Abstract
The Naples Prognostic Score (NPS), a comprehensive indicator of inflammation and nutrition, remains unstudied in relation to cataract prevalence. We analyzed 8612 adults (≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999–2008 in this cross-sectional study. Each 1-point increase in NPS was associated with 25% higher odds of cataract, and high NPS participants had 80% higher odds than low NPS. Restricted cubic spline (RCS) analysis indicated a significant linear association. Subgroup analysis strengthens the positive correlation between NPS and cataracts. Exploratory mediation analysis suggested that the triglyceride-glucose index-based A Body Shape Index (TyG-ABSI) may partially explain this association, particularly among nondiabetic individuals. These findings indicate that high NPS is independently and linearly associated with increased cataract prevalence in US adults, and exploratory analyses suggest that TyG-ABSI may partially mediate this association. These results support the potential benefit of maintaining optimal NPS values in mitigating cataract development.
Keywords: cataract, dietary status, Naples Prognostic Score, NHANES, systemic inflammation, TyG-ABSI
1. Introduction
Cataract, a major cause of global visual impairment, involves lens opacification that disrupts light transmission and retinal focus.[1] It is responsible for about 80% of all reversible blindness and 47.8% to 51% of blindness cases worldwide.[2,3] Although the availability of cataract surgery has improved recently, challenges with accessibility and expense still endure, especially for those from lower socioeconomic strata.[4] Identifying modifiable hazards for cataract is increasingly crucial as populations age and the prevalence of chronic diseases increases.
Systemic inflammation and dietary deficits have been implicated in the pathophysiology of cataracts in current studies. Oxidative stress and persistent low-grade inflammation can expedite the degradation and aggregation of lens proteins, which serve as the pathological foundation for cataract formation.[5,6] Furthermore, nutritional deficits, particularly in protein and lipid metabolism, are correlated with an increased prevalence of lens opacity.[7] These results highlight the necessity of identifying a component in cataract epidemiology that indicates both inflammation and nutritional condition.
Originally created as a predictive tool in cancer,[8] the Naples Prognostic Score (NPS) incorporates recognized immune-inflammatory markers such as serum albumin, total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR).[9] The utilization of NPS in non-tumor populations is progressively garnering interest since it reflects both systemic inflammation and nutritional reserves.[10,11] Nevertheless, its applicability to ocular diseases like cataracts is yet mostly unknown. NPS delivers a more in-depth evaluation than standard cataract assessment methods, facilitating the recognition of populations with elevated cataract prevalence.
Metabolic-adiposity markers, such as the triglyceride-glucose index (TyG) and A Body Shape Index (ABSI), have been proposed to assess insulin resistance and central obesity. TyG-ABSI, a novel composite index combining TyG and ABSI, captures both metabolic dysregulation and visceral adiposity. It has been shown to predict cardiovascular mortality and is associated with cognitive decline, indicating its relevance in metabolic-related conditions.[12-14] In this study, TyG-ABSI was examined as a potential explanatory factor in the association between systemic inflammation–nutritional status (measured by NPS) and cataract prevalence.
To address this research gap, we conducted a cross-sectional analysis of data from the 1999–2008 NHANES cycles. The primary objective was to examine the association between the Naples Prognostic Score (NPS) and cataract prevalence in US adults, thereby providing insight into the interplay between systemic health and ocular degeneration. In addition, we performed an exploratory mediation analysis to assess whether metabolic–adiposity indicators, particularly the triglyceride-glucose index–based A Body Shape Index (TyG-ABSI), may partially explain or suppress this association.
2. Materials and methods
2.1. Study population
The Centers for Disease Control and Prevention (CDC)’s National Center for Health Statistics (NCHS) administers the nationally representative NHANES survey.[15] Through standardized interviews, physical examinations, and laboratory testing carried out at mobile examination centers (MECs), it uses a stratified, multistage probability sampling methodology to gather extensive data. The National Center for Health Statistics Research Ethics Review Board approved the survey protocol, and each participant provided written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki. Public access to NHANES data is available at: https://www.cdc.gov/nchs/nhanes/.
