A link between sex hormones and B-cell activating factor (BAFF), a crucial immunoregulator of autoimmune thyroid disease (AITD), may exist. The study aimed to elucidate the role of estrogen (E2) in regulating BAFF in Graves' disease (GD). In clinical samples, serum BAFF levels were higher in women than in men in both the GD and control groups. serum BAFF levels were associated with thyroid-stimulating hormone receptor antibody levels and thyroid function only in women and not in men. BAFF transcripts in peripheral blood mononuclear cells were higher in women with GD than those in the control group. Among GD patients with the AA genotype of rs2893321, women had higher BAFF transcripts and protein levels than men. In the progression of a spontaneous autoimmune thyroiditis (SAT) murine model, NOD.H-2h4, serum free thyroxine and BAFF levels were higher in female than in male mice. Moreover, exogenous E2 treatment increased serum BAFF levels in male SAT mice. Meanwhile, female SAT mice exhibited higher thyroid BAFF transcripts levels than either the E2-treated or untreated male SAT mouse groups. Our results showed that E2 might be implicated in modulating BAFF expression, and support a possible mechanism for the higher incidence of AITD in women.

Background: Glucose effectiveness (GE) is the capacity of glucose to increase its own uptake and to maintain endogenous hepatic glucose output under basal insulin levels. In addition to decreased insulin sensitivity (IS) and impaired insulin secretion, GE plays a critical role in glucose balance in patients with type 2 diabetes (T2DM). In the study, we developed an equation for predicting GE. Methods: We enrolled 227 participants with glucose tolerances ranging from normal glucose tolerance to diabetes. Of the participants, 75% (171) participants were randomly assigned to the study group, whose data were used to construct the equation for estimating GE. The remaining 56 participants comprised the validation group. All participants underwent a frequently sampled intravenous glucose tolerance test; IS, GE, and the acute insulin response after the glucose load were determined. Results: Age, triglyceride (TG), and fasting plasma glucose (FPG) were independently correlated with GE and selected for inclusion in multiple linear regression analysis. We constructed the following equation: GE = (29.196 − 0.103 × age − 2.722 × TG − 0.592 × FPG) × 10⁻³. Using this same equation, we also calculated the GE of the validation group. The calculated GE was significantly correlated with the measured GE (r = 0.430, P = 0.001). Conclusions: Using the equation based on routine measurements enabled the GE to be predicted with acceptable accuracy (r = 0.430). This method of predicting GE may aid clinicians in further understanding the underlying pathological mechanisms of T2DM.

Objectives: The metabolic syndrome (MetS) is proposed to predict future occurrence of cardiovascular diseases and diabetes. There are some other “non-traditional” risk factors such as hematogram components that are also related to the same endpoints as MetS. In this four-year longitudinal study, we used hematogram components to build models for predicting future occurrence of MetS in older men and women separately. Methods: Subjects above 65 years without MetS and related diseases were enrolled. All subjects were followed up until they developed MetS or until up to four years from the day of entry, whichever was earlier. Results: Among the 4539 study participants, 1327 developed MetS. Models were built for men and women separately and the areas under the receiver operation curves were significant. The Kaplan–Meier plot showed that the models could predict future MetS. Finally, Cox regression analysis showed that the hematogram model was correlated to future MetS with hazard ratios of 1.567 and 1.738 in men and women, respectively. Conclusion: Our hematogram models could significantly predict future MetS in elderly and might be more practical and convenient for daily clinical practice.

Both decreased insulin sensitivity, and impaired insulin secretion are 2 major pathophyisologies for type 2 diabetes (T2DM). There are two phases of ISEC-the first (1st ISEC) and second phase (2nd ISEC). In this study, we tried to build an equation to predict 2nd ISEC. Totally, 82 subjects, including 15 with normal fasting glucose, 26 with pre-diabetes and 41 with T2DM were enrolled. They received a modified low dose graded glucose infusion (M-LDGGI). The M-LDGGI is a simplified version of Polonsky’s method. The results were interpreted as the slope of the changes of plasma insulin against the glucose levels. The slopes of these curves were regarded as the 2nd ISEC. If only metabolic syndrome (MetS) components were analyzed, the equation was built as the following: log (2nd ISEC) = -2.400- 0.088 • (fasting plasma glucose, FPG + 0.072 • (body mass index, BMI). After fasting plasma insulin (FPI) was added , the equation was shown as the following: log (2nd ISEC) = -2.316- 0.093˙FPG + 0.049 • BMI+ 0.434 • log(FPI). The second equation provided a greater accuracy to determine 2nd ISEC than first one in the external validation group (r2 = 0.545 vs r2 = 0.423). Using MetS components, 2nd ISEC could be predicted with good accuracy. After adding FPI into the equation, the predictive power further increases. These equations could be widely used in daily practice and clinical settings.