Dose-dependent suppression of hunger by a specific alginate in a low-viscosity drink formulation

Addition of specific types of alginates to drinks can enhance postmeal suppression of hunger, by forming strong gastric gels in the presence of calcium. However, some recent studies have not demonstrated an effect of alginate/calcium on appetite, perhaps because the selected alginates do not produce sufficiently strong gels or because the alginates were not sufficiently hydrated when consumed. Therefore, the objective of the study was to test effects on appetite of a strongly gelling and fully hydrated alginate in an acceptable, low-viscosity drink formulation. In a balanced order crossover design, 23 volunteers consumed a meal replacement drink containing protein and calcium and either 0 (control), 0.6, or 0.8% of a specific high-guluronate alginate. Appetite (six self-report scales) was measured for 5 h postconsumption. Relevant physicochemical properties of the drinks were measured, i.e., product viscosity and strength of gel formed under simulated gastric conditions. Hunger was robustly reduced (20-30% lower area under the curve) with 0.8% alginate (P < 0.001, analysis of covariance), an effect consistent across all appetite scales. Most effects were also significant with 0.6% alginate, and a clear dose-response observed. Gastric gel strength was 1.8 and 3.8 N for the 0.6 and 0.8% alginate drinks, respectively, while product viscosity was acceptable (<0.5 Pa.s at 10 s(-1)). We conclude that strongly gastric-gelling alginates at relatively low concentrations in a low-viscosity drink formulation produced a robust reduction in hunger responses. This and other related studies indicate that the specific alginate source and product matrix critically impacts upon apparent efficacy.     

Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis

BackgroundTimely interventions in women presenting with preterm labour can substantially improve health outcomes for preterm babies. However, establishing such a diagnosis is very challenging, as signs and symptoms of preterm labour are common and can be nonspecific. We aimed to develop and externally validate a risk prediction model using concentration of vaginal fluid fetal fibronectin (quantitative fFN), in combination with clinical risk factors, for the prediction of spontaneous preterm birth and assessed its cost-effectiveness.Methods and findingsPregnant women included in the analyses were 22⁺⁰ to 34⁺⁶ weeks gestation with signs and symptoms of preterm labour. The primary outcome was spontaneous preterm birth within 7 days of quantitative fFN test. The risk prediction model was developed and internally validated in an individual participant data (IPD) meta-analysis of 5 European prospective cohort studies (2009 to 2016; 1,783 women; mean age 29.7 years; median BMI 24.8 kg/m²; 67.6% White; 11.7% smokers; 51.8% nulliparous; 10.4% with multiple pregnancy; 139 [7.8%] with spontaneous preterm birth within 7 days). The model was then externally validated in a prospective cohort study in 26 United Kingdom centres (2016 to 2018; 2,924 women; mean age 28.2 years; median BMI 25.4 kg/m²; 88.2% White; 21% smokers; 35.2% nulliparous; 3.5% with multiple pregnancy; 85 [2.9%] with spontaneous preterm birth within 7 days). The developed risk prediction model for spontaneous preterm birth within 7 days included quantitative fFN, current smoking, not White ethnicity, nulliparity, and multiple pregnancy. After internal validation, the optimism adjusted area under the curve was 0.89 (95% CI 0.86 to 0.92), and the optimism adjusted Nagelkerke R² was 35% (95% CI 33% to 37%). On external validation in the prospective UK cohort population, the area under the curve was 0.89 (95% CI 0.84 to 0.94), and Nagelkerke R² of 36% (95% CI: 34% to 38%). Recalibration of the model’s intercept was required to ensure overall calibration-in-the-large. A calibration curve suggested close agreement between predicted and observed risks in the range of predictions 0% to 10%, but some miscalibration (underprediction) at higher risks (slope 1.24 (95% CI 1.23 to 1.26)). Despite any miscalibration, the net benefit of the model was higher than “treat all” or “treat none” strategies for thresholds up to about 15% risk. The economic analysis found the prognostic model was cost effective, compared to using qualitative fFN, at a threshold for hospital admission and treatment of ≥2% risk of preterm birth within 7 days. Study limitations include the limited number of participants who are not White and levels of missing data for certain variables in the development dataset.ConclusionsIn this study, we found that a risk prediction model including vaginal fFN concentration and clinical risk factors showed promising performance in the prediction of spontaneous preterm birth within 7 days of test and has potential to inform management decisions for women with threatened preterm labour. Further evaluation of the risk prediction model in clinical practice is required to determine whether the risk prediction model improves clinical outcomes if used in practice.Trial registrationThe study was approved by the West of Scotland Research Ethics Committee (16/WS/0068). The study was registered with ISRCTN Registry (ISRCTN 41598423) and NIHR Portfolio (CPMS: 31277).

