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The gout epidemic in French Polynesia: a modelling study of data from the Ma’i u’u epidemiological survey

Pascart, Tristan, Wasik, Kaja A, Preda, Cristian, Chune, Valérie, Torterat, Jérémie, Prud’homme, Nicolas, Nassih, Maryline, Martin, Agathe, Le Masson, Julien, Rodière, Vahinetua, Frogier, Sylvain, Canova, Georges, Pescheux, Jean-Paul, Shan Sei Fan, Charles, Jauffret, Charlotte, Claeys, Patrick, von Baeyer, Sarah LeBaron, Castel, Stephane E, Emde, Anne-Katrin, Yerges-Armstrong, Laura, Fox, Keolu, Leask, Megan, Vitagliano, Jean-Jacques, Graf, Sahara, Norberciak, Laurène, Raynal, Jacques, Dalbeth, Nicola, Merriman, Tony, Bardin, Thomas, Oehler, Erwan·2024·The Lancet Global Health

Abstract

Background Gout is the most common cause of inflammatory arthritis worldwide, particularly in Pacific regions. We aimed to establish the prevalence of gout and hyperuricaemia in French Polynesia, their associations with dietary habits, their comorbidities, the prevalence of the HLA-B*58:01 allele, and current management of the disease. Methods The Ma'i u'u survey was epidemiological, prospective, cross-sectional, and gout-focused and included a random sample of adults from the general adult population of French Polynesia. It was conducted and data were collected between April 13 and Aug 16, 2021. Participants were randomly selected to represent the general adult population of French Polynesia on the basis of housing data collected during the 2017 territorial census. Each selected household was visited by a research nurse from the Ma'i u'u survey who collected data via guided, 1-h interviews with participants. In each household, the participant was the individual older than 18 years with the closest upcoming birthday. To estimate the frequency of HLA-B*58:01 , we estimated HLA-B haplotypes on individuals who had whole-genome sequencing to approximately 5× average coverage (mid-pass sequencing). A subset of individuals who self-reported Polynesian ancestry and not European, Chinese, or other ancestry were used to estimate Polynesian-ancestry specific allele frequencies. Bivariate associations were reported for weighted participants; effect sizes were estimated through the odds ratio (OR) of the association calculated on the basis of a logistic model fitted with weighted observations. Findings Among the random sample of 2000 households, 896 participants were included, 140 individuals declined, and 964 households could not be contacted. 22 participants could not be weighted due to missing data, so the final weighted analysis included 874 participants (449 [51·4%] were female and 425 [48·6%] were male) representing the 196 630 adults living in French Polynesia. The estimated prevalence of gout was 14·5% (95% CI 9·9–19·2), representing 28 561 French Polynesian adults, that is 25·5% (18·2–32·8) of male individuals and 3·5% (1·0–6·0) of female individuals. The prevalence of hyperuricaemia was estimated at 71·6% (66·7–76·6), representing 128 687 French Polynesian adults. In multivariable analysis, age (OR 1·5, 95% CI 1·2–1·8 per year), male sex (10·3, 1·8–60·7), serum urate (1·6, 1·3–2·0 per 1 mg/dL), uraturia (0·8, 0·8–0·8 per 100 mg/L), type 2 diabetes (2·1, 1·4–3·1), BMI more than 30 kg/m 2 (1·1, 1·0–1·2 per unit), and percentage of visceral fat (1·7, 1·1–2·7 per 1% increase) were associated with gout. There were seven heterozygous HLA-B*58:01 carriers in the full cohort of 833 individuals (seven [0·4%] of 1666 total alleles) and two heterozygous carriers in a subset of 696 individuals of Polynesian ancestry (two [0·1%]). Interpretation French Polynesia has an estimated high prevalence of gout and hyperuricaemia, with gout affecting almost 15% of adults. Territorial measures that focus on increasing access to effective urate-lowering therapies are warranted to control this major public health problem. Funding Variant Bio, the French Polynesian Health Administration, Lille Catholic University Hospitals, French Society of Rheumatology, and Novartis.

