Explore the vast range of titles available.

Sex differences in dementia risk are unclear, but some studies have found greater risk for women. To determine associations between sex and cognitive decline in order to better understand sex differences in dementia risk. This cohort study used pooled analysis of individual participant data from 5 cohort studies for years 1971 to 2017: Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in Young Adults Study, Cardiovascular Health Study, Framingham Offspring Study, and Northern Manhattan Study. Linear mixed-effects models were used to estimate changes in each continuous cognitive outcome over time by sex. Data analysis was completed from March 2019 to October 2020. Sex. The primary outcome was change in global cognition. Secondary outcomes were change in memory and executive function. Outcomes were standardized as t scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition. Among 34 349 participants, 26 088 who self-reported Black or White race, were free of stroke and dementia, and had covariate data at or before the first cognitive assessment were included for analysis. Median (interquartile range) follow-up was 7.9 (5.3-20.5) years. There were 11 775 (44.7%) men (median [interquartile range] age, 58 [51-66] years at first cognitive assessment; 2229 [18.9%] Black) and 14 313 women (median [interquartile range] age, 58 [51-67] years at first cognitive assessment; 3636 [25.4%] Black). Women had significantly higher baseline performance than men in global cognition (2.20 points higher; 95% CI, 2.04 to 2.35 points; P < .001), executive function (2.13 points higher; 95% CI, 1.98 to 2.29 points; P < .001), and memory (1.89 points higher; 95% CI, 1.72 to 2.06 points; P < .001). Compared with men, women had significantly faster declines in global cognition (-0.07 points/y faster; 95% CI, -0.08 to -0.05 points/y; P < .001) and executive function (-0.06 points/y faster; 95% CI, -0.07 to -0.05 points/y; P < .001). Men and women had similar declines in memory (-0.004 points/y faster; 95% CI, -0.023 to 0.014; P = .61). The results of this cohort study suggest that women may have greater cognitive reserve but faster cognitive decline than men, which could contribute to sex differences in late-life dementia.

Diabetic kidney disease (DKD) progression is not well understood. Using high-throughput proteomics, biostatistical, pathway and machine learning tools, we examine the urinary Complement proteome in two prospective cohorts with type 1 or 2 diabetes and advanced DKD followed for 1,804 person-years. The top 5% urinary proteins representing multiple components of the Complement system (C2, C5a, CL-K1, C6, CFH and C7) are robustly associated with 10-year kidney failure risk, independent of clinical covariates. We confirm the top proteins in three early-to-moderate DKD cohorts (2,982 person-years). Associations are especially pronounced in advanced kidney disease stages, similar between the two diabetes types and far stronger for urinary than circulating proteins. We also observe increased Complement protein and single cell/spatial RNA expressions in diabetic kidney tissue. Here, our study shows Complement engagement in DKD progression and lays the groundwork for developing biomarker-guided treatments. Complement proteome engagement is strongly linked to kidney outcomes in diabetes. This translational study leveraged five cohorts of over 4,500 person-years and high-throughput proteomics to enable potential biomarker-guided drug development.

We previously described a cross-sectional association between serum uric acid and reduced glomerular filtration rate (GFR) in nonproteinuric patients with type 1 diabetes. Here, we prospectively investigated whether baseline uric acid impacts the risk of early progressive renal function loss (early GFR loss) in these patients. Patients with elevated urinary albumin excretion (n = 355) were followed for 4-6 years for changes in urinary albumin excretion and GFR. The changes were estimated by multiple determinations of albumin-to-creatinine ratios (ACRs) and serum cystatin C (GFRcystatin). At baseline, the medians (25th-75th percentiles) for uric acid, ACR, and GFRcystatin values were 4.6 mg/dl (3.8-5.4), 26.2 mg/g (15.1-56.0), and 129 ml/min per 1.73 m(2) (111-145), respectively. During the 6-year follow-up, significant association (P < 0.0002) was observed between serum uric acid and development of early GFR loss, defined as GFRcystatin decline exceeding 3.3% per year. In baseline uric acid concentration categories (in mg/dl: <3.0, 3.0-3.9, 4.0-4.9, 5.0-5.9, and >or=6), the risk of early GFR loss increased linearly (9, 13, 20, 29, and 36%, respectively). This linear increase corresponds to odds ratio 1.4 (95% CI 1.1-1.8) per 1 mg/dl increase of uric acid. The progression and regression of urinary albumin excretion were not associated with uric acid. We found a clear dose-response relation between serum uric acid and risk of early GFR loss in patients with type 1 diabetes. Clinical trials are warranted to determine whether uric acid-lowering drugs can halt renal function decline before it becomes clinically significant.

