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<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">PLoS Med</journal-id>
<journal-id journal-id-type="publisher-id">plos</journal-id>
<journal-id journal-id-type="pmc">plosmed</journal-id>
<journal-title-group>
<journal-title>PLOS Medicine</journal-title>
</journal-title-group>
<issn pub-type="ppub">1549-1277</issn>
<issn pub-type="epub">1549-1676</issn>
<publisher>
<publisher-name>Public Library of Science</publisher-name>
<publisher-loc>San Francisco, CA USA</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.1371/journal.pmed.1004337</article-id>
<article-id pub-id-type="publisher-id">PMEDICINE-D-23-01967</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Research Article</subject>
</subj-group>
<subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Alcohol consumption</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Alcohol consumption</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Milk</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Milk</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Anatomy</subject><subj-group><subject>Body fluids</subject><subj-group><subject>Milk</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Anatomy</subject><subj-group><subject>Body fluids</subject><subj-group><subject>Milk</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Physiology</subject><subj-group><subject>Body fluids</subject><subj-group><subject>Milk</subject></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Coffee</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Coffee</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Biology and life sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Tea</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Nutrition</subject><subj-group><subject>Diet</subject><subj-group><subject>Beverages</subject><subj-group><subject>Tea</subject></subj-group></subj-group></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Public and occupational health</subject><subj-group><subject>Physical activity</subject></subj-group></subj-group></subj-group><subj-group subj-group-type="Discipline-v3">
<subject>Medicine and health sciences</subject><subj-group><subject>Epidemiology</subject><subj-group><subject>Medical risk factors</subject></subj-group></subj-group></subj-group></article-categories>
<title-group>
<article-title>Association of a healthy beverage score with total mortality in the adult population of Spain: A nationwide cohort study</article-title>
<alt-title alt-title-type="running-head">A healthy beverage score is associated with lower total mortality</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-4690-7105</contrib-id>
<name name-style="western">
<surname>Rodríguez-Ayala</surname>
<given-names>Montserrat</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
<role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role content-type="http://credit.niso.org/contributor-roles/software/">Software</role>
<role content-type="http://credit.niso.org/contributor-roles/visualization/">Visualization</role>
<role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing – original draft</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff002"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-4523-4148</contrib-id>
<name name-style="western">
<surname>Donat-Vargas</surname>
<given-names>Carolina</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff003"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff004"><sup>4</sup></xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0003-0604-5042</contrib-id>
<name name-style="western">
<surname>Moreno-Franco</surname>
<given-names>Belén</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
<xref ref-type="aff" rid="aff005"><sup>5</sup></xref>
<xref ref-type="aff" rid="aff006"><sup>6</sup></xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-0323-2443</contrib-id>
<name name-style="western">
<surname>Mérida</surname>
<given-names>Diana María</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-6152-9862</contrib-id>
<name name-style="western">
<surname>Ramón Banegas</surname>
<given-names>José</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-9317-5755</contrib-id>
<name name-style="western">
<surname>Rodríguez-Artalejo</surname>
<given-names>Fernando</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
<role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff007"><sup>7</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes" xlink:type="simple">
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0003-4759-6713</contrib-id>
<name name-style="western">
<surname>Guallar-Castillón</surname>
<given-names>Pilar</given-names>
</name>
<role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
<role content-type="http://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
<role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
<role content-type="http://credit.niso.org/contributor-roles/supervision/">Supervision</role>
<role content-type="http://credit.niso.org/contributor-roles/validation/">Validation</role>
<xref ref-type="aff" rid="aff001"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff007"><sup>7</sup></xref>
<xref ref-type="corresp" rid="cor001">*</xref>
</contrib>
</contrib-group>
<aff id="aff001"><label>1</label> <addr-line>Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Madrid and CIBERESP (CIBER of Epidemiology and Public Health), Madrid, Spain</addr-line></aff>
<aff id="aff002"><label>2</label> <addr-line>Department of Microbiology and Parasitology, Hospital Universitario La Paz, Madrid, Spain</addr-line></aff>
<aff id="aff003"><label>3</label> <addr-line>ISGlobal, Campus Mar., Barcelona, Spain</addr-line></aff>
<aff id="aff004"><label>4</label> <addr-line>Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden</addr-line></aff>
<aff id="aff005"><label>5</label> <addr-line>Instituto de Investigación Sanitaria (IIS) Aragón, Hospital Universitario Miguel Servet, Zaragoza, Spain</addr-line></aff>
<aff id="aff006"><label>6</label> <addr-line>CIBERCV (CIBER of Cardiovascular), Instituto de Salud Carlos III, Madrid, Spain</addr-line></aff>
<aff id="aff007"><label>7</label> <addr-line>IMDEA-Food Institute, CEI UAM+CSIC., Madrid, Spain</addr-line></aff>
<author-notes>
<fn fn-type="conflict" id="coi001">
<p>The authors have declared that no competing interests exist.</p>
</fn>
<corresp id="cor001">* E-mail: <email xlink:type="simple">mpilar.guallar@uam.es</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>23</day>
<month>1</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<month>1</month>
<year>2024</year>
</pub-date>
<volume>21</volume>
<issue>1</issue>
<elocation-id>e1004337</elocation-id>
<history>
<date date-type="received">
<day>12</day>
<month>7</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>12</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-year>2024</copyright-year>
<copyright-holder>Rodríguez-Ayala et al</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
<license-p>This is an open access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">Creative Commons Attribution License</ext-link>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="info:doi/10.1371/journal.pmed.1004337"/>
<abstract>
<sec id="sec001">
<title>Background</title>
<p>Despite the substantial evidence of the relationship between diet and mortality, the role of beverage consumption patterns is not well known. The aim of this study was to assess the association of the adherence to a Healthy Beverage Score (HBS) and all-cause mortality in a representative sample of the Spanish adult population.</p>
</sec>
<sec id="sec002">
<title>Methods and findings</title>
<p>We conducted an observational cohort study using data from the Study on Nutrition and Cardiovascular Risk in Spain (ENRICA), which included 12,161 community-dwelling individuals aged ≥18 years recruited in 2008 to 2010 and followed until January 2022. At baseline, food consumption was collected using a validated diet history. The HBS consists of 7 items, each of which is scored from 1 to 4 (highest adherence). The HBS ranges from 7 to 28 points with a higher score representing a healthier pattern. Adherence was assigned as a higher consumption of low-fat milk, and coffee and tea, a lower consumption of whole-fat milk, no consumption of fruit juice, artificially sweetened beverages, or sugar-sweetened beverages, and no or moderate consumption of alcohol. Total mortality was ascertained by linkage to the Spanish National Death Index. Statistical analyses were performed with Cox models and adjusted for the main confounders, including sociodemographic, lifestyle, dietary variables, and morbidity.</p>
<p>After a mean follow-up of 12.5 years (SD: 1.7; range: 0.5 to 12.9), a total of 967 deaths occurred. For all-cause mortality, the fully adjusted hazard ratio (HR) for the highest versus lowest sex-specific quartiles of HBS was 0.72 (95% confidence interval [0.57, 0.91], p linear-trend = 0.015), corresponding to an 8.3% reduction in the absolute risk of death. A linear relationship between the risk of death and the adherence to the HBS was observed using restricted cubic splines. The results were robust to sensitivity analyses. The main limitation was that repeated measurements on beverage consumption were not available and beverage consumption could have changed during follow-up.</p>
</sec>
<sec id="sec003">
<title>Conclusions</title>
<p>In this study, we observed that higher adherence to the HBS was associated with lower total mortality. Adherence to a healthy beverage pattern could play a role in the prevention of premature mortality.</p>
</sec>
</abstract>
<abstract abstract-type="toc">
<p>Montserrat Rodríguez-Ayala and coworkers assess the relationship between a Healthy Beverage Score (HBS) and mortality in a representative sample of &gt;12,000 individuals from Spain.</p>
</abstract>
<abstract abstract-type="summary">
<title>Author summary</title>
<sec id="sec004">
<title>Why was this study done?</title>
<list list-type="bullet">
<list-item><p>Most dietary patterns focus solely on solid foods, and the role of beverages as a whole has received little attention.</p></list-item>
<list-item><p>Our aim was to assess the relationship between a Healthy Beverage Score (HBS) and mortality in a representative sample of community-dwelling individuals from Spain.</p></list-item>
<list-item><p>Our hypothesis was that high adherence to the HBS would be associated with lower mortality.</p></list-item>
</list>
</sec>
<sec id="sec005">
<title>What did the researchers do and find?</title>
<list list-type="bullet">
<list-item><p>We included a representative sample of 12,161 adults (18 years and older) from Spain who were recruited in 2008 to 2010 and followed up until 2022. A total of 967 deaths occurred.</p></list-item>
<list-item><p>Participants were categorized according to their adherence to the HBS.</p></list-item>
<list-item><p>A higher total score was achieved with a higher consumption of low-fat milk, and coffee and tea, no consumption of whole-fat milk, fruit juice, artificially sweetened beverages, sugar-sweetened beverages, and no consumption or moderate consumption of alcohol.</p></list-item>
<list-item><p>Each HBS item scored from 1 (minimum adherence) to 4 points (maximum adherence) and the HBS ranged from 7 to 28 points. The higher the HBS, the healthier.</p></list-item>
<list-item><p>When comparing extreme categories, higher adherence to the HBS was associated with lower all-cause mortality in the Spanish adult population, with an 8.3% reduction in the absolute risk of death.</p></list-item>
</list>
</sec>
<sec id="sec006">
<title>What do these findings mean?</title>
<list list-type="bullet">
<list-item><p>The adherence to the HBS could serve as a potential diet-based strategy to prevent premature mortality.</p></list-item>
<list-item><p>The quality of beverage patterns could influence health outcomes in the general population.</p></list-item>
</list>
</sec>
</abstract>
<funding-group>
<award-group id="award001">
<funding-source>
<institution>The Carlos III Health Institute, the Secretary of R+D+I, and the European Regional Development Fund/European Social Fund</institution>
</funding-source>
<award-id>FIS grants 17/1709 and 20/144</award-id>
<principal-award-recipient>
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0003-4759-6713</contrib-id>
<name name-style="western">
<surname>Guallar-Castillón</surname>
<given-names>Pilar</given-names>
</name>
</principal-award-recipient>
</award-group>
<award-group id="award002">
<funding-source>
<institution>The National Plan on Drugs grant 2020/17, Spanish Ministry of Health, Spain</institution>
</funding-source>
<award-id>2020/17</award-id>
<principal-award-recipient>
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-9317-5755</contrib-id>
<name name-style="western">
<surname>Rodríguez-Artalejo</surname>
<given-names>Fernando</given-names>
</name>
</principal-award-recipient>
</award-group>
<award-group id="award003">
<funding-source>
<institution>The FACINGLCOVID-CM project, Comunidad de Madrid and European Regional Development Fund (ERDF), European Union</institution>
</funding-source>
<principal-award-recipient>
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-9317-5755</contrib-id>
<name name-style="western">
<surname>Rodríguez-Artalejo</surname>
<given-names>Fernando</given-names>
</name>
</principal-award-recipient>
</award-group>
<award-group id="award004">
<funding-source>
<institution>The REACT EU Program, Comunidad de Madrid and European Regional Development Fund (ERDF)</institution>
</funding-source>
<principal-award-recipient>
<contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-9317-5755</contrib-id>
<name name-style="western">
<surname>Rodríguez-Artalejo</surname>
<given-names>Fernando</given-names>
</name>
</principal-award-recipient>
</award-group>
<funding-statement>This work was supported by FIS grants 17/1709, and 20/144 from the Carlos III Health Institute, the Secretary of R+D+I, and the European Regional Development Fund/European Social Fund (to P.G-C); by the National Plan on Drugs grant 2020/17, Spanish Ministry of Health, Spain (to F.R-A); by the FACINGLCOVID-CM project, Comunidad de Madrid and European Regional Development Fund (ERDF), European Union (to F.R-A); and by the REACT EU Program, Comunidad de Madrid and European Regional Development Fund (ERDF), European Union (to F.R-A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</funding-statement>
</funding-group>
<counts>
<fig-count count="2"/>
<table-count count="5"/>
<page-count count="20"/>
</counts>
<custom-meta-group>
<custom-meta id="data-availability">
<meta-name>Data Availability</meta-name>
