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| Name | Class |
|---|---|
| Novo Nordisk A/S | INDUSTRY |
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The Investigators will generate a repository of human biosamples across therapeutic areas that will be used to identify disease-associated biomarkers and potential targets with immune and multi-omics profiling. This sample collection and analysis from people living with type 2 diabetes, or chronic or diabetic kidney disease will lay the groundwork for an extensive network of biosample access and linked datasets that will provide an invaluable resource for translational research.
The Investigators will generate a repository of human biosamples across therapeutic areas that will be used to identify disease-associated biomarkers and potential targets with immune and multi-omics profiling. This sample collection and analysis from people living with type 2 diabetes, or chronic or diabetic kidney disease will lay the groundwork for an extensive network of biosample access and linked datasets that will provide an invaluable resource for translational research.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Diabetic Kidney Disease | Sample Collection:
| ||
| Chronic Kidney Disease | Sample Collection:
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| Type 2 Diabetes | Sample Collection:
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| Measure | Description | Time Frame |
|---|---|---|
| collect biospecimen samples | The study objective is to collect biospecimen samples (e.g., blood and urine) from participants diagnosed with diabetic kidney disease, chronic kidney disease, or type 2 diabetes. These samples will be used to generate a repository of human biosamples across therapeutic areas that will be used to identify disease-associated biomarkers and potential targets with immune and multi-omics profiling. This sample collection and analysis from people living with type 2 diabetes, or chronic or diabetic kidney disease will lay the groundwork for an extensive network of biosample access and linked datasets that will provide an invaluable resource for translational research. | 7 months |
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Cohort 1: Diabetic Kidney Disease
Inclusion:
Exclusion:
Cohort 2: Chronic Kidney Disease
Inclusion:
Exclusion:
Cohort 3: Type 2 Diabetes
Inclusion:
Exclusion:
Cohort 4: Healthy Matched The study will enroll participants considered healthy matched controls per the eligibility criteria. The healthy-matched controls must match each participant in the diseased cohorts by age (+/- 10 years).
Inclusion:
1. The participant is willing and able to provide written informed consent 2. The participant is willing and able to provide appropriate photo identification 3. Participants aged 18 to 85 4. Participants who are in generally good health are defined as: b. Participants may have a common/mild health condition(s) that are generally under control, including but not limited to: i. Hypertension, high cholesterol, asthma, anxiety, depression, attention deficit disorder (ADD), attention deficit hyperactivity disorder (ADHD), gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), allergies, eczema, migraines, osteoarthritis, sleep apnea, restless leg syndrome, and eye issues (e.g., myopia, astigmatism, etc.) ii. Participants with a previous diagnosis and have recovered from COVID-19 iii. Participants in general good health may also take nonsteroidal anti-inflammatory drugs (NSAIDS) (i.e., ibuprofen, Tylenol, aspirin, Excedrin) irregularly or semi-regularly due to conditions like headache, body aches, cold/flu treatment as long as the medications are not being used for the treatment of a major underlying condition.
Exclusion:
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Patients diagnosed with Diabetic Kidney Disease, Chronic Kidney Disease, and Diabetes Type 2. The 4th cohort will be a healthy match cohort of patients: 2 of the disease cohorts will have 3 healthy matched patieints and the final disease cohort (unspecified) will have 4 healthy patients matched.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sanguine Biosciences, Inc. | Woburn | Massachusetts | 01801 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | National Institute of Health (NIH): National Center for Advancing Translational Sciences. Understanding Translational Research Tools: Biorepository. Accessed: 19 October 2022. https://toolkit.ncats.nih.gov/module/discovery/developing-translational-research-tools/biorepository/ | ||
| 26444893 | Background | Siwek M. An Overview of Biorepositories-Past, Present, and Future. Mil Med. 2015 Oct;180(10 Suppl):57-66. doi: 10.7205/MILMED-D-15-00119. | |
| 20978388 |
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| Healthy Matched Controls | Sample Collection:
|
| Background |
| Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010 Nov;5(6):463-6. doi: 10.1097/COH.0b013e32833ed177. |
| 29382490 | Background | Quezada H, Guzman-Ortiz AL, Diaz-Sanchez H, Valle-Rios R, Aguirre-Hernandez J. Omics-based biomarkers: current status and potential use in the clinic. Bol Med Hosp Infant Mex. 2017 May-Jun;74(3):219-226. doi: 10.1016/j.bmhimx.2017.03.003. Epub 2017 May 10. |
| Background | Kreeger K. Immune Profiling: A New Opportunity for Drug Development. Penn Medicine News. February 14, 2019. Accessed: 20 October 2022. https://www.pennmedicine.org/news/news-blog/2019/february/immune-profiling-a-new-opportunity-for-drug-development |
| 32034066 | Background | Chuah S, Chew V. High-dimensional immune-profiling in cancer: implications for immunotherapy. J Immunother Cancer. 2020 Feb;8(1):e000363. doi: 10.1136/jitc-2019-000363. |
| 31561483 | Background | Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci. 2019 Sep 26;20(19):4781. doi: 10.3390/ijms20194781. |
| 33362867 | Background | Krassowski M, Das V, Sahu SK, Misra BB. State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing. Front Genet. 2020 Dec 10;11:610798. doi: 10.3389/fgene.2020.610798. eCollection 2020. |
| 32076369 | Background | Subramanian I, Verma S, Kumar S, Jere A, Anamika K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform Biol Insights. 2020 Jan 31;14:1177932219899051. doi: 10.1177/1177932219899051. eCollection 2020. |
| 32175717 | Background | Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes - Global Burden of Disease and Forecasted Trends. J Epidemiol Glob Health. 2020 Mar;10(1):107-111. doi: 10.2991/jegh.k.191028.001. |
| 34634970 | Background | Roy S, Schweiker-Kahn O, Jafry B, Masel-Miller R, Raju RS, O'Neill LMO, Correia CR, Trivedi A, Johnson C, Pilot C, Saddemi J, Memon A, Chen A, McHugh SP, Patel S, Daroshefski NM, Nguyen T, Wissler W, Sharma E, Hunter K. Risk Factors and Comorbidities Associated with Diabetic Kidney Disease. J Prim Care Community Health. 2021 Jan-Dec;12:21501327211048556. doi: 10.1177/21501327211048556. |
| 34198818 | Background | Garcia-Carro C, Vergara A, Bermejo S, Azancot MA, Sanchez-Fructuoso AI, Sanchez de la Nieta MD, Agraz I, Soler MJ. How to Assess Diabetic Kidney Disease Progression? From Albuminuria to GFR. J Clin Med. 2021 Jun 5;10(11):2505. doi: 10.3390/jcm10112505. |
| ID | Term |
|---|---|
| D003928 | Diabetic Nephropathies |
| D003924 | Diabetes Mellitus, Type 2 |
| D051436 | Renal Insufficiency, Chronic |
| ID | Term |
|---|---|
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D051437 | Renal Insufficiency |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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