Sex and Age Differences in the Effects of Food Frequency on Metabolic Parameters in Japanese Adults Article Swipe
Katsumi Iizuka
,
Kotone Yanagi
,
Kanako Deguchi
,
Chihiro Ushiroda
,
Risako Yamamoto-Wada
,
Kazuko Kobae
,
Yoshiko Yamada
,
Hiroyuki Naruse
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/nu16172931
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/nu16172931
Owing to differences in dietary preferences between men and women, the associations between dietary intake frequency and metabolic parameters may differ between the sexes. A retrospective observational study of the checkup findings of 3147 Japanese individuals (968 men, 2179 women) aged 20–59 years was conducted to examine differences in dietary habits and associations between food frequency and blood parameters (eGFR, HbA1c, uric acid, and lipids) by sex and age. Males were more likely to consume meat, fish, soft drinks, and alcohol, whereas women were more likely to consume soybeans, dairy products, vegetables, fruits, and snacks. Multivariate linear regression models adjusted for age and BMI revealed that meat intake frequency was positively associated with HbA1c (β = 0.007, p = 0.03) and negatively associated with eGFR (β = −0.3, p = 0.01) only in males, whereas fish intake frequency was positively associated with eGFR (β = 0.4, p = 0.005) only in females. Egg and soy intake frequencies were positively and negatively associated with non-HDL-C (egg: β = 0.6, p = 0.02; soy: β = −0.3, p = 0.03) only in females. Alcohol consumption frequency was associated with uric acid (M: β = 0.06, p < 0.001; F: β = 0.06, p < 0.001) and HDL-C (M: β = 1.0, p < 0.001; F: β = 1.3, p < 0.001) in both sexes. Future research is needed to determine whether varying the emphasis of dietary guidance by sex and age group is effective, since the effects of dietary preferences on metabolic parameters vary by age and sex.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/nu16172931
- OA Status
- gold
- Cited By
- 9
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402134004
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4402134004Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/nu16172931Digital Object Identifier
- Title
-
Sex and Age Differences in the Effects of Food Frequency on Metabolic Parameters in Japanese AdultsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-02Full publication date if available
- Authors
-
Katsumi Iizuka, Kotone Yanagi, Kanako Deguchi, Chihiro Ushiroda, Risako Yamamoto-Wada, Kazuko Kobae, Yoshiko Yamada, Hiroyuki NaruseList of authors in order
- Landing page
-
https://doi.org/10.3390/nu16172931Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/nu16172931Direct OA link when available
- Concepts
-
Uric acid, Medicine, Food frequency questionnaire, Refined grains, Fish
, Obesity, Food group, Red meat, Alcohol intake, Biology, Alcohol, Food science, Endocrinology, Internal medicine, Environmental health, Whole grains, Biochemistry, Fishery Top concepts (fields/topics) attached by OpenAlex - Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.intake | 14, 107, 136, 155 |
| abstract_inverted_index.likely | 72, 85 |
| abstract_inverted_index.linear | 96 |
| abstract_inverted_index.males, | 133 |
| abstract_inverted_index.models | 98 |
| abstract_inverted_index.needed | 225 |
| abstract_inverted_index.sexes. | 23, 221 |
| abstract_inverted_index.women) | 39 |
| abstract_inverted_index.women, | 9 |
| abstract_inverted_index.20–59 | 41 |
| abstract_inverted_index.Alcohol | 181 |
| abstract_inverted_index.between | 6, 12, 21, 53 |
| abstract_inverted_index.checkup | 30 |
| abstract_inverted_index.consume | 74, 87 |
| abstract_inverted_index.dietary | 4, 13, 49, 233, 246 |
| abstract_inverted_index.drinks, | 78 |
| abstract_inverted_index.effects | 244 |
| abstract_inverted_index.examine | 46 |
| abstract_inverted_index.fruits, | 92 |
| abstract_inverted_index.lipids) | 64 |
| abstract_inverted_index.snacks. | 94 |
| abstract_inverted_index.varying | 229 |
| abstract_inverted_index.whereas | 81, 134 |
| abstract_inverted_index.whether | 228 |
| abstract_inverted_index.−0.3, | 127, 174 |
| abstract_inverted_index.Japanese | 34 |
| abstract_inverted_index.adjusted | 99 |
| abstract_inverted_index.alcohol, | 80 |
| abstract_inverted_index.emphasis | 231 |
| abstract_inverted_index.females. | 151, 180 |
| abstract_inverted_index.findings | 31 |
| abstract_inverted_index.guidance | 234 |
| abstract_inverted_index.research | 223 |
| abstract_inverted_index.revealed | 104 |
| abstract_inverted_index.conducted | 44 |
| abstract_inverted_index.determine | 227 |
| abstract_inverted_index.frequency | 15, 55, 108, 137, 183 |
| abstract_inverted_index.metabolic | 17, 249 |
| abstract_inverted_index.non-HDL-C | 163 |
| abstract_inverted_index.products, | 90 |
| abstract_inverted_index.soybeans, | 88 |
| abstract_inverted_index.associated | 111, 122, 140, 161, 185 |
| abstract_inverted_index.effective, | 241 |
| abstract_inverted_index.negatively | 121, 160 |
| abstract_inverted_index.parameters | 18, 58, 250 |
| abstract_inverted_index.positively | 110, 139, 158 |
| abstract_inverted_index.regression | 97 |
| abstract_inverted_index.consumption | 182 |
| abstract_inverted_index.differences | 2, 47 |
| abstract_inverted_index.frequencies | 156 |
| abstract_inverted_index.individuals | 35 |
| abstract_inverted_index.preferences | 5, 247 |
| abstract_inverted_index.vegetables, | 91 |
| abstract_inverted_index.Multivariate | 95 |
| abstract_inverted_index.associations | 11, 52 |
| abstract_inverted_index.observational | 26 |
| abstract_inverted_index.retrospective | 25 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5046846190 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I145673806, https://openalex.org/I4210124875 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.96990635 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |