Associations of Dietary Nutrients with All-Cause and Disease-Specific Mortality: A Nutrient-Wide Analysis in Middle-Aged and Elderly Adults

Associations of Dietary Nutrients with All-Cause and Disease-Specific Mortality: A Nutrient-Wide Analysis in Middle-Aged and Elderly Adults | 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 Associations of Dietary Nutrients with All-Cause and Disease-Specific Mortality: A Nutrient-Wide Analysis in Middle-Aged and Elderly Adults Yuan Wang, Jing Wang, Quwen Li, Hangyu Chen, Shuqian Huang, Jing Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8718995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective: To our knowledge, a systematic comparison of nutrients contribution to mortality in large scale cohort of middle-aged to elderly individuals has not yet been done. We aim to investigate the associations between most of the available nutrients and all-cause and disease-specific mortality, and explored their joint effect on mortality risk. Methods: A total of 208,312 participants from the UK Biobank (UKB) with baseline 24-hour dietary recall data were enrolled. Cox proportional hazards models were used for a nutrients-wide association analysis of all-cause mortality and disease-specific mortality. Mixed-effects analyses were further conducted to evaluate the combined effects of nutrients significantly associated with mortality risk by BKMR and Qgcomp regression models. Results: No significant associations were found between total energy, total protein, total lipid, or total carbohydrate intake and all-cause mortality risk. However, energy density was moderately and positively associated with all-cause mortality (HR=1.017, 95%CI: 1.004-1.030). Nutrient type and quality exhibited significant impacts: plant-derived protein (HR=0.995, 95%CI: 0.992-0.998), plant-derived lipids (HR=0.997, 95%CI: 0.995-0.999), were negatively associated with all-cause mortality. Among carbohydrates, starch, lactose, and intrinsic/milk sugars showed protective effects, while free sugars, non-milk extrinsic sugars, sucrose, and maltose were positively associated with increased mortality risk. For minerals and vitamins, copper, manganese, total iron, non-haem iron, vitamin E, riboflavin, biotin, and pantothenic acid exhibited inverse associations with all-cause mortality. Mixed-effects analyses revealed cumulative inverse trends of beneficial nutrients and positive trends of harmful nutrients on mortality risk, with manganese, maltose, biotin, and niacin being key contributors. Disease-specific analysis showed that energy density and certain sugars were positively associated with neoplasms mortality; multiple sugars were linked to nervous system disease mortality; and alcohol, maltose were positively associated with digestive system disease mortality, while most macronutrients, minerals, vitamins, and fibre had protective effects. Sodium and chloride were positively associated with circulatory system disease mortality. Conclusion: Total intake of major macronutrients was not significantly associated with mortality risk, but nutrient type and quality played critical roles. Plant-derived nutrients, specific minerals, vitamins, dietary fibre, and natural carbohydrates were protective against mortality, whereas refined sugars and high energy density were detrimental. These findings highlight the importance of dietary quality in reducing mortality risk and provide evidence for developing targeted dietary recommendations. Figures Figure 1 Figure 2 Figure 3 Introduction For middle-aged and elderly individuals, accurately recognizing those with shortened life expectancy and appropriately stratifying their associated risks is a major public health imperative.. Over recent decades, epidemiological investigations have substantially advanced our understanding of how environmental exposures and lifestyle behaviors contribute to the etiology of age-related disorders and the risk of mortality[ 1 – 2 ]. Conventional modifiable risk factors linked to daily living habits-such as tobacco use, excessive alcohol consumption, lack of physical activity, unhealthy dietary patterns, and obesity-have been consistently linked to elevated mortality risk, particularly in the context of chronic disease-related deaths[ 3 ].Among various environmental determinants, diet stands out as one of the most influential. Mounting evidence indicates that nutritional status exerts profound effects on human health and the aging process[ 4 ]. Adopting a proper lifestyle and healthy dietary patterns may confer substantial health benefits, thereby contributing to prolonged life expectancy[ 5 , 6 ]. Currently, most initiatives designed to alleviate the chronic disease burden have centered primarily on curbing excessive consumption of unhealthy nutrients, while the significance of ensuring sufficient intake of essential and semi-essential nutrients, has been largely overlooked. Population-based surveys further reveal that the nutritional intake among older adults is often inadequate to fully sustain healthy aging[ 7 ]. Existing evidence from published studies indicates that targeted enhancements in the consumption of specific food categories or nutrients may hold considerable potential for slowing the progression of age-related chronic conditions, including musculoskeletal diseases, dementia, visual impairment, and cardiometabolic disorders.[ 8 , 9 ]. Our current understanding of how targeted nutritional interventions can slow the progression of chronic diseases remains evolving, with inconsistent effect estimates and heterogeneous levels of evidence across studies. In 2022, the US Preventive Services Task Force conducted a systematic review of randomized controlled trials examining vitamin supplementation and mortality outcomes. Their analysis concluded that the available evidence was inadequate to definitively assess the benefits or risks of such supplementation, partly due to constraints including short follow-up durations and limited generalizability of the included trials[ 10 ]. Traditionally, nutritional epidemiology has tended to examine the mortality associations of individual nutrients. The limited number of studies that did consider multiple nutrient categories failed to distinguish specific causes of death and were often constrained by small sample sizes[ 11 , 12 ]. Hence, previous studies have several major limitations. The nutrients included in the analyses are not comprehensive. Few studies have simultaneously compared the relative impacts of distinct nutrients on overall mortality risk within the same population, and few have explored the differential effects of nutrients on cause-specific mortality related to various chronic diseases. We aimed to conduct a systematic and untargeted investigation to quantify the relative contributions of 63 nutrients to mortality risk based on the UK Biobank project, which includes approximately 500,000 men and women aged 40–70 years. We identified individual nutrients that may be associated with mortality and compared the distinct roles of these nutrients in the mortality risk of 11 chronic diseases. In addition, we analyzed the mixed effects of these nutrients using Qgcomp regression and Bayesian kernel machine regression (BKMR) models. Methods Study population The study population was derived from the general population cohort of the UK Biobank (UKB), which enrolled more than 500,000 middle‑aged and elderly participants at baseline. We excluded individuals without available dietary questionnaire data (n=291,287). Furthermore, participants with extremely high or low daily energy intake were excluded, with thresholds defined as greater than 4,000 or less than 600 kcal/day for men, and greater than 3,500 or less than 500 kcal/day for women.. A total of 208,312 participants were included in the final analysis. Dietary and nutrients assessment Dietary intake information was obtained via the Oxford WebQ questionnaire, a well-established dietary assessment instrument with documented validity relative to interviewer-conducted 24-hour dietary recalls [13]. During each assessment, participants were asked to retrospectively report all foods items consumed over the preceding 24-hour period, including food types and portion sizes.Based on the reported food items and their estimated quantities, individual nutrient intake is calculated by linking each food to a standardized food composition database by a built-in algorithms as described in detail in previous publications [14, 15]. The present study included a total of 63 nutrients, with their corresponding field identifiers in the UK Biobank documented in Table S1. In total, participants could complete up to five dietary assessments: one baseline assessment administered between April 2009 and September 2010, followed by repeated follow-up assessments conducted at 3-4 month intervals from February 2011 to June 2012. For those who underwent more than one assessment, the average values of nutrient intakes were computed to better represent long-term dietary exposure. Ascertainment of the outcome variable The detailed protocols used to link participant records with national mortality and cause-of-death registries have been previously described in detail elsewhere[16]. Follow-up time for each participant was calculated from the date of enrollment into the UK Biobank study until January 1, 2023, or date of death, whichever occurred first. Follow‑up time for each participant was calculated from the date of enrollment into the UK Biobank study until January 1, 2023, or date of death, whichever occurred first. All-cause mortality was defined as any death recorded before the cutoff date of January 1, 2023. Cause-specific mortality was categorized using the ICD-10 coding system: infectious and parasitic diseases (A00-B99); neoplasms (C00-D48); blood and immune disorders (D55-D89); endocrine, nutritional and metabolic diseases (E00-E90); mental and behavioural disorders (F00-F89); nervous system diseases (G00-G99); circulatory system diseases (I05-I99); respiratory system diseases (J09-J99); digestive system diseases (K00-K93); musculoskeletal and connective tissue diseases (M00-M90); and genitourinary system diseases (N00-N98). Covariates A range of covariates were incorporated in the analyses, covering key demographic and socioeconomic indicators including age, sex, ethnicity, employment status, household income, and the Townsend deprivation index. Lifestyle-related variables such as smoking status, alcohol intake, and physical activity level, as well as overall health status, were all obtained through self-report at study baseline. Participants were classified into never, former, or current categories according to their smoking and alcohol consumption status. Ethnicity was dichotomized as white or other. With regard to employment status, participants were divided into four groups: employed, retired, unemployed, or other. Physical activity level was categorized as low, moderate, or high. Self-reported general health was rated on a four-point scale: excellent, good, fair, or poor. Statistical analysis Categorical variables were summarized as numbers and corresponding percentages, while continuous variables were reported as medians with interquartile ranges (IQR). Cox regression analysis were applied to evaluate the prospective associations between each nutrient and the risks of all-cause and cause-specific mortality. These analyses were conducted in R software with the qgcomp (version 1.1.0) and bkmr (version 0.2.0) packages, respectively. We adjusted for multiple comparisons by calculating false discovery rate (FDR)-corrected P-values using the Benjamini–Hochberg procedure. All statistical analyses were carried out using R software (version 4.2.2). Results Characteristics of study population Population characteristics are summarized in Table 1. A total of 208,312 participants with 24-hour dietary recall data at baseline were enrolled in this study. The mean age of the study population is 57 years (IQR: 50-63), and 114,912 (55.16%) are female. Of these, 92.0% of the population are White. There were 12,617 deaths from all causes after a median 16.8 years of follow-up. Women had a lower all-cause mortality rate compared with men (4.5% in women versus 7.9% in men). Mortality by cause of death for all participants is given in Table S2. The number of death caused by the eleven disease was 11,765, which accounted for 93.2% of the total number of reported deaths in UKB cohort. The leading causes of death in the population include neoplasms, diseases of the circulatory system, diseases of the nervous system, diseases of the respiratory system, with proportion of death causes of 55%, 19%, 6%, and 5.9%, respectively. The average total energy intake of the population was 1998.2 kcal/day. For the three major macronutrients, the median (IQR) for protein, fat, and carbohydrate were 78.40 (65.00, 93.22) g/day, 69.41 (53.35, 87.96) g/day, and 246.68 (201.87, 296.48) g/day, respectively. The distribution of 63 nutrient intakes is listed in Table S3. Table 1 Baseline characteristics of participants Characteristics Female (N =114912) Male (N = 93400) Participants (N = 208,312) Age at recruitment (year) 57.0（49.0-62.0） 58.0（50.0-63.0） 57.0（50.0-63.0） Ethnic background White 104,518（90.95%） 86,335（92.44%） 190,853（91.62%） others 10,394（9.05%） 7,065（7.56%） 17,459（8.38%） Education level High 47,335（41.19%） 41,439（44.37%） 88,774（42.62%） Median 37,039（32.23%） 32,376（34.66%） 69,415（33.32%） Low 20,342（17.70%） 11,020（11.80%） 31,362（15.06%） others 10,196（8.87%） 8,565（9.17%） 18,761（9.01%） Average total household income (£ per year) < 18,000 17,194（17.17%） 11,762（13.59%） 28,956（15.51%） ~ 31,000 25,570（25.54%） 19,820（22.90%） 45,390（24.32%） ~52,000 27,991（27.96%） 25,249（29.18%） 53,240（28.52%） ~100,000 22,812（22.78%） 22,765（26.31%） 45,577（24.42%） >100,000 6,557（6.55%） 6,936（8.02%） 13,493（7.23%） Townsend deprivation index -2.28（-3.70-0.11） -2.37（-3.77--0.01） -2.32（-3.73-0.05） Smoking status Never 69,661（60.77%） 47,970（51.50%） 117,631（56.62%） Former 37,317（32.56%） 36,613（39.31%） 73,930（35.58%） Current 7,644（6.67%） 8,559（9.19%） 16,203（7.80%） Alcohol drinker status Never 4,801（4.18%） 1,892（2.03%） 6,693（3.22%） Former 3,568（3.11%） 2,754（2.95%） 6,322（3.04%） Current 106,427（92.71%） 88,668（95.02%） 195,095（93.75%） Physical activity Low 15,986（17.51%） 15,230（18.92%） 31,216（18.17%） Moderate 40,180（44%） 32,482（40.35%） 72,662（42.29%） High 35,149（38.49%） 32,792（40.73%） 67,941（39.54%） General health status Excellent 3,120（2.72%） 3,359（3.61%） 6,479（3.12%） Good 18,290（15.96%） 18,300（19.64%） 36,590（17.61%） Fair 69,581（60.71%） 54,229（58.21%） 123,810（59.59%） Poor 23,615（20.61%） 17,272（18.54%） 40,887（19.68%） Nutrients-wide analysis of all-cause mortality Nutrients-wide association study analyses of all-cause mortality were conducted by serially testing 63 environmental exposures in relation to mortality via Cox proportional hazards models. The regression coefficient of each individual nutrient in the females and males group was presented in the heatmap (Fig. 1A). No notable differences were observed in regression coefficients for most nutrients when these were calculated separately in females and males (Fig. 1B), except for n-3 fatty acids, riboflavin, vitamin B6, copper, haem iron, manganese. Detailed Cox regression results of each individual nutrient in the females and males group were shown in Table S4. In a final mortality association analysis combining females and males, 26/63 exposures (41%) were significantly replicated with P< 0.05 (Fig. 1C). Details of the Cox regression between 63 nutrients and all-cause mortality was presented in Table 1 and Table S5. Fig. 1 Regarding the association between energy intake and all-cause mortality. No association was observed between total energy intake and mortality outcomes, whereas energy density was moderately and positively associated with mortality outcomes (HR = 1.017, 95%CI: 1.004-1.030). All three major macronutrients were found to be associated with mortality outcomes. Specifically, no association was observed between total protein intake or total lipid intake and mortality risk. However, plant-derived protein (HR = 0.995, 95%CI: 0.992-0.998) and plant-derived lipids (HR = 0.997, 95%CI: 0.995-0.999) were negatively associated with mortality risk. Among lipids, n-3 fatty acids (HR = 0.967, 95%CI: 0.943-0.992) and n-6 fatty acids (HR = 0.990, 95%CI: 0.984-0.996) were also negatively associated with mortality risk. No statistically significant association was found between total carbohydrate intake and mortality risk, but the effects varied by carbohydrate type. Starch (HR = 0.999, 95%CI: 0.998-0.999), lactose (HR = 0.996, 95%CI: 0.993-0.999), and intrinsic and milk sugars (HR = 0.998, 95%CI: 0.997-0.999) were negatively associated with mortality risk, while free sugars (HR = 1.002, 95%CI: 1.001-1.003), non-milk extrinsic sugars (HR = 1.002, 95%CI: 1.001-1.003), sucrose (HR = 1.002, 95%CI: 1.001-1.003), and maltose (HR = 1.006, 95%CI: 1.003-1.010) were positively associated with mortality risk. Among mineral elements, copper (HR = 0.899, 95%CI: 0.846-0.955), manganese (HR = 0.932, 95%CI: 0.915-0.949), total iron (HR = 0.988, 95%CI: 0.980-0.996), non-haem iron (HR = 0.986, 95%CI: 0.977-0.994), showed negative associations with mortality risk to varying degrees. Among all vitamins and nutrients, only vitamin E (HR = 0.992, 95%CI: 0.985-0.998) and the B vitamins including riboflavin (HR = 0.945, 95%CI: 0.905-0.986), biotin (HR = 0.997, 95%CI: 0.995-0.999), and pantothenic acid (HR = 0.979, 95%CI: 0.966-0.992) were found to have statistically significant negative associations with mortality risk. In addition, dietary fibre intake (HR = 0.992, 95%CI: 0.988-0.996) was negatively associated with overall mortality risk. Among all the tested nutrients, fat (vegetable fat, n-3 and n-6 polyunsaturated fatty acids), carbohydrates (non-milk extrinsic sugars, free sugars, and maltose), mineral elements (including manganese, magnesium, phosphorus, calcium, and copper), fibre, and biotin showed the strongest associations with overall mortality risk (Fig. 1D). Table 2 Nutrients-wide association analyses of all-cause mortality Nutrients Model 1 Model 2 Model 3 HR(95%CI) FDR-p HR(95%CI) FDR-p HR(95%CI) FDR-p Energy from beverages (kJ/day) 1.0001(1.0001,1.0001) <0.0001 1.0001(1.0000,1.0001) <0.0001 1.0000(1.0000,1.0001) 0.0034 Energy density (kJ/g per day) 1.0043(0.9941,1.0146) 0.4544 1.0490(1.0374,1.0607) <0.0001 1.0170(1.0040,1.0303) 0.0266 Vegetable protein (g/day) 0.9926(0.9902,0.9950) <0.0001 0.9913(0.9887,0.9938) <0.0001 0.9954(0.9924,0.9983) 0.0072 Vegetable fat (g/day) 0.9929(0.9913,0.9944) <0.0001 0.9970(0.9954,0.9986) 0.0005 0.9970(0.9952,0.9989) 0.0072 Free sugar (g/day) 1.0024(1.0018,1.0030) <0.0001 1.0031(1.0025,1.0037) <0.0001 1.0019(1.0012,1.0026) <0.0001 n-3 fatty acids (g/day) 0.9426(0.9224,0.9633) <0.0001 0.9437(0.9233,0.9646) <0.0001 0.9669(0.9429,0.9915) 0.0242 n-6 fatty acids (g/day) 0.9724(0.9675,0.9774) <0.0001 0.9866(0.9815,0.9917) <0.0001 0.9902(0.9843,0.9962) 0.0054 Englyst fibre (g/day) 0.9929(0.9897,0.9962) 0.0001 0.9830(0.9795,0.9865) <0.0001 0.9916(0.9876,0.9956) 0.0004 Calcium (mg/day) 0.9999(0.9998,1.0000) 0.0061 0.9998(0.9997,0.9999) <0.0001 0.9998(0.9997,0.9999) 0.0004 Iron (mg/day) 0.9897(0.9828,0.9968) 0.0072 0.9653(0.9581,0.9726) <0.0001 0.9879(0.9795,0.9964) 0.0152 Potassium (mg/day) 1.0000(1.0000,1.0000) 0.8480 0.9999(0.9999,0.9999) <0.0001 0.9999(0.9999,1.0000) 0.0010 Magnesium (mg/day) 0.9987(0.9984,0.9990) <0.0001 0.9977(0.9974,0.9980) <0.0001 0.9989(0.9985,0.9993) <0.0001 Vitamin E (mg/day) 0.9761(0.9707,0.9815) <0.0001 0.9880(0.9822,0.9937) 0.0001 0.9915(0.9849,0.9982) 0.0287 Starch (g/day) 0.9987(0.9982,0.9992) <0.0001 0.9996(0.9990,1.0001) 0.1754 0.9992(0.9985,0.9998) 0.0274 Riboflavin (mg/day) 1.0024(0.9681,1.0379) 0.8928 0.9108(0.8768,0.9461) <0.0001 0.9445(0.9046,0.9861) 0.0246 Phosphorus (mg/day) 0.9997(0.9997,0.9998) <0.0001 0.9996(0.9995,0.9997) <0.0001 0.9998(0.9997,0.9999) <0.0001 Biotin (ug/day) 0.9964(0.9950,0.9978) <0.0001 0.9930(0.9914,0.9946) <0.0001 0.9969(0.9951,0.9986) 0.0029 Copper (mg/day) 0.9192(0.8711,0.9700) 0.0039 0.8049(0.7582,0.8544) <0.0001 0.8985(0.8455,0.9547) 0.0031 Lactose (g/day) 1.0011(0.9987,1.0035) 0.4211 0.9964(0.9939,0.9990) 0.0080 0.9961(0.9932,0.9990) 0.0242 Maltose (g/day) 1.0162(1.0135,1.0189) <0.0001 1.0113(1.0083,1.0143) <0.0001 1.0056(1.0021,1.0092) 0.0065 Intrinsic and milk sugars (g/day) 0.9992(0.9985,0.9999) 0.0441 0.9969(0.9961,0.9976) <0.0001 0.9985(0.9976,0.9993) 0.0031 Manganese (mg/day) 0.9255(0.9120,0.9392) <0.0001 0.8894(0.8756,0.9034) <0.0001 0.9322(0.9154,0.9493) <0.0001 Sodium (mg/day) 1.0001(1.0000,1.0001) 0.0002 1.0001(1.0001,1.0001) <0.0001 1.0001(1.0000,1.0001) 0.0084 Niacin equivalent (mg/day) 0.9941(0.9919,0.9962) <0.0001 0.9920(0.9897,0.9944) <0.0001 0.9966(0.9939,0.9992) <0.0001 Non-haem iron (mg/day) 0.9860(0.9789,0.9932) 0.0003 0.9615(0.9541,0.9689) <0.0001 0.9855(0.9769,0.9942) 0.0052 Non-milk extrinsic sugars (g/day) 1.0024(1.0018,1.0030) <0.0001 1.0027(1.0021,1.0034) <0.0001 1.0018(1.0010,1.0025) <0.0001 Pantothenic acid (mg/day) 1.0057(0.9945,1.0169) 0.3819 0.9604(0.9491,0.9718) <0.0001 0.9788(0.9655,0.9923) 0.0072 Sucrose (g/day) 1.0037(1.0028,1.0045) <0.0001 1.0037(1.0028,1.0046) <0.0001 1.0018(1.0008,1.0028) 0.0034 Model 1 was the crude model. Model 2 was adjusted for sex, age, ethnicity. Model 3 was further adjusted for lifestyle factors, including smoking status, alcohol consumption status, education level , Townsend deprivation index, physical activity, general health status. Nutrients-wide analysis of disease specific mortality For disease-specific mortality, the diseases most closely associated with nutrient intake include neoplasms, diseases of the nervous system, digestive system, and the genitourinary system (Figure 2 and Table S6). Energy density and sugars including free sugars , maltose , non-milk extrinsic sugars , and sucrose were positively associated with neoplasms mortality. Protein , total nitrogen, n-3 fatty acids, intrinsic and milk sugars, fibre , magnesium , selenium , manganese, vitamin D, niacin, and pantothenic acid were negatively associated with neoplasms mortality. For diseases of the nervous system, various sugars are closely associated with the mortality of this disease, including total sugars, free sugars, carbohydrates, fructose, glucose, non-milk extrinsic sugars, and sucrose. For diseases of the digestive system, only alcohol and maltose were positively associated with the mortality risk of this disease. All three major macronutrients were negatively associated with this disease, including vegetable protein, total fat, as well as vegetable fat, n-3 and n-6 fatty acids, monounsaturated fatty acids, and intake of carbohydrates and starch. Among mineral elements, calcium, magnesium, especially copper and manganese, were also negatively associated with the mortality risk of this disease. Vitamins such as vitamin E and vitamins B including riboflavin, niacin equivalent, and pantothenic acid were negatively associated with the mortality risk of this disease as well. Finally, fibre was negatively associated with digestive system disease-related mortality. Intake of animal protein, total nitrogen, haem iron, iodine, selenium, and vitamin B12 was observed to be positively associated with genitourinary system-related mortality. As for other causes of death, only sodium and chloride were observed to be positively associated with the mortality risk of circulatory system diseases, while manganese was negatively associated with it. Intake of total sugars was positively associated with the mortality risk of mental and behavioural disorders. No significant association was observed for death caused by endocrine, nutritional and metabolic diseases, diseases of the blood and blood-forming organs, infectious and parasitic diseases, or diseases of the musculoskeletal system. Fig. 2 The mixed effects of individual nutrients on mortality risk We further conducted a mixed-effects analysis on the nutrients that were found to have a statistically significant association ( P < 1×10⁻³) with mortality risk in the previous step. In the BKMR regression model, although confidence intervals were wide, there was an inverse overall trend between the intake of the nutrients and mortality risk (Fig. 3A). The outcome of mortality showed decrease when all the nutrients were at their 60th percentile or above, compared to their 50th percentile, indicating a inverse association with mortality. The posterior inclusion probabilities (PIP) of each nutrient was presented in Fig. 3B, in which manganese, maltose, biotin, Energy from beverages, calcium, sucrose, niacin played most important role. Consistent with BKMR results, the cumulative effects of all the nutrients exhibited a inverse trend with mortality risk after adjusting for confounders by Qgcomp regression model as shown in Figure 3C. Results from of the fully adjusted models are presented in Table S7. The estimated weights for each nutrient are shown in Figure 3D and Table S8. Nutrients of manganese, niacin, biotin, calcium showed negative weights, while nutrients such as free sugar, iron, maltose showed positive weights in the model. Fig. 3 Discussion The study observed no statistically significant association between the total intake of energy and the three macronutrients and mortality risk, except for energy density. A potential explanation is that the average energy contribution ratios of carbohydrates, total fat, and total protein were 51.2%, 32.5%, and 16.2%, respectively, which are roughly consistent with the acceptable macronutrient distribution ranges. Thus, the risk of disease outcomes may not be sensitive to variations in the total intake of macronutrients in this population. Nevertheless, we found that the type and quality of nutrients were significantly associated with mortality risk. For instance, plant-derived protein and plant-derived lipids exerted a protective effect against all-cause mortality risk, particularly n-3 and n-6 fatty acids among lipids. Moreover, different types of carbohydrates exerted divergent effects on mortality risk: natural carbohydrates (e.g., starch, lactose, intrinsic sugars, and milk sugars) were associated with reduced mortality risk, whereas refined carbohydrates (e.g., free sugars, non-milk extrinsic sugars, sucrose, and maltose) increased mortality risk. These findings are generally consistent with the well-established mainstream evidence. A large body of research and reviews has addressed the question of whether the intake amount or quality of macronutrients is more critical to the incidence of chronic diseases and mortality risk. These studies have consistently demonstrated that both plant-derived proteins and lipids are associated with a reduced risk of chronic disease mortality, compared with their animal-derived counterparts. A network meta-analysis of prospective observational studies was conducted to analyze the association between macronutrient substitution and all-cause mortality. The findings indicated that replacing 5% of energy from animal monounsaturated fatty acids (MUFAs) with plant MUFAs, as well as replacing animal protein and saturated fatty acids with plant protein, was linked to a lower mortality risk [ 17 ]. In another prospective cohort study that examined the association between dietary fats and disease-specific mortality, MUFAs,especially plant MUFAs, were found to exert a protective effect and were inversely associated with mortality risk [ 18 ]. Collectively, these findings suggest that plant-derived proteins and lipids are generally associated with a reduced risk of mortality. Numerous studies have investigated the association between dietary carbohydrates and mortality risk. Dietary carbohydrates have long been demonized for their supposedly adverse effects on health and survival outcomes [ 19 ]. However, some studies have drawn opposite conclusions. data from the Atherosclerosis Risk in Communities (ARIC) study indicated that higher intakes of energy, animal fat, and animal protein were generally linked to an elevated mortality hazard, whereas higher carbohydrate intake was associated with a reduced mortality hazard [ 20 ]. In the present study, we found divergent associations between different types of carbohydrates and the risk of all-cause mortality. Extensive research has been conducted on the associations between added sugar consumption and the risks of diseases and mortality; notably, intake of sugar-sweetened beverages and artificially sweetened beverages has been shown to elevate the risks of diabetes, cardiovascular disease (CVD), and all-cause mortality [ 21 ]. By contrast, research on other carbohydrate types is relatively limited. A Japan cohort study assessed the associations of starch, total sugars, individual sugar types, and free sugars with risks of all-cause and cause-specific mortality and find that high starch intake was associated with decreased mortality, whereas high intakes of glucose, fructose, sucrose, maltose, and free sugars were linked to increased mortality among Japanese men [ 22 ]. The results of the present study are generally consistent with the conclusions of these aforementioned studies on carbohydrate subtypes. Controversy exists regarding the associations between vitamin and mineral intake and mortality risk. In a cohort study of US adults, data from 390,124 participants across three prospective cohort studies were analyzed, and results showed that multivitamin use was not associated with any mortality benefit [ 23 ]. In contrast, the study based on the National Health and Nutrition Examination Survey (NHANES) indicated that higher dietary intakes of vitamins A, B2, B6, C, E, and folic acid, combined with lower heme iron intake, were associated with reduced risks of all-cause and CVD mortality [ 24 ]. Associations between mineral intake and mortality have not been sufficiently investigated in non-Western countries. One study addressed this gap using data from the Golestan Cohort Study, which focused on a Middle Eastern population, and examined the roles of copper, manganese, total iron, non-heme iron, and vitamin E. It found that calcium, copper, selenium, and phosphorus were associated with all-cause mortality [ 25 ]. Consistent with some of these findings, our study also observed significant inverse associations between mortality risk and intake of the minerals copper, manganese, and iron, as well as the vitamins vitamin E, B2, biotin, and pantothenic acid (vitamin B5). However, no independent associations were found between mortality risk and intakes of vitamin A, vitamin C, or folic acid. Our study also compared the role of nutrients across different disease types. We observed that nutrients exerted more pronounced effects on certain systemic diseases than on others. The disease categories with the largest number of nutrients associated with mortality included neoplasms, diseases of the nervous system, and diseases of the digestive and genitourinary systems. As for circulatory system diseases, sodium and chloride were observed to be positively associated with mortality risk, while manganese was inversely associated with it. Of the 63 nutrients tested, 20 nutrients, inlcuding macronutrients, minerals, vitamins, and dietary fiber, showed association with cancer mortality risk. The vast majority of these nutrients were inversely correlated with cancer mortality risk; only energy density and sugars (including free sugars, extrinsic sugars, and sucrose) were positively associated with elevated cancer mortality risk. Similar complex associations were also observed for digestive system disease mortality risk: 28 out of the 63 nutrients were linked to mortality risk, with most (especially minerals and vitamins) showing inverse associations. Only alcohol, beverages, and maltose were found to significantly increase mortality risk. In contrast, carbohydrates were the nutrients most closely correlated with nervous system disease mortality risk. Currently, a large body of research has focused on the associations between nutrients and mortality risks from CVD and cancer. The Prospective Urban Rural Epidemiology (PURE) study reported that total fat and specific types of fat were not associated with CVD, myocardial infarction, or CVD mortality [ 26 ]. In the current study population, no statistically significant associations were found between intakes of lipids or carbohydrates and circulatory system disease mortality risk. However, our study revealed that sodium and chloride were positively associated with the mortality risk of circulatory system diseases, while manganese was inversely associated with this risk. Excessive sodium and chloride intake may contribute to hypertension, which is a key determinant of circulatory and CVD risk[ 27 ]. The relationship between carbohydrates and cancer has exhibited marked heterogeneity in research findings, which depends on the cancer type and the specific form of carbohydrates. Existing studies have indicated that the association between total carbohydrate intake and the risk of colorectal cancer as well as prostate cancer is weak. However, diets with glycemic load are positively correlated with the risk of colorectal cancer, and high intake of refined sugars, desserts and sugar-sweetened beverages is associated with increased mortality in patients with head and neck cancer[ 28 ]. This aligns with the findings of our study, which indicated that carbohydrate intake—especially that of free sugars and extrinsic sugars—could increase cancer mortality risk. Beyond its association with cancer mortality risk, evidence suggests that free sugars, extrinsic sugars, and sucrose are linked to cognitive disorders and neurodegenerative diseases, exerting detrimental effects on such conditions. A systematic analysis explored 58 detailed risk factors for dementia and Parkinson’s disease in older adults across 204 countries, revealing that diets high in sugar-sweetened beverages were specifically associated with elevated risks of dementia and Parkinson’s disease [ 29 ]. Current research on the associations between vitamins, minerals, and chronic disease mortality risk has largely focused on clinical intervention studies. A meta-analysis based on randomized controlled trials investigated the effects of multivitamin supplementation on mortality due to cancer, tumors, and malignancy, reporting no significant effect of multivitamin supplementation on cancer-related mortality[ 30 ]. Additionally, an evidence summary derived from US clinical studies reported that, except for vitamin D, which was associated with an 11% reduction in cancer mortality odds, vitamins E, C, and selenium did not confer any benefit in reducing cancer or CVD mortality [ 10 , 31 ]. These findings are inconsistent with the results observed in our study. This discrepancy may be attributed to the fact that clinical trials are often constrained by limited sample sizes, short intervention durations, and challenges associated with observing long-term effects. There are substantial differences between nutrient-disease mortality risk assessments based on dietary intake and those based on dietary supplements, in terms of intake dosage, absorption and utilization rates, and the concomitant intake of other beneficial dietary components. A recently published systematic review focusing on observational studies of food intake and colorectal cancer reported that dietary fiber, calcium, polyphenols, curcumin, selenium, zinc, magnesium, and vitamins A, C, D, E, and B (particularly B6, B9, and B2) exerted protective effects against colorectal cancer risk [ 32 ]. Similar reviews have summarized the associations of dietary patterns, foods, and nutrients with digestive system diseases, including chronic gastrointestinal diseases and non-alcoholic fatty liver disease [ 33 – 34 ]. These findings are consistent with the trends observed in our current population-based cohort study. Nevertheless, with the exception of data from the UK Biobank, there remains a paucity of high-quality, large-scale cohort study evidence to draw consistent conclusions regarding the associations between dietary intake of various nutrients and chronic disease mortality risk. Conclusion No significant association between total energy intake, total intake of the three major macronutrients, and mortality risk. However, the type and quality of nutrients are crucial: plant-derived nutrients, certain minerals, and vitamins exert a protective effect, while refined sugars and similar substances increase the risk. Additionally, nutrients have divergent impacts on mortality risks of different diseases. In the future, more large-scale cohort studies and mechanistic explorations are warranted to further validate the causal associations between nutrients and chronic diseases, thereby providing more targeted dietary strategies for chronic disease prevention and control. Abbreviations Interquartile ranges (IQR), Hazard Ratio (HR), Confidence Interval (CI), Bayesian kernel machine regression (BKMR), False discovery rate (FDR), Monounsaturated fatty acids (MUFAs), Cardiovascular disease(CVD) Declarations Ethics approval and consent to participate Prior to enrollment, all participants provided written informed consent in accordance with the guidelines outlined in the Helsinki Declaration. This study was reviewed and approved by the NHS National Research Ethics Service (Ref: 11/NW/0382). Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available from [in the UK Biobank (https://www.ukbiobank.ac.uk/) under application number 61083] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [UK Biobank]. Competing interests The authors declare that they have no competing interests Funding This research was conducted using the UKB Resource (https://www.ukbiobank.ac.uk/) under application number number 477103. This study was supported by the Fujian Natural Science Foundation Program (2020J01639). Authors' contributions FQL designed the research. YW, JW, QWL analyzed and interpreted the data. HYC and SQH assisted with the data analysis. JW and YNL helped to write and revise the manuscript for important intellectual content and contributed to the design of the study. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Kalia V, Belsky DW, Baccarelli AA, Miller GW: An exposomic framework to uncover environmental drivers of aging. Exposome 2022, 2(1):osac002. Ganna A, Ingelsson E: 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study. Lancet 2015, 386(9993):533-540. Zhang YB, Pan XF, Chen J, Cao A, Xia L, Zhang Y, Wang J, Li H, Liu G, Pan A: Combined lifestyle factors, all-cause mortality and cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. J Epidemiol Community Health 2021, 75(1):92-99. Collaborators GBDD: Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019, 393(10184):1958-1972. Carballo-Casla A, Ortola R, Garcia-Esquinas E, Oliveira A, Sotos-Prieto M, Lopes C, Lopez-Garcia E, Rodriguez-Artalejo F: The Southern European Atlantic Diet and all-cause mortality in older adults. BMC Med 2021, 19(1):36. Hu FB: Diet strategies for promoting healthy aging and longevity: An epidemiological perspective. J Intern Med 2024, 295(4):508-531. Bruins MJ, Van Dael P, Eggersdorfer M: The Role of Nutrients in Reducing the Risk for Noncommunicable Diseases during Aging. Nutrients 2019, 11(1). Lepp HL, Amrein K, Dizdar OS, Casaer MP, Gundogan K, de Man AME, Rezzi S, van Zanten ARH, Shenkin A, Berger MM et al: LLL 44 - Module 3: Micronutrients in Chronic disease. Clin Nutr ESPEN 2024, 62:285-295. Zhong VW, Van Horn L, Greenland P, Carnethon MR, Ning H, Wilkins JT, Lloyd-Jones DM, Allen NB: Associations of Processed Meat, Unprocessed Red Meat, Poultry, or Fish Intake With Incident Cardiovascular Disease and All-Cause Mortality. JAMA Intern Med 2020, 180(4):503-512. Force USPST, Mangione CM, Barry MJ, Nicholson WK, Cabana M, Chelmow D, Coker TR, Davis EM, Donahue KE, Doubeni CA et al: Vitamin, Mineral, and Multivitamin Supplementation to Prevent Cardiovascular Disease and Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2022, 327(23):2326-2333. Bajracharya R, Katzke V, Mukama T, Kaaks R: Effect of Iso-Caloric Substitution of Animal Protein for Other Macro Nutrients on Risk of Overall, Cardiovascular and Cancer Mortality: Prospective Evaluation in EPIC-Heidelberg Cohort and Systematic Review. Nutrients 2023, 15(3). Ros E: Contrasting Effects on Mortality of Monounsaturated Fatty Acid Intake Depending on Vegetable or Animal Sources. Circ Res 2019, 124(8):1154-1156. Greenwood DC, Hardie LJ, Frost GS, Alwan NA, Bradbury KE, Carter M, Elliott P, Evans CEL, Ford HE, Hancock N et al: Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers. Am J Epidemiol 2019, 188(10):1858-1867. Perez-Cornago A, Pollard Z, Young H, van Uden M, Andrews C, Piernas C, Key TJ, Mulligan A, Lentjes M: Description of the updated nutrition calculation of the Oxford WebQ questionnaire and comparison with the previous version among 207,144 participants in UK Biobank. Eur J Nutr 2021, 60(7):4019-4030. Piernas C, Perez-Cornago A, Gao M, Young H, Pollard Z, Mulligan A, Lentjes M, Carter J, Bradbury K, Key TJ et al: Describing a new food group classification system for UK biobank: analysis of food groups and sources of macro- and micronutrients in 208,200 participants. Eur J Nutr 2021, 60(5):2879-2890. Mortality Data: Linkage to Death Registries (UK Biobank, 2023); https://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=115559 Wallerer S, Papakonstantinou T, Morze J, Stadelmaier J, Kiesswetter E, Gorenflo L, Barbaresko J, Szczerba E, Neuenschwander M, Bell W et al: Association between substituting macronutrients and all-cause mortality: a network meta-analysis of prospective observational studies. EClinicalMedicine 2024, 75:102807. Zhuang P, Zhang Y, He W, Chen X, Chen J, He L, Mao L, Wu F, Jiao J: Dietary Fats in Relation to Total and Cause-Specific Mortality in a Prospective Cohort of 521 120 Individuals With 16 Years of Follow-Up. Circ Res 2019, 124(5):757-768. Qin P, Huang C, Jiang B, Wang X, Yang Y, Ma J, Chen S, Hu D, Bo Y: Dietary carbohydrate quantity and quality and risk of cardiovascular disease, all-cause, cardiovascular and cancer mortality: A systematic review and meta-analysis. Clin Nutr 2023, 42(2):148-165. Krakauer NY, Krakauer JC: Diet Composition, Anthropometrics, and Mortality Risk. Int J Environ Res Public Health 2022, 19(19). Meng Y, Li S, Khan J, Dai Z, Li C, Hu X, Shen Q, Xue Y: Sugar- and Artificially Sweetened Beverages Consumption Linked to Type 2 Diabetes, Cardiovascular Diseases, and All-Cause Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. Nutrients 2021, 13(8). Nagata C, Wada K, Yamakawa M, Konishi K, Goto Y, Koda S, Mizuta F, Uji T: Intake of starch and sugars and total and cause-specific mortality in a Japanese community: the Takayama Study. Br J Nutr 2019, 122(7):820-828. Loftfield E, O'Connell CP, Abnet CC, Graubard BI, Liao LM, Beane Freeman LE, Hofmann JN, Freedman ND, Sinha R: Multivitamin Use and Mortality Risk in 3 Prospective US Cohorts. JAMA Netw Open 2024, 7(6):e2418729. Wang W, Gao J, Li N, Han S, Wu L, Zhang Y, Han T, Shan R, Li Y, Sun C et al: Dietary iron and vitamins in association with mortality. Clin Nutr 2021, 40(4):2401-2409. Yazdanpanah MH, Sharafkhah M, Poustchi H, Etemadi A, Sheikh M, Kamangar F, Pourshams A, Boffetta P, Dawsey SM, Abnet CC et al: Mineral Intake and Cardiovascular Disease, Cancer, and All-Cause Mortality: Findings from the Golestan Cohort Study. Nutrients 2024, 16(3). Dehghan M, Mente A, Zhang X, Swaminathan S, Li W, Mohan V, Iqbal R, Kumar R, Wentzel-Viljoen E, Rosengren A et al: Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 2017, 390(10107):2050-2062. Kazi RNA: Silent Effects of High Salt: Risks Beyond Hypertension and Body's Adaptation to High Salt. Biomedicines 2025, 13(3). Maino Vieytes CA, Taha HM, Burton-Obanla AA, Douglas KG, Arthur AE: Carbohydrate Nutrition and the Risk of Cancer. Curr Nutr Rep 2019, 8(3):230-239. Chen W, Du M, Liu M, Liu J: Co-occurrence patterns and related risk factors of dementia and Parkinson's disease among older adults across 204 countries and territories: a spatial correspondence and systematic analysis. BMC Med 2026. Macpherson H, Pipingas A, Pase MP: Multivitamin-multimineral supplementation and mortality: a meta-analysis of randomized controlled trials. Am J Clin Nutr 2013, 97(2):437-444. Fortmann SP, Burda BU, Senger CA, Lin JS, Whitlock EP: Vitamin and mineral supplements in the primary prevention of cardiovascular disease and cancer: An updated systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 2013, 159(12):824-834. Kumar A, Chinnathambi S, Kumar M, Pandian GN: Food Intake and Colorectal Cancer. Nutr Cancer 2023, 75(9):1710-1742. Montemayor S, Garcia S, Monserrat-Mesquida M, Tur JA, Bouzas C: Dietary Patterns, Foods, and Nutrients to Ameliorate Non-Alcoholic Fatty Liver Disease: A Scoping Review. Nutrients 2023, 15(18). Corsello A, Pugliese D, Gasbarrini A, Armuzzi A: Diet and Nutrients in Gastrointestinal Chronic Diseases. Nutrients 2020, 12(9). Additional Declarations No competing interests reported. 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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-8718995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596770702,"identity":"c0d08ff0-1bc1-44e1-a25b-067edbaf9589","order_by":0,"name":"Yuan Wang","email":"","orcid":"","institution":"Fujian Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Wang","suffix":""},{"id":596770703,"identity":"0b15a287-cea8-4248-89fe-e5c6738407d6","order_by":1,"name":"Jing Wang","email":"","orcid":"","institution":"Laboratory Center, School of Public Health, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":596770704,"identity":"00f15818-f4ee-4c76-8dd8-ef92167fc3c9","order_by":2,"name":"Quwen Li","email":"","orcid":"","institution":"Fujian Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Quwen","middleName":"","lastName":"Li","suffix":""},{"id":596770705,"identity":"f6f2fd05-ce2b-4daf-b62c-efac261d2f45","order_by":3,"name":"Hangyu Chen","email":"","orcid":"","institution":"Fujian Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Hangyu","middleName":"","lastName":"Chen","suffix":""},{"id":596770706,"identity":"ec9c324d-d336-4de7-8922-63ba4ab9d24d","order_by":4,"name":"Shuqian Huang","email":"","orcid":"","institution":"Fujian Medical University School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Shuqian","middleName":"","lastName":"Huang","suffix":""},{"id":596770707,"identity":"4a20a444-746b-4640-b21b-a0b18cf6ed31","order_by":5,"name":"Jing Wang","email":"","orcid":"","institution":"Central Laboratory, Quanzhou First Hospital Affiliated to Fujian Medical University;","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":596770708,"identity":"0593e071-7f0e-47c1-9d60-a21d6017799b","order_by":6,"name":"Yanni Li","email":"","orcid":"","institution":"Department of‌ Disease Control and Prevention, Quanzhou First Hospital Affiliated to Fujian Medical University;","correspondingAuthor":false,"prefix":"","firstName":"Yanni","middleName":"","lastName":"Li","suffix":""},{"id":596770709,"identity":"80f647f3-89c5-4bcd-8d42-41c1077b0f65","order_by":7,"name":"Fengqiong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACPuYDjA8qeMBsA+K0sLElMBucIVULm8QZBtK0MG+rOCCzLbGBvXmbBEPNHWK0sJXdOMBzO7GB51iZBMOxZ0Roke8xu/0BpEUix0yCseEwMbbwmBWAbZF/Q4IWBrAWCR6itbAVSwC1GLfxpBVbJBwjQgs/G/PGDwd7bsv2sx/eeONDDRFaGEDRwdgDtA7ETCBKAzgGfxCpdBSMglEwCkYmAACtpTVEomH6IAAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Medical University School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Fengqiong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-01-28 10:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8718995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8718995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103613249,"identity":"bae16bb5-fdf2-4a16-8b90-7d9a9297a608","added_by":"auto","created_at":"2026-02-27 16:14:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":722959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNutrients-wide analysis of all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) A heat map of β coefficients representing associations between all nutrients and mortality by sex. B) The correlation (Pearson r) between regression coefficients (beta) for the association between each nutrient and mortality calculated separately in women and men. C) Volcano plot of log-transformed P values and fold change (calculated as log2 of the HR) for all nutrients associations for mortality. Each point represents the effect and P value for the association between a single nutrients and all-cause mortality from a Cox proportional hazard model. D) Importance of individual nutrients, as assessed by a multivariable model including all nutrients.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8718995/v1/db04b2661e2c32103dd38e67.png"},{"id":103613248,"identity":"97cbc4b7-1676-401f-94dd-684d3a8c941d","added_by":"auto","created_at":"2026-02-27 16:14:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between nutrient and chronic diseases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnly associations with statistical significance were presented.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8718995/v1/a4dc6c3cba8dd9127c689194.png"},{"id":103613250,"identity":"3980d160-c211-41be-9de6-06545fdf3c4b","added_by":"auto","created_at":"2026-02-27 16:14:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJoint effect of individual nutrients on all-cause mortality risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Joint effect (95% CI) of all the nutrients on mortality risk by BKMR model when all the nutrients at particular percentiles were compared to all the nutrients at their 50th percentile. B) The estimated weights for each nutrients in the BKMR regression model. C) The cumulative effects of all nutrientson all-cause mortalityby Qgcomp regression. D) The estimated weights for each nutrients in the Qgcomp regression model.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8718995/v1/7f14e3c47e56abc8a5823607.png"},{"id":104399628,"identity":"a43ab4bb-8376-4d71-b90c-3403278ef911","added_by":"auto","created_at":"2026-03-11 12:06:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2110323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8718995/v1/4b7b61a5-6391-4aea-b082-0fdca7b24fda.pdf"},{"id":103613251,"identity":"ae27c309-4bef-442b-9700-61fb91647729","added_by":"auto","created_at":"2026-02-27 16:14:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":249524,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8718995/v1/0b11bc7fe324ff01049ccf28.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of Dietary Nutrients with All-Cause and Disease-Specific Mortality: A Nutrient-Wide Analysis in Middle-Aged and Elderly Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor middle-aged and elderly individuals, accurately recognizing those with shortened life expectancy and appropriately stratifying their associated risks is a major public health imperative.. Over recent decades, epidemiological investigations have substantially advanced our understanding of how environmental exposures and lifestyle behaviors contribute to the etiology of age-related disorders and the risk of mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Conventional modifiable risk factors linked to daily living habits-such as tobacco use, excessive alcohol consumption, lack of physical activity, unhealthy dietary patterns, and obesity-have been consistently linked to elevated mortality risk, particularly in the context of chronic disease-related deaths[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Among various environmental determinants, diet stands out as one of the most influential. Mounting evidence indicates that nutritional status exerts profound effects on human health and the aging process[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Adopting a proper lifestyle and healthy dietary patterns may confer substantial health benefits, thereby contributing to prolonged life expectancy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, most initiatives designed to alleviate the chronic disease burden have centered primarily on curbing excessive consumption of unhealthy nutrients, while the significance of ensuring sufficient intake of essential and semi-essential nutrients, has been largely overlooked. Population-based surveys further reveal that the nutritional intake among older adults is often inadequate to fully sustain healthy aging[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Existing evidence from published studies indicates that targeted enhancements in the consumption of specific food categories or nutrients may hold considerable potential for slowing the progression of age-related chronic conditions, including musculoskeletal diseases, dementia, visual impairment, and cardiometabolic disorders.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Our current understanding of how targeted nutritional interventions can slow the progression of chronic diseases remains evolving, with inconsistent effect estimates and heterogeneous levels of evidence across studies. In 2022, the US Preventive Services Task Force conducted a systematic review of randomized controlled trials examining vitamin supplementation and mortality outcomes. Their analysis concluded that the available evidence was inadequate to definitively assess the benefits or risks of such supplementation, partly due to constraints including short follow-up durations and limited generalizability of the included trials[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditionally, nutritional epidemiology has tended to examine the mortality associations of individual nutrients. The limited number of studies that did consider multiple nutrient categories failed to distinguish specific causes of death and were often constrained by small sample sizes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Hence, previous studies have several major limitations. The nutrients included in the analyses are not comprehensive. Few studies have simultaneously compared the relative impacts of distinct nutrients on overall mortality risk within the same population, and few have explored the differential effects of nutrients on cause-specific mortality related to various chronic diseases.\u003c/p\u003e \u003cp\u003eWe aimed to conduct a systematic and untargeted investigation to quantify the relative contributions of 63 nutrients to mortality risk based on the UK Biobank project, which includes approximately 500,000 men and women aged 40\u0026ndash;70 years. We identified individual nutrients that may be associated with mortality and compared the distinct roles of these nutrients in the mortality risk of 11 chronic diseases. In addition, we analyzed the mixed effects of these nutrients using Qgcomp regression and Bayesian kernel machine regression (BKMR) models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population was derived from the general population cohort of the UK Biobank (UKB), which enrolled more than 500,000 middle‑aged and elderly participants at baseline. We excluded individuals without available dietary questionnaire data (n=291,287). Furthermore, participants with extremely high or low daily energy intake were excluded, with thresholds defined as greater than 4,000 or less than 600 kcal/day for men, and greater than 3,500 or less than 500 kcal/day for women.. A total of 208,312 participants were included in the final analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDietary and nutrients assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDietary intake information was obtained via the Oxford WebQ questionnaire, a well-established dietary assessment instrument with documented validity relative to interviewer-conducted 24-hour dietary recalls [13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDuring each assessment, participants were asked to retrospectively report all foods items consumed over the preceding 24-hour period, including food types and portion sizes.Based on the reported food items and their estimated quantities, individual nutrient intake is calculated by linking each food to a standardized food composition database by a built-in algorithms as described in detail in previous publications [14, 15].\u003c/p\u003e\n\u003cp\u003eThe present study included a total of 63 nutrients, with their corresponding field identifiers in the UK Biobank documented in Table S1. In total, participants could complete up to five dietary assessments: one baseline assessment administered between April 2009 and September 2010, followed by repeated follow-up assessments conducted at 3-4 month intervals from February 2011 to June 2012. For those who underwent more than one assessment, the average values of nutrient intakes were computed to better represent long-term dietary exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAscertainment of the outcome variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe detailed protocols used to link participant records with national mortality and cause-of-death registries have been previously described in detail elsewhere[16].\u003c/p\u003e\n\u003cp\u003eFollow-up time for each participant was calculated from the date of enrollment into the UK Biobank study until January 1, 2023, or date of death, whichever occurred first. Follow‑up time for each participant was calculated from the date of enrollment into the UK Biobank study until January 1, 2023, or date of death, whichever occurred first. All-cause mortality was defined as any death recorded before the cutoff date of January 1, 2023. Cause-specific mortality was categorized using the ICD-10 coding system: infectious and parasitic diseases (A00-B99); neoplasms (C00-D48); blood and immune disorders (D55-D89); endocrine, nutritional and metabolic diseases (E00-E90); mental and behavioural disorders (F00-F89); nervous system diseases (G00-G99); circulatory system diseases (I05-I99); respiratory system diseases (J09-J99); digestive system diseases (K00-K93); musculoskeletal and connective tissue diseases (M00-M90); and genitourinary system diseases (N00-N98).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA range of covariates were incorporated in the analyses, covering key demographic and socioeconomic indicators including age, sex, ethnicity, employment status, household income, and the Townsend deprivation index. Lifestyle-related variables such as smoking status, alcohol intake, and physical activity level, as well as overall health status, were all obtained through self-report at study baseline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants were classified into never, former, or current categories according to their smoking and alcohol consumption status. Ethnicity was dichotomized as white or other. With regard to employment status, participants were divided into four groups: employed, retired, unemployed, or other. Physical activity level was categorized as low, moderate, or high. Self-reported general health was rated on a four-point scale: excellent, good, fair, or poor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were summarized as numbers and corresponding percentages, while continuous variables were reported as medians with interquartile ranges (IQR). Cox regression analysis were applied to evaluate the prospective associations between each nutrient and the risks of all-cause and cause-specific mortality. These analyses were conducted in R software with the qgcomp (version 1.1.0) and bkmr (version 0.2.0) packages, respectively. We adjusted for multiple comparisons by calculating false discovery rate (FDR)-corrected P-values using the Benjamini\u0026ndash;Hochberg procedure. All statistical analyses were carried out using R software (version 4.2.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation characteristics are summarized in Table 1. A total of 208,312 participants with 24-hour dietary recall data at baseline were enrolled in this study. The mean age of the study population is 57 years (IQR: 50-63), and 114,912 (55.16%) are female. Of these, 92.0% of the population are White. There were 12,617 deaths from all causes after a median 16.8\u0026thinsp;years of follow-up. Women had a lower all-cause mortality rate compared with men (4.5% in women versus 7.9% in men).\u003c/p\u003e\n\u003cp\u003eMortality by cause of death for all participants is given in Table S2. The number of death caused by the eleven disease was 11,765, which accounted for 93.2% of the total number of reported deaths in UKB cohort. The leading causes of death in the population include neoplasms, diseases of the circulatory system, diseases of the nervous system, diseases of the respiratory system, with proportion of death causes of 55%, 19%, 6%, and 5.9%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average total energy intake of the population was 1998.2 kcal/day. For the three major macronutrients, the median (IQR) for protein, fat, and carbohydrate were 78.40 (65.00, 93.22) g/day, 69.41 (53.35, 87.96) g/day, and 246.68 (201.87, 296.48) g/day, respectively. The distribution of 63 nutrient intakes is listed in Table S3.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 635px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1 \u0026nbsp;Baseline characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale (N\u0026thinsp;=114912)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale (N\u0026thinsp;=\u0026thinsp;93400)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (N\u0026thinsp;=\u0026thinsp;208,312)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at recruitment (year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e57.0（49.0-62.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e58.0（50.0-63.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e57.0（50.0-63.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnic background\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e104,518（90.95%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e86,335（92.44%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e190,853（91.62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e10,394（9.05%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e7,065（7.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e17,459（8.38%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e47,335（41.19%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e41,439（44.37%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e88,774（42.62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e37,039（32.23%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e32,376（34.66%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e69,415（33.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e20,342（17.70%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e11,020（11.80%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e31,362（15.06%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e10,196（8.87%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e8,565（9.17%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e18,761（9.01%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage total household income (\u0026pound; per year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026lt; 18,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e17,194（17.17%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e11,762（13.59%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e28,956（15.51%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e~ 31,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e25,570（25.54%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e19,820（22.90%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e45,390（24.32%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e~52,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e27,991（27.96%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e25,249（29.18%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e53,240（28.52%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e~100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e22,812（22.78%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e22,765（26.31%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e45,577（24.42%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026gt;100,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e6,557（6.55%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e6,936（8.02%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e13,493（7.23%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTownsend deprivation index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e-2.28（-3.70-0.11）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e-2.37（-3.77--0.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e-2.32（-3.73-0.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e69,661（60.77%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e47,970（51.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e117,631（56.62%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e37,317（32.56%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e36,613（39.31%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e73,930（35.58%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e7,644（6.67%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e8,559（9.19%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e16,203（7.80%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol drinker status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e4,801（4.18%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e1,892（2.03%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6,693（3.22%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3,568（3.11%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e2,754（2.95%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6,322（3.04%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e106,427（92.71%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e88,668（95.02%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e195,095（93.75%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e15,986（17.51%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e15,230（18.92%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e31,216（18.17%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e40,180（44%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e32,482（40.35%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e72,662（42.29%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; High\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e35,149（38.49%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e32,792（40.73%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e67,941（39.54%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral health status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Excellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e3,120（2.72%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e3,359（3.61%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e6,479（3.12%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Good\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e18,290（15.96%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e18,300（19.64%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e36,590（17.61%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Fair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e69,581（60.71%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e54,229（58.21%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e123,810（59.59%）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e23,615（20.61%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e17,272（18.54%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e40,887（19.68%）\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\u003cstrong\u003eNutrients-wide analysis of all-cause mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNutrients-wide association study analyses of all-cause mortality were conducted by serially testing 63 environmental exposures in relation to mortality via Cox proportional hazards models. The regression coefficient of each individual nutrient in the females and males group was presented in the heatmap (Fig. 1A). No notable differences were observed in regression coefficients for most nutrients when these were calculated separately in females and males (Fig. 1B), except for n-3 fatty acids, \u0026nbsp;riboflavin, vitamin B6, copper, haem iron, manganese. Detailed Cox regression results of each individual nutrient in the females and males group were shown in Table S4. In a final mortality association analysis combining females and males, 26/63 exposures (41%) were significantly replicated with \u003cem\u003eP\u0026lt;\u003c/em\u003e0.05 (Fig. 1C). Details of the Cox regression between 63 nutrients and all-cause mortality was presented in Table 1 and Table S5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 1\u003c/p\u003e\n\u003cp\u003eRegarding the association between energy intake and all-cause mortality. No association was observed between total energy intake and mortality outcomes, whereas energy density was moderately and positively associated with mortality outcomes (HR = 1.017, 95%CI: 1.004-1.030). All three major macronutrients were found to be associated with mortality outcomes. Specifically, no association was observed between total protein intake or total lipid intake and mortality risk. However, plant-derived protein (HR = 0.995, 95%CI: 0.992-0.998) and plant-derived lipids (HR = 0.997, 95%CI: 0.995-0.999) were negatively associated with mortality risk. Among lipids, n-3 fatty acids (HR = 0.967, 95%CI: 0.943-0.992) and n-6 fatty acids (HR = 0.990, 95%CI: 0.984-0.996) were also negatively associated with mortality risk.