Gender and age disparities in risk factor burden in China: a GBD 2021 analysis with projections to 2050

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Methods Using GBD 2021 data, we analyzed exposure, mortality, and disability burdens for 68 risk factors across genders/age groups. Estimated Annual Percentage Change (EAPC) quantified 32-year trends (1990–2021). Future trends (2022–2050) were projected via ARIMA modeling. Results Ambient particulate matter pollution exposure increased most markedly (Female EAPC: 5.26%; Male: 4.50%), with SEV projected to exceed 88% across all ages by 2050. Associated deaths and YLDs will concentrate in those ≥ 70. Mortality from nutritional deficiencies declined significantly (e.g., vitamin A deficiency: Female EAPC − 17.37%; Male − 18.02%). Notable gender disparities existed in 2021: insufficient milk intake (Women: 97.97% SEV; Men: 51.76%) and secondhand smoke (Women: 72.82% SEV; Men: 33.34% vs. 43.38% smoking rate). Diets high in red meat (EAPC: 18.92%), sugar-sweetened beverages (EAPC: 4.18%), and processed meat increased mortality risk, exacerbating metabolic factors (BMI, FBG, LDL, SBP) in ages 15+, elevating future death/disability burdens. For children (5–14 years), nutrition (vitamin A deficiency, short gestation, low birth weight) and metabolic (high BMI) factors are projected to increase disability burden. Conclusion Risk factor profiles distinctly vary by gender and age in China, necessitating demographically tailored prevention for effective disease control and public health improvement. Risk factors China Sex factors Age factors Air pollution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Reducing modifiable risk factors is a critically important and successful strategy for averting ill health and early death from diseases and accidents. Effective risk-reduction policies and practices depend on data that is specific to a given location and population, including trends in the prevalence of leading risk factors, the percentage of disease-specific mortality and morbidity that can be attributed to specific risk factors, and the relationships between risk factors and health outcomes. Additionally, age-specific control of disease risk factors helps achieve more precise disease prevention and health management, enhancing the effectiveness and cost-effectiveness of intervention measures, as well as improving the population's overall health. 1 , 2 Timely and accurate prediction of the level of exposure to health risk factors is of great significance for the government to effectively adjust future environmental intervention policies, help health professionals and policy makers understand the health priorities, enhance public health awareness, and reduce the burden of diseases on the population. The Healthy China 2030 plan is the country's signature domestic population health program, and it has elevated population health to the top of the economic and political agenda. Two of the goals are to promote lifespan and healthy life expectancy, as well as to increase illness prevention. Other goals include lowering newborn mortality, improving the environment (eg, air quality), health services and insurance (eg, chronic disease mortality), changing one's lifestyle (eg, regular exercise), and reforming the health industry. Due to age differences in living environment and lifestyle, close forecast of population health metrics at the age-specific levels will be crucial to developing evidence­based policies and achieving the Healthy China 2030 goals. 3 Our study has reviewed the trends in exposure levels and disease burdens of 68 detailed risk factors across different age groups in the Chinese region from the GBD database over the past 32 years. Based on previous data, we also have predicted the development trends of the top 10 risk factors with higher exposure levels and higher disease burdens in 2021 over the next 20 years, which can provide evidence for the precise etiological prevention of diseases. Methods Data source and definitions The exposure level and impact of risk factors on mortality and health-related quality of life for China were obtained from GBD 2021 online results tool. Using the most recent epidemiological data and improved standardized methodologies, GBD 2021 provided a thorough evaluation of relevant metrics for 23 age groups from birth to age 95 years and older; for males, females, and all sexes combined; and for 204 countries and territories grouped into 21 regions and seven super-regions. Additionally, GBD 2021 produced risk-specific estimates of summary exposure value (SEV), RR, population attributable fraction (PAF), risk attributable burden measured in disability-adjusted life years (DALYs; the sum of years of life lost to premature mortality and years lived with disability), and deaths. We used above records of the most detailed risks, together with the related crude and age-standardized rates (ASRs) from the GBD 2021 by factors such as age group and gender. Since our study was based on publicly available data, there was no need for ethical approval or informed consent. Statistical analysis ASR was derived through weighted averaging of age-specific rates. To signify ASR trends of risk factors spanning from 1990 to 2021, we computed the EAPC (estimated annual percent change) utilizing the formula where Y = ln (ASR), X = calendar year, and ε = the error term. Y = α + βX + ε EAPC = 100 × (exp(β) − 1) Based on linear regression models, we obtained a 95% confidence interval (CI) for our findings. When both EAPC estimations and 95% CI lower limits were positive, the ASR trended upward. When both EAPC estimations and 95% CI upper limit were negative, the ASR decreased. The trend remained stable during the study period if neither of these conditions were met. The Auto-Regressive Integrated Moving Average (ARIMA) model is a popular technique used in epidemiology for forecasting time-series data. In our study, we constructed an ARIMA model using the R programming language to predict the SEVs and burden attributed to risks from 2022 to 2050. Given the variation in risk factors across different age and gender groups, this study focuses on the top 10 risk factors for each group in 2021 and their projected future trends. Results 3.1 SEVs of risk factors The SEV trend of risk factors over the past 32 years by gender The ASR changes from 1990 to 2021 of the SEVs of 68 detailed risks are shown in Fig. 1 , the SEV of diet low in vegetables had the most significant decline (EAPC of -8.16 [95% CI: -8.52, -7.80] in female and − 7.33 [95% CI: -7.60, 7.06] in male), followed by household air pollution from solid fuels and vitamin A deficiency in both genders. There is a significant gender difference in the control of iron-deficiency(EAPC of -5.56 [95% CI: -5.78, -5.37] in male while − 1.07 [95% CI: -1.12, 1.02] in female), Similarly, the SEV of diet low in calcium, (EAPC of -3.60 [95% CI: -3.70, -3.49] in male while − 1.65 [95% CI: -1.71, 1.58] in female). The SEV of ambient particulate matter pollution had the most significant increase in both genders (EAPC of 5.26 [95% CI: 4.81, 5.63] in female and 4.50 [95% CI: 4.11, 4.89] in male), followed by diet high in sugar-sweetened beverages, high BMI and diet high in processed meat. Compared to male groups, the trend of occupational exposure to benzene among female workers is more significant. (EAPC of 2.26 [95% CI: 2.24, 2.28])。Furthermore, the trajectory of occupational noise and asthmagens by gender is entirely opposite, with male showing a declining trend and female showing an upward trend. The top 10 risk factors for SEV among all age groups in 2021 Different age groups exhibited distinct high-risk exposure factors in 2021 ( Table 1 ), the top 10 SEV risk factors for children under 5 years of age and those aged 5–14 are predominantly environmental in nature, and the high-risk factors in these two groups exhibit a high degree of overlap. For the population aged over 15, which encompasses those aged 15–49, 50–69, and above 70, the types of risk factors are mainly lifestyle-related, and the types of risk factors in these three groups are also highly congruent. Some risk factors present significant gender disparities in SEVs. More than half of children under the age of 5 years were still highly exposed to secondhand smoke environments: the SEV rates for males and females were 57.21% and 64.16%, respectively. Inadequate feeding approaches encompass discontinued breastfeeding and non-exclusive breastfeeding, which still constitute a considerable proportion (the SEV: 48% and 31% respectively). Subsequently, environmental risks such as ambient particulate matter pollution, high temperature, unsafe sanitation, unsafe water sources, and low temperature come into play. Moreover, 18.64% of female infants and young children are exposed to nitrogen dioxide pollution, meanwhile, a proportion of 24% of the male children exhibited highly body-mass index. The leading 8 high exposure risk factors for the 5–14 age group are largely in accordance with those of the < 5 age group (excluding feeding conditions), yet with higher exposure probabilities. It is notable that 12.61% of males and 10.05% of females underwent bullying victimization. Additionally, 6.7% of the population in both sexes still lack access to handwashing facilities. Among the 15–49 age group, more than half of the high exposure risk factors pertain to dietary aspects. Over 82% of individuals of both sexes have a diet deficient in Omega-6 polyunsaturated fatty acids. Virtually all women (SEV: 97.97%, 95%CI [99.81%, 96.17%]) have an insufficient intake of milk, whereas the SEV of a diet low in milk for males was 51.76%. Another prominent gender gap risk factor is secondhand smoke, with SEVs being 33.34% for males and 72.82% for females, while 43.38% of men are smokers. Over half of the population has a diet high in sodium, with SEVs being 62.6% and 53.66% for males and females respectively, and other high-exposure dietary factors encompass a diet high in red meat and a diet low in whole grains. High LDL cholesterol metabolic factors also account for a considerable proportion in the 15–49 age group, with SEVs being 43.82% and 45.41% for males and females respectively. The exposure rate of the environmental factor, ambient particulate matter pollution, is also high (SEV: 46.27% for males, 45.22% for females). 34.77% of men have lead exposure. Additionally, 33.06% females exposed to high temperature and 28.9% females had low intake in nuts and seeds. The high-risk dietary factors for the 50–69 age group are largely in alignment with those of the 15–49 age group. The most critical dietary issue for males lies in high sodium intake, while for females, it is the insufficient intake of milk. Additionally, more than 82% of both genders have a deficiency in omega-6 polyunsaturated fatty acids. nevertheless, it is notable that the former has a higher level of lead exposure. The SEVs were 57.16% for males and 47.1% for females. In contrast to the 15–49 age group, metabolic factors led to an increase in high SBP in addition to high LDL cholesterol in the 50–69 age group. In comparison with the 50–69 age group, the elderly over 70 had an extra high-risk factor of low physical activity, with SEVs being 41.95% for males and 53.16% for females. Other dietary matters exhibit a high degree of consistency among individuals aged 50–69 and those aged 15–49. Exposure Trends of the Top 10 Risk Factors Across Different Age Groups Over the Next 30 Years We applied the ARIMA model to estimate the SEVs of the most prevalent risk factors across age groups from 2022 to 2050. As illustrated in Fig. 2, ambient particulate matter pollution is a major risk factor for children under 5 years old and those aged 5–14 years. The level of exposure is projected to rise sharply over the next three decades, surpassing 88% by 2050. Another risk factor with a steadily increasing trend is high BMI. By 2050, the SEV for boys under 5 years old is expected to reach 48%, while among both genders aged 5–14 years, it will increase to 38%. Importantly, secondhand smoke will continue to show high exposure levels across all subgroups under 15 years of age. Although a slight decline is anticipated for males under 5 years old, the SEV remains above 50%, and in other subgroups, it stays around 64%. Similarly, discontinued breastfeeding (48.7% in 2050) and non-exclusive breastfeeding (25% in 2050) among children under 5, as well as exposure to high temperatures (33% in 2050) and low temperatures (25% in 2050) in both age groups, are expected to remain consistently elevated but stable. Other risk factors either persist at low exposure levels or demonstrate a continuous decrease. As illustrated in Fig. 2 , among the three age groups above 15 years of age, ambient particulate matter pollution continues to be a prevalent risk factor with consistently rising exposure levels. By 2050, SEVs for all three groups are projected to surpass 93%. For other environmental risk factors such as lead exposure, distinct variations in decline trends are observed across age groups. The most rapid reduction occurs in the 15–49 age group, whereas only a minor decrease is anticipated in the population aged over 70. Exposure to high temperatures, in contrast, demonstrates no clear trend of change. Regarding dietary risk factors, the SEV of diets low in nuts and seeds exhibits a marked downward trajectory. Most other dietary risks, however, remain at elevated levels without significant reductions. Furthermore, aside from a moderate decline in the 15–49 age group (33.9% in 2050), the SEV for diets high in red meat is expected to rise across all other age groups. The patterns of change in behavior-related and metabolic risk factors align closely with those observed in dietary factors. 3.2 Disease Burden Associated with Risk Factors Trends in the disease burden attributable to various risk factors over the past 32 years are presented below. As illustrated in Figure_3A, the burden of death linked to nutritional deficiency-related risk factors have exhibited a marked decline. The most substantial reductions in population mortality burden were observed for vitamin A deficiency (EAPC: -17.37 [95% CI: -18.12, -16.61] in females and − 18.02 [95% CI: -18.47, -17.56] in males), zinc deficiency (EAPC: -15.24 [95% CI: -15.79, -14.67] in females and − 13.44 [95% CI: -13.89, -13.00] in males), discontinued breastfeeding (EAPC: -14.06 for both sexes), child stunting (EAPC: -13.03 for both sexes), and unsafe sanitation (EAPC: -12.85 for both sexes). In contrast, The burden of death associated with dietary behavior and occupational exposure risk factors has generally increased significantly. The most notable upward trends were seen in diet high in red meat (EAPC: 18.92 for both sexes), diet high in sugar-sweetened beverages (EAPC: 4.18 for both sexes), and diet high in processed meat, as well as environmental particulate matter pollution (EAPC: 1.54 for both sexes) and occupational exposure to trichloroethylene (EAPC: 1.79 for both sexes), with no significant gender differences in these trends. However, childhood sexual abuse (EAPC: -3.99 [95% CI: -4.15, -3.83] in females vs. 0.24 [95% CI: -0.27, 0.75] in males) and unsafe sex (EAPC: -0.66 [95% CI: -0.84, -0.49] in females vs. 6.87 [95% CI: 6.22, 7.53] in males) demonstrated clear gender disparities, with significant declines in females and either no change or a sharp increase in males. As illustrated in Figure_3B, the most substantial declines in risk factors contributing to female disability were observed for child wasting (EAPC: -13.89 [95% CI: -14.30, -13.48] in females) and child underweight (EAPC: -13.25 [95% CI: -13.64, -12.86] in females). In contrast, among males, the greatest reductions were seen in non-exclusive breastfeeding (EAPC: -11.91) and child stunting (EAPC: -10.27). For both genders, the risk factors with the most pronounced increases included high consumption of sugar-sweetened beverages (EAPC: 7.36 for both sexes), high red meat intake (EAPC: 5.79 for both sexes), and increased consumption of processed meat (EAPC: 4.84 for both sexes). Notably, secondhand smoke exposure (EAPC: -0.17 [95% CI: -0.29, -0.05] in females vs. 0.72 [95% CI: 0.63, 0.81] in males) and diets high in sodium (EAPC: -0.09 [95% CI: -0.17, -0.01] in females vs. 0.28 [95% CI: 0.22, 0.33] in males) exhibited gender-specific trends, with increasing patterns in males and decreasing trends in females. The Top 10 death burden associated risk factors by age group in 2021 As illustrated in Figure_4A, among males in the infant and toddler period (< 5 years old), leading causes of death are associated with birth defects and environmental exposures, including low birth weight (11,734 deaths), preterm birth (8,624 deaths), and exposure to environmental particulate matter pollution (3,449 deaths). For females in the same age group, the mortality-related risk factors are largely similar but occur at lower magnitudes—such as low birth weight, which accounts for 8,102 deaths. The number of deaths attributed to various risk factors declines significantly in the 5–14 age group; however, high alcohol use remains a concern for males, contributing to approximately 469.7 deaths. Among males aged over 15 years, smoking and hypertension are the primary risk factors across all three age groups, with mortality burdens increasing with advancing age. Additionally, high alcohol consumption and diets high in sodium also warrant attention. Other major contributors to mortality include environmental particulate matter pollution and metabolic risk factors. For females over 15 years of age, the two leading mortality-associated risk factors across all three age groups are hypertension and environmental particulate matter pollution, although the absolute number of deaths is considerably lower than in males. In the 15–49 age group, unsafe sex emerges as a significant cause of mortality among females, accounting for 12,500.4 deaths. Similar to males, metabolic risk factors remain prominent among females aged over 15 years. Notably, secondhand smoke consistently ranks among the top ten mortality-related risk factors across all female age groups. The Top 10 Risk Factors Contributing to YLDs by Age Group in 2021 As illustrated in Figure_4B, among children under the age of 5, there is no gender difference in the leading causes of disability-adjusted life years (YLDs), with iron deficiency and low birth weight being the primary contributors. In the 5–14 age group, the YLDs due to iron deficiency in females has increased more than tenfold, making it the most severe disabling factor (4,892.9 years for females under 5 years old vs. 85,333.1 years for females aged 5–14 years). For both males and females in this age group, short gestation ranks among the top two causes of disability, and bullying victimization also imposes a substantial burden (51,192.4 years for males and 42,291.7 years for females). Among individuals aged 15–49 years, metabolic risk factors show similar patterns between genders; however, notable differences exist in behavioral and dietary risks. Males are predominantly affected by alcohol use (1,409,149.2 years) and smoking (1,247,760.5 years), whereas females experience higher YLDs from iron deficiency (782,548.7 years) and intimate partner violence (369,256.2 years). For both age groups over 50 years, metabolic disorders and exposure to environmental particulate matter pollution are the leading causes of disability. However, smoking remains the most significant contributor to YLDs among males. Trends in Disease Burden Attributable to Risk Factors Across Age Groups Over the Next 30 Years Trends in Mortality Burden Caused by Risk Factors Using the ARIMA model, we further projected the trends of the top ten mortality-related risk factors for each age group from 2022 to 2050. As shown in Figure_5A , the mortality burden associated with environmental and childhood nutritional risks continues to decline. This includes deaths linked to unsafe water sources, indoor air pollution from solid fuels, child stunting, child wasting, and low birth weight. Notably, the mortality burden caused by secondhand smoke among children under five years old is projected to rise, with a higher impact on girls compared to boys (18,380 vs. 8,315 deaths in females and males, respectively, by 2050). Among individuals aged over 15 years, most risk factors are expected to lead to increasing mortality burdens, particularly in the over-70 age group. The main contributors include metabolic factors such as high BMI, behavioral factors like diets high in sodium and smoking, and ambient particulate matter pollution. In the female population, mortality burdens from unsafe sex and household air pollution due to solid fuels are predicted to decrease significantly, while secondhand smoke will continue to increase, primarily affecting those aged over 70 (projected to exceed 250,000 deaths by 2050). For males, the mortality burden from diets low in whole grains among the 15–49 age group is expected to decline, whereas lead exposure in men over 70 years old will rise sharply (projected to exceed 350,000 deaths by 2050). Additionally, alcohol use in the 50–69 age group will result in over 20,000 deaths by 2050. Trends in Disability Burden Caused by Risk Factors Across Age Groups As shown in Figure_5B, Nutrition-related factors—including vitamin A deficiency, short gestation, and low birth weight—as well as metabolic factors such as high BMI are projected to increase the disability burden among children aged 5–14 years. The disability burden attributed to vitamin A deficiency in children under five is also expected to rise, with a more pronounced increase observed in boys (projected to exceed 4,000 YLDs by 2050). Meanwhile, nitrogen dioxide pollution, secondhand smoke, and bullying victimization are not expected to show significant changes in their disability burden among children and adolescents; however, bullying victimization remains a persistent contributor at a high level. Iron deficiency and lead exposure demonstrate notable downward trends across both age groups. Importantly, bullying continues to impose a substantial disability burden on adult males aged 15–49, exceeding 30,000 YLDs after 2030. Among the three age groups over 15 years, outdoor particulate matter pollution is projected to cause a marked increase in disability burden. Similar upward trends are observed for metabolic risk factors such as high fasting blood glucose (FBG), high systolic blood pressure (SBP), high low-density lipoprotein (LDL), and kidney dysfunction. Women face an ongoing rise in disability burden from secondhand smoke and indoor solid fuel use, while men experience growing burdens related to lifestyle factors including high salt intake, excessive alcohol consumption, and tobacco use. Furthermore, individuals over 50 years of age should be vigilant about rising disability burdens associated with kidney dysfunction and reduced bone mineral density. Discussion Significant variations exist in the current and projected trends of risk factors across different age and gender groups. Secondhand smoke and iron deficiency continue to exert substantial health impacts, particularly on women and children. For females, unsafe sexual practices and intimate partner violence represent major contributors to disease burden. Among males, smoking and excessive alcohol consumption are driving an upward trend in disease burden. In children and adolescents, low birth weight, preterm birth, short gestation, and bullying victimization remain leading causes of mortality and disability. Concurrently, exposure to unhealthy dietary patterns—including high salt intake, increased consumption of red and processed meats, high sugar intake, and low whole grain consumption is rising among adults. These behaviors are expected to exacerbate metabolic conditions such as elevated BMI, FBG, LDL, and SBP, thereby increasing the future burden of death and disability. Over the past 32 years, the Chinese government has successfully reduced the exposure risks and associated health burdens of nutrition-related factors and poor sanitation. However, the exposure risk and health impact of outdoor particulate matter pollution have been steadily increasing across all age groups, indicating a growing public health challenge that requires urgent attention. Secondhand smoke imposes a substantial burden of mortality and disability across all female age groups. Although the implementation of public smoking bans over the past 32 years has led to a modest reduction in disease burden among women compared to 1990, projections indicate that the burden continues to rise among two specific subgroups: children under five years of age and women over 50 years old. The increasing trend among young children may be attributed to inadequate control of secondhand smoke exposure within household settings 4 , whereas the rising burden in older women is likely due to the cumulative effects of long-term disease progression. 5 Exposure to secondhand smoke poses significant health risks for both women and children, representing a critical public health concern. Accumulating evidence demonstrates that secondhand smoke is strongly associated with a range of adverse health outcomes, particularly in these vulnerable populations. Children exposed to secondhand smoke face elevated risks of developing asthma, respiratory infections, and cardiovascular diseases 6 . In addition, exposure has been linked to behavioral issues, sleep disturbances, and an increased likelihood of cancer 7 。