Comparison of energy and nutrient intake with dietary guidance recommendations for older adults in rural communities and its relationship with oxidative stress levels: A latent class analysis study

Comparison of energy and nutrient intake with dietary guidance recommendations for older adults in rural communities and its relationship with oxidative stress levels: A latent class analysis study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison of energy and nutrient intake with dietary guidance recommendations for older adults in rural communities and its relationship with oxidative stress levels: A latent class analysis study Weijuan Kong, Ting Jiang, Yanhua Ning, Xiongxiong LYU, Meiman Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3939030/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The level of oxidative stress in the human body is related to diet and nutrient intake, and it is the common pathogenic mechanism of chronic diseases. Understanding the nutrient intake status and its relationship with oxidative stress is beneficial for addressing elder’s nutritional issues in the context of aging. This study aimed to describe the status of energy intake and intake of different nutrients and their relationship with oxidative stress through latent class analysis. Methods We invited 376 older residents from 3 rural communities to complete a questionnaire survey and collect blood samples in Ningxia Hui Autonomous Region, China, between April and August 2021. The participants completed questionnaires regarding their general characteristics, and dietary status, and venous blood was collected to detect biomarkers of oxidative stress. Latent class analysis was employed to identify distinct energy and nutrient intake group subgroups. Results The results revealed three classes, “imbalanced nutrient—high energy” (37.50%, imbalanced in intake of energy and nutrients with high energy and protein intake), “sufficient nutrient—low energy and protein” (18.35%, sufficient and balanced intake of other nutrients except for energy and protein), and “low nutrient” (44.15%, low intake of energy and various nutrients). Among the oxidative stress biomarkers, imbalanced nutrient—high energy had higher value than did the other classes for 8-iso-PGF2 α ; sufficient nutrient—low energy and protein valued higher than imbalanced nutrient—high energy and low nutrient classes for SOD. Conclusion Oxidative stress can be measured based on the different energy and nutrient intake classes and their predictors. Nutrient intake Oxidative stress Older adults Latent class analysis Introduction The population is growing and aging worldwide. Several official investigations have shown that the proportion of people older than 65 years is supposed to double within the next 30 years, reaching 16% globally; 1 the results of the seventh national population census show that people aged 65 years accounted for 13.50% of the population in China. 2 Currently, the proportion of the population aged 65 and above in rural areas of China exceeds 17%, and its aging level far exceeds the overall national level. 3 Therefore, the health and nutritional status of older residents who live in rural communities should receive more attention in the context of healthy aging. Oxidative stress is induced by excessive levels of oxygen free radicals or reactive oxygen species (ROS) are present in the body, 4 their being highly reactive towards lipids, proteins and DNA, and severely harmful for cell survival when present at very high concentrations, both led to the concept of oxidative stress as detrimental condition occurring in all living systems and arising from the imbalance between oxidants species and antioxidant defense. 5 Many studies have shown that oxidative stress is associated with various chronic diseases. 6–9 Additionally, a study noted that oxidative stress is a central player in metabolic ailments associated with high-carbohydrate and animal-based protein diets and excessive fat consumption. 10 A deficiency of vitamin in the diet will negatively affect the antioxidant defense system, 11 and vitamin D supplementation may improve metabolic variables, and reduce oxidative stress and cardiovascular disease outcomes in certain risk groups. 12 Low folate and vitamin B 12 levels are associated with increased oxidative stress in chronic pancreatitis patients. 13 In a rat model of moderate environmental human exposure to cadmium, researchers found that zinc had a protective effect on the disruption of the oxidative/antioxidative balance. 14 Previous literatures have reported that DNA, lipids, and protein peroxidation products are commonly used to assess oxidative stress in humans. 15 According to previous relevant studies, 16–18 oxidative stress was measured using malondialdehyde (MDA), 8-iso-prostaglandin F2a (8-iso-PGF2a), superoxide dismutase (SOD), and the total antioxidant capacity (T-AOC). Among the above biomarkers, MDA is one of the most common biomarkers of protein and lipid peroxidation, 17 and 8-iso-PGF2a is considered to be the most comprehensive and reliable biomarker for evaluating oxidative damage to DNA and lipids. 19 The above two biomarkers are directly proportional to the level of oxidative stress, while SOD and T-AOC are inversely proportional to the level of oxidative stress. SOD is the most common antioxidant damage biomarker, and can reduce oxidative stress damage by clearing free hydrogen peroxide and oxygen free radicals in the body. 16 The T-AOC can better reflect the body’s antioxidant status. 18 On the basis of the 2022 version of the Dietary Guidelines for Chinese Residents, 20 the Chinese Nutrition Society released the Dietary Guidelines for the Chinese Elderly(2022), 21 which provides dietary guidance for older people aged 65 to 79 and people aged 80 and above respectively. This finding is consistent with the content of the Dietary Guide for Elderly Agents recommended by the National Health and Family Planning Commission. 22 Therefore, under the above two guidelines, our study aimed to explore the impact of energy, macronutrients, and vitamins with antioxidant functions on oxidative stress levels in the bodies of older residents living in rural areas. Latent class analysis (LCA), is a probabilistic modeling algorithm that allows clustering of data and statistical inference, and the unobserved, or “latent”, groups are inferred from patterns of the observed variables or “indicators” used in the modeling. This approach allows investigators to determine whether unmeasured or unobserved groups exist within a population. 23 24 Thus, this study intends to use latent class analysis to the latent variables of energy and nutrient intake groups in the elderly population. An important influencing factor of chronic diseases is diet and nutrient intake, and oxidative stress is a common pathogenic mechanism of chronic diseases. 25, 26 In previous studies, we demonstrated that dietary diversity and quality can affect oxidative stress levels in older adults. 27 In this study, we hypothesized that energy, macronutrient and vitamin intake combined with antioxidant function might affect the level of oxidative stress, and aimed to determine the latent classes of different energy and nutrient intakes groups in older adults through latent class analysis to explore their cross-sectional relationships with oxidative stress biomarkers. Methods Sample size calculating This study employed Study design The current study employed a descriptive cross-sectional design, and used a convenience sampling method. Setting The study was conducted at community health stations in 3 rural areas in Yinchuan and Wuzhong city, Ningxia, China, between April and August 2021. We contacted health workers and held lectures on nutritional knowledge for older residents at the above community health stations. Potential participants were provided with informational documents regarding our study, and they were given time to contemplate their participation. After the recruitment of older people and providing informed consent, the study visits were performed at the health stations. Participants Older people aged 65 years and older who were living in the rural community of Ningxia for more than one year and who voluntarily participated in this study. This study excluded participants who were diagnosed with speech and hearing disorders, cognitive dysfunction, Alzheimer’s disease or dementia; who reported a history of disease, such as severe cardiopulmonary dysfunction, kidney dysfunction, terminal stages of diseas; and who were taking immunosuppressants, vitamin C and vitamin E preparations, and other drugs that may have affected the measurement of biomarkers of oxidative stress in the past 3 months. Measures Nutritional data Data regarding diet were obtained from 3 d 24 h dietary records. The dietary records were filled in by trained investigators according to the participants’ descriptions, and the dietary models were used as a reference. The