<meta-value>The data are freely available upon request by contacting Esther López-García at the Department of Preventive Medicine and Public Health, Faculty of Medicine, Universidad Autónoma de Madrid (UAM)-IdiPaz, CIBERESP (CIBER of Epidemiology and Public Health), 28029, Madrid, Spain. E-mail address: <email xlink:type="simple">esther.lopez@uam.es</email>. UAM website: <ext-link ext-link-type="uri" xlink:href="https://www.uam.es/ss/Satellite/Medicina/es/1242658444664/subhome/Departamento_de_Medicina_Preventiva_y_Salud_Publica_y_Microbiologia.htm" xlink:type="simple">https://www.uam.es/ss/Satellite/Medicina/es/1242658444664/subhome/Departamento_de_Medicina_Preventiva_y_Salud_Publica_y_Microbiologia.htm</ext-link>.</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="sec007" sec-type="intro">
<title>Introduction</title>
<p>The influence of unhealthy dietary factors on adverse outcomes, including premature mortality, is a public health concern [<xref ref-type="bibr" rid="pmed.1004337.ref001">1</xref>]. Thus, the association between diet and mortality has been examined by using several approaches such as analyzing individual nutrients, food and, more recently, assessing dietary patterns and indexes [<xref ref-type="bibr" rid="pmed.1004337.ref002">2</xref>–<xref ref-type="bibr" rid="pmed.1004337.ref004">4</xref>]. Most of the indexes include mainly solid food (e.g., meat, poultry, fish, as well as fruit and vegetables) [<xref ref-type="bibr" rid="pmed.1004337.ref005">5</xref>], although some of them also comprise beverages [<xref ref-type="bibr" rid="pmed.1004337.ref006">6</xref>].</p>
<p>The mechanisms by which beverages influence health are complex and are not only based on the nutritional quality of their components (such as energy provided, macronutrients, fiber, minerals, and vitamins) [<xref ref-type="bibr" rid="pmed.1004337.ref007">7</xref>], but also rely on other factors such as satiety mechanisms and factors that affect the assimilation of beverages such as rapid gastric emptying and intestinal absorption [<xref ref-type="bibr" rid="pmed.1004337.ref008">8</xref>]. Moreover, the addition of artificial sweeteners influences mortality [<xref ref-type="bibr" rid="pmed.1004337.ref009">9</xref>]. On the other hand, some beverages are also an important source of other additives (e.g., phosphates) as well as contaminants from packaging or processing (e.g., organophosphate esters, phthalates) [<xref ref-type="bibr" rid="pmed.1004337.ref010">10</xref>].</p>
<p>In 2015, a healthy beverage index, based on commonly consumed drinks, was developed to evaluate the role of beverage quality on cardiometabolic risk in adult Americans. A low adherence to this index was associated with several detrimental cardiometabolic markers [<xref ref-type="bibr" rid="pmed.1004337.ref011">11</xref>]. Consistent results were obtained in another US study where the association between the adherence to a healthy beverage pattern and total mortality was evaluated. Data were obtained from a cohort of 2,283 adults, aged ≥21 years, with a previous diagnosis of mild to moderate chronic renal insufficiency. A healthier beverage index was inversely associated with the progression of chronic kidney disease and all-cause mortality [<xref ref-type="bibr" rid="pmed.1004337.ref012">12</xref>].</p>
<p>Although the association of specific beverages has been studied previously, the role of a healthy beverage index and its association with mortality has not been assessed in the general population yet. We hypothesized that higher adherence to a 7-item Healthy Beverage Score (HBS), previously proposed by Hu and colleagues [<xref ref-type="bibr" rid="pmed.1004337.ref012">12</xref>] and adapted to the Spanish beverage consumption, could be associated with lower mortality. Therefore, the aim of this study is to assess the association between the HBS and all-cause mortality in a representative cohort of Spanish adults.</p>
</sec>
<sec id="sec008" sec-type="materials|methods">
<title>Methods</title>
<sec id="sec009">
<title>Study design and participants</title>
<p>Data were obtained from the Study on Nutrition and Cardiovascular Risk in Spain (ENRICA) whose methods have been reported elsewhere [<xref ref-type="bibr" rid="pmed.1004337.ref013">13</xref>]. In brief, 13,105 individuals aged ≥18 years were recruited from 2008 to 2010. A stratified cluster sampling based on the census sections of Spain was performed to guarantee the representativeness of the sample. Sample weights were based on the size of municipalities, sex, and age. Three sequential stages were followed for data collection. First, sociodemographic, lifestyle characteristics, and morbidity information was obtained through a telephone interview. Second, blood and urine samples were collected on a first home visit. Third, a physical examination and a face-to-face dietary history (DH-ENRICA) were completed during a second home visit. The response rate was 51% and the main reasons for non-participation were refusal to provide a blood sample (51.7%) and not being interested in the study (37.8%).</p>
<p>From the initial sample (13,105 individuals), 944 participants were excluded: 60 (0.5%) without information on diet and 884 (6.8%) with implausible values for total energy intake (&lt;800 kcal/day or &gt;5,000 kcal/day in males; &lt;500 kcal/day or &gt;4,000 kcal/day in females). Therefore, a total of 12,161 participants were included in the analysis (<xref ref-type="supplementary-material" rid="pmed.1004337.s005">S1 Fig</xref>).</p>
<p>The Clinical Research Ethics Committee of La Paz University Hospital in Madrid provided ethical approval. All participants from the ENRICA Study gave written informed consent after explaining the details of the study.</p>
</sec>
<sec id="sec010">
<title>Study variables</title>
<sec id="sec011">
<title>Dietary history</title>
<p>Information on diet was obtained through a computerized dietary history (DH-ENRICA), conducted by trained and certified nonmedical interviewers. The DH-ENRICA collected information on 861 items of food, with 82 beverages included. Participants informed about all items of food and beverages consumed at least once every 2 weeks in the previous year. Food consumed during weekdays and weekends were considered. A total of 127 sets of digitalized photos, household measurements, as well as the usual proportion sizes of food from typical Spanish recipes were used to estimate portion sizes in grams per day. Regarding beverages, a total of 14 digitized photos and 23 household measurements were used to later estimate beverage consumption in milliliters per day. In addition, for alcoholic beverages, the consumption of ethanol in grams per day was calculated using Spanish food composition tables [<xref ref-type="bibr" rid="pmed.1004337.ref014">14</xref>]. The validity correlation coefficients in HD-ENRICA for beverages were: 0.71 for coffee, 0.69 for milk, 0.40 for soft drinks, and 0.64 for alcoholic beverages [<xref ref-type="bibr" rid="pmed.1004337.ref015">15</xref>].</p>
</sec>
<sec id="sec012">
<title>The Healthy Beverage Score (HBS)</title>
<p>A Healthy Beverage Score (HBS) was previously described by Hu and colleagues [<xref ref-type="bibr" rid="pmed.1004337.ref012">12</xref>]. Based on the HBS, we built a 7-item HBS modifying its cut-off points to fit with the beverage consumption of a representative sample of the Spanish adult population. Each item of the HBS scored from 1 (minimal adherence) to 4 points (maximal adherence) based on sex-specific categories of consumption. Thus, the HBS ranged from 7 (low adherence) to 28 points (high adherence). The higher the HBS, the healthier the pattern. Items were grouped in 2 main components: adequacy and moderation (<xref ref-type="table" rid="pmed.1004337.t001">Table 1</xref>). Two beverages were considered as adequacy components: low-fat milk as well as coffee and tea consumption. For these 2 components, the higher the score, the healthier the pattern. No low-fat milk consumption scored 1, while the remaining sample was divided into tertiles among consumers; coffee and tea consumption was grouped into quartiles. Five items were included as moderation components: whole-fat milk, fruit juice, artificially sweetened beverages, sugar-sweetened beverages, and alcohol. For these 5 items, the higher the consumption, the lower the score, with a specific classification for alcohol consumption. Whole-fat milk and sugar-sweetened beverages scored 4 for no consumption and the remaining sample was divided into tertiles among consumers. Fruit juice and artificially sweetened beverages consumption scored 4 for no consumption and 1 for any consumption. The scoring of fruit juices and artificially sweetened beverages was decided on the basis of the lack of a wide range of consumption, and to maintain the relative weight of these items in the score, as previously described by Hu and colleagues [<xref ref-type="bibr" rid="pmed.1004337.ref012">12</xref>]. Finally, for alcohol consumption, participants with no consumption or moderate drinkers (&lt;40 g/day for males and &lt;24 g/day for females) scored 4, and heavy drinkers (≥40 g/day for males and ≥24 g/day for females) scored 1.</p>
<table-wrap id="pmed.1004337.t001" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.t001</object-id>
<label>Table 1</label> <caption><title>Scoring criteria for the HBS in the ENRICA Study (2008–2010).</title></caption>
<alternatives>
<graphic id="pmed.1004337.t001g" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.t001" xlink:type="simple"/>
<table>
<colgroup>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
</colgroup>
<thead>
<tr>
<th align="left">Components</th>
<th align="center">Minimum score</th>
<th align="center"/>
<th align="center"/>
<th align="center">Maximum score</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><bold>Adequacy</bold></td>
<td align="left"/>
<td align="left"/>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left"><bold> Low-fat milk</bold></td>
<td align="center">1 (No consumption)</td>
<td align="center">2 (Tertile 1 among consumers)</td>
<td align="center">3 (Tertile 2 among consumers)</td>
<td align="center">4 (Tertile 3 among consumers)</td>
</tr>
<tr>
<td align="left"><bold> Coffee and tea</bold></td>
<td align="center">1 (Quartile 1)</td>
<td align="center">2 (Quartile 2)</td>
<td align="center">3 (Quartile 3)</td>
<td align="center">4 (Quartile 4)</td>
</tr>
<tr>
<td align="left"><bold>Moderation</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Whole-fat milk</bold></td>
<td align="center">1 (Tertile 3 among consumers)</td>
<td align="center">2 (Tertile 2 among consumers)</td>
<td align="center">3 (Tertile 1 among consumers)</td>
<td align="center">4 (No consumption)</td>
</tr>
<tr>
<td align="left"><bold> Fruit juice</bold></td>
<td align="center">1 (Any consumption)</td>
<td align="center">--</td>
<td align="center">--</td>
<td align="center">4 (No consumption)</td>
</tr>
<tr>
<td align="left"><bold> Artificially sweetened beverages</bold></td>
<td align="center">1 (Any consumption)</td>
<td align="center">--</td>
<td align="center">--</td>
<td align="center">4 (No consumption)</td>
</tr>
<tr>
<td align="left"><bold> Sugar-sweetened beverages</bold></td>
<td align="center">1 (Tertile 3 among consumers)</td>
<td align="center">2 (Tertile 2 among consumers)</td>
<td align="center">3 (Tertile 1 among consumers)</td>
<td align="center">4 (No consumption)</td>
</tr>
<tr>
<td align="left"><bold> Alcohol</bold><xref ref-type="table-fn" rid="t001fn001"><sup><bold>a</bold></sup></xref></td>
<td align="center">1 (Heavy drinkers)</td>
<td align="center">--</td>
<td align="center">--</td>
<td align="center">4 (No consumption or moderate drinkers)</td>
</tr>
<tr>
<td align="center"><bold>Range</bold></td>
<td align="center"><bold>7</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"><bold>28</bold></td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot>
<fn id="t001fn001"><p><sup>a</sup> Heavy drinkers were defined as consumption ≥40 g/day for males and ≥24 g/day for females. Among drinkers, a moderate alcohol consumption was defined as &lt;40 g/day for males and &lt;24 g/day for females.</p></fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec013">
<title>Mortality assessment</title>
<p>All-cause mortality was ascertained through a computerized linkage with the Spanish National Death Index. Participants were followed from baseline in 2008 to 2010 to January 31, 2022. Follow-up was censored at the date of death or at the end of follow-up, whichever occurred first.</p>
</sec>
<sec id="sec014">
<title>Confounders</title>
<p>Participants provided information regarding age, sex, educational level, and smoking status which were obtained through the computer-assisted telephone interview performed at baseline. On the second home visit and following standardized procedures, blood pressure, and weight and height were measured. Body mass index (BMI) was calculated as weight divided by the square of the height in meters (kg/m<sup>2</sup>). Leisure time and household physical activity were evaluated with the EPIC short questionnaire, collecting information on 17 activities. Then, each activity was multiplied by their respective energy expenditure rate in metabolic equivalents in hours per week (METs-hour/week) [<xref ref-type="bibr" rid="pmed.1004337.ref016">16</xref>] and total energy expenditure was obtained by summing up all activities. Hours spent watching television were used to account for sedentary activities. In order to control for good dietary quality, analyses were adjusted for total energy intake, fiber, fruit and vegetable consumption, as well as the Mediterranean Diet Adherence defined by Trichopoulou and colleagues [<xref ref-type="bibr" rid="pmed.1004337.ref017">17</xref>] without including alcohol. Blood samples collected on the first home visit were centrally analyzed in the CORE laboratory of La Paz University Hospital in Madrid. A colorimetric enzymatic method with lipase and glycerol kinase (for triglycerides) and a colorimetric enzymatic method with cholesterol-oxidase, esterase, and peroxidase (for cholesterol) were used. To define hypertriglyceridemia, we used a threshold of ≥150 mg/d in fasting plasma triglycerides levels, and for hypercholesterolemia, a fasting plasma total cholesterol level of ≥200 mg/dL or prescribed lipid-lowering medications. Hypertension was defined as ≥140/90 mmHg or taking antihypertensive medication. Analyses were also controlled for the number of chronic conditions (chronic obstructive pulmonary disease, coronary heart disease, stroke, heart failure, osteoarthritis, cancer, depression diagnosed by a physician, and diabetes), as well as the number of prescribed medications to consider prevalent morbidity.</p>
