Incidence and Risk Factors for Progression to Diabetes Mellitus: A Retrospective Cohort Study

Diabetic peripheral neuropathy and neuromodulation techniques: a systematic review of progress and prospects

Neuromodulation for diabetic peripheral neuropathy represents a significant area of interest in the management of chronic pain associated with this condition. Diabetic peripheral neuropathy, a common complication of diabetes, is characterized by nerve damage due to high blood sugar levels that lead to symptoms, such as pain, tingling, and numbness, primarily in the hands and feet. The aim of this systematic review was to evaluate the efficacy of neuromodulatory techniques as potential therapeutic interventions for patients with diabetic peripheral neuropathy, while also examining recent developments in this domain. The investigation encompassed an array of neuromodulation methods, including frequency rhythmic electrical modulated systems, dorsal root ganglion stimulation, and spinal cord stimulation. This systematic review suggests that neuromodulatory techniques may be useful in the treatment of diabetic peripheral neuropathy. Understanding the advantages of these treatments will enable physicians and other healthcare providers to offer additional options for patients with symptoms refractory to standard pharmacologic treatments. Through these efforts, we may improve quality of life and increase functional capacity in patients suffering from complications related to diabetic neuropathy.

Incidence and predictors of type 2 diabetes mellitus during 17 years of follow-up in the Golestan Cohort Study

In this study, we aimed to determine the incidence and predictors of type 2 Diabetes Mellitus (T2DM) in the Golestan Cohort Study (GCS). This study is a prospective population-based cohort study conducted in the Golestan province of Iran with the participation of 50,044 people aged 30 to 87 years between 2004 and 2008. Participants were followed up for 17 years for T2DM. The cumulative incidence of T2DM was 13.32% in the GCS. We observed hypertension (HTN) and dyslipidemia (DLP) increased the risk of T2DM 1.16 and 1.63 times relative to the healthy participants (RR: 1.16, 1.63, 95% CI : 1.102-1.222, 1.393-1.928, p < 0.001). For every one-unit increase in the body mass index (BMI), the risk of T2DM increased 1.09 times (RR: 1.09, 95% CI :1.086-1.106, p < 0.001). High-risk waist circumference (WC) increased the risk of T2DM by 1.89 times more than normal WC (RR: 1.89, 95% CI : 1.756-2.053, p < 0.001). Smokers had an 89% lower risk of T2DM than non-smokers (RR: 0.897, 95% CI : 0.814-0.989, p = 0.029). We conclude that environmental factors induce T2DM by affecting body fat. Also, other metabolic diseases could develop T2DM.

Driving into Diabetes: Risk Assessment among Interstate Truck Drivers in West Bengal

Diabetes mellitus has led to a huge increase in its number over recent decades. Due to their occupation, truck drivers are predisposed to higher risk of lifestyle disorders. Hence, this study intended to assess their risk of developing diabetes mellitus with respect to IDRS (Indian Diabetes Risk Score). A cross-sectional study was conducted in a selected logistic company for 3 months, where 160 interstate truck drivers were selected by simple random sampling. The collected data were analyzed using descriptive and inferential statistics using SPSS 16 version. The mean age of study participants was 38.4 ± 10.7 years. Of the total, 49.5% of the participants had a high IDRS. A significant increase in IDRS was associated with the occupational variables. Strict monitoring of government guidelines on fair working hours for truck drivers is required. They, being a high-risk group due to their occupation, require periodic health checkups, and appropriate measures should be taken to minimize the detrimental effects on their health.

The incidence of diabetes mellitus and its determining factors in a Kurdish population: insights from a cohort study in western Iran

Diabetes mellitus (DM) is among the most widespread non-communicable diseases and poses a substantial global health challenge. The aim of this study was to examine the incidence of DM and its nutritional, anthropometric, laboratory, demographic, and behavioral determinants, as well as comorbidities, within a Kurdish population residing in western Iran. This research was conducted in the Ravansar Non-Communicable Disease (RaNCD) cohort study, followed 9170 participants aged 35-65 years, for an average ± SD of 7.11 ± 1.26 years, from 2015 until 2023. A hierarchical Cox regression model was used to estimates the adjusted hazard ratios (HRs). The incidence of DM was 4.45 (95% CI 3.96, 4.99) per 1000 person-years. We found several significant predictors for DM incidence, including prediabetes, comorbidity, urban residence, total antioxidant capacity (TAC), and the interaction between gender and body mass index (BMI). Prediabetes emerged as the strongest predictor of DM incidence, with a hazard ratio of 10.13 (CI 7.84, 13.09). Additionally, having two diseases (HR = 2.18; 95% CI 1.44, 3.29) or three and more diseases (HR = 3.17; 95% CI 2.06, 4.90) increased the risk of developing DM. Also, the hazard ratios for the effects of gender on DM incidence in the normal, overweight, and obese BMI groups were 0.24, 0.81, and 1.01, respectively. The presence of prediabetes and obesity serve as the crucial indicators for the onset of DM, emphasizing the pressing need for interventions to prevent DM in these circumstances. Furthermore, there are notable disparities between urban and rural populations in this study, warranting further investigations to ascertain the underlying causes of such variations.

LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages

BACKGROUND

Formal risk assessment is crucial for diabetes prevention. We aimed to establish a practical nomogram for predicting the risk incidence of prediabetes and prediabetes conversion to diabetes.

METHODS

A cohort of 1428 subjects was collected to develop prediction models. The LASSO was used to screen for important risk factors in prediabetes and diabetes and was compared with other algorithms (LR, RF, SVM, LDA, NB, and Treebag). Multivariate logistic regression analysis was used to construct the prediction model of prediabetes and diabetes, and drawn the predictive nomogram. The performance of the nomograms was evaluated by receiver-operating characteristic curve and calibration.

RESULTS

These findings revealed that the other six algorithms were not as good as LASSO in terms of diabetes risk prediction. The nomogram for individualized prediction of prediabetes included "Age," "FH," "Insulin_F," "hypertension," "Tgab," "HDL-C," "Proinsulin_F," and "TG" and the nomogram of prediabetes to diabetes included "Age," "FH," "Proinsulin_E," and "HDL-C". The results showed that the two models had certain discrimination, with the AUC of 0.78 and 0.70, respectively. The calibration curve of the two models also indicated good consistency.

CONCLUSIONS

We established early warning models for prediabetes and diabetes, which can help identify prediabetes and diabetes high-risk populations in advance.

Association of sodium intake with diabetes in adults without hypertension: evidence from the National Health and Nutrition Examination Survey 2009-2018

Background

Sodium is essential for human health, however the prevalence of various diseases is associated with excessive sodium intake, particularly cardiovascular disorders. However, in most countries, salt intake is much higher than the World Health Organization recommends. Several studies in recent years have revealed that high salt intake is associated with diabetes in the general population, but the association is uncertain in people who do not have hypertension. In this study, we aimed to find out whether high sodium intake increases the risk of diabetes in this particular population.

Method

Data were extracted from the National Health and Nutrition Examination Survey (NHANES; 2009-2018). Participants included adults aged over 20 years old who have undergone the diabetes questionnaire, and the hypertension population was excluded. In order to adjust the confounders, multivariate analysis models were built. Finally, subgroup analysis were conducted to investigate the association between sodium intake and diabetes separately.

Result

In the present study, 7,907 participants are included (3,920 female and 3,987 male), and 512 (6.48%) individuals reported diabetes. The median sodium intake of the participants was 3,341 mg/d (IQR: 2498, 4,364 mg/d). A linear association between sodium intake and the prevalence of diabetes was found (p = 0.003). According to the multivariate analysis models, the odds ratio of diabetes for every 1,000 mg sodium intake increment is 1.20 (OR: 1.20, 95% CI 1.07-1.35). The highest sodium intake quartile was 1.80-fold more likely to have diabetes than the lowest quartile (OR: 1.80, 95% CI 1.17-2.76).

Conclusion

Our results suggest that higher sodium intake is associated with an increased risk of diabetes in the population without hypertension, and for every 1,000 mg sodium intake increment, the risk of diabetes increased by 1.20-fold. To sum up, we have provided the clue to the etiology of diabetes and further prospective research is needed to contribute recommendations for the primary prevention of diabetes in the US.

Influence of glimepiride plus sitagliptin on treatment outcome, blood glucose, and oxidative stress in diabetic patients

OBJECTIVE

This research sets out to investigate the influence of glimepiride (GLIM) plus sitagliptin (SITA) on the treatment outcome, blood glucose (BG), and oxidative stress (OS) in diabetic patients.

METHODS

In this retrospective study, 189 patient cases of type 2 diabetes mellitus (T2DM) admitted from July 2017 to July 2021 to the Affiliated Hospital of Nantong University were selected, of whom 99 cases treated with GLIM + SITA were assigned to the research group (RG) and 90 cases receiving GLIM monotherapy were set as the control group (CG). The two cohorts of patients were compared in terms of treatment outcomes, BG, islet function, OS, inflammatory responses (IRs), and safety. The BG indexes detected mainly included fasting blood glucose (FBG), 2-hour postprandial blood glucose (2hPG) and glycosylated hemoglobin (HbA1c). Islet function was mainly measured by Homeostasis Model Assessment of β-cell Function (HOMA-β) and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). The OS parameters measured primarily included malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione peroxidase (GSH-PX). Tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-18 were the inflammatory factors measured.

RESULTS

A statistically higher excellent or good rate of treatment was determined in the RG compared to the CG. After treatment, FBG, 2hPG, HbA1c, HOMA-IR, MDA, TNF-α, IL-6, and IL-18 were lower in the RG while HOMA-β, SOD, and GSH-PX were higher, compared to the levels before treatment and the CG. A non-significantly different incidence of adverse reactions between groups was determined.

CONCLUSIONS

Our findings demonstrated high efficacy of GLIM + SITA in the treatment of T2DM patients, which can effectively improve the BG and OS of patients and reduce inflammation without increasing the incidence of adverse reactions. This should have high clinical application value.