Construction of a nomogram for predicting the risk of all-cause mortality in patients with diabetic retinopathy

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 21, 2025

Diabetic retinopathy (DR) not only leads to visual impairment but also increases the risk of death in type 2 diabetes patients. This study aimed construct a nomogram assess all-cause mortality patients with DR. cross-sectional included 1004 from National Health and Nutrition Examination Survey database (NHANES) between 1999-2018. Participants were randomized 7:3 ratio into training set test set. We selected predictors by LASSO regression multifactorial Cox proportional analysis constructed nomograms, guided established clinical guidelines expert consensus as gold standard. used concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, decision (DCA) evaluate nomogram's discriminative power, quality, use. The sets consisted 703 301 participants median age 64 63 years, respectively. identified seven predictors, including age, marital status, congestive heart failure (CHF), coronary disease (CHD), stroke, creatinine level, taking insulin. C-index model was 0.738 (95% CI: 0.704-0.771), while 0.716 0.663-0.768). In set, model's AUC values for predicting at 3 5 10 years 0.739, 0.765, 0.808, these 0.737, 0.717, 0.732, ROC DCA all demonstrated excellent predictive performance, confirming effectiveness reliability applications. Our demonstrates high accuracy, enabling clinicians effectively predict overall DR, thereby significantly improving their prognosis.

Language: Английский

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Association of serum selenium levels with diabetic retinopathy: NHANES 2011–2016

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: April 4, 2025

Background Several studies have established a clear link between serum selenium levels and various health outcomes. However, to date, only few found an association diabetic retinopathy (DR). The exact them is unclear. We collected data from different patient populations. Methods Data 645 adults, through the National Health Nutrition Examination Survey (NHANES) 2011 2016, were analyzed. incidence of DR was assessed using binary logistic regression. Subgroup analysis, smoothed curve-fitting propensity score weighting used investigate further. Results According multivariate there no statistically significant linear probability developing ( p > 0.05). Segmented regression however, showed that chance considerably lower when reached threshold 106.8 μg/L (OR = 0.88, 0.0107). Conclusion A U-shaped curve represents DR. elevated in individuals with are either higher or than optimal range.

Language: Английский

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Exploring the relationship between heavy metals and diabetic retinopathy: a machine learning modeling approach

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 6, 2024

Abstract Diabetic retinopathy (DR) is one of the leading causes adult blindness in United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there a lack analyses based on machine learning (ML) to adequately explain complex relationship between and DR interactions variables. Based characteristic variables participants with without metal exposure data obtained from NHANES database (2003–2010), ML model was developed effective prediction DR. The best predictive selected 11 models by receiver operating curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) SHapley Additive exPlanations (SHAP) analysis were used assess capability key influencing factors. A total 1042 eligible individuals randomly assigned two groups training testing set model. ROC showed k-nearest neighbour (KNN) had highest performance, achieving close 100% accuracy set. Urinary Sb level identified as critical affecting predicted DR, contribution weight 1.730632 ± 1.791722, which much higher than other baseline results PDP SHAP also indicated antimony (Sb) more significant effect interaction age compared pairs. We found could serve potential predictor influence development mediating cellular systemic senescence. study monitoring urinary levels can useful early non-invasive screening intervention development, highlighted important role constructed explaining effects

Language: Английский

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Associations of heavy metal exposure with diabetic retinopathy in the U.S. diabetic population: a cross-sectional study

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 1, 2024

Background Mounting evidence suggests a correlation between heavy metals exposure and diabetes. Diabetic retinopathy (DR) is prevalent irreversible complication of diabetes that can result in blindness. However, studies focusing on the effects to DR remain scarce. Thus, this study aimed investigate potential DR. Methods A total 1,146 diabetics from National Health Nutrition Examination Survey (NHANES) 2005 2018 were included study. Heavy metal levels measured via urine testing. Weighted logistic regression, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS) restricted cubic spline (RCS) utilized relationships 10 Finally, subgroup analysis was conducted based glycemic control status. Results Among participants, 239 (20.86%) diagnosed with Those had worse higher prevalence chronic kidney disease compared those without Moreover, both WQS BKMR models demonstrated positive relationship mixed risk The results revealed cobalt (Co) antimony (Sb) (OR = 1.489, 95%CI: 1.064–2.082, p 0.021; OR 1.475, 95% CI: 1.084–2.008, 0.014), while mercury (Hg) found promote exclusively group good 1.509, 1.157–1.967, 0.003). These findings corroborated by RCS analysis. Conclusion associated an increased DR, especially Sb, Co, Hg exposure. Nevertheless, well-designed prospective are warranted validate these findings.

Language: Английский

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Association between blood heavy metals and diabetic kidney disease among type 2 diabetic patients: a cross-sectional study

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 5, 2024

Studies on the correlation of exposure to metals with diabetic kidney disease (DKD) is scarce, especially concerning impact mixed DKD. This study aimed explore association blood heavy DKD risk among type 2 diabetes mellitus (T2DM) patients. cross-sectional enrolled patients T2DM in NHANES 2011–2020. ICP‒MS was applied detect five metals, namely, Pb, Cd, Hg, Se and Mn, blood. At same time, impacts single were assessed using multivariable logistic regression, WQS, BKMR models. The relationship examined based age, sex, BMI, hypertension, smoking status PIR. Totally 2362 participants for final analysis. Among them, 634 (26.84%) undergoing had Logistic regression indicated that, Pb (Q4: OR [95% CI]: 1.557 [1.175, 2.064]) related when all covariates adjusted. WQS analysis, which set a positive directional mode, suggested that correlated positively higher incidence In linear dose‒response curves generated fixing other 50th percentile. addition, significantly Subgroup analysis during demonstrated females, over 50 years, those 25 kg/m2, no under Serum albumin (ALB) did not regulate indirect risk. results showed increased metal concentration may lead an T2DM. Blood patients, especially, PIR According our observations, absorption at least slightly influences occurrence progression. More studies are needed validate this work illustrate relevant biological mechanism.

Language: Английский

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TraceEyeDisease: a web-based database for investigating trace elements and their imbalances in eye diseases

BMC Research Notes, Journal Year: 2024, Volume and Issue: 17(1)

Published: Nov. 12, 2024

Eye diseases remain a significant global health concern, with trace elements crucial in maintaining ocular and preventing disorders. In health, have been recognized as critical factors influencing the development progression of multiple eye diseases. this study, we conducted thorough literature search through PubMed to acquire data concerning different associated elements. These are essential element imbalances or deficiencies their progression. Our approach included meticulous compilation information from various databases, systematically integrated into carefully curated database. total, identified 178 distinct genes that encode proteins linked fourteen comprehensive list. A web-based database designed formulate evidence-based hypotheses regarding impact deficiency imbalance on was presented using Shiny R. This study underscores vital role preserving health. The R application facilitates subsequent investigations, fostering enhanced insights public clinical practices, disease research. URL TraceEyeDiseas is https://tredis.shinyapps.io/TraceEyeDisease/ .

Language: Английский

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