<p>Independent variables with missing values were imputed by using multiple imputation [<xref ref-type="bibr" rid="pmed.1004337.ref018">18</xref>]: educational level (&lt;1%), smoking status (&lt;1%), BMI (1.5%), number of television hours (&lt;1%), hypertriglyceridemia (&lt;1%), hypercholesterolemia (&lt;1%), and high blood pressure (1.1%). The validity of imputed data was examined against analyses performed with variables that contained full information.</p>
</sec>
</sec>
<sec id="sec015">
<title>Statistical analysis</title>
<p>Across sex-specific quartiles of adherence to the HBS, age-adjusted baseline characteristics of participants were computed using marginals. Age-adjusted means for continuous variables and age-adjusted proportions (%) for categorical variables were provided. To estimate hazard ratios (HR) and their 95% confidence intervals (CIs), Cox proportional hazards regression models were built and age was considered as the underlying time metric. The survey command was applied to account for the complex sampling design. The lowest category of adherence to the HBS was used as reference.</p>
<p>Three sequential Cox models were used. Model 1 was an unadjusted model. Model 2 was additionally adjusted for age, sex, educational level, smoking status, ex-drinker status, BMI, physical activity in leisure time, total energy intake, fruit and vegetable consumption, total fiber intake, hypertriglyceridemia, hypercholesterolemia, hypertension, number of self-reported chronic conditions, and number of medications. Finally, Model 3 was adjusted for the Mediterranean index by Trichopoulou excluding alcohol (maximum score = 8), but excluding fruit, vegetable, and fiber consumption. Schoenfeld residuals were plotted against time to assess proportional hazards assumptions and visually no violations were found. To test for linear trend, categories of the HBS were modeled as a continuous variable. The dose–response relationship was assessed by using restricted cubic splines with 3 knots (at the 10th, 50th, and 90th percentiles). Interactions between the HBS and age (&lt;65 years versus ≥65 years), sex (male versus female), BMI (&lt;30 kg/m2 versus ≥30 kg/m2), physical activity (≤median 61.5 METs-hour/week versus &gt;median 61.5 METs-hour/week), vegetable consumption (≤median 183.5 g/d versus &gt;median 183.5 g/d), adherence to the Mediterranean diet without including alcohol (≤median 4 versus &gt;median 4), and the prevalence of chronic conditions (yes/no) were tested by including multiplicative terms in Model 3. Sensitivity analyses were conducted excluding deaths in the first 3 years of follow-up to account for the effect of subclinical conditions at baseline. Also, individual items of the HBS were assessed according to Model 3 plus adjustment for the remaining items that are part of the score.</p>
<p>This was a preplanned study and data analysis was conducted according to a prespecified plan (<xref ref-type="supplementary-material" rid="pmed.1004337.s002">S1 Text</xref>). The HBS was previously used to assess the association between adherence to HBS and age-related frailty in a sample of Spanish older adults [<xref ref-type="bibr" rid="pmed.1004337.ref019">19</xref>]. A minor modification was introduced in alcohol consumption classification. For older adults, moderate alcohol consumption was considered the healthy option due to their high cardiovascular risk [<xref ref-type="bibr" rid="pmed.1004337.ref019">19</xref>]. However, in the current analysis, which involves the adult general population (aged ≥18 years) with lower cardiovascular risk, the category of alcohol considered healthy was “no consumption or moderate consumption.”</p>
<p>This study was reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (<xref ref-type="supplementary-material" rid="pmed.1004337.s001">S1 STROBE Checklist</xref>).</p>
<p>A two-tailed <italic>p</italic> value less than 0.05 was considered statistically significant. All analyses were performed with Stata, version 17.0 (StataCorp, College Station, Texas, United States of America).</p>
</sec>
</sec>
<sec id="sec016" sec-type="results">
<title>Results</title>
<p>The median age of participants (<italic>N</italic> = 12,161) was 46 years old (interquartile range 35 to 61) and 52.6% were females. Compared with those in quartile 1 (less healthy) of adherence to the HBS, participants in quartile 4 (healthier) were older, more frequently females, with a higher level of education and with a less sedentary lifestyle, and were more physically active. Also, those in quartile 4 had lower energy intake, consumed more fiber, fruit and vegetables, showed a higher adherence to the Mediterranean diet, and had more frequently hypercholesterolemia (<xref ref-type="table" rid="pmed.1004337.t002">Table 2</xref>). Sex-specific cut-off points for individual items of the HBS are shown in <xref ref-type="supplementary-material" rid="pmed.1004337.s003">S1 Table</xref>.</p>
<table-wrap id="pmed.1004337.t002" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.t002</object-id>
<label>Table 2</label> <caption><title>Age-adjusted baseline characteristics of participants in the ENRICA Study (2008–2010) by quartiles of the HBS (<italic>N</italic> = 12,161).</title></caption>
<alternatives>
<graphic id="pmed.1004337.t002g" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.t002" xlink:type="simple"/>
<table>
<colgroup>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
</colgroup>
<thead>
<tr>
<th align="center" rowspan="2">Characteristics</th>
<th align="center" colspan="4">Quartiles of adherence to the HBS</th>
<th align="center" rowspan="2"><italic>p</italic> for trend</th>
</tr>
<tr>
<th align="center">Quartile 1<break/>(Less healthy)<break/><italic>n</italic> = 2,813</th>
<th align="center">Quartile 2<break/><italic>n</italic> = 2,985</th>
<th align="center">Quartile 3<break/><italic>n</italic> = 2,745</th>
<th align="center">Quartile 4<break/>(Healthier)<break/><italic>n</italic> = 3,618</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><bold>Age, mean, y</bold></td>
<td align="center">38.9</td>
<td align="center">45.3</td>
<td align="center">48.5</td>
<td align="center">53.9</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Female, %</bold></td>
<td align="center">53.4</td>
<td align="center">39.3</td>
<td align="center">50.7</td>
<td align="center">57.9</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Educational level, %</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Primary or less</bold></td>
<td align="center">29.5</td>
<td align="center">24.3</td>
<td align="center">23.8</td>
<td align="center">27.2</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Secondary</bold></td>
<td align="center">43.8</td>
<td align="center">46.1</td>
<td align="center">43.2</td>
<td align="center">40.0</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> University</bold></td>
<td align="center">26.7</td>
<td align="center">29.6</td>
<td align="center">32.9</td>
<td align="center">32.8</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Smoking, %</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Non-smoker</bold></td>
<td align="center">53.4</td>
<td align="center">45.6</td>
<td align="center">47.3</td>
<td align="center">49.6</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Former smoker</bold></td>
<td align="center">20.1</td>
<td align="center">25.8</td>
<td align="center">25.6</td>
<td align="center">26.5</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Current smoker</bold></td>
<td align="center">26.5</td>
<td align="center">28.6</td>
<td align="center">27.1</td>
<td align="center">23.9</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Ex-drinker, %</bold></td>
<td align="center">56.1</td>
<td align="center">50.0</td>
<td align="center">52.1</td>
<td align="center">48.3</td>
<td align="center">0.320</td>
</tr>
<tr>
<td align="left"><bold>BMI, %</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> &lt;25 kg/m</bold><sup><bold>2</bold></sup></td>
<td align="center">39.9</td>
<td align="center">33.2</td>
<td align="center">35.6</td>
<td align="center">38.0</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> 25-&lt;30 kg/m</bold><sup><bold>2</bold></sup></td>
<td align="center">38.6</td>
<td align="center">43.3</td>
<td align="center">42.9</td>
<td align="center">39.5</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> ≥ 30 kg/m</bold><sup><bold>2</bold></sup></td>
<td align="center">21.6</td>
<td align="center">23.6</td>
<td align="center">21.5</td>
<td align="center">22.5</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Time watching TV, mean, h</bold></td>
<td align="center">2.1</td>
<td align="center">1.9</td>
<td align="center">1.9</td>
<td align="center">1.9</td>
<td align="center">0.005</td>
</tr>
<tr>
<td align="left"><bold>Physical activity, mean, METs-hour/week</bold></td>
<td align="center">67.0</td>
<td align="center">65.4</td>
<td align="center">68.3</td>
<td align="center">72.1</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Energy consumption, mean, Kcal/d</bold></td>
<td align="center">2,331.9</td>
<td align="center">2,261.9</td>
<td align="center">2,135.5</td>
<td align="center">1,992.8</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Fiber consumption, mean, g/d</bold></td>
<td align="center">22.5</td>
<td align="center">22.9</td>
<td align="center">23.2</td>
<td align="center">23.0</td>
<td align="center">0.035</td>
</tr>
<tr>
<td align="left"><bold>Fruit consumption, mean, g/d</bold></td>
<td align="center">222.8</td>
<td align="center">233.7</td>
<td align="center">245.5</td>
<td align="center">247.5</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Vegetable consumption, mean, g/d</bold></td>
<td align="center">184.0</td>
<td align="center">197.2</td>
<td align="center">211.6</td>
<td align="center">209.6</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Mediterranean diet score (without alcohol), mean</bold></td>
<td align="center">3.7</td>
<td align="center">3.9</td>
<td align="center">4.1</td>
<td align="center">4.0</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Hypertriglyceridemia, %</bold></td>
<td align="center">17.8</td>
<td align="center">19.8</td>
<td align="center">18.3</td>
<td align="center">16.8</td>
<td align="center">0.127</td>
</tr>
<tr>
<td align="left"><bold>Hypercholesterolemia, %</bold></td>
<td align="center">46.9</td>
<td align="center">50.5</td>
<td align="center">52.7</td>
<td align="center">52.8</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Hypertension, %</bold></td>
<td align="center">29.2</td>
<td align="center">32.2</td>
<td align="center">27.6</td>
<td align="center">28.3</td>
<td align="center">0.109</td>
</tr>
<tr>
<td align="left"><bold>Number of chronic conditions</bold><xref ref-type="table-fn" rid="t002fn001"><sup><bold>a</bold></sup></xref><bold>, %</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> None</bold></td>
<td align="center">71.9</td>
<td align="center">74.8</td>
<td align="center">71.7</td>
<td align="center">69.0</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> One</bold></td>
<td align="center">22.3</td>
<td align="center">20.5</td>
<td align="center">22.2</td>
<td align="center">24.7</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Two or more</bold></td>
<td align="center">5.8</td>
<td align="center">4.6</td>
<td align="center">6.1</td>
<td align="center">6.3</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Number of medications, %</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> None</bold></td>
<td align="center">72.0</td>
<td align="center">69.4</td>
<td align="center">71.4</td>
<td align="center">71.0</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> One to 3</bold></td>
<td align="center">25.4</td>
<td align="center">27.6</td>
<td align="center">24.5</td>
<td align="center">25.5</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> More than 3</bold></td>
<td align="center">2.6</td>
<td align="center">3.0</td>
<td align="center">4.1</td>
<td align="center">3.5</td>
<td align="center"/>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot>
<fn id="t002fn001"><p><sup>a</sup> Chronic conditions included: chronic obstructive pulmonary disease, coronary heart disease, stroke, heart failure, osteoarthritis, cancer, depression diagnosed by a physician, and diabetes.</p></fn>
<fn id="t002fn002"><p>HBS, Healthy Beverage Score; BMI, body mass index; METs-hour/week, metabolic equivalents in hours per week.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>In accordance with the rules for the construction of the HBS, compared with those in quartile 1 (less healthy), participants in quartile 4 (healthier) consumed more low-fat milk, coffee and tea, and alcohol, but consumed less whole-fat milk, fruit juice, artificially sweetened beverages, and sugar-sweetened beverages (<xref ref-type="table" rid="pmed.1004337.t003">Table 3</xref>).</p>
<table-wrap id="pmed.1004337.t003" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.t003</object-id>
<label>Table 3</label> <caption><title>Beverage consumption by quartiles of the HBS in the ENRICA Study (2008–2010) (<italic>N</italic> = 12,161).</title></caption>
<alternatives>
<graphic id="pmed.1004337.t003g" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.t003" xlink:type="simple"/>
<table>
<colgroup>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
</colgroup>
<thead>
<tr>
<th align="center" rowspan="2">HBS components</th>
<th align="center" colspan="4">Quartiles of adherence to the HBS<xref ref-type="table-fn" rid="t003fn001"><sup>a</sup></xref></th>
<th align="center" rowspan="2"><italic>p</italic> value</th>
</tr>
<tr>
<th align="center">Quartile 1<break/>(Less healthy)<break/><italic>n</italic> = 2,813</th>
<th align="center">Quartile 2<break/><italic>n</italic> = 2,985</th>
<th align="center">Quartile 3<break/><italic>n</italic> = 2,745</th>
<th align="center">Quartile 4<break/>(Healthier)<break/><italic>n</italic> = 3,618</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><bold>Adequacy</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Low-fat milk, mean (SD), mL/d</bold></td>
<td align="center">50.0 (92.7)</td>
<td align="center">100.3 (128.2)</td>
<td align="center">149.3 (155.1)</td>
<td align="center">215.5 (148.3)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Coffee and tea, mean (SD), mL/d</bold></td>
<td align="center">64.3 (88.2)</td>
<td align="center">103.1 (121.8)</td>
<td align="center">127.5 (138.8)</td>
<td align="center">161.5 (154.6)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold>Moderation</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold> Whole-fat milk, mean (SD), mL/d</bold></td>
<td align="center">139.3 (159.3)</td>
<td align="center">82.8 (121.6)</td>
<td align="center">38.2 (76.4)</td>
<td align="center">8.8 (23.7)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Fruit juice, mean (SD), mL/d</bold></td>
