Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Nov 11;11(11):2733.
doi: 10.3390/nu11112733.

Dysregulated Iron Metabolism-Associated Dietary Pattern Predicts an Altered Body Composition and Metabolic Syndrome

Affiliations

Dysregulated Iron Metabolism-Associated Dietary Pattern Predicts an Altered Body Composition and Metabolic Syndrome

Anggun Rindang Cempaka et al. Nutrients. .

Abstract

Diet plays an important role in the development of obesity and may contribute to dysregulated iron metabolism (DIM). A cross-sectional survey of 208 adults was conducted in Taipei Medical University Hospital (Taipei, Taiwan). A reduced-rank regression from 31 food groups was used for a dietary pattern analysis. DIM was defined as at least four of the following criteria: serum hepcidin (men >200 ng/mL and women >140 ng/mL), hyperferritinemia (serum ferritin of >300 ng/mL in men and >200 ng/mL in women), central obesity, non-alcoholic fatty liver disease, and two or more abnormal metabolic profiles. Compared to non-DIM patients, DIM patients were associated with an altered body composition and had a 4.52-fold (95% confidence interval (CI): (1.95-10.49); p < 0.001) greater risk of metabolic syndrome (MetS) after adjusting for covariates. A DIM-associated dietary pattern (high intake of deep-fried food, processed meats, chicken, pork, eating out, coffee, and animal fat/skin but low intake of steamed/boiled/raw foods and dairy products) independently predicted central obesity (odds ratio (OR): 1.57; 95% CI: 1.05-2.34; p < 0.05) and MetS (OR: 1.89; 95% CI: 1.07-3.35; p < 0.05). Individuals with the highest DIM pattern scores (tertile 3) had a higher visceral fat mass (%) (β = 0.232; 95% CI: 0.011-0.453; p < 0.05) but lower skeletal muscle mass (%) (β = -1.208; 95% CI: -2.177--0.239; p < 0.05) compared to those with the lowest DIM pattern scores (tertile 1). In conclusion, a high score for the identified DIM-associated dietary pattern was associated with an unhealthier body composition and a higher risk of MetS.

Keywords: central obesity; dietary pattern; dysregulated iron metabolism; ferritin; hepcidin; metabolic syndrome; skeletal muscle mass; visceral fat.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Direct acyclic graph of the reduced-rank regression (RRR) conceptual framework. ALT, alanine aminotransferase; HDL-C, high-density lipoprotein cholesterol.
Figure 2
Figure 2
Adjusted multivariate linear and logistic regression and 95% confidence intervals of dysregulated iron metabolism (DIM) in terms of visceral fat mass (%) and skeletal muscle mass (%) (A) and metabolic syndrome (B). (A) the Beta coefficient (β-value) was adjusted by age and sex, and (B) odds ratio (OR) was adjusted by age, sex, and body-mass index (BMI) (N = 208). *** p ≤ 0.001.
Figure 3
Figure 3
Multivariate logistic analysis adjusted for age, gender, and body-mass index and linear regression and 95% confidence intervals (CIs) of dysregulated iron metabolism (DIM)-associated dietary pattern scores for predicting DIM (A), metabolic syndrome (MetS) (A), central obesity (A), visceral fat mass (%) (B), and skeletal muscle mass (%) (C). * p < 0.05; ** p < 0.01; *** p ≤ 0.001.

References

    1. Chang H.C., Yang H.C., Chang H.Y., Yeh C.J., Chen H.H., Huang K.C., Pan W.H. Morbid obesity in Taiwan: Prevalence, trends, associated social demographics, and lifestyle factors. PLoS ONE. 2017;12:e0169577. doi: 10.1371/journal.pone.0169577. - DOI - PMC - PubMed
    1. Zhao L., Zhang X., Shen Y., Fang X., Wang Y., Wang F. Obesity and iron deficiency: a quantitative meta-analysis. Obes. Rev. 2015;16:1081–1093. doi: 10.1111/obr.12323. - DOI - PubMed
    1. Chang J.S., Lin S.M., Huang T.C., Chao J.C., Chen Y.C., Pan W.H., Bai C.H. Serum ferritin and risk of the metabolic syndrome: a population-based study. Asia. Pac. J. Clin. Nutr. 2013;22:400–407. doi: 10.6133/apjcn.2013.22.3.07. - DOI - PubMed
    1. Recalcati S., Gammella E., Cairo G. Dysregulation of iron metabolism in cancer stem cells. Free Radic. Biol. Med. 2019;133:216–220. doi: 10.1016/j.freeradbiomed.2018.07.015. - DOI - PubMed
    1. Ginzburg Y.Z., Feola M., Zimran E., Varkonyi J., Ganz T., Hoffman R. Dysregulated iron metabolism in polycythemia vera: etiology and consequences. Leukemia. 2018;32:2105–2116. doi: 10.1038/s41375-018-0207-9. - DOI - PMC - PubMed