymond E, Ripley V, Stevens C, Tseng B, Olefsky JM, Barrett-Connor E, Mudaliar S, Araneta MR, Carrion-Petersen ML, Vejvoda K, Bassiouni S, Beltran M, Claravall LN, Dowden JM, Edelman SV, Garimella P, Henry RR, Horne J, Lamkin M, Janesch SS, Leos D, Polonsky W, Ruiz R, Smith J, Torio-Hurley J, Pi-Sunyer FX, Lee JE, Hagamen S, Allison DB, Agharanya N, Aronoff NJ, Baldo M, Crandall JP, Foo ST, Luchsinger JA, Pal C, Parkes K, Pena MB, Rooney ES, Van Wye GE, Viscovich KA, de Groot M, Marrero DG, Mather KJ, Prince MJ, Kelly SM, Jackson MA, McAtee G, Putenney P, Ackermann RT, Cantrell CM, Dotson YF, Fineberg ES, Fultz M, Guare JC, Hadden A, Ignaut JM, Kirkman MS, Phillips EO, Pinner KL, Porter BD, Roach PJ, Rowland ND, Wheeler ML, Aroda V, Magee M, Ratner RE, Youssef G, Shapiro S, Andon N, Bavido-Arrage C, Boggs G, Bronsord M, Brown E, Love Burkott H, Cheatham WW, Cola S, Evans C, Gibbs P, Kellum T, Leon L, Lagarda M, Levatan C, Lindsay M, Nair AK, Park J, Passaro M, Silverman A, Uwaifo G, Wells-Thayer D, Wiggins R, Saad MF, Watson K, Budget M, Jinagouda S, Botrous M, Sosa A, Tadros S, Akbar K, Conzues C, Magpuri P, Ngo K, Rassam A, Waters D, Xapthalamous K, Santiago JV, Brown AL, Das S, Khare-Ranade P, Stich T, Santiago A, Fisher E, Hurt E, Jones T, Kerr M, Ryder L, Wernimont C, Golden SH, Saudek CD, Bradley V, Sullivan E, Whittington T, Abbas C, Allen A, Brancati FL, Cappelli S, Clark JM, Charleston JB, Freel J, Horak K, Greene A, Jiggetts D, Johnson D, Joseph H, Loman K, Mathioudakis N, Mosley H, Reusing J, Rubin RR, Samuels A, Shields T, Stephens S, Stewart KJ, Thomas L, Utsey E, Williamson P, Schade DS, Adams KS, Canady JL, Johannes C, Hemphill C, Hyde P, Atler LF, Boyle PJ, Burge MR, Chai L, Colleran K, Fondino A, Gonzales Y, Hernandez-McGinnis DA, Katz P, King C, Middendorf J, Rubinchik S, Senter W, Crandall J, Shamoon H, Brown JO, Trandafirescu G, Powell D, Adorno E, Cox L, Duffy H, Engel S, Friedler A, Goldstein A, Howard-Century CJ, Lukin J, Kloiber S, Longchamp N, Martinez H, Pompi D, Scheindlin J, Violino E, Walker EA, Wylie-Rosett J, Zimmerman E, Zonszein J, Orchard T, Venditti E, Wing RR, Jeffries S, Koenning G, Kramer MK, Smith M, Barr S, Benchoff C, Boraz M, Clifford L, Culyba R, Frazier M, Gilligan R, Guimond S, Harrier S, Harris L, Kriska A, Manjoo Q, Mullen M, Noel A, Otto A, Pettigrew J, Rockette-Wagner B, Rubinstein D, Semler L, Smith CF, Weinzierl V, Williams KV, Wilson T, Mau MK, Baker-Ladao NK, Melish JS, Arakaki RF, Latimer RW, Isonaga MK, Beddow R, Bermudez NE, Dias L, Inouye J, Mikami K, Mohideen P, Odom SK, Perry RU, Yamamoto RE, Anderson H, Cooeyate N, Dodge C, Hoskin MA, Percy CA, Enote A, Natewa C, Acton KJ, Andre VL, Barber R, Begay S, Bennett PH, Benson MB, Bird EC, Broussard BA, Bucca BC, Chavez M, Cook S, Curtis J, Dacawyma T, Doughty MS, Duncan R, Edgerton C, Ghahate JM, Glass J, Glass M, Gohdes D, Grant W, Hanson RL, Horse E, Ingraham LE, Jackson M, Jay P, Kaskalla RS, Kavena K, Kessler D, Kobus KM, Krakoff J, Kurland J, Manus C, McCabe C, Michaels S, Morgan T, Nashboo Y, Nelson JA, Poirier S, Polczynski E, Piromalli C, Reidy M, Roumain J, Rowse D, Roy RJ, Sangster S, Sewenemewa J, Smart M, Spencer C, Tonemah D, Williams R, Wilson C, Yazzie M, Bain R, Fowler S, Temprosa M, Larsen MD, Brenneman T, Edelstein SL, Abebe S, Bamdad J, Barkalow M, Bethepu J, Bezabeh T, Bowers A, Butler N, Callaghan J, Carter CE, Christophi C, Dwyer GM, Foulkes M, Gao Y, Gooding R, Gottlieb A, Grimes KL, Grover-Fairchild N, Haffner L, Hoffman H, Jablonski K, Jones S, Jones TL, Katz R, Kolinjivadi P, Lachin JM, Ma Y, Mucik P, Orlosky R, Reamer S, Rochon J, Sapozhnikova A, Sherif H, Stimpson C, Hogan Tjaden A, Walker-Murray F, Venditti EM, Kriska AM, Weinzierl V, Marcovina S, Aldrich FA, Harting J, Albers J, Strylewicz G, Eastman R, Fradkin J, Garfield S, Lee C, Gregg E, Zhang P, O’Leary D, Evans G, Budoff M, Dailing C, Stamm E, Schwartz A, Navy C, Palermo L, Rautaharju P, Prineas RJ, Alexander T, Campbell C, Hall S, Li Y, Mills M, Pemberton N, Rautaharju F, Zhang Z, Soliman EZ, Hu J, Hensley S, Keasler L, Taylor T, Blodi B, Danis R, Davis M, Hubbard* L, Endres** R, Elsas** D, Johnson** S, Myers** D, Barrett N, Baumhauer H, Benz W, Cohn H, Corkery E, Dohm K, Gama V, Goulding A, Ewen A, Hurtenbach C, Lawrence D, McDaniel K, Pak J, Reimers J, Shaw R, Swift M, Vargo P, Watson S, Manly J, Mayer-Davis E, Moran RR, Ganiats T, David K, Sarkin AJ, Groessl E, Katzir N, Chong H, Herman WH, Brändle M, Brown MB, Altshuler D, Billings LK, Chen L, Harden M, Knowler WC, Pollin TI, Shuldiner AR, Franks PW, Hivert MF. Association of Metformin With the Development of Age-Related Macular Degeneration. JAMA Ophthalmol 2023; 141:140-147. [PMID: 36547967 PMCID: PMC9936345 DOI: 10.1001/jamaophthalmol.2022.5567] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/29/2022] [Indexed: 12/24/2022]
Abstract
Importance Age-related macular degeneration (AMD) is a leading cause of blindness with no treatment available for early stages. Retrospective studies have shown an association between metformin and reduced risk of AMD. Objective To investigate the association between metformin use and age-related macular degeneration (AMD). Design, Setting, and Participants The Diabetes Prevention Program Outcomes Study is a cross-sectional follow-up phase of a large multicenter randomized clinical trial, Diabetes Prevention Program (1996-2001), to investigate the association of treatment with metformin or an intensive lifestyle modification vs placebo with preventing the onset of type 2 diabetes in a population at high risk for developing diabetes. Participants with retinal imaging at a follow-up visit 16 years posttrial (2017-2019) were included. Analysis took place between October 2019 and May 2022. Interventions Participants were randomly distributed between 3 interventional arms: lifestyle, metformin, and placebo. Main Outcomes and Measures Prevalence of AMD in the treatment arms. Results Of 1592 participants, 514 (32.3%) were in the lifestyle arm, 549 (34.5%) were in the metformin arm, and 529 (33.2%) were in the placebo arm. All 3 arms were balanced for baseline characteristics including age (mean [SD] age at randomization, 49 [9] years), sex (1128 [71%] male), race and ethnicity (784 [49%] White), smoking habits, body mass index, and education level. AMD was identified in 479 participants (30.1%); 229 (14.4%) had early AMD, 218 (13.7%) had intermediate AMD, and 32 (2.0%) had advanced AMD. There was no significant difference in the presence of AMD between the 3 groups: 152 (29.6%) in the lifestyle arm, 165 (30.2%) in the metformin arm, and 162 (30.7%) in the placebo arm. There was also no difference in the distribution of early, intermediate, and advanced AMD between the intervention groups. Mean duration of metformin use was similar for those with and without AMD (mean [SD], 8.0 [9.3] vs 8.5 [9.3] years; P = .69). In the multivariate models, history of smoking was associated with increased risks of AMD (odds ratio, 1.30; 95% CI, 1.05-1.61; P = .02). Conclusions and Relevance These data suggest neither metformin nor lifestyle changes initiated for diabetes prevention were associated with the risk of any AMD, with similar results for AMD severity. Duration of metformin use was also not associated with AMD. This analysis does not address the association of metformin with incidence or progression of AMD.
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Affiliation(s)
- Amitha Domalpally
- Wisconsin Reading Center, Department of Ophthalmology, University of Wisconsin School of Medicine and Public and Health, Madison
| | - Samuel A. Whittier
- Wisconsin Reading Center, Department of Ophthalmology, University of Wisconsin School of Medicine and Public and Health, Madison
| | - Qing Pan
- Department of Statistics, George Washington University, Washington, DC
| | - Dana M. Dabelea
- Department of Epidemiology, University of Colorado School of Public Health, Denver
| | - Christine H. Darwin
- Department of Medicine, Ronald Reagan UCLA Medical Center, Los Angeles, California
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Christine G. Lee
- Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institutes of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland
| | - Jose A. Luchsinger
- Department of Medicine, Columbia University Medical Center, New York, New York
| | - Neil H. White
- Division of Endocrinology & Diabetes, Department of Pediatrics, Washington University in St Louis School of Medicine, St Louis, Missouri
| | - Emily Y. Chew
- Division of Epidemiology and Clinical Applications–Clinical Trials Branch, National Eye Institute - National Institutes of Health, Bethesda, Maryland
| | | | | | | | | | | | - Amber Dragg
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Crystal Duncan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Frank Greenway
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Erma Levy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Monica Lockett
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Donna H. Ryan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Lisa L. Smith
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | - Janet Tobian
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Bart Clark
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kirsten Czech
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Wylie McNabb
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Jose F. Caro
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kevin Furlong
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Jewel Mendoza
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Marsha Simmons
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Wendi Wildman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Renee Liberoni
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Constance Pepe
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Ronald Prineas
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Anna Giannella
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Patricia Rowe
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Rajesh Garg
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Olga Lara
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Carmen Larreal
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Jadell Mendez
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Arlette Perry
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Patrice Saab
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Bertha Veciana
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Kathy Hattaway
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Juan Isaac
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Carlos Lorenzo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Monica Salazar
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tatiana Walker
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | | | | | - Brian Bucca
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - B. Ned Calonge
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lynne Delve
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Martha Farago
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - James O. Hill
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Tonya Jenkins
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Dione Lenz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Marsha Miller
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Thomas Nilan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - David W. Price
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Helen Seagle
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Medha Munshi
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Kati Swift
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ronald A. Arky
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | - Om P. Ganda
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ashley Guidi
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Mathew Guido
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Lyn M. Kula
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Margaret Kocal
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lori Lambert
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Sarah Ledbury
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Jocelyn Pan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Ellen W. Seely
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Dana Schweizer
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Fannie Smith
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - James Warram
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Steven E. Kahn
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Basma Fattaleh
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | - Michelle Marr
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Anne Murillo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kayla O’Neal
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Dace Trence
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lonnese Taylor
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - April Thomas
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Elaine C. Tsai
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Mary E. Murphy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Laura Taylor
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Debra Clark
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Uzoma Ibebuogu
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Raed Imseis
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Helen Lambeth
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Hooman Oktaei
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Harriet Ricks
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Amy R. Sherman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Clara M. Smith
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Avnisha Patel
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | | | - Michelle Cook
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Mimi M. Giles
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Deloris Heard
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Diane Larsen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Anne Lowe
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Megan Lyman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Samsam C. Penn
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Thomas Pitts
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Renee Reinhart
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Roston
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Amisha Wallia
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Mary Larkin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Kathy Abbott
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ellen Anderson
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Laurie Bissett
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kristy Bondi
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Jose C. Florez
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Elaine Grassa
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lindsery Gurry
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kali D’Anna
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Peter Lou
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Elyse Raymond
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Valerie Ripley
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Beverly Tseng
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | - Karen Vejvoda
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | | | - Javiva Horne
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Marycie Lamkin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Diana Leos
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Rosa Ruiz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jean Smith
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Jane E. Lee
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Hagamen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Maria Baldo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Sandra T. Foo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Carmen Pal
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kathy Parkes
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Mary Beth Pena
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Mary de Groot
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Susie M. Kelly
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Gina McAtee
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Paula Putenney
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Megan Fultz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - John C. Guare
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Angela Hadden
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Kisha L Pinner
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Paris J. Roach
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Vanita Aroda
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Michelle Magee
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Sue Shapiro
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Natalie Andon
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | - Susan Cola
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Cindy Evans
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Peggy Gibbs
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tracy Kellum
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lilia Leon
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Milvia Lagarda
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Asha K. Nair
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jean Park
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Gabriel Uwaifo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Renee Wiggins
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Karol Watson
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Maria Budget
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Medhat Botrous
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Anthony Sosa
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Sameh Tadros
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Khan Akbar
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Kathy Ngo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Amer Rassam
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Debra Waters
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Samia Das
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Tamara Stich
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ana Santiago
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Edwin Fisher
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Emma Hurt
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tracy Jones
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Michelle Kerr
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lucy Ryder
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Emily Sullivan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Caroline Abbas
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Adrienne Allen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Janice Freel
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Alicia Greene
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Dawn Jiggetts
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Hope Joseph
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kimberly Loman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Henry Mosley
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - John Reusing
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Alafia Samuels
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Thomas Shields
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - LeeLana Thomas
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Evonne Utsey
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | - Penny Hyde
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Mark R. Burge
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lisa Chai
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Ateka Fondino
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ysela Gonzales
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Patricia Katz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Carolyn King
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Jill Crandall
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Harry Shamoon
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Janet O. Brown
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Elsie Adorno
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Liane Cox
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Helena Duffy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Samuel Engel
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Jennifer Lukin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Stacey Kloiber
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Helen Martinez
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Dorothy Pompi
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Elissa Violino
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Joel Zonszein
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Trevor Orchard
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Rena R. Wing
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Jeffries
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Gaye Koenning
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - M. Kaye Kramer
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Marie Smith
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Barr
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Miriam Boraz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lisa Clifford
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Rebecca Culyba
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Ryan Gilligan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Susan Harrier
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Louann Harris
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Andrea Kriska
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Monica Mullen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Alicia Noel
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Amy Otto
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Linda Semler
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Tara Wilson
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - John S. Melish
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Mae K. Isonaga
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ralph Beddow
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Lorna Dias
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jillian Inouye
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kathy Mikami
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Sharon K. Odom
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | - Mary A. Hoskin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Carol A. Percy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Alvera Enote
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Camille Natewa
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kelly J. Acton
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Rosalyn Barber
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Shandiin Begay
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Evelyn C. Bird
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Brian C. Bucca
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Sherron Cook
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jeff Curtis
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tara Dacawyma
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Roberta Duncan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Cyndy Edgerton
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Justin Glass
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Martia Glass
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Dorothy Gohdes
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Wendy Grant
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Ellie Horse
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Merry Jackson
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Priscilla Jay
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Karen Kavena
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - David Kessler
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Jason Kurland
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Cherie McCabe
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Sara Michaels
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tina Morgan
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Steven Poirier
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Mike Reidy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Debra Rowse
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Robert J. Roy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Miranda Smart
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Darryl Tonemah
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Raymond Bain
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Sarah Fowler
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Tina Brenneman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Solome Abebe
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Julie Bamdad
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Joel Bethepu
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Anna Bowers
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Nicole Butler
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Mary Foulkes
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Yuping Gao
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Robert Gooding
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | - Lori Haffner
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Steve Jones
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tara L. Jones
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Richard Katz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - John M. Lachin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Yong Ma
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Pamela Mucik
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Robert Orlosky
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Reamer
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - James Rochon
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Hanna Sherif
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | | | | | | | | | - John Albers
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - R. Eastman
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Judith Fradkin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Christine Lee
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Edward Gregg
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ping Zhang
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Dan O’Leary
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Gregory Evans
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Matthew Budoff
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Chris Dailing
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Ann Schwartz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Caroline Navy
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lisa Palermo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | - Sharon Hall
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Yabing Li
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Margaret Mills
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Zhuming Zhang
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Julie Hu
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Susan Hensley
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Lisa Keasler
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Tonya Taylor
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Barbara Blodi
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ronald Danis
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Matthew Davis
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Larry Hubbard*
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ryan Endres**
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Dawn Myers**
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Nancy Barrett
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Wendy Benz
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Holly Cohn
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ellie Corkery
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kristi Dohm
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Vonnie Gama
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Anne Goulding
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Andy Ewen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Kyle McDaniel
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jeong Pak
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - James Reimers
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Ruth Shaw
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Maria Swift
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Pamela Vargo
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Sheila Watson
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Jennifer Manly
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | - Ted Ganiats
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Kristin David
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Erik Groessl
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Naomi Katzir
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Helen Chong
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | | | | | | | | | - Ling Chen
- for the Diabetes Prevention Program Research (DPPOS) Group
| | - Maegan Harden
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Toni I. Pollin
- for the Diabetes Prevention Program Research (DPPOS) Group
| | | | - Paul W. Franks
- for the Diabetes Prevention Program Research (DPPOS) Group
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3
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Whyte K, Johnson J, Kelly K, Horowitz M, Widen EM, Toro-Ramos T, Gidwani S, Paley C, Crane J, Lin S, Rosenn B, Thornton J, Pi-Sunyer FX, Gallagher D. No sustained effects of an intervention to prevent excessive GWG on offspring fat and lean mass at 54 weeks: Yet a greater head circumference persists. Pediatr Obes 2021; 16:e12767. [PMID: 33394566 PMCID: PMC8178185 DOI: 10.1111/ijpo.12767] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 12/07/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND LIFT (Lifestyle Intervention for Two) trial found that intervening in women with overweight and obesity through promoting healthy diet and physical activity to control gestational weight gain (GWG) resulted in neonates with greater weight, lean mass and head circumference and similar fat mass at birth. Whether these neonate outcomes are sustained at 1-year was the focus of this investigation. METHODS Measures included body composition by PEA POD air displacement plethysmography (ADP) and Echo Infant quantitative magnetic resonance (QMR) and head circumference at birth (n = 169), 14 (n = 136) and 54 weeks (n = 137). Differences in fat and lean mass between lifestyle intervention (LI) and Usual care (UC) groups were examined using ANCOVA adjusting for maternal age and BMI, GWG, offspring sex and age. RESULTS Compared to UC, LI infants had similar weight (112 ± 131 g; P = .40), fat mass (14 ± 80 g; P = .86), lean mass (100 ± 63 g; P = .12) at 14 weeks and similar weight (168 ± 183 g; P = .36), fat mass (148 ± 124 g; P = .24), lean mass (117 ± 92 g; P = .21) at 54 weeks. Head circumference was greater in LI at 54 weeks (0.46 ± 2.1 cm P = .03). CONCLUSIONS Greater lean mass observed at birth in LI offspring was not sustained at 14 and 54 weeks, whereas the greater head circumference in LI offspring persisted at 54 weeks.
