Improvement of nutritional status enhances cognitive and physical functions in older adults with orthostatic hypotension

Orthostatic hypotension and neurocognitive disorders in older women: Results from the EPIDOS cohort study

BACKGROUND

Although it is well-admitted that cardiovascular health affects cognition, the association between orthostatic hypotension (OH) and cognition remains unclear. The objectives of the present study were i) to determine among the EPIDOS cohort (EPIdémiologie de l'OStéoporose) whether OH was cross-sectionally associated with cognitive impairment at baseline, and ii) whether baseline OH could predict incident cognitive decline after 7 years of follow-up.

METHODS

Systolic and Diastolic Blood Pressure (SBP and DBP) changes while standing (ie, ΔSBP and ΔDBP, in %) were measured at baseline among 2,715 community-dwelling older women aged 75 years and older using no antihypertensive drugs from the French EPIDOS cohort. OH was defined as a decrease in SBP ≥20 mmHg and/or a decrease in DBP ≥10 mmHg within 3 min after standing. Cognitive impairment was defined as a Short Portable Mental Status Questionnaire (SPMSQ) score <8 (/10). Among those without cognitive impairment at baseline, a possible incident onset of cognitive decline was then sought after 7 years of follow-up among 257 participants.

RESULTS

Baseline ΔSBP was associated with baseline cognitive impairment (adjusted OR = 1.01, p = 0.047), but not with incident onset of cognitive decline after 7 years (adjusted OR = 0.98, p = 0.371). Neither baseline OH nor baseline ΔDBP were associated with cognitive impairment neither at baseline (p = 0.426 and p = 0.325 respectively) nor after 7 years (p = 0.180 and p = 0.345 respectively).

CONCLUSIONS

SBP drop while standing, but neither OH per se nor DBP drop while standing, was associated with baseline cognitive impairment in older women. The relationship between OH and cognitive impairment appears more complex than previously expected.

Association between Cognitive Impairment and Malnutrition in Hemodialysis Patients: Two Sides of the Same Coin

Cognitive impairment and malnutrition are prevalent in patients on hemodialysis (HD), and they negatively affect the outcomes of HD patients. Evidence suggests that cognitive impairment and malnutrition may be associated, but clinical studies to assess this association in HD patients are lacking. The aim of this study was to evaluate the association between cognitive impairment evaluated by the Montreal Cognitive Assessment (MoCA) score and nutritional status evaluated by the malnutrition inflammation score (MIS) in HD patients. We enrolled 84 HD patients (44 males and 40 females; age: 75.8 years (63.5-82.7); HD vintage: 46.0 months (22.1-66.9)). The MISs identified 34 patients (40%) as malnourished; the MoCa scores identified 67 patients (80%) with mild cognitive impairment (MCI). Malnourished patients had a higher prevalence of MCI compared to well-nourished patients (85% vs. 70%; p = 0.014). MoCa score and MIS were negatively correlated (rho:-0.317; p < 0.01). Our data showed a high prevalence of MCI and malnutrition in HD patients. Low MoCA scores characterized patients with high MISs, and malnutrition was a risk factor for MCI. In conclusion, it is plausible that MCI and malnutrition are linked by common sociodemographic, clinical, and biochemical risk factors rather than by a pathophysiological mechanism.

Orthostatic hypotension is associated with malnutrition diagnosed by GLIM in elderly hypertensive patients

Background

Orthostatic Hypotension (OH) and malnutrition, are common health problems in elderly hypertensive patients. This study aimed to analyze the relationship between malnutrition and OH in elderly hypertensive patients.

Methods

This is a cross-sectional single-center study. All participants underwent a Comprehensive Geriatric Assessment (CGA), in which malnutrition was defined according to the Global Leadership Initiative on Malnutrition (GLIM) criteria based on four different methods of diagnosing muscle mass loss. Furthermore, the accuracy of these methods was verified by Receiver Operating Characteristic (ROC) analysis. Univariate and multivariate logistic regression analyses were used to identify risk factors for OH in elderly hypertensive patients.

