Serum Lipid Biomarkers as Predictors for Age-Related Macular Degeneration Risk: A Cross-Sectional Analysis from the Beichen Eye Study

Serum Lipid Biomarkers as Predictors for Age-Related Macular Degeneration Risk: A Cross-Sectional Analysis from the Beichen Eye Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum Lipid Biomarkers as Predictors for Age-Related Macular Degeneration Risk: A Cross-Sectional Analysis from the Beichen Eye Study Gang Zou, Fei Gao, Limin Zhang, Qianhui Yang, Boshi Liu, Xiaorong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6877444/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: This population-based study aimed to investigate the associations between serum lipid biomarkers and the prevalence of age-related macular degeneration (AMD). Methods: This cross-sectional study analyzed data from 4748 subjects over 50 years old who were enrolled in the Beichen Eye Study. TG/HDL-c, HDL-c/LDL-c, the neutrophil/HDL-c ratio (NHR), the lymphocyte/HDL-c ratio (LHR), the monocyte/HDL-c ratio (MHR), the platelet/HDL-c ratio (PHR), the platelet/lymphocyte ratio (PLR), and the neutrophil/lymphocyte ratio (NLR)were assessed. Additionally, basic information, BMI, history of disease related to lipid metabolism, living habits and history of statin use were collected. Results: This study included 4748 participants, and 1245 of whom were diagnosed with AMD. The overall prevalence of AMD was 26.22%. The prevalence of AMD increased significantly with age (Z=-6.58, P<0.001). TG/HDL-c and HDL-c/LDL-c were significantly associated with the incidence of AMD (Z=-2.71, P=0.007; Z=-1.98, P=0.047, respectively). Multivariate logistic regression revealed that a high TG/HDL-c ratio (OR=0.80, P<0.05) and high NHR (OR=0.83, P<0.05) were both inversely associated with AMD risk, indicating protective effects. Elevated TG levels were also found to be protective against AMD (OR=0.80, P<0.05). Elevated HDL-c was associated with a paradoxical increase in AMD risk, especially in the second tertile (OR=1.04, 95% CI=0.89–1.22; P<0.05). Conclusions: This study suggested that an elevated TG/HDL-c ratio and NHR serve as protective biomarkers for AMD, with higher TG levels showing a protective effect. Conversely, HDL-c levelsdemonstrated a paradoxical association with AMD risk.These findings provide insights into the complex role of lipid metabolism in AMD pathogenesis and suggest potential biomarkers for AMD risk prediction. Age-related macular degeneration Lipid metabolism TG HDL-c Biomarker Figures Figure 1 Introduction Age-related macular degeneration (AMD), the leading cause of irreversible central vision loss in people over 50 years of age, is a complex interplay between genetic predisposition, the aging process, and homeostasis of the internal environment risk factors, including altered lipid metabolism[ 1 – 3 ]. With the transformation of the metabolic state of the body, inflammation caused by abnormal lipid metabolism may become an important factor in the pathogenesis of AMD[ 4 ].Recent studies have shown that lipid metabolism disorders and the inflammatory response play an important roles in the pathogenesis of AMD[ 4 ]. The relationship between dyslipidemia and the pathogenesis of AMD has been extensively studied, but the results of epidemiological studies have been inconsistent[ 5 – 7 ]. Emerging evidence suggests that systemic comorbidities in the pathogenesis of AMD, particularly cardiometabolic disorders, such as hypertension, atherosclerosis, and hypercholesterolemia are independently associated with accelerated retinal pigment epithelium (RPE) dysfunction in multiple cohorts[ 8 – 10 ]. The association between high-density lipoprotein cholesterol (HDL-c) and AMD remains mechanistically ambiguous[ 11 , 12 ], with conflicting observations across epidemiological cohorts. While Mendelian randomization studies robustly implicate elevated HDL-c as a causal risk factor for advanced AMD in multiethnic populations[ 13 ], observational data exhibit marked heterogeneity, including positive correlations in European consortia (E3)[ 5 ], inverse associations in select cohorts and null effects in others[ 14 ]. This inconsistency likely stems from uncontrolled confounding factors (e.g., inflammation, oxidative stress), phenotypic heterogeneity across AMD stages, and HDL-c functional alterations under retinal microenvironment stresses[ 1 ]. Changes in the number of neutrophils, monocytes, lymphocytes and platelets in the complete blood count are indicators of inflammation in the body[ 15 ]. Given that systemic dyslipidemia alone may not be sufficient to explain the pathogenesis of AMD, systematic reviews of lipid biomarkers and AMD risk and population-based cohort epidemiological studies may reveal this association[ 10 ]. New hematological parameters related to lipid indices and whole blood cells, such as the NHR[ 16 ], MHR[ 17 ], LHR[ 18 ], PHR[ 19 ], PLR[ 20 ], and NLR[ 21 ], have been proposed as new inflammatory biomarkers and may provide insight into AMD inflammation. Hence, the objective of this study was to investigate the associations between serum lipid biomarkers and the prevalence of AMD. Methods 1.Study population This study is a population-based cross-sectional investigation, that targets adults aged 50 years and older residing in Beichen District. The Beichen Eye Study was an epidemiological survey of eye diseases in a community population conducted by Tianjin Medical University Eye Hospital from December 2019 to February 2022. This study follows the original Helsinki Declaration approved by the Ethics Committee of Tianjin Medical University Eye Hospital (Approval Number: 2019ky-22). Additionally, written informed consent was obtained from each participant. The data used in this study were from the Beichen Eye Study. From the initial population-based cohort of 5840 adults recruited; 4748 eligible participants were included in the final analysis. The inclusion criteria were as follows: aged > 50 years in 12 villages in 4 towns in Beichen District, Tianjin; communities were selected via multistage random sampling. The exclusion criteria were as follows: (1) inability to perform AMD grading (n = 1092: severe cataracts, ungradable fundus photography, or noncooperation); (2) ocular comorbidities (n eliminated with exclusion sequence: glaucoma/suspected glaucoma IOP > 21 mmHg (1 mmHg = 0.133 kPa), retinal detachment, and other vitreoretinal pathologies); and (3) missing hematological parameters (blood lipid profiles or inflammatory biomarkers). The final analytic cohort consisted of 1245 AMD patients (early/intermediate AMD = 1127; late AMD = 118) and 3503 age-matched controls. 2.Ophthalmic examination All examinations were performed at the community hospital to which the respondents belonged. The protocol included comprehensive clinical assessments, blood lipid detection indicators and standard questionnaires. Clinical investigations included visual acuity assessment, optometric assays, slit-lamp examination, intraocular pressure, axial length, mydriasis, direct ophthalmoscopy of the posterior segment, fundus photography, ultrawide field retinal imaging, and swept-source optical coherence tomography (SS-OCT). The detailed methodology of the epidemiology of the Beichen Eye Study has been published previously[ 22 ]. For patient screening procedures, detection methods and questionnaires, refer to the details in reference[ 23 ]. Venous blood samples (15 ml) were obtained from each participant in the morning after an 8-hour overnight fast. Blood lipid detection indicators include total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c). All blood samples were tested via an automatic blood analyzer (Sysmex, XN-9000). 3.Diagnostic criteria for AMD AMD was defined and graded via the Wisconsin AMD grading system[ 24 ], AMD was defined as the presence of drusen (> diameter of 63–125 µm), including early, intermediate, and late AMD and geographic atrophy. Late AMD was defined as the presence of neovascular AMD or geographic atrophy. Neovascular AMD includes serous or hemorrhagic detachment of the retinal pigment epithelium (RPE) or sensory retina, subretinal or sub-RPE hemorrhages, and subretinal fibrous scars. Geographic atrophy was defined as a discrete circular area of depigmentation of the RPE with a diameter of ≥ 175 µm. 4.Statistical Analysis Statistical analysis was conducted via a statistical software program (SPSS 25.0 for Windows (SPSS Inc., Chicago, USA)). The data are presented as the means ± standard deviations (SDs) and numerical (%) values. Continuous data with skewed distributions are presented as medians (interquartile distances), and comparisons between groups were performed via the Mann‒Whitney test. Categorical variables are presented as frequencies and percentages, and differences were tested via the χ2 test. The incidence of AMD is presented as a percentage with a 95% confidence interval (CI). Logistic regression analysis was conducted to estimate the odds ratio (OR) and 95% confidence interval (CI) of AMD values for the risk of biomarkers of serum lipids and inflammatory levels associated with AMD, and P < 0.05 was considered statistically significant. The variables included in the multivariable adjusted logistic regression model are as follows: Model 1: single biomarkers of serum lipids and inflammatory levels + age and sex; Model 2: Model 1 + BMI, fasting glucose, diabetes, hypertension, smoking, alcohol consumption, and coronary heart disease history. Model 3: Model 2 + history of statin use. P for trend was determined via a multivariate regression model linear trend test. Continuous variables were entered into the model after being divided into three categories, and the lowest quantile was used as the reference group. Results 1.Basic characteristics of the study population A total of 4748 participants were included in this study, of whom 1245 were diagnosed with AMD on the basis of fundus photography and macular optical coherence tomography (OCT). The prevalence of AMD was 26.22%, while the non-AMD group consisted of 3503 individuals (73.78%). The participant selection process is detailed in Fig. 1 . The age distribution significantly differed between the case and control groups, with the median age in the AMD group being 65 years compared with 60 years in the non-AMD group (P < 0.001). The prevalence of AMD increased markedly with age and was considerably higher in older adults (Z=-6.58, P < 0.001) (shown in Table 1 ). Compared with that in the non-AMD group, the median level triglyceride (TG) in the AMD group was significantly lower (Z=-2.70, P = 0.007). This difference suggests that TG may be associated with AMD risk. No significant differences were observed between the two groups with respect to other confounding factors, including BMI, diabetes, hypertension, fasting blood glucose, smoking history, alcohol consumption, coronary heart disease history, and statin use (P > 0.05). Table 1 Baseline cohort characteristic. Variables Total (n = 4748) Non-AMD (n = 3503) AMD (n = 1245) Statistic p-value Age, M (Q₁, Q₃) 63.00 (57.00, 67.00) 62.00 (57.00, 67.00) 64.00 (59.00, 68.00) Z=-6.58 < 0.001** BMI, M (Q₁, Q₃) 25.95 (23.83, 28.35) 25.95 (23.81, 28.36) 25.97 (23.88, 28.33) Z=-0.14 0.892 GLU, M (Q₁, Q₃) 4.60 (4.30, 5.20) 4.60 (4.30, 5.20) 4.60 (4.30, 5.30) Z=-0.41 0.684 TC, M (Q₁, Q₃) 5.30 (4.60, 5.90) 5.30 (4.60, 5.90) 5.30 (4.60, 6.00) Z=-0.16 0.872 TG, M (Q₁, Q₃) 1.42 (1.04, 1.93) 1.43 (1.05, 1.96) 1.38 (1.02, 1.86) Z=-2.70 0.007** LDL-c, M (Q₁, Q₃) 3.01 (2.49, 3.52) 3.02 (2.49, 3.52) 3.00 (2.46, 3.50) Z=-0.87 0.383 HDL-c, M (Q₁, Q₃) 1.11 (0.96, 1.31) 1.11 (0.95, 1.30) 1.12 (0.97, 1.32) Z=-1.71 0.087 Sex, n (%) χ²=0.34 0.561 Male 1665 (35.07) 1220 (34.83) 445 (35.74) Female 3083 (64.93) 2283 (65.17) 800 (64.26) DM, n (%) χ²=0.21 0.644 No 3900 (82.14) 2872 (81.99) 1028 (82.57) Yes 848 (17.86) 631 (18.01) 217 (17.43) HBP, n (%) χ²=0.97 0.324 No 1506 (31.72) 1125 (32.12) 381 (30.60) Yes 3242 (68.28) 2378 (67.88) 864 (69.40) CHD, n (%) χ²=3.66 0.056 No 3788 (79.78) 2818 (80.45) 970 (77.91) Yes 960 (20.22) 685 (19.55) 275 (22.09) Statin use, n (%) χ²=0.54 0.464 No 4397 (92.63) 3238 (92.46) 1159 (93.09) Yes 350 (7.37) 264 (7.54) 86 (6.91) Smoke, n (%) χ²=2.70 0.260 No smoking 3513 (73.99) 2613 (74.59) 900 (72.29) Quit smoking 285 (6.00) 208 (5.94) 77 (6.18) Smoking 950 (20.01) 682 (19.47) 268 (21.53) Drink, n (%) χ²=2.70 0.259 No drinking 3680 (77.51) 2720 (77.65) 960 (77.11) Quit drinking 150 (3.16) 102 (2.91) 48 (3.86) Drinking 918 (19.33) 681 (19.44) 237 (19.04) Tab. 1. Continuous and categorical variables are presented as medians (interquartile ranges) and numbers (%), respectively. The age distribution was skewed, with negative Z scores indicating an older age in the AMD group. Abbreviations: Z: Mann‒Whitney test; χ²: chi‒square test; M: median; Q₁: 1st quartile; Q₃: 3rd quartile. AMD: age-related macular degeneration; BMI: body mass index; GLU: fasting plasma glucose; TC: total cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; HDL-c: high-density lipoprotein cholesterol; DM: diabetes mellitus; HBP: hypertension; CHD: coronary heart disease. 2. Univariate Analysis of Serum Lipid and Inflammatory Biomarkers This study revealed a median triglyceride-to-HDL ratio (TG/HDL-c) of 1.28 (IQR: 0.84–1.91) and HDL-c /LDL-c ratio of 0.37 (IQR: 0.30–0.47). Mann-Whitney U tests revealed significant differences in the TG/HDL and HDL-c/LDL-c ratios between the AMD and non-AMD groups, with the AMD group exhibiting lower TG/HDL-c ratios (Z=-2.71, P = 0.007) but higher HDL-c /LDL-c ratios (Z=-1.98, P = 0.047) (shown in Table 2 ). No significant intergroup differences were detected for the NHR (Z=-1.50, P = 0.134), LHR (Z=-1.37, P = 0.171), or inflammatory markers including the PLR (Z=-0.31, P = 0.755), NLR (Z=-0.42, P = 0.674), and MHR (Z=-0.81, P = 0.420). The PHR showed borderline significance (Z=-1.71, P = 0.087). Table 2 Serum lipid and inflammatory level biomarkers Variables Total (n = 4748) Non-AMD (n = 3503) AMD (n = 1245) Statistic p-value TG/HDL-c, M (Q₁, Q₃) 1.28 (0.84, 1.91) 1.30 (0.85, 1.94) 1.21 (0.82, 1.80) Z=-2.71 0.007** HDL-c /LDL-c, M (Q₁, Q₃) 0.37 (0.30, 0.47) 0.37 (0.30, 0.47) 0.38 (0.31, 0.48) Z=-1.98 0.047* NHR, M (Q₁, Q₃) 2.88 (2.14, 3.87) 2.89 (2.15, 3.89) 2.85 (2.09, 3.77) Z=-1.50 0.134 LHR, M (Q₁, Q₃) 1.75 (1.35, 2.25) 1.76 (1.36, 2.27) 1.73 (1.34, 2.21) Z=-1.37 0.171 PHR, M (Q₁, Q₃) 219.81 (178.26, 272.08) 220.91 (178.79, 273.07) 216.51 (176.67, 268.07) Z=-1.71 0.087 PLR, M (Q₁, Q₃) 126.23 (102.69, 155.95) 125.84 (102.47, 156.16) 126.85 (103.87, 155.77) Z=-0.31 0.755 NLR, M (Q₁, Q₃) 1.63 (1.28, 2.10) 1.63 (1.29, 2.10) 1.64 (1.28, 2.10) Z=-0.42 0.674 MHR, M (Q₁, Q₃) 0.37 (0.28, 0.49) 0.37 (0.28, 0.49) 0.36 (0.28, 0.48) Z=-0.81 0.420 Tab. 2. Data are presented as medians with interquartile ranges (IQRs). Statistical significance was evaluated by the Mann‒Whitney U test (*P<0.05, ** P<0.01). Abbreviations: Z: Mann‒Whitney test; M: median; Q₁: 1st quartile; Q₃: 3rd quartile. TG/HDL-c: triglyceride/HDL-c: triglyceride/HDL-c ratio; HDL-c/LDL-c: high-density lipoprotein cholesterol/low-density lipoprotein cholesterol ratio; NHR: neutrophil/HDL-c ratio; LHR: lymphocyte/HDL-c ratio; PHR: platelet/HDL-c ratio; PLR: platelet/lymphocyte ratio; NLR: neutrophil/lymphocyte ratio; MHR: monocyte/HDL-c ratio. 3. Multivariable Associations b etween Lipid Profiles and AMD Risk The results of the multivariable-adjusted logistic regression models are shown in Tables 3 and 4. After adjusting for age, sex, BMI, fasting glucose, diabetes mellitus, hypertension, smoking, alcohol consumption, coronary heart disease history, and statin use (Model 3), lipid profiles exhibited differential associations with AMD risk. The TG/HDL-c ratio demonstrated a significant inverse linear association with AMD risk (P for trend=0.010). Compared with those in the lowest tertile, participants in the highest tertile of TG/HDL-c ratio (≥1.7) presented 20% lower odds of AMD compared to those in the lowest tertile (OR=0.80, 95% CI: 0.69–0.95). Similarly, the neutrophil-to-HDL-c ratio (NHR) exhibited a protective linear trend (p for trend=0.032), with the highest tertile (≥3.5) associated with AMD risk reduction in AMD risk (OR=0.83, 95% CI: 0.69–0.98). Sex-stratified HDL-c level analysis revealed a borderline significant risk elevation in the middle tertile (males 0.96–1.14/females 0.91–1.54 mmol/L) (OR=1.04, 95% CI: 0.89–1.22), although no overall linear trend was observed (P for trend=0.269). Total cholesterol (TC) and LDL-c were not significantly associated with AMD risk according to the fully adjusted models. Triglycerides (TGs) demonstrated a significant linear trend (p for trend=0.039), with the middle tertile (0.56–1.69 mmol/L) showing a 17% increased risk of AMD compared with the highest tertile (≥1.69 mmol/L) (OR=1.17, 95% CI: 1.01–1.34), suggesting that elevated TG levels may serve as a protective factor against AMD. Table 3: OR with 95% CI of biomarkers of serum lipid and inflammatory level for AMD risk analyzed by logistic models Model 1 OR （ 95%CI ） Model 2 OR （ 95%CI ） Model 3 OR （ 95%CI ） TG/HDL -c P for trend=0.101 P for trend=0.009* P for trend=0.010* Bottom tertile (≤1.0) Reference Reference Reference Middle tertile (1.0–1.7) 0.93 (0.79 ~ 1.09) 0.93 (0.79 ~ 1.09) 0.93 (0.79 ~ 1.09) Top tertile (≥1.7) 0.81 (0.69 ~ 0.95) * 0.80 (0.68 ~ 0.94) * 0.80 (0.69 ~ 0.95) * HDL -c /LDL -c P for trend=0.185 P for trend=0.120 P for trend=0.131 Bottom tertile (≤0.3) Reference Reference Reference Middle tertile (0.3–0.4) 1.17 (1.00 ~ 1.37) 1.17 (0.99 ~ 1.37) 1.17 (0.99 ~ 1.37) Top tertile (≥0.4) 1.14 (0.97 ~ 1.34) 1.14 (0.96 ~ 1.34) 1.14 (0.97 ~ 1.35) NHR P for trend=0.058 P for trend=0.027* P for trend=0.032* Bottom tertile (≤2.4) Reference Reference Reference Middle tertile (2.4–3.5) 0.93 (0.80 ~ 1.09) 0.93 (0.80 ~ 1.10) 0.94(0.80 ~ 1.10) Top tertile (≥3.5) 0.84 (0.71 ~ 0.99) * 0.82 (0.69 ~ 0.98) * 0.83 (0.69 ~ 0.98) * LHR P for trend=0.479 P for trend=0.390 P for trend=0.411 Bottom tertile (≤1.5) Reference Reference Reference Middle tertile (1.5–2.1) 1.04 (0.89 ~ 1.21) 1.04 (0.89 ~ 1.22) 1.04 (0.89 ~ 1.22) Top tertile (≥2.1) 0.93 (0.79 ~ 1.10) 0.93 (0.78 ~ 1.09) 0.93 (0.79 ~ 1.10) MHR P for trend=0.353 P for trend=0.249 P for trend=0.264 Bottom tertile (≤0.3) Reference Reference Reference Middle tertile (0.3–0.4) 0.95 (0.81 ~ 1.11) 0.95 (0.81 ~ 1.12) 0.95 (0.81 ~ 1.12) Top tertile (≥0.4) 0.91 (0.77 ~ 1.08) 0.90 (0.76 ~ 1.07) 0.90 (0.76 ~ 1.08) PHR P for trend=0.211 P for trend=0.084 P for trend=0.093 Bottom tertile (≤191.7) Reference Reference Reference Middle tertile (191.7–252.1) 0.95 (0.81 ~ 1.11) 0.95 (0.81 ~ 1.11) 0.95 (0.81 ~ 1.12) Top tertile (≥252.1) 0.87 (0.74 ~ 1.02) 0.86 (0.73 ~ 1.02) 0.87 (0.74 ~ 1.02) PLR P for trend=0.904 P for trend=0.884 P for trend=0.888 Bottom tertile (≤110.7) Reference Reference Reference Middle tertile (110.7–144) 1.13 (0.96 ~ 1.32) 1.13 (0.97 ~ 1.33) 1.14 (0.97 ~ 1.33) Top tertile (≥144) 1.01 (0.86 ~ 1.19) 1.01 (0.86 ~ 1.19) 1.01 (0.86 ~ 1.19) NLR P for trend=0.687 P for trend=0.243 P for trend=0.257 Bottom tertile (≤1.4) Reference Reference Reference Middle tertile (1.4–1.9) 0.96 (0.82 ~ 1.12) 0.96 (0.81 ~ 1.12) 0.96 (0.82 ~ 1.12) Top tertile (≥1.9) 0.91 (0.77 ~ 1.07) 0.91 (0.77 ~ 1.07) 0.91 (0.77 ~ 1.07) Tab. 3 . Asterisk (*) denotes P<0.05. Abbreviations: TG/HDL-c: triglyceride/ high-density lipoprotein cholesterol ratio; HDL/LDL-c: high-density lipoprotein cholesterol/ low-density lipoprotein cholesterol ratio; NHR: neutrophil/HDL-c ratio; LHR: lymphocyte/HDL-c ratio; MHR: monocyte/HDL-c ratio; PHR: platelet/HDL-c ratio; PLR: platelet/lymphocyte ratio; NLR: neutrophil/lymphocyte ratio Table 4: OR with 95% CI of lipidemia level indicators for AMD risk analyzed by logistic models Model 1 OR （ 95%CI ） Model 2 OR （ 95%CI ） Model 3 OR （ 95%CI ） TC P for trend=0.744 P for trend=0.693 P for trend=0.748 ≤ 2.8 Reference Reference Reference 2.8–5.16 1.09 (0.48 ~ 2.46) 1.11 (0.49 ~ 2.52) 1.12 (0.49 ~ 2.55) ≥ 5.16 1.11 (0.49 ~ 2.52) 1.14 (0.50 ~ 2.59) 1.14 (0.50 ~ 2.60) TG a P for trend=0.030* P for trend=0.034* P for trend=0.039* ≤ 0.56 1.22 (0.75 ~ 1.97) 1.21 (0.74 ~ 1.97) 1.20 (0.73 ~ 1.97) 0.56–1.69 1.17 (1.02 ~ 1.34) * 1.17 (1.01 ~ 1.35) * 1.17 (1.01 ~ 1.34) * ≥ 1.69 Reference Reference Reference LDL-c P for trend=0.483 P for trend=0.545 P for trend=0.491 ≤ 1.59 Reference Reference Reference 1.59–3.0 0.87 (0.62 ~ 1.23) 0.89 (0.63 ~ 1.26) 0.88 (0.62 ~ 1.25) ≥ 1.59 0.86 (0.61 ~ 1.21) 0.87 (0.62 ~ 1.24) 0.86 (0.61 ~ 1.22) HDL-c P for trend=0.097 P for trend=0.100 P for trend=0.269 ≤ 0.96 in male/ ≤ 0.9 in female Reference Reference Reference 0.96-1.14 in male/ 0.91-1.54 in female 1.24 (1.04 ~ 1.49) * 1.25 (1.04 ~ 1.50) * 1.04 (0.89 ~ 1.22) * ≥ 1.14 in male/ ≥ 1.54 in female 1.18 (0.96 ~ 1.45) 1.19 (0.96 ~ 1.47) 1.18 (0.95 ~ 1.47) Tab. 4. TG a : The highest quantile was used as the reference group. Asterisk (*) denotes P<0.05. Abbreviations: TC: total cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; HDL-c: high-density lipoprotein cholesterol. Discussion The overall prevalence