Restricted Cubic Spline Regression Research Articles

Sex-specific effects of maternal blood pressure on newborn telomere length: A prospective study.

To investigate the relationship between maternal blood pressure (BP) and neonatal cord blood telomere length (TL) during pregnancy, and to clarify the sensitive period. We conducted a prospective cohort study with 621 mother-newborn pairs from the Guangxi Zhuang Birth Cohort (GZBC) in China. Multiple informant models, restricted cubic spline regression (RCS) models, and quantile regression models were conducted to analyze the correlation between maternal BP and neonatal TL. Maternal diastolic blood pressure (DBP) was inversely related to neonatal cord blood TL in the second trimester (P = 0.015) and the third trimester (P = 0.011). There was a male-specific relationship between maternal BP and neonatal TL. A 1 mmHg increment in maternal systolic blood pressure (SBP) and DBP during the second trimester was related with 0.42% (95% CI: -0.80%, -0.04%) and 0.61% (95% CI: -1.13%, -0.09%) shorter TL in male newborns, respectively. Per unit increase of maternal DBP during the third trimester was related with 0.54% (95% CI: -1.03%, -0.05%) shorter TL in male newborns. Pregnant women with hypertensive disease of pregnancy (HDP) had male offspring with shorter TL (P = 0.003). However, no significant relationships were found in female newborns (P = 0.570). Maternal BP during pregnancy is inversely correlated with male neonatal TL and the second and third trimesters are sensitive windows.

A non-linear relationship between blood pressure and mild cognitive impairment in elderly individuals: A cohort study based on the Chinese longitudinal healthy longevity survey (CLHLS).

Hypertension is an established risk factor for mild cognitive impairment (MCI) in elderly individuals. Nevertheless, the impact of different levels of blood pressure on the progression of MCI remains uncertain. This study aims to investigate the non-linear relationship between blood pressure and MCI in the elderly and detect the critical blood pressure threshold, thus, improving blood pressure management for individuals at high risk of MCI. Data was obtained from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) cohort. We chose normal cognitive elderly individuals who entered the cohort in 2014 for a 5-year follow-up to observe the progression of MCI. Subsequently, we utilized the Cox regression model to identify risk factors for MCI and conducted a Cox-based restricted cubic spline regression (RCS) model to examine the non-linear relationship between systolic blood pressure (SBP) and diastolic blood pressure (DBP) with MCI, determining the critical blood pressure threshold for MCI progression. In the elderly population, female (HR = 1.489, 95% CI: 1.017-2.180), lacking of exercise in the past (HR = 1.714, 95% CI: 1.108-2.653), preferring animal fats (HR = 2.340, 95% CI: 1.348-4.061), increased age (HR = 1.061, 95% CI: 1.038-1.084), increased SBP (HR = 1.036, 95% CI: 1.024-1.048), and increased DBP (HR = 1.056, 95% CI: 1.031-1.081) were associated with MCI progression. After adjusting factors such as gender, exercise, preferred types of fats, and age, both SBP (P non-linear < 0.001) and DBP (P non-linear < 0.001) in elderly individuals exhibited a non-linear association with MCI. The risk of MCI rose when SBP exceeded 135mmHg and DBP was in the range of 80-88mmHg. However, when DBP exceeded 88mmHg, there was a declining trend in MCI progression, although the HR remained above 1. The identified critical blood pressure management threshold for MCI was 135/80mmHg. In this study, we discovered that risk factors affecting the progression of MCI in elderly individuals comprise gender (female), preferring to use animal fat, lack of exercise in the past, increased age, increased SBP, and increased DBP. Additionally, a non-linear relationship between blood pressure levels and MCI progression was confirmed, with the critical blood pressure management threshold for MCI onset falling within the prehypertensive range.

Association between eicosapentaenoic acid consumption and the risk of depressive symptoms in US adults: Analyses from NHANES 2005–2018

BackgroundThis study examines the relationship between eicosapentaenoic acid (EPA) intake from food and depression. EPA, an Omega-3 fatty acid commonly found in fish and seafood, has garnered attention for its potential role in depression prevention and treatment. MethodsWe selected 30,976 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018. Depressive symptoms were diagnosed using the Patient Health Questionnaire (PHQ-9). EPA intake was assessed through dietary evaluation. Logistic regression and restricted cubic spline regression (RCS) were employed to assess the correlation between EPA and depressive symptom. ResultsThe prevalence of depressive symptoms was 7.3 %. Participants with depressive symptoms exhibited lower EPA intake from food compared to non-depressed individuals. This negative association with depressive symptoms persisted even after accounting for various potential influencing factors (e.g., age, gender, body mass index, total energy intake, comorbidities). Notably, EPA demonstrated a nonlinear association with depressive symptoms, particularly in females. ConclusionsThis study emphasizes a significant negative correlation between EPA consumption and depressive symptoms, particularly in females. This suggests that maintaining a rich EPA diet may play a role in depression prevention and treatment.

Association between inflammation markers and all-cause mortality in critical ill patients with atrial fibrillation: Analysis of the Multi-Parameter Intelligent Monitoring in Intensive Care (MIMIC-IV) database

BackgroundInflammation is related to cardiovascular disease. Among the many inflammatory markers, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII) were considered as novel predictors for atherosclerosis outcomes. We aimed to investigate the impact of these inflammatory markers on the prognosis of patients with atrial fibrillation (AF). MethodsWe obtained data on AF patients from the Medical Information Mart for Intensive Care (MIMIC)-IV database. These patients were classified into two groups based on their survival status within 30 days. Then, they were divided into three groups based on the tertile of baseline NLR, PLR, and SII, respectively. We comprehensively explored the relationship between those inflammatory indicators and all-cause mortality in patients with AF by Kaplan-Meier analysis, multivariate Cox regression analysis, receiver operating characteristic (ROC) analyses, restricted cubic spline regression (RCS), and subgroup analysis. ResultsA total of 4562 patients with AF were included. Statistically significant differences were found between survivor and non-survivor groups for NLR, PLR and SII. Patients in the high tertile of the NLR had a higher mortality rate than those in the low and intermediate tertiles, as did patients in the PLR and the SII. NLR, PLR and SII were independently associated with increased risk of all-cause mortality. RCS showed that the 30-day and 365-day risk of death were linearly associated with increases in NLR, PLR, and SII, respectively. ConclusionNLR, PLR, and SII have the potential to be used as indicators for stratifying the risk of mortality in critically ill patients with AF.

Association between 2,4-dichlorophenoxyacetic acid and cognitive impairment in older adults: a cross-sectional study from NHANES 2001-2002 and 2011-2014.

2,4-Dichlorophenoxyacetic acid (2,4-D) is reported to be the most widely used herbicide in home and garden environments, rendering it commonly encountered in daily life. Despite being ubiquitous, there is a scarcity of studies that have comprehensively assessed the relationship between 2,4-D exposure and cognition using multiple models. To explore the association between 2,4-D exposure and cognition among older American people. This was a cross-sectional study that included 3 cycles of data from the National Health and Nutrition Examination Survey. Generalized linear models (GLMs), restricted cubic spline (RCS) regression, and generalized additive models (GAMs) were used to assess the relationship between exposure to 2,4-D and cognitive performance by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) word learning sub-test, Digit Symbol Substitution Test (DSST), and Animal Fluency Test (AFT). A total of 1364 older U.S. adults (60+ years) were included in the study. The GLMs revealed a negative association between median high levels(0.315-0.566μg/L) of 2,4-D and cognitive impairment on the DSST and AFT, with multivariate-adjusted ORs of 0.403 (95% CI: 0.208-0.781, P = 0.009) and 0.396 (95% CI: 0.159-0.986, P = 0.047); the RCS regression and GAMs revealed a "U" shaped curve, the left part of which is consistent with the result of the GLMs. There is a U-shaped relationship between human urinary 2,4-D concentrations andcognitive impairmentin older U.S. adults, especially in males, so controlling 2,4-D exposurewithin an appropriate range is particularly important for cognitive function.

