Age of Onset and Risk Factors for MRI-Detected White Matter Hyperintensities in a Lahore Population: A Cross-Sectional Study

Age of Onset and Risk Factors for MRI-Detected White Matter Hyperintensities in a Lahore Population: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Age of Onset and Risk Factors for MRI-Detected White Matter Hyperintensities in a Lahore Population: A Cross-Sectional Study Ali Sayedain Jaffar, Muhammad Sajeel Latif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7431094/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background White matter hyperintensities (WMHs) detected on MRI are established neuroimaging markers of cerebrovascular disease, vascular dementia, and cognitive decline. The age of onset and associated vascular risk factors for WMHs remain underexplored in South Asian populations. Identifying these associations may help guide early prevention and risk management strategies. Methods This cross-sectional study was conducted over seven months at Shalamar Hospital, Lahore. A total of 145 participants aged 30–86 years were included; individuals with a history of stroke or intracranial hemorrhage were excluded. Brain MRI scans were acquired using a 1.5T scanner. Demographic and clinical data were collected, and analysis of covariance (ANCOVA) was performed to assess the relationship between WMHs and vascular risk factors, including diabetes, hypertension, and smoking. Results The study cohort included both male and female participants with a mean age of 46 years. WMHs were observed as early as age 30. Increasing age, diabetes, hypertension, and the interaction between diabetes and hypertension were significantly associated with WMH burden. Participants with either diabetes or hypertension had a greater burden of WMHs compared to those without these conditions, and individuals with both risk factors demonstrated the highest burden. Smoking, however, did not show a statistically significant association with WMHs. Conclusion This study demonstrates that WMHs can occur in relatively young adults and are strongly associated with diabetes and hypertension in a South Asian population. These findings emphasize the importance of early screening, vascular risk factor management, and consideration of neuroimaging in at-risk individuals to mitigate future cerebrovascular and cognitive complications. White Matter Hyperintensities Magnetic Resonance Imaging Cerebrovascular Disorders Diabetes Mellitus Hypertension Risk Factors Neuroepidemiology South Asian Population Figures Figure 1 Figure 2 Figure 3 Background The human brain is a complex organ composed of various types of tissue, including gray matter, white matter, and cerebrospinal fluid. As individuals age, changes in the brain's structure and function occur, which may affect cognitive performance and increase the risk of developing neurological disorders. One of the most common agerelated changes in the brain is the presence of white matter hyperintensities (WMHs) in white matter [ 1 ].WMHs are areas of hyperintense signal intensity on magnetic resonance imaging (MRI) that reflect damage to small blood vessels in the brain, typically resulting from chronic hypoperfusion or ischemia. WMHs can lead to a variety of neurological symptoms, including cognitive impairment, depression, and gait disturbances, and have been associated with an increased risk of developing dementia [ 2 ]. Agerelated changes in the brain, particularly the development of senile MRI hyperintensities, have gained considerable attention owing to their correlation with cognitive decline and heightened risk of neurodegenerative disorders. These hyperintensities, which are common as white matter hyperintensities (WMHs) and periventricular hyperintensities (PVHs) on MR images, indicate areas of heightened signal intensity and are often indicative of underlying small vessel disease (SVD) or cerebrovascular pathology. As individuals age, the emergence of these hyperintensities becomes increasingly apparent, emphasizing the importance of comprehending their initiation and progression within the context of aging. While aging is generally associated with the presence of senile MRI hyperintensities, the specific age at which these alterations become noticeable remains under investigation. Identifying the onset age of senile MRI hyperintensities is vital for understanding the natural progression of cerebral small vessel disease and its potential impact on cognitive function and neurological wellbeing among aging individuals. Additionally, pinpointing the age of appearance of these hyperintensities has implications for implementing strategies aimed at early detection and intervention to uphold brain health in older populations [ 1 ]. In line with the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE), white matter hyperintensities (WMHs) are defined as signal abnormalities within cerebral white matter on T2weighted magnetic resonance imaging (MRI), devoid of cavitation and distinct from cerebrospinal fluid [ 2 ]. These WMHs are commonly detected via MRI scans of healthy elderly individuals and have been associated with various neurological and geriatric conditions [ 3 , 1 ]. MRI is highly sensitive to changes in cerebral white matter. Typically, damaged white matter exhibits prolonged T2 relaxation times due to increased tissue water content and degradation of the macromolecular structure of myelin. Consequently, WMHs are readily identifiable on conventional proton density (PD) and T2weighted spinecho or fast spinecho sequences, with increased visibility on fluidattenuated inversion recovery (FLAIR) images. The presence, morphology, and severity of WMH offer valuable insights into healthy aging and the pathophysiology of diverse disorders. However, classifying WMHs is challenging because of their heterogeneous patterns, ranging from large confluent periventricular WMHs to punctate lesions in deep white matter. Visual rating scales such as the Scheltens or Fazekas scales often lack comparability, are sensitive to clinical differences, and exhibit significant variability and ceiling/floor effects, leading to inconsistencies in WMH studies [ 4 – 7 ]. Given the growing interest in brain research and the need for efficient clinical studies involving large cohorts, an automated approach to WMH detection is desirable. While several fully automated methods for WMH segmentation exist, none have clearly demonstrated superiority over other methods. Each method described in the literature has strengths and weaknesses, which are often associated with the imaging modalities used and the abnormalities detected. Although some techniques have been developed for lesion detection in multiple sclerosis (MS) patients, they exhibit moderate performance when applied to geriatric populations because of agerelated decreases in gray matter white matter contrast and differences in lesion boundary characteristics [ 8 – 10 ]. The prevalence of WMH increases with age, with some studies suggesting that up to 95% of individuals over the age of 65 have some degree of WMH. These areas of increased signal intensity in the white matter are thought to reflect damage to small blood vessels and have been associated with a variety of neurological symptoms, including cognitive impairment, gait disturbances, and depression. As the population ages, understanding the prevalence and clinical implications of WMH is becoming increasingly important for both clinical and research purposes. Understanding the risk factors and pathophysiology underlying the development of WMH is essential for developing effective prevention and treatment strategies for agerelated neurological disorders [ 11 ]. The pathophysiology of WMH is not fully understood, but it is believed that chronic hypoperfusion and ischemia lead to damage to the small vessels in the white matter. Other factors that may contribute to the development of WMH include hypertension, diabetes, and smoking. WMHs are associated with an increased risk of developing cognitive decline and dementia and may be early markers of cerebrovascular disease [ 12 ]. Magnetic resonance imaging (MRI) is a noninvasive technique that has become a valuable tool for studying white matter hyperintensities (WMHs) of the brain in aging individuals. WMHs appear as areas of increased signal intensity in fluidattenuated inversion recovery (FLAIR) images, and their presence and severity can be quantified via various rating scales. MRI has also been used to study the relationship between WMH and cognitive function, as well as the progression of WMH over time. MRI is particularly useful in identifying the location and distribution of WMHs, which can provide clues about the underlying pathophysiology. Additionally, MRI has the advantage of being able to detect other brain abnormalities that may be associated with WMHs, such as cortical atrophy, lacunar infarcts, and microbleeds. These findings can help clinicians better understand the potential clinical implications of WMH and identify individuals at high risk for developing neurological disorders [ 13 ]. Magnetic resonance imaging (MRI) hyperintensities, particularly those observed in cerebral white matter, have garnered significant attention as potential biomarkers for cerebrovascular pathology and are associated with various neurological and cognitive conditions. Among the tools used for their assessment, the Fazekas scale stands out as a widely employed method for the qualitative grading of these hyperintensities, offering a standardized approach to their characterization and severity assessment. Recently, a new study by Smith et al. (2023) has shed further light on the clinical utility of the Fazekas scale in assessing MRI hyperintensity. In their investigation, Smith et al. explored the interrater reliability and diagnostic accuracy of the Fazekas scale in a large cohort of elderly individuals with varying degrees of cognitive impairment. The study revealed strong interrater agreement among experienced neuroradiologists using the Fazekas scale, highlighting its reliability as a tool for assessing white matter hyperintensities (WMHs) across different raters and settings. The Fazekas scale, originally proposed by Fazekas et al. in 1987, categorizes MRI hyperintensities on the basis of their appearance in T2weighted or fluidattenuated inversion recovery (FLAIR) images, with scores ranging from 0–3 corresponding to increasing severity. Grade 0 denotes the absence of hyperintensities, whereas grades 1 to 3 signify mild, moderate, and severe hyperintensities, respectively [ 14 ]. The scale considers the location and distribution of hyperintensities, distinguishing between periventricular and deep white matter involvement. The findings of the study by Smith et al. underscore the relevance of the Fazekas scale in clinical research and practice, particularly in the context of neuroimaging studies focusing on cerebrovascular disease and cognitive impairment. By providing a standardized method for assessing and grading WMHs, the Fazekas scale facilitates comparability and consistency in evaluating the burden of MRI hyperintensities across different patient populations. This review aimed to provide an overview of the Fazekas scale, its application in the assessment of MRI hyperintensities, and its implications for clinical practice and research, with particular emphasis on the recent findings of Smith et al. (2023). MRI hyperintensities play crucial roles in risk stratification, prognosis, and treatment planning in older patients. Their assessment aids clinicians in identifying individuals at greater risk of developing cognitive impairment or stroke, guiding preventive interventions and therapeutic strategies to mitigate adverse outcomes [ 14 ]. Magnetic resonance imaging (MRI) hyperintensities, particularly those found in cerebral white matter, have emerged as critical markers for understanding the interplay between cerebrovascular pathology and various vascular risk factors, such as diabetes, hypertension, and smoking. These hyperintensities signify structural changes within the brain and are often associated with an increased risk of cerebrovascular events and cognitive decline in older adults. The relationship between MRI hyperintensities and vascular risk factors has garnered considerable attention in clinical research. Diabetes, characterized by chronic hyperglycemia and insulin resistance, has been implicated in the pathogenesis of cerebral small vessel disease, leading to the formation of white matter hyperintensities (WMHs) and microvascular lesions [ 15 ]. Similarly, hypertension, a wellestablished risk factor for stroke and cardiovascular disease, contributes to the development and progression of WMH through its effects on cerebral blood flow regulation and vascular remodeling [ 2 ]. Additionally, smoking, known for its detrimental effects on vascular health, has been linked to an increased burden of MRI hyperintensity and a greater risk of cerebrovascular events [ 12 ]. WMHs are common findings on brain MRI in older individuals, and their prevalence increases with age and is associated with a variety of risk factors, including hypertension, diabetes, smoking, and cardiovascular disease. The pathophysiology of WMH is not fully understood. By studying the location and distribution of WMHs on MRI, as well as their relationship with other brain abnormalities, researchers may gain insights into the underlying mechanisms that contribute to the development and progression of WMHs. Methods This is a cross-sectional descriptive study conducted at the Radiology Department of Shalamar Hospital, Lahore, over a period of seven months, from December 2024 to June 2025. 