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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2024.1363299</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Nutrition</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Associations of dietary selenium intake with the risk of chronic diseases and mortality in US adults</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Zhang</surname> <given-names>Yuchen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2614813/overview"/>
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</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Meng</surname> <given-names>Shixin</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name><surname>Yu</surname> <given-names>Yuexin</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author">
<name><surname>Bi</surname> <given-names>Liangwen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Tian</surname> <given-names>Jihong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Zhang</surname> <given-names>Lizhen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>The Second Affiliated Hospital of Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>The Basic Medical Sciences College of Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Shanghai General Hospital</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Shanghai Jiao Tong University School of Medicine</institution>, <addr-line>Shanghai</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0002"><p>Edited by: Samrat Singh, Imperial College London, United Kingdom</p></fn>
<fn fn-type="edited-by" id="fn0003"><p>Reviewed by: Luciana Neri Nobre, Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM), Brazil</p><p>Xianli Gao, Jiangsu University, China</p></fn>
<corresp id="c001">&#x002A;Correspondence: Lizhen Zhang, <email>zhanglizhen2010@126.com</email></corresp>
<fn fn-type="equal" id="fn0001"><p><sup>&#x2020;</sup>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1363299</elocation-id>
<history>
<date date-type="received">
<day>03</day>
<month>01</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>06</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Zhang, Meng, Yu, Bi, Tian and Zhang.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Zhang, Meng, Yu, Bi, Tian and Zhang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Objective</title>
<p>Selenium is an essential micronutrient and a type of dietary antioxidant. This study aimed to investigate the associations of dietary selenium intake with the risk of human chronic disease [cardiovascular disease (CVD), diabetes mellitus (DM), and cancer] and mortality among US general adults.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>The dietary and demographic data in this study were collected from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018. Death outcomes were determined by associating with the National Death Index (NDI) records as of December 31, 2019. Logistic regression analyses were used to investigate the relationship of selenium intake with the risk of CVD, DM, and cancer. The effect of dietary selenium on all-cause and disease-specific mortality was estimated with restricted cubic spline (RCS) curves based on the univariate and multivariate Cox proportional hazard models.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>Among the 25,801 participants, dietary selenium intake was divided into quintiles (Q1&#x2013;Q5). After covariate adjustment, the results showed that the participants with higher quintiles (Q4 and Q5) of selenium intake tended to have a low risk of CVD (OR&#x2009;=&#x2009;0.97, 95% CI: 0.96, 0.99; OR&#x2009;=&#x2009;0.98, 95% CI: 0.97, 1.00, respectively). Moreover, the RCS curves showed a significant nonlinear association between selenium intake and the risk of all-cause (with a HR of 0.82, 95% CI: 0.68, 0.99) and DM-specific mortality (with the lowest HR of 0.30; 95% CI, 0.12&#x2013;0.75). Furthermore, we conducted a subgroup analysis and found a negative correlation between the highest quartile of selenium intake and all-cause mortality among participants aged 50 and above (HR&#x2009;=&#x2009;0.75, 95% CI: 0.60&#x2013;0.93, <italic>p</italic>&#x2009;=&#x2009;0.009).</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>Our results indicated that a moderate dietary selenium supplement decreased the risk of CVD and displayed a nonlinear trend in association with the risk of all-cause and DM-specific mortality among US adults. In addition, we found that participants aged 50 and older may benefit from higher selenium intake. However, these findings still need to be confirmed through further mechanism exploration.</p>
</sec>
</abstract>
<kwd-group>
<kwd>selenium intake</kwd>
<kwd>cardiovascular disease</kwd>
<kwd>diabetes mellitus</kwd>
<kwd>mortality</kwd>
<kwd>NHANES</kwd>
</kwd-group>
<contract-num rid="cn1">81972484</contract-num>
<contract-num rid="cn2">20220005</contract-num>
<contract-sponsor id="cn1">National Nature Science Foundation of China</contract-sponsor>
<contract-sponsor id="cn2">Nanjing Medical University School Fund</contract-sponsor>
<counts>
<fig-count count="3"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="65"/>
<page-count count="12"/>
<word-count count="8573"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Nutritional Epidemiology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<title>Introduction</title>
<p>Non-communicable chronic diseases (NCDs) mainly include CVD, DM and cancer, which are the leading causes of premature death and harm to human health (<xref ref-type="bibr" rid="ref1">1</xref>). Numerous studies have found that excessive accumulation of reactive oxygen species (ROS) in the human body is an important risk factor for promoting the onset of chronic diseases and endangering human health (<xref ref-type="bibr" rid="ref2">2</xref>). ROS originate from molecular oxygen, which typically includes superoxide (O2&#x00B7;&#x2013;), hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), and hydroxyl radical (&#x00B7;OH). Overproduction of ROS disrupts the normal cellular biological processes by changing the activity of various signaling pathways, such as NRF2&#x2013;KEAP1, NF-&#x03BA;B, and AMPK, and mitochondrial signaling pathways, which can cause fatal damage to cells (<xref ref-type="bibr" rid="ref3">3</xref>).</p>
<p>Previous studies have identified oxidative stress as a risk factor for the development of some NCDs, such as CVD, DM, and cancer. Atherosclerosis caused by oxidative stress and chronic inflammatory cascade reaction is the most important etiology of CVD, as reported by Macvanin et al. (<xref ref-type="bibr" rid="ref4">4</xref>). Another study indicated that one of the reasons for insulin resistance may be the destruction of pancreatic &#x03B2;-cells caused by oxidative stress. Furthermore, excessive ROS production plays a crucial role in the pathophysiology of DM, leading to cell damage and disease progression (<xref ref-type="bibr" rid="ref5">5</xref>). Chronic oxidative stress is a common condition in patients with cancers. Excessive ROS production can induce oxidative damage and inflammation, which, in turn, lead to the progression of various kinds of cancer, including hepatocellular carcinoma, lung cancer, and colon cancer (<xref ref-type="bibr" rid="ref6 ref7 ref8">6&#x2013;8</xref>).</p>
<p>Various treatment methods have been continuously proposed to reduce the global disease burden and human mortality rate. In addition to medication, dietary intervention is gradually being recognized as a reliable treatment strategy to prevent the occurrence of chronic diseases and prolong human survival (<xref ref-type="bibr" rid="ref9">9</xref>). Antioxidants can limit the adverse effects (oxidative stress and chronic inflammation) caused by ROS accumulation (<xref ref-type="bibr" rid="ref10">10</xref>). It is considered that insufficient consumption of antioxidant-rich foods, such as vegetables and fruits, is closely associated with the occurrence of NCDs, especially CVD, DM, and cancer (<xref ref-type="bibr" rid="ref11">11</xref>, <xref ref-type="bibr" rid="ref12">12</xref>). Dietary antioxidants are mainly composed of vitamin A, vitamin C, vitamin E, carotenoids, zinc, and selenium (<xref ref-type="bibr" rid="ref13">13</xref>). Related studies have found that a high-level dietary intake of vitamin A, vitamin E, and carotenoids is associated with a lower all-cause mortality compared with a low-level intake of these micronutrients (<xref ref-type="bibr" rid="ref14">14</xref>). These studies may provide individuals with a safe and convenient choice, namely, by having an adequate dietary antioxidant intake, the occurrence of NCDs and the risk of all-cause and disease-specific mortality can be reduced.</p>
