The aim of this research is to characterise the genetic and molecular landscape of gout, inflammation and metabolic diseases, as well as the associated molecular, anthropomorphic and pathological characteristics.
Recent work by the GHICL has highlighted the imminent public health problem represented by gout and hyperuricaemia in French Polynesia, with gout affecting around 15% and hyperuricaemia 71.6% of the adult population. Hyperuricaemia is defined as a serum urate level above the saturation point, leading to its crystallisation under biological conditions. It is generally accepted that hyperuricaemia (HU) begins at 6.0 mg/dL (360 µmol/L). The best-known consequence of hyperuricaemia is the development of gout, which results from the inflammatory response to the presence of monosodium urate (MSU) crystals in and around the joints. The factors modulating the deposition of MSU crystals in HU are poorly understood and a potential genetic contribution is unknown. It is known that the inflammatory response to the presence of MSU crystal deposits causing gout flares is mediated by activation of the NLRP33 inflammasome, but there is significant variability in this inflammatory response within the same individual (irregular recurrence of flares), and between individuals. The exceptionally high prevalence of HU that have been identified in French Polynesia represents a major health burden, as hyperuricaemia and gout are associated with many other cardiometabolic diseases. For example, hyperuricaemia, BMI and type 2 diabetes are all associated with the risk of gout. The association between gout and cardiovascular events is also well known, and is thought to be mediated by a persistent inflammatory state. These data are particularly relevant in the context of French Polynesia where, in addition to high rates of gout and HU, a prevalence of obesity of over 40% (BMI \>30kg/m2) and a prevalence of diabetes (based on HBA1c measurements) of over 13% was previously identified by the same team. This last point is particularly striking as the prevalence of diabetes based on self-reported diagnosis was only 7%, indicating that many people are probably undiagnosed and therefore untreated. A better understanding not only of the mechanism and causal link between these conditions and risk factors, but also of the genetic basis of the disease, could lead to the development of therapies and to the improvement in diagnosis. This is particularly crucial in French Polynesia, where hyperuricaemia and its pathological effects on cardiometabolic health affect a large proportion of the adult population. Here a multi-pronged approach to take advantage of a previous study named 'TOPATA' (NCT04812886) performed previously in French Polynesia, is proposed. The idea is to expand the existing cohort, in order to identify the genetic effectors contributing to the high rates of metabolic diseases previously observed, and to track the longitudinal impacts of HU.
Study Type
OBSERVATIONAL
Enrollment
2,750
Collection of sociodemographic data, collection of treatment data, standard and clinical ophthalmological examination, physical and biological measurements, clinical characteristics specific to chronic and metabolic diseases, questionnaires (gout questionnaire, health assessment questionnaire (HAQ-II), EuroQol (EQ)-5D-5L, joint pain, state of health, diet and physical activity, access to care, addictions, sleep quality, pain scale (EVA), personal and family history) will be performed.
Collection of blood and urine samples for genetic, biochemical, omics, and biobank analyses, in order to characterize cardiometabolic health markers will be performed.
Medical Office Dr Antoine Scuiller
Faa'a, French Polynesia
RECRUITINGMedical Office Dr Cedric Gueguen
Papeete, French Polynesia
RECRUITINGMedical Office Dr Voradeth Nouanesengsy
Papeete, French Polynesia
GWAS-identified associations between population-enriched genetic variants and health-related traits
Effect estimates (odds ratios or beta coefficients) for statistically significant associations between population-enriched genetic variants and predefined health-related traits identified through GWAS.
Time frame: 12 month
Multiple correlation between gout, hyperuricemia, type 2 diabetes and obesity
Anthropometric and biological characteristics of gout, hyperuricemia, type 2 diabetes, and obesity will be assessed, and comorbidity associations analyzed using multivariate logistic regression. Measurements will include waist and hip circumferences, blood pressure, muscle properties by myotonometry, lower-limb strength via chair stand test, and handgrip strength. Blood analyses will assess lipid profile, liver function, inflammation, renal function and metabolism, glucose metabolism, and complete blood count. Urine analyses will also be performed.
