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COVID-19, Aging, and Cardiometabolic Risk Factors Study (CARAMEL)

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ClinicalTrials.gov Identifier: NCT04802044
Recruitment Status : Recruiting
First Posted : March 17, 2021
Last Update Posted : March 17, 2021
Sponsor:
Collaborator:
Leiden University Medical Center
Information provided by (Responsible Party):
Dicky L. Tahapary, Indonesia University

Brief Summary:
COVID-19 pandemic has made a tremendous impact on Indonesian economic and health care system especially with the double burden of diseases facing by Indonesia as a developing country. The prevalence of non-communicable diseases such as obesity, type diabetes, and cardiovascular diseases is increasing. These diseases along with older age have been known as an established risk factors for higher mortality and severe clinical disease entity in COVID-19 infection. Although, there is still some part of patients with these co-morbidities that only present with mild symptoms when infected with SARS-CoV-2, even for some without any symptoms. Thus, it would be very interesting to evaluate how are these role of aging and cardiometabolic parameters in the clinical disease course of COVID-19 infection, and how are the relationship with the immune system.

Condition or disease
Covid19 Obesity Diabetes Mellitus Aging Cardiometabolic Syndrome Immune System Disorder

Detailed Description:

Indonesia is a country in transition where the burden of non-communicable diseases is taking over the infectious diseases problem, mostly due to the changes in lifestyle and increase in life expectancy.

However, the unprecedented rising numbers of COVID-19 patients in Indonesia has impacted the Indonesian healthcare system heavily. It has been reported that older age and the presence of cardiometabolic risk factors pose a poor prognostic factor of COVID-19. It is also important to note that in Indonesia, the presence of cardiometabolic risk factors is often observed at a younger age. Thus, this might also contribute to the higher mortality of COVID19 infected patients despite their relatively younger age in comparison to other countries. Nevertheless, specific data on the impact of aging and cardiometabolic risk factors on COVID-19 are fragmentary, justifying the achievement of a dedicated prospective observational study.

The CARAMEL study aims to specifically describe the phenotypic aging and cardiometabolic characteristics of patients with COVID-19 infection, in relation with the changes in the mucosal and systemic immune system. Particular attention will be devoted to obesity, central obesity, prediabetes, diabetes, hypertension, dyslipidemia, as well as anti-diabetic, antihypertensive, and anti-dyslipidemia therapies.

This study will provide answers to researchers, medical professionals, and especially patients, regarding the impact of aging and cardiometabolic risk factors for COVID-19 prognosis. This pilot study will be used for the development of new studies and for the establishment of recommendations for the care of patients with cardiometabolic risk factors and COVID-19.

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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: COVID-19, Aging, and Cardiometabolic Risk Factors (CARAMEL) Study: Integrating Aging, Cardiovascular, Metabolic, and Immunological Studies to Unravel COVID-19 Pathophysiology
Actual Study Start Date : December 8, 2020
Estimated Primary Completion Date : February 28, 2022
Estimated Study Completion Date : December 31, 2022



Primary Outcome Measures :
  1. Correlation of Body Mass Index with Clinical Disease Severity [ Time Frame: Baseline ]
    To compare the body mass index, which calculated from body height (in meters) and body weight (in kilograms), in groups of COVID-19 patients with various disease severity based on WHO criteria

  2. Correlation of Visceral Fat with Clinical Disease Severity [ Time Frame: Baseline ]
    To compare the visceral fat that measures using a bio-impedance analyzer, in groups of COVID-19 patients with various disease severity based on WHO criteria

  3. Correlation of Blood Glucose Levels with Clinical Disease Severity [ Time Frame: Baseline ]
    To compare the random blood glucose levels during admission in groups of COVID-19 patients with various disease severity based on WHO criteria

  4. Correlation of HbA1c with Clinical Disease Severity [ Time Frame: Baseline ]
    To compare the HbA1c levels during admission in groups of COVID-19 patients with various disease severity based on WHO criteria


Secondary Outcome Measures :
  1. Changes of Insulin Resistance Levels in COVID-19 Patients Overtime [ Time Frame: Baseline, 6, and 12 month ]
    To compare the changes of HOMA-IR, a surrogate marker for whole-body insulin resistance which calculated from fasting blood glucose (IU/mL) and fasting insulin (mg/dL), between COVID-19 patients and healthy control subjects

  2. Changes of Leptin/Adiponectin Ratio in COVID-19 Patients Overtime [ Time Frame: Baseline, 6, and 12 month ]
    To compare the changes of leptin/adiponectin ratio, which calculated from leptin levels (ng/mL) divided by adiponectin levels (mikrogram/dL), between COVID-19 patients and healthy control subjects

  3. Systemic Immune Profiles in Diabetic COVID-19 Patients [ Time Frame: Baseline ]
    To compare the systemic immune profiles using mass cytometry between diabetic/COVID-19, non-diabetic/COVID-19, and healthy control subjects

