OCT Analysis in Patients with Relapsing-Remitting Multiple Sclerosis during Fingolimod Therapy: 2-Year Longitudinal Retrospective Study

Many studies have demonstrated the usefulness of some optical coherence tomography (OCT) parameters, like total macular volume (TMV) and retinal nerve fiber layer thickness (RNFL-T), for monitoring patients with multiple sclerosis (MS). However, there are no real-world, long-term studies on patients with relapsing-remitting MS (RR-MS) treated with fingolimod. Therefore, the purpose of this study was to describe retinal changes associated with fingolimod therapy during a two-year follow-up while considering previous episodes of optic neuritis (ON). Patients diagnosed with RR-MS and treated with fingolimod (46 in total) underwent a two-year follow-up. Based on previous ON history, we identified 16 ON+ and 30 ON− patients. The ophthalmological evaluations, including visual field (VF) examination and OCT, were performed at a baseline at 3–6, 12 and 24 months to evaluate the progression rate for each parameter. When analyzing the whole sample, OCT showed no cases of macular edema. Instead, we observed a significant reduction rate in the central retinal thickness (CRT) (p < 0.001), TMV (p < 0.001) and RNFL (p < 0.05). Moreover, we observed a significant difference in the progression rate between ON+ and ON− patients, relative to the VF and RNFL (p < 0.05) examinations. OCT highlighted a significant progression rate of retinal damage in MS patients despite fingolimod therapy, especially in MS ON+ patients

Epidemiologic Data of Vitamin D Deficiency and Its Implication in Cardio-Cerebrovascular Risk in a Southern Italian Population

Background. Vitamin D (25(OH)D) deficiency is a prevalent condition worldwide. However, the highest prevalence rates of 25(OH)D deficiency have been attributed to regions with higher latitude. A close association between 25(OH)D and cardio-cerebrovascular (CCV) risk factors and major health problems has been identified. Aim. To establish the prevalence of 25(OH)D deficiency and to investigate the relationship between 25(OH)D levels and CCV risk factors (blood cholesterol, triglycerides, glucose concentrations, body mass index, and systolic and diastolic blood pressure) in a cohort representative of Southern Italy. Methods. The prevalence of 25(OH)D levels was evaluated in 1200 subjects aged 25–74 years (600 males and 600 females), enrolled in the “VIP” (from Italian for Irno Valley Prevention) Project, whereas multiple linear regression analysis was used to determine the relationship between 25(OH)D levels and CCV risk factors. Results. Only 13.3% of females and 11.1% of males showed adequate serum concentrations of 25(OH)D (≥30 ng/ml), while 59.3% of females and 55.1% of males showed 25(OH)D deficient levels (<20 ng/ml). We observed an independent association between 25(OH)D concentrations and metabolic syndrome score, LDL cholesterol, HDL cholesterol, and corrected QT (cQT). Conclusions. We report a high prevalence of 25(OH)D deficiency across the largest Italian adult population studied so far and, in particular, the first across Southern Italy; furthermore, we provide data on the association between 25(OH)D deficiency and higher CCV risk factors

Cognitive performance in multiple sclerosis: the contribution of intellectual enrichment and brain MRI measures

Cognitive reserve (CR) is a construct that originates from the observation of poor correspondence between brain damage and clinical symptoms. The aim of the study was to investigate the association between cognitive reserve (CR), brain reserve (BR) and cognitive functions and to evaluate whether CR might attenuate/moderate the negative impact of brain atrophy and lesion load on cognitive functions in multiple sclerosis (MS). To achieve these aims, ninety-eight relapsing-remitting MS patients underwent the brief repeatable battery of neuropsychological tests and Stroop test (ST). CR was assessed by vocabulary-based estimate of lifetime intellectual enrichment. All patients underwent a 3T MRI to assess T2-lesion load and atrophy measures, including normalized gray matter and white matter (nWMV) volumes. The BR was evaluated by maximal lifetime brain volume expressed by intracranial volume (ICV). Hierarchical regressions were used to investigate whether higher BR and/or CR is related to better cognitive performances after controlling for potentially confounding factors. The ICV was not associated with any cognitive tests. Intellectual enrichment was positively associated with performance on tests assessing memory, attention and information processing speed, verbal fluency and inhibitory control. Significant relationship between nWMV and ST was moderated by intellectual enrichment. In conclusion, the findings suggested that CR seems to mitigate cognitive dysfunction in MS patients and can reduce the negative impact of brain atrophy on inhibitory control, relevant for integrity of instrumental activities of daily living

Attention and processing speed performance in multiple sclerosis is mostly related to thalamic volume

Cognitive impairment (CI), mainly involving attention and processing speed (A-PS), is a common and disabling symptom in multiple sclerosis (MS). Symbol Digit Modalities Test (SDMT) is one of the more sensitive and reliable tests to assess A-PS deficits in MS. Structural MRI correlates of A-PS in MS still need to be clarified. This study aimed to investigate, in a large group of MS patients, the relationship between regional gray matter (GM) atrophy and SDMT performance. 125 relapsing remitting MS patients and 52 healthy controls (HC) underwent a 3 T–MRI protocol including high-resolution 3D–T1 imaging. All subjects underwent a neurological evaluation and SDMT. A Voxel Based Morphometry analysis was performed to assess: 1) correlations between regional GM volume and SDMT performance in MS patients; 2) regional differences in GM volume between MS patients and HC. Thalamic, putamen and cerebellar volumes were also calculated using FIRST tool from the FMRIB Software Library. A linear regression analysis was performed to assess the contribution of each one of these structures to A-PS performance. A significant negative correlation was found between regional GM volume and SDMT score at the level of the thalamus, cerebellum, putamen, and occipital cortex in MS patients. Thalamus, cerebellum and putamen also showed significant GM atrophy in MS patients compared to HC. Thalamic atrophy is also an independent and additional contributor to A-PS deficits in MS patients. These findings support the role of thalamus as the most relevant GM structure subtending A-PS performance in MS, as measured by SDMT

Fatigue in multiple sclerosis: The contribution of resting-state functional connectivity reorganization

Objectives: To investigate resting-state functional connectivity (RS-FC) of the default-mode network (DMN) and of sensorimotor network (SMN) network in relapsing remitting (RR) multiple sclerosis (MS) patients with fatigue (F) and without fatigue(NF). Methods: In all, 59 RRMS patients and 29 healthy controls (HC) underwent magnetic resonance imaging (MRI) protocol including resting-state fMRI (RS-fMRI). Functional connectivity of the DMN and SMN was evaluated by independent component analysis (ICA). A linear regression analysis was performed to explore whether fatigue was mainly driven by changes observed in the DMN or in the SMN. Regional gray matter atrophy was assessed by voxel-based morphometry (VBM). Results: Compared to HC, F-MS patients showed a stronger RS-FC in the posterior cingulate cortex (PCC) and a reduced RS-FC in the anterior cingulated cortex (ACC) of the DMN. F-MS patients, compared to NF-MS patients, revealed (1) an increased RS-FC in the PCC and a reduced RS-FC in the ACC of the DMN and (2) an increased RS-FC in the primary motor cortex and in the supplementary motor cortex of the SMN. The regression analysis suggested that fatigue is mainly driven by RS-FC changes of the DMN. Conclusions: Fatigue in RRMS is mainly associated to a functional rearrangement of non-motor RS networks

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

BackgroundSerum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.MethodsHere, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.ResultsA pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis.ConclusionWe show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting

Serum steroid ratio profiles in prostate cancer: A new diagnostic tool toward a personalized medicine approach

Background: Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach. Methods: Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association. Results: A pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis. Conclusion: We show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting