Association Between Neuropsychiatric Symptom Burden and Chronic Kidney Disease Among Older Adults

Association Between Neuropsychiatric Symptom Burden and Chronic Kidney Disease Among Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Neuropsychiatric Symptom Burden and Chronic Kidney Disease Among Older Adults ilker Atay, Cihan Heybeli, Erhan Eröz, İrem Tanrıverdi, Raye Sevra Ozmen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8933624/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Methods This cross-sectional study was conducted in a university hospital geriatrics outpatient clinic in Istanbul, Turkey. Participants underwent standardized neuropsychiatric assessment using the Neuropsychiatric Inventory (NPI) and a comprehensive geriatric evaluation, including activities of daily living, Mini-Nutritional Assessment, and Mini-Mental State Examination. Linear regression analyses were performed to examine associations between CKD and NPI total and domain scores. Results A total of 535 older adults were included, of whom 277 (52%) had CKD. Median NPI total score was higher in the CKD group than in those without CKD (19 vs 17, p = 0.013). In unadjusted analyses, CKD was associated with higher NPI total score and higher appetite/feeding, exaltation/euphoria, and delusion scores. After adjustment for demographic and clinical variables, CKD remained independently associated with NPI total score and appetite/feeding symptoms. Following further adjustment for laboratory and geriatric assessment parameters, the association with total NPI score was attenuated, whereas appetite/feeding symptoms remained independently associated with CKD. Appetite-related symptoms increased progressively with advancing CKD stages, while other neuropsychiatric domains showed no consistent trend. Conclusions Older adults with CKD exhibit a higher neuropsychiatric symptom burden, predominantly driven by appetite and feeding disturbances. These findings highlight the importance of systematic assessment of appetite-related symptoms in the routine clinical evaluation of older patients with CKD. Aged Appetite Neuropsychiatric Symptoms Chronic Kidney Disease Key summary points Aim To investigate the association between chronic kidney disease (CKD) and overall neuropsychiatric symptom burden in older adults. Findings Older adults with CKD had higher total Neuropsychiatric Inventory (NPI) scores compared with those without CKD. CKD was independently associated with increased neuropsychiatric symptom burden in multivariable regression analyses. Message CKD may contribute to a greater burden of neuropsychiatric symptoms in older adults and should be considered during comprehensive geriatric assessment and clinical management. Introduction Chronic kidney disease (CKD) is a global public health burden characterized by progressive loss of renal function, systemic metabolic derangements, and increased risk for morbidity and mortality. Beyond its somatic complications, CKD has profound effects on the central nervous system and mental health. A growing body of evidence indicates that neuropsychiatric conditions such as cognitive impairment, depression, and anxiety are more common in CKD patients than in the general population. Observational studies and systematic reviews have documented high prevalence rates of cognitive dysfunction and mood disorders across the CKD spectrum, with proposed mechanisms including uremic toxin accumulation, chronic inflammation, oxidative stress, and cerebrovascular dysregulation affecting brain function and behavior [ 1 ]. Depressive symptoms in CKD have been reported in approximately one-quarter of patients in large meta-analyses, and may be even more prevalent when assessed by symptom scales rather than structured clinical interviews [ 2 ]. Cognitive impairment is also frequent across all stages of CKD and is thought to result from a combination of uremic neurotoxicity, vascular injury, endothelial dysfunction, and shared risk factors such as aging and cardiovascular disease [ 3 ]. These observations support the concept of a kidney–brain axis, in which reduced renal function contributes to neuropsychiatric and cognitive sequelae through multiple overlapping biological pathways, including immune-mediated neuroinflammation [ 4 ]. While most prior studies have focused on isolated domains such as cognition or depression, neuropsychiatric symptoms rarely occur in isolation in clinical practice. Geriatric research has increasingly emphasized that neuropsychiatric symptoms in older adults often cluster with functional impairment and nutritional vulnerability, particularly in populations with chronic systemic diseases such as CKD [ 5 , 6 ]. The Neuropsychiatric Inventory (NPI) is a validated instrument that assesses a broad spectrum of behavioral and psychological symptoms, including delusions, hallucinations, agitation, depression, anxiety, euphoria, apathy, disinhibition, irritability, aberrant motor behavior, sleep disturbance, and appetite changes. The NPI total score provides an integrated measure of overall neuropsychiatric symptom burden rather than single-symptom outcomes. In older adults with CKD, behavioral symptoms frequently coexist with systemic illness and functional decline, which supports the use of multidomain rather than single-symptom assessments [ 7 ]. Despite extensive literature on selected psychiatric and cognitive outcomes in CKD, research evaluating the overall burden of neuropsychiatric symptoms using a multidomain instrument such as the NPI is scarce. To date, no study has systematically examined the association between CKD itself and the NPI total score as a comprehensive neuropsychiatric battery. Understanding whether CKD is independently associated with higher overall NPI scores, beyond the effects of age, sex, and dementia, would extend existing knowledge beyond single-domain findings and help clarify the broad neuropsychiatric impact of CKD. Methods This study included patients admitted to the outpatient geriatric clinic at Bezmialem University Hospital, Istanbul, Turkey. All of the included patients underwent a comprehensive geriatric assessment. Exclusions included lack of available NPI evaluation (n = 1520), unavailable kidney function assessment (n = 619), and severe CKD with an estimated glomerular filtration rate of < 15 ml/min/1.73 m2 (n = 25). Patients with acute health problems, terminal illnesses, severe dementia, delirium, diagnosed with psychiatric diseases and/or severe visual or hearing impairments were also excluded, as they did not undergo a comprehensive geriatric assessment. The study was approved by the ethics committee of Bezmialem University. Written informed consent was obtained from patients or their relatives or caregivers. Demographic characteristics, comorbidities, and laboratory evaluations were retrospectively obtained from patients' medical files. Age, sex, years of education, drug count, comorbidities, laboratory evaluations and scores of each geriatric assessment test were recorded. Comprehensive geriatric assessments were performed within the same week of the first visit, and laboratory evaluations were also performed at the same time. The laboratory test panel included Haemoglobin, serum levels of sodium, potassium, magnesium, calcium, phosphorus, albumin, glucose, creatinine, HbA1c, Vitamin D, blood lipids, vitamin B12, and folate. Glomerular filtration rate was estimated based on serum creatinine measurements using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [ 8 ]. The definition of CKD required a persistent estimated GFR < 60 mL/min/1.73 m² for at least 3 months [ 9 ]. Staging of CKD was according to the Kidney Disease: Improving Global Outcomes (KDIGO) Guidelines. Neuropsychiatric battery test evaluations Neuropsychiatric symptoms were evaluated using the NPI, a validated, caregiver-based instrument designed to assess behavioral and psychological symptoms in older adults [ 10 ]. This was previously validated in the Turkish population [ 11 ]. The NPI evaluates 12 symptom domains: delusions, hallucinations, agitation/aggression, depression/dysphoria, anxiety, exaltation/euphoria, apathy/indifference, disinhibition, irritability/lability, aberrant motor behavior, sleep/nighttime behaviour, and appetite/eating disturbances. For each domain, frequency and severity are rated and multiplied to generate a domain score; higher scores indicate greater symptom burden. The NPI total score was calculated as the sum of all domain scores, providing a global measure of neuropsychiatric symptom burden, which was used as the primary outcome in the present analyses. Comprehensive geriatric assessments The comprehensive geriatric assessment was performed by a gerontologist and a geriatrician for all patients. Basic Activities of Daily Living (BADL, Barthel): The BADL score was calculated based on 10 questions about the ability to provide self-care, use toilet, get dressed, eat, urinary and fecal continence, use the stairs, move from bed to chair, and mobility [ 12 ]. Instrumental Activities of Daily Living (IADL, Lawton): The Lawton- Brody IADL index has been proposed as a means to determine the instrumental activities of daily living (IADL). IADL was calculated according to 8 questions about telephone usage, preparing meals, shopping, carrying out daily house works, laundry, transportation, taking pills, and money management [ 13 ]. Mini-Nutritional risk Assessment score: The tool contains 18 items and evaluates 4 different aspects: anthropometric assessment (body mass index, weight loss, and arm and calf circumferences); general assessment (lifestyle, medication, mobility and presence of signs of depression or dementia); short dietary assessment (number of meals, food and fluid intake and autonomy of feeding); and subjective assessment (self-perception of health and nutrition) [ 14 ]. The Mini-Mental State examination (MMSE) was used to evaluate cognitive functions of all patients [ 15 ]. The diagnosis of dementia was made according to the DSM-5 criteria [ 16 ]. In accordance with the ESPEN guidelines [ 17 ], plasma osmolarity was calculated with the following formula: 1.86 × (serum sodium + serum potassium) + 1.15 × plasma glucose + urea + 14 (all in mmol/L). A calculated osmolarity of over 295 mmol/L was defined as dehydration. Statistical Analyses Data normality was tested using the Kolmogorov-Smirnov test. Numerical variables were presented as mean with the standard deviation if normally distributed, and as median with the interquartile range (25%-75%) if non-normally distributed. For variables exhibiting highly skewed or zero-inflated distributions, particularly individual neuropsychiatric symptom scores, dispersion was instead presented using the 10th -90th percentiles to improve interpretability and avoid misleading compression of the interquartile range. Categorical variables are given as counts and percentages. We used chi-squared tests to compare categorical variables, and Mann-Whitney U tests to compare continuous variables between the CKD and non-CKD groups. We ran linear regression analysis to determine associations between demographic and clinical characteristics with NPI symptom scores. Although NPI scores were right-skewed and zero-inflated, linear regression was used to estimate absolute differences in symptom burden; inspection of residuals indicated an approximately normal distribution. Because of the large number of potential covariates, multivariate models were built in a staged fashion based on clinical domains rather than a single fully saturated model. Model 1 was adjusted for demographic characteristics and comorbidities; model 2 was adjusted for age, sex, and laboratory assessments; and model 3 was adjusted for age, sex, and scores on geriatric assessment tests. Results Baseline characteristics A total of 535 participants were included, of whom 278 (52%) had CKD and 257 (48%) did not. Patients with an EGFR of > 60 (ml/min/1.73 m 2 ), 45–59, 30–44, and < 30 comprised 48% (257 patients), 26.7% (143 patients), 19.6% (105 patients), and 5.6% (30 patients) of the cohort. Patients with CKD were significantly older than those without CKD (86 ± 7 vs 82 ± 7 years, p < 0.001), while sex distribution and education level were similar between groups. Diabetes mellitus and higher BMI tended to be more frequent in the CKD group, although these differences did not reach statistical significance. Chronic heart disease was more prevalent among patients with CKD (30.3% vs 22.7%, p = 0.045). Table 1 shows other characteristics of the CKD and non-CKD groups. Table 1 Comparison of demographic and clinical characteristics, and geriatric assessments between CKD and Non-CKD patients. Variables CKD (n = 278) Non-CKD (n = 257) P Demographics, comorbidities Age, years, mean ± SD 86 ± 7 82 ± 7 < 0.001 Female sex, n (%) 201/277 (72.6) 186/256 (72.7) 0.981 Education, years, median (IQR) 5 (0–8) 5 (0–6) 0.860 Diabetes mellitus, n (%) 113/272 (41.5) 85/253 (33.6) 0.060 Hypertension, n (%) 204/277 (73.6) 177/256 (69.6) 0.250 COPD, n (%) 17/277 (6.1) 9/256 (3.5) 0.160 Chronic heart disease, n (%) 84/277 (30.3) 58/256 (22.7) 0.045 Cerebrovascular disease, n (%) 40/276 (14.5) 31/256 (12.1) 0.419 Dementia, n (%) 150/277 (54.2) 129/256 (50.4) 0.385 Parkinson’s disease, n (%) 37/276 (13.4) 28/255 (11) 0.394 BMI, kg/m 2 , mean ± SD 29.5 ± 5.8 28.6 ± 5.9 0.071 Laboratory eGFR, ml/min/1.73 m 2 43 ± 10 75 ± 9 < 0.001 Sodium, mmol/L 139 ± 3 139 ± 9 0.074 Potassium, mEq/L 4.4 ± 0.5 4.3 ± 0.4 0.044 Calcium, mg/dl 9.3 (9.0-9.8) 9.4 (9.1–9.7) 0.295 Phosphorus, mg/dl 3.5 (3.1–4.0) 3.5 (3.1–3.9) 0.327 Magnesium, mg/dl 1.9 (1.6–2.1) 1.9 (1.8-2.0) 0.027 Hemoglobin, g/dl 12.0 ± 1.7 12.6 ± 1.6 < 0.001 Albumin, g/dl 4.1 ± 0.5 4.2 ± 0.4 0.001 Vitamin D 25 (16–36) 20 (13–29) < 0.001 CRP, mg/L 3.2 (0.5–12.6) 1.4 (0.2–5.2) < 0.001 LDL-cholesterol, mg/dl 128 (97–154) 122 (102–145) 0.448 Vitamin B12 396 (286–616) 394 (283–563) 0.582 Folate 6.7 (4.8–9.3) 6.6 (5.1–9.3) 0.761 NPI symptoms Delusion, (median, P10-90%)* 0 (0–9) 0 (0–6) 0.030 Hallucination 0 (0–6) 0 (0–4) 0.222 Agitation/aggression 0 (0–9) 0 (0–6) 0.099 Depression 4 (0–12) 2 (0–12) 0.135 Anxiety/dysphoria 0 (0–4) 0 (0–4) 0.947 Exaltation/euphoria 0 (0–12) 0 (0–9) 0.045 Apathy/indifference 0 (0–8) 0 (0–6) 0.070 Lack of inhibition 0 (0–2) 0 (0–1) 0.918 Irritability/lability 0 (0–6) 0 (0–6) 0.797 Aberrant motor behavior 0 (0–8) 0 (0–6) 0.764 Sleep 1 (0–12) 1 (0–12) 0.534 Appetite/feeding 0 (0–12) 0 (0–8) < 0.001 NPI total score, (median, P10-90%) 19 (2–55) 17 (1–48) 0.013 Geriatric assessment scores MMSE 20 ± 7 21 ± 6 < 0.001 BADL 67 ± 27 77 ± 27 < 0.001 IADL 5 (1–13) 11 (4–17) < 0.001 Drug count 6 (4–9) 8 (5–10) 0.005 MNA 20 ± 5 23 ± 5 0.018 Dehydration, n (%) 192/247 (77.7) 131/235 (55.7) < 0.001 *While most continuous variables that are not normally-distributed are shown with the median and interquartile range (25–75%), the NPI symptoms were given as percentiles 10% to 90% (P10-90%) with the median since these variables are highly right skewed. BADL: Basic Activities of Daily Living (Barthel Index); BMI: Body-Mass Index; COPD: Chronic Obstructive Lung Disease; eGFR: Estimated Glomerular Filtration Rate; IADL: Instrumental Activities of Daily Living (Lawton Index); MMSE: Mini-Mental State Examination; MNA: Mini-Nutritional Assessment (long-form); NPI: Neuro-Psychiatric Inventory. As expected, eGFR was markedly lower in the CKD group (43 ± 10 vs 75 ± 9 mL/min/1.73 m², p < 0.001). CKD patients had higher potassium levels and lower hemoglobin and albumin concentrations. Vitamin D levels were paradoxically higher in the CKD group, whereas CRP levels were significantly elevated, indicating greater inflammatory burden. No significant differences were observed in sodium, calcium, phosphorus, LDL-cholesterol, vitamin B12, or folate. In geriatric assessments, patients with CKD had lower MMSE scores, worse functional status (BADL and IADL), lower MNA scores, higher prevalence of dehydration, and used fewer medications. Dementia prevalence did not differ significantly between groups. Neuropsychiatric symptoms in CKD versus non-CKD The median NPI total score was higher in patients with CKD than in those without CKD (19 vs 17, p = 0.013). Among individual NPI domains, appetite/feeding disturbances were significantly more severe in the CKD group (p < 0.001). Delusions and exaltation/euphoria also showed statistically significant, albeit small, differences between groups. Other domains, including hallucinations, agitation, depression, anxiety, apathy, disinhibition, irritability, aberrant motor behavior, and sleep disturbance, did not differ significantly. Unadjusted associations with NPI scores Table 2 shows unadjusted associations between demographic and clinical factors with each NPI domain score. In unadjusted linear regression, higher age, dementia, CKD, lower BMI, lower hemoglobin, lower vitamin D, lower albumin, higher potassium, lower MMSE, worse BADL and IADL, poorer nutritional status (MNA), and dehydration were all associated with higher total NPI scores. CKD was associated with a 4.43-point higher NPI total score (95% CI 0.94–7.92, p = 0.013). For appetite/feeding symptoms, CKD showed a strong association (β = 1.52, 95% CI 0.82–2.21, p < 0.001), along with age, dementia, DM, BMI, hemoglobin, albumin, potassium, CRP, BADL, IADL, MNA, and dehydration. For exaltation/euphoria, CKD was also associated in unadjusted analysis (β = 0.284, 95% CI 0.014–0.553, p = 0.039), together with dementia, MMSE, and IADL. Table 2 Unadjusted linear regression analysis for associations between demographic and clinical conditions with neuropsychiatric symptom scores Variable NPI total score Appetite/feeding Exaltation/euphoria Delusion Beta CI P Beta CI P Beta CI P Beta CI P Demographics, comorbidities Age 0.48 0.24–0.72 < 0.001 0.071 0.023–0.12 0.004 0.003 -0.016–0.021 0.788 0.09 0.05–0.13 0.045 Female 1.32 -2.62–5.26 0.510 0.017 -0.77–0.81 0.965 0.184 -0.12–0.487 0.235 0.50 -0.16-1.16 0.136 Dementia 12.0 8.59–15.3 < 0.001 0.863 0.161–1.57 0.016 0.471 0.20–0.74 0.001 2.01 1.46–2.58 < 0.001 Chronic heart disease 0.66 -3.3–4.63 0.744 0.34 -0.46-1.14 0.402 -0.20 -0.51–0.11 0.201 0.23 -0.44–0.89 0.504 Diabetes mellitus -4.10 -7.76 - -0.45 0.028 -0.88 -1.62 - -0.15 0.018 -0.11 -0.38–0.16 0.427 -0.37 -0.98-0.24 0.237 Chronic kidney dis. 4.43 0.94–7.92 0.013 1.52 0.82–2.21 < 0.001 0.284 0.014–0.553 0.039 0.62 0.03–1.20 0.038 BMI -0.36 -0.68 - -0.04 0.028 -0.13 -0.19 - -0.07 < 0.001 -0.01 -0.03–0.02 0.499 -0.02 -0.07-0.04 0.573 Laboratory Hemoglobin -1.93 -3.0 - -0.86 < 0.001 -0.34 -0.55 - -0.12 0.002 -0.07 -0.15–0.012 0.097 -0.14 -0.32-0.04 0.129 Vitamin D -0.17 -0.30 - -0.05 0.007 0.003 -0.02–0.027 0.818 0.002 -0.008–0.011 0.735 -0.02 -0.04—0.001 0.045 Albumin -5.9 -11 - -1.93 0.005 -2.27 -3.12 - -1.43 < 0.001 0.11 -0.217–0.442 0.504 -1.12 -1.89—0.36 0.004 Sodium 0.19 -0.08–0.46 0.158 0.001 -0.05–0.054 0.968 0.011 -0.01–0.031 0.297 0.04 -0.01-0.08 0.122 Potassium -4.04 -7.57 - -0.52 0.025 -1.17 -1.87 - -0.48 0.001 -0.02 -0.29–0.25 0.884 -0.22 -0.81-0.37 0.462 Magnesium 3.87 -1.50–9.23 0.157 0.38 -0.68–1.44 0.480 0.048 -0.33–0.42 0.799 0.14 -0.78-1.05 0.767 CRP -0.002 -0.09–0.08 0.960 0.03 0.01–0.04 0.002 -0.003 -0.01–0.003 0.295 -0.01 -0.02-0.01 0.389 Geriatric evaluations MMSE -1.04 -1.33 - -0.75 < 0.001 -0.06 -0.12–0.004 0.065 -0.03 -0.06 – -0.01 0.007 -0.15 -0.20 --0.1 < 0.001 BADL -0.25 -0.32 - -0.19 < 0.001 -0.03 -0.04 – -0.018 < 0.001 -0.003 -0.007–0.002 0.311 -0.03 -0.04—0.02 < 0.001 IADL -1.02 -1.24 - -0.80 < 0.001 -0.124 -0.169 - -0.08 < 0.001 -0.02 -0.03 – -0.01 0.019 -0.13 -0.17—0.1 < 0.001 Drug count 0.10 -0.37–0.57 0.680 0.063 -0.03–0.16 0.191 -0.006 -0.04–0.031 0.754 -0.01 -0.09-0.07 0.861 MNA -1.44 -1.77 - -1.12 < 0.001 -0.42 -0.48 - -0.36 < 0.001 0.003 -0.023–0.029 0.812 -0.08 -0.13 - -0.02 0.008 Dehydration 3.35 -0.67–7.34 0.102 0.80 0.01–1.58 0.047 0.16 -0.15–0.47 0.302 0.53 -0.15-1.20 0.126 BADL: Basic Activities of Daily Living (Barthel Index); BMI: Body-Mass Index; IADL: Instrumental Activities of Daily Living (Lawton Index); MMSE: Mini-Mental State Examination; MNA: Mini-Nutritional Assessment (long-form); NPI: Neuro-Psychiatric Inventory. Table 3 Multiple regression Variable NPI total score Appetite/feeding Exaltation/euphoria Delusion Beta CI p Beta CI p Beta CI P Beta CI P Model 1 4.16 0.4–8.0 0.034 1.67 0.96–2.39 < 0.001 0.29 0.013–0.58 0.040 0.27 -0.31-0.85 0.362 Model 2 4.68 0.001–9.36 0.050 1.47 0.57–2.36 0.001 0.31 0.001–0.62 0.056 0.18 -0.58–0.94 0.646 Model 3 2.32 -2.62–7.26 0.356 1.07 0.16–1.98 0.021 0.31 -0.03–0.65 0.073 -0.19 -0.84-0.47 0.574 Model 1 (Demographics model). Variables selected from demographic variables, comorbidities, and body mass index. Each with a p-value of < 0.1 in Table 2 were included. Age and sex were included in all adjustments. Model 2 (Laboratory model). In addition to age and sex, laboratory values that had a p-value of < 0.1 in Table 2 were included. Model 3 (Geriatric assessment model). In addition to age and sex, geriatric assessments with p-values < 0.1 in Table 2 were included. Dementia was not included here as MMSE score was a covariate. Multivariable models NPI total score In Model 1 (demographics and comorbidities), adjustment was performed for age and sex (forced into all models), as well as diabetes mellitus, dementia, and BMI, which were associated with NPI total score at p < 0.1 in univariate analyses. After these adjustments, CKD remained independently associated with a higher NPI total score (β = 4.16, 95% CI 0.40–8.00, p = 0.034). In Model 2 (laboratory-adjusted model), age and sex were retained, and laboratory variables associated with NPI total score at p < 0.1 were included, namely hemoglobin, vitamin D, serum albumin, serum potassium, and CRP. In this model, the association between CKD and NPI total score became borderline significant (β = 4.68, 95% CI 0.001–9.36, p = 0.050). In Model 3 (geriatric assessment model), age and sex were retained and geriatric parameters associated with NPI total score at p < 0.1 were included, specifically MMSE, MNA, BADL, and IADL scores. Dementia was not included in this model due to collinearity with MMSE. After adjustment for these geriatric factors, CKD was no longer significantly associated with NPI total score (β = 2.32, 95% CI − 2.62 to 7.26, p = 0.356). Appetite/feeding score In Model 1, adjustment was performed for age and sex, as well as diabetes mellitus and dementia, which were associated with appetite/feeding score in univariate analyses. CKD remained independently associated with higher appetite/feeding score (β = 1.67, 95% CI 0.96–2.39, p < 0.001). In Model 2, age and sex were retained, and laboratory variables associated with appetite/feeding score at p < 0.1 were included, namely hemoglobin, serum albumin, serum potassium, and CRP. The association between CKD and appetite/feeding score remained significant (β = 1.47, 95% CI 0.57–2.36, p = 0.001). In Model 3, age and sex were retained, and geriatric parameters associated with appetite/feeding score were included, specifically MNA, BADL, IADL, and MMSE. CKD remained significantly associated with appetite/feeding score after these adjustments (β = 1.07, 95% CI 0.16–1.98, p = 0.021). Exaltation/euphoria score In Model 1, adjustment was performed for age and sex, as well as dementia, which was the only comorbidity associated with exaltation/euphoria score in univariate analyses. CKD remained significantly associated with higher exaltation/euphoria score (β = 0.29, 95% CI 0.01–0.58, p = 0.040). In Model 2, age and sex were retained, and laboratory variables associated with exaltation/euphoria score at p < 0.1 (hemoglobin) were included. In this model, the association between CKD and exaltation/euphoria score became borderline significant (β = 0.31, 95% CI 0.001–0.62, p = 0.056). In Model 3, age and sex were retained and geriatric parameters associated with exaltation/euphoria score were included, namely MMSE and IADL. After these adjustments, the association between CKD and exaltation/euphoria score was no longer statistically significant (β = 0.31, 95% CI − 0.03 to 0.65, p = 0.073). Delusion score In Model 1, adjustment was performed for age and sex, as well as dementia, which showed a strong association with delusion score in univariate analyses. CKD was not significantly associated with delusion score after adjustment (β = 0.27, p = 0.362). In Models 2 and 3, CKD remained not significantly associated with delusion score after additional adjustment for laboratory variables (vitamin D and albumin) and geriatric parameters (MMSE, BADL, IADL, and MNA), respectively. Neuropsychiatric symptoms across CKD stages When stratified by CKD stage, the NPI total score increased progressively from GFR > 60 to GFR < 30 (median 17 to 26; p = 0.041), although post hoc comparisons were not statistically significant (Table 4 ). Among individual symptoms, appetite/feeding disturbances showed a clear stepwise increase with worsening renal function and remained significant in post-hoc comparisons between GFR > 60 and 45–59, and between GFR > 60 and 30–44 mL/min/1.73 m². Other NPI domains, including euphoria, did not show significant differences across CKD stages, although euphoria demonstrated a borderline trend (p = 0.063). Table 4 Neuropsychiatric symptom scores across different chronic kidney disease stages Symptom GFR > 60 (n = 257) GFR 45–59 (n = 143) GFR 30–44 (n = 105) GFR < 30 (n = 30) P Delusion 0 (0–6) 0 (0–11) 0 (0–7) 0 (0–12) 0.173 Hallucination 0 (0–4) 0 (0–6) 0 (0–4) 0 (0–12) 0.595 Agitation/aggression 0 (0–6) 0 (0–11) 0 (0–8) 1 (0–9) 0.230 Depression 2 (0–12) 2 (0–12) 4 (0–12) 6 (0–12) 0.123 Anxiety/dysphoria 0 (0–9) 1 (0–9) 0 (0–8) 1 (0–9) 0.609 Exaltation/euphoria 0 (0–9) 0 (0–12) 0 (0–12) 0 (0–4) 0.063 Apathy/indifference 0 (0–6) 0 (0–8) 0 (0–8) 0 (0–12) 0.227 Lack of inhibition 0 (0–1) 0 (0–4) 0 (0–1) 0 (0–2) 0.909 Irritability/lability 0 (0–6) 0 (0–6) 0 (0–4) 0 (0–4) 0.545 Aberrant motor behavior 0 (0–6) 0 (0–9) 0 (0–6) 0 (0–4) 0.478 Sleep 1 (0–12) 1 (0–12) 1 (0–12) 1 (0–12) 0.906 Appetite/feeding 0 (0–8) 0 (0–12) 1 (0–12) 1 (0–12) 60 versus 45–59 ml/min/1.73 m 2 . b: significantly different between groups > 60 versus 30–44 ml/min/.1.73 m 2 . NS: Not significant (p > 0.05) in post-hoc tests. The NPI symptoms were given as percentiles 10% to 90% with the median since these variables are highly right skewed. Overall, these results indicate that CKD is associated with a higher overall neuropsychiatric symptom burden, driven predominantly by appetite and feeding disturbances, while the association with exaltation/euphoria appears weaker and sensitive to adjustment for clinical and geriatric factors. Associated Factors within CKD and Non-CKD cohorts In the CKD cohort, univariate analysis showed that older age (β = 0.55, 95% CI 0.16–0.93, p = 0.005), dementia (β = 11.82, 95% CI 6.79–16.85, p < 0.001), lower hemoglobin (β = −1.56, 95% CI − 3.13 to − 0.001, p = 0.050), lower albumin (β = −7.14, 95% CI − 13.42 to − 0.86, p = 0.026), lower vitamin D levels (β = −0.17, 95% CI − 0.33 to − 0.002, p = 0.047), higher potassium (β = −4.87, 95% CI − 9.58 to − 0.16, p = 0.043), poorer cognitive performance (MMSE; β = −1.07, 95% CI − 1.48 to − 0.67, p < 0.001), worse nutritional status (MNA; β = −1.32, 95% CI − 1.83 to − 0.81, p < 0.001), and lower functional scores (Barthel and Lawton; both p < 0.001) were associated with higher NPI total scores. BMI showed a borderline association (β = −0.47, p = 0.056), whereas sex, diabetes, cardiovascular disease, CRP, dehydration, and drug count were not significantly associated with NPI score (Table 5 ). Table 5 Associations between demographics, laboratory results, and geriatric evaluation scores with the Neuro-Psychiatric Inventory score based on linear regression analysis. CKD Non-CKD Beta 95% CI P Beta 95% CI P Univariate Age 0.55 0.16–0.93 0.005 0.31 -0.02-0.63 0.065 Female 0.65 -5.17-6.47 0.826 2.11 -3.11-7.34 0.427 DM -4.56 -9.88-0.78 0.094 -4.45 -9.37-0.48 0.076 Dementia 11.82 6.79–16.85 < 0.001 11.77 7.36–16.19 < 0.001 BMI -0.47 -0.96-0.01 0.056 -0.31 -0.72-0.09 0.132 Cardiovascular disease 1.78 -3.87-7.43 0.536 -1.82 -7.37-3.73 0.520 Hb -1.56 -3.13-0.001 0.050 -1.92 -3.42–0.42 0.012 Albumin -7.14 -13.42—0.86 0.026 -4.1 -10.7–2.56 0.226 D vit -0.17 -0.33—0.002 0.047 -0.30 -0.50 - -0.94 0.004 Potassium -4.87 -9.58—0.16 0.043 -3.41 -8.8–1.98 0.213 CRP -0.01 -0.11-0.10 0.911 -0.05 -0.20-0.113 0.575 MMSE -1.07 -1.48 - -0.67 < 0.001 -0.93 -1.36 - -0.50 < 0.001 MNA score -1.32 -1.83 - -0.81 < 0.001 -1.50 -1.91 - -1.10 < 0.001 Barthel -0.28 -0.37 - -0.19 < 0.001 -0.22 -0.30 - -0.14 < 0.001 Lawton -1.17 -1.51- -0.82 < 0.001 -0.86 -1.14–0.57 < 0.001 Dehydration 2.23 -4.61–9.07 0.521 2.37 -2.58–7.33 0.346 Drug count 0.088 -0.59-0.76 0.798 -0.06 -0.74–0.61 0.851 Multivariate* Age 0.46 0.08–0.83 0.018 0.23 -0.09–0.54 0.154 Dementia 10.9 5.9–15.9 < 0.001 11.3 6.87–15.8 < 0.001 BMI -0.36 -0.85-0.13 0.146 -0.19 -0.60-0.22 0.351 Hb -1.82 -3.39 - -0.25 0.023 -1.53 -3.01-0.006 0.051 Alb -4.78 -11–1.46 0.132 -1.34 -8.04-5.36 0.693 D vitamini -0.19 -0.34 - -0.026 0.023 -0.31 -0.51 - -0.11 0.002 Potassium -4.1 -8.57-0.53 0.083 -1.12 -6.39-4.14 0.675 MMSE -0.75 -1.23 - -0.27 0.002 -0.57 -1.11–0.02 0.041 MNA -1.21 -1.71–0.69 < 0.001 -1.30 -1.72 - -0.89 < 0.001 Barthel -0.22 -0.31 - -0.13 < 0.001 -0.16 -0.25 - -0.08 < 0.001 Lawton -0.91 -1.31–0.51 < 0.001 -0.67 -0.99 - -0.34 < 0.001 Each variable was included in a multivariable model including age, sex, and dementia. In multivariable models adjusted for age, sex, and dementia, older age (β = 0.46, 95% CI 0.08–0.83, p = 0.018), dementia (β = 10.9, 95% CI 5.9–15.9, p < 0.001), lower hemoglobin (β = −1.82, 95% CI − 3.39 to − 0.25, p = 0.023), lower vitamin D levels (β = −0.19, 95% CI − 0.34 to − 0.026, p = 0.023), poorer cognitive function (MMSE; β = −0.75, p = 0.002), worse nutritional status (MNA; β = −1.21, p < 0.001), and lower functional capacity (Barthel and Lawton; both p < 0.001) remained independently associated with higher NPI scores. Albumin, BMI, potassium, and CRP were no longer significant after adjustment. In the non-CKD cohort, univariate analysis demonstrated that dementia (β = 11.77, 95% CI 7.36–16.19, p < 0.001), lower hemoglobin (β = −1.92, 95% CI − 3.42 to − 0.42, p = 0.012), lower vitamin D levels (β = −0.30, 95% CI − 0.50 to − 0.94, p = 0.004), poorer cognitive performance (MMSE; β = −0.93, p < 0.001), worse nutritional status (MNA; β = −1.50, p < 0.001), and lower functional scores (Barthel and Lawton; both p < 0.001) were associated with higher NPI total scores. Age and diabetes showed borderline associations, while sex, BMI, cardiovascular disease, potassium, CRP, dehydration, and drug count were not significant. After adjustment for age, sex, and dementia, dementia (β = 11.3, 95% CI 6.87–15.8, p < 0.001), lower vitamin D (β = −0.31, 95% CI − 0.51 to − 0.11, p = 0.002), poorer cognitive performance (MMSE; β = −0.57, p = 0.041), worse nutritional status (MNA; β = −1.30, p < 0.001), and lower functional scores (Barthel and Lawton; both p < 0.001) remained independently associated with higher NPI scores. The association with hemoglobin became borderline (p = 0.051), while age, BMI, albumin, potassium, and CRP were not independently associated. Discussion CKD is commonly accompanied by advanced age, multiple comorbid conditions, electrolyte disturbances, neurohormonal dysregulation, inflammation, and nutritional abnormalities, rendering these patients clinically complex. In the present study, appetite and feeding disturbances, delusions, and exaltation/euphoria were more frequent in patients with CKD, and overall NPI total scores were significantly higher compared with those without CKD. After adjustment for potential confounders, appetite and feeding disturbances remained independently associated with CKD, whereas associations with delusion and exaltation/euphoria were attenuated. CKD was associated with a mean increase of 4.16, 4.68, and 2.32 points in total NPI score compared with non-CKD patients after adjustment for demographic and comorbidity variables, laboratory parameters, and geriatric assessment measures, respectively. However, this association was no longer statistically significant after final adjustment for geriatric and functional variables, suggesting partial mediation by these factors. Both malnutrition and CKD are highly prevalent in older adults and frequently coexist. The overall impact of nutritional problems may be broader than commonly appreciated. For example, we have previously demonstrated that loss of appetite is common in CKD, affecting nearly 60% of patients, and is associated with depressive symptoms and sleep disturbances [ 18 ]. Associations between excessive daytime sleepiness and nutritional factors have also been reported in other populations [ 19 ]. In the present study, MNA score was significantly associated with total NPI score in both CKD and non-CKD groups. Furthermore, several variables related to nutritional status -including lower vitamin D levels, lower body mass index, lower hemoglobin, and lower serum albumin and potassium concentrations- were associated with higher total NPI scores, although some associations lost statistical significance after multivariable adjustment. Collectively, these findings support a meaningful link between nutritional vulnerability and neuropsychiatric symptoms in older adults with CKD. Notably, most of these associations were also observed in non-CKD patients and were likely contributors to higher NPI scores in the broader geriatric population. Although nutritional problems are well recognized in CKD, their specific relationship with neuropsychiatric symptom burden appears relatively novel and remains insufficiently explored. Importantly, several of these abnormalities (e.g., vitamin D deficiency, anemia, and hypokalemia) are potentially modifiable; however, it is currently unclear whether correction of these factors leads to improvement in neuropsychiatric symptom burden. Interventional studies are needed to address this question. Careful evaluation of nutritional status, particularly in patients with prominent neuropsychiatric symptoms, may therefore have important clinical implications. Higher NPI total scores observed in patients with CKD are consistent with prior literature demonstrating an increased burden of neuropsychiatric symptoms in individuals with impaired renal function. Previous studies have reported that CKD is associated with a higher prevalence of cognitive impairment, depressive symptoms, and anxiety compared with the general population [ 1 ]. In the present study, although several individual NPI domains tended to be higher in patients with CKD, others -including anxiety and depression- did not show marked or statistically significant differences. These discrepancies within the current study and previous literature may be explained by differences in study populations. Our cohort consisted of very old, ambulatory patients referred for comprehensive geriatric assessment, with a relatively low proportion of advanced kidney disease. In contrast, many prior studies focused on younger adults or patients with end-stage kidney disease, particularly those receiving dialysis [ 1 ]. Accordingly, our findings may be most applicable to older outpatients with mild to moderate CKD. Consistent with other geriatric syndromes [ 15 ], cognitive impairment has been shown to correlate with declining estimated glomerular filtration rate and increasing albuminuria, while depressive symptoms worsen as kidney function deteriorates [ 20 ]. Several pathophysiological mechanisms have been proposed to explain neuropsychiatric manifestations in CKD, including accumulation of uremic toxins, chronic systemic inflammation, anemia, oxidative stress, and shared microvascular pathology affecting both the brain and kidneys [ 1 , 21 ]. Although some individual NPI domains demonstrated higher scores with worsening kidney function, most symptoms did not exhibit a clear stage-dependent gradient. The most consistent associations were observed for appetite and feeding disturbances, which were robustly elevated in CKD, and for exaltation/euphoria, which was unexpectedly higher in CKD in unadjusted analyses and selected adjusted models. Among individual NPI domains, appetite and feeding disturbances emerged as the most prominent and consistently associated with CKD across models. Appetite loss and anorexia are well-recognized clinical features of CKD and play a central role in protein–energy wasting and cachexia [ 22 , 23 ]. Recent studies in CKD populations have further highlighted malnutrition and appetite-related vulnerability as key contributors to broader neuropsychiatric and functional symptom clusters [ 24 ]. Chronic inflammation and uremic neurotoxins may disrupt central appetite regulation, thereby linking somatic disease burden with behavioral and neuropsychiatric manifestations. These findings suggest that appetite impairment in CKD represents not only a nutritional issue but also a marker of broader neuropsychiatric involvement. In contrast, the association between CKD and exaltation/euphoria is more difficult to interpret, given the absence of well-established biological mechanisms linking CKD with elevated or euphoric mood states. Although this association reached statistical significance in some analyses, it was not consistently robust and may reflect chance findings, residual confounding, or unmeasured behavioral or psychosocial factors. Accordingly, this result should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Resting-state functional MRI studies in CKD demonstrate significant disruption of frontoparietal, cingulate, and prefrontal networks that are critical for cognitive control and emotional modulation [ 25 ]. Given evidence from human pharmacologic studies showing that altered nucleus accumbens–prefrontal connectivity is associated with subjective euphoria [ 26 ], dysregulation of these reward-related circuits in CKD might contribute to abnormal elevations in exaltation/euphoria symptoms. Similarly, the association between CKD and delusion scores observed in unadjusted analyses became non-significant after adjustment for confounders, suggesting that this relationship may be mediated by cognitive, functional, or metabolic factors. Although electrolyte and metabolic disturbances are common in CKD and have been implicated in neuropsychiatric manifestations, the present findings do not allow causal inferences, and whether correction of such abnormalities improves delusional symptoms requires further study. Notably, the association between CKD and total NPI score was attenuated after inclusion of geriatric, cognitive, and functional measures. This finding implies that cognitive impairment, functional dependency, nutritional status, and anemia may partially mediate the relationship between CKD and neuropsychiatric symptom burden. This interpretation is supported by prior evidence demonstrating that metabolic and inflammatory disturbances characteristic of CKD contribute to both neuropsychiatric symptoms and functional decline [ 27 ]. Lower hemoglobin levels appeared to be particularly relevant in both CKD and non-CKD groups, potentially reflecting shared nutritional or inflammatory pathways. This study has several limitations. Its cross-sectional design precludes causal inference, and the retrospective use of medical records introduces the potential for missing or inaccurately documented data. The single-centre setting within a university-based geriatrics outpatient clinic may limit generalizability to other populations, including hospitalized patients or community-dwelling older adults. Residual confounding cannot be excluded, particularly from unmeasured factors such as medication classes, CKD duration, or specific uremic toxin levels. In addition, neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory based on caregiver reports, which may be subject to recall bias or informant-related variability. Finally, the relatively small number of patients with advanced CKD may have limited statistical power to detect stage-dependent associations. Despite these limitations, the study has notable strengths. It includes a relatively large cohort of very old adults evaluated in a real-world clinical setting. All participants underwent a standardized neuropsychiatric inventory alongside a comprehensive geriatric assessment, enabling integrated evaluation of cognitive, functional, nutritional, and metabolic domains. Use of the NPI total score allowed assessment of overall neuropsychiatric burden rather than isolated symptoms. Finally, the stepwise modelling approach provided insight into how demographic, laboratory, and geriatric factors influence the association between CKD and neuropsychiatric symptoms. In conclusion, among older outpatients, CKD was associated with a higher overall neuropsychiatric symptom burden, predominantly driven by appetite and feeding disturbances. Clinical factors commonly accompanying CKD -such as advanced age, cognitive impairment, comorbidity burden, and metabolic or laboratory abnormalities- appear to contribute to this association and should be carefully evaluated in routine practice. These findings underscore the importance of comprehensive neuropsychiatric and nutritional assessment in older adults with CKD. Future studies are needed to clarify the biological and clinical pathways linking kidney dysfunction to multidimensional behavioral symptoms and to determine whether interventions targeting nutritional, metabolic, or inflammatory factors can meaningfully reduce neuropsychiatric symptom burden in this population. Declarations Compliance with Ethical Standards Ethical approval: This study was approved by the Institutional Review Board of Bezmialem University Hospital. We certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Informed consent: Written informed consent was obtained from patients or their caregivers or relatives. Conflicts of Interest: None. Funding: None. Data availability: Data is available and could be shared upon reasonable request from the corresponding author. References Simoes ESAC, Miranda AS, Rocha NP et al (2019) Neuropsychiatric Disorders in Chronic Kidney Disease. Front Pharmacol 10:932 Palmer S, Vecchio M, Craig JC et al (2013) Prevalence of depression in chronic kidney