Community-based Cohort Research Articles

High-sensitivity C-reactive protein is associated with cardiac dysfunction and morbidity in a community-based cohort

Abstract Background Inflammation is increasingly recognized as a risk factor for cardiac morbidity in the general population. Biomarkers like high-sensitivity C-reactive protein (hs-CRP) can aid in the detection of adverse systemic inflammation. The relationship between hs-CRP and cardiac structure and function is therefore a target of interest. Purpose The purpose of this study was to investigate the association of inflammation with cardiac structure and function in individuals free of overt cardiac disease. In addition, we examined if hs-CRP provided prognostic information regarding incident heart failure and myocardial infarction independent of echocardiographic measures. Methods In a community-based cohort study, participants were randomly selected for invitation from the Capital Region of Denmark. Echocardiography was performed on all the participants and measures of cardiac structure and function were obtained by blinded investigators. Participants with a history of cardiac disease were excluded from the analysis. Associations between log-transformed hs-CRP and echocardiographic measures were analyzed through uni- and multivariable linear regressions. Results A total of 3,379 individuals were included. Mean age was 55 years, 42% were female and the median hs-CRP was 1.13 (interquartile range: 0.65 - 1.47) mg/L. In the univariable analyses, increasing levels of hs-CRP were associated with worse left ventricular (LV) function including LV ejection fraction, global longitudinal strain (GLS), worse diastolic function (E/e’), worse right heart function (tricuspid regurgitation (TR) velocity and tricuspid annular plane systolic excursion (TAPSE)) and higher left ventricular (LV) mass index (p < 0.001 for all measures) (figure). When adjusting for sex, age, physical activity, smoking status, body mass index, blood pressure, hypercholesterolemia, diabetes, hypertension, creatinine, and heart rate, only LVEF (p < 0.001), GLS, (p < 0.001), E/e’ (p = 0.027), TAPSE (p = 0.008) and peak TR velocity (p = 0.015) remained significantly associated with increasing hs-CRP levels. In multivariable Cox regression adjusting for clinical characteristics and echocardiographic measures including LV ejection fraction, LV mass index, E/e’, TAPSE and GLS, higher hs-CRP was significantly associated with both heart failure and myocardial infarction (table) (HR for heart failure: 1.30, 95% CI: 1.03–1.66 p = 0.030; HR for acute myocardial infarction: 1.40, 95% CI: 1.04 – 1.89, p = 0.028, per doubling of hs-CRP). We did not find any interactions between hs-CRP and outcome among different types of echocardiographic phenotypes. Conclusion In the general population, higher levels of hs-CRP were associated with declining systolic, diastolic and right heart function. The associations persisted when adjusting for clinical characteristics. In addition, hs-CRP was associated with cardiac events independent of cardiac function as assessed by echocardiography.Linear regression splinesCox regressions

The association between left ventricular mass index and all-cause mortality

Abstract Background Left ventricular hypertrophy (LVH) has been associated with a higher risk of all-cause mortality. However, there is a lack of optimal cut-off values for grading the severity of LVH in the general population. Purpose We aim to investigate the association between the left ventricular mass index (LVMi) estimated by echocardiography and all-cause mortality and determine the optimal cut-off values of LVMi for the general population to predict outcomes. Methods Consecutive participants undergoing echocardiography in a tertiary medical center in Taiwan during 2005-2021 were enrolled. LVMi was calculated using the cube formula from the linear method. The primary outcome was all-cause mortality by linking to the National Death Registry. The association between LVMi and all-cause mortality was assessed using the joinpoint linear regression analysis to derive the optimal cut-off values for LVMi. Using the Kaplan-Meier methods, we validated our results with a community-based cohort with a long-term follow-up period. Results 85,503 participants (age 64.3 ± 16.9 years, 53.7% men) were enrolled. During a mean follow-up of 67.6 ± 48.5 months, there were 14,565 deaths (17%). A U-shaped relationship between LVMi and the risk of all-cause mortality could be observed. The joinpoint linear regression analysis revealed that LVMi could be categorized into the following groups (less than 66 gm/m2, 66-87 gm/m2, 88-132 gm/m2, 133-170 gm/m2, and more than 171 gm/m2), with each group representing a different risk for all-cause mortality. (Figure 1) In the validated cohort categorized by the cut-off values of LVMi, the Kaplan-Meier analysis showed divergent risks for all-cause mortality between different groups in the community-indwelled populations (Figure 2, log-rank P-value< 0.001). Conclusion There was a U-shaped relationship between LVMi and the risk of all-cause mortality. Grading for the severity of LVH using the LVMi is warranted for the risk assessment in the general population.Figure 1Figure 2

An artificial intelligence-enabled electrocardiogram model outperforms the revised cardiac risk index score for predicting major adverse cardiovascular events in patients undergoing noncardiac surgery

Abstract Background Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery. Objective We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries. Methods We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values. Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance. Conclusion AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.

Long-term exposure to PM2.5 and mortality among Japanese community-dwelling adults

Abstract Background Long-term exposure to PM2.5 (particulate matter with a diameter of 2.5 μm or smaller) is a risk factor for mortality and cardiovascular disease including stroke. However, there was limited evidence for the association between PM2.5 exposure and stroke in Asia where the major cardiovascular disease is stroke. Additionally, the causal association between PM2.5 and respiratory disease is still unresolved. The aim of this study was to investigate the association between long-term exposure to PM2.5 and all-cause, cardiovascular, and respiratory disease mortality in a Japanese community-based prospective cohort. Methods This study included 46,974 participants (19,707 men; 27,267 women) from the second cohort of the Ibaraki Prefectural Health Study. The participants were followed up from baseline survey (in 2009) to 31 December 2019. We confirmed underlying causes by death certificates. Using the PM2.5 concentrations estimated by the inverse distance weighting methods based on ambient air monitoring data, administrative area level concentrations were assigned to each participant. The hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality using a Cox proportional hazard model were presented. Results We identified 2,789 all-cause deaths during the 470,178 person-years of follow-up. PM2.5 exposure had no significant association with all-cause and cause-specific mortality. The multivariable-adjusted HRs per 1 μg/m3 increase in the PM2.5 concentration were 0.94 (95% CI = 0.80-1.12) for coronary heart disease, 0.92 (95% CI = 0.81-1.04) for stroke and 1.09 (95% CI = 0.97-1.23) for non-malignant respiratory disease. Conclusions We observed little evidence of increased risk of all-cause and cause-specific mortality by long-term exposure to PM2.5 whose concentrations ranged from 8.3 to 13.1 μg/m3. A somewhat increased trend for non-malignant respiratory disease mortality may not be negligible. Key messages • We found little evidence of increased risk of all-cause and cause-specific mortality by long-term exposure to PM2.5. • Somewhat increased tendency for non-malignant respiratory disease mortality associated with PM2.5 exposure was observed.

Modifying effect of obesity status on the association between low-density lipoprotein cholesterol with incident hemorrhagic stroke: evidence from a community-based longitudinal cohort study in China

Abstract Background Previous results on the association between lipids and hemorrhagic stroke were controversial. Modifying effect of obesity on the association between low-density lipoprotein cholesterol (LDL-C) and hemorrhagic stroke risk in the Chinese community-based population remains unclear. Objectives We aimed to investigate the association between LDL-C and hemorrhagic stroke risk in the Chinese community-based population. In addition, we aimed to explore the potential modifying effect of obesity status, particularly abdominal obesity, on this relationship. Methods A total of 7899 eligible subjects, without history of stroke and myocardial infarction, and any use of lipid-lowering medication at baseline, were enrolled in this study. Levels of LDL-C, total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) were assessed at baseline. The primary endpoint was the hemorrhagic stroke. Abdominal obesity was defined as waist circumference > 90 cm in men and > 85 cm in women. Results Of all participants, the mean age was 56.13 ± 8.87 years, 2848 (36.1%) were male. During a mean 9.71-year follow-up, 66 hemorrhagic strokes occurred. After multivariable adjustment (Figure 1), compared to participants with LDL-C levels 2.6 – 3.4 mmol/L, those with LDL-C levels < 1.8 mmol/L had 3.32 times the risk (hazard ratio [HR]: 3.32; 95% confidence interval [CI]: 1.24, 8.84) of incident hemorrhagic stroke. Compared with participants with TC levels 3.1 – 5.2 mmol/L, HR (95% CI) was 7.82 (2.36, 25.90) for hemorrhagic stroke in those with TC < 3.1 mmol/L. We observed no significant associations between TG or HDL-C levels and hemorrhagic stroke risk. Interaction effects were found between obesity and LDL-C for incident hemorrhagic stroke. Accordingly, significant associations between low LDL-C level (< 1.8 mmol/L) and the risk of incident hemorrhagic stroke were found only among abdominal obesity participants (HR: 3.76; 95% CI: 1.01, 13.97), or those with body mass index (BMI) > = 24 kg/m2 (HR: 3.74; 95% CI: 1.24, 11.34) (Figure 2). Conclusions LDL-C levels < 1.8mmol/L and TC levels < 3.1mmol/L were associated with increased risk of hemorrhagic stroke. Modifying effect of obesity status on the association between low LDL-C level and hemorrhagic stroke was observed in our study.