Strategies to prevent or delay Alzheimer’s disease and related dementias (AD/ADRD) are urgently needed, and blood pressure (BP) management is a promising strategy. Yet the effects of different BP control strategies across the life course on AD/ADRD are unknown. Randomized trials may be infeasible due to prolonged follow-up and large sample sizes. Simulation analysis is a practical approach to estimating these effects using the best available existing data. However, existing simulation frameworks cannot estimate the effects of BP control on both dementia and cardiovascular disease. This manuscript describes the design principles, implementation details, and population-level validation of a novel population-health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP-COG) study of the effect of BP levels over the life course on dementia and cardiovascular disease. MICROSIM is an agent-based Monte Carlo simulation designed using computer programming best practices. MICROSIM estimates annual vascular risk factor levels and transition probabilities in all-cause dementia, stroke, myocardial infarction, and mortality in a nationally representative sample of US adults 18+ using the National Health and Nutrition Examination Survey (NHANES). MICROSIM models changes in risk factors over time, cognition and dementia using changes from a pooled dataset of individual participant data from 6 US prospective cardiovascular cohort studies. Cardiovascular risks were estimated using a widely used risk model and BP treatment effects were derived from meta-analyses of randomized trials. MICROSIM is an extensible, open-source framework designed to estimate the population-level impact of different BP management strategies and reproduces US population-level estimates of BP and other vascular risk factors levels, their change over time, and incident all-cause dementia, stroke, myocardial infarction, and mortality.

Diabetic kidney disease causes significant morbidity and mortality among people with type 1 diabetes (T1D). Intensive glucose and blood pressure control have thus far failed to adequately curb this problem and therefore a major need for novel treatment approaches exists. Multiple observations link serum uric acid levels to kidney disease development and progression in diabetes and strongly argue that uric acid lowering should be tested as one such novel intervention. A pilot of such a trial, using allopurinol, is currently being conducted by the Preventing Early Renal Function Loss (PERL) Consortium. Although the PERL trial targets T1D individuals at highest risk of kidney function decline, the use of allopurinol as a renoprotective agent may also be relevant to a larger segment of the population with diabetes. As allopurinol is inexpensive and safe, it could be cost-effective even for relatively low-risk patients, pending the completion of appropriate trials at earlier stages.

At present, there are many software procedures available that enable statisticians to fit linear mixed models (LMMs) to continuous dependent variables in clustered or longitudinal datasets. LMMs are flexible tools for analyzing relationships among variables in these types of datasets, in that a variety of covariance structures can be used depending on the subject matter under study. The explicit random effects in LMMs allow analysts to make inferences about the variability between clusters or subjects in larger hypothetical populations, and examine cluster- or subject-level variables that explain portions of this variability. These models can also be used to analyze longitudinal or clustered datasets with data that are missing at random (MAR), and can accommodate time-varying covariates in longitudinal datasets. Although the software procedures currently available have many features in common, more specific analytic aspects of fitting LMMs (e.g., crossed random effects, appropriate hypothesis testing for variance components, diagnostics, incorporating sampling weights) may only be available in selected software procedures. With this article, we aim to perform a comprehensive and up-to-date comparison of the current capabilities of software procedures for fitting LMMs, and provide statisticians with a guide for selecting a software procedure appropriate for their analytic goals.

Elite sprinters offer insights into the fastest whole body auditory reaction times. When, however, is a reaction so fast that it represents a false start? Currently, a false start is awarded if an athlete increases the force on their starting block above a given threshold before 100 ms has elapsed after the starting gun. To test the hypothesis that the fastest valid reaction times of sprinters really is 100 ms and that no sex difference exists in that time, we analyzed the fastest reaction times achieved by each of the 425 male and female sprinters who competed at the 2008 Beijing Olympics. After power transformation of the skewed data, a fixed effects ANOVA was used to analyze the effects of sex, race, round and lane position. The lower bounds of the 95, 99 and 99.9% confidence intervals were then calculated and back transformed. The mean fastest reaction time recorded by men was significantly faster than women (p<0.001). At the 99.9% confidence level, neither men nor women can react in 100 ms, but they can react in as little as 109 ms and 121 ms, respectively. However, that sex difference in reaction time is likely an artifact caused by using the same force threshold in women as men, and it permits a woman to false start by up to 21 ms without penalty. We estimate that female sprinters would have similar reaction times to male sprinters if the force threshold used at Beijing was lowered by 22% in order to account for their lesser muscle strength.

Reaction times of Olympic sprinters provide insights into the most rapid of human response times. To determine whether minimum reaction times have changed as athlete training has become ever more specialized, we analyzed the results from the Olympic Games between 2004 and 2016. The results for the 100 m and 110 m hurdle events show that minimum reaction times have systematically decreased between 2004 and 2016 for both sexes, with women showing a marked decrease since 2008 that eliminated the sex difference in 2012. Because overall race times have not systematically decreased between 2004 and 2016, the most likely explanation for the apparent decrease in reaction times is a reduction in the proprietary force thresholds used to calculate the reaction times based on force sensors in starting blocks-and not the result of more specialized or effective training.

Clostridium difficile infection (CDI) is a major cause of morbidity and biomarkers that predict severity of illness are needed. Procalcitonin (PCT), a serum biomarker with specificity for bacterial infections, has been little studied in CDI. We hypothesized that PCT associated with CDI severity. Serum PCT levels were measured for 69 cases of CDI. Chart review was performed to evaluate the presence of severity markers and concurrent acute bacterial infection (CABI). We defined the binary variables clinical score as having fever (T >38°C), acute organ dysfunction (AOD), and/or WBC >15,000 cells/mm(3) and expanded score, which included the clinical score plus the following: ICU admission, no response to therapy, colectomy, and/or death. In univariate analysis log10 PCT associated with clinical score (OR 3.13, 95% CI 1.69-5.81, P<.001) and expanded score (OR 3.33, 95% CI 1.77-6.23, P<.001). In a multivariable model including the covariates log10 PCT, enzyme immunoassay for toxin A/B, ribotype 027, age, weighted Charlson-Deyo comorbidity index, CABI, and extended care facility residence, log10 PCT associated with clinical score (OR 3.09, 95% CI 1.5-6.35, P = .002) and expanded score (OR 3.06, 95% CI 1.49-6.26, P = .002). PCT >0.2 ng/mL was 81% sensitive/73% specific for a positive clinical score and had a negative predictive value of 90%. An elevated PCT level associated with the presence of CDI severity markers and CDI was unlikely to be severe with a serum PCT level below 0.2 ng/mL. The extent to which PCT changes during CDI therapy or predicts recurrent CDI remains to be quantified.

Objective: Pain, fatigue, and depression commonly co-occur as a symptom cluster in pathological inflammatory states. Psychosocial stressors such as loneliness may lead to similar states through shared mechanisms. We investigated the association of loneliness with pain, fatigue, and depression in older adults. Methods: Using Health and Retirement Study data (N = 11,766), we measured cross-sectional prevalence of frequent, moderate to severe pain; severe fatigue; depressive symptoms; and co-occurrence of symptoms surpassing threshold levels (i.e., symptom cluster). Logistic regression models evaluated associations with loneliness. Results: Pain, fatigue, and depression were reported in 19.2%, 20.0%, and 15.3% of the total sample, respectively. The symptom cluster was seen in 4.9% overall; prevalence in lonely individuals was significantly increased (11.6% vs. 2.3%, p < .0001). After adjusting for demographic variables, loneliness associated with the symptom cluster (adjusted OR = 3.39, 95% CI = 2.91, 3.95) and each symptom (pain adjusted OR = 1.61, 95% CI = 1.48, 1.76; fatigue adjusted OR = 2.02, 95% CI = 1.85, 2.20; depression adjusted OR = 4.34, 95% CI = 3.93, 4.79). Discussion: Loneliness strongly associates with the symptom cluster of pain, fatigue, and depression. Further research should examine causal relationships and investigate whether interventions targeting loneliness mitigate pain, fatigue, and depression.