Serum Nutritional Biomarkers and All-Cause and Cause-Specific Mortality in U.S. Adults with Metabolic Syndrome: The Results from National Health and Nutrition Examination Survey 2001-2006

Abstract

Background: There is limited research on the associations between serum nutritional biomarkers and mortality risk in patients with metabolic syndrome (MetS). Existing studies merely investigated the single-biomarker effect. Thus, this study aimed to investigate the combined effect of nutritional biomarker mixtures and mortality risk using the Bayesian kernel machine regression (BKMR) model in patients with MetS.

Methods: We included the MetS patients, defined according to the 2018 Guideline on the Management of Blood Cholesterol from the National Health and Nutrition Examination Survey (NHANES) 2001-2006. A total of 20 serum nutritional biomarkers were measured and evaluated in this study. The Cox proportional hazard model and restricted cubic spline models were used to evaluate the individual linear and non-linear association of 20 nutritional biomarkers with mortality risk. Bayesian kernel machine regression (BKMR) was used to assess the associations between mixture of nutritional biomarkers and mortality risk.

Results: A total of 1455 MetS patients had a median age of 50 years (range: 20-85). During a median of 17.1-year follow-up, 453 (24.72%) died: 146 (7.20%) caused by CVD and 87 (5.26%) by cancer. Non-linear and linear analyses indicated that, in total, eight individual biomarkers (α-carotene, β-carotene, bicarbonate, lutein/zeaxanthin, lycopene, potassium, protein, and vitamin A) were significantly associated with all-cause mortality (all p-values < 0.05). Results from BKMR showed an association between the low levels of the mixture of nutritional biomarkers and high risk of all-cause mortality with the estimated effects ranging from 0.04 to 0.14 (referent: medians). α-Carotene (PIP = 0.971) and potassium (PIP = 0.796) were the primary contributors to the combined effect of the biomarker mixture. The nutritional mixture levels were found to be negatively associated with the risk of cardiovascular disease (CVD) mortality and positively associated with the risk of cancer mortality. After it was stratified by nutrients, the mixture of vitamins showed a negative association with all-cause and CVD mortality, whereas the mixture of mineral-related biomarkers was positively associated with all-cause and cancer mortality.

Conclusion: Our findings support the evidence that nutritional status was associated with long-term health outcomes in MetS patients. It is necessary for MetS patients to be concerned with certain nutritional status (i.e., vitamins and mineral elements).

Keywords: Bayesian analysis; NHANES; biomarkers; carotenoid; metabolic syndrome; mortality; potassium.

Figure 1

The estimated associations of all biomarker mixtures with all-cause, cancer, and CVD mortality risk with 95% confidence intervals. The X−axis shows percentiles of biomarkers mixtures. The Y−axis shows the estimated change in risk of all−cause mortality. The lines of the plot show the overall effects of the mixture (estimates and 95% CIs) in mortality risk when biomarkers are all at a particular percentile compared to when biomarker are s set at the 50th percentile.

Figure 2

Single−biomarker health effects (95% CI), defined as the change in the response associated with a change in a particular biomarker from its 25th to its 75th percentile, where all of the other biomarkers are fixed at a specific quartile. Red, green, and blue represent the 25th percentile, 50th percentile, and 75th percentile, respectively.

Figure 3

The estimated associations of (A) vitamin biomarkers mixture, (B) trace metal biomarker mixtures, and (C) other biomarker mixtures with all−cause and cause−specific mortality risk with 95% confidence intervals. The X−axis shows percentiles of biomarkers mixtures. The Y−axis shows the estimated change in risk of all−cause mortality. The lines of the plot show the overall effects of the mixture (estimates and 95% CIs) in mortality risk when biomarkers are all at a particular percentile compared to when biomarkers are set at the 50th percentile.