Gout, Renal Insufficiency, Chronic
Conditions
Keywords
Gout, Renal Insufficiency Chronic, New Caledonia
Brief summary
The goal of this research is to study the associations of genetic variants of gout and kidney failure, which are very common in the Melanesian population in New Caledonia
Detailed description
Gout is a chronic pathology linked to the deposition in the tissues of monosodium urate (MSU) crystals, secondary to hyperuricemia (high blood levels of urate). Gout causes very painful joint attacks that are first acute and then lead to chronic pain, and disabling deforming manifestations called tophus. The disease is strongly associated with cardiovascular comorbidities and chronic renal failure. In New Caledonia, the prevalence of chronic kidney disease (CKD) (according to the glomerular filtration rate (GFR) \< 60 ml/min) was of 7.4% in 2015 (according to the epidemiological study Barometer Health 2015). In the Loyalty Islands, which has overall significantly more Melanesian population, a local database showed that in 2018 the prevalence of patients having at least one blood test reporting kidney disease (GFR CKD\< 60 ml/min) and seen at least once in the previous two years was as follows: * 7.7% in Lifou (9,200 inhabitants) * 8.4% in Maré (5,700 inhabitants) * 9.1% in Ouvéa (3,400 inhabitants) In summary, inflammatory diseases such as CKD and gout have high prevalence in New Caledonia and the Loyalty Islands, and constitute a major health issue. Although the high prevalence of these diseases is probably due in part to non-genetic factors (environment, diet, etc.), it is likely, given the demographic history of this region, that undetected genetic risk alleles among the Melanesian population contribute to the appearance and progression of diseases. Performing genetic and epidemiological studies in an as yet understudied region is essential to identify these variants, which could lead to improved diagnoses and health outcomes.
Interventions
Sociodemographic data collection, treatments collection, physical assessment, clinical examination and physical and biological measurements, biological evaluation (blood and urine samples), CKD-specific clinical features collection, gout-specific clinical features collection, clinical characteristics specific to chronic diseases, questionnaires (Health Assessment Questionnaire (HAQ-II), EuroQol (EQ)-5D-5L, joint pain, state of health, diet and physical activity, access to care, addictions, pain scale (EVA), personal and family history)
Sponsors
Variant Bio, Inc.
Lille Catholic University
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE
Intervention model description
3 groups of participants : * Patients with gout * Patients with CKD * Controls
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
Yes
Inclusion criteria
Criteria common to the 3 cohorts : \- Consenting to participate in the study and having signed the informed consent \- Claiming to be of Melanesian ethnicity 1. Patients with gout : * Age: 18 - 70 years old * To be included in the study, a patient with a diagnosis of gout in his medical file or declaring to have gout will have to satisfy to the ACR/EULAR (ref) classification criteria : 1. have had at least one episode of swelling, pain spontaneous, or triggered by pressure, of a joint peripheral or a bursa AND evidence of sodium urate crystals in a joint or bursitis symptomatic or by puncture of a tophus reported in his medical file. 2. Or Score \> or =8 according to ACR/EULAR clinical criteria 2. Patients with CKD * Age: 18 - 70 years old * Patients on dialysis or CKD clinically diagnosed on the basis of: 1. Markers of kidney damage (one or more) : Albuminuria (ACR ≥ 30 mg/g), Urinary sediment abnormalities (e.g., casts urinary), Electrolyte abnormalities and other, abnormalities due to tubular disorders (eg, hyperkalemia), abnormalities detected by histology, structural abnormalities detected by imaging (e.g.,USG), history of kidney transplantation 2. Decreased kidney function: GFR \< 60 ml/min/1.73 m² (calculated according to the Chronic Kidney Disease - EPIdemiology formula: CKD-EPI) 3. Controls cohort * Absence of gout or CKD * Age: 30 - 80 years old
Exclusion criteria
* Pregnant women * Individuals under guardianship / curatorship / judicially incapacitated
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Genome-wide association study (GWAS) in gout | 8 months | GWAS is a research approach used to identify genomic variants that are statistically associated with a risk for a disease or a particular trait. The method involves surveying the genomes of many people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait. Once such genomic variants are identified, they are typically used to search for nearby variants that contribute directly to the disease or trait. |
| Phenome-wide association studies (PheWAS) in gout | 8 months | PheWAS will be used to analyze many phenotypes compared to a single genetic variant (or other attribute). |