Among women, studies have shown a significant association between secondhand smoke exposure and the development of chronic bronchitis, particularly in Taiwanese women, where such exposure markedly increases disease risk 8 . Furthermore, secondhand smoke may impair lung function in non-smoking women; however, some research suggests that these women may exhibit greater resilience to smoke-induced pulmonary decline 9 . Emerging evidence also indicates that secondhand smoke negatively affects children's oral health. Postnatal exposure has been found to increase the incidence of dental caries, with this association remaining statistically significant after controlling for confounding variables 10 . Therefore, reducing secondhand smoke exposure not only improves public health outcomes but may also yield substantial economic benefits by lowering healthcare expenditures 11 . In addition to secondhand smoke, the mortality burden attributable to unsafe sexual behavior among women remains a significant public health concern. Unsafe sexual practices expose women to multiple reproductive tract infections, with sexually transmitted infections (STIs) representing a primary consequence. High-risk pathogens such as HIV, HPV, and Chlamydia trachomatis are strongly associated with increased risks of AIDS, cervical cancer, and ectopic pregnancy, all of which have profound long-term impacts on women's quality of life. Over the past 32 years, the mortality burden resulting from unsafe sexual behavior among Chinese women has remained consistently high. However, recent widespread HPV vaccination has led to a modest decline in related deaths, and projections suggest this downward trend will continue in the future. A similar pattern is observed in the disability burden caused by intimate partner violence: although historically high, it has shown a marked decrease following targeted policy interventions. Notably, in contrast to the declining trends observed in women, the mortality burden associated with unsafe sexual behavior among men has significantly increased over the same period. Multiple data sources indicate that male-to-male sexual contact, drug use, inconsistent condom use, and commercial sex are the primary contributors to unsafe sexual behavior among men 12 , 13 , Nevertheless, due to traditional cultural norms in China, the availability of exposure and disease data remains limited, leading to substantial underestimation of both risk and disease burden. The most critical contributing factor to this situation is the lack of comprehensive sex education combined with the gradual liberalization of sexual attitudes driven by internet penetration. Research highlights that schools serve as optimal settings for delivering sex education to students and play a pivotal role in standardizing HIV/AIDS health education, expanding outreach, and enhancing the effectiveness of public awareness campaigns. 13 Exposure risks associated with nutrient deficiencies and their corresponding disease burdens have shown substantial improvement among the Chinese population over the past 32 years. For instance, both the exposure risk and health impact of vitamin A deficiency have significantly declined. While iron deficiency has also seen improvements, these gains are uneven across genders. Notably, the disability burden attributable to iron deficiency, particularly among adolescent girls, continues to rise. A similar pattern is observed for calcium deficiency, where low bone mineral density is increasingly contributing to disability, especially among elderly women. Research on mineral intake among lactating women in China indicates that many do not meet the estimated average requirements for calcium and iron. This shortfall may be linked to long-term inadequate milk consumption, as dairy products are key dietary sources of these nutrients. More concerning is that insufficient mineral intake often persists during lactation, potentially compromising maternal health and infant development. Therefore, increasing consumption of milk and other calcium- and iron-rich foods could help alleviate this issue and reduce the health consequences of mineral deficiencies 14 . Early childhood iron deficiency is strongly associated with impaired cognitive and motor development in children and adolescents 15 , 16 , underscoring the importance of early-life interventions. In recent years, China has implemented several staple food fortification programs, such as promoting iron-fortified infant rice cereal and iron-enriched flour, which have effectively lowered population-wide exposure to iron deficiency. Nevertheless, targeted interventions focusing on infants and adolescent girls remain necessary to further mitigate the disease burden caused by deficiencies in essential minerals like iron and calcium. Smoking and high alcohol use, as two major unhealthy lifestyle factors, constitute significant contributors to mortality and disability among Chinese men. These findings highlight the urgent need to prioritize individual health-related risk behaviors in disease prevention strategies. Effective interventions should focus on guiding male populations toward healthier behaviors through measures such as restricting the availability of tobacco and alcohol and reducing related advertising. Notably, excessive alcohol consumption also imposes a measurable mortality burden on adolescents aged 5–14 years. Adolescent alcohol use disorder has increasingly become a focal point in public health due to its direct physiological harm and indirect social consequences, including increased risks of school violence. Therefore, implementing legal restrictions on alcohol access for minors is critically important 17 . In addition to governmental policy interventions, school-based programs for managing health risk behaviors play an essential role in addressing these issues and promoting long-term behavioral change 18 . In addition to alcohol use, bullying victimization imposes a significant disability burden on adolescents, with this burden persisting at alarmingly high levels into adulthood, particularly among males. Although efforts over the past 32 years have led to some control of this issue, the burden remains substantial, indicating the need for continued and enhanced preventive measures to safeguard adolescent health. Bullying victimization has wide-ranging negative effects on youth well-being. Moore et al. conducted a comprehensive systematic review of extensive literature and found robust evidence of a causal relationship between bullying victimization and multiple adverse outcomes in adolescents, including anxiety, depression, mental and physical health problems, non-suicidal self-injury, suicidal ideation, and suicide attempts 19 , Furthermore, numerous studies have demonstrated that the detrimental effects of childhood bullying often persist into adulthood, underscoring the long-term public health significance of this issue 20 , 21 . Over the past decade, rapid economic development has largely eliminated nutritional deficiency risks associated with insufficient food intake among the Chinese population. However, a new set of dietary challenges has emerged, characterized by excessive consumption of sugar, salt, and fat, combined with low whole grain intake. This unbalanced nutrient profile is contributing to a growing prevalence of metabolic diseases and is accompanied by widespread deficiencies in key micronutrients such as unsaturated fatty acids, calcium, iron, and dietary fiber 22 These dietary issues are not only prevalent among adults but are also increasingly observed among adolescents, showing a rapidly rising trend. Data from the China Health and Nutrition Survey (CHNS) indicate that fat intake among Chinese residents is generally high, particularly in terms of saturated and trans fatty acids, which exceed recommended levels. The proportion of energy derived from fat has surpassed 30%, significantly exceeding the recommended range of 20–30%. Saturated fatty acids primarily originate from animal-based fats such as fatty meat and lard, while trans fatty acids are mainly found in foods containing partially hydrogenated vegetable oils, including margarine and fried foods. Excessive intake of these fats is strongly associated with increased risks of cardiovascular disease and obesity. Moreover, whole grain consumption accounts for only about 10% of total grain intake among Chinese residents. This imbalanced carbohydrate intake pattern not only results in inadequate dietary fiber consumption but may also impair glucose regulation and gut health. Vitamin deficiencies remain prevalent across the population, particularly for vitamins A, B₂, and C, with a large proportion of individuals failing to meet recommended intake levels. CHNS data show that approximately 70% of residents do not achieve the recommended daily intake of vitamin A 23 , 24 . Collectively, these findings highlight the urgent need for comprehensive dietary interventions targeting the entire population. Strategies should include strengthening public health education, implementing policy-level regulations, and providing tailored interventions for vulnerable groups. Additionally, the catering industry must play an active role by reducing the availability of high-salt, high-sugar, and high-fat foods and promoting healthier dietary behaviors to support long-term improvements in national nutrition. Currently, the exposure risk and disease burden associated with outdoor air particulate matter among the general population in China are increasing, with the elderly—particularly those of advanced age—facing the highest risks of morbidity and mortality. PM2.5 (fine particulate matter) remains one of the primary pollutants contributing to urban air pollution in China. Although PM2.5 concentrations have significantly declined in many cities in recent years, levels in certain regions still exceed the World Health Organization (WHO) air quality guideline value of 10 µg/m³ annual average concentration) 25 . In winter 2023, PM2.5 concentrations in some northern Chinese cities reached 50–100 µg/m³, far exceeding the WHO-recommended threshold. Prolonged exposure to elevated PM2.5 levels is strongly linked to increased risks of cardiovascular diseases, respiratory illnesses, and lung cancer.PM10 (inhalable particulate matter) concentrations also remain high, particularly in industrialized regions and urban centers with heavy traffic. The annual average PM10 concentration in several Chinese cities exceeds 70 µg/m³, posing adverse effects on both the respiratory and cardiovascular systems. Chronic exposure to high PM10 levels has been associated with conditions such as chronic bronchitis and impaired lung function. In addition to natural factors like wind-blown dust and meteorological conditions such as precipitation, major anthropogenic sources of particulate matter in urban areas include traffic emissions, construction dust, and industrial activities. In rural areas, particulate pollution is closely linked to the combustion of solid fuels 26 – 28 , Therefore, it remains critically important to promote clean energy use nationwide, reduce reliance on solid fuels, and control industrial emissions. Furthermore, government agencies and meteorological departments should enhance public health protection by strengthening air quality monitoring and early warning systems. Timely dissemination of air quality information can help inform the public and encourage protective behaviors, thereby reducing the health impacts of particulate matter exposure 29 . Population-specific control of disease risk factors constitutes a critical component of modern public health and disease prevention strategies. Its significance can be summarized across six key dimensions. First, this approach enhances the targeting and effectiveness of preventive interventions. Different population groups—defined by age, gender, ethnicity, occupation, and other demographic characteristics—are exposed to distinct disease risk profiles. For example, in China, smoking remains a major risk factor for cardiovascular diseases and stroke among men aged 15–49, whereas hypertension, hyperlipidemia, and hyperglycemia are more critical among individuals over 50 years of age. Tailoring interventions to specific subpopulations enables more efficient allocation of resources and improves overall prevention outcomes. Second, it contributes to reducing health disparities by addressing the unique needs of vulnerable populations. Among women, persistent inequalities in household resource distribution often result in widespread deficiencies in iron and calcium intake. Additionally, domestic violence and exposure to second-hand smoke represent significant long-term health burdens for women and children. Targeted identification and management of these risk factors can help mitigate existing health inequities. Third, monitoring and evaluating the distribution of risk factors across different population groups allows for the identification of those requiring greater access to health education, screening, and treatment services. This not only optimizes resource utilization but also supports equitable health service delivery. Fourth, stratified risk factor control serves as the foundation for implementing multi-level disease prevention strategies—primary, secondary, and tertiary prevention. In primary prevention, health education and lifestyle modification programs tailored to high-risk groups (e.g., smokers or individuals with hypertension) can significantly reduce disease incidence. Secondary prevention relies on early detection and timely intervention to delay disease progression. Tertiary prevention focuses on disease management and rehabilitation for diagnosed patients, aiming to minimize the individual and societal burden of chronic illness.Fifth, effective implementation of population-based risk factor control requires intersectoral collaboration involving health departments, educational institutions, community organizations, and policymakers. For instance, addressing adolescent bullying necessitates coordinated efforts from schools, families, and communities through comprehensive strategies such as health education and policy reform. Such cross-sector engagement enhances intervention effectiveness and fosters a holistic model of health promotion. Finally, analyzing risk factor distributions and intervention outcomes across diverse populations enables the identification of most effective practices. These insights provide empirical support for the formulation of evidence-based public health policies and regulations. By ensuring that policy decisions are grounded in robust data, this approach strengthens the scientific basis of public health initiatives and facilitates the achievement of broader health objectives. 30 , 31 Declarations Ethics approval and consent to participate: None Availability of data and materials: None Competing interests: None Funding ： Science and Technology Research Project of Education Department of Hubei Province, Q20221604; University Scientific Research Fund of Wuhan Polytechnic University, 2022Y37 Authors' contributions ： Hua Hu contributed to the research design, data analysis, and visualization, as well as the initial drafting and subsequent revision of the manuscript; Jing Zhang contributed to the revision and enhancement of the manuscript; Wang was responsible for the final proofreading of the manuscript and the review of the supporting materials. Acknowledgements ： During the preparation of this work, the author used “QuillBot” in order to refine the paper to make it more idiomatic. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication. References Ferrari AJ, Santomauro DF, Aali A, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133-2161. doi:10.1016/s0140-6736(24)00757-8 Brauer M, Roth GA, Aravkin AY, et al. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2162-2203. doi:10.1016/s0140-6736(24)00933-4 Zhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145-1158. doi:10.1016/s0140-6736(19)30427-1 Zheng P, Berg C, Kegler M, et al. Smoke-Free Homes and Home Exposure to Secondhand Smoke in Shanghai, China. IJERPH. 2014;11(11):12015-12028. doi:10.3390/ijerph111112015 Štěpánek L, Ševčíková J, Horáková D, Patel MS, Durďáková R. Public Health Burden of Secondhand Smoking: Case Reports of Lung Cancer and a Literature Review. IJERPH. 2022;19(20):13152. doi:10.3390/ijerph192013152 Simons E, To T, Moineddin R, Stieb D, Dell SD. Maternal second-hand smoke exposure in pregnancy is associated with childhood asthma development. J Allergy Clin Immunol Pract. 2014;2(2):201-207. doi:10.1016/j.jaip.2013.11.014 Treyster Z, Gitterman B. Second hand smoke exposure in children: environmental factors, physiological effects, and interventions within pediatrics. Rev Environ Health. 2011;26(3):187-195. doi:10.1515/reveh.2011.026 Wu CF, Feng NH, Chong IW, et al. Second-hand smoke and chronic bronchitis in Taiwanese women: a health-care based study. BMC Public Health. 2010;10:44. doi:10.1186/1471-2458-10-44 Kim Y, Cho WK, Evangelista LS. Effect of Second-Hand Smoke Exposure on Lung Function among Non-Smoking Korean Women. Iran J Public Health. 2013;42(12):1363-1373. Dearing BA, Katz RV, Weitzman M. Prenatal tobacco and postbirth second-hand smoke exposure and dental caries in children. Community Dent Oral Epidemiol. 2021;50(2):130-138. doi:10.1111/cdoe.12642 Batscheider A, Zakrzewska S, Heinrich J, et al. Exposure to second-hand smoke and direct healthcare costs in children - results from two German birth cohorts, GINIplus and LISAplus. BMC Health Serv Res. 2012;12:344. doi:10.1186/1472-6963-12-344 Chow EPF, Tucker JD, Wong FY, et al. Disparities and Risks of Sexually Transmissible Infections among Men Who Have Sex with Men in China: A Meta-Analysis and Data Synthesis. PLoS One. 2014;9(2):e89959. doi:10.1371/journal.pone.0089959 Yang Z, Chen W, Ma Q, et al. Factors Associated with Commercial Sex Behavior among Male College Students Who Engaged in Temporary Heterosexual Behavior in Zhejiang Province, China. Biomed Res Int. 2022;2022(1). doi:10.1155/2022/4319194 Zhao A, Xue Y, Zhang Y, Li W, Yu K, Wang P. Nutrition Concerns of Insufficient and Excessive Intake of Dietary Minerals in Lactating Women: A Cross-Sectional Survey in Three Cities of China. PLoS One. 2016;11(1):e0146483. doi:10.1371/journal.pone.0146483 Samantha M, Marta PA, Sophie E M. The Role of Iron in Brain Development: A Systematic Review. Nutrients. 2020;12(7). doi:10.3390/nu12072001 Frank R G, Robert D B. Early Childhood Chronic Iron Deficiency and Later Cognitive Function: The Conundrum Continues. Pediatrics. 2022;150(6). doi:10.1542/peds.2022-058591 Xie Z, Zhong G, Xu C, et al. Trends and cross-country inequalities of alcohol use disorders: findings from the global burden of disease study 2021. Globalization and health. 2025;21(1):30. doi:10.1186/s12992-025-01124-5 Park S, Lee M, Park S, Lee. Health risk behaviors and psychological problems among South Korean, North Korean, and other multicultural family adolescents (2011-2016). Psychiatry research. 2018;268:373-380. doi:10.1016/j.psychres.2018.07.042 Moore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: A systematic review and meta-analysis. WJP. 2017;7(1):60. doi:10.5498/wjp.v7.i1.60 Hemphill SA, Kotevski A, Herrenkohl TI, et al. Longitudinal consequences of adolescent bullying perpetration and victimisation: A study of students in Victoria, Australia. Criminal Behav Ment Health. 2011;21(2):107-116. doi:10.1002/cbm.802 Klomek AB, Sourander A, Elonheimo H. Bullying by peers in childhood and effects on psychopathology, suicidality, and criminality in adulthood. Lancet Psychiatry. 2015;2(10):930-941. doi:10.1016/s2215-0366(15)00223-0 Wu M, Lv Y, Liu W, et al. Exploring environmental and cardiometabolic impacts associated with adherence to the sustainable EAT-Lancet reference diet: findings from the China Health and Nutrition Survey. Environ Health Perspect. Published online April 30, 2025. doi:10.1289/ehp15006 Liu X, Wen Y, Zhou Q. Gender differences in adolescent food preferences and their association with parent food preferences: data from the China Health and Nutrition Survey (CHNS). Eur J Nutr. 2024;63(7):2611-2619. doi:10.1007/s00394-024-03450-7 Wei X, Zhao L, Fang H, et al. Deficiency of Energy and Nutrient and Gender Differences among Chinese Adults: China Nutrition and Health Survey (2015–2017). Nutrients. 2024;16(14):2371. doi:10.3390/nu16142371 Brook RD, Rajagopalan S, Pope CA, et al. Particulate Matter Air Pollution and Cardiovascular Disease. Circulation. 2010;121(21):2331-2378. doi:10.1161/cir.0b013e3181dbece1 Rodríguez S, Querol X, Alastuey A, et al. Comparative PM10-PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain. Sci Total Environ. 2004;328(1-3):95-113. doi:10.1016/s0048-9697(03)00411-x Bo M, Salizzoni P, Clerico M, Buccolieri R. Assessment of Indoor-Outdoor Particulate Matter Air Pollution: A Review. Atmosphere (Basel). 2017;8(8):136. doi:10.3390/atmos8080136 Muyemeki L, Burger R, Piketh SJ, Language B, Beukes JP, Van Zyl PG. Source apportionment of ambient PM10−2.5 and PM2.5 for the Vaal Triangle, South Africa. S Afr J Sci. 2021;117(5/6). doi:10.17159/sajs.2021/8617 Carnevale C, De Angelis E, Tagliani FL, Turrini E, Volta M. A Short-Term Air Quality Control for PM10 Levels. Electron (Switz). 2020;9(9):1409. doi:10.3390/electronics9091409 Park TH, Ko Y, Lee SJ, et al. Identifying Target Risk Factors Using Population Attributable Risks of Ischemic Stroke by Age and Sex. J Stroke. 2015;17(3):302-311. doi:10.5853/jos.2015.17.3.302 Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45-52. doi:10.1016/s0140-6736(14)60648-6 Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers invited by journal 21 Aug, 2025 Editor invited by journal 19 Aug, 2025 Editor assigned by journal 17 Aug, 2025 Submission checks completed at journal 17 Aug, 2025 First submitted to journal 09 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7332902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506048425,"identity":"476333b5-e904-41af-9bfe-64073d6abe18","order_by":0,"name":"Hua Hu","email":"","orcid":"","institution":"Wuhan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Hu","suffix":""},{"id":506048426,"identity":"149487f6-ce02-4a8b-ac01-c14d3d23e698","order_by":1,"name":"Jing Zhang","email":"","orcid":"","institution":"Wuhan Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":506048427,"identity":"58e426fd-4bac-4311-ba7d-c23adb267ea7","order_by":2,"name":"Mengni Wang","email":"data:image/png;base64,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","orcid":"","institution":"Wuhan Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Mengni","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-09 09:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7332902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7332902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90313394,"identity":"bf54cb3b-ce92-4339-9091-ab4495d23ae5","added_by":"auto","created_at":"2025-09-01 10:07:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2531318,"visible":true,"origin":"","legend":"\u003cp\u003eThe ASR (age-standardized rates) from 1990 to 2021 of the SEVs of 68 detailed risks.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/e6700e68050abccfd17eae0f.jpg"},{"id":90315755,"identity":"16f0be4d-0e3f-4834-a0ec-57877e163097","added_by":"auto","created_at":"2025-09-01 10:15:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1458865,"visible":true,"origin":"","legend":"\u003cp\u003eExposure Trends of the Top 10 Risk Factors Across Different Age Groups Over the Next 30 Years.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/bd3f0dd8b8b759283e574d95.jpg"},{"id":90313391,"identity":"d9d272fd-4c29-4936-a2a2-04ab2eacd8f9","added_by":"auto","created_at":"2025-09-01 10:07:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1933471,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the disease burden attributable to various risk factors over the past 32 years.\u003c/p\u003e\n\u003cp\u003e(A) The trends of death number attributable to detailed risks.\u003c/p\u003e\n\u003cp\u003e(B) The trends of YLDs attributable to detailed risks.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/549ea3e039c39036fefed030.jpg"},{"id":90315760,"identity":"2c59ff65-3772-40e3-a614-5ca4330b6d04","added_by":"auto","created_at":"2025-09-01 10:15:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2392134,"visible":true,"origin":"","legend":"\u003cp\u003eDisease burden related to the top 10 risk factors in China in 2021\u003c/p\u003e\n\u003cp\u003e(A) The Top 10 death burden associated risk factors by age group in 2021\u003c/p\u003e\n\u003cp\u003e(B) The Top 10 disability burden associated risk factors by age group in 2021.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/2666f46e6869b044b6f9279c.jpg"},{"id":90315756,"identity":"cf687b76-b9e5-4c79-a7d7-2af2a402c488","added_by":"auto","created_at":"2025-09-01 10:15:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":919684,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in Disease Burden Attributable to Risk Factors Across Age Groups Over the Next 30 Years\u003c/p\u003e\n\u003cp\u003e(A) Trends in Deaths number Caused by Risk Factors\u003c/p\u003e\n\u003cp\u003e(B) Trends in YLDs Caused by Risk Factors\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/ae7ca3910d025f59714a0b13.jpg"},{"id":90317046,"identity":"6e60babc-26b4-47b1-8d66-e642435721af","added_by":"auto","created_at":"2025-09-01 10:23:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10016596,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/e261f8b1-d587-4e0d-aefb-b018fddeef55.pdf"},{"id":90313387,"identity":"45208b86-be2e-40e9-aff0-0e52e062cd74","added_by":"auto","created_at":"2025-09-01 10:07:33","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12798,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7332902/v1/9ea5c0d1f1360eb03f19a00b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender and age disparities in risk factor burden in China: a GBD 2021 analysis with projections to 2050","fulltext":[{"header":"Introduction","content":"\u003cp\u003eReducing modifiable risk factors is a critically important and successful strategy for averting ill health and early death from diseases and accidents. Effective risk-reduction policies and practices depend on data that is specific to a given location and population, including trends in the prevalence of leading risk factors, the percentage of disease-specific mortality and morbidity that can be attributed to specific risk factors, and the relationships between risk factors and health outcomes. Additionally, age-specific control of disease risk factors helps achieve more precise disease prevention and health management, enhancing the effectiveness and cost-effectiveness of intervention measures, as well as improving the population's overall health.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTimely and accurate prediction of the level of exposure to health risk factors is of great significance for the government to effectively adjust future environmental intervention policies, help health professionals and policy makers understand the health priorities, enhance public health awareness, and reduce the burden of diseases on the population. The Healthy China 2030 plan is the country's signature domestic population health program, and it has elevated population health to the top of the economic and political agenda. Two of the goals are to promote lifespan and healthy life expectancy, as well as to increase illness prevention. Other goals include lowering newborn mortality, improving the environment (eg, air quality), health services and insurance (eg, chronic disease mortality), changing one's lifestyle (eg, regular exercise), and reforming the health industry. Due to age differences in living environment and lifestyle, close forecast of population health metrics at the age-specific levels will be crucial to developing evidence\u0026shy;based policies and achieving the Healthy China 2030 goals.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOur study has reviewed the trends in exposure levels and disease burdens of 68 detailed risk factors across different age groups in the Chinese region from the GBD database over the past 32 years. Based on previous data, we also have predicted the development trends of the top 10 risk factors with higher exposure levels and higher disease burdens in 2021 over the next 20 years, which can provide evidence for the precise etiological prevention of diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eData source and definitions\u003c/h2\u003e\u003cp\u003eThe exposure level and impact of risk factors on mortality and health-related quality of life for China were obtained from GBD 2021 online results tool. Using the most recent epidemiological data and improved standardized methodologies, GBD 2021 provided a thorough evaluation of relevant metrics for 23 age groups from birth to age 95 years and older; for males, females, and all sexes combined; and for 204 countries and territories grouped into 21 regions and seven super-regions. Additionally, GBD 2021 produced risk-specific estimates of summary exposure value (SEV), RR, population attributable fraction (PAF), risk attributable burden measured in disability-adjusted life years (DALYs; the sum of years of life lost to premature mortality and years lived with disability), and deaths. We used above records of the most detailed risks, together with the related crude and age-standardized rates (ASRs) from the GBD 2021 by factors such as age group and gender. Since our study was based on publicly available data, there was no need for ethical approval or informed consent.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eASR was derived through weighted averaging of age-specific rates. To signify ASR trends of risk factors spanning from 1990 to 2021, we computed the EAPC (estimated annual percent change) utilizing the formula where Y = ln (ASR), X = calendar year, and ε = the error term.\u003c/p\u003e\u003cp\u003eY = α + βX + ε\u003c/p\u003e\u003cp\u003eEAPC = 100 × (exp(β) − 1)\u003c/p\u003e\u003cp\u003eBased on linear regression models, we obtained a 95% confidence interval (CI) for our findings. When both EAPC estimations and 95% CI lower limits were positive, the ASR trended upward. When both EAPC estimations and 95% CI upper limit were negative, the ASR decreased. The trend remained stable during the study period if neither of these conditions were met.\u003c/p\u003e\u003cp\u003eThe Auto-Regressive Integrated Moving Average (ARIMA) model is a popular technique used in epidemiology for forecasting time-series data. In our study, we constructed an ARIMA model using the R programming language to predict the SEVs and burden attributed to risks from 2022 to 2050. Given the variation in risk factors across different age and gender groups, this study focuses on the top 10 risk factors for each group in 2021 and their projected future trends.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e3.1 SEVs of risk factors\u003c/b\u003e\u003c/p\u003e\u003ch3\u003eThe SEV trend of risk factors over the past 32 years by gender\u003c/h3\u003e\u003cp\u003eThe ASR changes from 1990 to 2021 of the SEVs of 68 detailed risks are shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e, the SEV of diet low in vegetables had the most significant decline (EAPC of -8.16 [95% CI: -8.52, -7.80] in female and − 7.33 [95% CI: -7.60, 7.06] in male), followed by household air pollution from solid fuels and vitamin A deficiency in both genders. There is a significant gender difference in the control of iron-deficiency(EAPC of -5.56 [95% CI: -5.78, -5.37] in male while − 1.07 [95% CI: -1.12, 1.02] in female), Similarly, the SEV of diet low in calcium, (EAPC of -3.60 [95% CI: -3.70, -3.49] in male while − 1.65 [95% CI: -1.71, 1.58] in female).\u003c/p\u003e\u003cp\u003eThe SEV of ambient particulate matter pollution had the most significant increase in both genders (EAPC of 5.26 [95% CI: 4.81, 5.63] in female and 4.50 [95% CI: 4.11, 4.89] in male), followed by diet high in sugar-sweetened beverages, high BMI and diet high in processed meat. Compared to male groups, the trend of occupational exposure to benzene among female workers is more significant. (EAPC of 2.26 [95% CI: 2.24, 2.28])。Furthermore, the trajectory of occupational noise and asthmagens by gender is entirely opposite, with male showing a declining trend and female showing an upward trend.\u003c/p\u003e\u003ch3\u003eThe top 10 risk factors for SEV among all age groups in 2021\u003c/h3\u003e\u003cp\u003eDifferent age groups exhibited distinct high-risk exposure factors in 2021 (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e), the top 10 SEV risk factors for children under 5 years of age and those aged 5–14 are predominantly environmental in nature, and the high-risk factors in these two groups exhibit a high degree of overlap. For the population aged over 15, which encompasses those aged 15–49, 50–69, and above 70, the types of risk factors are mainly lifestyle-related, and the types of risk factors in these three groups are also highly congruent. Some risk factors present significant gender disparities in SEVs.\u003c/p\u003e\u003cp\u003eMore than half of children under the age of 5 years were still highly exposed to secondhand smoke environments: the SEV rates for males and females were 57.21% and 64.16%, respectively. Inadequate feeding approaches encompass discontinued breastfeeding and non-exclusive breastfeeding, which still constitute a considerable proportion (the SEV: 48% and 31% respectively). Subsequently, environmental risks such as ambient particulate matter pollution, high temperature, unsafe sanitation, unsafe water sources, and low temperature come into play. Moreover, 18.64% of female infants and young children are exposed to nitrogen dioxide pollution, meanwhile, a proportion of 24% of the male children exhibited highly body-mass index.\u003c/p\u003e\u003cp\u003eThe leading 8 high exposure risk factors for the 5–14 age group are largely in accordance with those of the \u0026lt; 5 age group (excluding feeding conditions), yet with higher exposure probabilities. It is notable that 12.61% of males and 10.05% of females underwent bullying victimization. Additionally, 6.7% of the population in both sexes still lack access to handwashing facilities.\u003c/p\u003e\u003cp\u003eAmong the 15–49 age group, more than half of the high exposure risk factors pertain to dietary aspects. Over 82% of individuals of both sexes have a diet deficient in Omega-6 polyunsaturated fatty acids. Virtually all women (SEV: 97.97%, 95%CI [99.81%, 96.17%]) have an insufficient intake of milk, whereas the SEV of a diet low in milk for males was 51.76%. Another prominent gender gap risk factor is secondhand smoke, with SEVs being 33.34% for males and 72.82% for females, while 43.38% of men are smokers. Over half of the population has a diet high in sodium, with SEVs being 62.6% and 53.66% for males and females respectively, and other high-exposure dietary factors encompass a diet high in red meat and a diet low in whole grains. High LDL cholesterol metabolic factors also account for a considerable proportion in the 15–49 age group, with SEVs being 43.82% and 45.41% for males and females respectively. The exposure rate of the environmental factor, ambient particulate matter pollution, is also high (SEV: 46.27% for males, 45.22% for females). 34.77% of men have lead exposure. Additionally, 33.06% females exposed to high temperature and 28.9% females had low intake in nuts and seeds.\u003c/p\u003e\u003cp\u003eThe high-risk dietary factors for the 50–69 age group are largely in alignment with those of the 15–49 age group. The most critical dietary issue for males lies in high sodium intake, while for females, it is the insufficient intake of milk. Additionally, more than 82% of both genders have a deficiency in omega-6 polyunsaturated fatty acids. nevertheless, it is notable that the former has a higher level of lead exposure. The SEVs were 57.16% for males and 47.1% for females. In contrast to the 15–49 age group, metabolic factors led to an increase in high SBP in addition to high LDL cholesterol in the 50–69 age group.\u003c/p\u003e\u003cp\u003eIn comparison with the 50–69 age group, the elderly over 70 had an extra high-risk factor of low physical activity, with SEVs being 41.95% for males and 53.16% for females. Other dietary matters exhibit a high degree of consistency among individuals aged 50–69 and those aged 15–49.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure Trends of the Top 10 Risk Factors Across Different Age Groups Over the Next 30 Years\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe applied the ARIMA model to estimate the SEVs of the most prevalent risk factors across age groups from 2022 to 2050. As illustrated in Fig.\u0026nbsp;2, ambient particulate matter pollution is a major risk factor for children under 5 years old and those aged 5–14 years. The level of exposure is projected to rise sharply over the next three decades, surpassing 88% by 2050. Another risk factor with a steadily increasing trend is high BMI. By 2050, the SEV for boys under 5 years old is expected to reach 48%, while among both genders aged 5–14 years, it will increase to 38%. Importantly, secondhand smoke will continue to show high exposure levels across all subgroups under 15 years of age. Although a slight decline is anticipated for males under 5 years old, the SEV remains above 50%, and in other subgroups, it stays around 64%. Similarly, discontinued breastfeeding (48.7% in 2050) and non-exclusive breastfeeding (25% in 2050) among children under 5, as well as exposure to high temperatures (33% in 2050) and low temperatures (25% in 2050) in both age groups, are expected to remain consistently elevated but stable. Other risk factors either persist at low exposure levels or demonstrate a continuous decrease.\u003c/p\u003e\u003cp\u003eAs illustrated in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e, among the three age groups above 15 years of age, ambient particulate matter pollution continues to be a prevalent risk factor with consistently rising exposure levels. By 2050, SEVs for all three groups are projected to surpass 93%. For other environmental risk factors such as lead exposure, distinct variations in decline trends are observed across age groups. The most rapid reduction occurs in the 15–49 age group, whereas only a minor decrease is anticipated in the population aged over 70. Exposure to high temperatures, in contrast, demonstrates no clear trend of change. Regarding dietary risk factors, the SEV of diets low in nuts and seeds exhibits a marked downward trajectory. Most other dietary risks, however, remain at elevated levels without significant reductions. Furthermore, aside from a moderate decline in the 15–49 age group (33.9% in 2050), the SEV for diets high in red meat is expected to rise across all other age groups. The patterns of change in behavior-related and metabolic risk factors align closely with those observed in dietary factors.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.2 Disease Burden Associated with Risk Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends in the disease burden attributable to various risk factors over the past 32 years are presented below.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs illustrated in Figure_3A, the burden of death linked to nutritional deficiency-related risk factors have exhibited a marked decline. The most substantial reductions in population mortality burden were observed for vitamin A deficiency (EAPC: -17.37 [95% CI: -18.12, -16.61] in females and − 18.02 [95% CI: -18.47, -17.56] in males), zinc deficiency (EAPC: -15.24 [95% CI: -15.79, -14.67] in females and − 13.44 [95% CI: -13.89, -13.00] in males), discontinued breastfeeding (EAPC: -14.06 for both sexes), child stunting (EAPC: -13.03 for both sexes), and unsafe sanitation (EAPC: -12.85 for both sexes). In contrast, The burden of death associated with dietary behavior and occupational exposure risk factors has generally increased significantly. The most notable upward trends were seen in diet high in red meat (EAPC: 18.92 for both sexes), diet high in sugar-sweetened beverages (EAPC: 4.18 for both sexes), and diet high in processed meat, as well as environmental particulate matter pollution (EAPC: 1.54 for both sexes) and occupational exposure to trichloroethylene (EAPC: 1.79 for both sexes), with no significant gender differences in these trends. However, childhood sexual abuse (EAPC: -3.99 [95% CI: -4.15, -3.83] in females vs. 0.24 [95% CI: -0.27, 0.75] in males) and unsafe sex (EAPC: -0.66 [95% CI: -0.84, -0.49] in females vs. 6.87 [95% CI: 6.22, 7.53] in males) demonstrated clear gender disparities, with significant declines in females and either no change or a sharp increase in males.\u003c/p\u003e\u003cp\u003eAs illustrated in Figure_3B, the most substantial declines in risk factors contributing to female disability were observed for child wasting (EAPC: -13.89 [95% CI: -14.30, -13.48] in females) and child underweight (EAPC: -13.25 [95% CI: -13.64, -12.86] in females). In contrast, among males, the greatest reductions were seen in non-exclusive breastfeeding (EAPC: -11.91) and child stunting (EAPC: -10.27). For both genders, the risk factors with the most pronounced increases included high consumption of sugar-sweetened beverages (EAPC: 7.36 for both sexes), high red meat intake (EAPC: 5.79 for both sexes), and increased consumption of processed meat (EAPC: 4.84 for both sexes). Notably, secondhand smoke exposure (EAPC: -0.17 [95% CI: -0.29, -0.05] in females vs. 0.72 [95% CI: 0.63, 0.81] in males) and diets high in sodium (EAPC: -0.09 [95% CI: -0.17, -0.01] in females vs. 0.28 [95% CI: 0.22, 0.33] in males) exhibited gender-specific trends, with increasing patterns in males and decreasing trends in females.\u003c/p\u003e\u003ch3\u003eThe Top 10 death burden associated risk factors by age group in 2021\u003c/h3\u003e\u003cp\u003eAs illustrated in Figure_4A, among males in the infant and toddler period (\u0026lt; 5 years old), leading causes of death are associated with birth defects and environmental exposures, including low birth weight (11,734 deaths), preterm birth (8,624 deaths), and exposure to environmental particulate matter pollution (3,449 deaths). For females in the same age group, the mortality-related risk factors are largely similar but occur at lower magnitudes—such as low birth weight, which accounts for 8,102 deaths. The number of deaths attributed to various risk factors declines significantly in the 5–14 age group; however, high alcohol use remains a concern for males, contributing to approximately 469.7 deaths. Among males aged over 15 years, smoking and hypertension are the primary risk factors across all three age groups, with mortality burdens increasing with advancing age. Additionally, high alcohol consumption and diets high in sodium also warrant attention. Other major contributors to mortality include environmental particulate matter pollution and metabolic risk factors. For females over 15 years of age, the two leading mortality-associated risk factors across all three age groups are hypertension and environmental particulate matter pollution, although the absolute number of deaths is considerably lower than in males. In the 15–49 age group, unsafe sex emerges as a significant cause of mortality among females, accounting for 12,500.4 deaths. Similar to males, metabolic risk factors remain prominent among females aged over 15 years. Notably, secondhand smoke consistently ranks among the top ten mortality-related risk factors across all female age groups.\u003c/p\u003e\u003ch3\u003eThe Top 10 Risk Factors Contributing to YLDs by Age Group in 2021\u003c/h3\u003e\u003cp\u003eAs illustrated in Figure_4B, among children under the age of 5, there is no gender difference in the leading causes of disability-adjusted life years (YLDs), with iron deficiency and low birth weight being the primary contributors. In the 5–14 age group, the YLDs due to iron deficiency in females has increased more than tenfold, making it the most severe disabling factor (4,892.9 years for females under 5 years old vs. 85,333.1 years for females aged 5–14 years). For both males and females in this age group, short gestation ranks among the top two causes of disability, and bullying victimization also imposes a substantial burden (51,192.4 years for males and 42,291.7 years for females). Among individuals aged 15–49 years, metabolic risk factors show similar patterns between genders; however, notable differences exist in behavioral and dietary risks. Males are predominantly affected by alcohol use (1,409,149.2 years) and smoking (1,247,760.5 years), whereas females experience higher YLDs from iron deficiency (782,548.7 years) and intimate partner violence (369,256.2 years). For both age groups over 50 years, metabolic disorders and exposure to environmental particulate matter pollution are the leading causes of disability. However, smoking remains the most significant contributor to YLDs among males.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrends in Disease Burden Attributable to Risk Factors Across Age Groups Over the Next 30 Years\u003c/b\u003e\u003c/p\u003e\u003ch2\u003eTrends in Mortality Burden Caused by Risk Factors\u003c/h2\u003e\u003cp\u003eUsing the ARIMA model, we further projected the trends of the top ten mortality-related risk factors for each age group from 2022 to 2050. As shown in \u003cb\u003eFigure_5A\u003c/b\u003e, the mortality burden associated with environmental and childhood nutritional risks continues to decline. This includes deaths linked to unsafe water sources, indoor air pollution from solid fuels, child stunting, child wasting, and low birth weight. Notably, the mortality burden caused by secondhand smoke among children under five years old is projected to rise, with a higher impact on girls compared to boys (18,380 vs. 8,315 deaths in females and males, respectively, by 2050). Among individuals aged over 15 years, most risk factors are expected to lead to increasing mortality burdens, particularly in the over-70 age group. The main contributors include metabolic factors such as high BMI, behavioral factors like diets high in sodium and smoking, and ambient particulate matter pollution. In the female population, mortality burdens from unsafe sex and household air pollution due to solid fuels are predicted to decrease significantly, while secondhand smoke will continue to increase, primarily affecting those aged over 70 (projected to exceed 250,000 deaths by 2050). For males, the mortality burden from diets low in whole grains among the 15–49 age group is expected to decline, whereas lead exposure in men over 70 years old will rise sharply (projected to exceed 350,000 deaths by 2050). Additionally, alcohol use in the 50–69 age group will result in over 20,000 deaths by 2050.\u003c/p\u003e\u003ch3\u003eTrends in Disability Burden Caused by Risk Factors Across Age Groups\u003c/h3\u003e\u003cp\u003eAs shown in Figure_5B, Nutrition-related factors—including vitamin A deficiency, short gestation, and low birth weight—as well as metabolic factors such as high BMI are projected to increase the disability burden among children aged 5–14 years. The disability burden attributed to vitamin A deficiency in children under five is also expected to rise, with a more pronounced increase observed in boys (projected to exceed 4,000 YLDs by 2050). Meanwhile, nitrogen dioxide pollution, secondhand smoke, and bullying victimization are not expected to show significant changes in their disability burden among children and adolescents; however, bullying victimization remains a persistent contributor at a high level. Iron deficiency and lead exposure demonstrate notable downward trends across both age groups. Importantly, bullying continues to impose a substantial disability burden on adult males aged 15–49, exceeding 30,000 YLDs after 2030. Among the three age groups over 15 years, outdoor particulate matter pollution is projected to cause a marked increase in disability burden. Similar upward trends are observed for metabolic risk factors such as high fasting blood glucose (FBG), high systolic blood pressure (SBP), high low-density lipoprotein (LDL), and kidney dysfunction. Women face an ongoing rise in disability burden from secondhand smoke and indoor solid fuel use, while men experience growing burdens related to lifestyle factors including high salt intake, excessive alcohol consumption, and tobacco use. Furthermore, individuals over 50 years of age should be vigilant about rising disability burdens associated with kidney dysfunction and reduced bone mineral density.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSignificant variations exist in the current and projected trends of risk factors across different age and gender groups. Secondhand smoke and iron deficiency continue to exert substantial health impacts, particularly on women and children. For females, unsafe sexual practices and intimate partner violence represent major contributors to disease burden. Among males, smoking and excessive alcohol consumption are driving an upward trend in disease burden. In children and adolescents, low birth weight, preterm birth, short gestation, and bullying victimization remain leading causes of mortality and disability. Concurrently, exposure to unhealthy dietary patterns\u0026mdash;including high salt intake, increased consumption of red and processed meats, high sugar intake, and low whole grain consumption is rising among adults. These behaviors are expected to exacerbate metabolic conditions such as elevated BMI, FBG, LDL, and SBP, thereby increasing the future burden of death and disability. Over the past 32 years, the Chinese government has successfully reduced the exposure risks and associated health burdens of nutrition-related factors and poor sanitation. However, the exposure risk and health impact of outdoor particulate matter pollution have been steadily increasing across all age groups, indicating a growing public health challenge that requires urgent attention.\u003c/p\u003e\u003cp\u003eSecondhand smoke imposes a substantial burden of mortality and disability across all female age groups. Although the implementation of public smoking bans over the past 32 years has led to a modest reduction in disease burden among women compared to 1990, projections indicate that the burden continues to rise among two specific subgroups: children under five years of age and women over 50 years old. The increasing trend among young children may be attributed to inadequate control of secondhand smoke exposure within household settings \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, whereas the rising burden in older women is likely due to the cumulative effects of long-term disease progression. \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eExposure to secondhand smoke poses significant health risks for both women and children, representing a critical public health concern. Accumulating evidence demonstrates that secondhand smoke is strongly associated with a range of adverse health outcomes, particularly in these vulnerable populations. Children exposed to secondhand smoke face elevated risks of developing asthma, respiratory infections, and cardiovascular diseases \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, exposure has been linked to behavioral issues, sleep disturbances, and an increased likelihood of cancer \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e。Among women, studies have shown a significant association between secondhand smoke exposure and the development of chronic bronchitis, particularly in Taiwanese women, where such exposure markedly increases disease risk \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, secondhand smoke may impair lung function in non-smoking women; however, some research suggests that these women may exhibit greater resilience to smoke-induced pulmonary decline\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Emerging evidence also indicates that secondhand smoke negatively affects children's oral health. Postnatal exposure has been found to increase the incidence of dental caries, with this association remaining statistically significant after controlling for confounding variables\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, reducing secondhand smoke exposure not only improves public health outcomes but may also yield substantial economic benefits by lowering healthcare expenditures \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to secondhand smoke, the mortality burden attributable to unsafe sexual behavior among women remains a significant public health concern. Unsafe sexual practices expose women to multiple reproductive tract infections, with sexually transmitted infections (STIs) representing a primary consequence. High-risk pathogens such as HIV, HPV, and Chlamydia trachomatis are strongly associated with increased risks of AIDS, cervical cancer, and ectopic pregnancy, all of which have profound long-term impacts on women's quality of life. Over the past 32 years, the mortality burden resulting from unsafe sexual behavior among Chinese women has remained consistently high. However, recent widespread HPV vaccination has led to a modest decline in related deaths, and projections suggest this downward trend will continue in the future. A similar pattern is observed in the disability burden caused by intimate partner violence: although historically high, it has shown a marked decrease following targeted policy interventions. Notably, in contrast to the declining trends observed in women, the mortality burden associated with unsafe sexual behavior among men has significantly increased over the same period. Multiple data sources indicate that male-to-male sexual contact, drug use, inconsistent condom use, and commercial sex are the primary contributors to unsafe sexual behavior among men \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, Nevertheless, due to traditional cultural norms in China, the availability of exposure and disease data remains limited, leading to substantial underestimation of both risk and disease burden. The most critical contributing factor to this situation is the lack of comprehensive sex education combined with the gradual liberalization of sexual attitudes driven by internet penetration. Research highlights that schools serve as optimal settings for delivering sex education to students and play a pivotal role in standardizing HIV/AIDS health education, expanding outreach, and enhancing the effectiveness of public awareness campaigns. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eExposure risks associated with nutrient deficiencies and their corresponding disease burdens have shown substantial improvement among the Chinese population over the past 32 years. For instance, both the exposure risk and health impact of vitamin A deficiency have significantly declined. While iron deficiency has also seen improvements, these gains are uneven across genders. Notably, the disability burden attributable to iron deficiency, particularly among adolescent girls, continues to rise. A similar pattern is observed for calcium deficiency, where low bone mineral density is increasingly contributing to disability, especially among elderly women. Research on mineral intake among lactating women in China indicates that many do not meet the estimated average requirements for calcium and iron. This shortfall may be linked to long-term inadequate milk consumption, as dairy products are key dietary sources of these nutrients. More concerning is that insufficient mineral intake often persists during lactation, potentially compromising maternal health and infant development. Therefore, increasing consumption of milk and other calcium- and iron-rich foods could help alleviate this issue and reduce the health consequences of mineral deficiencies \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Early childhood iron deficiency is strongly associated with impaired cognitive and motor development in children and adolescents \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, underscoring the importance of early-life interventions. In recent years, China has implemented several staple food fortification programs, such as promoting iron-fortified infant rice cereal and iron-enriched flour, which have effectively lowered population-wide exposure to iron deficiency. Nevertheless, targeted interventions focusing on infants and adolescent girls remain necessary to further mitigate the disease burden caused by deficiencies in essential minerals like iron and calcium.\u003c/p\u003e\u003cp\u003eSmoking and high alcohol use, as two major unhealthy lifestyle factors, constitute significant contributors to mortality and disability among Chinese men. These findings highlight the urgent need to prioritize individual health-related risk behaviors in disease prevention strategies. Effective interventions should focus on guiding male populations toward healthier behaviors through measures such as restricting the availability of tobacco and alcohol and reducing related advertising. Notably, excessive alcohol consumption also imposes a measurable mortality burden on adolescents aged 5\u0026ndash;14 years. Adolescent alcohol use disorder has increasingly become a focal point in public health due to its direct physiological harm and indirect social consequences, including increased risks of school violence. Therefore, implementing legal restrictions on alcohol access for minors is critically important \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition to governmental policy interventions, school-based programs for managing health risk behaviors play an essential role in addressing these issues and promoting long-term behavioral change\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition to alcohol use, bullying victimization imposes a significant disability burden on adolescents, with this burden persisting at alarmingly high levels into adulthood, particularly among males. Although efforts over the past 32 years have led to some control of this issue, the burden remains substantial, indicating the need for continued and enhanced preventive measures to safeguard adolescent health. Bullying victimization has wide-ranging negative effects on youth well-being. Moore et al. conducted a comprehensive systematic review of extensive literature and found robust evidence of a causal relationship between bullying victimization and multiple adverse outcomes in adolescents, including anxiety, depression, mental and physical health problems, non-suicidal self-injury, suicidal ideation, and suicide attempts \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, Furthermore, numerous studies have demonstrated that the detrimental effects of childhood bullying often persist into adulthood, underscoring the long-term public health significance of this issue \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOver the past decade, rapid economic development has largely eliminated nutritional deficiency risks associated with insufficient food intake among the Chinese population. However, a new set of dietary challenges has emerged, characterized by excessive consumption of sugar, salt, and fat, combined with low whole grain intake. This unbalanced nutrient profile is contributing to a growing prevalence of metabolic diseases and is accompanied by widespread deficiencies in key micronutrients such as unsaturated fatty acids, calcium, iron, and dietary fiber \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These dietary issues are not only prevalent among adults but are also increasingly observed among adolescents, showing a rapidly rising trend.\u003c/p\u003e\u003cp\u003eData from the China Health and Nutrition Survey (CHNS) indicate that fat intake among Chinese residents is generally high, particularly in terms of saturated and trans fatty acids, which exceed recommended levels. The proportion of energy derived from fat has surpassed 30%, significantly exceeding the recommended range of 20\u0026ndash;30%. Saturated fatty acids primarily originate from animal-based fats such as fatty meat and lard, while trans fatty acids are mainly found in foods containing partially hydrogenated vegetable oils, including margarine and fried foods. Excessive intake of these fats is strongly associated with increased risks of cardiovascular disease and obesity. Moreover, whole grain consumption accounts for only about 10% of total grain intake among Chinese residents. This imbalanced carbohydrate intake pattern not only results in inadequate dietary fiber consumption but may also impair glucose regulation and gut health. Vitamin deficiencies remain prevalent across the population, particularly for vitamins A, B₂, and C, with a large proportion of individuals failing to meet recommended intake levels. CHNS data show that approximately 70% of residents do not achieve the recommended daily intake of vitamin A\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Collectively, these findings highlight the urgent need for comprehensive dietary interventions targeting the entire population. Strategies should include strengthening public health education, implementing policy-level regulations, and providing tailored interventions for vulnerable groups. Additionally, the catering industry must play an active role by reducing the availability of high-salt, high-sugar, and high-fat foods and promoting healthier dietary behaviors to support long-term improvements in national nutrition.\u003c/p\u003e\u003cp\u003eCurrently, the exposure risk and disease burden associated with outdoor air particulate matter among the general population in China are increasing, with the elderly\u0026mdash;particularly those of advanced age\u0026mdash;facing the highest risks of morbidity and mortality. PM2.5 (fine particulate matter) remains one of the primary pollutants contributing to urban air pollution in China. Although PM2.5 concentrations have significantly declined in many cities in recent years, levels in certain regions still exceed the World Health Organization (WHO) air quality guideline value of 10 \u0026micro;g/m\u0026sup3; annual average concentration)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In winter 2023, PM2.5 concentrations in some northern Chinese cities reached 50\u0026ndash;100 \u0026micro;g/m\u0026sup3;, far exceeding the WHO-recommended threshold. Prolonged exposure to elevated PM2.5 levels is strongly linked to increased risks of cardiovascular diseases, respiratory illnesses, and lung cancer.PM10 (inhalable particulate matter) concentrations also remain high, particularly in industrialized regions and urban centers with heavy traffic. The annual average PM10 concentration in several Chinese cities exceeds 70 \u0026micro;g/m\u0026sup3;, posing adverse effects on both the respiratory and cardiovascular systems. Chronic exposure to high PM10 levels has been associated with conditions such as chronic bronchitis and impaired lung function. In addition to natural factors like wind-blown dust and meteorological conditions such as precipitation, major anthropogenic sources of particulate matter in urban areas include traffic emissions, construction dust, and industrial activities. In rural areas, particulate pollution is closely linked to the combustion of solid fuels\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, Therefore, it remains critically important to promote clean energy use nationwide, reduce reliance on solid fuels, and control industrial emissions. Furthermore, government agencies and meteorological departments should enhance public health protection by strengthening air quality monitoring and early warning systems. Timely dissemination of air quality information can help inform the public and encourage protective behaviors, thereby reducing the health impacts of particulate matter exposure \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePopulation-specific control of disease risk factors constitutes a critical component of modern public health and disease prevention strategies. Its significance can be summarized across six key dimensions. First, this approach enhances the targeting and effectiveness of preventive interventions. Different population groups\u0026mdash;defined by age, gender, ethnicity, occupation, and other demographic characteristics\u0026mdash;are exposed to distinct disease risk profiles. For example, in China, smoking remains a major risk factor for cardiovascular diseases and stroke among men aged 15\u0026ndash;49, whereas hypertension, hyperlipidemia, and hyperglycemia are more critical among individuals over 50 years of age. Tailoring interventions to specific subpopulations enables more efficient allocation of resources and improves overall prevention outcomes. Second, it contributes to reducing health disparities by addressing the unique needs of vulnerable populations. Among women, persistent inequalities in household resource distribution often result in widespread deficiencies in iron and calcium intake. Additionally, domestic violence and exposure to second-hand smoke represent significant long-term health burdens for women and children. Targeted identification and management of these risk factors can help mitigate existing health inequities. Third, monitoring and evaluating the distribution of risk factors across different population groups allows for the identification of those requiring greater access to health education, screening, and treatment services. This not only optimizes resource utilization but also supports equitable health service delivery. Fourth, stratified risk factor control serves as the foundation for implementing multi-level disease prevention strategies\u0026mdash;primary, secondary, and tertiary prevention. In primary prevention, health education and lifestyle modification programs tailored to high-risk groups (e.g., smokers or individuals with hypertension) can significantly reduce disease incidence. Secondary prevention relies on early detection and timely intervention to delay disease progression. Tertiary prevention focuses on disease management and rehabilitation for diagnosed patients, aiming to minimize the individual and societal burden of chronic illness.Fifth, effective implementation of population-based risk factor control requires intersectoral collaboration involving health departments, educational institutions, community organizations, and policymakers. For instance, addressing adolescent bullying necessitates coordinated efforts from schools, families, and communities through comprehensive strategies such as health education and policy reform. Such cross-sector engagement enhances intervention effectiveness and fosters a holistic model of health promotion. Finally, analyzing risk factor distributions and intervention outcomes across diverse populations enables the identification of most effective practices. These insights provide empirical support for the formulation of evidence-based public health policies and regulations. By ensuring that policy decisions are grounded in robust data, this approach strengthens the scientific basis of public health initiatives and facilitates the achievement of broader health objectives. \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eScience and Technology Research Project of Education Department of Hubei Province, Q20221604; University Scientific Research Fund of Wuhan Polytechnic University, 2022Y37\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eHua Hu contributed to the research design, data analysis, and visualization, as well as the initial drafting and subsequent revision of the manuscript; Jing Zhang contributed to the revision and enhancement of the manuscript; Wang was responsible for the final proofreading of the manuscript and the review of the supporting materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eDuring the preparation of this work, the author used \u0026ldquo;QuillBot\u0026rdquo; in order to refine the paper to make it more idiomatic. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003eFerrari AJ, Santomauro DF, Aali A, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133-2161. doi:10.1016/s0140-6736(24)00757-8\u003c/li\u003e\n\u003cli\u003eBrauer M, Roth GA, Aravkin AY, et al. Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2162-2203. doi:10.1016/s0140-6736(24)00933-4\u003c/li\u003e\n\u003cli\u003eZhou M, Wang H, Zeng X, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145-1158. doi:10.1016/s0140-6736(19)30427-1\u003c/li\u003e\n\u003cli\u003eZheng P, Berg C, Kegler M, et al. Smoke-Free Homes and Home Exposure to Secondhand Smoke in Shanghai, China. IJERPH. 2014;11(11):12015-12028. doi:10.3390/ijerph111112015\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;těp\u0026aacute;nek L, \u0026Scaron;evč\u0026iacute;kov\u0026aacute; J, Hor\u0026aacute;kov\u0026aacute; D, Patel MS, Durď\u0026aacute;kov\u0026aacute; R. Public Health Burden of Secondhand Smoking: Case Reports of Lung Cancer and a Literature Review. IJERPH. 2022;19(20):13152. doi:10.3390/ijerph192013152\u003c/li\u003e\n\u003cli\u003eSimons E, To T, Moineddin R, Stieb D, Dell SD. Maternal second-hand smoke exposure in pregnancy is associated with childhood asthma development. J Allergy Clin Immunol Pract. 2014;2(2):201-207. doi:10.1016/j.jaip.2013.11.014\u003c/li\u003e\n\u003cli\u003eTreyster Z, Gitterman B. Second hand smoke exposure in children: environmental factors, physiological effects, and interventions within pediatrics. Rev Environ Health. 2011;26(3):187-195. doi:10.1515/reveh.2011.026\u003c/li\u003e\n\u003cli\u003eWu CF, Feng NH, Chong IW, et al. Second-hand smoke and chronic bronchitis in Taiwanese women: a health-care based study. BMC Public Health. 2010;10:44. doi:10.1186/1471-2458-10-44\u003c/li\u003e\n\u003cli\u003eKim Y, Cho WK, Evangelista LS. Effect of Second-Hand Smoke Exposure on Lung Function among Non-Smoking Korean Women. Iran J Public Health. 2013;42(12):1363-1373.\u003c/li\u003e\n\u003cli\u003eDearing BA, Katz RV, Weitzman M. Prenatal tobacco and postbirth second-hand smoke exposure and dental caries in children. 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Nutrition Concerns of Insufficient and Excessive Intake of Dietary Minerals in Lactating Women: A Cross-Sectional Survey in Three Cities of China. PLoS One. 2016;11(1):e0146483. doi:10.1371/journal.pone.0146483\u003c/li\u003e\n\u003cli\u003eSamantha M, Marta PA, Sophie E M. The Role of Iron in Brain Development: A Systematic Review. Nutrients. 2020;12(7). doi:10.3390/nu12072001\u003c/li\u003e\n\u003cli\u003eFrank R G, Robert D B. Early Childhood Chronic Iron Deficiency and Later Cognitive Function: The Conundrum Continues. Pediatrics. 2022;150(6). doi:10.1542/peds.2022-058591\u003c/li\u003e\n\u003cli\u003eXie Z, Zhong G, Xu C, et al. Trends and cross-country inequalities of alcohol use disorders: findings from the global burden of disease study 2021. Globalization and health. 2025;21(1):30. doi:10.1186/s12992-025-01124-5\u003c/li\u003e\n\u003cli\u003ePark S, Lee M, Park S, Lee. Health risk behaviors and psychological problems among South Korean, North Korean, and other multicultural family adolescents (2011-2016). Psychiatry research. 2018;268:373-380. doi:10.1016/j.psychres.2018.07.042\u003c/li\u003e\n\u003cli\u003eMoore SE, Norman RE, Suetani S, Thomas HJ, Sly PD, Scott JG. Consequences of bullying victimization in childhood and adolescence: A systematic review and meta-analysis. WJP. 2017;7(1):60. doi:10.5498/wjp.v7.i1.60\u003c/li\u003e\n\u003cli\u003eHemphill SA, Kotevski A, Herrenkohl TI, et al. Longitudinal consequences of adolescent bullying perpetration and victimisation: A study of students in Victoria, Australia. Criminal Behav Ment Health. 2011;21(2):107-116. doi:10.1002/cbm.802\u003c/li\u003e\n\u003cli\u003eKlomek AB, Sourander A, Elonheimo H. Bullying by peers in childhood and effects on psychopathology, suicidality, and criminality in adulthood. Lancet Psychiatry. 2015;2(10):930-941. doi:10.1016/s2215-0366(15)00223-0\u003c/li\u003e\n\u003cli\u003eWu M, Lv Y, Liu W, et al. Exploring environmental and cardiometabolic impacts associated with adherence to the sustainable EAT-Lancet reference diet: findings from the China Health and Nutrition Survey. Environ Health Perspect. Published online April 30, 2025. doi:10.1289/ehp15006\u003c/li\u003e\n\u003cli\u003eLiu X, Wen Y, Zhou Q. Gender differences in adolescent food preferences and their association with parent food preferences: data from the China Health and Nutrition Survey (CHNS). Eur J Nutr. 2024;63(7):2611-2619. doi:10.1007/s00394-024-03450-7\u003c/li\u003e\n\u003cli\u003eWei X, Zhao L, Fang H, et al. Deficiency of Energy and Nutrient and Gender Differences among Chinese Adults: China Nutrition and Health Survey (2015\u0026ndash;2017). Nutrients. 2024;16(14):2371. doi:10.3390/nu16142371\u003c/li\u003e\n\u003cli\u003eBrook RD, Rajagopalan S, Pope CA, et al. 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A Short-Term Air Quality Control for PM10 Levels. Electron (Switz). 2020;9(9):1409. doi:10.3390/electronics9091409\u003c/li\u003e\n\u003cli\u003ePark TH, Ko Y, Lee SJ, et al. Identifying Target Risk Factors Using Population Attributable Risks of Ischemic Stroke by Age and Sex. J Stroke. 2015;17(3):302-311. doi:10.5853/jos.2015.17.3.302\u003c/li\u003e\n\u003cli\u003eBauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014;384(9937):45-52. doi:10.1016/s0140-6736(14)60648-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Risk factors, China, Sex factors, Age factors, Air pollution","lastPublishedDoi":"10.21203/rs.3.rs-7332902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7332902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo identify gender- and age-specific risk factors in China, informing targeted prevention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing GBD 2021 data, we analyzed exposure, mortality, and disability burdens for 68 risk factors across genders/age groups. Estimated Annual Percentage Change (EAPC) quantified 32-year trends (1990\u0026ndash;2021). Future trends (2022\u0026ndash;2050) were projected via ARIMA modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmbient particulate matter pollution exposure increased most markedly (Female EAPC: 5.26%; Male: 4.50%), with SEV projected to exceed 88% across all ages by 2050. Associated deaths and YLDs will concentrate in those\u0026thinsp;\u0026ge;\u0026thinsp;70. Mortality from nutritional deficiencies declined significantly (e.g., vitamin A deficiency: Female EAPC \u0026minus;\u0026thinsp;17.37%; Male \u0026minus;\u0026thinsp;18.02%). Notable gender disparities existed in 2021: insufficient milk intake (Women: 97.97% SEV; Men: 51.76%) and secondhand smoke (Women: 72.82% SEV; Men: 33.34% vs. 43.38% smoking rate). Diets high in red meat (EAPC: 18.92%), sugar-sweetened beverages (EAPC: 4.18%), and processed meat increased mortality risk, exacerbating metabolic factors (BMI, FBG, LDL, SBP) in ages 15+, elevating future death/disability burdens. For children (5\u0026ndash;14 years), nutrition (vitamin A deficiency, short gestation, low birth weight) and metabolic (high BMI) factors are projected to increase disability burden.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eRisk factor profiles distinctly vary by gender and age in China, necessitating demographically tailored prevention for effective disease control and public health improvement.\u003c/p\u003e","manuscriptTitle":"Gender and age disparities in risk factor burden in China: a GBD 2021 analysis with projections to 2050","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:07:28","doi":"10.21203/rs.3.rs-7332902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-21T09:30:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334334673838348628735872395420049617402","date":"2025-11-21T08:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T12:07:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-19T06:54:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-17T23:42:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-17T23:42:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-09T09:23:05+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":"45a5a000-0238-4f14-b426-03a9ba7eba65","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T10:07:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:07:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7332902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7332902","identity":"rs-7332902","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}