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Random treatment assignment is essential in demonstrating a causal relationship between a treatment and the outcome of interest. Randomisation ensures that animals assigned to different treatment groups do not differ from each other systematically, except for the randomly assigned treatment. The randomisation pattern should also dictate the statistical analysis. [ABSTRACT FROM AUTHOR]

Background: We investigated whether repeatedly measured left atrial reservoir strain (LASr) in heart failure with reduced ejection fraction (HFrEF) patients provides incremental prognostic value over a single baseline LASr value, and whether temporal patterns of LASr provide incremental prognostic value over temporal patterns of other echocardiographic markers and NT-proBNP. Methods: In this prospective observational study, 153 patients underwent 6-monthly echocardiography, during a median follow-up of 2.5 years. Speckle tracking echocardiography was used to measure LASr. Hazard ratios (HRs) were calculated for LASr from Cox models (baseline) and joint models (repeated measurements). The primary endpoint (PEP) comprised HF hospitalization, left ventricular assist device, heart transplantation, and cardiovascular death. Results: Mean age was 58 ± 11 years, 76% were men, 82% were in NYHA class I/II, mean LASr was 20.9% ± 11.3%, and mean LVEF was 29% ± 10%. PEP was reached by 50 patients. Baseline and repeated measurements of LASr (HR per SD change (95% CI) 0.20 (0.10–0.41) and (0.13 (0.10–0.29), respectively) were both significantly associated with the PEP, independent of both baseline and repeated measurements of other echo-parameters and NT-proBNP. Although LASr was persistently lower over time in patients with PEP, temporal trajectories did not diverge in patients with versus without the PEP as the PEP approached. Conclusion: LASr was associated with adverse events in HFrEF patients, independent of baseline and repeated other echo-parameters and NT-proBNP. Temporal trajectories of LASr showed decreased but stable values in patients with the PEP, and do not provide incremental prognostic value for clinical practice compared to single measurements of LASr. [ABSTRACT FROM AUTHOR]

Abstract The aim of the study was to analyze the change trend of serum ALP over time and identify factors influencing its levels in peritoneal dialysis patients. Then to investigate the impact of serum ALP changes on calcium and phosphorus metabolism in single peritoneal dialysis center utilizing repeated measurement data. A retrospective cohort study was conducted with a total follow-up duration of 30 months. Serum ALP and other biomarkers, including calcium (Ca), phosphorus (P), 25(OH)D, intact parathyroid hormone (iPTH), albumin(ALB), and hemoglobin(Hb) were measured every 3 months. The generalized estimation equation (GEE) was utilized to analyze the change trend of serum ALP over time, and to assess whether there were differences in changes over time between different genders and different primary disease groups. Additionally, factors influencing serum ALP levels were analyzed, and the impact of serum ALP changes on calcium and phosphorus metabolism was also explored. A total of 34 patients were included in the study. Serum ALP and other indicators were measured repeatedly, with a maximum of 8 times and a minimum of 4 times. The median of serum ALP values at all measurement times for all selected patients was 89 U/L. The GEE analysis revealed that serum ALP gradually increased with time, and patients in diabetes group increased faster than those in non-diabetes group. A positive correlation was observed between serum ALP and dialysis duration, also between serum ALP and hemoglobin. However, variations in serum ALP did not significantly affect serum corrected calcium, phosphorus, or iPTH concentrations. The serum ALP levels of peritoneal dialysis patients increase gradually over time, and the concentrations are influenced by dialysis duration. The changes in serum ALP values do not have a significant impact on serum calcium, phosphorus, and iPTH levels.

Aims: Heart failure (HF), a global pandemic affecting millions of individuals, calls for adequate predictive guidance for improved therapy. Congestion, a key factor in HF‐related hospitalizations, further underscores the need for timely interventions. Proactive monitoring of intracardiac pressures, guided by pulmonary artery (PA) pressure, offers opportunities for efficient early‐stage intervention, since haemodynamic congestion precedes clinical symptoms. Methods: The BioMEMS study, a substudy of the MONITOR‐HF trial, proposes a multifaceted approach integrating blood biobank data with traditional and novel HF parameters. Two additional blood samples from 340 active participants in the MONITOR‐HF trial were collected at baseline, 3‐, 6‐, and 12‐month visits and stored for the BioMEMS biobank. The main aims are to identify the relationship between temporal biomarker patterns and PA pressures derived from the CardioMEMS‐HF system, and to identify the biomarker profile(s) associated with the risk of HF events and cardiovascular death. Conclusion: Since the prognostic value of single baseline measurements of biomarkers like N‐terminal pro‐B‐type natriuretic peptide is limited, with the BioMEMS study we advocate a dynamic, serial approach to better capture HF progression. We will substantiate this by relating repeated biomarker measurements to PA pressures. This design rationale presents a comprehensive review on cardiac biomarkers in HF, and aims to contribute valuable insights into personalized HF therapy and patient risk assessment, advancing our ability to address the evolving nature of HF effectively. [ABSTRACT FROM AUTHOR]

The aim of the study was to analyze the change trend of serum ALP over time and identify factors influencing its levels in peritoneal dialysis patients. Then to investigate the impact of serum ALP changes on calcium and phosphorus metabolism in single peritoneal dialysis center utilizing repeated measurement data. A retrospective cohort study was conducted with a total follow-up duration of 30 months. Serum ALP and other biomarkers, including calcium (Ca), phosphorus (P), 25(OH)D, intact parathyroid hormone (iPTH), albumin(ALB), and hemoglobin(Hb) were measured every 3 months. The generalized estimation equation (GEE) was utilized to analyze the change trend of serum ALP over time, and to assess whether there were differences in changes over time between different genders and different primary disease groups. Additionally, factors influencing serum ALP levels were analyzed, and the impact of serum ALP changes on calcium and phosphorus metabolism was also explored. A total of 34 patients were included in the study. Serum ALP and other indicators were measured repeatedly, with a maximum of 8 times and a minimum of 4 times. The median of serum ALP values at all measurement times for all selected patients was 89 U/L. The GEE analysis revealed that serum ALP gradually increased with time, and patients in diabetes group increased faster than those in non-diabetes group. A positive correlation was observed between serum ALP and dialysis duration, also between serum ALP and hemoglobin. However, variations in serum ALP did not significantly affect serum corrected calcium, phosphorus, or iPTH concentrations. The serum ALP levels of peritoneal dialysis patients increase gradually over time, and the concentrations are influenced by dialysis duration. The changes in serum ALP values do not have a significant impact on serum calcium, phosphorus, and iPTH levels. [ABSTRACT FROM AUTHOR]

