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Covid19CARAMELNCT04802044

COVID-19, Aging, and Cardiometabolic Risk Factors Study

An observational study of Covid19 and Obesity, run by Universitas Indonesia at 2 sites in Indonesia.

Unknown status

Status not confirmed. The sponsor has not verified this record for over two years, so it may be out of date.

Official record
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Trial 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. Read more about this study

Who is it for?
Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for
Covid19, Obesity, Diabetes Mellitus
How many people
440 participants
Where
2 sitesIndonesia
Study dates
8 Dec 2020to 31 Dec 2023
Phase
Observational
Are there results?
None posted in the registry

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

Study population: COVID-19 patients in hospital and community setting

Who may be able to join

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

Who cannot join

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

Trial locations and contacts

2 sites in 1 country.

Indonesia2 sites

  • Dr. Cipto Mangunkusumo National General Hospital

    Jakarta Pusat, DKI Jakarta, 10430

  • Metabolic Disorder, Cardiovascular, and Aging Research Cluster IMERI-FKUI, Research Tower, 5th Floor

    Jakarta Pusat, DKI Jakarta, 10430

Other trials in Indonesia

About this study

Official title: COVID-19, Aging, and Cardiometabolic Risk Factors (CARAMEL) Study: Integrating Aging, Cardiovascular, Metabolic, and Immunological Studies to Unravel COVID-19 Pathophysiology

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.

Read the 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.

Keywords
COVID-19, adiposity, diabetes, insulin resistance, aging, inflammation, immune system
Medical subject headings
COVID-19, Obesity, Diabetes Mellitus, Metabolic Syndrome, Immune System Diseases, Insulin Resistance, Inflammation

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Correlation of Body Mass Index with Clinical Disease Severity

    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

    Measured: Baseline

  2. Correlation of Visceral Fat with Clinical Disease Severity

    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

    Measured: Baseline

  3. Correlation of Blood Glucose Levels with Clinical Disease Severity

    To compare the random blood glucose levels during admission in groups of COVID-19 patients with various disease severity based on WHO criteria

    Measured: Baseline

  4. Correlation of HbA1c with Clinical Disease Severity

    To compare the HbA1c levels during admission in groups of COVID-19 patients with various disease severity based on WHO criteria

    Measured: Baseline

Secondary outcomes (11)

  1. Changes of Insulin Resistance Levels in COVID-19 Patients Overtime

    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

    Measured: Baseline, 6, and 12 month

  2. Changes of Leptin/Adiponectin Ratio in COVID-19 Patients Overtime

    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

    Measured: Baseline, 6, and 12 month

  3. Systemic Immune Profiles in Diabetic COVID-19 Patients

    To compare the systemic immune profiles using mass cytometry between diabetic/COVID-19, non-diabetic/COVID-19, and healthy control subjects

    Measured: Baseline

  4. Nasal Mucosal Immune Profiles in Diabetic COVID-19 Patients

    To compare the nasal-mucosal immune profiles using mass cytometry between diabetic/COVID-19, non-diabetic/COVID-19, and healthy control subjects

    Measured: Baseline

  5. Aging Parameter (ACE-2 gene expression) in COVID-19 Patients

    To compare the nasal epithelial ACE-2 gene expression in groups of COVID-19 patients with various disease severity based on WHO criteria

    Measured: Baseline

  6. Aging Parameter (Telomere Length) in COVID-19 Patients

    To compare the aging parameter using telomere length in groups of COVID-19 patients with various disease severity based on WHO criteria

    Measured: Baseline

  7. Immune Cells Exhaustion in COVID-19 Patients

    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

    Measured: Baseline

  8. Changes of Pro-Inflammatory Cytokine (IL-6) in COVID-19 Patients

    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

    Measured: Baseline, 1, 3, and 6 months

  9. Changes of Anti-Inflammatory Cytokine (IL-10) in COVID-19 Patients

    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

    Measured: Baseline, 1, 3, and 6 months

  10. Antibody Kinetics in COVID-19 Patients

    To compare the changes of antibody titers in groups of patients with various clinical disease severity based on WHO criteria

    Measured: Baseline, 1, 3, and 6 months

  11. Proportion of Long COVID Syndrome

    Percentage of COVID-19 patients still present with symptoms compared to whole study subjects

    Measured: 3, 6, and 12 months

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted17 Mar 2021
  2. Study start8 Dec 2020
  3. Primary completion31 Dec 2022 (estimated)
  4. Study completion31 Dec 2023 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Observational
Observational model
Cohort
Time perspective
Prospective
Enrolment
440 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT04802044
Sponsor's protocol ID
CARAMEL
Acronym
CARAMEL
Status last verified
Apr 2022
First submitted
30 Jan 2021
Last updated
18 Apr 2022
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No

Publications

  • Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. J Infect. 2020 Jun;80(6):e14-e18. doi: 10.1016/j.jinf.2020.03.005. Epub 2020 Mar 27.

