Introduction: Genome-wide association studies have identified numerous glycemic traits-associated SNPs with myocardial infarction (MI) and stroke. However, conventional regression methods combining ...these large numbers of highly significant SNPs in a weighted genetic risk score (wGRS) have only explained a small proportion of variance in MI and stroke. Therefore, we used a machine-learning algorithm (gradient boosting) to test the association of genetic risk for glycemic traits with MI and stroke in the UK Biobank population.
Methods: We identified 4626 SNPs associated with glycemic traits from the NGHRI catalogue for GWAS studies. wGRS were calculated using the effect estimates from the GWAS studies. We used a gradient-boosting machine-learning (GBM) model to identify the relative influence (RI) of baseline variables and wGRS of the glycemic traits on prevalent stroke and MI in the UK Biobank population.
Results: The study consisted of 409,633 individuals (53% females) with a median age of 58 (51-63) years and a median BMI of 26.7 (24.1-29.8) kg/m2. The prevalence rates of MI and stroke were 3.6% and 2.3%. In the GBM model, top wGRS associated with MI were type 2 diabetes (RI=14.6) , blood glucose levels adjusted for fasting time (RI=12.2) and beta-cell glucose sensitivity (RI=10.06) . In contrast, the top wGRS associated with stroke were HbA1c (RI=12.13) and fasting insulin levels (RI=10.31) . The addition of wGRS’s to the model comprising the baseline characteristics slightly improved the AUC for predicting stroke and MI.
Conclusion: We showed a differential effect of wGRS for various glycemic traits on the risk of MI and stroke in the UK Biobank population. However, even in machine learning models, these wGRS for various glycemic traits had a limited utility in predicting stroke and MI in the general population.
Disclosure
H.Deshmukh: None. M.Miles: None. A.Bhaiji: None. N.Shah: None. S.Akbar: None. M.Papageorgiou: None. T.Sathyapalan: n/a.
Funding
NIHR Clinical Lectureship
Introduction: We used a machine-learning algorithm (gradient boosting) to test the association of genetic risk for glycemic traits with osteoporosis and fracture in the UK Biobank population.
...Methods: The study was performed with 409,633 participants in the UKBIobank. We identified 4626 SNPs associated with glycemic traits from the NGHRI catalogue for GWAS studies. Weighted genetic risk scores (wGRS) were calculated using the effect estimates from the GWAS studies. We used a gradient-boosting machine-learning (GBM) model to identify the relative influence (RI) of baseline variables and wGRS of the glycemic traits on osteoporosis and self-reported all-cause fractures in the UK Biobank population. We split the data into training (2/3) and testing set (1/3) and calculated the discriminatory power of the models using the area under the curve (AUC) with the testing model.
Results: The study consisted of 409,633 individuals (53% females) with a median age of 58 (51-63) years and a median BMI of 26.7 (24.1-29.8) kg/m2. The study population had 41954 (10.2%) all-cause fractures and 4995 (1.2%) participants with osteoporosis. In the GBM model, top wGRS associated with all-cause fractures were wGRS for Type 1 diabetes (RI=4.49) and fasting glucose (4.17) . In contrast, the top wGRS associated with osteoporosis were wGRS for acute insulin response to glucose (RI=6.74) and Type 1 diabetes (RI=5.62) . Both models showed low to moderate discriminatory power with the area under the AUC of 0.57 (CI: 0.56-0.57) for fractures and 0.75 (CI: 0.74-0.76) for osteoporosis.
Conclusion: We showed a differential effect of wGRS for various glycemic traits on the risk of fractures and osteoporosis in the UK Biobank population. However, the machine-learning model with wGRS for glycemic traits demonstrated limited capacity to predict fractures and osteoporosis in the general population.
Disclosure
H.Deshmukh: None. M.Papageorgiou: None. C.Mark-wagstaff: None. S.Akbar: None. A.Bhaiji: None. N.Shah: None. T.Sathyapalan: n/a.
Funding
Dr Harshal Deshmukh is funded by NIHR clinical lectureship
Introduction
Some but not all women with polycystic ovary syndrome (PCOS) develop the metabolic syndrome (MS). The objective of this study was to determine if a subset of women with PCOS had higher ...androgen levels predisposing them to MS and whether routinely measured hormonal parameters impacted the metabolic syndrome score (siMS).
Methods
We included data from a discovery (PCOS clinic data) and a replication cohort (Hull PCOS Biobank) and utilized eight routinely measured hormonal parameters in our clinics (free androgen index FAI, sex hormone‐binding globulin, dehydroepiandrosterone sulphate (DHEAS), androstenedione, luteinizing hormone LH, follicular stimulating hormone, anti‐Müllerian hormone and 17 hydroxyprogesterone 17‐OHP) to perform a K‐means clustering (an unsupervised machine learning algorithm). We used NbClust Package in R to determine the best number of clusters. We estimated the siMS in each cluster and used regression analysis to evaluate the effect of hormonal parameters on SiMS.