main food types included staple food and non-staple food, such as snacks, fruits, and drinks, which were eaten by the participants and their family members. The nutritional calculator developed by the Institute of Nutrition and Food Safety of the Chinese Center for Disease Control and Prevention and Beijing Feihua Communication Technology Co., Ltd. was used to input the 3 d 24 h dietary information, this information was used to determine the energy and nutrient intake of each participant. Nutrient intake grouping The use of energy, macronutrients, and vitamins was compared with the antioxidant function of participants according to the recommended intake in the Dietary Guide for Elderly Adults, 22 and the intake was divided into three groups: insufficiency, moderation, and excessiveness (Supplemental Materials Table 1 and Table 2 .). Laboratory examination Refer to relevant experimental methods, 15 after 10 h overnight fasting; participants’ venous blood was collected by qualified health workers. The blood samples were left at 37°C for 2 h and separated by centrifugation at 3000 r/min for 10 min. The liquid supernatant was extracted and stored at -80°C until analysis. The concentrations of MDA, 8-iso-PGF2a, SOD, and T-AOC in the blood were determined by the following methods. MDA: thiobarbituric acid colorimetric method (Nanjing Jiancheng Bioengineering Institute); 8-iso-PGF2a: enzyme-linked immunosorbent assay (Elabscience Biotechnology Co., Ltd); SOD: water-soluble tetrazole salt colorimetric method (Nanjing Jiancheng Bioengineering Institute); and T-AOC: chemiluminescence method (Nanjing Jiancheng Bioengineering Institute). 27, 28 Statistical analysis Latent class analysis (LCA) Using Mplus vision 8.3 for latent class analysis in examining the number of unobserved classes (the latent class of energy and nutrient intakes), the characteristics of the classes were described, and the probabilities of class memberships were estimated for each individual. 29 A latent class analysis (LCA) was performed to identify distinct homogeneous groups (latent classes), from categorical multivariate data. In the case of this study, 30 the LCA results identified specific groups of energy and nutrient intake present in the sample, and the analysis included data pertaining to meeting grouping the following grouping: ( 1 ) energy, ( 2 ) protein, ( 3 ) fat, ( 4 ) carbohydrate, ( 5 ) vitamin A, ( 6 ) vitamin C, and ( 7 ) vitamin E. Five models with 1–5 classes were tested, and model selection was based on the results of a number of fit criteria: 31, 32 Low values for the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) indicate superior model fit among competing models. The entropy value indicates the distinctiveness of the latent classes when compared to one another and values closer to one suggest clear classification. In addition, the Lo—Mendell—Rubin adjusted likelihood ratio test (LMR-A) and parametric bootstrapped likelihood ratio test (BLRT) were used to compare the k class model to the k-1 model, where k is the number of latent classes. If the probability P - value is < 0.05, the k class model is considered superior. Subsequent analysis The statistical analyses of the latent class results and oxidative stress biomarkers were performed using IBM SPSS Statistics 25.0 for Windows. The means and standard deviations were used to describe the measurement data, and the frequency, constituent ratio, and percentage were used to describe the counting data. Analysis of variance (ANOVA) was used to compare the differences between different groups of nutrient intake and oxidative stress biomarkers; comparisons between different classes were performed using the least significant difference (LSD) method of post hoc comparison. A value of 0.05 was used as a standard test; P - values are the probabilities of both sides. Results Participants and energy and nutrient intake grouping This study included blood samples and general data from 376 older residents in 3 rural communities in Ningxia. The age of the participants was 65~89 (72.06±5.95) years (Table 1). The grouping of participants’ energy and nutrient intakes is shown in Table 2. The grouping method is described in supplemental material Table 2. Table 1 Baseline characteristics of the participants（ N =376）. Variable Numbers Frequency (%) Gender Men 178 47.3 Women 198 52.7 Age（ , years） 72.06±5.95 Education Uneducated 204 54.3 Elementary 101 26.9 Intermediate and above 71 18.8 Monthly income Below 1000 273 72.6 1000-2000 44 11.7 2001 and above 59 15.7 Marital status Married with surviving spouse 293 77.9 Unmarried/divorced/widowed 83 22.1 Employment Physical labor 297 79.0 Mental labor 51 13.6 Unemployment 28 7.4 Table 2 Grouping comparison among the participants’ nutrient intake and the recommendation in the Dietary Guidelines Energy and nutrients Men Women RNI a /AI b Nutrients intake c Occupy RNI%/AI% Group RNI a /AI b Nutrients intake c Occupy RNI%/AI% Group Energy/(kcal/d) 1900 1753.79±668.54 110, excessiveness 1 2 3 1500 1576.83±586.92 110, excessiveness 1 2 3 Protein RNI/(g/d) 65 54.28±27.98 55 45.78±24.62 Fat(%E d ) 42 36.52±3.31 33 33.14±20.72 Carbohydrate(%E d ) 238 224.15±87.39 188 205.35±76.48 Vitamin A (μg RAE e /d) 800 420.64±198.90 110, excessiveness 1 2 3 700 439.85±212.56 110, excessiveness 1 2 3 Vitamin C RNI/(mg/d) 100 75.31±40.71 100 77.32±34.19 Vitamin E AI/(mgα-TE f /d) 14 11.77±8.30 14 12.62±8.78 Note. RNI a : Recommended intake; AI b : Adequate Intake; c Average intake; d %E: The percentage of total energy; e Retinol activity equivalent (RAE, μg) =Dietary or supplement source all trans retinol（μg) +1/2 Supplement Pure All Trans β- Carotene（μg)+1/12 Dietary all trans β- Carotene（μg) +1/24 Other Dietary Vitamin A Procarotenoids（μg); f α- Tocopherol equivalent（α- TE, mg), total in diet α- TE equivalent (mg)=1 ×α- Tocopherol (mg)+0.5 ×β- Tocopherol (mg)+0.1 × γ – tocopherol (mg)+0.2 ×δ- Tocopherol (mg)+0.3 ×α- Triene tocopherol (mg); *Cholesterol intake should not be excessive, so only distinguishing between moderateness and excessiveness. The participants of this study are all individuals with light physical activity Oxidative stress results The values and reference ranges of oxidative stress biomarkers among the participants are shown in Table 3. Table 3 Measurement values and reference ranges of oxidative stress biomarkers（ N =376）. Biomarkers of oxidative stress Range MDA （nmol/mL） 1.58~11.29 4.82 1.74 8-iso-PGF2 α （pg/mL） 247.59~1788.34 782.13±245.82 SOD（U/mL） 9.21~72.12 38.59 T-AOC （mmol/L） 0.60~1.34 1.00±0.15 Abbreviations: T-AOC, alternate Total Antioxidant Capacity; 8-iso-PGF2α, 15-F2t-isoprostane 8-isoprostanes-F2α; MDA, Malondialdehyde; SOD, Superoxide Dismutase. Latent class model selection The fit statistics of the LCA models are presented in Table 4. The AIC and BIC values are the smallest in class 3, the BIC value begins to increase in class 4, the LMR-LRT and BLRT values support a three-class model, and the model is characterized by sufficient Entropy (0.932). The structure of the three-class model is presented in Fig. 1. Class 1 (C1: n = 141, 37.50% of the total samples) was characterized by an imbalance in the intake of energy and nutrients, and the probability of energy and protein intake was much greater than that of other nutrients. For the purposes of this analysis, the sample was assigned to the ‘imbalanced nutrient—high energy’ class. Class 2 ( n = 69, 18.35% of the total samples) was characterized by sufficient and balanced levels of nutrients except for energy and protein. For this reason, the plants were assigned to the ‘sufficient nutrient—low energy and protein’ class. Class 3 ( n = 166, 44.15% of the total samples) was characterized by low intake of energy with significant differences in intake of various nutrients and was designated the ‘low nutrient’ class. Table 4 Results of the LCA – fit statistics（ N =376）. Model (Numbers of latent classes) LL AIC BIC aBIC Entropy LMR LR P-values BLRT P-values 1 -2269.384 4566.768 3874.935 4577.363 - - - 2 -1851.488 3760.977 4431.29 3782.925 0.899 <0.001 <0.001 3 -1735.821 3559.641 3732.543 3592.942 0.932 <0.001 <0.001 4 -1692.886 3503.772 3735.618 3548.426 0.951 <0.001 <0.001 5 -1664.006 3476.011 3766.801 3532.018 0.947 0.684 <0.001 Note. LL = Loglikelihood; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = Ajusted BIC; LMR LR = Vuong-Lo-Mendell-Rubin Likelihood Ratio Test; BLRT = Parametric Bootstrapped Likelihood Ratio Test. Class chosen is shown in bold. Oxidative stress across the latent class of energy and nutrient intake The ANOVA results showed that the concentrations of 8-iso-PGF2 α and SOD, which are oxidative stress biomarkers, were significantly different ( P < 0.05). In other words, oxidative stress is significantly different among the classes, and the post test results revealed that for 8-iso-PGF2 α , class 1 had significantly greater values than class 2 and class 3. For SOD, class 2 had significantly greater values than did class 1 and class 3. The results are shown in Table 5. Table 5 Comparison of different classes of energy and nutrient intakes with oxidative stress biomarkers. ( N = 376, x̄ ±s) Oxidative stress biomarkers Class P Difference between class C1 C2 C3 MDA（nmol/mL） 4.88±1.62 4.72±2.03 4.82±1.71 0.847 - 8-iso-PGF2 α （pg/mL） 834.59±280.37 721.03±211.65 762.96±218.85 0.004 1>2, 1>3 SOD（U/mL） 36.82±10.10 42.01±11.27 38.68±10.65 0.004 2>1, 2>3 T-AOC （mmol/L） 1.00±0.14 1.00±0.16 1.02±0.15 0.120 - Abbreviations: T-AOC, alternate Total Antioxidant Capacity; 8-iso-PGF2α, 15-F2t-isoprostane Discussion This study conducted a questionnaire survey and blood sample collection of aged residents in 3 rural communities in Ningxia, China, to determine their dietary status and oxidative stress levels. We calculated the energy and nutrient intakes of the participants through the nutrition calculator and compared them with the Dietary Guide for Elderly Adults. According to the ratio of their intake to the Recommended Intake or Adequate Intake, the participants were divided into three categories: insufficiency, moderation, and excessiveness. The results showed that the proportion of people with insufficient energy and total nutrients was the highest. Researchers have shown that older Chinese people living in the East Coast region have the highest energy intake, those living in the Western region have the lowest total energy intake, 33 and older people living in urban areas have better nutrient intake than do those living in rural areas. 34 These findings indicate that the preliminary results of our study are consistent with those of the above studies. Our results showed that the energy and nutrient intake of older adults are heterogeneous, and have potential diagnostic value. There are three types of nutrients in the population; imbalanced nutrient-high energy class, sufficient nutrient - low energy and protein class, and low nutrient class. Among the four oxidative stress biomarkers, MDA and T-AOC did not differ among the three latent classes, indicating that further exploration is needed. The imbalanced nutrient-high energy class had the highest value of 8-iso-PGF2 α , the probability of energy and protein intake was much greater than that of other nutrients, while the probability of fat and vitamin intake was slightly lower than that of sufficient nutrient-low energy and protein class. Both domestic and international studies have confirmed that vitamins and their supplements can effectively reduce the level of oxidative stress in the human body. 35-37 For diseases, existing studies have revealed that the vitamins can reduce ROS levels in chronic kidney disease patients and in those with critical illness. 38, 39 And insufficient fat intake can have adverse effects on the health of older people, 40 so a balanced diet with sufficient vitamins and moderate fat intake is beneficial for the health of the elder and can reduce their levels of oxidative stress. For SOD, the value of sufficient nutrient - low energy and protein class higher than imbalanced nutrient-high energy and low nutrient class. Different dietary nutrient patterns have different effects on metabolic syndrome incidence. 41 The specific nutrients in dietary regimen can largely influence overall aging health and changes in risk factors such as cholesterol level and blood pressure. 42 Therefore, compared to people with imbalanced or low nutrient intake, older people with sufficient or balanced nutrient intake have higher SOD concentrations and less oxidative stress damage. A review of Chinese and English literature showed that while foods such as milk and sugary beverages can increase plasma inflammatory factor or oxidative stress levels. 43 According to our study, for classes with sufficient nutrient intake and lower intake of energy and protein within a reasonable range, these conditions result in higher SOD concentrations, which are beneficial for enhancing the body’s antioxidant capacity. Most of the previous studies examining the relationship between nutrients and oxidative stress levels have focused on animal experiments on the dietary supplements of vitamins E, C, and β -carotene to reduce oxidative stress in horses 44-47 . To our knowledge, few studies have specifically focused on the nutrient intake of older residents in rural areas of Northwest China. On the basis of previous research on diet, we further explored the relationships of intake of different classes of energy, macronutrients and vitamins with antioxidant function and oxidative stress levels. Thus, our study provides a useful reference for further exploration of the effects of nutrient intake on human oxidative stress levels. Like in domestic research in China, nutrient intake needs to be strengthened for aged people. In particular, nutrients including vitamins, beta-carotene, polyphenols, selenium and zinc, are considered natural antioxidants and should be added to the daily diet for older adults. 48 Specialized knowledge of dietary intake and the role of supplements in achieving recommended intakes should be integrated into patient care. 49 Overall, balanced nutrient intake is crucial for older people, but more challenges are getting people to adhere to it, which is an important aspect that community health workers should consider in elder nursing care in the community. Limitations Despite its practical significance, this study has the following limitations. First, we investigated only individual rural communities in two cities in Ningxia; further studies are necessary to examine these findings. Second, as this was a cross-sectional study, we could not conclude causality between nutrient intake groups and oxidative stress. Third, our study concluded that nutrient intake is related only to 8-iso-PGF2 α and SOD; however, the relationships between other oxidative stress biomarkers and nutrient intake need further exploration by expanding the sample size. Conclusion In summary, we found that intake of latent classes of energy, macronutrients and vitamins is related to the level of oxidative stress, and related to two oxidative stress indicators: 8-iso-PGF2 α and SOD. The two different oxidative stress biomarkers mentioned above are related to the different types of nutrient grouping based on the dietary guidelines for elderly adults. Declarations Acknowledgement All named in this manuscript have given permission for the submission of this manuscript for publication. Author contributions Weijuan Kong, Ting Jiang and Yanhua Ning conceived the study, designed the research, Yanhua Ning obtained funding. Weijuan Kong wrote the primary drafts of the manuscript. Weijuan Kong, Xiongxiong LYU, Meiman Li, Haiyan Liu, Yahong Guo performed statistical analyses. Jing shi and Lingna Liu performed data management. All authors discussed the findings, interpretations, and cowrote the final versions of the manuscript. Funding This study was funded by the Natural Science Foundation of Ningxia Province. Fund number: 2023AAC03192. Data availability The raw data required to reproduce the above findings cannot be shared at this time due to ethical reasons. Ethical approval and consent to participate This study was approved by the Ethics Committee of Ningxia Medical University, China (approval number: Ningxia Medical University Ethics No. 2022-N046). Written informed consent was obtained from all participants before the survey. Competing interests There are no conflicts of interest in this study. References Affairs UNDoEaS. World Population Ageing 2020 Highlights: Living Arrangements of Older Persons. . New York 2020. Statistics CNBo. 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Effect of nutrition on human inflammatory and oxidative stress markers. Zhonghua Yi Xue Za Zhi. Dec 29 2020;100(48):3897-3902. Li W, He E, Zhang P, Li Y, Qiu H. Multiomics analyses uncover nanoceria triggered oxidative injury and nutrient imbalance in earthworm Eisenia fetida. J Hazard Mater. Sep 5 2022;437(1):129354-129365. Gismondi E, Daneels L, Damseaux F, Lehnert K, Siebert U, Das K. Preliminary study of oxidative stress biomarkers and trace elements in North Sea Harbour Seals. Mar Pollut Bull. Feb 2021;163(1):111905-111921. Bloomer RJ, Schriefer JHM, Gunnels TA, et al . Nutrient Intake and Physical Exercise Significantly Impact Physical Performance, Body Composition, Blood Lipids, Oxidative Stress, and Inflammation in Male Rats. Nutrients. Aug 17 2018;10(8):1109-1121. Garcia EIC, Elghandour M, Khusro A, et al . Dietary Supplements of Vitamins E, C, and β-Carotene to Reduce Oxidative Stress in Horses: An Overview. J Equine Vet Sci. Mar 2022;110(1):103863-103874. Neves MF, Cunha MR, de Paula T. Effects of Nutrients and Exercises to Attenuate Oxidative Stress and Prevent Cardiovascular Disease. Current pharmaceutical design. 2018;24(40):4800-4806. L.Ford. K, Jorgenson. DJ, J.L.Landry. E, Whiting. SJ. Vitamin and mineral supplement use in medically complex, community-living, older adults. Appl Physiol Nutr Metab. Apr 2019;44(4):450-453. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial.docx Supplementalmaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3939030","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272007875,"identity":"50eb4b3b-51b2-4729-a717-6ca0ac755e85","order_by":0,"name":"Weijuan Kong","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weijuan","middleName":"","lastName":"Kong","suffix":""},{"id":272007877,"identity":"dea342fe-c0ad-4dbe-95a0-3b00785049f7","order_by":1,"name":"Ting Jiang","email":"","orcid":"","institution":"Ningxia Medical 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06:34:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3939030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3939030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51194188,"identity":"ba52425f-c42e-4d1f-a9e3-6b5606ef1f37","added_by":"auto","created_at":"2024-02-15 18:09:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":368052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3939030/v1/3db6732f-e55a-4e1b-8ab9-9950b5d7a18f.pdf"},{"id":50983084,"identity":"db5ee905-da40-488a-be6a-6f5dbe622db1","added_by":"auto","created_at":"2024-02-12 06:47:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23978,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3939030/v1/a8dab5f7a69bb13ec474550a.docx"},{"id":50983085,"identity":"73137d04-6b1b-42d5-98b0-b0479faa5e17","added_by":"auto","created_at":"2024-02-12 06:47:52","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23978,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3939030/v1/15342e71b1c3fe0d00a8e2d5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of energy and nutrient intake with dietary guidance recommendations for older adults in rural communities and its relationship with oxidative stress levels: A latent class analysis study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe population is growing and aging worldwide. Several official investigations have shown that the proportion of people older than 65 years is supposed to double within the next 30 years, reaching 16% globally;\u003csup\u003e1\u003c/sup\u003e the results of the seventh national population census show that people aged 65 years accounted for 13.50% of the population in China.\u003csup\u003e2\u003c/sup\u003e Currently, the proportion of the population aged 65 and above in rural areas of China exceeds 17%, and its aging level far exceeds the overall national level.\u003csup\u003e3\u003c/sup\u003e Therefore, the health and nutritional status of older residents who live in rural communities should receive more attention in the context of healthy aging.\u003c/p\u003e \u003cp\u003eOxidative stress is induced by excessive levels of oxygen free radicals or reactive oxygen species (ROS) are present in the body,\u003csup\u003e4\u003c/sup\u003e their being highly reactive towards lipids, proteins and DNA, and severely harmful for cell survival when present at very high concentrations, both led to the concept of oxidative stress as detrimental condition occurring in all living systems and arising from the imbalance between oxidants species and antioxidant defense.\u003csup\u003e5\u003c/sup\u003e Many studies have shown that oxidative stress is associated with various chronic diseases.\u003csup\u003e6\u0026ndash;9\u003c/sup\u003e Additionally, a study noted that oxidative stress is a central player in metabolic ailments associated with high-carbohydrate and animal-based protein diets and excessive fat consumption.\u003csup\u003e10\u003c/sup\u003e A deficiency of vitamin in the diet will negatively affect the antioxidant defense system,\u003csup\u003e11\u003c/sup\u003e and vitamin D supplementation may improve metabolic variables, and reduce oxidative stress and cardiovascular disease outcomes in certain risk groups.\u003csup\u003e12\u003c/sup\u003e Low folate and vitamin B\u003csub\u003e12\u003c/sub\u003e levels are associated with increased oxidative stress in chronic pancreatitis patients.\u003csup\u003e13\u003c/sup\u003e In a rat model of moderate environmental human exposure to cadmium, researchers found that zinc had a protective effect on the disruption of the oxidative/antioxidative balance.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious literatures have reported that DNA, lipids, and protein peroxidation products are commonly used to assess oxidative stress in humans.\u003csup\u003e15\u003c/sup\u003e According to previous relevant studies,\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e oxidative stress was measured using malondialdehyde (MDA), 8-iso-prostaglandin F2a (8-iso-PGF2a), superoxide dismutase (SOD), and the total antioxidant capacity (T-AOC). Among the above biomarkers, MDA is one of the most common biomarkers of protein and lipid peroxidation,\u003csup\u003e17\u003c/sup\u003e and 8-iso-PGF2a is considered to be the most comprehensive and reliable biomarker for evaluating oxidative damage to DNA and lipids.\u003csup\u003e19\u003c/sup\u003e The above two biomarkers are directly proportional to the level of oxidative stress, while SOD and T-AOC are inversely proportional to the level of oxidative stress. SOD is the most common antioxidant damage biomarker, and can reduce oxidative stress damage by clearing free hydrogen peroxide and oxygen free radicals in the body.\u003csup\u003e16\u003c/sup\u003e The T-AOC can better reflect the body\u0026rsquo;s antioxidant status.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOn the basis of the 2022 version of the Dietary Guidelines for Chinese Residents,\u003csup\u003e20\u003c/sup\u003e the Chinese Nutrition Society released the Dietary Guidelines for the Chinese Elderly(2022),\u003csup\u003e21\u003c/sup\u003e which provides dietary guidance for older people aged 65 to 79 and people aged 80 and above respectively. This finding is consistent with the content of the Dietary Guide for Elderly Agents recommended by the National Health and Family Planning Commission.\u003csup\u003e22\u003c/sup\u003e Therefore, under the above two guidelines, our study aimed to explore the impact of energy, macronutrients, and vitamins with antioxidant functions on oxidative stress levels in the bodies of older residents living in rural areas.\u003c/p\u003e \u003cp\u003eLatent class analysis (LCA), is a probabilistic modeling algorithm that allows clustering of data and statistical inference, and the unobserved, or \u0026ldquo;latent\u0026rdquo;, groups are inferred from patterns of the observed variables or \u0026ldquo;indicators\u0026rdquo; used in the modeling. This approach allows investigators to determine whether unmeasured or unobserved groups exist within a population.\u003csup\u003e23 24\u003c/sup\u003e Thus, this study intends to use latent class analysis to the latent variables of energy and nutrient intake groups in the elderly population.\u003c/p\u003e \u003cp\u003eAn important influencing factor of chronic diseases is diet and nutrient intake, and oxidative stress is a common pathogenic mechanism of chronic diseases.\u003csup\u003e25, 26\u003c/sup\u003e In previous studies, we demonstrated that dietary diversity and quality can affect oxidative stress levels in older adults.\u003csup\u003e27\u003c/sup\u003e In this study, we hypothesized that energy, macronutrient and vitamin intake combined with antioxidant function might affect the level of oxidative stress, and aimed to determine the latent classes of different energy and nutrient intakes groups in older adults through latent class analysis to explore their cross-sectional relationships with oxidative stress biomarkers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample size calculating\u003c/h2\u003e \u003cp\u003eThis study employed\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe current study employed a descriptive cross-sectional design, and used a convenience sampling method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThe study was conducted at community health stations in 3 rural areas in Yinchuan and Wuzhong city, Ningxia, China, between April and August 2021. We contacted health workers and held lectures on nutritional knowledge for older residents at the above community health stations. Potential participants were provided with informational documents regarding our study, and they were given time to contemplate their participation. After the recruitment of older people and providing informed consent, the study visits were performed at the health stations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eOlder people aged 65 years and older who were living in the rural community of Ningxia for more than one year and who voluntarily participated in this study. This study excluded participants who were diagnosed with speech and hearing disorders, cognitive dysfunction, Alzheimer\u0026rsquo;s disease or dementia; who reported a history of disease, such as severe cardiopulmonary dysfunction, kidney dysfunction, terminal stages of diseas; and who were taking immunosuppressants, vitamin C and vitamin E preparations, and other drugs that may have affected the measurement of biomarkers of oxidative stress in the past 3 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eNutritional data\u003c/h2\u003e \u003cp\u003e Data regarding diet were obtained from 3 d 24 h dietary records. The dietary records were filled in by trained investigators according to the participants\u0026rsquo; descriptions, and the dietary models were used as a reference. The main food types included staple food and non-staple food, such as snacks, fruits, and drinks, which were eaten by the participants and their family members. The nutritional calculator developed by the Institute of Nutrition and Food Safety of the Chinese Center for Disease Control and Prevention and Beijing Feihua Communication Technology Co., Ltd. was used to input the 3 d 24 h dietary information, this information was used to determine the energy and nutrient intake of each participant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eNutrient intake grouping\u003c/h2\u003e \u003cp\u003eThe use of energy, macronutrients, and vitamins was compared with the antioxidant function of participants according to the recommended intake in the Dietary Guide for Elderly Adults,\u003csup\u003e22\u003c/sup\u003e and the intake was divided into three groups: insufficiency, moderation, and excessiveness (Supplemental Materials Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory examination\u003c/h2\u003e \u003cp\u003eRefer to relevant experimental methods,\u003csup\u003e15\u003c/sup\u003e after 10 h overnight fasting; participants\u0026rsquo; venous blood was collected by qualified health workers. The blood samples were left at 37\u0026deg;C for 2 h and separated by centrifugation at 3000 r/min for 10 min. The liquid supernatant was extracted and stored at -80\u0026deg;C until analysis. The concentrations of MDA, 8-iso-PGF2a, SOD, and T-AOC in the blood were determined by the following methods.\u003c/p\u003e \u003cp\u003eMDA: thiobarbituric acid colorimetric method (Nanjing Jiancheng Bioengineering Institute); 8-iso-PGF2a: enzyme-linked immunosorbent assay (Elabscience Biotechnology Co., Ltd); SOD: water-soluble tetrazole salt colorimetric method (Nanjing Jiancheng Bioengineering Institute); and T-AOC: chemiluminescence method (Nanjing Jiancheng Bioengineering Institute).\u003csup\u003e27, 28\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eLatent class analysis (LCA)\u003c/h2\u003e \u003cp\u003eUsing Mplus vision 8.3 for latent class analysis in examining the number of unobserved classes (the latent class of energy and nutrient intakes), the characteristics of the classes were described, and the probabilities of class memberships were estimated for each individual.\u003csup\u003e29\u003c/sup\u003e A latent class analysis (LCA) was performed to identify distinct homogeneous groups (latent classes), from categorical multivariate data. In the case of this study,\u003csup\u003e30\u003c/sup\u003e the LCA results identified specific groups of energy and nutrient intake present in the sample, and the analysis included data pertaining to meeting grouping the following grouping: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) energy, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) protein, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) fat, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) carbohydrate, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) vitamin A, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) vitamin C, and (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) vitamin E. Five models with 1\u0026ndash;5 classes were tested, and model selection was based on the results of a number of fit criteria:\u003csup\u003e31, 32\u003c/sup\u003e Low values for the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) indicate superior model fit among competing models. The entropy value indicates the distinctiveness of the latent classes when compared to one another and values closer to one suggest clear classification. In addition, the Lo\u0026mdash;Mendell\u0026mdash;Rubin adjusted likelihood ratio test (LMR-A) and parametric bootstrapped likelihood ratio test (BLRT) were used to compare the k class model to the k-1 model, where k is the number of latent classes. If the probability \u003cem\u003eP\u003c/em\u003e- value is \u0026lt;\u0026thinsp;0.05, the k class model is considered superior.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubsequent analysis\u003c/h2\u003e \u003cp\u003eThe statistical analyses of the latent class results and oxidative stress biomarkers were performed using IBM SPSS Statistics 25.0 for Windows. The means and standard deviations were used to describe the measurement data, and the frequency, constituent ratio, and percentage were used to describe the counting data. Analysis of variance (ANOVA) was used to compare the differences between different groups of nutrient intake and oxidative stress biomarkers; comparisons between different classes were performed using the least significant difference (LSD) method of post hoc comparison. A value of 0.05 was used as a standard test; \u003cem\u003eP\u003c/em\u003e- values are the probabilities of both sides.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants and energy and nutrient intake grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included blood samples and general data from 376 older residents in 3 rural communities in Ningxia. The age of the participants was 65~89 (72.06\u0026plusmn;5.95) years (Table 1). The grouping of participants\u0026rsquo; energy and nutrient intakes is shown in Table 2. The grouping method is described in supplemental material Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Baseline characteristics of the participants（\u003cem\u003eN\u003c/em\u003e=376）.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"541\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"61.737523105360445%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003eNumbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.44731977818854%\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" valign=\"top\"\u003e\n \u003cp\u003eAge（\u0026nbsp;, years）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.70979667282809%\" colspan=\"3\"\u003e\n \u003cp\u003e72.06\u0026plusmn;5.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEducation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.44731977818854%\"\u003e\n \u003cp\u003eUneducated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eIntermediate and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMonthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.44731977818854%\"\u003e\n \u003cp\u003eBelow 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003e72.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003e1000-2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003e2001 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.44731977818854%\"\u003e\n \u003cp\u003eMarried with surviving spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eUnmarried/divorced/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.290203327171906%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eEmployment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.44731977818854%\"\u003e\n \u003cp\u003ePhysical labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.005545286506468%\" valign=\"top\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.256931608133087%\" valign=\"top\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eMental labor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.12048192771084%\"\u003e\n \u003cp\u003eUnemployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.16867469879518%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.710843373493976%\" valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Grouping comparison among the participants\u0026rsquo; nutrient intake and the recommendation in the Dietary Guidelines\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"997\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.45235707121364%\" rowspan=\"2\"\u003e\n \u003cp\u003eEnergy and nutrients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.82246740220662%\" colspan=\"4\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.72517552657974%\" colspan=\"4\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.436893203883495%\" valign=\"top\"\u003e\n \u003cp\u003eRNI \u003csup\u003ea\u003c/sup\u003e /AI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.927184466019417%\" valign=\"top\"\u003e\n \u003cp\u003eNutrients intake \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.233009708737864%\" valign=\"top\"\u003e\n \u003cp\u003eOccupy RNI%/AI%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.315533980582524%\" valign=\"top\"\u003e\n \u003cp\u003eRNI \u003csup\u003ea\u003c/sup\u003e /AI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.927184466019417%\" valign=\"top\"\u003e\n \u003cp\u003eNutrients intake \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.325242718446603%\" valign=\"top\"\u003e\n \u003cp\u003eOccupy RNI%/AI%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" valign=\"top\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.43486973947896%\" valign=\"top\"\u003e\n \u003cp\u003eEnergy/(kcal/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.617234468937875%\"\u003e\n \u003cp\u003e1900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324649298597194%\"\u003e\n \u003cp\u003e1753.79\u0026plusmn;668.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.228456913827655%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80,insufficiency\u003c/p\u003e\n \u003cp\u003e80~110, moderateness\u003c/p\u003e\n \u003cp\u003e\u0026gt;110, excessiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.7114228456913825%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.517034068136272%\" valign=\"top\"\u003e\n \u003cp\u003e1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324649298597194%\" valign=\"top\"\u003e\n \u003cp\u003e1576.83\u0026plusmn;586.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.130260521042084%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80,insufficiency\u003c/p\u003e\n \u003cp\u003e80~110, moderateness\u003c/p\u003e\n \u003cp\u003e\u0026gt;110, excessiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.7114228456913825%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.441624365482234%\"\u003e\n \u003cp\u003eProtein RNI/(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.551607445008461%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e54.28\u0026plusmn;27.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e45.78\u0026plusmn;24.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.441624365482234%\" valign=\"top\"\u003e\n \u003cp\u003eFat(%E \u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.551607445008461%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e36.52\u0026plusmn;3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e33.14\u0026plusmn;20.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.441624365482234%\" valign=\"top\"\u003e\n \u003cp\u003eCarbohydrate(%E \u003csup\u003ed\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.551607445008461%\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e224.15\u0026plusmn;87.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e205.35\u0026plusmn;76.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.43486973947896%\" valign=\"top\"\u003e\n \u003cp\u003eVitamin A (\u0026mu;g RAE \u003csup\u003ee\u003c/sup\u003e/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.617234468937875%\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324649298597194%\" valign=\"top\"\u003e\n \u003cp\u003e420.64\u0026plusmn;198.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.228456913827655%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80,insufficiency\u003c/p\u003e\n \u003cp\u003e80~110, moderateness\u003c/p\u003e\n \u003cp\u003e\u0026gt;110, excessiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.7114228456913825%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.517034068136272%\" valign=\"top\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.324649298597194%\" valign=\"top\"\u003e\n \u003cp\u003e439.85\u0026plusmn;212.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.130260521042084%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80,insufficiency\u003c/p\u003e\n \u003cp\u003e80~110, moderateness\u003c/p\u003e\n \u003cp\u003e\u0026gt;110, excessiveness\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.7114228456913825%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.441624365482234%\"\u003e\n \u003cp\u003eVitamin C RNI/(mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.551607445008461%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e75.31\u0026plusmn;40.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e77.32\u0026plusmn;34.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.441624365482234%\"\u003e\n \u003cp\u003eVitamin E AI/(mg\u0026alpha;-TE \u003csup\u003ef\u003c/sup\u003e/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.551607445008461%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e11.77\u0026plusmn;8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.382402707275803%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.81218274111675%\"\u003e\n \u003cp\u003e12.62\u0026plusmn;8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote. RNI \u003csup\u003ea\u003c/sup\u003e: Recommended intake; AI \u003csup\u003eb\u003c/sup\u003e: Adequate Intake; \u003csup\u003ec\u003c/sup\u003e Average intake; \u003csup\u003ed\u003c/sup\u003e %E: The\u003csup\u003e\u0026nbsp;\u003c/sup\u003epercentage of total energy; \u003csup\u003ee\u003c/sup\u003e Retinol activity equivalent (RAE, \u0026mu;g) =Dietary or supplement source all trans retinol（\u0026mu;g) +1/2 Supplement Pure All Trans \u0026beta;- Carotene（\u0026mu;g)+1/12 Dietary all trans \u0026beta;- Carotene（\u0026mu;g) +1/24 Other Dietary Vitamin A Procarotenoids（\u0026mu;g); \u003csup\u003ef\u003c/sup\u003e\u0026alpha;- Tocopherol equivalent（\u0026alpha;- TE, mg), total in diet \u0026alpha;- TE equivalent (mg)=1 \u0026times;\u0026alpha;- Tocopherol (mg)+0.5 \u0026times;\u0026beta;- Tocopherol (mg)+0.1 \u0026times; \u0026gamma; \u0026ndash; tocopherol (mg)+0.2 \u0026times;\u0026delta;- Tocopherol (mg)+0.3 \u0026times;\u0026alpha;- Triene tocopherol (mg); *Cholesterol intake should not be excessive, so only distinguishing between moderateness and excessiveness.\u003c/p\u003e\n\u003cp\u003eThe participants of this study are all individuals with light physical activity\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOxidative stress results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe values and reference ranges of oxidative stress biomarkers among the participants are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Measurement values and reference ranges of oxidative stress biomarkers（\u003cem\u003eN\u003c/em\u003e=376）.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.03966597077244%\" valign=\"top\"\u003e\n \u003cp\u003eBiomarkers of oxidative stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.64509394572025%\" valign=\"top\"\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.315240083507305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.03966597077244%\" valign=\"top\"\u003e\n \u003cp\u003eMDA\u0026nbsp;（nmol/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.64509394572025%\" valign=\"top\"\u003e\n \u003cp\u003e1.58~11.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.315240083507305%\" valign=\"top\"\u003e\n \u003cp\u003e4.82\u0026nbsp;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.03966597077244%\" valign=\"top\"\u003e\n \u003cp\u003e8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.64509394572025%\" valign=\"top\"\u003e\n \u003cp\u003e247.59~1788.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.315240083507305%\" valign=\"top\"\u003e\n \u003cp\u003e782.13\u0026plusmn;245.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.03966597077244%\" valign=\"top\"\u003e\n \u003cp\u003eSOD（U/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.64509394572025%\" valign=\"top\"\u003e\n \u003cp\u003e9.21~72.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.315240083507305%\" valign=\"top\"\u003e\n \u003cp\u003e38.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.03966597077244%\" valign=\"top\"\u003e\n \u003cp\u003eT-AOC\u0026nbsp;（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.64509394572025%\" valign=\"top\"\u003e\n \u003cp\u003e0.60~1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.315240083507305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: T-AOC, alternate Total Antioxidant Capacity; 8-iso-PGF2\u0026alpha;, 15-F2t-isoprostane 8-isoprostanes-F2\u0026alpha;; MDA, Malondialdehyde; SOD, Superoxide Dismutase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLatent class model selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fit statistics of the LCA models are presented in Table 4. The AIC and BIC values are the smallest in class 3, the BIC value begins to increase in class 4, the LMR-LRT and BLRT values support a three-class model, and the model is characterized by sufficient Entropy (0.932). The structure of the three-class model is presented in Fig. 1. Class 1 (C1: \u003cem\u003en\u0026nbsp;\u003c/em\u003e= 141, 37.50% of the total samples) was characterized by an imbalance in the intake of energy and nutrients, and the probability of energy and protein intake was much greater than that of other nutrients. For the purposes of this analysis, the sample was assigned to the \u0026lsquo;imbalanced nutrient\u0026mdash;high energy\u0026rsquo; class. Class 2 (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 69, 18.35% of the total samples) was characterized by sufficient and balanced levels of nutrients except for energy and protein. For this reason, the plants were assigned to the \u0026lsquo;sufficient nutrient\u0026mdash;low energy and protein\u0026rsquo; class. Class 3 (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 166, 44.15% of the total samples) was characterized by low intake of energy with significant differences in intake of various nutrients and was designated the \u0026lsquo;low nutrient\u0026rsquo; class.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Results of the LCA \u0026ndash; fit statistics（\u003cem\u003eN\u003c/em\u003e=376）.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003cp\u003e(Numbers of latent classes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\"\u003e\n \u003cp\u003eLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\"\u003e\n \u003cp\u003eaBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\"\u003e\n \u003cp\u003eEntropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\"\u003e\n \u003cp\u003eLMR LR \u003cem\u003eP-values\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\"\u003e\n \u003cp\u003eBLRT\u003cem\u003e\u0026nbsp;P-values\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e-2269.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e4566.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3874.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e4577.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e-1851.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3760.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e4431.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3782.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1735.821\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3559.641\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3732.543\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3592.942\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.932\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e-1692.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3503.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3735.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3548.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.42528735632184%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e-1664.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3476.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3766.801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e3532.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.919540229885058%\" valign=\"top\"\u003e\n \u003cp\u003e0.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.775862068965518%\" valign=\"top\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.201149425287356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. LL = Loglikelihood; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; aBIC = Ajusted BIC; LMR LR = Vuong-Lo-Mendell-Rubin Likelihood Ratio Test; BLRT = Parametric Bootstrapped Likelihood Ratio Test. Class chosen is shown in bold.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOxidative stress across the latent class of energy and nutrient intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ANOVA results showed that the concentrations of\u0026nbsp;8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e and SOD, which are oxidative stress biomarkers, were significantly different (\u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). In other words, oxidative stress is significantly different among the classes, and the post test results revealed that for\u0026nbsp;8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e\u003csub\u003e,\u003c/sub\u003e class 1 had significantly greater values than class 2 and class 3. For SOD, class 2 had significantly greater values than did class 1 and class 3. The results are shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Comparison of different classes of energy and nutrient intakes with oxidative stress biomarkers. (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 376, x̄ \u0026plusmn;s)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.190332326283986%\" rowspan=\"2\"\u003e\n \u003cp\u003eOxidative stress biomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"45.01510574018127%\" colspan=\"3\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.157099697885196%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.637462235649547%\" rowspan=\"2\"\u003e\n \u003cp\u003eDifference between class\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.23146747352496%\"\u003e\n \u003cp\u003eMDA（nmol/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e4.88\u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e4.72\u0026plusmn;2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e4.82\u0026plusmn;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.169440242057489%\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.23146747352496%\"\u003e\n \u003cp\u003e8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e（pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e834.59\u0026plusmn;280.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e721.03\u0026plusmn;211.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e762.96\u0026plusmn;218.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.169440242057489%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\"\u003e\n \u003cp\u003e1\u0026gt;2, 1\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.23146747352496%\"\u003e\n \u003cp\u003eSOD（U/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e36.82\u0026plusmn;10.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e42.01\u0026plusmn;11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e38.68\u0026plusmn;10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.169440242057489%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\"\u003e\n \u003cp\u003e2\u0026gt;1, 2\u0026gt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.23146747352496%\"\u003e\n \u003cp\u003eT-AOC\u0026nbsp;（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e1.00\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.97730711043873%\"\u003e\n \u003cp\u003e1.02\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.169440242057489%\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.667170953101362%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: T-AOC, alternate Total Antioxidant Capacity; 8-iso-PGF2\u0026alpha;, 15-F2t-isoprostane\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducted a questionnaire survey and blood sample collection of aged residents in 3 rural communities in Ningxia, China, to determine their dietary status and oxidative stress levels.\u0026nbsp;We calculated the energy and nutrient intakes of the participants through the nutrition calculator and compared them with the Dietary Guide for Elderly Adults. According to the ratio of their intake to the Recommended Intake or Adequate Intake, the participants were divided into three categories: insufficiency, moderation, and excessiveness. The results showed that the proportion of people with insufficient energy and total nutrients was the highest. Researchers have shown that older Chinese people living in the East Coast region have the highest energy intake, those living in the Western region have the lowest total energy intake,\u003csup\u003e33\u003c/sup\u003e and older people living in urban areas have better nutrient intake than do those living in rural areas.\u003csup\u003e34\u003c/sup\u003e These findings indicate that the preliminary results of our study are consistent with those of the above studies.\u003c/p\u003e\n\u003cp\u003eOur results showed that the energy and nutrient intake of older adults are heterogeneous, and have potential diagnostic value. There are three types of nutrients in the population; imbalanced nutrient-high energy class, sufficient nutrient - low energy and protein class, and low nutrient class. Among the four oxidative stress biomarkers, MDA and T-AOC did not differ among the three latent classes, indicating that further exploration is needed. The imbalanced nutrient-high energy class had the highest value of\u0026nbsp;8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e, the probability of energy and protein intake was much greater than that of other nutrients, while the probability of fat and vitamin intake was slightly lower than that of sufficient nutrient-low energy and protein class. Both domestic and international studies have confirmed that vitamins and their supplements can effectively reduce the level of oxidative stress in the human body.\u003csup\u003e35-37\u003c/sup\u003e For diseases, existing studies have revealed that the vitamins can reduce ROS levels in chronic kidney disease patients and in those with critical illness.\u003csup\u003e38, 39\u003c/sup\u003e And insufficient fat intake can have adverse effects on the health of older people,\u003csup\u003e40\u003c/sup\u003e so a balanced diet with sufficient vitamins and moderate fat intake is beneficial for the health of the elder and can reduce their levels of oxidative stress.