We examined the relationship between different patterns of meat and seafood consumption and plasma metabolic profiles in an Asian population. We selected 270 ethnic Chinese men and women from the Singapore Prospective Study Program based on their dietary habits assessed with a validated food frequency questionnaire. Participants were divided into four subgroups: high meat and high seafood (n = 60), high meat and low seafood (n = 64), low meat and high seafood (n = 60), and low meat and low seafood (n = 86) consumers. Plasma metabolites were measured using both targeted and untargeted mass spectroscopy-based analyses. A total of 42 metabolites differed significantly by dietary group. Higher concentrations of essential amino acids, polyunsaturated fatty acids, and d-glucose were found in high meat and/or seafood consumers as compared with the group with a low consumption of these animal foods. Red meat, poultry, fish, shellfish, soy products, and dairy were each correlated with at least one differential metabolite (r = −0.308 to 0.448). Some observations, such as the correlation between fish and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), confirmed previous studies. Other observations, such as the correlation between shellfish and phosphatidylethanolamine (p36:4), were novel. We also observed significant correlations between plasma metabolites and clinical characteristics, such as CMPF with fasting blood glucose (r = 0.401). These findings demonstrate a significant influence of meat and seafood consumption on metabolic profiles in the Asian population. Full article

High arachidonic acid (AA; 20:4 n − 6) status may have adverse effects on inflammation and risk of cardiovascular diseases. Concerns about high intake of n − 6 polyunsaturated fatty acids (PUFAs) are based on the premise that endogenous conversion from linoleic acid (LA; 18:2 n − 6) is an important source of AA, but few population-based studies have investigated dietary determinants of AA status. In this study, we examined habitual food consumption in relation to plasma concentrations of AA and other PUFAs in population-based studies. We used cross-sectional data from 269 healthy, ethnic Chinese participants (25–80 years old) with contrasting intakes of fish and red meat from the Singapore Prospective Study Program and 769 healthy participants (44–74 years old) from the Singapore Chinese Health Study as a validation set. Multivariable linear regression was used to examine PUFA intake (% energy) and food sources of PUFA (fish, red meat, poultry, soy and cooking oils) in relation to plasma PUFAs (AA, LA, dihomo-gamma-linolenic acid (DGLA; 20:3 n − 6), alpha-linolenic acid (ALA; 18:3 n − 3), eicosapentaenoic acid (EPA; 20:5 n − 3), and docosahexaenoic acid (DHA; 22:6 n − 3)) concentrations. Higher intake of red meat was associated with higher plasma AA concentrations. High intake of PUFA or PUFA-rich oils was associated with higher plasma ALA but not with plasma AA. Higher intakes of soy were associated with higher ALA and fish with higher DHA and EPA concentrations. These associations were statistically significant (p < 0.05) in both studies. Red meat consumption, but not PUFA or PUFA-rich cooking oil, was associated with circulating AA suggesting that intake of pre-formed AA rather than LA is an important determinant of AA status. A diet high in fish, soy products and polyunsaturated cooking oil, and low in red meat may be associated with an optimal plasma profile of PUFA in this Chinese population. Full article

Objectives: The aim of this study was to systematically evaluate the relationship between urinary excretion of cadmium (U-Cd) and biomarkers of renal dysfunction. Methods: One hundred eighty five non-smoking female farmers (aged from 44 to 71 years) were recruited from two rural areas with different cadmium levels of exposure in southern China. Morning spot urine samples were collected for detecting U-Cd, urinary creatinine (U-cre), β₂-microglobulin (β₂-MG), α₁-microglobulin (α₁-MG), metallothionein (MT), retinol binding protein (RBP), albumin (AB), N-acetyl-β-D-glucosaminidase (NAG), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT) and kidney injury molecule-1 (KIM-1). Spearman’s rank correlation was carried out to assess pairwise bivariate associations between continuous variables. Three different models of multiple linear regression (the cre-corrected, un-corrected and cre-adjusted model) were used to model the dose-response relationships between U-Cd and nine urine markers. Results: Spearman’s rank correlation showed that NAG, ALP, RBP, β₂-MG and MT were significantly associated with U-Cd for both cre-corrected and observed data. Generally, NAG correlated best with U-Cd among the nine biomarkers studied, followed by ALP and MT. In the un-corrected model and cre-adjusted model, the regression coefficients and R² of nine biomarkers were larger than the corresponding values in the cre-corrected model, indicating that the use of observed data was better for investigating the relationship between biomarkers and U-Cd than cre-corrected data. Conclusions: Our results suggest that NAG, MT and ALP in urine were better biomarkers for long-term environmental cadmium exposure assessment among the nine biomarkers studied. Further, data without normalization with creatinine show better relationships between cadmium exposure and renal dysfunction. Full article

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs. Full article