<p>Selenium is a type of dietary antioxidant. Chen&#x2019;s et al. (<xref ref-type="bibr" rid="ref15">15</xref>) study showed that an appropriate concentration of selenium (6&#x2009;mg/L nanoselenium) can enhance the antioxidant activity of soy sauce. As an essential micronutrient, selenium is generally considered to play an important role in maintaining human health. A selenium supplement (nanoselenium) has been shown to improve the health of various species (<xref ref-type="bibr" rid="ref16">16</xref>). It has been proved that organic selenium is characterized by higher absorption, better antioxidant properties, and lower toxicity to the human body (<xref ref-type="bibr" rid="ref17">17</xref>). However, even though both organic and inorganic selenium exists within our daily dietary, the phenomenon of imbalanced dietary nutritional intake still exists (<xref ref-type="bibr" rid="ref18">18</xref>). Insufficient consumption of grains and animal foods leads to selenium deficiency, as the daily intake of selenium for a normal adult is approximately 75&#x2009;&#x00B1;&#x2009;1&#x2009;&#x03BC;g (<xref ref-type="bibr" rid="ref19">19</xref>). Notably, Bama Yao Autonomous County is famous for the longevity of its residents. Li et al. (<xref ref-type="bibr" rid="ref20">20</xref>) proved that a high level of selenium intake (82.54&#x2009;&#x03BC;g/day) is one of the main factors for maintaining the normal physical function and longevity of the older adults in Bama Yao County. Several studies have shown that selenium deficiency increases the risk of metabolic disorders, such as dyslipidemia, abnormal glucose metabolism, and thyroid hormone dysfunction (<xref ref-type="bibr" rid="ref21">21</xref>), through oxidative stress and abnormal activation of the PI3K/Akt signaling pathway (<xref ref-type="bibr" rid="ref22">22</xref>). Moreover, Yildirim et al. (<xref ref-type="bibr" rid="ref23">23</xref>) showed that selenium can prevent oxidative stress-related damage in acrylamide-induced testicular toxicity in rats. In another study, Ibrahim et al. (<xref ref-type="bibr" rid="ref24">24</xref>) indicated that binaphthyl diselenide could significantly restore the defects of antioxidant defense mechanisms and exert anti-ulcer activity against ethanol-induced gastric injury.</p>
<p>However, few studies are dedicated to elucidating the harm of selenium deficiency in the US population. Therefore, it is critical to explore the effects of dietary selenium intake on human health. In this study, we systematically analyzed the association of selenium intake with the occurrence and prognosis of CVD, DM, and cancer, aiming to provide a theoretical foundation for a moderate selenium supplement in daily diet.</p>
</sec>
<sec sec-type="materials|methods" id="sec6">
<title>Materials and methods</title>
<p>This is a large sample retrospective study that include 25,801 participates, with information sourced from NHANES 2007&#x2013;2018. NHANES is a comprehensive cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) in all 50 states and the District of Columbia in the United States. NHANES began in the 1980s and its data has been updated every 2&#x2009;years since the end of the 20th century. NHANES database contain a large amount of survey questionnaire data, biological samples measurement indicators, physical examination markers and other content (<xref ref-type="bibr" rid="ref25">25</xref>, <xref ref-type="bibr" rid="ref26">26</xref>). Detailed information and method of application can be obtained from the website: <ext-link xlink:href="https://www.cdc.gov/nchs/nhanes/index.htm" ext-link-type="uri">https://www.cdc.gov/nchs/nhanes/index.htm</ext-link>. The survival status and cause of death in NHANES can be determined by matching National Death Index (NDI) records (<xref ref-type="bibr" rid="ref27">27</xref>).</p>
<sec id="sec7">
<title>Study design</title>
<p>Flow chart of this study is shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>. We utilized dietary and demographic information from NHANES 2007&#x2013;2018 (<italic>N</italic>&#x2009;=&#x2009;59,842). In this analysis, CVD is defined as meeting any of the following criteria: (1) Ever told had congestive heart failure; (2) Ever told you had coronary heart disease; (3) Ever told you had angina/angina pectoris; (4) Ever told you had heart attack; (5) Ever told you had a stroke. TDM is defined as satisfy the questionnaire: (1) doctor told you have diabetes, or (2) taking insulin now. And the population who answered yes in the questionnaire: ever told you had cancer or malignancy is considered as cancer patients. Participants aged 20&#x2009;years or above were included in this study (<italic>N</italic>&#x2009;=&#x2009;36,580). Individuals who missed selenium intake information in their diet and dietary supplements, without important covariate, and those with unavailable follow-up data on survival status were excluded. Ultimately, our analysis included a total of 25,801 participants.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Flow chart of the sample selection in this study.</p>
</caption>
<graphic xlink:href="fnut-11-1363299-g001.tif"/>
</fig>
</sec>
<sec id="sec8">
<title>Measurement of selenium intake</title>
<p>The information on selenium intake in diet and dietary supplements assembled from two 24-h interviews. The first dietary recall interview was personally collected at the Mobile Examination Center (MEC), and the second interview was collected over the telephone 3 to 10&#x2009;days later. In this analysis, total intake of selenium is derived from the sum of dietary and dietary supplement intake. The average dietary selenium intake was calculated through two 24-h dietary recalls, and the first 24-h value will be used instead of the average value if information is unavailable during the second 24-h recall.</p>
</sec>
<sec id="sec9">
<title>Assessment of covariates</title>
<p>We organized and analyzed the covariates of the included population. Based on previous researches, the covariates involved in this study are as follows: age, gender (males and females), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black and Other), education level (High school graduate or above), family poverty income ratio (&#x003C;1.3; 1.3&#x2013;3.5; &#x2265;3.5), smoke status (current smoker, former smoker and never smoker), drink status (drinker and never drinker) and BMI (Underweight, Normal, Overweight and Obese). BMI was calculated as weight (kg)/[height(m)]<sup>2</sup>.</p>
</sec>
<sec id="sec10">
<title>Survival analysis</title>
<p>The outcome information of participants is recorded in National Death Index (NDI) and linked to NHANES through SEQN. The median follow-up time of this study was 81&#x2009;months. All-cause mortality includes deaths caused by any reason during the follow-up period. Specific disease related deaths were determined using ICD-10. CVD-related death was represented as ICD 054-068, cancer-related death was represented as ICD 019-043 and T2DM-related death was represented as ICD 046. There were total of 2,829 CVD-related mortality, 2,578 cancer-related mortality and 3,417 T2DM-related mortality.</p>
</sec>
<sec id="sec11">
<title>Statistical analysis</title>
<p>We extracted data from six NHANES cycles (2007&#x2013;2018) and preformed all analyses in accordance with the complex multistage probability design offered by NHANES officials. The characteristics of participants were exhibited using survey-weighted means and proportions. A total of selenium intake was divided into quintiles. Wilcoxon rank sum test and Kruskal&#x2013;Wallis test were used to compare the baseline characteristic differences between populations with different quintiles of selenium intake. We performed survey-weighted logistic regression model to illustrate the association between selenium intake and chronic disease, and survey-weighted cox proportional hazards model was used to investigate the role of dietary selenium intake in survival outcomes. Results of the model were presented as odds ratio (OR) or hazard ratio (HR), and 95% confidence interval (CI) were calculated to estimate the range of values for variables. During the process of establishing the model, we stepwise adjusted for confounding factors. Model 1 represents a crude model without any variable adjustments. In Model 2, age and gender were adjusted, while in Model 3, covariates were fully adjusted, including age, gender, race, education level, PIR, smoking status, alcohol intake, and BMI. All data were extracted and analyzed using the R software (version 4.3.0), and <italic>p</italic>&#x2009;&#x003C;&#x2009;0.05 was defined as statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="sec12">
<title>Results</title>
<sec id="sec13">
<title>Characteristics of the participants</title>
<p>The study collected data from the National Health and Nutrition Examination Survey (NHANES) 2007&#x2013;2018. A total of 25,801 US adults with dietary selenium intake information and mortality data were included in the analysis. <xref ref-type="table" rid="tab1">Table 1</xref> shows the baseline characteristics of the eligible participants. The mean age of these individuals was 47&#x2009;&#x00B1;&#x2009;17&#x2009;years, and 49% were male. Non-Hispanic White was the most common race in this research (68%). Among the subjects, people suffering from hypertension, diabetes, and CVD accounted for 32, 9.8, and 8.6%, respectively. Selenium intake was classified into quartiles (Q1&#x2013;Q5) based on the values of two 24-h dietary interviews. Compared with Q1, the participants in Q2&#x2013;Q5 tended to have higher levels of education and PIR, there were fewer smokers among them, and they were more likely to drink alcohol. There was no significant difference in body mass index (BMI) and cancer morbidity between the groups.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Characteristics of the participants in this study stratified by selenium intake.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="center" valign="top" colspan="8">Dietary selenium intake (quintile)</th>
</tr>
<tr>
<th align="left" valign="top">Characteristic</th>
<th align="center" valign="top">Overall, <italic>N</italic> =&#x2009;25,801 (100%)<sup>2</sup></th>
<th align="center" valign="top">Q1, <italic>N</italic> =&#x2009;5,680 (20%)<sup>2</sup></th>
<th align="center" valign="top">Q2, <italic>N</italic> =&#x2009;5,363 (20%)<sup>2</sup></th>
<th align="center" valign="top">Q3, <italic>N</italic> =&#x2009;5,082 (20%)<sup>2</sup></th>
<th align="center" valign="top">Q4, <italic>N</italic> =&#x2009;4,996 (20%)<sup>2</sup></th>
<th align="center" valign="top">Q5, <italic>N</italic> =&#x2009;4,680 (20%)<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value<sup>3</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">
<bold>Age (years)</bold>
</td>
<td align="center" valign="top">47 [47 (17)]</td>
<td align="center" valign="top">48 [48 (18)]</td>
<td align="center" valign="top">47 [48 (17)]</td>
<td align="center" valign="top">47 [48 (17)]</td>
<td align="center" valign="top">46 [47 (17)]</td>
<td align="center" valign="top">47 [47 (16)]</td>
<td align="center" valign="top">0.005</td>
</tr>
<tr>
<td align="left" valign="top">
<bold>Gender (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>Female</italic>
</td>
<td align="center" valign="top">13,009 (51%)</td>
<td align="center" valign="top">4,022 (75%)</td>
<td align="center" valign="top">3,269 (63%)</td>
<td align="center" valign="top">2,571 (53%)</td>
<td align="center" valign="top">2,018 (42%)</td>
<td align="center" valign="top">1,129 (24%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Male</italic>
</td>
<td align="center" valign="top">12,792 (49%)</td>
<td align="center" valign="top">1,658 (25%)</td>
<td align="center" valign="top">2,094 (37%)</td>
<td align="center" valign="top">2,511 (47%)</td>
<td align="center" valign="top">2,978 (58%)</td>
<td align="center" valign="top">3,551 (76%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Race (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>Mexican American</italic>
</td>
<td align="center" valign="top">3,745 (8.2%)</td>
<td align="center" valign="top">826 (7.7%)</td>
<td align="center" valign="top">795 (8.7%)</td>
<td align="center" valign="top">732 (7.9%)</td>
<td align="center" valign="top">722 (8.4%)</td>
<td align="center" valign="top">670 (8.1%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Other Hispanic</italic>
</td>
<td align="center" valign="top">2,551 (5.4%)</td>
<td align="center" valign="top">617 (6.0%)</td>
<td align="center" valign="top">542 (5.4%)</td>
<td align="center" valign="top">522 (5.5%)</td>
<td align="center" valign="top">474 (5.4%)</td>
<td align="center" valign="top">396 (4.8%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Non-Hispanic White</italic>
</td>
<td align="center" valign="top">11,354 (68%)</td>
<td align="center" valign="top">2,310 (66%)</td>
<td align="center" valign="top">2,343 (68%)</td>
<td align="center" valign="top">2,260 (69%)</td>
<td align="center" valign="top">2,271 (69%)</td>
<td align="center" valign="top">2,170 (70%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Non-Hispanic Black</italic>
</td>
<td align="center" valign="top">5,424 (11%)</td>
<td align="center" valign="top">1,454 (14%)</td>
<td align="center" valign="top">1,159 (11%)</td>
<td align="center" valign="top">1,008 (10%)</td>
<td align="center" valign="top">942 (9.6%)</td>
<td align="center" valign="top">861 (8.5%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Other</italic>
</td>
<td align="center" valign="top">2,727 (7.4%)</td>
<td align="center" valign="top">473 (6.5%)</td>
<td align="center" valign="top">524 (6.9%)</td>
<td align="center" valign="top">560 (7.7%)</td>
<td align="center" valign="top">587 (7.9%)</td>
<td align="center" valign="top">583 (8.2%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Educational level (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>&#x2264;High school</italic>
</td>
<td align="center" valign="top">5,879 (15%)</td>
<td align="center" valign="top">1,678 (20%)</td>
<td align="center" valign="top">1,292 (15%)</td>
<td align="center" valign="top">1,050 (13%)</td>
<td align="center" valign="top">1,014 (13%)</td>
<td align="center" valign="top">845 (12%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>&#x003E;High school</italic>
</td>
<td align="center" valign="top">19,922 (85%)</td>
<td align="center" valign="top">4,002 (80%)</td>
<td align="center" valign="top">4,071 (85%)</td>
<td align="center" valign="top">4,032 (87%)</td>
<td align="center" valign="top">3,982 (87%)</td>
<td align="center" valign="top">3,835 (88%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>PIR (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>&#x003C; 1.3</italic>
</td>
<td align="center" valign="top">8,138 (22%)</td>
<td align="center" valign="top">2,194 (28%)</td>
<td align="center" valign="top">1,806 (25%)</td>
<td align="center" valign="top">1,499 (20%)</td>
<td align="center" valign="top">1,469 (21%)</td>
<td align="center" valign="top">1,170 (17%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>&#x2265;3.5</italic>
</td>
<td align="center" valign="top">7,868 (43%)</td>
<td align="center" valign="top">1,288 (35%)</td>
<td align="center" valign="top">1,521 (40%)</td>
<td align="center" valign="top">1,626 (46%)</td>
<td align="center" valign="top">1,677 (45%)</td>
<td align="center" valign="top">1,756 (51%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>1.3&#x2013;3.5</italic>
</td>
<td align="center" valign="top">9,795 (35%)</td>
<td align="center" valign="top">2,198 (37%)</td>
<td align="center" valign="top">2,036 (36%)</td>
<td align="center" valign="top">1,957 (35%)</td>
<td align="center" valign="top">1,850 (35%)</td>
<td align="center" valign="top">1,754 (32%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Smoke status (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>Current smoker</italic>
</td>
<td align="center" valign="top">5,346 (20%)</td>
<td align="center" valign="top">1,316 (25%)</td>
<td align="center" valign="top">1,097 (20%)</td>
<td align="center" valign="top">988 (19%)</td>
<td align="center" valign="top">990 (19%)</td>
<td align="center" valign="top">955 (18%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Former smoker</italic>
</td>
<td align="center" valign="top">6,421 (25%)</td>
<td align="center" valign="top">1,211 (21%)</td>
<td align="center" valign="top">1,262 (23%)</td>
<td align="center" valign="top">1,279 (26%)</td>
<td align="center" valign="top">1,346 (28%)</td>
<td align="center" valign="top">1,323 (28%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Never smoker</italic>
</td>
<td align="center" valign="top">14,034 (55%)</td>
<td align="center" valign="top">3,153 (55%)</td>
<td align="center" valign="top">3,004 (56%)</td>
<td align="center" valign="top">2,815 (56%)</td>
<td align="center" valign="top">2,660 (53%)</td>
<td align="center" valign="top">2,402 (54%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Alcohol intake (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>Drinker</italic>
</td>
<td align="center" valign="top">17,819 (74%)</td>
<td align="center" valign="top">3,407 (66%)</td>
<td align="center" valign="top">3,512 (71%)</td>
<td align="center" valign="top">3,588 (75%)</td>
<td align="center" valign="top">3,648 (77%)</td>
<td align="center" valign="top">3,664 (82%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Never drinker</italic>
</td>
<td align="center" valign="top">7,982 (26%)</td>
<td align="center" valign="top">2,273 (34%)</td>
<td align="center" valign="top">1,851 (29%)</td>
<td align="center" valign="top">1,494 (25%)</td>
<td align="center" valign="top">1,348 (23%)</td>
<td align="center" valign="top">1,016 (18%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>BMI group (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.2</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>Normal</italic>
</td>
<td align="center" valign="top">6,768 (26%)</td>
<td align="center" valign="top">1,426 (24%)</td>
<td align="center" valign="top">1,390 (26%)</td>
<td align="center" valign="top">1,362 (26%)</td>
<td align="center" valign="top">1,333 (27%)</td>
<td align="center" valign="top">1,257 (27%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Obese</italic>
</td>
<td align="center" valign="top">10,323 (40%)</td>
<td align="center" valign="top">2,325 (41%)</td>
<td align="center" valign="top">2,196 (41%)</td>
<td align="center" valign="top">1,951 (39%)</td>
<td align="center" valign="top">1,973 (39%)</td>
<td align="center" valign="top">1,878 (40%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Overweight</italic>
</td>
<td align="center" valign="top">8,325 (32%)</td>
<td align="center" valign="top">1,824 (32%)</td>
<td align="center" valign="top">1,693 (31%)</td>
<td align="center" valign="top">1,716 (34%)</td>
<td align="center" valign="top">1,600 (32%)</td>
<td align="center" valign="top">1,492 (32%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Underweight</italic>
</td>
<td align="center" valign="top">385 (1.5%)</td>
<td align="center" valign="top">105 (1.8%)</td>
<td align="center" valign="top">84 (1.5%)</td>
<td align="center" valign="top">53 (1.2%)</td>
<td align="center" valign="top">90 (1.8%)</td>
<td align="center" valign="top">53 (1.1%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Hypertension (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.2</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>No</italic>
</td>
<td align="center" valign="top">16,356 (68%)</td>
<td align="center" valign="top">3,435 (67%)</td>
<td align="center" valign="top">3,366 (67%)</td>
<td align="center" valign="top">3,257 (69%)</td>
<td align="center" valign="top">3,270 (69%)</td>
<td align="center" valign="top">3,028 (67%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Yes</italic>
</td>
<td align="center" valign="top">9,445 (32%)</td>
<td align="center" valign="top">2,245 (33%)</td>
<td align="center" valign="top">1,997 (33%)</td>
<td align="center" valign="top">1,825 (31%)</td>
<td align="center" valign="top">1,726 (31%)</td>
<td align="center" valign="top">1,652 (33%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Cardiovascular disease (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>No</italic>
</td>
<td align="center" valign="top">22,972 (91%)</td>
<td align="center" valign="top">4,901 (89%)</td>
<td align="center" valign="top">4,765 (91%)</td>
<td align="center" valign="top">4,550 (92%)</td>
<td align="center" valign="top">4,519 (93%)</td>
<td align="center" valign="top">4,237 (92%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Yes</italic>
</td>
<td align="center" valign="top">2,829 (8.6%)</td>
<td align="center" valign="top">779 (11%)</td>
<td align="center" valign="top">598 (8.8%)</td>
<td align="center" valign="top">532 (8.3%)</td>
<td align="center" valign="top">477 (7.3%)</td>
<td align="center" valign="top">443 (8.1%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Diabetes (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.037</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>No</italic>
</td>
<td align="center" valign="top">22,384 (90%)</td>
<td align="center" valign="top">4,814 (89%)</td>
<td align="center" valign="top">4,615 (89%)</td>
<td align="center" valign="top">4,441 (91%)</td>
<td align="center" valign="top">4,395 (91%)</td>
<td align="center" valign="top">4,119 (91%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Yes</italic>
</td>
<td align="center" valign="top">3,417 (9.8%)</td>
<td align="center" valign="top">866 (11%)</td>
<td align="center" valign="top">748 (11%)</td>
<td align="center" valign="top">641 (9.3%)</td>
<td align="center" valign="top">601 (9.3%)</td>
<td align="center" valign="top">561 (8.9%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<bold>Cancer (%)</bold>
</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td align="center" valign="top">0.6</td>
</tr>
<tr>
<td align="left" valign="top">
<italic>No</italic>
</td>
<td align="center" valign="top">23,223 (89%)</td>
<td align="center" valign="top">5,082 (89%)</td>
<td align="center" valign="top">4,796 (89%)</td>
<td align="center" valign="top">4,604 (90%)</td>
<td align="center" valign="top">4,505 (90%)</td>
<td align="center" valign="top">4,236 (90%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">
<italic>Yes</italic>
</td>
<td align="center" valign="top">2,578 (11%)</td>
<td align="center" valign="top">598 (11%)</td>
<td align="center" valign="top">567 (11%)</td>
<td align="center" valign="top">478 (10%)</td>
<td align="center" valign="top">491 (10%)</td>
<td align="center" valign="top">444 (10%)</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Q1, quintile 1; Q2&#x2013;Q5, higher quintiles; PIR, poverty index; BMI, body mass index. <sup>2</sup>The percentage of participates with different quintiles of selenium intake to the total number of participants. <sup>3</sup>The <italic>p</italic>-value of Wilcoxon rank sum test and Kruskal Wallis test for calculating baseline characteristic differences between populations with different quintiles of selenium intake.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec14">
<title>Association between selenium intake and human chronic disease</title>
<p>A multivariate logistic regression model was created to evaluate the relationship between selenium intake and human chronic diseases, such as CVD, DM, and cancer (<xref ref-type="table" rid="tab2">Table 2</xref>). In the crude model, compared with Q1, the risk of CVD decreased in participants with higher quintiles (Q2&#x2013;Q5) of selenium consumption (with OR&#x2009;=&#x2009;0.98, 95% CI: 0.97&#x2013;1.00, <italic>p</italic>&#x2009;=&#x2009;0.022; OR&#x2009;=&#x2009;0.98, 95% CI: 0.96&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.008; OR&#x2009;=&#x2009;0.97, 95% CI: 0.95&#x2013;0.98, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001; OR&#x2009;=&#x2009;0.98, 95% CI: 0.96&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.005, respectively). In Model 2, adjusted for age and gender, the OR of CVD still decreased with increasing selenium intake (the ORs for Q1&#x2013;Q4 are 0.98, 95% CI: 0.97&#x2013;1.00, <italic>p</italic>&#x2009;=&#x2009;0.025; 0.98, 95% CI: 0.96&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.008; 0.97, 95% CI: 0.95&#x2013;0.98, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001 and 0.97, 95% CI: 0.95&#x2013;0.99, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001, respectively). In addition, the results of the logistic regression analysis showed that the risk of DM decreased when selenium intake reached 0.12920 (g) and above (Q4 and Q5), regardless of age and gender adjustment (the ORs for Q4 and Q5 in Model 1 are 0.98, 95% CI: 0.97&#x2013;1.00, <italic>p</italic>&#x2009;=&#x2009;0.047; 0.98, 95% CI: 0.97&#x2013;1.00, <italic>p</italic>&#x2009;=&#x2009;0.009, respectively, and in Model 2 are 0.98, 95% CI: 0.97&#x2013;1.00, <italic>p</italic>&#x2009;=&#x2009;0.039; 0.98, 95% CI: 0.96&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.001, respectively). After adjusting all the possible confounders (Model 3), Q4 and Q5 of selenium intake still had a protective effect on preventing the occurrence of CVD. However, selenium supplementation did not show a statistically significant reduction in OR for DM when all of the suspected covariates were adjusted. Moreover, there was no statistically significant correlation between any level of selenium consumption and cancer risk.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>The association between dietary selenium intake and the risk of CVD, TDM and cancer.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Dietary selenium intake (quintile)</th>
<th align="center" valign="top" colspan="3">Model 1</th>
<th align="center" valign="top" colspan="3">Model 2</th>
<th align="center" valign="top" colspan="3">Model 3</th>
</tr>
<tr>
<th align="center" valign="top">Exp (Beta)</th>
<th align="center" valign="top">95% CI<sup>1</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">Exp (Beta)</th>
<th align="center" valign="top">95% CI<sup>1</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">Exp (Beta)</th>
<th align="center" valign="top">95% CI<sup>1</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" colspan="10">
<bold>The risk of CVD</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.022</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.025</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.11</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.96, 0.99</td>
<td align="center" valign="top">0.008</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.96, 0.99</td>
<td align="center" valign="top">0.008</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.10</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.97</td>
<td align="center" valign="top">0.95, 0.98</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.97</td>
<td align="center" valign="top">0.95, 0.98</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.97</td>
<td align="center" valign="top">0.96, 0.99</td>
<td align="center" valign="top">0.002</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.96, 0.99</td>
<td align="center" valign="top">0.005</td>
<td align="center" valign="top">0.97</td>
<td align="center" valign="top">0.95, 0.99</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.038</td>
</tr>
<tr>
<td align="left" valign="top" colspan="10">
<bold>The risk of TDM</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.4</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.99, 1.01</td>
<td align="center" valign="top">&#x003E;0.9</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.7</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.01</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.99, 1.02</td>
<td align="center" valign="top">0.8</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.6</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.13</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.99, 1.02</td>
<td align="center" valign="top">0.5</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.9</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.01</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">1.01</td>
<td align="center" valign="top">0.99, 1.03</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.99, 1.02</td>
<td align="center" valign="top">0.8</td>
</tr>
<tr>
<td align="left" valign="top" colspan="10">
<bold>The risk of cancer</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.7</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.8</td>
<td align="center" valign="top">1.00</td>
<td align="center" valign="top">0.99, 1.02</td>
<td align="center" valign="top">0.7</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.060</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.056</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.4</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.047</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.039</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.98, 1.01</td>
<td align="center" valign="top">0.3</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.009</td>
<td align="center" valign="top">0.98</td>
<td align="center" valign="top">0.96, 0.99</td>
<td align="center" valign="top">0.001</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.97, 1.00</td>
<td align="center" valign="top">0.10</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Model 1: crude model. Model 2: adjusted for age and gender. Model 3: adjusted for age, gender (male and female), race (non-Hispanic white, non-Hispanic black, Mexican American, Other Hispanic, or other ethnicity), educational level (&#x2264;high school, or &#x003E;high school), family poverty income ratio (&#x003C;1.3, 1.3&#x2013;3.5, or &#x003E;3.5), smoking status (current smoker, former smoker, or never smoker), alcohol intake (drinker, or never drinker), body mass index (underweight, normal overweight, or obese). <sup>1</sup>CI, confidence interval; Q1, quintile 1; Q2&#x2013;Q5, higher quintiles; CVD, cardiovascular disease; TDM, Diabetes mellitus.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec15">
<title>Association between selenium intake with all-cause and disease-specific mortality</title>
<p>The participants in this study had a median follow-up period of 81&#x2009;months, and a total of 2,436 deaths events were recorded at the endpoint. As shown in <xref ref-type="table" rid="tab3">Table 3</xref>, the association between selenium intake and the risk of all-cause and disease-specific mortality was calculated based on a multivariate Cox proportional hazard model. The dietary selenium intake showed a significant negative correlation with the all-cause mortality risk in the unadjusted model (Model 1). Compared with Q1 (reference), Q2&#x2013;Q5 of selenium intake were linked with reduced mortality risk, with all <italic>p</italic>-values &#x003C;0.05. These correlations also were observed in Model 2 (adjusting for age and race). The results of Model 3 (adjusting for all covariates) indicated that Q5 selenium intake was significantly correlated with a lower risk of all-cause mortality (HR&#x2009;=&#x2009;0.82, 95% CI: 0.68&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.037). After adjusting for all covariates, the disease-specific mortality results indicated that moderate to high levels of selenium intake were associated with decreased risk of CVD and T2DM-specific death. In detail, the participants with Q4 selenium intake had a lower HR for CVD-specific mortality compared with the participants in Q1 (HR&#x2009;=&#x2009;0.40, 95% CI: 0.16&#x2013;0.97, <italic>p</italic>&#x2009;=&#x2009;0.043). Moreover, the DM-specific HR was decreased in participants with Q2&#x2013;Q4 of selenium (HR&#x2009;=&#x2009;0.30, 95% CI: 0.12&#x2013;0.75, <italic>p</italic>&#x2009;=&#x2009;0.011; HR&#x2009;=&#x2009;0.44, 95% CI: 0.21&#x2013;0.94, <italic>p</italic>&#x2009;=&#x2009;0.035; HR&#x2009;=&#x2009;0.36, 95% CI: 0.14&#x2013;0.92, <italic>p</italic>&#x2009;=&#x2009;0.033, respectively). On the contrary, there was no significant correlation among the different groups in the crude model of CVD and DM mortality analysis. This may be attributed to smoking, alcohol consumption, and BMI, which are considered important risk factors for accelerating the progression of CVD and DM. After adjusting for these variables, the protective effect of selenium on disease-specific mortality can be more clearly demonstrated. However, there were no significant associations between selenium intake and cancer-related mortality in either the original model or the model adjusted for confounding factors.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Association between selenium intake with all-cause and disease specific mortality.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Dietary selenium intake (quintile)</th>
<th align="center" valign="top" colspan="3">Model 1</th>
<th align="center" valign="top" colspan="3">Model 2</th>
<th align="center" valign="top" colspan="3">Model 3</th>
</tr>
<tr>
<th align="center" valign="top">HR<sup>1</sup></th>
<th align="center" valign="top">95% CI<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">HR<sup>1</sup></th>
<th align="center" valign="top">95% CI<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">HR<sup>1</sup></th>
<th align="center" valign="top">95% CI<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" colspan="10">
<bold>All-cause mortality</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.83</td>
<td align="center" valign="top">0.70, 0.99</td>
<td align="center" valign="top">0.035</td>
<td align="center" valign="top">0.83</td>
<td align="center" valign="top">0.71, 0.96</td>
<td align="center" valign="top">0.013</td>
<td align="center" valign="top">0.86</td>
<td align="center" valign="top">0.73, 1.00</td>
<td align="center" valign="top">0.056</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.74</td>
<td align="center" valign="top">0.63, 0.88</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.74</td>
<td align="center" valign="top">0.63, 0.88</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.71, 1.01</td>
<td align="center" valign="top">0.070</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.79</td>
<td align="center" valign="top">0.66, 0.93</td>
<td align="center" valign="top">0.006</td>
<td align="center" valign="top">0.80</td>
<td align="center" valign="top">0.68, 0.95</td>
<td align="center" valign="top">0.013</td>
<td align="center" valign="top">0.92</td>
<td align="center" valign="top">0.78, 1.09</td>
<td align="center" valign="top">0.3</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.67</td>
<td align="center" valign="top">0.56, 0.79</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.68</td>
<td align="center" valign="top">0.57, 0.81</td>
<td align="center" valign="top">&#x003C;0.001</td>
<td align="center" valign="top">0.82</td>
<td align="center" valign="top">0.68, 0.99</td>
<td align="center" valign="top">0.037</td>
</tr>
<tr>
<td align="left" valign="top" colspan="10">
<bold>CVD-specific mortality</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">1.51</td>
<td align="center" valign="top">0.78, 2.90</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">1.42</td>
<td align="center" valign="top">0.77, 2.62</td>
<td align="center" valign="top">0.3</td>
<td align="center" valign="top">1.10</td>
<td align="center" valign="top">0.45, 2.66</td>
<td align="center" valign="top">0.8</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.77</td>
<td align="center" valign="top">0.37, 1.59</td>
<td align="center" valign="top">0.5</td>
<td align="center" valign="top">0.76</td>
<td align="center" valign="top">0.36, 1.59</td>
<td align="center" valign="top">0.5</td>
<td align="center" valign="top">0.76</td>
<td align="center" valign="top">0.38, 1.50</td>
<td align="center" valign="top">0.4</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.59</td>
<td align="center" valign="top">0.26, 1.33</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">0.58</td>
<td align="center" valign="top">0.24, 1.42</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">0.40</td>
<td align="center" valign="top">0.16, 0.97</td>
<td align="center" valign="top">0.043</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">1.45</td>
<td align="center" valign="top">0.88, 2.38</td>
<td align="center" valign="top">0.14</td>
<td align="center" valign="top">1.36</td>
<td align="center" valign="top">0.70, 2.63</td>
<td align="center" valign="top">0.4</td>
<td align="center" valign="top">1.21</td>
<td align="center" valign="top">0.66, 2.23</td>
<td align="center" valign="top">0.5</td>
</tr>
<tr>
<td align="left" valign="top" colspan="10">
<bold>TDM-specific mortality</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.52</td>
<td align="center" valign="top">0.25, 1.07</td>
<td align="center" valign="top">0.075</td>
<td align="center" valign="top">0.46</td>
<td align="center" valign="top">0.22, 0.96</td>
<td align="center" valign="top">0.039</td>
<td align="center" valign="top">0.30</td>
<td align="center" valign="top">0.12, 0.75</td>
<td align="center" valign="top">0.011</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">1.02</td>
<td align="center" valign="top">0.55, 1.90</td>
<td align="center" valign="top">&#x003E;0.9</td>
<td align="center" valign="top">0.95</td>
<td align="center" valign="top">0.51, 1.77</td>
<td align="center" valign="top">0.9</td>
<td align="center" valign="top">0.44</td>
<td align="center" valign="top">0.21, 0.94</td>
<td align="center" valign="top">0.035</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.82</td>
<td align="center" valign="top">0.45, 1.50</td>
<td align="center" valign="top">0.5</td>
<td align="center" valign="top">0.79</td>
<td align="center" valign="top">0.44, 1.42</td>
<td align="center" valign="top">0.4</td>
<td align="center" valign="top">0.36</td>
<td align="center" valign="top">0.14, 0.92</td>
<td align="center" valign="top">0.033</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.58</td>
<td align="center" valign="top">0.31, 1.10</td>
<td align="center" valign="top">0.10</td>
<td align="center" valign="top">0.51</td>
<td align="center" valign="top">0.23, 1.14</td>
<td align="center" valign="top">0.10</td>
<td align="center" valign="top">0.52</td>
<td align="center" valign="top">0.17, 1.60</td>
<td align="center" valign="top">0.3</td>
</tr>
<tr>
<td align="left" valign="top" colspan="10">
<bold>Cancer-specific mortality</bold>
</td>
</tr>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.65, 1.51</td>
<td align="center" valign="top">&#x003E;0.9</td>
<td align="center" valign="top">0.95</td>
<td align="center" valign="top">0.64, 1.42</td>
<td align="center" valign="top">0.8</td>
<td align="center" valign="top">0.95</td>
<td align="center" valign="top">0.66, 1.38</td>
<td align="center" valign="top">0.8</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.69, 1.41</td>
<td align="center" valign="top">&#x003E;0.9</td>
<td align="center" valign="top">0.96</td>
<td align="center" valign="top">0.70, 1.34</td>
<td align="center" valign="top">0.8</td>
<td align="center" valign="top">0.94</td>
<td align="center" valign="top">0.68, 1.29</td>
<td align="center" valign="top">0.7</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.87</td>
<td align="center" valign="top">0.64, 1.19</td>
<td align="center" valign="top">0.4</td>
<td align="center" valign="top">0.88</td>
<td align="center" valign="top">0.66, 1.17</td>
<td align="center" valign="top">0.4</td>
<td align="center" valign="top">0.91</td>
<td align="center" valign="top">0.68, 1.22</td>
<td align="center" valign="top">0.5</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">1.06</td>
<td align="center" valign="top">0.72, 1.57</td>
<td align="center" valign="top">0.8</td>
<td align="center" valign="top">1.01</td>
<td align="center" valign="top">0.69, 1.47</td>
<td align="center" valign="top">&#x003E;0.9</td>
<td align="center" valign="top">1.05</td>
<td align="center" valign="top">0.71, 1.55</td>
<td align="center" valign="top">0.8</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Model 1: crude model. Model 2: adjusted for age and sex. Model 3: adjusted for age, sex (male and female), race (non-Hispanic white, non-Hispanic black, Mexican American, Other Hispanic, or other ethnicity), educational level (&#x2264; high school, or &#x003E;high school), family poverty income ratio (&#x003C;1.3, 1.3&#x2013;3.5, or &#x003E;3.5), smoking status (current smoker, former smoker, or never smoker), alcohol intake (drinker, or never drinker), body mass index (underweight, normal overweight, or obese). <sup>1</sup>HR, hazard ratio; <sup>2</sup>CI, confidence interval; Q1, quintile 1; Q2&#x2013;Q5, higher quintiles; CVD, cardiovascular disease; TDM, Diabetes mellitus.</p>
</table-wrap-foot>
</table-wrap>
<p>We performed a restricted cubic spline (RCS) to further illustrate the relationship between selenium intake and all-cause and cause-specific mortality. The results of the RCS analysis based on Cox regression models indicated that selenium had a statistically significant nonlinear correlation with all-cause and DM-specific death (<italic>p</italic> for nonlinearity in all-cause mortality&#x2009;=&#x2009;0.0289; <italic>p</italic> for nonlinearity in diabetes-specific mortality&#x2009;=&#x2009;0.0481). After multivariate adjustment, the RCS curves presented different dose-response relationships depending on the cause of death (<xref ref-type="fig" rid="fig2">Figure 2</xref>).</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>The RCS analysis between selenium intake and all-cause, CVD-specific, DM-specific and cancer-specific mortality. The red line represents hazard ratio, and blue area represent the 95% confidence interval of estimated HR. <bold>(A)</bold> All-cause mortality; <bold>(B)</bold> CVD-specific mortality; <bold>(C)</bold> DM-specific mortality; <bold>(D)</bold> cancer-specific mortality.</p>
</caption>
<graphic xlink:href="fnut-11-1363299-g002.tif"/>
</fig>
</sec>
<sec id="sec16">
<title>Subgroup analysis</title>
<p>Finally, we investigated the relationships in the subgroups divided by age and sex. A significant association was observed between the highest quartile of selenium intake and all-cause mortality among participants aged 50 and above (HR&#x2009;=&#x2009;0.75, 95% CI: 0.60&#x2013;0.93, <italic>p</italic>&#x2009;=&#x2009;0.009). No statistical interactions were found between the different quintiles of selenium intake and the risk of mortality among people younger than 50&#x2009;years. Different genders have no effect on the relationship between selenium intake and all-cause mortality (<xref ref-type="table" rid="tab4">Table 4</xref> and <xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Subgroup analyses.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Dietary selenium intake (quintile)</th>
<th align="center" valign="top" colspan="3">Age &#x003C;50</th>
<th align="center" valign="top" colspan="3">Age &#x2265;50</th>
</tr>
<tr>
<th align="center" valign="top">HR<sup>1</sup></th>
<th align="center" valign="top">95% CI<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">HR<sup>1</sup></th>
<th align="center" valign="top">95% CI<sup>2</sup></th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">1.04</td>
<td align="center" valign="top">0.62, 1.73</td>
<td align="center" valign="top">0.9</td>
<td align="center" valign="top">0.83</td>
<td align="center" valign="top">0.69, 1.01</td>
<td align="center" valign="top">0.058</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.62</td>
<td align="center" valign="top">0.34, 1.12</td>
