CD8 T-cell reactivity to islet antigens is unique to type 1 while CD4 T-cell reactivity exists in both type 1 and type 2 diabetes.

Previous cross-sectional analyses demonstrated that CD8(+) and CD4(+) T-cell reactivity to islet-specific antigens was more prevalent in T1D subjects than in healthy donors (HD). Here, we examined T1D-associated epitope-specific CD4(+) T-cell cytokine production and autoreactive CD8(+) T-cell frequency on a monthly basis for one year in 10 HD, 33 subjects with T1D, and 15 subjects with T2D. Autoreactive CD4(+) T-cells from both T1D and T2D subjects produced more IFN-γ when stimulated than cells from HD. In contrast, higher frequencies of islet antigen-specific CD8(+) T-cells were detected only in T1D. These observations support the hypothesis that general beta-cell stress drives autoreactive CD4(+) T-cell activity while islet over-expression of MHC class I commonly seen in T1D mediates amplification of CD8(+) T-cells and more rapid beta-cell loss. In conclusion, CD4(+) T-cell autoreactivity appears to be present in both T1D and T2D while autoreactive CD8(+) T-cells are unique to T1D. Thus, autoreactive CD8(+) cells may serve as a more T1D-specific biomarker.

Anti-Mullerian Hormone-Based Phenotyping Identifies Subgroups of Women with Polycystic Ovary Syndrome with Differing Clinical and Biochemical Characteristics.

Even though polycystic ovary syndrome (PCOS) was originally defined as "amenorrhea associated with bilateral polycystic ovaries", women without PCO morphology can be included in this diagnosis. This may contribute to the clinical heterogeneity seen in PCOS. Serum anti-Mullerian hormone (AMH) correlates with the number of ovarian cysts. We investigated whether phenotyping based on serum AMH can distinguish subgroups of PCOS with different clinical and biochemical characteristics. The electronic medical records of 108 women with PCOS (Rotterdam criteria) were reviewed. The serum AMH value correlated inversely (0.03 < p < 0.0001) with age, weight, and BMI values and directly with serum total testosterone (T), free T, and bioavailable T values. When divided into quartiles based on serum AMH values, the women in the highest quartile (AMH: 18.5 ± 9.9 ng/mL; n = 27) had lower BMI (29.4 ± 6.9 vs. 34.0 ± 10.6-36.7 ± 7.2 kg/m2) but higher total T (51.3 ± 27.2 vs. 26.5 ± 10.4-35.1 ± 16.3 ng/dL), free T (7.7 ± 6.0 vs. 4.4 ± 2.3-5.7 ± 3.2 ng/dL), and bioavailable T (22.1 ± 17.0 vs. 12.2 ± 6.6-16.5 ± 8.7 ng/dL) values. The combination of high AMH and high testosterone values may point to the ovaries and reproductive etiology for PCOS in this subgroup. Thus, AMH-based phenotyping may provide a practical and cost-effective tool to explore the heterogeneity in PCOS.

Temporal intra-individual variation of immunological biomarkers in type 1 diabetes patients: implications for future use in cross-sectional assessment.

Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies.