BackgroundIt has been well documented that obesity is closely associated with metabolic syndrome (MetS). Although body mass index (BMI) is the most frequently used method to assess overweightness and obesity, this method has been criticized because BMI does not always reflect true body fatness, which may be better evaluated by assessment of body fat and fat-free mass. The objective of this study was to investigate the best indicator to predict the presence of MetS among fat mass index, BMI and percentage of body fat (BF %) and determine its optimal cut-off value in the screening of MetS in practice.MethodsA cross-sectional study of 1698 subjects (aged 20–79 years) who participated in the annual health check-ups was employed. Body composition was measured by bioelectrical impedance analysis (BIA). Fat mass index (FMI) was calculated. Sex-specific FMI quartiles were defined as follows: Q1: <4.39, Q2:4.39- < 5.65, Q3:5.65- < 7.03, Q4:≥7.03,in men; and Q1:<5.25, Q2:5.25- < 6.33, Q3:6.33- < 7.93,Q4:≥7.93, in women. MetS was defined by National Cholesterol Education Program/Adult Treatment Panel III criteria. The association between FMI quartiles and MetS was assessed using Binary logistic regression. Receiver operating curve(ROC) analysis was used to determine optimal cutoff points for BMI,BF% and FMI in relation to the area under the curve(AUC),sensitivity and specificity in men and women.ResultsThe adjusted odds ratios (95% CI) for the presence of MetS in the highest FMI quartile versus lowest quartile were 79.143(21.243-294.852) for men( P < 0.01) and 52.039(4.144-653.436) for women( P < 0.01) after adjusting age, BMI, BF%, TC, LDL, CRP, smoking status and exercise status, and the odds ratios were 9.166(2.157-38.952) for men( P < 0.01) and 25.574(1.945-336.228) for women( P < 0.05) when WC was also added into the adjustment. It was determined that BMI values of 27.45 and 23.85 kg/m2, BF% of 23.95% and 31.35% and FMI of 7.00 and 7.90 kg/m2 were the optimal cutoff values to predict the presence of MetS among men and women according to the ROC curve analysis. Among the indicators used to predict MetS, FMI was the index that showed the greatest area under the ROC curve in both sexes.ConclusionsHigher FMI levels appear to be independently and positively associated with the presence of MetS regardless of BMI and BF%. FMI seems to be a better screening tool in prediction of the presence of metabolic syndrome than BMI and percentage of body fat in men and women.
α-Synuclein (a-Syn), a protein implicated in Parkinson disease, contributes significantly to dopamine metabolism. a-Syn binding inhibits the activity of tyrosine hydroxylase (TH), the rate-limiting enzyme in catecholamine synthesis. Phosphorylation of TH stimulates its activity, an effect that is reversed by protein phosphatase 2A (PP2A). In cells, a-Syn overexpression activates PP2A. Here we demonstrate that a-Syn significantly inhibited TH activity in vitro and in vivo and that phosphorylation of a-Syn serine 129 (Ser-129) modulated this effect. In MN9D cells, a-Syn overexpression reduced TH serine 19 phosphorylation (Ser(P)-19). In dopaminergic tissues from mice overexpressing human a-Syn in catecholamine neurons only, TH-Ser-19 and TH-Ser-40 phosphorylation and activity were also reduced, whereas PP2A was more active. Cerebellum, which lacks excess a-Syn, had PP2A activity identical to controls. Conversely, a-Syn knock-out mice had elevated TH-Ser-19 phosphorylation and activity and less active PP2A in dopaminergic tissues. Using an a-Syn Ser-129 dephosphorylation mimic, with serine mutated to alanine, TH was more inhibited, whereas PP2A was more active in vitro and in vivo. Phosphorylation of a-Syn Ser-129 by Polo-like-kinase 2 in vitro reduced the ability of a-Syn to inhibit TH or activate PP2A, identifying a novel regulatory role for Ser-129 on a-Syn. These findings extend our understanding of normal a-Syn biology and have implications for the dopamine dysfunction of Parkinson disease.
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