Effect of dietary inflammatory potential on the aging acceleration for cardiometabolic disease: A population-based study

Abstract

Background/aim: Optimized dietary patterns have been considered an important determinant of delaying aging in cardiometabolic disease (CMD). Dietary pattern with high-level dietary inflammatory potential is a key risk factor for cardiometabolic disease, and has drawn increasing attention. The aim of this study was to investigate whether dietary pattern with high dietary inflammatory potential was associated with aging acceleration in cardiometabolic disease.

Materials and methods: We analyzed the cross-sectional data from six survey cycles (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) of the National Health and Nutritional Examination Surveys (NHANES). A total of 16,681 non-institutionalized adults and non-pregnant females with CMD were included in this study. Dietary inflammatory index (DII) was used to assess the dietary inflammatory potential. The two age acceleration biomarkers were calculated by the residuals from regressing chronologic age on Klemera-Doubal method biological age (KDM BioAge) or Phenotypic Age (PhenoAge), termed "KDMAccel" and "PhenoAgeAccel." A multivariable linear regression accounting for multistage survey design and sampling weights was used in different models to investigate the association between DII and aging acceleration. Four sensitivity analyses were used to ensure the robustness of our results. Besides, we also analyzed the anti-aging effects of DASH-type dietary pattern and "Life's Simple 7".

Results: For 16,681 participants with CMD, compared with the first tertile of DII after adjusting for all potential confounders, the patients with second tertile of DII showed a 1.02-years increase in KDMAccel and 0.63-years increase in PhenoAgeAccel (KDMAccel, β = 1.02, 95% CI = 0.64 to 1.41, P < 0.001; PhenoAgeAccel, β = 0.63, 95% CI = 0.44 to 0.82, P < 0.001), while the patients with the third tertile of DII showed a 1.48-years increase in KDMAccel and 1.22-years increase in PhenoAgeAccel (KDMAccel, β = 1.48, 95% CI = 1.02 to 1.94, P < 0.001; PhenoAgeAccel, β = 1.22, 95% CI = 1.01 to 1.43, P < 0.001). In addition, DASH-type dietary pattern was associated with a 0.57-years reduction in KDMAccel (β = -0.57, 95% CI = -1.08 to -0.06, P = 0.031) and a 0.54-years reduction in PhenoAgeAccel (β = -0.54, 95% CI = -0.80 to -0.28, P < 0.001). The each one-unit increase in CVH score was associated with a 1.58-years decrease in KDMAccel (β = -1.58, 95% CI = -1.68 to -1.49, P < 0.001) and a 0.36-years in PhenoAgeAccel (β = -0.36, 95% CI = -0.41 to -0.31, P < 0.001).

Conclusion: Among CMD, the dietary pattern with high dietary inflammatory potential was association with aging acceleration, and the anti-aging potential of DASH-type dietary pattern and "Life's Simple 7" should also be given attention, but these observations require future prospective validation.

Keywords: aging acceleration; biological aging; cardiometabolic disease; dietary inflammatory index; dietary inflammatory potential.

FIGURE 1

Flowchart of conforms the analytic sample.

FIGURE 2

Forest plot for the effect of dietary inflammatory index (DII) on aging acceleration. Model 1 adjusted for age, sex, ethnicity, and household income; Model 2 adjusted for sex, BMI, and total energy intake; Model 3 adjusted age, sex, ethnicity, household income, smoking status, BMI, total energy intake, CCI, CVD, hypertension, dysglycemia, MetS, hyperlipidemia, asthma, CKD, anti-hypertensive drug use, and anti-hyperlipidemic drug use. P-value: ***, < 0.001.

FIGURE 3

Fitting curves for restricted cubic splines models. (A) Effect of dietary inflammatory index on KDMAccel. (B) Effect of dietary inflammatory index on PhenoAgeAccel.