2.1 MRI equipment and imaging technique : MRI scans were performed using a 1.5 Tesla Philips MRI scanner. Patients are instructed to remove all metal objects or clothing containing metal, such as jewelry or underwire bras, prior to the examination. Each patient is positioned supine on the scanner bed, and a dedicated head coil is used to ensure proper alignment and minimize motion artifacts. Images are acquired via a fast spinecho (FSE) pulse sequence with the following parameters: Repetition time (TR): 3000–5000 ms Echo Time (TE): 80–120 ms Slice Thickness:3–5 mm Field of view (FOV): 22–26 cm Axial scan planes are used, covering the entire brain from the top to the skull base. The total scan time ranged from approximately 10 to 20 minutes, depending on the number of slices acquired. Postprocessing included adjustments to brightness and contrast and the application of noise-reduction filters. 2.2 Integration of MRI findings On the basis of the radiological assessment, each participant was assigned a categorical code representing the degree of white matter hyperintensity observed on MRI. These scores, referred to as Code_Hyperintensities, are used as outcome variables in the statistical analysis. 2.3 Ethical considerations Written informed consent was obtained from all participants prior to their enrollment in the study. All collected data were kept strictly confidential, and participant anonymity was maintained throughout. Participants were fully informed about the study objectives and were assured that the procedures posed no adverse effects or risks. They were also informed of their right to withdraw from the study at any time without consequences. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki, and ethical approval was obtained from the Ethical Review Committee of Faisalabad Medical University, Faisalabad, Pakistan. 2.4 Data collection Data are collected via structured data collection sheets on the basis of operational definitions of the study variables. Demographic information and clinical history, including age, sex, presence of diabetes, hypertension, and smoking status, were recorded. 2.5 Statistical analysis The data were analyzed via MedCalc version 20.215. Quantitative variables are expressed as the means and standard deviations, whereas qualitative variables are presented as frequencies and percentages. Analysis of covariance (ANCOVA) was used to assess the effects of age, diabetes, hypertension, and their interaction on the severity of WMHs. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of age for WMH presence. Odds ratios are calculated to measure associations between clinical risk factors and WMHs. The results are presented in the form of tables, charts, and graphs. Results A total of 145 participants were included in the study, with 67 females (46.2%) and 78 males (53.8%). The participants ranged in age from 30–86 years, with a mean age of 46 years. ANCOVA revealed significant effects of age (F = 7.42, p = 0.01), diabetes (F = 14.29, p < 0.001), hypertension (F = 69.52, p < 0.001), and the interaction between diabetes and hypertension (F = 6.65, p = 0.01) on white matter hyperintensity. These variables explained 41.9% of the variance in WMH severity (R² = 0.419; adjusted R² = 0.4024). Odds ratio analysis revealed that diabetic participants had significantly greater odds of WMH (OR = 3.38, 95% CI: 1.63–7.02; z = 3.297, p = 0.001), whereas hypertensive individuals demonstrated a much stronger association (OR = 16.26, 95% CI: 6.54–40.44; z = 6.000, p < 0.001). Table 1 Effects of Diabetes and Hypertension on Code Hyperintensities (Adjusted for Age) Source Sum of Squares df Mean Square F p value Corrected Model 14.74 4.00 3.68 25.24 < 0.001*** Intercept 11.12 1.00 11.12 76.17 < 0.001*** Age 1.08 1.00 1.08 7.42 0.01** Diabetes 2.09 1.00 2.09 14.29 < 0.001*** Hypertension 10.15 1.00 10.15 69.52 < 0.001*** Diabetes × Hypertension 0.97 1.00 0.97 6.65 0.01** Residual 20.44 140.00 0.15 Total 85.00 145.00 Corrected Total 35.17 144.00 R² = 0.4190, Adjusted R² = 0.4024 (Table 1 : Effects of Diabetes and Hypertension on Code Hyperintensities (Adjusted for Age) ANCOVA results showing the effects of age, diabetes, hypertension, and their interaction on code hyperintensities. Fstatistic (variance ratio), df = degrees of freedom, R² = coefficient of determination, Adj. R² = adjusted coefficient of determination, p = probability value. If p < 0.05*, p < 0.01**, p < 0.001***.) Table 1 shows the results of the ANCOVA, which revealed that age, diabetes, hypertension, and their interaction had significant impacts on the presence of hyperintensities, even after controlling for age. The overall model is statistically significant (F = 25.24, p < 0.001) and explains approximately 42% of the variance in hyperintensities (adjusted R² = 0.4024). The most influential factor was hypertension (F = 69.52, p < 0.001), followed by diabetes (F = 14.29, p < 0.001) and age (F = 7.42, p = 0.01). The significant interaction between diabetes and hypertension (F = 6.65, p = 0.01) indicates that the combined presence of both conditions has a distinct effect on hyperintensities compared with having only one. These findings indicate that both diabetes and hypertension independently and interactively influence hyperintensity outcomes, even when controlling for age. Table 2 Test of Homogeneity of Slopes Source Sum of Squares df Mean Square F-Stats p value Heterogeneity of Slopes 0.87 3.00 0.29 2.02 0.11 Individual Residual 19.57 137.00 0.14 If p < 0.05*, p < 0.01**,p < 0.001***, *Fstatistic (variance ratio), df (degrees of freedom) (Table 2 : Test of Homogeneity of Slopes Test of homogeneity of regression slopes assessing whether the relationship between age and hyperintensity scores differs across groups defined by diabetes and hypertension status. Significance levels: p < 0.05, p < 0.01**, p < 0.001***. F = Fstatistic (variance ratio); df = degrees of freedom.*) The test of homogeneity of regression slopes in Table 2 shows that the relationship between age and white matter hyperintensity is consistent across both groups being compared. The findings indicate that there is no statistically significant interaction in terms of the heterogeneity of slopes. The influence of age on hyperintensities (diabetes, hypertension, and their interaction) did not substantially differ between the groups, indicating that the assumption of homogeneity of regression slopes was met. This confirms that using ANCOVA to analyze adjusted group differences in hyperintensities is valid. Since the significance level for the heterogeneity of slopes is 11%, which is higher than the 5% threshold, it is concluded that the assumption of homogeneity of regression slopes is upheld. Hence, the results of ANCOVA are consistent regardless of how age and hyperintensity vary among groups. Table 3 Descriptive Statistics for Code Hyperintensities by Diabetes Status Diabetes Status n Mean Standard Error 95% Confidence Interval Diabetic 60.00 0.77 0.05 0.67to 0.86 Non-Diabetic 85.00 0.52 0.04 0.43 to 0.60 (Table 3 : Descriptive Statistics for Code Hyperintensities by Diabetes Status Descriptive statistics showing the mean white matter hyperintensity scores among diabetic and nondiabetic participants. CI = confidence interval; n = number of participants.) Table 3 presents descriptive statistics for white matter hyperintensities by diabetes status. The results revealed that individuals with diabetes have a higher mean hyperintensity score than nondiabetic individuals do. Similarly, the confidence intervals between groups do not overlap much, indicating the likelihood of a real difference between the groups. This descriptive difference aligns with the statistically significant findings. Table 4 Pairwise comparison of Code Hyperintensities by Diabetes Status Comparison Mean Difference Standard Error p-value 95% CI Diabetic – Nondiabetic 0.25 0.07 0.00*** 0.12 to 0.38 If p < 0.05*, p < 0.01**,p < 0.001*** (Table 4 : Pairwise comparison of Code Hyperintensities by Diabetes Status Pairwise comparison of the mean hyperintensity scores between the diabetic and nondiabetic groups, including confidence intervals (CIs) and statistical significance. CI = confidence interval. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.*) Table 4 shows the pairwise comparisons between diabetic and nondiabetic individuals. The mean hyperintensity score in diabetic patients was 48% higher than that in nondiabetic patients; this difference was highly statistically significant. The confidence interval indicates that the true difference is unlikely to be zero and is consistent with higher hyperintensity levels in the diabetic group. This finding also aligns with previous results, highlighting a meaningful difference in hyperintensity levels between diabetic and nondiabetic individuals. Table 5 Descriptive Statistics for Code Hyperintensities by Hypertension Status Hypertension Status n Mean Standard Error 95% Confidence Interval Hypertensive 65.00 0.93 0.05 0.82 to 1.02 Non-Hypertensive 80.00 0.36 0.04 0.27 to 0.45 Here: n = number of individuals (Table 5 : Descriptive Statistics for Code Hyperintensities by Hypertension Status Descriptive statistics comparing the mean hyperintensity scores between hypertensive and nonhypertensive individuals. n = number of individuals. CI = confidence interval.) The results in Table 5 indicate that individuals with hypertension have greater hyperintensities than nonhypertensive individuals do. On average, the hyperintensity levels in the hypertensive group were approximately 2.5 times higher than those in the nonhypertensive group. This substantial difference suggests that hyperintensities are much more common among people with hypertension. These findings support the conclusion that hypertension is strongly associated with increased hyperintensity. Table 6 Pairwise comparison of Code Hyperintensities by Hypertension Status Comparison Mean Difference Standard Error p value 95% Confidence Interval Hypertensive - nonhypertensive 0.56 0.07 < 0.0001*** 0.43 to 0.70 If p < 0.05*, p < 0.01**,p < 0.001*** (Table 6 : Pairwise comparison of Code Hyperintensities by Hypertension Status Pairwise comparison of mean hyperintensity scores between hypertensive and nonhypertensive participants, including confidence intervals and statistical significance. CI = confidence interval. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.*) The results in Table 6 show that individuals with hypertension have significantly greater hyperintensities than do those without hypertension. On average, their hyperintensity scores are approximately 0.56 points higher. This difference is clear and highly statistically significant. These results confirm that hyperintensities are more common in individuals with high blood pressure. Table 7 Descriptive Statistics for the Combined Diabetes and Hypertension Groups Group n Mean Standard Error 95% Confidence Interval Diabetic & Hypertensive 30.00 0.97 0.07 0.82 to 1.10 Diabetic &non hypertensive 30.00 0.57 0.07 0.43 to 0.71 Non-Diabetic & Hypertensive 35.00 0.89 0.07 0.75 to 1.01 Non-Diabetic & Non-Hypertensive 50.00 0.16 0.06 0.04to 0.26 (Table 7 : Descriptive Statistics for Patients with Combined Diabetes and Hypertension descriptive statistics showing the mean hyperintensity scores across the four groups on the basis of combined diabetes and hypertension status. n = number of individuals. CI = confidence interval.) The results in Table 7 show that individuals with both diabetes and hypertension have the highest levels of hyperintensity. In comparison, those with neither condition have the lowest levels. Individuals with hypertension only present high hyperintensity levels, whereas those with diabetes only present moderate hyperintensity levels. These findings suggest that having both diabetes and hypertension leads to the greatest increase in hyperintensities. Hypertension alone causes a substantial increase, whereas diabetes alone has a smaller effect. Individuals with neither diabetes nor hypertension presented the lowest hyperintensity levels overall. Table 8 Descriptive Statistics for the Study Variables Variable Mean Standard Deviation Code Hyperintensities 0.59 0.49 Age (years) 49.88 17.59 (Table 8 : Descriptive Statistics for the Study Variables Descriptive statistics for code hyperintensity scores and participant age, including means and standard deviations. SD = standard deviation.) Table 8 shows that the average hyperintensity score is 0.59, with a standard deviation of 0.49, indicating that hyperintensity levels vary across individuals. The average age of the participants was approximately 50 years, with a standard deviation of 17.6 years, suggesting that the sample included both younger and older individuals. Table 9 Shapiro‒Wilk test for normality (code hyperintensities) Statistic W p value Code Hyperintensities 0.98 0.04* If p < 0.05*, p < 0.01**,p < 0.001*** (Table 9 : Shapiro‒Wilk test for normality (code hyperintensities) Shapiro‒Wilk test for normality of code hyperintensity scores to assess the assumption of a normal distribution. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.*) The Shapiro‒Wilk test was used to assess whether the hyperintensity scores were normally distributed. The test result is statistically significant, which means that the data do not perfectly follow a normal distribution. However, the deviation from normality is relatively small. These results suggest that the assumption of normality is slightly violated for the hyperintensity variable. ROC and Association Analysis Table 10 Descriptive statistics of associations for hypertension and Code Hyperintensities Code Hyperintensities Hypertensive (n = 65) Non-Hypertensive (n = 80) Total (n = 145) Percentage No 7 53 60 41.4% Yes 58 27 85 58.6% Total 65 80 145 100% (Table 10 : Descriptive statistics of associations for hypertension and Code Hyperintensities Distribution of white matter hyperintensity among hypertensive and nonhypertensive individuals. n = number of participants) The results revealed a clear difference in hyperintensity levels between hypertensive and nonhypertensive individuals. Among the hypertensive group (n = 65), the majority (58 individuals, or approximately 89%) had hyperintensities. In contrast, among the nonhypertensive group (n = 80), most individuals (53, or approximately 66%) had no hyperintensities. Overall, of all the 145 participants, 58.6% had hyperintensities, whereas 41.4% did not. Table 11 Association measures for hypertension and code intensities Measure Value 95% Confidence Interval z-Statistic p Value Relative Risk (RR) 2.64 1.92 to 3.64 5.98 < 0.001*** Odds Ratio (OR) 16.26 6.54 to 40.44 6.00 < 0.001*** If p < 0.05*, p < 0.01**,p < 0.001*** (Table 11 : Association measures for hypertension and code intensities) Relative risk and odds ratio estimates showing the association between hypertension and the presence of white matter hyperintensities, with confidence intervals and statistical significance. CI = confidence interval; OR = odds ratio; RR = relative risk; z = zstatistic. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.) The results revealed a strong association between hypertension and hyperintensity. Individuals with hypertension were approximately 2.6 times more likely to have hyperintensities than were those without hypertension, as shown in Fig. 1 . The odds of having hyperintensities were approximately 16 times greater in the hypertensive group. Both of these results are highly statistically significant. Similarly, the large zvalues provide strong statistical evidence that hypertension is linked to an increased likelihood of hyperintensities. Frequency bar graph of hypertensive and nonhypertensive individuals showing the distribution of white matter hyperintensity across both groups) Table 12 ROC analysis summary for age Variable AGE Sample Size 145 Positive Group (Code Hyperintensities = Yes) 85 (58.62%) Negative Group (Code Hyperintensities = No) 60 (41.38%) AUC (Area Under ROC Curve) 0.53 Standard Error 0.05 95% Confidence Interval 0.45 to 0.61 z-statistic 0.60 p value (Area = 0.5) 0.55 Youden Index (J) 0.11 Optimal Cutoff (Criterion) Age > 59 Sensitivity at Cutoff (%) 37.65 Specificity at Cutoff (%) 73.33 (Table 12 : ROC Analysis Summary for Age ROC analysis assessing the ability of age to predict the presence of white matter hyperintensity. The variables included the area under the curve (AUC), sensitivity, specificity, optimal age cutoff, and Youden index. AUC = area under the curve; CI = confidence interval; J = Youden Index. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.) ROC analysis was conducted to assess whether age could predict the presence of hyperintensities. The results revealed an AUC (area under the curve) of 0.53, which is very close to 0.5. This finding indicates that age is not a strong or reliable predictor of hyperintensity. The pvalue is 0.55, confirming that the finding is not statistically significant. The analysis suggested that the best cutoff point is age over 59 years; however, even at this threshold, the ability to correctly identify individuals with hyperintensities (sensitivity) is only 37.65%, whereas the ability to correctly identify those without hyperintensities (specificity) is 73.33%. The Youden index was 0.11, further indicating that age alone was not a good predictor of hyperintensity in this study. Table 13 Detailed Age Cutoff Performance for Code Hyperintensity Prediction Age Criterion Sensitivity (%) 95% CI Specificity (%) 95% CI +LR -LR ≥ 7 100.00 95.8–100.0 0.00 0.0–6.0 1.00 > 7 100.00 95.8–100.0 1.67 0.04–8.9 1.02 0.00 > 9 98.82 93.6–100.0 3.33 0.4–11.5 1.02 0.35 > 13 96.47 90.0–99.3 3.33 0.4–11.5 1.00 1.06 > 17 96.47 90.0–99.3 8.33 2.8–18.4 1.05 0.42 > 18 95.29 88.4–98.7 11.67 4.8–22.6 1.08 0.40 > 20 94.12 86.8–98.1 11.67 4.8–22.6 1.07 0.50 > 21 94.12 86.8–98.1 13.33 5.9–24.6 1.09 0.44 > 23 92.94 85.3–97.4 13.33 5.9–24.6 1.07 0.53 > 25 92.94 85.3–97.4 16.67 8.3–28.5 1.12 0.42 > 30 85.88 76.6–92.5 16.67 8.3–28.5 1.03 0.85 > 31 81.18 71.2–88.8 18.33 9.5–30.4 0.99 1.03 > 35 74.12 63.5–83.0 18.33 9.5–30.4 0.91 1.41 > 38 74.12 63.5–83.0 21.67 12.1–34.2 0.95 1.19 > 39 70.59 59.7–80.0 23.33 13.4–36.0 0.92 1.26 > 40 69.41 58.5–79.0 28.33 17.5–41.4 0.97 1.08 (Table 13 : Detailed Age Cutoff Performance for Code Hyperintensity Prediction Diagnostic performance of different age cutoff points for predicting white matter hyperintensity, including sensitivity, specificity, and positive and negative likelihood ratios. +LR = positive likelihood ratio; −LR = negative likelihood ratio; CI = confidence interval.) The detailed analysis in Table 13 of different age cutoff points shows how age performs in predicting hyperintensities. At very low cutoffs, such as age ≥ 7 years, the sensitivity was 100%, indicating that all individuals with hyperintensities were correctly identified. However, the specificity is only 0–1.7%, indicating a failure to identify individuals without hyperintensities. As the age cutoff increases, the sensitivity gradually decreases, and the specificity increases slightly. For example, at age > 40, the sensitivity decreases to 69%, whereas the specificity increases to approximately 28%. Throughout the table, the positive likelihood ratios (+ LR) remain close to 1, and the negative likelihood ratios (− LR) are also weak, indicating that age alone does not meaningfully improve the prediction of hyperintensities at any cutoff point. No age cutoff provides a good balance of sensitivity and specificity. Age alone is not a strong or reliable predictor for identifying individuals with or without hyperintensities. This trend is also depicted in Fig. 2 . The AUC was 0.529 (p = 0.548), indicating poor discriminatory ability. The optimal cutoff was age > 59 years, with 37.6% sensitivity and 78.3% specificity. Table 14 Descriptive statistics of the associations between smoking and code hyperintensities (n = 145) Code Hyperintensities Non-Smoker (n = 103) Smoker (n = 42) Total (n = 145) Percentage No 47 13 60 41.4% Yes 56 29 85 58.6% Total 103 42 145 100.0% (Table 14 : Descriptive statistics of the associations between smoking and code hyperintensities (n = 145). Number of smokers and nonsmokers with and without white matter hyperintensity. n = number of participants.) Table 14 shows the associations between smoking status and hyperintensity. Among nonsmokers, just over half had hyperintensities, whereas the remaining did not. In contrast, a greater proportion of smokers had hyperintensities, with the rest not affected. Overall, 58.6% of the total sample had hyperintensities, as shown in Fig. 3 . These results suggest that hyperintensities were somewhat more common in smokers than in nonsmokers within this sample. The figure displays the distribution of white matter hyperintensities among smokers and nonsmokers. A greater proportion of hyperintensities was observed in the smoking group than in the nonsmoking group. Table 15 Association Measures between Smoking and Code Hyperintensities Measure Value 95% Confidence Interval z-Statistic p Value Relative Risk (RR) 1.27 0.97 to 1.66 1.74 0.08 Odds Ratio (OR) 1.87 0.88 to 4.01 1.62 0.11 If p < 0.05*, p < 0.01**,p < 0.001*** (Table 15 : Association Measures between Smoking and Code Hyperintensities Estimates of relative risk and odds ratios for the presence of white matter hyperintensities among smokers compared with nonsmokers, including confidence intervals and pvalues. CI = confidence interval; OR = odds ratio; RR = relative risk; z = zstatistic. Significance levels: p < 0.05, p < 0.01**, p < 0.001***.*) Table 15 shows the associations between smoking and hyperintensities, which were examined via relative risk and odds rratios. The relative risk (RR) is 1.27, indicating that smokers are approximately 27% more likely to have hyperintensities than nonsmokers are. However, the 95% confidence interval (0.97–1.66) includes 1, and the pvalue is 0.08, which means that this result is not statistically significant. Similarly, the odds ratio (OR) was 1.87, suggesting that smokers had nearly double the odds of having hyperintensities compared with nonsmokers. However, again, the confidence interval (0.88–4.01) includes 1, and the pvalue is 0.11, so this finding is also not statistically significant. Discussion Compared with Zhuang et al. (2018) [ 16 ] and Olowolafe et al. (2022) [ 17 ], our study offers new insights into the relationship between senile MRI hyperintensities and vascular risk factors in a South Asian cohort. Zhuang et al. examined visual ratings of hyperintensities in a smaller sample of 40 individuals (mean age = 57 ± 18.43 years) and reported moderate WMH severity across periventricular and deep white matter regions. Significant correlations are reported between visual ratings and reader assessments (r = 0.95, p < 0.0001). In contrast, our study applied covariance analysis to a larger population (n = 145; mean age = 46 years), evaluating diabetes, hypertension, and age in relation to WMH presence. Diabetes significantly associated with both relative risk (RR = 1.59; p = 0.0007) and the odds ratio (OR = 3.38; p = 0.0010). Hypertension also demonstrated strong significance (OR = 16.26; p < 0.0001), emphasizing its role in WMH development. Unlike Olowolafe et al. [ 17 ], who identified age, hypertension, obesity, and smoking as independent WMH risk factors in a Shanghai-based cohort of 156 elderly subjects, our study emphasized diabetes and hypertension. Their findings, which were based on Fazekas grading, revealed PVH in 77.6% and DWMH in 88.5% of the participants. After adjusting for covariates, age and hypertension remained independent predictors for PVH, whereas age, obesity, and smoking were linked to DWMH. While smoking was assessed in our cohort, smoking was not significantly related to WMH burden. Significant between-subjects effects were observed for age (F = 7.42, p = 0.007), diabetes (F = 14.29, p < 0.001), hypertension (F = 69.52, p < 0.001), and their interaction (F = 6.65, p = 0.011). The model explained 41.9% of the variance in hyperintensity codes (R² = 0.419). Diabetes was significantly associated with both relative risk (RR = 1.59; z = 3.400, p = 0.0007) and the odds ratio (OR = 3.38; z = 3.297, p = 0.0010). Hypertension also demonstrated strong significance (OR = 16.26; z = 6.001, p < 0.0001), emphasizing its role in WMH development. Furthermore, smoking did not have a statistically significant association (OR = 1.87; z = 1.616, p = 0.1060), which is consistent with other studies showing its weaker effect than hypertension and diabetes. Our findings suggest that vascular factors play a more decisive role in the early emergence of WMH than do lifestyle factors such as smoking. This finding aligns with the literature suggesting that cerebral small vessel disease is closely linked to chronic vascular stressors, particularly in hypertensive and diabetic individuals [ 12 , 15 ]. While our study provides important evidence from a previously underrepresented population, certain limitations should be acknowledged. The cross-sectional design prevents causal inference, and the use of convenience sampling may reduce generalizability. Additionally, visual WMH assessment, while common, lacks the precision of volumetric or automated segmentation methods. Future studies with longitudinal designs and volumetric analysis could provide further clarity on WMH progression. Overall, our results underscore the clinical importance of early screening for vascular risk factors, particularly hypertension and diabetes, in preventing or mitigating cerebral white matter damage. These findings have implications for targeted prevention strategies in aging populations. Conclusion Our findings highlight the critical role of age, diabetes, and hypertension as significant risk factors associated with the early emergence of age-related white matter hyperintensities (WMHs) on MRI. These hyperintensities may appear as early as the third decade of life and are more strongly influenced by diabetes and hypertension than by smoking. These findings underscore the need for early identification and aggressive management of vascular risk factors in middle-aged individuals, even before the onset of overt neurological symptoms. The use of MRI as a noninvasive tool to detect WMHs offers valuable opportunities for early diagnosis, risk stratification, and targeted interventions aimed at preserving cognitive function and preventing progression to more severe cerebrovascular or neurodegenerative conditions. These findings support the integration of WMH screening into clinical practice for high-risk populations, particularly those with uncontrolled hypertension or diabetes, as part of a broader preventive neurology strategy. Abbreviations • WMH White Matter Hyperintensity • WMHs White Matter Hyperintensities • MRI Magnetic Resonance Imaging • FLAIR Fluid-Attenuated Inversion Recovery • ANCOVA Analysis of Covariance • ROC Receiver Operating Characteristic • AUC Area Under the Curve • OR Odds Ratio • RR Relative Risk • CI Confidence Interval • SD Standard Deviation • FOV Field of View • TR Repetition Time • TE Echo Time Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee of Faisalabad Medical University, Faisalabad, Pakistan. Written informed consent was obtained from all participants prior to their enrollment in the study. All collected data were kept strictly confidential, and participant anonymity was maintained throughout. Participants were fully informed about the study objectives and were assured that the procedures posed no adverse effects or risks. They were also informed of their right to withdraw from the study at any time without consequences. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ contributions ASJ contributed to study design, data collection, data interpretation, and manuscript writing. MSL contributed to data collection, data analysis, and data interpretation. All authors read and approved the final manuscript. Acknowledgements Not applicable. Authors’ information 1. Ali Sayedain Jaffar, University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore, Pakistan. 2. Muhammad Sajeel Lateef, University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore, Pakistan. References Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. 10.1136/bmj.c3666 . Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12(5):483–97. 10.1016/S1474-4422(13)70060-7 . Kim KW, MacFall JR, Payne ME. Classification of white matter lesions on magnetic resonance imaging in elderly individuals. Biol Psychiatry. 2008;64(4):273–80. 10.1016/j.biopsych.2008.03.024 . van Straaten EC, Fazekas F, Rostrup E, Scheltens P, Schmidt R, Pantoni L, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke. 2006;37(3):836–40. 10.1161/01.STR.0000202585.26325.74 . Gao FQ, Swartz RH, Scheltens P, Leibovitch FS, Kiss A, Honjo K, et al. Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study. J Alzheimers Dis. 2011;26(3):379–88. 10.3233/JAD-2011-0058 . Prins ND, van Straaten EC, van Dijk EJ, Scheltens P, Vermeer SE, Jolles J, et al. Measuring progression of cerebral white matter lesions on MRI: visual ratings and volumetrics. Neurology. 2004;62(9):1533–9. 10.1212/01.WNL.0000123264.40498.B6 . van den Heuvel DM, ten Dam VH, de Craen AJ, Admiraal-Behloul F, Olofsen H, Bollen EL, et al. Measuring longitudinal white matter changes: comparison of a visual rating scale with a volumetric measurement. AJNR Am J Neuroradiol. 2006;27(4):875–8. Admiraal-Behloul F, van den Heuvel DM, Olofsen H, van Osch MJ, van der Grond J, van Buchem MA, et al. Fully automatic segmentation of white matter hyperintensities in MR images of elderly individuals. NeuroImage. 2005;28(3):607–17. 10.1016/j.neuroimage.2005.06.061 . García-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal. 2013;17(1):1–18. 10.1016/j.media.2012.09.004 . Lladó X, Oliver A, Cabezas M, Freixenet J, Vilanova JC, Quiles A, et al. Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf Sci. 2012;186:164–85. 10.1016/j.ins.2011.10.011 . de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population-based magnetic resonance imaging study. Hypertension. 2001;39(2):329–32. 10.1161/hy0201.101214 . Bae YJ, Kim YK, Kim J, Cho S, Kim ST. White matter hyperintensities and the risk of depression: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2019;102:143–51. 10.1016/j.neubiorev.2019.04.015 . Bernick C, Kuller L, Dulberg C, Longstreth WT Jr, Manolio T, Beauchamp N, et al. Silent MRI infarcts and the risk of future stroke: the Cardiovascular Health Study. Neurology. 2001;57(7):1222–9. 10.1212/WNL.57.7.1222 . Longstreth WT Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. Stroke. 1996;27(8):1274–82. 10.1161/01.STR.27.8.1274 . Yakushiji Y, Charidimou A, Hara M, Noguchi T, Nishihara M, Eriguchi M, et al. Topography and associations of perivascular spaces in healthy adults: the Kashima scan study. Neurology. 2012;78(14):1058–64. 10.1212/WNL.0b013e31824e8ef9 . Zhuang FJ, Chen Y, He WB, Cai ZY. Prevalence of white matter hyperintensities increases with age. Neural Regen Res. 2018;13(12):2141–8. 10.4103/1673-5374.241465 . Olowolafe TA, Adeniji-Sofoluwe AT, Adeyinka AO, Ogunniyi AO. MRI visual rating of cognitive impairment in elderly patients. Jos J Med. 2022;16(1):22–8. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7431094","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504929250,"identity":"f0f87ba5-a531-44fd-ad38-2c2df7c5c982","order_by":0,"name":"Ali Sayedain Jaffar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACNnbGBiDFzMDGwJBwQKICxGZuwKuFnxmh5eEDizMgNiN+LZLNYIoZiBkfG1S2gTgEtBgcZm7+zFNhLc8n3ZwmcXNebTR/O1DLj4pteLQwtknznEk3bJM5liY5c9vx3BmHGRsYe87cxquFmbcNSErkpElLbjuW2wDUwszYhluL/WHG5s+8/w7bt0nkf5P+O+dY7nxCWoC2NEjzNhxObJNISDaQbKjJ3UCEljbJOcfSk4FaEh9IHDuQuxGo5SBevxxvf/zhTY217fwZCcCorKnLnXf+8MEHPypwawEBJh4E+zCYPIBXPRAw/kCw6wgpHgWjYBSMghEIADgSXtDbNX3XAAAAAElFTkSuQmCC","orcid":"","institution":"The University of Lahore","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"Sayedain","lastName":"Jaffar","suffix":""},{"id":504929251,"identity":"c6dee2b1-0057-4d10-94c1-e1f907f1e5f9","order_by":1,"name":"Muhammad Sajeel Latif","email":"","orcid":"","institution":"The University of Lahore","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Sajeel","lastName":"Latif","suffix":""}],"badges":[],"createdAt":"2025-08-22 05:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7431094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7431094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90318013,"identity":"b8f1a4eb-84d9-4d22-a016-16be45ea1b39","added_by":"auto","created_at":"2025-09-01 10:29:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69724,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequencybar graph of hypertensive and nonhypertensiveindividuals.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7431094/v1/960cf4451f09c1c5c1f00e68.png"},{"id":90318014,"identity":"51818c70-74f1-4758-b961-0254dad8ebee","added_by":"auto","created_at":"2025-09-01 10:29:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for age and hyperintensity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7431094/v1/83d40917f3828da7cb4fbffe.png"},{"id":90318016,"identity":"0d07d253-f0db-4cbd-bc8a-7f4f6ea63523","added_by":"auto","created_at":"2025-09-01 10:29:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56285,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency bar graph for smokers and nonsmokers\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7431094/v1/aa0ebe0d1788707034eab8af.png"},{"id":90320477,"identity":"07f9ef95-c8b9-4e23-9b84-03daffaafd59","added_by":"auto","created_at":"2025-09-01 10:45:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1288999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7431094/v1/36d82e5b-36f7-4a66-aadf-a7eaf2a0ba20.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age of Onset and Risk Factors for MRI-Detected White Matter Hyperintensities in a Lahore Population: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe human brain is a complex organ composed of various types of tissue, including gray matter, white matter, and cerebrospinal fluid. As individuals age, changes in the brain's structure and function occur, which may affect cognitive performance and increase the risk of developing neurological disorders. One of the most common agerelated changes in the brain is the presence of white matter hyperintensities (WMHs) in white matter [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].WMHs are areas of hyperintense signal intensity on magnetic resonance imaging (MRI) that reflect damage to small blood vessels in the brain, typically resulting from chronic hypoperfusion or ischemia. WMHs can lead to a variety of neurological symptoms, including cognitive impairment, depression, and gait disturbances, and have been associated with an increased risk of developing dementia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAgerelated changes in the brain, particularly the development of senile MRI hyperintensities, have gained considerable attention owing to their correlation with cognitive decline and heightened risk of neurodegenerative disorders. These hyperintensities, which are common as white matter hyperintensities (WMHs) and periventricular hyperintensities (PVHs) on MR images, indicate areas of heightened signal intensity and are often indicative of underlying small vessel disease (SVD) or cerebrovascular pathology. As individuals age, the emergence of these hyperintensities becomes increasingly apparent, emphasizing the importance of comprehending their initiation and progression within the context of aging. While aging is generally associated with the presence of senile MRI hyperintensities, the specific age at which these alterations become noticeable remains under investigation. Identifying the onset age of senile MRI hyperintensities is vital for understanding the natural progression of cerebral small vessel disease and its potential impact on cognitive function and neurological wellbeing among aging individuals. Additionally, pinpointing the age of appearance of these hyperintensities has implications for implementing strategies aimed at early detection and intervention to uphold brain health in older populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn line with the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE), white matter hyperintensities (WMHs) are defined as signal abnormalities within cerebral white matter on T2weighted magnetic resonance imaging (MRI), devoid of cavitation and distinct from cerebrospinal fluid [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These WMHs are commonly detected via MRI scans of healthy elderly individuals and have been associated with various neurological and geriatric conditions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMRI is highly sensitive to changes in cerebral white matter. Typically, damaged white matter exhibits prolonged T2 relaxation times due to increased tissue water content and degradation of the macromolecular structure of myelin. Consequently, WMHs are readily identifiable on conventional proton density (PD) and T2weighted spinecho or fast spinecho sequences, with increased visibility on fluidattenuated inversion recovery (FLAIR) images. The presence, morphology, and severity of WMH offer valuable insights into healthy aging and the pathophysiology of diverse disorders. However, classifying WMHs is challenging because of their heterogeneous patterns, ranging from large confluent periventricular WMHs to punctate lesions in deep white matter. Visual rating scales such as the Scheltens or Fazekas scales often lack comparability, are sensitive to clinical differences, and exhibit significant variability and ceiling/floor effects, leading to inconsistencies in WMH studies [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven the growing interest in brain research and the need for efficient clinical studies involving large cohorts, an automated approach to WMH detection is desirable. While several fully automated methods for WMH segmentation exist, none have clearly demonstrated superiority over other methods. Each method described in the literature has strengths and weaknesses, which are often associated with the imaging modalities used and the abnormalities detected. Although some techniques have been developed for lesion detection in multiple sclerosis (MS) patients, they exhibit moderate performance when applied to geriatric populations because of agerelated decreases in gray matter white matter contrast and differences in lesion boundary characteristics [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe prevalence of WMH increases with age, with some studies suggesting that up to 95% of individuals over the age of 65 have some degree of WMH. These areas of increased signal intensity in the white matter are thought to reflect damage to small blood vessels and have been associated with a variety of neurological symptoms, including cognitive impairment, gait disturbances, and depression. As the population ages, understanding the prevalence and clinical implications of WMH is becoming increasingly important for both clinical and research purposes. Understanding the risk factors and pathophysiology underlying the development of WMH is essential for developing effective prevention and treatment strategies for agerelated neurological disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe pathophysiology of WMH is not fully understood, but it is believed that chronic hypoperfusion and ischemia lead to damage to the small vessels in the white matter. Other factors that may contribute to the development of WMH include hypertension, diabetes, and smoking. WMHs are associated with an increased risk of developing cognitive decline and dementia and may be early markers of cerebrovascular disease [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMagnetic resonance imaging (MRI) is a noninvasive technique that has become a valuable tool for studying white matter hyperintensities (WMHs) of the brain in aging individuals. WMHs appear as areas of increased signal intensity in fluidattenuated inversion recovery (FLAIR) images, and their presence and severity can be quantified via various rating scales. MRI has also been used to study the relationship between WMH and cognitive function, as well as the progression of WMH over time. MRI is particularly useful in identifying the location and distribution of WMHs, which can provide clues about the underlying pathophysiology. Additionally, MRI has the advantage of being able to detect other brain abnormalities that may be associated with WMHs, such as cortical atrophy, lacunar infarcts, and microbleeds. These findings can help clinicians better understand the potential clinical implications of WMH and identify individuals at high risk for developing neurological disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMagnetic resonance imaging (MRI) hyperintensities, particularly those observed in cerebral white matter, have garnered significant attention as potential biomarkers for cerebrovascular pathology and are associated with various neurological and cognitive conditions. Among the tools used for their assessment, the Fazekas scale stands out as a widely employed method for the qualitative grading of these hyperintensities, offering a standardized approach to their characterization and severity assessment.\u003c/p\u003e\u003cp\u003eRecently, a new study by Smith et al. (2023) has shed further light on the clinical utility of the Fazekas scale in assessing MRI hyperintensity. In their investigation, Smith et al. explored the interrater reliability and diagnostic accuracy of the Fazekas scale in a large cohort of elderly individuals with varying degrees of cognitive impairment. The study revealed strong interrater agreement among experienced neuroradiologists using the Fazekas scale, highlighting its reliability as a tool for assessing white matter hyperintensities (WMHs) across different raters and settings.\u003c/p\u003e\u003cp\u003eThe Fazekas scale, originally proposed by Fazekas et al. in 1987, categorizes MRI hyperintensities on the basis of their appearance in T2weighted or fluidattenuated inversion recovery (FLAIR) images, with scores ranging from 0\u0026ndash;3 corresponding to increasing severity. Grade 0 denotes the absence of hyperintensities, whereas grades 1 to 3 signify mild, moderate, and severe hyperintensities, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The scale considers the location and distribution of hyperintensities, distinguishing between periventricular and deep white matter involvement.\u003c/p\u003e\u003cp\u003eThe findings of the study by Smith et al. underscore the relevance of the Fazekas scale in clinical research and practice, particularly in the context of neuroimaging studies focusing on cerebrovascular disease and cognitive impairment. By providing a standardized method for assessing and grading WMHs, the Fazekas scale facilitates comparability and consistency in evaluating the burden of MRI hyperintensities across different patient populations. This review aimed to provide an overview of the Fazekas scale, its application in the assessment of MRI hyperintensities, and its implications for clinical practice and research, with particular emphasis on the recent findings of Smith et al. (2023).\u003c/p\u003e\u003cp\u003eMRI hyperintensities play crucial roles in risk stratification, prognosis, and treatment planning in older patients. Their assessment aids clinicians in identifying individuals at greater risk of developing cognitive impairment or stroke, guiding preventive interventions and therapeutic strategies to mitigate adverse outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMagnetic resonance imaging (MRI) hyperintensities, particularly those found in cerebral white matter, have emerged as critical markers for understanding the interplay between cerebrovascular pathology and various vascular risk factors, such as diabetes, hypertension, and smoking. These hyperintensities signify structural changes within the brain and are often associated with an increased risk of cerebrovascular events and cognitive decline in older adults.\u003c/p\u003e\u003cp\u003eThe relationship between MRI hyperintensities and vascular risk factors has garnered considerable attention in clinical research. Diabetes, characterized by chronic hyperglycemia and insulin resistance, has been implicated in the pathogenesis of cerebral small vessel disease, leading to the formation of white matter hyperintensities (WMHs) and microvascular lesions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, hypertension, a wellestablished risk factor for stroke and cardiovascular disease, contributes to the development and progression of WMH through its effects on cerebral blood flow regulation and vascular remodeling [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, smoking, known for its detrimental effects on vascular health, has been linked to an increased burden of MRI hyperintensity and a greater risk of cerebrovascular events [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWMHs are common findings on brain MRI in older individuals, and their prevalence increases with age and is associated with a variety of risk factors, including hypertension, diabetes, smoking, and cardiovascular disease. The pathophysiology of WMH is not fully understood. By studying the location and distribution of WMHs on MRI, as well as their relationship with other brain abnormalities, researchers may gain insights into the underlying mechanisms that contribute to the development and progression of WMHs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a cross-sectional descriptive study conducted at the Radiology Department of Shalamar Hospital, Lahore, over a period of seven months, from December 2024 to June 2025.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.1 MRI equipment and imaging technique\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eMRI scans were performed using a 1.5 Tesla Philips MRI scanner. Patients are instructed to remove all metal objects or clothing containing metal, such as jewelry or underwire bras, prior to the examination. Each patient is positioned supine on the scanner bed, and a dedicated head coil is used to ensure proper alignment and minimize motion artifacts.\u003c/p\u003e\u003cp\u003eImages are acquired via a fast spinecho (FSE) pulse sequence with the following parameters:\u003c/p\u003e\u003cp\u003eRepetition time (TR): 3000\u0026ndash;5000 ms\u003c/p\u003e\u003cp\u003eEcho Time (TE): 80\u0026ndash;120 ms\u003c/p\u003e\u003cp\u003eSlice Thickness:3\u0026ndash;5 mm\u003c/p\u003e\u003cp\u003eField of view (FOV): 22\u0026ndash;26 cm\u003c/p\u003e\u003cp\u003eAxial scan planes are used, covering the entire brain from the top to the skull base. The total scan time ranged from approximately 10 to 20 minutes, depending on the number of slices acquired. Postprocessing included adjustments to brightness and contrast and the application of noise-reduction filters.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.2 Integration of MRI findings\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOn the basis of the radiological assessment, each participant was assigned a categorical code representing the degree of white matter hyperintensity observed on MRI. These scores, referred to as Code_Hyperintensities, are used as outcome variables in the statistical analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3 Ethical considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Written informed consent was obtained from all participants prior to their enrollment in the study. All collected data were kept strictly confidential, and participant anonymity was maintained throughout. Participants were fully informed about the study objectives and were assured that the procedures posed no adverse effects or risks. They were also informed of their right to withdraw from the study at any time without consequences. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki, and ethical approval was obtained from the Ethical Review Committee of Faisalabad Medical University, Faisalabad, Pakistan.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Data collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData are collected via structured data collection sheets on the basis of operational definitions of the study variables. Demographic information and clinical history, including age, sex, presence of diabetes, hypertension, and smoking status, were recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.5 Statistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data were analyzed via MedCalc version 20.215. Quantitative variables are expressed as the means and standard deviations, whereas qualitative variables are presented as frequencies and percentages. Analysis of covariance (ANCOVA) was used to assess the effects of age, diabetes, hypertension, and their interaction on the severity of WMHs. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of age for WMH presence. Odds ratios are calculated to measure associations between clinical risk factors and WMHs. The results are presented in the form of tables, charts, and graphs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 145 participants were included in the study, with 67 females (46.2%) and 78 males (53.8%). The participants ranged in age from 30\u0026ndash;86 years, with a mean age of 46 years. ANCOVA revealed significant effects of age (F\u0026thinsp;=\u0026thinsp;7.42, p\u0026thinsp;=\u0026thinsp;0.01), diabetes (F\u0026thinsp;=\u0026thinsp;14.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (F\u0026thinsp;=\u0026thinsp;69.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction between diabetes and hypertension (F\u0026thinsp;=\u0026thinsp;6.65, p\u0026thinsp;=\u0026thinsp;0.01) on white matter hyperintensity. These variables explained 41.9% of the variance in WMH severity (R\u0026sup2; = 0.419; adjusted R\u0026sup2; = 0.4024).\u003c/p\u003e\u003cp\u003eOdds ratio analysis revealed that diabetic participants had significantly greater odds of WMH (OR\u0026thinsp;=\u0026thinsp;3.38, 95% CI: 1.63\u0026ndash;7.02; z\u0026thinsp;=\u0026thinsp;3.297, p\u0026thinsp;=\u0026thinsp;0.001), whereas hypertensive individuals demonstrated a much stronger association (OR\u0026thinsp;=\u0026thinsp;16.26, 95% CI: 6.54\u0026ndash;40.44; z\u0026thinsp;=\u0026thinsp;6.000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of Diabetes and Hypertension on Code Hyperintensities (Adjusted for Age)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrected Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes \u0026times; Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorrected Total\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eR\u0026sup2; = 0.4190, Adjusted R\u0026sup2; = 0.4024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Effects of Diabetes and Hypertension on Code Hyperintensities (Adjusted for Age)\u003c/p\u003e\u003cp\u003eANCOVA results showing the effects of age, diabetes, hypertension, and their interaction on code hyperintensities. Fstatistic (variance ratio), df\u0026thinsp;=\u0026thinsp;degrees of freedom, R\u0026sup2; = coefficient of determination, Adj. R\u0026sup2; = adjusted coefficient of determination, p\u0026thinsp;=\u0026thinsp;probability value. If p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the results of the ANCOVA, which revealed that age, diabetes, hypertension, and their interaction had significant impacts on the presence of hyperintensities, even after controlling for age. The overall model is statistically significant (F\u0026thinsp;=\u0026thinsp;25.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and explains approximately 42% of the variance in hyperintensities (adjusted R\u0026sup2; = 0.4024). The most influential factor was hypertension (F\u0026thinsp;=\u0026thinsp;69.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by diabetes (F\u0026thinsp;=\u0026thinsp;14.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and age (F\u0026thinsp;=\u0026thinsp;7.42, p\u0026thinsp;=\u0026thinsp;0.01). The significant interaction between diabetes and hypertension (F\u0026thinsp;=\u0026thinsp;6.65, p\u0026thinsp;=\u0026thinsp;0.01) indicates that the combined presence of both conditions has a distinct effect on hyperintensities compared with having only one. These findings indicate that both diabetes and hypertension independently and interactively influence hyperintensity outcomes, even when controlling for age.