COVID-19, Aging, and Cardiometabolic Risk Factors Study (CARAMEL)
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ClinicalTrials.gov Identifier: NCT04802044 |
Recruitment Status :
Active, not recruiting
First Posted : March 17, 2021
Last Update Posted : April 18, 2022
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Condition or disease |
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Covid19 Obesity Diabetes Mellitus Aging Cardiometabolic Syndrome Immune System Disorder |
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.
Study Type : | Observational |
Actual Enrollment : | 440 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 : | December 31, 2022 |
Estimated Study Completion Date : | December 31, 2023 |

- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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

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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 |
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

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
Indonesia | |
Dr. Cipto Mangunkusumo National General Hospital | |
Jakarta Pusat, DKI Jakarta, Indonesia, 10430 | |
Metabolic Disorder, Cardiovascular, and Aging Research Cluster IMERI-FKUI, Research Tower, 5th Floor | |
Jakarta Pusat, DKI Jakarta, Indonesia, 10430 |
Principal Investigator: | Dicky L Tahapary | Indonesia University |
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: | April 18, 2022 |
Last Verified: | April 2022 |
Studies a U.S. FDA-regulated Drug Product: | No |
Studies a U.S. FDA-regulated Device Product: | No |
COVID-19 adiposity diabetes insulin resistance |
aging inflammation immune system |
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 |