Greedy knot selection algorithm for restricted cubic spline regression.

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one such method, for example, highly relevant to Cox proportional hazard regression model analysis. RCS regression uses third-order polynomials joined at knot points to model non-linear relationships. The standard approach is to place knots by a regular sequence of quantiles between the outer boundaries. A regression curve can easily be fitted to the sample using a relatively high number of knots. The problem is then overfitting, where a regression model has a good fit to the given sample but does not generalize well to other samples. A low knot count is thus preferred. However, the standard knot selection process can lead to underperformance in the sparser regions of the predictor variable, especially when using a low number of knots. It can also lead to overfitting in the denser regions. We present a simple greedy search algorithm using a backward method for knot selection that shows reduced prediction error and Bayesian information criterion scores compared to the standard knot selection process in simulation experiments. We have implemented the algorithm as part of an open-source R-package, knutar.

The association between atherogenic index of plasma and stroke in Chinese middle-aged and elderly population: a national cross-sectional study

Objective: To investigate the association between atherogenic index of plasma (AIP) and stroke. Methods: The 2015 China Health and Retirement Longitudinal Study (CHARLS) survey was used in this study. Logistic regression was adopted to examine the relationship between AIP, calculated as log (TG/HDL), and stroke. Subgroup analysis was used to further explore the associated factors. Restricted cubic spline (RCS) regression was adopted to estimate the non-linear association. Receiver operator characteristic (ROC) analysis was utilized to calculate the cutoff value of AIP. Results: In this study, 13,027 participants aged 45 years above were enrolled. The odds ratios in the fully adjusted logistic model across the quartiles of AIP were 1.00 (reference), 1.09 (95% CI 0.69–1.72), 1.62 (95% CI 1.06–2.49), 1.07 (1.08–2.58), respectively. Non-linear association between AIP and stroke was not found using RCS regression (p for non-linearity &gt; 0.05). The subgroup analyses showed that the association between AIP and stroke varied in different gender, alcohol and cigarette consumption, but the interaction effects were insignificant (all p &gt; 0.05). The cutoff value of AIP was 0.82, and the area under the curve (AUC) was 0.57. Conclusion: Elevated AIP is significantly associated with the increased risk of stroke in the middle- aged and elderly Chinese, indicating that AIP may be a predictive factor for stroke among the Chinese middle-aged and elderly.

Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study

BackgroundThe triglyceride-glucose index (TyG), an established indicator of insulin resistance, is closely correlated with the prognosis of several metabolic disorders. This study aims to investigate the association between the TyG index and the incidence of critical delirium in patients aged 65 years and older.MethodsWe focused on evaluating patients aged 65 years and older diagnosed with critical delirium. Data were obtained from the Medical Information Database for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Multivariate logistic regression and restricted cubic spline (RCS) regression were used to determine the relationship between the TyG index and the risk of delirium.ResultsParticipants aged 65 years and older were identified from the MIMIC-IV (n = 4,649) and eICU-CRD (n = 1,844) databases. Based on optimal thresholds derived from RCS regression, participants were divided into two cohorts: Q1 (< 8.912), Q2 (≥ 8.912). The logistic regression analysis showed a direct correlation between the TyG index and an increased risk of critical delirium among ICU patients aged 65 and older. These findings were validated in the eICU-CRD dataset, and sensitivity analysis further strengthened our conclusions. In addition, the subgroup analysis revealed certain differences.ConclusionThis study highlights a clear, independent relationship between the TyG index and the risk of critical delirium in individuals aged 65 years and older, suggesting the importance of the TyG index as a reliable cardio-cerebrovascular metabolic marker for risk assessment and intervention.

