Background: Excessive fructose intake causes metabolic syndrome in animals and can be partially prevented by lowering the uric acid level. We tested the hypothesis that fructose might induce features ...of metabolic syndrome in adult men and whether this is protected by allopurinol. Methods: A randomized, controlled trial of 74 adult men who were administered 200 g fructose daily for 2 weeks with or without allopurinol. Primary measures included changes in ambulatory blood pressure (BP), fasting lipids, glucose and insulin, homeostatic model assessment (HOMA) index, body mass index and criteria for metabolic syndrome. Results: The ingestion of fructose resulted in an increase in ambulatory BP (7±2 and 5±2 mm Hg for systolic (SBP) and diastolic BP (DBP), P<0.004 and P<0.007, respectively). Mean fasting triglycerides increased by 0.62±0.23 mmol l−1 (55±20 mg per 100 ml), whereas high-density lipoprotein cholesterol decreased by 0.06±0.02 mmol l−1 (2.5±0.7 mg per 100 ml), P<0.002 and P<0.001, respectively. Fasting insulin and HOMA indices increased significantly, whereas plasma glucose level did not change. All liver function tests showed an increase in values. The metabolic syndrome increased by 25–33% depending on the criteria. Allopurinol lowered the serum uric acid level (P<0.0001) and prevented the increase in 24-h ambulatory DBP and daytime SBP and DBP. Allopurinol treatment did not reduce HOMA or fasting plasma triglyceride levels, but lowered low-density lipoprotein cholesterol relative to control (P<0.02) and also prevented the increase in newly diagnosed metabolic syndrome (0–2%, P=0.009). Conclusions: High doses of fructose raise the BP and cause the features of metabolic syndrome. Lowering the uric acid level prevents the increase in mean arterial blood pressure. Excessive intake of fructose may have a role in the current epidemics of obesity and diabetes.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The novel coronavirus (severe acute respiratory syndrome CoV-2 SARS-CoV-2), also known as COVID-19, is a single-stranded enveloped RNA virus that created a Public Health Emergency of International ...Concern in January 2020, with a global case burden of over 15 million in just 7 months. Infected patients develop a wide range of clinical manifestations—typically presenting with fever, cough, myalgia, and fatigue. Severely ill patients may fall victim to acute respiratory distress syndrome, acute heart injuries, neurological manifestations, or complications due to secondary infections. These critically ill patients are also found to have disrupted coagulation function, predisposing them to consumptive coagulopathies, and both venous and thromboembolic complications. Common laboratory findings include thrombocytopenia, elevated D-dimer, fibrin degradation products, and fibrinogen, all of which have been associated with greater disease severity. Many cases of pulmonary embolism have been noted, along with deep vein thrombosis, ischemic stroke, myocardial infarction, and systemic arterial embolism. The pathogenesis of coronavirus has not been completely elucidated, but the virus is known to cause excessive inflammation, endothelial injury, hypoxia, and disseminated intravascular coagulation, all of which contribute to thrombosis formation. These patients are also faced with prolonged immobilization while staying in the hospital or intensive care unit. It is important to have a high degree of suspicion for thrombotic complications as patients may rapidly deteriorate in severe cases. Evidence suggests that prophylaxis with anticoagulation may lead to a lower risk of mortality, although it does not eliminate the possibility. The risks and benefits of anticoagulation treatment should be considered in each case. Patients should be regularly evaluated for bleeding risks and thrombotic complications.
Background
Due to insufficient scientific evidence, panels of tumour markers (TMs) are currently not recommended for use in suspected cancer. However, recent well‐designed studies have revealed a ...potential clinical value in lung cancer. We analysed the diagnostic accuracy of a panel of 11 circulating TMs with clinically controlled thresholds in the differentiation of cancer from nonmalignant diseases.
Methods
We prospectively recruited 4776 consecutive patients presenting with focal or nonspecific symptoms suggestive of cancer who underwent testing for 11 serum TMs before diagnosis was known. The study abided by 2015 STARD guidelines. Tumour markers included, among others, carbohydrate antigen 19‐9, carcinoembryonic antigen, alpha‐fetoprotein, squamous cell carcinoma‐associated antigen, prostate‐specific antigen (males), neuron‐specific enolase, progastrin‐releasing peptide and carbohydrate antigen 125. Thresholds were adjusted for the presence of kidney failure, liver disease, effusions and dermatological disorders. Results showing ≥1 TMs with concentrations above threshold were considered positive.
Results
Benign diseases were diagnosed in 3281 (68.7%) patients and cancer in 1495 (31.3%), with epithelial cancers in 1214 (77% at stage IV). When applying criteria for controlled thresholds, overall specificity was 98%. Overall sensitivity of the panel in epithelial cancers was 72.2%, positive predictive value 93% and negative predictive value 90.5%. The area under the receiver operating characteristic curve was 0.920 (95% confidence interval, 0.902‐0.924).
Conclusions
By using clinically controlled cut‐offs, the combined panel demonstrated an excellent ability to discriminate epithelial cancers from nonmalignant diseases. However, its use in clinical practice would need formal validation through a multicentre controlled trial assessing a panel‐guided strategy vs. standard diagnosis.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Postmenopausal osteoporosis is an important metabolic bone disease characterized by rapid bone loss occurring in the postmenopausal period. Recently, the most prevalent form of clinically significant ...osteopenia and osteoporosis involves various inflammatory conditions. The aim of the study is to evaluate the association between proinflammatory markers (interleukin IL-1β, IL-6, TNF-α) with bone turnover markers (BTMs) in postmenopausal Saudi women with and without osteoporosis. A total of 200 postmenopausal Saudi women ≥50 years old, 100 with osteoporosis and 100 without osteoporosis (control) were recruited under the supervision of qualified physicians in King Salman Hospital and King Fahd Medical City, Riyadh, Saudi Arabia. Serum tumor necrosis factor alpha (TNF-α), IL-1, IL-4, IL-6, and parathyroid hormone (PTH) were determined using Luminex xMAP technology. N-telopeptides of collagen type I (NTx) was assessed using ELISA, 25(OH) vitamin D and osteocalcin were determined using electrochemiluminescence, serum calcium and inorganic phosphate (Pi) were measured by a chemical analyzer. Serum IL-1β, IL-6, NTx, and PTH levels in women with osteoporosis were significantly higher than controls. Although IL-4 and osteocalcin were significantly lower than controls. IL-1β and TNF-α were positively associated with NTx in osteoporosis women. TNF-α, IL-6, and TNF-α were positively correlated with IL-lβ in both groups. A significant negative correlation between osteocalcin and IL-1β in healthy women and women with osteoporosis were observed. Findings of the present study implicate a role for cytokine pattern-mediated inflammation in patients with osteoporosis.
Trace Elements in Obese Turkish Children Tascilar, Mehmet Emre; Ozgen, Ilker Tolga; Abaci, Ayhan ...
Biological trace element research,
10/2011, Volume:
143, Issue:
1
Journal Article
Peer reviewed
The quality of the diet of obese children is poor. Eating habits may alter micronutrient status in obese patients. In this study, we determined the serum levels of selenium, zinc, vanadium, ...molybdenum, iron, copper, beryllium, boron, chromium, manganese, cobalt, silver, barium, aluminum, nickel, cadmium, mercury, and lead in obese Turkish children. Thirty-four obese and 33 healthy control subjects were enrolled in the study. Serum vanadium and cobalt levels of obese children were significantly lower than those of the control group (0.244 ± 0.0179 vs. 0.261 ± 0.012 μg/l,
p
< 0.001, and 0.14 ± 0.13 vs. 0.24 ± 0.15 μg/l,
p
= 0.011, respectively). There was no significant difference between groups regarding the other serum trace element levels. In conclusion, there may be alterations in the serum levels of trace elements in obese children and these alterations may have a role in the pathogenesis of obesity.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background
Several biomarkers have individually been shown to be useful for risk stratification in patients with acute myocardial infarction (MI). The optimal multimarker strategy remains undefined.