  4. Nasal Mucosal Immune Profiles in Diabetic COVID-19 Patients [ Time Frame: Baseline ]
    To compare the nasal-mucosal immune profiles using mass cytometry between diabetic/COVID-19, non-diabetic/COVID-19, and healthy control subjects

  5. Aging Parameter (ACE-2 gene expression) in COVID-19 Patients [ Time Frame: Baseline ]
    To compare the nasal epithelial ACE-2 gene expression in groups of COVID-19 patients with various disease severity based on WHO criteria

  6. Aging Parameter (Telomere Length) in COVID-19 Patients [ Time Frame: Baseline ]
    To compare the aging parameter using telomere length in groups of COVID-19 patients with various disease severity based on WHO criteria

  7. Immune Cells Exhaustion in COVID-19 Patients [ Time Frame: Baseline ]
    To compare the immune cells exhaustion marker (T-cell immunoglobulin mucin-3/TIM-3 expressions) in groups of COVID-19 patients with various disease severity based on WHO criteria

  8. Changes of Pro-Inflammatory Cytokine (IL-6) in COVID-19 Patients [ Time Frame: Baseline, 1, 3, and 6 months ]
    To compare the changes of pro-inflammatory cytokine (IL-6) levels overtime, measured from the supernatant of stimulated PBMC isolation in groups of patients with various clinical disease severity based on WHO criteria

  9. Changes of Anti-Inflammatory Cytokine (IL-10) in COVID-19 Patients [ Time Frame: Baseline, 1, 3, and 6 months ]
    To compare the changes of anti-inflammatory cytokine (IL-10) levels overtime, measured from the supernatant of stimulated PBMC isolation in groups of patients with various clinical disease severity based on WHO criteria

  10. Antibody Kinetics in COVID-19 Patients [ Time Frame: Baseline, 1, 3, and 6 months ]
    To compare the changes of antibody titers in groups of patients with various clinical disease severity based on WHO criteria

  11. Proportion of Long COVID Syndrome [ Time Frame: 3, 6, and 12 months ]
    Percentage of COVID-19 patients still present with symptoms compared to whole study subjects


Biospecimen Retention:   Samples With DNA
Serum PBMC Nasal scrappe Nasal swab


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population
COVID-19 patients in hospital and community setting
Criteria

Inclusion Criteria:

  • Patients newly diagnosed with COVID-19 at hospital setting or community screening, confirmed with biological proof (RT-PCR)

Exclusion Criteria:

  • Subjects opposed to the use of their data
  • Minors, adults under guardianship, protected persons
  • History of malignancy
  • History of autoimmune disease
  • Pregnancy

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04802044


Contacts
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Contact: Dicky L Tahapary, MD, PhD +62 21 29189160 ext 201201 dicky.tahapary@ui.ac.id
Contact: Farid Kurniawan, MD +62 21 29189162 farid.kurniawan01@ui.ac.id

Locations
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Indonesia
Dr. Cipto Mangunkusumo National General Hospital Recruiting
Jakarta Pusat, DKI Jakarta, Indonesia, 10430
Contact: Farid Kurniawan, MD    +62 21 29189162    farid.kurniawan01@ui.ac.id   
Metabolic Disorder, Cardiovascular, and Aging Research Cluster IMERI-FKUI, Research Tower, 5th Floor Recruiting
Jakarta Pusat, DKI Jakarta, Indonesia, 10430
Contact: Tika Pradnaparamita, M. Biomed    +62 21 29189162    mva.imeri@gmail.com   
Sponsors and Collaborators
Indonesia University
Leiden University Medical Center
Investigators
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Principal Investigator: Dicky L Tahapary Indonesia University
Publications:
Telles S, Reddy SK, Nagendra HR. Obesity, Inflammation and Endothelial Dysfunction. J Chem Inf Model. 2019;53(9):1689-99.

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Responsible Party: Dicky L. Tahapary, Principal Investigator, Indonesia University
ClinicalTrials.gov Identifier: NCT04802044    
Other Study ID Numbers: CARAMEL
First Posted: March 17, 2021    Key Record Dates
Last Update Posted: March 17, 2021
Last Verified: March 2021

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Dicky L. Tahapary, Indonesia University:
COVID-19
adiposity
diabetes
insulin resistance
aging
inflammation
immune system
Additional relevant MeSH terms:
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COVID-19
Metabolic Syndrome
Immune System Diseases
Respiratory Tract Infections
Infections
Pneumonia, Viral
Pneumonia
Virus Diseases
Coronavirus Infections
Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Lung Diseases
Respiratory Tract Diseases
Insulin Resistance
Hyperinsulinism
Glucose Metabolism Disorders
Metabolic Diseases