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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT02226640
Other study ID # TRIMDFH 238153
Secondary ID
Status Completed
Phase N/A
First received May 16, 2014
Last updated February 21, 2018
Start date November 2010
Est. completion date June 2014

Study information

Verified date February 2018
Source Translational Research Institute for Metabolism and Diabetes, Florida
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

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.


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.


Recruitment information / eligibility

Status Completed
Enrollment 80
Est. completion date June 2014
Est. primary completion date June 2014
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility 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 Design


Related Conditions & MeSH terms


Locations

Country Name City State
United States Translational Research Institute for Metabolism and Diabetes Orlando Florida

Sponsors (5)

Lead Sponsor Collaborator
Translational Research Institute for Metabolism and Diabetes, Florida Children's Hospital & Research Center Oakland, Duke Univeristy Sarah W. Stedman Nutrition & Metabolism Center, Florida Hospital, Sanford-Burnham Medical Research Institute

Country where clinical trial is conducted

United States, 

References & Publications (30)

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. — View Citation

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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. Review. — View Citation

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. — View Citation

Barrès 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. — View Citation

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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. — View Citation

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. Review. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. Epub 2007 Nov 14. Review. — View Citation

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

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. Review. — View Citation

Perou CM, Sørlie 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, Lønning PE, Børresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000 Aug 17;406(6797):747-52. — View Citation

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. — View Citation

Reitman ML, Arioglu E, Gavrilova O, Taylor SI. Lipoatrophy revisited. Trends Endocrinol Metab. 2000 Dec;11(10):410-6. Review. — View Citation

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. — View Citation

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. Review. — View Citation

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).

Sørlie 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. — View Citation

Sørlie T. Molecular portraits of breast cancer: tumour subtypes as distinct disease entities. Eur J Cancer. 2004 Dec;40(18):2667-75. Review. — View Citation

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. — View Citation

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. — View Citation

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. Review. — View Citation

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. — View Citation

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. — View Citation

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. Epub 2001 Sep 18. — View Citation

* Note: There are 30 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Perform 'deep' clinical and molecular phenotyping of 360 adults- Diabetic and Non-Diabetic 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. up to Day 9
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