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Affiliations
1 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. rocio.barragan@uv.es.
2 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. rocio.barragan@uv.es.
3 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. oscar.coltell@uji.es.
4 Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castellón, Spain. oscar.coltell@uji.es.
5 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Olga.Portoles@uv.es.
6 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Olga.Portoles@uv.es.
7 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. eva.m.asensio@uv.es.
8 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. eva.m.asensio@uv.es.
9 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. jose.sorli@uv.es.
10 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. jose.sorli@uv.es.
11 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. carolina.ortega@uv.es.
12 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. carolina.ortega@uv.es.
13 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Ignacio.Glez-Arraez@uv.es.
14 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Ignacio.Glez-Arraez@uv.es.
15 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. carmen.saiz@uv.es.
16 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. carmen.saiz@uv.es.
17 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Rebeca.Fernandez@uv.es.
18 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Rebeca.Fernandez@uv.es.
19 Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging Tufts University, Boston, MA 02155, USA. jose.ordovas@tufts.edu.
20 Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain. jose.ordovas@tufts.edu.
21 IMDEA Alimentación, 28049 Madrid, Spain. jose.ordovas@tufts.edu.
22 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Dolores.corella@uv.es.
23 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Dolores.corella@uv.es.
Rocio Barragán et al. Nutrients. 2018.
Affiliations
1 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. rocio.barragan@uv.es.
2 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. rocio.barragan@uv.es.
3 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. oscar.coltell@uji.es.
4 Department of Computer Languages and Systems, Universitat Jaume I, 12071 Castellón, Spain. oscar.coltell@uji.es.
5 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Olga.Portoles@uv.es.
6 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Olga.Portoles@uv.es.
7 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. eva.m.asensio@uv.es.
8 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. eva.m.asensio@uv.es.
9 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. jose.sorli@uv.es.
10 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. jose.sorli@uv.es.
11 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. carolina.ortega@uv.es.
12 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. carolina.ortega@uv.es.
13 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Ignacio.Glez-Arraez@uv.es.
14 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Ignacio.Glez-Arraez@uv.es.
15 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. carmen.saiz@uv.es.
16 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. carmen.saiz@uv.es.
17 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Rebeca.Fernandez@uv.es.
18 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Rebeca.Fernandez@uv.es.
19 Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging Tufts University, Boston, MA 02155, USA. jose.ordovas@tufts.edu.
20 Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares (CNIC), 28029 Madrid, Spain. jose.ordovas@tufts.edu.
21 IMDEA Alimentación, 28049 Madrid, Spain. jose.ordovas@tufts.edu.
22 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, 28029 Madrid, Spain. Dolores.corella@uv.es.
23 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, 46010 Valencia, Spain. Dolores.corella@uv.es.
There is growing interest in relating taste perception to diet and healthy aging. However, there is still limited information on the influence of age, sex and genetics on taste acuity as well as on the relationship between taste perception and taste preferences. We have analysed the influence of age on the intensity rating of the five basic tastes: sweet, salty, bitter, sour and umami (separately and jointly in a "total taste score") and their modulation by sex and genetics in a relatively healthy population (men and women) aged 18⁻80 years (n = 1020 Caucasian European participants). Taste perception was determined by challenging subjects with solutions of the five basic tastes using standard prototypical tastants (6-n-propylthiouracil (PROP), NaCl, sucrose, monopotassium glutamate and citric acid) at 5 increasing concentrations (I to V). We also measured taste preferences and determined the polymorphisms of the genes taste 2 receptor member 38 (TAS2R38), taste 1 receptor member 2 (TAS2R38) and sodium channel epithelial 1 beta subunit (SCNN1B), as TAS2R38-rs713598, TAS1R2-rs35874116 and SCNN1B-rs239345 respectively. We found a statistically significant decrease in taste perception ("total taste score") with increasing age for all the concentrations analysed. This association was stronger for the higher concentrations (p = 0.028; p = 0.012; p = 0.005; p = 4.20 × 10-5 and p = 1.48 × 10-7, for I to V in the multivariable-adjusted models). When we analysed taste qualities (using concentration V), the intensity rating of all the 5 tastes was diminished with age (p < 0.05 for all). This inverse association differed depending on the test quality, being higher for bitter (PROP) and sour. Women perceived taste significantly more intense than men (p = 1.4 × 10-8 for total taste score). However, there were differences depending on the taste, umami being the lowest (p = 0.069). There was a complex association between the ability to perceive a taste and the preference for the same. Significant associations were, nevertheless, found between a higher perception of sour taste and a higher preference for it in women. In contrast, the higher perception of sweet was significantly associated with a higher preference for bitter in both, men and women. The TAS2R38-rs713598 was strongly associated with bitter (PROP) taste (p = 1.38 × 10-50), having a significant interaction with sex (p = 0.030). The TAS1R2-rs35874116 was not significantly associated with sweet, whereas the SCNN1B-rs239345 was associated (p = 0.040) with salty taste. In conclusion, the inverse association between age and perceived taste intensity as well as the additional influence of sex and some genetic polymorphisms give rise to large inter-individual differences in taste perception and taste preferences that should be taken into account in future studies and for applications in precision nutrition for healthy aging.
