Uncovering the 'ORIGINS' of Diabetes

NCT ID: NCT02226640

Last Updated: 2018-02-22

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

80 participants

Study Classification

OBSERVATIONAL

Study Start Date

2010-11-30

Study Completion Date

2014-06-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

This is a study to identify different subtypes of type 2 diabetes. The investigators will look for information at the molecular level, which may lead to personalized diagnosis and therapies.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Type 2 diabetes mellitus (T2DM) is approaching epidemic prevalence in the US adult population (over 1 in 10 of all US adults over 20). Diabetes is diagnosed based on fasting hyperglycemia, oral glucose intolerance or markers of hyperglycemia such as HbA1c. However, we now recognize that diabetes is a heterogeneous disorder. With the existing overly simplistic diagnostic criteria, treatment failure rates are high for virtually every agent currently in the drug arsenal - including insulin. In the late 1990's oncologists pioneered the use of high-throughput molecular technologies, such as transcriptome profiling and more recently metabolomics to identify discrete sub-classes of cancers that cannot be distinguished histologically or by a small number of biochemical markers. That effort rapidly accelerated the pace of scientific discovery and quickly led to the development of personalized cancer therapeutics. We believe that those cancer efforts provide a roadmap for biomarker discovery and personalized therapy in diabetes. molecular phenotyping (profiling the metabolome, transcriptome, and epigenome) with advanced bioinformatics analysis will identify discrete subtypes of diabetes - ushering in a new era of personalized diagnosis and therapy in diabetes.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Type 2 Diabetes

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Non-Diabetic Lean Athletes

Athletes with a Body Mass Index (BMI) less than or equal to 25 kg/m2.

No interventions assigned to this group

Non-Diabetic Lean No Diabetes History

Adults with a BMI \< 25 kg/m2 and no family history of diabetes

No interventions assigned to this group

Non-Diabetic Lean Yes Diabetes History

Adults with a BMI \< 25 kg/m2 and family history of diabetes

No interventions assigned to this group

Non-Diabetic Obese

Adults with BMI greater than or equal to 30 kg/m2.

No interventions assigned to this group

Non-Diabetic Obese Female PCOS

Female adults with BMI \> 30 kg/m2 and Polycystic Ovarian Syndrome (PCOS).

No interventions assigned to this group

Diabetic No Medication

Have diabetes and currently receiving no medication or early treatment with one medication. Some participants receiving insulin may also be included in this study

No interventions assigned to this group

Diabetic GAD Ab+

Have diabetes with Latent Autoimmune Diabetes in Adults (LADA).

No interventions assigned to this group

Diabetic With NASH

Have diabetes with Nonalcoholic Steatohepatitis (NASH), which is chronic liver disease with fat in the liver, inflammation, and damage not associated with drinking alcohol.

No interventions assigned to this group

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* Age \> 18
* HbA1C \< 8.0% \*
* You have not gained or lost more than 3 kg or 6.6 pounds in the last 8 weeks
* You have not lost more than 10% of your heaviest body weight in your lifetime
* BMI \< 25 kg/m2 or \> 30 kg/m2
* Women: more than 1 year post-partum
* Have diabetes and are able to maintain accurate and reliable home glucose monitoring logs

Exclusion Criteria

* Treatment with more than 2 of the following: metformin (Fortamet, Glucophage, Glumetza, Riomet), sulfonylureas (Glucotrol, Diabeta, Glynase, Micronase), Glucagon-like peptide-1 analogs (Byetta) and/or Dipeptidyl peptidase IV inhibitors (Januvia, Onglyza)
* Treatment with long acting Glucagon-like peptide-1 agonists within the last 3 months (i.e. exenatide once weekly)
* Treatment with thiazolidinediones (TZDs) (i.e. Avandia, Actos, Rezulin) within the last 3 months
* Known, untreated thyroid disease or abnormal thyroid function blood test.\*
* Known diagnosis of liver disease (except NASH) or elevated liver function blood test
* Known diagnosis of kidney disease or elevated kidney function blood test
* Uncontrolled high blood pressure (BP \> 140 systolic or \> 90 diastolic)
* Start of or changes in oral contraceptives or hormone replacement therapy within the last 3 months
* Use of drugs or alcohol (\> 3 drinks per day) within the last 5 years.
* Uncontrolled psychiatric disease that would interfere with study participation.
* History of cancer within the last 5 years (skin cancers, with the exception of melanoma, may be acceptable)
* History of organ transplant
* History of heart attack within the last 6 months
* Current treatment with blood thinners or antiplatelet medications that cannot be safely stopped for testing procedures
* Current anemia
* History of HIV, active Hepatitis B or C, or Tuberculosis
* Presence of clinically significant abnormalities on electrocardiogram.
* Current smokers (smoking any nicotine or non-nicotine product within the past 3 months)
* Use of any medications known to influence glucose, fat and/or energy metabolism within the last 3 months (e.g., growth hormone therapy, glucocorticoids \[steroids\], etc.)
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

AdventHealth

OTHER

Sponsor Role collaborator

Sanford-Burnham Medical Research Institute

OTHER

Sponsor Role collaborator

UCSF Benioff Children's Hospital Oakland

OTHER

Sponsor Role collaborator

Duke Univeristy Sarah W. Stedman Nutrition & Metabolism Center

UNKNOWN

Sponsor Role collaborator

AdventHealth Translational Research Institute

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Principal Investigators

Learn about the lead researchers overseeing the trial and their institutional affiliations.

Steven R Smith, MD

Role: PRINCIPAL_INVESTIGATOR

Translational Research Institute for Metabolism and Diabetes

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

Translational Research Institute for Metabolism and Diabetes

Orlando, Florida, United States

Site Status

Countries

Review the countries where the study has at least one active or historical site.

United States

References

Explore related publications, articles, or registry entries linked to this study.

Shoelson SE, Lee J, Yuan M. Inflammation and the IKK beta/I kappa B/NF-kappa B axis in obesity- and diet-induced insulin resistance. Int J Obes Relat Metab Disord. 2003 Dec;27 Suppl 3:S49-52. doi: 10.1038/sj.ijo.0802501.

Reference Type BACKGROUND
PMID: 14704745 (View on PubMed)

Reitman ML, Arioglu E, Gavrilova O, Taylor SI. Lipoatrophy revisited. Trends Endocrinol Metab. 2000 Dec;11(10):410-6. doi: 10.1016/s1043-2760(00)00309-x.

Reference Type BACKGROUND
PMID: 11091118 (View on PubMed)

Itani SI, Ruderman NB, Schmieder F, Boden G. Lipid-induced insulin resistance in human muscle is associated with changes in diacylglycerol, protein kinase C, and IkappaB-alpha. Diabetes. 2002 Jul;51(7):2005-11. doi: 10.2337/diabetes.51.7.2005.

Reference Type BACKGROUND
PMID: 12086926 (View on PubMed)

Moro C, Bajpeyi S, Smith SR. Determinants of intramyocellular triglyceride turnover: implications for insulin sensitivity. Am J Physiol Endocrinol Metab. 2008 Feb;294(2):E203-13. doi: 10.1152/ajpendo.00624.2007. Epub 2007 Nov 14.

Reference Type BACKGROUND
PMID: 18003718 (View on PubMed)

Kelley DE, He J, Menshikova EV, Ritov VB. Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes. Diabetes. 2002 Oct;51(10):2944-50. doi: 10.2337/diabetes.51.10.2944.

Reference Type BACKGROUND
PMID: 12351431 (View on PubMed)

Ritov VB, Menshikova EV, Azuma K, Wood R, Toledo FG, Goodpaster BH, Ruderman NB, Kelley DE. Deficiency of electron transport chain in human skeletal muscle mitochondria in type 2 diabetes mellitus and obesity. Am J Physiol Endocrinol Metab. 2010 Jan;298(1):E49-58. doi: 10.1152/ajpendo.00317.2009. Epub 2009 Nov 3.

Reference Type BACKGROUND
PMID: 19887598 (View on PubMed)

Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000 Feb 3;403(6769):503-11. doi: 10.1038/35000501.

Reference Type BACKGROUND
PMID: 10676951 (View on PubMed)

Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, Yu J, Laxman B, Mehra R, Lonigro RJ, Li Y, Nyati MK, Ahsan A, Kalyana-Sundaram S, Han B, Cao X, Byun J, Omenn GS, Ghosh D, Pennathur S, Alexander DC, Berger A, Shuster JR, Wei JT, Varambally S, Beecher C, Chinnaiyan AM. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009 Feb 12;457(7231):910-4. doi: 10.1038/nature07762.

Reference Type BACKGROUND
PMID: 19212411 (View on PubMed)

van't Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature. 2008 Apr 3;452(7187):564-70. doi: 10.1038/nature06915.

Reference Type BACKGROUND
PMID: 18385730 (View on PubMed)

Wang S, Sparks LM, Xie H, Greenway FL, de Jonge L, Smith SR. Subtyping obesity with microarrays: implications for the diagnosis and treatment of obesity. Int J Obes (Lond). 2009 Apr;33(4):481-9. doi: 10.1038/ijo.2008.277. Epub 2009 Feb 3.

Reference Type BACKGROUND
PMID: 19188926 (View on PubMed)

Ptitsyn A, Hulver M, Cefalu W, York D, Smith SR. Unsupervised clustering of gene expression data points at hypoxia as possible trigger for metabolic syndrome. BMC Genomics. 2006 Dec 19;7:318. doi: 10.1186/1471-2164-7-318.

Reference Type BACKGROUND
PMID: 17178004 (View on PubMed)

Naik RG, Brooks-Worrell BM, Palmer JP. Latent autoimmune diabetes in adults. J Clin Endocrinol Metab. 2009 Dec;94(12):4635-44. doi: 10.1210/jc.2009-1120. Epub 2009 Oct 16.

Reference Type BACKGROUND
PMID: 19837918 (View on PubMed)

West M, Blanchette C, Dressman H, Huang E, Ishida S, Spang R, Zuzan H, Olson JA Jr, Marks JR, Nevins JR. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11462-7. doi: 10.1073/pnas.201162998. Epub 2001 Sep 18.

Reference Type BACKGROUND
PMID: 11562467 (View on PubMed)

Sorlie T. Molecular portraits of breast cancer: tumour subtypes as distinct disease entities. Eur J Cancer. 2004 Dec;40(18):2667-75. doi: 10.1016/j.ejca.2004.08.021.

Reference Type BACKGROUND
PMID: 15571950 (View on PubMed)

Sorlie T. Introducing molecular subtyping of breast cancer into the clinic? J Clin Oncol. 2009 Mar 10;27(8):1153-4. doi: 10.1200/JCO.2008.20.6276. Epub 2009 Feb 9. No abstract available.

Reference Type BACKGROUND
PMID: 19204193 (View on PubMed)

Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000 Aug 17;406(6797):747-52. doi: 10.1038/35021093.

Reference Type BACKGROUND
PMID: 10963602 (View on PubMed)

Mutch DM, Temanni MR, Henegar C, Combes F, Pelloux V, Holst C, Sorensen TI, Astrup A, Martinez JA, Saris WH, Viguerie N, Langin D, Zucker JD, Clement K. Adipose gene expression prior to weight loss can differentiate and weakly predict dietary responders. PLoS One. 2007 Dec 19;2(12):e1344. doi: 10.1371/journal.pone.0001344.

Reference Type BACKGROUND
PMID: 18094752 (View on PubMed)

Henry RR, Abrams L, Nikoulina S, Ciaraldi TP. Insulin action and glucose metabolism in nondiabetic control and NIDDM subjects. Comparison using human skeletal muscle cell cultures. Diabetes. 1995 Aug;44(8):936-46. doi: 10.2337/diab.44.8.936.

Reference Type BACKGROUND
PMID: 7622000 (View on PubMed)

Ukropcova B, McNeil M, Sereda O, de Jonge L, Xie H, Bray GA, Smith SR. Dynamic changes in fat oxidation in human primary myocytes mirror metabolic characteristics of the donor. J Clin Invest. 2005 Jul;115(7):1934-41. doi: 10.1172/JCI24332.

Reference Type BACKGROUND
PMID: 16007256 (View on PubMed)

Aagaard-Tillery KM, Grove K, Bishop J, Ke X, Fu Q, McKnight R, Lane RH. Developmental origins of disease and determinants of chromatin structure: maternal diet modifies the primate fetal epigenome. J Mol Endocrinol. 2008 Aug;41(2):91-102. doi: 10.1677/JME-08-0025. Epub 2008 May 30.

Reference Type BACKGROUND
PMID: 18515302 (View on PubMed)

Cox J, Williams S, Grove K, Lane RH, Aagaard-Tillery KM. A maternal high-fat diet is accompanied by alterations in the fetal primate metabolome. Am J Obstet Gynecol. 2009 Sep;201(3):281.e1-9. doi: 10.1016/j.ajog.2009.06.041.

