Management of Patients with Newly Diagnosed Diabetic Mellitus: Ophthalmologic Outcomes in Intensive versus Conventional Glycemic Control

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables

OBJECTIVE

Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario.

METHODS

In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction.

RESULTS

The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively.

CONCLUSION

The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Hand grip strength is inversely associated with total daily insulin dose requirement in patients with type 2 diabetes mellitus: a cross-sectional study

Background

Short-term (2 weeks to 3 months) insulin intensive therapy using continuous subcutaneous insulin infusion (CSII) can improve islet beta cell function and prolong glycemic remission in patients with newly diagnosed type 2 diabetes mellitus (T2DM). However, the total daily insulin dose (TDD, IU/kg/d) required to achieve near-normoglycemic control with CSII still needs to be frequently adjusted based on blood glucose monitoring. Although real-time continuous glucose monitoring (rtCGM), which measures the interstitial fluid glucose concentration continuously without much difficulty, facilitates the adjustment of insulin dosage, its adoption in the T2DM population is strictly limited by insurance coverage and lack of awareness of rtCGM among clinicians. Thus, it is of clinical significance to identify easy-to-use parameters that may allow a more rapid and accurate prediction of TDD requirement. This study aimed to explore the association between hand grip strength (HGS) and TDD requirement in patients with T2DM receiving CSII therapy.

Methods

A total of 180 eligible patients with T2DM were enrolled in the study and divided into three groups based on their HGS: low (L), medium (M), and high (H). The TDD requirement was calculated on day 7 or 8 of CSII treatment. Anthropometric parameters, including HGS, skeletal muscle mass, skeletal muscle index (SMI) and 6-m gait speed, and laboratory data, were collected on the morning of the second day after admission, within the first 24 h of CSII therapy. These parameters were used to identify significant predictors of TDD requirement using Pearson or Spearman correlation test, and stepwise multiple regression analysis.

Results

There were no significant differences in age, duration of T2DM, waist-to-hip ratio (WHR), body mass index (BMI), blood pressure, liver function, estimated glomerular filtration rate, triglyceride, total cholesterol, glycosylated hemoglobin A1c (HbA1c), homeostatic model assessment of insulin resistance (HOMA-IR), and homeostasis model assessment of beta cell function (HOMA-β) among the groups. The H group had higher body muscle mass-to-fat ratio (BMFR), skeletal muscle mass-to-fat ratio (SMFR), SMI, 6-m gait speed, and lower TDD requirement than the M and L groups. The HGS negatively correlated with TDD requirement (r = -0.33, p < 0.001) after adjusting for sex, age, BMI, WHR, HbA1c, Ln (HOMA-β), Ln (HOMA-IR), Ln (BMFR), Ln (SMFR), SMI, and 6-m gait speed. Multivariate stepwise regression analysis indicated that HGS was an independent predictor of TDD requirement in patients with T2DM (β = -0.45, p < 0 001).

Conclusion

Lower HGS is associated with an increased TDD requirement in T2DM patients. HGS may facilitate the prediction of TDD requirement in T2DM patients receiving CSII therapy.

Effects of intensive insulin therapy on the retinal microvasculature in patients with type 2 diabetes mellitus: a prospective observational study

Background

We examined the retinal microvascular changes and associated factors in type 2 diabetes mellitus (T2DM) before and after intensive insulin therapy.

Methods

This prospective observational study recruited patients with T2DM and divided them into intensive insulin therapy and oral hypoglycemic agent groups. All patients enrolled in this study had diabetes without retinopathy or non-proliferative diabetic retinopathy. Optical coherence tomography angiography (OCTA) was used in all patients before treatment and at 1, 3, and 6 months after treatment. Vessel density (VD) and thickness changes in the macular and optic disc areas were assessed.

Results

The study included 36 eyes in the intensive insulin therapy group and 36 in the oral hypoglycemic agent group. One month after treatment, VD in the deep capillary plexus (DCP) and peripapillary capillary VD (ppVD) were significantly decreased by intensification (P = 0.009, 0.000). At three months after treatment, decreases in VD induced by intensification were found in the superficial capillary plexus (SCP), DCP, foveal density in a 300-μm-wide region around the foveal avascular area (FD-300), and ppVD (P = 0.032, 0.000, 0.039, 0.000). Six months after treatment, decreases in VD by intensification were observed in the DCP and ppVD groups (P = 0.000, 0.000). Vessel density showed no significant change in the oral hypoglycemic agent group after treatment. The amount of DCP-VD reduction was correlated with macular thickening (r = 0.348, P = 0.038; r = 0.693, P = 0.000 and r = 0.417, P = 0.011, respectively) after intensive insulin therapy.

Conclusions

Insulin-intensive treatment caused a transient reduction in vessel density in the macular and optic disc areas. DCP-VD and ppVD were more susceptible at an earlier stage. Retinal microvasculature monitoring using OCTA is vital for patients with type 2 diabetes receiving intensive insulin therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12886-022-02397-9.