Time frame: 12 month
Incidence rate of gout or HU in French Polynesia between 2021 and 2025
Incidence rate of gout or HU in French Polynesia between 2021 and 2025 in a sample of participants in the 2021 cohort with asymptomatic HU and no gout.
Time frame: 4 years
Proportion of each type of urate-lowering therapy prescribed annually between 2019 and 2025
Breakdown of types of THU prescribed between 2019 and 2025 based on the database of healthcare claims submitted to the Caisse de Prévoyance Sociale (CPS)
Time frame: 4 years
Incidence of major adverse cardiovascular events (MACE) in patients with gout, diabetes, or obesity
Occurrence of MACE related to gout, diabetes or obesity on the Healthcare Claims Database (HCD).
Time frame: 12 month
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CH de Polynesie Française Papeete
Pirae, French Polynesia
NOT_YET_RECRUITINGMedical Office Dr Nanethida Nouanesengsy
Pirae, French Polynesia
RECRUITINGMedical Office Dr Raphael Habib
Pirae, French Polynesia
RECRUITINGDeterminants of progression from asymptomatic hyperuricemia (in 2021) to gout (in 2025)
Factors influencing the transition from asymptomatic HU to gout will be sought among demographic, anthropomorphic and biological characteristics, comorbidities, current treatments, genetic profiles, gene expression, proteomic profiles and metabolomic profiles.
Time frame: 4 years
Multiple correlation between metabolic changes and disease evolution
Severe gout is defined by subcutaneous tophi. Clinical characteristics will be assessed, including disease features (age at onset, duration), topography (affected joints, mono-/polyarticular and axial involvement), number, duration, and severity of flares, serum uric acid, and presence, number, and size of tophi. Treatment history and response (urate-lowering therapy, flare management), comorbidities (cardiovascular disease, chronic kidney disease, metabolic syndrome, diabetes, obesity, hypertension), and lifestyle factors (diet, alcohol, physical activity) will also be recorded. Molecular and biological characterization will include genetic, transcriptomic, proteomic, and metabolomic analyses to identify biomarkers associated with disease severity and progression.
Time frame: 12 month
Multiple correlation between molecular, clinical features and gout severity
Severe gout is defined by subcutaneous tophi. Clinical characteristics will be evaluated, including disease features (age at onset, duration), topography (affected joints, mono-/polyarticular and axial involvement), number, duration, and severity of flares, serum uric acid, and presence, number, and size of tophi. Treatment history and response (urate-lowering therapy, flare management), comorbidities (cardiovascular disease, chronic kidney disease, metabolic syndrome, diabetes, obesity, hypertension), and lifestyle factors (diet, alcohol, physical activity) will also be recorded. Molecular and biological characterization will include genetic, transcriptomic, proteomic, and metabolomic analyses to identify biomarkers linked to disease severity and progression.
Time frame: 12 month
Multiple correlation between gout attacks, clinical manifestations, gene expression, proteomic, and metabolomic profiles.
Gout attack characteristics will be investigated, including attack location (affected joints, mono- or polyarticular), pain intensity (VAS or NRS), and inflammatory parameters (CRP, ESR if available, leukocyte count and differential). Synovial fluid analysis will include total and differential cell counts and presence of monosodium urate crystals. Triggering factors (diet, alcohol, trauma, infection, medications), associated acute conditions (infection, surgery, cardiovascular events), and flare duration and severity will be recorded. Treatments and clinical response will also be documented. Molecular characterization will include genetic analyses (urate transport and inflammation variants), gene expression, proteomic profiling of blood and synovial fluid, and metabolomic analyses of serum, urine, and synovial fluid to identify biomarkers of acute flares.
Time frame: 12 month
Change in clinical and molecular characteristics of acute gout flares between baseline and 1-week visit
Composite assessment of acute gout flares including clinical characteristics (attack location, mono- or polyarticular involvement, pain intensity measured by VAS or NRS, inflammatory markers \[CRP, ESR if available, leukocyte count and differential\]), synovial fluid findings (total and differential cell counts and presence of monosodium urate crystals), triggering factors and associated acute conditions, flare duration and severity, treatments administered and clinical response, and molecular biomarkers identified through genetic analyses, gene expression profiling, proteomic analyses, and metabolomic profiling of blood, urine, and synovial fluid.
Time frame: one week