Abdominal Obesity, Healthy, Hypercholesterolemia, Hypertension, Low-grade Inflammation, Metabolically Healthy Controls, Metabolic Syndrome, Normal Weight Adults, Obesity, Overweight, Risk Factor, Cardiovascular
Conditions
Keywords
nutrition, microbiome, lifestyle factors, low-grade inflammation, personalized nutrition, precision nutrition
Brief summary
The goal of this observational study is to learn about low-grade inflammation in healthy individuals and individuals with overweight or obesity. The main questions it aims to answer are: * Whether it is possible to predict low-grade inflammation * What are the medical, biological, and lifestyle variables related to low-grade inflammation? Participants will be asked to: 1. Attend a general medical visit to collect vital signs, anthropometric measurements, and collect blood samples. 2. Complete questionnaires and collect a stool sample at home.
Detailed description
Cardiometabolic diseases (CMDs) are a heterogeneous spectrum of nutrition-related chronic diseases, ranging from obesity to diabetes and, ultimately, to acute and chronic cardiovascular diseases. Once established, these diseases are usually irreversible and evolve over time. Since these diseases are born out of societal and lifestyle changes, the cornerstones of prevention and management are changes in nutrition and lifestyle. This inevitable increase in CMDs, including obesity, particularly affects socially vulnerable populations. The etiology of cardiometabolic diseases is complex and involves environmental, biological and genetic elements. Weight gain is at the heart of these pathologies: it frequently precedes their development or contributes to the progression of these diseases. To this end, even modest weight loss is suggested as an important line of prevention or treatment of cardiometabolic diseases. For example, diabetes remission can be achieved with weight loss and is directly correlated with the amount of weight lost. Despite the beneficial effects of weight loss on preventing the progression of cardiometabolic diseases, maintaining weight loss is difficult, with only 30% of individuals achieving long-term weight loss (5 years). The same is true with the development of anti-obesity treatments (new analogues of glucagon-like peptide 1 (GLP1)); Discontinuation of treatment is accompanied by weight gain. In the case of diabetes, weight gain is associated with the recurrence of previously remitted diabetes. Chronic low-grade inflammation is tightly linked with obesity and a central feature of cardiometabolic diseases and associated diseases. Furthermore, it paves the way for future comorbidities. This inflammation is characterized by a rise of systemic or circulating inflammatory molecules. However, no single cytokine can reflect the inflammatory state seen in cardiometabolic diseases and these systemic factors are highly variable from subject to subject. Recently, combinatorial indexes, using multiple inflammatory markers have been strongly associated with coronary risks and Metabolic alterations. Over the past 10 years, the gut microbiome has become a recognized contributor to our metabolic health. Accumulating evidence has shown that the gut microbiome strongly reflects environmental and lifestyle changes (including nutrition) by altering its diversity and composition as well as its functions by producing molecules that interact with host organs, including the brain. The excess or deficit production of molecules produced by the microbiota, bacterial metabolites (such as trimethylamine oxide (TMAO), Imidazole propionate, branched-chain amino acids (BCAAs), or short-chain fatty acids (SCFAs), etc.) are molecules implicated in the link between the environment, microbiota and metabolic and inflammatory disturbances. Current strong evidence indicates that the gut microbiota is altered early in people with inflammatory diseases that include CMDs. Relationships between the inflammatory component of the diet and the gut microbiome have also been identified. In an effort to predict chronic-low grade inflammation in a real-world population and decipher the relationships between chronic low-grade inflammation and individual factors, comprising lifestyle, diet, behavior, environment, the gut microbiome, and health-related clinical data, the present study recruits a cohort of participants across age, sex, body mass index, and metabolic health spectra. Chronic low-grade inflammation markers of interest will be measured to establish a multi-component index of inflammation relative in the population.