<td align="center">100.4 (139.9)</td>
<td align="center">66.1 (124.2)</td>
<td align="center">36.9 (87.7)</td>
<td align="center">7.6 (42.6)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Artificially sweetened beverages, mean (SD), mL/d</bold></td>
<td align="center">48.9 (156.2)</td>
<td align="center">34.5 (219.7)</td>
<td align="center">17.7 (115.0)</td>
<td align="center">3.5 (51.3)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Sugar-sweetened beverages, mean (SD), mL/d</bold></td>
<td align="center">162.0 (249.4)</td>
<td align="center">71.0 (168.2)</td>
<td align="center">43.2 (125.7)</td>
<td align="center">10.2 (68.7)</td>
<td align="center">&lt;0.001</td>
</tr>
<tr>
<td align="left"><bold> Alcohol, mean (SD), g/d</bold><xref ref-type="table-fn" rid="t003fn002"><sup>b</sup></xref></td>
<td align="center">11.4 (19.2)</td>
<td align="center">10.7 (17.8)</td>
<td align="center">7.9 (14.0)</td>
<td align="center">5.8 (10.4)</td>
<td align="center">&lt;0.001</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot>
<fn id="t003fn001"><p><sup>a</sup> Cut-off points for the HBS = for males: Q1 10–18; Q2 19–21; Q3 22–23; Q4 24–28; for females: Q1 10–19; Q2 20–21; Q3 22–23; Q4 24–28.</p></fn>
<fn id="t003fn002"><p><sup>b</sup> Alcohol was defined as the consumption of ethanol in grams per day.</p></fn>
<fn id="t003fn003"><p>HBS, Healthy Beverage Score; SD, standard deviation.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>After a mean follow-up of 12.5 years (SD: 1.7; range: 0.5 to 12.9) and 151,459 person-years of follow-up, a total of 967 deaths occurred. The HR for all-cause mortality when comparing extreme quartiles of the adherence to the HBS was 0.72 (95% CI, 0.57 to 0.91, <italic>p</italic> for linear trend = 0.015) in the fully adjusted model (<xref ref-type="table" rid="pmed.1004337.t004">Table 4</xref>). The decrease in absolute risk of death was 4.3% for quartile 2, 6.3% for quartile 3, and 8.3% for quartile 4. No significant interactions were found for age, sex, BMI, physical activity, vegetable consumption, or adherence to the Mediterranean diet (without including alcohol). However, a statistically significant interaction was found when stratifying for the presence of at least 1 chronic condition (<italic>p</italic> = 0.030). Among those with at least 1 chronic condition, higher adherence to the HBS was associated with lower mortality. No association was observed among those with no chronic conditions (<xref ref-type="table" rid="pmed.1004337.t005">Table 5</xref>). After excluding the first 3 years of follow-up, the inverse association between the adherence to the HBS and total mortality remained similar (<xref ref-type="supplementary-material" rid="pmed.1004337.s004">S2 Table</xref>). When assessing dose–response, a linear relationship was observed using restricted cubic splines (<italic>p</italic> value for non-linearity = 0.010) (<xref ref-type="fig" rid="pmed.1004337.g001">Fig 1</xref>).</p>
<fig id="pmed.1004337.g001" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.g001</object-id>
<label>Fig 1</label>
<caption>
<title>Adjusted restricted cubic splines of the association of the HBS with mortality risk in the ENRICA Study from baseline (2008–2010) to January 2022 (<italic>N</italic> = 12,161).</title>
<p>Lines are restricted cubic splines, showing the dose–response association of the HBS with mortality. The solid line represents the HR, and the dashed lines indicate the lower and upper 95% CIs. The knots were located at the 10th, 50th, and 90th percentiles (corresponding to HBS scores 17, 22, and 25, respectively); <italic>p</italic> for non-linearity = 0.010. Adjusted as in Model 3. Data were adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (&lt;25, ≥25 and ≤30, &gt;30 kg/m<sup>2</sup>), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and ≥2), number of medications (0, 1–3, &gt;3), and adherence to the Mediterranean diet without including alcohol (maximum score = 8). Age was the underlying time metric.</p>
</caption>
<graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.g001" xlink:type="simple"/>
</fig>
<table-wrap id="pmed.1004337.t004" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.t004</object-id>
<label>Table 4</label> <caption><title>Mortality risk according to quartiles of the adherence to the HBS in the ENRICA Study from baseline (2008–2010) to January 2022 (<italic>N</italic> = 12,161).</title></caption>
<alternatives>
<graphic id="pmed.1004337.t004g" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.t004" xlink:type="simple"/>
<table>
<colgroup>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
</colgroup>
<thead>
<tr>
<th align="center">Total mortality</th>
<th align="center">Quartile 1<break/>HR (95% CI)<break/>(Less healthy)</th>
<th align="center">Quartile 2<break/>HR (95% CI)</th>
<th align="center">Quartile 3<break/>HR (95% CI)</th>
<th align="center">Quartile 4<break/>HR (95% CI)<break/>(Healthier)</th>
<th align="center"><italic>p</italic> for linear trend<xref ref-type="table-fn" rid="t004fn004"><sup>d</sup></xref></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><bold>Deaths/n</bold></td>
<td align="center">141/2,813</td>
<td align="center">227/2,985</td>
<td align="center">228/2,745</td>
<td align="center">371/3,618</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Person-years</bold></td>
<td align="center">36,216</td>
<td align="center">38,058</td>
<td align="center">32,860</td>
<td align="center">44,325</td>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>Model 1</bold><xref ref-type="table-fn" rid="t004fn001"><sup>a</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.86 [0.68, 1.10]</td>
<td align="center">0.84 [0.65, 1.08]</td>
<td align="center">0.75 [0.59, 0.94]</td>
<td align="center">0.011</td>
</tr>
<tr>
<td align="left"><bold>Model 2</bold><xref ref-type="table-fn" rid="t004fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.79 [0.61, 1.02]</td>
<td align="center">0.77 [0.59, 1.00]</td>
<td align="center">0.72 [0.57, 0.92]</td>
<td align="center">0.017</td>
</tr>
<tr>
<td align="left"><bold>Model 3</bold><xref ref-type="table-fn" rid="t004fn003"><sup>c</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.79 [0.61, 1.02]</td>
<td align="center">0.78 [0.60, 1.02]</td>
<td align="center">0.72 [0.57, 0.91]</td>
<td align="center">0.015</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot>
<fn id="t004fn001"><p><sup>a</sup>Model 1 was an unadjusted model. Age was the underlying time metric.</p></fn>
<fn id="t004fn002"><p><sup>b</sup>Model 2 was adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (&lt;25, ≥25 and ≤30, &gt;30 kg/m<sup>2</sup>), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), fiber intake (g/d continuous), fruit and vegetable consumption (g/d, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and ≥2), and number of medications (0, 1–3, &gt;3). Age was the underlying time metric.</p></fn>
<fn id="t004fn003"><p><sup>c</sup>Model 3 was adjusted for factors in Model 2 plus adherence to the Mediterranean diet without including alcohol (maximum score = 8) and excluding fruit, vegetable, and fiber consumption. Age was the underlying time metric.</p></fn>
<fn id="t004fn004"><p><sup>d</sup><italic>p</italic> value for quartile 4 vs. quartile 1: Model 1 <italic>p</italic> = 0.012, Model 2 <italic>p</italic> = 0.007; Model 3 <italic>p</italic> = 0.007.</p></fn>
<fn id="t004fn005"><p>BMI; body mass index; CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio.</p></fn>
</table-wrap-foot>
</table-wrap>
<table-wrap id="pmed.1004337.t005" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.t005</object-id>
<label>Table 5</label> <caption><title>Mortality risk according to quartiles of the adherence to the HBS in the ENRICA Study from baseline (2008–2010) to January 2022 by age, sex, BMI, physical activity, vegetable consumption, adherence to the Mediterranean diet without including alcohol and prevalence of chronic conditions (<italic>N</italic> = 12,161).</title></caption>
<alternatives>
<graphic id="pmed.1004337.t005g" mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.t005" xlink:type="simple"/>
<table>
<colgroup>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
<col align="left" valign="middle"/>
</colgroup>
<thead>
<tr>
<th align="center">Total mortality</th>
<th align="center">Quartile 1<break/>HR<break/>(95% CI)<break/>(Less healthy)</th>
<th align="center">Quartile 2<break/>HR<break/>(95% CI)</th>
<th align="center">Quartile 3<break/>HR<break/>(95% CI)</th>
<th align="center">Quartile 4<break/>HR<break/>(95% CI)<break/>(Healthier)</th>
<th align="center"><italic>p</italic> for linear trend</th>
<th align="center"><italic>p</italic> for interaction<xref ref-type="table-fn" rid="t005fn001"><sup>a</sup></xref></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left"><bold>Age</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left" colspan="2"><bold>&lt;65 years, <italic>n</italic> = 9,774</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center" rowspan="6">0.364</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">39/2,514</td>
<td align="center">67/2,486</td>
<td align="center">58/2,193</td>
<td align="center">79/2,581</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">1.19 [0.74, 1.90]</td>
<td align="center">1.11 [0.70, 1.78]</td>
<td align="center">0.99 [0.64, 1.54]</td>
<td align="center">0.680</td>
</tr>
<tr>
<td align="left" colspan="2"><bold>≥65 years, <italic>n</italic> = 2,387</bold></td>
<td align="center"/>
<td align="center"/>
<td align="left"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">102/299</td>
<td align="center">160/499</td>
<td align="center">170/552</td>
<td align="center">292/1,037</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.71 [0.53, 0.95]</td>
<td align="center">0.73 [0.53, 0.99]</td>
<td align="center">0.68 [0.51, 0.89]</td>
<td align="center">0.025</td>
</tr>
<tr>
<td align="left"><bold>Sex</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left" colspan="2"><bold>Male, <italic>n</italic> = 5,760</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center" rowspan="6">0.287</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">63/1,243</td>
<td align="center">141/1,750</td>
<td align="center">125/1,294</td>
<td align="center">206/1,473</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.93 [0.65, 1.33]</td>
<td align="center">0.90 [0.62, 1.31]</td>
<td align="center">0.89 [0.63, 1.24]</td>
<td align="center">0.482</td>
</tr>
<tr>
<td align="left" colspan="2"><bold>Female, <italic>n</italic> = 6,401</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">78/1,570</td>
<td align="center">86/1,235</td>
<td align="center">103/1,451</td>
<td align="center">165/2,145</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.67 [0.47, 0.97]</td>
<td align="center">0.68 [0.47, 0.98]</td>
<td align="center">0.58 [0.42, 0.81]</td>
<td align="center">0.004</td>
</tr>
<tr>
<td align="left"><bold>BMI</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left" colspan="2"><bold>&lt;30 kg/m</bold><sup><bold>2</bold></sup><bold>, <italic>n</italic> = 9,513</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center" rowspan="6">0.932</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">104/2,332</td>
<td align="center">144/2,338</td>
<td align="center">158/2,148</td>
<td align="center">244/2,695</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.80 [0.60, 1.07]</td>
<td align="center">0.82 [0.61, 1.10]</td>
<td align="center">0.72 [0.55, 0.95]</td>
<td align="center">0.038</td>
</tr>
<tr>
<td align="left" colspan="2"><bold>≥30 kg/m</bold><sup><bold>2</bold></sup><bold>, <italic>n</italic> = 2,648</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">37/481</td>
<td align="center">83/647</td>
<td align="center">70/597</td>
<td align="center">127/923</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.72 [0.44, 1.18]</td>
<td align="center">0.65 [0.38, 1.09]</td>
<td align="center">0.65 [0.40, 1.05]</td>
<td align="center">0.144</td>
</tr>
<tr>
<td align="left" colspan="2"><bold>Physical activity</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left" colspan="5"><bold>≤Median (61.5 METs-hour/week), <italic>n</italic> = 6,082</bold></td>
<td align="center"/>
<td align="center" rowspan="6">0.603</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">89/1,451</td>
<td align="center">142/1,557</td>
<td align="center">148/1,373</td>
<td align="center">246/1,701</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.79 [0.58, 1.09]</td>
<td align="center">0.80 [0.59, 1.09]</td>
<td align="center">0.76 [0.58, 1.02]</td>
<td align="center">0.140</td>
</tr>
<tr>
<td align="left" colspan="4"><bold>&gt;Median (61.5 METs-hour/week), <italic>n</italic> = 6,079</bold></td>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">52/1,362</td>
<td align="center">85/1,428</td>
<td align="center">80/1,372</td>
<td align="center">125/1,917</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.84 [0.55, 1.27]</td>
<td align="center">0.79 [0.50, 1.26]</td>
<td align="center">0.65 [0.43, 0.99]</td>
<td align="center">0.038</td>
</tr>
<tr>
<td align="left" colspan="7"><bold>Vegetable consumption</bold></td>
</tr>
<tr>
<td align="left" colspan="3"><bold>≤Median (183.5 g/d), <italic>n</italic> = 6,083</bold></td>
<td align="left"/>
<td align="left"/>
<td align="center"/>
<td align="center" rowspan="6">0.284</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">72/1,576</td>
<td align="left">121/1,532</td>
<td align="left">125/1,287</td>
<td align="left">198/1,688</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">82 [0.58, 1.17]</td>
<td align="center">0.93 [0.65, 1.32]</td>
<td align="center">0.75 [0.54, 1.03]</td>
<td align="center">0.113</td>
</tr>
<tr>
<td align="left" colspan="3"><bold>&gt;Median (183.5 g/d), <italic>n</italic> = 6,078</bold></td>
<td align="left"/>
<td align="left"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, n</td>
<td align="center">69/1,237</td>
<td align="left">106/1,453</td>
<td align="left">103/1,458</td>
<td align="left">173/1,930</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.71 [0.50, 1.02]</td>
<td align="center">0.58 [0.39, 0.85]</td>
<td align="center">0.64 [0.45, 0.92]</td>
<td align="center">0.043</td>
</tr>
<tr>
<td align="left" colspan="5"><bold>Adherence to the Mediterranean diet without including alcohol</bold></td>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left" colspan="3"><bold>≤Median (4), <italic>n</italic> = 7,400</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center" rowspan="6">0.325</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">82/2,002</td>
<td align="center">123/1,843</td>
<td align="center">117/1,557</td>
<td align="center">181/1,998</td>
<td align="left"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.84 [0.59, 1.19]</td>
<td align="center">0.94 [0.66, 1.35]</td>
<td align="center">0.77 [0.55, 1.07]</td>
<td align="center">0.175</td>
</tr>
<tr>
<td align="left" colspan="3"><bold>&gt;Median (4), <italic>n</italic> = 4,761</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">59/811</td>
<td align="center">104/1,142</td>
<td align="center">111/1,188</td>
<td align="center">190/1,620</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.74 [0.51, 1.08]</td>
<td align="center">0.63 [0.43, 0.90]</td>
<td align="center">0.66 [0.47, 0.92]</td>
<td align="center">0.033</td>
</tr>
<tr>
<td align="left" colspan="4"><bold>Prevalence of chronic conditions</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left"><bold>No, <italic>n</italic> = 8,151</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center" rowspan="6">0.030</td>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">45/2,143</td>
<td align="center">82/2,124</td>
<td align="center">70/1,811</td>
<td align="center">114/2,073</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">1.15 [0.73, 1.81]</td>
<td align="center">1.18 [0.75, 1.87]</td>
<td align="center">1.16 [0.74, 1.81]</td>
<td align="center">0.616</td>
</tr>
<tr>
<td align="left" colspan="2"><bold>Yes, <italic>n</italic> = 4,010</bold></td>
<td align="center"/>
<td align="center"/>
<td align="center"/>
<td align="center"/>
</tr>
<tr>
<td align="left">Deaths, <italic>n</italic></td>
<td align="center">96/670</td>
<td align="center">145/861</td>
<td align="center">158/934</td>
<td align="center">257/1,545</td>
<td align="center"/>
</tr>
<tr>
<td align="left">Model 3<xref ref-type="table-fn" rid="t005fn002"><sup>b</sup></xref></td>
<td align="center">1 (ref.)</td>
<td align="center">0.67 [0.49, 0.91]</td>
<td align="center">0.63 [0.46, 0.86]</td>
<td align="center">0.57 [0.43, 0.76]</td>
<td align="center">&lt;0.001</td>
</tr>
</tbody>
</table>
</alternatives>
<table-wrap-foot>
<fn id="t005fn001"><p><sup>a</sup><italic>p</italic> for interaction was calculated using the Wald test.</p></fn>
<fn id="t005fn002"><p><sup>b</sup>Model 3 was adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (&lt;25, ≥25 and ≤30, &gt;30 kg/m<sup>2</sup>), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and ≥2), number of medications (0, 1–3, &gt;3), adherence to the Mediterranean diet without including alcohol (maximum score = 8) as appropriate. Age was the underlying time metric.</p></fn>
<fn id="t005fn003"><p>BMI, body mass index; CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio; METs-hour/week, metabolic equivalents in hours per week.</p></fn>
</table-wrap-foot>
</table-wrap>
<p>When individual HBS items were analyzed using Model 3 for adjustment, a higher consumption of coffee and tea, and no consumption of fruit juices and artificially sweetened beverages contributed most to the association with lower all-cause mortality (<xref ref-type="fig" rid="pmed.1004337.g002">Fig 2</xref>). Unadjusted results are also shown (<xref ref-type="supplementary-material" rid="pmed.1004337.s007">S3 Fig</xref>).</p>
<fig id="pmed.1004337.g002" position="float">
<object-id pub-id-type="doi">10.1371/journal.pmed.1004337.g002</object-id>
<label>Fig 2</label>
<caption>
<title>Adjusted mortality risk for individual items of the HBS when comparing extreme categories (quartile 4 vs. quartile 1) in the ENRICA Study from baseline (2008–2010) to January 2022 (<italic>N</italic> = 12,161).</title>
<p>Adjusted as in Model 3. Data were adjusted for age (years, continuous), sex (male, female), educational level (primary or less, secondary, university), smoking (non-smoker, former smoker, current smoker), ex-drinker (yes/no), BMI (&lt;25, ≥25 and ≤30, &gt;30 kg/m<sup>2</sup>), time watching TV (hours, continuous), physical activity (METs-hour/week, continuous), energy intake (kcal/day, continuous), hypertriglyceridemia (yes/no), hypercholesterolemia (yes/no), hypertension (yes/no), number of chronic conditions (0, 1, and ≥2), number of medications (0, 1–3, &gt;3), adherence to the Mediterranean diet without including alcohol (maximum score = 8), and for the rest of items of the HBS (as appropriate). Age was the underlying time metric. CI, confidence interval; HBS, Healthy Beverage Score; HR, hazard ratio.</p>
</caption>
<graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.g002" xlink:type="simple"/>
</fig>
</sec>
<sec id="sec017" sec-type="conclusions">
<title>Discussion</title>
<p>In this large population-based study of Spanish adults with a mean follow-up of 12.5 years, a higher adherence to the HBS was inversely associated with total mortality, after adjusting for potential confounders. Those with higher adherence to the HBS had an 8.3% reduction in the absolute risk of death compared to those with lower adherence. The association was linear and robust. It may also be of particular interest to people with preexisting chronic conditions, as they had lower mortality, although these findings need to be confirmed in future research.</p>
<p>Regarding to items of the HBS, 2 recent prospective studies performed in Spain found an inverse association between coffee consumption and all-cause mortality [<xref ref-type="bibr" rid="pmed.1004337.ref020">20</xref>,<xref ref-type="bibr" rid="pmed.1004337.ref021">21</xref>]. Results from the EPIC study (with 500,000 participants from 10 European countries) [<xref ref-type="bibr" rid="pmed.1004337.ref022">22</xref>] and from the UK Biobank study were also similar to the findings in this study [<xref ref-type="bibr" rid="pmed.1004337.ref023">23</xref>]. Our results are also in line with meta-analyses comprising cross-sectional studies, longitudinal cohorts, as well as interventional studies [<xref ref-type="bibr" rid="pmed.1004337.ref024">24</xref>–<xref ref-type="bibr" rid="pmed.1004337.ref026">26</xref>]. The beneficial effect of coffee might rely, among others, on the antioxidant and anti-inflammatory activity exhibited by its bioactive components, mainly melanoidins, chlorogenic acids, and caffeine [<xref ref-type="bibr" rid="pmed.1004337.ref027">27</xref>]. These compounds reduce oxidative stress and inflammation [<xref ref-type="bibr" rid="pmed.1004337.ref028">28</xref>], enhance endothelial function [<xref ref-type="bibr" rid="pmed.1004337.ref029">29</xref>], and counteract carcinogenesis on in vitro studies [<xref ref-type="bibr" rid="pmed.1004337.ref030">30</xref>]. Coffee also increases the metabolic rate [<xref ref-type="bibr" rid="pmed.1004337.ref031">31</xref>], improves the glucose metabolism [<xref ref-type="bibr" rid="pmed.1004337.ref032">32</xref>], and lowers long-term blood pressure [<xref ref-type="bibr" rid="pmed.1004337.ref033">33</xref>]. Moreover, coffee could reduce mortality even in those with impaired caffeine metabolism [<xref ref-type="bibr" rid="pmed.1004337.ref034">34</xref>] and independently to the addition of sweeteners [<xref ref-type="bibr" rid="pmed.1004337.ref035">35</xref>]. However, high coffee consumption has been associated with an increase in serum levels of total cholesterol, LDL-cholesterol, and triglycerides [<xref ref-type="bibr" rid="pmed.1004337.ref036">36</xref>]. On the other hand, evidence suggests that coffee consumption above 4 cups/day is not associated with further lower mortality [<xref ref-type="bibr" rid="pmed.1004337.ref025">25</xref>].</p>
<p>Spain is included among the European countries with the lowest tea consumption [<xref ref-type="bibr" rid="pmed.1004337.ref037">37</xref>] and we did not find studies that evaluated its relationship with mortality among Spanish adults. Therefore, it is unlikely that tea consumption accounts for our results. However, in literature, both all-cause and cardiovascular mortality were reduced among tea consumers [<xref ref-type="bibr" rid="pmed.1004337.ref038">38</xref>].</p>
<p>The effect of milk on health has been widely studied due to its fatty acid composition [<xref ref-type="bibr" rid="pmed.1004337.ref039">39</xref>]. Two recent cohort studies showed that low-fat milk consumption was associated with lower all-cause mortality when compared to whole-fat milk consumption [<xref ref-type="bibr" rid="pmed.1004337.ref040">40</xref>,<xref ref-type="bibr" rid="pmed.1004337.ref041">41</xref>]. Whole-fat milk has a higher content of saturated fats that has been related to an increase in LDL-cholesterol and atherosclerosis [<xref ref-type="bibr" rid="pmed.1004337.ref042">42</xref>]. As a result, whole-fat milk consumption could be particularly harmful among individuals with known cardiovascular risk. However, in a clinical trial among normocholesterolemic individuals, whole-fat milk consumption showed no impairment in lipid profile nor in glucose-insulin metabolism when compared to low-fat milk consumption [<xref ref-type="bibr" rid="pmed.1004337.ref043">43</xref>]. It is of note that, milk could modulate satiety mechanisms [<xref ref-type="bibr" rid="pmed.1004337.ref044">44</xref>] and also has several components with potential beneficial effects, such as caseins with antioxidant properties [<xref ref-type="bibr" rid="pmed.1004337.ref045">45</xref>]. Milk is also rich in minerals, vitamins, and other bioactive compounds involved in anti-inflammatory and immune regulation [<xref ref-type="bibr" rid="pmed.1004337.ref046">46</xref>]. A recent meta-analysis showed that whole-fat milk consumption was associated with a higher risk of all-cause, cardiovascular disease and cancer mortality; however, low-fat milk showed a protective but nonsignificant association [<xref ref-type="bibr" rid="pmed.1004337.ref047">47</xref>].</p>
<p>Results on fruit juice consumption and mortality mostly depend on the distinction between processed or fresh fruit juice [<xref ref-type="bibr" rid="pmed.1004337.ref048">48</xref>]. A recent meta-analysis showed that processed fruit juice consumption was associated with a higher risk of type 2 diabetes and total mortality [<xref ref-type="bibr" rid="pmed.1004337.ref049">49</xref>]. Evidence on fresh fruit juice consumption and health, however, is insufficient to draw conclusions. Results of a cohort study from the US with 13,440 participants showed that a higher consumption of 100% fruit juice was associated with a higher mortality [<xref ref-type="bibr" rid="pmed.1004337.ref050">50</xref>]. Conversely, a study with 198,285 individuals from the UK found a positive association between sugar-sweetened beverages and mortality, but not for 100% fruit juice consumption [<xref ref-type="bibr" rid="pmed.1004337.ref051">51</xref>]. Similarly, a recent meta-analysis of prospective cohorts concluded that there was no association between 100% fruit juice consumption and all-cause mortality [<xref ref-type="bibr" rid="pmed.1004337.ref052">52</xref>]. Several studies have also found that, compared with 100% fruit juice, sugary or processed fruit beverages produce harmful glucose levels after ingestion, mainly due to the higher content of free sugars [<xref ref-type="bibr" rid="pmed.1004337.ref053">53</xref>]. In our study, bottled, sweetened, as well as fresh fruit juices were analyzed together as a unique item because of their rapid absorption [<xref ref-type="bibr" rid="pmed.1004337.ref008">8</xref>] and similar effect on postprandial glucose levels [<xref ref-type="bibr" rid="pmed.1004337.ref054">54</xref>]. Fructose intake, particularly from sugar-sweetened beverages at any dose, or from fruit juice at higher doses, contributes a rapid extra dietary energy source that could explain its detrimental effect on health [<xref ref-type="bibr" rid="pmed.1004337.ref055">55</xref>]. However, food-based dietary guidelines from various countries from Europe Union, including Spain, consent to replace occasionally 1 daily portion of fruit with fresh fruit juice [<xref ref-type="bibr" rid="pmed.1004337.ref056">56</xref>,<xref ref-type="bibr" rid="pmed.1004337.ref057">57</xref>].</p>
<p>In order to lower calorie intake and control body weight, artificially sweetened beverages could be adequate short-term substitutes [<xref ref-type="bibr" rid="pmed.1004337.ref058">58</xref>]. However, when considering the long-term influence of artificially sweetened beverage consumption, several studies have found associations with higher obesity, hypertension, type 2 diabetes, stroke, cardiovascular disease incidence and mortality, and all-cause mortality [<xref ref-type="bibr" rid="pmed.1004337.ref059">59</xref>–<xref ref-type="bibr" rid="pmed.1004337.ref061">61</xref>]. Since these beverages contain few to no calories nor sugars [<xref ref-type="bibr" rid="pmed.1004337.ref051">51</xref>], some investigations have related them with weight gain as a result of an increased consumption of sweet food due to a greater affinity for sweet flavors or the perception of eating fewer calories [<xref ref-type="bibr" rid="pmed.1004337.ref062">62</xref>]. In addition, their flavoring components have been associated with the formation of advanced glycation end-products [<xref ref-type="bibr" rid="pmed.1004337.ref063">63</xref>], which are involved in the development of metabolic diseases [<xref ref-type="bibr" rid="pmed.1004337.ref064">64</xref>]. Moreover, some sweeteners such as sucralose and saccharin could induce glucose intolerance and alterations in gut microbiota [<xref ref-type="bibr" rid="pmed.1004337.ref065">65</xref>] that are linked to obesity [<xref ref-type="bibr" rid="pmed.1004337.ref066">66</xref>].</p>
<p>In literature, a low to moderate alcohol consumption is related to a reduction in all-cause mortality [<xref ref-type="bibr" rid="pmed.1004337.ref067">67</xref>]. Biological explanations for this protective role on health are based on lipid regulation, insulin response, and endothelial function [<xref ref-type="bibr" rid="pmed.1004337.ref068">68</xref>] resulting from the modulation of some anti-inflammatory biomarkers [<xref ref-type="bibr" rid="pmed.1004337.ref069">69</xref>]. However, at high doses, alcohol is detrimental to cardiovascular health and is related to several types of cancer [<xref ref-type="bibr" rid="pmed.1004337.ref070">70</xref>]. A harmful alcohol consumption is associated to neurodegenerative processes [<xref ref-type="bibr" rid="pmed.1004337.ref071">71</xref>], microbial dysbiosis [<xref ref-type="bibr" rid="pmed.1004337.ref070">70</xref>], and an increased intestinal permeability that leads to a permanent hepatic exposure to bacterial translocation, oxidative stress, and other inflammatory components [<xref ref-type="bibr" rid="pmed.1004337.ref072">72</xref>]. In addition, alcohol use could result in hepatic steatosis and de novo lipogenesis, and also could reduce the utilization of lipids [<xref ref-type="bibr" rid="pmed.1004337.ref073">73</xref>]. Lastly, alcohol may injure myocardium with potential cardiomyopathy and heart failure [<xref ref-type="bibr" rid="pmed.1004337.ref074">74</xref>], and increase the risk of hypertension [<xref ref-type="bibr" rid="pmed.1004337.ref075">75</xref>]. On the other hand, the beneficial association of alcohol consumption with mortality found in some studies may rely on abstinence bias, insufficient adjustment for covariates, or consumption changes due to disease detection [<xref ref-type="bibr" rid="pmed.1004337.ref076">76</xref>,<xref ref-type="bibr" rid="pmed.1004337.ref077">77</xref>]. However, recent studies from an epigenetic perspective have proposed that alcohol at restricted doses could be particularly beneficial in older adults based on changes in alcohol metabolism related to age [<xref ref-type="bibr" rid="pmed.1004337.ref078">78</xref>]. In this regard, a meta-analysis for the Global Burden of Disease Study has proposed a change from sex-specific to age-specific recommendations on alcohol consumption [<xref ref-type="bibr" rid="pmed.1004337.ref079">79</xref>]. Then, low alcohol consumption could be beneficial among older adults, but not for younger adults. For our analyses, we considered that being a heavy drinker was harmful because of its well-established association.</p>