Validation and development of a scoring system for comprehensively evaluating arterial damage in the elderly chinese

Abstract Background and aims Existing assessments on arterial damage focus on specific segments of arterial system (e.g., carotid artery), lacking a comprehensive index to evaluate overall arterial health. We aimed to integrate existing assessments for arterial damage and develop a new score, named Arterial Damage Index (ADI), to comprehensively evaluate arterial health status of the whole-body arterial system. Methods A total of 3075 community-dwelling elderly Chinese (age ≥65 years) from the North Shanghai Study (2014–2022) were included. Assessments on arteries including ankle-brachial index (ABI), pulse wave velocity (PWV), carotid intima-media thickness (CIMT) and plaques, pulse pressure (PP), and inter-arm (IASBPD) and -leg (ILSBPD) systolic blood pressure difference were included for screening. Cox proportional hazards regressions of MACEs (non-fatal myocardial infarction, non-fatal stroke, and all-cause mortality) were conducted to acquire the ADI algorithm. Internal and external validation on ADI were also performed. Results Over a median follow-up period of 6.4 years, 316 individuals (10.3%) experienced an incident of MACE. After screening, PWV, ABI, PP, CIMT, and carotid plaque were included for ADI scoring system. Survival analysis revealed a significant difference in the rates of composite events across different ADI stratifications (p for trend < .05). ADI showed superior discriminative performance (C-index 0.65±0.02) compared to any individual arterial-related indicator. Calibration curve shows the predictive model showed that the predicted and actual probabilities were broadly consistent. Additionally, ADI provided better incremental value when added to the base model (net reclassification improvement, 36.2% [95% CI, 15.3%-52.1%]; p < .05, integrated discrimination improvement, 7.9% [95% CI, 4.9%-11.0%]; p < .05) compared to any individual arterial-related indicator. These findings were confirmed in internal validation sets (p for DeLong tests >0.05), and was externally validated in 2155 participants from a community-based cardiovascular disease cohort among residents in Beijing, China. Conclusion ADI can comprehensively assess arterial damage and provide a valuable tool for further assessment of future cardiovascular risk.central illustration

Left ventricular pressure-strain loops for the assessment of myocardial work in a low-risk community-based cohort: associations with long-term prognosis

Abstract Background Non-invasive left ventricular (LV) pressure-strain loop analysis is a novel technique for the evaluation of global myocardial work (GMW) that provides additional insights on LV contractility and energetics beyond traditional metrics, such as ejection fraction (EF) and global longitudinal strain (GLS). Despite its established value in numerous cardiac diseases, there remains limited data on its predictive power in a low-risk population. Purpose Our objective was to determine the long-term prognostic value of GMW in a population-based screening sample comprising low-risk adult individuals. Methods We retrospectively identified 1562 volunteers from a population-based screening program (mean age 54±15 years, 59% female), with a median follow-up time of 11 years. All subjects underwent 2D echocardiography to measure LV EF, GLS, peak atrial longitudinal strain (PALS), GMW index (GMWI), global wasted work (GWW), and GMW efficiency (GMWE). The primary endpoint was all-cause mortality. Results 191 subjects (12,2%) met the primary endpoint. Subjects who experienced adverse outcomes exhibited significantly lower LV EF (dead vs. alive; 49.7±8.3 vs. 52.7 ±6.4 %, p<0.001), GLS (-18.7±4.1 vs. -20.4±3.6 %, p<0.001), PALS (33.6±15.7 vs. 44.2±15.2 %, p<0.001), and GMWI (2181.7±591.3 vs. 2286.8±482.5 mmHg%, p=0.008). Furthermore, subjects with adverse outcomes had significantly higher GWW (132.6±249.4 vs 95.1±113.4 mmHg%, p<0.001) and, consequently, lower GMWE (0.95±0.05 vs. 0.96±0.03, p<0.001). Univariate Cox regression analysis revealed GMWI to be a significant predictor of mortality (hazard ratio for 100 units 0.957 [95% CI: 0.929-0.986], p=0.004), alongside GMWE (hazard ratio for 1 percent 0.940 [95% CI: 0.912-0.968], p<0.001), and GWW (hazard ratio for 100 units 1.124 [95% CI: 1.063-1.188], p<0.001). By multivariable Cox regression encompassing age, sex, GMWI, LVEF, GLS, PALS, and systolic blood pressure, GMWI remained an independent predictor of all-cause mortality (hazard ratio 0.932 [95% CI: 0.874-0.995], p=0.035), while GLS was not an independent predictor. Moreover, integrating the Atherosclerosis Risk in Communities (ARIC)-HF risk factors (age, sex, race, coronary artery disease, antihypertensive medication, blood pressure, diabetes mellitus, body mass index, heart rate, and smoking status), we constructed a multivariate model with GMWI. GMWI was still an independent predictor of mortality (hazard ratio for 100 units 0.941 [95% CI: 0.903-0.980], p=0.004). Using the GMWI cut-off value of 1576 mm Hg% (based on a large-scale healthy population study), the population was divided into 2 groups. Using Kaplan-Meier curves (Figure), the outcomes of the groups differed significantly (log-rank p<0.001). Conclusions GMW provides added value in clinical outcome prediction, even in low-risk individuals. Myocardial work analysis is a promising approach for a comprehensive evaluation of cardiac function and for cardiovascular risk stratification.

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

A novel electrocardiogram-based model for prediction of dementia: the Atherosclerosis Risk in Communities (ARIC) study

Abstract Background Current dementia prediction models, such as the UK Biobank Dementia Risk Score (UKBDRS, 12 variables), are limited by having variables that are difficult to obtain, subjectively measured, and self-reported. Although individual ECG parameters are linked to higher dementia risk, no study has combined multiple ECG parameters to create an ECG model for dementia prediction. Purpose Using data from the ARIC study, a prospective community-based cohort of Black and White participants in the USA that started in 1987-89, we created an ECG model (based on 7 pre-defined ECG parameters linked to dementia) and compared its model discrimination with the UKBDRS. Methods Participants without prevalent dementia from 2 baselines were included: Visit 4 (V4, 1996-98) and V5 (2011-13). Seven ECG parameters were examined: heart rate, abnormal P-wave terminal force in lead V1 (PTFV1), prolonged P wave duration, abnormal P wave axis, advanced interatrial block, left ventricular hypertrophy (LVH, Cornell voltage criteria), and corrected QT interval (QTc). Dementia cases were identified through 2019 using in-person and phone cognitive assessments, hospitalisation codes, and death certificates. Using a backward selection method (keeping variables with P<0.10 and age, since it is an important dementia risk factor), parsimonious Cox regression models were constructed for prediction of dementia over the longest available follow-up (V4: 23 years, V5: 8 years). Model discrimination of the ECG models and UKBDRS variables was evaluated by the Harrell’s C-statistic (95% CI). Results At V4, 11,520 participants (mean age, 62.8 ± 5.7 years; 56% women; 22% Black) were included; 2,427 developed dementia. At V5, 5,667 participants (mean age, 75.4 ± 5.1 years; 59% women; 22% Black) were included; 830 developed dementia. Figures 1 and 2 show the C-statistic (95%) and area under the ROC curve for the ECG model and UKBDRS variables at V4 and V5, respectively. The C-statistic (95% CI) for both ECG models were >0.70 and comparable with the corresponding UKBDRS models. Conclusion For middle-aged and older adults, a dementia prediction model comprising age and 4 ECG variables has good discrimination for dementia that is comparable with the 12-variable UKBDRS model. Since ECG variables are easily obtainable and objectively measured, these ECG models will be easier to adopt than the UKBDRS model. Further work to validate this model in an external cohort and develop a risk score will facilitate clinical translation.

Independent and joint effects of self-reported physical activity and sedentary behaviors on mortality in community-dwelling older persons: a prospective cohort study

BackgroundThis study aims to assess the joint and independent effects of self-reported physical activity and sedentary behavior on mortality in older persons.MethodsA prospective community-based cohort study was conducted to examine physical activity (PA) level and sitting time (ST) in relation to mortality among 1,786 older persons aged 65 years and above. PA was assessed by a checklist of 26 self-reported items about PA and hours per week, and the metabolic equivalent hours/week was derived, and ST was measured by a self-reported item asking the average number of hours spent sitting per day. The participants were divided into four combination groups of PA and ST based on WHO guideline and values found in literature: high PA/short ST group, high PA/long ST group, low PA/long ST group, and low PA/short ST group. Data on death ascertainment were obtained through linkage with the national death datasets and expanded cardiovascular disease (CVD) included cardiovascular disease, diabetes, and chronic kidney disease.ResultsAfter follow-up for a median 11.1 years, 599 mortality cases were recorded, giving a crude all-cause mortality of 32.5/1,000 person-years, CVD mortality of 8.6/1,000 person-years, expanded CVD mortality of 11.9/1,000 person-years, and nonexpanded CVD mortality of 20.8/1,000 person-years. For all-cause, and expanded CVD, the hazards ratios (HRs) for the low PA/long ST group remained significant compared with that for the high PA/short ST group after all covariates were considered [HRs for all-cause mortality: 1.4 [95% confidence interval (CI) 1.1, 1.8]; and expanded CVD mortality: 1.7 (95% CI 1.1, 2.4).ConclusionsThe independent effect of PA and the joint effects of PA and ST are associated with all-cause and expanded CVD death risks. Expanded CVD mortality may be minimized by engaging in PA and reducing sedentary behaviors.

Multiplex PCR detection of enteric pathogens in a community-based birth cohort in Ecuador: comparison of xTAG-GPP and TaqMan array card assays.

We compared the performance of two multiplex platforms, Luminex xTAG Gastrointestinal Pathogen PanelⓇ and TaqMan Array Card, against a panel of 14 enteric pathogen targets in a community-based birth cohort in Ecuador. We found high levels of agreement and similar prevalence estimates across most pathogens.

The risk of depression, anxiety, and suicidal behavior in patients with obesity on glucagon like peptide-1 receptor agonist therapy.

This large community-based cohort study investigates the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RAs), specifically Liraglutide and Semaglutide, on the risk of developing psychiatric conditions such as depression, anxiety, and suicidal behaviors in patients with obesity. Utilizing post-marketing data, this research compares patients prescribed GLP-1 RAs (cases) with those not taking these medications (controls). The analysis spanned data from January 1, 2015, to December 31, 2023. To minimize selection bias, we employed 1:1 propensity score matching to account for demographic factors such as age, sex, race, and comorbidities. After matching, the study included 162,253 case and control patients. This study showed a significant association between GLP-1 RA treatment and an 98% increased risk of any psychiatric disorders. Notably, patients on GLP-1 RAs exhibited a 195% higher risk of major depression, a 108% increased risk for anxiety, and a 106% elevated risk for suicidal behavior. These findings underscore the critical need for physicians to thoroughly assess patient history before prescribing GLP-1 RAs and highlight the urgent requirement for further prospective clinical trials to fully understand the implications of GLP-1 RA use on mental health in the obese patient population.

Artificial intelligence-assisted oculo-gait measurements for cognitive impairment in cerebral small vessel disease.

