Comorbid conditions commonly affect people with multiple sclerosis (MS). Population-based studies indicate that people with MS have an increased incidence of ischemic heart disease, cerebrovascular disease, peripheral vascular disease, and psychiatric disorders as compared to people without MS. People with MS from underrepresented minority and immigrant groups have higher comorbidity burdens. Comorbidities exert effects throughout the disease course, from symptom onset through diagnosis to the end of life. At the individual level, comorbidity is associated with higher relapse rates, greater physical and cognitive impairments, lower health-related quality of life, and increased mortality. At the level of the health system and society, comorbidity is associated with increased health care utilization, costs and work impairment. A nascent literature suggests that MS affects outcomes from comorbidities. Comorbidity management needs to be integrated into MS care, and this would be facilitated by determining optimal models of care.

https://www.frontiersin.org/articles/10.3389/fimmu.2023.1197195/pdf

Etiology, effects and management of comorbidities in multiple sclerosis: recent advances

Purpose of reviewTo review recent studies which assess comorbidity, that is, prevalence of co-occurrence, relationships and treatment implications between anxiety disorders and various medical illnesses.Recent findingsThe prevalence rates of anxiety disorders in patients with medical illnesses are h

Anxiety disorders and medical illness comorbidity and treatment implications.

Background:People with multiple sclerosis (MS) experience myriad symptoms that negatively affect their quality of life. Despite significant progress in rehabilitation strategies for people living w...

https://journals.sagepub.com/doi/pdf/10.1177/1352458521999970

Prioritizing progressive MS rehabilitation research: A call from the International Progressive MS Alliance:

Digitalization is changing medicinal services today. A lot of information alludes to Big Data gathered by digitizing everything. Data accumulated from different sources centers around changing human services advancement utilizing innovation. Medicinal services investigation is the capacity to analyze and propose approaches to decrease costs, give better patient results, and forestall preventable maladies. Man-made brainpower (AI), additionally called man-made reasoning, is data that is shown by a machine in contrast with the characteristic data shown by people and different creatures. It stresses the formation of shrewd automatic devices that respond to people. This is a change in perspective to social insurance by improving access to the conveyance of restorative data and the fast advances in diagnostic systems. Simulated intelligence advancements are present on an increasing basis in the different enterprises on the planet, e.g., money, agribusiness, vehicle transportation, vitality, and social insurance. Machine learning is a developed innovative apparatus that utilizes computerized reasoning to catch unreliable regions of plans of action. The square chain is another hot innovation that assumes a significant job in deciding and guaranteeing the security and straightforwardness of pharmaceutical items showcased by pharmaceutical organizations. This will guarantee that the information is recorded on the structure. These lead to better degrees of treatment techniques, which increase the life expectancy of people. The well-being-observing gadget may enable the client to be further prepared and offer this data to the doctor (and the AI framework) to obtain extra data about the patient’s needs and propensities. Diabetes is a noteworthy infection. The illness is brought about by the failure to retain vitality from sustenance, particularly glucose. Over the long haul, people with diabetes will in general have long-haul issues with regard to obesity, CAD, HTN, eyes, kidneys, heart, cerebrum, feet, and nerves. The reason for this examination was to explore diabetes investigation from CAD and different maladies utilizing the most recent innovations to dissect IoT, Big Data, and stress correlation (TTH) on human well-being. In general, TTH individuals for the most part have HTN. Six out of eight subjects (patients) to have HTN in TTH, and individuals with 75% TTH. These figures plainly demonstrate that HTN is related with TTH. On the off chance that a subject experiences diabetes and HTN on some day, TTH is in all respects prone to happen, and the subject is probably going to be male. The most ideal approach to stop or postpone these issues is to control one’s glucose and deal with oneself.

Examining diabetic subjects on their correlation with TTH and CAD: a statistical approach on exploratory results

Objective Vascular comorbidities are associated with reduced cognitive performance and with changes in brain structure in people with multiple sclerosis (MS). Understanding causal pathways is necessary to support the design of interventions to mitigate the impacts of comorbidities, and to monitor their effectiveness. We assessed the inter-relationships among vascular comorbidity, cognition and brain structure in people with MS. Methods Adults with neurologist-confirmed MS reported comorbidities, and underwent assessment of their blood pressure, HbA1c, and cognitive functioning (i.e., Symbol Digit Modalities Test, California Verbal Learning Test, Brief Visuospatial Memory Test-Revised, and verbal fluency). Test scores were converted to age-, sex-, and education-adjusted z-scores. Whole brain magnetic resonance imaging (MRI) was completed, from which measures of thalamic and hippocampal volumes, and mean diffusivity of gray matter and normal-appearing white matter were converted to age and sex-adjusted z-scores. Canonical correlation analysis was used to identify linear combinations of cognitive measures (cognitive variate) and MRI measures (MRI variate) that accounted for the most correlation between the cognitive and MRI measures. Regression analyses were used to test whether MRI measures mediated the relationships between the number of vascular comorbidities and cognition measures. Results Of 105 participants, most were women (84.8%) with a mean (SD) age of 51.8 (12.8) years and age of symptom onset of 29.4 (10.5) years. Vascular comorbidity was common, with 35.2% of participants reporting one, 15.2% reporting two, and 8.6% reporting three or more. Canonical correlation analysis of the cognitive and MRI variables identified one pair of variates (Pillai's trace = 0.45, p = 0.0035). The biggest contributors to the cognitive variate were the SDMT and CVLT-II, and to the MRI variate were gray matter MD and thalamic volume. The correlation between cognitive and MRI variates was 0.50; these variates were used in regression analyses. On regression analysis, vascular comorbidity was associated with the MRI variate, and with the cognitive variate. After adjusting for the MRI variate, vascular comorbidity was not associated with the cognitive variate. Conclusion Vascular comorbidity is associated with lower cognitive function in people with MS and this association is partially mediated via changes in brain macrostructure and microstructure.

