Association of disease duration and sensorimotor function in type 2 diabetes mellitus: beyond diabetic peripheral neuropathy

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus

Objective

Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition.

Methods

In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency.

Results

Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN.

Conclusion

This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.

An IoT-based cognitive impairment detection device: A newly proposed method in older adults care-choice reaction time-device development and data-driven validation

Background

Research shows that older adults' performance on choice reaction time (CRT) tests can predict cognitive decline. A simple CRT tool could help detect mild cognitive impairment (MCI) and preclinical dementia, allowing for further stratification of cognitive disorders on-site or via telemedicine.

Objective

The primary objective was to develop a CRT testing device and protocol to differentiate between two cognitive impairment categories: (a) subjective cognitive decline (SCD) and non-amnestic mild cognitive impairment (na-MCI), and (b) amnestic mild cognitive impairment (a-MCI) and multiple-domain a-MCI (a-MCI-MD).

Methods

A pilot study in Italy and Romania with 35 older adults (ages 61-85) assessed cognitive function using the Mini-Mental State Examination (MMSE) and a CRT color response task. Reaction time, accuracy, and demographics were recorded, and machine learning classifiers analyzed performance differences to predict preclinical dementia and screen for mild cognitive deficits.

Results

Moderate correlations were found between the MMSE score and both mean reaction time and mean accuracy rate. There was a significant difference between the two groups' reaction time for blue light, but not for any other colors or for mean accuracy rate. SVM and RUSBoosted trees were found to have the best preclinical dementia prediction capabilities among the tested classifier algorithms, both presenting an accuracy rate of 77.1%.

Conclusions

CRT testing with machine learning effectively differentiates cognitive capacities in older adults, facilitating early diagnosis and stratification of neurocognitive diseases and can also identify impairments from stressors like dehydration and sleep deprivation. This study highlights the potential of portable CRT devices for monitoring cognitive function, including SCD and MCI.

Diabetic peripheral neuropathy is associated with diabetic kidney disease and cardiovascular disease – The Silesia Diabetes-Heart Project

Microvascular complications of diabetes seem to be clustered and put patients at higher risk of developing cardiovascular disease (CVD). This was a questionnaire-based study designed to screen for the presence of diabetic peripheral neuropathy (DPN), defined as the score in the Michigan Neuropathy Screening Instrument (MNSI) above 2, and to evaluate its association with other complication of diabetes, including CVD. There were 184 patients included into the study. The prevalence of DPN in the study group was 37.5%. The regression model analysis revealed that the presence of DPN was significantly associated with the presence of diabetic kidney disease (DKD) (P = 0.0034;) and patient's age (P < 0.0001). Thirty-four patients (49.3%) with MNSI score >2 were diagnosed with CVD in comparison to 24 (20.1%) subjects with MNSI score ≤ 2 (P = 0.00006). In case of having one diabetes complication diagnosed, it is important to screen for others, including macrovascular ones.

Glycated albumin and HbA1c as markers of lower extremity disease inUS adults with and without diabetes

AIM

We evaluated the associations of two biomarkers of hyperglycemia-hemoglobin A1c(HbA1c) and glycated albumin-with lower extremity disease in US adultsoverall and by diabetes status.

METHODS

We conducted a cross-sectional study of adult participants aged ≥ 40 years who attended the National Health and Nutrition Examination Survey (NHANES) 1999-2004 (unweighted N = 5,785). We used logistic regression to evaluate the associations of HbA1c and glycated albumin with lower extremity disease: peripheral neuropathy (assessed by monofilament test), peripheral artery disease (assessed by ankle-brachial index), history of foot ulcer, or amputation. All analyses were weighted and accounted for the complex NHANES sample survey design.

RESULTS

The prevalence of lower extremity disease was 17.4% (15.9% in adults without diabetes and 33.2% in adults with diabetes). HbA1c and glycated albumin were not significantly associated with lower extremity disease in adults without diabetes. However, we observed significant associations of both HbA1c (OR 1.19 per 1-% point increase, 95 %CI 1.06-1.34) and glycated albumin (OR 1.06 per 1-% point increase, 95 %CI 1.02-1.10) with lower extremity disease in adults with diabetes after adjustment. The patterns of association were similar for HbA1c and glycated albumin (P-for-seemingly-unrelated-regression = 0.60), with strong linear associations observed at high (diabetic) levels of both biomarkers.

CONCLUSIONS

Our study suggests the importance of diabetes prevention and glycemic control in adults with diabetes to reduce the burden of lower extremity disease.

Aberrant Interhemispheric Functional Connectivity in Diabetic Retinopathy Patients

Background: Accumulating lines of evidence demonstrated that diabetic retinopathy (DR) patients trigger abnormalities in brain’s functional connectivity (FC), whereas the alterations of interhemispheric coordination pattern occurring in DR are not well understood. Our study was to investigate alterations of interhemispheric coordination in DR patients.

Methods: Thirty-four DR individuals (19 males and 15 females: mean age: 52.97 ± 8.35 years) and 37 healthy controls (HCs) (16 males and 21 females; mean age: 53.78 ± 7.24 years) were enrolled in the study. The voxel-mirrored homotopic connectivity (VMHC) method was conducted to investigate the different interhemispheric FC between two groups. Then, the seed-based FC method was applied to assess the different FCs with region of interest (ROI) in the brain regions of decreased VMHC between two groups.

Results: Compared with HC groups, DR groups showed decreased VMHC values in the bilateral middle temporal gyrus (MTG), lingual/calcarine/middle occipital gyrus (LING/CAL/MOG), superior temporal gyrus (STG), angular (ANG), postcentral gyrus (PosCG), inferior parietal lobule (IPL), and precentral gyrus (PreCG). Meanwhile, altered FC includes the regions of auditory network, visual network, default mode network, salience network, and sensorimotor network. Moreover, a significant positive correlation was observed between the visual acuity-oculus dexter (OD) and zVMHC values in the bilateral LING/CAL/MOG (r = 0.551, p = 0.001), STG (r = 0.426, p = 0.012), PosCG (r = 0.494, p = 0.003), and IPL (r = 0.459, p = 0.006) in DR patients.

Conclusion: Our results highlighted that DR patients were associated with substantial impairment of interhemispheric coordination in auditory network, visual network, default mode network, and sensorimotor network. The VMHC might be a promising therapeutic target in the intervention of brain functional dysfunction in DR patients.