Search Results
Search for other papers by Yang Lv in
Google Scholar
PubMed
Search for other papers by Ning Pu in
Google Scholar
PubMed
Search for other papers by Wei-lin Mao in
Google Scholar
PubMed
Search for other papers by Wen-qi Chen in
Google Scholar
PubMed
Search for other papers by Huan-yu Wang in
Google Scholar
PubMed
Search for other papers by Xu Han in
Google Scholar
PubMed
Search for other papers by Yuan Ji in
Google Scholar
PubMed
Search for other papers by Lei Zhang in
Google Scholar
PubMed
Search for other papers by Da-yong Jin in
Google Scholar
PubMed
Search for other papers by Wen-Hui Lou in
Google Scholar
PubMed
Search for other papers by Xue-feng Xu in
Google Scholar
PubMed
Aim
We aim to investigate the clinical characteristics of the rectal NECs and the prognosis-related factors and construct a nomogram for prognosis prediction.
Methods
The data of 41 patients and 1028 patients with rectal NEC were retrieved respectively from our institution and SEER database. OS or PFS was defined as the major study outcome. Variables were compared by chi-square test and t-test when appropriate. Kaplan–Meier analysis with log-rank test was used for survival analysis and the Cox regression analysis was applied. The nomogram integrating risk factors for predicting OS was constructed by R to achieve superior discriminatory ability. Predictive utility of the nomogram was determined by concordance index (C-index) and calibration curve.
Results
In the univariate and multivariate analyses, tumor differentiation, N stage, M stage and resection of primary site were identified as independent prognostic indicators. The linear regression relationship was found between the value of Ki-67 index and the duration of OS (P < 0.05). Furthermore, the independent prognostic factors were added to formulate prognostic nomogram. The constructed nomogram showed good performance according to the C-index.
Conclusions
Contrary to WHO classification guideline, we found that the rectal NEC diseases are heterogeneous and should be divided as different categories according to the pathological differentiation. Besides, the nomogram formulated in this study showed excellent discriminative capability to predict OS for those patients. More advanced predictive model for this disease is required to assist risk stratification via the formulated nomogram.
Search for other papers by Yun Cai in
Google Scholar
PubMed
Search for other papers by Jieni Yan in
Google Scholar
PubMed
Search for other papers by Yong Gu in
Google Scholar
PubMed
Search for other papers by Heng Chen in
Google Scholar
PubMed
Search for other papers by Yang Chen in
Google Scholar
PubMed
Search for other papers by Xinyu Xu in
Google Scholar
PubMed
Search for other papers by Mei Zhang in
Google Scholar
PubMed
Search for other papers by Liping Yu in
Google Scholar
PubMed
Search for other papers by Xuqin Zheng in
Google Scholar
PubMed
Search for other papers by Tao Yang in
Google Scholar
PubMed
Objective
The most common coexisting organ-specific autoimmune disease in patients with type 1 diabetes mellitus (T1DM) is autoimmune thyroid disease (AITD). However, there have been few clinical reports based on a large population about the prevalence of zinc transporter 8 autoantibody (ZnT8A) and other islet autoantibodies in AITD patients. We aimed to explore the presence of islet autoantibodies, ZnT8A, glutamic acid decarboxylase autoantibodies (GADA) and insulinoma-associated antigen 2 autoantibodies (IA-2A) compared with thyroid autoantibodies, thyroid peroxidase autoantibodies (TPOAb) and thyroglobulin autoantibodies (TGAb) and thyrotropin receptor autoantibodies (TRAb) in patients with Graves’ disease (GD), Hashimoto’s thyroiditis (HT) and T1DM patients with AITD.
Methods
Totally, 389 patients with GD, 334 patients with HT, 108 T1DM patients with AITD and 115 healthy controls (HC) were recruited in the study. Islet autoantibodies (ZnT8A, GADA and IA-2A) were detected by radioligand binding assay. Thyroid autoantibodies, TPOAb and TGAb were detected by chemiluminescence assay, and TRAb was detected by RIA.
Results
The prevalence of ZnT8A, GADA and IA-2A was higher in GD and HT patients than that of HC (ZnT8A: GD 8.48%, HT 10.8% vs HC 1.74%; GADA: GD 7.46%, HT 7.74% vs HC 0.870%; IA-2A: GD 4.88%, HT 3.59% vs HC 0%; All P < 0.05) but lower than that of T1DM subjects with AITD (ZnT8A: 42.6%; IA-2A: 44.4%; GADA: 74.1%; all P < 0.0001).
Conclusions
An increased prevalence of ZnT8A as well as GADA and IA-2A was found in both GD and HT patients, indicating that there is a potential link between thyroid autoimmunity and islet autoimmunity.
Search for other papers by Xue-Jiao Yang in
Google Scholar
PubMed
Search for other papers by Le-Yang Zhang in
Google Scholar
PubMed
Search for other papers by Qing-Hua Ma in
Google Scholar
PubMed
Search for other papers by Hong-Peng Sun in
Google Scholar
PubMed
Search for other papers by Yong Xu in
Google Scholar
PubMed
Search for other papers by Xing Chen in
Google Scholar
PubMed
Search for other papers by Chen-Wei Pan in
Google Scholar
PubMed
Purpose:
We aimed to examine the associations of platelet parameters with the presence of metabolic syndrome in community-dwelling older Chinese adults.
Methods:
Study sample was from the Weitang Geriatric Diseases Study, which included 4338 individuals aged 60 years or above. The mean age of the participants was 68 years. Metabolic syndrome was defined based on the Adult Treatment Panel III criteria. Platelet parameters were assessed using an automated hematology analyzer. Multiple logistic regression models were fitted to examine relationships between the platelet parameters and the presence of metabolic syndrome after adjusting for potential confounders.
Results:
The adjusted odds ratio (95% CI) of metabolic syndrome for the highest quartile of platelet parameters (platelet count, mean platelet volume, plateletcrit, platelet distribution width, platelet larger cell ratio) when compared to the lowest quartile were 1.32 (1.06, 1.64), 1.00 (0.81, 1.24), 1.37 (1.10, 1.71), 1.45 (1.14, 1.83), 1.11 (0.89, 1.39), respectively. Hypertension and diabetes modified the relationship between platelet distribution width and metabolic syndrome with the associations being significant in hypertensive and non-diabetic groups. The levels of platelet distribution width increased with the risk of metabolic syndrome in men but not in women.
Conclusion:
The levels of platelet count, plateletcrit and platelet distribution width increased in older adults with metabolic syndrome, suggesting that these parameters may be useful biomarkers for further risk appraisal of metabolic syndrome in aged population.
