- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT02226640
Uncovering the 'ORIGINS' of Diabetes (ORIGINS)
February 21, 2018 updated by: AdventHealth Translational Research Institute
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.
Study Overview
Status
Completed
Conditions
Detailed Description
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.
Study Type
Observational
Enrollment (Actual)
80
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
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Florida
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Orlando, Florida, United States, 32804
- Translational Research Institute for Metabolism and Diabetes
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Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
Yes
Genders Eligible for Study
All
Sampling Method
Probability Sample
Study Population
Community sample
Description
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.)
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
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Non-Diabetic Lean Athletes
Athletes with a Body Mass Index (BMI) less than or equal to 25 kg/m2.
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Non-Diabetic Lean No Diabetes History
Adults with a BMI < 25 kg/m2 and no family history of diabetes
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Non-Diabetic Lean Yes Diabetes History
Adults with a BMI < 25 kg/m2 and family history of diabetes
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Non-Diabetic Obese
Adults with BMI greater than or equal to 30 kg/m2.
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Non-Diabetic Obese Female PCOS
Female adults with BMI > 30 kg/m2 and Polycystic Ovarian Syndrome (PCOS).
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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
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Diabetic GAD Ab+
Have diabetes with Latent Autoimmune Diabetes in Adults (LADA).
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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.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Perform 'deep' clinical and molecular phenotyping of 360 adults- Diabetic and Non-Diabetic
Time Frame: up to Day 9
|
Phenotyping will include whole body composition (DEXA), substrate metabolism, classic diabetes phenotypes, and detailed molecular phenotyping of circulating mononuclear cells/ plasma, muscle and adipose tissue.
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up to Day 9
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Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Collaborators
Investigators
- Principal Investigator: Steven R Smith, MD, Translational Research Institute for Metabolism and Diabetes
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. Erratum In: Nature. 2013 Jul 25;499(7459):504.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start
November 1, 2010
Primary Completion (Actual)
June 1, 2014
Study Completion (Actual)
June 1, 2014
Study Registration Dates
First Submitted
May 16, 2014
First Submitted That Met QC Criteria
August 26, 2014
First Posted (Estimate)
August 27, 2014
Study Record Updates
Last Update Posted (Actual)
February 22, 2018
Last Update Submitted That Met QC Criteria
February 21, 2018
Last Verified
February 1, 2018
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- TRIMDFH 238153
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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