Mathematical modeling for the prediction of cerebral white matter lesions based on clinical examination data

Developing a Personalized Approach to Obstructive Sleep Apnea

All areas of medicine are focused on developing a personalized approach to diagnosis and treatment of specific conditions. This is based on the fundamental concept that all subjects with apparently the same disorder are different. There are multiple reasons for these differences. These include differences in the sequence of DNA, differences in the environment, differences in epigenetics, some of which may be driven by environmental differences and differences in the microbiome. These different factors will result in variations in multiple aspects of the phenotype. This includes different pathways to disease, different symptoms, different pattern of comorbidities and risk for adverse outcomes, and different physiological changes during sleep as a result of breathing episodes.

Asymmetric U-shaped relationship between blood glucose and white matter lesions: results of a cross-sectional study

BACKGROUND

Elderly individuals are susceptible to the accrual of White Matter Lesions (WMLs), a subcategory of cerebral small-vessel disease. WMLs are strongly linked to an increased risk of strokes, intracerebral hemorrhages, and dementia. While the relationship between blood glucose levels and the development of WMLs has been investigated in previous studies, the findings remain inconsistent. Some evidence suggests that glucose dysregulation, including both hypo- and hyperglycemia, may contribute to WML formation through mechanisms such as endothelial dysfunction and chronic inflammation. However, other studies report no significant correlation. This inconsistency underscores the need for further investigation.

METHODS

In this investigation, the primary data were derived from a predictive mathematical model designed to estimate WMLs based on parameters obtained from routine medical examinations, with head MRI scans serving as the reference standard for WML diagnosis and quantification. We leveraged multivariable logistic regression analysis to scrutinize the relationship between blood glucose concentrations and WMLs. Additionally, we employed a restricted cubic spline regression model to investigate a potential non-linear relationship between these variables.

RESULTS

There were 1904 participants who underwent medical check-ups which included a head MRI. Generally, the relationship between blood glucose levels and white matter lesions followed an asymmetric U-shaped curve (P for non-linearity = 0.004). A consistent finding was that compared to the individuals in the 2nd and 3rd quartiles (95 to 107 mg/dl), the 1st quartile (OR, 1.71; 95% CI: 1.26-2.30) and 4th quartile (OR, 1.57; 95%CI: 1.12-2.20) had white matter lesions were significantly higher.

CONCLUSION

An asymmetric U-shaped relationship exists between blood glucose and WMLs, with the lowest risk occurring at 95-107 mg/dl. Management of blood glucose can help prevent the occurrence and development of WMLs. However, the study's cross-sectional design limits causal inference, and the reliance on pre-existing data constrained the availability of variables.

Grade prediction of lesions in cerebral white matter using a convolutional neural network

We established a diagnostic method for cerebral white matter lesions using MRI images and examined the relationship between the MRI images and the medical checkup data. There were approximately 25 MRI images for each patient's head, from the top of the head to near the eyes. To order these images, we defined the unit of axial for convenience. We varied conditions, such as the location and extent of the images to be loaded, into a convolutional neural network model and verified the changes in discrimination performance on the test data. Co-occurrence network diagrams were also used to determine the relationship between the grade of cerebral white matter lesions and the biochemical test items, which were treated as categorical variables, the progression of cerebral white matter lesions, and patient health status. The convolutional neural network showed the highest discrimination performance when the images were loaded into the model with 80 pixels per side, axial from 9 to 15, along with FLAIR and T1-weighted images. The area under the curve for each grade was 0.9814 for grade 0, 0.9800 for grade 1, 0.9905 for grade 2, 0.9977 for grade 3, and 0.9998 for grade 4. In the co-occurrence network diagram, patients with no or mild cerebral white matter lesions, such as grade 0 and grade 1, had near normal blood pressure, whereas grade 2 patients were closer to (isolated) systolic hypertension. This indicates that patients with higher-grade cerebral white matter lesions tend to experience more severe hypertension.

A nomogram for predicting cerebral white matter lesions in elderly men

Objective

This study aimed to develop a nomogram tool to predict cerebral white matter lesions (WMLs) in elderly men.

Methods

Based on a retrospective cohort from January 2017 to December 2019, a multivariate logistic analysis was performed to construct a nomogram for predicting WMLs. The nomogram was further validated using a follow-up cohort between January 2020 and December 2022. The calibration curve, receiver operating characteristics (ROC) curves, and the decision curves analysis (DCA) were used to evaluate discrimination and calibration of this nomogram.

Result

A total of 436 male patients were enrolled in this study, and all 436 patients were used as the training cohort and 163 follow-up patients as the validation cohort. A multivariate logistic analysis showed that age, cystatin C, uric acid, total cholesterol, platelet, and the use of antiplatelet drugs were independently associated with WMLs. Based on these variables, a nomogram was developed. The nomogram displayed excellent predictive power with the area under the ROC curve of 0.951 [95% confidence interval (CI), 0.929-0.972] in the training cohort and 0.915 (95% CI, 0.864-0.966) in the validation cohort. The calibration of the nomogram was also good, as indicated by the Hosmer-Lemeshow test with p-value of 0.594 in the training cohort and 0.178 in the validation cohort. The DCA showed that the nomogram holds good clinical application value.

Conclusion

We have developed and validated a novel nomogram tool for identifying elderly men at high risk of WMLs, which exhibits excellent predictive power, discrimination, and calibration.

Development and validation of a predictive model for white matter lesions in young- and middle-aged people

BACKGROUND

White matter lesion (WML) is an age-related disorder associated with stroke and cognitive impairment. This study aimed to investigate the risk factors and build a predictive model of WML in young- and middle-aged people.

METHODS

We performed a second analysis of the data from the Dryad Digital Repository. We selected those people who are <60 years old and randomly divided them into the training group and the validation group. We investigated the risk factors of WML in the training group with logistic regression analysis and built a prediction nomogram based on multivariate logistic regression analysis; finally, the performance of the prediction nomogram was evaluated for discrimination, accuracy, and clinical utility.

RESULTS

There were 308 people in the training group and 723 people in the validation group. Multivariate regression analysis showed that the age (OR = 1.49, 95% CI: 1.31-1.70), diastolic blood pressure (OR = 1.02, 95% CI: 1.00-1.03), carotid plaque score (OR = 1.31, 95% CI: 1.14-1.50), female gender (OR = 2.27, 95% CI: 1.56-3.30), and metabolic syndrome (OR = 2.12, 95% CI: 1.22-3.70) were significantly associated with white matter lesions. The area under the curve value (AUC) of the receiver operating curve (ROC) was 0.734 for the training group and 0.642 for the validation group. The calibration curve and clinical impact curve showed that the prediction nomogram has good accuracy and clinical application value.

CONCLUSION

Age, diastolic blood pressure, carotid plaque score, female gender, and metabolic syndrome were risk factors in young- and middle-aged people <60 years old with WML, and the nomogram based on these risk factors showed good discrimination, accuracy, and clinical utility.

Association between Triglyceride-Glucose Index and carotid plaque in Japanese population: a cross-sectional study

OBJECTIVE

Previous studies linking Triglyceride Glucose (TyG) Index to carotid plaque have yielded inconsistent results. Moreover, related studies on the population of Japan are rare. This study aims to provide further results.

DESIGN

A hospital-based cross-sectional study.

SETTING

The Shin Takeo Hospital.

PARTICIPANTS

We assessed 1904 Japanese participants (988 men and 916 women) whose mean age was 57±11.9 years, and those participants underwent health check-ups at Shinbuf Hospital at Shin Takeo Hospital from 1 April 2016 to 31 October 2017.

METHODOLOGY

Carotid plaque, triglyceride and fasting glucose and other relevant indicators were collected. We used ultrasonography to evaluate carotid plaque. A multivariable logistic regression model and generalised additive model were used to evaluate the association between the TyG Index and carotid plaque. Subgroup and interaction analyses were validated for the consistency of these correlations.

RESULTS

Following the adjustment of traditional carotid plaque risk factors, the non-linear relationship between the TyG Index and carotid plaque was investigated. Using a two-piecewise regression model, we calculated the inflection point to be 9.06. The OR and 95% CIs for the inflection points on the left and right sides were 1.70 (1.27 to 2.29) and 0.88 (0.52 to 1.47), respectively. According to the variables tested, the interactions between the TyG Index and all subgroup factors were analysed and significant interactions were not observed.

CONCLUSION

In individuals who underwent a comprehensive check-up in Japan, the relationship between the TyG Index and carotid plaque is non-linear. When the TyG Index is less than 9.06, it is associated with carotid plaque.

Association of Carotid Atherosclerosis With White Matter Hyperintensity in an Asymptomatic Japanese Population: A Cross-Sectional Study

Objective: A limited number of scholars concentrated on the relationship between carotid atherosclerosis (CAS) and white matter hyperintensity (WMH) (i.e., CAS-WMH relationship). The current research aimed to clarify the CAS-WMH relationship in Japanese population.

Methods: All participants underwent MRI of head and ultrasonography of the carotid artery. WMH was diagnosed from MRI results. The carotid ultrasound findings, carotid artery plaque score (PS), and plaque number (PN) could be achieved to indicate the severity of CAS. We also employed multivariate logistic regression models to estimate the CAS-WMH relationship. Interaction and stratified analyses were undertaken on the basis of a number of factors (e.g., gender, age, smoking status, drinking habit, and history of chronic diseases).

Results: A total of 1,904 Japanese subjects were included, and the prevalence of WMH was 54.8% (1,044/1,904). It was unveiled that frequency of CAS was greater in cases with WMH. In a fully adjusted model, high PS was associated with the frequency of WMH, followed by high PN. Further analyses revealed a dose-response relationship between PS and incidence of WMH.

Conclusion: PS and PN exhibited the greatest influences on determining the frequency of WMH, highlighting the potentially important pathophysiological role of large artery atherosclerosis in intensifying WMH.