To explore sleep structure in participants with obstructive sleep apnea (OSA) and comorbid insomnia (COMISA) and participants with OSA without insomnia (OSA-only) using both single-night polysomnography and multi-night wrist-worn photoplethysmography/accelerometry. Multi-night 4-class sleep-staging was performed with a validated algorithm based on actigraphy and heart rate variability, in 67 COMISA (23 women, median age: 51 years) and 50 OSA-only (15 women, median age: 51) participants. Sleep statistics were compared using linear regression models and mixed-effects models. Multi-night variability was explored using a clustering approach and between- and within-participant analysis. Polysomnographic parameters showed no significant group differences. Multi-night measurements, during 13.4 ± 5.2 nights per subject, demonstrated a longer sleep onset latency and lower sleep efficiency for the COMISA group. Detailed analysis of wake parameters revealed longer mean durations of awakenings in COMISA, as well as higher numbers of awakenings lasting 5 min and longer (WKN ≥5min) and longer wake after sleep onset containing only awakenings of 5 min or longer. Within-participant variance was significantly larger in COMISA for sleep onset latency, sleep efficiency, mean duration of awakenings and WKN ≥5min. Unsupervised clustering uncovered three clusters; participants with consistently high values for at least one of the wake parameters, participants with consistently low values, and participants displaying higher variability. Patients with COMISA more often showed extended, and more variable periods of wakefulness. These observations were not discernible using single night polysomnography, highlighting the relevance of multi-night measurements to assess characteristics indicative for insomnia. • Single-night sleep measurements show limited value in the differentiation between OSA and COMISA. • COMISA patients more often show extended periods of wakefulness over multiple nights. • Combined photoplethysmography and actigraphy can adequately capture these differences. • Multi-night assessment is useful to objectively identify comorbid insomnia and prompt further investigation. [ABSTRACT FROM AUTHOR]

Single-case experimental designs (SCEDs) have grown in popularity in the fields such as education, psychology, medicine, and rehabilitation. Although SCEDs are valid experimental designs for determining evidence-based practices, they encounter some challenges in analyses of data. One of these challenges, missing data, is likely to be occurred frequently in SCEDs research due to repeated measurements over time. Since missing data is a critical factor that can weaken the validity and generalizability of a study, it is important to determine the characteristics of missing data in SCEDs, which are especially conducted with a small number of participants. In this regard, this study aimed to describe missing data features in SCEDs studies in detail. To accomplish this goal, 465 published SCEDs studies within the recent 5 years in six journals were included in the investigation. The overall results showed that the prevalence of missing data among SCEDs articles in at least one phase, as at least one data point, was approximately 30%. In addition, the results indicated that the missing data rates were above 10% within most studies where missing data occurred. Although missing data is so common in SCEDs research, only a handful of studies (5%) have handled missing data; however, their methods are traditional. In analyzing SCEDs data, several methods are proposed considering missing data ratios in the literature. Therefore, missing data rates determined in this study results can shed light on the analyses of SCEDs data with proper methods by improving the validity and generalizability of study results. [ABSTRACT FROM AUTHOR]

Heterogeneity exists in populations, and people may benefit differently from the same treatments or services. Correctly identifying subgroups corresponding to outcomes such as treatment response plays an important role in data‐based decision making. As few discussions exist on subgroup analysis with measurement error, we propose a new estimation method to consider these two components simultaneously under the linear regression model. First, we develop an objective function based on unbiased estimating equations with two repeated measurements and a concave penalty on pairwise differences between coefficients. The proposed method can identify subgroups and estimate coefficients simultaneously when considering measurement error. Second, we derive an algorithm based on the alternating direction method of multipliers algorithm and demonstrate its convergence. Third, we prove that the proposed estimators are consistent and asymptotically normal. The performance and asymptotic properties of the proposed method are evaluated through simulation studies. Finally, we apply our method to data from the Lifestyle Education for Activity and Nutrition study and identify two subgroups, of which one has a significant treatment effect. [ABSTRACT FROM AUTHOR]

Background The association between soy isoflavones intake and cardiometabolic health remains inconclusive. We investigated the associations of urinary biomarkers of isoflavones including daidzein, glycitein, genistein, equol (a gut microbial metabolite of daidzein), and equol‐predicting microbial species with cardiometabolic risk markers. Methods and Results In a 1‐year study of 305 Chinese community‐dwelling adults aged ≥18 years, urinary isoflavones, fecal microbiota, blood pressure, blood glucose and lipids, and anthropometric data were measured twice, 1 year apart. Brachial‐ankle pulse wave velocity was also measured after 1 year. A linear mixed‐effects model was used to analyze repeated measurements. Logistic regression was used to calculate the adjusted odds ratio (aOR) and 95% CI for the associations for arterial stiffness. Each 1 μg/g creatinine increase in urinary equol concentrations was associated with 1.47%, 0.96%, and 3.32% decrease in triglycerides, plasma atherogenic index, and metabolic syndrome score, respectively (all P

Clinically assessing arterial stiffness is valuable because it aids in predicting future cardiovascular events. There are several methods for measuring arterial stiffness, including pulse wave velocity (PWV), augmentation index, and pulse pressure. Numerous studies have shown that these indicators of arterial stiffness possess prognostic value for various patient groups as well as the general population. In cross-sectional studies, arterial stiffness was also linked to organ damage indices. However, most studies related to arterial stiffness have relied on a single measurement. Taking multiple serial measurements of arterial stiffness offers several advantages. Through repeated assessments, one can confirm the variability of arterial stiffness and observe changes over time, which is beneficial for understanding its pathophysiology. Such repeated measurements are also invaluable in evaluating the efficacy of interventions aimed at improving arterial stiffness. However, caution is needed, as there is no standardized method for measuring arterial stiffness. For instance, with PWV, the values can be influenced by numerous external factors. Therefore, the external conditions during the measurement must be noted. It's essential to recognize the pros and cons of repeated arterial stiffness measurements and integrate them effectively into clinical practice. [ABSTRACT FROM AUTHOR]

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys—at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies. [ABSTRACT FROM AUTHOR]

The collection of multiple responses is common in longitudinal studies. It is of interest to assess the effects of covariates on multiple responses simultaneously using regression models. In some longitudinal studies, response variables are related to hidden states during the time-course study. The trend of responses for each subject can be segmented by hidden states. In the present study, we proposed a multivariate hidden semi-Markov regression model for longitudinal data having a multivariate normal distribution for responses in each hidden state. Based on the model, the effects of covariates on multiple responses have been calculated in the presence of hidden states. Simulation studies were conducted to evaluate the properties of the suggested model under different scenarios. The application of the suggested method was shown in hemodialysis patients’ data. [ABSTRACT FROM AUTHOR]

Identification of a subset of patients who may be sensitive to a specific treatment is an important step towards personalized medicine. We consider the case where the effect of a treatment is assessed by longitudinal measurements, which may be continuous or categorical, such as quality of life scores assessed over the duration of a clinical trial. We assume that multiple baseline covariates, such as age and expression levels of genes, are available, and propose a generalized single‐index linear threshold model to identify the treatment‐sensitive subset and assess the treatment‐by‐subset interaction after combining these covariates. Because the model involves an indicator function with unknown parameters, conventional procedures are difficult to apply for inferences of the parameters in the model. We define smoothed generalized estimating equations and propose an inference procedure based on these equations with an efficient spectral algorithm to find their solutions. The proposed procedure is evaluated through simulation studies and an application to the analysis of data from a randomized clinical trial in advanced pancreatic cancer. [ABSTRACT FROM AUTHOR]

IntroductionRecent studies have identified inflammatory mediators as potential biomarkers for monitoring or diagnosing periodontitis. However, the brief half-life of these mediators, coupled with their variability among different individuals and across different stages of periodontal disease, may limit their reliability as biomarkers.MethodsIn this study, we assessed the concentration profile of salivary biomarkers (IL-6, IL-8, and total protein) through repeated measurements within the same day and across different days in 79 patients exhibiting various states of periodontal health: intact periodontium, stable periodontitis, and active periodontitis. Additionally, we explored how daily variations, such as the interval between toothbrushing and eating, impact the levels of these salivary biomarkers and their diagnostic efficacy for periodontitis activity.ResultsOur results showed high salivary levels of IL-6 and total proteins in periodontitis patients (p

Although animal studies have shown the reproductive toxicity of vanadium, less is known about its effects on semen quality in humans. Among 1135 healthy men who were screened as potential semen donors, we investigated the relationships of semen quality with urinary and seminal plasma vanadium levels via inductively coupled plasma-mass spectrometry (ICP-MS). Spearman rank correlation tests and linear regression models were used to assess the correlations between average urinary and within-individual pooled seminal plasma vanadium concentrations (n = 1135). We utilized linear mixed-effects models to evaluate the associations of urinary and seminal plasma vanadium levels (n = 1135) with repeated sperm quality parameters (n = 5576). Seminal plasma vanadium concentrations were not significantly correlated with urinary vanadium concentrations (r = 0.03). After adjusting for possible confounders, we observed inverse relationships of within-individual pooled seminal plasma vanadium levels with total count, semen volume, and sperm concentration (all P values for trend < 0.05). Specifically, subjects in the highest (vs. lowest) tertile of seminal plasma vanadium concentrations had − 11.3% (−16.4%, −5.9%), − 11.1% (−19.1%, −2.4%), and − 20.9% (−29.0%, −11.8%) lower sperm volume, concentration, and total count, respectively; moreover, urinary vanadium levels appeared to be negatively associated with sperm motility. These relationships showed monotonically decreasing dose-response patterns in the restricted cubic spline analyses. Our results demonstrated a poor correlation between urinary and seminal plasma levels of vanadium, and elevated vanadium concentrations in urine and seminal plasma may be adversely related to male semen quality.

Introduction: The reason why maximum tongue pressure (MTP) decreases in patients with Parkinson’s disease (PD) remains unclear. Repeated measurements of isometric force and MTP may be useful for analyzing muscle wasting and force generation. The purpose of this pilot study was to evaluate the clinical characteristics and temporal transition of MTP in PD and normal control (NC) groups. Methods: There were 18 participants in this study: 10 with PD and 8 NCs. The MTP was measured 20 times at regular intervals. The area under the curve of MTP temporal transitions, time to reach MTP, and total transition time of the tongue pressure (time to return to baseline) were compared between the groups. Results: MTP decreased from baseline in PD subjects. Unlike NCs, PD subjects showed diverse and inconsistent temporal transitions. The decrease in MTP and delays in time to reach MTP and time to return to baseline were significantly greater in PD subjects (p

Background We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high‐sensitivity cardiac troponin T, N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity C‐reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long‐term mortality risk. Methods and Results BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high‐frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker‐based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all‐cause death were evaluated using accelerated failure time models (median follow‐up, 9.1 years; 141 deaths). Three biomarker‐based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long‐term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44–0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39–1.32; P=0.281) compared with patients with a repeat ACS. Conclusions Patients with subphenotypes of post‐ACS with different all‐cause mortality risks during long‐term follow‐up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

