Modelling wine astringency from its chemical composition using machine learning algorithms

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms. Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190. Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity. Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines

Modelling wine astringency from its chemical composition using machine learning algorithms

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms. Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190. Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity. Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines

A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study

Background: The benefit of pharmacogenetic testing before starting drug therapy has been well documented for several single gene–drug combinations. However, the clinical utility of a pre-emptive genotyping strategy using a pharmacogenetic panel has not been rigorously assessed. Methods: We conducted an open-label, multicentre, controlled, cluster-randomised, crossover implementation study of a 12-gene pharmacogenetic panel in 18 hospitals, nine community health centres, and 28 community pharmacies in seven European countries (Austria, Greece, Italy, the Netherlands, Slovenia, Spain, and the UK). Patients aged 18 years or older receiving a first prescription for a drug clinically recommended in the guidelines of the Dutch Pharmacogenetics Working Group (ie, the index drug) as part of routine care were eligible for inclusion. Exclusion criteria included previous genetic testing for a gene relevant to the index drug, a planned duration of treatment of less than 7 consecutive days, and severe renal or liver insufficiency. All patients gave written informed consent before taking part in the study. Participants were genotyped for 50 germline variants in 12 genes, and those with an actionable variant (ie, a drug–gene interaction test result for which the Dutch Pharmacogenetics Working Group [DPWG] recommended a change to standard-of-care drug treatment) were treated according to DPWG recommendations. Patients in the control group received standard treatment. To prepare clinicians for pre-emptive pharmacogenetic testing, local teams were educated during a site-initiation visit and online educational material was made available. The primary outcome was the occurrence of clinically relevant adverse drug reactions within the 12-week follow-up period. Analyses were irrespective of patient adherence to the DPWG guidelines. The primary analysis was done using a gatekeeping analysis, in which outcomes in people with an actionable drug–gene interaction in the study group versus the control group were compared, and only if the difference was statistically significant was an analysis done that included all of the patients in the study. Outcomes were compared between the study and control groups, both for patients with an actionable drug–gene interaction test result (ie, a result for which the DPWG recommended a change to standard-of-care drug treatment) and for all patients who received at least one dose of index drug. The safety analysis included all participants who received at least one dose of a study drug. This study is registered with ClinicalTrials.gov, NCT03093818 and is closed to new participants. Findings: Between March 7, 2017, and June 30, 2020, 41 696 patients were assessed for eligibility and 6944 (51·4 % female, 48·6% male; 97·7% self-reported European, Mediterranean, or Middle Eastern ethnicity) were enrolled and assigned to receive genotype-guided drug treatment (n=3342) or standard care (n=3602). 99 patients (52 [1·6%] of the study group and 47 [1·3%] of the control group) withdrew consent after group assignment. 652 participants (367 [11·0%] in the study group and 285 [7·9%] in the control group) were lost to follow-up. In patients with an actionable test result for the index drug (n=1558), a clinically relevant adverse drug reaction occurred in 152 (21·0%) of 725 patients in the study group and 231 (27·7%) of 833 patients in the control group (odds ratio [OR] 0·70 [95% CI 0·54–0·91]; p=0·0075), whereas for all patients, the incidence was 628 (21·5%) of 2923 patients in the study group and 934 (28·6%) of 3270 patients in the control group (OR 0·70 [95% CI 0·61–0·79]; p <0·0001). Interpretation: Genotype-guided treatment using a 12-gene pharmacogenetic panel significantly reduced the incidence of clinically relevant adverse drug reactions and was feasible across diverse European health-care system organisations and settings. Large-scale implementation could help to make drug therapy increasingly safe. Funding: European Union Horizon 2020

Potato consumption does not increase blood pressure or incident hypertension in 2 cohorts of Spanish adults

5 TablasBackground: Potatoes have a high glycemic load but also antioxidants, vitamins, and minerals. It is unclear what mechanisms are involved in relation to their effect on blood pressure (BP) and hypertension. Objectives: This study aimed to assess the association between potato consumption, BP changes, and the risk of hypertension in 2 Spanish populations. Methods: Separate analyses were performed in PREDIMED (PREvención con DIeta MEDiterránea), a multicenter nutrition intervention trial of adults aged 55-80 y, and the SUN (Seguimiento Universidad de Navarra) project, a prospective cohort made up of university graduates and educated adults with ages (means±SDs) of 42.7±13.3 y for men and 35.1± 10.7 y for women. In PREDIMED, generalized estimating equations adjusted for lifestyle and dietary characteristics were used to assess changes in BP across quintiles of total potato consumption during a 4-y follow-up. Controlled BP values (systolic BP < 140 mm Hg and diastolic BP < 90 mm Hg) during follow-up were also assessed. For SUN, multivariateadjusted HRs for incident hypertension during a mean 6.7-y follow-up were calculated. Results: In PREDIMED, the total potato intake was 81.9 ± 40.6 g/d. No overall differences in systolic or diastolic BP changes were detected based on consumption of potatoes. For total potatoes, the mean difference in change between quintile 5 (highest intake) and quintile 1 (lowest intake) in systolic BP after multivariate adjustment was 20.90 mm Hg (95% CI: -2.56, 0.76 mm Hg; P-trend = 0.1) and for diastolic BP was 20.02 mm Hg (95% CI: -0.93, 0.89 mm Hg; P-trend = 0.8). In SUN, the total potato consumption was 52.7 ± 33.6 g/d, and no significant association between potato consumption and hypertension incidence was observed in the fully adjusted HR for total potato consumption (quintile 5 compared with quintile 1: 0.98; 95% CI: 0.80, 1.19; P-trend = 0.8). Conclusions: Potato consumption is not associated with changes over 4 y in blood pressure among older adults in Spain or with the risk of hypertension among Spanish adults.Supported by the official funding agency for biomedical research of the Spanish Government, Instituto de Salud Carlos III through grants provided to research networks specifically developed for the trial (RTIC G03/140, to RE; RTIC RD 06/0045, to MAM-G) and through Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERobn), and by grants from Centro Nacional de Investigaciones Cardiovasculares (CNIC 06/2007), Fondo de Investigación Sanitaria–Fondo Europeo de Desarrollo Regional [Proyecto de Investigación (PI) 04-2239, PI 05/2584, CP06/00100, PI07/0240, PI07/1138, PI07/0954, PI 07/0473, PI10/01407, PI10/02658, PI11/01647, P11/02505 and PI13/00462], Ministerio de Ciencia e Innovación [Recursos y teconologia agroalimentarias (AGL)-2009-13906-C02 and AGL2010-22319-C03 and AGL2013-49083-C3-1-R], Fundación Mapfre 2010, the Consejería de Salud de la Junta de Andalucía (PI0105/2007), the Public Health Division of the Department of Health of the Autonomous Government of Catalonia, Generalitat Valenciana [Generalitat Valenciana Ayuda Complementaria (GVACOMP) 06109, GVACOMP2010-181, GVACOMP2011-151], Conselleria de Sanitat y AP; Atención Primaria (CS) 2010-AP-111 and CS2011-AP-042, and Regional Government of Navarra (P27/2011)