    PubMed 32171866

  • Iannelli A, Favre G, Frey S, Esnault V, Gugenheim J, Bouam S, Schiavo L, Tran A, Alifano M. Obesity and COVID-19: ACE 2, the Missing Tile. Obes Surg. 2020 Nov;30(11):4615-4617. doi: 10.1007/s11695-020-04734-7. No abstract available.

    PubMed 32451913

  • Apicella M, Campopiano MC, Mantuano M, Mazoni L, Coppelli A, Del Prato S. COVID-19 in people with diabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol. 2020 Sep;8(9):782-792. doi: 10.1016/S2213-8587(20)30238-2. Epub 2020 Jul 17.

    PubMed 32687793

  • Wu H, Ballantyne CM. Metabolic Inflammation and Insulin Resistance in Obesity. Circ Res. 2020 May 22;126(11):1549-1564. doi: 10.1161/CIRCRESAHA.119.315896. Epub 2020 May 21.

    PubMed 32437299

  • Andersen CJ, Murphy KE, Fernandez ML. Impact of Obesity and Metabolic Syndrome on Immunity. Adv Nutr. 2016 Jan 15;7(1):66-75. doi: 10.3945/an.115.010207. Print 2016 Jan.

    PubMed 26773015

  • Exley MA, Hand L, O'Shea D, Lynch L. Interplay between the immune system and adipose tissue in obesity. J Endocrinol. 2014 Nov;223(2):R41-8. doi: 10.1530/JOE-13-0516. Epub 2014 Sep 16.

    PubMed 25228503

  • Agrawal M, Kern PA, Nikolajczyk BS. The Immune System in Obesity: Developing Paradigms Amidst Inconvenient Truths. Curr Diab Rep. 2017 Aug 15;17(10):87. doi: 10.1007/s11892-017-0917-9.

    PubMed 28812211

  • Gerriets VA, MacIver NJ. Role of T cells in malnutrition and obesity. Front Immunol. 2014 Aug 11;5:379. doi: 10.3389/fimmu.2014.00379. eCollection 2014.

    PubMed 25157251

  • Han JM, Levings MK. Immune regulation in obesity-associated adipose inflammation. J Immunol. 2013 Jul 15;191(2):527-32. doi: 10.4049/jimmunol.1301035.

    PubMed 23825387

  • Rebello CJ, Kirwan JP, Greenway FL. Obesity, the most common comorbidity in SARS-CoV-2: is leptin the link? Int J Obes (Lond). 2020 Sep;44(9):1810-1817. doi: 10.1038/s41366-020-0640-5. Epub 2020 Jul 9.

    PubMed 32647360

  • Misumi I, Starmer J, Uchimura T, Beck MA, Magnuson T, Whitmire JK. Obesity Expands a Distinct Population of T Cells in Adipose Tissue and Increases Vulnerability to Infection. Cell Rep. 2019 Apr 9;27(2):514-524.e5. doi: 10.1016/j.celrep.2019.03.030.

    PubMed 30970254

  • Kwaifa IK, Bahari H, Yong YK, Noor SM. Endothelial Dysfunction in Obesity-Induced Inflammation: Molecular Mechanisms and Clinical Implications. Biomolecules. 2020 Feb 13;10(2):291. doi: 10.3390/biom10020291.

    PubMed 32069832

  • Li M, Qian M, Xu J. Vascular Endothelial Regulation of Obesity-Associated Insulin Resistance. Front Cardiovasc Med. 2017 Aug 9;4:51. doi: 10.3389/fcvm.2017.00051. eCollection 2017.

    PubMed 28848738

  • Engin A. Endothelial Dysfunction in Obesity. Adv Exp Med Biol. 2017;960:345-379. doi: 10.1007/978-3-319-48382-5_15.

    PubMed 28585207

  • Meyers MR, Gokce N. Endothelial dysfunction in obesity: etiological role in atherosclerosis. Curr Opin Endocrinol Diabetes Obes. 2007 Oct;14(5):365-9. doi: 10.1097/MED.0b013e3282be90a8.

    PubMed 17940464

Questions about this trial

What is this clinical trial studying?

An observational study of Covid19 and Obesity, run by Universitas Indonesia at 2 sites in Indonesia.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 1 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: status not confirmed, as of the sponsor's last update on 18 Apr 2022. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 2 sites in Indonesia. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Results are not available in the source record.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Correlation of Body Mass Index with Clinical Disease Severity (time frame: Baseline). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT04802044, which also lists 32 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Apr 2022.

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