disease: systematic review and meta-analysis of observational studies. Kidney Int 84:179–191 Bossola M, Picconi B (2024) Uremic toxins and the brain in chronic kidney disease. J Nephrol 37:1391–1395 Chagas YW, Vaz de Castro PAS, Simoes ESAC (2025) Neuroinflammation in kidney disease and dialysis. Behav Brain Res 483:115465 Chang J, Hou W, Li Y et al (2022) Prevalence and associated factors of cognitive frailty in older patients with chronic kidney disease: a cross-sectional study. BMC Geriatr 22:681 Luo B, Luo Z, Zhang X et al (2022) Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study. BMJ Open 12:e060633 Yuan Y, Chang J, Sun Q (2024) Research Progress on Cognitive Frailty in Older Adults with Chronic Kidney Disease. Kidney Blood Press Res 49:302–309 Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612 Levey AS, de Jong PE, Coresh J et al (2011) , . The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int ; 80: 17–28. Cummings J (2020) The Neuropsychiatric Inventory: Development and Applications. J Geriatr Psychiatry Neurol 33:73–84 Sahin Cankurtaran E, Danişman M, Tutar H et al (2015) The reliability and validity of the Turkish version of the Neuropsychiatric Inventory-Clinician. Turk J Med Sci 45:1087–1093 Mahoney FI, Barthel DW (1965) Functional Evaluation: The Barthel Index. Md State Med J 14:61–65 Lawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9:179–186 Guigoz Y, Lauque S, Vellas BJ (2002) Identifying the elderly at risk for malnutrition. The Mini Nutritional Assessment. Clin Geriatr Med 18:737–757 Soysal P, Heybeli C, Koc Okudur S et al (2023) Prevalence and co-incidence of geriatric syndromes according to glomerular filtration rate in older patients. Int Urol Nephrol 55:469–476 Regier DA, Kuhl EA, Kupfer DJ (2013) The DSM-5: Classification and criteria changes. World Psychiatry 12:92–98 Volkert D, Beck AM, Cederholm T et al (2022) ESPEN practical guideline: Clinical nutrition and hydration in geriatrics. Clin Nutr 41:958–989 Yildiz S, Heybeli C, Smith L et al (2023) The prevalence and clinical significance of loss of appetite in older patients with chronic kidney disease. Int Urol Nephrol 55:2295–2302 Heybeli C, Soysal P, Oktan MA et al (2022) Associations between nutritional factors and excessive daytime sleepiness in older patients with chronic kidney disease. Aging Clin Exp Res 34:573–581 Moldovan D, Kacso I, Avram L et al (2025) Mental Health and Kidneys: The Interplay Between Cognitive Decline, Depression, and Kidney Dysfunction in Hospitalized Older Adults. J Clin Med ; 14 Kim DS, Kim SW, Gil HW (2022) Emotional and cognitive changes in chronic kidney disease. Korean J Intern Med 37:489–501 Cıngar Alpay K, Heybeli C, Bilgic I et al (2025) Appetite loss in older adults without undernutrition: associated factors and clinical implications. BMC Geriatr 25:641 Mitch WE (2005) Cachexia in chronic kidney disease: a link to defective central nervous system control of appetite. J Clin Invest 115:1476–1478 Ho MH, Chen PC, Liu MF et al (2025) Cognitive Frailty and Its Risk Factors Among Patients With Chronic Kidney Disease Receiving Hemodialysis: A Cross-Sectional Study. Nurs Health Sci 27:e70197 Liu Y, Wang Y, Peng L et al (2025) Investigating Brain Functional Connectivity and Its Correlation With Cognitive Dysfunction in Chronic Kidney Disease Patients via Resting-State fMRI. Brain Behav 15:e70947 Crane NA, Phan KL (2021) Effect of ∆9-Tetrahydrocannabinol on frontostriatal resting state functional connectivity and subjective euphoric response in healthy young adults. Drug Alcohol Depend 221:108565 Michou V, Tsamos G, Vasdeki D et al (2024) Unraveling of Molecular Mechanisms of Cognitive Frailty in Chronic Kidney Disease: How Exercise Makes a Difference. J Clin Med ; 13 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8933624","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597092000,"identity":"178e211a-ea11-408b-944c-957e1c5f9fdd","order_by":0,"name":"ilker Atay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACNnY2GJP5AJCQkCGshRmsxQDETABp4SFsDUILD4hgIKyFj5kt8XFFzR95gxs5n1/dqLHgYWA/fHQDAYcdNjxzzMBww43cbdY5x4AO40lLu4FfC3ubZAObASNIi3EOG1CLBI8ZIS3tPxv+GdhvuJHzzDjnH1Fa2I4xNrYZJAK1MD/ObSNOS7JkY59x8swzz8yYc/skeNgI+UW+vc3wY8M3Odu+48mPP+d8q5PjZz98DK8WOFC4kMAmAbaXKOVg6/oPMH8gWvUoGAWjYBSMKAAA10ZEMO3UjSEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-4558-2152","institution":"Dokuz Eylul University Hospital: Dokuz Eylul Universitesi Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"ilker","middleName":"","lastName":"Atay","suffix":""},{"id":597092001,"identity":"e8face78-fd5b-4427-8fd8-0378969cd1d7","order_by":1,"name":"Cihan Heybeli","email":"","orcid":"","institution":"Dokuz Eylul University Hospital: Dokuz Eylul Universitesi Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Cihan","middleName":"","lastName":"Heybeli","suffix":""},{"id":597092002,"identity":"3fe13134-6fe7-4097-a761-a1e5b4bcf10e","order_by":2,"name":"Erhan Eröz","email":"","orcid":"","institution":"Dokuz Eylul University Hospital: Dokuz Eylul Universitesi Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Erhan","middleName":"","lastName":"Eröz","suffix":""},{"id":597092003,"identity":"29cf5ce2-6b44-436a-b291-045b49746315","order_by":3,"name":"İrem Tanrıverdi","email":"","orcid":"","institution":"Bezmialem Vakif Universitesi Tip Fakultesi Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"İrem","middleName":"","lastName":"Tanrıverdi","suffix":""},{"id":597092004,"identity":"b9711a98-2b40-45a8-be2a-ab0271675a60","order_by":4,"name":"Raye Sevra Ozmen","email":"","orcid":"","institution":"Bezmialem Vakif Universitesi Tip Fakultesi Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Raye","middleName":"Sevra","lastName":"Ozmen","suffix":""},{"id":597092005,"identity":"3d3b26e8-1115-4e71-8d68-ff7ae44ebbfe","order_by":5,"name":"Lee Smith","email":"","orcid":"","institution":"Anglia Ruskin University - Cambridge Campus","correspondingAuthor":false,"prefix":"","firstName":"Lee","middleName":"","lastName":"Smith","suffix":""},{"id":597092006,"identity":"6490c9a5-e86d-4625-a416-e209f987a738","order_by":6,"name":"Pınar Soysal","email":"","orcid":"","institution":"Bezmialem Vakif Universitesi Tip Fakultesi Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Pınar","middleName":"","lastName":"Soysal","suffix":""}],"badges":[],"createdAt":"2026-02-21 12:56:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8933624/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8933624/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104402442,"identity":"c25cccf7-3f93-4574-ac50-6fa83636d26b","added_by":"auto","created_at":"2026-03-11 12:15:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1396333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8933624/v1/21da366b-e2cc-41e2-8d84-44729d355872.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssociation Between Neuropsychiatric Symptom Burden and Chronic Kidney Disease Among Older Adults\u003c/p\u003e","fulltext":[{"header":"Key summary points","content":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the association between chronic kidney disease (CKD) and overall neuropsychiatric symptom burden in older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOlder adults with CKD had higher total Neuropsychiatric Inventory (NPI) scores compared with those without CKD. CKD was independently associated with increased neuropsychiatric symptom burden in multivariable regression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMessage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCKD may contribute to a greater burden of neuropsychiatric symptoms in older adults and should be considered during comprehensive geriatric assessment and clinical management.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) is a global public health burden characterized by progressive loss of renal function, systemic metabolic derangements, and increased risk for morbidity and mortality. Beyond its somatic complications, CKD has profound effects on the central nervous system and mental health. A growing body of evidence indicates that neuropsychiatric conditions such as cognitive impairment, depression, and anxiety are more common in CKD patients than in the general population. Observational studies and systematic reviews have documented high prevalence rates of cognitive dysfunction and mood disorders across the CKD spectrum, with proposed mechanisms including uremic toxin accumulation, chronic inflammation, oxidative stress, and cerebrovascular dysregulation affecting brain function and behavior [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepressive symptoms in CKD have been reported in approximately one-quarter of patients in large meta-analyses, and may be even more prevalent when assessed by symptom scales rather than structured clinical interviews [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cognitive impairment is also frequent across all stages of CKD and is thought to result from a combination of uremic neurotoxicity, vascular injury, endothelial dysfunction, and shared risk factors such as aging and cardiovascular disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These observations support the concept of a kidney\u0026ndash;brain axis, in which reduced renal function contributes to neuropsychiatric and cognitive sequelae through multiple overlapping biological pathways, including immune-mediated neuroinflammation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile most prior studies have focused on isolated domains such as cognition or depression, neuropsychiatric symptoms rarely occur in isolation in clinical practice. Geriatric research has increasingly emphasized that neuropsychiatric symptoms in older adults often cluster with functional impairment and nutritional vulnerability, particularly in populations with chronic systemic diseases such as CKD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Neuropsychiatric Inventory (NPI) is a validated instrument that assesses a broad spectrum of behavioral and psychological symptoms, including delusions, hallucinations, agitation, depression, anxiety, euphoria, apathy, disinhibition, irritability, aberrant motor behavior, sleep disturbance, and appetite changes. The NPI total score provides an integrated measure of overall neuropsychiatric symptom burden rather than single-symptom outcomes. In older adults with CKD, behavioral symptoms frequently coexist with systemic illness and functional decline, which supports the use of multidomain rather than single-symptom assessments [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite extensive literature on selected psychiatric and cognitive outcomes in CKD, research evaluating the overall burden of neuropsychiatric symptoms using a multidomain instrument such as the NPI is scarce. To date, no study has systematically examined the association between CKD itself and the NPI total score as a comprehensive neuropsychiatric battery. Understanding whether CKD is independently associated with higher overall NPI scores, beyond the effects of age, sex, and dementia, would extend existing knowledge beyond single-domain findings and help clarify the broad neuropsychiatric impact of CKD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study included patients admitted to the outpatient geriatric clinic at Bezmialem University Hospital, Istanbul, Turkey. All of the included patients underwent a comprehensive geriatric assessment. Exclusions included lack of available NPI evaluation (n\u0026thinsp;=\u0026thinsp;1520), unavailable kidney function assessment (n\u0026thinsp;=\u0026thinsp;619), and severe CKD with an estimated glomerular filtration rate of \u0026lt;\u0026thinsp;15 ml/min/1.73 m2 (n\u0026thinsp;=\u0026thinsp;25). Patients with acute health problems, terminal illnesses, severe dementia, delirium, diagnosed with psychiatric diseases and/or severe visual or hearing impairments were also excluded, as they did not undergo a comprehensive geriatric assessment. The study was approved by the ethics committee of Bezmialem University. Written informed consent was obtained from patients or their relatives or caregivers.\u003c/p\u003e \u003cp\u003e Demographic characteristics, comorbidities, and laboratory evaluations were retrospectively obtained from patients' medical files. Age, sex, years of education, drug count, comorbidities, laboratory evaluations and scores of each geriatric assessment test were recorded.\u003c/p\u003e \u003cp\u003eComprehensive geriatric assessments were performed within the same week of the first visit, and laboratory evaluations were also performed at the same time. The laboratory test panel included Haemoglobin, serum levels of sodium, potassium, magnesium, calcium, phosphorus, albumin, glucose, creatinine, HbA1c, Vitamin D, blood lipids, vitamin B12, and folate. Glomerular filtration rate was estimated based on serum creatinine measurements using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The definition of CKD required a persistent estimated GFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; for at least 3 months [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Staging of CKD was according to the Kidney Disease: Improving Global Outcomes (KDIGO) Guidelines.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychiatric battery test evaluations\u003c/h2\u003e \u003cp\u003eNeuropsychiatric symptoms were evaluated using the NPI, a validated, caregiver-based instrument designed to assess behavioral and psychological symptoms in older adults [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This was previously validated in the Turkish population [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The NPI evaluates 12 symptom domains: delusions, hallucinations, agitation/aggression, depression/dysphoria, anxiety, exaltation/euphoria, apathy/indifference, disinhibition, irritability/lability, aberrant motor behavior, sleep/nighttime behaviour, and appetite/eating disturbances. For each domain, frequency and severity are rated and multiplied to generate a domain score; higher scores indicate greater symptom burden. The NPI total score was calculated as the sum of all domain scores, providing a global measure of neuropsychiatric symptom burden, which was used as the primary outcome in the present analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComprehensive geriatric assessments\u003c/h3\u003e\n\u003cp\u003eThe comprehensive geriatric assessment was performed by a gerontologist and a geriatrician for all patients. Basic Activities of Daily Living (BADL, Barthel): The BADL score was calculated based on 10 questions about the ability to provide self-care, use toilet, get dressed, eat, urinary and fecal continence, use the stairs, move from bed to chair, and mobility [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Instrumental Activities of Daily Living (IADL, Lawton): The Lawton- Brody IADL index has been proposed as a means to determine the instrumental activities of daily living (IADL). IADL was calculated according to 8 questions about telephone usage, preparing meals, shopping, carrying out daily house works, laundry, transportation, taking pills, and money management [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mini-Nutritional risk Assessment score: The tool contains 18 items and evaluates 4 different aspects: anthropometric assessment (body mass index, weight loss, and arm and calf circumferences); general assessment (lifestyle, medication, mobility and presence of signs of depression or dementia); short dietary assessment (number of meals, food and fluid intake and autonomy of feeding); and subjective assessment (self-perception of health and nutrition) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Mini-Mental State examination (MMSE) was used to evaluate cognitive functions of all patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The diagnosis of dementia was made according to the DSM-5 criteria [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In accordance with the ESPEN guidelines [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], plasma osmolarity was calculated with the following formula: 1.86 \u0026times; (serum sodium\u0026thinsp;+\u0026thinsp;serum potassium)\u0026thinsp;+\u0026thinsp;1.15 \u0026times; plasma glucose\u0026thinsp;+\u0026thinsp;urea\u0026thinsp;+\u0026thinsp;14 (all in mmol/L). A calculated osmolarity of over 295 mmol/L was defined as dehydration.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eData normality was tested using the Kolmogorov-Smirnov test. Numerical variables were presented as mean with the standard deviation if normally distributed, and as median with the interquartile range (25%-75%) if non-normally distributed. For variables exhibiting highly skewed or zero-inflated distributions, particularly individual neuropsychiatric symptom scores, dispersion was instead presented using the 10th -90th percentiles to improve interpretability and avoid misleading compression of the interquartile range. Categorical variables are given as counts and percentages. We used chi-squared tests to compare categorical variables, and Mann-Whitney U tests to compare continuous variables between the CKD and non-CKD groups. We ran linear regression analysis to determine associations between demographic and clinical characteristics with NPI symptom scores. Although NPI scores were right-skewed and zero-inflated, linear regression was used to estimate absolute differences in symptom burden; inspection of residuals indicated an approximately normal distribution. Because of the large number of potential covariates, multivariate models were built in a staged fashion based on clinical domains rather than a single fully saturated model. Model 1 was adjusted for demographic characteristics and comorbidities; model 2 was adjusted for age, sex, and laboratory assessments; and model 3 was adjusted for age, sex, and scores on geriatric assessment tests.\u003c/p\u003e"},{"header":"Results","content":"\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 535 participants were included, of whom 278 (52%) had CKD and 257 (48%) did not. Patients with an EGFR of \u0026gt;\u0026thinsp;60 (ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), 45\u0026ndash;59, 30\u0026ndash;44, and \u0026lt;\u0026thinsp;30 comprised 48% (257 patients), 26.7% (143 patients), 19.6% (105 patients), and 5.6% (30 patients) of the cohort. Patients with CKD were significantly older than those without CKD (86\u0026thinsp;\u0026plusmn;\u0026thinsp;7 vs 82\u0026thinsp;\u0026plusmn;\u0026thinsp;7 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while sex distribution and education level were similar between groups. Diabetes mellitus and higher BMI tended to be more frequent in the CKD group, although these differences did not reach statistical significance. Chronic heart disease was more prevalent among patients with CKD (30.3% vs 22.7%, p\u0026thinsp;=\u0026thinsp;0.045). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows other characteristics of the CKD and non-CKD groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of demographic and clinical characteristics, and geriatric assessments between CKD and Non-CKD patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCKD (n\u0026thinsp;=\u0026thinsp;278)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-CKD (n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics, comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201/277 (72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186/256 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113/272 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85/253 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204/277 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177/256 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/277 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9/256 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84/277 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58/256 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40/276 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31/256 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150/277 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129/256 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParkinson\u0026rsquo;s disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37/276 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/255 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mEq/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3 (9.0-9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 (9.1\u0026ndash;9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphorus, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 (3.1\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (3.1\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (1.6\u0026ndash;2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.8-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (16\u0026ndash;36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (13\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (0.5\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (0.2\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-cholesterol, mg/dl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (97\u0026ndash;154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (102\u0026ndash;145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396 (286\u0026ndash;616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394 (283\u0026ndash;563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFolate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7 (4.8\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (5.1\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPI symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelusion, (median, P10-90%)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHallucination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgitation/aggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety/dysphoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaltation/euphoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApathy/indifference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrritability/lability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAberrant motor behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite/feeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPI total score, (median, P10-90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (2\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (1\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeriatric assessment scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (4\u0026ndash;17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehydration, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192/247 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131/235 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*While most continuous variables that are not normally-distributed are shown with the median and interquartile range (25\u0026ndash;75%), the NPI symptoms were given as percentiles 10% to 90% (P10-90%) with the median since these variables are highly right skewed. BADL: Basic Activities of Daily Living (Barthel Index); BMI: Body-Mass Index; COPD: Chronic Obstructive Lung Disease; eGFR: Estimated Glomerular Filtration Rate; IADL: Instrumental Activities of Daily Living (Lawton Index); MMSE: Mini-Mental State Examination; MNA: Mini-Nutritional Assessment (long-form); NPI: Neuro-Psychiatric Inventory.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs expected, eGFR was markedly lower in the CKD group (43\u0026thinsp;\u0026plusmn;\u0026thinsp;10 vs 75\u0026thinsp;\u0026plusmn;\u0026thinsp;9 mL/min/1.73 m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). CKD patients had higher potassium levels and lower hemoglobin and albumin concentrations. Vitamin D levels were paradoxically higher in the CKD group, whereas CRP levels were significantly elevated, indicating greater inflammatory burden. No significant differences were observed in sodium, calcium, phosphorus, LDL-cholesterol, vitamin B12, or folate.\u003c/p\u003e \u003cp\u003eIn geriatric assessments, patients with CKD had lower MMSE scores, worse functional status (BADL and IADL), lower MNA scores, higher prevalence of dehydration, and used fewer medications. Dementia prevalence did not differ significantly between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychiatric symptoms in CKD versus non-CKD\u003c/h2\u003e \u003cp\u003eThe median NPI total score was higher in patients with CKD than in those without CKD (19 vs 17, p\u0026thinsp;=\u0026thinsp;0.013). Among individual NPI domains, appetite/feeding disturbances were significantly more severe in the CKD group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Delusions and exaltation/euphoria also showed statistically significant, albeit small, differences between groups. Other domains, including hallucinations, agitation, depression, anxiety, apathy, disinhibition, irritability, aberrant motor behavior, and sleep disturbance, did not differ significantly.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnadjusted associations with NPI scores\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows unadjusted associations between demographic and clinical factors with each NPI domain score. In unadjusted linear regression, higher age, dementia, CKD, lower BMI, lower hemoglobin, lower vitamin D, lower albumin, higher potassium, lower MMSE, worse BADL and IADL, poorer nutritional status (MNA), and dehydration were all associated with higher total NPI scores. CKD was associated with a 4.43-point higher NPI total score (95% CI 0.94\u0026ndash;7.92, p\u0026thinsp;=\u0026thinsp;0.013). For appetite/feeding symptoms, CKD showed a strong association (β\u0026thinsp;=\u0026thinsp;1.52, 95% CI 0.82\u0026ndash;2.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), along with age, dementia, DM, BMI, hemoglobin, albumin, potassium, CRP, BADL, IADL, MNA, and dehydration. For exaltation/euphoria, CKD was also associated in unadjusted analysis (β\u0026thinsp;=\u0026thinsp;0.284, 95% CI 0.014\u0026ndash;0.553, p\u0026thinsp;=\u0026thinsp;0.039), together with dementia, MMSE, and IADL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnadjusted linear regression analysis for associations between demographic and clinical conditions with neuropsychiatric symptom scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNPI total score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAppetite/feeding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eExaltation/euphoria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eDelusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics, comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.023\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.016\u0026ndash;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.05\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.62\u0026ndash;5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.77\u0026ndash;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.12\u0026ndash;0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.16-1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.59\u0026ndash;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.161\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u0026ndash;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.46\u0026ndash;2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.3\u0026ndash;4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.46-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.51\u0026ndash;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.44\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.76 - -0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.62 - -0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.38\u0026ndash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.98-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney dis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;7.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026ndash;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u0026ndash;0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.03\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.68 - -0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.19 - -0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.07-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.0 - -0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.55 - -0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.15\u0026ndash;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.32-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.30 - -0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u0026ndash;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.008\u0026ndash;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.04\u0026mdash;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11 - -1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.12 - -1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.217\u0026ndash;0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.89\u0026mdash;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u0026ndash;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u0026ndash;0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u0026ndash;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.01-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.57 - -0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.87 - -0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.29\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.81-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.50\u0026ndash;9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.68\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.33\u0026ndash;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.78-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u0026ndash;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.02-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeriatric evaluations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.33 - -0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u0026ndash;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.06 \u0026ndash; -0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.20 --0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.32 - -0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.04 \u0026ndash; -0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.007\u0026ndash;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.04\u0026mdash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.24 - -0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.169 - -0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.03 \u0026ndash; -0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.17\u0026mdash;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37\u0026ndash;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u0026ndash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.04\u0026ndash;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.09-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.77 - -1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.48 - -0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.023\u0026ndash;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.13 - -0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehydration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.67\u0026ndash;7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.15\u0026ndash;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.15-1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eBADL: Basic Activities of Daily Living (Barthel Index); BMI: Body-Mass Index; IADL: Instrumental Activities of Daily Living (Lawton Index); MMSE: Mini-Mental State Examination; MNA: Mini-Nutritional Assessment (long-form); NPI: Neuro-Psychiatric Inventory.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNPI total score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAppetite/feeding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eExaltation/euphoria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eDelusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u0026ndash;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026ndash;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.013\u0026ndash;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.31-0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u0026ndash;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u0026ndash;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u0026ndash;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.58\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.62\u0026ndash;7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u0026ndash;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.03\u0026ndash;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.84-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eModel 1 (Demographics model). Variables selected from demographic variables, comorbidities, and body mass index. Each with a p-value of \u0026lt;\u0026thinsp;0.1 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were included. Age and sex were included in all adjustments.\u003c/p\u003e \u003cp\u003eModel 2 (Laboratory model). In addition to age and sex, laboratory values that had a p-value of \u0026lt;\u0026thinsp;0.1 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were included.\u003c/p\u003e \u003cp\u003eModel 3 (Geriatric assessment model). In addition to age and sex, geriatric assessments with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were included. Dementia was not included here as MMSE score was a covariate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariable models\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNPI total score\u003c/h2\u003e \u003cp\u003eIn Model 1 (demographics and comorbidities), adjustment was performed for age and sex (forced into all models), as well as diabetes mellitus, dementia, and BMI, which were associated with NPI total score at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in univariate analyses. After these adjustments, CKD remained independently associated with a higher NPI total score (β\u0026thinsp;=\u0026thinsp;4.16, 95% CI 0.40\u0026ndash;8.00, p\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e \u003cp\u003eIn Model 2 (laboratory-adjusted model), age and sex were retained, and laboratory variables associated with NPI total score at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included, namely hemoglobin, vitamin D, serum albumin, serum potassium, and CRP. In this model, the association between CKD and NPI total score became borderline significant (β\u0026thinsp;=\u0026thinsp;4.68, 95% CI 0.001\u0026ndash;9.36, p\u0026thinsp;=\u0026thinsp;0.050).\u003c/p\u003e \u003cp\u003eIn Model 3 (geriatric assessment model), age and sex were retained and geriatric parameters associated with NPI total score at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included, specifically MMSE, MNA, BADL, and IADL scores. Dementia was not included in this model due to collinearity with MMSE. After adjustment for these geriatric factors, CKD was no longer significantly associated with NPI total score (β\u0026thinsp;=\u0026thinsp;2.32, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;2.62 to 7.26, p\u0026thinsp;=\u0026thinsp;0.356).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAppetite/feeding score\u003c/h2\u003e \u003cp\u003eIn Model 1, adjustment was performed for age and sex, as well as diabetes mellitus and dementia, which were associated with appetite/feeding score in univariate analyses. CKD remained independently associated with higher appetite/feeding score (β\u0026thinsp;=\u0026thinsp;1.67, 95% CI 0.96\u0026ndash;2.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn Model 2, age and sex were retained, and laboratory variables associated with appetite/feeding score at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were included, namely hemoglobin, serum albumin, serum potassium, and CRP. The association between CKD and appetite/feeding score remained significant (β\u0026thinsp;=\u0026thinsp;1.47, 95% CI 0.57\u0026ndash;2.36, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn Model 3, age and sex were retained, and geriatric parameters associated with appetite/feeding score were included, specifically MNA, BADL, IADL, and MMSE. CKD remained significantly associated with appetite/feeding score after these adjustments (β\u0026thinsp;=\u0026thinsp;1.07, 95% CI 0.16\u0026ndash;1.98, p\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExaltation/euphoria score\u003c/h2\u003e \u003cp\u003eIn Model 1, adjustment was performed for age and sex, as well as dementia, which was the only comorbidity associated with exaltation/euphoria score in univariate analyses. CKD remained significantly associated with higher exaltation/euphoria score (β\u0026thinsp;=\u0026thinsp;0.29, 95% CI 0.01\u0026ndash;0.58, p\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e \u003cp\u003eIn Model 2, age and sex were retained, and laboratory variables associated with exaltation/euphoria score at p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (hemoglobin) were included. In this model, the association between CKD and exaltation/euphoria score became borderline significant (β\u0026thinsp;=\u0026thinsp;0.31, 95% CI 0.001\u0026ndash;0.62, p\u0026thinsp;=\u0026thinsp;0.056).\u003c/p\u003e \u003cp\u003eIn Model 3, age and sex were retained and geriatric parameters associated with exaltation/euphoria score were included, namely MMSE and IADL. After these adjustments, the association between CKD and exaltation/euphoria score was no longer statistically significant (β\u0026thinsp;=\u0026thinsp;0.31, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.03 to 0.65, p\u0026thinsp;=\u0026thinsp;0.073).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDelusion score\u003c/h2\u003e \u003cp\u003eIn Model 1, adjustment was performed for age and sex, as well as dementia, which showed a strong association with delusion score in univariate analyses. CKD was not significantly associated with delusion score after adjustment (β\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.362).\u003c/p\u003e \u003cp\u003eIn Models 2 and 3, CKD remained not significantly associated with delusion score after additional adjustment for laboratory variables (vitamin D and albumin) and geriatric parameters (MMSE, BADL, IADL, and MNA), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychiatric symptoms across CKD stages\u003c/h2\u003e \u003cp\u003eWhen stratified by CKD stage, the NPI total score increased progressively from GFR\u0026thinsp;\u0026gt;\u0026thinsp;60 to GFR\u0026thinsp;\u0026lt;\u0026thinsp;30 (median 17 to 26; p\u0026thinsp;=\u0026thinsp;0.041), although post hoc comparisons were not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among individual symptoms, appetite/feeding disturbances showed a clear stepwise increase with worsening renal function and remained significant in post-hoc comparisons between GFR\u0026thinsp;\u0026gt;\u0026thinsp;60 and 45\u0026ndash;59, and between GFR\u0026thinsp;\u0026gt;\u0026thinsp;60 and 30\u0026ndash;44 mL/min/1.73 m\u0026sup2;. Other NPI domains, including euphoria, did not show significant differences across CKD stages, although euphoria demonstrated a borderline trend (p\u0026thinsp;=\u0026thinsp;0.063).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNeuropsychiatric symptom scores across different chronic kidney disease stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFR\u0026thinsp;\u0026gt;\u0026thinsp;60 (n\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGFR 45\u0026ndash;59 (n\u0026thinsp;=\u0026thinsp;143)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGFR 30\u0026ndash;44 (n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHallucination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgitation/aggression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety/dysphoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExaltation/euphoria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApathy/indifference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of inhibition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrritability/lability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAberrant motor behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppetite/feeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003ea, b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPI total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (1\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (2\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (2\u0026ndash;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (7\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea: significantly different between groups, estimated glomerular filtration rate of \u0026gt;\u0026thinsp;60 versus 45\u0026ndash;59 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. b: significantly different between groups\u0026thinsp;\u0026gt;\u0026thinsp;60 versus 30\u0026ndash;44 ml/min/.1.73 m\u003csup\u003e2\u003c/sup\u003e. NS: Not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in post-hoc tests. The NPI symptoms were given as percentiles 10% to 90% with the median since these variables are highly right skewed.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, these results indicate that CKD is associated with a higher overall neuropsychiatric symptom burden, driven predominantly by appetite and feeding disturbances, while the association with exaltation/euphoria appears weaker and sensitive to adjustment for clinical and geriatric factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociated Factors within CKD and Non-CKD cohorts\u003c/h2\u003e \u003cp\u003eIn the CKD cohort, univariate analysis showed that older age (β\u0026thinsp;=\u0026thinsp;0.55, 95% CI 0.16\u0026ndash;0.93, p\u0026thinsp;=\u0026thinsp;0.005), dementia (β\u0026thinsp;=\u0026thinsp;11.82, 95% CI 6.79\u0026ndash;16.