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Affiliation(s)
- Kathryn Whyte
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Jill Johnson
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Kim Kelly
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Michelle Horowitz
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Elizabeth M. Widen
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York,Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University, New York, New York,Department of Nutritional Sciences, University of Texas at Austin, New York, New York
| | - Tatiana Toro-Ramos
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Sonia Gidwani
- Department Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine, New York, New York
| | - Charles Paley
- Department Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine, New York, New York
| | - Janet Crane
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | - Susan Lin
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York,Center for Family and Community Medicine, Columbia University Irving Medical Center, New York, New York
| | - Barak Rosenn
- Department of Obstetrics and Gynecology, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine, New York, New York
| | | | - F. Xavier Pi-Sunyer
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York,Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University, New York, New York
| | - Dympna Gallagher
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, New York, New York,Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University, New York, New York
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4
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Kwee LC, Ilkayeva O, Muehlbauer MJ, Bihlmeyer N, Wolfe B, Purnell JQ, Xavier Pi-Sunyer F, Chen H, Bahnson J, Newgard CB, Shah SH, Laferrère B. Metabolites and diabetes remission after weight loss. Nutr Diabetes 2021; 11:10. [PMID: 33627633 PMCID: PMC7904757 DOI: 10.1038/s41387-021-00151-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 12/01/2020] [Accepted: 01/04/2021] [Indexed: 12/16/2022] Open
Abstract
There is marked heterogeneity in the response to weight loss interventions with regards to weight loss amount and metabolic improvement. We sought to identify biomarkers predictive of type 2 diabetes remission and amount of weight loss in individuals with severe obesity enrolled in the Longitudinal Assessment of Bariatric Surgery (LABS) and the Look AHEAD (Action for Health in Diabetes) studies. Targeted mass spectrometry-based profiling of 135 metabolites was performed in pre-intervention blood samples using a nested design for diabetes remission over five years (n = 93 LABS, n = 80 Look AHEAD; n = 87 remitters), and for extremes of weight loss at five years (n = 151 LABS; n = 75 with high weight loss). Principal components analysis (PCA) was used for dimensionality reduction, with PCA-derived metabolite factors tested for association with both diabetes remission and weight loss. Metabolic markers were tested for incremental improvement to clinical models, including the DiaRem score. Two metabolite factors were associated with diabetes remission: one primarily composed of branched chain amino acids (BCAA) and tyrosine (odds ratio (95% confidence interval) [OR (95% CI)] = 1.4 [1.0–1.9], p = 0.045), and one with betaine and choline (OR [95% CI] = 0.7 [0.5–0.9], p = 0.02).These results were not significant after adjustment for multiple tests. Inclusion of these two factors in clinical models yielded modest improvements in model fit and performance: in a constructed clinical model, the C-statistic improved from 0.87 to 0.90 (p = 0.02), while the net reclassification index showed improvement in prediction compared to the DiaRem score (NRI = 0.26, p = 0.0013). No metabolite factors associated with weight loss at five years. Baseline levels of metabolites in the BCAA and trimethylamine-N-oxide (TMAO)-microbiome-related pathways are independently and incrementally associated with sustained diabetes remission after weight loss interventions in individuals with severe obesity. These metabolites could serve as clinically useful biomarkers to identify individuals who will benefit the most from weight loss interventions.
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Affiliation(s)
| | - Olga Ilkayeva
- Duke Molecular Physiology Institute, Durham, NC, USA.,Sarah W. Stedman Nutrition and Metabolism Center, Durham, NC, USA
| | - Michael J Muehlbauer
- Duke Molecular Physiology Institute, Durham, NC, USA.,Sarah W. Stedman Nutrition and Metabolism Center, Durham, NC, USA
| | | | - Bruce Wolfe
- Departments of Surgery and Medicine, Oregon Health & Science University,, Portland, OR, USA
| | - Jonathan Q Purnell
- Departments of Surgery and Medicine, Oregon Health & Science University,, Portland, OR, USA
| | - F Xavier Pi-Sunyer
- New York Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Haiying Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine Medical Center, Winston-Salem, NC, USA
| | - Judy Bahnson
- Department of Biostatistics and Data Science, Wake Forest School of Medicine Medical Center, Winston-Salem, NC, USA
| | - Christopher B Newgard
- Duke Molecular Physiology Institute, Durham, NC, USA.,Sarah W. Stedman Nutrition and Metabolism Center, Durham, NC, USA.,Department of Pharmacology & Cancer Biology and Division of Endocrinology, Department of Medicine, Duke University, Durham, DC, USA
| | - Svati H Shah
- Duke Molecular Physiology Institute, Durham, NC, USA.,Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, DC, USA
| | - Blandine Laferrère
- New York Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University College of Physicians and Surgeons, New York, NY, USA.
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5
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Kuna ST, Reboussin DM, Strotmeyer ES, Millman RP, Zammit G, Walkup MP, Wadden TA, Wing RR, Pi-Sunyer FX, Spira AP, Foster GD. Effects of Weight Loss on Obstructive Sleep Apnea Severity. Ten-Year Results of the Sleep AHEAD Study. Am J Respir Crit Care Med 2021; 203:221-229. [PMID: 32721163 DOI: 10.1164/rccm.201912-2511oc] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Rationale: Weight loss is recommended to treat obstructive sleep apnea (OSA).Objectives: To determine whether the initial benefit of intensive lifestyle intervention (ILI) for weight loss on OSA severity is maintained at 10 years.Methods: Ten-year follow-up polysomnograms of 134 of 264 adults in Sleep AHEAD (Action for Health in Diabetes) with overweight/obesity, type 2 diabetes mellitus, and OSA were randomized to ILI for weight loss or diabetes support and education (DSE).Measurements and Main Results: Change in apnea-hypopnea index (AHI) was measured. Mean ± SE weight losses of ILI participants of 10.7 ± 0.7, 7.4 ± 0.7, 5.1 ± 0.7, and 7.1 ± 0.8 kg at 1, 2, 4, and 10 years, respectively, were significantly greater than the 1-kg weight loss at 1, 2, and 4 years and 3.5 ± 0.8 kg weight loss at 10 years for the DSE group (P values ≤ 0.0001). AHI was lower with ILI than DSE by 9.7, 8.0, and 7.9 events/h at 1, 2, and 4 years, respectively (P values ≤ 0.0004), and 4.0 events/h at 10 years (P = 0.109). Change in AHI over time was related to amount of weight loss, baseline AHI, visit year (P values < 0.0001), and intervention independent of weight change (P = 0.01). OSA remission at 10 years was more common with ILI (34.4%) than DSE (22.2%).Conclusions: Participants with OSA and type 2 diabetes mellitus receiving ILI for weight loss had reduced OSA severity at 10 years. No difference in OSA severity was present between ILI and DSE groups at 10 years. Improvement in OSA severity over the 10-year period with ILI was related to change in body weight, baseline AHI, and intervention independent of weight change.
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Affiliation(s)
- Samuel T Kuna
- University of Pennsylvania, Philadelphia, Pennsylvania.,Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania
| | | | | | | | | | | | | | | | | | | | - Gary D Foster
- Temple University, Philadelphia, Pennsylvania; and.,WW (formerly Weight Watchers), New York, New York
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6
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Carmichael OT, Neiberg RH, Dutton GR, Hayden KM, Horton E, Pi-Sunyer FX, Johnson KC, Rapp SR, Spira AP, Espeland MA. Long-term Change in Physiological Markers and Cognitive Performance in Type 2 Diabetes: The Look AHEAD Study. J Clin Endocrinol Metab 2020; 105:5897494. [PMID: 32845968 PMCID: PMC7566388 DOI: 10.1210/clinem/dgaa591] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 08/24/2020] [Indexed: 12/20/2022]
Abstract
CONTEXT The effects of physiological improvements on cognitive function among persons with type 2 diabetes mellitus (T2DM) are not fully understood. OBJECTIVE To determine whether improvements in physiological markers (body weight, blood sugar control, and physical activity) during intensive lifestyle intervention (ILI) are associated with enhancements in cognitive function in older adults with T2DM. DESIGN Multisite randomized controlled trial. SETTING Academic research centers. PATIENTS OR OTHER PARTICIPANTS Participants were aged 45-76 years, with T2DM. INTERVENTION The Action for Health in Diabetes (Look AHEAD) study, a randomized, controlled clinical trial of ILI. MAIN OUTCOME MEASURE Two to 3 cognitive assessments were collected from 1089 participants, the first and last occurring a mean (standard deviation) of 8.6 (1.0) and 11.5 (0.7) years after enrollment. RESULTS Greater improvement in blood sugar control was associated with better cognitive scores (fasting glucose and Rey Auditory Verbal Learning Test [AVLT]: P = 0.0148; fasting glucose and Digit Symbol Coding (DSC): P = 0.0360; HbA1C and DSC: P = 0.0477); but weight loss had mixed associations with cognitive scores (greater body mass index [BMI] reduction and worse AVLT overall: P = 0.0053; and greater BMI reduction and better DSC scores among those overweight but not obese at baseline: P = 0.010). Associations were strongest among those who were overweight (not obese) at baseline, and among those with a history of cardiovascular disease (CVD) at baseline. CONCLUSIONS Improvements in glycemic control, but not necessarily weight status, during ILI may be associated with better subsequent cognitive performance. These associations may differ by adiposity and CVD history.
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Affiliation(s)
- Owen T Carmichael
- Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, Louisiana
- Correspondence and Reprint Requests: Owen T. Carmichael, PhD, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70808, USA. E-mail:
| | - Rebecca H Neiberg
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Gareth R Dutton
- Department of Medicine, Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Kathleen M Hayden
- Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Edward Horton
- Joslin Diabetes Center, Harvard University, Boston, Massachusetts
| | - F Xavier Pi-Sunyer
- Division of Endocrinology, Obesity/Nutrition Research Center, Columbia University College of Physicians and Surgeons, New York, NY
| | - Karen C Johnson
- Department of Preventive Medicine, The University of Tennessee Health Science Center, Memphis, Tennessee
| | - Stephen R Rapp
- Department of Psychiatry & Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Adam P Spira
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD
| | - Mark A Espeland
- Division of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
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Yeh HC, Bantle JP, Cassidy-Begay M, Blackburn G, Bray GA, Byers T, Clark JM, Coday M, Egan C, Espeland MA, Foreyt JP, Garcia K, Goldman V, Gregg EW, Hazuda HP, Hesson L, Hill JO, Horton ES, Jakicic JM, Jeffery RW, Johnson KC, Kahn SE, Knowler WC, Korytkowski M, Kure A, Lewis CE, Mantzoros C, Meacham M, Montez MG, Nathan DM, Pajewski N, Patricio J, Peters A, Xavier Pi-Sunyer F, Pownall H, Ryan DH, Safford M, Sedjo RL, Steinburg H, Vitolins M, Wadden TA, Wagenknecht LE, Wing RR, Wolff AC, Wyatt H, Yanovski SZ. Intensive Weight Loss Intervention and Cancer Risk in Adults with Type 2 Diabetes: Analysis of the Look AHEAD Randomized Clinical Trial. Obesity (Silver Spring) 2020; 28:1678-1686. [PMID: 32841523 PMCID: PMC8855671 DOI: 10.1002/oby.22936] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 05/18/2020] [Accepted: 05/19/2020] [Indexed: 12/30/2022]
Abstract
OBJECTIVE This study was designed to determine whether intensive lifestyle intervention (ILI) aimed at weight loss lowers cancer incidence and mortality. METHODS Data from the Look AHEAD trial were examined to investigate whether participants randomized to ILI designed for weight loss would have reduced overall cancer incidence, obesity-related cancer incidence, and cancer mortality, as compared with the diabetes support and education (DSE) comparison group. This analysis included 4,859 participants without a cancer diagnosis at baseline except for nonmelanoma skin cancer. RESULTS After a median follow-up of 11 years, 684 participants (332 in ILI and 352 in DSE) were diagnosed with cancer. The incidence rates of obesity-related cancers were 6.1 and 7.3 per 1,000 person-years in ILI and DSE, respectively, with a hazard ratio (HR) of 0.84 (95% CI: 0.68-1.04). There was no significant difference between the two groups in total cancer incidence (HR, 0.93; 95% CI: 0.80-1.08), incidence of nonobesity-related cancers (HR, 1.02; 95% CI: 0.83-1.27), or total cancer mortality (HR, 0.92; 95% CI: 0.68-1.25). CONCLUSIONS An ILI aimed at weight loss lowered incidence of obesity-related cancers by 16% in adults with overweight or obesity and type 2 diabetes. The study sample size likely lacked power to determine effect sizes of this magnitude and smaller.