Keywords: aging; sex; taste perception; taste polymorphisms; taste preferences.
The authors declare no conflict of interest.
Total taste score (sum of intensity ratings of the five tastes) by age…
Total taste score (sum of intensity ratings of the five tastes) by age group and the increasing concentrations (from I to V) of the tastants tested (n = 1019, 1016, 1014, 1016 and 1020 for the concentrations I, II, III, IV and V), respectively in the whole population). Values are adjusted means (model 3) including sex, age, diabetes, body mass index, smoking and medications. Age groups are based on tertiles. p-values show the statistical significance for the age differences in total taste perception for each concentration after adjustment for covariates (model 3). Error bars are SE. Age groups: 18–36 years (n = 342), 37–50 years (n = 329) and 51–80 years (n = 349).
Intensity rating (points) for the five basic tastes (bitter (PROP), sweet, salty, sour…
Intensity rating (points) for the five basic tastes (bitter (PROP), sweet, salty, sour and umami) by age group (18–36 years (n = 342), 37–50 years (n = 329) and 51–80 years (n = 349)) at the maximum concentration (concentration V) of the tastants tested (PROP 5.5 mM; sucrose 400 mM, NaCl 200 mM; citric acid 34 mM; and MPG 200 mM), in the whole population (n = 1020). Values are adjusted means (model 3) including sex, age, diabetes, body mass index, smoking and medications. Age groups are based on tertiles. p-values show the statistical significance for the age differences in taste perception among groups for each taste after adjustment for covariates (model 3). Error bars are SE.
Total taste score (sum of intensity ratings of the five tastes) at the…
Total taste score (sum of intensity ratings of the five tastes) at the maximum concentration of the tastants tested (concentration V) by age group (18–36 years (n = 342), 37–50 years (n = 329) and 51–80 years (n = 349)) in men (n = 365) and women (n = 655). Values are adjusted means (model 3) including sex, age, diabetes, body mass index, smoking and medications. p-values show the statistical significance of the differences in total taste perception by sex in each age group, after adjustment for covariates (model 3). Error bars are SE.
Taste perception (intensity rating) of…
Taste perception (intensity rating) of bitter (PROP, 5.5 mM), sweet (sucrose, 400 mM)…
Taste perception (intensity rating) of bitter (PROP, 5.5 mM), sweet (sucrose, 400 mM) and salty (NaCl, 200 mM) in the whole population depending on the genotype for the corresponding taste polymorphism: TAS2R38-rs713598 for bitter (PROP); TAS1R2-rs35874116 for sweet; and SCNN1B-rs239345 for salty. Means were adjusted for sex, age, diabetes, body mass index, smoking and medications (model 3). p-values show the statistical significance for the corresponding taste polymorphism (3 genotypes as codominant) in the multivariate adjusted model 3. n = 945 for TAS2R38-rs713598; n = 949 for TAS1R2-rs35874116; and n = 927 for SCNN1B-rs239345. Error bars are SE.
Bitter taste perception (intensity rating…
Bitter taste perception (intensity rating of PROP, 5.5 mM) in the whole population…
Bitter taste perception (intensity rating of PROP, 5.5 mM) in the whole population (n = 949) by the TAS2R38-rs713598 polymorphism (n = 304 for Ala/Ala, n = 464 for Ala/Pro and n = 177 for Pro/Pro) and age group. Means were adjusted for sex, age, diabetes, body mass index, smoking and medications (model 3). p-values show the statistical significance of the TAS2R38-rs713598 polymorphism, in each age group, in the multivariate adjusted model 3. The p-value for interaction term between the TAS2R38-rs713598 and age groups was additionally tested in model 3. Error bars are SE.