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Figure 1 Age pyramid showing the prevalence of gout according to the classification algorithm and hyperuricaemia (serum urate level >6·0 mg/dL or receiving urate-lowering therapy) in male and female adult age groups in the weighted population
Figure 1 Age pyramid showing the prevalence of gout according to the classification algorithm and hyperuricaemia (serum urate level >6·0 mg/dL or receiving urate-lowering therapy) in male and female adult age groups in the weighted populationCC-BY-NC-ND · From the PDF
Figure 2 HLA-B allele frequencies in individuals with Polynesian ancestry in the unweighted population (A) Prevalence of HLA-B alleles at >1% population allele frequency and HLA-B*58:01 for the full cohort, individuals who did not self-report any European or Chinese ancestry, and individuals with both WGS and RNA-seq showing both WGS-based and RNA-seq-based calls. (B) Forest plot showing odds ratios and p values from a logistic regression model of having at least one copy of the HLA-B*58:01 allele with two binary variables corresponding to self-reported Chinese or French Polynesian ancestry compared with individuals who did not self-report Chinese or French Polynesian ancestry. HLA-B*58:01 allele frequency is shown in individuals self-reporting each ancestry. Participants were able to self-report more than one ancestry. WGS=whole-genome sequencing.
Figure 2 HLA-B allele frequencies in individuals with Polynesian ancestry in the unweighted population (A) Prevalence of HLA-B alleles at >1% population allele frequency and HLA-B*58:01 for the full cohort, individuals who did not self-report any European or Chinese ancestry, and individuals with both WGS and RNA-seq showing both WGS-based and RNA-seq-based calls. (B) Forest plot showing odds ratios and p values from a logistic regression model of having at least one copy of the HLA-B*58:01 allele with two binary variables corresponding to self-reported Chinese or French Polynesian ancestry compared with individuals who did not self-report Chinese or French Polynesian ancestry. HLA-B*58:01 allele frequency is shown in individuals self-reporting each ancestry. Participants were able to self-report more than one ancestry. WGS=whole-genome sequencing.CC-BY-NC-ND · From the PDF

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  1. Bertrand, 2013, Health survey 2010 in French Polynesia: surveillance of risk factors for non-communicable diseases, Bull Epidemiol Hebd (Paris), 28–29, 326

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Metadata

Authors
Pascart, T., Wasik, K. A., Preda, C., Chune, V., Torterat, J., Prud’homme, N., Nassih, M., Martin, A., Le Masson, J., Rodière, V., Frogier, S., Canova, G., Pescheux, J.-P., Shan Sei Fan, C., Jauffret, C., Claeys, P., von Baeyer, S. L., Castel, S. E., Emde, A.-K., Yerges-Armstrong, L., Fox, K., Leask, M., Vitagliano, J.-J., Graf, S., Norberciak, L., Raynal, J., Dalbeth, N., Merriman, T., Bardin, T., & Oehler, E.
Venue
The Lancet Global Health
Publisher
Elsevier BV
Published
2024-04
Volume
12
Issue
4
Pages
e685-e696
ISSN
2214-109X
Cited by
39
References
56
Language
en
Update notices
erratum (2024-06-01)
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CoChat. (2026). Citation check: The gout epidemic in French Polynesia: a modelling study of data from the Ma’i u’u epidemiological survey. https://app.cochat.ai/papers/10.1016/s2214-109x(24)00012-3
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Pascart, T., Wasik, K. A., Preda, C., Chune, V., Torterat, J., Prud’homme, N., Nassih, M., Martin, A., Le Masson, J., Rodière, V., Frogier, S., Canova, G., Pescheux, J.-P., Shan Sei Fan, C., Jauffret, C., Claeys, P., von Baeyer, S. L., Castel, S. E., Emde, A.-K., . . . Oehler, E. (2024). The gout epidemic in French Polynesia: a modelling study of data from the Ma’i u’u epidemiological survey. The Lancet Global Health. https://doi.org/10.1016/s2214-109x(24)00012-3

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