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Affiliation(s)
- Hsin-Chieh Yeh
- Departments of Medicine, Epidemiology, and Oncology, Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | - John P Bantle
- Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Maria Cassidy-Begay
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix Epidemiology and Clinical Research Branch, Phoenix, Arizona, USA
| | - George Blackburn
- Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - George A Bray
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Tim Byers
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jeanne M Clark
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Mace Coday
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Caitlin Egan
- Psychiatry and Human Behavior, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Mark A Espeland
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - John P Foreyt
- Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Katelyn Garcia
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Valerie Goldman
- Diabetes Clinical Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Edward W Gregg
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Helen P Hazuda
- Department of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Louise Hesson
- Center for Weight and Eating Disorders, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - James O Hill
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Edward S Horton
- Department of Medicine, Joslin Diabetes Center, Boston, Massachusetts, USA
| | - John M Jakicic
- Department of Health and Physical Activity, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Robert W Jeffery
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Karen C Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Steven E Kahn
- Department of Medicine, VA Puget Sound Health Care System / University of Washington, Seattle, Washington, USA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix Epidemiology and Clinical Research Branch, Phoenix, Arizona, USA
| | - Mary Korytkowski
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Anne Kure
- Department of Medicine, VA Puget Sound Health Care System / University of Washington, Seattle, Washington, USA
| | - Cora E Lewis
- Division of Preventive Medicine, School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | | | - Maria Meacham
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix Epidemiology and Clinical Research Branch, Phoenix, Arizona, USA
| | - Maria G Montez
- Department of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - David M Nathan
- Diabetes Clinical Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nicholas Pajewski
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | | | - Anne Peters
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | | | - Henry Pownall
- Division of Cardiology, Baylor College of Medicine, Houston, Texas, USA
| | - Donna H Ryan
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Monika Safford
- Department of Medicine, Weill Cornell Medical College of Cornell University, New York, New York, USA
| | - Rebecca L Sedjo
- Department of Community and Behavioral Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Helmut Steinburg
- Department of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Mara Vitolins
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Thomas A Wadden
- Center for Weight and Eating Disorders, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Lynne E Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Rena R Wing
- Psychiatry and Human Behavior, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Antonio C Wolff
- Department of Oncology, The Johns Hopkins Sydney Kimmel Cancer Center, Baltimore, Maryland, USA
| | - Holly Wyatt
- Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Susan Z Yanovski
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
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Hamm JD, Dotel J, Tamura S, Shechter A, Herzog M, Brunstrom JM, Albu J, Pi-Sunyer FX, Laferrère B, Kissileff HR. Reliability and responsiveness of virtual portion size creation tasks: Influences of context, foods, and a bariatric surgical procedure. Physiol Behav 2020; 223:113001. [PMID: 32522683 PMCID: PMC7370306 DOI: 10.1016/j.physbeh.2020.113001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/22/2020] [Accepted: 06/02/2020] [Indexed: 12/01/2022]
Abstract
Food portion size influences energy intake and sustained high-energy intake often leads to obesity. Virtual portion creation tasks (VPCTs), in which a participant creates portions of food on a computer screen, predict intake in healthy individuals. The objective of this study was to determine whether portions created in VPCTs are stable over time (test-retest reliability) and responsive to factors known to influence food intake, such as eating contexts and food types, and to determine if virtual portions can predict weight loss. Patients with obesity scheduled for bariatric surgery (n = 29), and individuals with a normal BMI (18.5-24.9 kg/m2, controls, n = 29), were instructed to create virtual portions of eight snack foods, which varied in energy density (low and high) and taste (sweet and salty). Portions were created in response to the following eating situations, or "contexts": What they would a) eat to stay healthy (healthy), b) typically eat (typical), c) eat to feel comfortably satisfied (satisfied), d) consider the most that they could tolerate eating (maximum), and e) eat if nothing was limiting them (desired). Tasks were completed before, and 3 months after, surgery in patients, and at two visits, 3 months apart, in controls. Body weight (kg) was recorded at both visits. Virtual portions differed significantly across groups, visits, eating contexts, energy densities (low vs. high), and tastes (sweet vs. salty). Portions created by controls did not change over time, while portions created by patients decreased significantly after surgery, for all contexts except healthy. For patients, desired and healthy portions predicted 3-month weight loss. VPCTs are replicable, responsive to foods and eating contexts, and predict surgical weight loss. These tasks could be useful for individual assessment of expectations of amounts that are eaten in health and disease and for prediction of weight loss.
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Affiliation(s)
- Jeon D Hamm
- Institute of Human Nutrition, Vagelos College of Physicians & Surgeons, Columbia University, 630 W 168th Street #1512, New York 10032, NY, United States; Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, United States; Division of Endocrinology, Department of Medicine, Mount Sinai - Morningside Hospital, 1111 Amsterdam Avenue, New York 10025, NY, United States.
| | - Jany Dotel
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, United States; Division of Endocrinology, Department of Medicine, Mount Sinai - Morningside Hospital, 1111 Amsterdam Avenue, New York 10025, NY, United States
| | - Shoran Tamura
- New York Obesity Nutrition Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, 1150 St. Nicholas Avenue #121, New York 10032, NY, United States
| | - Ari Shechter
- Institute of Human Nutrition, Vagelos College of Physicians & Surgeons, Columbia University, 630 W 168th Street #1512, New York 10032, NY, United States; Center for Behavioral Cardiovascular Health, Columbia University, 622 W 168th Street, New York, 10032, NY, United States
| | - Musya Herzog
- Teachers College, Columbia University, 525 W 120th Street, New York 10027, NY, United States
| | - Jeffrey M Brunstrom
- Nutrition and Behaviour Unit, School of Psychological Science, University of Bristol, 12a Priory Road, Bristol BS8 1TU, UK
| | - Jeanine Albu
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, United States; Division of Endocrinology, Department of Medicine, Mount Sinai - Morningside Hospital, 1111 Amsterdam Avenue, New York 10025, NY, United States
| | - F Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, 1150 St. Nicholas Avenue #121, New York 10032, NY, United States
| | - Blandine Laferrère
- New York Obesity Nutrition Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center, 1150 St. Nicholas Avenue #121, New York 10032, NY, United States
| | - Harry R Kissileff
- Diabetes, Obesity, and Metabolism Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York 10029, NY, United States; Division of Endocrinology, Department of Medicine, Mount Sinai - Morningside Hospital, 1111 Amsterdam Avenue, New York 10025, NY, United States.
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9
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Ashby-Thompson M, Ji Y, Wang J, Yu W, Thornton JC, Wolper C, Weil R, Chambers EC, Laferrère B, Pi-Sunyer FX, Gallagher D. High-Resolution Three-Dimensional Photonic Scan-Derived Equations Improve Body Surface Area Prediction in Diverse Populations. Obesity (Silver Spring) 2020; 28:706-717. [PMID: 32100449 PMCID: PMC7375836 DOI: 10.1002/oby.22743] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 12/20/2019] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Equations for predicting body surface area (BSA) produce flawed estimates, especially for individuals with obesity. This study aimed to compare BSA measured by a three-dimensional photonic scanner (3DPS) with BSA predicted by six commonly cited prediction equations and to develop new prediction equations if warranted. METHODS The 3DPS was validated against manual measurements by breadth caliper for body thicknesses measured at three anatomical sites on a mannequin. BSA was derived from 3DPS whole-body scans of 67 males and 201 females, aged 18 to 83 years, with BMI between 17.8 and 77.8 kg/m2 and varied races/ethnicities. RESULTS Width and depth measurements by 3DPS and caliper were within 1%, except for hip, with an error of 1.8%. BSA3DPS differed from BSA predicted by each equation (P < 0.05), except for males by DuBois and DuBois (P = 0.60), Tikuisis (P = 0.27), and Yu (P = 0.45) and for females by Tikuisis (P = 0.70). The combined and sex-specific equations obtained by regressing ln(BSA) on ln(weight in kilograms [W]) and ln(height in meters [H]) are as follows (R2 and SEE correspond to ln[BSA]): combined, BSA3DPS = 0.03216 × W0.4904 × H0.3769 , R2 = 0.982, SEE = 0.021; males, BSA3DPS = 0.01624 × W0.4725 × H0.5231 ; and females, BSA3DPS = 0.01522 × W0.4921 × H0.5231 , R2 = 0.986, SEE = 0.019. CONCLUSIONS New height and weight BSA equations improve BSA estimation in individuals with BMI ≥ 40 and in African Americans, Hispanic Americans, and Asian Americans.
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Affiliation(s)
- Maxine Ashby-Thompson
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Ying Ji
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Jack Wang
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Wen Yu
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | | | - Carla Wolper
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Richard Weil
- Division of Endocrinology, Diabetes, and Bone Disease, Mount Sinai Health System, Icahn School of Medicine, New York, New York, USA
| | - Earle C Chambers
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Blandine Laferrère
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- Division of Endocrinology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - F Xavier Pi-Sunyer
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- Institute of Human Nutrition, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Dympna Gallagher
- New York Obesity Research Center, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
- Institute of Human Nutrition, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, USA
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10
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Janumala I, Toro-Ramos T, Widen E, Rosenn B, Crane J, Horowitz M, Lin S, Gidwani S, Paley C, Thornton J, Pi-Sunyer FX, Gallagher D. Increased Visceral Adipose Tissue Without Weight Retention at 59 Weeks Postpartum. Obesity (Silver Spring) 2020; 28:552-562. [PMID: 32030911 PMCID: PMC7042094 DOI: 10.1002/oby.22736] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 11/18/2019] [Indexed: 11/11/2022]
Abstract
OBJECTIVE This study aimed to determine whether controlling maternal gestational weight gain (GWG) influences adipose tissue distribution at 1 year postpartum. METHODS Women with overweight or obesity (n = 210, BMI ≥ 25 or ≥ 30) were randomized to a lifestyle intervention (LI) designed to control GWG or to usual obstetrical care (UC). Measures included anthropometry, whole-body magnetic resonance imaging for visceral (VAT), intermuscular, and subcutaneous adipose tissue, and cardiometabolic risk factors in pregnancy (15 and 35 weeks) and after delivery (15 and 59 weeks). RESULTS Baseline (15 weeks) characteristics were similar (mean [SD]: age, 33.8 [4.3] years; weight, 81.9 [13.7] kg; BMI, 30.4 [4.5]; gestational age at randomization, 14.9 [0.8] weeks). LI had less GWG (1.79 kg; P = 0.003) and subcutaneous adipose tissue gain at 35 weeks gestation (P < 0.01). UC postpartum weight (2.92 kg) was higher at 15 weeks but not different from baseline or LI at 59 weeks postpartum. Postpartum VAT increased from baseline in LI by 0.23 kg at 15 weeks and 0.55 kg at 59 weeks; in UC, it increased by 0.34 kg at 15 and 59 weeks. Intermuscular adipose tissue remained elevated in LI (0.22 kg) at 59 weeks. VAT was associated with several cardiometabolic risk factors at 59 weeks. CONCLUSIONS Despite no weight retention at 59 weeks postpartum, women had increased VAT by ~30%. Postpartum modifiable behaviors are warranted to lower the risk of VAT retention.
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Affiliation(s)
- Isaiah Janumala
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
| | - Tatiana Toro-Ramos
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
| | - Elizabeth Widen
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
- Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University
- Department of Nutritional Sciences, University of Texas at Austin
| | - Barak Rosenn
- Department Obstetrics and Gynecology, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | - Janet Crane
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
| | - Michelle Horowitz
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
| | - Susan Lin
- Center for Family and Community Medicine, Columbia University
| | - Sonia Gidwani
- Department of Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | - Charles Paley
- Department of Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | | | - F. Xavier Pi-Sunyer
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
- Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University
| | - Dympna Gallagher
- New York Obesity Research Center, Dept. of Medicine, College of Physicians and Surgeons, Columbia University
- Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University
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11
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Jakicic JM, Horton ES, Curtis JM, Killean TM, Bray GA, Cheskin LJ, Johnson KC, Middelbeek RJW, Pi-Sunyer FX, Regensteiner JG, Ribisl PM, Wagenknecht L, Espeland MA. Abnormal Exercise Test or CVD History on Weight Loss or Fitness: the Look AHEAD Trial. Transl J Am Coll Sports Med 2020; 5:e000134. [PMID: 34017914 PMCID: PMC8130141 DOI: 10.1249/tjx.0000000000000134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Obesity and type 2 diabetes are associated with an increased risk of cardiovascular disease (CVD) and the combination of weight loss and increased physical exercise are commonly recommended to reduce CVD. This study examined whether people with obesity and type 2 diabetes with an abnormal graded exercise tolerance test (GXT) or a history of CVD would have less success in achieving weight loss and improved fitness, compared to adults without these conditions. METHODS The Look AHEAD Study examined whether an intensive lifestyle intervention (ILI) compared with diabetes support and education (DSE) reduced cardiovascular events in adults with overweight/obesity and type 2 diabetes. Participants underwent a baseline maximal GXT and provided medical history data. Weight loss and fitness change were examined in 5011 participants over four years in those with or without an abnormal baseline GXT and/or history of CVD. RESULTS After four years, weight loss in both ILI and DSE were significantly greater in those without a prior history of CVD than in those with a CVD history (6.69% vs 5.98%, p=0.02, in ILI and 0.73 vs -.07% (weight gain), p=0.01, in DSE). Likewise, those without a prior history of CVD experienced greater improvements in fitness in both ILI and DSE relative to those with a history of CVD. Having an abnormal GXT at baseline did not affect weight loss or fitness. CONCLUSIONS A history of CVD at baseline modestly lessened weight loss and fitness changes at 4 years, whereas having any abnormality on the baseline GXT did not affect these outcomes. Thus, weight loss and improved fitness are achievable in adults with a history of CVD or ECG abnormalities.
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Affiliation(s)
| | | | - Jeffrey M. Curtis
- NIDDK, Phoenix, AZ
- St. Joseph’s Hospital and Medical Center, Phoenix, AZ
| | - Tina M. Killean
- NIDDK, Phoenix, AZ
- Northern Navajo Medical Center, Shiprock, NM
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Hamm JD, Herzog M, Shechter A, Albu J, Laferrère B, Brunstrom J, Pi-Sunyer FX, Kissileff HR. Use of a virtual portion selection instrument in persons undergoing bariatric surgery. Appetite 2018. [DOI: 10.1016/j.appet.2018.05.194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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13
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Vasselli JR, Pi-Sunyer FX, Wall DG, John CS, Chapman CD, Currie PJ. Central effects of insulin detemir on feeding, body weight, and metabolism in rats. Am J Physiol Endocrinol Metab 2017; 313:E613-E621. [PMID: 28720583 PMCID: PMC5792141 DOI: 10.1152/ajpendo.00111.2016] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Revised: 07/11/2017] [Accepted: 07/11/2017] [Indexed: 12/20/2022]
Abstract
Insulin detemir (DET) is a basal insulin analog that, in contrast to other long-acting forms of insulin, has significant weight-gain-sparing effects in diabetic patients. We hypothesized that this effect of DET may be due to its enhanced catabolic action in the central nervous system. We investigated the long-term effects of single third ventricular (3V) microinjections of equimolar doses of DET and regular insulin in normal male rats on feeding, body weight, energy expenditure (EE), and respiratory quotient (RQ). Also, in acute testing, we assessed the ability of lower doses of DET to alter feeding, EE, and RQ when microinjected directly into the paraventricular nucleus (PVN). The anabolic peptide ghrelin served as a positive control in acute testing. 3V administration of both DET (0.5-2.0 mU) and regular insulin (2.0-8.0 mU) significantly reduced feeding and body weight over 48 and 120 h, respectively, with DET yielding greater inhibitory effects. DET also stimulated greater elevations of EE and reductions of RQ over 72 and 48 h postinjection, respectively. In acute (4 h) testing, microinjections of DET (0.5 mU) into the PVN reduced feeding, increased EE, and reduced RQ, while ghrelin (100 pmol) had the opposite effects. When administered sequentially into the PVN, DET (0.25 and 0.5 mU) reversed ghrelin-induced feeding, EE, and RQ effects. These data support the notion that the weight-sparing effect of DET is at least in part based on its central catabolic action and that enhanced EE and reduced RQ may participate in this effect.
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Affiliation(s)
- Joseph R Vasselli
- Obesity Nutrition Research Center, Department of Medicine, Columbia University, New York, New York; and
| | - F Xavier Pi-Sunyer
- Obesity Nutrition Research Center, Department of Medicine, Columbia University, New York, New York; and
| | - Daniel G Wall
- Department of Psychology, Reed College, Portland, Oregon
| | | | | | - Paul J Currie
- Department of Psychology, Reed College, Portland, Oregon
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14
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Toro-Ramos T, Paley C, Wong WW, Pi-Sunyer FX, Yu W, Thornton J, Gallagher D. Reliability of the EchoMRI Infants System for Water and Fat Measurements in Newborns. Obesity (Silver Spring) 2017; 25:1577-1583. [PMID: 28712143 PMCID: PMC5669386 DOI: 10.1002/oby.21918] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 05/22/2017] [Accepted: 05/25/2017] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The precision and accuracy of a quantitative magnetic resonance (EchoMRI Infants) system in newborns were determined. METHODS Canola oil and drinking water phantoms (increments of 10 g to 1.9 kg) were scanned four times. Instrument reproducibility was assessed from three scans (within 10 minutes) in 42 healthy term newborns (12-70 hours post birth). Instrument precision was determined from the coefficient of variation (CV) of repeated scans for total water, lean mass, and fat measures for newborns and the mean difference between weight and measurement for phantoms. In newborns, the system accuracy for total body water (TBW) was tested against deuterium dilution (D2 O). RESULTS In phantoms, the repeatability and accuracy of fat and water measurements increased as the weight of oil and water increased. TBW was overestimated in amounts >200 g. In newborns weighing 3.14 kg, fat, lean mass, and TBW were 0.52 kg (16.48%), 2.28 kg, and 2.40 kg, respectively. EchoMRI's reproducibility (CV) was 3.27%, 1.83%, and 1.34% for total body fat, lean mass, and TBW, respectively. EchoMRI-TBW values did not differ from D2 O; mean difference, -1.95 ± 6.76%, P = 0.387; mean bias (limits of agreement), 0.046 kg (-0.30 to 0.39 kg). CONCLUSIONS The EchoMRI Infants system's precision and accuracy for total body fat and lean mass are better than established techniques and equivalent to D2 O for TBW in phantoms and newborns.
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Affiliation(s)
- Tatiana Toro-Ramos
- New York Obesity Nutrition Research Center, Dept. of Medicine, Columbia University, New York, New York, USA
- Institute of Human Nutrition; Columbia University, New York, New York, USA
| | - Charles Paley
- New York Obesity Nutrition Research Center, Dept. of Medicine, Columbia University, New York, New York, USA
- Department of Pediatrics, Mount Sinai-Roosevelt Hospital, New York, New York, USA
| | - William W. Wong
- USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, Texas, USA
| | - F. Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, Dept. of Medicine, Columbia University, New York, New York, USA
- Institute of Human Nutrition; Columbia University, New York, New York, USA
| | - W. Yu
- New York Obesity Nutrition Research Center, Dept. of Medicine, Columbia University, New York, New York, USA
| | | | - Dympna Gallagher
- New York Obesity Nutrition Research Center, Dept. of Medicine, Columbia University, New York, New York, USA
- Institute of Human Nutrition; Columbia University, New York, New York, USA
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15
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Luchsinger JA, Ma Y, Christophi CA, Florez H, Golden SH, Hazuda H, Crandall J, Venditti E, Watson K, Jeffries S, Manly JJ, Pi-Sunyer FX. Metformin, Lifestyle Intervention, and Cognition in the Diabetes Prevention Program Outcomes Study. Diabetes Care 2017; 40:958-965. [PMID: 28500216 PMCID: PMC5481986 DOI: 10.2337/dc16-2376] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 04/03/2017] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We examined the association of the Diabetes Prevention Program (DPP) intervention arms (lifestyle intervention, metformin, and placebo) with cognition in the Diabetes Prevention Program Outcomes Study (DPPOS). We also examined metformin use, incident type 2 diabetes, and glycemia as exposures. RESEARCH DESIGN AND METHODS The DPP lasted 2.8 years, followed by a 13-month bridge to DPPOS. Cognition was assessed in DPPOS years 8 and 10 (12 and 14 years after randomization) with the Spanish English Verbal Learning Test (SEVLT), letter fluency and animal fluency tests, Digit Symbol Substitution Test (DSST), and a composite cognitive score. RESULTS A total of 2,280 participants (749 lifestyle, 776 metformin, and 755 placebo) aged 63.1 ± 10.7 years underwent cognitive assessments; 67.7% women, 54.6% non-Hispanic white, 20.7% non-Hispanic black, 14.6% Hispanic, 5.5% American Indian, and 4.6% Asian; 26.6% were homozygous or heterozygous for APOE-ε4. At the time of cognitive assessment, type 2 diabetes was higher in the placebo group (57.9%; P < 0.001) compared with lifestyle (47.0%) and metformin (50.4%). Metformin exposure was higher in the metformin group (8.72 years; P < 0.001) compared with placebo (1.43 years) and lifestyle (0.96 years). There were no differences in cognition across intervention arms. Type 2 diabetes was not related to cognition, but higher glycated hemoglobin at year 8 was related to worse cognition after confounder adjustment. Cumulative metformin exposure was not related to cognition. CONCLUSIONS Exposure to intensive lifestyle intervention or metformin was not related to cognition among DPPOS participants. Higher glycemia was related to worse cognitive performance. Metformin seemed cognitively safe among DPPOS participants.
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Affiliation(s)
| | - Yong Ma
- Biostatistics Center, The George Washington University, Washington, DC
| | | | | | | | - Helen Hazuda
- University of Texas Health Science Center, San Antonio, TX
| | | | | | - Karol Watson
- University of California, Los Angeles, Los Angeles, CA
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16
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Shechter A, Foster GD, Lang W, Reboussin DM, St-Onge MP, Zammit G, Newman AB, Millman RP, Wadden TA, Jakicic JM, Strotmeyer ES, Wing RR, Pi-Sunyer FX, Kuna ST. Effects of a lifestyle intervention on REM sleep-related OSA severity in obese individuals with type 2 diabetes. J Sleep Res 2017; 26:747-755. [PMID: 28560832 DOI: 10.1111/jsr.12559] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 04/11/2017] [Indexed: 11/28/2022]
Abstract
The aim of this study was to determine if an intensive lifestyle intervention (ILI) reduces the severity of obstructive sleep apnea (OSA) in rapid-eye movement (REM) sleep, and to determine if longitudinal changes in glycaemic control are related to changes in OSA severity during REM sleep over a 4-year follow-up. This was a randomized controlled trial including 264 overweight/obese adults with type 2 diabetes (T2D) and OSA. Participants were randomized to an ILI targeted to weight loss or a diabetes support and education (DSE) control group. Measures included anthropometry, apnea-hypopnea index (AHI) during REM sleep (REM-AHI) and non-REM sleep (NREM-AHI) and glycated haemoglobin (HbA1c) at baseline and year 1, year 2 and year 4 follow-ups. Mean baseline values of REM-AHI were significantly higher than NREM-AHI in both groups. Both REM-AHI and NREM-AHI were reduced significantly more in ILI versus DSE, but these differences were attenuated slightly after adjustment for weight changes. Repeated-measure mixed-model analyses including data to year 4 demonstrated that changes in HbA1c were related significantly to changes in weight, but not to changes in REM-AHI and NREM-AHI. Compared to control, the ILI reduced REM-AHI and NREM-AHI during the 4-year follow-up. Weight, as opposed to REM-AHI and NREM-AHI, was related to changes in HbA1c. The findings imply that weight loss from a lifestyle intervention is more important than reductions in AHI for improving glycaemic control in T2D patients with OSA.
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Affiliation(s)
- Ari Shechter
- Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University, New York, NY, USA
| | - Gary D Foster
- Center for Weight and Eating Disorders, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.,Weight Watchers International, New York, NY, USA
| | - Wei Lang
- Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - David M Reboussin
- Division of Public Health Sciences, Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marie-Pierre St-Onge
- New York Obesity Nutrition Research Center, Department of Medicine, Columbia University, New York, NY, USA
| | | | - Anne B Newman
- Center for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Richard P Millman
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Alpert Medical School of Brown University, Providence, RI, USA
| | - Thomas A Wadden
- Center for Weight and Eating Disorders, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - John M Jakicic
- Physical Activity and Weight Management Research Center, Department of Health and Physical Activity, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elsa S Strotmeyer
- Center for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Rena R Wing
- Department of Psychiatry and Human Behavior, Weight Control and Diabetes Research Center, The Miriam Hospital, Alpert Medical School of Brown University, Providence, RI, USA
| | - F Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, Department of Medicine, Columbia University, New York, NY, USA
| | - Samuel T Kuna
- Division of Sleep Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
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17
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Weil R, Kovacs B, Miller N, McDermott MP, Wall M, Kupersmith M, Pi-Sunyer FX. A 6-month telephone-based weight loss intervention in overweight and obese subjects with idiopathic intracranial hypertension. Obes Sci Pract 2016; 2:95-103. [PMID: 29071096 PMCID: PMC5523694 DOI: 10.1002/osp4.34] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 02/03/2016] [Accepted: 02/04/2016] [Indexed: 01/05/2023] Open
Abstract
Objectives The purpose of this paper is to measure the change in body weight after a 6‐month telephone‐based weight loss intervention in overweight and obese subjects with idiopathic intracranial hypertension (IIH) and mild visual loss randomized to receive either acetazolamide or placebo. Methods One hundred sixty‐five subjects with IIH, aged 29.1 ± 7.5 (mean ± SD) and BMI 39.9 + 8.3 kg/m2, enrolled at 38 academic and private practice sites in North America, participated in this trial. This was a randomized, double‐masked, placebo‐controlled trial of acetazolamide in subjects with IIH and mild visual loss. All participants received a reduced‐sodium, weight‐reduction diet and a 6‐month telephone‐based weight loss intervention. Six‐month changes from baseline in body weight, perimetric mean deviation as assessed by automated perimetry and quality of life using the National Eye Institute Visual Function Questionnaire 25 and the 36‐item Short Form Health Survey were measured. Results Mean percent weight change at 6 months was −5.9% ± 6.7% of initial body weight overall, −3.5% ± 5.9% in the placebo group and −7.8% ± 6.8% in the acetazolamide group. Weight change was not associated with changes in either mean deviation or quality of life scores. Conclusion Patients with IIH and mild visual loss assigned to either acetazolamide or placebo, all of whom received a 6‐month telephone‐based weight loss intervention, lost an average of 5.9% of initial body weight, consistent with NHLBI guidelines of 5% to 10% of body weight loss for clinically significant health benefit.
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Affiliation(s)
- Richard Weil
- Department of Endocrinology, Diabetes and Nutrition Mt Sinai St Luke's Hospital New York NY USA
| | - Betty Kovacs
- Department of Endocrinology, Diabetes and Nutrition Mt Sinai St Luke's Hospital New York NY USA
| | - Neil Miller
- Depts of Ophthalmology, Neurology and Neurosurgery Johns Hopkins University School of Medicine Baltimore MD USA
| | - Michael P McDermott
- Department of Biostatistics and Computational Biology University of Rochester Medical Center Rochester NY USA
| | - Michael Wall
- University of Iowa Carver College of Medicine Iowa City IA USA
| | - Mark Kupersmith
- Department of Neuro-Ophthalmology Mt. Sinai Roosevelt Hospital and New York Eye and Ear Infirmary New York NY USA
| | - F Xavier Pi-Sunyer
- Obesity Research Center, Department of Medicine Columbia University Medical Center New York NY USA
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18
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Kline CE, Reboussin DM, Foster GD, Rice TB, Strotmeyer ES, Jakicic JM, Millman RP, Pi-Sunyer FX, Newman AB, Wadden TA, Zammit G, Kuna ST. The Effect of Changes in Cardiorespiratory Fitness and Weight on Obstructive Sleep Apnea Severity in Overweight Adults with Type 2 Diabetes. Sleep 2016; 39:317-25. [PMID: 26446118 DOI: 10.5665/sleep.5436] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 09/04/2015] [Indexed: 11/03/2022] Open
Abstract
STUDY OBJECTIVES To examine the effect of changes in cardiorespiratory fitness on obstructive sleep apnea (OSA) severity prior to and following adjustment for changes in weight over the course of a 4-y weight loss intervention. METHODS As secondary analyses of a randomized controlled trial, 263 overweight/obese adults with type 2 diabetes and OSA participated in an intensive lifestyle intervention or education control condition. Measures of OSA severity, cardiorespiratory fitness, and body weight were obtained at baseline, year 1, and year 4. Change in the apnea-hypopnea index (AHI) served as the primary outcome. The percentage change in fitness (submaximal metabolic equivalents [METs]) and change in weight (kg) were the primary independent variables. Primary analyses collapsed intervention conditions with statistical adjustment for treatment group and baseline METs, weight, and AHI among other relevant covariates. RESULTS At baseline, greater METs were associated with lower AHI (B [SE] = -1.48 [0.71], P = 0.038), but this relationship no longer existed (B [SE] = -0.24 [0.73], P = 0.75) after adjustment for weight (B [SE] = 0.31 [0.07], P < 0.0001). Fitness significantly increased at year 1 (+16.53 ± 28.71% relative to baseline), but returned to near-baseline levels by year 4 (+1.81 ± 24.48%). In mixed-model analyses of AHI change over time without consideration of weight change, increased fitness at year 1 (B [SE] = -0.15 [0.04], P < 0.0001), but not at year 4 (B [SE] = 0.04 [0.05], P = 0.48), was associated with AHI reduction. However, with weight change in the model, greater weight loss was associated with AHI reduction at years 1 and 4 (B [SE] = 0.81 [0.16] and 0.60 [0.16], both P < 0.0001), rendering the association between fitness and AHI change at year 1 nonsignificant (B [SE] = -0.04 [0.04], P = 0.31). CONCLUSIONS Among overweight/obese adults with type 2 diabetes, fitness change did not influence OSA severity change when weight change was taken into account. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov identification number NCT00194259.
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Affiliation(s)
| | | | - Gary D Foster
- Temple University, Philadelphia, PA.,Weight Watchers International, New York, NY
| | | | | | | | | | | | | | | | | | - Samuel T Kuna
- University of Pennsylvania, Philadelphia PA.,Philadelphia Veterans Affairs Medical Center, Philadelphia, PA
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19
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Lin WY, Peng CY, Lin CC, Davidson LE, Pi-Sunyer FX, Sung PK, Huang KC. General and Abdominal Adiposity and Risk of Death in HBV Versus Non-HBV Carriers: A 10-Year Population-based Cohort Study. Medicine (Baltimore) 2016; 95:e2162. [PMID: 26765398 PMCID: PMC4718224 DOI: 10.1097/md.0000000000002162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Revised: 10/31/2015] [Accepted: 11/04/2015] [Indexed: 12/13/2022] Open
Abstract
Both obesity and hepatitis B virus (HBV) infection increase the risk of death. We investigate the association between general and central obesity and all-cause mortality among adult Taiwanese HBV versus non-HBV carriers.A total of 19,850 HBV carriers and non-hepatitis C virus (HCV) carriers, aged 20 years and older at enrollment in 1998 to 1999 in Taiwan, were matched to 79,400 non-HBV and non-HCV carriers (1:4). Cox proportional-hazards models were used to estimate the relative risks for all-cause mortality during a maximum follow-up period of 10 years. Four obesity-related anthropometric indices-body mass index (BMI), waist circumference, waist-to-hip ratio, and waist-to-height ratio-were the main variables of interest.During the follow-up period, 628 and 2366 participants died among HBV and non-HBV carriers, respectively. Both underweight and general obesity were associated with an increased risk of death. The highest risk of all-cause death in relation to BMI was found in the HBV carriers with underweight (BMI <18.5 kg/m2) and non-HBV carriers with obesity (BMI ≥30 kg/m2). The lowest risks of all-cause death in relation to abdominal adiposity were found at the third quartiles of waist circumference, waist-to-hip ratio, and waist-to-height ratio among HBV carriers, but in the second quartiles among non-HBV carriers. For those with pre-existing liver disease among HBV carriers, patients with underweight have higher risk of death than those with obesity.Hepatitis B virus carriers with underweight have higher risk of death than non-HBV carriers. HBV carriers with mild abdominal obesity have the lowest risk of death, but not in the non-HBV carriers.
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Affiliation(s)
- Wen-Yuan Lin
- From the Department of Family Medicine (W-YL, C-CL); Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan (C-YP); School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan(W-YL, C-CL, C-YP); Department of Family Medicine, National Taiwan University Hospital, Taipei, Taiwan (W-YL, K-CH); Institute of Health Care Administration, College of Health Science, Asia University, Taichung, Taiwan (C-CL); MJ Health Screening Center, Taipei, Taiwan (P-KS); College of Life Sciences, Brigham Young University, Utah, USA (LED); and New York Presbyterian Hospital, Columbia University, New York, USA (FXP-S)
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20
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Toro-Ramos T, Paley C, Pi-Sunyer FX, Gallagher D. Body composition during fetal development and infancy through the age of 5 years. Eur J Clin Nutr 2015; 69:1279-89. [PMID: 26242725 PMCID: PMC4680980 DOI: 10.1038/ejcn.2015.117] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Revised: 06/08/2015] [Accepted: 06/11/2015] [Indexed: 02/07/2023]
Abstract
Fetal body composition is an important determinant of body composition at birth, and it is likely to be an important determinant at later stages in life. The purpose of this work is to provide a comprehensive overview by presenting data from previously published studies that report on body composition during fetal development in newborns and the infant/child through 5 years of age. Understanding the changes in body composition that occur both in utero and during infancy and childhood, and how they may be related, may help inform evidence-based practice during pregnancy and childhood. We describe body composition measurement techniques from the in utero period to 5 years of age, and identify gaps in knowledge to direct future research efforts. Available literature on chemical and cadaver analyses of fetal studies during gestation is presented to show the timing and accretion rates of adipose and lean tissues. Quantitative and qualitative aspects of fetal lean and fat mass accretion could be especially useful in the clinical setting for diagnostic purposes. The practicality of different pediatric body composition measurement methods in the clinical setting is discussed by presenting the assumptions and limitations associated with each method that may assist the clinician in characterizing the health and nutritional status of the fetus, infant and child. It is our hope that this review will help guide future research efforts directed at increasing the understanding of how body composition in early development may be associated with chronic diseases in later life.
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Affiliation(s)
- T Toro-Ramos
- Department of Medicine, New York Obesity Nutrition Research Center, St Luke’s-Roosevelt Hospital, New York, NY, USA
- Department of Medicine, Institute of Human Nutrition, Columbia University, New York, NY, USA
| | - C Paley
- Department of Medicine, New York Obesity Nutrition Research Center, St Luke’s-Roosevelt Hospital, New York, NY, USA
- Department of Pediatrics, St Luke’s-Roosevelt Hospital, New York, NY, USA
| | - FX Pi-Sunyer
- Department of Medicine, New York Obesity Nutrition Research Center, St Luke’s-Roosevelt Hospital, New York, NY, USA
- Department of Medicine, Institute of Human Nutrition, Columbia University, New York, NY, USA
| | - D Gallagher
- Department of Medicine, New York Obesity Nutrition Research Center, St Luke’s-Roosevelt Hospital, New York, NY, USA
- Department of Medicine, Institute of Human Nutrition, Columbia University, New York, NY, USA
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21
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Belalcazar LM, Lang W, Haffner SM, Schwenke DC, Kriska A, Balasubramanyam A, Hoogeveen RC, Pi-Sunyer FX, Tracy RP, Ballantyne CM. Improving Adiponectin Levels in Individuals With Diabetes and Obesity: Insights From Look AHEAD. Diabetes Care 2015; 38:1544-50. [PMID: 25972574 PMCID: PMC4512135 DOI: 10.2337/dc14-2775] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Accepted: 04/20/2015] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This study investigated whether fitness changes resulting from lifestyle interventions for weight loss may independently contribute to the improvement of low adiponectin levels in obese individuals with diabetes. RESEARCH DESIGN AND METHODS Look AHEAD (Action for Health in Diabetes) randomized overweight/obese individuals with type 2 diabetes to intensive lifestyle intervention (ILI) for weight loss or to diabetes support and education (DSE). Total and high-molecular weight adiponectin (adiponectins), weight, and cardiorespiratory fitness (submaximal exercise stress test) were measured in 1,397 participants at baseline and at 1 year, when ILI was most intense. Regression analyses examined the associations of 1-year weight and fitness changes with change in adiponectins. RESULTS ILI resulted in greater improvements in weight, fitness, and adiponectins at 1 year compared with DSE (P < 0.0001). Weight loss and improved fitness were each associated with changes in adiponectins in men and women (P < 0.001 for all), after adjusting for baseline adiponectins, demographics, clinical variables, and treatment arm. Weight loss contributed an additional 4-5% to the variance of change in adiponectins than did increased fitness in men; in women, the contributions of improved fitness (1% greater) and of weight loss were similar. When weight and fitness changes were both accounted for, weight loss in men and increased fitness in women retained their strong associations (P < 0.0001) with adiponectin change. CONCLUSIONS Improvements in fitness and weight with ILI were favorably but distinctly associated with changes in adiponectin levels in overweight/obese men and women with diabetes. Future studies need to investigate whether sex-specific biological determinants contribute to the observed associations.
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Affiliation(s)
- L Maria Belalcazar
- Department of Medicine, University of Texas Medical Branch at Galveston, Galveston, TX
| | - Wei Lang
- Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Steven M Haffner
- Department of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Dawn C Schwenke
- College of Nursing & Health Innovation, Arizona State University, Phoenix, AZ
| | - Andrea Kriska
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA
| | | | - Ron C Hoogeveen
- Department of Medicine, Baylor College of Medicine, Houston, TX
| | - F Xavier Pi-Sunyer
- Department of Medicine, Columbia University, St. Luke's-Roosevelt Hospital, New York, NY
| | - Russell P Tracy
- Department of Pathology and Laboratory Medicine, University of Vermont, Burlington, VT
| | - Christie M Ballantyne
- Department of Medicine, Baylor College of Medicine, Houston, TX Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart & Vascular Center, Houston, TX
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22
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Cefalu WT, Bray GA, Home PD, Garvey WT, Klein S, Pi-Sunyer FX, Hu FB, Raz I, Van Gaal L, Wolfe BM, Ryan DH. Advances in the Science, Treatment, and Prevention of the Disease of Obesity: Reflections From a Diabetes Care Editors' Expert Forum. Diabetes Care 2015; 38:1567-82. [PMID: 26421334 PMCID: PMC4831905 DOI: 10.2337/dc15-1081] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
As obesity rates increase, so too do the risks of type 2 diabetes, cardiovascular disease, and numerous other detrimental conditions. The prevalence of obesity in U.S. adults more than doubled between 1980 and 2010, from 15.0 to 36.1%. Although this trend may be leveling off, obesity and its individual, societal, and economic costs remain of grave concern. In June 2014, a Diabetes Care Editors' Expert Forum convened to review the state of obesity research and discuss the latest prevention initiatives and behavioral, medical, and surgical therapies. This article, an outgrowth of the forum, offers an expansive view of the obesity epidemic, beginning with a discussion of its root causes. Recent insights into the genetic and physiological factors that influence body weight are reviewed, as are the pathophysiology of obesity-related metabolic dysfunction and the concept of metabolically healthy obesity. The authors address the crucial question of how much weight loss is necessary to yield meaningful benefits. They describe the challenges of behavioral modification and predictors of its success. The effects of diabetes pharmacotherapies on body weight are reviewed, including potential weight-neutral combination therapies. The authors also summarize the evidence for safety and efficacy of pharmacotherapeutic and surgical obesity treatments. The article concludes with an impassioned call for researchers, clinicians, governmental agencies, health policymakers, and health-related industries to collectively embrace the urgent mandate to improve prevention and treatment and for society at large to acknowledge and manage obesity as a serious disease.
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Affiliation(s)
- William T. Cefalu
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | - George A. Bray
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | | | - W. Timothy Garvey
- Department of Nutrition Sciences, University of Alabama at Birmingham and Birmingham Veterans Affairs Medical Center, Birmingham, AL
| | - Samuel Klein
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, MO
| | - F. Xavier Pi-Sunyer
- Obesity Research Center, Department of Medicine, Columbia University, New York, NY
| | - Frank B. Hu
- Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Itamar Raz
- Department of Internal Medicine, Diabetes Unit, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Luc Van Gaal
- Department of Endocrinology, Diabetology, and Metabolism, Antwerp University Hospital, Antwerp, Belgium
| | - Bruce M. Wolfe
- Department of Surgery, Oregon Health and Science University, Portland, OR
| | - Donna H. Ryan
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
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Belalcazar LM, Papandonatos GD, McCaffery JM, Peter I, Pajewski NM, Erar B, Allred ND, Balasubramanyam A, Bowden DW, Brautbar A, Pi-Sunyer FX, Ballantyne CM, Huggins GS. A common variant in the CLDN7/ELP5 locus predicts adiponectin change with lifestyle intervention and improved fitness in obese individuals with diabetes. Physiol Genomics 2015; 47:215-24. [PMID: 25759378 DOI: 10.1152/physiolgenomics.00109.2014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 03/10/2015] [Indexed: 01/19/2023] Open
Abstract
Overweight/obese individuals with Type 2 diabetes have low adiponectin levels, which may improve with lifestyle changes. We investigated whether genetic variants associated with adiponectin levels in genome-wide association studies (GWAS) would also be related with adiponectin changes in response to an intensive lifestyle intervention (ILI), potentially through mechanisms altering the adipose microenvironment via weight loss and/or improved cardiorespiratory fitness. Look AHEAD was a randomized trial comparing the cardiovascular benefits of ILI-induced weight loss and physical activity compared with diabetes support and education among overweight/obese individuals with Type 2 diabetes. In a subsample of Look AHEAD with adiponectin data and genetic consent (n=1,351), we evaluated the effects of 24 genetic variants, demonstrated by GWAS to be cross-sectionally associated with adiponectin, on adiponectin change 1-yr postintervention. We explored via mediational analyses whether any differential effects by treatment arm were occurring through weight loss and/or improved fitness. A variant, rs222857, in the CLDN7 locus, potentially associated with epithelial barrier integrity and tight junction physiology, and a putative cis expression quantitative trail locus for elongator acetyltransferase complex subunit 5 (ELP5), predicted adiponectin increases within ILI (log-adiponectin in overall sample per copy: β±SE=0.05±0.02, P=0.008; in non-Hispanic whites: 0.06±0.02, P=0.009). The favorable effects of rs222857 (minor allele frequency 45.5%) appeared to be mediated by mechanisms associated with improved fitness, and not weight loss. This is the first study to identify a genetic variant that modifies adiponectin response to lifestyle intervention in overweight/obese diabetic individuals.
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Affiliation(s)
- L Maria Belalcazar
- Department of Medicine, University of Texas Medical Branch, Galveston, Texas;
| | | | - Jeanne M McCaffery
- Weight Control and Diabetes Research Center, Department of Psychiatry and Human Behavior, The Miriam Hospital and Brown Medical School, Providence, Rhode Island
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Nicholas M Pajewski
- Department of Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina
| | - Bahar Erar
- Department of Biostatistics, Brown University, Providence, Rhode Island
| | - Nicholette D Allred
- Department of Biochemistry and Center for Genomics and Personalized Medicine Research, Wake Forest University Health Sciences, Winston-Salem, North Carolina
| | | | - Donald W Bowden
- Department of Biochemistry and Center for Genomics and Personalized Medicine Research, Wake Forest University Health Sciences, Winston-Salem, North Carolina
| | - Ariel Brautbar
- Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - F Xavier Pi-Sunyer
- Department of Medicine, Columbia University, St. Luke's-Roosevelt Hospital, New York, New York
| | - Christie M Ballantyne
- Department of Medicine, Baylor College of Medicine, Houston, Texas; Center for Cardiovascular Disease Prevention, Methodist De Bakey Heart and Vascular Center, Houston, Texas; and
| | - Gordon S Huggins
- MCRI Center for Translational Genomics, Tufts Medical Center, Boston, Massachusetts
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Gallagher D, Heshka S, Kelley DE, Thornton J, Boxt L, Pi-Sunyer FX, Patricio J, Mancino J, Clark JM. Changes in adipose tissue depots and metabolic markers following a 1-year diet and exercise intervention in overweight and obese patients with type 2 diabetes. Diabetes Care 2014; 37:3325-32. [PMID: 25336745 PMCID: PMC4237982 DOI: 10.2337/dc14-1585] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We aim to characterize the effects on total body fat and distribution of a 1-year intensive lifestyle intervention (ILI) for weight loss in overweight and obese adults with type 2 diabetes and to examine whether changes in adipose tissue (AT) depots were associated with changes in metabolic biomarkers. RESEARCH DESIGN AND METHODS Participants were 54 females and 38 males (age 57.8 ± 6.7 years [mean ± SD]; BMI 31.7 ± 3.5 kg/m(2)) enrolled in the Look AHEAD (Action for Health in Diabetes) trial randomized to ILI or diabetes support and education (DSE) from whom baseline and 1-year MRI measures of total AT (TAT) and regional (arm, trunk, leg) AT, including subcutaneous AT (SAT), visceral AT (VAT), and intermuscular AT (IMAT), were acquired. We tested whether mean changes in ILI and DSE were equal and, within groups, whether changes were different from zero. Regression models tested whether changes in AT compartments were associated with metabolic variable changes. RESULTS Body weight changed -0.52 ± 3.62 kg (P = 0.31) in DSE and -7.24 ± 5.40 kg (P < 0.0001) in ILI. Mean ILI changes were different from DSE (P < 0.001 for TAT, SAT, and IMAT and P < 0.01 for VAT in females). Within ILI, SAT and VAT decreased in males and females (P < 0.0001), but IMAT was unchanged (0.00 ± 0.54 kg; P = 0.99). In DSE, SAT and VAT did not change, but IMAT increased by 0.46 ± 0.55 kg (P < 0.001). Controlling for weight loss, reduction of specific AT depots was associated with improvement in metabolic biomarkers. CONCLUSIONS Weight loss of 7-10% from an ILI over 1 year reduced SAT and VAT and prevented an increase in IMAT. Reductions in AT depots were associated with improvements in biomarkers.
