Frontiers in Nutrition (Jan 2022)

Association Between Dietary Patterns and Different Metabolic Phenotypes in Japanese Adults: WASEDA'S Health Study

  • Kumpei Tanisawa,
  • Tomoko Ito,
  • Tomoko Ito,
  • Ryoko Kawakami,
  • Chiyoko Usui,
  • Takuji Kawamura,
  • Takuji Kawamura,
  • Katsuhiko Suzuki,
  • Shizuo Sakamoto,
  • Kaori Ishii,
  • Isao Muraoka,
  • Koichiro Oka,
  • Mitsuru Higuchi

DOI
https://doi.org/10.3389/fnut.2022.779967
Journal volume & issue
Vol. 9

Abstract

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Although many studies have reported that a posteriori dietary pattern is associated with metabolic health, there is little evidence of an association between dietary patterns and different metabolic phenotypes. The present study aimed to examine the association between major dietary patterns and different metabolic phenotypes (metabolically healthy non-obese [MHNO], metabolically unhealthy non-obese [MUNO], metabolically healthy obese [MHO], and metabolically unhealthy obese [MUO]) in middle-aged and elderly Japanese adults. This cross-sectional study enrolled 2,170 Japanese adults aged ≥40 years. The four different metabolic phenotypes were determined based on the presence of obesity, abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. The major dietary patterns were determined using principal component analysis based on energy-adjusted food intake. Two dietary patterns were identified: the healthy dietary pattern, which was characterized by a high intake of vegetables, fruits, potatoes, soy products, mushrooms, seaweeds, and fish; and the alcohol dietary pattern, which was characterized by a high intake of alcoholic beverages, liver, chicken, and fish. The healthy dietary pattern was associated with the MHNO and MHO phenotypes (MUNO and MUO as reference groups, respectively), and the multivariate-adjusted odds ratios (ORs) (95% confidence intervals [CIs]) in the highest quartile of healthy dietary pattern score with the lowest quartile as the reference category were 2.10 (1.40–3.15) and 1.86 (1.06–3.25), respectively. Conversely, the alcohol dietary pattern was inversely associated with the MHNO and MHO phenotypes, while the multivariate-adjusted ORs (95% CIs) in the highest quartile of the alcohol dietary pattern score with the lowest quartile as the reference category were 0.63 (0.42–0.94) and 0.45 (0.26–0.76), respectively. There were no significant interactions between sex and healthy/alcohol dietary patterns in the prevalence of the MHNO and MHO phenotypes. In conclusion, the present study's findings suggest that major dietary patterns are associated with different metabolic phenotypes in middle-aged and elderly Japanese adults. These findings provide useful evidence for maintaining metabolic health through diet regardless of obesity status.

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