Nutritional Status Assessed with Objective Data Assessment Correlates with a High-Risk Foot in Patients with Type 2 Diabetes.

Malnutrition and diabetes are likely to co-occur. There are few reports on the association between nutritional status and foot risk in patients with type 2 diabetes (T2D). Therefore, we aimed to investigate this relationship in this cross-sectional study. We investigated the relationships between objective data assessment (ODA), especially Controlling Nutritional Status (CONUT) score and foot risk, evaluated by the International Working Group on the Diabetic Foot (IWGDF), in consecutive patients with T2D. Patients were divided into groups 0 to 3 by IWGDF, and groups 1 to 3 were defined as high-risk groups. Among 469 patients, 42.6% (n = 200) of them had high-risk foot. Patients with high-risk foot were significantly older (71.2 ± 11.3 vs. 64.2 ± 13.4 years, p < 0.001) and had a longer duration of diabetes (18.0 ± 12.0 vs. 11.5 ± 10.0 years, p < 0.001) than those in the low-risk group. In the high-risk group, serum albumin level, total lymphocyte count, hemoglobin, and CONUT score were significantly worse, especially in older patients (≥75 years). Multivariate logistic regression analysis showed that there was a positive correlation between CONUT score and high-risk foot in older patients (OR, 1.37; 95% CI, 1.05−1.86; p = 0.021). Our results indicated that nutritional status, assessed by ODA, correlated with high-risk foot, especially in older patients with T2D.

Trigger finger is associated with risk of incident cardiovascular disease in individuals with type 2 diabetes: a retrospective cohort study.

INTRODUCTION: Trigger finger is one of the complications affecting the upper extremity in patients with diabetes. Diabetes is also a well-known risk factor that predisposes individuals to cardiovascular diseases (CVDs). This retrospective cohort study aimed to establish the association between trigger finger and the patients with incident CVD with type 2 diabetes. MATERIALS AND METHODS: Trigger finger was diagnosed by palpating a thickened tendon during flexion or on the manifestation of a locking phenomenon during extension or flexion of either finger. The relationship between trigger finger and other clinical parameters or complications of diabetes was examined by a comparative analysis. Cox regression analysis was used to evaluate the association between trigger finger and incidence of CVD. We calculated the propensity scores using sex, body mass index, age, smoking status, duration of diabetes, estimated glomerular filtration rate, hypertension, dyslipidemia, and hemoglobin A1c as the number of patients with incident CVD during the follow-up period was low. RESULTS: Among the 399 patients with type 2 diabetes, 54 patients had trigger finger. Patients with trigger finger were significantly older in age and had been suffering from diabetes for a longer duration. They also displayed worse renal function and glycemic control, along with a higher incidence of hypertension, neuropathy and nephropathy. During the average 5.66±1.12 years of follow-up, a total of 18 incidents occurred. According to the Cox regression analysis, trigger finger was shown to be associated with enhanced risk of the incidence of CVD after adjustment for the covariates (adjusted HR=3.33 (95% CI 1.25 to 8.66), p=0.017). CONCLUSIONS: Trigger finger is associated with the risk of incident CVD in patients with type 2 diabetes. Thus, clinicians must consider these factors at the time of diagnosis of such patients.

Cross-Domain Matching with Squared-Loss Mutual Information.

The goal of cross-domain matching (CDM) is to find correspondences between two sets of objects in different domains in an unsupervised way. CDM has various interesting applications, including photo album summarization where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system, and temporal alignment which aligns sequences such as videos that are potentially expressed using different features. In this paper, we propose an information-theoretic CDM framework based on squared-loss mutual information (SMI). The proposed approach can directly handle non-linearly related objects/sequences with different dimensions, with the ability that hyper-parameters can be objectively optimized by cross-validation. We apply the proposed method to several real-world problems including image matching, unpaired voice conversion, photo album summarization, cross-feature video and cross-domain video-to-mocap alignment, and Kinect-based action recognition, and experimentally demonstrate that the proposed method is a promising alternative to state-of-the-art CDM methods.