The absolute risk of gout by clusters of gout-associated comorbidities and lifestyle factors—30 years follow-up of the Malmö Preventive Project
(2020) In Arthritis Research and Therapy 22(1).- Abstract
Background: Gout is predicted by a number of comorbidities and lifestyle factors. We aimed to identify discrete phenotype clusters of these factors in a Swedish population-based health survey. In these clusters, we calculated and compared the incidence and relative risk of gout. Methods: Cluster analyses were performed to group variables with close proximity and to obtain homogenous clusters of individuals (n = 22,057) in the Malmö Preventive Project (MPP) cohort. Variables clustered included obesity, kidney dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking, and the use of diuretics. Incidence rates and hazard ratios (HRs) for gout, adjusted for age and... (More)
Background: Gout is predicted by a number of comorbidities and lifestyle factors. We aimed to identify discrete phenotype clusters of these factors in a Swedish population-based health survey. In these clusters, we calculated and compared the incidence and relative risk of gout. Methods: Cluster analyses were performed to group variables with close proximity and to obtain homogenous clusters of individuals (n = 22,057) in the Malmö Preventive Project (MPP) cohort. Variables clustered included obesity, kidney dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking, and the use of diuretics. Incidence rates and hazard ratios (HRs) for gout, adjusted for age and sex, were computed for each cluster. Results: Five clusters (C1–C5) were identified. Cluster C1 (n = 16,063) was characterized by few comorbidities. All participants in C2 (n = 750) had kidney dysfunction (100%), and none had CVD. In C3 (n = 528), 100% had CVD and most participants were smokers (74%). C4 (n = 3673) had the greatest fractions of obesity (34%) and dyslipidemia (74%). In C5 (n = 1043), proportions with DM (51%), hypertension (54%), and diuretics (52%) were highest. C1 was by far the most common in the population (73%), followed by C4 (17%). These two pathways included 86% of incident gout cases. The four smaller clusters (C2–C5) had higher incidence rates and a 2- to 3-fold increased risk for incident gout. Conclusions: Five distinct clusters based on gout-related comorbidities and lifestyle factors were identified. Most incident gout cases occurred in the cluster of few comorbidities, and the four comorbidity pathways had overall a modest influence on the incidence of gout.
(Less)
- author
- Fatima, Tahzeeb LU ; Nilsson, Peter M. LU ; Turesson, Carl LU ; Dehlin, Mats ; Dalbeth, Nicola ; Jacobsson, Lennart T.H. LU and Kapetanovic, Meliha C. LU
- organization
- publishing date
- 2020-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Clusters, Comorbidities, Epidemiology, Gout, Risk, Urate
- in
- Arthritis Research and Therapy
- volume
- 22
- issue
- 1
- article number
- 244
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:85092785116
- pmid:33066806
- ISSN
- 1478-6354
- DOI
- 10.1186/s13075-020-02339-0
- language
- English
- LU publication?
- yes
- id
- 05234969-b266-4e1b-ae28-de642d4d4f1b
- date added to LUP
- 2020-11-04 11:48:38
- date last changed
- 2024-07-11 00:47:00
@article{05234969-b266-4e1b-ae28-de642d4d4f1b,
abstract = {{<p>Background: Gout is predicted by a number of comorbidities and lifestyle factors. We aimed to identify discrete phenotype clusters of these factors in a Swedish population-based health survey. In these clusters, we calculated and compared the incidence and relative risk of gout. Methods: Cluster analyses were performed to group variables with close proximity and to obtain homogenous clusters of individuals (n = 22,057) in the Malmö Preventive Project (MPP) cohort. Variables clustered included obesity, kidney dysfunction, diabetes mellitus (DM), hypertension, cardiovascular disease (CVD), dyslipidemia, pulmonary dysfunction (PD), smoking, and the use of diuretics. Incidence rates and hazard ratios (HRs) for gout, adjusted for age and sex, were computed for each cluster. Results: Five clusters (C1–C5) were identified. Cluster C1 (n = 16,063) was characterized by few comorbidities. All participants in C2 (n = 750) had kidney dysfunction (100%), and none had CVD. In C3 (n = 528), 100% had CVD and most participants were smokers (74%). C4 (n = 3673) had the greatest fractions of obesity (34%) and dyslipidemia (74%). In C5 (n = 1043), proportions with DM (51%), hypertension (54%), and diuretics (52%) were highest. C1 was by far the most common in the population (73%), followed by C4 (17%). These two pathways included 86% of incident gout cases. The four smaller clusters (C2–C5) had higher incidence rates and a 2- to 3-fold increased risk for incident gout. Conclusions: Five distinct clusters based on gout-related comorbidities and lifestyle factors were identified. Most incident gout cases occurred in the cluster of few comorbidities, and the four comorbidity pathways had overall a modest influence on the incidence of gout.</p>}},
author = {{Fatima, Tahzeeb and Nilsson, Peter M. and Turesson, Carl and Dehlin, Mats and Dalbeth, Nicola and Jacobsson, Lennart T.H. and Kapetanovic, Meliha C.}},
issn = {{1478-6354}},
keywords = {{Clusters; Comorbidities; Epidemiology; Gout; Risk; Urate}},
language = {{eng}},
month = {{12}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{Arthritis Research and Therapy}},
title = {{The absolute risk of gout by clusters of gout-associated comorbidities and lifestyle factors—30 years follow-up of the Malmö Preventive Project}},
url = {{http://dx.doi.org/10.1186/s13075-020-02339-0}},
doi = {{10.1186/s13075-020-02339-0}},
volume = {{22}},
year = {{2020}},
}