Introduction Methods Results Discussion and Conclusions The social gradient in consumption behaviours has been suggested to partly explain health inequalities. The majority of previous studies have only included baseline measurements and not considered potential changes in behaviours over time. The study aimed to investigate the contribution of alcohol consumption and smoking to the social gradient in mortality and to assess whether the use of repeated measurements results in larger attenuations of the main association compared to using single baseline assessments.Longitudinal survey data from the population‐based Stockholm Public Health Cohort from 2006 to 2014 was linked to register data on mortality until 2018 for 13,688 individuals and analysed through Cox regression.Low socioeconomic position (SEP) was associated with increased mortality compared with high SEP; hazard ratios 1.56 (95% CI 1.30–1.88) for occupational status and 1.77 (95% CI 1.49–2.11) for education, after adjustment for demographic characteristics. Using repeated measurements, alcohol consumption and smoking explained 44% of the association between occupational status and all‐cause mortality. Comparing repeated and baseline measures, the percentage attenuation due to alcohol consumption increased from 11% to 18%, whereas it remained similar for smoking (25–23%).Smoking and alcohol consumption explained a large part of the association between SEP and mortality. Comparing results from time‐fixed and time‐varying models, there was an increase in overall percentage attenuation that was mainly due to the increased proportion explained by alcohol consumption. Repeated measurements provide a better estimation of the contribution of alcohol consumption, but not smoking, for the association between SEP and mortality. [ABSTRACT FROM AUTHOR]

Simulation studies were performed to investigate for which conditions of sample size of patients (n) and number of repeated measurements (k) (e.g., raters) the optimal (i.e., balance between precise and efficient) estimations of intraclass correlation coefficients (ICCs) and standard error of measurements (SEMs) can be achieved. Subsequently, we developed an online application that shows the implications for decisions about sample sizes in reliability studies. We simulated scores for repeated measurements of patients, based on different conditions of n, k, the correlation between scores on repeated measurements (r), the variance between patients' test scores (v), and the presence of systematic differences within k. The performance of the reliability parameters (based on one-way and two-way effects models) was determined by the calculation of bias, mean squared error (MSE), and coverage and width of the confidence intervals (CI). We showed that the gain in precision (i.e., largest change in MSE) of the ICC and SEM parameters diminishes at larger values of n or k. Next, we showed that the correlation and the presence of systematic differences have most influence on the MSE values, the coverage and the CI width. This influence differed between the models. As measurements can be expensive and burdensome for patients and professionals, we recommend to use an efficient design, in terms of the sample size and number of repeated measurements to come to precise ICC and SEM estimates. Utilizing the results, a user-friendly online application is developed to decide upon the optimal design, as 'one size fits all' doesn't hold. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) and changes in cognition and global brain structure. Methods: In the Rotterdam Study, baseline NT‐proBNP was assessed at baseline from 1997 to 2008. Between 1997 and 2016, participants without dementia or stroke at baseline (n = 9566) had repeated cognitive tests (every 3–6 years) for global cognitive function, executive cognitive function, fine manual dexterity, and memory. Magnetic resonance imaging of the brain was performed repeatedly at re‐examination visits between 2005 and 2015 for 2607 participants to obtain brain volumes, focal brain lesions, and white matter microstructural integrity as measures of brain structure. Results: Among 9566 participants (mean age 65.1 ± 9.8 years), 5444 (56.9%) were women, and repeated measures of cognition were performed during a median follow‐up time of 5.5 (range 1.1–17.9) years, of whom 2607 participants completed at least one brain imaging scan. Higher levels of NT‐proBNP were associated with a faster decline of scores in the global cognitive function (p value = 0.003) and the Word‐Fluency test (p value = 0.003) but were not related to a steeper deterioration in brain volumes, global fractional anisotropy, and mean diffusivity, as indicators of white matter microstructural integrity, or focal brain lesions. Conclusions: Higher baseline NT‐proBNP levels were associated with a faster decline in cognition; however, no association with global brain structure was found. [ABSTRACT FROM AUTHOR]

Reproducibility of body fat (BF) and fat-free mass (FFM) measurements obtained by two indirect methods (bioimpedance analysis (BIA) and ultrasound scanning (US)) was performed in a group of young adults. To evaluate reproducibility, the repeated body composition measurements obtained using an ABC-02 Medas bioimpedance analyzer and BodyMetrixTM ultrasound scanner were carried out in the group of adult men and women. The study indicates the reliability of both individual and group estimates, as well as high reproducibility of measurements obtained with BIA and US. Strong positive significant correlations were found between the body composition measurements obtained with an ABC-02 Medas and BodyMetrixTM, as well as between repeated measurements of each of the devices. The analysis carried out for the ABC-02 Medas and BodyMetrixTM devices indicates good reproducibility of BF and FFM measurements in young men and women. The highest reproducibility was shown for the results of FFM measurements. [ABSTRACT FROM AUTHOR]

Background: The ambient and indoor environment are pivotal to our health. We spend most of our time indoors within our home, why our home is where we are most exposed to indoor pollutants and indoor air quality (IAQ). Populations within social housing areas are more vulnerable due to advanced age, co-morbidity and social economic status. Commonly, studies within social housing are cross-sectional, few Nordic longitudinal studies exist, and fewer studies combine quantitative and qualitative measurements in a mixed method approach. Method: This research proposal provides an extensive detailed description of the design and methodology of the HOME-Health study. The study is a longitudinal study and is a natural experiment employing structured surveys, objective measurements of indoor air parameters, lung function test and qualitative semi-structured interviews. Data collection are conducted seasonally (winter and summer), before and after building energy renovation (BER). Generalisability: The study population before BER (n = 432) was explored and found similar to the Danish social housing population in terms of age, gender, persons per apartment and migration status. Future analyses should be stratified by multi-family apartments and terraced houses. Research aim: The aim of the HOME-Health study is to provide knowledge about residents' seasonal state of health, perception of indoor enviromental quality (IEQ), IEQ-related behaviours and practices, and objective measurements of IAQ before and after BER. By applying a design with repeated measurement before and after BER, and combining both objective and subjective quantitative as well as qualitative data the study is expected to create in-depth knowledge. Future results will provide evidence of both energy-savings and non-energy savings from different BER projects. Knowledge which are expected to benefit future renovation projects within social housing areas. [ABSTRACT FROM AUTHOR]