Interventions
None listed
Sponsors
Assistance Publique - Hôpitaux de Paris
Assistance Publique Hopitaux De Marseille
University Hospital, Bordeaux
Centre Hospitalier Universitaire Dijon
Institut Pasteur de Lille
Hopitaux Civils de Colmar
Integrative Phenomics
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
Yes
Inclusion criteria
* Male or female between the ages of 18 and 70 included, * One of the following two criteria: * Clinically at-risk group Body Mass Index between 25 (included) and up to 35 kg/m2 (excluded) * Non-clinically at-risk group Body Mass Index between 18.5 (included) and up to 25 kg/m2 (excluded) and absence of metabolic syndrome criteria * Subject covered by social security or a similar system. * Ability to use a mobile phone application on a daily basis (food intake). * Subject, after being informed of the contents of this study, fully understanding and accepting its purpose; and able to personally sign a written informed consent
Exclusion criteria
* Subject with diagnosed inflammatory disease or infection-related inflammation (viral or bacterial) or medical history (viral) within the last 2 months: * Rheumatoid arthritis, reactive or psoriatic arthritis (non-osteoarthritis) * Inflammatory bowel disease (IBD) (Crohn's disease or ulcerative colitis) or irritable bowel syndrome * Systemic lupus erythematosus * Uncontrolled psoriasis * Viral hepatitis or ongoing viral infection * Seasonal virus (influenza-like illness) * Subjects who have taken antibiotics in the last 2 months * Subject under treatment within the last 2 months of an: * Antiviral (for HIV, hepatitis, influenza, chickenpox/shingles) * Oral, topical, or injectable treatment of a drug that modulates the inflammatory response (e.g. Corticosteroid, non-steroidal anti-inflammatory drugs (e.g. ibuprofen, diclofenac, celecoxib, naproxen, aspirin, etc.) * Dietary supplement that can modulate the inflammatory response (e.g. * Omega 3 fatty acid, curcuma/turmeric, probiotic, prebiotics) * Subject with diabetes (type 1 or 2) known treated prior to the inclusion visit (specifically subjects recently diagnosed or diagnosed with diabetes at the time of the laboratory assessment may be retained in the study if they are not taking anti-diabetic treatment): i.e. exclusion of subject with diabetes diagnosed with fasting blood glucose ≥ 126 mg/dL (7.0 mmol measured twice/L OR glycated hemoglobin ≥ 6.5% (48 mmol/mol) AND anti-diabetic therapy (metformin, GLP-1 receptor agonist, insulin, sulphonylurea, alpha-glucosidase inhibitor) * Subject with severe or unstable hepatic, renal, cardiovascular, respiratory, endocrine, or metabolic disorders or cancer diagnosed with or without treatment * Subject suffering from gastrointestinal disorders resulting in the use of laxatives or drugs for intestinal transit (e.g., loperamide) in the last 2 months. * Subject with a complication or procedure in the last 2 months that could result in inflammation * Minor or acute tendonitis, sprain, or contusion * Severe contusion (e.g. Bone contusion) * Major or invasive surgery * Subject in a situation that, in the opinion of the investigator, could interfere with optimal participation in the present study or pose a particular risk to the subject. * Subject currently participating in an interventional clinical study * Subject not affiliated to the Social Security scheme * Subject who did not comply with the exclusion period of the study in which they would have previously participated * Subject not being able to use the internet
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Low-grade inflammation | Baseline | Assessed as a z-score composed of six markers (C reactive protein (CRP), interleukin (IL)-6, serum amyloid-A (SAA), soluble intracellular adhesion molecule (sICAM), tumor necrosis factor alpha (TNF)-alpha) and categorized into 3 tertiles: Low/ Moderate/High |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Body fat mass | Baseline | Percentage of bodymass measured by impedance |
| Water body mass | Baseline | Percentage of body mass measured by impedance |
| Lean body mass | Baseline | Percentage of body mass measured by impedance |