...Methods and Results
Biomarkers representing different pathobiological axes were studied, including myocardial stress/structural changes (NT‐pro B‐type natriuretic peptide NT‐proBNP, midregional proatrial natriuretic peptide MR‐proANP, suppression of tumorigenicity 2 ST2, galectin‐3, midregional proadrenomedullin MR‐proADM, and copeptin), myonecrosis (troponin T), and inflammation (myeloperoxidase MPO, high sensitivity C‐reactive protein hsCRP, pregnancy‐associated plasma protein A PAPP‐A, and growth‐differentiation factor‐15 GDF‐15), in up to 1258 patients from Clopidogrel as Adjunctive Reperfusion Therapy‐Thrombolysis in Myocardial Infarction 28 (CLARITY‐TIMI 28), a randomized trial of clopidogrel in ST‐elevation MI (STEMI). Patients were followed for 30 days. Biomarker analyses were adjusted for traditional clinical variables. Forward step‐wise selection was used to assess a multimarker strategy. After adjustment for clinical variables and using a dichotomous cutpoint, 7 biomarkers were each significantly associated with a higher odds of cardiovascular death or heart failure (HF) through 30 days, including NT‐proBNP (adjusted odds ratio ORadj, 2.54; 95% CI, 1.47–4.37), MR‐proANP (2.18; 1.27–3.76), ST2 (2.88; 1.72–4.81), troponin T (4.13; 1.85–9.20), MPO (2.75; 1.20–6.27), hsCRP (1.96, 1.17–3.30), and PAPP‐A (3.04; 1.17–7.88). In a multimarker model, 3 biomarkers emerged as significant and complementary predictors of cardiovascular death or HF: ST2 (ORadj, 2.87; 1.61–5.12), troponin T (2.34; 1.09–5.01 and 4.13, 1.85–9.20, respectively for intermediate and high levels), and MPO (2.49; 1.04–5.96). When added to the TIMI STEMI Risk Score alone, the multimarker risk score significantly improved the C‐statistic (area under the curve, 0.75 95% CI, 0.69–0.81 to 0.82 0.78–0.87; P=0.001), net reclassification index (0.93; P<0.001), and integrated discrimination index (0.09; P<0.001).
Conclusions
In patients with STEMI, a multimarker strategy that combines biomarkers across pathobiological axes of myocardial stress, myocyte necrosis, and inflammation provides incremental prognostic information for prediction of cardiovascular death or HF.
CONTEXT Prior studies have demonstrated conflicting results regarding how much information novel biomarkers add to cardiovascular risk assessment. OBJECTIVE To evaluate the utility of contemporary ...biomarkers for predicting cardiovascular risk when added to conventional risk factors. DESIGN, SETTING, AND PARTICIPANTS Cohort study of 5067 participants (mean age, 58 years; 60% women) without cardiovascular disease from Malmö, Sweden, who attended a baseline examination between 1991 and 1994. Participants underwent measurement of C-reactive protein (CRP), cystatin C, lipoprotein-associated phospholipase 2, midregional proadrenomedullin (MR-proADM), midregional proatrial natriuretic peptide, and N-terminal pro-B-type natriuretic peptide (N-BNP) and underwent follow-up until 2006 using the Swedish national hospital discharge and cause-of-death registers and the Stroke in Malmö register for first cardiovascular events (myocardial infarction, stroke, coronary death). MAIN OUTCOME MEASURES Incident cardiovascular and coronary events. RESULTS During median follow-up of 12.8 years, there were 418 cardiovascular and 230 coronary events. Models with conventional risk factors had C statistics of 0.758 (95% confidence interval CI, 0.734 to 0.781) and 0.760 (0.730 to 0.789) for cardiovascular and coronary events, respectively. Biomarkers retained in backward-elimination models were CRP and N-BNP for cardiovascular events and MR-proADM and N-BNP for coronary events, which increased the C statistic by 0.007 (P = .04) and 0.009 (P = .08), respectively. The proportion of participants reclassified was modest (8% for cardiovascular risk, 5% for coronary risk). Net reclassification improvement was nonsignificant for cardiovascular events (0.0%; 95% CI, −4.3% to 4.3%) and coronary events (4.7%; 95% CI, −0.76% to 10.1%). Greater improvements were observed in analyses restricted to intermediate-risk individuals (cardiovascular events: 7.4%; 95% CI, 0.7% to 14.1%; P = .03; coronary events: 14.6%; 95% CI, 5.0% to 24.2%; P = .003). However, correct reclassification was almost entirely confined to down-classification of individuals without events rather than up-classification of those with events. CONCLUSIONS Selected biomarkers may be used to predict future cardiovascular events, but the gains over conventional risk factors are minimal. Risk classification improved in intermediate-risk individuals, mainly through the identification of those unlikely to develop events.
Adipokines and inflammation may provide a mechanistic link between obesity and postmenopausal breast cancer, yet epidemiologic data on their associations with breast cancer risk are limited.
In a ...case-cohort analysis nested within the Women's Health Initiative Observational Study, a prospective cohort of postmenopausal women, baseline plasma samples from 875 incident breast cancer case patients and 839 subcohort participants were tested for levels of seven adipokines, namely leptin, adiponectin, resistin, interleukin-6, tumor necrosis factor-α, hepatocyte growth factor, and plasminogen activator inhibitor-1, and for C-reactive protein (CRP), an inflammatory marker. Data were analyzed by multivariable Cox modeling that included established breast cancer risk factors and previously measured estradiol and insulin levels. All statistical tests were two-sided.