Sarah J Stock and colleagues develop and validate a risk prediction model of spontaneous preterm birth for women with preterm labour symptoms, based on vaginal fluid fetal fibronectin and clinical risk factors.     

Assessment of PLSDA cross validation

Classifying groups of individuals based on their metabolic profile is one of the main topics in metabolomics research. Due to the low number of individuals compared to the large number of variables, this is not an easy task. PLSDA is one of the data analysis methods used for the classification. Unfortunately this method eagerly overfits the data and rigorous validation is necessary. The validation however is far from straightforward. Is this paper we will discuss a strategy based on cross model validation and permutation testing to validate the classification models. It is also shown that too optimistic results are obtained when the validation is not done properly. Furthermore, we advocate against the use of PLSDA score plots for inference of class differences.     

Implication of lipoprotein associated phospholipase A2 activity in oxLDL uptake by macrophages

Recognition and uptake of oxidized LDL (oxLDL) by scavenger receptors of macrophages and foam cell formation are mediated by the oxidatively modified apolipoprotein B (ApoB) and lipid moiety of oxLDL. A great amount of oxidized phosphatidylcholine (oxPC) of oxLDL is hydrolyzed at the sn-2 position by lipoprotein associated phospholipase A₂ (Lp-PLA₂) to lysophosphatidylcholine and small oxidation products. This study examines the involvement of Lp-PLA₂ in the uptake of oxLDL by mouse peritoneal macrophages. LDL with intact Lp-PLA₂ activity [LDL (+)] and LDL with completely inhibited Lp-PLA₂ activity [LDL (-)] were subjected to oxidation with 5 μM CuSO₄ for 6 h [moderately oxLDL (MoxLDL)], or 24 h [heavily oxLDL (HoxLDL)] and peritoneal macrophages were incubated with these preparations. The uptake of MoxLDL(-) was about 30% increased compared with that of MoxLDL(+), and HoxLDL(-) uptake was about 20% increased compared with that of HoxLDL(+). Inhibition of Lp-PLA₂ activity had no effect on the uptake of ApoB-liposomes conjugates with ApoB isolated from MoxLDL(-), MoxLDL(+), HoxLDL(-), and HoxLDL(+). Liposomes prepared from the lipid extract of MoxLDL(-), MoxLDL(+), HoxLDL(-), and HoxLDL(+) exhibited a similar pattern to that observed in the uptake of the corresponding intact lipoproteins. This study suggests that the progressive inactivation of Lp-PLA₂ during LDL oxidation leads to an increased uptake of oxLDL by macrophages, which could be primarily attributed to the increased uptake of the oxidized phospholipids enriched lipid moiety of oxLDL.     

Post-Stroke Epilepsy in Young Adults: A Long-Term Follow-Up Study

Background

Little is known about the incidence and risk of seizures after stroke in young adults. Especially in the young seizures might dramatically influence prognosis and quality of life. We therefore investigated the long-term incidence and risk of post-stroke epilepsy in young adults with a transient ischemic attack (TIA), ischemic stroke (IS) or intracerebral hemorrhage (ICH).

Methods and Findings

We performed a prospective cohort study among 697 consecutive patients with a first-ever TIA, IS or ICH, aged 18–50 years, admitted to our hospital between 1-1-1980 till 1-11-2010. The occurrence of epilepsy was assessed by standardized questionnaires and verified by a neurologist. Cumulative risks were estimated with Kaplan-Meier analysis. Cox proportional hazard models were used to calculate relative risks. After mean follow-up of 9.1 years (SD 8.2), 79 (11.3%) patients developed post-stroke epilepsy and 39 patients (5.6%) developed epilepsy with recurrent seizures. Patients with an initial late seizure more often developed recurrent seizures than patients with an initial early seizure. Cumulative risk of epilepsy was 31%, 16% and 5% for patients with an ICH, IS and TIA respectively (Logrank test ICH and IS versus TIA p<0.001). Cumulative risk of epilepsy with recurrent seizures was 23%, 8% and 4% respectively (Logrank ICH versus IS p = 0.05, ICH versus TIA p<0.001, IS versus TIA p = 0.01). In addition a high NIHSS was a significant predictor of both epilepsy and epilepsy with recurrent seizures (HR 1.07, 95% CI 1.03–1.11 and 1.08, 95% CI 1.02–1.14).