<p>In a previous study, a 10-item Healthy Beverage Index was constructed using an a priori approach based on the US recommendations for beverage consumption [<xref ref-type="bibr" rid="pmed.1004337.ref011">11</xref>]. Similar to this index, Hu and colleagues described the HBS as a more suitable score for use in large epidemiological studies [<xref ref-type="bibr" rid="pmed.1004337.ref012">12</xref>]. The HBS excluded water consumption as well as 2 items on total energy from beverages and calculations of daily fluid intake. In our study, the same scoring weights (from 1 to 4) were maintained for all items in the HBS. However, in contrast to Hu and colleagues, we considered both no alcohol consumption and moderate alcohol consumption as healthy. Additionally, we also modified the HBS cut-off points of the items to fit with the beverage consumption of the Spanish adult population. As a result, the HBS used in our study retained the same items as originally described by Hu and colleagues, as well as the relative weight of the items.</p>
<p>The use of the HBS as an overall measure of beverage consumption has several advantages. First, the use of this score overcomes the limitations of analyses of relationships between individual beverages and diseases, as beverage consumption may be correlated, and an increase in consumption of one beverage may be associated with a decrease in the others. Secondly, the HBS could be a complementary tool for assessing adherence to dietary patterns that include only solid foods, in order to assess dietary quality as a whole. Thus, the HBS could serve as a simple and rapid screener to obtain information on the quality of beverage consumption from the general population, similar to other indexes used to assess adherence to certain diets, such as the Mediterranean diet. Also, the use of this 7-item pattern may be an optimal choice when dealing with patients in time-constrained clinical settings. Finally, the HBS includes items on commonly consumed beverages and could be easily adapted to other populations with only minor modifications to account for their specific beverage consumption.</p>
<p>We have used the HBS in the general population and caution should be exercised in deriving beverage consumption recommendations from this score in specific populations, especially those with restricted fluid requirements, long-term liquid diets, and other preexisting conditions involving fluid consumption. Further studies are also needed in specific population subgroups. In addition, a future study could consider intercorrelations and specific population-based patterns of beverage consumption using an a posteriori approach.</p>
<p>This study has some limitations. First, when measuring diet, non-differential misclassification of the exposure is always possible, in general, resulting in an underestimation of the associations found. Second, no information concerning behavioral changes or repeated measurements on beverage consumption were available, and beverage consumption could have changed during follow-up. Third, water consumption was not available in this study. Water is universally recommended as a safe beverage and as the main source of hydration. As water does not provide energy, macronutrients or micronutrients, its consumption is considered free for the general population.</p>
<p>There are also some strengths. To our knowledge, this is the first examination on the relation between a healthy beverage score and all-cause mortality among the Spanish adult population. In addition, we used a dietary history that allowed us to collect information on beverages with validity and reproducibility in a Spanish population. Also, the national vital statistics records, accessed through linkage to the Spanish National Death Index, ensured an extensive follow-up of the cohort for mortality assessment. Finally, several confounders were considered in more adjusted models.</p>
<p>In conclusion, in this representative study of the Spanish adult population, higher adherence to the HBS was associated with a reduction in total mortality. As the consumption of a healthy solid diet should be encouraged, adherence to a healthy beverage consumption pattern may also play an important role in the prevention of premature mortality as part of public health nutrition prevention strategies.</p>
</sec>
<sec id="sec018" sec-type="supplementary-material">
<title>Supporting information</title>
<supplementary-material id="pmed.1004337.s001" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s001" xlink:type="simple">
<label>S1 STROBE Checklist</label>
<caption>
<title>STROBE Checklist.</title>
<p>(DOCX)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s002" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s002" xlink:type="simple">
<label>S1 Text</label>
<caption>
<title>Prespecified analysis plan and modifications.</title>
<p>(DOCX)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s003" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s003" xlink:type="simple">
<label>S1 Table</label>
<caption>
<title>Sex-specific cut-off points for individual items of the Healthy Beverage Score (HBS) in the ENRICA Study (2008–2010) (N = 12,161).</title>
<p>(DOCX)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s004" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s004" xlink:type="simple">
<label>S2 Table</label>
<caption>
<title>Mortality risk according to quartiles of the adherence to the Healthy Beverage Score (HBS) in the ENRICA Study from baseline (2008–2010) to January 2022 (N = 12,161) excluding the first 3 years of follow-up.</title>
<p>(DOCX)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s005" mimetype="image/tiff" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s005" xlink:type="simple">
<label>S1 Fig</label>
<caption>
<title>Flow diagram.</title>
<p>(TIF)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s006" mimetype="image/tiff" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s006" xlink:type="simple">
<label>S2 Fig</label>
<caption>
<title>Unadjusted restricted cubic splines of the association of the Healthy Beverage Score (HBS) with mortality risk in the ENRICA Study from baseline (2008-2010) to January 2022 (N = 12,161).</title>
<p>Lines are restricted cubic splines, showing the dose-response association of the Healthy Beverage Score (HBS) with mortality. The solid line represents the hazard ratio (HR), and the dashed lines indicate the lower and upper 95% confidence intervals. The knots were located at the 10th, 50th, and 90th percentiles (corresponding to HBS scores 17, 22 and 25, respectively). p for non-linearity = 0.003.</p>
<p>(TIF)</p>
</caption>
</supplementary-material>
<supplementary-material id="pmed.1004337.s007" mimetype="image/tiff" position="float" xlink:href="info:doi/10.1371/journal.pmed.1004337.s007" xlink:type="simple">
<label>S3 Fig</label>
<caption>
<title>Unadjusted mortality risk for individual items of the Healthy Beverage Score (HBS) when comparing extreme categories (quartile 4 vs. quartile 1) in the ENRICA Study from baseline (2008-2010) to January 2022 (N = 12,161).</title>
<p>HBS, Healthy Beverage Score; HR, hazard ratio; CI, confidence interval.</p>
<p>(TIF)</p>
</caption>
</supplementary-material>
</sec>
</body>
<back>
<glossary>
<title>Abbreviations</title>
<def-list>
<def-item><term>BMI</term>
<def><p>body mass index</p></def>
</def-item>
<def-item><term>CI</term>
<def><p>confidence interval</p></def>
</def-item>
<def-item><term>ENRICA</term>
<def><p>Study on Nutrition and Cardiovascular Risk in Spain</p></def>
</def-item>
<def-item><term>HBS</term>
<def><p>Healthy Beverage Score</p></def>
</def-item>
<def-item><term>HR</term>
<def><p>hazard ratio</p></def>
</def-item>
<def-item><term>METs-hour/week</term>
<def><p>metabolic equivalents in hours per week</p></def>
</def-item>
<def-item><term>SD</term>
<def><p>standard deviation</p></def>
</def-item>
</def-list>
</glossary>
<ref-list>
<title>References</title>
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<named-content content-type="letter-date">14 Jul 2023</named-content>
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<p>Dear Dr Guallar-Castillón, </p>
<p>Thank you for submitting your manuscript entitled "Adherence to a healthy beverage score is associated with lower mortality in the adult population of Spain" for consideration by PLOS Medicine.</p>
<p>Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.</p>
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<named-content content-type="letter-date">11 Oct 2023</named-content>
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<p>Dear Dr. Guallar-Castillón,</p>
<p>Thank you very much for submitting your manuscript "Adherence to a healthy beverage score is associated with lower mortality in the adult population of Spain" (PMEDICINE-D-23-01967R1) for consideration at PLOS Medicine. </p>
<p>Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:</p>
<p>[LINK]</p>
<p>In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.  </p>
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<p>PLOS Medicine</p>
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<p>-----------------------------------------------------------</p>
<p>COMMENTS FROM THE ACADEMIC EDITOR</p>
<p>I am unsure of the value because of some problematic scoring. This is not the first such study but one with a strong outcome. I do see some issues but let’s see how the authors respond.</p>
<p>COMMENTS FROM THE EDITORS</p>
<p>GENERAL</p>
<p>Please respond to all editor and reviewer requests detailed below in full.</p>
<p>The editorial team are in agreement that your manuscript could offer some contribution to the field but, we have concerns regarding novelty/advance and require that you clearly define this in the relevant parts of the manuscript.</p>
<p>We agree with the reviewers and the academic editor that clear justification for the scoring system you apply here as well as the advantages/disadvantages of the HBS as opposed to the HBI should be clearly detailed and discussed.</p>
<p>Your study is observational and therefore causality cannot be inferred. Throughout, please remove language that implies causality and refer to associations instead.</p>
<p>Please add line numbers starting at 1 on the title page and in continuous sequence throughout thereafter.</p>
<p>Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information.  Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."</p>
<p>The STROBE guideline can be found here: <ext-link ext-link-type="uri" xlink:href="http://www.equator-network.org/reporting-guidelines/strobe/" xlink:type="simple">http://www.equator-network.org/reporting-guidelines/strobe/</ext-link></p>
<p>When completing the checklist, please use section and paragraph numbers, rather than page numbers as these often change in the event of publication.</p>
<p>DATA AVAILABILITY</p>
<p>PLOS Medicine requires that the de-identified data underlying the specific results in a published article be made available, without restrictions on access, in a public repository or as Supporting Information at the time of article publication, provided it is legal and ethical to do so. Please see the policy at </p>
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<p>PLOS defines the “minimal data set” to consist of the data set used to reach the conclusions drawn in the manuscript with related metadata and methods, and any additional data required to replicate the reported study findings in their entirety. Authors do not need to submit their entire data set, or the raw data collected during an investigation. Please submit the following data:</p>
<p>The values behind the means, standard deviations and other measures reported;</p>
<p>The values used to build graphs;</p>
<p>The points extracted from images for analysis.</p>
<p>The Data Availability Statement (DAS) requires revision. For each data source used in your study: </p>
<p>a) If the data are freely or publicly available, note this and state the location of the data: within the paper, in Supporting Information files, or in a public repository (include the DOI or accession number).</p>
<p>b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.</p>
<p>c) If the data are not freely available, please describe briefly the ethical, legal, or contractual restriction that prevents you from sharing it. Please also include an appropriate contact (web or email address) for inquiries (again, this cannot be a study author).</p>
<p>COMPETING INTERESTS</p>
<p>All authors must declare their relevant competing interests per the PLOS policy, which can be seen here:</p>
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<p>For authors with ties to industry, please indicate whether any of the interests has a financial stake in the results of the current study.</p>
<p>TITLE</p>
<p>Please revise your title according to PLOS Medicine's style. Your title must be nondeclarative and not a question. It should begin with main concept if possible. "Effect of" should be used only if causality can be inferred, i.e., for an RCT. Please place the study design ("A randomized controlled trial," "A retrospective study," "A modelling study," etc.) in the subtitle (ie, after a colon).</p>
<p>ABSTRACT</p>
<p>Background:</p>
<p>Line 1 – suggest ‘relationship’.</p>
<p>Abstract Methods and Findings:</p>
<p>Please ensure that all numbers presented in the abstract are present and identical to numbers presented in the main manuscript text.</p>
<p>Please include the study design (observational cohort study), population and setting and, please clearly define the main outcome measures.</p>