\u003c/p\u003e\n\u003cp\u003eNo statistically significant association was found between total carbohydrate intake and mortality risk, but the effects varied by carbohydrate type. Starch (HR = 0.999, 95%CI: 0.998-0.999), lactose (HR = 0.996, 95%CI: 0.993-0.999), and intrinsic and milk sugars (HR = 0.998, 95%CI: 0.997-0.999) were negatively associated with mortality risk, while free sugars (HR = 1.002, 95%CI: 1.001-1.003), non-milk extrinsic sugars (HR = 1.002, 95%CI: 1.001-1.003), sucrose (HR = 1.002, 95%CI: 1.001-1.003), and maltose (HR = 1.006, 95%CI: 1.003-1.010) were positively associated with mortality risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong mineral elements, copper (HR = 0.899, 95%CI: 0.846-0.955), manganese (HR = 0.932, 95%CI: 0.915-0.949), total iron (HR = 0.988, 95%CI: 0.980-0.996), non-haem iron (HR = 0.986, 95%CI: 0.977-0.994), showed negative associations with mortality risk to varying degrees. Among all vitamins and nutrients, only vitamin E (HR = 0.992, 95%CI: 0.985-0.998) and the B vitamins including riboflavin (HR = 0.945, 95%CI: 0.905-0.986), biotin (HR = 0.997, 95%CI: 0.995-0.999), and pantothenic acid (HR = 0.979, 95%CI: 0.966-0.992) were found to have statistically significant negative associations with mortality risk. In addition, dietary fibre intake (HR = 0.992, 95%CI: 0.988-0.996) was negatively associated with overall mortality risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong all the tested nutrients, fat (vegetable fat, n-3 and n-6 polyunsaturated fatty acids), carbohydrates (non-milk extrinsic sugars, free sugars, and maltose), mineral elements (including manganese, magnesium, phosphorus, calcium, and copper), fibre, and biotin showed the strongest associations with overall mortality risk (Fig. 1D).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"705\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 705px;\"\u003e\n \u003cp\u003eTable 2 \u0026nbsp;Nutrients-wide association analyses of all-cause mortality \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003eNutrients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 184px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 178px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 174px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eFDR-p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003eFDR-p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eHR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003eFDR-p\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eEnergy from beverages (kJ/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0001(1.0001,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0001(1.0000,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0000(1.0000,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0034\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eEnergy density (kJ/g per day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0043(0.9941,1.0146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.4544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0490(1.0374,1.0607)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0170(1.0040,1.0303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0266\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eVegetable protein (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9926(0.9902,0.9950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9913(0.9887,0.9938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9954(0.9924,0.9983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eVegetable fat (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9929(0.9913,0.9944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9970(0.9954,0.9986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9970(0.9952,0.9989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eFree sugar (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0024(1.0018,1.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0031(1.0025,1.0037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0019(1.0012,1.0026)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003en-3 fatty acids (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9426(0.9224,0.9633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9437(0.9233,0.9646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9669(0.9429,0.9915)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0242\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003en-6 fatty acids (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9724(0.9675,0.9774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9866(0.9815,0.9917)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9902(0.9843,0.9962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0054\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eEnglyst fibre (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9929(0.9897,0.9962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9830(0.9795,0.9865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9916(0.9876,0.9956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eCalcium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9999(0.9998,1.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0061\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9998(0.9997,0.9999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9998(0.9997,0.9999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eIron (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9897(0.9828,0.9968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9653(0.9581,0.9726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9879(0.9795,0.9964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0152\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003ePotassium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0000(1.0000,1.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.8480\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9999(0.9999,0.9999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9999(0.9999,1.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eMagnesium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9987(0.9984,0.9990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9977(0.9974,0.9980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9989(0.9985,0.9993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eVitamin E (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9761(0.9707,0.9815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9880(0.9822,0.9937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9915(0.9849,0.9982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0287\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eStarch (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9987(0.9982,0.9992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9996(0.9990,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.1754\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9992(0.9985,0.9998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0274\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eRiboflavin (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0024(0.9681,1.0379)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.8928\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9108(0.8768,0.9461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9445(0.9046,0.9861)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0246\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003ePhosphorus (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9997(0.9997,0.9998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9996(0.9995,0.9997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9998(0.9997,0.9999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eBiotin (ug/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9964(0.9950,0.9978)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9930(0.9914,0.9946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9969(0.9951,0.9986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eCopper (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9192(0.8711,0.9700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.8049(0.7582,0.8544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.8985(0.8455,0.9547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eLactose (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0011(0.9987,1.0035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.4211\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9964(0.9939,0.9990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.0080\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9961(0.9932,0.9990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0242\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eMaltose (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0162(1.0135,1.0189)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0113(1.0083,1.0143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0056(1.0021,1.0092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0065\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eIntrinsic and milk sugars (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9992(0.9985,0.9999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0441\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9969(0.9961,0.9976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9985(0.9976,0.9993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eManganese (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9255(0.9120,0.9392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.8894(0.8756,0.9034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9322(0.9154,0.9493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eSodium (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0001(1.0000,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0001(1.0001,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0001(1.0000,1.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0084\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eNiacin equivalent (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9941(0.9919,0.9962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9920(0.9897,0.9944)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9966(0.9939,0.9992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eNon-haem iron (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.9860(0.9789,0.9932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.0003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9615(0.9541,0.9689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9855(0.9769,0.9942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0052\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eNon-milk extrinsic sugars (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0024(1.0018,1.0030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0027(1.0021,1.0034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0018(1.0010,1.0025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003ePantothenic acid (mg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0057(0.9945,1.0169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.3819\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e0.9604(0.9491,0.9718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e0.9788(0.9655,0.9923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eSucrose (g/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.0037(1.0028,1.0045)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1.0037(1.0028,1.0046)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e1.0018(1.0008,1.0028)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.0034\u0026nbsp;\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\u003eModel 1 was the crude model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 2 was adjusted for sex, age, ethnicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 3 was further adjusted for lifestyle factors, including smoking status, alcohol consumption status, education level , Townsend deprivation index, physical activity, general health status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNutrients-wide analysis of disease specific mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor disease-specific mortality, the diseases most closely associated with nutrient intake include neoplasms, diseases of the nervous system, digestive system, and the genitourinary system (Figure 2 and Table S6).\u003c/p\u003e\n\u003cp\u003eEnergy density and sugars including free sugars , maltose , non-milk extrinsic sugars , and sucrose were positively associated with neoplasms mortality. Protein , total nitrogen, n-3 fatty acids, intrinsic and milk sugars, fibre , magnesium , selenium , manganese, vitamin D, niacin, and pantothenic acid were negatively associated with neoplasms mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor diseases of the nervous system, various sugars are closely associated with the mortality of this disease, including total sugars, free sugars, carbohydrates, fructose, glucose, non-milk extrinsic sugars, and sucrose.