| Serum fasting low-density lipoprotein | Baseline | mmol/L |
| Fasting serum high-density lipoprotein | Baseline | mmol/L |
| Fasting total serum cholesterol | Baseline | mmol/L |
| Stool consistency | Baseline | Stool consistency assessed and self-reported by Bristol Stool Scale |
| Stress | Baseline | Self-perceived stress assessed by questionnaire |
| Deprivation | Baseline | Economic, material, and social deprivation assessed by questionnaire |
| Sleep | Baseline | Sleep latency, duration, efficiency, quality, disturbances, and daytime dysfunction assessed by questionnaire |
| Waist circumference | Baseline | Centimeters |
| Neck circumference | Baseline | Centimeters |
| Gut microbiome metabolites | Baseline | Consumption and production in mmol/day assessed through in silico metabolic modeling |
| Fasting glucose | Baseline | Serum glucose in mg/dl |
| Stool microbiome composition | Baseline | Relative abundance of microbiome taxonomies (Phyla, Order, Class, Family, Genus, Species), metagenomic species (MGS), and co-abundance genes (CAGs) in stool samples assessed through shot-gun sequencing |
| Stool microbiome functional pathways | Baseline | Relative abundances of microbiome functional pathways assessed through metagenomics and in silico metabolic modeling |
| Systolic blood pressure | Baseline | mmHg |
| Resting heart rate | Baseline | Beats per minute |
| Serum glycated hemoglobin (HbA1c) | Baseline | Percentage of HbA1c or mmol/L |
| Diastolic blood pressure | Baseline | millimeters mercury (mmHg) |
| Height | Baseline | Centimeters |
| Consumption of dietary macronutrients | Baseline | Dietary macronutrient consumption assessed in g/day from dietary records and food frequency questionnaires |
| Consumption of dietary micronutrients | Baseline | Daily micronutrient consumption (mg/d) assessed by dietary records and food frequency questionnaire |
| Consumption of dietary metabolites | Baseline | Dietary metabolite consumption expressed in mmol/day assessed by dietary records and food frequency questionnaire |
| Food item consumption | Baseline | Consumption of food items in g/day assessed by dietary records and food frequency questionnaires |
| Food group consumption | Baseline | Consumption of food groups in g/day assessed by dietary records and food frequency questionnaires |
| Body weight | Baseline | Kilograms |
| Serum Alanine Transaminase (ALT) | Baseline | Serum Units per Liter (U/L) |
| Serum Aspartate Aminotransferase (ALT) | Baseline | Serum Units per Liter (U/L) |
| Serum gamma-glutamyl transferase (GGT) | Baseline | Serum Units per Liter (U/L) |
| Fasting serum triglycerides | Baseline | mmol/L |
| Fasting serum uric acid | Baseline | mmol/L |
| Fasting serum creatinine | Baseline | mmol/L |
| Fasting serum insulin | Baseline | mmol/L |
| Blood hemoglobin | Baseline | grams per 100 milliliters (g/100ml) |
| Blood hematocrit | Baseline | Percentage (%) of whole blood sample |
| Red blood cells | Baseline | Cell counts in 10\^9 per liter (10\^9/L) |
| Red blood cell volume | Baseline | Mean volume in cubic micrometers (um\^3) |
| Hemoglobin relative red blood cell size | Baseline | Mean relative hemoglobin relative to red blood cell size in percentage |
| Mean cell hemoglobin (MCH) | Baseline | Mass of hemoglobin per red blood cell in picograms (pg) |
| Hip circumference | Baseline | Centimeters |
| White blood cells | Baseline | Cell counts expressed in billions/L (10\^9/L) and differential |
| Perceived quality of life | Baseline | Self-perceived measurements of mental, physical, emotional, social, and general quality of life, fatigue, energy assessed by questionnaire |
| Eating behavior | Baseline | Self-perceived emotional, uncontrolled, and eating restriction assessed by questionnaire |
| Physical activity | Baseline | Total, leisure, work, and sports physical activity assessed by questionnaire |
| Sleep apnea | Baseline | Binary value (yes/no) assessed from questionnaire |
| Blood platelets | Baseline | Cell counts expressed in billions/L (10\^9/L) |
Contacts
Primary ContactKarine Clément, MD, PhD
Outcome results
None listed