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Scientific Reports volume 15, Article number: 222 (2025) Cite this article
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To investigate the relationship between the Chinese Visceral Adiposity Index (CVAI) and the risk of developing Benign Prostatic Hyperplasia (BPH). Using data from the China Health and Retirement Longitudinal Study (CHARLS), we included 3,295 men aged 45 years and older. Multivariate logistic regression and restricted cubic spline models were employed to analyze the association between CVAI and the risk of BPH. During the 4-year follow-up period, 267 cases of BPH were identified. CVAI was positively associated with the risk of developing BPH (OR = 1.23, 95% CI: 1.07–1.42), with a significant dose-response relationship (P < 0.001). Stratified analysis showed that the effect of CVAI on BPH risk was consistent across various subgroups. There is a positive correlation between CVAI and the risk of developing BPH. Managing visceral fat content and maintaining a healthy fat distribution pattern may help reduce the risk of BPH.
Benign Prostatic Hyperplasia (BPH) refers to the non-cancerous enlargement of the prostate gland and is one of the most common conditions among middle-aged and elderly men. With the acceleration of the aging process, the incidence of BPH is also increasing. In China, the prevalence of BPH exceeds 50% among men aged 60 and over, and reaches as high as 83% among men aged 80 and over¹. According to statistics, there were 11.26 million new cases of BPH worldwide in 2019, leading to 1.86 million Disability-Adjusted Life Years (DALYs)². As a significant public health concern, BPH severely impacts the quality of life and health status of middle-aged and elderly patients, while also increasing global healthcare expenditures^3,4. BPH can lead to various adverse effects, such as Lower Urinary Tract Symptoms (LUTS), which include frequent urination, urgency, nocturia, weak urine stream, dribbling, and incomplete bladder emptying⁵. Studies have reported that moderate to severe LUTS can increase the risk of adverse cardiac events⁶. BPH can cause urethral obstruction, leading to urinary retention and urinary tract infections. Prolonged urinary obstruction can result in decreased bladder contractility, exacerbating difficulties in urination. Persistent urinary obstruction and bladder dysfunction can further lead to hydronephrosis and renal insufficiency, and in severe cases, renal failure^7,8. Therefore, identifying high-risk populations for BPH and implementing early interventions and treatments are crucial for reducing its incidence and improving overall health outcomes.
The Chinese Visceral Adiposity Index (CVAI) is a novel adiposity assessment model tailored for the Chinese population⁹. It is based on age, Body Mass Index (BMI), Waist Circumference (WC), Triglycerides (TG), and High-Density Lipoprotein (HDL) levels, and it can be used to estimate visceral fat content and predict the risk of metabolic diseases⁹. Currently, the relationship between CVAI and BPH remains unclear. Therefore, we conducted a prospective study using data from the China Health and Retirement Longitudinal Study (CHARLS)¹⁰ database to investigate the association between CVAI and the risk of developing BPH.
This study utilized the baseline data from 2011 and follow-up data from 2015 from the CHARLS database (http://charls.pku.edu.cn/), which is publicly accessible. CHARLS is a large, nationwide, interdisciplinary survey covering 450 villages/communities in 28 provinces (autonomous regions and municipalities) in China, targeting participants aged 45 years and older. The survey encompasses a wide range of topics, including demographic information, family structure, health status, healthcare and insurance, employment and pensions, income and expenditures, housing conditions, and laboratory test results. Participants provided fasting venous blood samples after fasting for over 12 h, which were immediately analyzed for complete blood cell counts on-site. Whole blood samples were stored at 4 °C, and remaining samples were transported to a central laboratory for further analysis of glucose, Total Cholesterol (TC), TG, Low-Density Lipoprotein Cholesterol (LDL-C), and High-Density Lipoprotein Cholesterol (HDL-C) levels using enzymatic colorimetric methods. Diagnostic information on BPH was collected from the 2015 follow-up data.
The CHARLS study received approval from the Institutional Review Board (IRB) of Peking University in 2008 (Approval No. IRB00001052-11015). This study strictly adhered to all CHARLS protocols and requirements. All participants voluntarily signed informed consent forms prior to the survey.
For this study, the inclusion criteria were defined as follows: age ≥ 45 years, complete socio-demographic information (including educational level, marital status, and residence), and availability of both 2011 baseline and 2015 follow-up data. Participants with missing values for exposure or outcome variables, as well as those who were diagnosed with BPH at baseline, were excluded from the analysis.
The CVAI equation is derived from a regression model that was validated in previous studies⁹. The equation incorporates five variables: age, BMI, WC, TG, and HDL-C. These variables were chosen because they are closely linked to visceral fat accumulation and metabolic disturbances, which are key components of metabolic syndrome and contribute to BPH pathogenesis. The equation is designed to capture the combined effects of these factors, providing a composite measure of visceral fat and metabolic risk. While CVAI is an indirect estimate of visceral fat, it has been shown to correlate well with direct imaging methods, such as CT or MRI, and has been validated in large-scale cohort studies.
The CVAI score for Chinese males is calculated using the following formula: CVAI = − 267.93 + 0.68×Age + 0.03×BMI + 4.00×WC + 22.00×log10(TG) − 16.32×HDL-C.
Men who were not diagnosed with BPH during the 2011 baseline survey but were newly diagnosed with BPH in the 2015 follow-up were defined as BPH cases. BPH diagnosis was based on self-reported data from participants. Specifically, participants were asked in a questionnaire whether they had ever been diagnosed with a prostate illness, such as prostate hyperplasia, by a healthcare professional. The diagnosis was confirmed through participant responses to questions regarding prior medical evaluations and physician diagnoses¹⁰.
The analysis included socio-demographic characteristics, lifestyle factors, and physical examination indicators as covariates. Socio-demographic variables comprised age, educational level (middle school or below / high school / college or above), residence (urban / rural), and marital status (married / unmarried or divorced or widowed). Lifestyle factors included smoking status (never smoked / currently smoking), alcohol consumption frequency (never / less than once a month / at least once a month), and sleep duration. This information was collected through self-reported questionnaires completed by participants under the guidance of trained interviewers. Physical examination indicators included Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), measured three times using an Omron HEM-7200 electronic sphygmomanometer, with the average values used for analysis.