\u003c/p\u003e\n\u003cp\u003eFor SOD, the value of\u0026nbsp;sufficient nutrient - low energy and protein class higher than imbalanced nutrient-high energy and low nutrient class. Different dietary nutrient patterns have different effects on metabolic syndrome incidence.\u003csup\u003e41\u003c/sup\u003e The specific nutrients in dietary regimen can largely influence overall aging health and changes in risk factors such as cholesterol level and blood pressure.\u0026nbsp;\u003csup\u003e42\u003c/sup\u003e Therefore, compared to people with imbalanced or low nutrient intake, older people with sufficient or balanced nutrient intake have higher SOD concentrations and less oxidative stress damage. A review of Chinese and English literature showed that while foods such as milk and sugary beverages can increase plasma inflammatory factor or oxidative stress levels.\u0026nbsp;\u003csup\u003e43\u003c/sup\u003e According to our study, for classes with sufficient nutrient intake and lower intake of energy and protein within a reasonable range, these conditions result in higher SOD concentrations, which are beneficial for enhancing the body\u0026rsquo;s antioxidant capacity.\u003c/p\u003e\n\u003cp\u003eMost of the previous studies examining the relationship between nutrients and oxidative stress levels have focused on animal experiments on the dietary supplements of vitamins E, C, and \u003cem\u003e\u0026beta;\u003c/em\u003e-carotene to reduce oxidative stress in horses\u003csup\u003e44-47\u003c/sup\u003e. To our knowledge, few studies have specifically focused on the nutrient intake of older residents in rural areas of Northwest China. On the basis of previous research on diet, we further explored the relationships of intake of different classes of energy, macronutrients and vitamins with antioxidant function and oxidative stress levels. Thus, our study provides a useful reference for further exploration of the effects of nutrient intake on human oxidative stress levels.\u003c/p\u003e\n\u003cp\u003eLike in domestic research in China, nutrient intake needs to be strengthened for aged people. In particular,\u0026nbsp;nutrients including vitamins, beta-carotene, polyphenols, selenium and zinc, are considered natural antioxidants and should be added to the daily diet for older adults.\u003csup\u003e48\u003c/sup\u003e Specialized knowledge of dietary intake and the role of supplements in achieving recommended intakes should be integrated into patient care.\u003csup\u003e49\u003c/sup\u003e Overall, balanced nutrient intake is crucial for older people, but more challenges are getting people to adhere to it, which is an important aspect that community health workers should consider in elder nursing care in the community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite its practical significance, this study has the following limitations. First, we investigated only individual rural communities in two cities in Ningxia; further studies are necessary to examine these findings. Second, as this was a cross-sectional study, we could not conclude causality between nutrient intake groups and oxidative stress. Third, our study concluded that nutrient intake is related only to\u0026nbsp;8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e and SOD; however, the relationships between other oxidative stress biomarkers and nutrient intake need further exploration by expanding the sample size.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we found that intake of latent classes of energy, macronutrients and vitamins is related to the level of oxidative stress, and related to two oxidative stress indicators:\u0026nbsp;8-iso-PGF2\u003csub\u003e\u0026alpha;\u003c/sub\u003e and SOD. The two different oxidative stress biomarkers mentioned above are related to the different types of nutrient grouping based on the dietary guidelines for elderly adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll named in this manuscript have given permission for the submission of this manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeijuan Kong, Ting Jiang and Yanhua Ning conceived the study, designed the research, Yanhua Ning obtained funding. Weijuan Kong wrote the primary drafts of the manuscript. Weijuan Kong, Xiongxiong LYU, Meiman Li, Haiyan Liu, Yahong Guo performed statistical analyses. Jing shi and Lingna Liu performed data management. All authors discussed the findings, interpretations, and cowrote the final versions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Natural Science Foundation of Ningxia Province.\u003c/p\u003e\n\u003cp\u003eFund number: 2023AAC03192.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data required to reproduce the above findings cannot be shared at this time due to ethical reasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Ningxia Medical University, China (approval number: Ningxia Medical University Ethics No. 2022-N046). Written informed consent was obtained from all participants before the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts of interest in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAffairs UNDoEaS. \u003cem\u003eWorld Population Ageing 2020 Highlights: Living Arrangements of Older Persons. .\u003c/em\u003e New York 2020.\u003c/li\u003e\n\u003cli\u003eStatistics CNBo. The Seventh National Census. 2021-5-21. Available at: http://www.stats.gov.cn/xxgk/jd/sjjd2020/202105/t20210512_1817342. html.\u003c/li\u003e\n\u003cli\u003eJournal CB. National People\u0026apos;s Congress Representative Zhang Yi: Suggest Transforming Vacant Village Primary Schools into Elderly Service Centers. 2023-03-10. Available at: https://baijiahao.baidu.com/s?id=1759982745458919063\u0026amp;wfr=spider\u0026amp;for=pc.\u003c/li\u003e\n\u003cli\u003eOpara EC. 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Vitamin and mineral supplement use in medically complex, community-living, older adults. \u003cem\u003eAppl Physiol Nutr Metab. \u003c/em\u003eApr 2019;44(4):450-453.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nutrient intake, Oxidative stress, Older adults, Latent class analysis","lastPublishedDoi":"10.21203/rs.3.rs-3939030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3939030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe level of oxidative stress in the human body is related to diet and nutrient intake, and it is the common pathogenic mechanism of chronic diseases. Understanding the nutrient intake status and its relationship with oxidative stress is beneficial for addressing elder\u0026rsquo;s nutritional issues in the context of aging. This study aimed to describe the status of energy intake and intake of different nutrients and their relationship with oxidative stress through latent class analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe invited 376 older residents from 3 rural communities to complete a questionnaire survey and collect blood samples in Ningxia Hui Autonomous Region, China, between April and August 2021. The participants completed questionnaires regarding their general characteristics, and dietary status, and venous blood was collected to detect biomarkers of oxidative stress. Latent class analysis was employed to identify distinct energy and nutrient intake group subgroups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed three classes, \u0026ldquo;imbalanced nutrient\u0026mdash;high energy\u0026rdquo; (37.50%, imbalanced in intake of energy and nutrients with high energy and protein intake), \u0026ldquo;sufficient nutrient\u0026mdash;low energy and protein\u0026rdquo; (18.35%, sufficient and balanced intake of other nutrients except for energy and protein), and \u0026ldquo;low nutrient\u0026rdquo; (44.15%, low intake of energy and various nutrients). Among the oxidative stress biomarkers, imbalanced nutrient\u0026mdash;high energy had higher value than did the other classes for 8-iso-PGF2\u003csub\u003eα\u003c/sub\u003e; sufficient nutrient\u0026mdash;low energy and protein valued higher than imbalanced nutrient\u0026mdash;high energy and low nutrient classes for SOD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOxidative stress can be measured based on the different energy and nutrient intake classes and their predictors.\u003c/p\u003e","manuscriptTitle":"Comparison of energy and nutrient intake with dietary guidance recommendations for older adults in rural communities and its relationship with oxidative stress levels: A latent class analysis study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 06:47:47","doi":"10.21203/rs.3.rs-3939030/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75ccaa21-a453-43cb-b007-8fed95b37a8e","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-15T18:01:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 06:47:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3939030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3939030","identity":"rs-3939030","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}