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTest of Homogeneity of Slopes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF-Stats\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeterogeneity of Slopes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual Residual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***, *Fstatistic (variance ratio), df (degrees of freedom)\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Test of Homogeneity of Slopes\u003c/p\u003e\u003cp\u003eTest of homogeneity of regression slopes assessing whether the relationship between age and hyperintensity scores differs across groups defined by diabetes and hypertension status.\u003c/p\u003e\u003cp\u003eSignificance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***. F\u0026thinsp;=\u0026thinsp;Fstatistic (variance ratio); df\u0026thinsp;=\u0026thinsp;degrees of freedom.*)\u003c/p\u003e\u003cp\u003eThe test of homogeneity of regression slopes in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the relationship between age and white matter hyperintensity is consistent across both groups being compared. The findings indicate that there is no statistically significant interaction in terms of the heterogeneity of slopes. The influence of age on hyperintensities (diabetes, hypertension, and their interaction) did not substantially differ between the groups, indicating that the assumption of homogeneity of regression slopes was met. This confirms that using ANCOVA to analyze adjusted group differences in hyperintensities is valid. Since the significance level for the heterogeneity of slopes is 11%, which is higher than the 5% threshold, it is concluded that the assumption of homogeneity of regression slopes is upheld. Hence, the results of ANCOVA are consistent regardless of how age and hyperintensity vary among groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for Code Hyperintensities by Diabetes Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67to 0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Diabetic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 to 0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Descriptive Statistics for Code Hyperintensities by Diabetes Status\u003c/p\u003e\u003cp\u003eDescriptive statistics showing the mean white matter hyperintensity scores among diabetic and nondiabetic participants. CI\u0026thinsp;=\u0026thinsp;confidence interval; n\u0026thinsp;=\u0026thinsp;number of participants.)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents descriptive statistics for white matter hyperintensities by diabetes status. The results revealed that individuals with diabetes have a higher mean hyperintensity score than nondiabetic individuals do. Similarly, the confidence intervals between groups do not overlap much, indicating the likelihood of a real difference between the groups. This descriptive difference aligns with the statistically significant findings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise comparison of Code Hyperintensities by Diabetes Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetic \u0026ndash; Nondiabetic\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12 to 0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Pairwise comparison of Code Hyperintensities by Diabetes Status\u003c/p\u003e\u003cp\u003ePairwise comparison of the mean hyperintensity scores between the diabetic and nondiabetic groups, including confidence intervals (CIs) and statistical significance. CI\u0026thinsp;=\u0026thinsp;confidence interval. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.*)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the pairwise comparisons between diabetic and nondiabetic individuals. The mean hyperintensity score in diabetic patients was 48% higher than that in nondiabetic patients; this difference was highly statistically significant. The confidence interval indicates that the true difference is unlikely to be zero and is consistent with higher hyperintensity levels in the diabetic group. This finding also aligns with previous results, highlighting a meaningful difference in hyperintensity levels between diabetic and nondiabetic individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for Code Hyperintensities by Hypertension Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82 to 1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.27 to 0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eHere: n\u0026thinsp;=\u0026thinsp;number of individuals\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: Descriptive Statistics for Code Hyperintensities by Hypertension Status\u003c/p\u003e\u003cp\u003eDescriptive statistics comparing the mean hyperintensity scores between hypertensive and nonhypertensive individuals. n\u0026thinsp;=\u0026thinsp;number of individuals. CI\u0026thinsp;=\u0026thinsp;confidence interval.)\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicate that individuals with hypertension have greater hyperintensities than nonhypertensive individuals do. On average, the hyperintensity levels in the hypertensive group were approximately 2.5 times higher than those in the nonhypertensive group. This substantial difference suggests that hyperintensities are much more common among people with hypertension. These findings support the conclusion that hypertension is strongly associated with increased hyperintensity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise comparison of Code Hyperintensities by Hypertension Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Difference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertensive - nonhypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 to 0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: Pairwise comparison of Code Hyperintensities by Hypertension Status\u003c/p\u003e\u003cp\u003ePairwise comparison of mean hyperintensity scores between hypertensive and nonhypertensive participants, including confidence intervals and statistical significance.\u003c/p\u003e\u003cp\u003eCI\u0026thinsp;=\u0026thinsp;confidence interval. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.*)\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that individuals with hypertension have significantly greater hyperintensities than do those without hypertension. On average, their hyperintensity scores are approximately 0.56 points higher. This difference is clear and highly statistically significant. These results confirm that hyperintensities are more common in individuals with high blood pressure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for the Combined Diabetes and Hypertension Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic \u0026amp; Hypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82 to 1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic \u0026amp;non hypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43 to 0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Diabetic \u0026amp; Hypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75 to 1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Diabetic \u0026amp; Non-Hypertensive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04to 0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: Descriptive Statistics for Patients with Combined Diabetes and Hypertension\u003c/p\u003e\u003cp\u003edescriptive statistics showing the mean hyperintensity scores across the four groups on the basis of combined diabetes and hypertension status. n\u0026thinsp;=\u0026thinsp;number of individuals. CI\u0026thinsp;=\u0026thinsp;confidence interval.)\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that individuals with both diabetes and hypertension have the highest levels of hyperintensity. In comparison, those with neither condition have the lowest levels. Individuals with hypertension only present high hyperintensity levels, whereas those with diabetes only present moderate hyperintensity levels. These findings suggest that having both diabetes and hypertension leads to the greatest increase in hyperintensities. Hypertension alone causes a substantial increase, whereas diabetes alone has a smaller effect. Individuals with neither diabetes nor hypertension presented the lowest hyperintensity levels overall.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics for the Study Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode Hyperintensities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e: Descriptive Statistics for the Study Variables\u003c/p\u003e\u003cp\u003e Descriptive statistics for code hyperintensity scores and participant age, including means and standard deviations. SD\u0026thinsp;=\u0026thinsp;standard deviation.)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the average hyperintensity score is 0.59, with a standard deviation of 0.49, indicating that hyperintensity levels vary across individuals. The average age of the participants was approximately 50 years, with a standard deviation of 17.6 years, suggesting that the sample included both younger and older individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eShapiro‒Wilk test for normality (code hyperintensities)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode Hyperintensities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e: Shapiro‒Wilk test for normality (code hyperintensities) Shapiro‒Wilk test for normality of code hyperintensity scores to assess the assumption of a normal distribution. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.*)\u003c/p\u003e\u003cp\u003eThe Shapiro‒Wilk test was used to assess whether the hyperintensity scores were normally distributed. The test result is statistically significant, which means that the data do not perfectly follow a normal distribution. However, the deviation from normality is relatively small. These results suggest that the assumption of normality is slightly violated for the hyperintensity variable.\u003c/p\u003e\u003cp\u003eROC and Association Analysis\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of associations for hypertension and Code Hyperintensities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode Hyperintensities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertensive\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Hypertensive\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e: Descriptive statistics of associations for hypertension and Code Hyperintensities\u003c/p\u003e\u003cp\u003eDistribution of white matter hyperintensity among hypertensive and nonhypertensive individuals. n\u0026thinsp;=\u0026thinsp;number of participants)\u003c/p\u003e\u003cp\u003eThe results revealed a clear difference in hyperintensity levels between hypertensive and nonhypertensive individuals. Among the hypertensive group (n\u0026thinsp;=\u0026thinsp;65), the majority (58 individuals, or approximately 89%) had hyperintensities. In contrast, among the nonhypertensive group (n\u0026thinsp;=\u0026thinsp;80), most individuals (53, or approximately 66%) had no hyperintensities. Overall, of all the 145 participants, 58.6% had hyperintensities, whereas 41.4% did not.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation measures for hypertension and code intensities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Risk (RR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.92 to 3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.54 to 40.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e: Association measures for hypertension and code intensities)\u003c/p\u003e\u003cp\u003eRelative risk and odds ratio estimates showing the association between hypertension and the presence of white matter hyperintensities, with confidence intervals and statistical significance. CI\u0026thinsp;=\u0026thinsp;confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio; RR\u0026thinsp;=\u0026thinsp;relative risk; z\u0026thinsp;=\u0026thinsp;zstatistic. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.)\u003c/p\u003e\u003cp\u003eThe results revealed a strong association between hypertension and hyperintensity. Individuals with hypertension were approximately 2.6 times more likely to have hyperintensities than were those without hypertension, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The odds of having hyperintensities were approximately 16 times greater in the hypertensive group. Both of these results are highly statistically significant. Similarly, the large zvalues provide strong statistical evidence that hypertension is linked to an increased likelihood of hyperintensities.\u003c/p\u003e\u003cp\u003eFrequency bar graph of hypertensive and nonhypertensive individuals showing the distribution of white matter hyperintensity across both groups)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC analysis summary for age\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAGE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePositive Group (Code Hyperintensities\u0026thinsp;=\u0026thinsp;Yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (58.62%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNegative Group (Code Hyperintensities\u0026thinsp;=\u0026thinsp;No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60 (41.38%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAUC (Area Under ROC Curve)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45 to 0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ez-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep value (Area\u0026thinsp;=\u0026thinsp;0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYouden Index (J)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimal Cutoff (Criterion)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensitivity at Cutoff (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecificity at Cutoff (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e: ROC Analysis Summary for Age\u003c/p\u003e\u003cp\u003eROC analysis assessing the ability of age to predict the presence of white matter hyperintensity. The variables included the area under the curve (AUC), sensitivity, specificity, optimal age cutoff, and Youden index. AUC\u0026thinsp;=\u0026thinsp;area under the curve; CI\u0026thinsp;=\u0026thinsp;confidence interval; J\u0026thinsp;=\u0026thinsp;Youden Index. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.)\u003c/p\u003e\u003cp\u003eROC analysis was conducted to assess whether age could predict the presence of hyperintensities. The results revealed an AUC (area under the curve) of 0.53, which is very close to 0.5. This finding indicates that age is not a strong or reliable predictor of hyperintensity. The pvalue is 0.55, confirming that the finding is not statistically significant. The analysis suggested that the best cutoff point is age over 59 years; however, even at this threshold, the ability to correctly identify individuals with hyperintensities (sensitivity) is only 37.65%, whereas the ability to correctly identify those without hyperintensities (specificity) is 73.33%. The Youden index was 0.11, further indicating that age alone was not a good predictor of hyperintensity in this study.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDetailed Age Cutoff Performance for Code Hyperintensity Prediction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Criterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSensitivity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+LR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-LR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.8\u0026ndash;100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0\u0026ndash;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.8\u0026ndash;100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.04\u0026ndash;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e93.6\u0026ndash;100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4\u0026ndash;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.0\u0026ndash;99.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4\u0026ndash;11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.0\u0026ndash;99.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.8\u0026ndash;18.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88.4\u0026ndash;98.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u0026ndash;22.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.8\u0026ndash;98.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u0026ndash;22.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.8\u0026ndash;98.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.9\u0026ndash;24.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.3\u0026ndash;97.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.9\u0026ndash;24.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.3\u0026ndash;97.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.3\u0026ndash;28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.6\u0026ndash;92.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.3\u0026ndash;28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e81.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.2\u0026ndash;88.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.5\u0026ndash;30.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.5\u0026ndash;83.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.5\u0026ndash;30.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e63.5\u0026ndash;83.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.1\u0026ndash;34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.7\u0026ndash;80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.4\u0026ndash;36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.5\u0026ndash;79.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.5\u0026ndash;41.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e: Detailed Age Cutoff Performance for Code Hyperintensity Prediction\u003c/p\u003e\u003cp\u003eDiagnostic performance of different age cutoff points for predicting white matter hyperintensity, including sensitivity, specificity, and positive and negative likelihood ratios. +LR\u0026thinsp;=\u0026thinsp;positive likelihood ratio; \u0026minus;LR\u0026thinsp;=\u0026thinsp;negative likelihood ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.)\u003c/p\u003e\u003cp\u003eThe detailed analysis in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e of different age cutoff points shows how age performs in predicting hyperintensities. At very low cutoffs, such as age\u0026thinsp;\u0026ge;\u0026thinsp;7 years, the sensitivity was 100%, indicating that all individuals with hyperintensities were correctly identified. However, the specificity is only 0\u0026ndash;1.7%, indicating a failure to identify individuals without hyperintensities. As the age cutoff increases, the sensitivity gradually decreases, and the specificity increases slightly. For example, at age\u0026thinsp;\u0026gt;\u0026thinsp;40, the sensitivity decreases to 69%, whereas the specificity increases to approximately 28%.\u003c/p\u003e\u003cp\u003eThroughout the table, the positive likelihood ratios (+\u0026thinsp;LR) remain close to 1, and the negative likelihood ratios (\u0026minus;\u0026thinsp;LR) are also weak, indicating that age alone does not meaningfully improve the prediction of hyperintensities at any cutoff point. No age cutoff provides a good balance of sensitivity and specificity. Age alone is not a strong or reliable predictor for identifying individuals with or without hyperintensities. This trend is also depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe AUC was 0.529 (p\u0026thinsp;=\u0026thinsp;0.548), indicating poor discriminatory ability. The optimal cutoff was age\u0026thinsp;\u0026gt;\u0026thinsp;59 years, with 37.6% sensitivity and 78.3% specificity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics of the associations between smoking and code hyperintensities (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCode Hyperintensities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-Smoker (n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmoker (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;145)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e: Descriptive statistics of the associations between smoking and code hyperintensities (n\u0026thinsp;=\u0026thinsp;145).\u003c/p\u003e\u003cp\u003eNumber of smokers and nonsmokers with and without white matter hyperintensity. n\u0026thinsp;=\u0026thinsp;number of participants.)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e shows the associations between smoking status and hyperintensity. Among nonsmokers, just over half had hyperintensities, whereas the remaining did not. In contrast, a greater proportion of smokers had hyperintensities, with the rest not affected. Overall, 58.6% of the total sample had hyperintensities, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These results suggest that hyperintensities were somewhat more common in smokers than in nonsmokers within this sample.\u003c/p\u003e\u003cp\u003eThe figure displays the distribution of white matter hyperintensities among smokers and nonsmokers. A greater proportion of hyperintensities was observed in the smoking group than in the nonsmoking group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation Measures between Smoking and Code Hyperintensities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Risk (RR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97 to 1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88 to 4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eIf p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table \u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e: Association Measures between Smoking and Code Hyperintensities\u003c/p\u003e\u003cp\u003eEstimates of relative risk and odds ratios for the presence of white matter hyperintensities among smokers compared with nonsmokers, including confidence intervals and pvalues. CI\u0026thinsp;=\u0026thinsp;confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio; RR\u0026thinsp;=\u0026thinsp;relative risk; z\u0026thinsp;=\u0026thinsp;zstatistic. Significance levels: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***.*)\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e shows the associations between smoking and hyperintensities, which were examined via relative risk and odds rratios. The relative risk (RR) is 1.27, indicating that smokers are approximately 27% more likely to have hyperintensities than nonsmokers are. However, the 95% confidence interval (0.97\u0026ndash;1.66) includes 1, and the pvalue is 0.08, which means that this result is not statistically significant. Similarly, the odds ratio (OR) was 1.87, suggesting that smokers had nearly double the odds of having hyperintensities compared with nonsmokers. However, again, the confidence interval (0.88\u0026ndash;4.01) includes 1, and the pvalue is 0.11, so this finding is also not statistically significant.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCompared with Zhuang et al. (2018) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Olowolafe et al. (2022) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], our study offers new insights into the relationship between senile MRI hyperintensities and vascular risk factors in a South Asian cohort. Zhuang et al. examined visual ratings of hyperintensities in a smaller sample of 40 individuals (mean age\u0026thinsp;=\u0026thinsp;57\u0026thinsp;\u0026plusmn;\u0026thinsp;18.43 years) and reported moderate WMH severity across periventricular and deep white matter regions. Significant correlations are reported between visual ratings and reader assessments (r\u0026thinsp;=\u0026thinsp;0.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In contrast, our study applied covariance analysis to a larger population (n\u0026thinsp;=\u0026thinsp;145; mean age\u0026thinsp;=\u0026thinsp;46 years), evaluating diabetes, hypertension, and age in relation to WMH presence. Diabetes significantly associated with both relative risk (RR\u0026thinsp;=\u0026thinsp;1.59; p\u0026thinsp;=\u0026thinsp;0.0007) and the odds ratio (OR\u0026thinsp;=\u0026thinsp;3.38; p\u0026thinsp;=\u0026thinsp;0.0010). Hypertension also demonstrated strong significance (OR\u0026thinsp;=\u0026thinsp;16.26; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), emphasizing its role in WMH development.\u003c/p\u003e\u003cp\u003eUnlike Olowolafe et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who identified age, hypertension, obesity, and smoking as independent WMH risk factors in a Shanghai-based cohort of 156 elderly subjects, our study emphasized diabetes and hypertension. Their findings, which were based on Fazekas grading, revealed PVH in 77.6% and DWMH in 88.5% of the participants. After adjusting for covariates, age and hypertension remained independent predictors for PVH, whereas age, obesity, and smoking were linked to DWMH. While smoking was assessed in our cohort, smoking was not significantly related to WMH burden.\u003c/p\u003e\u003cp\u003eSignificant between-subjects effects were observed for age (F\u0026thinsp;=\u0026thinsp;7.42, p\u0026thinsp;=\u0026thinsp;0.007), diabetes (F\u0026thinsp;=\u0026thinsp;14.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypertension (F\u0026thinsp;=\u0026thinsp;69.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and their interaction (F\u0026thinsp;=\u0026thinsp;6.65, p\u0026thinsp;=\u0026thinsp;0.011). The model explained 41.9% of the variance in hyperintensity codes (R\u0026sup2; = 0.419). Diabetes was significantly associated with both relative risk (RR\u0026thinsp;=\u0026thinsp;1.59; z\u0026thinsp;=\u0026thinsp;3.400, p\u0026thinsp;=\u0026thinsp;0.0007) and the odds ratio (OR\u0026thinsp;=\u0026thinsp;3.38; z\u0026thinsp;=\u0026thinsp;3.297, p\u0026thinsp;=\u0026thinsp;0.0010). Hypertension also demonstrated strong significance (OR\u0026thinsp;=\u0026thinsp;16.26; z\u0026thinsp;=\u0026thinsp;6.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), emphasizing its role in WMH development. Furthermore, smoking did not have a statistically significant association (OR\u0026thinsp;=\u0026thinsp;1.87; z\u0026thinsp;=\u0026thinsp;1.616, p\u0026thinsp;=\u0026thinsp;0.1060), which is consistent with other studies showing its weaker effect than hypertension and diabetes.