\u003c/p\u003e\n\u003cp\u003eFor diseases of the digestive system, only alcohol and maltose were positively associated with the mortality risk of this disease. All three major macronutrients were negatively associated with this disease, including vegetable protein, total fat, as well as vegetable fat, n-3 and n-6 fatty acids, monounsaturated fatty acids, and intake of carbohydrates and starch. Among mineral elements, calcium, magnesium, especially copper and manganese, were also negatively associated with the mortality risk of this disease. Vitamins such as vitamin E and vitamins B including riboflavin, niacin equivalent, and pantothenic acid were negatively associated with the mortality risk of this disease as well. Finally, fibre was negatively associated with digestive system disease-related mortality.\u003c/p\u003e\n\u003cp\u003eIntake of animal protein, total nitrogen, haem iron, iodine, selenium, and vitamin B12 was observed to be positively associated with genitourinary system-related mortality. As for other causes of death, only sodium and chloride were observed to be positively associated with the mortality risk of circulatory system diseases, while manganese was negatively associated with it. Intake of total sugars was positively associated with the mortality risk of mental and behavioural disorders.\u003c/p\u003e\n\u003cp\u003eNo significant association was observed for death caused by endocrine, nutritional and metabolic diseases, diseases of the blood and blood-forming organs, infectious and parasitic diseases, or diseases of the musculoskeletal system.\u003c/p\u003e\n\u003cp\u003eFig. 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe mixed effects of individual nutrients on mortality risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further conducted a mixed-effects analysis on the nutrients that were found to have a statistically significant association (\u003cem\u003eP\u003c/em\u003e \u0026lt; 1\u0026times;10⁻\u0026sup3;) with mortality risk in the previous step.\u003c/p\u003e\n\u003cp\u003eIn the BKMR regression model, although confidence intervals were wide, there was an inverse overall trend between the intake of the nutrients and mortality risk (Fig. 3A). The outcome of mortality showed decrease when all the nutrients were at their 60th percentile or above, compared to their 50th percentile, indicating a inverse association with mortality. The posterior inclusion probabilities (PIP) of each nutrient was presented in Fig. 3B, in which manganese, maltose, biotin, Energy from beverages, calcium, sucrose, niacin played most important role. Consistent with BKMR results, the cumulative effects of all the nutrients exhibited a inverse trend with mortality risk after adjusting for confounders by Qgcomp regression model as shown in Figure 3C. Results from of the fully adjusted models are presented in Table S7. The estimated weights for each nutrient are shown in Figure 3D and Table S8. Nutrients of manganese, niacin, biotin, calcium showed negative weights, while nutrients such as free sugar, iron, maltose showed positive weights in the model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 3\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study observed no statistically significant association between the total intake of energy and the three macronutrients and mortality risk, except for energy density. A potential explanation is that the average energy contribution ratios of carbohydrates, total fat, and total protein were 51.2%, 32.5%, and 16.2%, respectively, which are roughly consistent with the acceptable macronutrient distribution ranges. Thus, the risk of disease outcomes may not be sensitive to variations in the total intake of macronutrients in this population. Nevertheless, we found that the type and quality of nutrients were significantly associated with mortality risk. For instance, plant-derived protein and plant-derived lipids exerted a protective effect against all-cause mortality risk, particularly n-3 and n-6 fatty acids among lipids. Moreover, different types of carbohydrates exerted divergent effects on mortality risk: natural carbohydrates (e.g., starch, lactose, intrinsic sugars, and milk sugars) were associated with reduced mortality risk, whereas refined carbohydrates (e.g., free sugars, non-milk extrinsic sugars, sucrose, and maltose) increased mortality risk. These findings are generally consistent with the well-established mainstream evidence.\u003c/p\u003e \u003cp\u003eA large body of research and reviews has addressed the question of whether the intake amount or quality of macronutrients is more critical to the incidence of chronic diseases and mortality risk. These studies have consistently demonstrated that both plant-derived proteins and lipids are associated with a reduced risk of chronic disease mortality, compared with their animal-derived counterparts. A network meta-analysis of prospective observational studies was conducted to analyze the association between macronutrient substitution and all-cause mortality. The findings indicated that replacing 5% of energy from animal monounsaturated fatty acids (MUFAs) with plant MUFAs, as well as replacing animal protein and saturated fatty acids with plant protein, was linked to a lower mortality risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In another prospective cohort study that examined the association between dietary fats and disease-specific mortality, MUFAs,especially plant MUFAs, were found to exert a protective effect and were inversely associated with mortality risk [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Collectively, these findings suggest that plant-derived proteins and lipids are generally associated with a reduced risk of mortality.\u003c/p\u003e \u003cp\u003eNumerous studies have investigated the association between dietary carbohydrates and mortality risk. Dietary carbohydrates have long been demonized for their supposedly adverse effects on health and survival outcomes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, some studies have drawn opposite conclusions. data from the Atherosclerosis Risk in Communities (ARIC) study indicated that higher intakes of energy, animal fat, and animal protein were generally linked to an elevated mortality hazard, whereas higher carbohydrate intake was associated with a reduced mortality hazard [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the present study, we found divergent associations between different types of carbohydrates and the risk of all-cause mortality. Extensive research has been conducted on the associations between added sugar consumption and the risks of diseases and mortality; notably, intake of sugar-sweetened beverages and artificially sweetened beverages has been shown to elevate the risks of diabetes, cardiovascular disease (CVD), and all-cause mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy contrast, research on other carbohydrate types is relatively limited. A Japan cohort study assessed the associations of starch, total sugars, individual sugar types, and free sugars with risks of all-cause and cause-specific mortality and find that high starch intake was associated with decreased mortality, whereas high intakes of glucose, fructose, sucrose, maltose, and free sugars were linked to increased mortality among Japanese men [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The results of the present study are generally consistent with the conclusions of these aforementioned studies on carbohydrate subtypes.\u003c/p\u003e \u003cp\u003eControversy exists regarding the associations between vitamin and mineral intake and mortality risk. In a cohort study of US adults, data from 390,124 participants across three prospective cohort studies were analyzed, and results showed that multivitamin use was not associated with any mortality benefit [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, the study based on the National Health and Nutrition Examination Survey (NHANES) indicated that higher dietary intakes of vitamins A, B2, B6, C, E, and folic acid, combined with lower heme iron intake, were associated with reduced risks of all-cause and CVD mortality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Associations between mineral intake and mortality have not been sufficiently investigated in non-Western countries. One study addressed this gap using data from the Golestan Cohort Study, which focused on a Middle Eastern population, and examined the roles of copper, manganese, total iron, non-heme iron, and vitamin E. It found that calcium, copper, selenium, and phosphorus were associated with all-cause mortality [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Consistent with some of these findings, our study also observed significant inverse associations between mortality risk and intake of the minerals copper, manganese, and iron, as well as the vitamins vitamin E, B2, biotin, and pantothenic acid (vitamin B5). However, no independent associations were found between mortality risk and intakes of vitamin A, vitamin C, or folic acid.\u003c/p\u003e \u003cp\u003eOur study also compared the role of nutrients across different disease types. We observed that nutrients exerted more pronounced effects on certain systemic diseases than on others. The disease categories with the largest number of nutrients associated with mortality included neoplasms, diseases of the nervous system, and diseases of the digestive and genitourinary systems. As for circulatory system diseases, sodium and chloride were observed to be positively associated with mortality risk, while manganese was inversely associated with it.\u003c/p\u003e \u003cp\u003eOf the 63 nutrients tested, 20 nutrients, inlcuding macronutrients, minerals, vitamins, and dietary fiber, showed association with cancer mortality risk. The vast majority of these nutrients were inversely correlated with cancer mortality risk; only energy density and sugars (including free sugars, extrinsic sugars, and sucrose) were positively associated with elevated cancer mortality risk. Similar complex associations were also observed for digestive system disease mortality risk: 28 out of the 63 nutrients were linked to mortality risk, with most (especially minerals and vitamins) showing inverse associations. Only alcohol, beverages, and maltose were found to significantly increase mortality risk. In contrast, carbohydrates were the nutrients most closely correlated with nervous system disease mortality risk.\u003c/p\u003e \u003cp\u003eCurrently, a large body of research has focused on the associations between nutrients and mortality risks from CVD and cancer. The Prospective Urban Rural Epidemiology (PURE) study reported that total fat and specific types of fat were not associated with CVD, myocardial infarction, or CVD mortality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In the current study population, no statistically significant associations were found between intakes of lipids or carbohydrates and circulatory system disease mortality risk. However, our study revealed that sodium and chloride were positively associated with the mortality risk of circulatory system diseases, while manganese was inversely associated with this risk. Excessive sodium and chloride intake may contribute to hypertension, which is a key determinant of circulatory and CVD risk[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between carbohydrates and cancer has exhibited marked heterogeneity in research findings, which depends on the cancer type and the specific form of carbohydrates. Existing studies have indicated that the association between total carbohydrate intake and the risk of colorectal cancer as well as prostate cancer is weak. However, diets with glycemic load are positively correlated with the risk of colorectal cancer, and high intake of refined sugars, desserts and sugar-sweetened beverages is associated with increased mortality in patients with head and neck cancer[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This aligns with the findings of our study, which indicated that carbohydrate intake\u0026mdash;especially that of free sugars and extrinsic sugars\u0026mdash;could increase cancer mortality risk. Beyond its association with cancer mortality risk, evidence suggests that free sugars, extrinsic sugars, and sucrose are linked to cognitive disorders and neurodegenerative diseases, exerting detrimental effects on such conditions. A systematic analysis explored 58 detailed risk factors for dementia and Parkinson\u0026rsquo;s disease in older adults across 204 countries, revealing that diets high in sugar-sweetened beverages were specifically associated with elevated risks of dementia and Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research on the associations between vitamins, minerals, and chronic disease mortality risk has largely focused on clinical intervention studies. A meta-analysis based on randomized controlled trials investigated the effects of multivitamin supplementation on mortality due to cancer, tumors, and malignancy, reporting no significant effect of multivitamin supplementation on cancer-related mortality[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, an evidence summary derived from US clinical studies reported that, except for vitamin D, which was associated with an 11% reduction in cancer mortality odds, vitamins E, C, and selenium did not confer any benefit in reducing cancer or CVD mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings are inconsistent with the results observed in our study. This discrepancy may be attributed to the fact that clinical trials are often constrained by limited sample sizes, short intervention durations, and challenges associated with observing long-term effects. There are substantial differences between nutrient-disease mortality risk assessments based on dietary intake and those based on dietary supplements, in terms of intake dosage, absorption and utilization rates, and the concomitant intake of other beneficial dietary components.\u003c/p\u003e \u003cp\u003eA recently published systematic review focusing on observational studies of food intake and colorectal cancer reported that dietary fiber, calcium, polyphenols, curcumin, selenium, zinc, magnesium, and vitamins A, C, D, E, and B (particularly B6, B9, and B2) exerted protective effects against colorectal cancer risk [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similar reviews have summarized the associations of dietary patterns, foods, and nutrients with digestive system diseases, including chronic gastrointestinal diseases and non-alcoholic fatty liver disease [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings are consistent with the trends observed in our current population-based cohort study. Nevertheless, with the exception of data from the UK Biobank, there remains a paucity of high-quality, large-scale cohort study evidence to draw consistent conclusions regarding the associations between dietary intake of various nutrients and chronic disease mortality risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNo significant association between total energy intake, total intake of the three major macronutrients, and mortality risk. However, the type and quality of nutrients are crucial: plant-derived nutrients, certain minerals, and vitamins exert a protective effect, while refined sugars and similar substances increase the risk. Additionally, nutrients have divergent impacts on mortality risks of different diseases. In the future, more large-scale cohort studies and mechanistic explorations are warranted to further validate the causal associations between nutrients and chronic diseases, thereby providing more targeted dietary strategies for chronic disease prevention and control.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eInterquartile ranges (IQR), Hazard Ratio (HR), Confidence Interval (CI), Bayesian kernel machine regression (BKMR), False discovery rate (FDR), Monounsaturated fatty acids (MUFAs), Cardiovascular disease(CVD)\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to enrollment, all participants provided written informed consent in accordance with the guidelines outlined in the Helsinki Declaration. This study was reviewed and approved by the NHS National Research Ethics Service (Ref: 11/NW/0382).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from [in the UK Biobank (https://www.ukbiobank.ac.uk/) under application number 61083] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [UK Biobank].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted using the UKB Resource (https://www.ukbiobank.ac.uk/) under application number number 477103. This study was supported by the Fujian Natural Science Foundation Program (2020J01639).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFQL designed the research. YW, JW, QWL analyzed and interpreted the data. HYC and SQH assisted with the data analysis. JW and YNL helped to write and revise the manuscript for important intellectual content and contributed to the design of the study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKalia V, Belsky DW, Baccarelli AA, Miller GW: An exposomic framework to uncover environmental drivers of aging. Exposome 2022, 2(1):osac002.\u003c/li\u003e\n\u003cli\u003eGanna A, Ingelsson E: 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study. Lancet 2015, 386(9993):533-540.\u003c/li\u003e\n\u003cli\u003eZhang YB, Pan XF, Chen J, Cao A, Xia L, Zhang Y, Wang J, Li H, Liu G, Pan A: Combined lifestyle factors, all-cause mortality and cardiovascular disease: a systematic review and meta-analysis of prospective cohort studies. 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JAMA 2022, 327(23):2326-2333.\u003c/li\u003e\n\u003cli\u003eBajracharya R, Katzke V, Mukama T, Kaaks R: Effect of Iso-Caloric Substitution of Animal Protein for Other Macro Nutrients on Risk of Overall, Cardiovascular and Cancer Mortality: Prospective Evaluation in EPIC-Heidelberg Cohort and Systematic Review. Nutrients 2023, 15(3).\u003c/li\u003e\n\u003cli\u003eRos E: Contrasting Effects on Mortality of Monounsaturated Fatty Acid Intake Depending on Vegetable or Animal Sources. Circ Res 2019, 124(8):1154-1156.\u003c/li\u003e\n\u003cli\u003eGreenwood DC, Hardie LJ, Frost GS, Alwan NA, Bradbury KE, Carter M, Elliott P, Evans CEL, Ford HE, Hancock N et al: Validation of the Oxford WebQ Online 24-Hour Dietary Questionnaire Using Biomarkers. Am J Epidemiol 2019, 188(10):1858-1867.\u003c/li\u003e\n\u003cli\u003ePerez-Cornago A, Pollard Z, Young H, van Uden M, Andrews C, Piernas C, Key TJ, Mulligan A, Lentjes M: Description of the updated nutrition calculation of the Oxford WebQ questionnaire and comparison with the previous version among 207,144 participants in UK Biobank. Eur J Nutr 2021, 60(7):4019-4030.\u003c/li\u003e\n\u003cli\u003ePiernas C, Perez-Cornago A, Gao M, Young H, Pollard Z, Mulligan A, Lentjes M, Carter J, Bradbury K, Key TJ et al: Describing a new food group classification system for UK biobank: analysis of food groups and sources of macro- and micronutrients in 208,200 participants. Eur J Nutr 2021, 60(5):2879-2890.\u003c/li\u003e\n\u003cli\u003eMortality Data: Linkage to Death Registries (UK Biobank, 2023); https://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=115559\u003c/li\u003e\n\u003cli\u003eWallerer S, Papakonstantinou T, Morze J, Stadelmaier J, Kiesswetter E, Gorenflo L, Barbaresko J, Szczerba E, Neuenschwander M, Bell W et al: Association between substituting macronutrients and all-cause mortality: a network meta-analysis of prospective observational studies. EClinicalMedicine 2024, 75:102807.\u003c/li\u003e\n\u003cli\u003eZhuang P, Zhang Y, He W, Chen X, Chen J, He L, Mao L, Wu F, Jiao J: Dietary Fats in Relation to Total and Cause-Specific Mortality in a Prospective Cohort of 521 120 Individuals With 16 Years of Follow-Up. Circ Res 2019, 124(5):757-768.\u003c/li\u003e\n\u003cli\u003eQin P, Huang C, Jiang B, Wang X, Yang Y, Ma J, Chen S, Hu D, Bo Y: Dietary carbohydrate quantity and quality and risk of cardiovascular disease, all-cause, cardiovascular and cancer mortality: A systematic review and meta-analysis. Clin Nutr 2023, 42(2):148-165.\u003c/li\u003e\n\u003cli\u003eKrakauer NY, Krakauer JC: Diet Composition, Anthropometrics, and Mortality Risk. Int J Environ Res Public Health 2022, 19(19).\u003c/li\u003e\n\u003cli\u003eMeng Y, Li S, Khan J, Dai Z, Li C, Hu X, Shen Q, Xue Y: Sugar- and Artificially Sweetened Beverages Consumption Linked to Type 2 Diabetes, Cardiovascular Diseases, and All-Cause Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies. Nutrients 2021, 13(8).\u003c/li\u003e\n\u003cli\u003eNagata C, Wada K, Yamakawa M, Konishi K, Goto Y, Koda S, Mizuta F, Uji T: Intake of starch and sugars and total and cause-specific mortality in a Japanese community: the Takayama Study. Br J Nutr 2019, 122(7):820-828.\u003c/li\u003e\n\u003cli\u003eLoftfield E, O\u0026apos;Connell CP, Abnet CC, Graubard BI, Liao LM, Beane Freeman LE, Hofmann JN, Freedman ND, Sinha R: Multivitamin Use and Mortality Risk in 3 Prospective US Cohorts. JAMA Netw Open 2024, 7(6):e2418729.\u003c/li\u003e\n\u003cli\u003eWang W, Gao J, Li N, Han S, Wu L, Zhang Y, Han T, Shan R, Li Y, Sun C et al: Dietary iron and vitamins in association with mortality. Clin Nutr 2021, 40(4):2401-2409.\u003c/li\u003e\n\u003cli\u003eYazdanpanah MH, Sharafkhah M, Poustchi H, Etemadi A, Sheikh M, Kamangar F, Pourshams A, Boffetta P, Dawsey SM, Abnet CC et al: Mineral Intake and Cardiovascular Disease, Cancer, and All-Cause Mortality: Findings from the Golestan Cohort Study. Nutrients 2024, 16(3).\u003c/li\u003e\n\u003cli\u003eDehghan M, Mente A, Zhang X, Swaminathan S, Li W, Mohan V, Iqbal R, Kumar R, Wentzel-Viljoen E, Rosengren A et al: Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study. Lancet 2017, 390(10107):2050-2062.\u003c/li\u003e\n\u003cli\u003eKazi RNA: Silent Effects of High Salt: Risks Beyond Hypertension and Body\u0026apos;s Adaptation to High Salt. Biomedicines 2025, 13(3).\u003c/li\u003e\n\u003cli\u003eMaino Vieytes CA, Taha HM, Burton-Obanla AA, Douglas KG, Arthur AE: Carbohydrate Nutrition and the Risk of Cancer. Curr Nutr Rep 2019, 8(3):230-239.\u003c/li\u003e\n\u003cli\u003eChen W, Du M, Liu M, Liu J: Co-occurrence patterns and related risk factors of dementia and Parkinson\u0026apos;s disease among older adults across 204 countries and territories: a spatial correspondence and systematic analysis. BMC Med 2026.\u003c/li\u003e\n\u003cli\u003eMacpherson H, Pipingas A, Pase MP: Multivitamin-multimineral supplementation and mortality: a meta-analysis of randomized controlled trials. Am J Clin Nutr 2013, 97(2):437-444.\u003c/li\u003e\n\u003cli\u003eFortmann SP, Burda BU, Senger CA, Lin JS, Whitlock EP: Vitamin and mineral supplements in the primary prevention of cardiovascular disease and cancer: An updated systematic evidence review for the U.S. Preventive Services Task Force. Ann Intern Med 2013, 159(12):824-834.\u003c/li\u003e\n\u003cli\u003eKumar A, Chinnathambi S, Kumar M, Pandian GN: Food Intake and Colorectal Cancer. Nutr Cancer 2023, 75(9):1710-1742.\u003c/li\u003e\n\u003cli\u003eMontemayor S, Garcia S, Monserrat-Mesquida M, Tur JA, Bouzas C: Dietary Patterns, Foods, and Nutrients to Ameliorate Non-Alcoholic Fatty Liver Disease: A Scoping Review. Nutrients 2023, 15(18).\u003c/li\u003e\n\u003cli\u003eCorsello A, Pugliese D, Gasbarrini A, Armuzzi A: Diet and Nutrients in Gastrointestinal Chronic Diseases. Nutrients 2020, 12(9).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8718995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8718995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To our knowledge, a systematic comparison of nutrients contribution to mortality in large scale cohort of middle-aged to elderly individuals has not yet been done. We aim to investigate the associations between most of the available nutrients and all-cause and disease-specific mortality, and explored their joint effect on mortality risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 208,312 participants from the UK Biobank (UKB) with baseline 24-hour dietary recall data were enrolled. Cox proportional hazards models were used for a nutrients-wide association analysis of all-cause mortality and disease-specific mortality. Mixed-effects analyses were further conducted to evaluate the combined effects of nutrients significantly associated with mortality risk by BKMR and Qgcomp regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e No significant associations were found between total energy, total protein, total lipid, or total carbohydrate intake and all-cause mortality risk. However, energy density was moderately and positively associated with all-cause mortality (HR=1.017, 95%CI: 1.004-1.030). Nutrient type and quality exhibited significant impacts: plant-derived protein (HR=0.995, 95%CI: 0.992-0.998), plant-derived lipids (HR=0.997, 95%CI: 0.995-0.999), were negatively associated with all-cause mortality. Among carbohydrates, starch, lactose, and intrinsic/milk sugars showed protective effects, while free sugars, non-milk extrinsic sugars, sucrose, and maltose were positively associated with increased mortality risk. For minerals and vitamins, copper, manganese, total iron, non-haem iron, vitamin E, riboflavin, biotin, and pantothenic acid exhibited inverse associations with all-cause mortality. Mixed-effects analyses revealed cumulative inverse trends of beneficial nutrients and positive trends of harmful nutrients on mortality risk, with manganese, maltose, biotin, and niacin being key contributors. Disease-specific analysis showed that energy density and certain sugars were positively associated with neoplasms mortality; multiple sugars were linked to nervous system disease mortality; and alcohol, maltose were positively associated with digestive system disease mortality, while most macronutrients, minerals, vitamins, and fibre had protective effects. Sodium and chloride were positively associated with circulatory system disease mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eTotal intake of major macronutrients was not significantly associated with mortality risk, but nutrient type and quality played critical roles. Plant-derived nutrients, specific minerals, vitamins, dietary fibre, and natural carbohydrates were protective against mortality, whereas refined sugars and high energy density were detrimental. These findings highlight the importance of dietary quality in reducing mortality risk and provide evidence for developing targeted dietary recommendations.\u003c/p\u003e","manuscriptTitle":"Associations of Dietary Nutrients with All-Cause and Disease-Specific Mortality: A Nutrient-Wide Analysis in Middle-Aged and Elderly Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 16:14:06","doi":"10.21203/rs.3.rs-8718995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T06:07:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T03:52:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T10:04:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107598083695893435517754163055287893920","date":"2026-03-22T03:27:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"312145004874245395195283169427234238236","date":"2026-03-19T23:58:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T17:15:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T13:53:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T11:38:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T15:06:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-10T13:49:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb52d574-959d-41fc-8493-4452d4fa7f71","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T07:25:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 16:14:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8718995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8718995","identity":"rs-8718995","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}