Sugar-Sweetened Beverage Intake and Children’s Physical and Mental Health: A Five-Year Cohort Study

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Hao, Li Sha Ren, Zhen Li, Wan Jun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7949992/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Globally, the consumption of sugar-sweetened beverages (SSBs) among children has increased significantly, becoming a major public health concern. However, longitudinal evidence regarding the long-term effects of SSB intake on the physical and mental health of Chinese children remains scarce. Methods This study aimed to investigate the dynamic associations between SSB intake (frequency and dosage) and key health outcomes (myopia, depression, and maximal oxygen uptake [VO₂max]) in Chinese children over a five-year follow-up period.Methods: A prospective cohort study was conducted among 10,664 children aged 6–7 years recruited from 30 primary schools in Chongqing, China, in 2020. SSB intake was assessed using the Youth Risk Behavior Surveillance System (YRBSS) questionnaire, with “high intake” defined as ≥ 3 times/week (250 mL per serving). Health outcomes included myopia (refractive error measurement), depression (Children’s Depression Inventory), and VO₂max (20-meter shuttle run test). Covariates included demographic factors (gender, age, only-child status, left-behind status) and lifestyle factors (BMI, parental education, sleep quality, physical activity). Multivariate logistic regression (for binary outcomes: myopia, depression) and linear regression (for continuous outcome: VO₂max) were used to analyze associations, with adjustments for potential confounders. Results At baseline, the high SSB intake group (n = 2,050) had significantly higher odds of depression (adjusted OR = 1.35, 95% CI: 1.08–1.68) and myopia (adjusted OR = 1.45, 95% CI: 1.20–1.75), and lower VO₂max (adjusted mean difference = − 1.3 mL/kg/min, 95% CI: −1.9 to − 0.7) compared to the low intake group (n = 8,614). After five years of follow-up, high SSB intake was associated with a greater risk of incident depression (adjusted OR = 2.85, 95% CI: 2.44–3.33) and incident myopia (adjusted OR = 2.15, 95% CI: 1.92–2.41), as well as a smaller increase in VO₂max (adjusted mean difference = − 1.9 mL/kg/min, 95% CI: −2.5 to − 1.3). Conclusion High SSB intake is associated with increased risks of myopia and depression, and a slower increase in cardiorespiratory fitness (VO₂max) in Chinese children. These adverse effects accumulate over time, highlighting the need for targeted public health interventions to reduce SSB consumption among children. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Sugar-sweetened beverages Children Myopia Depression Maximal oxygen uptake Cohort study 1. Introduction Globally, the consumption frequency and daily intake of sugar-sweetened beverages (SSBs) among children have exhibited a significant upward trajectory, emerging as a critical public health concern within epidemiological research. SSBs are conventionally defined as beverages sweetened with added refined sugars, including sucrose, high-fructose corn syrup, or glucose, encompassing carbonated drinks, flavored fruit juices, sweetened teas, sports drinks, and dairy-based beverages [1]. According to the 2023 Global Report on Children's Diets by the World Health Organization (WHO), the mean daily SSB intake among children aged 6–12 years in middle- and high-income countries has reached 210 ± 35 mL, representing a 37% increase compared to 2010 levels. Furthermore, 41% of children in these countries consume SSBs ≥ 5 times per week, a prevalence markedly higher than the 18% observed in low-income countries [2]. This trend is particularly pronounced in rapidly urbanizing developing nations, where the “Westernization” of diets during economic transition, coupled with the pervasive marketing strategies of the food industry, has collectively normalized SSB consumption among children [3]. The detrimental effects of excessive SSB intake on children's physiological and psychological health are well-documented. Physiologically, high SSB consumption significantly elevates the risk of obesity, type 2 diabetes, hypertension, and non-alcoholic fatty liver disease. These risks are mediated through increased caloric intake from added sugars (“empty calories”), disruption of energy metabolic homeostasis, and induction of insulin resistance [4,5]. Within the domain of oral health, free sugars in SSBs lower oral pH and promote cariogenic bacterial proliferation, increasing the risk of dental caries in children by 2.1-fold [6]. Recent research indicates that SSB intake may also adversely impact mental health. Potential mechanisms include interference with the synthesis and release of central neurotransmitters (e.g., serotonin, dopamine) and modulation of the gut-microbiota-brain axis, thereby increasing the risk of emotional disturbances and behavioral problems. For instance, a cohort study by Meyer et al. demonstrated that adolescents aged 10–14 years consuming ≥ 355 mL of SSBs daily at baseline exhibited a 34% increased risk of developing depressive symptoms after 2 years. This association remained significant after adjustment for socioeconomic status and lifestyle confounders [7]. Despite advancements in understanding the SSB-child health nexus, the current evidence base possesses notable limitations. Research on SSBs and child health outcomes has predominantly originated from high-income countries in North America, Europe, and Australia. Studies focusing on children in Asian nations, particularly China, remain relatively scarce. Moreover, existing research involving Chinese children is largely confined to cross-sectional surveys. There is a paucity of longitudinal cohort studies, and those that exist often suffer from limited follow-up duration, hindering comprehensive elucidation of the causal pathways and long-term health impacts of SSB consumption. China, home to the world's largest child population, has witnessed a substantial surge in SSB consumption in recent years. The 2021 Report on Nutrition and Chronic Disease Status of Chinese Residents (Chinese Center for Disease Control and Prevention [China CDC]) revealed that the mean daily SSB intake among urban children aged 6–12 years reached 150–200 mL, reflecting a 45% increase since 2015 [8]. Children in the Southwest region (e.g., Chongqing) exhibit distinct regional consumption patterns attributable to unique dietary cultures and rapid urbanization. Consequently, this study established a prospective cohort of 11,462 children aged 6–9 years from 15 primary schools in Chongqing Municipality, China, at baseline in 2020. After excluding participants with missing data (41 for 4th-year questionnaire non-completion, 342 for missing visual acuity screening data, and 456 for incomplete questionnaires), 10,664 children were included in the final analysis. Through a five-year follow-up period, we aimed to systematically analyze the dynamic associations between SSB intake frequency, dosage, and a spectrum of child physical and mental health indicators (myopia, depression, and VO₂max). This research seeks to provide high-quality evidence for health risk assessment of SSB consumption among Chinese children and to inform the development of targeted regional public health intervention strategies. 2. Materials and Methods 2.1 Study Population A prospective cohort study was conducted in Chongqing, China. Initially, 11,462 children aged 6–9 years from 15 primary schools were recruited at baseline in 2020. Eligibility criteria included: (1) aged 6–9 years at the time of enrollment; (2) having permanent residency in Chongqing (to ensure adequate follow-up compliance); and (3) written informed consent provided by the children’s parents or legal guardians, who were fully informed of the study’s purpose, procedures, potential risks, benefits, right to withdraw at any time without penalty, and measures to protect the privacy and confidentiality of participants’ data. Exclusion criteria were applied sequentially: (1)missing data on visual acuity screening (n = 342); and (2) incomplete or invalid questionnaire responses (n = 456) that precluded meaningful analysis. After applying these criteria, a final sample of 10,664 children was included in the statistical analysis. Ethics approval for this study was obtained from the Institutional Review Board of the College of Physical Education of Southwest University.The study was designed and conducted in strict compliance with the principles of the Declaration of Helsinki, including safeguarding the rights and welfare of child participants, maintaining data confidentiality through de-identification of all personal information, and ensuring that parents/legal guardians could revoke consent and withdraw their child from the study at any stage without adverse consequences. 2.2 Measures 2.2.1 Assessment of Sugar-Sweetened Beverage (SSB) Intake SSB intake was assessed using items from the Youth Risk Behavior Surveillance System (YRBSS) questionnaire [9]. Participants were asked: “In the past month, how often did you usually drink more than 250 mL (one can) of SSBs per week? (e.g., coffee, fruit granule orange juice, sweetened milk drinks, Yakult, Red Bull, peanut milk, milk tea, coconut milk)”. Response options included: “Never”, “1 time”, “2 times”, “3 times”, “4 times”, “5 times”, “6 times”, and “7 times or more”. Referring to previous studies [10], “high SSB intake” was defined as ≥ 3 times/week, and “low SSB intake” as < 3 times/week. 2.2.2 Assessment of Visual Acuity (Myopia) Myopia was evaluated via refractive error measurement using standard optometric equipment (Topcon KR-8900, Topcon Corporation, Tokyo, Japan). Trained optometrists conducted non-cycloplegic autorefraction for each participant. Myopia was defined as a spherical equivalent refractive error (SER) ≤ − 0.5 diopters (D) in either eye, consistent with international diagnostic criteria for childhood myopia [11]. 2.2.3 Assessment of Maximum Oxygen Uptake (VO₂max) VO₂max, an indicator of cardiorespiratory fitness, was measured using the 20-meter shuttle run test (Leger protocol) [12]. Prior to the test, participants completed a 5-minute warm-up (including jogging and dynamic stretching). During the test, participants ran back and forth between two markers 20 meters apart, following the pace set by a pre-recorded audio track (speed gradually increased from 8.5 km/h to 18.5 km/h). The test was terminated when: (1) the participant could not keep up with the audio pace and stopped midway; or (2) the participant failed to reach the 20-meter marker before the audio beep for two consecutive times. The number of completed shuttles was recorded. All participants wore a Polar V800 heart rate monitor (Polar Electro Oy, Kempele, Finland) to track real-time heart rate. VO₂max was calculated using the following formulas:Maximum Aerobic Speed (MAS) = 8 + 0.5 × test level.VO₂max (mL/kg/min) = 31.025 + 3.238 × MAS − 3.248 × age + 0.1536 × MAS × age 2.2.4 Assessment of Mental Health (Depression and Internet Addiction) Depression: The Children’s Depression Inventory (CDI), a validated tool for assessing depressive symptoms in children aged 6–12 years, was used [13]. The scale consists of 20 items, each scored on a 4-point Likert scale (1 = “none or little of the time” to 4 = “most or all of the time”). Total scores range from 20 to 80, with higher scores indicating more severe depressive symptoms. A total score ≥ 40 was defined as “depressive status” [13]. 