Results
The study consisted of 310 women with PCOS (discovery cohort: n = 199, replication cohort: n = 111). The cluster analysis identified two clusters in both the discovery and replication cohorts. The discovery cohort identified a larger cluster (n = 137) and a smaller cluster (n = 62), with 31% of the study participants. Similarly, the replication cohort identified a larger cluster (n = 74) and a smaller cluster (n = 37) with 33% of the study participants. The smaller cluster in the discovery cohort had significantly higher levels of LH (7.26 vs. 16.1 IU/L, p < .001), FAI (5.21 vs. 9.22, p < .001), androstenedione (3.93 vs. 7.56 nmol/L, p < .001) and 17‐OHP (1.59 vs. 3.12 nmol/L, p < .001). These findings were replicated in the replication cohort. The mean (±SD) siMS score was higher in the smaller cluster, 3.1 (±1.1) versus 2.8 (±0.8); however, this was not statistically significant (p = .20). In the regression analysis, higher FAI (β = .05, p = .003) and androstenedione (β = .03, p = .02) were independently associated with a higher risk of SiMS score, while higher DHEAS levels were associated with a lower siMS score (β = −.07, p = .03)
Conclusion
We identified a subset of women in our PCOS cohort with significantly higher LH, FAI, and androstenedione levels. We show that higher levels of androstenedione and FAI are associated with a higher siMS, while higher DHEAS levels were associated with lower siMS.
Apium leptophyllum (Pers.) is an annual herb with traditional appreciation for various pharmacological properties; however, the scientific information on this herb is insufficient. The aim of the ...present investigation was undertaken to evaluate flavonoidal fraction of A. leptophyllum fruit (FFALF) against diarrhoea on albino rats.
The antidiarrhoeal study was conducted by castor oil induce diarrhoea, prostaglandin E
(PGE
) induced enteropooling and intestinal transit by charcoal meal test. The rats were divided into five groups (six/group). Group I served as control and received orally 2% acacia suspension; Group II served as standard and received orally loperamide (3 mg/kg) or atropine sulphate (5 mg/kg); Group III, IV and V served as test groups and received the FFALF at doses of 5, 10 and 20 mg/kg orally, respectively.
In castor oil-induced diarrhoeal model, the FFALF significantly (p < 0.001) reduced the frequency of diarrhoea, defecation and weight of faeces as well as increased the sodium-potassium ATPase (Na
K
ATPase) activity and decreased nitric oxide (NO) content in the small intestine. In prostaglandin induced enteropooling model, it significantly (p < 0.01) and dose dependently slowed the intestinal fluid accumulation by decreasing the masses and volumes of intestinal fluid where as in charcoal meal test, it decreased charcoal meal transit in gastrointestinal tract as compared with control.
The study reveals that the FFALF possess anti-diarrhoeal properties mediated through inhibition of hyper secretion and gastrointestinal motility which support the traditional use of the plant.
The potential of algal biofuels has been technically and experimentally confirmed with laboratory- and pilot-scale studies in past literature. However, the most important factor now is to confirm ...that algal cultivation for biofuels and other end-products is economically feasible on the large, commercial scale. The ALGADISK project aimed to produce a novel biofilm-based photo-bioreactor with the aims of CO2 capture and making valuable products such as biofuel, economically viable. This thesis aimed to investigate and provide substrates in which algae biofilm is stimulated and increased. Polyelectrolyte (PE) coatings adsorbed onto cost-effective polymers were investigated, based on the strategy of electrostatic attraction. It was found that the algae species charge density and cell wall functional groups composition affected attachment onto charged PE coatings. Two coatings labeled C1 and C3 were selected due to their promising growth results with the strains C.sorokiniana, C.vulgaris and S.obliquus. Harvesting growth results showed inconsistent regrowth due to the lack of textured structure. Sandpapering the surface with certain grades was found to improve regrowth and consistency. Surface roughness did not show correlation to initial attachment of algae or strength of attachment. It was shown instead that surface roughness improved long-term growth As part of the aims of the ALGADISK project, the coatings large scale potential and cost was optimized. It was found that airbrushing rather than dip-coating, reduced the amount of PE solution needed drastically. Furthermore, photo-cross- linking with UV exposure enhanced the strength of C1 according to scratch and wear data. Lastly, the physico-chemical properties of both algae and substrates were examined in order to examine the thermodynamic model for algae adhesion prediction. It was found that the two thermodynamic approaches tested did not predict algae adhesion results with good accuracy. However, it was revealed that there could be a possible link between the substrate physico-chemistry and lipid content found in the biofilm attached. It was found that the less favorable the predicted thermodynamic conditions the higher the lipid content.
PDF