Reference Type BACKGROUND
PMID: 19733280 (View on PubMed)

Barker DJ, Gluckman PD, Godfrey KM, Harding JE, Owens JA, Robinson JS. Fetal nutrition and cardiovascular disease in adult life. Lancet. 1993 Apr 10;341(8850):938-41. doi: 10.1016/0140-6736(93)91224-a.

Reference Type BACKGROUND
PMID: 8096277 (View on PubMed)

Barker DJ, Hales CN, Fall CH, Osmond C, Phipps K, Clark PM. Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth. Diabetologia. 1993 Jan;36(1):62-7. doi: 10.1007/BF00399095.

Reference Type BACKGROUND
PMID: 8436255 (View on PubMed)

Barres R, Osler ME, Yan J, Rune A, Fritz T, Caidahl K, Krook A, Zierath JR. Non-CpG methylation of the PGC-1alpha promoter through DNMT3B controls mitochondrial density. Cell Metab. 2009 Sep;10(3):189-98. doi: 10.1016/j.cmet.2009.07.011.

Reference Type BACKGROUND
PMID: 19723495 (View on PubMed)

Freda PU, Shen W, Reyes-Vidal CM, Geer EB, Arias-Mendoza F, Gallagher D, Heymsfield SB. Skeletal muscle mass in acromegaly assessed by magnetic resonance imaging and dual-photon x-ray absorptiometry. J Clin Endocrinol Metab. 2009 Aug;94(8):2880-6. doi: 10.1210/jc.2009-0026. Epub 2009 Jun 2.

Reference Type BACKGROUND
PMID: 19491226 (View on PubMed)

Welch S, Gebhart SS, Bergman RN, Phillips LS. Minimal model analysis of intravenous glucose tolerance test-derived insulin sensitivity in diabetic subjects. J Clin Endocrinol Metab. 1990 Dec;71(6):1508-18. doi: 10.1210/jcem-71-6-1508.

Reference Type BACKGROUND
PMID: 2229309 (View on PubMed)

Bergman RN, Ider YZ, Bowden CR, Cobelli C. Quantitative estimation of insulin sensitivity. Am J Physiol. 1979 Jun;236(6):E667-77. doi: 10.1152/ajpendo.1979.236.6.E667.

Reference Type BACKGROUND
PMID: 443421 (View on PubMed)

Elia M, Livesey G. Energy expenditure and fuel selection in biological systems: the theory and practice of calculations based on indirect calorimetry and tracer methods. World Rev Nutr Diet. 1992;70:68-131. doi: 10.1159/000421672. No abstract available.

Reference Type BACKGROUND
PMID: 1292242 (View on PubMed)

Brody DL, Magnoni S, Schwetye KE, Spinner ML, Esparza TJ, Stocchetti N, Zipfel GJ, Holtzman DM. Amyloid-beta dynamics correlate with neurological status in the injured human brain. Science. 2008 Aug 29;321(5893):1221-4. doi: 10.1126/science.1161591.

Reference Type BACKGROUND
PMID: 18755980 (View on PubMed)

Smith, S.R., Martin, C., Katzmarzyk, P. & Church, T. Obesity and Diabetes: Implications for Management. in 2009 Educational Review Manual in Endocrinology FOCUS: Diabetes (ed. Kendall, D.M.) (Castle Connolly Graduate Medical Publishing, New York, NY 2009).

Reference Type BACKGROUND

Divoux A, Eroshkin A, Erdos E, Sandor K, Osborne TF, Smith SR. DNA Methylation as a Marker of Body Shape in Premenopausal Women. Front Genet. 2021 Jul 29;12:709342. doi: 10.3389/fgene.2021.709342. eCollection 2021.

Reference Type DERIVED
PMID: 34394195 (View on PubMed)

Pachori AS, Madan M, Nunez Lopez YO, Yi F, Meyer C, Seyhan AA. Reduced skeletal muscle secreted frizzled-related protein 3 is associated with inflammation and insulin resistance. Obesity (Silver Spring). 2017 Apr;25(4):697-703. doi: 10.1002/oby.21787. Epub 2017 Feb 27.

Reference Type DERIVED
PMID: 28240822 (View on PubMed)

Related Links

Access external resources that provide additional context or updates about the study.

http://www.tri-md.org

Florida Hospital Translational Research Institute for Metabolism and Diabetes

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

TRIMDFH 238153

Identifier Type: -

Identifier Source: org_study_id

More Related Trials

Additional clinical trials that may be relevant based on similarity analysis.

AI Ready and Exploratory Atlas for Diabetes Insights
NCT06002048 ENROLLING_BY_INVITATION