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Affiliation(s)
- Dympna Gallagher
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, New York, NY Institute of Human Nutrition and Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY
| | - Stanley Heshka
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, New York, NY Institute of Human Nutrition and Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY
| | - David E Kelley
- Obesity/Nutrition Research Center, University of Pittsburgh, Pittsburgh, PA Diabetes and Endocrinology, Merck Research Laboratories, Rahway, NJ
| | - John Thornton
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, New York, NY
| | - Lawrence Boxt
- Department of Radiology, St. Luke's-Roosevelt Hospital, New York, NY
| | - F Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, New York, NY
| | - Jennifer Patricio
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, New York, NY
| | - Juliet Mancino
- Obesity/Nutrition Research Center, University of Pittsburgh, Pittsburgh, PA
| | - Jeanne M Clark
- The Johns Hopkins University School of Medicine, Department of Medicine, Baltimore, MD
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Shechter A, St-Onge MP, Kuna ST, Zammit G, RoyChoudhury A, Newman AB, Millman RP, Reboussin DM, Wadden TA, Jakicic JM, Pi-Sunyer FX, Wing RR, Foster GD. Sleep architecture following a weight loss intervention in overweight and obese patients with obstructive sleep apnea and type 2 diabetes: relationship to apnea-hypopnea index. J Clin Sleep Med 2014; 10:1205-11. [PMID: 25325608 DOI: 10.5664/jcsm.4202] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 07/20/2014] [Indexed: 12/18/2022]
Abstract
STUDY OBJECTIVES To determine if weight loss and/ or changes in apnea-hypopnea index (AHI) improve sleep architecture in overweight/ obese adults with type 2 diabetes (T2D) and obstructive sleep apnea (OSA). METHODS This was a randomized controlled trial including 264 overweight/ obese adults with T2D and OSA. Participants were randomized to an intensive lifestyle intervention (ILI) or a diabetes and support education (DSE) control group. Measures included anthropometry, AHI, and sleep at baseline and year-1, year-2, and year-4 follow-ups. RESULTS Changes in sleep duration (total sleep time [TST]), continuity [wake after sleep onset (WASO)], and architecture stage 1, stage 2, slow wave sleep, and REM sleep) from baseline to year 1, 2, and 4 did not differ between ILI and DSE. Repeated-measure mixed-model analyses including data from baseline through year-4 for all participants demonstrated a significant positive association between AHI and stage 1 sleep (p < 0.001), and a significant negative association between AHI and stage 2 (p = 0.01) and REM sleep (p < 0.001), whereas changes in body weight had no relation to any sleep stages or TST. WASO had a significant positive association with change in body weight (p = 0.009). CONCLUSIONS Compared to control, the ILI did not induce significant changes in sleep across the 4-year follow-up. In participants overall, reduced AHI in overweight/ obese adults with T2D and OSA was associated with decreased stage 1, and increased stage 2 and REM sleep. These sleep architecture changes are more strongly related to reductions in AHI than body weight, whereas WASO may be more influenced by weight than AHI. CLINICAL TRIAL REGISTRATION NUMBER NCT00194259.
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Affiliation(s)
| | | | - Samuel T Kuna
- University of Pennsylvania, Philadelphia, PA and Philadelphia Veterans Affairs Medical Center, Philadelphia, PA
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Belalcazar LM, Anderson AM, Lang W, Schwenke DC, Haffner SM, Yatsuya H, Rushing J, Vitolins MZ, Reeves R, Pi-Sunyer FX, Tracy RP, Ballantyne CM. Fiber intake and plasminogen activator inhibitor-1 in type 2 diabetes: Look AHEAD (Action for Health in Diabetes) trial findings at baseline and year 1. J Acad Nutr Diet 2014; 114:1800-10.e2. [PMID: 25131348 DOI: 10.1016/j.jand.2014.06.357] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Accepted: 06/11/2014] [Indexed: 12/22/2022]
Abstract
Plasminogen activator inhibitor 1 (PAI-1) is elevated in obese individuals with type 2 diabetes and may contribute, independently of traditional factors, to increased cardiovascular disease risk. Fiber intake may decrease PAI-1 levels. We examined the associations of fiber intake and its changes with PAI-1 before and during an intensive lifestyle intervention (ILI) for weight loss in 1,701 Look AHEAD (Action for Health in Diabetes) participants with dietary, fitness, and PAI-1 data at baseline and 1 year. Look AHEAD was a randomized cardiovascular disease trial in 5,145 overweight/obese patients with type 2 diabetes, comparing ILI (goal of ≥7% reduction in baseline weight) with a control arm of diabetes support and education. ILI participants were encouraged to consume vegetables, fruits, and grain products low in sugar and fat. At baseline, median fiber intake was 17.9 g/day. Each 8.3 g/day higher fiber intake was associated with a 9.2% lower PAI-1 level (P=0.008); this association persisted after weight and fitness adjustments (P=0.03). Higher baseline intake of fruit (P=0.019) and high-fiber grain and cereal (P=0.029) were related to lower PAI-1 levels. Although successful in improving weight and physical fitness at 1 year, the ILI in Look AHEAD resulted in small increases in fiber intake (4.1 g/day, compared with -2.35 g/day with diabetes support and education) that were not related to PAI-1 change (P=0.34). Only 31.3% of ILI participants (39.8% of women, 19.1% of men) met daily fiber intake recommendations. Increasing fiber intake in overweight/obese individuals with diabetes interested in weight loss is challenging. Future studies evaluating changes in fiber consumption during weight loss interventions are warranted.
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Unick JL, Hogan PE, Neiberg RH, Cheskin LJ, Dutton GR, Evans-Hudnall G, Jeffery R, Kitabchi AE, Nelson JA, Pi-Sunyer FX, West DS, Wing RR. Evaluation of early weight loss thresholds for identifying nonresponders to an intensive lifestyle intervention. Obesity (Silver Spring) 2014; 22:1608-16. [PMID: 24771618 PMCID: PMC4077939 DOI: 10.1002/oby.20777] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 04/13/2014] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Weight losses in lifestyle interventions are variable, yet prediction of long-term success is difficult. The utility of using various weight loss thresholds in the first 2 months of treatment for predicting 1-year outcomes was examined. METHODS Participants included 2327 adults with type 2 diabetes (BMI:35.8 ± 6.0) randomized to the intensive lifestyle intervention (ILI) of the Look AHEAD trial. ILI included weekly behavioral sessions designed to increase physical activity and reduce caloric intake. 1-month, 2-month, and 1-year weight changes were calculated. RESULTS Participants failing to achieve a ≥2% weight loss at Month 1 were 5.6 (95% CI:4.5, 7.0) times more likely to also not achieve a ≥10% weight loss at Year 1, compared to those losing ≥2% initially. These odds were increased to 11.6 (95% CI:8.6, 15.6) when using a 3% weight loss threshold at Month 2. Only 15.2% and 8.2% of individuals failing to achieve the ≥2% and ≥3% thresholds at Months 1 and 2, respectively, go on to achieve a ≥10% weight loss at Year 1. CONCLUSIONS Given the association between initial and 1-year weight loss, the first few months of treatment may be an opportune time to identify those who are unsuccessful and utilize rescue efforts. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00017953.
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Affiliation(s)
- Jessica L Unick
- Weight Control and Diabetes Research Center, The Miriam Hospital and Brown Medical School, Providence, Rhode Island, USA
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Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM, Kushner RF, Loria CM, Millen BE, Nonas CA, Pi-Sunyer FX, Stevens J, Stevens VJ, Wadden TA, Wolfe BM, Yanovski SZ, Jordan HS, Kendall KA, Lux LJ, Mentor-Marcel R, Morgan LC, Trisolini MG, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC, Tomaselli GF. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. Circulation 2014; 129:S102-S138. [PMID: 24222017 DOI: 10.1161/01.cir.0000437739.71477.ee/-/dc1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
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Davidson LE, Kelley DE, Heshka S, Thornton J, Pi-Sunyer FX, Boxt L, Balasubramanyam A, Gallagher D. Skeletal muscle and organ masses differ in overweight adults with type 2 diabetes. J Appl Physiol (1985) 2014; 117:377-82. [PMID: 24947030 DOI: 10.1152/japplphysiol.01095.2013] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Whether lean body mass (LBM) composition, especially skeletal muscle and abdominal organs, differs in adults with type 2 diabetes (T2DM) compared with nondiabetic healthy controls has not been investigated. A subset of African-American and Caucasian participants with T2DM from the Look AHEAD (Action for Health in Diabetes) trial had body composition assessed and compared with a sample of healthy controls. Skeletal muscle mass (SMM), liver, kidneys, and spleen mass were quantified using a contiguous slice magnetic resonance imaging (MRI) protocol. Cardiac mass was quantified by either a cardiac gated MRI protocol or by echocardiography. MRI volumes were converted to mass using assumed densities. Dual-energy X-ray absorptiometry assessed LBM. Using general linear models adjusted for height, weight, sex, age, race, and interactions of diabetes status with race or sex, persons with T2DM (n = 95) had less LBM (49.7 vs. 51.6 kg) and SMM (24.1 vs. 25.4 kg) and larger kidneys (0.40 vs. 0.36 kg) than controls (n = 76) (all P < 0.01). Caucasians with T2DM had larger livers (1.90 vs. 1.60 kg, P < 0.0001) and spleens (0.29 vs. 0.22 kg, P < 0.01), and T2DM men had less cardiac mass than controls (0.25 vs. 0.30 kg, P < 0.001). In this sample, T2DM is characterized by less relative skeletal muscle and cardiac mass in conjunction with larger kidneys, liver, and spleen. Further investigation is needed to establish the causes and metabolic consequences of these race- and sex-specific organ mass differences in T2DM.
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Affiliation(s)
- Lance E Davidson
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University, New York, New York; Institute of Human Nutrition, Columbia University, New York, New York
| | - David E Kelley
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Stanley Heshka
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University, New York, New York
| | - John Thornton
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University, New York, New York
| | - F Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University, New York, New York; Institute of Human Nutrition, Columbia University, New York, New York
| | | | - Ashok Balasubramanyam
- Diabetes and Endocrinology Research Center, Baylor College of Medicine, Houston, Texas
| | - Dympna Gallagher
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University, New York, New York; Institute of Human Nutrition, Columbia University, New York, New York;
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Shen W, Velasquez G, Chen J, Jin Y, Heymsfield SB, Gallagher D, Pi-Sunyer FX. Comparison of the relationship between bone marrow adipose tissue and volumetric bone mineral density in children and adults. J Clin Densitom 2014; 17:163-9. [PMID: 23522982 PMCID: PMC3770790 DOI: 10.1016/j.jocd.2013.02.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2013] [Accepted: 02/13/2013] [Indexed: 02/05/2023]
Abstract
Several large-scale studies have reported the presence of an inverse relationship between bone mineral density (BMD) and bone marrow adipose tissue (BMAT) in adults. We aim to determine if there is an inverse relationship between pelvic volumetric BMD (vBMD) and pelvic BMAT in children and to compare this relationship in children and adults. Pelvic BMAT and bone volume (BV) was evaluated in 181 healthy children (5-17yr) and 495 healthy adults (≥18yr) with whole-body magnetic resonance imaging (MRI). Pelvic vBMD was calculated using whole-body dual-energy X-ray absorptiometry to measure pelvic bone mineral content and MRI-measured BV. An inverse correlation was found between pelvic BMAT and pelvic vBMD in both children (r=-0.374, p<0.001) and adults (r=-0.650, p<0.001). In regression analysis with pelvic vBMD as the dependent variable and BMAT as the independent variable, being a child or adult neither significantly contribute to the pelvic BMD (p=0.995) nor did its interaction with pelvic BMAT (p=0.415). The inverse relationship observed between pelvic vBMD and pelvic BMAT in children extends previous findings that found the inverse relationship to exist in adults and provides further support for a reciprocal relationship between adipocytes and osteoblasts.
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Affiliation(s)
- Wei Shen
- Department of Pediatrics, College of Physicians and Surgeons, Columbia University, New York, NY, USA; New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA.
| | - Gilbert Velasquez
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA
| | - Jun Chen
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA
| | - Ye Jin
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA
| | | | - Dympna Gallagher
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA
| | - F Xavier Pi-Sunyer
- New York Obesity Nutrition Research Center, St. Luke's-Roosevelt Hospital and Institute of Human Nutrition, Columbia University, New York, NY, USA
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Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, Hubbard VS, Jakicic JM, Kushner RF, Loria CM, Millen BE, Nonas CA, Pi-Sunyer FX, Stevens J, Stevens VJ, Wadden TA, Wolfe BM, Yanovski SZ. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol 2013; 63:2985-3023. [PMID: 24239920 DOI: 10.1016/j.jacc.2013.11.004] [Citation(s) in RCA: 1375] [Impact Index Per Article: 125.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Ochner CN, Barrios DM, Lee CD, Pi-Sunyer FX. Biological mechanisms that promote weight regain following weight loss in obese humans. Physiol Behav 2013; 120:106-13. [PMID: 23911805 DOI: 10.1016/j.physbeh.2013.07.009] [Citation(s) in RCA: 119] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2012] [Revised: 04/05/2013] [Accepted: 07/23/2013] [Indexed: 01/25/2023]
Abstract
Weight loss dieting remains the treatment of choice for the vast majority of obese individuals, despite the limited long-term success of behavioral weight loss interventions. The reasons for the near universal unsustainability of behavioral weight loss in [formerly] obese individuals have not been fully elucidated, relegating researchers to making educated guesses about how to improve obesity treatment, as opposed to developing interventions targeting the causes of weight regain. This article discusses research on several factors that may contribute to weight regain following weight loss achieved through behavioral interventions, including adipose cellularity, endocrine function, energy metabolism, neural responsivity, and addiction-like neural mechanisms. All of these mechanisms are engaged prior to weight loss, suggesting that these so called "anti-starvation" mechanisms are activated via reductions in energy intake, rather than depletion of energy stores. Evidence suggests that these mechanisms are not necessarily part of a homeostatic feedback system designed to regulate body weight, or even anti-starvation mechanisms per se. Although they may have evolved to prevent starvation, they appear to be more accurately described as anti-weight loss mechanisms, engaged with caloric restriction irrespective of the adequacy of energy stores. It is hypothesized that these factors may combine to create a biological disposition that fosters the maintenance of an elevated body weight and works to restore the highest sustained body weight, thus precluding the long-term success of behavioral weight loss. It may be necessary to develop interventions that attenuate these biological mechanisms in order to achieve long-term weight reduction in obese individuals.
