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Clusters of carbohydrate-rich foods and associations with type 2 diabetes incidence : a prospective cohort study

Olsson, Kjell LU orcid ; González-Padilla, Esther LU ; Janzi, Suzanne LU ; Stubbendorff, Anna LU orcid ; Borné, Yan LU ; Ramne, Stina LU orcid ; Ericson, Ulrika LU and Sonestedt, Emily LU orcid (2023) In Nutrition Journal 22(1).
Abstract

Background: About one in ten adults are living with diabetes worldwide. Intake of carbohydrates and carbohydrate-rich foods are often identified as modifiable risk factors for incident type 2 diabetes. However, strong correlation between food variables can make it difficult to identify true associations. The purpose of this study was to identify clusters of carbohydrate-rich foods and analyse their associations with type 2 diabetes incidence in the Malmö Diet and Cancer Study cohort in southern Sweden. Methods: Dietary intake of 26 622 participants was assessed using a validated three-part diet history method: a 7-day food diary, a 168-item food frequency questionnaire, and a 60-minute interview. K-means clustering analysis identified... (More)

Background: About one in ten adults are living with diabetes worldwide. Intake of carbohydrates and carbohydrate-rich foods are often identified as modifiable risk factors for incident type 2 diabetes. However, strong correlation between food variables can make it difficult to identify true associations. The purpose of this study was to identify clusters of carbohydrate-rich foods and analyse their associations with type 2 diabetes incidence in the Malmö Diet and Cancer Study cohort in southern Sweden. Methods: Dietary intake of 26 622 participants was assessed using a validated three-part diet history method: a 7-day food diary, a 168-item food frequency questionnaire, and a 60-minute interview. K-means clustering analysis identified five clusters from 21 food variables. The Cox proportional hazard regression model was applied to calculate hazard ratios (HR) and 95% confidence intervals (CI) of the association between clusters and incident type 2 diabetes. Results: The cluster analysis resulted in five clusters; high vegetables/low added sugar, high sugar-sweetened beverages, high juice, high fruit, and high refined carbohydrates/low fruit & vegetables (reference). During mean follow-up of 18 years, 4046 type 2 diabetes cases were identified. After adjustment for potential confounding (including lifestyle, body mass index, and diet), a high fruit cluster (HR 0.86; 95% CI 0.78, 0.94) was inversely associated with type 2 diabetes compared to the reference cluster. No other significant associations were identified. Conclusions: A dietary pattern defined by a high intake of fruits was associated with a lower incidence of type 2 diabetes. The findings provide additional evidence of a potential protective effect from fruit intake in reducing type 2 diabetes risk. Future studies are needed to explore this association further.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Diet, Epidemiology, K-means clustering, Malmö Diet and Cancer Study, Nutrition, Type 2 Diabetes
in
Nutrition Journal
volume
22
issue
1
article number
71
publisher
BioMed Central (BMC)
external identifiers
  • scopus:85179929988
  • pmid:38111004
ISSN
1475-2891
DOI
10.1186/s12937-023-00906-0
language
English
LU publication?
yes
id
78d6c496-306a-4f18-b8dc-90593828b085
date added to LUP
2024-01-03 15:16:36
date last changed
2024-09-20 03:01:09
@article{78d6c496-306a-4f18-b8dc-90593828b085,
  abstract     = {{<p>Background: About one in ten adults are living with diabetes worldwide. Intake of carbohydrates and carbohydrate-rich foods are often identified as modifiable risk factors for incident type 2 diabetes. However, strong correlation between food variables can make it difficult to identify true associations. The purpose of this study was to identify clusters of carbohydrate-rich foods and analyse their associations with type 2 diabetes incidence in the Malmö Diet and Cancer Study cohort in southern Sweden. Methods: Dietary intake of 26 622 participants was assessed using a validated three-part diet history method: a 7-day food diary, a 168-item food frequency questionnaire, and a 60-minute interview. K-means clustering analysis identified five clusters from 21 food variables. The Cox proportional hazard regression model was applied to calculate hazard ratios (HR) and 95% confidence intervals (CI) of the association between clusters and incident type 2 diabetes. Results: The cluster analysis resulted in five clusters; high vegetables/low added sugar, high sugar-sweetened beverages, high juice, high fruit, and high refined carbohydrates/low fruit &amp; vegetables (reference). During mean follow-up of 18 years, 4046 type 2 diabetes cases were identified. After adjustment for potential confounding (including lifestyle, body mass index, and diet), a high fruit cluster (HR 0.86; 95% CI 0.78, 0.94) was inversely associated with type 2 diabetes compared to the reference cluster. No other significant associations were identified. Conclusions: A dietary pattern defined by a high intake of fruits was associated with a lower incidence of type 2 diabetes. The findings provide additional evidence of a potential protective effect from fruit intake in reducing type 2 diabetes risk. Future studies are needed to explore this association further.</p>}},
  author       = {{Olsson, Kjell and González-Padilla, Esther and Janzi, Suzanne and Stubbendorff, Anna and Borné, Yan and Ramne, Stina and Ericson, Ulrika and Sonestedt, Emily}},
  issn         = {{1475-2891}},
  keywords     = {{Diet; Epidemiology; K-means clustering; Malmö Diet and Cancer Study; Nutrition; Type 2 Diabetes}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Nutrition Journal}},
  title        = {{Clusters of carbohydrate-rich foods and associations with type 2 diabetes incidence : a prospective cohort study}},
  url          = {{http://dx.doi.org/10.1186/s12937-023-00906-0}},
  doi          = {{10.1186/s12937-023-00906-0}},
  volume       = {{22}},
  year         = {{2023}},
}