Continuous variables following a normal distribution were expressed as mean ± standard deviation (SD), while those with skewed distribution were expressed as median (interquartile range, IQR). Categorical variables were presented as frequencies (percentages). Differences in baseline characteristics and BPH incidence across CVAI quartiles (Q1-Q4) were compared using one-way ANOVA, Kruskal-Wallis H test, or chi-square test as appropriate. Three logistic regression models were employed to evaluate the association between CVAI and BPH risk. CVAI was analyzed both as a continuous variable (per SD increase) and as a categorical variable (quartiles). Model 1 was unadjusted; Model 2 adjusted for socio-demographic factors such as age, education level, residence, and marital status; Model 3 further adjusted for lifestyle and physiological indicators, including smoking status, alcohol consumption frequency, sleep duration, SBP, and diastolic DBP. For the stratified analysis, we employed Model 3 (adjusting for confounders) and assessed the outcome variable as categorized. The strength of the association was expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Interaction analyses were conducted by including interaction terms [CVAI × (covariate)] in the models to examine the potential effect modification of socio-demographic characteristics and lifestyle factors on the relationship between CVAI and BPH. Additionally, restricted cubic spline (RCS) regression was used to explore the non-linear dose-response relationship between CVAI and BPH risk.
All analyses were performed using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and Free Statistics analysis platform (Version 2.0, Beijing, China). The “rms” package was utilized for restricted cubic spline modeling; the “rcs” function was used to define the restricted cubic spline terms for the independent variable and to fit the regression model. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant.
A total of 3,295 eligible participants were included in the final analysis after excluding participants with missing data or those diagnosed with BPH at baseline (Fig. 1), with an average age of 59.4 ± 8.7 years. During the follow-up period, 297 men developed BPH. The baseline median CVAI for those who developed BPH was 99.5 (70.6, 133.9), compared to 85.1 (57.1, 119.8) for those who did not develop BPH. Baseline characteristics of BPH patients differed significantly from those without BPH, with BPH patients being older, having lower educational levels, more likely to reside in rural areas, and having higher BMI and waist circumference (Table 1).
Flowchart of participants selection.
Three logistic regression models were constructed to evaluate the association between CVAI and BPH risk, with adjustments for different covariates. After adjusting for covariates, the analysis was conducted using Models 1, 2, and 3. In Model 3, CVAI was significantly associated with BPH risk, with an adjusted OR of 1.93 (95% CI: 1.32–2.84). When CVAI was included in the model as a continuous variable (per SD increase), it was positively associated with BPH risk (OR = 1.23, 95% CI: 1.07–1.42, p = 0.004). Furthermore, the risk of BPH increased across CVAI quartiles (p < 0.001) (Table 2; Fig. 2).
Restricted cubic spline of the association between CVAI and the risk of BPH. The model was adjusted for age, education level, location and marital status, smoking status, drinking status, sleep time, SBP, and DBP. The plot shows a relationship between CVAI and the risk of BPH. The reference point is the median of all the data.
To explore the heterogeneity of the association between CVAI and BPH risk across different subgroups, we conducted a stratified analysis based on age, education level, and residence. The results of the stratified analysis indicated that the positive association between CVAI and BPH risk was consistent across all subgroups, with no significant effect modification observed. Within each subgroup, higher CVAI values were associated with an increased risk of BPH. For the stratified analysis, we employed Model 3 (adjusting for confounders) and assessed the outcome variable as categorized (Fig. 3).
Forest plot of stratified analysis of the association of CVAI with the risk of BPH.
This study, based on data from the CHARLS, investigated the relationship between the CVAI and the risk of developing BPH. The findings revealed a significant positive correlation between CVAI and BPH incidence, demonstrating a clear dose-response relationship. As CVAI quartiles increased, the risk of BPH progressively rose, suggesting that CVAI might be an independent risk factor for BPH. Further stratified analysis showed that the impact of CVAI on BPH risk was consistent across subgroups defined by age, education level, and residence, with no significant effect modification detected. This study contributes to the current understanding of BPH by identifying CVAI as a potentially valuable marker for BPH risk stratification. This study is the first to establish a positive association between CVAI and the risk of BPH, indicating that managing visceral fat content and maintaining a healthy fat distribution pattern may help reduce the incidence of BPH.
CVAI is not only a marker of visceral fat but also incorporates factors such as insulin resistance and inflammatory markers¹¹, both of which are implicated in the development of BPH¹². Recent studies have emphasized the critical role of insulin resistance and inflammation in BPH development¹¹. These metabolic factors promote prostate cell proliferation and inhibit cell apoptosis¹³, thus facilitating the growth of prostatic tissue¹⁴. Our results align with these findings, suggesting that CVAI can offer more nuanced insights into the relationship between obesity, metabolic dysfunction, and BPH risk. In addition to metabolic dysfunction and inflammation, hormonal changes also contribute significantly to BPH development¹⁵. The conversion of circulating testosterone to estrogen in prostate tissue disrupts the balance of these hormones, which further promotes prostatic hyperplasia¹⁶. Furthermore, studies have shown that aging and changes in steroid hormone levels are major factors influencing prostate enlargement¹⁷. Interestingly, reducing body fat percentage in obese individuals has been shown to alleviate male LUTS, highlighting the importance of managing obesity and metabolic dysfunction in preventing or mitigating BPH. CVAI, as a more comprehensive marker of visceral fat and metabolic dysfunction, offers important insights into the pathophysiology of BPH. Our study supports the potential of CVAI as an effective tool for identifying individuals at higher risk for BPH, particularly those with metabolic disturbances that predispose them to prostate enlargement. Dietary factors, such as a preference for fatty foods, may play a role in BPH development, and while our study did not account for these variables, future research should consider their potential impact on BPH risk¹⁸. Additionally, family history is an established risk factor for BPH, and while we did not have access to detailed family history data, this factor could confound the observed associations. We recommend that future studies incorporate dietary habits and family history to better isolate the effects of metabolic indices like CVAI on BPH. Future survey should explore the role of CVAI in larger, more diverse populations and investigate its utility in clinical risk stratification for BPH.
