20 resultados para C21 - Cross-Sectional Models


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β-Arrestin2 (ARRB2) is a component of the G-protein-coupled receptor complex and is involved in μ-opioid and dopamine D(2) receptor signaling, two central processes in methadone signal transduction. We analyzed 238 patients in methadone maintenance treatment (MMT) and identified a haplotype block (rs34230287, rs3786047, rs1045280 and rs2036657) spanning almost the entire ARRB2 locus. Although none of these single nucleotide polymorphisms (SNPs) leads to a change in amino-acid sequence, we found that for all the SNPs analyzed, with exception of rs34230287, homozygosity for the variant allele confers a nonresponding phenotype (n=73; rs1045280C and rs2036657G: OR=3.1, 95% CI=1.5-6.3, P=0.004; rs3786047A: OR=2.5, 95% CI=1.2-5.1, P=0.02) also illustrated by a 12-fold shorter period of negative urine screening (P=0.01). The ARRB2 genotype may thus contribute to the interindividual variability in the response to MMT and help to predict response to treatment.

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BACKGROUND: The presence of cognitive and structural deficits in euthymic elderly depressed patients remains a matter of debate. Integrative aetiological models assessing concomitantly these parameters as well as markers of psychological vulnerability such as persistent personality traits, are still lacking for this age group. METHODS: Cross-sectional comparisons of 38 elderly remitted patients with early-onset depression (EOD) and 62 healthy controls included detailed neuropsychological assessment, estimates of brain volumes in limbic areas and white matter hyperintensities, as well as evaluation of the Five-Factor personality dimensions. RESULTS: Both cognitive performances and brain volumes were preserved in euthymic EOD patients. No significant group differences were observed in white matter hyperintensity scores between the two groups. In contrast, EOD was associated with significant increase of Neuroticism and decrease of Extraversion facet scores. LIMITATIONS: Results concern the restricted portion of EOD patients without psychiatric and physical comorbidities. Future longitudinal studies are necessary to determine the temporal relationship between the occurrence of depression and personality dimensions. CONCLUSIONS: After remission from acute depressive symptoms, cognitive performances remain intact in elderly patients with EOD. In contrast to previous observations, these patients display neither significant brain volume loss in limbic areas nor increased vascular burden compared to healthy controls. Further clinical investigations on EOD patterns of vulnerability in old age will gain from focusing on psychological features such as personality traits rather than neurocognitive clues.

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We aimed to compare physical activity level and cardiorespiratory fitness in children with different chronic diseases, such as type 1 diabetes mellitus (T1DM), obesity (OB) and juvenile idiopathic arthritis (JIA), with healthy controls (HC). We performed a cross-sectional study including 209 children: OB: n = 45, T1DM: n = 48, JIA: n = 31, and HC: n = 85. Physical activity level was assessed by accelerometer and cardiorespiratory fitness by a treadmill test. ANOVA, linear regressions and Pearson correlations were used. Children with chronic diseases had reduced total daily physical activity counts (T1DM 497 +/- 54 cpm, p = 0.003; JIA 518 +/- 28, p < 0.001, OB 590 +/- 25, p = 0.003) and cardiorespiratory fitness (JIA 39.3 +/- 1.7, p = 0.001, OB 41.7 +/- 1.2, p = 0.020) compared to HC (668 +/- 35 cpm; 45.3 +/- 0.9 ml kg(-1) min(-1), respectively). Only 60.4% of HC, 51.6% of OB, 38.1% of JIA and 38.5% of T1DM children met the recommended daily 60 min of moderate-to-vigorous physical activity. Low cardiorespiratory fitness was associated with female gender and low daily PA. Children with chronic diseases had reduced physical activity and cardiorespiratory fitness. As the benefits of PA on health have been well demonstrated during growth, it should be encouraged in those children to prevent a reduction of cardiorespiratory fitness and the development of comorbidities.

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INTRODUCTION: Several studies have shown an increased risk of type 2 diabetes among smokers. Therefore, the aim of this analysis was to assess the relationship between smoking, cumulative smoking exposure and nicotine dependence with pre-diabetes. METHODS: We performed a cross-sectional analysis of healthy adults aged 25-41 in the Principality of Liechtenstein. Individuals with known diabetes, Body Mass Index (BMI) >35 kg/m² and prevalent cardiovascular disease were excluded. Smoking behaviour was assessed by self-report. Pre-diabetes was defined as glycosylated haemoglobin between 5.7% and 6.4%. Multivariable logistic regression models were done. RESULTS: Of the 2142 participants (median age 37 years), 499 (23.3%) had pre-diabetes. There were 1,168 (55%) never smokers, 503 (23%) past smokers and 471 (22%) current smokers, with a prevalence of pre-diabetes of 21.2%, 20.9% and 31.2%, respectively (p <0.0001). In multivariable regression models, current smokers had an odds ratio (OR) of pre-diabetes of 1.82 (95% confidential interval (CI) 1.39; 2.38, p <0.0001). Individuals with a smoking exposure of <5, 5-10 and >10 pack-years had an OR (95% CI) for pre-diabetes of 1.34 (0.90; 2.00), 1.80 (1.07; 3.01) and 2.51 (1.80; 3.59) (p linear trend <0.0001) compared with never smokers. A Fagerström score of 2, 3-5 and >5 among current smokers was associated with an OR (95% CI) for pre-diabetes of 1.27 (0.89; 1.82), 2.15 (1.48; 3.13) and 3.35 (1.73; 6.48) (p linear trend <0.0001). DISCUSSION: Smoking is strongly associated with pre-diabetes in young adults with a low burden of smoking exposure. Nicotine dependence could be a potential mechanism of this relationship.

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The association between adiposity measures and dyslipidemia has seldom been assessed in a multipopulational setting. 27 populations from Europe, Australia, New Zealand and Canada (WHO MONICA project) using health surveys conducted between 1990 and 1997 in adults aged 35-64 years (n = 40,480). Dyslipidemia was defined as the total/HDL cholesterol ratio >6 (men) and >5 (women). Overall prevalence of dyslipidemia was 25% in men and 23% in women. Logistic regression showed that dyslipidemia was strongly associated with body mass index (BMI) in men and with waist circumference (WC) in women, after adjusting for region, age and smoking. Among normal-weight men and women (BMI<25 kg/m(2)), an increase in the odds for being dyslipidemic was observed between lowest and highest WC quartiles (OR = 3.6, p < 0.001). Among obese men (BMI ≥ 30), the corresponding increase was smaller (OR = 1.2, p = 0.036). A similar weakening was observed among women. Classification tree analysis was performed to assign subjects into classes of risk for dyslipidemia. BMI thresholds (25.4 and 29.2 kg/m(2)) in men and WC thresholds (81.7 and 92.6 cm) in women came out at first stages. High WC (>84.8 cm) in normal-weight men, menopause in women and regular smoking further defined subgroups at increased risk. standard categories of BMI and WC, or their combinations, do not lead to optimal risk stratification for dyslipidemia in middle-age adults. Sex-specific adaptations are necessary, in particular by taking into account abdominal obesity in normal-weight men, post-menopausal age in women and regular smoking in both sexes.