Inverse Association of Plasma Molybdenum with Metabolic Syndrome in a Chinese Adult Population: A Case-Control Study

Abstract

Molybdenum has been found to be associated with metabolic disorders. However, the relationship between molybdenum and metabolic syndrome (MetS) is still unclear. A large case-control study was conducted in a Chinese population from the baseline of Ezhou-Shenzhen cohort. A total of 5356 subjects were included with 2678 MetS and 2678 controls matched by sex and age (±2 years). Medians (IQRs) of plasma molybdenum concentrations were 1.24 μg/L for MetS cases and 1.46 μg/L for controls. After adjustment for multiple covariates, the odds ratio (OR) and 95% confidence intervals (CIs) for MetS were 1.00 (reference), 0.71 (0.59-0.84), 0.56 (0.46-0.68), and 0.47 (0.39-0.58) across quartiles of plasma molybdenum, and per SD increment of log-transformed molybdenum was associated with a 23% lower risk of MetS. In the spline analysis, the risk of MetS and its components decreased steeply with increasing molybdenum and followed by a plateau when the cutoff point was observed around 2.0 μg/L. The dose-dependent relationship of molybdenum with MetS remained consistent when considering other essential elements in the Bayesian kernel machine regression (BKMR) model. In our study, higher plasma molybdenum was significantly associated with a lower risk of MetS, as well as its components, in a dose-response manner.

Keywords: case-control study; dose-response relationship; metabolic syndrome; molybdenum.

Figure 1

The restricted cubic spline for the associations of log-transformed plasma molybdenum with MetS and its components. (A) MetS; (B) abdominal obesity; (C) hypertriglyceridemia; (D) low HDL-C; (E) hypertension; (F) hyperglycemia. Knots were placed at the 5th, 35th, 65th, and 95th percentiles of the plasma molybdenum distribution. Results were adjusted for sex, age (≤40, 40–49, 50–59, ≥60), body mass index (≤18, 18–23.9, 24–27.9, ≥28), smoking (current, former, and never), drinking (current, former, and never), physical activity (yes or no), an education level (none or elementary school, middle school, and high school or college), family history of diabetes (yes or no), survey center (Ezhou and Shenzhen), and multiple essential elements (log-transformed plasma calcium, magnesium, iron, copper, chromium, selenium, and cobalt).

Figure 2

Joint effect of the essential elements mixture (calcium, magnesium, iron, copper, chromium, selenium, molybdenum, and cobalt) on MetS by using Bayesian kernel machine regression (BKMR) model. (A) Overall effect of the essential elements mixture estimates and 95% confidence intervals. (B) Single element association (estimate and 95% confidence intervals); this plot compares ORs of MetS when a single element was at the 75th vs. 25th percentile when all the other elements were fixed at either the 25th (red line), 50th (green line), or 75th percentile (blue line). Results were adjusted for sex, age (≤40, 40–49, 50–59, ≥60), body mass index (≤18, 18–23.9, 24–27.9, ≥28), smoking (current, former, and never), drinking (current, former, and never), physical activity (yes or no), an education level (none or elementary school, middle school, and high school or college), family history of diabetes (yes or no), and survey center (Ezhou and Shenzhen).