Oculomotor and gait dysfunctions are closely associated with cognition. However, oculo-gait patterns and their correlation with cognition in cerebral small vessel disease (CSVD) remain unclear. Patients with CSVD from a hospital-based cohort (n = 194) and individuals with presumed early CSVD from a community-based cohort (n = 319) were included. Oculo-gait patterns were measured using the artificial intelligence (AI) -assisted 'EyeKnow' eye-tracking and 'ReadyGo' motor evaluation systems. Multivariable linear and logistic regression models were employed to investigate the association between the oculo-gait parameters and cognition. Anti-saccade accuracy, stride velocity, and swing velocity were significantly associated with cognition in both patients and community dwellers with CSVD, and could identify cognitive impairment in CSVD with moderate accuracy (area under the curve [AUC]: hospital cohort, 0.787; community cohort, 0.810) after adjusting for age and education. The evaluation of oculo-gait features (anti-saccade accuracy, stride velocity, and swing velocity) may help screen cognitive impairment in CSVD. Oculo-gait features (lower anti-saccade accuracy, stride velocity, and swing velocity) were associated with cognitive impairment in cerebral small vessel disease (CSVD). Logistic model integrating the oculo-gait features, age, and education level moderately distinguished cognitive status in CSVD. Artificial intelligence-assisted oculomotor and gait measurements provide quick and accurate evaluation in hospital and community settings.

Telomere length and cognitive function among middle-aged and older participants from communities underrepresented in aging research: A preliminary study.

Accelerated biological aging is a plausible and modifiable determinant of dementia burden facing minoritized communities, but is not well-studied in these historically underrepresented populations. Our objective was to preliminarily characterize relationships between telomere length and cognitive health among American Indian/Alaska Native (AI/AN) and Black/African American (B/AA) middle-aged and older adults. This study included data on telomere length and cognitive test performance from 187 participants, enrolled in one of two community-based cognitive aging cohorts and who identified their primary race as AI/AN or B/AA. Nested multivariable regression models revealed preliminary evidence for associations between telomere length and cognitive performance, and these associations were partially independent of chronological age. Small sample size limited estimate precision, however, findings suggest future work on telomere length and cognitive health in underrepresented populations at high risk for dementia is feasible and valuable as a foundation for social and behavioral intervention research.

Association of lifestyle trajectory and total lifestyle change score with risk of metabolic syndrome: The prospective community-based Ansung-Ansan cohort study

Background&AimsWe aimed to examine the association of lifestyle trajectory and total lifestyle change score with the risk of metabolic syndrome (MetS). MethodsWe analyzed 2,048 participants aged 40 to 69 years without MetS at baseline in the Ansung-Ansan cohort study. Lifestyle trajectories were identified using group based trajectory analysis, and total lifestyle change score were identified using index analysis. Healthy lifestyle trajectory and total lifestyle change score as exposure, and MetS as outcome. Cox proportional-hazards regression was used to examine the hazard ratios (HRs) for the exposure-outcome association. ResultsDuring the median 9.8-year follow-up, 756 cases were recorded. Compared with those in the the stable low healthy lifestyle trajectory, the stable high healthy lifestyle trajectory showed a protective effect on reducing the risk of MetS (men: HR, 0.47; 95% confidence interval [95% CI], 0.34-0.66; women: HR, 0.62; 95% CI, 0.43-0.91). Similar results were observed in the index based analysis, compared with those with lower total lifestyle change scores, men and women with higher scores had 46% and 47% lower risks of developing MetS, respectively (men: HR, 0.54; 95%CI, 0.41-0.71; women: HR, 0.53; 95% CI, 0.41-0.68). ConclusionsStable healthy lifestyle trajectory was associated with a reduced risk of MetS among Korean adults. Furthermore, a higher total lifestyle change score was inversely associated with the risk of MetS.