Bitter taste perception (intensity rating…
Bitter taste perception (intensity rating of PROP, 5.5 mM) in men ( n…
Bitter taste perception (intensity rating of PROP, 5.5 mM) in men (n = 342) and women (n = 603) by the TAS2R38-rs713598 polymorphism (men: n = 116 for Ala/Ala, n = 157 for Ala/Pro and n = 69 for Pro/Pro; women: n = 188 for Ala/Ala, n = 307 for Ala/Pro and n = 108 for Pro/Pro). Means were adjusted for sex, age, diabetes, body mass index, smoking and medications (model 3). p-values show the statistical significance of the TAS2R38-rs713598 polymorphism stratified by sex, in the multivariate adjusted model 3. The p-value for interaction term between the TAS2R38-rs713598 and sex was additionally tested in model 3. Error bars are SE.
Coltell O, Sorlí JV, Asensio EM, Fernández-Carrión R, Barragán R, Ortega-Azorín C, Estruch R, González JI, Salas-Salvadó J, Lamon-Fava S, Lichtenstein AH, Corella D. Coltell O, et al. Am J Clin Nutr. 2019 Jun 1;109(6):1709-1723. doi: 10.1093/ajcn/nqz038. Am J Clin Nutr. 2019. PMID: 31005965
Chamoun E, Carroll NA, Duizer LM, Qi W, Feng Z, Darlington G, Duncan AM, Haines J, Ma DWL; Guelph Family Health Study. Chamoun E, et al. Nutrients. 2018 Jul 29;10(8):990. doi: 10.3390/nu10080990. Nutrients. 2018. PMID: 30060620 Free PMC article.
Mennella JA, Pepino MY, Reed DR. Mennella JA, et al. Pediatrics. 2005 Feb;115(2):e216-22. doi: 10.1542/peds.2004-1582. Pediatrics. 2005. PMID: 15687429 Free PMC article.
Garcia-Bailo B, Toguri C, Eny KM, El-Sohemy A. Garcia-Bailo B, et al. OMICS. 2009 Feb;13(1):69-80. doi: 10.1089/omi.2008.0031. OMICS. 2009. PMID: 18687042 Review.
Feeney E, O'Brien S, Scannell A, Markey A, Gibney ER. Feeney E, et al. Proc Nutr Soc. 2011 Feb;70(1):135-43. doi: 10.1017/S0029665110003976. Epub 2010 Nov 22. Proc Nutr Soc. 2011. PMID: 21092367 Review.
Jeon S, Kim Y, Min S, Song M, Son S, Lee S. Jeon S, et al. Nutrients. 2021 May 17;13(5):1693. doi: 10.3390/nu13051693. Nutrients. 2021. PMID: 34067560 Free PMC article.
Martin C, Neyraud E. Martin C, et al. Foods. 2021 May 20;10(5):1139. doi: 10.3390/foods10051139. Foods. 2021. PMID: 34065269 Free PMC article.
Ferraris C, Turner A, Scarlett C, Veysey M, Lucock M, Bucher T, Beckett EL. Ferraris C, et al. Nutrients. 2021 Feb 24;13(3):719. doi: 10.3390/nu13030719. Nutrients. 2021. PMID: 33668367 Free PMC article.
Parsa S, Mogharab V, Ebrahimi M, Ahmadi SR, Shahi B, Mehramiz NJ, Foroughian M, Zarenezhad M, Kalani N, Abdi MH, Javdani F, Keshavarz P, Hatami N. Parsa S, et al. Int J Biol Macromol. 2021 Apr 30;177:204-210. doi: 10.1016/j.ijbiomac.2021.02.070. Epub 2021 Feb 11. Int J Biol Macromol. 2021. PMID: 33582215 Free PMC article.
Morita R, Ohta M, Umeki Y, Nanri A, Tsuchihashi T, Hayabuchi H. Morita R, et al. Nutrients. 2021 Feb 9;13(2):577. doi: 10.3390/nu13020577. Nutrients. 2021. PMID: 33572364 Free PMC article.

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Subject: 1 selected item: 33045617 - PubMed
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Affiliations
1 Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi'an, China.
2 Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi'an, China. Electronic address: mujjun@163.com.
Yue-Yuan Liao et al. Atherosclerosis. 2020 Nov.
Affiliations
1 Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi'an, China.
2 Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China; Key Laboratory of Molecular Cardiology of Shaanxi Province, Xi'an, China. Electronic address: mujjun@163.com.
Background and aims: Obesity and hypertension play important roles in the development of arterial stiffness. We aimed to examine the sex differences in the impact of the cumulative long-term burden and trends of body mass index (BMI) and blood pressure (BP) since childhood on adult arterial stiffness (AS).
Methods: This longitudinal study consisted of 1553 individuals aged 6-15 years, who were examined 4 or more times for BMI and BP since childhood, with a follow-up period of 30 years. The area under the curve (AUC) was calculated as a measure of the long-term b
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Sex Smotret Salt Mir Sex