<p>Please quantify the main results with 95% CIs and p values. Please report p as &lt;0.001 and where higher the exact p values as p=0.002, for example. Please separate upper and lower bounds of 95% CIs with commas as opposed to hyphens as these can be confused with negative values.</p>
<p>Please include any important dependent variables that are adjusted for in the analyses.</p>
<p>Please include the actual amounts and/or absolute risk(s) of relevant outcomes not just relative risks or correlation coefficients. (example for absolute risks: PMID: 28399126). </p>
<p>In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.</p>
<p>Abstract Conclusions:</p>
<p>Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful.</p>
<p>Please interpret the study based on the results presented in the abstract, emphasizing what is new without overstating your conclusions.</p>
<p>Please avoid vague statements such as "these results have major implications for policy/clinical care".  Mention only specific implications substantiated by the results.</p>
<p>In revising your conclusions, please ensure to avoid assertions of primacy ("We report for the first time....")</p>
<p>AUTHOR SUMMARY</p>
<p>At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The authors summary should consist of 2-3 succinct bullet points under each of the following headings:</p>
<p>• Why Was This Study Done? Authors should reflect on what was known about the topic before the research was published and why the research was needed.</p>
<p>• What Did the Researchers Do and Find? Authors should briefly describe the study design that was used and the study’s major findings. Do include the headline numbers from the study, such as the sample size and key findings.    </p>
<p>• What Do These Findings Mean? Authors should reflect on the new knowledge generated by the research and the implications for practice, research, policy, or public health. Authors should also consider how the interpretation of the study’s findings may be affected by the study limitations. In the final bullet point of ‘What Do These Findings Mean?’, please describe the main limitations of the study in non-technical  language.</p>
<p>Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: <ext-link ext-link-type="uri" xlink:href="https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary" xlink:type="simple">https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary</ext-link></p>
<p>INTRODUCTION</p>
<p>As above, please indicate whether your study is novel and how you determined that. If there has been a systematic review of the evidence related to your study (or you have conducted one), please refer to and reference that review and indicate whether it supports the need for your study.</p>
<p>Please conclude the Introduction with a clear description of the study question or hypothesis as in the current version.</p>
<p>METHODS and RESULTS</p>
<p>As above, please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."</p>
<p>Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.</p>
<p>a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript. </p>
<p>b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.  </p>
<p>c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.</p>
<p>For all observational studies, in the manuscript text, please indicate: </p>
<p>(1) the specific hypotheses you intended to test, </p>
<p>(2) the analytical methods by which you planned to test them, </p>
<p>(3) the analyses you actually performed, and </p>
<p>(4) when reported analyses differ from those that were planned, transparent explanations for differences that affect the reliability of the study's results. If a reported analysis was performed based on an interesting but unanticipated pattern in the data, please be clear that the analysis was data-driven.</p>
<p>As per reviewer comments (see below), please include further details of how this score differs from the HBI and the added advantage and please justify/explain your chosen scoring system/criteria.</p>
<p>Please define the length of follow up (eg, in mean, SD, and range).</p>
<p>As for the abstract, please quantify the main results with 95% CIs and p values. Please report p as &lt;0.001 and where higher the exact p values as p=0.002, for example. Please separate upper and lower bounds of 95% CIs with commas as opposed to hyphens as these can be confused with negative values. </p>
<p>Page 13 – ‘(95% CI, 0.55-0.88, P for linear trend=.003)’ please report as follows, in line with previous formatting requests/requirements, ‘(95% CI [0.55,0.88], p for linear trend=0.003),</p>
<p>TABLES</p>
<p>Thank you for indicating that your analyses are adjusted and detailing the factors adjusted for. To help facilitate transparent data reporting, please also include unadjusted analyses for comparison.</p>
<p>Please ensure that all abbreviations re defined in the caption/footnote including those used for statistical reporting – CI, HR, BMI, for example. Please check and amend throughout including in the supporting files where relevant.</p>
<p>Throughout, please separate upper and lower bounds of 95% CIs with commas as opposed to hyphens as the latter can be confused with the reporting of negative values.</p>
<p>Table 1 – the footnote appears to detail how the scoring system is applied to different beverages. This would be better presented as a table. Please revise accordingly and in line with the guidance above regarding the requirement for more nuanced details of the scoring system.</p>
<p>Table 2 (and others) – please report p as &lt;0.001 as opposed to &lt;.001. Please check and amend throughout all sections of the manuscript including text, tables, figures including in the supporting files.</p>
<p>FIGURES</p>
<p>Please ensure that all abbreviations including those used for statistical reporting are clearly defined in an appropriate caption/footnote.</p>
<p>Please ensure that p values are reported as &lt;0.001 and where higher the exact p values as p=0.002, for example. </p>
<p>Please replace uppercase ‘P’ with lowercase ‘p’.</p>
<p>Where adjusted analyses are presented to help facilitate transparent data reporting, please also present unadjusted analyses for comparison.</p>
<p>Where 95% CIs are reported, please also report p values as detailed above.</p>
<p>DISCUSSION</p>
<p>Please present and organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion. Please refrain from using sub-headings such that the discussion reads as continuous prose.</p>
<p>Please see reviewer #2 comments detailed below regarding wider discussion of the HBS which we agree with. </p>
<p>Page 19 – please remove the sub-heading ‘conclusions’.</p>
<p>DECLARATIONS</p>
<p>Page 22 – please remove all declarations from the main manuscript and include only in the masncuript submission form. In the event of publication, these details will be compiled as metadata.</p>
<p>REFERENCES</p>
<p>PLOS uses the reference style outlined by the International Committee of Medical Journal Editors (ICMJE), also referred to as the “Vancouver” style. </p>
<p>Please see our website for further guidelines here <ext-link ext-link-type="uri" xlink:href="https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references" xlink:type="simple">https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references</ext-link></p>
<p>Journal name abbreviations should be those found in the National Center for Biotechnology Information (NCBI) databases. </p>
<p>Please ensure that all web references include an ‘Accessed [date]’ as opposed to cited. Journal publications accessed on-line should not be cited as web references but as journal publications using the format detailed above.</p>
<p>SUPPORTING INFORMATION</p>
<p>As above, please include the completed STROBE checklist as Supporting Information.  When completing the checklist please refer to section and paragraph numbers as opposed to page or line numbers as these often change in the event of publication.</p>
<p>S1 table – as for the tables in the main manuscript, please separate upper and lower CI bounds with commas as opposed to hyphens. Please replace ‘P’ with ‘p’ in the column header. Please report p &lt;0.001 and where higher the exact p value as p=0.002, for example. Please ensure to amend the footnote also. To help facilitate transparent data reporting, please also present unadjusted analyses for comparison.</p>
<p>Comments from the reviewers:</p>
<p>Reviewer #1: Alex McConnachie, Statistical Review</p>
<p>The paper by Rodríguez-Ayala looks at the association between a derived healthy drinking score and mortality, using a large cohort study from Spain. This review considers the use of statistics in the paper.</p>
<p>Overall, the analysis is very good. The use of Cox PH models is appropriate, and it is nice to not have to remind the authors to check the PH assumption. Models with different levels of adjustment for confounders are reported. the exposure of interest is analysed both in categories but also as a continuous measure using cubic splines. A landmark analysis is done to check for reverse causality. The individual components of the main exposure variable are also assessed. Everything is explained well and presented clearly. My comments are fairly minor.</p>
<p>As ever, care should be taken to avoid language that implies a causal association, such as the use of the words "resulted in", as seen in the conclusion of the abstract.</p>
<p>Missing data are imputed using stochastic regression. What is "stochastic regression"? The reference is a book that I could not access. However, looking online, it appears to be another term for multiple imputation - if so, then this would be a more widely used term.</p>
<p>One of the results in Table 4 looked odd. For younger ages, the trend is nearly significant at p&lt;0.05, with an increasing trend; for older ages, the trend is clearly decreasing. For Q3 and Q4, the confidence intervals are actually non-overlapping. Yet the interaction p-value is 0.65, which is a surprise. Could there be an error somewhere? The interaction p-values come from Wald tests, but I would normally use likelihood ratio tests in this situation. Do these give the same results?</p>
<p>There was a strange sentence in the discussion. "First, the number of deaths was low to evaluate cause specific mortality in subgroups of participants and sensitivity analyses were only performed with total mortality." As far as I can see, no analyses were done on cause specific mortality - everything was done with total mortality.</p>
<p>In Figure 1, what is the right-hand axis showing?</p>
<p>Reviewer #2: The authors explore the association of a Healthy Beverage Score with mortality risk in the ENRICA Study. Beverage consumption is an important domain of dietary intake that is often left out (with the exception of alcohol intake) of dietary guidance. Overall, the manuscript is well-written, and the study is of potential public health interest; however, there are some major concerns that should be addressed.</p>
<p>* As indicated in the text, the ENRICA study was drawn from a stratified cluster sample. Are the estimates provided weighted to be nationally representative? If so, please describe the sample weights and how they were applied. If not, was the enrolled sample in fact representative? Additional information on response rate is also needed.</p>
<p>* The text suggests that the HBS varies slightly from Duffey and Davy's Healthy Beverage Index HBI; however, the HBI is a 10-item index that includes water, total beverage energy, and fluid requirements in addition to the 7 items included in the current HBS. Please comment on these differences and how they likely impacted the scores and results.</p>
<p>* What is the rationale behind 4-point scoring system for components?  It is unclear why alcohol, fruit juice, and artificially sweetened beverages are only assigned values 1 and 4. Scoring these components as either 1 or 4 appears to have resulted in some scores being more probable than others such that Q4 has almost 900 more individuals than Q3. Additionally, according to the footnote for table 2, the quartiles are very similar for men and women. If this is correct, what is reason for sex-specific quartiles?</p>
<p>* The alcohol consumption cutoff for women does not include women who drink 20-23 g/day. As it stands, it is &lt;20 g/day for a score of 4 and &gt;=24 g/day for a score of 1. Please redefine/clarify the alcohol consumption cutoff for women. Also, alcohol consumption (table 2) seems quite low in comparison to other beverage consumption. How is alcohol intake defined, as ethanol or volume of alcoholic beverages?  were there a large proportion of zeros (i.e., nondrinkers) for certain beverages? If so, it would be more informative to show the median and IQR rather than mean (SD).</p>
<p>* It is unclear what the footnote under table 1 is specifying. Are these the scores of the participants within those tertiles and quartiles? </p>
<p>* The cutoff values for the interaction tests appear to be designed to preserve sample size in strata but do not necessarily make sense if a biological interaction is suspected. The authors should consider more clinically meaningful categories or, if statistical power is the main concern, run models with continuous variables and their interactions. </p>
<p>* Given the wide age range of the population, age-standardized descriptive statistics for table 2 would be more helpful in understanding what other covariates are potential confounders. For this reason (and given the strong potential of age to confound the HBS-mortality association), age should also be considered as the underlying time metric.</p>
<p>* Presently, one would expect multivariable adjustment for these variables to alter HR estimates; however, in Table 3, adjustment does not meaningfully shift estimates. This is surprising, given that coffee consumption, which tends to be highly correlated with smoking, is highlighted as explaining most of the association. </p>
<p>* Table 4 - the number of deaths for each quartile do not equal the number of deaths provided in table 3. If correct, please explain why the cohort is different between the full analysis and stratified analyses. </p>
<p>* Table S1: Why are there more deaths now in Quartile 1 and Quartile 3 than in the original analysis? Were the quartiles redefined for the sensitivity analysis? </p>
<p>* There is no discussion on the actual HBS scale itself, only on its component beverages. The novelty of this study is the use of a HBS score, and advantages, implications, and limitations of the score should be more directly discussed.</p>
<p>Minor comment:</p>
<p>* Visually inspecting the Schoenfeld residuals is not a statistical test for the proportional hazards assumption. Please replace the word "test" with "assess". </p>