Results

For GLIM criteria, when Fat-Free Mass Index (FFMI) was the gold standard for muscle mass loss, the Area Under ROC Curve (AUC) values for Upper Arm Circumference (UAC), Calf Circumference (CC), and Hand Grip Strength (HGS) were 0.784, 0.805, and 0.832, with moderate accuracy in diagnosing malnutrition. Multivariate analysis showed that females, Diabetes Mellitus (DM), diuretics, and malnutrition diagnosed by GLIM-UAC were risk factors for OH in elderly hypertensive patients.

Conclusion

Prompt detection of malnutrition in the elderly and attention to changes in UAC may be critical. Similarly, we should strengthen medication and disease management in elderly hypertensive patients.

The identification and prediction of frailty based on Bayesian network analysis in a community-dwelling older population

Background

We have witnessed frailty, which characterized by a decline in physiological reserves, become a major public health issue in older adults. Understanding the influential factors associated with frailty may help prevent or if possible reverse frailty. The present study aimed to investigate factors associated with frailty status and frailty transition in a community-dwelling older population.

Methods

A prospective cohort study on community-dwelling subjects aged ≥ 60 years was conducted, which was registered beforehand (ChiCTR 2,000,032,949). Participants who had completed two visits during 2020–2021 were included. Frailty status was evaluated using the Fried frailty phenotype. The least absolute shrinkage and selection operator (LASSO) regression was applied for variable selection. Bayesian network analysis with the max-min hill-climbing (MMHC) algorithm was used to identify factors related to frailty status and frailty transition.

Results

Of 1,981 subjects at baseline, 1,040 (52.5%) and 165 (8.33%) were classified as prefrailty and frailty. After one year, improved, stable, and worsening frailty status was observed in 460 (35.6%), 526 (40.7%), and 306 (23.7%) subjects, respectively. Based on the variables screened by LASSO regression, the Bayesian network structure suggested that age, nutritional status, instrumental activities of daily living (IADL), balance capacity, and social support were directly related to frailty status. The probability of developing frailty is 14.4% in an individual aged ≥ 71 years, which increases to 20.2% and 53.2% if the individual has balance impairment alone, or combined with IADL disability and malnutrition. At a longitudinal level, ADL/IADL decline was a direct predictor of worsening in frailty state, which further increased the risk of hospitalization. Low high-density lipoprotein cholesterol (HDL-C) and diastolic blood pressure (DBP) levels were related to malnutrition, and further had impacts on ADL/IADL decline, and ultimately led to the worsening of the frailty state. Knowing the status of any one or more of these factors can be used to infer the risk of frailty based on conditional probabilities.

Conclusion

Older age, malnutrition, IADL disability, and balance impairment are important factors for identifying frailty. Malnutrition and ADL/IADL decline further predict worsening of the frailty state.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03520-7.

An Increased Anticholinergic Drug Burden Index Score Negatively Affect Nutritional Status in Older Patients Without Dementia

Introduction/Aim

Anticholinergic drugs, which have severe central and peripheric side effects, are frequently prescribed to older adults. Increased anticholinergic drug burden is associated with poor physical and cognitive functions. On the other side, the impact of anticholinergics on nutritional status is not elaborated in the literature. Therefore, this study was aimed to investigate the effect of the anticholinergic burden on nutrition.

Materials and Methods

Patients who underwent comprehensive geriatric assessment (CGA) 6 months apart were included in the study. Patients diagnosed with dementia were excluded because of the difference in the course of cognition, physical performance and nutrition. Nutritional status and global cognition were evaluated using Mini Nutritional Assessment-short form (MNA-SF), Mini-Mental State Examination (MMSE). Anticholinergic drug burden was assessed with the Drug Burden Index (DBI), enabling a precise dose-related cumulative exposure. Patients were divided into three groups according to DBI score: 0, no DBI exposure; 0–1, low risk; and ≥1, high risk. