Association Between Urinary Flow Rate and Cognition in the Elderly: A Cross-Sectional Study

Age-related lower urinary tract symptoms (LUTS) is a common disease in the elderly. The reduction of urinary ow rate (UFR) as an assessment of LUTS is associated with cognitive impairment. The association between UFR and cognitive performance has not been studied to date.


Introduction
The global population aging has largely led to the increase of morbidity and disability [1] . Age-related cognitive decline begins in middle age and continues with time [2] . Elderly cognitive impairment has a great impact on the daily life of patients and their families, increases the mortality of the elderly, and brings a huge burden to the society [3][4][5] . In view of the occult incidence of cognitive decline and the slow progress to dementia, and the fact that there is no effective treatment for this disorder at present, it is very important to screen high-risk groups and nd early cognitive decline markers for primary prevention [6] .
Urinary ow rate (UFR) represents the amount of urine excreted from the urethra per unit time in a natural state, which is mainly in uenced by detrusor contraction strength and bladder outlet resistance [7] . As a common noninvasive examination, the determination of UFR has the advantages of rapidity, low cost and high repeatability, which is widely used in the preliminary screening of lower urinary tract symptoms (LUTS). As far as we knew, the low UFR was considered to be a bladder dysuria, which is manifested as the underactive bladder (UAB) caused by detrusor underactivity [8] . According to the de nition of the International Continence Society, the detrusor underactivity refers to bladder weakness or prolonged bladder emptying time due to decreased contractility [9] . Many previous studies have shown that poor urination is caused by both myogenic and neurogenic mechanisms [10][11][12][13] . Therefore, as a disease characterized by neurologic impairment, cognitive impairment plays an important role in urination.
At present, some studies have proved that the cognitive decline of the elderly is related to the dysfunction of urination. Jellinger [14] found that LUTS, which consist of storage (frequency, urgency, nocturia) and voiding symptoms (delayed urination, poor or prolonged urine ow), were common in patients with Parkinson's disease (PD) (prevalence 27% -85%). Picillo et al [15] also mentioned that dysuria is an early marker of progression of PD, and suggested that urinary dysfunction can be used to predict the development of the disease, which may represent a valuable variable in neuroprotective clinical trials. However, so far, there is no relevant predictor to indicate the occurrence of cognitive impairment.
In this study, we aimed to examined the association between UFR and cognitive performance based on the U.S. National Health and Nutrition Examination Survey (NHANES) database, which objective to provide predictive indicators for the occurrence of cognitive impairment in the elderly in order to prevent and control earlier.

Data Collection and participants selection
The NHANES study is a nationally representative study of population in the United States, which is also a cross-sectional survey based on a national sample of non-institutionalized population in the USA. It is conducted by the U.S. National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The survey consist of three main parts. Initial screening of quali ed participants through the questionnaire. Then, extensive interviews are conducted, including age, gender, race, medical history and health status. What is more, physical examination and clinical evaluation are performed in specially designed mobile examination centers (MECs). In the process of data acquisition, all interviewers have received the training plan and reached the required standards. NHANES started in 1999 and is an ongoing annual survey with data published every 2 years and made publicly available online. This study gained Institutional Review Board (IRB; project identi cation code protocol #2011-17) approval by the NCHS in line with the revised Helsinki Declaration [16] . Informed consent was provided by all study participants before the data collection and examination procedures. More NHANES data and information are available at https://www.cdc.gov/nchs/nhanes/index.htm.
Participants from the NHANES were included in this population-based cross-sectional research. There were 19,932 participants in the NHANES from two 2-year survey cycles: 2011-2012 and 2013-2014. We screen participants according to the exclusion criteria listed below: (1) subjects without cognitive performance score (n =16,997 ); (2) subjects without UFR data (n =166 ); (3) subjects with missing data for covariate (n=45 ). Eventually, 2,724 eligible individuals of the NHANES survey were included in our study ( Figure 1).
The whole informed consents from each eligible participant were obtained after explaining the whole process of the research. All experimental methods were performed in accordance with the relevant guidelines and regulations of the CDC.

Measurement of Urinary Flow Rate
The UFR was measured by uro owmetry (mL/min). The calculation formula of UFR is UFR=V/t, where V is the volume of the present urine sample and t is the time duration between the former urination and the present urine collection [17] . The participants had to record their last urination time before coming to the MECs. Then, at the centers, they would record the voiding time and volume of the urine sample and calculate the UFR for three times. The specimens were collected in different containers to guaranteeing enough data for various analyses. The composite UFR (mL/min) was measured by dividing the total urine volume collected by the total time covered by all collected voids [18] .

Measurement of Cognitive Function
The following 3 cognitive function measurements designed to assess a wide range of neurocognitive function across a variety of demo graphic backgrounds were studied: the Digit Symbol Substitution Test (DSST), the Animal Fluency Test (AFT) and the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) immediate recall test. The three assessment methods of cognitive function score are detailed in the Supplementary File.

Statistical analysis
The statistical analysis was performed according to the CDC analytical reporting guidelines for complex NHANES data analysis (https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx). A sample weight was assigned to each person participating in NHANES. Therefore, we accounted for masked variance and used the proposed recommended weighting methodology. Continuous variables were expressed as mean ±standard deviation. Categorical variables were expressed in frequency or as a percentage. Weighted linear regression model (for continuous variables) or weighted chi-square test (for categorical variables) were used to calculate the differences among different UFR groups (tertiles). To investigate whether UFR is correlated with cognitive function in selected participants, our statistical analysis consisted of two main steps.
First, weighted multivariate logistic regression model were employed. We estimated three models: crude model, no covariates were adjusted; model I,only adjusted for gender, age and BMI data; in the nal model (model II), model I + other covariates presented in Table 1 (i.e. PIR; marital status; comorbidity index; alcohol intake per week and smoking status).
Moreover, the subgroup analyses were then performed using weighted strati ed logistic regression models to further determine the correlation between UFR and cognitive function. To ensure the robustness of data analysis, we did the sensitivity analysis.
All analyses were performed using the statistical software packages R (http://www.R-project.org, The R Foundation) and EmpowerStats (http://www.empower stats.com, X&Y Solutions, Inc., Boston, MA). All P values less than 0.05 (two-sided) were considered statistically signi cant.

Baseline characteristics of participants
The weighted distribution of selective participants sociodemographic characteristics and other related covariates for the selected 2,724 NHANES participants from 2011 to 2014, according to UFR tertiles, is shown in Table 1. The average age of the participants was 69.26±6.65, and 54.56% were women. Among different tertile groups (T1-T3), there was no obvious difference in the following distributions: smoking status, BMI, comorbidity index and alcohol intake per week. We found that both UFR declined and cognitive performance scores (DSST, AFT and CERAD immediate recall test) decreased with age (p 0.0001).

Multivariate Logistic Regression Analysis
Associations between UFR as a continuous variable and cognitive performance (DSST, AFT and CERAD immediate recall test) is demonstrated in Table 2 In addition, to further test the correlation between UFR and cognitive function, participants were divided into three groups according to UFR (0.01-0.51, 0.51-0.92 and 0.92-3.45). Regarding UFR as a categorical variable (tertile), we found the similar trend (AFT: P for trend <0.0001; DSST: P for trend <0.0001; CERAD immediate: P for trend =0.0403) (see Table 2). Table 3 shows the results of our subgroup analysis. We found that after adjusting for potential confounding factors, the interaction test had no signi cant effect on age (grouped by 65 years old), gender, smoking status, marital status, comorbidity index, and alcohol intake per week (P > 0.05), except for BMI and PIR. However, due to the large difference in the number of patients between groups, and the lack of clinical signi cance. We think that there is still a positive relationship between UFR and cognitive ability.

Discussion
In the elderly population of the United States, we found that the UFR decline was positively correlated with the decrease of cognitive function after adjusting for a variety of potential confounders in men and women.
Previous studies have noted the high incidence of LUST in age-related cognitive impairment [21][22][23] . Consistent with our ndings, some previous studies have found that LUST caused by cognitive impairment in the elderly are common in Alzheimer's disease (AD), PD and Lewy body syndrome [24][25][26] . In addition, multiple system atrophy (MSA) is another cognitive disorder in the elderly with typical LUST, which is urinary retention [27,28] . Sakakibara et al used pressure-ow analysis in PD during urination, and the results showed that detrusor activity was weak during urination (40% of men; 66% of women) [29] .
The nervous system controls many essential aspects of the normal urination cycle (storage and urination). Especially important are cognition (e.g, decision-making, anticipation, perception of environmental / social context and conscious perception of sensation), sensory nerve activity and autonomic nerve functions (e.g, regulation of detrusor and sphincter) and motor function (e.g, mobility, balance and dexterity). Neurologic functions work together to ensure that urine storage and voiding re ect timings and environment appropriately, which are completely controlled voluntarily [30] . The neurogenic dysuria in the elderly may come from two ways: one is the disturbance of sensory consciousness, the other is the degeneration of nervous system, which leads to the loss of the function of the muscles. By default, the lower urinary tract remains in "store" mode during bladder lling. When the bladder is full, there is a "switch" to "urinate" mode. Default mode network is the key functional basis of PD cognitive impairment, for PD patients, there is the dysfunction of the corticobasal ganglia associative areas nigrostriatal and mesocortical dopamine (DA) depletion with impairment of the frontostriatal circuitry, which is important for executive problems [31] . On the other hand, detrusor underactivity is an important cause of dysuria in MSA. It may be caused by degeneration of primary motor cortex (PMC) and locus coeruleus, pontobronchial raphe and cerebellar vermis, all of which regions are considered to be important areas of brain autonomic control [32] .
This study has multiple advantages. This is the rst study to explore the relationship between UFR and cognitive impairment in the elderly, so as to provide a feasible marker for clinical prediction of cognitive impairment in the elderly. Second, the multi-ethnic, nationally representative data from NHANES enable our ndings to be extrapolated to a broader population.
However, this study still has several imitations. First of all, NHANES is a cross-sectional study that examines cognitive scores and UFR at a speci c point in time, rather than continuously collecting data over a long period of time. As a single measurement may produce biased results, causality can not be established. Secondly, although we use three cognitive assessment methods, we still can not cover the overall cognitive ability. Accompanying tests, such as mini mental state examination (MMSE), help to assess cognitive function more comprehensively. Thirdly, we used the method in the NHANES database to calculate the average urine ow rate, which can not represent the detrusor contraction and diastolic pressure values and the maximum urinary ow rate. The combination of the average UFR and the peak UFR certainly provides more comprehensive urodynamic studies, but the peak UFR requires more complex calculation with uro owmetry. However, the average UFR data can be used as a reference index of urinary re ex and the overall bladder muscle function. In addition, previous comparative studies have used the average UFR [16], which provide us with methods that can be used for reference. In the future, more clinical studies are needed to determine the relationship between UFR and cognitive decline, which may provide a new method for predicting cognitive impairment in the elderly.

Conclusion
Using the NHANES database, this study found a positive correlation between UFR and cognitive ability in the elderly after adjusting for potential confounding factors, which may suggest that UFR can be used as a marker to predict cognitive impairment in the elderly. In order to better understand the pathophysiology of this correlation, further high-quality studies are needed.