<p>Reviewer #3: </p>
<p>The article proposes the evaluation of the association of the adherence to a Healthy Beverage Score (HBS) and all-cause mortality in the ENRICA-1 cohort, a representative sample of the adult Spanish population. The article concerns an argument of current interest and is in general clear and well-written; the data come from a well-designed and well-conducted study with updated references. Few points should be addressed before considering for publication. </p>
<p>1. In Figure 2, each item should be adjusted by the rest of the items that are part of the index (each beverage by the rest of the beverages).</p>
<p>2. Sensitivity analyses could be done for chronically ill patients to test if HBS also predicts mortality among the sickest.</p>
<p>3. Another sensitivity analysis would be to evaluate if the results are modified among those who have a low consumption of vegetables.</p>
<p>Any attachments provided with reviews can be seen via the following link:</p>
<p>[LINK]</p>
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<named-content content-type="letter-date">14 Dec 2023</named-content>
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<p>Dear Dr. Guallar-Castillón,</p>
<p>Thank you very much for re-submitting your manuscript "Association of a healthy beverage score with total mortality in the adult population of Spain: A nationwide prospective cohort study" (PMEDICINE-D-23-01967R2) for review by PLOS Medicine.</p>
<p>I have discussed the paper with my colleagues and the academic editor and it was also seen again by 2 reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.</p>
<p>The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:</p>
<p>[LINK]</p>
<p>***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***</p>
<p>In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.</p>
<p>Please also check the guidelines for revised papers at <ext-link ext-link-type="uri" xlink:href="http://journals.plos.org/plosmedicine/s/revising-your-manuscript" xlink:type="simple">http://journals.plos.org/plosmedicine/s/revising-your-manuscript</ext-link> for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.</p>
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<p>We look forward to receiving the revised manuscript by Dec 21 2023 11:59PM.   </p>
<p>Sincerely,</p>
<p>Philippa Dodd, MBBS MRCP PhD</p>
<p>PLOS Medicine</p>
<p><ext-link ext-link-type="uri" xlink:href="http://plosmedicine.org" xlink:type="simple">plosmedicine.org</ext-link></p>
<p><email xlink:type="simple">pdodd@plos.org</email></p>
<p>------------------------------------------------------------</p>
<p>COMMENTS FROM THE ACADEMIC EDITOR</p>
<p>I think the authors have taken their data as far as they can. Their measure is really quite crude but pending the reviews, I am reluctantly ok with this. The authors really must be cautious in their findings. This points toward an impact but more refined measures are needed to totally test this relationship.</p>
<p>COMMENTS FROM THE EDITORS</p>
<p>GENERAL</p>
<p>Thank you for detailed responses to previous editor and reviewer comments. </p>
<p>The Editorial team agree with the Academic Editor that you need to be cautious about how you report your findings. Specifically, we require greater consideration and contextualization of actual versus relative risk when reporting and discussing your results. Please see below for further comments which we require you address in full prior to publication.</p>
<p>TITLE</p>
<p>Please revise your title, specifically description as ‘prospective’ is misleading. Suggest the following, “Healthy beverage consumption and total mortality in Spanish adults: A nationwide cohort study”.</p>
<p>DATA AVAILABILITY STATEMENT</p>
<p>Thank you for updating your statement in line with point ‘b)’ below. Your statement requires revision. Please also indicate where these data are held i.e., where does the contact for data requests reside?</p>
<p>‘b) If the data are owned by a third party but freely available upon request, please note this and state the owner of the data set and contact information for data requests (web or email address). Note that a study author cannot be the contact person for the data.’</p>
<p>ABSTRACT</p>
<p>Line 29 – suggest removing ‘…the ENRICA-1 cohort…’</p>
<p>Line 31 – suggest making reference to the ENRICA-1 cohort here instead. Perhaps, ‘We conducted an observational cohort study leveraging data from the ENRICA-1 cohort, a study of community dwelling adults in Spain set up to…[please provide brief details]…’ . </p>
<p>Please also define the acronym, ‘ENRICA-1’ at first use.</p>
<p>Line 38 – suggest moving sentence beginning ‘The HBS ranges from…’ to line 35 following ‘…(highest adherence)’.</p>
<p>Line 45 – please place CIs in brackets as follows, ‘[0.57, 0.91]’.</p>
<p>Line 48 – this statement is true of all observational studies. Please detail the limitations of the methodology as specific to your study.</p>
<p>Line 51 – ‘…associated with a one-quarter reduction in total mortality.’ This is an overstatement of your findings. Please revise and temper this statement (and later in your discussion, please see below). </p>
<p>As above, it is vital that risk is contextualized appropriately in relation to actual and relative measures. While the reported percentage change is correct in proportional terms it does not reflect the true change in risk. Please amend (and throughout) this is a prerequisite to publication.</p>
<p>AUTHOR SUMMARY</p>
<p>Thank you for including an author summary. As written, it is lacking sufficient detail. Please revise.</p>
<p>Line 74 – this would be better placed under the section at line 79 when detailing what you did.</p>
<p>Please also revise this statement to improve clarity. Would it not be better to refer to the healthy beverage score (for consistency with the rest of the manuscript)?  Suggest, ‘We describe a healthy beverage dietary score based on patterns of consumption of low-fat milk, coffee and tea, whole-fat milk, fruit juice, artificially-sweetened beverages, sugar-sweetened beverages and alcohol.’ Or similar</p>
<p>Line 79 – what did the researchers do and find? This section is very vague. It is impossible to understand how you conducted your study in whom or in how many when reading this section. Please revise including [in brief] the critical details.</p>
<p>INTRODUCTION</p>
<p>Final paragraph – it might be helpful to indicate that you develop a modified HBS (per methods section, line 158 onwards) as part of your study.</p>
<p>METHODS and RESULTS</p>
<p>Please see reviewer comments below regarding the description (and discussion later) of the HBI and HBS which we agree with. Please revise accordingly.</p>
<p>Throughout, please ensure that you do not overstate your findings. Please ensure adequate contextualization of the actual Vs relative risk. As reported the true risk could be over interpreted.</p>
<p>Line 140 - Ethics statement – can you please detail that this statement regarding consent applies to entry into the ENRICA-2 cohort study, as we understand things. Apologies if I have misunderstood but perhaps all the more reason to clarify!</p>
<p>TABLES</p>
<p>Throughout please replace ‘Men’ and ‘Women’ with ‘Male’ and ‘Female’ respectively. Please also ensure to amend footnotes/caption where relevant.</p>
<p>Please also see statistical reviewer comments below regarding table 2, which we agree with.</p>
<p>FIGURES</p>
<p>Figure 2 (forest plot) – please revise the presentation of p values which should be reported as &lt;0.001 and where higher the exact p value as 0.442, for example. Please revise.</p>
<p>DISCUSSION</p>
<p>Please include a more nuanced discussion of the HBI Vs HBS as per reviewer comments below.</p>
<p>Line 376 – ‘In this large prospective population-based study…’ as for the title, please remove the word ‘prospective’, whilst the data from the ENRICA-1 cohort were collected prospectively, the data were analyzed retrospectively and as such your study cannot be described as a prospective study.</p>
<p>Line 378 – ‘the risk of death was reduced by a quarter’. As above please temper this statement and contextualize your findings in terms of actual risk – not just proportional change.</p>
<p>Line 380 – it be helpful to further quantify this statement based on the data generated.</p>
<p>Line 499 – please remove the word ‘remarkable’.</p>
<p>Line 501 – the phrase ‘on the other hand’ is in appropriately placed here. Please remove and perhaps replace with ‘in addition’, or similar.</p>
<p>Line 502 – what are the ‘vital statistics records’ you refer to here? This phrase has not been used elsewhere in the manuscript. Please revie and be in explicit.</p>
<p>Line 506 – as above the statement, ‘one quarter reduction in total mortality’ is an over interpretation and could be misleading, please revise.</p>
<p>SUPPORTING INFORMATION</p>
<p>Thank you for indicating that you analyses were preplanned. Did you document your analysis plan as part of funding proposal, for example? If so, please include a copy of the statistical analysis plan as supporting information.</p>
<p>Figures: S3 Figure (forest plot) – as for the main manuscript, please revise the presentation of p values which should be reported as &lt;0.001 and where higher the exact p value as 0.442, for example.</p>
<p>Tables: Throughout please replace ‘Men’ and ‘Women’ with ‘Male’ and ‘Female’ respectively. For example, S2 table. Please also ensure to amend footnotes/caption where relevant.</p>
<p>SOCIAL MEDIA</p>
<p>To help us extend the reach of your research, please detail any X (formerly Twitter) handles you wish to be included when we tweet this paper (including your own, your coauthors’, your institution, funder, or lab) in the manuscript submission form when you re-submit the manuscript.</p>
<p>COMMENTS FROM THE REVIEWERS:</p>
<p>Reviewer #1: Alex McConnachie, Statistical Review</p>
<p>I thank the authors for their consideration of my previous comments. I am satisfied with their responses. My remaining observations are very minor.</p>
<p>I note that Table 2 has been changed. It now shows standard errors for the mean of the continuous measures. I am not keen on this, since the aim of this table should be to show the distribution of the data, whereas the SE is a measure of precision. Could these SE's be replaced with standard deviations, or simply omitted, showing the mean only?</p>
<p>Incidentally, the values reported as the SE for mean age do not look right - they seem far too large.</p>
<p>There is an oddly phrased sentence on lines 308-309: "…with lower mortality among those who were chronically ill" - I believe something like "with higher HBS associated with lower mortality among those who were chronically ill, but no association in those without a chronic illness" would be more accurate.</p>
<p>Reviewer #2: The authors were generally responsive to reviewer comments. I have a few additional questions and comments for their consideration.</p>
<p>1. I disagree with the authors that the HBS varies "slightly" from HBI as there are a handful of differences that could each be considered substantial. I think the methods could simply cite the HBS applied by Hu et al and the HBI could be left to the discussion.</p>
<p>2. The authors' response to my question about the scoring rationale suggests that had there been a wider distribution of intake for either fruit juice or artificially sweetened beverages then they would have been scored similarly to the other items. It should be noted in the methods that lack of consumption of these beverages is the reason for the difference in scoring.</p>
<p>3. No where in the methods or tables do the authors define alcohol as grams of ethanol (rather than grams of alcoholic beverages). Given that all other beverages are defined based on the total beverage, and not just a component, I would expect the authors to add this detail.</p>
<p>4. The authors now use age the underlying time metric but include age as a confounder in the model. Though it wouldn't impact HRs much (if at all), I am unsure if this is a typo in the tables or in fact how the model was constructed. Also, it should be added to the footnotes that age is the underlying time metric as model 1 does account for age if this is the case.</p>
<p>5. The authors observe an inverse association among those with a chronic disease but not among those without. In line with this finding, there is an inverse association in older but not younger individuals. Given, the wide age range in this study, did the authors look at how the leading causes of deaths differed in these subgroups? Could it be that the HBS is important for chronic disease-specific death only? This is alluded to in the discussion as the potential mechanism discussed are related to chronic disease. The subgroup analyses are also somewhat at odds with the interpretation of HBS being inversely associated with "premature" mortality or mortality in the "general population".</p>
<p>6. The abstract and discussion state "a one-quarter reduction in total mortality" this is misleading as results in this analysis are relative and not absolute risk reductions.</p>
<p>Any attachments provided with reviews can be seen via the following link:</p>
<p>[LINK]</p>
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<named-content content-type="letter-date">21 Dec 2023</named-content>
</p>
<p>Dear Dr Guallar-Castillón, </p>
<p>On behalf of my colleagues and the Academic Editor, Professor Barry Popkin, I am pleased to inform you that we have agreed to publish your manuscript "Association of a healthy beverage score with total mortality in the adult population of Spain: A nationwide cohort study" (PMEDICINE-D-23-01967R3) in PLOS Medicine.</p>
<p>Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.</p>
<p>In the meantime, please log into Editorial Manager at <ext-link ext-link-type="uri" xlink:href="http://www.editorialmanager.com/pmedicine/" xlink:type="simple">http://www.editorialmanager.com/pmedicine/</ext-link>, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. </p>
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<p>To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at <ext-link ext-link-type="uri" xlink:href="https://plos.org/protocols?utm_medium=editorial-email&amp;utm_source=authorletters&amp;utm_campaign=protocols" xlink:type="simple">https://plos.org/protocols?utm_medium=editorial-email&amp;utm_source=authorletters&amp;utm_campaign=protocols</ext-link></p>
<p>Thank you again for submitting to PLOS Medicine, it has been a pleasure handling your manuscript. We look forward to publishing your paper. </p>
<p>Best wishes,</p>
<p>Pippa </p>
<p>Philippa Dodd, MBBS MRCP PhD </p>
<p>PLOS Medicine</p>
<p><email xlink:type="simple">pdodd@plos.org</email></p>
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