RESUMO
Aims/hypothesis: Diabetes mellitus (DM) is associated with comorbid brain disorders. Neuroimaging studies in DM revealed neuronal degeneration in several cortical and subcortical brain regions. Previous studies indicate more pronounced brain alterations in type 2 diabetes mellitus (T2DM) than in type 1 diabetes mellitus (T1DM). However, a comparison of both types of DM in a single analysis has not been done so far. The aim of this meta-analysis was to conduct an unbiased objective investigation of neuroanatomical differences in DM by combining voxel-based morphometry (VBM) studies of T1DM and T2DM using dual disorder anatomical likelihood estimation (ALE) quantification. Methods: PubMed, Web of Science and Medline were systematically searched for publications until June 15, 2020. VBM studies comparing gray matter volume (GMV) differences between DM patients and controls at the whole-brain level were included. Study coordinates were entered into the ALE meta-analysis to investigate the extent to which T1DM, T2DM, or both conditions contribute to gray matter volume differences compared to controls. Results: Twenty studies (comprising of 1,175 patients matched with 1,013 controls) were included, with seven studies on GMV alterations in T1DM and 13 studies on GMV alterations in T2DM. ALE analysis revealed seven clusters of significantly lower GMV in T1DM and T2DM patients relative to controls across studies. Both DM subtypes showed GMV reductions in the left caudate, right superior temporal lobe, and left cuneus. Conversely, GMV reductions associated exclusively with T2DM (>99% contribution) were found in the left cingulate, right posterior lobe, right caudate and left occipital lobe. Meta-regression revealed no significant influence of study size, disease duration, and HbA1c values. Conclusions/interpretation: Our findings suggest a more pronounced gray matter atrophy in T2DM compared to T1DM. The increased risk of microvascular or macrovascular complications, as well as the disease-specific pathology of T2DM may contribute to observed GMV reductions. Systematic Review Registration: [PROSPERO], identifier [CRD42020142525].
RESUMO
OBJECTIVE: To investigate the effects of robot-assisted training on the recovery of people with spinal cord injury (SCI). DATA SOURCES: Randomized controlled trials (RCTs) or quasi-RCTs involving people with SCI that compared robot-assisted upper limbs or lower limbs training with a control of other treatment approach or no treatment. We included studies involving people with complete or incomplete SCIs. STUDY SELECTION: We searched MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials (Cochrane Library), and Embase to August 2016. Bibliographies of relevant articles on the effect of body-weight-supported treadmill training on subjects with SCI were screened to avoid missing relevant articles from the search of databases. DATA EXTRACTION: All kinds of objective assessments concerning physical ability, mobility, and/or functional ability were included. Assessments could be clinical tests (ie, 6-minute walk test, FIM) or laboratory tests (ie, gait analysis). Subjective outcome measures were excluded from this review. DATA SYNTHESIS: Eleven RCT studies involving 443 subjects were included in the study. Meta-analysis was performed on the included studies. Walking independence (3.73; 95% confidence interval [CI], -4.92 to -2.53; P<.00001; I2=38%) and endurance (53.32m; 95% CI, -73.15 to -33.48; P<.00001; I2=0%) were found to have better improvement in robot-assisted training groups. Lower limb robot-assisted training was also found to be as effective as other types of body-weight-supported training. There is a lack of upper limb robot-assisted training studies; therefore, performing a meta-analysis was not possible. CONCLUSIONS: Robot-assisted training is an adjunct therapy for physical and functional recovery for patients with SCI. Future high-quality studies are warranted to investigate the effects of robot-assisted training on functional and cardiopulmonary recovery of patients with SCI.