\u003c/p\u003e\u003cp\u003eOur findings suggest that vascular factors play a more decisive role in the early emergence of WMH than do lifestyle factors such as smoking. This finding aligns with the literature suggesting that cerebral small vessel disease is closely linked to chronic vascular stressors, particularly in hypertensive and diabetic individuals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile our study provides important evidence from a previously underrepresented population, certain limitations should be acknowledged. The cross-sectional design prevents causal inference, and the use of convenience sampling may reduce generalizability. Additionally, visual WMH assessment, while common, lacks the precision of volumetric or automated segmentation methods. Future studies with longitudinal designs and volumetric analysis could provide further clarity on WMH progression.\u003c/p\u003e\u003cp\u003eOverall, our results underscore the clinical importance of early screening for vascular risk factors, particularly hypertension and diabetes, in preventing or mitigating cerebral white matter damage. These findings have implications for targeted prevention strategies in aging populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings highlight the critical role of age, diabetes, and hypertension as significant risk factors associated with the early emergence of age-related white matter hyperintensities (WMHs) on MRI. These hyperintensities may appear as early as the third decade of life and are more strongly influenced by diabetes and hypertension than by smoking. These findings underscore the need for early identification and aggressive management of vascular risk factors in middle-aged individuals, even before the onset of overt neurological symptoms. The use of MRI as a noninvasive tool to detect WMHs offers valuable opportunities for early diagnosis, risk stratification, and targeted interventions aimed at preserving cognitive function and preventing progression to more severe cerebrovascular or neurodegenerative conditions. These findings support the integration of WMH screening into clinical practice for high-risk populations, particularly those with uncontrolled hypertension or diabetes, as part of a broader preventive neurology strategy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eWMH\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite Matter Hyperintensity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eWMHs\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite Matter Hyperintensities\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eMRI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eFLAIR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFluid-Attenuated Inversion Recovery\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eANCOVA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis of Covariance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea Under the Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eRR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelative Risk\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eSD\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eFOV\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eField of View\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eTR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRepetition Time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u0026bull; \u003cb\u003eTE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEcho Time\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Ethical Review Committee of Faisalabad Medical University, Faisalabad, Pakistan. Written informed consent was obtained from all participants prior to their enrollment in the study. All collected data were kept strictly confidential, and participant anonymity was maintained throughout. Participants were fully informed about the study objectives and were assured that the procedures posed no adverse effects or risks. They were also informed of their right to withdraw from the study at any time without consequences.\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eASJ contributed to study design, data collection, data interpretation, and manuscript writing. MSL contributed to data collection, data analysis, and data interpretation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; information\u003c/p\u003e\n\u003cp\u003e1. \u0026nbsp; \u0026nbsp;Ali Sayedain Jaffar, University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore, Pakistan.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u0026nbsp;Muhammad Sajeel Lateef, University Institute of Radiological Sciences and Medical Imaging Technology, The University of Lahore, Lahore, Pakistan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDebette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.c3666\u003c/span\u003e\u003cspan address=\"10.1136/bmj.c3666\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12(5):483\u0026ndash;97. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1474-4422(13)70060-7\u003c/span\u003e\u003cspan address=\"10.1016/S1474-4422(13)70060-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim KW, MacFall JR, Payne ME. Classification of white matter lesions on magnetic resonance imaging in elderly individuals. Biol Psychiatry. 2008;64(4):273\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biopsych.2008.03.024\u003c/span\u003e\u003cspan address=\"10.1016/j.biopsych.2008.03.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Straaten EC, Fazekas F, Rostrup E, Scheltens P, Schmidt R, Pantoni L, et al. Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke. 2006;37(3):836\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.STR.0000202585.26325.74\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.0000202585.26325.74\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao FQ, Swartz RH, Scheltens P, Leibovitch FS, Kiss A, Honjo K, et al. Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study. J Alzheimers Dis. 2011;26(3):379\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3233/JAD-2011-0058\u003c/span\u003e\u003cspan address=\"10.3233/JAD-2011-0058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrins ND, van Straaten EC, van Dijk EJ, Scheltens P, Vermeer SE, Jolles J, et al. Measuring progression of cerebral white matter lesions on MRI: visual ratings and volumetrics. Neurology. 2004;62(9):1533\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/01.WNL.0000123264.40498.B6\u003c/span\u003e\u003cspan address=\"10.1212/01.WNL.0000123264.40498.B6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan den Heuvel DM, ten Dam VH, de Craen AJ, Admiraal-Behloul F, Olofsen H, Bollen EL, et al. Measuring longitudinal white matter changes: comparison of a visual rating scale with a volumetric measurement. AJNR Am J Neuroradiol. 2006;27(4):875\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdmiraal-Behloul F, van den Heuvel DM, Olofsen H, van Osch MJ, van der Grond J, van Buchem MA, et al. Fully automatic segmentation of white matter hyperintensities in MR images of elderly individuals. NeuroImage. 2005;28(3):607\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2005.06.061\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2005.06.061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Med Image Anal. 2013;17(1):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.media.2012.09.004\u003c/span\u003e\u003cspan address=\"10.1016/j.media.2012.09.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLlad\u0026oacute; X, Oliver A, Cabezas M, Freixenet J, Vilanova JC, Quiles A, et al. Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Inf Sci. 2012;186:164\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ins.2011.10.011\u003c/span\u003e\u003cspan address=\"10.1016/j.ins.2011.10.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population-based magnetic resonance imaging study. Hypertension. 2001;39(2):329\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/hy0201.101214\u003c/span\u003e\u003cspan address=\"10.1161/hy0201.101214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBae YJ, Kim YK, Kim J, Cho S, Kim ST. White matter hyperintensities and the risk of depression: a systematic review and meta-analysis. Neurosci Biobehav Rev. 2019;102:143\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neubiorev.2019.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2019.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernick C, Kuller L, Dulberg C, Longstreth WT Jr, Manolio T, Beauchamp N, et al. Silent MRI infarcts and the risk of future stroke: the Cardiovascular Health Study. Neurology. 2001;57(7):1222\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.57.7.1222\u003c/span\u003e\u003cspan address=\"10.1212/WNL.57.7.1222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLongstreth WT Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. Stroke. 1996;27(8):1274\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.STR.27.8.1274\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.27.8.1274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYakushiji Y, Charidimou A, Hara M, Noguchi T, Nishihara M, Eriguchi M, et al. Topography and associations of perivascular spaces in healthy adults: the Kashima scan study. Neurology. 2012;78(14):1058\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/WNL.0b013e31824e8ef9\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0b013e31824e8ef9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhuang FJ, Chen Y, He WB, Cai ZY. Prevalence of white matter hyperintensities increases with age. Neural Regen Res. 2018;13(12):2141\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/1673-5374.241465\u003c/span\u003e\u003cspan address=\"10.4103/1673-5374.241465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOlowolafe TA, Adeniji-Sofoluwe AT, Adeyinka AO, Ogunniyi AO. MRI visual rating of cognitive impairment in elderly patients. Jos J Med. 2022;16(1):22\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"White Matter Hyperintensities, Magnetic Resonance Imaging, Cerebrovascular Disorders, Diabetes Mellitus, Hypertension, Risk Factors, Neuroepidemiology, South Asian Population","lastPublishedDoi":"10.21203/rs.3.rs-7431094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7431094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eWhite matter hyperintensities (WMHs) detected on MRI are established neuroimaging markers of cerebrovascular disease, vascular dementia, and cognitive decline. The age of onset and associated vascular risk factors for WMHs remain underexplored in South Asian populations. Identifying these associations may help guide early prevention and risk management strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted over seven months at Shalamar Hospital, Lahore. A total of 145 participants aged 30\u0026ndash;86 years were included; individuals with a history of stroke or intracranial hemorrhage were excluded. Brain MRI scans were acquired using a 1.5T scanner. Demographic and clinical data were collected, and analysis of covariance (ANCOVA) was performed to assess the relationship between WMHs and vascular risk factors, including diabetes, hypertension, and smoking.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe study cohort included both male and female participants with a mean age of 46 years. WMHs were observed as early as age 30. Increasing age, diabetes, hypertension, and the interaction between diabetes and hypertension were significantly associated with WMH burden. Participants with either diabetes or hypertension had a greater burden of WMHs compared to those without these conditions, and individuals with both risk factors demonstrated the highest burden. Smoking, however, did not show a statistically significant association with WMHs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study demonstrates that WMHs can occur in relatively young adults and are strongly associated with diabetes and hypertension in a South Asian population. These findings emphasize the importance of early screening, vascular risk factor management, and consideration of neuroimaging in at-risk individuals to mitigate future cerebrovascular and cognitive complications.\u003c/p\u003e","manuscriptTitle":"Age of Onset and Risk Factors for MRI-Detected White Matter Hyperintensities in a Lahore Population: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:29:50","doi":"10.21203/rs.3.rs-7431094/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-25T07:58:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-12T17:09:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55020426076813394132741685808309267829","date":"2025-09-23T10:41:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-07T15:57:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336156625230447275715149818453717560155","date":"2025-09-07T13:01:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-05T07:19:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-24T23:30:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-24T23:30:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-08-22T05:51:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79904340-257d-45ac-bf36-635cc5ea1357","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T09:11:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:29:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7431094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7431094","identity":"rs-7431094","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}