2.2. Inclusion and exclusion criteria
Exclusions were applied as follows from 51,623 participants in the 1999–2008 NHANES cycles: missing data on age and age <20 years (N = 26,618); missing data on cataract (N = 2811) and data on NPS (N = 1378); lack of information on key covariates, including gender, race, education, marriage, poverty-income ratio (PIR), body mass index (BMI), smoking, drinking, and comorbidities (N = 2957); and missing data on mediating variables (N = 9247). Following the application of these criteria, the final analysis contained 8612 eligible subjects (Fig. 1).
Figure 1.
Flowchart of participant selection from NHANES 1999–2008. Of 51,623 initial participants, those with age < 20 years, or missing cataract, NPS, covariates, or mediator data were excluded, leaving 8612 eligible participants. NHANES = National Health and Nutrition Examination Survey, NPS = Naples Prognostic Score.
2.3. Cataract evaluation
Self-reported history of cataract surgery was used to infer the occurrence of cataract, which is in line with previous studies.[16] If a member answers “yes” to the question “Have you had cataract surgery?” (VIQ071), they are classified as cataracts, while those who answered “No” were regarded as controls.[17]
2.4. NPS assessment
According to Galizia et al,[8] serum albumin, TC, NLR, and LMR are the 4 clinical markers that were used to calculate the NPS. The scoring criteria were as follows: Scores of 0 were assigned to results with serum albumin ≥ 40 g/L, TC > 180 mg/dL, NLR ≤ 2.96, and LMR > 4.44, whereas those outside these parameters received a score of 1. The 4 component values were added to determine the NPS, which has a range of 0 to 4. Members were divided into 3 groups: low (score = 0), intermediate (score = 1–2), and high (score = 3–4; Fig. 2).
Figure 2.
Scoring criteria for serum albumin (ALB), total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR). Points (0 or 1) were assigned according to predefined cutoffs. ALB = serum albumin, LMR = lymphocyte-to-monocyte ratio, NLR = neutrophil-to-lymphocyte ratio, TC = total cholesterol.
2.5. Covariates
A variety of factors that may be linked to the prevalence of cataract were added based on the existing information. Sociodemographic characteristics (age, gender, race, education, marriage, and PIR), anthropometric information (BMI), lifestyle factors (alcohol consumption and smoking), and comorbidities (diabetes and hypertension) were among them.
People self-reported their demographics. A lifetime cigarette usage of 100 or more was considered smoking, and consuming 12 or more alcoholic beverages over the previous 12 months was considered alcohol consumption. BMI was further divided into 3 levels (≤18.5, 18.5–25, >25 kg/m2). PIR was categorized as high (>3), intermediate (1–3), or low (≤1). Use the doctor’s self reported diagnosis of hypertension and diabetes.
2.6. Mediating variables
The triglyceride-glucose index–based A Body Shape Index (TyG-ABSI), a composite marker of metabolic dysregulation and central adiposity, was calculated as shown in Figure 3.[12]
Figure 3.
Formulas for calculating the triglyceride-glucose (TyG) index and TyG-based A Body Shape Index (TyG-ABSI). TyG uses fasting triglycerides and glucose, while TyG-ABSI integrates TyG with waist circumference, BMI, and height. ABSI = A Body Shape Index, BMI = body mass index, TyG = triglyceride-glucose index, TyG-ABSI = triglyceride-glucose index-based A Body Shape Index, WC = waist circumference.
2.7. Analytical statistics
The intricate sample design and survey weights were taken into consideration in all analyses in accordance with NHANES rules. Categorize individuals according to the occurrence of cataracts and encapsulate their characteristics utilizing descriptive statistics. Employ a weighted linear regression model to examine continuous variables, and assess categorical variables using a weighted chi-square test.
We employed weighted multivariable logistic regression to evaluate the association between NPS and cataract prevalence, presenting the results as odds ratios (ORs) with 95% confidence intervals (CIs). Model 1 was a crude model and adjusted for nothing. Model 2 was adjusted for age, gender, and race. Model 3 included the covariates of model 2 with additional adjustment for hypertension, diabetes, smoking, alcohol consumption, PIR, education level, marital status, and BMI. To explore potential nonlinear relationships, restricted cubic spline (RCS) models were further applied. The overall association and nonlinear trend were evaluated using Wald chi-square statistics. A dual-axis spline plot was generated, illustrating the odds ratio with 95% confidence intervals and the distribution of NPS. Effect modification was examined using subgroup and interaction analysis. The fully adjusted model was employed to assess subgroup differences, and interactions between NPS and potential effect modifiers were evaluated.
To evaluate the robustness of the observed association between NPS and cataract, we performed 3 complementary sensitivity analyses: participants with missing mediator data (TyG-ABSI) but complete information on NPS and cataract were retained, and survey-weighted logistic regression was applied; the same expanded sample was analyzed using unweighted logistic regression to assess the impact of sampling weights; the original analytic sample (excluding those with missing TyG-ABSI) was reanalyzed with unweighted models to isolate the effect of weighting from changes in sample composition. All models were adjusted for the same covariates as model 3.
Additionally, to investigate potential pathways, causal mediation analysis was subsequently performed to evaluate whether TyG-ABSI and height mediated the association between NPS and cataract. Direct, indirect, and total effects, as well as the proportion mediated, were estimated using regression-based methods. Covariate adjustment in mediation models was consistent with model 3 of the main analysis, excluding diabetes and BMI to avoid overadjustment of potential mediators.
All statistical analyses were conducted using EmpowerStats (www.empowerstats.com) and R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria), with a 2-sided P-value < .05 considered statistically significant.
3. Results
3.1. Baseline characteristics
The final study comprised 8612 members from the NHANES cycles of 1999–2008. As shown in Table 1, cataract prevalence was significantly associated with NPS (P < .01). Notably, compared to the non-cataract group, the proportion of individuals with high NPS in the cataract group was higher (18.89% vs 13.07%), indicating a positive correlation between NPS and the prevalence of cataracts.
Table 1.
Baseline characteristics of participants in NHANES 1999–2008 (N = 8612).
| Characteristics | Cataract (N = 593) |
Non-cataract (N = 8019) |
P-value |
|---|---|---|---|
| Age, mo, mean (SE) | 853.03 (6.13) | 539.41 (4.12) | <.01 |
| Age, n (%) | <.01 | ||
| ≤50 | 16 (2.70) | 4624 (57.66) | |
| 50–65 | 88 (14.84) | 1992 (24.84) | |
| >65 | 489 (82.46) | 1403 (17.50) | |
| Gender, n (%) | <.01 | ||
| Male | 279 (47.05) | 3997 (49.84) | |
| Female | 314 (52.95) | 4022 (50.16) | |
| Ethnicity, n (%) | <.01 | ||
| Mexican American | 78 (13.15) | 1698 (21.17) | |
| Other Hispanic | 24 (4.05) | 423 (5.27) | |
| Non-Hispanic White | 402 (67.79) | 4039 (50.37) | |
| Non-Hispanic Black | 78 (13.15) | 1561 (19.47) | |
| Other race | 11 (1.85) | 298 (3.72) | |
| Education, n% | <.01 | ||
| Less than 9th grade | 127 (21.42) | 936 (11.67) | |
| 9–11th grade | 109 (18.38) | 1275 (15.90) | |
| High school grade | 141 (23.78) | 1935 (24.13) | |
| Some college or AA degree | 128 (21.58) | 2239 (27.92) | |
| College graduate or above | 88 (14.84) | 1634 (20.38) | |
| Marital status, n (%) | <.01 | ||
| Married or living with partner | 339 (57.17) | 5249 (65.46) | |
| Non-married | 14 (2.36) | 1286 (16.04) | |
| Other | 240 (40.47) | 1484 (18.51) | |
| PIR, n (%) | <.01 | ||
| ≤1 | 91 (15.35) | 1375 (17.15) | |
| 1–3 | 323 (54.47) | 3234 (40.33) | |
| >3 | 179 (30.18) | 3410 (42.52) | |
| BMI, n (%) | .79 | ||
| ≤18.5 | 7 (1.18) | 114 (1.42) | |
| 18.5–25 | 170 (28.67) | 2315 (28.87) | |
| >25 | 416 (70.15) | 5590 (69.71) | |
| Alcohol drinking, n (%) | <.01 | ||
| Yes | 361 (60.88) | 5727 (71.42) | |
| No | 232 (39.12) | 2292 (28.58) | |
| Smoking, n (%) | <.01 | ||
| Yes | 363 (61.21) | 3918 (48.86) | |
| No | 230 (38.79) | 4101 (51.14) | |
| Diabetes, n (%) | <.01 | ||
| Yes | 143 (24.11) | 711 (8.87) | |
| No | 450 (75.89) | 7308 (91.13) | |
| Hypertension, n (%) | <.01 | ||
| Yes | 343 (57.84) | 2471 (30.81) | |
| No | 250 (42.16) | 5548 (69.19) | |
| ALB, g/L, mean (SE) | 42.69 (0.07) | 41.44 (0.16) | <.01 |
| TC, mg/dL, mean (SE) | 199.02 (0.72) | 200.72 (1.87) | .39 |
| NLR, mean (SE) | 2.20 (0.02) | 2.59 (0.07) | <.01 |
| LMR, mean (SE) | 3.95 (0.03) | 3.35 (0.06) | <.01 |
| NPS, n (%) | <.01 | ||
| 0 | 60 (10.12) | 1359 (16.95) | |
| 1–2 | 421 (71.00) | 5612 (69.98) | |
| 3–4 | 112 (18.89) | 1048 (13.07) |
ALB = serum albumin, BMI = body mass index, LMR = lymphocyte-to-monocyte ratio, NLR = neutrophil-to-lymphocyte ratio, NPS = Naples Prognostic Score, PIR = poverty-to-income ratio, SE = standard error, TC = total cholesterol.
3.2. Correlation between NPS and cataract
Multivariable logistic regression analysis revealed a strong and independent positive correlation between NPS and the prevalence of cataracts (Table 2). Each 1-point increase in NPS was associated with a 25% higher odds of cataract (OR = 1.25; 95% CI: 1.12–1.40; model 3). The high NPS group demonstrated 1.80-fold higher odds of cataract (OR = 1.80; 95% CI: 1.13–2.86; model 3). The findings indicate that NPS, which encompasses systemic inflammation and nutritional status, was closely connected to the prevalence of cataract. Figure 4 illustrates the linear positive correlation between NPS and cataract.
Table 2.
Survey-weighted association between Naples Prognostic Score and cataract.
| Characteristic | Model 1 OR (95% CI) | Model 2 OR (95% CI) | Model 3 OR (95% CI) |
|---|---|---|---|
| NPS continuous | 1.49 (1.33, 1.67) | 1.31 (1.17, 1.48) | 1.25 (1.12, 1.40) |
| Categories | |||
| 0 | 1.00 | 1.00 | 1.00 |
| 1–2 | 1.82 (1.30, 2.54) | 1.49 (1.03, 2.17) | 1.42 (0.96, 2.12) |
| 3–4 | 3.26 (2.16, 4.92) | 2.09 (1.34, 3.26) | 1.80 (1.13, 2.86) |
| P for trend | <.01 | <.01 | <.01 |
CI = confidence interval, NPS = Naples Prognostic Score, OR = odds ratio.
Figure 4.
Restricted cubic spline (RCS) logistic regression of NPS and cataract, adjusted for covariates. The red line represents adjusted odds ratios (ORs) with 95% confidence intervals across NPS levels; gray bars indicate NPS distribution. P-overall < 0.0001, P-nonlinear = 0.3593. CI = confidence interval, NPS = Naples Prognostic Score, OR = odds ratio, RCS = restricted cubic spline.
3.3. Subgroup analyses and sensitivity analyses
Subgroup analyses indicated that the positive association remained largely consistent across demographic and clinical strata, although the strength of the relationship varied. The association appeared stronger among older adults (>65 years), females, Non-Hispanic Whites, participants with intermediate income, and those with a high school education. Comparable associations were observed regardless of smoking and alcohol status, hypertension, diabetes, or BMI categories. Importantly, no significant interactions were detected (all P for interaction > .05), suggesting that the observed relationship was broadly stable across subpopulations (Table 3).
Table 3.
Stratified analysis of the correlation between Naples Prognostic Score and cataract in adults in NHANES 1999–2008.
| Subgroup | Model OR (95% CI) | P-value | P for interaction | |
|---|---|---|---|---|
| Age | .54 | |||
| ≤50 | 1.38 (1.07, 1.78) | .02 | ||
| 50–65 | 1.10 (0.79, 1.53) | .56 | ||
| >65 | 1.30 (1.15, 1.47) | <.01 | ||
| Gender | .65 | |||
| Male | 1.21 (1.03, 1.43) | .02 | ||
| Female | 1.28 (1.09, 1.50) | <.01 | ||
| Race | .20 | |||
| Mexican American | 1.15 (0.85, 1.55) | .36 | ||
| Other Hispanic | 1.80 (0.96, 3.38) | .07 | ||
| Non-Hispanic White | 1.25 (1.08, 1.44) | <.01 | ||
| Non-Hispanic Black | 1.21 (0.93, 1.57) | .16 | ||
| Other race | 1.06 (0.64, 1.76) | .81 | ||
| Marital status | .46 | |||
| Married or living with partner | 1.20 (1.03, 1.39) | .02 | ||
| Non-married | 1.01 (0.61, 1.67) | .96 | ||
| Other | 1.37 (1.13, 1.66) | <.01 | ||
| Education status | .46 | |||
| Less than 9th grade | 1.22 (0.98, 1.53) | .09 | ||
| 9–11th grade | 1.10 (0.83, 1.44) | .51 | ||
| High school grade | 1.53 (1.15, 2.03) | <.01 | ||
| Some college or AA degree | 1.15 (0.98, 1.35) | .10 | ||
| College graduate or above | 1.17 (0.95, 1.44) | .14 | ||
| Poverty-to-income ratio | .33 | |||
| ≤1 | 1.11 (0.86, 1.43) | .43 | ||
| 1–3 | 1.40 (1.20, 1.62) | <.01 | ||
| >3 | 1.14 (0.94, 1.39) | .19 | ||
| Smoking status | .65 | |||
| Yes | 1.28 (1.10, 1.48) | <.01 | ||
| No | 1.21 (1.04, 1.42) | .02 | ||
| Alcohol drinking | .48 | |||
| Yes | 1.32 (1.10, 1.57) | <.01 | ||
| No | 1.22 (1.06, 1.40) | <.01 | ||
| Hypertension | .30 | |||
| Yes | 1.31 (1.13, 1.52) | <.01 | ||
| No | 1.18 (1.01, 1.38) | .04 | ||
| Diabetes | .20 | |||
| Yes | 1.43 (1.17, 1.76) | <.01 | ||
| No | 1.21 (1.06, 1.39) | <.01 | ||
| BMI | .30 | |||
| ≤18.5 | 2.10 (1.08, 4.08) | .03 | ||
| 18.5–25 | 1.27 (1.07, 1.51) | <.01 | ||
| >25 | 1.22 (1.04, 1.44) | .02 | ||
BMI = body mass index, CI = confidence interval, NPS = Naples Prognostic Score, OR = odds ratio, PIR = poverty-to-income ratio.
All 3 sensitivity analyses indicate that the association between NPS and cataract was robust to both sample inclusion criteria and the application of survey weights (Tables S1–S3, Supplemental Digital Content 1–3).
3.4. Mediation analysis
Exploratory mediation analysis suggested that TyG-ABSI acted as a suppressor in the association between NPS and cataract (indirect effect = –0.0003; mediation proportion = –7.78%). In stratified analysis, a stronger suppressor effect was observed among participants without diabetes (mediation proportion = –15.74%; Fig. 5).
Figure 5.
Mediation analysis of the NPS–cataract association via TyG-ABSI. (A) Mediation effect of TyG-ABSI in the entire study population; (B) mediation effect of TyG-ABSI in participants without diabetes. Models adjusted for age, sex, race, socioeconomic and lifestyle factors, and hypertension. ABSI = A Body Shape Index, CI = confidence interval, NPS = Naples Prognostic Score, TyG = triglyceride-glucose index, TyG-ABSI = triglyceride-glucose index-based A Body Shape Index.
4. Discussion
To our knowledge, this is the first study to examine the association between NPS and cataract prevalence using nationally representative NHANES data. After controlling for confounders, a positive association was observed between increasing NPS and cataract prevalence, with each additional point in NPS corresponding to a 25% increase in odds. Individuals in the high NPS group had 80% higher odds compared with the reference group. RCS analyses illustrated the linear positive correlation between NPS and cataract. Subgroup analyses indicated that the positive association between NPS and cataract was generally consistent across demographic and clinical subgroups, with no significant interactions observed, suggesting the relationship is robust across populations. The results of all sensitivity analyses consistently supported the association between NPS and cataract prevalence, further confirming the robustness of our findings. Mediation analysis indicated that the observed association between NPS and cataract could be partly explained by obesity- and metabolism-related mechanisms, as reflected by the mediating role of TyG-ABSI. In stratified analyses, this suppressor effect of TyG-ABSI appeared more pronounced among participants without diabetes.
NPS is a novel composite biomarker integrating inflammatory and nutritional parameters. Unlike isolated markers, NPS reflects the cumulative burden of chronic inflammation, immune dysregulation, metabolic disturbance, and nutritional deficiency, all of which have been individually implicated in cataract pathogenesis.[18] Chronic low-grade inflammation is a key driver of age-related cataract. Elevated NLR reflects increased oxidative stress and impaired immune surveillance, promoting reactive oxygen species (ROS) production, lens epithelial cell apoptosis, and crystalline protein denaturation, which accelerate lens opacity.[19,20] Conversely, a low LMR indicates impaired immune tolerance and augmented monocyte-driven inflammation with elevated pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6), which exacerbate structural damage within the lens.[21] In addition, serum albumin serves as a key antioxidant and free radical scavenger. Hypoalbuminemia compromises lens metabolism and antioxidant defenses, correlating with increased incidence of cortical and posterior subcapsular cataracts, especially in elderly and metabolically compromised individuals.[22,23] Elevated total cholesterol contributes to lens membrane lipid instability and lipid peroxidation, as shown in animal models where hypercholesterolemia accelerates lens opacification via oxidative and osmotic damage.[24] Behavioral factors linked to elevated NPS components, including poor diet, smoking, and alcohol consumption, are well-known cataract risk factors that further aggravate systemic inflammation and metabolic dysfunction, indirectly contributing to lens pathology.[25,26] Thus, NPS likely captures not only biological but also lifestyle-related determinants of cataract prevalence.
Our findings are consistent with prior studies reporting low serum albumin and elevated NLR as independent risk factors for cataract, further supporting the clinical relevance of NPS.[19,22,27]
Exploratory mediation analysis suggested that the observed association between NPS and cataract may be partly attributable to obesity- and metabolism-related pathways, as reflected by TyG-ABSI. TyG-ABSI captures the combined burden of insulin resistance and central adiposity, which have been linked in prior studies to oxidative stress, inflammation, and advanced protein glycation in the lens.[28,29] These features may help explain why TyG-ABSI exhibited a stronger statistical suppressor effect than single anthropometric indices in our analyses. Notably, the suppressor effect was more pronounced among nondiabetic participants, indicating heterogeneity by diabetes status. In nondiabetic individuals, higher TyG-ABSI may reflect subclinical insulin resistance, visceral adiposity, and metabolic stress; in our cross-sectional data, this was associated with attenuation of the direct NPS–cataract link. Previous studies have shown that TyG and other non–insulin-based indices are strongly related to early metabolic disturbances in nondiabetic adults,[30] and that TyG-ABSI outperforms traditional anthropometric indices in predicting cardiometabolic outcomes.[31] Moreover, even mild metabolic impairments, below the diagnostic threshold for diabetes, have been associated with increased cataract prevalence.[32] Collectively, these findings highlight TyG-ABSI as a sensitive marker in prediabetic states, where it may statistically suppress rather than reinforce the observed NPS–cataract association.[33] Importantly, these mediation results are descriptive and based on cross-sectional data, and thus do not establish causal relationships.
This study benefits from the large, diverse, nationally representative NHANES cohort, enhancing the generalizability of results. The multidimensional nature of NPS, based on routinely available blood tests, also supports its potential clinical utility for cataract.
However, several limitations merit consideration. The cross-sectional design precludes causal inference and temporal assessment of NPS in cataract development. NHANES lacks detailed ophthalmologic data on cataract subtypes and severity, limiting phenotype-specific analyses. Potential confounders such as ultraviolet light exposure, genetic susceptibility, and acute illnesses were not fully accounted for. Moreover, inflammatory and nutritional markers can fluctuate with transient health conditions, which may affect NPS reliability. Future longitudinal and mechanistic studies are needed to validate NPS as a predictive biomarker and to elucidate its role in cataract pathophysiology.
Future longitudinal and mechanistic studies are needed to verify whether TyG-ABSI acts as a potential mediator and to further evaluate NPS as a biomarker for cataract risk stratification. Interventions targeting chronic inflammation, metabolic dysfunction, and nutritional deficits could be explored as potential strategies for cataract prevention in high NPS populations.
5. Conclusion
This nationally representative analysis identified a significant linear association between the Naples Prognostic Score (NPS) and cataract prevalence in US adults, and TyG-ABSI partly explained this link. The findings underscore the potential value of inflammation control and nutritional optimization in cataract, particularly among individuals with elevated NPS. Further longitudinal research is warranted to validate these associations and evaluate targeted interventions.
Acknowledgments
We thank the National Center for Health Statistics for providing open access to the NHANES database.
Author contributions
Conceptualization: Qiwen Yu.
Data curation: Qiwen Yu.
Methodology: Qiwen Yu.
Writing – original draft: Qiwen Yu, Zhicheng Ma.
Writing – review & editing: Qiwen Yu, Zhicheng Ma, Huiying Rao.
Abbreviations:
- ABSI
- A Body Shape Index
- ALB
- serum albumin
- BMI
- body mass index
- CDC
- Centers for Disease Control and Prevention
- CI
- confidence interval
- IL-6
- interleukin 6
- LMR
- lymphocyte-to-monocyte ratio
- MEC
- mobile examination center
- NCHS
- National Center for Health Statistics
- NHANES
- National Health and Nutrition Examination Survey
- NLR
- neutrophil-to-lymphocyte ratio
- NPS
- Naples Prognostic Score
- OR
- odds ratio
- PIR
- poverty–income ratio
- RCS
- restricted cubic spline
- ROS
- reactive oxygen species
- TC
- total cholesterol
- TNF-α
- tumor necrosis factor alpha
- TyG
- triglyceride-glucose index
- TyG-ABSI
- triglyceride-glucose index-based A Body Shape Index
This research were funded by the Natural Science Foundation Project of the Fujian Provincial Health Commission (grant number 2024J011026) and the Joint Funds for the Innovation of Science and Technology of Fujian Province (2024Y9082).
The NHANES protocol was approved by the National Center for Health Statistics Research Ethics Review Board. All participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki. As this analysis was based on publicly available de-identified data, no additional ethical approval was required.
The authors have no conflicts of interest to declare.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000050615).
How to cite this article: Yu Q, Ma Z, Rao H. TyG-ABSI mediates the association between Naples Prognostic Score and cataract in US adults: A cross-sectional study of NHANES 1999–2008. Medicine 2026;105:39(e50615).
Contributor Information
Qiwen Yu, Email: 422650877@qq.com.
Zhicheng Ma, Email: 574170181@qq.com.
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