The Relationship between Cadmium Exposure and Mortality in Postmenopausal Females: A Cohort Study of 2001–2018 NHANES

Cadmium is one of the most harmful elements to human health, and the health of postmenopausal females is an important public health issue. However, the correlation between exposure to cadmium and the survival status of postmenopausal women is currently not fully clear. This research intended to explore the correlation between cadmium exposure and mortality among postmenopausal females using a representative sample of the population in the U.S. We drew upon the data of the National Health and Nutrition Examination Survey (2001–2018). Cox’s proportional hazards models and a restricted cubic spline regression (RCS) model were utilized to analyze the correlation between blood and urine cadmium and the mortality of postmenopausal women. Stratified analyses also were conducted to identify the highest risk factor of mortality for the participants. The mean concentration of blood cadmium was 0.59 μg/L, and the mean concentration of urine cadmium was 0.73 μg/g creatinine. Higher cadmium concentrations in blood and urine were significantly related to an increase in all-cause mortality for postmenopausal females after adjustment for multivariate covariates. Furthermore, there was a linear positive correlation between urine cadmium concentrations and cancer mortality, while there was no correlation between blood cadmium and cancer death. The correlation between cadmium concentrations and all-cause mortality is stronger in older, more overweight women with a history of hypertension or smoking. We propose that cadmium remains an important risk factor of all-cause and cancer mortality among postmenopausal females in the U.S. Further decreases in cadmium exposure in the population can promote the health of postmenopausal women and prolong their lifespan.

Exposure to polycyclic aromatic hydrocarbons increases the risk of poor sleep pattern in US adults: results from the NHANES (2005-2010).

Recently, polycyclic aromatic hydrocarbons (PAHs) were found to be linked to various diseases. The current study's objective was to explore whether or not there was a relation between PAH exposure and poor sleep pattern. We evaluated nine urine PAH metabolites as exposures in our cross-sectional research based on the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2010. Logistic regression, restricted cubic spline regression (RCS) model, weighted quantile sum (WQS) regression, subgroup analysis, and mediation analysis were used to assess the associations between PAH metabolism and poor sleep pattern risk. After controlling for all confounding variables, several primary PAH metabolites, namely 1-hydroxynapthalene (1-NAP, OR 1.32, 95% CI 1.04-1.68), 2-hydroxyfluorene (2-FLU, OR 1.34, 95% CI 1.05-1.71), 1-hydroxyphenanthrene (1-PHE, OR 1.30, 95% CI 1.03-1.64), 9-hydroxyfluorene (9-FLU, OR 1.38, 95% CI 1.09-1.74), and ∑PAHs (OR 1.33, 95% CI 1.05-1.69), compared to the bottom tertile, were associated with increased risk of poor sleep pattern. The WQS regression analysis showed that 9-FLU and 1-NAP comprised the two most important factors related to poor sleep pattern. Mediation analysis revealed that inflammation acted as a mediator between PAHs and the prevalence of poor sleep pattern. In conclusion, exposure to PAHs may be associated with poor sleep pattern. Inflammation is a mediator of the effects of PAH exposure on poor sleep pattern.

Association of glyphosate exposure with multiple adverse outcomes and potential mediators