Associations between leisure time, non-leisure time physical activity, and kidney function in Japanese adults: a cross-sectional study

BackgroundChronic kidney disease (CKD) contributes to decreased life expectancy. We examined the association between leisure-time physical activity (LTPA), non-leisure-time physical activity (non-LTPA) and kidney function.MethodsThis was a cross-sectional study including 32 162 community-dwelling adults aged ≥ 20 years from the Tohoku Medical MegaBank community-based cohort study. Kidney function was evaluated using cystatin C-based estimated glomerular filtration rate (eGFR) as well as self-reported LTPA and non-LTPA. CKD was defined as either eGFR decline (≤ 60 mL/min/1.73 m2) or presence of albuminuria (albumin-creatinine ≥ 30 mg/g). The association between domain-specific physical activity and kidney function, and CKD prevalence was examined using multivariable-adjusted ordinary least squares and modified Poisson models.ResultsThe mean eGFR was 98.1 (± 13.2) mL/min/1.73 m2. 3 185 (9.9%) participants were classified as having CKD. The mean LTPA and non-LTPA levels were 2.9 (± 4.2) and 16.6 (± 14.2) METs-hour/day, respectively. For LTPA, in the adjusted model, the quartile groups with higher levels had a higher kidney function (β, 0.36; 95% confidence intervals [CI], [0.06, 0.66]; p = 0.019 for the 2nd quartile, β, 0.82; 95% CI, [0.51, 1.14]; p < 0.001 for the 3rd quartile, and β, 1.16; 95% CI, [0.83, 1.49]; p < 0.001 for the 4th quartile), whereas there were no apparent associations for prevalence of CKD. For non-LTPA, 4th quartile was associated with decreased eGFR (β, -0.42; 95% CI, [-0.72, -0.11]; p = 0.007) and higher prevalence of CKD prevalence (Prevalence ratio, 1.12; 95% CI, [1.02, 1.24]; p = 0.022). These associations with kidney function remained consistent in the subgroup analyses divided by demographic and biological variables.ConclusionsWe observed a positive association between higher LTPA levels and better kidney function, but not association with CKD prevalence. In contrast, higher non-LTPA was negatively associated with both kidney function and CKD prevalence. These findings suggest that promoting LTPA is beneficial for kidney function.

The triglyceride glucose related index is an indicator of Sarcopenia

The triglyceride glucose (TyG) related index, a metric used to evaluate assessing insulin resistance (IR), has received limited attention in its association with sarcopenia. Our study aims to explore the predictive potential of the TyG index for sarcopenia. This study utilized data from the China Health and Retirement Longitudinal Study, a nationally representative, community-based cohort study, including a sample size of 10,537 participants aged 45 years and older. Associations between TyG related index and sacopenia was explored using multivariate logistic regression. Analysis of the predictive value of TyG related index for sarcopenia using receiver-operating characteristic curve (ROC). We evaluated the correlation between the TyG related index and the risk of sarcopenia using Cox proportional hazards models. Additionally, we utilized restricted cubic spline (RCS) regression analyses to explore the connections between the TyG-related index and sarcopenia. Logistic regression analysis showed an association between TyG (OR 0.961[0.955,0.968], P < 0.001), TyG-body mass index (TyG-BMI) (OR 0.872[0.867,0.878], P < 0.001), TyG- waist circumference (TyG-WC) (OR 0.896[0.890,0.902], P < 0.001) and sarcopenia. The results of the ROC analysis indicated that the area under the curve values for TyG, TyG-BMI, and TyG-WC were 0.659, 0.903, and 0.819, respectively. Compared to those without sarcopenia, patients with sarcopenia had a 37.7% (HR 0.623[0.502,0.774], P < 0.001), 4.8% (HR 0.952[0.947,0.958], P < 0.001), and 0.4% (HR 0.996[0.995,0.996], P < 0.001) lower risk with increasing TyG, TyG-BMI, and TyG-WC, respectively. RCS results show nonlinear relationship between TyG-BMI (P < 0.001) and TyG-WC (P < 0.001) and risk of sarcopenia. We observed a correlation between the TyG-related index and sarcopenia, with the TyG-BMI index demonstrating strong predictive capability for sarcopenia.

Neutrophil-to-High-Density Lipoprotein Ratio (NHR) and Neutrophil-to-Lymphocyte Ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study

Cardiovascular diseases (CVD) remain a leading cause of global mortality, with atherosclerosis and inflammation playing pivotal roles in their development. The neutrophil-to-lymphocyte ratio (NLR) and neutrophil-to-HDL cholesterol ratio (NHR) have emerged as potential biomarkers for assessing CVD risk. In this community-based cohort study conducted in Taiwan, involving 3278 participants, we investigated the associations between NHR, NLR, and the risks of CVD and all-cause mortality. Our findings revealed that both NHR and NLR were effective in identifying individuals at high risk for CVD. However, when assessing their joint effect, NHR alone demonstrated a stronger predictive value for CVD prognosis than NLR or the combination of both markers. Furthermore, NLR alone showed potential as a predictor of all-cause mortality when compared with NHR alone or in combination with NLR and NHR. These findings underscore the complex interplay between inflammation and lipid metabolism in the pathogenesis of CVD. While NHR shows promise as a cost-effective tool for CVD risk assessment, NLR emerges potential as a prognostic marker for mortality. Further research is warranted to explore the dynamic changes in these markers and their implications for clinical practice.

Associations of Regular Internet Usage with All-Cause and Cause-Specific Mortality: A Prospective Cohort Study

ObjectivesThe impact of internet usage on mortality is not widely known. This study intended to investigate the associations between regular internet usage and the risks of all-cause and cause-specific mortality, while also ascertaining potential factors that may modify these correlations. DesignA community-based prospective cohort study. Setting and ParticipantsThe study included 21,481 individuals [mean (SD) age, 64.1 (11.0) years] from the Health and Retirement Study, with data collected between 2006 and 2020. MethodsThe Cox proportional hazards regression model was used to evaluate the associations between regular internet usage and the risks of all-cause and cause-specific mortality, adjusting for demographic factors, lifestyle behaviors, and other potential confounding factors. Moreover, we explored the association between daily hours of internet usage and the risk of outcomes. ResultsRegular internet usage was significantly associated with a lower risk of all-cause mortality (hazard ratio, 0.78; 95% CI, 0.74-0.83) and cardiovascular mortality (hazard ratio, 0.72; 95% CI, 0.64-0.82). No significant interaction effects were observed for age, sex, regular exercise, or current alcohol consumption (all P interactions > .05). Additionally, estimations for daily hours of usage indicated a U-shaped relationship with all-cause mortality. Adults who used 2.1 to 4 hours per day had the lowest risk; however, not all estimations showed their significance on account of the limited sample size. Conclusions and ImplicationsRegular internet usage was associated with a lower risk of all-cause and cardiovascular mortality, which may prompt consideration of the beneficial impact of internet usage on lifespan.

Effectiveness of Antileishmanial Treatments in Three Endemic Areas of Colombia: A Community-Based Cohort Study.

The effectiveness of treatment of cutaneous leishmaniasis (CL) in rural settings remains underexplored. This study assessed the effectiveness of standard antileishmanial treatments using a community-based approach supported by mobile health (mHealth) in three rural areas of Colombia. From January 2018 to September 2021, we assessed treatment outcomes, adherence, and adverse drug reactions in CL patients, with the support of the Guaral+ST app. Treatment decisions were made by providers at health facilities at each site in accordance with national guidelines, whereas treatment follow-up and presumptive case identification were made by trained community leaders and health agents at the community level. In total, 231 participants received antileishmanial treatment (63 received miltefosine, 110 meglumine antimoniate, and 58 pentamidine). Disease presentation was mild (median number of lesions = 1, interquartile range [IQR]: 1-2) and of short duration (1.5 months, IQR: 1-3). The strategy yielded information on the therapeutic outcomes in 81% of study participants. Effectiveness, measured as the proportion of cure at 90 to 180 days, was 86.3% (95% CI: 73.3-93.48) for miltefosine; 77.6% (67.5-85.3) for meglumine antimoniate, and 73.1% (59.0-83.6) for pentamidine. The effectiveness of pentamidine in children ≤10 years old was 79.4% (61.6-90.3). This is one of the few reports of effectiveness of pentamidine in children with prospective data collection in the Americas. Adverse drug reactions occurred in 32% of patients, most frequently with meglumine antimoniate. Our findings demonstrate that standard antileishmanial treatments are effective in rural areas where the disease is endemic and that mHealth has a pivotal role in improving patient follow-up and data collection on therapeutic outcomes.