| GWAS in CKD | 8 months | Genome-wide association study (GWAS) and Phenome-wide association studies (PheWAS) will be used. GWAS is a research approach used to identify genomic variants that are statistically associated with a risk for a disease or a particular trait. The method involves surveying the genomes of many people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait. Once such genomic variants are identified, they are typically used to search for nearby variants that contribute directly to the disease or trait. PheWAS will be used to analyze many phenotypes compared to a single genetic variant (or other attribute). |
| PheWAS in CKD | 8 months | PheWAS will be used to analyze many phenotypes compared to a single genetic variant (or other attribute). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Relation between CKD and sex | 8 months | Relation between CKD and sex (Male/ Female) |
| Relation between gout and sex | 8 months | Relation between gout and sex (Male/ Female) |
| Relation between CKD and age | 8 months | Relation between CKD and age |
| Relation between gout and age | 8 months | Relation between gout and age |
| Relation between CKD and Body Mass Index (BMI) | 8 months | Relation between CKD and Body Mass Index (BMI) in kg/m\^2 |
| Link between gout and Body Mass Index (BMI) | 8 months | Link between gout and Body Mass Index (BMI) in kg/m\^2 |
| Relation between CKD and the Body Fat Percentage | 8 months | Relation between CKD and the Body Fat Percentage in Percentage (%) |
| Relation between gout and the Body Fat Percentage | 8 months | Relation between gout and the Body Fat Percentage in Percentage (%) |
| Relation between CKD and Muscle Mass Percentage | 8 months | Relation between CKD and Muscle Mass Percentage in Percentage (%) |
| Relation between gout and Muscle Mass Percentage | 8 months | Relation between gout and Muscle Mass Percentage in Percentage (%) |
| Relation between CKD and Basal Metabolism | 8 months | Relation between CKD and Basal Metabolism in Joules |
| Metabolomic profile in gout | 8 months | Metabolomics profiling will be conducted using ultrahigh-performance liquid chromatography-tandem mass-spectrometry by the metabolomics provider Metabolon Inc. (USA) on fasting serum samples from participants. The metabolomic dataset measured by Metabolon includes known metabolites containing the following broad categories - amino-acids, peptides, carbohydrates, energy intermediates, lipids, nucleotides, cofactors and vitamins, and xenobiotics. |
| Relation between CKD and Visceral Fat Percentage | 8 months | Relation between CKD and Visceral Fat Percentage in Percentage |
| Relation between gout and Visceral Fat Percentage | 8 months | Relation between gout and Visceral Fat Percentage in Percentage |
| Relation between CKD and height | 8 months | Relation between CKD and height in meters |
| Relation between gout and height | 8 months | Relation between gout and height in centimeters |
| Relation between CKD and waist size | 8 months | Relation between CKD and waist size in centimeters |
| Relation between gout and waist size | 8 months | Relationbetween gout and waist size in centimeters |
| Relation between CKD and hip circumference | 8 months | Relation between CKD and hip circumference in centimeters |
| Relation between gout and hip circumference | 8 months | Relation between gout and hip circumference in centimeters |
| Relation between CKD and waist/hip ratio | 8 months | Relation between CKD and waist/hip ratio |
| Relation between gout and waist/hip ratio | 8 months | Relation between gout and waist/hip ratio |
| Mendelian randomization | 8 months | Mendelian randomization uses measured variation in genes to interrogate the causal effect of an exposure on comorbidities development |
| Relation between gout and Basal Metabolism | 8 months | Relation between gout and Basal Metabolism in Joules |
| Metabolomic profile in CKD | 8 months | Metabolomics profiling will be conducted using ultrahigh-performance liquid chromatography-tandem mass-spectrometry by the metabolomics provider Metabolon Inc. ( USA) on fasting serum samples from participants. The metabolomic dataset measured by Metabolon includes known metabolites containing the following broad categories - amino-acids, peptides, carbohydrates, energy intermediates, lipids, nucleotides, cofactors and vitamins, and xenobiotics. |
| GWAS and severity of CKD according to glomerular filtration rate (GFR) | 8 months | GWAS will be used to identify genomic variants that are statistically associated with severity. Stage of severity according to GFR (mL/min/1.73m2): 1. Mild renal impairment with normal or increased filtration : Over 90 2. Mild decrease in renal function : 60-89 3. Moderate decrease in renal function : 30-59 4. Severe decrease in renal function : 15-29 5. Renal failure: Less than 15 (or dialysis) |
| GWAS and severity of gout | 8 months | GWAS will be used to identify genomic variants that are statistically associated with severity. Severity will be defined using four binary variables: number of attacks (cutoff six attacks per year), presence of clinical tophus (yes or no), uric acid level (\>80 mg/L or 480 micromol/L) and age of onset (\<30 years) . |
Countries
New Caledonia
Outcome results
None listed