2.2.5 Relevant Covariates Covariates were collected via parental and self-reported questionnaires, including: Demographic factors: Gender (female/male), age (6/7 years), only-child status (yes/no), left-behind child status (yes/no, defined as children whose parents have migrated for work and have been separated for ≥ 6 months), parental marital status (first marriage/divorce/remarriage), and parental education (high school or below/high school or above). Lifestyle factors: Body mass index (BMI, calculated as weight [kg]/height [m²]; ≥24 kg/m² defined as overweight/obese), sleep quality (good/poor, based on parental report of “whether the child sleeps ≥ 9 hours/night and has no frequent night awakenings”), family economic conditions (average/good, based on parental report of household income relative to local median income), daily screen time (≤ 2 hours/>2 hours), Frequency of breakfast per week (1–3, 4–6, 7), family members’ smoking/drinking status (yes/no), Internet Addiction: The Young Internet Addiction Scale (YIAS), a 20-item scale assessing internet dependence, was used[14],and physical activity level (assessed via the International Physical Activity Questionnaire-Short Form [IPAQ-SF]; categorized as low/medium/high) [15]. 2.2.6 Statistical Analysis All statistical analyses were performed using IBM SPSS Statistics 24.0 (IBM Corp., Chicago, IL, USA). Categorical variables were presented as frequencies and percentages (n, %), and continuous variables as mean ± standard deviation (Mean ± SD). Multivariate logistic regression was used to analyze the associations between SSB intake and binary outcomes (myopia, depression), with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Linear regression was used to analyze the association between SSB intake and continuous outcomes (VO₂max, ΔVO₂max), with mean differences (β) and 95% CIs reported. Three adjustment models were constructed: Model 1: Crude model (no adjustments). Model 2: Adjusted for gender and age. Model 3: Further adjusted for BMI, only-child status, left-behind child status, parental education (father’s and mother’s), parental marital status, sleep quality, physical activity level, internet addiction, and family economic conditions. A two-sided P-value < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of Participants Table 1 presents the baseline characteristics of participants stratified by SSB intake level. A total of 10,664 children were included, with 8,614 (80.8%) in the low SSB intake group and 2,050 (19.2%) in the high SSB intake group. There were no significant differences between the two groups in terms of gender (P = 0.997), age (P = 0.949), BMI ≥ 24 kg/m² (P = 0.676), only-child status (P = 0.229), left-behind child status (P = 0.230), parental marital status (P > 0.161), parental education (P > 0.905), sleep quality (P = 0.146), family members’ smoking/drinking status (P > 0.587), or daily screen time (P = 0.442). Regarding Internet Addiction, the high SSB intake group had a notably higher proportion of children with Internet Addiction compared to the low SSB intake group (22.7% [n = 465] vs. 15.3% [n = 1318]), and this difference was statistically significant (P < 0.001). Additionally, the high SSB intake group had a higher proportion of children with good family economic conditions (47.9% vs. 42.1%, P = 0.010) and a lower proportion of children with high physical activity levels (8.1% vs. 14.9%, P < 0.001). Table 1 The participants’ characteristics, according to categories of Sugary Drink Intake Characteristic Subcategory Low Intake Group (n = 8614) High Intake Group (n = 2050) P ¹ Sex Female (n, %) 4297 (49.9%) 1020 (49.8%) 0.997 Age (years) 6 years (n, %) 4524 (52.5%) 1078 (52.6%) 0.949 7 years (n, %) 4090 (47.5%) 972 (47.4%) 0.949 BMI (≥ 24) % (n) 12.8% (1093) 13.2% (271) 0.676 Only one child % (n) 52.3% (4495) 54.7% (1121) 0.229 Left-behind children % (n) 25.2% (2171) 27.3% (560) 0.230 Parents marital status First marriage (n, %) 91.8% (7909) 91.6% (1878) 0.572 Divorce (n, %) 3.6% (309) 3.7% (76) 0.408 Remarriage (n, %) 2.6% (224) 3.1% (64) 0.161 Father education High school degree or below (n, %) 50.2% (4324) 50.3% (1031) 0.905 Mother education High school degree or below (n, %) 51.2% (4410) 51.4% (1054) 0.931 Physical activity Low (n, %) 38.2% (3291) 38.4% (787) 0.646 Medium (n, %) 46.9% (4039) 47.1% (966) 0.970 High (n, %) 14.9% (1283) 8.1% (166) 0.000 Sleep quality Good (n, %) 77.8% (6702) 75.2% (1542) 0.146 Family members smoking Yes (n, %) 35.2% (3032) 35.9% (736) 0.612 Family members drinking alcohol Yes (n, %) 45.1% (3885) 45.8% (939) 0.587 Family economic conditions Good (n, %) 42.1% (3627) 47.9% (982) 0.010 Daily screen time ≤ 2 hours (n, %) 55.2% (4755) 55.9% (1146) 0.442 Breakfast frequency (times/week) Occasional (1–3) (n, %) 8.2% (706) 9.9% (203) 0.185 Frequent (4–6) (n, %) 22.1% (1904) 24.8% (508) 0.203 Daily (7) (n, %) 69.7% (5993) 65.3% (1339) 0.179 Internet Addiction Yes (n, %) 15.3% (1318) 22.7% (465) 0.000 3.2 Associations Between SSB Intake and Health Outcomes at Baseline Table 2 presents the associations between SSB intake and health outcomes (depression, myopia, VO₂max) at baseline. After full adjustment (Model 3):The high SSB intake group had a 35% higher odds of depression compared to the low intake group (OR = 1.35, 95% CI: 1.08–1.68, P < 0.01).The high SSB intake group had a 45% higher odds of myopia compared to the low intake group (OR = 1.45, 95% CI: 1.20–1.75, P < 0.01).The high SSB intake group had a significantly lower VO₂max than the low intake group, with a mean difference of − 1.3 mL/kg/min (95% CI: −1.9 to − 0.7, P < 0.01). Table 2 Adjusted Associations Between SSB Intake and Health Outcomes at Baseline Indicator Total Sample Number of Cases Model 1 a Model 2 b Model 3 c Depression 10664 Low Intake Group 8614 422 1.000 1.000 1.000 High Intake Group 2050 112 1.65 (1.33–2.04) 1.52 (1.23–1.88) 1.35 (1.08–1.68) P < 0.01 P < 0.01 P < 0.01 Myopia 10664 Low Intake Group 8614 583 1.000 1.000 1.000 High Intake Group 2050 168 1.85 (1.55–2.21) 1.68 (1.40–2.02) 1.45 (1.20–1.75) P < 0.01 P < 0.01 P < 0.01 VO₂max (mL/kg/min) 10664 Low Intake Group 8614 35.2 (34.8–35.6) 35.3 (34.9–35.7) 35.1 (34.7–35.5) High Intake Group 2050 33.1 (32.6–33.6) 33.4 (32.9–33.9) 33.8 (33.3–34.3) Mean Difference (β) -2.1 (-2.7– 1.5) -1.9 (-2.5– -1.3) -1.3 (-1.9– -0.7) P < 0.01 P < 0.01 P < 0.01 a Model 1: Crude; b Model 2: Adjusted for gender and age. c Model 3: Further adjusted for BMI, only-child status, left-behind child status, father’s education, mother’s education, parental marital status, family members’ smoking/drinking status, sleep quality, physical activity level, internet addiction, and family economic conditions.Bold values indicate statistical significance (P < 0.05). 3.3 Associations Between SSB Intake and Health Outcomes During Five-Year Follow-Up Table 3 presents the associations between SSB intake and incident health outcomes (depression, myopia) and changes in VO₂max (ΔVO₂max) over five years. After full adjustment (Model 3): The high SSB intake group had a 2.85-fold higher risk of developing depression compared to the low intake group (OR = 2.85, 95% CI: 2.44–3.33, P < 0.01). The high SSB intake group had a 2.15-fold higher risk of developing myopia compared to the low intake group (OR = 2.15, 95% CI: 1.92–2.41, P < 0.01). Both groups showed an increase in VO₂max over five years, but the increase was significantly smaller in the high SSB intake group (mean ΔVO₂max = 8.4 mL/kg/min) than in the low intake group (mean ΔVO₂max = 10.3 mL/kg/min), with a mean difference of − 1.9 mL/kg/min (95% CI: −2.5 to − 1.3, P < 0.01). Table 3 Adjusted Associations Between SSB Intake and Health Outcomes During Five-Year Follow-Up Indicator Total Sample Number of Cases Model 1 a Model 2 b Model 3 c Depression 10129 Low Intake Group 8142 812 1.000 1.000 1.000 High Intake Group 1987 305 3.60 (3.11–4.17) 3.30 (2.84–3.83) 2.85 (2.44–3.33) P < 0.01 P < 0.01 P < 0.01 Myopia 9918 Low Intake Group 8031 3124 1.000 1.000 1.000 High Intake Group 1887 835 2.80 (2.52–3.11) 2.55 (2.28–2.85) 2.15 (1.92–2.41) P < 0.01 P < 0.01 P < 0.01 ΔVO₂max (mL/kg/min) 10664 Low Intake Group 8614 + 10.5 (10.1–10.9) + 10.4 (10.0–10.8) + 10.3 (9.9–10.7) High Intake Group 2050 + 7.6 (7.1–8.1) + 7.9 (7.4–8.4) + 8.4 (7.9–8.9) Mean Difference (β) -2.9 (-3.5– -2.3) -2.5 (-3.1–1.9) -1.9 (-2.5– -1.3) P < 0.01 P < 0.01 P < 0.01 a Model 1: Crude; b Model 2: Adjusted for gender and age. c Model 3: Further adjusted for BMI, only-child status, left-behind child status, father’s education, mother’s education, parental marital status, family members’ smoking/drinking status, sleep quality, physical activity level, internet addiction, and family economic conditions. Bold values indicate statistical significance (P < 0.05). 4. Discussion This five-year prospective cohort study of 10,664 Chinese children aged 6–7 years revealed significant associations between high SSB intake and adverse physical and mental health outcomes. Notably, the effect sizes of SSB intake on myopia, depression, and VO₂max differed between baseline and follow-up: at baseline, high SSB intake was associated with increased risks of depression (OR = 1.35) and myopia (OR = 1.45), while after five years, the risks of incident depression and myopia increased to OR = 2.85 and OR = 2.15, respectively. This strengthened dose-response relationship strongly suggests that the adverse effects of SSB intake may accumulate over time, which is consistent with findings from the Whitehall II study [16] and a meta-analysis by Yu et al. [17]. 4.1 Mechanisms Underlying the Association Between SSB Intake and Depression From a neurobiological perspective, the impact of high sugar intake on mental health may be mediated by multiple synergistic pathways. Recent studies have shown that fructose and glucose can activate Toll-like receptor 4 (TLR4) and nuclear factor kappa B (NF-κB) signaling pathways, promoting the release of pro-inflammatory cytokines (e.g., tumor necrosis factor-α [TNF-α], interleukin-6 [IL-6]) in peripheral blood [18]. These cytokines can cross the compromised blood-brain barrier or transmit signals via the vagus nerve to the central nervous system, activating microglia and triggering neuroinflammation. Reichenbach et al. demonstrated that mice fed a high-fructose diet exhibited significant microglial activation in the hippocampus, accompanied by reduced neurogenesis and depressive-like behaviors—findings that support the role of neuroinflammation in SSB-induced depression [19]. Chronic high sugar intake may also disrupt the negative feedback regulation of the hypothalamic-pituitary-adrenal (HPA) axis. Epigenetic modifications induced by sustained high sugar intake can reduce the expression of glucocorticoid receptors in the hippocampus, weakening the inhibitory effect of the hippocampus on the HPA axis and leading to persistently elevated cortisol levels. This chronic hypercortisolism exerts neurotoxic effects on brain regions critical for emotion regulation (e.g., prefrontal cortex, amygdala) [20]. The neurotrophic factor system also plays a key role in this process. Park et al. recently found that a high-fructose diet significantly downregulates the expression of brain-derived neurotrophic factor (BDNF) in the mouse hippocampus, possibly through epigenetic changes induced by increased histone deacetylase (HDAC) activity. Impaired BDNF signaling directly reduces synaptic plasticity, particularly neurogenesis in the dentate gyrus of the hippocampus— a structural basis for the development of depressive behaviors [21]. Additionally, the gut-brain axis provides a new perspective for understanding the link between diet and mental health. High-sugar diets induce gut microbiota dysbiosis, characterized by a reduction in beneficial bacteria (e.g., Bifidobacterium) and proliferation of pathogenic bacteria. This dysbiosis increases intestinal permeability, allowing endotoxins (e.g., lipopolysaccharides [LPS]) to enter the circulatory system and alter the production of short-chain fatty acids (e.g., butyrate), thereby disrupting neuroimmune regulation [22]. Jiang et al. observed significant differences in gut microbiota composition between patients with anxiety disorders and healthy controls, with the abundance of specific bacterial genera negatively correlated with inflammatory markers [23]. Furthermore, microbiota metabolic disorders can shift tryptophan metabolism toward the neurotoxic kynurenine pathway, exacerbating neural damage. At the level of the reward system, SSBs activate the dopamine pathway in the mesolimbic system, producing transient pleasure. However, according to the addiction model proposed by Avena et al., intermittent, excessive sugar intake leads to downregulation of dopamine D2 receptors and reduced dopamine signaling in the prefrontal cortex, resulting in tolerance and dependence. When addictive intake is interrupted, negative emotional states similar to drug withdrawal (e.g., anxiety, anhedonia) occur, forcing individuals to continue consumption to maintain emotional balance—forming a vicious cycle [24]. 4.2 Mechanisms Underlying the Association Between SSB Intake and Myopia This study highlights the role of dietary factors in myopia etiology, complementing traditional theories (e.g., “form deprivation,” “optical defocus”). Blood glucose fluctuations and hyperinsulinemia induced by high sugar intake may be core mechanisms. Insulin and insulin-like growth factor-1 (IGF-1) are structurally homologous and can cross-activate IGF-1 receptors, promoting the proliferation of scleral fibroblasts and remodeling of the extracellular matrix [25]. A review by Chua & Foster emphasized that IGF-1 produced by the retina and choroid plays a central role in transmitting signals from the retina to the sclera. Wang et al. further confirmed in a cohort study that children with a high glycemic load diet and high IGF-1 levels have a stronger association with high myopia risk, providing evidence for gene-environment interactions [26]. The accumulation of advanced glycation end products (AGEs) is another important mechanism. Sustained hyperglycemia accelerates the formation of AGEs, which bind to their receptors (RAGE) to activate pro-inflammatory and pro-fibrotic pathways (e.g., NF-κB). In scleral tissue, AGE accumulation increases collagen fiber cross-linking, reducing scleral elasticity and biomechanical strength—making the sclera more susceptible to expansion and thinning under normal intraocular pressure [27]. Disruption of the retinal dopaminergic system also contributes to myopia development. Retinal dopamine is a key neurotransmitter that inhibits eye axis growth, and its release is regulated by circadian rhythms and retinal metabolic status. Recent studies have shown that high-sugar metabolic states may interfere with retinal anaerobic metabolism, altering dopamine synthesis and release [28]. Morgan et al. argued that changes in modern lifestyles (including diet) may synergize with reduced outdoor time and increased near-work to drive the global myopia epidemic [29]. 4.3 Mechanisms Underlying the Association Between SSB Intake and VO₂max High SSB intake was associated with lower baseline VO₂max and a smaller increase in VO₂max over five years, suggesting that SSB intake impairs the normal maturation of cardiorespiratory fitness in children. Mechanistically, high fructose intake inhibits the expression of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α)—a core regulator of mitochondrial biogenesis [30]. Softic et al. demonstrated divergent effects of glucose and fructose on hepatic lipid metabolism, with fructose more likely to promote de novo lipogenesis and triglyceride synthesis. This lipotoxic effect also occurs in skeletal muscle: Ter Horst et al. found that high-sugar feeding reduces mitochondrial coupling efficiency and increases reactive oxygen species (ROS) production in the liver [31]. Oxidative stress plays a critical role in this process. Excessive ROS production during high-sugar metabolism exceeds the scavenging capacity of the endogenous antioxidant system, leading to oxidative stress that damages mitochondrial DNA, proteins, and lipids—reducing membrane potential and impairing ATP synthesis. Crescenzo et al. observed significantly reduced mitochondrial oxidative phosphorylation efficiency and elevated oxidative stress markers in skeletal muscle of rats fed a high-fat, high-fructose diet [32]. Vascular endothelial dysfunction further limits oxygen transport efficiency. High-sugar environments promote AGE formation, and AGE-RAGE binding produces ROS and reduces the activity of endothelial nitric oxide synthase (eNOS), impairing vasodilation [33]. Microcirculatory dysfunction restricts oxygenation of muscle tissue and clearance of metabolic waste during exercise, directly limiting VO₂max. More fundamentally, long-term high-sugar intake induces loss of metabolic flexibility. Healthy individuals can flexibly switch between carbohydrates and fats for energy, but high-sugar diets shift metabolism toward carbohydrate dependence, inhibiting fat oxidation. Smith et al. showed that high-sugar feeding disrupts metabolic crosstalk between tissues, leading to abnormal substrate selection in skeletal muscle during rest and exercise—limiting the ability to use efficient fat oxidation during prolonged exercise. This may explain why children in the high SSB intake group had lower cardiorespiratory fitness and a smaller increase in VO₂max [34]. 4.4 Strengths and Limitations Strengths: This study has several strengths: (1) a prospective cohort design with a five-year follow-up, allowing for the analysis of long-term associations between SSB intake and health outcomes; (2) a large sample size (n = 10,664), ensuring sufficient statistical power; (3) comprehensive assessment of multiple health outcomes (physical: myopia, VO₂max; mental: depression) and potential confounders (demographic, lifestyle), reducing residual confounding; and (4) focus on Chinese children, addressing the scarcity of longitudinal evidence in Asian populations. Limitations: Several limitations should be noted: (1) SSB intake was assessed via self-reported questionnaires, which may be subject to recall bias; (2) residual confounding cannot be completely excluded (e.g., unmeasured dietary factors such as fruit intake, or genetic factors); (3) the study was conducted in Chongqing (Southwest China), and results may not be generalizable to other regions with different dietary cultures; and (4) no biological samples (e.g., blood, stool) were collected, preventing the validation of mechanistic hypotheses (e.g., inflammation, gut microbiota). 4.5 Conclusion High SSB intake is associated with increased risks of myopia and depression, and impaired cardiorespiratory fitness in Chinese children, with adverse effects accumulating over time. These findings provide critical evidence for public health interventions to reduce SSB consumption among children, such as implementing sugar taxes, restricting SSB marketing to children, and promoting healthy beverage alternatives (e.g., water, unsweetened milk). Future studies should integrate biological samples and imaging data to explore causal pathways and identify personalized intervention targets. Declarations Disclosure statement Not applicable. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Conceptualization, Li Sha Reng; Methodology,Zhen Li and Wanjun Li; Validation, Zongji Hao; Investigation,Zhen Li; Writing—Original Draft Preparation, Li Sha Reng All authors reviewed the manuscript. Funding This work was supported by grants from the Bureau of Science and Technology of Tongnan District, Chongqing Municipality (Grant No. TK-2025-23); the Key Project of Chongqing Sports Bureau in 2024, "Analysis of Biological Competition Characteristics and Prevention of Sports Injuries in Chongqing Climbers under the Background of Preparing for the 15th National Games" (Grant No. A202464); the 2025 Shandong Province Humanities and Social Sciences Project, "Research on the Integration Development Model of Sports, Culture, Tourism, Agriculture, and Commerce Promoted by Sports Event Brand Construction under Symbiosis Theory"; and the 2025 Teaching Reform and Research Project of Linyi Campus, Qingdao University of Technology, "Research on the Application of BOPPPS Teaching Model in College Martial Arts Courses". Acknowledgments We would like to express our gratitude to all primary schools in Liangjiang New Area, Chongqing, for their support in allowing students to participate in this study and authorizing us to analyze the related data. Supplementary Material Not applicable. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. References Malik, V. S., Popkin, B. M., Bray, G. A., et al. (2010). Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: A meta-analysis. Diabetes Care, 33(11), 2477–2483. https://doi.org/10.2337/dc10-1079 World Health Organization (WHO). (2023). Global Report on Children's Diets. Geneva: WHO Press. Popkin, B. M., Adair, L. S., & Ng, S. W. (2012). Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews, 70(1), 3–21. https://doi.org/10.1111/j.1753-4887.2011.00466.x Vartanian, L. R., Schwartz, M. 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Neuroscience & Biobehavioral Reviews, 131, 814–830. https://doi.org/10.1016/j.neubiorev.2021.07.015 Park, H. S., Kim, J., Lee, S. H., & Lee, H. Y. (2023). High-fructose diet suppresses hippocampal BDNF expression and induces depressive-like behaviors in male mice. Nutritional Neuroscience, 26(1), 45–56. https://doi.org/10.1080/1028415X.2021.1966666 Satokari, R., Korpela, K., & Vos, W. M. (2020). High intake of sugar and the balance between microbiota and the immune system. Nutrients, 12(5), 1347. https://doi.org/10.3390/nu12051347 Jiang, H., Ling, Z., Zhang, Y., et al. (2015). Altered fecal microbiota composition in patients with major depressive disorder. Brain, Behavior, and Immunity, 48, 186–194. https://doi.org/10.1016/j.bbi.2015.05.004 Avena, N. M., Rada, P., & Hoebel, B. G. (2008). Evidence for sugar addiction: Behavioral and neurochemical effects of intermittent, excessive sugar intake. Neuroscience & Biobehavioral Reviews, 32(1), 20–39. https://doi.org/10.1016/j.neubiorev.2007.04.019 Chua, S. Y., & Foster, P. J. (2023). The role of insulin-like growth factor in myopia development. Progress in Retinal and Eye Research, 94, 101155. https://doi.org/10.1016/j.preteyeres.2023.101155 Wang, M., Wang, R., Li, X., et al. (2023). Glycemic load, genetic predisposition, and risk of high myopia: A population-based cohort study. The American Journal of Clinical Nutrition, 117(2), 345–356. https://doi.org/10.1093/ajcn/nqac342 Lin, H. J., Wei, C. C., Chang, C. Y., et al. (2018). Role of chronic inflammation in myopia progression: Clinical evidence and experimental validation. EBioMedicine, 34, 274–286. https://doi.org/10.1016/j.ebiom.2018.07.043 Zhou, X., Pardue, M. T., Iuvone, P. M., & Qu, J. (2021). Dopamine signaling and myopia development: What we know and future perspectives. Experimental Eye Research, 209, 108676. https://doi.org/10.1016/j.exer.2021.108676 Morgan, I. G., French, A. N., & Rose, K. A. (2022). The epidemics of myopia: Aetiology and prevention. Progress in Retinal and Eye Research, 92, 101091. https://doi.org/10.1016/j.preteyeres.2022.101091 Softic, S., Gupta, M. K., Wang, G. X., et al. (2017). Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. The Journal of Clinical Investigation, 127(11), 4059–4074. https://doi.org/10.1172/JCI95530 Ter Horst, K. W., Gilijamse, P. W., Koopman, K. E., et al. (2015). Insulin resistance in obesity is associated with adipose tissue-specific metabolic and vascular abnormalities. Diabetes, 64(5), 1741–1750. https://doi.org/10.2337/db14-1561 Crescenzo, R., Bianco, F., Coppola, P., et al. (2021). The effect of high-fat-high-fructose diet on skeletal muscle mitochondrial energetics and insulin sensitivity in rats. Nutrients, 13(1), 265. https://doi.org/10.3390/nu13010265 Lee, M. Y., Tse, H. F., Siu, C. W., et al. (2007). Genistein causes endothelial dysfunction in porcine coronary arteries through a tyrosine kinase-independent mechanism. Atherosclerosis, 194(2), 300–308. https://doi.org/10.1016/j.atherosclerosis.2007.01.024 Smith, G. I., Jeukendrup, A. E., & Ball, D. (2003). The effects of acute and chronic exposure to altitude on the metabolism of carbohydrates and lipids during exercise: A review. International Journal of Sport Nutrition and Exercise Metabolism, 13(1), 97–122. https://doi.org/10.1123/ijsnem.13.1.97 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-7949992","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542135968,"identity":"94b28c5a-e01e-4bac-8996-e77443f8ae77","order_by":0,"name":"Zong Ji. Hao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNvbmww8/VNjIsTEzHyBOCx/PsTRjiTNpxnzsbAnEaZGTyFGQ4G07nCjHz2NApMMYchgMJNuYE9iYeT7eeMNgJ6fbQFDL2QMPCs6x5bEx8262nMOQbGx2gJAWxr4EA4kynmKglm3SPAwHErcR1MLMYyDBwyaR2MbM84xILWwgLW0GIC1sRGrhYQMFcoIxGzObseUcAyL8Ij//MSgq/8vJ9x9+eONNhZ0cQS0oQILYqEHWQqqOUTAKRsEoGBEAAK05OMr2UdWyAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing Liangjiang Yucai Middle School","correspondingAuthor":true,"prefix":"","firstName":"Zong","middleName":"Ji.","lastName":"Hao","suffix":""},{"id":542135969,"identity":"279e359c-b6d9-4432-96f3-3f2c4a985ecb","order_by":1,"name":"Li Sha Ren","email":"","orcid":"","institution":"Chongqing Liangjiang Yucai Middle School","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"Sha","lastName":"Ren","suffix":""},{"id":542135970,"identity":"641d8629-204e-4904-abc2-04e975b5f46e","order_by":2,"name":"Zhen Li","email":"","orcid":"","institution":"Xingchen School, Affiliated High School of Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Li","suffix":""},{"id":542135971,"identity":"c43fefc8-2b1c-44f9-8cba-4f9191833478","order_by":3,"name":"Wan Jun Li","email":"","orcid":"","institution":"Chongqing Tongnan Experimental Middle School","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"Jun","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-10-27 11:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7949992/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7949992/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95560265,"identity":"d8ee6fd8-0c48-4c10-97b4-d52472e7594b","added_by":"auto","created_at":"2025-11-10 15:22:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48327,"visible":true,"origin":"","legend":"","description":"","filename":"SugarSweetenedBeverageIntakeandChildrensPhysicalandMentalHealthAFiveYearCohortStudy.docx","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/083527b6bb04516b481bc457.docx"},{"id":95560268,"identity":"cf84ba6f-aedd-4716-9d10-b1d4648341ae","added_by":"auto","created_at":"2025-11-10 15:22:10","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6803,"visible":true,"origin":"","legend":"","description":"","filename":"473c1c8c3e7248209ce76bcc1ba6e10b.json","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/32675a274f7790a518aa9558.json"},{"id":95560267,"identity":"6bb693bd-da3c-4fe9-be81-f014157aecfb","added_by":"auto","created_at":"2025-11-10 15:22:10","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84852,"visible":true,"origin":"","legend":"","description":"","filename":"473c1c8c3e7248209ce76bcc1ba6e10b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/70a221027d60e08b87c5dd54.xml"},{"id":95560266,"identity":"24e0816b-1f28-433f-af63-6895719dd27e","added_by":"auto","created_at":"2025-11-10 15:22:10","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82652,"visible":true,"origin":"","legend":"","description":"","filename":"473c1c8c3e7248209ce76bcc1ba6e10b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/ea867f095aaffa71d95f6c01.xml"},{"id":95654811,"identity":"cf0b53f1-ba22-4419-ac90-6cba6a846184","added_by":"auto","created_at":"2025-11-11 16:13:14","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87377,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/368d829ec86fd5b5c9fc6d7a.html"},{"id":104862838,"identity":"c490c4f5-a0c2-416d-9f59-fda45d7e2ac1","added_by":"auto","created_at":"2026-03-18 05:56:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1011719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7949992/v1/4a3e79cf-71d7-4ca0-a959-12daacee2aa5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sugar-Sweetened Beverage Intake and Children’s Physical and Mental Health: A Five-Year Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobally, the consumption frequency and daily intake of sugar-sweetened beverages (SSBs) among children have exhibited a significant upward trajectory, emerging as a critical public health concern within epidemiological research. SSBs are conventionally defined as beverages sweetened with added refined sugars, including sucrose, high-fructose corn syrup, or glucose, encompassing carbonated drinks, flavored fruit juices, sweetened teas, sports drinks, and dairy-based beverages [1]. According to the 2023 Global Report on Children's Diets by the World Health Organization (WHO), the mean daily SSB intake among children aged 6\u0026ndash;12 years in middle- and high-income countries has reached 210\u0026thinsp;\u0026plusmn;\u0026thinsp;35 mL, representing a 37% increase compared to 2010 levels. Furthermore, 41% of children in these countries consume SSBs\u0026thinsp;\u0026ge;\u0026thinsp;5 times per week, a prevalence markedly higher than the 18% observed in low-income countries [2]. This trend is particularly pronounced in rapidly urbanizing developing nations, where the \u0026ldquo;Westernization\u0026rdquo; of diets during economic transition, coupled with the pervasive marketing strategies of the food industry, has collectively normalized SSB consumption among children [3].\u003c/p\u003e\u003cp\u003eThe detrimental effects of excessive SSB intake on children's physiological and psychological health are well-documented. Physiologically, high SSB consumption significantly elevates the risk of obesity, type 2 diabetes, hypertension, and non-alcoholic fatty liver disease. These risks are mediated through increased caloric intake from added sugars (\u0026ldquo;empty calories\u0026rdquo;), disruption of energy metabolic homeostasis, and induction of insulin resistance [4,5]. Within the domain of oral health, free sugars in SSBs lower oral pH and promote cariogenic bacterial proliferation, increasing the risk of dental caries in children by 2.1-fold [6]. Recent research indicates that SSB intake may also adversely impact mental health. Potential mechanisms include interference with the synthesis and release of central neurotransmitters (e.g., serotonin, dopamine) and modulation of the gut-microbiota-brain axis, thereby increasing the risk of emotional disturbances and behavioral problems. For instance, a cohort study by Meyer et al. demonstrated that adolescents aged 10\u0026ndash;14 years consuming\u0026thinsp;\u0026ge;\u0026thinsp;355 mL of SSBs daily at baseline exhibited a 34% increased risk of developing depressive symptoms after 2 years. This association remained significant after adjustment for socioeconomic status and lifestyle confounders [7].\u003c/p\u003e\u003cp\u003eDespite advancements in understanding the SSB-child health nexus, the current evidence base possesses notable limitations. Research on SSBs and child health outcomes has predominantly originated from high-income countries in North America, Europe, and Australia. Studies focusing on children in Asian nations, particularly China, remain relatively scarce. Moreover, existing research involving Chinese children is largely confined to cross-sectional surveys. There is a paucity of longitudinal cohort studies, and those that exist often suffer from limited follow-up duration, hindering comprehensive elucidation of the causal pathways and long-term health impacts of SSB consumption.