Population attributable risk (PAR%) reflects the preventable fraction of disease. However, PAR% estimates of cancer have shown large variation across populations, methods, data sources, and timing of measurements. Three statistical methods to estimate PAR% were identified from a systematic literature review: the Levin's formula, the comparative incidence rate method, and the comparative risk assessment method. We compared the variations in PAR% of postmenopausal breast cancer in the Nurses' Health Study to evaluate the influence by method choice, source of prevalence data, use of single vs repeated exposure measurements, and potential joint effects of obesity, alcohol, physical activity, fruit and vegetable intake. Across models of the three methods, the estimated PAR% using repeated measurements were higher than that using baseline measurement; overall PAR% for the baseline, simple update, and cumulative average models were 13.8%, 21.1%, 18.6% by Levin's formula; 13.7%, 28.0%, 31.2% by comparative risk assessment; and 17.4%, 25.2%, 29.3% by comparative incidence rate method. The estimated PAR% of the combination of multiple risk factors was higher than the product of the individual PAR%: 18.9% when assuming independence and 31.2% when considering the risk factors jointly. The three methods provided similar PAR% based on the same data source, timing of measurements, and target populations. However, sizable increases in the PAR% were observed for repeated measures over a single measure and for calculations based on achieving all recommendations jointly rather than individually. [ABSTRACT FROM AUTHOR]

Background: Clinical guidelines categorize atrial fibrillation (AF) based on the temporality of AF events. Due to its dependence on event duration, this classification is not applicable to population-based cohort settings. We aimed to develop a simple and standardized method to classify AF patterns at population level. Additionally, we compared the longitudinal trajectories of cardiovascular risk factors preceding the AF patterns, and between men and women. Methods: Between 1990 and 2014, participants from the population-based Rotterdam study were followed for AF status, and categorized into 'single-documented AF episode', 'multiple-documented AF episodes', or 'long-standing persistent AF'. Using repeated measurements we created linear mixed-effects models to assess the longitudinal evolution of risk factors prior to AF diagnosis. Results: We included 14,061 participants (59.1% women, mean age 65.4 ± 10.2 years). After a median follow-up of 9.4 years (interquartile range 8.27), 1,137 (8.1%) participants were categorized as 'single-documented AF episode', 208 (1.5%) as 'multiple-documented AF episodes', and 57 (0.4%) as 'long-standing persistent AF'. In men, we found poorer trajectories of weight and waist circumference preceding 'long-standing persistent AF' as compared to the other patterns. In women, we found worse trajectories of all risk factors between 'long-standing persistent AF' and the other patterns. Conclusion: We developed a standardized method to classify AF patterns in the general population. Participants categorized as 'long-standing persistent AF' showed poorer trajectories of cardiovascular risk factors prior to AF diagnosis, as compared to the other patterns. Our findings highlight sex differences in AF pathophysiology and provide insight into possible risk factors of AF patterns. [ABSTRACT FROM AUTHOR]

Abstract Background When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner. Methods For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists. Results As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis. Conclusions This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.

Background & Aims: Data regarding health‐related quality of life (HRQoL) in primary sclerosing cholangitis (PSC) are sparse and have only been studied cross‐sectionally in a disease which runs a fluctuating and unpredictable course. We aim to describe HRQoL longitudinally by using repeated measurements in a population‐based cohort. Methods: Every 3 months from May 2017 up to August 2020, patients received digital questionnaires at home. These included the EQ‐5D, 5‐D Itch, patient‐based SCCAI and patient‐based HBI. The SF‐36, measuring HRQoL over eight dimensions as well as a physical component summary (PCS) and mental component summary (MCS) score, was sent annually. Data were compared with Dutch reference data and a matched IBD disease control from the population‐based POBASIC cohort. Mixed‐effects modelling was performed to identify factors associated with HRQoL. Results: Three hundred twenty‐eight patients completed 2576 questionnaires. A significant reduction of small clinical relevance in several mean HRQoL scores was found compared with the Dutch reference population: 46.4 versus 48.0, p =.018 for PCS and 47.5 versus 50.5, p =.004 for MCS scores. HRQoL outcomes were significantly negatively associated with coexisting active IBD (PCS −12.2, p <.001 and MCS −12.0, p <.001), which was not the case in case of quiescent IBD. Decreasing HRQoL scores were also negatively associated with increasing age (PCS −0.1 per 10 years, p =.002), female sex (PCS ‐2.8, p <.001), diagnosis of AIH overlap (PCS ‐3.7, p =.059), end‐stage liver disease (PCS ‐3.7, p =.015) and presence of itch (PCS ‐9.2, p <.001 and MCS −3.1, p =.078). The odds of reporting a clinically relevant reduction in EQ‐5D scores showed seasonal variation, being lowest in summer (OR = 0.48 relative to spring, p =.037). In patients with liver transplant, HRQoL outcomes were comparable to the Dutch general population. Conclusions: PSC patients report impaired HRQoL of small clinical relevance compared with the general population. After liver transplantation, HRQoL scores are at comparable levels to the general population. HRQoL scores are associated with potentially modifiable factors such as itch and IBD activity. [ABSTRACT FROM AUTHOR]

Background: Cancer is a leading cause of premature death worldwide and incidence is expected to rise in the coming decades. Many cohort studies, measuring lifestyle factors at one time-point, have observed that overall healthy lifestyles were inversely related to cancer incidence. However, there is little knowledge on the impact of lifestyle modification within adulthood. Methods: Using the Norwegian Women and Cancer study, two repeated self-reported assessments of lifestyle behaviours were used to calculate healthy lifestyle index scores at each time-point (N = 66 233). The associations between change in healthy lifestyle index score and lifestyle-related cancer incidence, including alcohol-, tobacco-, obesity-, and reproductive-related, and site-specific breast and colorectal cancer incidence were estimated using Cox proportional hazard regression models. To assess nonlinearity in the dose–response relationships, restricted cubic spline models were used. Results: Independent of baseline lifestyle, positive lifestyle changes were inversely related to the incidence of overall lifestyle-related cancers, as well as alcohol-related, tobacco-related, obesity-related, and reproductive-related cancers, but not breast and colorectal site-specific cancers. An association between lifestyle worsening and cancer incidence compared to stable lifestyle was observed. Conclusions: This study provides evidence that overall lifestyle changes among cancer-free women between the ages of 41 and 76 impact the incidence of many cancer types. Regardless of baseline lifestyle, there was a negative dose–response relationship between magnitude of positive lifestyle change and the incidence of overall lifestyle-related cancers. We observed that underlying this trend was an especially clear association between lifestyle worsening and increased risk compared to stable lifestyle. For adult women, maintaining a stable healthy lifestyle and lifestyle improvement are important for preventing the occurrence of many cancer types. [ABSTRACT FROM AUTHOR]

Jatropha curcas has become a prominent source of biofuel, especially because of the high oil content in its fruit. The aim of this study was to test different statistic models and compare the best-fitted model with the compound symmetry model and study the grain yield persistence of J. curcas progenies. A total of 730 individuals from 73 half-sib families were evaluated for the fruit yield trait over six crop years. Repeated measures models with different covariance structures for the genetic and non-genetic effects were tested. Results show an increase up to in accuracy upon modeling the genetic and non-genetic effects when compared to the compound symmetry model. The selection gain obtained via the best-fit model for 10, 15, 20, and 25 selected best progenies was around 3 to 2% higher than gain obtained via the standard statistical model used by breeders (compound symmetry model). The harvests evaluated exhibited accuracies of high magnitude. The ten progenies that stood out with the best genetic performance are also those with the greatest persistence and greatest accumulated yield. Combining modeling of covariance structures for grain yield and selecting for persistence of production can sustain a successful long-term J. curcas breeding program. [ABSTRACT FROM AUTHOR]

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions. [ABSTRACT FROM AUTHOR]

Introduction Patients who have experienced an acute coronary syndrome (ACS) are at risk of a recurrent event, but their level of risk varies. Because of their close temporal relationship with vascular injury, longitudinal measurements of circulating endothelial cells (CECs) carry potential to improve individual risk assessment. Methods We conducted an explorative nested case-control study within our multicenter, prospective, observational biomarker study (BIOMArCS) of 844 ACS patients. Following an index ACS, high-frequency blood sampling was performed during 1-year follow-up. CECs were identified using flow cytometric analyses in 15 cases with recurrent event, and 30 matched controls. Results Cases and controls had a median (25th-75thpercentile) age of 64.1 (58.1-75.1) years and 80% were men. During the months preceding the endpoint, the mean (95%CI) CEC concentration in cases was persistently higher than in controls (12.8 [8.2-20.0] versus 10.0 [7.0-14.4] cells/ml), although this difference was non-significant (P = 0.339). In controls, the mean cell concentration was significantly (P = 0.030) lower in post 30-day samples compared to samples collected within one day after index ACS: 10.1 (7.5-13.6) versus 17.0 (10.8-26.6) cells/ml. Similar results were observed for CEC subsets co-expressing CD133 and CD309 (VEGFR-2) or CD106 (VCAM-1). Conclusion Despite their close relation to vascular damage, no increase in cell concentrations were found prior to the occurrence of a secondary adverse cardiac event. [ABSTRACT FROM AUTHOR]

Studies examining long-term longitudinal metabolomic data and their reliability in large-scale populations are limited. Therefore, we aimed to evaluate the reliability of repeated measurements of plasma metabolites in a prospective cohort setting and to explore intra-individual concentration changes at three time points over a 6-year period. The study participants included 2999 individuals (1317 men and 1682 women) from the Tsuruoka Metabolomics Cohort Study, who participated in all three surveys—at baseline, 3 years, and 6 years. In each survey, 94 plasma metabolites were quantified for each individual and quality control (QC) sample. The coefficients of variation of QC, intraclass correlation coefficients, and change rates of QC were calculated for each metabolite, and their reliability was classified into three categories: excellent, fair to good, and poor. Seventy-six percent (71/94) of metabolites were classified as fair to good or better. Of the 39 metabolites grouped as excellent, 29 (74%) in men and 26 (67%) in women showed significant intra-individual changes over 6 years. Overall, our study demonstrated a high degree of reliability for repeated metabolome measurements. Many highly reliable metabolites showed significant changes over the 6-year period, suggesting that repeated longitudinal metabolome measurements are useful for epidemiological studies.

Repeated size measurements of individual animals are valuable data for many research questions, but it is often hard to obtain without causing stress or damage to the animal. We developed a video-based approach called Zoobooth to size individual zooplankton, which involves a low risk of handling accidents and stress. Here we describe the process of assembling the instrument we used to acquire video recordings of single zooplankton and the procedure to obtain size estimates from the recorded videos. Our setup produces accurate size estimates for Daphnia magna (correlation to manual measurements = 0.97), and was also tested with other zooplankton species. Zoobooth is especially advantageous when one needs size measurements of live, individual mesozooplankton. The device is small, portable, and comprised of very affordable and readily available components. It can easily be modified for other purposes, such as studies of coloration or behavior of micro-and macro-plankton. We share all the files to build and use Zoobooth.

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene–environment interaction studies. This study introduces “springer,” an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package “springer” has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the “springer” package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package “springer” is available at https://cran.r-project.org/package=springer.