While our study provides valuable insights into the association between CVAI and BPH, several limitations should be acknowledged. First, due to the observational design of the study, we cannot establish causality between CVAI and the risk of BPH. Second, the reliance on self-reported BPH diagnoses introduces the potential for misclassification and recall bias, which could affect the accuracy of the findings. Third, the cross-sectional nature of our study restricts our ability to assess changes in CVAI or BPH risk over time, limiting our understanding of the long-term effects of these factors. Additionally, while we adjusted for a range of confounders, there may still be residual confounding factors that were not captured in the analysis. This study did not account for potential confounders such as dietary preferences (e.g., fatty food consumption) or family history of BPH, both of which may influence the development of the disease. Future studies should consider these factors to provide a more comprehensive understanding of the relationship between CVAI and BPH.
The generalizability of our findings is also limited by the study’s sample population, which consists solely of Chinese men aged 45 and older. Further research is needed to determine whether these results are applicable to younger populations or other demographic groups. These limitations highlight the need for further studies, ideally with longitudinal data and more diverse cohorts, to confirm the validity and broader applicability of our findings. Although the current study offers valuable insights, future research should aim to validate these results using independent datasets. Conducting the same analysis on external validation cohorts would increase the robustness and generalizability of our conclusions, allowing them to be more confidently applied to a broader range of populations and clinical settings.
This study is the first to establish a positive correlation between the CVAI and the risk of developing BPH, demonstrating a clear dose-response relationship. These findings suggest that managing visceral fat content and maintaining a healthy fat distribution pattern may help reduce the risk of BPH, offering new insights for the early prevention and intervention of BPH.
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.
Chinese Visceral Adiposity Index
Benign Prostatic Hyperplasia
China Health and Retirement Longitudinal Study
Disability-Adjusted Life Years
Lower Urinary Tract Symptoms
Body Mass Index
Waist Circumference
Triglycerides
High-Density Lipoprotein
Total Cholesterol
Low-Density Lipoprotein Cholesterol
High-Density Lipoprotein Cholesterol
Institutional Review Board
Systolic Blood Pressure
Diastolic Blood Pressure
Odds Ratio
Confidence Intervals
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We sincerely thank all members of our research team for their invaluable support and contributions throughout this study. We are grateful to the CHARLS project and Peking University for providing the dataset that made this research possible.
None.
Bing Li and Junping Li have contributed equally to this work.
Department of Urology, Tianjin Medical University General Hospital, Tianjin, China
Bing Li
Department of Oncology, Zibo City Municipal Hospital, Zibo, China
Junping Li
Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin, China
Chao Sun
Department of Cardiovascular surgery, Tianjin Medical University General Hospital, Tianjin, China
Yaodong Sun
Graduate School of Tianjin Medical University, Tianjin Medical University, Tianjin, China
Zhiqiang Zhang
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BL and ZZ: Conceptualization and design of the study, data acquisition and interpretation, drafting the manuscript. CS and YS: Conceptualization and design of the study, formal analysis, and methodology. JL: Preparation of figures and tables, drafting the manuscript. All authors contributed to the final version of the manuscript and approved it.
Correspondence to Zhiqiang Zhang.
The authors declare no competing interests.
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Li, B., Li, J., Sun, C. et al. Association between the Chinese Visceral Adiposity Index and the risk of Benign Prostatic Hyperplasia: a national prospective cohort study. Sci Rep 15, 222 (2025). https://doi.org/10.1038/s41598-024-83960-w
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DOI: https://doi.org/10.1038/s41598-024-83960-w
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SAN ANTONIO — A one-year-old girl has died two weeks after she was critically injured in a crash outside her preschool. The crash also claimed the life of her 22-year-old teacher, Alexia Rosales.
It happened at ExcellED Montessori Plus Daycare Preschool on 1-10 near Fair Oaks following a holiday performance.
On December 19, the Bexar County Sheriff’s Office said a parent appeared to have some sort of medical episode, causing them to crash into another car and then into a fenced in play area.
Now, the Bexar County Sheriff’s Office reports the driver they believe caused the crash is not answering their questions. Instead, they are referring investigators to their attorney. No charges have been filed in the case.
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He said since then, he was weighing his legal options.
“We had the darkest days of our life,” Amrani said Monday.
Amrani and his wife were not at the preschool when the crash happened. When they got the call, they rushed over.
“Unfortunately, all the kids were recovered except one of the babies and we kind of thought it was her,” Amrani said.
Amrani said he watched as paramedics carried his daughter to the ambulance. He said she was unconscious and appeared lifeless. He rode along and watched as they worked to bring her to life. He said for several minutes MKaya lost oxygen to her brain.
But shortly after getting to the hospital, he said doctors got a pulse.
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Since then, he said her condition only worsened. He said the family tried their best to hold out hope.
“We don’t want to give up on her,” He said Monday.
But on Thursday, he said MKaya had passed away. Now as the family grieves, they are also looking for answers. He said he believes his child should have never been in a play area so close to the parking lot.
“We’re going to dedicate to spread awareness and if that means we got to drive by every daycare and make sure we raise the concerns of it being too close to high traffic areas, we’ll do that,” Amrani said.
He said he also is waiting to learn from investigators what medical episode the driver suffered that allegedly caused the crash.
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Scientific Reports volume 15, Article number: 499 (2025) Cite this article
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The objective of this study is to investigate the association between the Systemic Immune-Inflammation Index (SII) and cataracts. This cross-sectional study analyzed data from the 2005–2008 NHANES to examine the relationship between the SII and cataract prevalence. Covariates included age, race/ethnicity, gender, education level, marital status, Body Mass Index (BMI), smoking, alcohol consumption, hypertension, hyperlipidemia, and diabetes. Multivariable logistic regression was used to assess the association, while spline curve fitting explored potential non-linear relationships. Threshold analysis identified critical inflection points. To address age-related bias, Propensity Score Matching (PSM) was performed, aligning cataract patients with comparable non-cataract individuals for further evaluation. Our study included 3,623 participants, of whom 730 (20.15%) were diagnosed with cataracts. After adjusting for all covariates, multivariable logistic regression analysis demonstrated that elevated levels of the SII were significantly associated with increased odds of cataracts (Model1: OR = 1.56; 95%CI [1.33–1.85]; Model2: OR = 1.55; 95%CI [1.32–1.84]; Model3: OR = 1.57; 95%CI [1.33–1.86]). In the spline curve fitting model, the relationship between ln-SII and cataract prevalence was non-linear (P < 0.001), with a critical inflection point identified at an SII of 428.38. SII levels remained significantly associated with cataract prevalence following PSM adjustments (Model 1: OR = 1.48; 95% CI [1.21–1.80]; Model 2: OR = 1.48; 95% CI [1.21–1.80]; Model 3: OR = 1.46; 95% CI [1.20–1.78]). Elevated SII levels are associated with a higher prevalence of cataracts, underscoring the pivotal role of systemic inflammation in cataract development. These findings indicate that SII could serve as a valuable biomarker for assessing cataract risk, further emphasizing the significance of managing systemic inflammation as a potential strategy for cataract prevention.
The lens, positioned behind the iris yet in front of the vitreous body and retina, plays a crucial role in directing light onto the retina. Originating from ectodermal tissue, it comprises epithelial cells that produce lens fibers, causing the lens to thicken over time. Cataracts, characterized by reduced lens clarity, impair vision, decrease contrast sensitivity, alter color perception, and cause glare^1,2,3. The World Health Organization reports that cataracts represent approximately 46% of the nearly 180 million global cases of visual impairments³. Although treatable, cataracts remain a leading cause of vision loss worldwide, posing significant public health challenges^4,5. Factors such as aging, smoking, diabetes, and exposure to ultraviolet light contribute to the development of age-related cataracts⁶. Although cataract surgery can significantly improve vision, its affordability and the limited availability of surgeons in some regions continue to pose challenges⁷.
Immune cells play a crucial role in the systemic inflammatory response, which is implicated in a variety of diseases. Researchers have found that the combined count of lymphocytes, neutrophils, and platelets in peripheral blood provides a more accurate indication of inflammatory status^8,9. Recent ophthalmologic studies have demonstrated a significant association between Systemic Immune-Inflammation Index (SII) and primary open-angle glaucoma¹⁰. Additionally, inflammation and immune responses are increasingly recognized as key factors in cataract development. Chronic inflammation induces oxidative stress, which promotes lens protein modification and ultimately leads to lens opacity¹¹. Moreover, immune responses involving cytokines and inflammatory mediators have been implicated in the progression of cataracts.
Initially, SII was identified as a prognostic marker for gastrointestinal tumors in elderly patients¹². Subsequent studies have also linked it to the occurrence of pseudophakic cystoid macular edema (PCME) following uncomplicated phacoemulsification cataract surgery in patients without risk factors, highlighting its potential as a predictive biomarker for PCME. This suggests its utility in enhancing clinical assessments and refining risk stratification¹³. However, the relationship between SII and cataracts remains unexplored. To address this gap, our research team conducted a population-based cross-sectional analysis using NHANES data to investigate the potential association between SII and cataract prevalence in adults.
The NHANES is a cross-sectional study that collects comprehensive data on the health and nutritional status of U.S. households¹⁴. It employs a complex, stratified, multistage probability cluster sampling method to ensure an accurate representation of the U.S. population. For further insights into NHANES’s detailed methodology, please refer to their website at http://www.cdc.gov/nchs/nhanes/index.htm. Ethical clearance for the study was granted by the National Center for Health Statistics’ Ethics Committee, with all subjects providing written informed consent⁸. Our study excluded individuals with missing SII data, cataract information, or other necessary covariate details. Given the strong correlation between cataracts and age, we further excluded participants under the age of 50 to enhance the reliability and validity of our findings. This approach targeted an age group with a higher prevalence of cataracts, thereby minimizing the confounding effect of age on the association of the SII with cataract, and the effect of age on all potential confounders included in the model. Ultimately, the study population comprised 3,623 individuals, with the sample selection process illustrated in Fig. 1.
Screening process of the included studies.
According to the NHANES Vision Procedures Manual¹⁵, participants aged 20 and older were asked whether they had undergone cataract surgery. To minimize confounding factors associated with age, participants under the age of 50 were further excluded. Given the high accessibility and low barriers to cataract surgery in the United States, self-reported cataract surgery is considered a surrogate indicator for clinically significant cataracts. Participants completed a questionnaire (VIQ071: 2005–2008), which included the question, “Have you ever had cataract surgery?” An affirmative response was taken as indicative of cataracts¹⁶. This method of identifying cataracts is consistent with the methodologies used in previous studies^16,17.
The Complete Blood Count (CBC) metrics employ the Beckman Coulter methodology, which integrates counting and sizing techniques, an automated system for diluting and mixing samples, and a single-beam photometer for measuring hemoglobin levels. White Blood Cell (WBC) differentials utilize VCS technology. Positioned within the NHANES mobile examination center (MEC), the Beckman Coulter DxH 800 instrument conducts CBC analyses on participant blood samples, providing a comprehensive cell distribution profile. The SII is derived from these three circulating immune cell types and is calculated using the formula: platelets (PC) × neutrophils (NC) / lymphocytes (LC) ¹⁸.
Demographic variables such as age, race/ethnicity, gender, education level, marital status, and Body Mass Index (BMI) were selected as covariates in our study. This demographic information was collected via computer-assisted personal interviews¹⁹. The comorbidities considered included hypertension, hyperlipidemia, and diabetes mellitus. Hypertension was defined as either a doctor’s diagnosis, the use of antihypertensive medication, or having a systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg. Hyperlipidemia was identified in participants with a doctor’s diagnosis, those taking lipid-lowering medications, or those with a total cholesterol level ≥ 240 mg/dL during NHANES assessments. Diabetes mellitus was determined by a doctor’s diagnosis, the use of glucose-lowering medication or insulin, or an HbA1c level ≥ 6.5% during NHANES testing. In terms of lifestyle, the covariates included were smoking and alcohol consumption. Smoking was categorized into two groups: non-smokers (those who have never smoked or have smoked fewer than 100 cigarettes in their lifetime) and smokers (those who have smoked at least 100 cigarettes in their lifetime)²⁰. Similarly, alcohol consumption was divided into two categories: non-drinkers (those who have never consumed alcohol or have consumed fewer than 12 drinks in their lifetime) and drinkers (those who have consumed at least 12 drinks in their lifetime)²¹.
Data analysis was performed using R2 and EmpowerStats software, developed by X&Y Solutions, Inc., based in Boston, MA, and available at http://www.empowerstats.com. This analysis accounted for the complex sampling framework of NHANES by incorporating sampling weights, strata, and primary sampling units. Continuous variables were presented as means ± standard errors (SE), and categorical variables as percentages ± SE. Chi-square tests or T-tests were utilized to assess demographic differences.
The SII data exhibited a right-skewed distribution, necessitating a natural logarithm transformation for statistical analysis. Logistic regression models were utilized to assess the association between SII levels and cataract risk. Model 1 adjusted for age, gender, race, and BMI. Model 2 included additional adjustments for education level, marital status, smoking, and alcohol consumption. Model 3, building on Model 1, also accounted for diabetes, hypertension, and hyperlipidemia. Quantile regression analyses were conducted to further explore these associations. The logistic regression results were visually represented through forest plots, and smooth curve fitting was utilized to investigate potential relationships between SII levels and cataract risk. Analysis of threshold and saturation effects were performed to identify the optimal inflection point. In order to mitigate potential age-related effects on the outcomes, cataract patients and non-cataract individuals were matched in a 1:1 ratio based on average age in the propensity score matching (PSM) analysis. Statistical significance was established at p-values less than 0.05.
The study encompassed a total of 3,623 participants, including 2,893 individuals without cataracts and 730 who were diagnosed with cataracts following screening. The characteristics of these participants are summarized in Table 1.
The average age of participants was 65.9 years, consisting of 1,786 males (49.30%) and 1,837 females (50.70%). Participants who had undergone cataract surgery were predominantly older, unmarried females with lower levels of education. Additionally, it was observed that patients with a history of smoking or alcohol consumption were more susceptible to developing cataracts. Furthermore, participants diagnosed with hypertension, hyperlipidemia, or diabetes exhibited a higher incidence of cataract formation. Table 1 demonstrates that individuals with cataracts presented higher SII scores, corroborating our initial hypothesis.
We performed a weighted multivariate logistic regression analysis (illustrated in Fig. 2), which revealed a significant positive association between elevated ln-SII scores and the likelihood of cataract prevalence. This association was consistently significant across all models: Model 1 (OR = 1.56; 95% CI = 1.33–1.85, p < 0.001), Model 2 (OR = 1.55; 95% CI = 1.32–1.84, p < 0.001), and Model 3 (OR = 1.57; 95% CI = 1.33–1.86, p < 0.001). Further analysis using smoothing spline techniques delineated a non-linear relationship between ln-SII and the odds of having cataracts across various covariates (as depicted in Fig. 3, p < 0.001). Additionally, threshold and saturation effect analyses identified an inflection point at an ln-SII of 6.06 (equivalent to an SII of 428.38). Below this threshold, the association between SII and the prevalence of cataracts is weaker; however, when SII exceeds this value, its positive correlation with the prevalence of cataracts significantly strengthens.
Forest plot of logistic regression results. Note: Model 1: adjusted for Gender; Age; Race; BMI. Model 2: adjusted for Gender; Age; Race; BMI; Education; Marital Status; Smoke; Drink. Model 3: adjusted for Gender; Age; Race; BMI; Hypertension; Hyperlipemia; Diabetes.
Relationship between ln-SII and cataract prevalence. Note: The red solid line represents a smoothed curve fit of SII to cataract prevalence. The blue dashed line represents the 95% confidence interval of the smoothed curve fit.
For sensitivity analysis, we transformed ln-SII from a continuous variable into categorical quartiles, as depicted in Table 2. Compared to the lowest quartile (Q1), participants in the highest quartile (Q4) exhibited 96% higher odds of having cataracts (OR = 1.96; 95% CI = 1.51–2.54, p < 0.001) in Model 1, 95% higher odds in Model 2 (OR = 1.95; 95% CI = 1.49–2.53, p < 0.001), and 99% higher odds in Model 3 (OR = 1.99; 95% CI = 1.53–2.59, p < 0.001). Further analysis using smoothing spline techniques demonstrated a linear relationship between ln-SII (values above 6.06) and the odds of developing cataracts, adjusted for all covariates, as illustrated in Fig. 4 (p < 0.001). This analysis substantiates the strong association between elevated ln-SII (values above 6.06) levels and increased odds of having cataracts..
Relationship between ln-SII(values above 6.06) and cataract prevalence. Note: Based on the turning point (ln-SII > 6.06) in Fig. 3, the linear relationship between ln-SII and cataract adjusted for all covariates was further explored.
Given the age and numerical discrepancies between the cataract and normal groups, a 1:1 propensity score matching (PSM) analysis was conducted based on age to mitigate age-related effects. A total of 1,254 participants were enrolled and classified into the cataract and non-cataract groups. The baseline characteristics of each group, post-PSM, are presented in Table 3. Significant differences in the SII were observed between the groups after PSM, with the cataract group displaying elevated SII levels (P < 0.001). Following this, the logistic regression model, which assessed the relationship between SII and the incidence of cataracts post-PSM, indicated a significant positive correlation between higher SII scores and the prevalence of cataracts. This correlation remained consistently significant across all models (Model1:1.48 (1.21, 1.80); Model2:1.48 (1.21, 1.80); Model3:1.46 (1.20, 1.78), adhering to the inclusion criteria and details that align with those previously described, as further detailed in Supplementary Table 1.
In this study, we leveraged data from the NHANES database to investigate the potential link between the SII and cataract development. Our findings from this nationally representative cross-sectional analysis revealed a nonlinear relationship between SII levels and cataract prevalence. We identified a critical inflection point for ln-SII at 6.06 (SII = 428.38), beyond which SII levels positively correlate with increased cataract prevalence. To the best of our knowledge, this is the inaugural study to explore the association between SII and cataracts, offering a novel perspective in the understanding of this relationship.
Systemic inflammation can be measured through various biochemical or hematological markers commonly assessed in routine blood analyses, or via ratios derived from these markers²². The SII, a novel and consistent marker of inflammation, is computed using the formula: PC × NC / LC⁸.
Elevated SII levels indicate an inflammatory environment marked by increased NC and decreased LC, potentially contributing to the onset and progression of various diseases. Such elevation not only reflects an inflammatory state but may also indicate a disruption in immune regulation. Cells like NC and LC play crucial roles in managing inflammation and immune responses. Consequently, a high SII may denote persistent immune activation, a condition frequently observed in the pathogenesis of various chronic diseases, including age-related disorders such as cataracts²³.
The connection between inflammatory states and cataract formation may be mediated by inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6)²⁴. Released during inflammatory responses, these cytokines directly alter the intraocular environment, promoting oxidative damage and apoptosis in lens epithelial cells, thereby accelerating the development of cataracts. Concurrently, elevated levels of SII lead to an increase in inflammatory mediators within the bloodstream. These mediators, penetrating the eye via ocular circulation, trigger inflammatory pathways within the lens. This activation induces further cellular responses, including the release of additional cytokines, which exacerbate oxidative stress and cellular damage in the lens²⁵.
Alterations in the SII may also indicate the infiltration status of immune cells, particularly NC and LC, within the eye. The activation and subsequent infiltration of these cells can directly damage the lens or indirectly lead to lens cell injury and death through the release of inflammatory mediators and enzymes. Such processes significantly accelerate the development of cataracts.
Oxidative stress is intricately linked to mitochondrial function. Under conditions of inflammation and elevated SII, oxidative stress can escalate, leading to mitochondrial dysfunction—a key factor in the development of cataracts. This dysfunction may decrease ATP production and disrupt cellular metabolism, impairing the normal functioning and viability of lens cells. Furthermore, mitochondrial dysfunction can enhance the production of reactive oxygen species (ROS) within cells, further exacerbating oxidative stress²⁶.
Additionally, prolonged inflammatory responses and oxidative stress can deplete the body’s antioxidant defense mechanisms. Notably, the activities of antioxidant enzymes, such as superoxide dismutase (SOD) and glutathione peroxidase (GP), may diminish. Concurrently, the overall levels of antioxidants may decrease, impairing the eye’s capacity to neutralize ROS. This reduction in antioxidant defense can lead to increased oxidative damage to the lens, exacerbating conditions conducive to cataract formation.
Considering the established correlation between the SII and cataracts, future research should focus on therapeutic strategies that target inflammation and oxidative stress. Potential approaches could include the development of novel pharmacological agents or nutritional supplements designed to mitigate inflammatory responses, enhance antioxidant defenses, or directly scavenge ROS. Additionally, subsequent studies ought to evaluate the utility of SII in clinically assessing cataract risk and monitoring disease progression. With an enhanced understanding of the precise relationship between SII and cataract formation, SII could serve as a pivotal marker for predicting cataract risk and directing appropriate intervention strategies.
Our study is bolstered by a substantial, nationally representative sample and adjusts for critical demographic, examination, and laboratory factors, thereby enhancing the credibility and generalizability of our findings. However, the study also exhibits limitations, chiefly its cross-sectional design, which constrains our capacity to establish causality. Furthermore, the NHANES survey administrators did not thoroughly account for the age-specific characteristics of cataracts, including comprehensive records of bilateral conditions during ophthalmological screenings. To address these challenges, future research employing a robust sample size is essential to clarify causal relationships. Additionally, it is imperative to further investigate the possible association between SII and the history of bilateral cataract surgery.
This study, drawing on data from the NHANES database, unveils new insights into the correlation between the SII and cataract development. Typically, SII is positively correlated with inflammatory conditions, such as cataract. A critical SII value of 428.38 emerges with significant clinical implications. These findings propose SII as a novel biomarker for the risk assessment and early prevention of cataracts, highlighting the pivotal role of systemic inflammation in cataract pathogenesis.
Publicly available datasets were analyzed in this study. This data can be found at: https://www.cdc.gov/nchs/nhanes/index.htm.
Systemic immune-inflammation index
National health and nutrition examination survey
Propensity score matching
Complete blood count
White blood cell
Mobile examination center
Body mass index
Standard errors
Odds ratio
Tumor necrosis factor-alpha
Interleukin-6
Reactive oxygen species
Super oxide dismutase
Glutathione peroxidase
Lymphocytes
Neutrophils
Platelets
Pseudophakic cystoid macular edema
Logarithmic
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The authors would like to thank all reviewers for their valuable comments.
The study described was supported by grants from a Key Project and a Lab Project at Chongqing Three Gorges Medical College, China (SYS20210021), and a project supported by the Chongqing Education Commission Science and Technology Research Program (KJQN202302715).
Xiang Li, Guo-lei Du and Shi-Nan Wu, contributed equally to this work.
Eye Institute & Affiliated Xiamen Eye Center, School of Medicine, Xiamen University, XiaMen, China
Xiang Li & Shi-Nan Wu
Chongqing Key Laboratory of Development and Utilization of Genuine Medicinal Materials in Three Gorges Reservoir Area, Chongqing Three Gorges Medical College, No. 366, Tian Xing Rd, Bai’anba, Wanzhou, chongqing, China
Xiang Li & Jia-feng Tang
Weihai Institute for Bionics-Jilin University, Weihai, China
Guo-lei Du & Si-Qi Zhang
The First Affiliated Hospital of Xi’an Jiao Tong University, Shaanxi, 710061, China
Yi-qing Sun
Kunming Medical University, Kunming, 650500, Yunnan, China
Zhi-Jie Zhang
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Xiang Li conceived the research idea. Xiang Li, Guo-lei Du, Shi-Nan Wu, Yi-qing Sun, Si-Qi Zhang, Zhi-Jie Zhang conducted data cleaning and literature review. Xiang Li, Guo-lei Du, Shi-Nan Wu, and Si-Qi Zhang, contributed to drafting and critically revising the work for intellectual content. Xiang Li, Guo-lei Du, Shi-Nan Wu, and Si-Qi Zhang, conducted the analysis and created the figures and tables. Jia-feng Tang provided a critical review of the manuscript. All authors have read and approved the manuscript.
Correspondence to Jia-feng Tang.
The authors declare no competing interests.
Considering that the NHANES database is publicly accessible, and patient records are anonymous and de-identified, it does not involve informed consent or ethical approval.
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Li, X., Du, Gl., Wu, SN. et al. Association between systemic immune inflammation index and cataract incidence from 2005 to 2008. Sci Rep 15, 499 (2025). https://doi.org/10.1038/s41598-024-84204-7
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OpinionBrett Younger  |  January 2, 2025
My mother had light in her eyes.
She was a delightful combination of my grandparents. My grandmother had a strong sense of right and wrong, a strong sense that her way was right and yours was wrong. So much was off limits — drinking, dancing and Methodists. My grandfather had a strong sense of joy. He was always about to laugh. My mom’s father kept things lively, even as my grandmother kept things orderly.
Mom had a brief but glorious hoops career. Grandma would not let my mother play basketball for the purple and gold of Itawamba High School because the team’s short pants were 2 feet too short. On one famous night in 1948, several Lady Indians fouled out in the third quarter of a tight game. The coach went into the stands to beg Clarice Graham to play. In a dress that hit just below the ankles, she scored several key baskets, leading the Indians to a celebrated victory.
Clarice Graham Younger: 1932-2024
Mom was the salutatorian at her high school. She only made one B at Southwestern Seminary, which J. Leo Garrett gave her in systematic theology.
When I met Garrett 50 years later, I told him, “My mother still complains about that B you gave her.”
He said, “I feel bad about that.”
He should have felt bad. A better teacher would have recognized an A student.
Mom had the sense of adventure to go to California as a summer missionary, where she cared for migrant children. Twenty-two-year-old Clarice would have been amazed at what the next 70 years would bring.
My dad knew he was blessed to marry my mother. The wedding was at Tombigbee Baptist Church (Landmark). I do not know if my grandfather, who preached the sermon, told my father he better treat his daughter right, but the entire congregation was thinking it. The service was on a Monday night at 8:00. The newlyweds spent the night at the Travelers Motel in Tupelo, Miss., which they say looks better now than it did in 1956.
My mother and father were partners in ministry. They went to Washington, where they learned to care for each other far from home. Then South Dakota, where they had their first child. Six years into their marriage, Mom got back to Mississippi. Four years later, the second son. Then at 44, surprising everyone who knew her, she lived in Cleveland, Ohio.
She raised two sons who adore her. She read to her children. She played with her children. She stared at her children from the choir.
She put up with a lot. My little brother once hung a pork chop on a string from the ceiling in the dining room. He set up a hidden camera to record my mom waving a broom at the pork chop trying to knock it down. If it had been 30 years later, it would have gone viral.
Clarice and Lynton Younger
She watched more baseball, basketball and football games than she wanted to see. She went to a lot of games to see Marshal play and a lot of games to watch me sit on the bench.
In the story of Jesus visiting Mary and Martha, my mom is Mary and Martha. She was Martha because she stayed busy. She could not sit still. She kept moving.
But she also was Mary. She cared about everyone in the house. She made sure everyone felt loved. She knew when to listen, when to laugh, when to hug.
At the end of the story in Luke, Jesus says, “There is need of only one thing.”
If the one thing is love, my mother was great at the one thing.
She was a bank teller who worked the drive-through and consistently had the longest line. She knew everyone’s name and asked about their health. She prayed with them. At Christmas she received gifts from her customers. Did you give your bank teller a gift?
She sent thank you notes. She wrote letters. She sent so many Christmas cards. She received so many Christmas cards. Mom kept the U.S. Postal service alive by herself. They ought to put her picture on a stamp.
“She made the church more Christian.”
When churches went through hard times, she was hopeful. When there were disagreements, she was a voice for peace. When people were sick, she offered concern. When days were dark, she was light. When people felt left out, she invited them in. She made the church more Christian.
She was a great mother-in-law. My wife wanted to spend as much time as possible with my mother. I am glad my mother never had to choose between Carol and me. That would not have gone well for me.
My mother passed down good gifts to my children. Graham and Caleb are smart like my mom. They are good cooks. They love the best parts of the South. They worry about the ones who are left out.
Someone asked, “What was your mother like?” and I did not know where to start. I finally decided to talk about how she laughed. When mom giggled, which she did frequently, she would begin to shake, and her voice would go to a pitch audible only to dogs. Her face would turn a beautiful shade of red and her bright blue eyes would start dancing.
At 66, she went home to Mississippi, to a house 100 yards from the spot where she was born. Mom knew it was a good gift to live where people knew and loved her family.
My mother suffered with dementia. Two and a half years ago, my brother and sister-in-law welcomed Mom into their home. They cared for her through the long, hard goodbye. Even with that terrible disease, Mom kept being light. When she did not know anyone’s name, she would say, “I want you to meet my dear, dear friend.”
At her funeral, surrounded by family, friends, and a great cloud of witnesses, the light was still shining.
 
Brett Younger
Brett Younger serves as senior minister at Plymouth Church in Brooklyn, N.Y.

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CLEVELAND, Ohio — The Saturday, Jan. 4, 2025, Powerball jackpot is an estimated $200 million, making it the top lottery prize this weekend.
Saturday’s Classic Lotto jackpot is $1.5 million, while the Friday, Jan. 3 Mega Millions prize is $42 million.
Thursday’s winning Ohio Lottery numbers are:
Pick 3 evening: 306 (midday, 926)
Pick 4 evening: 9634 (midday, 6444)
Pick 5 evening: 95275 (midday, 68508)
Rolling Cash 5: 6-18-29-33-35
Pick 3 winners receive $500 for a $1 straight bet and the odds of winning are 1-in-1,000. Pick 4 winners receive $5,000 for a $1 straight bet and the odds of winning are 1-in-10,000.
The Rolling Cash 5 jackpot for the next drawing is $120,000 for hitting all five numbers and the odds of winning are 1-in-575,757.
Drawings for Pick 3, Pick 4 and Pick 5 are twice daily, at 12:29 p.m. and 7:29 p.m. The drawing for the Rolling Cash 5 is each night at 7:35 p.m.
Classic Lotto draws at 7:05 p.m. on Mondays, Wednesdays and Saturdays. The odds of winning the jackpot with a $1 ticket are 1-in-13,983,816.
Lucky for Life draws at 10:30 p.m. each night. The odds of winning the jackpot with a $2 ticket are 1 in 30,821,472.
Powerball draws on Mondays, Wednesdays and Saturdays at 10:59 p.m. The odds of winning the jackpot with a $2 ticket are 1 in 292,201,338.
Mega Millions draws on Tuesdays and Fridays at 11 p.m. The odds of winning the jackpot with a $2 ticket are 1-in-302,575,350.
The official Ohio Lottery site offers more information on instant tickets, raffles & other lottery games.
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Dating Apps

Could the punchlines of tomorrow be crafted by machines? As artificial intelligence makes unprecedented strides, particularly in creative fields, comedy is experiencing a technological revolution.
Traditionally, comedy has thrived on human quirks and cultural idioms, making it deeply personal and spontaneous. However, AI-powered comedians are beginning to emerge, with algorithms capable of analyzing humor patterns and generating jokes. A recent study conducted by tech innovators has shown AI can create jokes that mimic human-made humor with surprising accuracy. With platforms like OpenAI’s GPT models, these AI comedians are now being tested in standalone acts and even incorporated into human performances to offer improvised responses.
But does AI threaten the uniqueness and spontaneity of human comedy? Experts argue that while AI can replicate and even innovate in joke-telling, it lacks the emotional depth and cultural context that define stand-up comedy and other comedic forms. AI’s role, they suggest, might lie in enhancing rather than replacing; assisting writers with prompts, exploring new comedic styles, or breaking language barriers in global comedy.
As audiences become more tech-savvy, their openness to AI-driven content is growing, marking a potential shift in the comedic landscape. While AI comedians might not be ready to headline a Saturday night at the Apollo, their increasing prominence cannot be ignored. The question remains: how will traditional comedians adapt, and what new comedic formats will emerge in this tech-fueled future?
As artificial intelligence continues to advance, it is making inroads into areas once thought exclusive to human creativity, including the field of comedy. The rise of AI comedians could fundamentally reshape how humor is experienced and delivered.
**Features of AI Comedy**
AI-driven comedy relies on sophisticated algorithms to analyze humor patterns and generate jokes. These models, particularly those developed by platforms like OpenAI, leverage vast amounts of data to craft humor that mimics traditional joke structures and delivery. The evolution of AI humor has progressed from simple puns to more nuanced and contextually aware jokes that border on human-like wit.
**Potential Use Cases**
AI’s potential role in comedy is multifaceted. It can serve as a creative collaborator, providing writers with a treasure trove of ideas and prompts to inspire new material. Moreover, AI can help comedians explore innovative comedic styles that might not be immediately apparent through traditional methods. Its capability to break language barriers also opens new horizons for global comedy, allowing humor to transcend cultural limitations and reach wider audiences.
**Limitations of AI-Generated Humor**
Despite impressive strides, AI-generated jokes have significant limitations. One key shortcoming is the lack of emotional depth, which is essential for impactful comedy. AI lacks the ability to understand and react to the cultural nuances and spontaneous interactions that often give life to a stand-up routine. These limitations suggest that while AI can contribute to the comedic process, it is unlikely to replace the human touch anytime soon.
**Market Analysis and Trends**
The integration of AI into comedy is a burgeoning market trend, reflecting broader shifts in how audiences consume entertainment. As digital natives become more comfortable with technology-driven content, the interest in AI-enhanced or AI-generated performances is expected to grow. This shift presents both challenges and opportunities for traditional comedians, who must adapt to new formats and technologies to stay relevant.
**Security and Innovation Aspects**
AI in comedy also necessitates discussions around security and ethical considerations. As with any AI application, ensuring that humorous content generated by these systems is appropriate and non-offensive is crucial. Innovations in this field must prioritize the development of mechanisms to prevent the propagation of harmful or insensitive jokes.
**Sustainability and Future Predictions**
The sustainability of AI in comedy hinges on continuing innovations that harness its strengths without undermining the unique elements of human humor. Future predictions suggest a hybrid model where AI and human comedians collaborate, blending the computational power of AI with the emotive and improvisational skills of humans to create a more diverse comedic landscape.
In conclusion, while AI-generated comedians are not poised to oust human comics from the stage, their emerging presence is undeniable. The future of comedy will likely involve a symbiotic relationship between humans and AI, exploring new territories of humor that were previously unimaginable.
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India's Space Odyssey Awaits

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There are several ways to obtain spins in Coin Master. You can earn them by completing albums, finishing missions in daily events, participating and ranking higher in tournaments, landing three lightning bolt symbols on the virtual slot machine, purchasing from the in-app store, and more. However, the simplest method is by redeeming the daily links released by developer Moon Active.
There are three Moon Active links that grant spins at no cost on January 3, 2025. However, none of them offers free coins.
Looking for Crossword hints & solutions? Check out latest NYT Mini Crossword Answers, LA Times Crossword Answers, and USA Today Crossword Answers
Moon Active has posted two Coin Master links on the game’s official Facebook page and one on X. Each link grants 25 free spins, totaling 75 spins.
Below are all the links for January 3, 2025, with their respective rewards:
The process of redeeming Coin Master links is simple and takes only a few seconds to complete. Here are the steps:
Keep in mind that the rewards from the links above are available only if you redeem them within three days. After that, the links will expire and won’t provide freebies upon redemption.
Additionally, each link grants rewards only once per account.
Quick Links

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