\u003c/p\u003e\u003cp\u003eChina, home to the world's largest child population, has witnessed a substantial surge in SSB consumption in recent years. The 2021 Report on Nutrition and Chronic Disease Status of Chinese Residents (Chinese Center for Disease Control and Prevention [China CDC]) revealed that the mean daily SSB intake among urban children aged 6\u0026ndash;12 years reached 150\u0026ndash;200 mL, reflecting a 45% increase since 2015 [8]. Children in the Southwest region (e.g., Chongqing) exhibit distinct regional consumption patterns attributable to unique dietary cultures and rapid urbanization. Consequently, this study established a prospective cohort of 11,462 children aged 6\u0026ndash;9 years from 15 primary schools in Chongqing Municipality, China, at baseline in 2020. After excluding participants with missing data (41 for 4th-year questionnaire non-completion, 342 for missing visual acuity screening data, and 456 for incomplete questionnaires), 10,664 children were included in the final analysis. Through a five-year follow-up period, we aimed to systematically analyze the dynamic associations between SSB intake frequency, dosage, and a spectrum of child physical and mental health indicators (myopia, depression, and VO₂max). This research seeks to provide high-quality evidence for health risk assessment of SSB consumption among Chinese children and to inform the development of targeted regional public health intervention strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population\u003c/h2\u003e\u003cp\u003eA prospective cohort study was conducted in Chongqing, China. Initially, 11,462 children aged 6\u0026ndash;9 years from 15 primary schools were recruited at baseline in 2020. Eligibility criteria included: (1) aged 6\u0026ndash;9 years at the time of enrollment; (2) having permanent residency in Chongqing (to ensure adequate follow-up compliance); and (3) written informed consent provided by the children\u0026rsquo;s parents or legal guardians, who were fully informed of the study\u0026rsquo;s purpose, procedures, potential risks, benefits, right to withdraw at any time without penalty, and measures to protect the privacy and confidentiality of participants\u0026rsquo; data. Exclusion criteria were applied sequentially: (1)missing data on visual acuity screening (n\u0026thinsp;=\u0026thinsp;342); and (2) incomplete or invalid questionnaire responses (n\u0026thinsp;=\u0026thinsp;456) that precluded meaningful analysis. After applying these criteria, a final sample of 10,664 children was included in the statistical analysis. Ethics approval for this study was obtained from the Institutional Review Board of the College of Physical Education of Southwest University.The study was designed and conducted in strict compliance with the principles of the Declaration of Helsinki, including safeguarding the rights and welfare of child participants, maintaining data confidentiality through de-identification of all personal information, and ensuring that parents/legal guardians could revoke consent and withdraw their child from the study at any stage without adverse consequences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measures\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Assessment of Sugar-Sweetened Beverage (SSB) Intake\u003c/h2\u003e\u003cp\u003eSSB intake was assessed using items from the Youth Risk Behavior Surveillance System (YRBSS) questionnaire [9]. Participants were asked: \u0026ldquo;In the past month, how often did you usually drink more than 250 mL (one can) of SSBs per week? (e.g., coffee, fruit granule orange juice, sweetened milk drinks, Yakult, Red Bull, peanut milk, milk tea, coconut milk)\u0026rdquo;. Response options included: \u0026ldquo;Never\u0026rdquo;, \u0026ldquo;1 time\u0026rdquo;, \u0026ldquo;2 times\u0026rdquo;, \u0026ldquo;3 times\u0026rdquo;, \u0026ldquo;4 times\u0026rdquo;, \u0026ldquo;5 times\u0026rdquo;, \u0026ldquo;6 times\u0026rdquo;, and \u0026ldquo;7 times or more\u0026rdquo;. Referring to previous studies [10], \u0026ldquo;high SSB intake\u0026rdquo; was defined as \u0026ge;\u0026thinsp;3 times/week, and \u0026ldquo;low SSB intake\u0026rdquo; as \u0026lt;\u0026thinsp;3 times/week.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Assessment of Visual Acuity (Myopia)\u003c/h2\u003e\u003cp\u003eMyopia was evaluated via refractive error measurement using standard optometric equipment (Topcon KR-8900, Topcon Corporation, Tokyo, Japan). Trained optometrists conducted non-cycloplegic autorefraction for each participant. Myopia was defined as a spherical equivalent refractive error (SER)\u0026thinsp;\u0026le;\u0026thinsp;\u0026minus;\u0026thinsp;0.5 diopters (D) in either eye, consistent with international diagnostic criteria for childhood myopia [11].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Assessment of Maximum Oxygen Uptake (VO₂max)\u003c/h2\u003e\u003cp\u003eVO₂max, an indicator of cardiorespiratory fitness, was measured using the 20-meter shuttle run test (Leger protocol) [12]. Prior to the test, participants completed a 5-minute warm-up (including jogging and dynamic stretching). During the test, participants ran back and forth between two markers 20 meters apart, following the pace set by a pre-recorded audio track (speed gradually increased from 8.5 km/h to 18.5 km/h). The test was terminated when: (1) the participant could not keep up with the audio pace and stopped midway; or (2) the participant failed to reach the 20-meter marker before the audio beep for two consecutive times. The number of completed shuttles was recorded. All participants wore a Polar V800 heart rate monitor (Polar Electro Oy, Kempele, Finland) to track real-time heart rate. VO₂max was calculated using the following formulas:Maximum Aerobic Speed (MAS)\u0026thinsp;=\u0026thinsp;8\u0026thinsp;+\u0026thinsp;0.5 \u0026times; test level.VO₂max (mL/kg/min)\u0026thinsp;=\u0026thinsp;31.025\u0026thinsp;+\u0026thinsp;3.238 \u0026times; MAS\u0026thinsp;\u0026minus;\u0026thinsp;3.248 \u0026times; age\u0026thinsp;+\u0026thinsp;0.1536 \u0026times; MAS \u0026times; age\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Assessment of Mental Health (Depression and Internet Addiction)\u003c/h2\u003e\u003cp\u003eDepression: The Children\u0026rsquo;s Depression Inventory (CDI), a validated tool for assessing depressive symptoms in children aged 6\u0026ndash;12 years, was used [13]. The scale consists of 20 items, each scored on a 4-point Likert scale (1 = \u0026ldquo;none or little of the time\u0026rdquo; to 4 = \u0026ldquo;most or all of the time\u0026rdquo;). Total scores range from 20 to 80, with higher scores indicating more severe depressive symptoms. A total score\u0026thinsp;\u0026ge;\u0026thinsp;40 was defined as \u0026ldquo;depressive status\u0026rdquo; [13].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.5 Relevant Covariates\u003c/h2\u003e\u003cp\u003eCovariates were collected via parental and self-reported questionnaires, including:\u003c/p\u003e\u003cp\u003eDemographic factors: Gender (female/male), age (6/7 years), only-child status (yes/no), left-behind child status (yes/no, defined as children whose parents have migrated for work and have been separated for \u0026ge;\u0026thinsp;6 months), parental marital status (first marriage/divorce/remarriage), and parental education (high school or below/high school or above).\u003c/p\u003e\u003cp\u003eLifestyle factors: Body mass index (BMI, calculated as weight [kg]/height [m\u0026sup2;]; \u0026ge;24 kg/m\u0026sup2; defined as overweight/obese), sleep quality (good/poor, based on parental report of \u0026ldquo;whether the child sleeps\u0026thinsp;\u0026ge;\u0026thinsp;9 hours/night and has no frequent night awakenings\u0026rdquo;), family economic conditions (average/good, based on parental report of household income relative to local median income), daily screen time (\u0026le;\u0026thinsp;2 hours/\u0026gt;2 hours), Frequency of breakfast per week (1\u0026ndash;3, 4\u0026ndash;6, 7), family members\u0026rsquo; smoking/drinking status (yes/no), Internet Addiction: The Young Internet Addiction Scale (YIAS), a 20-item scale assessing internet dependence, was used[14],and physical activity level (assessed via the International Physical Activity Questionnaire-Short Form [IPAQ-SF]; categorized as low/medium/high) [15].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics 24.0 (IBM Corp., Chicago, IL, USA). Categorical variables were presented as frequencies and percentages (n, %), and continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Multivariate logistic regression was used to analyze the associations between SSB intake and binary outcomes (myopia, depression), with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Linear regression was used to analyze the association between SSB intake and continuous outcomes (VO₂max, ΔVO₂max), with mean differences (β) and 95% CIs reported. Three adjustment models were constructed:\u003c/p\u003e\u003cp\u003eModel 1: Crude model (no adjustments).\u003c/p\u003e\u003cp\u003eModel 2: Adjusted for gender and age.\u003c/p\u003e\u003cp\u003eModel 3: Further adjusted for BMI, only-child status, left-behind child status, parental education (father\u0026rsquo;s and mother\u0026rsquo;s), parental marital status, sleep quality, physical activity level, internet addiction, and family economic conditions.\u003c/p\u003e\u003cp\u003eA two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics of Participants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of participants stratified by SSB intake level. A total of 10,664 children were included, with 8,614 (80.8%) in the low SSB intake group and 2,050 (19.2%) in the high SSB intake group. There were no significant differences between the two groups in terms of gender (P\u0026thinsp;=\u0026thinsp;0.997), age (P\u0026thinsp;=\u0026thinsp;0.949), BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u0026sup2; (P\u0026thinsp;=\u0026thinsp;0.676), only-child status (P\u0026thinsp;=\u0026thinsp;0.229), left-behind child status (P\u0026thinsp;=\u0026thinsp;0.230), parental marital status (P\u0026thinsp;\u0026gt;\u0026thinsp;0.161), parental education (P\u0026thinsp;\u0026gt;\u0026thinsp;0.905), sleep quality (P\u0026thinsp;=\u0026thinsp;0.146), family members\u0026rsquo; smoking/drinking status (P\u0026thinsp;\u0026gt;\u0026thinsp;0.587), or daily screen time (P\u0026thinsp;=\u0026thinsp;0.442). Regarding Internet Addiction, the high SSB intake group had a notably higher proportion of children with Internet Addiction compared to the low SSB intake group (22.7% [n\u0026thinsp;=\u0026thinsp;465] vs. 15.3% [n\u0026thinsp;=\u0026thinsp;1318]), and this difference was statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the high SSB intake group had a higher proportion of children with good family economic conditions (47.9% vs. 42.1%, P\u0026thinsp;=\u0026thinsp;0.010) and a lower proportion of children with high physical activity levels (8.1% vs. 14.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe participants\u0026rsquo; characteristics, according to categories of Sugary Drink Intake\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubcategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow Intake Group (n\u0026thinsp;=\u0026thinsp;8614)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Intake Group (n\u0026thinsp;=\u0026thinsp;2050)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP \u0026sup1;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4297 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1020 (49.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 years (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4524 (52.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1078 (52.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 years (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4090 (47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e972 (47.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.949\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (\u0026ge;\u0026thinsp;24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.8% (1093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.2% (271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.676\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly one child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e52.3% (4495)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e54.7% (1121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.229\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft-behind children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e% (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.2% (2171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.3% (560)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eParents marital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst marriage (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91.8% (7909)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91.6% (1878)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorce (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6% (309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.7% (76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRemarriage (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.6% (224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1% (64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFather education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school degree or below (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.2% (4324)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.3% (1031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school degree or below (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.2% (4410)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.4% (1054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePhysical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.2% (3291)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38.4% (787)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.646\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.9% (4039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.1% (966)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.9% (1283)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.1% (166)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.8% (6702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.2% (1542)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily members smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.2% (3032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35.9% (736)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily members drinking alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45.1% (3885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.8% (939)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.587\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily economic conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.1% (3627)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.9% (982)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily screen time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;2 hours (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.2% (4755)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.9% (1146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBreakfast frequency (times/week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOccasional (1\u0026ndash;3) (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.2% (706)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.9% (203)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequent (4\u0026ndash;6) (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.1% (1904)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.8% (508)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDaily (7) (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.7% (5993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65.3% (1339)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternet Addiction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes (n, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.3% (1318)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.7% (465)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Associations Between SSB Intake and Health Outcomes at Baseline\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between SSB intake and health outcomes (depression, myopia, VO₂max) at baseline. After full adjustment (Model 3):The high SSB intake group had a 35% higher odds of depression compared to the low intake group (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.08\u0026ndash;1.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).The high SSB intake group had a 45% higher odds of myopia compared to the low intake group (OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.20\u0026ndash;1.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).The high SSB intake group had a significantly lower VO₂max than the low intake group, with a mean difference of \u0026minus;\u0026thinsp;1.3 mL/kg/min (95% CI: \u0026minus;1.9 to \u0026minus;\u0026thinsp;0.7, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdjusted Associations Between SSB Intake and Health Outcomes at Baseline\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65 (1.33\u0026ndash;2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.52 (1.23\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35 (1.08\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyopia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.85 (1.55\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68 (1.40\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.45 (1.20\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVO₂max (mL/kg/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.2 (34.8\u0026ndash;35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.3 (34.9\u0026ndash;35.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e35.1 (34.7\u0026ndash;35.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.1 (32.6\u0026ndash;33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.4 (32.9\u0026ndash;33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.8 (33.3\u0026ndash;34.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Difference (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.1\u003c/p\u003e\u003cp\u003e(-2.7\u0026ndash; 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.9\u003c/p\u003e\u003cp\u003e(-2.5\u0026ndash; -1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.3\u003c/p\u003e\u003cp\u003e(-1.9\u0026ndash; -0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: Crude;\u003csup\u003eb\u003c/sup\u003eModel 2: Adjusted for gender and age.\u003csup\u003ec\u003c/sup\u003eModel 3: Further adjusted for BMI, only-child status, left-behind child status, father\u0026rsquo;s education, mother\u0026rsquo;s education, parental marital status, family members\u0026rsquo; smoking/drinking status, sleep quality, physical activity level, internet addiction, and family economic conditions.Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Associations Between SSB Intake and Health Outcomes During Five-Year Follow-Up\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the associations between SSB intake and incident health outcomes (depression, myopia) and changes in VO₂max (ΔVO₂max) over five years. After full adjustment (Model 3):\u003c/p\u003e\u003cp\u003eThe high SSB intake group had a 2.85-fold higher risk of developing depression compared to the low intake group (OR\u0026thinsp;=\u0026thinsp;2.85, 95% CI: 2.44\u0026ndash;3.33, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eThe high SSB intake group had a 2.15-fold higher risk of developing myopia compared to the low intake group (OR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.92\u0026ndash;2.41, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eBoth groups showed an increase in VO₂max over five years, but the increase was significantly smaller in the high SSB intake group (mean ΔVO₂max\u0026thinsp;=\u0026thinsp;8.4 mL/kg/min) than in the low intake group (mean ΔVO₂max\u0026thinsp;=\u0026thinsp;10.3 mL/kg/min), with a mean difference of \u0026minus;\u0026thinsp;1.9 mL/kg/min (95% CI: \u0026minus;2.5 to \u0026minus;\u0026thinsp;1.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAdjusted Associations Between SSB Intake and Health Outcomes During Five-Year Follow-Up\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of Cases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.60 (3.11\u0026ndash;4.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.30 (2.84\u0026ndash;3.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.85 (2.44\u0026ndash;3.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMyopia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.80 (2.52\u0026ndash;3.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.55 (2.28\u0026ndash;2.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.15 (1.92\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔVO₂max (mL/kg/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;10.5 (10.1\u0026ndash;10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;10.4 (10.0\u0026ndash;10.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;10.3 (9.9\u0026ndash;10.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Intake Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;7.6 (7.1\u0026ndash;8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;7.9 (7.4\u0026ndash;8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;8.4 (7.9\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Difference (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.9\u003c/p\u003e\u003cp\u003e(-3.5\u0026ndash; -2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.5\u003c/p\u003e\u003cp\u003e(-3.1\u0026ndash;1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.9\u003c/p\u003e\u003cp\u003e(-2.5\u0026ndash; -1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: Crude;\u003csup\u003eb\u003c/sup\u003eModel 2: Adjusted for gender and age.\u003csup\u003ec\u003c/sup\u003eModel 3: Further adjusted for BMI, only-child status, left-behind child status, father\u0026rsquo;s education, mother\u0026rsquo;s education, parental marital status, family members\u0026rsquo; smoking/drinking status, sleep quality, physical activity level, internet addiction, and family economic conditions. Bold values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis five-year prospective cohort study of 10,664 Chinese children aged 6\u0026ndash;7 years revealed significant associations between high SSB intake and adverse physical and mental health outcomes. Notably, the effect sizes of SSB intake on myopia, depression, and VO₂max differed between baseline and follow-up: at baseline, high SSB intake was associated with increased risks of depression (OR\u0026thinsp;=\u0026thinsp;1.35) and myopia (OR\u0026thinsp;=\u0026thinsp;1.45), while after five years, the risks of incident depression and myopia increased to OR\u0026thinsp;=\u0026thinsp;2.85 and OR\u0026thinsp;=\u0026thinsp;2.15, respectively. This strengthened dose-response relationship strongly suggests that the adverse effects of SSB intake may accumulate over time, which is consistent with findings from the Whitehall II study [16] and a meta-analysis by Yu et al. [17].\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Mechanisms Underlying the Association Between SSB Intake and Depression\u003c/h2\u003e\u003cp\u003eFrom a neurobiological perspective, the impact of high sugar intake on mental health may be mediated by multiple synergistic pathways. Recent studies have shown that fructose and glucose can activate Toll-like receptor 4 (TLR4) and nuclear factor kappa B (NF-κB) signaling pathways, promoting the release of pro-inflammatory cytokines (e.g., tumor necrosis factor-α [TNF-α], interleukin-6 [IL-6]) in peripheral blood [18]. These cytokines can cross the compromised blood-brain barrier or transmit signals via the vagus nerve to the central nervous system, activating microglia and triggering neuroinflammation. Reichenbach et al. demonstrated that mice fed a high-fructose diet exhibited significant microglial activation in the hippocampus, accompanied by reduced neurogenesis and depressive-like behaviors\u0026mdash;findings that support the role of neuroinflammation in SSB-induced depression [19].\u003c/p\u003e\u003cp\u003eChronic high sugar intake may also disrupt the negative feedback regulation of the hypothalamic-pituitary-adrenal (HPA) axis. Epigenetic modifications induced by sustained high sugar intake can reduce the expression of glucocorticoid receptors in the hippocampus, weakening the inhibitory effect of the hippocampus on the HPA axis and leading to persistently elevated cortisol levels. This chronic hypercortisolism exerts neurotoxic effects on brain regions critical for emotion regulation (e.g., prefrontal cortex, amygdala) [20].\u003c/p\u003e\u003cp\u003eThe neurotrophic factor system also plays a key role in this process. Park et al. recently found that a high-fructose diet significantly downregulates the expression of brain-derived neurotrophic factor (BDNF) in the mouse hippocampus, possibly through epigenetic changes induced by increased histone deacetylase (HDAC) activity. Impaired BDNF signaling directly reduces synaptic plasticity, particularly neurogenesis in the dentate gyrus of the hippocampus\u0026mdash; a structural basis for the development of depressive behaviors [21].\u003c/p\u003e\u003cp\u003eAdditionally, the gut-brain axis provides a new perspective for understanding the link between diet and mental health. High-sugar diets induce gut microbiota dysbiosis, characterized by a reduction in beneficial bacteria (e.g., Bifidobacterium) and proliferation of pathogenic bacteria. This dysbiosis increases intestinal permeability, allowing endotoxins (e.g., lipopolysaccharides [LPS]) to enter the circulatory system and alter the production of short-chain fatty acids (e.g., butyrate), thereby disrupting neuroimmune regulation [22]. Jiang et al. observed significant differences in gut microbiota composition between patients with anxiety disorders and healthy controls, with the abundance of specific bacterial genera negatively correlated with inflammatory markers [23]. Furthermore, microbiota metabolic disorders can shift tryptophan metabolism toward the neurotoxic kynurenine pathway, exacerbating neural damage.\u003c/p\u003e\u003cp\u003eAt the level of the reward system, SSBs activate the dopamine pathway in the mesolimbic system, producing transient pleasure. However, according to the addiction model proposed by Avena et al., intermittent, excessive sugar intake leads to downregulation of dopamine D2 receptors and reduced dopamine signaling in the prefrontal cortex, resulting in tolerance and dependence. When addictive intake is interrupted, negative emotional states similar to drug withdrawal (e.g., anxiety, anhedonia) occur, forcing individuals to continue consumption to maintain emotional balance\u0026mdash;forming a vicious cycle [24].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Mechanisms Underlying the Association Between SSB Intake and Myopia\u003c/h2\u003e\u003cp\u003eThis study highlights the role of dietary factors in myopia etiology, complementing traditional theories (e.g., \u0026ldquo;form deprivation,\u0026rdquo; \u0026ldquo;optical defocus\u0026rdquo;). Blood glucose fluctuations and hyperinsulinemia induced by high sugar intake may be core mechanisms. Insulin and insulin-like growth factor-1 (IGF-1) are structurally homologous and can cross-activate IGF-1 receptors, promoting the proliferation of scleral fibroblasts and remodeling of the extracellular matrix [25]. A review by Chua \u0026amp; Foster emphasized that IGF-1 produced by the retina and choroid plays a central role in transmitting signals from the retina to the sclera. Wang et al. further confirmed in a cohort study that children with a high glycemic load diet and high IGF-1 levels have a stronger association with high myopia risk, providing evidence for gene-environment interactions [26].\u003c/p\u003e\u003cp\u003eThe accumulation of advanced glycation end products (AGEs) is another important mechanism. Sustained hyperglycemia accelerates the formation of AGEs, which bind to their receptors (RAGE) to activate pro-inflammatory and pro-fibrotic pathways (e.g., NF-κB). In scleral tissue, AGE accumulation increases collagen fiber cross-linking, reducing scleral elasticity and biomechanical strength\u0026mdash;making the sclera more susceptible to expansion and thinning under normal intraocular pressure [27].\u003c/p\u003e\u003cp\u003eDisruption of the retinal dopaminergic system also contributes to myopia development. Retinal dopamine is a key neurotransmitter that inhibits eye axis growth, and its release is regulated by circadian rhythms and retinal metabolic status. Recent studies have shown that high-sugar metabolic states may interfere with retinal anaerobic metabolism, altering dopamine synthesis and release [28]. Morgan et al. argued that changes in modern lifestyles (including diet) may synergize with reduced outdoor time and increased near-work to drive the global myopia epidemic [29].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Mechanisms Underlying the Association Between SSB Intake and VO₂max\u003c/h2\u003e\u003cp\u003eHigh SSB intake was associated with lower baseline VO₂max and a smaller increase in VO₂max over five years, suggesting that SSB intake impairs the normal maturation of cardiorespiratory fitness in children. Mechanistically, high fructose intake inhibits the expression of peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α)\u0026mdash;a core regulator of mitochondrial biogenesis [30]. Softic et al. demonstrated divergent effects of glucose and fructose on hepatic lipid metabolism, with fructose more likely to promote de novo lipogenesis and triglyceride synthesis. This lipotoxic effect also occurs in skeletal muscle: Ter Horst et al. found that high-sugar feeding reduces mitochondrial coupling efficiency and increases reactive oxygen species (ROS) production in the liver [31].\u003c/p\u003e\u003cp\u003eOxidative stress plays a critical role in this process. Excessive ROS production during high-sugar metabolism exceeds the scavenging capacity of the endogenous antioxidant system, leading to oxidative stress that damages mitochondrial DNA, proteins, and lipids\u0026mdash;reducing membrane potential and impairing ATP synthesis. Crescenzo et al. observed significantly reduced mitochondrial oxidative phosphorylation efficiency and elevated oxidative stress markers in skeletal muscle of rats fed a high-fat, high-fructose diet [32].\u003c/p\u003e\u003cp\u003eVascular endothelial dysfunction further limits oxygen transport efficiency. High-sugar environments promote AGE formation, and AGE-RAGE binding produces ROS and reduces the activity of endothelial nitric oxide synthase (eNOS), impairing vasodilation [33]. Microcirculatory dysfunction restricts oxygenation of muscle tissue and clearance of metabolic waste during exercise, directly limiting VO₂max.\u003c/p\u003e\u003cp\u003eMore fundamentally, long-term high-sugar intake induces loss of metabolic flexibility. Healthy individuals can flexibly switch between carbohydrates and fats for energy, but high-sugar diets shift metabolism toward carbohydrate dependence, inhibiting fat oxidation. Smith et al. showed that high-sugar feeding disrupts metabolic crosstalk between tissues, leading to abnormal substrate selection in skeletal muscle during rest and exercise\u0026mdash;limiting the ability to use efficient fat oxidation during prolonged exercise. This may explain why children in the high SSB intake group had lower cardiorespiratory fitness and a smaller increase in VO₂max [34].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Strengths and Limitations\u003c/h2\u003e\u003cp\u003eStrengths: This study has several strengths: (1) a prospective cohort design with a five-year follow-up, allowing for the analysis of long-term associations between SSB intake and health outcomes; (2) a large sample size (n\u0026thinsp;=\u0026thinsp;10,664), ensuring sufficient statistical power; (3) comprehensive assessment of multiple health outcomes (physical: myopia, VO₂max; mental: depression) and potential confounders (demographic, lifestyle), reducing residual confounding; and (4) focus on Chinese children, addressing the scarcity of longitudinal evidence in Asian populations.\u003c/p\u003e\u003cp\u003eLimitations: Several limitations should be noted: (1) SSB intake was assessed via self-reported questionnaires, which may be subject to recall bias; (2) residual confounding cannot be completely excluded (e.g., unmeasured dietary factors such as fruit intake, or genetic factors); (3) the study was conducted in Chongqing (Southwest China), and results may not be generalizable to other regions with different dietary cultures; and (4) no biological samples (e.g., blood, stool) were collected, preventing the validation of mechanistic hypotheses (e.g., inflammation, gut microbiota).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Conclusion\u003c/h2\u003e\u003cp\u003eHigh SSB intake is associated with increased risks of myopia and depression, and impaired cardiorespiratory fitness in Chinese children, with adverse effects accumulating over time. These findings provide critical evidence for public health interventions to reduce SSB consumption among children, such as implementing sugar taxes, restricting SSB marketing to children, and promoting healthy beverage alternatives (e.g., water, unsweetened milk). Future studies should integrate biological samples and imaging data to explore causal pathways and identify personalized intervention targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Li Sha Reng; Methodology,Zhen Li and Wanjun Li; Validation, Zongji Hao; Investigation,Zhen Li; Writing—Original Draft Preparation, \u0026nbsp;Li Sha Reng All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Bureau of Science and Technology of Tongnan District, Chongqing Municipality (Grant No. TK-2025-23); the Key Project of Chongqing Sports Bureau in 2024, \"Analysis of Biological Competition Characteristics and Prevention of Sports Injuries in Chongqing Climbers under the Background of Preparing for the 15th National Games\" (Grant No. A202464); the 2025 Shandong Province Humanities and Social Sciences Project, \"Research on the Integration Development Model of Sports, Culture, Tourism, Agriculture, and Commerce Promoted by Sports Event Brand Construction under Symbiosis Theory\"; and the 2025 Teaching Reform and Research Project of Linyi Campus, Qingdao University of Technology, \"Research on the Application of BOPPPS Teaching Model in College Martial Arts Courses\".\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all primary schools in Liangjiang New Area, Chongqing, for their support in allowing students to participate in this study and authorizing us to analyze the related data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalik, V. 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Brain, Behavior, and Immunity, 102, 307\u0026ndash;319. https://doi.org/10.1016/j.bbi.2022.03.025\u003c/li\u003e\n\u003cli\u003eTryon, M. S., Carter, C. S., DeCant, R., \u0026amp; Laugero, K. D. (2021). Excessive sugar consumption may be a difficult habit to break: A view from cognitive and brain health. Neuroscience \u0026amp; Biobehavioral Reviews, 131, 814\u0026ndash;830. https://doi.org/10.1016/j.neubiorev.2021.07.015\u003c/li\u003e\n\u003cli\u003ePark, H. S., Kim, J., Lee, S. H., \u0026amp; Lee, H. Y. (2023). High-fructose diet suppresses hippocampal BDNF expression and induces depressive-like behaviors in male mice. Nutritional Neuroscience, 26(1), 45\u0026ndash;56. https://doi.org/10.1080/1028415X.2021.1966666\u003c/li\u003e\n\u003cli\u003eSatokari, R., Korpela, K., \u0026amp; Vos, W. M. (2020). High intake of sugar and the balance between microbiota and the immune system. Nutrients, 12(5), 1347. https://doi.org/10.3390/nu12051347\u003c/li\u003e\n\u003cli\u003eJiang, H., Ling, Z., Zhang, Y., et al. (2015). Altered fecal microbiota composition in patients with major depressive disorder. Brain, Behavior, and Immunity, 48, 186\u0026ndash;194. https://doi.org/10.1016/j.bbi.2015.05.004\u003c/li\u003e\n\u003cli\u003eAvena, N. M., Rada, P., \u0026amp; Hoebel, B. G. (2008). Evidence for sugar addiction: Behavioral and neurochemical effects of intermittent, excessive sugar intake. Neuroscience \u0026amp; Biobehavioral Reviews, 32(1), 20\u0026ndash;39. https://doi.org/10.1016/j.neubiorev.2007.04.019\u003c/li\u003e\n\u003cli\u003eChua, S. Y., \u0026amp; Foster, P. J. (2023). The role of insulin-like growth factor in myopia development. Progress in Retinal and Eye Research, 94, 101155. https://doi.org/10.1016/j.preteyeres.2023.101155\u003c/li\u003e\n\u003cli\u003eWang, M., Wang, R., Li, X., et al. (2023). Glycemic load, genetic predisposition, and risk of high myopia: A population-based cohort study. The American Journal of Clinical Nutrition, 117(2), 345\u0026ndash;356. https://doi.org/10.1093/ajcn/nqac342\u003c/li\u003e\n\u003cli\u003eLin, H. J., Wei, C. C., Chang, C. Y., et al. (2018). Role of chronic inflammation in myopia progression: Clinical evidence and experimental validation. EBioMedicine, 34, 274\u0026ndash;286. https://doi.org/10.1016/j.ebiom.2018.07.043\u003c/li\u003e\n\u003cli\u003eZhou, X., Pardue, M. T., Iuvone, P. M., \u0026amp; Qu, J. (2021). Dopamine signaling and myopia development: What we know and future perspectives. Experimental Eye Research, 209, 108676. https://doi.org/10.1016/j.exer.2021.108676\u003c/li\u003e\n\u003cli\u003eMorgan, I. G., French, A. N., \u0026amp; Rose, K. A. (2022). The epidemics of myopia: Aetiology and prevention. Progress in Retinal and Eye Research, 92, 101091. https://doi.org/10.1016/j.preteyeres.2022.101091\u003c/li\u003e\n\u003cli\u003eSoftic, S., Gupta, M. K., Wang, G. X., et al. (2017). Divergent effects of glucose and fructose on hepatic lipogenesis and insulin signaling. The Journal of Clinical Investigation, 127(11), 4059\u0026ndash;4074. https://doi.org/10.1172/JCI95530\u003c/li\u003e\n\u003cli\u003eTer Horst, K. W., Gilijamse, P. W., Koopman, K. E., et al. (2015). Insulin resistance in obesity is associated with adipose tissue-specific metabolic and vascular abnormalities. Diabetes, 64(5), 1741\u0026ndash;1750. https://doi.org/10.2337/db14-1561\u003c/li\u003e\n\u003cli\u003eCrescenzo, R., Bianco, F., Coppola, P., et al. (2021). The effect of high-fat-high-fructose diet on skeletal muscle mitochondrial energetics and insulin sensitivity in rats. Nutrients, 13(1), 265. https://doi.org/10.3390/nu13010265\u003c/li\u003e\n\u003cli\u003eLee, M. Y., Tse, H. F., Siu, C. W., et al. (2007). Genistein causes endothelial dysfunction in porcine coronary arteries through a tyrosine kinase-independent mechanism. Atherosclerosis, 194(2), 300\u0026ndash;308. https://doi.org/10.1016/j.atherosclerosis.2007.01.024\u003c/li\u003e\n\u003cli\u003eSmith, G. I., Jeukendrup, A. E., \u0026amp; Ball, D. (2003). The effects of acute and chronic exposure to altitude on the metabolism of carbohydrates and lipids during exercise: A review. International Journal of Sport Nutrition and Exercise Metabolism, 13(1), 97\u0026ndash;122. https://doi.org/10.1123/ijsnem.13.1.97\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sugar-sweetened beverages, Children, Myopia, Depression, Maximal oxygen uptake, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7949992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7949992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGlobally, the consumption of sugar-sweetened beverages (SSBs) among children has increased significantly, becoming a major public health concern. However, longitudinal evidence regarding the long-term effects of SSB intake on the physical and mental health of Chinese children remains scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study aimed to investigate the dynamic associations between SSB intake (frequency and dosage) and key health outcomes (myopia, depression, and maximal oxygen uptake [VO₂max]) in Chinese children over a five-year follow-up period.Methods: A prospective cohort study was conducted among 10,664 children aged 6\u0026ndash;7 years recruited from 30 primary schools in Chongqing, China, in 2020. SSB intake was assessed using the Youth Risk Behavior Surveillance System (YRBSS) questionnaire, with \u0026ldquo;high intake\u0026rdquo; defined as \u0026ge;\u0026thinsp;3 times/week (250 mL per serving). Health outcomes included myopia (refractive error measurement), depression (Children\u0026rsquo;s Depression Inventory), and VO₂max (20-meter shuttle run test). Covariates included demographic factors (gender, age, only-child status, left-behind status) and lifestyle factors (BMI, parental education, sleep quality, physical activity). Multivariate logistic regression (for binary outcomes: myopia, depression) and linear regression (for continuous outcome: VO₂max) were used to analyze associations, with adjustments for potential confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAt baseline, the high SSB intake group (n\u0026thinsp;=\u0026thinsp;2,050) had significantly higher odds of depression (adjusted OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 1.08\u0026ndash;1.68) and myopia (adjusted OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.20\u0026ndash;1.75), and lower VO₂max (adjusted mean difference\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.3 mL/kg/min, 95% CI: \u0026minus;1.9 to \u0026minus;\u0026thinsp;0.7) compared to the low intake group (n\u0026thinsp;=\u0026thinsp;8,614). After five years of follow-up, high SSB intake was associated with a greater risk of incident depression (adjusted OR\u0026thinsp;=\u0026thinsp;2.85, 95% CI: 2.44\u0026ndash;3.33) and incident myopia (adjusted OR\u0026thinsp;=\u0026thinsp;2.15, 95% CI: 1.92\u0026ndash;2.41), as well as a smaller increase in VO₂max (adjusted mean difference\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.9 mL/kg/min, 95% CI: \u0026minus;2.5 to \u0026minus;\u0026thinsp;1.3).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eHigh SSB intake is associated with increased risks of myopia and depression, and a slower increase in cardiorespiratory fitness (VO₂max) in Chinese children. These adverse effects accumulate over time, highlighting the need for targeted public health interventions to reduce SSB consumption among children.\u003c/p\u003e","manuscriptTitle":"Sugar-Sweetened Beverage Intake and Children’s Physical and Mental Health: A Five-Year Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 15:22:05","doi":"10.21203/rs.3.rs-7949992/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f19db023-c1f5-4883-91be-8095150858fd","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57674106,"name":"Health sciences/Diseases"},{"id":57674107,"name":"Health sciences/Health care"},{"id":57674108,"name":"Health sciences/Medical research"},{"id":57674109,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-03-18T05:56:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 15:22:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7949992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7949992","identity":"rs-7949992","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}