<td align="center" valign="top">0.11</td>
<td align="center" valign="top">0.84</td>
<td align="center" valign="top">0.69, 1.01</td>
<td align="center" valign="top">0.063</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.47, 1.53</td>
<td align="center" valign="top">0.6</td>
<td align="center" valign="top">0.84</td>
<td align="center" valign="top">0.69, 1.01</td>
<td align="center" valign="top">0.059</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.66</td>
<td align="center" valign="top">0.35, 1.26</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">0.74</td>
<td align="center" valign="top">0.60, 0.92</td>
<td align="center" valign="top">0.008</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="middle" rowspan="2">Dietary selenium intake (quintile)</th>
<th align="center" valign="middle" colspan="3">Male</th>
<th align="center" valign="middle" colspan="3">Female</th>
</tr>
<tr>
<th align="center" valign="middle">HR<sup>1</sup></th>
<th align="center" valign="middle">95% CI<sup>2</sup></th>
<th align="center" valign="middle"><italic>p</italic>-value</th>
<th align="center" valign="middle">HR<sup>1</sup></th>
<th align="center" valign="middle">95% CI<sup>2</sup></th>
<th align="center" valign="middle"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Q1</td>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
<td align="center" valign="top">&#x2014;</td>
<td align="center" valign="top">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Q2</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.69, 1.04</td>
<td align="center" valign="top">0.12</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.68, 1.06</td>
<td align="center" valign="top">0.14</td>
</tr>
<tr>
<td align="left" valign="top">Q3</td>
<td align="center" valign="top">0.83</td>
<td align="center" valign="top">0.65, 1.06</td>
<td align="center" valign="top">0.13</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.69, 1.06</td>
<td align="center" valign="top">0.2</td>
</tr>
<tr>
<td align="left" valign="top">Q4</td>
<td align="center" valign="top">0.85</td>
<td align="center" valign="top">0.67, 1.09</td>
<td align="center" valign="top">0.2</td>
<td align="center" valign="top">0.99</td>
<td align="center" valign="top">0.79, 1.24</td>
<td align="center" valign="top">&#x003E;0.9</td>
</tr>
<tr>
<td align="left" valign="top">Q5</td>
<td align="center" valign="top">0.81</td>
<td align="center" valign="top">0.64, 1.04</td>
<td align="center" valign="top">0.10</td>
<td align="center" valign="top">0.74</td>
<td align="center" valign="top">0.53, 1.03</td>
<td align="center" valign="top">0.078</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Subgroup analysis of the association between selenium intake (classification) and all-cause mortality. Adjusted for age, sex, race, educational level, family poverty income ratio, smoking status, alcohol intake and body mass index.<sup>1</sup>HR, hazard ratio; <sup>2</sup>CI, confidence interval.</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Forest plot of the associations between selenium intake and the risk of all-cause mortality as age-stratified <bold>(A)</bold> and gender-stratified <bold>(B)</bold>. The value of ORs was calculated using multivariable Cox proportional hazards regression.</p>
</caption>
<graphic xlink:href="fnut-11-1363299-g003.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec17">
<title>Discussion</title>
<p>Although several studies have been conducted on the relationship between selenium intake and human health, this study has a very high sample size. In this retrospective study based on the NHANES database, we noted a negative correlation between higher levels of dietary selenium intake and the morbidity of CVD. However, this relationship was not significant in T2DM and cancer. Furthermore, our research also found that selenium intake was nonlinearly associated with all-cause mortality, CVD-related mortality, and T2DM-related mortality after multivariable adjustment. These findings provide a theoretical basis for reducing the incidence of CVD and mortality by moderate dietary selenium intake.</p>
<p>As an essential micronutrient, selenium has always been considered to play an important role in decreasing the occurrence of CVD. As early as 1979, the results of an epidemiological study reported that selenium deficiency is one of the causes of the Keshan disease, marking the first time humans noticed the relationship between selenium deficiency and human diseases (<xref ref-type="bibr" rid="ref28">28</xref>). In recent decades, several prospective studies have generally shown a significant negative correlation between dietary selenium intake or serum selenium levels and the greater protection of CVD (<xref ref-type="bibr" rid="ref29 ref30 ref31">29&#x2013;31</xref>). Selenium and selenoprotein mainly block the development of CVD by inhibiting the inflammatory response of the aortic endothelial cells, weakening ROS-induced oxidative stress, and preventing vascular calcification (<xref ref-type="bibr" rid="ref32 ref33 ref34">32&#x2013;34</xref>). In our present study, higher quintiles of dietary selenium intake resulted in the up-regulated risk of CVD, consistent with previous research. It is worth noting that after multivariate adjustment, higher levels of selenium intake were still associated with decreased ORs of CVD, indicating that selenium intake was an independent risk factor for CVD. Furthermore, the results from several randomized controlled trials and experimental studies have demonstrated that selenium consumption above 200&#x2009;&#x03BC;g/day has a linear relationship with TDM incidence (<xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref36">36</xref>). However, in our analysis, there was no statistically significant correlation between higher levels of selenium intake and the risk of TDM after covariate adjustment. This may be due to the differences in baseline characteristics among the participants included in different studies. Additionally, other studies have found that dietary selenium plays a role in preventing cancer incidence, especially colorectal cancer, prostatic cancer, and non-melanoma skin cancer (<xref ref-type="bibr" rid="ref37 ref38 ref39">37&#x2013;39</xref>). At present, some selenium-based compounds or selenium derivatives have shown excellent anticancer activity in experiments (<xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref41">41</xref>). These studies provided a basis for the application of selenium in future cancer treatment and further elucidated the protective effects of selenium on preventing cancer occurrence. However, in our study, we did not draw a similar conclusion that selenium intake can reduce the morbidity of cancer, which may be attributed to the fact that we did not make a detailed distinction between cancer types.</p>
<p>Previous studies have proven that adequate selenium consumption promotes human health by improving metabolism, enhancing immunity, and delaying aging, which is strongly negatively correlated with all-cause mortality (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref43">43</xref>). In another study, Jenkins et al. (<xref ref-type="bibr" rid="ref44">44</xref>) indicated that supplementing selenium helps to enhance the protective effect of antioxidant mixtures on cardiovascular disease and reduce all-cause mortality. Our findings also show that moderate to high levels of selenium intake reduces the risk of all-cause mortality. Some studies have shown that selenium can increase the expression and phosphorylation of endothelial nitric oxide synthase (eNOS), thereby protecting the vitality and migration ability of endothelial cells (<xref ref-type="bibr" rid="ref45">45</xref>). Supplementing selenium and coenzyme Q10 can improve the systemic redox state and is significantly associated with a reduced risk of cardiovascular mortality in older individuals (<xref ref-type="bibr" rid="ref46">46</xref>). According to the results of our Cox proportional hazards model, dietary selenium intake was nonlinearly correlated with all-cause mortality and CVD-related mortality, which is consistent with previous studies. The correlation between selenium intake and the occurrence or prognosis of diabetes has always been controversial. The results of a cross-sectional study from Wei et al. (<xref ref-type="bibr" rid="ref47">47</xref>) showed that dietary selenium intake had a moderately negative correlation with diabetes related metabolic comorbidities (MetS). A recent research conducted by Kamali et al. (<xref ref-type="bibr" rid="ref48">48</xref>) indicated that an appropriate selenium supplementation improved glucose metabolism by reducing fasting plasma glucose (FPG). While some studies even found that high selenium intake can promote insulin resistance. In this study, we observed that an appropriate higher selenium intake contributes to reducing diabetes-related mortality but has no effect on cancer-related mortality (after all covariate adjustments). Although several retrospective analyses have shown the protective effect of dietary selenium intake on cancer (especially digestive system cancers) (<xref ref-type="bibr" rid="ref49">49</xref>), whereas its underlying mechanisms are still unclear, and further exploration is required.</p>
