Abstract
Background
The aim of this study was to investigate the association between maternal blood lipid trajectories during pregnancy and myopia in preschool offspring as well as the combined effects of maternal blood lipid trajectories and gestational diabetes mellitus (GDM) on myopia in offspring.
Methods
A prospective cohort design with a nested case‒control component was employed in this study. Visual outcomes in children aged 3–6 years, including both noncycloplegic refraction screening results and clinically validated diagnoses from medical records, were obtained. Maternal blood lipid trajectories were classified using group-based multi-trajectory modelling from repeat measurements of total cholesterol (TC) and triglycerides (TGs) during pregnancy in mothers with and without GDM as a complication. Modified Poisson regression models and generalized linear models were used to assess the associations between the maternal lipid trajectory and offspring myopia.
Results
Among the 4043 included mother‒child pairs, four distinct maternal lipid trajectories were identified: Group 1, “consistently normal TC and TG”; Group 2, “keeping normal TC but high TG”; Group 3, “moderately increasing TC and TG”; and Group 4, “highly increasing TC and TG”, accounting for 50.0% (n = 2078), 4.9% (n = 193), 41.0% (n = 1629), and 4.1% (n = 143) of the pairs, respectively. In the prospective cohort analysis, compared with membership in Group 1, membership in Groups 3 and 4 was associated with 21% (95% CI: 1.02–1.44) and 52% (95% CI: 1.06–2.20) increases in the risk of suspected myopia, respectively; when the mother had GDM as a complication, the relative risk was further elevated (RR = 1.52, 95% CI: 1.16–1.99 for “GDM + Group 3”). In the nested case‒control analysis, significant associations were also observed between the maternal lipid trajectory (Group 3: OR = 1.66, 95% CI: 1.02–2.72; Group 4: OR = 9.22, 95% CI: 1.05–80.66) and clinically diagnosed myopia; when the mother had GDM as a complication, these risks substantially increased (OR = 2.83, 95% CI: 1.06–7.59 for “GDM + Group 3”).
Conclusion
The results of this cohort study suggest that elevated gestational maternal blood lipid levels (including TC and TG levels) throughout pregnancy are associated with an increased risk of preschool myopia in the offspring. When these levels were considered in the context of GDM, the risks were further increased.
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-026-02863-z.
Keywords: Blood lipids, Pregnancy, Cohort study, Myopia, Refractive error, Child, Preschool
Introduction
Myopia constitutes more than 60% of refractive errors among children and adolescents [1, 2]. A shift toward earlier age of myopia onset has been widely documented across East Asia and Southeast Asia [3–5]. Unlike school-age myopia, preschool myopia often triggers strabismus and amblyopia, significantly increasing the lifelong risk of high myopia and macular degeneration [6, 7]. These findings underscore the urgent need to improve and popularize myopia prevention and control strategies [8]. Emerging evidence indicates that the presence of maternal cardiovascular metabolic diseases—such as diabetes and hypertensive disorders—may increase the risk of myopia in offspring [9, 10]. The identification of myopia risk factors related to early metabolic changes during pregnancy may provide key insights into the epidemic trends of myopia [7]. In particular, maternal lipid levels represent crucial intrauterine environmental factors, as lipids can selectively cross the placental barrier to support the development of the foetal brain and visual system [11, 12]. The adverse health outcomes associated with gestational dyslipidaemia in offspring may persist into adulthood [13, 14]. However, few studies have investigated the associations between maternal lipid levels and myopia risk.
Lipid levels are crucial for maintaining ocular function, protecting retinal health, and preserving the integrity of visual signal transduction [15]. Previous studies have demonstrated that hyperlipidaemia is associated with increased risks of meibomian gland dysfunction [16], retinopathy development [17], and choroidal thickening [18]. Hypercholesterolemia may disrupt the retinal vasculature through multiple mechanisms [19]. Clinical evidence indicates that infants with hypertriglyceridemia have an increased risk of retinopathy of prematurity [20]. Recent studies have suggested that gestational lipid profiles may serve as predictive biomarkers for visual development trajectories up to 2 years of age [21]. However, the associations between maternal gestational lipid levels and offspring myopia remain elusive, highlighting the urgent need for additional investigations. Moreover, given the profound fluctuations in blood lipids during pregnancy, the objective and comprehensive characterization of blood lipid dynamics in pregnant women is a significant methodological challenge. Group-based trajectory modelling (GBTM) effectively captures complex longitudinal patterns and characterizes their temporal profiles [22]. This approach, which serves as a robust analytical tool for repeated-measures data, provides clinically actionable guidance for optimizing prenatal care practices. Notably, maternal dyslipidaemia during pregnancy frequently co-occurs with abnormalities in glucose metabolism, such as gestational diabetes mellitus (GDM) [23, 24]. Therefore, investigating the combined influence of maternal lipid levels and comorbid GDM on myopia development in offspring is a critical research priority.
In this study, it was hypothesized that prenatal exposure to elevated maternal lipid levels is associated with an increased risk of myopia in preschool children and that the increase is exacerbated by concurrent GDM. The aims of this study, therefore, were to investigate the associations between longitudinal maternal lipid trajectories (specifically, total cholesterol (TC) and triglycerides (TG)) and myopia in offspring at 3–6 years of age and to explore the contributions of comorbid GDM to these associations.
Methods
Study population
This study involved the use of data from the Shanghai Maternal‒Child Pairs Cohort (MCPC). Participant recruitment was conducted at two regional maternity hospitals in Shanghai, China, with 6,714 pregnant women enrolled between 2016 and 2018. The study was approved by the Fudan University Institutional Review Board (IRB number 201604-0587-EX), and all participants provided written informed consent. The methods used for the initial and follow-up MCPC studies have been described in detail previously [25]. Among the recruited mother‒child pairs, 2,671 were excluded because of multiple births, loss to follow-up, major pre-existing chronic conditions including diabetes or severe hyperlipidaemia, or missing lipid data. The current study comprises two sub-analyses, namely, a prospective cohort analysis and a nested case‒control analysis, which collectively address the research objectives.
(1) A total of 3,310 preschool children aged 3–6 years and their mothers were included in the prospective cohort analysis. Visual outcomes were assessed using visual screening data obtained from the Shanghai Child and Adolescent Large-scale Eye Study (SCALE) through February 23, 2024 [26]. (2) While noncycloplegic refraction is suitable for large-scale population studies, it may overestimate the prevalence of myopia in preschool children [27]. Therefore, a nested case‒control study was conducted using cycloplegic refraction data for validation. The nested case‒control analysis was conducted through one-to-one propensity score matching involving age (within ≤1.0 years) and sex. From clinical records obtained through the electronic medical registry, 159 children who were diagnosed with myopia by February 22, 2023, were selected as the case group. The control group consisted of 159 children with no documented history of a myopia diagnosis and no suspected myopia identified during vision screenings at 3–6 years of age. The participant selection process is detailed in Fig. 1.
Fig. 1.
Flow diagram of study participants. GBTM Group-based multi-trajectory modeling, TC total cholesterol, TG Triglycerides
Assessment of maternal lipid levels during pregnancy
The exposure in both analyses was defined as the maternal blood lipid trajectory (comprising TC and TG) throughout pregnancy combined with GDM status. During routine prenatal visits, blood was collected from participants after 8 to 10 h of fasting (water permitted). TC and TG levels were quantified through enzymatic assays on an Atellica Solution system (ADVIA 2120; Siemens Healthineers, Germany) using morning fasting samples. Maternal lipid levels during pregnancy were categorized into three stages according to the time of measurement: < 24 weeks, 24–32 weeks, and ≥ 33 weeks. All examinations were performed by trained medical staff. Gestational hyperlipidaemia (GHL) was defined according to the trimester-specific reference intervals for lipid profiles established by Zheng et al. [28]. GDM diagnoses were retrieved from the subjects’ electronic medical records. Lipid trajectories were analysed using GBTM. GBTM is a generalization of univariate group-based trajectory modelling introduced by Nagin [22]. Trajectory models were constructed using the STATA Traj plugin with lipid indicators (TC and TG) measured during first, second🗍, and third pregnancy. The optimal latent class model was selected on the basis of a suite of fit indices, including the Bayesian information criterion (BIC), Akaike information criterion (AIC), entropy, and average posterior probability of assignment (APPA) [29]. Specifically, it met the following criteria: minimized BIC and AIC values; entropy > 0.7; APPA > 0.8; all class proportions > 2%; and sound theoretical interpretability.
Assessment of vision in children aged 3 to 6 years
The outcomes included suspected myopia and a diagnosis of myopia in children. Vision screening data for the prospective cohort analysis were sourced from the SCALE study. The detailed methods have been previously reported [26]. Noncycloplegic refraction was performed to measure the spherical equivalent (SE) with an autorefractor (KR-8900; Topcon, Tokyo, Japan); the SE was calculated as sphere + cylinder/2. Owing to the strong inter-eye correlation in the SE (r = 0.74, P < 0.001), only the right-eye data were included in subsequent analyses; the left-eye data were used for sensitivity analysis. Suspected myopia was defined as an SE ≤ −0.75 D [30].
The myopia diagnosis data for the nested case‒control analysis were acquired from the electronic medical records of the subjects. The diagnoses were made in accordance with the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10 code H52.1). For clinical diagnosis, cycloplegic refraction was measured after the administration of 0.5% tropicamide eye drops five times at 5-minute intervals. Subjective refraction was measured by hospital-based optometrists using the standardized maximum plus to best visual acuity method 30 min post-instillation. A diagnosis of myopia was defined as an SE ≤ −0.50 D.
Covariates
Parental characteristics included maternal age, maternal education level (middle school or below, high school, and college or above), pre-pregnancy body mass index (BMI, calculated as weight in kilograms divided by height in metres squared; normal: 18.5–23.9, underweight: < 18.5, and overweight: ≥ 24.0), last menstrual period, father’s age, total annual family income (renminbi (RMB) < 100,000 [<$13,806], RMB 100,000–200,000 [$13,806–$27,613], and RMB > 200,000 [>$27,613]), and history of parental myopia (none or at least one). Child factors included gestational age at delivery (not preterm [≥ 37 weeks] and preterm [< 37 weeks]), birth weight (normal [2500–4000 g], low birth weight [< 2500 g], and macrosomia [> 4000 g]), sex (male or female), age, daily outdoor time (< 1.0, 1.0–1.9, and ≥ 2.0 h), near-work time (< 1.0, 1.0–1.9, and ≥ 2.0 h), screen time (< 1.0, 1.0–1.9, and ≥ 2.0 h), and sleeping time. The mother’s age, pre-pregnancy BMI, last menstrual period, and gestational age at delivery, and the child’s sex and age were extracted from medical records. Gestational age was calculated using the last menstrual period and delivery date. Maternal education, paternal age, family income, and parental myopia data were obtained via baseline questionnaires. Data on children’s myopia-related behaviours (outdoor time, near work, screen time, and sleep duration) were collected through follow-up questionnaires for participants aged 3–6 years, with follow-up completed on March 6, 2024.
Statistical analysis
Statistical analysis was performed from May 12, 2024, to November 23, 2024. Continuous normally distributed variables are reported as the mean ± standard deviation (SD), whereas non-normally distributed data are reported as the median and interquartile range (IQR). Categorical variables are presented as counts (%). All significance tests were 2-tailed, and P < 0.05 was considered to indicate statistical significance. All analyses were conducted using R software version 4.2.2 and Stata version 15.1.
Comparisons between maternal lipid trajectory groups and overall myopia prevalence were conducted using both chi-square tests and linear trend analyses. In the prospective cohort analysis, modified Poisson regression models [31] were used to estimate risk ratios (RRs) with 95% confidence intervals (CIs) to evaluate associations among maternal lipid trajectories, GDM complications, and suspected myopia in offspring. For the nested case‒control analysis, generalized linear models were employed to examine differences in myopia diagnosis on the basis of maternal lipid trajectories and GDM status. All the models were adjusted for offspring sex, age, gestational age, birth weight, outdoor activity time, near-work duration, screen exposure time, and sleep duration; maternal education and pre-pregnancy BMI; parental ages and myopia history; and family income. Left-eye data were also used to identify suspected myopia cases as part of a sensitivity analysis.
Results
A total of 4,043 mother‒offspring pairs with complete blood lipid data during pregnancy were enrolled in this study. Boys accounted for 50.51% (n = 1672) of the children participating in the prospective cohort analysis and 52.20% (n = 166) of the children participating in the nested case‒control analysis. The mean (SD) ages in the two analyses were 4.70 (0.40) years and 4.99 (0.59) years, respectively. During follow-up at 3–6 years of age, among all 4043 children, 491 (12.1%) were suspected to have myopia, including 82 with a diagnosis of myopia. The baseline characteristics of the parents and children are presented in Table 1. Table S1 in the Supplement shows the characteristics of the included and excluded parents and offspring; the included parents had a higher economic status, educational attainment and prevalence of myopia, and age was greater among both parents and children.
Table 1.
Characteristics of parents and offspring
| Characteristic | Prospective cohort analysis | Nested case control analysis |
|---|---|---|
| (n = 3310) | (n = 318) | |
| Parents’ characteristics | ||
| Maternal age, y | 29.25 ± 4.13 | 29.13 ± 3.89 |
| Maternal lipid level < 24 GW | ||
| TC, mmol/L | 4.66(4.04, 5.17) | 4.75(4.19, 5.17) |
| TG, mmol/L | 1.46(1.12, 1.94) | 1.55(1.16, 1.95) |
| Maternal lipid level at 24–32 GW | ||
| TC, mmol/L | 6.16(5.48, 6.81) | 6.15(5.47, 6.76) |
| TG, mmol/L | 2.24(1.8, 2.78) | 2.15(1.77, 2.78) |
| Maternal lipid level in ≥ 33 GW | ||
| TC, mmol/L | 6.18(5.39, 7.09) | 5.95(5.29, 6.72) |
| TG, mmol/L | 2.87(2.25, 3.65) | 2.85(2.25, 3.52) |
| GDM | 686(20.73) | 72(22.64) |
| Prepregnancy BMI, kg/m² | ||
| 18.5–23.9 | 2225(67.22) | 209(65.72) |
| < 18.5 | 520(15.71) | 38(11.95) |
| ≥ 24.0 | 565(17.07) | 71(22.33) |
| Maternal educational level | ||
| Middle school and below | 213(6.44) | 39(12.26) |
| High School | 1556(47.01) | 157(49.37) |
| College and above | 1541(46.56) | 122(38.36) |
| Total family income per year, RMB | ||
| <100,000 | 814(24.59) | 106(33.33) |
| 100,000–200,000 | 1445(43.66) | 146(45.91) |
| >200,000 | 1051(31.75) | 66(20.75) |
| Father’s age, y | 30.31 ± 4.67 | 30.06 ± 4.55 |
| Parental myopia | ||
| None | 576(17.40) | 52(16.35) |
| At least one | 2734(82.60) | 266(83.65) |
| Offspring characteristics | ||
| Sex | ||
| Male | 1672(50.51) | 166(52.20) |
| Female | 1638(49.49) | 152(47.80) |
| Age, y | 4.75 ± 0.40 | 4.99 ± 0.59 |
| Birth weight | ||
| Normal | 3039(91.81) | 280(88.05) |
| Low birth weight | 104(3.14) | 12(3.77) |
| Macrosomia | 167(5.05) | 26(8.18) |
| Gestational age at delivery | ||
| Not preterm | 3168(95.71) | 304(95.60) |
| Preterm | 142(4.29) | 14(4.40) |
| Daily outdoor time, h/d | ||
| < 1.0 | 1523(46.01) | 148(46.54) |
| 1.0–1.9.0.9 | 1241(37.49) | 120(37.74) |
| ≥ 2.0 | 546(16.50) | 50(15.72) |
| Daily near work time, h/d | ||
| < 1.0 | 2063(62.33) | 169(53.14) |
| 1.0–1.9.0.9 | 940(28.40) | 108(33.96) |
| ≥ 2.0 | 307(9.27) | 41(12.89) |
| Daily screen time, h/d | ||
| < 1.0 | 2016(60.91) | 206(64.78) |
| 1.0–1.9.0.9 | 904(27.31) | 74(23.27) |
| ≥ 2.0 | 390(11.78) | 38(11.95) |
| Daily sleeping time, h/d | 9.7(9.34, 10.07) | 9.77(9.36, 10.14) |
| Overall myopia | 491(14.83) | - |
GW Gestational weeks, TC Total cholesterol, TG Triglycerides, GDM Gestational diabetes, IQR Interquartile range, RMB Renminbi. Parental and children’s age are expressed as mean ± standard deviation, while other continuous variables are presented as medians (IQR) and categorical variables as counts (%)
On the basis of the model-adequacy criteria, four distinct blood lipid trajectories were identified (descriptive classifications of lipid trajectories are shown in Fig. 2, and model fit statistics are shown in Table S2 in the Supplement): Group 1 (“consistently normal TC and TG”)—accounting for 50.0% (n = 2,078) of the sample, this group remained far below the GHL criteria, with sustained low total cholesterol (mean ≈ 5.0 mmol/L) and triglyceride (mean ≈ 2.1 mmol/L) levels; Group 2 (“keeping normal TC but high TG”)—accounting for 4.9% (n = 193), these participants presented triglyceride levels that exceeded the GHL criteria throughout pregnancy (mean ≈ 4.7 mmol/L) while maintaining normal total cholesterol levels (mean ≈ 5.5 mmol/L); Group 3 (“moderately increasing TC and TG”)—accounting for 41.0% (n = 1,629), this group showed progressive increases in total cholesterol (range 5.3–7.1 mmol/L) and triglyceride (range 1.6–3.1 mmol/L) levels over time; and Group 4 (“highly increasing TC and TG”)—accounting for 4.1% (n = 143), these participants demonstrated steep elevations in total cholesterol (range 7.2–8.8 mmol/L) and triglyceride (range 2.4–4.8 mmol/L) levels. Longitudinal analysis revealed that the total cholesterol level tended to increase from Group 1 to Group 4 throughout gestation (P < 0.05). The peak triglyceride levels in Group 3 progressively increased with increasing gestational age relative to those in the other three groups (P < 0.05; Figure S1 in the Supplement).
Fig. 2.
Four latent classifications of maternal lipid trajectory through whole pregnancy. TC Total cholesterol, TG Triglycerides, GHL Gestational hyperlipidemia. The grey dotted line is the reference of GHL represents the recommended value for hyperlipidemia through three pregnant trimesters
In comparisons between lipid trajectory groups and myopia outcomes, offspring in both the “moderately increasing TC and TG” group and the “highly increasing TC and TG” group presented significantly greater prevalences of both suspected myopia and diagnosed myopia than those in the reference “consistently normal TC and TG” group (all P values < 0.05). Significant linear trends were observed from Group 1 to Group 4, with all P values < 0.01 (Figure S2 in the Supplement).
In the prospective cohort analysis with adjustment for key confounders, compared with offspring in the “consistently normal TC and TG” reference group, offspring in the “moderately increasing TC and TG” group had a 20% increase in their risk of suspected myopia (RR = 1.20, 95% CI: 1.02–1.44, P = 0.031), whereas those in the “highly increasing TC and TG” group presented a 52% increase in risk (RR = 1.52, 95% CI: 1.06–2.20, P = 0.024). Compared with the offspring of GDM-free with consistently normal TC and TG levels (reference group), the offspring of other groups had the following fully adjusted RRs for suspected myopia: RR = 1.26, 95% CI: 0.95–1.67 for GDM and consistently normal TC and TG; RR = 0.69, 95% CI: 0.38–1.25 for no GDM and normal TC but high TG; RR = 1.00, 95% CI: 0.50–2.02 for GDM and normal TC but high TG; RR = 1.20, 95% CI: 0.98–1.47 for no GDM and moderately elevated TC and TG; RR = 1.52, 95% CI: 1.16–1.99 for GDM and moderately elevated TC and TG; and RR = 1.61, 95% CI: 1.11–2.33 for highly increasing TC and TG (independent of GDM status) (Table 2).
Table 2.
Associations between maternal lipid trajectory, GDM and offspring suspected myopia in prospective cohort analysis
| Lipid trajectories and GDM status | No. with outcome/total No. (%) | RR (95% CI) | P value |
|---|---|---|---|
| Lipid trajectories | |||
| Consistently normal TC and TG | 222/1649 (13.46) | 1 [Reference] | NA |
| Keeping normal TC but high TG | 17/162 (10.49) | 0.75(0.47, 1.18) | 0.215 |
| Moderately increasing TC and TG | 218/1337 (16.31) | 1.21(1.02, 1.44) | 0.031 |
| Highly increasing TC and TG | 26/122 (21.31) | 1.52(1.06, 2.20) | 0.024 |
| Lipid trajectories combined with GDM status | |||
| Consistently normal TC and TG & No GDM | 168/1330 (12.63) | 1 [Reference] | NA |
| Consistently normal TC and TG & GDM | 54/319 (16.93) | 1.26(0.95, 1.67) | 0.111 |
| Keeping normal TC but high TG & No GDM | 10/110 (9.09) | 0.69(0.38, 1.25) | 0.223 |
| Keeping normal TC but high TG & GDM | 7/52 (13.46) | 1.00(0.50, 2.02) | 1 |
| Moderately increasing TC and TG & No GDM | 161/1048 (15.36) | 1.20(0.98, 1.47) | 0.072 |
| Moderately increasing TC and TG & GDM | 57/289 (19.72) | 1.52(1.16, 1.99) | 0.003 |
| Highly increasing TC and TG | 26/122 (21.31) | 1.61(1.11, 2.33) | 0.013 |
TC Total cholesterol, TG Triglycerides, NA Not applicable. All models adjusted for children sex, children age, family income, parental education, parental age, parental myopia, prepregnancy BMI, gestational week, birth weight, outdoor time, near work time, screen time and sleeping time
In the nested case‒control analysis with identical covariate adjustments, the “moderately increasing TC and TG” group was associated with a 66% increase in the risk of diagnosed myopia (OR = 1.66, 95% CI: 1.02–2.72, P = 0.043), whereas the “highly increasing TC and TG” group presented an 8.22-fold increase in risk (OR = 9.22, 95% CI: 1.05–80.66, P = 0.045). With respect to offspring whose mothers did not have GDM and had consistently normal TC and TG levels, the fully adjusted ORs for diagnosed myopia were as follows: OR = 1.19, 95% CI: 0.58–2.44 for offspring whose mothers had GDM and consistently normal TC and TG levels; OR = 2.90, 95% CI: 0.68–12.41 for offspring whose mothers had no GDM and maintained normal TC but high TG levels; OR = 0.65, 95% CI: 0.11–3.76 for offspring whose mothers had GDM and maintained normal TC but high TG levels; OR = 1.54, 95% CI: 0.89–2.66 for offspring whose mothers had no GDM and moderately elevated TC and TG levels; OR = 2.83, 95% CI: 1.06–7.59 for offspring whose mothers had GDM and moderately increasing TC and TG levels; and OR = 9.71, 95% CI: 1.10–85.81 for offspring whose mothers had high TC and TG levels (Table 3).
Table 3.
Associations between maternal lipid trajectory, GDM and offspring diagnosed myopia in nested case-control analysis
| Lipids trajectories and GDM status | No. of myopia cases | Total No. | OR (95% CI) | P value |
|---|---|---|---|---|
| Lipid trajectories | ||||
| Consistently normal TC and TG | 82 | 186 | 1 [Reference] | NA |
| Keeping normal TC but high TG | 8 | 15 | 1.50(0.51, 4.42) | 0.461 |
| Moderately increasing TC and TG | 62 | 109 | 1.66(1.02, 2.72) | 0.043 |
| Highly increasing TC and TG | 7 | 8 | 9.22(1.05, 80.66) | 0.045 |
| Lipid trajectories combined with GDM status | ||||
| Consistently normal TC and TG & No GDM | 61 | 144 | 1 [Reference] | NA |
| Consistently normal TC and TG & GDM | 21 | 42 | 1.19(0.58, 2.44) | 0.637 |
| Keeping normal TC but high TG & No GDM | 6 | 9 | 2.90(0.68, 12.41) | 0.152 |
| Keeping normal TC but high TG & GDM | 2 | 6 | 0.65(0.11, 3.76) | 0.627 |
| Moderately increasing TC and TG & No GDM | 47 | 87 | 1.54(0.89, 2.66) | 0.123 |
| Moderately increasing TC and TG & GDM | 15 | 22 | 2.83(1.06, 7.59) | 0.038 |
| Highly increasing TC and TG | 7 | 8 | 9.71(1.10, 85.81) | 0.041 |
TC Total cholesterol, TG Triglycerides, GDM Gestational diabetes, NA Not applicable. All models adjusted for children sex, children age, family income, parental education, parental age, parental myopia, prepregnancy BMI, gestational week, birth weight, outdoor time, near work time, screen time and sleeping time. The numbers of the “highly increasing TC and TG” group was too limited and they were analyzed as one group without further subcategorization based on GDM
Sensitivity analysis using left-eye data to screen for suspected myopia confirmed the stable association with maternal lipid trajectory (Supplementary Table S3). Maternal GDM was significantly associated with elevated risks of both suspected and diagnosed myopia in offspring (Supplementary Table S4). The myopia distribution patterns of offspring under different concurrent maternal GDM and lipid trajectory conditions are detailed in Supplementary Table S5.
Discussion
In this cohort study, four distinct maternal lipid trajectories were identified: “consistently normal TC and TG” (50.0%), “keeping normal TC but high TG” (4.9%), “moderately increasing TC and TG” (41.0%), and “highly increasing TC and TG” (4.1%), wherein the TC and TG levels gradually increased from the “consistently normal TC and TG” group to the “highly increasing TC and TG” group. Children in the “moderately increasing TC and TG” and “highly increasing TC and TG” groups presented a significantly greater risk of myopia than did children in the “consistently normal TC and TG” group in both the prospective cohort analysis and the nested case‒control analysis. When GDM was present as a complication, the risk of myopia further increased in all lipid trajectory groups.
Among the four maternal lipid trajectories identified in this economically developed city in China, only half of mothers followed the trajectory in which both TC and TG levels remained below the recommended maximum throughout the pregnancy. Nearly 5% of pregnant women consistently presented high TG levels throughout their pregnancy, while their TC levels remained relatively low. More than 40% of the pregnant women tended to have increased levels of TC and TG. Another 4% of pregnant women maintained high levels of both TC and TG that were higher than the reference values. Despite significant lipid fluctuations during pregnancy, the current national guidelines lack established diagnostic criteria for gestational hyperlipidaemia [32–34]. Notably, the present study revealed elevated myopia risk in offspring even at subthreshold maternal lipid levels, a finding that suggests that standardized lipid monitoring throughout pregnancy is necessary.
There has been limited research investigating the relationship between gestational lipid levels in mothers and myopia in offspring; however, some studies have suggested a potential correlation between visual acuity and lipid levels within the same individual. A population-based study demonstrated statistically significant correlations between serum lipid indicators and the spherical equivalent [35]. The accumulation of lipids and oxidative stress in the macula represent key pathogenic mechanisms in age-related macular degeneration [36, 37]. Hypercholesterolemia has been shown to impair retinal function by diminishing retinal ganglion cell density and reducing the thickness of the photoreceptor layer and the inner nuclear layer in animal models [38]. In murine models, isolated dyslipidaemia has been shown to cause a reduction in retinal function and severely reduce b/a ratios [39]. Elevated low-density lipoprotein cholesterol (LDL-C) is positively associated with vascular endothelial damage, and high high-density lipoprotein cholesterol (HDL-C) may increase the risk of glaucoma [40]. However, while this study provides crucial epidemiological data for establishing pregnancy-specific lipid reference ranges, its clinical applicability is still limited: the unavailability of HDL/LDL measurements constrains the scope of the analysis and may affect the generalizability of the results.
Several mechanisms underlying the association between maternal lipid levels and myopia in offspring have been proposed. First, foetal genetic studies have demonstrated inverse correlations between elevated maternal total cholesterol and low-density lipoprotein (LDL)/triglyceride levels in umbilical cord blood lipid profiles [41]. Given the strong dependence of the foetal visual system on maternally derived fatty acids [42, 43], this nutritional imbalance may coincide with the observed abnormalities in the arachidonic acid and glycerophospholipid pathways in myopic children [44, 45]. The protective effects of omega-3 polyunsaturated fatty acids (ω−3 PUFAs) on choroidal thickness have been consistently shown in both animal models and Mendelian randomization analyses [46, 47]. Second, maternal hyperlipidaemia upregulates the expression of inflammatory mediators (including cytosolic phospholipase A2 and cyclooxygenase-2) in the uterus, an effect that can be ameliorated by ω−3 PUFA supplementation [48]. This proinflammatory milieu may predispose the developing retina to oxygen-induced vascular endothelial growth factor (VEGF) dysregulation [49, 50], given the biphasic changes in VEGF mRNA induced by hyperlipidaemia [51] and neurovascular developmental alterations, as VEGF critically regulates retinal vascular permeability [52]. Genetic evidence has confirmed that VEGF-A, cluster of differentiation 6, and monocyte chemoattractant protein-2 have causal relationships with myopia [53], with VEGF-A mediating myopia progression through villous vascular regulation [54]. This is corroborated by elevated placental VEGF-A in lipid-disordered pregnancies complicated with GDM [55, 56], a response that is mechanistically linked to retinal inflammation and vascular leakage [57]. Collectively, these intrauterine stressors may lead to photoreceptor degeneration and permanent retinal damage in offspring [58]. These are considered hallmark features of axial myopia, and their manifestations include a reduced density of photoreceptor and retinal pigment epithelial cells, as well as retinal thinning [59].
This study demonstrated that both GDM and maternal hyperlipidaemia had independent effects on myopia risk in offspring. Although some intergroup differences lacked statistical significance (likely due to the limited sample size), the co-occurrence of elevated lipid levels with GDM had greater risk effects. Mechanistically, GDM and dyslipidaemia may synergistically impair placental development through suppression of placental glycolysis, dysregulation of lipid processing [60], and acceleration of placental age [61]. Notably, maternal GDM independently predisposes offspring to hyperlipidaemia [62], and hyperlipidaemia under hyperglycaemic conditions exacerbates mitochondrial dysfunction, a known driver of retinopathy [63]. A cohort study by Ting et al. [64] revealed that the presence of dyslipidaemia increases early retinal microvascular injury risk by 882% in patients with diabetes. These findings align with existing reports that GDM–dyslipidaemia comorbidity increases perinatal risks [65, 66], suggesting the existence of shared pathogenic pathways.
Strengths and limitations
The primary strength of the present study is its prospective cohort design, which enabled the collection of accurate data on exposure, outcomes, and covariates. The possible influence of various covariates has been adjusted in multivariable models. Multiple potential confounding variables related to myopia, including family history of myopia, birth outcomes, and behavioural factors, were considered. A comprehensive approach was employed to classify lipid levels during pregnancy, providing evidence for establishing reference values for gestational hyperlipidaemia.
Several limitations should also be noted. First, other refractive errors were not included in the analysis because of the inability to differentiate between hyperopia, astigmatism, and other types of refractive errors in the medical diagnosis records. Second, the number of children exposed to maternal GDM in the highly increasing TC and TG group was relatively small, limiting the ability to directly compare GDM, maternal lipid trajectories, or their combinations and potentially weakening the statistical power of the study while increasing the risk of errors. Third, the primary outcome was noncycloplegic refraction rather than cycloplegic refraction, which may have led to an overestimation of the prevalence of myopia in young children. To address this limitation, a nested case‒control analysis based on clinically diagnosed myopia was integrated as an adjunct to the prospective cohort analysis. However, cycloplegic studies are still needed to validate the results and compensate for this limitation. Fourth, this study focused solely on maternal TC and TG levels during pregnancy; further research is necessary to explore the associations of HDL-C, LDL-C, and other lipid markers with offspring myopia. Fifth, residual confounding factors, such as postnatal environmental exposure and dietary nutrition, might exist. Sixth, owing to the limited follow-up duration, the findings cannot be extrapolated past the preschool period and do not allow the prediction of myopia risk beyond this stage. Seventh, the participants included in this study had a higher socioeconomic status than the excluded candidates, which may limit the generalizability of the findings. For pregnant women with pre-existing severe hyperlipidaemia, further specialized cohort studies are warranted.
Conclusion
Elevated maternal lipid levels, including TC and TG levels, are associated with an increased risk of myopia in offspring; when GDM is present as a complication, this risk is further elevated. These findings provide novel insights into the long-term prognosis of gestational glycaemic and lipid management and the clinical care of early-onset myopia in children. It is therefore imperative to monitor maternal lipid levels during pregnancy, especially in cases where blood glucose and lipid levels are concurrently elevated. Targeting these metabolic factors as an intervention strategy for early-onset myopia may contribute to improved long-term ocular health outcomes in offspring. Early vision screening is recommended for children born to mothers with glucose and lipid metabolism disorders.
Supplementary Information
Acknowledgements
We would like to thank all the members of Shanghai MCPC, all the teachers, students, and staff from local government, hospitals and community health who participated in the cohort baseline and follow-up the cohort.
Abbreviations
- AIC
Akaike information criterion
- APPA
Average posterior probability of assignment
- BIC
Bayesian information criterion
- BMI
Body mass index
- CI
Confidence interval
- D
Dioptre
- GDM
Gestational diabetes mellitus
- GHL
Gestational hyperlipidaemia
- GW
Gestational week
- GBTM
Group-based trajectory modelling
- HDL-C
High-density lipoprotein cholesterol
- ICD-10
The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision
- IQR
Interquartile range
- LDL-C
Low-density lipoprotein cholesterol
- NA
Not applicable
- OR
Odds ratio
- ω-3 PUFAs
Omega-3 polyunsaturated fatty acids
- RMB
Renminbi
- RR
Risk ratio
- SE
Spherical equivalent
- SD
Standard deviation
- SCALE
The Shanghai Child and Adolescent Large-scale Eye Study
- MCPC
The Shanghai Maternal‒Child Pairs Cohort
- TC
Total cholesterol
- TG
Triglycerides
- VEGF
Vascular endothelial growth factor
Authors’ contributions
JJS and HJS designed and conceptualized this study. JJS conducted and drafted the manuscript, HJS and GZJ critically revised and reviewed the manuscript. JJS, GZJ, JJW, XGH, YHZ and HJS collected data. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. HJS and JJS are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Funding
This study was supported by the National Natural Science Foundation of China (82473654) and the Key Discipline Program of Sixth Round of the Three-year Public Health Action Plan (Year 2023–2025) of Shanghai, China: GWVI-11.1-32.
Data availability
Data are available for sharing upon reasonable request to the corresponding author.
Declarations
Ethics approval and consent to participate
Ethical approval was granted by the Ethics Committee of the School of Public Health at Fudan University (IRB# 2016-04-0587-EX). Written informed consent was acquired from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
Data are available for sharing upon reasonable request to the corresponding author.