Glyphosate (GLY) is a widely used herbicide with potential adverse effects on public health. However, the current epidemiological evidence is limited. This study aimed to investigate the potential associations between exposure to GLY and multiple health outcomes. The data on urine GLY concentration and nine health outcomes, including type 2 diabetes mellitus (T2DM), hypertension, cardiovascular disease (CVD), obesity, chronic kidney disease (CKD), hepatic steatosis, cancers, chronic obstructive pulmonary disease (COPD), and neurodegenerative diseases (NGDs), were extracted from NHANES (2013–2016). The associations between GLY exposure and each health outcome were estimated using reverse-scale Cox regression and logistic regression. Furthermore, mediation analysis was conducted to identify potential mediators in the significant associations. The dose-response relationships between GLY exposure with health outcomes and potential mediators were analyzed using restricted cubic spline (RCS) regression. The findings of the study revealed that individuals with higher urinary concentrations of GLY had a higher likelihood of having T2DM, hypertension, CVD and obesity (p < 0.001, p = 0.005, p < 0.001 and p = 0.005, respectively). In the reverse-scale Cox regression, a notable association was solely discerned between exposure to GLY and the risk of T2DM (adjusted HR = 1.22, 95% CI: 1.10, 1.36). Consistent outcomes were also obtained via logistic regression analysis, wherein the adjusted OR and 95% CI for T2DM were determined to be 1.30 (1.12, 1.52). Moreover, the present investigation identified serum high-density lipoprotein cholesterol (HDL) as a mediator in this association, with a mediating effect of 7.14% (p = 0.040). This mediating effect was further substantiated by RCS regression, wherein significant dose-response associations were observed between GLY exposure and an increased risk of T2DM (p = 0.002) and reduced levels of HDL (p = 0.001). Collectively, these findings imply an association between GLY exposure and an increased risk of T2DM in the general adult population.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BackgroundExposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods. ObjectsWe aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data. MethodsPre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011–2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA. ResultsThe prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02–1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13–1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA. ConclusionsHeavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

Negative association between triglyceride glucose index and BMI-adjusted skeletal muscle mass index in hypertensive adults

BackgroundThe triglyceride glucose (TyG) index, an indicator of insulin resistance, is often associated with adverse outcomes in various cardiovascular diseases, while hypertension is associated with an increased risk of cardiovascular diseases. As the loss of muscle mass in people with hypertension is poorly understood, the current study aimed to explore the relationship between TyG index and muscle mass in hypertensive population.MethodsWe analyzed data from hypertensive adult participants in the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. The TyG index and body mass index (BMI)-adjusted skeletal muscle mass index (SMI) were calculated and the relationship between the two was evaluated using multivariable linear regression and restricted cubic spline (RCS) regression models.ResultsA total of 1633 participants in the dataset were included for the final analysis. In the multivariable regression analysis, the adjusted β of SMI with a 95% confidence interval (CI) for the highest TyG index quartile was − 5.27 (− 9.79 to − 0.75), compared with the lowest quartile. A negative linear relationship between TyG index and SMI was plotted by RCS regression (nonlinear P = 0.128). Stratified models of non-smoking women of different ages also demonstrated that SMI decreased as TyG index increased (all P for trend < 0.05).ConclusionThis linear and negative correlation between TyG index and SMI in hypertensive patients suggests that insulin resistance adversely affects muscle mass.

Age is a significant biomarker for the selection of neoadjuvant chemotherapy plus radiotherapy versus concurrent chemoradiotherapy in patients with advanced nasopharyngeal carcinoma.

The optimal timing of combined chemotherapy with radiotherapy for locally advanced nasopharyngeal carcinoma (LA-NPC) is undetermined. This study aimed to compare the therapeutic efficacy of neoadjuvant chemotherapy (NACT) followed by radiotherapy (RT) and concurrent chemoradiotherapy (CCRT). Five hundred and thirty-eight patients diagnosed with LA-NPC and treated with NACT + RT or CCRT alone were enrolled in the study. Restricted cubic spline regression (RCS) was used to determine the relationship between age and the hazard Ratio of death. A Kaplan-Meier analysis was performed to evaluate overall survival (OS) related to NACT + RT or CCRT alone. Cox proportional hazards models were used to adjust for potential confounding factors. Compared with the CCRT alone regimen, the NACT + RT regimen showed a significantly better OS rate with a 62% decreased risk of death in a subgroup of patients aged ⩾ 45 years (hazard ratio, HR: 0.38; 95% confidence interval, CI: 0.24-0.61). In patients aged < 45 years, the risk of death was significantly increased when NACT + RT was chosen compared with CCRT (HR: 4.10; 95% CI: 2.09-8.07). Age is a significant biomarker when selecting NACT + RT or CCRT alone in patients with advanced NPC.

Associations between smoke exposure and osteoporosis or osteopenia in a US NHANES population of elderly individuals.

Tobacco exposure is considered to be a risk factor for reduced bone mineral density (BMD), which may result in osteopenia. Cotinine, a metabolite of nicotine, is commonly utilized as a marker of tobacco exposure. Nevertheless, there are limited clinical data on the associations between osteoporosis (OP) or osteopenia and smoking status or serum cotinine level. We thoroughly examined the NHANES cross-sectional data from 2005 to 2010, 2013 to 2014, and 2017 to 2018. Multivariate logistic regression models were applied to assess the associations among smoking status and serum cotinine levels as well as OP and osteopenia. The relationships between serum cotinine level and OP and osteopenia were also assessed using the restricted cubic spline (RCS) method. A total of 10,564 participants were included in this cross-sectional study. The mean age of the study population was 64.85 ± 9.54 years, and the patients were predominantly male (51.9%). We found that the relationships between higher serum cotinine levels (≥3 ng/ml) and the prevalence of osteoporosis (Model 1: OR=2.27 [1.91-2.69]; Model 2: OR=2.03 [1.70-2.43]; Model 3: OR=2.04 [1.70-2.45]; all p for trend <0.001) remained significant after adjustment for covariates by applying the lowest serum cotinine levels (<0.05 ng/ml) as the reference. Similar results were observed for current smokers, who were more likely to develop OP compared with nonsmokers (Model 1: OR=2.30 [1.90-2.79]; Model 2: OR=2.16 [1.77-2.64]; Model 3: OR=2.16 [1.77-2.65]). Moreover, higher serum cotinine levels were found to be strongly and positively correlated with the prevalence of osteopenia (OR=1.60 [1.42-1.80]). A similar relationship was observed between current smokers and the prevalence of osteopenia compared with nonsmokers (OR=1.70 [1.49-1.94]). RCS regression also showed that serum cotinine levels were nonlinearly and positively correlated with OP and osteopenia, with inflection points of 5.82 ng/ml and 3.26 ng/ml, respectively. This study showed that being a smoker was associated with the prevalence of OP or osteopenia compared with being a nonsmoker and that there was a strong nonlinear positive dose-response relationship between serum cotinine levels and OP and osteopenia.

Frailty and hyperactive delirium in hospitalized older patients with COVID-19: an insight from GeroCovid registry.

Delirium is an acute neuropsychiatric condition associated with unfavourable outcomes, frequent in older hospitalized people. In the context of the SARS-CoV-2 pandemic, few studies have specifically focused on the inflammatory status of older, frail patients with hyperactive delirium (HD) hospitalized for COVID-19. To identify biological correlates of HD at hospital admission and to assess the independent effect of delirium and physical frailty on in-hospital mortality. Data were retrospectively extracted by the multicenter registry GeroCovid Observational Study. Individuals aged ≥ 60years were included if the information on the presence of HD, frailty based on themodified Fried criteria and inflammatory status had been collected. The risk of mortality was evaluated using a Kaplan-Meier estimator, according to frailty and delirium. Logistic and restricted cubic-spline regressions were employed to assess the relationship between inflammatory markers and HD. Three-hundred-thirty-seven older adults were included in the analysis [mean age (SD) 77.1 (9.5) years, 50.1% females], and 11.5% presented with HD. A significant association of both PaO2/FiO2 ratio (p = 0.015) and serum lactate dehydrogenase (p = 0.04) with delirium was observed. By Cox multivariable regression, frail and non-frail patients with HD had a 4.42 and 2.85 higher mortality risk compared with non-frail, non-delirious patients. Hyperactive delirium at hospital admission is related with markers of lung failure among older adults, especially when physical frailty coexists. Delirium is associated with increased in-hospital mortality risk, which is doubled by the coexistence of physical frailty.

Assessing the longitudinal association between the GGT/HDL-C ratio and NAFLD: a cohort study in a non-obese Chinese population

BackgroundA cross-sectional association between the combination indicator of high-density lipoprotein cholesterol (HDL-C) and gamma-glutamyl transferase (GGT) and fatty liver has been described in several recent studies, and this study aims to further evaluate the longitudinal relationship between the ratio of GGT to HDL-C (GGT/HDL-C ratio) and nonalcoholic fatty liver disease (NAFLD).MethodsThis cohort study included 12,126 individuals without NAFLD at baseline, followed prospectively for 5 years, and the endpoint of interest was new-onset NAFLD. The relationship of the GGT/HDL-C ratio with new-onset NAFLD and the shape of the association was assessed by Cox regression models and restricted cubic spline (RCS) regression, respectively. Time-dependent receiver operator characteristics (ROC) curves were constructed to evaluate the predictive value of GGT, HDL-C, GGT/HDL-C ratio and BMI for the occurrence of NAFLD at different time points in the future.ResultsThe prevalence of NAFLD was 72.46/1000 person-years during the 5-year follow-up period. Results of multivariate Cox regression analysis showed a positive association of the GGT/HDL-C ratio with new-onset NAFLD after adequate adjustment of the related confounding factors, and the degree of correlation was slightly higher than that of GGT, and further subgroup analysis found that this association was more significant in the population with elevated systolic blood pressure (SBP). In addition, we also found a nonlinear relationship of the GGT/HDL-C ratio with the risk of new-onset NAFLD using the RCS regression, where the saturation threshold was about 31.79 U/mmol. Time-dependent ROC analysis results showed that the GGT/HDL-C ratio was increasingly valuable in predicting NAFLD over time, and was better than HDL-C in predicting NAFLD in the early stage (1–3 years), but was not superior to BMI and GGT.ConclusionsIn this large longitudinal cohort study based on a Chinese population, our results supported that the GGT/HDL-C ratio was positively and nonlinearly associated with the risk of new-onset NAFLD in a non-obese population. In the assessment of future NAFLD risk, the GGT/HDL-C ratio was slightly better than GGT alone; However, the GGT/HDL-C ratio did not appear to have a significant advantage over GGT and BMI alone in predicting NAFLD.

The impact of sleep-corrected social jetlag on mental health, behavioral problems, and daytime sleepiness in adolescents

Study objectivesThis study aimed to examine the effect of sleep-corrected social jetlag (SJLsc) on mental health, behavioral problems, and daytime sleepiness in adolescents. MethodsThis was a cross-sectional study which included 4787 adolescents (Mean age: 14.83±1.6y, 56.0% girls) recruited from 15 secondary schools in Hong Kong. SJLsc was defined as the absolute difference between sleep-corrected midsleep on weekdays and weekends, at which the sleep debt has been considered. It was classified into three groups: low-level (“LSJLsc”, <1h), mid-level (“MSJLsc”, ≥1h and <2h), and high-level of SJLsc (“HSJLsc”, ≥2h). Adolescents’ mental health, behavioral problems and daytime sleepiness were measured by the General Health Questionnaire (GHQ-12), the Strengths and Difficulties Questionnaire (SDQ) and the Pediatric Daytime Sleepiness Scale (PDSS). Logistic regression analysis and restricted cubic spline regression (RCS) analysis were applied with consideration of confounders including age, gender, puberty and sleep problems. ResultsNearly half (46.9%) of adolescents had SJLsc for at least 1 h. Greater SJLsc was associated with more behavioral difficulties (MSJLsc: OR: 1.20, p = 0.03; HSJLsc: OR: 1.34, p = 0.02) when controlling for age, sex, puberty, chronotype, insomnia, and time in bed. There was a dose-response relationship in which higher SJLsc had an increased risk of conduct problems and hyperactivity, while only high-level SJLsc was associated with a peer relationship problem. In RCS analysis, SJLsc was associated with a higher likelihood of behavioral difficulties (p = 0.03) but not poor mental health or daytime sleepiness. ConclusionsSleep-corrected social jetlag was a unique risk factor for behavioral problems in adolescents. Our findings highlighted the need for interventions to promote healthy sleep-wake patterns in school adolescents.

Modeling non-linear relationships in epidemiological data: The application and interpretation of spline models.

Traditional methods to deal with non-linearity in regression analysis often result in loss of information or compromised interpretability of the results. A recommended but underutilized method for modeling non-linear associations in regression models is spline functions. We explain spline functions in a non-mathematical way and illustrate the application and interpretation to an empirical data example. Using data from the Amsterdam Growth and Health Longitudinal Study, we examined the non-linear relationship between the sum of four skinfolds and VO2max, which are measures of body fat and cardiorespiratory fitness, respectively. We compared traditional methods (i.e., quadratic regression and categorization) to spline methods [1- and 3-knot linear spline (LSP) models and a 3-knot restricted cubic spline (RCS) model] in terms of the interpretability of the results and their explained variance (). The spline models fitted the data better than the traditional methods. Increasing the number of knots in the LSP model increased the explained variance (from for the 1-knot model to for the 3-knot model). The RCS model fitted the data best (), but results in regression coefficients that are harder to interpret. Spline functions should be considered more often as they are flexible and can be applied in commonly used regression analysis. RCS regression is generally recommended for prediction research (i.e., to obtain the predicted outcome for a specific exposure value), whereas LSP regression is recommended if one is interested in the effects in a population.

Association between kaolin-induced maximum amplitude and slow-flow/no-reflow in ST elevation myocardial infarction patients treated with primary percutaneous coronary intervention

BackgroundST-segment elevation myocardial infarction (STEMI) patients with a high thrombus burden have a relatively high slow-flow/no-reflow risk. However, the association between kaolin-induced maximum amplitude (MAthrombin) and slow-flow/no-reflow has been scarcely explored. MethodsSTEMI patients treated with primary percutaneous coronary intervention (PCI) were retrospectively enrolled from January 2015 to December 2019 at China-Japan Friendship Hospital. MAthrombin levels were measured using thromboelastography before the PCI procedure. The patients were divided into two groups according to thrombolysis in myocardial infarction (TIMI) flow grade after primary PCI: the normal flow group (TIMI flow grade = 3) and slow-flow/no-reflow (TIMI flow grade ≤ 2). The logistic regression model and restricted cubic spline regression (RCS) were used to analyze the predictive value of MAthrombin for slow-flow/no-reflow. All patients were followed up after discharge and observed the adverse cardiovascular events between the two groups. ResultsA total of 690 patients were enrolled, with 108(15.7%) having slow-flow/no-reflow. The multivariate logistic regression model analysis showed that MAthrombin level was an independent risk factor for slow-flow/no-reflow. The RCS analysis showed a nonlinear relationship between MAthrombin levels and slow-flow/no-reflow. The cut-off value of MAthrombin levels for predicting slow-flow/no-reflow was 68 mm. During a median follow-up time of 4.4 years, slow-flow/no-reflow (hazard ratio 1.93, 95% confidence interval 1.27–2.93, P = 0.002) and MAthrombin levels (hazard ratio 1.06, 95% confidence interval 1.03–1.08, P < 0.001) were independent risk factors for predicting the long-term of adverse clinical cardiovascular events. ConclusionMAthrombin was an independent risk factor for predicting slow-flow/ no-reflow in STEMI patients who underwent primary PCI.