<p>To further verify the effect of selenium on all-cause mortality in different groups, we performed a subgroup analysis. The participants in this study were stratified by age and gender. As shown in <xref ref-type="table" rid="tab4">Table 4</xref>, we found that among the individuals aged &#x2265;50&#x2009;years, the highest quintile of selenium intake was significantly correlated with a decrease in all-cause mortality. Part of the reason for this finding may be attributed to changes in the ability of older adults to utilize micronutrients. Previous studies have shown that older adult people are affected by factors such as chronic diseases and medications intake, leading to a decrease in their ability to utilize nutrition (<xref ref-type="bibr" rid="ref50">50</xref>). Additionally, the distribution of selenium in plasma selenoproteins is also influenced by age (<xref ref-type="bibr" rid="ref51">51</xref>). Blood selenium concentration is negatively correlated with the risk of malnutrition in older adults (<xref ref-type="bibr" rid="ref52">52</xref>). Insufficient selenium intake may affect physical activity and self-awareness, thereby influencing the quality of life of older individuals (<xref ref-type="bibr" rid="ref53">53</xref>). Therefore, we speculate that older adult people consuming selenium rich diets such as fish and meat may have better protective effects on health, but this hypothesis still needs further experimental verification. Meanwhile, the effect of selenium intake on all-cause mortality was similar in males and females. In summary, the results of our subgroup analysis indicated that older adults may benefit better from dietary selenium supplementation, providing a theoretical basis for rational selenium supplementation for US adults, especially the older population. In addition, we have noticed that some studies suggest a risk of selenium deficiency in the daily diet of infants and preschool children, which seriously endangers their growth, development, and health (<xref ref-type="bibr" rid="ref54">54</xref>). However, currently epidemiological studies lack of dietary selenium intake data of infants and preschool children. Therefore, it is necessary to comprehensively analyze the selenium intake status of children in different age groups in the future, and provide nutritional recommendations for whether selenium supplement is needed in the children&#x2019;s diets.</p>
<p>Selenium has a wide range of sources in our daily diet, including meat (especially beef, pork and fish) (<xref ref-type="bibr" rid="ref55">55</xref>, <xref ref-type="bibr" rid="ref56">56</xref>), nuts (<xref ref-type="bibr" rid="ref57">57</xref>), and grains (<xref ref-type="bibr" rid="ref58">58</xref>). In addition, some multivitamins, such as Australian multivitamins (Elevit) and microbial supplements, are also part of the dietary selenium source (<xref ref-type="bibr" rid="ref59">59</xref>). Environment and dietary habits are important factors affecting human selenium intake. Long-term vegetarianism or living in low-selenium areas may lead to selenium deficiency in human body (<xref ref-type="bibr" rid="ref20">20</xref>, <xref ref-type="bibr" rid="ref60">60</xref>). The World Health Organization (WHO) recommends an intake of 55&#x2013;60&#x2009;&#x03BC;g of selenium per day (<xref ref-type="bibr" rid="ref61">61</xref>). In this study, our results demonstrate that selenium intake &#x2265;0.129 &#x03BC;g/day may be a protective factor in reducing all-cause and cardiovascular disease-related mortality, consistent with previous studies. However, excessive consumption of selenium is proven to be poisonous (<xref ref-type="bibr" rid="ref62">62</xref>, <xref ref-type="bibr" rid="ref63">63</xref>). Therefore, it is not recommended for the general population to take additional selenium supplements to prevent diseases.</p>
<p>There are several advantages of this study. First, we collected information from the NHANES database covering six cycles and ultimately included 25,801 individuals, making our data in this analysis large and representative. Second, we divided selenium intake in diet and dietary supplements into quintiles and provided a detailed dose-response relationship between selenium intake and all-cause or disease-specific mortality by plotting the RCS curves. Furthermore, given the controversy over the impact of selenium intake on diabetes in previous research, this study explored the relationship between the two using a large sample of people and found that higher levels of selenium intake can reduce the risk of diabetes-specific mortality. Although the potential mechanisms of this conclusion still need to be explored, our research undoubtedly provides a new perspective for dietary intervention in diabetes patients. Finally, this study systematically analyzed the impact of selenium intake on the occurrence, development, and prognosis of three major chronic diseases (CVD, TDM and cancer) in humans, emphasizing the importance of moderate selenium intake in diet and dietary supplements for maintaining human health.</p>
<p>Admittedly, our study also has several deficiencies. First, this is a retrospective study, and the research findings may have been influenced by unmeasured and unnoticed biases and confounding factors, which cannot be completely avoided despite complex statistical adjustments. Second, the dietary data in this study are sourced from 24-h telephone recalls and do not represent the long-term selenium intake of the participants. Therefore, the conclusions of this study need to be carefully interpreted. Third, when exploring the correlation between selenium intake and the occurrence or prognosis of cancer, we did not provide a detailed classification of cancer types. Cancer is a highly heterogeneous disease (<xref ref-type="bibr" rid="ref64">64</xref>, <xref ref-type="bibr" rid="ref65">65</xref>); thus, our conclusion may dilute the relationship between the two. Finally, the participants in this study are adults in the US. In the future, we need to further explore whether these conclusions are applicable to other populations, especially children.</p>
</sec>
<sec sec-type="conclusions" id="sec18">
<title>Conclusion</title>
<p>The findings suggest that moderate to high levels of selenium (&#x2265; 0.129&#x2009;&#x03BC;g/day) intake are associated with decreased risk for CVD incidence. In addition, appropriate selenium intake may be a protective factor in reducing the risk of all-cause and CVD-related mortality. Therefore, dietary intervention based on the consumption of a healthy diet rich in selenium is a promising measure for maintaining health and prolonging survival time for US adults.</p>
</sec>
<sec sec-type="data-availability" id="sec19">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: <ext-link xlink:href="https://www.cdc.gov/nchs/nhanes/index.htm" ext-link-type="uri">https://www.cdc.gov/nchs/nhanes/index.htm</ext-link>.</p>
</sec>
<sec sec-type="ethics-statement" id="sec20">
<title>Ethics statement</title>
<p>The studies involving humans were approved by the National Center for Health Statistics, CDC. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="sec21">
<title>Author contributions</title>
<p>YZ: Formal analysis, Methodology, Writing &#x2013; original draft. SM: Methodology, Software, Visualization, Writing &#x2013; original draft. YY: Formal analysis, Investigation, Writing &#x2013; review &#x0026; editing. LB: Methodology, Writing &#x2013; review &#x0026; editing. JT: Conceptualization, Formal analysis, Investigation, Writing &#x2013; review &#x0026; editing. LZ: Supervision, Validation, Writing &#x2013; review &#x0026; editing.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec22">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work was supported by the National Nature Science Foundation of China (81972484), Nanjing Medical University School Fund (20220005).</p>
</sec>
<ack>
<p>The authors thank all participants in NHANES and the staff of NHANES.</p>
</ack>
<sec sec-type="COI-statement" id="sec23">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="sec24">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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