of AMD in this study was 26.22%, with age identified as the most significant risk factor. Notably, distinct lipid profile signature discrepancies emerged between the AMD and non-AMD groups, particularly in the TG/HDL-c and HDL-c/LDL-c ratios (Table 2 ). These findings are consistent with emerging evidence suggesting that dysregulation of lipid homeostasis plays a crucial role in the pathogenesis of AMD. Previous studies have highlighted the importance of the dynamic balance between TG and HDL-c/LDL-c in AMD pathophysiology[ 11 ]. Multivariable logistic regression analysis revealed that individuals in the highest TG/HDL-c tertile (≥ 1.7) had a 20% reduction in AMD risk compared with those in the lowest tertile (P for trend = 0.010), reinforcing the paradoxical protective role of TG observed in multivariable Mendelian randomization studies[ 13 ]. The TG/HDL-c ratio reflects the balance between the detrimental effects of HDL-c dysfunction, which can lead to inflammation and oxidative stress, and the beneficial effects of TG-mediated protection of the RPE. Recent studies suggest that oxidized phospholipid derivatives from systemic TG metabolism may help mitigate complement-mediated retinal inflammation in AMD models, warranting further functional validation[ 25 ]. In this study, the median triglyceride (TG) level in the AMD group was lower than that in the non-AMD group (Table 1 ), suggesting that certain mechanisms may lead to a higher risk of AMD in individuals with low TG levels. This finding also supports the aforementioned conclusions. A paradoxical association was observed between elevated HDL-c levels and increased AMD risk in this study which aligns with findings from European registry data[ 5 ], but contrasts with conventional cardiovascular risk models[ 26 ]. This discrepancy likely reflects the specificity of the retinal microenvironment, as HDL-c may exhibit bidirectional effects under different conditions of the disease. Under physiological conditions, HDL-c plays a protective role in cardiovascular health through reverse cholesterol transport and anti-inflammatory effects[ 27 ]. However, in the presence of chronic inflammation (like AMD), diabetes, or oxidative stress, HDL-c may adopt a proinflammatory phenotype, losing its normal protective function[ 28 ]. In this study, the complex impact of HDL-c heterogeneity, particularly in the mid-quantile group, might have been influenced by inadequate adjustment for additional risk factors such as disease stages of AMD. Elevated plasma HDL-c levels are causally linked to an increased risk of AMD, suggesting that strategies aimed at lowering HDL-c levels could play a role in the prevention and management of AMD, especially in the early and middle stages of elevated HDL-c. This represents a crucial area for future research. The protective effect of the NHR on AMD risk (OR = 0.83 for the top tertile) provides novel insight into the interplay between neutrophils and lipid metabolism[ 16 , 29 ]. The NHR appears to capture both the anti-inflammatory properties of HDL-c and the pro-inflammatory activity of neutrophils, contributing to its predictive value. Although other inflammatory markers, such as the PLR, NLR and MHR, were not significantly associated, the protective effect of the NHR further underscores the role of neutrophils in the chronic inflammatory processes underlying AMD. These findings suggest that lipid profile ratios, particularly TG/HDL-c and NHR ratios, could serve as independent biomarkers for AMD risk, reflecting the underlying metabolic and inflammatory pathways involved in AMD pathogenesis and expanding the theoretical framework of lipid-inflammation interactions in AMD pathogenesis. In the development of risk models, both Model II and Model III (which included additional adjustment for statin use history) demonstrated highly consistent effect estimates, with differences in odds ratios (ORs) less than 5%. Statin use, as a significant lipid regulator, may exhibit collinearity with other disease states (such as a history of coronary heart disease), potentially limiting the independent contribution of this variable[ 30 ]. The protective effects of TG/HDL-c and the NHR against AMD remained statistically significant after accounting for statin use (p < 0.001). These findings suggest that these markers may reflect intrinsic metabolic regulatory mechanisms that are independent of pharmacological intervention, such as the antioxidant properties of HDL-c or alternative TG transport pathways not influenced by statin therapy. This finding can be used as an emphasis marker to predict the reliability of AMD. The limitations of this study include the following: (1) its cross-sectional design, which limits causal inference; (2) the lack of follow-up data to differentiate between dry and wet AMD subtypes; and (3) the functional heterogeneity of HDL-c (e.g., anti-inflammatory vs. proinflammatory subtypes) not being fully explored, and potential confounders such as omega-3 fatty acid intake were not adjusted for. Further mechanistic studies using lipidomics are needed. We recommend that future research employ nested case‒control designs to improve exposure measurements and explore the protective mechanisms of specific lipid species through lipidomics. Despite these limitations, our study is the first to systematically analyze lipid‒inflammation composite markers and their associations with AMD in a Chinese community cohort, providing important evidence for the development of targeted risk prediction models. In conclusion, our population-based data lend support to the emerging hypothesis that an elevated TG/HDL-c ratio and NHR serve as protective biomarkers for AMD. TG levels exhibit dose-dependent protective effects, whereas the inverse relationship between HDL-c and AMD risk suggests a complex role of traditional cardiovascular markers in AMD. Future research should validate the risk stratification capabilities of these biomarkers in longitudinal cohorts. Declarations A cknowledgment We would like to express our gratitude to the doctors, nurses, statisticians and all other contributors of the Beichen Eye Hospital research team. Statement of Ethics This study follows the original Helsinki Declaration approved by the Ethics Committee of Tianjin Medical University Eye Hospital (Approval Number: 2019ky–22). Written informed consent was obtained from each participant. Conflict of interest statement The authors have no conflicts of interest to declare. Funding Sources This work was supported by the Tianjin Key Medical Discipline (Specialty) Construction Project（TJYXZDXK-037A） Author Contributions F.G. conceived and planned the data statistics. G.Z wrote the manuscript while F.G., L.M.Z., B.S.L., and Q.H.Y. provided feedback. The corresponding author reviewed and provided feedback on the manuscript. All authors approved the final manuscript for submission. Data Availability Statement All the data generated or analyzed during this study are included in this published article. Further inquiries can be directed to the authors. References Fleckenstein M, Keenan TDL, Guymer RH, Chakravarthy U, Schmitz-Valckenberg S, Klaver CC, et al. Age-related macular degeneration. Nat Rev Dis Primers. 2021;7(1):31. Omarova S, Charvet CD, Reem RE, Mast N, Zheng W, Huang S, et al. Abnormal vascularization in mouse retina with dysregulated retinal cholesterol homeostasis. J Clin Invest. 2012;122(8):3012–23. Cheung CMG, Gan A, Fan Q, Chee ML, Apte RS, Khor CC, et al. Plasma lipoprotein subfraction concentrations are associated with lipid metabolism and age-related macular degeneration. J Lipid Res. 2017;58(9):1785–96. Jun S, Datta S, Wang L, Pegany R, Cano M, Handa JT. The impact of lipids, lipid oxidation, and inflammation on AMD, and the potential role of miRNAs on lipid metabolism in the RPE. Exp Eye Res. 2019;181:346–55. Colijn JM, den Hollander AI, Demirkan A, Cougnard-Gregoire A, Verzijden T, Kersten E, et al. Increased High-Density Lipoprotein Levels Associated with Age-Related Macular Degeneration: Evidence from the EYE-RISK and European Eye Epidemiology Consortia. Ophthalmology. 2019;126(3):393–406. Abalain JH, Carre JL, Leglise D, Robinet A, Legall F, Meskar A, et al. Is age-related macular degeneration associated with serum lipoprotein and lipoparticle levels? Clin Chim Acta. 2002;326(1–2):97–104. Erke MG, Bertelsen G, Peto T, Sjolie AK, Lindekleiv H, Njolstad I. Cardiovascular risk factors associated with age-related macular degeneration: the Tromso Study. Acta Ophthalmol. 2014;92(7):662–9. Feng J, Xie F, Wu Z, Wu Y. Age-related macular degeneration and cardiovascular disease in US population: an observational study. Acta Cardiol. 2024;79(6):665–71. Han G, Wei P, He M, Jia L, Su Q, Yang X, Hao R. Role of plasma fatty acid in age-related macular degeneration: insights from a mendelian randomization analysis. Lipids Health Dis. 2024;23(1):206. Bucan K, Lukic M, Bosnar D, Kopic A, Jukic T, Konjevoda S, et al. Analysis of association of risk factors for age-related macular degeneration. Eur J Ophthalmol. 2022;32(1):410–6. Wang Y, Wang M, Zhang X, Zhang Q, Nie J, Zhang M et al. The Association between the Lipids Levels in Blood and Risk of Age-Related Macular Degeneration. Nutrients. 2016;8(10). Cougnard-Gregoire A, Delyfer MN, Korobelnik JF, Rougier MB, Le Goff M, Dartigues JF, et al. Elevated high-density lipoprotein cholesterol and age-related macular degeneration: the Alienor study. PLoS ONE. 2014;9(3):e90973. Han X, Ong JS, Hewitt AW, Gharahkhani P, MacGregor S. The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study. Int J Epidemiol. 2021;50(1):325–36. Kananen F, Strandberg T, Loukovaara S, Immonen I. Early middle age cholesterol levels and the association with age-related macular degeneration. Acta Ophthalmol. 2021;99(7):e1063–9. Rye KA, Bursill CA, Lambert G, Tabet F, Barter PJ. The metabolism and anti-atherogenic properties of HDL. J Lipid Res. 2009;50(SupplSuppl):S195–200. Huang JB, Chen YS, Ji HY, Xie WM, Jiang J, Ran LS, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids Health Dis. 2020;19(1):59. Liu Z, Fan Q, Wu S, Wan Y, Lei Y. Compared with the monocyte to high-density lipoprotein ratio (MHR) and the neutrophil to lymphocyte ratio (NLR), the neutrophil to high-density lipoprotein ratio (NHR) is more valuable for assessing the inflammatory process in Parkinson's disease. Lipids Health Dis. 2021;20(1):35. Yu S, Guo X, Li G, Yang H, Zheng L, Sun Y. Lymphocyte to High-Density Lipoprotein Ratio but Not Platelet to Lymphocyte Ratio Effectively Predicts Metabolic Syndrome Among Subjects From Rural China. Front Cardiovasc Med. 2021;8:583320. Jialal I, Jialal G, Adams-Huet B. The platelet to high density lipoprotein -cholesterol ratio is a valid biomarker of nascent metabolic syndrome. Diabetes Metab Res Rev. 2021;37(6):e3403. Sengul EA, Artunay O, Kockar A, Afacan C, Rasier R, Gun P, et al. Correlation of neutrophil/lymphocyte and platelet/lymphocyte ratio with visual acuity and macular thickness in age-related macular degeneration. Int J Ophthalmol. 2017;10(5):754–9. Vergroesen JE, Thee EF, de Crom TOE, Kiefte-de Jong JC, Meester-Smoor MA, Voortman T, et al. The inflammatory potential of diet is associated with the risk of age-related eye diseases. Clin Nutr. 2023;42(12):2404–13. Gao F, Chen C, Hu L, Shi Y, Zhu X, Wang X, et al. Rationale, Design and Methodology of a Population-Based Ocular Study in a Suburbanization Region in Tianjin, China: The Beichen Eye Study. Ophthalmic Epidemiol. 2024;31(2):178–87. Yang Z, Liu Q, Wen D, Yu Z, Zheng C, Gao F, et al. Risk of diabetic retinopathy and retinal neurodegeneration in individuals with type 2 diabetes: Beichen Eye Study. Front Endocrinol (Lausanne). 2023;14:1098638. Joachim N, Mitchell P, Burlutsky G, Kifley A, Wang JJ. The Incidence and Progression of Age-Related Macular Degeneration over 15 Years: The Blue Mountains Eye Study. Ophthalmology. 2015;122(12):2482–9. Shaw PX, Zhang L, Zhang M, Du H, Zhao L, Lee C, et al. Complement factor H genotypes impact risk of age-related macular degeneration by interaction with oxidized phospholipids. Proc Natl Acad Sci U S A. 2012;109(34):13757–62. Gordon T, Castelli WP, Hjortland MC, Kannel WB, Dawber TR. High density lipoprotein as a protective factor against coronary heart disease. The Framingham Study. Am J Med. 1977;62(5):707–14. Barter P, Gotto AM, LaRosa JC, Maroni J, Szarek M, Grundy SM, et al. HDL cholesterol, very low levels of LDL cholesterol, and cardiovascular events. N Engl J Med. 2007;357(13):1301–10. Tricorache DF, Dascalu AM, Alexandrescu C, Bobirca A, Grigorescu C, Tudor C, Cristea BM. Correlations Between the Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio, and Serum Lipid Fractions With Neovascular Age-Related Macular Degeneration. Cureus. 2024;16(6):e62503. Hansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685–95. Klein R, Peto T, Bird A, Vannewkirk MR. The epidemiology of age-related macular degeneration. Am J Ophthalmol. 2004;137(3):486–95. 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-6877444","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472733716,"identity":"8f1da7bc-d0d5-40dd-ae4b-6293165c0269","order_by":0,"name":"Gang Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDADfmbGhgMfgAw+BgZm4rRItjcffDiDgUGCjWgtBmeOJRvzEKNFvr3H+MXPHbUMDDdyzKRtKg7XsbE3HzZgqLGJxqWFseeMmWXvmeMMjDOAWnLOHJZg4zmWnMBwLC23AYcWZokcMwPetmNghnRuG1CLRI7xAcaGwzi1ABWYGf4FagExpC2J0cIDVPCYt62GgQfoHmNGqJYEfFokeI6VMcu2HWCQYAcGcs+ZdMk2oF6DBDx+kW9v3vzxbVsdg/1hYFT+qLDm5weGmMSHGhucWsDeYWA4XI+qIAG3chBgBiaTOvxKRsEoGAWjYGQDAI8AVIPIZ0Y3AAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zou","suffix":""},{"id":472733717,"identity":"991d3146-e81b-4d7a-899b-667206c84d36","order_by":1,"name":"Fei Gao","email":"","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Gao","suffix":""},{"id":472733718,"identity":"61d611aa-6ede-4ef4-931a-18cb0e16c6f4","order_by":2,"name":"Limin Zhang","email":"","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Limin","middleName":"","lastName":"Zhang","suffix":""},{"id":472733719,"identity":"afdb02af-fa65-4d17-99b6-48e64587a170","order_by":3,"name":"Qianhui Yang","email":"","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qianhui","middleName":"","lastName":"Yang","suffix":""},{"id":472733720,"identity":"9e39906c-c868-430c-91b7-03e56be9b22f","order_by":4,"name":"Boshi Liu","email":"","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Boshi","middleName":"","lastName":"Liu","suffix":""},{"id":472733721,"identity":"1ec7e8d2-0aa2-4171-a021-d4f720a44ca3","order_by":5,"name":"Xiaorong Li","email":"","orcid":"","institution":"Tianjin Medical University Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaorong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-12 07:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6877444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6877444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85171239,"identity":"9e1b2734-1335-496d-a3c4-b4dcd880c53e","added_by":"auto","created_at":"2025-06-23 05:41:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100920,"visible":true,"origin":"","legend":"\u003cp\u003eProcessing flowchart for selecting participant data\u003c/p\u003e\n\u003cp\u003eDR: diabetic retinopathy.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6877444/v1/0718c6bc21433f19e6adf467.png"},{"id":86228825,"identity":"7aab39de-cb48-47e5-9cb8-9365082ccb34","added_by":"auto","created_at":"2025-07-08 08:24:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1402666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6877444/v1/756c0234-4281-4ce4-8e02-834841625501.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum Lipid Biomarkers as Predictors for Age-Related Macular Degeneration Risk: A Cross-Sectional Analysis from the Beichen Eye Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge-related macular degeneration (AMD), the leading cause of irreversible central vision loss in people over 50 years of age, is a complex interplay between genetic predisposition, the aging process, and homeostasis of the internal environment risk factors, including altered lipid metabolism[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the transformation of the metabolic state of the body, inflammation caused by abnormal lipid metabolism may become an important factor in the pathogenesis of AMD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Recent studies have shown that lipid metabolism disorders and the inflammatory response play an important roles in the pathogenesis of AMD[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The relationship between dyslipidemia and the pathogenesis of AMD has been extensively studied, but the results of epidemiological studies have been inconsistent[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that systemic comorbidities in the pathogenesis of AMD, particularly cardiometabolic disorders, such as hypertension, atherosclerosis, and hypercholesterolemia are independently associated with accelerated retinal pigment epithelium (RPE) dysfunction in multiple cohorts[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The association between high-density lipoprotein cholesterol (HDL-c) and AMD remains mechanistically ambiguous[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], with conflicting observations across epidemiological cohorts. While Mendelian randomization studies robustly implicate elevated HDL-c as a causal risk factor for advanced AMD in multiethnic populations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], observational data exhibit marked heterogeneity, including positive correlations in European consortia (E3)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], inverse associations in select cohorts and null effects in others[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This inconsistency likely stems from uncontrolled confounding factors (e.g., inflammation, oxidative stress), phenotypic heterogeneity across AMD stages, and HDL-c functional alterations under retinal microenvironment stresses[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChanges in the number of neutrophils, monocytes, lymphocytes and platelets in the complete blood count are indicators of inflammation in the body[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Given that systemic dyslipidemia alone may not be sufficient to explain the pathogenesis of AMD, systematic reviews of lipid biomarkers and AMD risk and population-based cohort epidemiological studies may reveal this association[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. New hematological parameters related to lipid indices and whole blood cells, such as the NHR[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], MHR[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], LHR[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], PHR[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], PLR[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and NLR[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], have been proposed as new inflammatory biomarkers and may provide insight into AMD inflammation. Hence, the objective of this study was to investigate the associations between serum lipid biomarkers and the prevalence of AMD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.Study population\u003c/h2\u003e \u003cp\u003eThis study is a population-based cross-sectional investigation, that targets adults aged 50 years and older residing in Beichen District. The Beichen Eye Study was an epidemiological survey of eye diseases in a community population conducted by Tianjin Medical University Eye Hospital from December 2019 to February 2022. This study follows the original Helsinki Declaration approved by the Ethics Committee of Tianjin Medical University Eye Hospital (Approval Number: 2019ky-22). Additionally, written informed consent was obtained from each participant. The data used in this study were from the Beichen Eye Study. From the initial population-based cohort of 5840 adults recruited; 4748 eligible participants were included in the final analysis. The inclusion criteria were as follows: aged\u0026thinsp;\u0026gt;\u0026thinsp;50 years in 12 villages in 4 towns in Beichen District, Tianjin; communities were selected via multistage random sampling. The exclusion criteria were as follows: (1) inability to perform AMD grading (n\u0026thinsp;=\u0026thinsp;1092: severe cataracts, ungradable fundus photography, or noncooperation); (2) ocular comorbidities (n eliminated with exclusion sequence: glaucoma/suspected glaucoma IOP\u0026thinsp;\u0026gt;\u0026thinsp;21 mmHg (1 mmHg\u0026thinsp;=\u0026thinsp;0.133 kPa), retinal detachment, and other vitreoretinal pathologies); and (3) missing hematological parameters (blood lipid profiles or inflammatory biomarkers). The final analytic cohort consisted of 1245 AMD patients (early/intermediate AMD\u0026thinsp;=\u0026thinsp;1127; late AMD\u0026thinsp;=\u0026thinsp;118) and 3503 age-matched controls.