The association between plasma CRP levels and breast cancer risk was dependent on hormone therapy (HT) use at baseline (P interaction = .003). In a model that controlled for multiple breast cancer risk factors including body mass index (BMI), estradiol, and insulin, CRP level was positively associated with breast cancer risk among HT nonusers (hazard ratio for high vs low CRP levels = 1.67, 95% confidence interval = 1.04 to 2.68, P trend = .029). None of the other adipokines were statistically significantly associated with breast cancer risk. Following inclusion of CRP, insulin, and estradiol in a multivariable model, the association of BMI with breast cancer was attenuated by 115%.
These data indicate that CRP is a risk factor for postmenopausal breast cancer among HT nonusers. Inflammatory mediators, together with insulin and estrogen, may play a role in the obesity-breast cancer relation.
Background
Following the SEPSIS‐3 consensus, detection of organ failure as assessed by the SOFA (Sequential Organ Failure Assessment) score, is mandatory to detect sepsis. Calculating SOFA outside of ...the Intensive Care Unit (ICU) is challenging. The alternative in this scenario, the quick SOFA, is very specific but less sensible. Biomarkers could help to detect the presence of organ failure secondary to infection either in ICU and non‐ICU settings.
Materials and methods
We evaluated the ability of four biomarkers (C‐Reactive protein (CRP), lactate, mid‐regional proadrenomedullin (MR‐proADM) and procalcitonin (PCT)) to detect each kind of organ failure considered in the SOFA in 213 patients with infection, sepsis or septic shock, by using multivariate regression analysis and calculation of the area under the receiver operating curve (AUROC).
Results
In the multivariate analysis, MR‐proADM was an independent predictor of five different failures (respiratory, coagulation, cardiovascular, neurological and renal). In turn, lactate predicted three (coagulation, cardiovascular and neurological) and PCT two (cardiovascular and renal). CRP did not predict any of the individual components of SOFA. The highest AUROCs were those of MR‐proADM and PCT to detect cardiovascular (AUROC, CI95%): MR‐proADM (0.82 0.76‐0.88), PCT (0.81 0.75‐0.87 (P < .05) and renal failure: MR‐proADM (0.87 0.82‐0.92), PCT (0.81 0.75‐0.86), (P < .05). None of the biomarkers tested was able to detect hepatic failure.
Conclusions
In patients with infection, MR‐proADM was the biomarker detecting the largest number of SOFA score components, with the exception of hepatic failure.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
Imprecise measurement of physical activity variables might attenuate estimates of the beneficial effects of activity on health-related outcomes. We aimed to compare the cardiometabolic risk factor ...dose-response relationships for physical activity and sedentary behaviour between accelerometer- and questionnaire-based activity measures.
Physical activity and sedentary behaviour were assessed in 317 adults by 7-day accelerometry and International Physical Activity Questionnaire (IPAQ). Fasting blood was taken to determine insulin, glucose, triglyceride and total, LDL and HDL cholesterol concentrations and homeostasis model-estimated insulin resistance (HOMA(IR)). Waist circumference, BMI, body fat percentage and blood pressure were also measured.
For both accelerometer-derived sedentary time (<100 counts.min(-1)) and IPAQ-reported sitting time significant positive (negative for HDL cholesterol) relationships were observed with all measured risk factors--i.e. increased sedentary behaviour was associated with increased risk (all p ≤ 0.01). However, for HOMA(IR) and insulin the regression coefficients were >50% lower for the IPAQ-reported compared to the accelerometer-derived measure (p<0.0001 for both interactions). The relationships for moderate-to-vigorous physical activity (MVPA) and risk factors were less strong than those observed for sedentary behaviours, but significant negative relationships were observed for both accelerometer and IPAQ MVPA measures with glucose, and insulin and HOMA(IR) values (all p<0.05). For accelerometer-derived MVPA only, additional negative relationships were seen with triglyceride, total cholesterol and LDL cholesterol concentrations, BMI, waist circumference and percentage body fat, and a positive relationship was evident with HDL cholesterol (p = 0.0002). Regression coefficients for HOMA(IR), insulin and triglyceride were 43-50% lower for the IPAQ-reported compared to the accelerometer-derived MVPA measure (all p≤0.01).
Using the IPAQ to determine sitting time and MVPA reveals some, but not all, relationships between these activity measures and metabolic and vascular disease risk factors. Using this self-report method to quantify activity can therefore underestimate the strength of some relationships with risk factors.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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