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Affiliation(s)
- Christopher N Ochner
- New York Obesity Nutrition Research Center, St. Luke's Roosevelt Hospital, Columbia University College of Physicians and Surgeons, New York, NY, USA; Mount Sinai Adolescent Health Center, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Kuna ST, Reboussin DM, Borradaile KE, Sanders MH, Millman RP, Zammit G, Newman AB, Wadden TA, Jakicic JM, Wing RR, Pi-Sunyer FX, Foster GD. Long-term effect of weight loss on obstructive sleep apnea severity in obese patients with type 2 diabetes. Sleep 2013; 36:641-649A. [PMID: 23633746 DOI: 10.5665/sleep.2618] [Citation(s) in RCA: 157] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
STUDY OBJECTIVES To examine whether the initial benefit of weight loss on obstructive sleep apnea (OSA) severity at 1 year is maintained at 4 years. DESIGN Randomized controlled trial with follow-up at 1, 2, and 4 years. SETTING 4 Look AHEAD clinical centers. PARTICIPANTS Two hundred sixty-four obese adults with type 2 diabetes and OSA. INTERVENTIONS Intensive lifestyle intervention with a behavioral weight loss program or diabetes support and education. MEASUREMENTS Change in apnea-hypopnea index on polysomnogram. RESULTS The intensive lifestyle intervention group's mean weight loss was 10.7 ± 0.7 (standard error), 7.4 ± 0.7, and 5.2 ± 0.7 kg at 1, 2, and 4 years respectively, compared to a less than 1-kg weight loss for the control group at each time (P < 0.001). Apnea-hypopnea index difference between groups was 9.7 ± 2.0, 8.0 ± 2.0, and 7.7 ± 2.3 events/h at 1, 2 and 4 years respectively (P < 0.001). Change in apnea-hypopnea index over time was related to the amount of weight loss (P < 0.0001) and intervention, independent of weight loss (P = 0.001). Remission of OSA at 4 years was 5 times more common with intensive lifestyle intervention (20.7%) than diabetes support and education (3.6%). CONCLUSIONS Among obese adults with type 2 diabetes and OSA, intensive lifestyle intervention produced greater reductions in weight and apnea-hypopnea index over a 4 year period than did diabetes support and education. Beneficial effects of intensive lifestyle intervention on apneahypopnea index at 1 year persisted at 4 years, despite an almost 50% weight regain. Effect of intensive lifestyle intervention on apnea-hypopnea index was largely, but not entirely, due to weight loss.
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Belalcazar LM, Haffner SM, Lang W, Hoogeveen RC, Rushing J, Schwenke DC, Tracy RP, Pi-Sunyer FX, Kriska AM, Ballantyne CM. Lifestyle intervention and/or statins for the reduction of C-reactive protein in type 2 diabetes: from the look AHEAD study. Obesity (Silver Spring) 2013; 21:944-50. [PMID: 23512860 PMCID: PMC3689862 DOI: 10.1002/oby.20431] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2012] [Accepted: 02/09/2013] [Indexed: 01/14/2023]
Abstract
OBJECTIVE Cardiovascular risk remains high despite statin use. Overweight/obese diabetic persons usually have normal/low LDL-cholesterol but high C-reactive protein (CRP) levels. We aimed to examine the effects of intensive lifestyle intervention for weight loss (ILI) on CRP levels in overweight/obese diabetic individuals by statin use. DESIGN AND METHODS Look AHEAD was a randomized trial in overweight/obese type 2 diabetic individuals testing whether ILI would reduce cardiovascular mortality, when compared to usual care. CRP changes in 1,431 participants with biomarker levels, who remained on or off statin treatment for 1 year, were evaluated. RESULTS The reduction in CRP levels with ILI at 1 year in men and women on statins was -44.9 and -42.3%, respectively, compared to -13.7 and -21.0% for those on statins and usual care (P < 0.0001). At 1 year, median CRP levels were: 1.8 mg L(-1) in participants randomized to ILI on statin therapy; 2.6 mg L(-1) for those on statins randomized to usual care and 2.9 mg L(-1) for participants not on statins but randomized to ILI. Weight loss was associated with 1-year CRP reduction (P < 0.0001) in statin and nonstatin users. CONCLUSIONS Our findings suggest that in overweight/obese diabetic persons, ILI and statin therapy may have substantial additive anti-inflammatory benefits.
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Affiliation(s)
- L Maria Belalcazar
- Department of Medicine, University of Texas Medical Branch, Galveston, Texas, USA.
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Hollander P, Lasko B, Barnett AH, Bengus M, Kanitra L, Pi-Sunyer FX, Balena R. Effects of taspoglutide on glycemic control and body weight in obese patients with type 2 diabetes (T-emerge 7 study). Obesity (Silver Spring) 2013; 21:238-47. [PMID: 23404788 DOI: 10.1002/oby.20042] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2012] [Accepted: 07/07/2012] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Therapies that lower blood glucose and provide weight loss may provide meaningful benefits for obese patients with type 2 diabetes mellitus (T2DM). This study assessed the efficacy of taspoglutide compared with placebo on glycemic control and weight in obese patients with T2DM inadequately controlled with metformin monotherapy. DESIGN AND METHODS In a 24-week, randomized, double-blind, placebo-controlled, multicenter trial, obese adults with T2DM were randomized (1:1) to weekly subcutaneous taspoglutide 20 mg (10 mg for first 4 weeks) (n = 154) or placebo (n = 151) for 24 weeks. Efficacy measures included hemoglobin A1c (HbA1c) levels, body weight, percentage of patients achieving HbA1c ≤6.5 and ≤7.0%, and fasting plasma glucose (FPG). Adverse events (AEs) were assessed. RESULTS Mean baseline HbA1c was 7.55% and mean baseline BMI was 36.7 kg/m(2) . HbA1c reductions from baseline were significantly greater with taspoglutide than placebo (least square mean [LSMean], -0.81% vs. -0.09%; P < 0.0001). Weight loss at week 24 was significantly greater with taspoglutide than placebo (LSMean, -3.16 vs. -1.85 kg; P < 0.01). In the taspoglutide and placebo groups, target HbA1c levels (≤6.5%) were achieved by 49 and 16% of patients, respectively, while 72 and 36% achieved HbA1c levels ≤7%. Decreases in FPG were significantly greater with taspoglutide than placebo (-23.59 vs. 0.09 mg/dl; P < 0.0001). Nausea and vomiting were the most common AEs associated with taspoglutide, but tended to be transient and generally mild or moderate. CONCLUSIONS In obese patients with T2DM, once-weekly taspoglutide provided the combined benefits of glycemic control and weight loss.
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Ochner CN, Jochner MCE, Caruso EA, Teixeira J, Xavier Pi-Sunyer F. Effect of preoperative body mass index on weight loss after obesity surgery. Surg Obes Relat Dis 2013; 9:423-7. [PMID: 23434275 DOI: 10.1016/j.soard.2012.12.009] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2012] [Revised: 10/24/2012] [Accepted: 12/05/2012] [Indexed: 01/08/2023]
Abstract
BACKGROUND Previous studies suggest that individuals with body mass index (BMI) above versus below 60 kg/m(2) attain lower percentage of excess weight loss (%EWL) after bariatric surgery. The objectives of this study were to (1) test whether conclusions drawn about the effect of preoperative BMI on postoperative weight loss depend on the outcome measure, (2) test for evidence of a threshold effect at BMI = 60 kg/m(2), and (3) test the effect from surgery to 12-month follow-up, relative to 12- to 36-month follow-up. METHODS Retrospective analyses of participants grouped according to preoperative BMI: 35-39.9 (n = 232); 40-49.9 (n = 1166); 50-59.9 (n = 429);≥60 (n = 166). RESULTS As anticipated, individuals with higher versus lower preoperative BMI had greater total weight loss but lower %EWL at all postoperative time points (all, P<.0005). However, these individuals also had lower percentage of initial weight loss (%IWL) at all time points beyond 1 month postsurgery (all, P<.0005). From 12- to 36-months, individuals with BMI 35-39.9 had 3.2±14.3 %IWL (P<.0001); 40-49.9 had 1.0±8.9 %IWL (P<.0005); 50-59.9 had-2.4±10.0 %IWL (P<.0005); and≥60 had-3.6±11.5 %IWL (P<.0005). Overall F3,1989 = 20.2, P< .0005. CONCLUSIONS Conclusions drawn about the effect of preoperative BMI may depend on the outcome measure. A dosage effect of preoperative BMI was apparent, with heavier individuals showing lower percentages of initial and excess weight loss, regardless of BMI above or below 60 kg/m(2). Finally, this effect was particularly apparent after the initial 12-month rapid weight loss phase, when less obese (BMI<50) individuals continued losing weight, while heavier individuals (BMI≥50) regained significant weight.
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Affiliation(s)
- Christopher N Ochner
- Center for Bariatric Surgery and Metabolic Disease, Department of Minimally Invasive Surgery, St. Luke's Roosevelt Hospital Center, New York, NY 10025, USA.
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Gregg EW, Chen H, Wagenknecht LE, Clark JM, Delahanty LM, Bantle J, Pownall HJ, Johnson KC, Safford MM, Kitabchi AE, Pi-Sunyer FX, Wing RR, Bertoni AG. Association of an intensive lifestyle intervention with remission of type 2 diabetes. JAMA 2012; 308:2489-96. [PMID: 23288372 PMCID: PMC4771522 DOI: 10.1001/jama.2012.67929] [Citation(s) in RCA: 441] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
CONTEXT The frequency of remission of type 2 diabetes achievable with lifestyle intervention is unclear. OBJECTIVE To examine the association of a long-term intensive weight-loss intervention with the frequency of remission from type 2 diabetes to prediabetes or normoglycemia. DESIGN, SETTING, AND PARTICIPANTS Ancillary observational analysis of a 4-year randomized controlled trial (baseline visit, August 2001-April 2004; last follow-up, April 2008) comparing an intensive lifestyle intervention (ILI) with a diabetes support and education control condition (DSE) among 4503 US adults with body mass index of 25 or higher and type 2 diabetes. INTERVENTIONS Participants were randomly assigned to receive the ILI, which included weekly group and individual counseling in the first 6 months followed by 3 sessions per month for the second 6 months and twice-monthly contact and regular refresher group series and campaigns in years 2 to 4 (n=2241) or the DSE, which was an offer of 3 group sessions per year on diet, physical activity, and social support (n=2262). MAIN OUTCOME MEASURES Partial or complete remission of diabetes, defined as transition from meeting diabetes criteria to a prediabetes or nondiabetic level of glycemia (fasting plasma glucose <126 mg/dL and hemoglobin A1c <6.5% with no antihyperglycemic medication). RESULTS Intensive lifestyle intervention participants lost significantly more weight than DSE participants at year 1 (net difference, -7.9%; 95% CI, -8.3% to -7.6%) and at year 4 (-3.9%; 95% CI, -4.4% to -3.5%) and had greater fitness increases at year 1 (net difference, 15.4%; 95% CI, 13.7%-17.0%) and at year 4 (6.4%; 95% CI, 4.7%-8.1%) (P < .001 for each). The ILI group was significantly more likely to experience any remission (partial or complete), with prevalences of 11.5% (95% CI, 10.1%-12.8%) during the first year and 7.3% (95% CI, 6.2%-8.4%) at year 4, compared with 2.0% for the DSE group at both time points (95% CIs, 1.4%-2.6% at year 1 and 1.5%-2.7% at year 4) (P < .001 for each). Among ILI participants, 9.2% (95% CI, 7.9%-10.4%), 6.4% (95% CI, 5.3%-7.4%), and 3.5% (95% CI, 2.7%-4.3%) had continuous, sustained remission for at least 2, at least 3, and 4 years, respectively, compared with less than 2% of DSE participants (1.7% [95% CI, 1.2%-2.3%] for at least 2 years; 1.3% [95% CI, 0.8%-1.7%] for at least 3 years; and 0.5% [95% CI, 0.2%-0.8%] for 4 years). CONCLUSIONS In these exploratory analyses of overweight adults, an intensive lifestyle intervention was associated with a greater likelihood of partial remission of type 2 diabetes compared with diabetes support and education. However, the absolute remission rates were modest. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00017953.
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Affiliation(s)
- Edward W Gregg
- Centers for Disease Control and Prevention, Atlanta, Georgia 30341, USA.
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Lin WY, Pi-Sunyer FX, Liu CS, Li CI, Davidson LE, Li TC, Lin CC. Central obesity and albuminuria: both cross-sectional and longitudinal studies in Chinese. PLoS One 2012; 7:e47960. [PMID: 23251329 PMCID: PMC3520991 DOI: 10.1371/journal.pone.0047960] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2012] [Accepted: 09/19/2012] [Indexed: 11/18/2022] Open
Abstract
Background Albuminuria is recognized as a marker of vascular dysfunction. Central obesity increases the risk of cardiovascular disease. Little is known about the association between albuminuria and central obesity in Chinese. We aimed to assess the association between central obesity and prevalence and incidence of albuminuria in a middle-aged population-based cohort study. Methods This is a cross-sectional and longitudinal cohort study. A total of 2350 subjects aged ≥40 years were recruited in 2004 in Taiwan for cross-sectional analysis. Longitudinal analysis included 1432 baseline normoalbuminuria subjects with a mean 2.8 years follow-up, 67 of whom exhibited incident albuminuria. Albuminuria was defined as urinary albumin-to-creatinine ratio ≥30 mg/g creatinine. Multiple logistic regression analyses were used to evaluate the relationship between central obesity and prevalence and incidence of albuminuria after adjustment for age, gender, body mass index, blood pressure, renal function, glucose, high sensitivity c-reactive protein, smoking, betel nut chewing, alcohol drinking, and physical activity. Results At baseline, albuminuria is significantly associated with central obesity. The adjusted odds ratio of having albuminuria among subjects with central obesity was 1.73(95% confidence interval (CI): 1.04–2.85), compared to the subjects without central obesity. In multivariable models, participants with central obesity at baseline had a 112% increase in risk of incident albuminuria (adjusted incidence rate ratio (95% CI): 2.12(1.01–4.44)) compared with participants with non-central obesity. Conclusions Abdominal adiposity was independently associated with increased prevalence and incidence of albuminuria in Chinese. The mechanisms linking adiposity and albuminuria need to be addressed.
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Affiliation(s)
- Wen-Yuan Lin
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Graduate Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
| | - F. Xavier Pi-Sunyer
- New York Obesity and Nutrition Research Center, St. Luke's-Roosevelt Hospital, Columbia University–College of Physicians and Surgeons, New York, New York, United States of America
| | - Chiu-Shong Liu
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Chia-Ing Li
- Medical Research, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Lance E. Davidson
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, United States of America
| | - Tsai-Chung Li
- Medical Research, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan
- Institute of Health Care Administration, College of Health Science, Asia University, Taichung, Taiwan
| | - Cheng-Chieh Lin
- Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Graduate Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
- Institute of Health Care Administration, College of Health Science, Asia University, Taichung, Taiwan
- * E-mail:
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Freedman DS, Thornton JC, Pi-Sunyer FX, Heymsfield SB, Wang J, Pierson RN, Blanck HM, Gallagher D. The body adiposity index (hip circumference ÷ height(1.5)) is not a more accurate measure of adiposity than is BMI, waist circumference, or hip circumference. Obesity (Silver Spring) 2012; 20:2438-44. [PMID: 22484365 PMCID: PMC3477292 DOI: 10.1038/oby.2012.81] [Citation(s) in RCA: 104] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Based on cross-sectional analyses, it was suggested that hip circumference divided by height(1.5) -18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual-energy X-ray absorptiometry (PBF(DXA)), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBF(DXA) was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBF(DXA) among men (2.5%) and overestimated PBF(DXA) among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBF(DXA), but none of the models based on BAI could more accurately predict PBF(DXA) than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBF(DXA), estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference.
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Affiliation(s)
- David S Freedman
- Division of Nutrition, Physical Activity, and Obesity Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
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Mather KJ, Christophi CA, Jablonski KA, Knowler WC, Goldberg RB, Kahn SE, Spector T, Dastani Z, Waterworth D, Richards JB, Funahashi T, Pi-Sunyer FX, Pollin TI, Florez JC, Franks PW. Common variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/2), adiponectin concentrations, and diabetes incidence in the Diabetes Prevention Program. Diabet Med 2012; 29:1579-88. [PMID: 22443353 PMCID: PMC3499646 DOI: 10.1111/j.1464-5491.2012.03662.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AIMS Baseline adiponectin concentrations predict incident Type 2 diabetes mellitus in the Diabetes Prevention Program. We tested the hypothesis that common variants in the genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1, ADIPOR2) would associate with circulating adiponectin concentrations and/or with diabetes incidence in the Diabetes Prevention Program population. METHODS Seventy-seven tagging single-nucleotide polymorphisms (SNPs) in ADIPOQ (24), ADIPOR1 (22) and ADIPOR2 (31) were genotyped. Associations of SNPs with baseline adiponectin concentrations were evaluated using linear modelling. Associations of SNPs with diabetes incidence were evaluated using Cox proportional hazards modelling. RESULTS Thirteen of 24 ADIPOQ SNPs were significantly associated with baseline adiponectin concentrations. Multivariable analysis including these 13 SNPs revealed strong independent contributions of rs17366568, rs1648707, rs17373414 and rs1403696 with adiponectin concentrations. However, no ADIPOQ SNPs were directly associated with diabetes incidence. Two ADIPOR1 SNPs (rs1342387 and rs12733285) were associated with ∼18% increased diabetes incidence for carriers of the minor allele without differences across treatment groups, and without any relationship with adiponectin concentrations. CONCLUSIONS ADIPOQ SNPs are significantly associated with adiponectin concentrations in the Diabetes Prevention Program cohort. This observation extends prior observations from unselected populations of European descent into a broader multi-ethnic population, and confirms the relevance of these variants in an obese/dysglycaemic population. Despite the robust relationship between adiponectin concentrations and diabetes risk in this cohort, variants in ADIPOQ that relate to adiponectin concentrations do not relate to diabetes risk in this population. ADIPOR1 variants exerted significant effects on diabetes risk distinct from any effect of adiponectin concentrations.
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Affiliation(s)
- K J Mather
- Division of Endocrinology and Metabolism, Indiana University, Indianapolis, IN, USA.
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Abstract
The metabolic syndrome has been characterized by a cluster of abnormalities that include obesity, hyperglycemia, dyslipidemia, and hypertension. Other conditions associated with this syndrome include microalbuminuria, inflammation, a prothrombotic state, and a fatty liver. Together, these abnormalities lead to an environment where the risk of developing both type 2 diabetes and atherosclerotic cardiovascular disease are greatly enhanced. Recognition of this syndrome by practitioners, early treatment, and long-term management are crucial for disease prevention. Successful treatment requires the introduction of lifestyle changes initially and pharmacotherapy subsequently if lifestyle changes are not sufficient.
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Affiliation(s)
- F Xavier Pi-Sunyer
- Department of Medicine, St. Luke's/Roosevelt Hospital Center, 1111 Amsterdam Avenue, Room 1020, New York, NY 10025, USA.
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Belalcazar LM, Lang W, Haffner SM, Hoogeveen RC, Pi-Sunyer FX, Schwenke DC, Balasubramanyam A, Tracy RP, Kriska AP, Ballantyne CM. Adiponectin and the mediation of HDL-cholesterol change with improved lifestyle: the Look AHEAD Study. J Lipid Res 2012; 53:2726-33. [PMID: 22956782 DOI: 10.1194/jlr.m030213] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Adipose tissue dysfunction plays a key role in the development of the metabolic abnormalities characteristic of type 2 diabetes (T2DM) and participates actively in lipid metabolism. Adiponectin, found abundantly in circulation and a marker of adipose health, is decreased in obese persons with T2DM. We investigated whether the changes in adiponectin with an intensive lifestyle intervention (ILI) for weight loss could potentially mediate the increase in low HDL-cholesterol (HDL-C) with ILI. Adiponectin and its fractions were determined using an ELISA with selective protease treatment in 1,397 participants from Look AHEAD, a trial examining whether ILI will reduce cardiovascular events in overweight/obese subjects with T2DM when compared with a control arm, diabetes support and education (DSE). Multivariable regression and mediational analyses were performed for adiponectin and its high-molecular-weight (HMW) and non-HMW fractions. ILI increased baseline HDL-C by 9.7% and adiponectin by 11.9%; changes with DSE were 1.3% and 0.2%, respectively (P < 0.0001). In a model including changes in weight, fitness, triglycerides, and glucose control and that adjusted for demographics and medical history, adiponectin changes remained significantly associated with HDL-C change. Data supported the contribution of changes in both HMW- and non-HMW-adiponectin to the improvement in HDL-C with ILI.
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Affiliation(s)
- L Maria Belalcazar
- Department of Medicine, University of Texas Medical Branch, Galveston, TX, USA
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Dodell GB, Albu JB, Attia L, McGinty J, Pi-Sunyer FX, Laferrère B. The bariatric surgery patient: lost to follow-up; from morbid obesity to severe malnutrition. Endocr Pract 2012; 18:e21-5. [PMID: 22138075 DOI: 10.4158/ep11200.cr] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To describe the potential long-term risk of malnutrition after Roux-en-Y gastric bypass (GBP) through an uncommon occurrence of inflammatory bowel disease (IBD) postoperatively, which posed a serious threat to the nutritional status and the life of the patient. METHODS We present a case report of a 44-year-old woman in whom Crohn disease developed 4 years after she had undergone GBP. The double insult of IBD and GBP resulted in severe malnutrition, with a serum albumin concentration of 0.9 g/dL (reference range, 3.5 to 5.0), weight loss, and watery diarrhea necessitating 6 hospital admissions during a period of 7 months. RESULTS Ultimately, the administration of total parenteral nutrition with aggressive macronutrient, vitamin, and mineral repletion resulted in substantial improvement in the patient's strength, function, and quality of life, in parallel with diminished symptoms of IBD. CONCLUSION Rarely, IBD develops after GBP, but the relationship between the 2 conditions remains unclear. Regardless, in addition to the altered anatomy after bariatric surgery, the further insult of IBD poses a severe threat to the nutritional status of affected patients. Malnutrition needs to be recognized and aggressively treated. Nutritional markers should be followed closely in this population of bariatric patients in an effort to avert the onset of severe malnutrition.
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Affiliation(s)
- Gregory B Dodell
- Division of Endocrinology, Diabetes and Nutrition, St. Luke's Roosevelt Hospital Center, New York, New York 10025, USA.
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St-Onge MP, Zammit G, Reboussin DM, Kuna ST, Sanders MH, Millman R, Newman AB, Wadden TA, Wing RR, Pi-Sunyer FX, Foster GD. Associations of sleep disturbance and duration with metabolic risk factors in obese persons with type 2 diabetes: data from the Sleep AHEAD Study. Nat Sci Sleep 2012; 4:143-50. [PMID: 23620687 PMCID: PMC3630980 DOI: 10.2147/nss.s35797] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
PURPOSE Some studies have found an association between sleep disturbances and metabolic risk, but none has examined this association in individuals with type 2 diabetes. The objective of this study was to determine the relationship between sleep disturbances and metabolic risk factors in obese patients with type 2 diabetes. PATIENTS AND METHODS This study was a cross-sectional examination of the relationship between sleep parameters (apnea/hypopnea index [AHI], time spent in various sleep stages) and metabolic risk markers (fasting glucose, hemoglobin A1c, lipids) using baseline data of the Sleep AHEAD cohort. Subjects (n = 305) were participants in Sleep AHEAD (Action for Health in Diabetes), a four-center ancillary study of the Look AHEAD study, a 16-center clinical trial of overweight and obese participants with type 2 diabetes, designed to assess the long-term effects of an intensive lifestyle intervention on cardiovascular events. All participants underwent one night of in-home polysomnography and provided a fasting blood sample. Regression analyses estimated the relationship between sleep variables and metabolic risk factors. Models were adjusted for study center, age, sex, race/ethnicity, waist circumference, smoking, alcohol intake, diabetes duration, and relevant medications. RESULTS Of 60 associations tested, only one was significant: fasting glucose was associated with sleep efficiency (estimate -0.53 ± [standard error] 0.26, P = 0.041). No associations were found between any of the sleep variables and lipid profile or hemoglobin A1c. CONCLUSIONS The present data show only weak associations between select sleep variables and metabolic risk factors and do not provide strong support for a role of sleep on metabolic abnormalities in obese patients with type 2 diabetes.
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Affiliation(s)
- Marie-Pierre St-Onge
- New York Obesity Research Center, St Luke's/Roosevelt Hospital, New York, NY, USA
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Javed F, Aziz EF, Sabharwal MS, Nadkarni GN, Khan SA, Cordova JP, Benjo AM, Gallagher D, Herzog E, Messerli FH, Pi-Sunyer FX. Association of BMI and cardiovascular risk stratification in the elderly African-American females. Obesity (Silver Spring) 2011; 19:1182-6. [PMID: 21183933 PMCID: PMC3319033 DOI: 10.1038/oby.2010.307] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We aimed to estimate the association of BMI and risk of systemic hypertension in African-American females aged 65 years and older. In this retrospective, cross-sectional study, medical charts were randomly reviewed after obtaining institutional review board approval and data collection was conducted for height, weight, BMI, age, ethnicity, gender, and hypertension. A multivariable logistic regression analysis was performed. The mean BMI was significantly higher in hypertensive subjects than normotensives (30.3 vs. 29 kg/m2; P = 0.003). A higher proportion of hypertensive subjects had a BMI >23 kg/m2 as compared to normotensives (88.9% vs. 83.5%; P = 0.023). When the log odds of having a history of hypertension was plotted against BMI as a continuous variable, we found that the odds showed an increasing trend with increasing BMI and a steep increase after a BMI of 23 kg/m2. When BMI was analyzed as a categorical variable, a BMI of 23-30 kg/m2 was found to have an odds ratio of 1.43 (95% confidence interval 1.01-2.13; P = 0.05) and a BMI of >30 kg/m2 had an odds ratio of 1.76 (95% confidence interval 1.17-2.65; P = 0.007) when compared to a BMI of <23 kg/m2. This association remained significant in both univariate and multivariate analysis. We conclude that BMI is an independent predictor of hypertension in elderly African-American females. Our results indicate that the risk of hypertension increased significantly at BMI of >23 kg/m2 in this ethnic group. Weight reduction to a greater extent than previously indicated could play an integral role in prevention and control of high blood pressure in this particular population.
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Affiliation(s)
- Fahad Javed
- New York Obesity Research Center, Division of Endocrinology, Diabetes, and Nutrition, St. Luke's-Roosevelt Hospital Center, Columbia University College of Physicians and Surgeons, New York, New York, USA.
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Gary-Webb TL, Baptiste-Roberts K, Pham L, Wesche-Thobaben J, Patricio J, Pi-Sunyer FX, Brown AF, Jones-Corneille L, Brancati FL. Neighborhood socioeconomic status, depression, and health status in the Look AHEAD (Action for Health in Diabetes) study. BMC Public Health 2011; 11:349. [PMID: 22182286 PMCID: PMC3111582 DOI: 10.1186/1471-2458-11-349] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2010] [Accepted: 05/19/2011] [Indexed: 11/25/2022] Open
Abstract
Background Depression and diminished health status are common in adults with diabetes, but few studies have investigated associations with socio-economic environment. The objective of this manuscript was to evaluate the relationship between neighborhood-level SES and health status and depression. Methods Individual-level data on 1010 participants at baseline in Look AHEAD (Action for Health in Diabetes), a trial of long-term weight loss among adults with type 2 diabetes, were linked to neighborhood-level SES (% living below poverty) from the 2000 US Census (tracts). Dependent variables included depression (Beck Inventory), and health status (Medical Outcomes Study (SF-36) scale). Multi-level regression models were used to account simultaneously for individual-level age, sex, race, education, personal yearly income and neighborhood-level SES. Results Overall, the % living in poverty in the participants' neighborhoods varied, mean = 11% (range 0-67%). Compared to their counterparts in the lowest tertile of neighborhood poverty (least poverty), those in the highest tertile (most poverty) had significantly lower scores on the role-limitations(physical), role limitations(emotional), physical functioning, social functioning, mental health, and vitality sub-scales of the SF-36 scale. When evaluating SF-36 composite scores, those living in neighborhoods with more poverty had significantly lower scores on the physical health (β-coefficient [β] = -1.90 units, 95% CI: -3.40,-0.039), mental health (β = -2.92 units, -4.31,-1.53) and global health (β = -2.77 units, -4.21,-1.33) composite scores. Conclusion In this selected group of weight loss trial participants, lower neighborhood SES was significantly associated with poorer health status. Whether these associations might influence response to the Look AHEAD weight loss intervention requires further investigation.
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Affiliation(s)
- Tiffany L Gary-Webb
- Department of Epidemiology, Columbia Mailman School of Public Health, New York, NY, USA.
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Belalcazar LM, Ballantyne CM, Lang W, Haffner SM, Rushing J, Schwenke DC, Pi-Sunyer FX, Tracy RP. Metabolic factors, adipose tissue, and plasminogen activator inhibitor-1 levels in type 2 diabetes: findings from the look AHEAD study. Arterioscler Thromb Vasc Biol 2011; 31:1689-95. [PMID: 21512162 DOI: 10.1161/atvbaha.111.224386] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Plasminogen activator inhibitor-1 (PAI-1) production by adipose tissue is increased in obesity, and its circulating levels are high in type 2 diabetes. PAI-1 increases cardiovascular risk by favoring clot stability, interfering with vascular remodeling, or both. We investigated in obese diabetic persons whether an intensive lifestyle intervention for weight loss (ILI) would decrease PAI-1 levels independently of weight loss and whether PAI-1 reduction would be associated with changes in fibrinogen, an acute phase reactant, or fibrin fragment D-dimer (D-dimer), a marker of ambient coagulation balance. METHODS AND RESULTS We examined 1-year changes in PAI-1, D-dimer, and fibrinogen levels; adiposity; fitness; glucose; and lipid control with ILI in 1817 participants from Look AHEAD, a randomized trial investigating the effects of ILI, compared with usual care, on cardiovascular events in overweight or obese diabetic persons. Median PAI-1 levels decreased 29% with ILI and 2.5% with usual care (P < 0.0001). Improvements in fitness, glucose control, and high-density lipoprotein cholesterol were associated with decreased PAI-1, independently of weight loss (P = 0.03 for fitness, P < 0.0001 for others). Fibrinogen and D-dimer remained unchanged. CONCLUSIONS Reductions in PAI-1 levels with ILI in obese diabetic individuals may reflect an improvement in adipose tissue health that could affect cardiovascular risk without changing fibrinogen or d-dimer levels. Clinical Trial Registration- URL: http://clinicaltrials.gov/ct2/show/NCT00017953. Unique identifier: NCT00017953.
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Affiliation(s)
- L Maria Belalcazar
- Department of Medicine, University of Texas Medical Branch, Galveston, TX 77555-1060, USA.
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