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.Ophthalmic examination\u003c/h3\u003e\n\u003cp\u003eAll examinations were performed at the community hospital to which the respondents belonged. The protocol included comprehensive clinical assessments, blood lipid detection indicators and standard questionnaires. Clinical investigations included visual acuity assessment, optometric assays, slit-lamp examination, intraocular pressure, axial length, mydriasis, direct ophthalmoscopy of the posterior segment, fundus photography, ultrawide field retinal imaging, and swept-source optical coherence tomography (SS-OCT). The detailed methodology of the epidemiology of the Beichen Eye Study has been published previously[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For patient screening procedures, detection methods and questionnaires, refer to the details in reference[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVenous blood samples (15 ml) were obtained from each participant in the morning after an 8-hour overnight fast. Blood lipid detection indicators include total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c). All blood samples were tested via an automatic blood analyzer (Sysmex, XN-9000).\u003c/p\u003e\n\u003ch3\u003e3.Diagnostic criteria for AMD\u003c/h3\u003e\n\u003cp\u003eAMD was defined and graded via the Wisconsin AMD grading system[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], AMD was defined as the presence of drusen (\u0026gt;\u0026thinsp;diameter of 63\u0026ndash;125 \u0026micro;m), including early, intermediate, and late AMD and geographic atrophy. Late AMD was defined as the presence of neovascular AMD or geographic atrophy. Neovascular AMD includes serous or hemorrhagic detachment of the retinal pigment epithelium (RPE) or sensory retina, subretinal or sub-RPE hemorrhages, and subretinal fibrous scars. Geographic atrophy was defined as a discrete circular area of depigmentation of the RPE with a diameter of \u0026ge;\u0026thinsp;175 \u0026micro;m.\u003c/p\u003e\n\u003ch3\u003e4.Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analysis was conducted via a statistical software program (SPSS 25.0 for Windows (SPSS Inc., Chicago, USA)). The data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) and numerical (%) values. Continuous data with skewed distributions are presented as medians (interquartile distances), and comparisons between groups were performed via the Mann‒Whitney test. Categorical variables are presented as frequencies and percentages, and differences were tested via the χ2 test. The incidence of AMD is presented as a percentage with a 95% confidence interval (CI). Logistic regression analysis was conducted to estimate the odds ratio (OR) and 95% confidence interval (CI) of AMD values for the risk of biomarkers of serum lipids and inflammatory levels associated with AMD, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The variables included in the multivariable adjusted logistic regression model are as follows: Model 1: single biomarkers of serum lipids and inflammatory levels\u0026thinsp;+\u0026thinsp;age and sex; Model 2: Model 1\u0026thinsp;+\u0026thinsp;BMI, fasting glucose, diabetes, hypertension, smoking, alcohol consumption, and coronary heart disease history. Model 3: Model 2\u0026thinsp;+\u0026thinsp;history of statin use. P for trend was determined via a multivariate regression model linear trend test. Continuous variables were entered into the model after being divided into three categories, and the lowest quantile was used as the reference group.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e1.Basic characteristics of the study population\u003c/h2\u003e\n \u003cp\u003eA total of 4748 participants were included in this study, of whom 1245 were diagnosed with AMD on the basis of fundus photography and macular optical coherence tomography (OCT). The prevalence of AMD was 26.22%, while the non-AMD group consisted of 3503 individuals (73.78%). The participant selection process is detailed in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The age distribution significantly differed between the case and control groups, with the median age in the AMD group being 65 years compared with 60 years in the non-AMD group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of AMD increased markedly with age and was considerably higher in older adults (Z=-6.58, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with that in the non-AMD group, the median level triglyceride (TG) in the AMD group was significantly lower (Z=-2.70, P\u0026thinsp;=\u0026thinsp;0.007). This difference suggests that TG may be associated with AMD risk. No significant differences were observed between the two groups with respect to other confounding factors, including BMI, diabetes, hypertension, fasting blood glucose, smoking history, alcohol consumption, coronary heart disease history, and statin use (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline cohort characteristic.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;4748)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-AMD (n\u0026thinsp;=\u0026thinsp;3503)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMD (n\u0026thinsp;=\u0026thinsp;1245)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.00 (57.00, 67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.00 (57.00, 67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.00 (59.00, 68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.95 (23.83, 28.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.95 (23.81, 28.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.97 (23.88, 28.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGLU, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.60 (4.30, 5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.60 (4.30, 5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.60 (4.30, 5.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.30 (4.60, 5.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.30 (4.60, 5.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.30 (4.60, 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42 (1.04, 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43 (1.05, 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.38 (1.02, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-c, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.01 (2.49, 3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.02 (2.49, 3.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.00 (2.46, 3.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-c, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11 (0.96, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.11 (0.95, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12 (0.97, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1665 (35.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1220 (34.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e445 (35.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3083 (64.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2283 (65.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e800 (64.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3900 (82.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2872 (81.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1028 (82.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e848 (17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e631 (18.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217 (17.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHBP, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1506 (31.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1125 (32.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e381 (30.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3242 (68.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2378 (67.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e864 (69.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHD, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3788 (79.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2818 (80.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e970 (77.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e960 (20.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e685 (19.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275 (22.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatin use, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4397 (92.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3238 (92.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1159 (93.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264 (7.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86 (6.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoke, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3513 (73.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2613 (74.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e900 (72.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuit smoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285 (6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208 (5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77 (6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e950 (20.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e682 (19.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268 (21.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrink, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3680 (77.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2720 (77.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e960 (77.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuit drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48 (3.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e918 (19.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e681 (19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e237 (19.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTab. 1.\u003c/strong\u003e Continuous and categorical variables are presented as medians (interquartile ranges) and numbers (%), respectively. The age distribution was skewed, with negative Z scores indicating an older age in the AMD group. Abbreviations: Z: Mann‒Whitney test; \u0026chi;\u0026sup2;: chi‒square test; M: median; Q₁: 1st quartile; Q₃: 3rd quartile. AMD: age-related macular degeneration; BMI: body mass index; GLU: fasting plasma glucose; TC: total cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; HDL-c: high-density lipoprotein cholesterol; DM: diabetes mellitus; HBP: hypertension; CHD: coronary heart disease.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. Univariate Analysis of Serum Lipid and Inflammatory Biomarkers\u003c/h3\u003e\n\u003cp\u003eThis study revealed a median triglyceride-to-HDL ratio (TG/HDL-c) of 1.28 (IQR: 0.84\u0026ndash;1.91) and HDL-c /LDL-c ratio of 0.37 (IQR: 0.30\u0026ndash;0.47). Mann-Whitney U tests revealed significant differences in the TG/HDL and HDL-c/LDL-c ratios between the AMD and non-AMD groups, with the AMD group exhibiting lower TG/HDL-c ratios (Z=-2.71, P\u0026thinsp;=\u0026thinsp;0.007) but higher HDL-c /LDL-c ratios (Z=-1.98, P\u0026thinsp;=\u0026thinsp;0.047) (shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). No significant intergroup differences were detected for the NHR (Z=-1.50, P\u0026thinsp;=\u0026thinsp;0.134), LHR (Z=-1.37, P\u0026thinsp;=\u0026thinsp;0.171), or inflammatory markers including the PLR (Z=-0.31, P\u0026thinsp;=\u0026thinsp;0.755), NLR (Z=-0.42, P\u0026thinsp;=\u0026thinsp;0.674), and MHR (Z=-0.81, P\u0026thinsp;=\u0026thinsp;0.420). The PHR showed borderline significance (Z=-1.71, P\u0026thinsp;=\u0026thinsp;0.087).\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSerum lipid and inflammatory level biomarkers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;4748)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-AMD (n\u0026thinsp;=\u0026thinsp;3503)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAMD (n\u0026thinsp;=\u0026thinsp;1245)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG/HDL-c, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.28 (0.84, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.30 (0.85, 1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.21 (0.82, 1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-c /LDL-c, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37 (0.30, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37 (0.30, 0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38 (0.31, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.88 (2.14, 3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.89 (2.15, 3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.85 (2.09, 3.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLHR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.75 (1.35, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76 (1.36, 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.73 (1.34, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219.81 (178.26, 272.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220.91 (178.79, 273.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216.51 (176.67, 268.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.23 (102.69, 155.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125.84 (102.47, 156.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126.85 (103.87, 155.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63 (1.28, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63 (1.29, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.64 (1.28, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHR, M (Q₁, Q₃)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37 (0.28, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37 (0.28, 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36 (0.28, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZ=-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 2.\u003c/strong\u003e Data are presented as medians with interquartile ranges (IQRs). Statistical significance was evaluated by the Mann‒Whitney U test (*P\u0026lt;0.05, ** P\u0026lt;0.01). Abbreviations: Z: Mann‒Whitney test; M: median; Q₁: 1st quartile; Q₃: 3rd quartile. TG/HDL-c: triglyceride/HDL-c: triglyceride/HDL-c ratio; HDL-c/LDL-c: high-density lipoprotein cholesterol/low-density lipoprotein cholesterol ratio; NHR: neutrophil/HDL-c ratio; LHR: lymphocyte/HDL-c ratio; PHR: platelet/HDL-c ratio; PLR: platelet/lymphocyte ratio; NLR: neutrophil/lymphocyte ratio; MHR: monocyte/HDL-c ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e \u003cstrong\u003eMultivariable Associations\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eetween Lipid Profiles and AMD Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the multivariable-adjusted logistic regression models are shown in Tables 3 and 4. After adjusting for age, sex, BMI, fasting glucose, diabetes mellitus, hypertension, smoking, alcohol consumption, coronary heart disease history, and statin use (Model 3), lipid profiles exhibited differential associations with AMD risk. The TG/HDL-c ratio demonstrated a significant inverse linear association with AMD risk (P for trend=0.010). Compared with those in the lowest tertile, participants in the highest tertile of TG/HDL-c ratio (\u0026ge;1.7) presented 20% lower odds of AMD compared to those in the lowest tertile (OR=0.80, 95% CI: 0.69\u0026ndash;0.95). Similarly, the neutrophil-to-HDL-c ratio (NHR) exhibited a protective linear trend (p for trend=0.032), with the highest tertile (\u0026ge;3.5) associated with AMD risk reduction in AMD risk (OR=0.83, 95% CI: 0.69\u0026ndash;0.98). Sex-stratified HDL-c level analysis revealed a borderline significant risk elevation in the middle tertile (males 0.96\u0026ndash;1.14/females 0.91\u0026ndash;1.54 mmol/L) (OR=1.04, 95% CI: 0.89\u0026ndash;1.22), although no overall linear trend was observed (P for trend=0.269). Total cholesterol (TC) and LDL-c were not significantly associated with AMD risk according to the fully adjusted models. Triglycerides (TGs) demonstrated a significant linear trend (p for trend=0.039), with the middle tertile (0.56\u0026ndash;1.69 mmol/L) showing a 17% increased risk of AMD compared with the highest tertile (\u0026ge;1.69 mmol/L) (OR=1.17, 95% CI: 1.01\u0026ndash;1.34), suggesting that elevated TG levels may serve as a protective factor against AMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u0026nbsp;\u003c/strong\u003eOR with 95% CI of biomarkers of serum lipid and inflammatory level for AMD risk analyzed by logistic models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG/HDL\u003c/strong\u003e-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.101\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.009*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.010*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (1.0\u0026ndash;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.79 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.79 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.79 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.81 (0.69 ~ 0.95)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.80 (0.68 ~ 0.94)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.80 (0.69 ~ 0.95) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e-c\u003cstrong\u003e\u0026nbsp;/LDL\u003c/strong\u003e-c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.185\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.120\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.131\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (0.3\u0026ndash;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.17 (1.00 ~ 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.17 (0.99 ~ 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.17 (0.99 ~ 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.97 ~ 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.96 ~ 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.97 ~ 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.058\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.027*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.032*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (2.4\u0026ndash;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.80 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.80 ~ 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.94(0.80 ~ 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.84 (0.71 ~ 0.99)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.82 (0.69 ~ 0.98)\u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.83 (0.69 ~ 0.98) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.479\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.390\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.411\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (1.5\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.04 (0.89 ~ 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.04 (0.89 ~ 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.04 (0.89 ~ 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.79 ~ 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.78 ~ 1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.93 (0.79 ~ 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.353\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.249\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.264\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (0.3\u0026ndash;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.91 (0.77 ~ 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.90 (0.76 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.90 (0.76 ~ 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.211\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.084\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.093\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;191.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (191.7\u0026ndash;252.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.95 (0.81 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;252.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.87 (0.74 ~ 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.86 (0.73 ~ 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.87 (0.74 ~ 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.904\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.884\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.888\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;110.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (110.7\u0026ndash;144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.13 (0.96 ~ 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.13 (0.97 ~ 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.14 (0.97 ~ 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.01 (0.86 ~ 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.01 (0.86 ~ 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1.01 (0.86 ~ 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.687\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.243\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.257\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBottom tertile (\u0026le;1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMiddle tertile (1.4\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.96 (0.82 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.96 (0.81 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.96 (0.82 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eTop tertile (\u0026ge;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.91 (0.77 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.91 (0.77 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e0.91 (0.77 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 3\u003c/strong\u003e. Asterisk (*) denotes P\u0026lt;0.05.\u0026nbsp;Abbreviations: TG/HDL-c:\u0026nbsp;triglyceride/ high-density lipoprotein cholesterol ratio; HDL/LDL-c: high-density lipoprotein cholesterol/ low-density lipoprotein cholesterol ratio; NHR: neutrophil/HDL-c ratio; LHR: lymphocyte/HDL-c ratio; MHR: monocyte/HDL-c ratio; PHR: platelet/HDL-c ratio; PLR: platelet/lymphocyte ratio; NLR: neutrophil/lymphocyte ratio\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eOR with 95% CI of lipidemia level indicators for AMD risk analyzed by logistic models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"100%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 34.1472%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45.1514%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.693\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.748\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e2.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.8\u0026ndash;5.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.09 (0.48 ~ 2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.11 (0.49 ~ 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.12 (0.49 ~ 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e5.16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.11 (0.49 ~ 2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.14 (0.50 ~ 2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.14 (0.50 ~ 2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.030*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.034*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.039*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e0.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.22 (0.75 ~ 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.21 (0.74 ~ 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.20 (0.73 ~ 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;0.56\u0026ndash;1.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.17 (1.02 ~ 1.34) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.17 (1.01 ~ 1.35) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.17 (1.01 ~ 1.34) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e1.69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL-c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.483\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.545\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.491\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e1.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.59\u0026ndash;3.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e0.87 (0.62 ~ 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.89 (0.63 ~ 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.88 (0.62 ~ 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e1.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e0.86 (0.61 ~ 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.87 (0.62 ~ 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.86 (0.61 ~ 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHDL-c\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.097\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP for trend=0.269\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e0.96 in male/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;\u003c/strong\u003e\u003cstrong\u003e0.9 in female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.96-1.14 in male/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.91-1.54 in female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.24 (1.04 ~ 1.49) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.25 (1.04 ~ 1.50) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.04 (0.89 ~ 1.22) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69.9849%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e1.14 in male/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;\u003c/strong\u003e\u003cstrong\u003e1.54 in female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.3137%;\"\u003e\n \u003cp\u003e1.18 (0.96 ~ 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.19 (0.96 ~ 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.18 (0.95 ~ 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 4.\u003c/strong\u003e TG\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e: The highest quantile was used as the reference group. Asterisk (*) denotes P\u0026lt;0.05.\u0026nbsp;Abbreviations: TC: total cholesterol; TG: triglyceride; LDL-c: low-density lipoprotein cholesterol; HDL-c: high-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe overall prevalence of AMD in this study was 26.22%, with age identified as the most significant risk factor. Notably, distinct lipid profile signature discrepancies emerged between the AMD and non-AMD groups, particularly in the TG/HDL-c and HDL-c/LDL-c ratios (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings are consistent with emerging evidence suggesting that dysregulation of lipid homeostasis plays a crucial role in the pathogenesis of AMD. Previous studies have highlighted the importance of the dynamic balance between TG and HDL-c/LDL-c in AMD pathophysiology[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Multivariable logistic regression analysis revealed that individuals in the highest TG/HDL-c tertile (\u0026ge;\u0026thinsp;1.7) had a 20% reduction in AMD risk compared with those in the lowest tertile (P for trend\u0026thinsp;=\u0026thinsp;0.010), reinforcing the paradoxical protective role of TG observed in multivariable Mendelian randomization studies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The TG/HDL-c ratio reflects the balance between the detrimental effects of HDL-c dysfunction, which can lead to inflammation and oxidative stress, and the beneficial effects of TG-mediated protection of the RPE. Recent studies suggest that oxidized phospholipid derivatives from systemic TG metabolism may help mitigate complement-mediated retinal inflammation in AMD models, warranting further functional validation[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this study, the median triglyceride (TG) level in the AMD group was lower than that in the non-AMD group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting that certain mechanisms may lead to a higher risk of AMD in individuals with low TG levels. This finding also supports the aforementioned conclusions.\u003c/p\u003e \u003cp\u003eA paradoxical association was observed between elevated HDL-c levels and increased AMD risk in this study which aligns with findings from European registry data[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], but contrasts with conventional cardiovascular risk models[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This discrepancy likely reflects the specificity of the retinal microenvironment, as HDL-c may exhibit bidirectional effects under different conditions of the disease. Under physiological conditions, HDL-c plays a protective role in cardiovascular health through reverse cholesterol transport and anti-inflammatory effects[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, in the presence of chronic inflammation (like AMD), diabetes, or oxidative stress, HDL-c may adopt a proinflammatory phenotype, losing its normal protective function[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, the complex impact of HDL-c heterogeneity, particularly in the mid-quantile group, might have been influenced by inadequate adjustment for additional risk factors such as disease stages of AMD. Elevated plasma HDL-c levels are causally linked to an increased risk of AMD, suggesting that strategies aimed at lowering HDL-c levels could play a role in the prevention and management of AMD, especially in the early and middle stages of elevated HDL-c. This represents a crucial area for future research.\u003c/p\u003e \u003cp\u003eThe protective effect of the NHR on AMD risk (OR\u0026thinsp;=\u0026thinsp;0.83 for the top tertile) provides novel insight into the interplay between neutrophils and lipid metabolism[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The NHR appears to capture both the anti-inflammatory properties of HDL-c and the pro-inflammatory activity of neutrophils, contributing to its predictive value. Although other inflammatory markers, such as the PLR, NLR and MHR, were not significantly associated, the protective effect of the NHR further underscores the role of neutrophils in the chronic inflammatory processes underlying AMD.\u003c/p\u003e \u003cp\u003eThese findings suggest that lipid profile ratios, particularly TG/HDL-c and NHR ratios, could serve as independent biomarkers for AMD risk, reflecting the underlying metabolic and inflammatory pathways involved in AMD pathogenesis and expanding the theoretical framework of lipid-inflammation interactions in AMD pathogenesis.\u003c/p\u003e \u003cp\u003eIn the development of risk models, both Model II and Model III (which included additional adjustment for statin use history) demonstrated highly consistent effect estimates, with differences in odds ratios (ORs) less than 5%. Statin use, as a significant lipid regulator, may exhibit collinearity with other disease states (such as a history of coronary heart disease), potentially limiting the independent contribution of this variable[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The protective effects of TG/HDL-c and the NHR against AMD remained statistically significant after accounting for statin use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that these markers may reflect intrinsic metabolic regulatory mechanisms that are independent of pharmacological intervention, such as the antioxidant properties of HDL-c or alternative TG transport pathways not influenced by statin therapy. This finding can be used as an emphasis marker to predict the reliability of AMD.\u003c/p\u003e \u003cp\u003eThe limitations of this study include the following: (1) its cross-sectional design, which limits causal inference; (2) the lack of follow-up data to differentiate between dry and wet AMD subtypes; and (3) the functional heterogeneity of HDL-c (e.g., anti-inflammatory vs. proinflammatory subtypes) not being fully explored, and potential confounders such as omega-3 fatty acid intake were not adjusted for. Further mechanistic studies using lipidomics are needed. We recommend that future research employ nested case‒control designs to improve exposure measurements and explore the protective mechanisms of specific lipid species through lipidomics. Despite these limitations, our study is the first to systematically analyze lipid‒inflammation composite markers and their associations with AMD in a Chinese community cohort, providing important evidence for the development of targeted risk prediction models.\u003c/p\u003e \u003cp\u003eIn conclusion, our population-based data lend support to the emerging hypothesis that an elevated TG/HDL-c ratio and NHR serve as protective biomarkers for AMD. TG levels exhibit dose-dependent protective effects, whereas the inverse relationship between HDL-c and AMD risk suggests a complex role of traditional cardiovascular markers in AMD. Future research should validate the risk stratification capabilities of these biomarkers in longitudinal cohorts.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the doctors, nurses, statisticians and all other contributors of the Beichen Eye Hospital research team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study follows the original Helsinki Declaration approved by the Ethics Committee of Tianjin Medical University Eye Hospital (Approval Number: 2019ky–22). Written informed consent was obtained from each participant.\u003c/p\u003e\n\u003cp id=\"_Toc472330565\"\u003eConflict of interest statement\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003eFunding Sources\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Tianjin Key Medical Discipline (Specialty) Construction Project（TJYXZDXK-037A）\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eF.G. conceived and planned the data statistics. G.Z wrote the manuscript while F.G., L.M.Z., B.S.L., and Q.H.Y. provided feedback. The corresponding author reviewed and provided feedback on the manuscript. All authors approved the final manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this published article. Further inquiries can be directed to the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFleckenstein M, Keenan TDL, Guymer RH, Chakravarthy U, Schmitz-Valckenberg S, Klaver CC, et al. Age-related macular degeneration. Nat Rev Dis Primers. 2021;7(1):31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOmarova S, Charvet CD, Reem RE, Mast N, Zheng W, Huang S, et al. Abnormal vascularization in mouse retina with dysregulated retinal cholesterol homeostasis. J Clin Invest. 2012;122(8):3012\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung CMG, Gan A, Fan Q, Chee ML, Apte RS, Khor CC, et al. Plasma lipoprotein subfraction concentrations are associated with lipid metabolism and age-related macular degeneration. J Lipid Res. 2017;58(9):1785\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJun S, Datta S, Wang L, Pegany R, Cano M, Handa JT. The impact of lipids, lipid oxidation, and inflammation on AMD, and the potential role of miRNAs on lipid metabolism in the RPE. Exp Eye Res. 2019;181:346\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColijn JM, den Hollander AI, Demirkan A, Cougnard-Gregoire A, Verzijden T, Kersten E, et al. Increased High-Density Lipoprotein Levels Associated with Age-Related Macular Degeneration: Evidence from the EYE-RISK and European Eye Epidemiology Consortia. Ophthalmology. 2019;126(3):393\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbalain JH, Carre JL, Leglise D, Robinet A, Legall F, Meskar A, et al. Is age-related macular degeneration associated with serum lipoprotein and lipoparticle levels? Clin Chim Acta. 2002;326(1\u0026ndash;2):97\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErke MG, Bertelsen G, Peto T, Sjolie AK, Lindekleiv H, Njolstad I. Cardiovascular risk factors associated with age-related macular degeneration: the Tromso Study. Acta Ophthalmol. 2014;92(7):662\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng J, Xie F, Wu Z, Wu Y. Age-related macular degeneration and cardiovascular disease in US population: an observational study. Acta Cardiol. 2024;79(6):665\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan G, Wei P, He M, Jia L, Su Q, Yang X, Hao R. Role of plasma fatty acid in age-related macular degeneration: insights from a mendelian randomization analysis. Lipids Health Dis. 2024;23(1):206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBucan K, Lukic M, Bosnar D, Kopic A, Jukic T, Konjevoda S, et al. Analysis of association of risk factors for age-related macular degeneration. Eur J Ophthalmol. 2022;32(1):410\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Wang M, Zhang X, Zhang Q, Nie J, Zhang M et al. The Association between the Lipids Levels in Blood and Risk of Age-Related Macular Degeneration. Nutrients. 2016;8(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCougnard-Gregoire A, Delyfer MN, Korobelnik JF, Rougier MB, Le Goff M, Dartigues JF, et al. Elevated high-density lipoprotein cholesterol and age-related macular degeneration: the Alienor study. PLoS ONE. 2014;9(3):e90973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Ong JS, Hewitt AW, Gharahkhani P, MacGregor S. The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study. Int J Epidemiol. 2021;50(1):325\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKananen F, Strandberg T, Loukovaara S, Immonen I. 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Correlation of neutrophil/lymphocyte and platelet/lymphocyte ratio with visual acuity and macular thickness in age-related macular degeneration. Int J Ophthalmol. 2017;10(5):754\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVergroesen JE, Thee EF, de Crom TOE, Kiefte-de Jong JC, Meester-Smoor MA, Voortman T, et al. The inflammatory potential of diet is associated with the risk of age-related eye diseases. Clin Nutr. 2023;42(12):2404\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao F, Chen C, Hu L, Shi Y, Zhu X, Wang X, et al. Rationale, Design and Methodology of a Population-Based Ocular Study in a Suburbanization Region in Tianjin, China: The Beichen Eye Study. Ophthalmic Epidemiol. 2024;31(2):178\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Liu Q, Wen D, Yu Z, Zheng C, Gao F, et al. 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Am J Med. 1977;62(5):707\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarter P, Gotto AM, LaRosa JC, Maroni J, Szarek M, Grundy SM, et al. HDL cholesterol, very low levels of LDL cholesterol, and cardiovascular events. N Engl J Med. 2007;357(13):1301\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTricorache DF, Dascalu AM, Alexandrescu C, Bobirca A, Grigorescu C, Tudor C, Cristea BM. Correlations Between the Neutrophil-Lymphocyte Ratio, Platelet-Lymphocyte Ratio, and Serum Lipid Fractions With Neovascular Age-Related Macular Degeneration. Cureus. 2024;16(6):e62503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansson GK. Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med. 2005;352(16):1685\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein R, Peto T, Bird A, Vannewkirk MR. The epidemiology of age-related macular degeneration. Am J Ophthalmol. 2004;137(3):486\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\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":"Age-related macular degeneration, Lipid metabolism, TG, HDL-c, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-6877444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6877444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This population-based study aimed to investigate the associations between serum lipid biomarkers and the prevalence of age-related macular degeneration (AMD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This cross-sectional study analyzed data from 4748 subjects over 50 years old who were enrolled in the Beichen Eye Study. TG/HDL-c, HDL-c/LDL-c, the neutrophil/HDL-c ratio (NHR), the lymphocyte/HDL-c ratio (LHR), the monocyte/HDL-c ratio (MHR), the platelet/HDL-c ratio (PHR), the platelet/lymphocyte ratio (PLR), and the neutrophil/lymphocyte ratio (NLR)were assessed. Additionally, basic information, BMI, history of disease related to lipid metabolism, living habits and history of statin use were collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e This study included 4748 participants, and 1245 of whom were diagnosed with AMD. The overall prevalence of AMD was 26.22%. The prevalence of AMD increased significantly with age (Z=-6.58, P\u0026lt;0.001). TG/HDL-c and HDL-c/LDL-c were significantly associated with the incidence of AMD (Z=-2.71, P=0.007; Z=-1.98, P=0.047, respectively). Multivariate logistic regression revealed that a high TG/HDL-c ratio (OR=0.80, P\u0026lt;0.05) and high NHR (OR=0.83, P\u0026lt;0.05) were both inversely associated with AMD risk, indicating protective effects. Elevated TG levels were also found to be protective against AMD (OR=0.80, P\u0026lt;0.05). Elevated HDL-c was associated with a paradoxical increase in AMD risk, especially in the second tertile (OR=1.04, 95% CI=0.89–1.22; P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study suggested that an elevated TG/HDL-c ratio and NHR serve as protective biomarkers for AMD, with higher TG levels showing a protective effect. Conversely, HDL-c levelsdemonstrated a paradoxical association with AMD risk.These findings provide insights into the complex role of lipid metabolism in AMD pathogenesis and suggest potential biomarkers for AMD risk prediction.\u003c/p\u003e","manuscriptTitle":"Serum Lipid Biomarkers as Predictors for Age-Related Macular Degeneration Risk: A Cross-Sectional Analysis from the Beichen Eye Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 05:40:57","doi":"10.21203/rs.3.rs-6877444/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":"0213e3e4-119f-4d1b-bbf3-870e269e0e53","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T08:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-23 05:40:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6877444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6877444","identity":"rs-6877444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}