Conclusions

Post-stroke epilepsy is much more common than previously thought. Especially patients with an ICH and a high NIHSS are at high risk. This calls upon the question whether a subgroup could be identified which benefits from the use of prophylactic antiepileptic medication. Future studies should be executed to investigate risk factors and the effect of post-stroke epilepsy on quality of life.     

Growth stimulation of UV-induced DNA damage retaining epidermal basal cells gives rise to clusters of p53 overexpressing cells

Ultraviolet (UV) radiation induces cyclobutane pyrimidine dimers (CPDs) and (6-4) photoproducts ((6-4)PPs) in DNA, which may give rise to clusters of cells expressing mutant p53 ('p53 patches') and eventually to skin carcinomas. We have previously reported that some basal cells in murine skin accumulate CPDs upon chronic low-level UV exposure and that these CPD-retaining basal cells (CRBCs) encompass epidermal stem and progenitor cells. Through replication of their damaged DNA CRBCs may become mutagenic foci from which tumors might form. We therefore investigated whether CRBCs may give rise to p53 patches after forced proliferation by repeated applications of 12-O-tetradecanoylphorbol-13-acetate (TPA). CRBCs, induced in SKH-1 hairless mice by chronic low-level UV exposure (70 J/m(2) daily for 40 days), disappeared in the TPA-induced epidermal hyperplasia within 2 weeks and numerous clusters of epidermal cells with overexpressed p53 appeared after 4 weeks. Neither mutant p53 patches nor any foci of pErk1/2-overexpressing cells that could have caused reactive wild type p53 expression were found. In skin exposed to a single high UV dose (2.8 kJ/m(2)) no CRBCs occurred, and no p53 clusters were observed after TPA treatment. These experiments suggest that CRBCs are a prerequisite for the formation of clusters of p53-overexpressing cells. The high frequency of these clusters (about 1 for every 3 CRBCs) precludes mutations in p53 as a likely cause. We surmise that forced proliferation of CRBCs gives rise to genomic instability that is propagated in daughter cells and evokes wild type p53 overexpression, signifying a potentially oncogenic process different from classic UV carcinogenesis involving mutant p53.     

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

In this paper we introduce discriminant Q² (DQ²) as an improvement for the Q² value used in the validation of PLSDA models. DQ² does not penalize class predictions beyond the class label value. With rigorous Monte Carlo simulations we show that when DQ² is used, a smaller effect can be found statistically significant than when the standard Q² is used.     

A lipidomic analysis approach to evaluate the response to cholesterol-lowering food intake

Plant sterols (PS) are well known to reduce serum levels of total cholesterol and LDL-cholesterol. Lipidomics potentially provides detailed information on a wide range of individual serum lipid metabolites, which may further add to our understanding of the biological effects of PS. In this study, lipidomics analysis was applied to serum samples from a placebo-controlled, parallel human intervention study (n?=?97) of 4-week consumption of two PS-enriched, yoghurt drinks differing in fat content (based on 0.1% vs. 1.5% dairy fat). A comprehensive data analysis strategy was developed and implemented to assess and compare effects of two different PS-treatments and placebo treatment. The combination of univariate and multivariate data analysis approaches allowed to show significant effects of PS intake on the serum lipidome, and helped to distinguish them from fat content and non-specific effects. The PS-enriched 0.1% dairy fat yoghurt drink had a stronger impact on the lipidome than the 1.5% dairy fat yoghurt drink, despite similar LDL-cholesterol lowering effects. The PS-enriched 0.1% dairy fat yoghurt drink reduced levels of several sphingomyelins which correlated well with the reduction in LDL-cholesterol and can be explained by co-localization of sphingomyelins and cholesterol on the surface of LDL lipoprotein. Statistically significant reductions in serum levels of two lysophosphatidylcholines (LPC(16:1), LPC(20:1)) and cholesteryl arachidonate may suggest reduced inflammation and atherogenic potential.