scholarly journals Development and validation of two composite aging measures using clinical biomarkers in the Chinese population

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 887-887
Author(s):  
Zuyun Liu
Keyword(s):  
Mahalanobis Distance ◽  
Chinese Population ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Clinical Biomarkers ◽  
Physiological Dysregulation ◽  
And Gender ◽  
Cox Proportional Hazard Regression ◽  
One Year ◽  
Development And Validation

Abstract Quantifying aging is crucial for addressing aging and related issues. This study aimed to: 1) develop two composite aging measures in the Chinese population using two recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and 2) validate the two measures by examining their associations with mortality and disease counts. Based on data from the China Nutrition and Health Survey 2009 wave (N=8,119, aged 20-79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 routine clinical biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N=9,304, aged 45-99 years, 53.4% women). We found that both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per one-year, HR=1.14, 95%CI=1.08, 1.19; PD, per one-SD, HR=1.50, 95%CI=1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The two aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although four biomarkers were not available. In summary, we successfully developed and validated two composite aging measures‒‒KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging related diseases in China.

scholarly journals Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies

2020 ◽  
Author(s):  
Zuyun Liu
Keyword(s):  
Mahalanobis Distance ◽  
Prospective Cohort ◽  
Chinese Population ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Clinical Biomarkers ◽  
Physiological Dysregulation ◽  
And Gender ◽  
Cox Proportional Hazard Regression ◽  
Development And Validation

Abstract Background This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts. Methods Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20–79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45–99 years, 53.4% women). Results Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available. Conclusions We successfully developed and validated 2 composite aging measures—KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.

scholarly journals Predicting drowning from sea and weather forecasts: development and validation of a model on surf beaches of southwestern France

2021 ◽  
pp. injuryprev-2020-044092
Author(s):  
Éric Tellier ◽  
Bruno Simonnet ◽  
Cédric Gil-Jardiné ◽  
Marion Lerouge-Bailhache ◽  
Bruno Castelle ◽  
...  
Keyword(s):  
Regression Models ◽  
Area Under The Curve ◽  
Temperature Wave ◽  
Medical Emergency ◽  
Emergency Center ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Step Model ◽  
Daily Risk ◽  
Development And Validation

ObjectiveTo predict the coast-wide risk of drowning along the surf beaches of Gironde, southwestern France.MethodsData on rescues and drownings were collected from the Medical Emergency Center of Gironde (SAMU 33). Seasonality, holidays, weekends, weather and metocean conditions were considered potentially predictive. Logistic regression models were fitted with data from 2011 to 2013 and used to predict 2015–2017 events employing weather and ocean forecasts.ResultsAir temperature, wave parameters, seasonality and holidays were associated with drownings. Prospective validation was performed on 617 days, covering 232 events (rescues and drownings) reported on 104 different days. The area under the curve (AUC) of the daily risk prediction model (combined with 3-day forecasts) was 0.82 (95% CI 0.79 to 0.86). The AUC of the 3-hour step model was 0.85 (95% CI 0.81 to 0.88).ConclusionsDrowning events along the Gironde surf coast can be anticipated up to 3 days in advance. Preventative messages and rescue preparations could be increased as the forecast risk increased, especially during the off-peak season, when the number of available rescuers is low.

Practice Patterns in Perioperative Nonopioid Analgesic Administration by Anesthesiologists in a Veterans Affairs Hospital

Pain Medicine ◽  
2019 ◽  
Author(s):  
Jereen Z Kwong ◽  
Seshadri C Mudumbai ◽  
Tina Hernandez-Boussard ◽  
Rita A Popat ◽  
Edward R Mariano
Keyword(s):  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Multimodal Analgesia ◽  
Veterans Affairs ◽  
Future Directions ◽  
Logistic Regression Models ◽  
Risks And Benefits ◽  
Perioperative Pain Management ◽  
One Year ◽  
Nonopioid Analgesic

Abstract Objective Although multimodal analgesia (MMA) is recommended for perioperative pain management, previous studies have found substantial variability in its utilization. To better understand the factors that influence anesthesiologists’ choices, we assessed the associations between patient or surgical characteristics and number of nonopioid analgesic modes received intraoperatively across a variety of surgeries in a university-affiliated Veteran Affairs hospital. Methods We included elective inpatient surgeries (orthopedic, thoracic, spine, abdominal, and pelvic procedures) that used at least one nonopioid analgesic within a one-year period. Multivariable multinomial logistic regression models were used to estimate adjusted odds ratios and 95% confidence intervals (CIs). We also described the combinations of analgesia used in each surgical subtype and conducted exploratory analyses to test the associations between the number of modes used and postoperative outcomes. Results Of the 1,087 procedures identified, 33%, 53%, and 14% were managed with one, two, and three or more modes, respectively. Older patients had lower odds of receiving three or more modes (adjusted odds ratio [aOR] = 0.28, 95% confidence interval [CI] = 0.15–0.52), as were patients with more comorbidities (two modes: aOR = 0.87, 95% CI = 0.79–0.96; three or more modes: aOR = 0.81, 95% CI = 0.71–0.94). Utilization varied across surgical subtypes P < 0.0001). Increasing the number of modes, particularly use of regional anesthesia, was associated with shorter length of stay. Conclusions Our study suggests that age, comorbidities, and surgical type contribute to variability in MMA utilization. Risks and benefits of multiple modes should be carefully considered for older and sicker patients. Future directions include developing patient- and procedure-specific perioperative MMA recommendations.

scholarly journals One-year outcomes of early-crossover patients in a cohort receiving nonoperative care for lumbar disc herniation

2017 ◽  
Vol 27 (4) ◽  
pp. 391-396 ◽  
Author(s):  
Anurekha Ramakrishnan ◽  
K. Michael Webb ◽  
Matthew C. Cowperthwaite
Keyword(s):  
Lumbar Disc Herniation ◽  
Disc Herniation ◽  
Regression Models ◽  
Lumbar Disc ◽  
Recursive Partitioning ◽  
Chi Square ◽  
Logistic Regression Models ◽  
Spine Intervention ◽  
Euroqol 5D ◽  
One Year

OBJECTIVEThe authors comprehensively studied the recovery course and 1-year outcomes of early-crossover patients who were randomized to the nonoperative care arm of the Leiden–The Hague Spine Intervention Prognostic Study. The primary goal was to gain insight into the differences in the recovery patterns of early-crossover patients and those treated nonoperatively; secondary goals were to identify predictors of good 1-year outcomes, and to understand when and why patients were likely to cross over.METHODSIndividual EuroQol-5D scores were obtained at baseline and at 2, 4, 8, 12, 26, 38, and 52 weeks for 142 patients. Early-crossover patients were defined as those electing to undergo surgery during the first 12 weeks of treatment. Crossover and noncrossover groups were compared using Kruskal-Wallis, Wilcoxon-Mann-Whitney, and chi-square tests. Linear mixed-effects models were used to examine the growth trajectories of crossover and noncrossover groups. Recursive partitioning trees were used to model crossover events and the timing of crossover decisions. Multivariable logistic regression models were used to identify predictors of good 1-year outcomes.RESULTSOf the 142 patients randomized to receive prolonged nonoperative care, 136 were selected for the study. In this cohort, 43/136 (32%) opted for surgery, and 31/43 (72%) of crossover events occurred before the 12-week time point. Early-crossover patients had significantly greater functional impairment at Week 2 than noncrossover patients (p = 0.031), but experienced greater recovery by 26 weeks and better 1-year outcomes (p = 0.045). Patients who did not experience an improvement in their symptoms between 2 and 8 weeks were more likely to cross over (OR 3.5, 95% CI 1.2–10.1; p = 0.01). Recursive partitioning trees were able to identify crossover patients with 76% accuracy. Regression models suggested that better recovery at 26 weeks (p < 0.01) was predictive of good 1-year outcome; declining health status between Weeks 4 and 8 was negatively predictive of good outcome (p < 0.01).CONCLUSIONSThis study is the first to comprehensively analyze the recovery and outcomes of crossover patients, and compare them to nonoperatively treated patients. The results suggest that patients who have a low EuroQol-5D score during the early weeks of treatment and who do not respond to nonoperative care during the first few weeks of treatment are most likely to cross over. Early-crossover patients experience a greater rate of recovery and more frequently have a good 1-year outcome when compared with nonoperatively treated patients. The current results motivate a broader investigation into the timing of surgery and the identification of patient populations that will be most benefited by early surgical treatment for lumbar disc herniation.

scholarly journals Association of Sodium, Potassium and Sodium-to-Potassium Ratio with Urine Albumin Excretion among the General Chinese Population

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3456
Author(s):  
Yuewen Sun ◽  
Puhong Zhang ◽  
Yuan Li ◽  
Feng J. He ◽  
Jing Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Renal Function ◽  
Chinese Population ◽  
Regression Models ◽  
Mixed Effects ◽  
Sodium Potassium ◽  
Logistic Regression Models ◽  
Albumin Excretion ◽  
Renal Function Impairment ◽  
Function Impairment

Mixed evidence was published regarding the association of sodium, potassium and sodium-to-potassium ratio (Na/K ratio) with renal function impairment. This study was conducted to further explore the relationship between sodium, potassium, NA/K ratio and kidney function in the general adult Chinese population. We performed a cross-sectional analysis using the baseline data from the Action on Salt China (ASC) study. 5185 eligible general adult participants from the baseline investigation of the ASC study were included in this analysis. Sodium, potassium and albumin excretion were examined from 24-h urine collection. Albuminuria was defined as albumin excretion rate (AER) greater than or equal to 30 mg/24-h. Mixed linear regression models, adjusted for confounders, were fitted to analyze the association between sodium, potassium and Na/K ratio, and natural log transformed AER. Mixed effects logistic regression models were performed to analyze the odds ratio of albuminuria at each quintile of sodium, potassium and Na/K ratio. The mean age of the participants was 49.5 ± 12.8 years, and 48.2% were male. The proportion of albuminuria was 7.5%.The adjusted mixed linear models indicated that sodium and Na/K ratio was positively associated with natural log transformed AER (Sodium: β = 0.069, 95%CI [0.050, 0.087], p < 0.001; Na/K ratio: β = 0.026, 95%CI [0.012, 0.040], p < 0.001). Mixed effects logistic regression models showed that the odds of albuminuria significantly increased with the quintiles of sodium (p < 0.001) and Na/K ratio (p = 0.001). No significant association was found between potassium and the outcome indicators. Higher sodium intake and higher Na/K ratio are associated with early renal function impairment, while potassium intake was not associated with kidney function measured by albumin excretion.

scholarly journals Differential Methylation in APOE Genes (Chr19; exon four; from 44,909,188 to 44,909,373) and Increased Apolipoprotein E Plasmatic Levels in Subjects with Mild Cognitive Impairment”

2019 ◽  
Author(s):  
Oscar Mancera Páez ◽  
Kelly Estrada Orozco ◽  
Maria Fernanda Mahecha ◽  
Francy Cruz Sanabria ◽  
Kely Bonilla-Vargas ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Apolipoprotein E ◽  
Social History ◽  
Regression Models ◽  
Neuropsychological Testing ◽  
Logistic Regression Models ◽  
And Gender ◽  
Epigenetic Patterns ◽  
Risk Association

Background: Biomarkers are essential for identification of individuals at high risk of mild cognitive impairment (MCI) for potential prevention of dementia. We investigated DNA methylation in the ApoE gene and plasmatic apolipoprotein E (ApoE) levels as MCI biomarkers in Colombian subjects with MCI and controls. Methods: 100 participants were included (71% women, average age, 70 yrs., range 43-91). MCI was diagnosed by neuropsychological testing, medical and social history, activities of daily living, cognitive symptoms and neuroimaging. Multivariate logistic regression models adjusted by age and gender were performed to examine the risk association of MCI with plasma ApoE and APOE methylation Results: MCI was diagnosed in 41 subjects (average age, 66.5±9.6 yrs.) and compared with 59 controls. Elevated plasma ApoE and APOE methylation of CpGs 165, 190, and 198 were risk factors for MCI (P&lt;0.05). Higher CpG-227 methylation correlated with lower risk for MCI (P=0.002). Only CpG-227 was significantly correlated with plasmatic ApoE levels (correlation coefficient=-0.665; P=0.008). Conclusion: Differential APOE methylation and increased plasma ApoE levels were correlated with MCI. These epigenetic patterns can be used as potential biomarkers to identify early stages of MCI.

scholarly journals Association Between Dietary Patterns and Prediabetes Risk in A Middle-Aged Chinese Population

2020 ◽  
Author(s):  
Xiao-Ming Shen ◽  
Yi-Qian Huang ◽  
Xiao-Yan Zhang ◽  
Xiao-Qing Tong ◽  
Pei-Fen Zheng ◽  
...  
Keyword(s):  
Dietary Patterns ◽  
Chinese Population ◽  
Regression Models ◽  
Multivariate Logistic Regression ◽  
Middle Aged ◽  
Food Frequency ◽  
Logistic Regression Models ◽  
Southern Chinese ◽  
Bottom Quartile ◽  
Confounding Variables

Abstract Background: Information regarding dietary patterns associated with prediabetes in the Chinese population is lacking. The objective of the present study was to explore the association between major dietary patterns and risk of prediabetes in a middle-aged Chinese population. Methods: A total of 1761 participants (aged 45 to 59 years) were recruited in Hangzhou city, the capital of Zhejiang Province, China from June 2015 to December 2016. Dietary information was obtained by interview using a 138-item, validated semi-quantitative food frequency questionnaire(SQFFQ). Multivariate logistic regression models were used to analyze the association between dietary patterns and the risk of prediabetes with adjustment of potential confounding variables. Results: Three dietary patterns were ascertained by factor analysis and labeled as traditional southern Chinese, Western, and grains-vegetables patterns. After controlling of the potential confounders, participants in the top quartile of the Western pattern scores had greater odds ratio(OR) for prediabetes (OR=1.54; 95% confidence interval(CI):1.068-2.059; P =0.025) than did those in the bottom quartile. Compared with those in the bottom quartile, participants in the top quartile of the grains-vegetables pattern scores had a lower OR for prediabetes(OR=0.83; 95% CI:0.747-0.965; P =0.03). Besides, no statistically significant association was observed in the association between the traditional southern Chinese pattern and prediabetes risk ( P >0.05). Conclusions: The findings of this study showed that the Western pattern was associated with a higher risk, and the grains-vegetables pattern was associated with a lower risk of prediabetes. Future prospective studies are required to validate our findings.

scholarly journals Relationship between Kidney Stone Disease and Arterial Stiffness in a Taiwanese Population

2020 ◽  
Vol 9 (6) ◽  
pp. 1693
Author(s):  
Zih-Jie Sun ◽  
Hsuan-Jung Hsiao ◽  
Hsiang-Ju Cheng ◽  
Chieh-Ying Chou ◽  
Feng-Hwa Lu ◽  
...  
Keyword(s):  
Arterial Stiffness ◽  
Kidney Stone ◽  
Regression Models ◽  
Pulse Wave ◽  
Stone Disease ◽  
Taiwanese Population ◽  
Kidney Stone Disease ◽  
Logistic Regression Models ◽  
And Gender ◽  
The Relationship

Previous studies examining the association between kidney stone disease (KSD) and arterial stiffness have been limited. Both age and gender have been found to have an impact on KSD, but their influence on the relationship between KSD and increased arterial stiffness is unclear. This study included 6694 subjects from October 2006 to August 2009. The diagnosis of kidney stone was based on the results of ultrasonographic examination. Increased arterial stiffness was defined as right-sided brachial-ankle pulse wave velocity (baPWV) ≥ 14 m/s. Associations between KSD and increased arterial stiffness were analyzed using multiple logistic regression models. KSD was positively related to increased arterial stiffness in both male and female groups (males: odds ratio [OR], 1.306; 95% confidence interval [CI], 1.035–1.649; females: OR, 1.585; 95% CI, 1.038–2.419) after adjusting for confounding factors. Subgroup analysis by age group (<50 and ≥50 years) showed a significant positive relationship only in the groups ≥ 50 years for both genders (males: OR, 1.546; 95% CI, 1.111–2.151; females: OR, 1.783; 95% CI, 1.042–3.054), but not in the groups < 50 years. In conclusion, KSD is associated with a higher risk of increased arterial stiffness in individuals aged ≥ 50 years, but not in those aged < 50 years for both genders.

The Determinants and Subsequent Effect of Self-Rated Health Status on Survival among Elderly Americans

2014 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Guangming Han
Keyword(s):  
Health Status ◽  
Regression Models ◽  
Survival Function ◽  
The Elderly ◽  
Mortality Prediction ◽  
Logistic Regression Models ◽  
Self Rated Health ◽  
Elderly Populations ◽  
Impact On Survival ◽  
Cox Proportional Hazard Regression

The main aim of this study is to explore the patterns, determinants and subsequent mortality prediction of change in self-rated health in the elderly American population. To achieve this purpose, we constructed logistic regression models and Cox proportional hazard regression models with the complex survey dataset from the National Second Longitudinal Study of Aging (LSOA II) to calculate the odds ratios (OR)/ hazard ratios (HR) and confidence intervals (CI) of risk factors. Our results show that chronic disease condition and difficulty in daily activities are the main reasons for change in self-rated health status. Furthermore, change in self-rated health has significant impact on survival function in the elderly populations. When change in self-rated health status was considered, self-rated health was a stronger and more flexible predictor of mortality for elderly populations. These findings will provide important information to establish effective strategies for prolonging lifespan by improving self-rated health status for elderly populations.

Sensor-based Fall Risk Assessment – an Expert ‘to go’

2011 ◽  
Vol 50 (05) ◽  
pp. 420-426 ◽  
Author(s):  
A. Rehwald ◽  
K. H. Wolf ◽  
M. Gietzelt ◽  
G. Nemitz ◽  
H. Meyer zu Schwabedissen ◽  
...  
Keyword(s):  
Physical Activity ◽  
Fall Risk ◽  
Regression Models ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Risk Models ◽  
Logistic Regression Models ◽  
Follow Up Study ◽  
One Year

SummaryBackground: Falls are a predominant problem in our aging society, often leading to severe somatic and psychological consequences, and having an incidence of about 30% in the group of persons aged 65 years or above. In order to identify persons at risk, many assessment tools and tests have been developed, but most of these have to be conducted in a supervised setting and are dependent on an expert rater.Objectives: The overall aim of our research work is to develop an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. The aims of our work for this paper are to derive a fall risk model based on sensor data that may potentially be measured during typical activities of daily life (aim #1), and to evaluate the resulting model with data from a one-year follow-up study (aim #2).Methods: A sample of n = 119 geriatric inpatients wore an accelerometer on the waist during a Timed ‘Up & Go’ test and a 20 m walk. Fifty patients were included in a one-year follow-up study, assessing fall events and scoring average physical activity at home in telephone interviews. The sensor data were processed to extract gait and dynamic balance parameters, from which four fall risk models – two classification trees and two logistic regression models – were computed: models CT#1 and SL#1 using accelerometer data only, models CT#2 and SL#2 including the physical activity score. The risk models were evaluated in a ten-times tenfold cross-validation procedure, calculating sensitivity (SENS), speci ficity (SPEC), positive and negative predictive values (PPV, NPV), classification accuracy, area under the curve (AUC) and the Brier score.Results: Both classification trees show a fair to good performance (models CT#1/ CT#2): SENS 74% / 58%, SPEC 96% / 82%, PPV 92% / 74%, NPV 77%/82%, accuracy 80% / 78%, AUC 0.83 / 0.87 and Brier scores 0.14 / 0.14. The logistic regression models (SL#1/ SL#2) perform worse: SENS 42% / 58%, SPEC 82% / 78%, PPV 62% / 65%, NPV 67% / 72%, accuracy 65% /70%, AUC 0.65 / 0.72 and Brier scores 0.23 / 0.21.Conclusions: Our results suggest that accelerometer data may be used to predict falls in an unsupervised setting. Furthermore, the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. These promising results have to be validated in a larger, long-term prospective trial.

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