Background: Diabetic foot ulcer (DFU) in patients with type 2 diabetes mellitus (T2D) often leads to amputation. Early intervention to prevent DFU is urgently necessary. So far, there have been no studies on predictive models associated with DFU risk factors. Our study aimed to quantify the predictive risk value of DFU, promote health education, and further develop behavioral interventions to reduce the incidence of DFU.

Methods: Data from 973 consecutive patients with T2D was collected from two hospitals. Patients from the Guangxi Medical University First Affiliated Hospital formed the training cohort (n = 853), and those from the Wuming Hospital of Guangxi Medical University formed the validation cohort (n = 120). Independent variable grouping analysis and multivariate logistic regression analysis were used to determine the risk factors of DFUs. The prediction model was established according to the related risk factors. In addition, the accuracy of the model was evaluated by specificity, sensitivity, predictive value, and predictive likelihood ratio.

Results: In total, 369 of the 853 patients (43.3%) and 60 of the 120 (50.0%) were diagnosed with DFUs in the two hospitals. The factors associated with DFU were old age, male gender, lower body mass index (BMI), longer duration of diabetes, history of foot disease, cardiac insufficiency, no use of oral hypoglycemic agent (OHA), high white blood cell count, high platelet count, low hemoglobin level, low lymphocyte absolute value, and high postprandial blood glucose. After incorporating these 12 factors, the nomogram drawn achieved good concordance indexes of 0.89 [95% confidence interval (CI): 0.87 to 0.91] in the training cohort and 0.84 (95% CI: 0.77 to 0.91) in the validation cohort in predicting DFUs and had well-fitted calibration curves. Patients who had a nomogram score of ≥180 were considered to have a low risk of DFU, whereas those having ≥180 were at high risk.

Conclusions: A nomogram was constructed by combining 12 identified risk factors of DFU. These 12 risk factors are easily available in hospitalized patients, so the prediction of DFU in hospitalized patients with T2D has potential clinical significance. The model provides a reliable prediction of the risk of DFU in patients with T2D.

There is an emerging Muslim and diabetic population in the United States and other Western countries and majority of pregnant women and patients with diabetes mellitus choose to fast during Ramadan. Fasting during Ramadan in pregnant women with diabetes may represent a ‘perfect storm’ of metabolic disturbances including hyperglycemia, hypoglycemia and ketosis. Recent continuous and flash glucose monitoring data suggests increased glycemic variability (fasting hypo- and post-Iftar hyperglycemia) in non-pregnant patients with diabetes during Ramadan. Only five small-scale studies, predominantly focused on women with gestational diabetes mellitus in Muslim-majority nations have explored maternal glycemic outcomes during Ramadan which is associated with lower mean blood glucose levels and higher frequency of fasting hypoglycemia. Data is limited however on important clinical outcomes such as symptomatic and serious hypoglycemia requiring hospitalization. Results have been conflicting regarding maternal Ramadan fasting and association with fetal outcomes in women without diabetes. Only one recently published study reported on perinatal outcomes in pregnant women with gestational diabetes which found no effect of Ramadan exposure on mean birthweight or macrosomia frequency but lower neonatal hypoglycemia prevalence, however a significant limitation was lack of documentation of maternal fasting status. At this stage, due to paucity of data, the current medical recommendation is against Ramadan fasting for pregnant Muslim women with diabetes. Large-scale population-based studies are warranted regarding maternal and fetal outcomes in pregnant fasting women with diabetes and such studies should characterize maternal fasting status and have meaningful and consistent clinical outcomes. High-quality data derived from these studies can assist clinicians in providing more evidence-based advice to safely navigate both mother and fetus through a potentially challenging pregnancy.