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower hemoglobin (β = \u0026minus;1.56, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;3.13 to \u0026minus;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.050), lower albumin (β = \u0026minus;7.14, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;13.42 to \u0026minus;\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.026), lower vitamin D levels (β = \u0026minus;0.17, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.33 to \u0026minus;\u0026thinsp;0.002, p\u0026thinsp;=\u0026thinsp;0.047), higher potassium (β = \u0026minus;4.87, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;9.58 to \u0026minus;\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.043), poorer cognitive performance (MMSE; β = \u0026minus;1.07, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.48 to \u0026minus;\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), worse nutritional status (MNA; β = \u0026minus;1.32, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.83 to \u0026minus;\u0026thinsp;0.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower functional scores (Barthel and Lawton; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with higher NPI total scores. BMI showed a borderline association (β = \u0026minus;0.47, p\u0026thinsp;=\u0026thinsp;0.056), whereas sex, diabetes, cardiovascular disease, CRP, dehydration, and drug count were not significantly associated with NPI score (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between demographics, laboratory results, and geriatric evaluation scores with the Neuro-Psychiatric Inventory score based on linear regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNon-CKD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.02-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.17-6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.11-7.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.88-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-9.37-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.79\u0026ndash;16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.36\u0026ndash;16.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.96-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.72-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.87-7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.37-3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.13-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.42\u0026ndash;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.42\u0026mdash;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.7\u0026ndash;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD vit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.33\u0026mdash;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50 - -0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.58\u0026mdash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.8\u0026ndash;1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.20-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.48 - -0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.36 - -0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.83 - -0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.91 - -1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarthel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37 - -0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.30 - -0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLawton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.51- -0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.14\u0026ndash;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDehydration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.61\u0026ndash;9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.58\u0026ndash;7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.59-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.74\u0026ndash;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultivariate*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09\u0026ndash;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u0026ndash;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.87\u0026ndash;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.85-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.60-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.39 - -0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.01-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-8.04-5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD vitamini\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34 - -0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.51 - -0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.57-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.39-4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.23 - -0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.11\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.71\u0026ndash;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.72 - -0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarthel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31 - -0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25 - -0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLawton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.31\u0026ndash;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.99 - -0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEach variable was included in a multivariable model including age, sex, and dementia.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn multivariable models adjusted for age, sex, and dementia, older age (β\u0026thinsp;=\u0026thinsp;0.46, 95% CI 0.08\u0026ndash;0.83, p\u0026thinsp;=\u0026thinsp;0.018), dementia (β\u0026thinsp;=\u0026thinsp;10.9, 95% CI 5.9\u0026ndash;15.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower hemoglobin (β = \u0026minus;1.82, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;3.39 to \u0026minus;\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;0.023), lower vitamin D levels (β = \u0026minus;0.19, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.34 to \u0026minus;\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.023), poorer cognitive function (MMSE; β = \u0026minus;0.75, p\u0026thinsp;=\u0026thinsp;0.002), worse nutritional status (MNA; β = \u0026minus;1.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower functional capacity (Barthel and Lawton; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained independently associated with higher NPI scores. Albumin, BMI, potassium, and CRP were no longer significant after adjustment.\u003c/p\u003e \u003cp\u003eIn the non-CKD cohort, univariate analysis demonstrated that dementia (β\u0026thinsp;=\u0026thinsp;11.77, 95% CI 7.36\u0026ndash;16.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower hemoglobin (β = \u0026minus;1.92, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;3.42 to \u0026minus;\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;0.012), lower vitamin D levels (β = \u0026minus;0.30, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.50 to \u0026minus;\u0026thinsp;0.94, p\u0026thinsp;=\u0026thinsp;0.004), poorer cognitive performance (MMSE; β = \u0026minus;0.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), worse nutritional status (MNA; β = \u0026minus;1.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower functional scores (Barthel and Lawton; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with higher NPI total scores. Age and diabetes showed borderline associations, while sex, BMI, cardiovascular disease, potassium, CRP, dehydration, and drug count were not significant.\u003c/p\u003e \u003cp\u003eAfter adjustment for age, sex, and dementia, dementia (β\u0026thinsp;=\u0026thinsp;11.3, 95% CI 6.87\u0026ndash;15.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower vitamin D (β = \u0026minus;0.31, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.51 to \u0026minus;\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.002), poorer cognitive performance (MMSE; β = \u0026minus;0.57, p\u0026thinsp;=\u0026thinsp;0.041), worse nutritional status (MNA; β = \u0026minus;1.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lower functional scores (Barthel and Lawton; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) remained independently associated with higher NPI scores. The association with hemoglobin became borderline (p\u0026thinsp;=\u0026thinsp;0.051), while age, BMI, albumin, potassium, and CRP were not independently associated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCKD is commonly accompanied by advanced age, multiple comorbid conditions, electrolyte disturbances, neurohormonal dysregulation, inflammation, and nutritional abnormalities, rendering these patients clinically complex. In the present study, appetite and feeding disturbances, delusions, and exaltation/euphoria were more frequent in patients with CKD, and overall NPI total scores were significantly higher compared with those without CKD. After adjustment for potential confounders, appetite and feeding disturbances remained independently associated with CKD, whereas associations with delusion and exaltation/euphoria were attenuated. CKD was associated with a mean increase of 4.16, 4.68, and 2.32 points in total NPI score compared with non-CKD patients after adjustment for demographic and comorbidity variables, laboratory parameters, and geriatric assessment measures, respectively. However, this association was no longer statistically significant after final adjustment for geriatric and functional variables, suggesting partial mediation by these factors.\u003c/p\u003e \u003cp\u003eBoth malnutrition and CKD are highly prevalent in older adults and frequently coexist. The overall impact of nutritional problems may be broader than commonly appreciated. For example, we have previously demonstrated that loss of appetite is common in CKD, affecting nearly 60% of patients, and is associated with depressive symptoms and sleep disturbances [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Associations between excessive daytime sleepiness and nutritional factors have also been reported in other populations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the present study, MNA score was significantly associated with total NPI score in both CKD and non-CKD groups. Furthermore, several variables related to nutritional status -including lower vitamin D levels, lower body mass index, lower hemoglobin, and lower serum albumin and potassium concentrations- were associated with higher total NPI scores, although some associations lost statistical significance after multivariable adjustment. Collectively, these findings support a meaningful link between nutritional vulnerability and neuropsychiatric symptoms in older adults with CKD. Notably, most of these associations were also observed in non-CKD patients and were likely contributors to higher NPI scores in the broader geriatric population. Although nutritional problems are well recognized in CKD, their specific relationship with neuropsychiatric symptom burden appears relatively novel and remains insufficiently explored. Importantly, several of these abnormalities (e.g., vitamin D deficiency, anemia, and hypokalemia) are potentially modifiable; however, it is currently unclear whether correction of these factors leads to improvement in neuropsychiatric symptom burden. Interventional studies are needed to address this question. Careful evaluation of nutritional status, particularly in patients with prominent neuropsychiatric symptoms, may therefore have important clinical implications.\u003c/p\u003e \u003cp\u003eHigher NPI total scores observed in patients with CKD are consistent with prior literature demonstrating an increased burden of neuropsychiatric symptoms in individuals with impaired renal function. Previous studies have reported that CKD is associated with a higher prevalence of cognitive impairment, depressive symptoms, and anxiety compared with the general population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the present study, although several individual NPI domains tended to be higher in patients with CKD, others -including anxiety and depression- did not show marked or statistically significant differences. These discrepancies within the current study and previous literature may be explained by differences in study populations. Our cohort consisted of very old, ambulatory patients referred for comprehensive geriatric assessment, with a relatively low proportion of advanced kidney disease. In contrast, many prior studies focused on younger adults or patients with end-stage kidney disease, particularly those receiving dialysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Accordingly, our findings may be most applicable to older outpatients with mild to moderate CKD.\u003c/p\u003e \u003cp\u003eConsistent with other geriatric syndromes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], cognitive impairment has been shown to correlate with declining estimated glomerular filtration rate and increasing albuminuria, while depressive symptoms worsen as kidney function deteriorates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several pathophysiological mechanisms have been proposed to explain neuropsychiatric manifestations in CKD, including accumulation of uremic toxins, chronic systemic inflammation, anemia, oxidative stress, and shared microvascular pathology affecting both the brain and kidneys [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Although some individual NPI domains demonstrated higher scores with worsening kidney function, most symptoms did not exhibit a clear stage-dependent gradient. The most consistent associations were observed for appetite and feeding disturbances, which were robustly elevated in CKD, and for exaltation/euphoria, which was unexpectedly higher in CKD in unadjusted analyses and selected adjusted models.\u003c/p\u003e \u003cp\u003eAmong individual NPI domains, appetite and feeding disturbances emerged as the most prominent and consistently associated with CKD across models. Appetite loss and anorexia are well-recognized clinical features of CKD and play a central role in protein\u0026ndash;energy wasting and cachexia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Recent studies in CKD populations have further highlighted malnutrition and appetite-related vulnerability as key contributors to broader neuropsychiatric and functional symptom clusters [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Chronic inflammation and uremic neurotoxins may disrupt central appetite regulation, thereby linking somatic disease burden with behavioral and neuropsychiatric manifestations. These findings suggest that appetite impairment in CKD represents not only a nutritional issue but also a marker of broader neuropsychiatric involvement.\u003c/p\u003e \u003cp\u003eIn contrast, the association between CKD and exaltation/euphoria is more difficult to interpret, given the absence of well-established biological mechanisms linking CKD with elevated or euphoric mood states. Although this association reached statistical significance in some analyses, it was not consistently robust and may reflect chance findings, residual confounding, or unmeasured behavioral or psychosocial factors. Accordingly, this result should be interpreted as exploratory and hypothesis-generating rather than confirmatory. Resting-state functional MRI studies in CKD demonstrate significant disruption of frontoparietal, cingulate, and prefrontal networks that are critical for cognitive control and emotional modulation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Given evidence from human pharmacologic studies showing that altered nucleus accumbens\u0026ndash;prefrontal connectivity is associated with subjective euphoria [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], dysregulation of these reward-related circuits in CKD might contribute to abnormal elevations in exaltation/euphoria symptoms.\u003c/p\u003e \u003cp\u003eSimilarly, the association between CKD and delusion scores observed in unadjusted analyses became non-significant after adjustment for confounders, suggesting that this relationship may be mediated by cognitive, functional, or metabolic factors. Although electrolyte and metabolic disturbances are common in CKD and have been implicated in neuropsychiatric manifestations, the present findings do not allow causal inferences, and whether correction of such abnormalities improves delusional symptoms requires further study.\u003c/p\u003e \u003cp\u003eNotably, the association between CKD and total NPI score was attenuated after inclusion of geriatric, cognitive, and functional measures. This finding implies that cognitive impairment, functional dependency, nutritional status, and anemia may partially mediate the relationship between CKD and neuropsychiatric symptom burden. This interpretation is supported by prior evidence demonstrating that metabolic and inflammatory disturbances characteristic of CKD contribute to both neuropsychiatric symptoms and functional decline [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Lower hemoglobin levels appeared to be particularly relevant in both CKD and non-CKD groups, potentially reflecting shared nutritional or inflammatory pathways.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Its cross-sectional design precludes causal inference, and the retrospective use of medical records introduces the potential for missing or inaccurately documented data. The single-centre setting within a university-based geriatrics outpatient clinic may limit generalizability to other populations, including hospitalized patients or community-dwelling older adults. Residual confounding cannot be excluded, particularly from unmeasured factors such as medication classes, CKD duration, or specific uremic toxin levels. In addition, neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory based on caregiver reports, which may be subject to recall bias or informant-related variability. Finally, the relatively small number of patients with advanced CKD may have limited statistical power to detect stage-dependent associations.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study has notable strengths. It includes a relatively large cohort of very old adults evaluated in a real-world clinical setting. All participants underwent a standardized neuropsychiatric inventory alongside a comprehensive geriatric assessment, enabling integrated evaluation of cognitive, functional, nutritional, and metabolic domains. Use of the NPI total score allowed assessment of overall neuropsychiatric burden rather than isolated symptoms. Finally, the stepwise modelling approach provided insight into how demographic, laboratory, and geriatric factors influence the association between CKD and neuropsychiatric symptoms.\u003c/p\u003e \u003cp\u003eIn conclusion, among older outpatients, CKD was associated with a higher overall neuropsychiatric symptom burden, predominantly driven by appetite and feeding disturbances. Clinical factors commonly accompanying CKD -such as advanced age, cognitive impairment, comorbidity burden, and metabolic or laboratory abnormalities- appear to contribute to this association and should be carefully evaluated in routine practice. These findings underscore the importance of comprehensive neuropsychiatric and nutritional assessment in older adults with CKD. Future studies are needed to clarify the biological and clinical pathways linking kidney dysfunction to multidimensional behavioral symptoms and to determine whether interventions targeting nutritional, metabolic, or inflammatory factors can meaningfully reduce neuropsychiatric symptom burden in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e This study was approved by the Institutional Review Board of Bezmialem University Hospital. We certify that the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e Written informed consent was obtained from patients or their caregivers or relatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e Data is available and could be shared upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSimoes ESAC, Miranda AS, Rocha NP et al (2019) Neuropsychiatric Disorders in Chronic Kidney Disease. Front Pharmacol 10:932\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmer S, Vecchio M, Craig JC et al (2013) Prevalence of depression in chronic kidney disease: systematic review and meta-analysis of observational studies. Kidney Int 84:179\u0026ndash;191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBossola M, Picconi B (2024) Uremic toxins and the brain in chronic kidney disease. J Nephrol 37:1391\u0026ndash;1395\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChagas YW, Vaz de Castro PAS, Simoes ESAC (2025) Neuroinflammation in kidney disease and dialysis. Behav Brain Res 483:115465\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang J, Hou W, Li Y et al (2022) Prevalence and associated factors of cognitive frailty in older patients with chronic kidney disease: a cross-sectional study. BMC Geriatr 22:681\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo B, Luo Z, Zhang X et al (2022) Status of cognitive frailty in elderly patients with chronic kidney disease and construction of a risk prediction model: a cross-sectional study. BMJ Open 12:e060633\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Chang J, Sun Q (2024) Research Progress on Cognitive Frailty in Older Adults with Chronic Kidney Disease. Kidney Blood Press Res 49:302\u0026ndash;309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604\u0026ndash;612\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevey AS, de Jong PE, Coresh J et al (2011) ,\u003cem\u003e. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int ; 80: 17\u0026ndash;28.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCummings J (2020) The Neuropsychiatric Inventory: Development and Applications. J Geriatr Psychiatry Neurol 33:73\u0026ndash;84\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahin Cankurtaran E, Danişman M, Tutar H et al (2015) The reliability and validity of the Turkish version of the Neuropsychiatric Inventory-Clinician. Turk J Med Sci 45:1087\u0026ndash;1093\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahoney FI, Barthel DW (1965) Functional Evaluation: The Barthel Index. Md State Med J 14:61\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawton MP, Brody EM (1969) Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9:179\u0026ndash;186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuigoz Y, Lauque S, Vellas BJ (2002) Identifying the elderly at risk for malnutrition. The Mini Nutritional Assessment. Clin Geriatr Med 18:737\u0026ndash;757\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoysal P, Heybeli C, Koc Okudur S et al (2023) Prevalence and co-incidence of geriatric syndromes according to glomerular filtration rate in older patients. Int Urol Nephrol 55:469\u0026ndash;476\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegier DA, Kuhl EA, Kupfer DJ (2013) The DSM-5: Classification and criteria changes. World Psychiatry 12:92\u0026ndash;98\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkert D, Beck AM, Cederholm T et al (2022) ESPEN practical guideline: Clinical nutrition and hydration in geriatrics. Clin Nutr 41:958\u0026ndash;989\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYildiz S, Heybeli C, Smith L et al (2023) The prevalence and clinical significance of loss of appetite in older patients with chronic kidney disease. Int Urol Nephrol 55:2295\u0026ndash;2302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeybeli C, Soysal P, Oktan MA et al (2022) Associations between nutritional factors and excessive daytime sleepiness in older patients with chronic kidney disease. Aging Clin Exp Res 34:573\u0026ndash;581\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoldovan D, Kacso I, Avram L et al (2025) Mental Health and Kidneys: The Interplay Between Cognitive Decline, Depression, and Kidney Dysfunction in Hospitalized Older Adults. J Clin Med ; 14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DS, Kim SW, Gil HW (2022) Emotional and cognitive changes in chronic kidney disease. Korean J Intern Med 37:489\u0026ndash;501\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCıngar Alpay K, Heybeli C, Bilgic I et al (2025) Appetite loss in older adults without undernutrition: associated factors and clinical implications. BMC Geriatr 25:641\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitch WE (2005) Cachexia in chronic kidney disease: a link to defective central nervous system control of appetite. J Clin Invest 115:1476\u0026ndash;1478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo MH, Chen PC, Liu MF et al (2025) Cognitive Frailty and Its Risk Factors Among Patients With Chronic Kidney Disease Receiving Hemodialysis: A Cross-Sectional Study. Nurs Health Sci 27:e70197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Wang Y, Peng L et al (2025) Investigating Brain Functional Connectivity and Its Correlation With Cognitive Dysfunction in Chronic Kidney Disease Patients via Resting-State fMRI. Brain Behav 15:e70947\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrane NA, Phan KL (2021) Effect of ∆9-Tetrahydrocannabinol on frontostriatal resting state functional connectivity and subjective euphoric response in healthy young adults. Drug Alcohol Depend 221:108565\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichou V, Tsamos G, Vasdeki D et al (2024) Unraveling of Molecular Mechanisms of Cognitive Frailty in Chronic Kidney Disease: How Exercise Makes a Difference. J Clin Med ; 13\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aged, Appetite, Neuropsychiatric Symptoms, Chronic Kidney Disease","lastPublishedDoi":"10.21203/rs.3.rs-8933624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8933624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted in a university hospital geriatrics outpatient clinic in Istanbul, Turkey. Participants underwent standardized neuropsychiatric assessment using the Neuropsychiatric Inventory (NPI) and a comprehensive geriatric evaluation, including activities of daily living, Mini-Nutritional Assessment, and Mini-Mental State Examination. Linear regression analyses were performed to examine associations between CKD and NPI total and domain scores.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 535 older adults were included, of whom 277 (52%) had CKD. Median NPI total score was higher in the CKD group than in those without CKD (19 vs 17, p\u0026thinsp;=\u0026thinsp;0.013). In unadjusted analyses, CKD was associated with higher NPI total score and higher appetite/feeding, exaltation/euphoria, and delusion scores. After adjustment for demographic and clinical variables, CKD remained independently associated with NPI total score and appetite/feeding symptoms. Following further adjustment for laboratory and geriatric assessment parameters, the association with total NPI score was attenuated, whereas appetite/feeding symptoms remained independently associated with CKD. Appetite-related symptoms increased progressively with advancing CKD stages, while other neuropsychiatric domains showed no consistent trend.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOlder adults with CKD exhibit a higher neuropsychiatric symptom burden, predominantly driven by appetite and feeding disturbances. These findings highlight the importance of systematic assessment of appetite-related symptoms in the routine clinical evaluation of older patients with CKD.\u003c/p\u003e","manuscriptTitle":"Association Between Neuropsychiatric Symptom Burden and Chronic Kidney Disease Among Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 12:52:48","doi":"10.21203/rs.3.rs-8933624/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd1ddd8f-573f-4436-8441-a28c9c2a2663","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T14:51:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 12:52:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8933624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8933624","identity":"rs-8933624","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}