Cognitive Functions across the GNB3<i>C825T</i>Polymorphism in an Elderly Italian Population

Casíglia, Edoardo; Giordano, Nunzia; Tikhonoff, Valérie; Boschetti, Giovanni; Mazza, Alberto; Caffi, Sandro; Guidotti, Federica; Bisiacchi, Patrizia

doi:10.1155/2013/597034

Cited by 10 publications

(6 citation statements)

References 35 publications

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“…Participants were seen by a neuropsychologist at an ad hoc hospital unit. Cognitive assessment was performed by mini‐mental state examination (MMSE) and a comprehensive neuropsychological battery of eight validated paper and pencil tests (Figure ) administered in a single session of approximately 2 h .…”

Section: Methodsmentioning

confidence: 99%

Body fat and the cognitive pattern: A population-based study

Tikhonoff

Casíglia

Guidotti

et al. 2015

Obesity

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Objective:The relationship between body fatness and cognitive pattern at a population level was investigated. Methods: Among 500 unselected subjects from the general population, the role of body mass index (BMI) and body fat mass (BFM) on a mini-mental state examination (MMSE) and on a battery of paper and pencil neuropsychological tests was analyzed. Multiple linear regressions, accounting for potential confounders, were used. Results: In fully adjusted models, MMSE (coefficient 10.027, 95% confidence intervals, 0.017-0.177), the clock drawing test (10.141, 0.053-0.226), and the trail making test A (11.542, 0.478-2.607) were positively associated with BMI. Adding BFM to the models, no associations were observed. The tests were also positively associated with BFM (10.056, 0.021-0.091; 10.063, 0.025-0.101; 10.592, 0.107-1.077; respectively). At analysis of covariance, the same tests were significantly better performed over 29.4 kg m 22 of BMI. After adding BFM as further confounder, all differences in performance across BMI were no longer significant. The three tests were better performed over 34.6 kg of BFM. Conclusions: Higher BMI and particularly higher BFM are positively associated with better performance at the cognitive tasks exploring selective attention and executive functions.

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Section: Methodsmentioning

confidence: 99%

Body fat and the cognitive pattern: A population-based study

Tikhonoff

Casíglia

Guidotti

et al. 2015

Obesity

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“…At the initial visit, cognitive assessment was performed by a MiniMental State Examination 32 and by a comprehensive neuropsychological battery of validated paper-and-pencil tests standardized for Italian people 33 administered in a single session lasting ≈2 hours. Details are described Cognitive Pattern Assessment section of onlineonly Data Supplement and in Table S1 in the online-only Data Supplement.…”

Section: Neuropsychological Assessmentmentioning

confidence: 99%

Poor Reliability of Wrist Blood Pressure Self-Measurement at Home

et al. 2016

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Blood pressure (BP) independently predicts cardiovascular risk in affluent countries. [1][2][3][4][5] However, several studies have shown that awareness of hypertension and BP control is still suboptimal, 1 and thus effective strategies should be developed to improve BP control and adherence to therapy. One of the methods used to achieve these goals is represented by self-measurement of BP at home by automatic devices. Many studies have shown that BP self-measurement at home allows a better BP control in hypertension and has a greater prognostic value than office BP. 6,7 In addition, self BP measurement is more appealing to the patient than the customary procedure in the physician's surgery often causing long waits. Upper-arm automatic devices gave fresh impetus to home self-measurement because of their user friendliness, 8 the only conditions to be respected being adequate cuff dimensions and correct positioning of the cuff on the patient's arm. [9][10][11][12][13][14] For reasons of market penetration and following a general tendency to miniaturization, many wrist devices recently appeared on the market 15 having the advantage of being smaller and easier to fit than upper-arm monitors, 16 and today wrist devices are used by a large portion of people who measure their BP at home. 17 However, in spite of technical improvement, their reliability in real-life conditions is not unanimously accepted. [18][19][20] Accuracy of BP measurement at wrist largely depends on the difference in height between the wrist and the heart because of the confounding effect of the hydrostatic pressure caused by the limb blood column.21-23 Wrists kept at a higher level in comparison with the heart lead to a false lower, and wrists at a lower level lead to a false higher BP values. Very rarely do patients receive appropriate training from family doctors or other healthcare personnel. Instructions attached to commercial packages require a certain degree of personal discernment, i.e., a good cognitive pattern. Only a few models have a position sensor to verify that the wrist is placed properly at heart level, 24 but even for these devices, no study has evaluated their reliability in real-life conditions.The aim of the present study was to ascertain whether wrist home BP is performed reliably or an error is present in measurement, and to identify the determinants of this error if any.To verify the reliability of wrist BP self-measurement, we determined at a population level the upper-arm−wrist BP difference in the office under a doctor's supervision and then verified whether this difference was maintained during home self-measurement.Abstract-The reliability of blood pressure measurement with wrist devices, which has not previously been assessed under real-life circumstances in general population, is dependent on correct positioning of the wrist device at heart level. We determined whether an error was present when blood pressure was self-measured at the wrist in 721 unselected subjects from the general population. After training, blood p...

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“…Studies have also reported that CHRFAM7A [58], ACHE (acetylcholinesterase (Cartwright blood group) [305], HP (haptoglobin) [306], SELENBP1 [63], BASP1 [72], CHRNA1 [307], RPTN (repetin) [76], IL33 [308], TACR1[309], SHANK3 [310], ALOX12B [311], HPN (hepsin) [312], GFAP (glial fibrillary acidic protein) [84], CACNB2 [313], MYT1 [314], NOTCH3 [315], TERT (telomerase reverse transcriptase) [316], GNB3 [317], EGR3 [318], BSN (bassoon presynaptic cytomatrix protein) [319], CUX2 [320], GDF15 [321], CXCL8 [322], MAGI1 [95] and PAH (phenylalanine hydroxylase) [323] are necessary for BD development. S100A12 [324], CDH1 [325], S100A9 [326], ANK1 [327], KCNH2 [328], HP (haptoglobin) [329], FRMD4A [330], SNCA (synuclein alpha) [331], FBXO7 [332], PINK1 [333], B2M [334], KCNH3 [335], CA2 [336], FUZ (fuzzy planar cell polarity protein) [337], UBB (ubiquitin B) [338], C5AR1 [339], CBS (cystathionine beta-synthase) [340], F12 [341], TSPAN5 [342], NQO2 [343], NDUFA1 [344], SRXN1 [345], BASP1 [346], GPX1 [347], OLIG2 [348], EFHC2 [349], FOXA1 [350], PAX4 [351], BGN (biglycan) [352], IL33 [353], LRIG3 [354], GJA1 [355], SHANK3 [356], ARC (activity regulated cytoskeleton associated protein) [357], GFAP (glial fibrillary acidic protein) [358], UNC13A [359], MSTN (myostatin) [360], HSPG2 [361], TERT (telomerase reverse transcriptase) [362], GNB3 [363], L1CAM [364], CALB1 [365], RYR2 [366], MYO15A [367], MYH11 [368], GDF15 […”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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Cognitive Functions across the GNB3C825TPolymorphism in an Elderly Italian Population

Cited by 10 publications

References 35 publications

Body fat and the cognitive pattern: A population-based study

Body fat and the cognitive pattern: A population-based study

Poor Reliability of Wrist Blood Pressure Self-Measurement at Home

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

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