Heart failure (HF) is considered as a chronic long-term and lethal disease and will continue to be a major public health problem. Studying (circulating) biomarkers is a promising field of research and could be the first step toward HF tailored prognostic strategies as well as understanding the response to HF drugs in CHF patients. In literature, there has been considerable research on elevated biomarker levels that are related to a poor prognosis for HF. Since biomarker levels change over time, it is important to study serial (repeated) biomarker measurements which may help us better understand the dynamic course of HF illness. However, the majority of research focuses predominantly on baseline values of biomarkers. Additionally, remote monitoring devices, like sensors, can be used to link hemodynamic information to freshen biomarker data in order to further ameliorate the management of HF. Novel biomarkers and additional scientific insights with hemodynamic feedback strongly aid in the prognostication and risk prediction of chronic HF. [ABSTRACT FROM AUTHOR]

Background: When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner. Methods: For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists. Results: As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis. Conclusions: This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications. [ABSTRACT FROM AUTHOR]

Collecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases, it is desirable to model these outcomes jointly. However, in large data sets, with many outcomes, computational burden often prevents the simultaneous modeling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson, or binary longitudinal markers within a multivariate generalized linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis), we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters. [ABSTRACT FROM AUTHOR]

QT interval length is an important risk factor for adverse cardiovascular outcomes; however, the genetic architecture of QT interval remains incompletely understood. We conducted a genome-wide association study of 76,995 ancestrally diverse Kaiser Permanente Northern California members enrolled in the Genetic Epidemiology Research on Adult Health and Aging cohort using 448,517 longitudinal QT interval measurements, uncovering 9 novel variants, most replicating in 40,537 individuals in the UK Biobank and Population Architecture using Genomics and Epidemiology studies. A meta-analysis of all 3 cohorts (n = 117,532) uncovered an additional 19 novel variants. Conditional analysis identified 15 additional variants, 3 of which were novel. Little, if any, difference was seen when adjusting for putative QT interval lengthening medications genome-wide. Using multiple measurements in Genetic Epidemiology Research on Adult Health and Aging increased variance explained by 163%, and we show that the 6 measurements in Genetic Epidemiology Research on Adult Health and Aging was equivalent to a 2.4 increase in sample size of a design with a single measurement. The array heritability was estimated at 17%, approximately half of our estimate of 36% from family correlations. Heritability enrichment was estimated highest and most significant in cardiovascular tissue (enrichment 7.2, 95% CI= 5.7–8.7, P = 2.1e-10), and many of the novel variants included expression quantitative trait loci in heart and other relevant tissues. Comparing our results to other cardiac function traits, it appears that QT interval has a multifactorial genetic etiology. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.Methods: We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means.Results: The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005).Conclusion: Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved. [ABSTRACT FROM AUTHOR]

Although anxiety and depression have been associated with adverse outcomes in chronic heart failure (HF), data on temporal evolution of these symptoms are scarce. We aimed to investigate the association between repeatedly measured depression and anxiety symptoms and clinical outcome in chronic HF patients. In this prospective observational study, outpatients with chronic HF were included and followed-up for a maximum of 2.5 years. The hospital anxiety and depression scale (HADS) questionnaire was conducted every six months. The primary endpoint was a composite of HF hospitalization, cardiovascular death, heart transplantation and left ventricular assist device (LVAD) implantation. Cox and joint models were used to investigate the association between the HADS score and the endpoint. A total of 362 patients filled out a median (25th–75th percentile) of 3 [2–4] questionnaires each. Mean ± SD age was 63 ± 13 years, 72% were men. Anxiety scores remained relatively stable leading up to the endpoint, while depression scores increased. Higher baseline depression scores were significantly associated with the endpoint (hazard ratio [HR] 1.68 and 95% confidence interval [CI] 1.19–2.36 per log(score+1), p = 0.003), while higher baseline anxiety scores did not reach statistical significance (HR [95% CI] 1.34 [0.99–1.83], p = 0.061). When repeatedly measured, both higher anxiety (HR [95% CI] 1.57[1.07–2.30], p = 0.022) and depression (HR [95% CI] 2.04 [1.39–3.06], p < 0.001) scores were significantly associated with the endpoint. Serial measurements of depression and anxiety symptoms identify chronic HF patients with increased risk of adverse clinical outcomes. Screening for both disorders should be considered in clinical practice. [Display omitted] • This study investigated the associations of anxiety and depression with clinical outcomes in HF patients. • Over time, anxiety scores remained relatively stable, while depression scores increased. • When repeatedly measured, both higher anxiety and depression scores were linked with higher risk of adverse outcomes. • Results suggest that screening for these symptoms may be beneficial for clinical practice. [ABSTRACT FROM AUTHOR]

Accumulating epidemiological evidence shows that handgrip strength provides predictive potential in physical, mental, and reproductive health status. However, the associations between handgrip strength and semen characteristics have not been explored. We recruited 1382 eligible men at the Hubei Province Human Sperm Bank (Wuhan, China) who had their handgrip strength measured at recruitment and provided 6458 repeated semen specimens within a 6-month period. Semen characteristics, including semen volume, sperm motility parameters (immotility, nonprogressive motility, and progressive motility), and sperm concentration, were assessed. Mixed-effect models and restricted cubic spline functions were applied to investigate the relationship of handgrip strength with repeated measurements of semen characteristics. After adjusting for confounding factors, the mixed-effect models revealed that handgrip strength was positively associated with semen volume, sperm concentration, progressive motility, total motility, and total count (all P for trend < 0.05). Compared to men in the lowest quartile, those in the highest quartile of handgrip strength had higher semen volume, sperm concentration, progressive motility, total motility, and total count, with measurements of 14.2% (95% confidence interval [CI]: 5.9%–23.2%), 19.5% (95% CI: 7.3%‒33.1%), 9.5% (95% CI: 3.4%‒15.9%), 8.8% (95% CI: 3.2%‒14.6%), and 36.4% (95% CI: 18.9%‒56.5%), respectively. These positive dose-response relationships were further confirmed in restricted cubic splines, where handgrip strength was modeled as a continuous variable. Handgrip strength, as an indicator of muscular function and strength, was positively associated with semen characteristics in a dose-dependent manner.

ABSTRACT The use of longitudinal measurements is an essential practice both in Psidium guajava L. breeding and in other perennial crops in which covariance structures can be introduced to explain the form of dependence between measurements. Hence, this study aimed to analyze six covariance structures to identify one that best described the correlation between the repeated measurements in time in traits of guava full-sib families. The repeatability coefficient for each trait was estimated and the minimum number of evaluations required for estimates representing the population was determined. The work was performed based on average data of three yield-related variables from nine harvests of a guava tree population evaluated from 2011 to 2018. The best model was chosen based on the Akaike and Schwarz Bayesian information criterion. The autoregressive covariance structure best represented the dependencies among families between crops for all traits. The number of variables of fruits and total yield per plant presented repeatability estimates higher than 0.5 and may be essential traits for indirect selection of others, such as fruit mass, which had an estimated repeatability of 0.24, proving low regularity in the repetition of the character from one cycle to another. It was also possible to define four harvests as the minimum acceptable number of observations necessary on the same individual for these traits; therefore, the repetitions represented the individuals.

BackgroundWe investigated whether repeatedly measured global longitudinal strain (GLS) has incremental prognostic value over repeatedly measured left ventricular ejection fraction (LVEF) and N-terminal pro B-type natriuretic peptide (NT-proBNP), and a single “baseline” GLS value, in chronic heart failure (HF) patients.MethodsIn this prospective observational study, echocardiography was performed in 173 clinically stable chronic HF patients every six months during follow up. During a median follow-up of 2.7 years, a median of 3 (25th–75th percentile:2–4) echocardiograms were obtained per patient. The endpoint was a composite of HF hospitalization, left ventricular assist device, heart transplantation, cardiovascular death. We compared hazard ratios (HRs) for the endpoint from Cox models (used to analyze the first available GLS measurements) with HRs from joint models (which links repeated measurements to the time-to-event data).ResultsMean age was 58 ± 11 years, 76% were men, 81% were in New York Heart Association functional class I/II, and all had LVEF < 50% (mean ± SD: 27 ± 9%). The endpoint was reached by 53 patients. GLS was persistently decreased over time in patients with the endpoint. However, temporal GLS trajectories did not further diverge in patients with versus without the endpoint and remained stable during follow-up. Both single measurements and temporal trajectories of GLS were significantly associated with the endpoint [HR per SD change (95%CI): 2.15(1.34–3.46), 3.54 (2.01–6.20)]. In a multivariable model, repeatedly measured GLS maintained its prognostic value while repeatedly measured LVEF did not [HR per SD change (95%CI): GLS:4.38 (1.49–14.70), LVEF:1.14 (0.41–3.23)]. The association disappeared when correcting for repeatedly measured NT-proBNP.ConclusionTemporal evolution of GLS was associated with adverse events, independent of LVEF but not independent of NT-proBNP. Since GLS showed decreased but stable values in patients with adverse prognosis, single measurements of GLS provide sufficient information for determining prognosis in clinical practice compared to repeated measurements, and temporal GLS patterns do not add prognostic information to NT-proBNP.

We evaluated the clinical significance of the new non-invasive vascular indices to explore their potential utility using repeated cuff-oscillometric inflation. In 250 consecutive outpatients, we performed a cross-sectional, retrospective, single-center, observational study to investigate sequential differences in arterial stiffness using blood pressure, arterial velocity pulse index (AVI), and arterial pressure volume index (API) with repeated measurements. Males accounted for 62.7% of the patients, and the mean age was 68.1 ± 12.1 years. The mean systolic blood pressure (SBP) and diastolic blood pressure (DBP) of the first reading in repeated measurements were 133.07 ± 21.20 mmHg and 73.94 ± 13.56 mmHg, respectively. The mean AVI and API were 23.83 ± 8.30 and 31.12 ± 7.86, respectively. In each measurement of these parameters, although DBP and AVI did not show significant changes throughout repeated measurements, SBP and API decreased significantly according to the measurement orders. Furthermore, changes in SBP and API were significantly correlated in several of the models. In this study, it was concluded that upper-arm SBP decline associated with repeated cuff-oscillometric inflation was significantly correlated with the arterial stiffness index. The findings of this study will allow clinicians to easily recognize the progression of atherosclerosis through regular, routine practice. In conclusion, this study suggests that changes in repeated SBP measurements may be predictive of arterial stiffness and atherosclerosis. [ABSTRACT FROM AUTHOR]

This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis. Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted. Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4–2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12–0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only. Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication. • We repeatedly measured 92 circulating proteins in 250 ambulant patients with HFrEF. • Clustering of individual biomarker trajectories identified 3 HFrEF subphenotypes. • Subphenotypes were clinically diverse and associated with adverse events. • Thus temporal biomarker pattern subphenotypes may aid in personalized prognostication. [ABSTRACT FROM AUTHOR]

Background: Many studies have investigated the intake of dietary isoflavones in relation to obesity risk, whereas the association using objective biomarkers of isoflavones, particularly equol (a gut-derived metabolite of daidzein with greater bioavailability than other isoflavones) has been less studied. In addition, the associations between equol and gut microbiota profile at the population level remain to be fully characterized., Objectives: We aimed to identify equol-predicting microbial species and to investigate the associations of equol-predicting microbial species and urinary excretion of isoflavones including glycitein, genistein, daidzein, and equol with diverse obesity markers in free living-individuals., Methods: In this 1-y longitudinal study of 754 community-dwelling adults, urinary isoflavones, fecal microbiota, height, weight, and circumferences of waist and hip were measured at baseline and again after 1 y. Liver fat [indicated by the controlled attenuation parameter (CAP)] and other body composition were also measured after 1 y. Linear models and linear mixed-effects models were used to analyze the associations for single measure and repeated measures, respectively., Results: Among 305 participants (median age: 50 y, IQR, 37-59 y) including 138 males and 167 females, higher urinary excretion of equol was associated with lower CAP (β = -0.013, P < 0.001) and body fat mass (β= -0.014, P = 0.046). No association was found between any other urinary isoflavones and obesity markers (all P > 0.05). We identified 21 bacterial genera whose relative abundance were positively associated with urinary equol concentrations (all P false discovery rate < 0.05), and constructed an equol-predicting microbial score to reflect the overall equol-producing potential of host gut microbiota. This score was inversely associated with CAP (β = -0.040, P = 0.011)., Conclusions: High urinary equol concentrations and equol-predicting microbial species could be favorably associated with liver fat and other obesity markers., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19. Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period. Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively. Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality. [ABSTRACT FROM AUTHOR]