/pdf/effects-of-vascular-comorbidity-on-cognition-in-multiple-22jg38ku.pdf

Effects of Vascular Comorbidity on Cognition in Multiple Sclerosis Are Partially Mediated by Changes in Brain Structure

Objective To determine whether comorbid diabetes and hypertension are associated with cognition in multiple sclerosis (MS) after accounting for psychiatric comorbidities. Methods Participants completed a structured psychiatric interview, the Hospital Anxiety and Depression Scale (HADS), a comorbidity questionnaire, and cognitive testing including the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT-II), Brief Visuospatial Memory Test-Revised (BVMT-R), and verbal fluency. Test scores were converted to age-, sex- and education-adjusted z-scores. We evaluated associations between diabetes and hypertension and the four cognitive z-scores using a multivariate linear model, adjusting for comorbid depression and anxiety disorders, psychotropic medications, disease-modifying therapies, smoking status and body mass index. Results Of 111 participants, most were women (82.9%) with relapsing remitting MS (83.5%), of mean (SD) age 49.6 (12.7) years. Comorbidity was common; 22.7% participants had hypertension, 10.8% had diabetes, 9.9% had current major depression, and 9.9% had current anxiety disorders. Mean (SD) z-scores were: SDMT −0.66 (1.15), CVLT-II −0.43 (1.32), BVMT-R −0.49 (1.07) and fluency −0.59 (0.86). Diabetes (p = 0.02) and anxiety disorder (p = 0.02) were associated with cognitive function overall. Diabetes was associated with lower BVMT-R (β = −1.18, p = 0.0015) and fluency (β = −0.63, p = 0.037) z-scores. Anxiety was associated with lower SDMT (β = -1.07, p = 0.0074) z-scores. Elevated anxiety symptoms (HADS-A ≥ 11) were associated with lower z-scores on the SDMT and CVLT-II. Conclusion Comorbidities, including diabetes and anxiety, are associated with cognitive dysfunction in MS. Their presence may contribute to the heterogeneous pattern of impairments seen across individuals and they may represent targets for improved management of cognitive symptoms.

Diabetes and anxiety adversely affect cognition in multiple sclerosis.

Background Few studies have evaluated the association between comorbidities associated with increased vascular risk and brain volume changes in multiple sclerosis (MS). To date, findings have not been consistent with respect to which comorbidities are associated with lower brain volumes or whether comorbidities associated with increased vascular risk are associated with greater brain volume loss over time. Objectives We aimed to evaluate the association between the Framingham Risk Score (FRS) which evaluates vascular risk and normalized whole brain volume in MS. Methods We included 98 participants with MS who underwent two brain MRIs two years apart, from which whole brain volumes were calculated. Each participant reported their comorbidities and medications taken. Blood pressure, height and weight were recorded and we calculated the FRS. We tested the association between the FRS at baseline and brain volume at the second time point using quantile regression adjusting for baseline normalized brain volume, age, gender and use of disease-modifying therapy. Results As the FRS increased, brain volume was lower, both at enrollment (β= -0.24; 95%CI: -0.42, -0.04) and at follow-up (-0.27; 95%CI: -0.45, -0.08). After further adjustment for age, gender, and use of disease modifying therapy, higher FRS remained associated with lower brain volume at follow-up at the 90th percentile of brain volume (β= -2.22; 95%CI: -3.40, -1.04) but not at the 10th or 50th percentiles. Conclusion Higher FRS were associated with lower brain volumes in persons with MS at baseline, and with brain volume loss over time. This effect was most pronounced for persons with higher brain volumes at baseline, which suggests that prevention, detection and effective management of comorbidities associated with vascular risk in people with MS is particularly important early in the disease course.

Higher Framingham Risk Scores are associated with greater loss of brain volume over time in multiple sclerosis

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Diabetes and anxiety adversely affect cognition in multiple sclerosis.

Higher Framingham Risk Scores are associated with greater loss of brain volume over time in multiple sclerosis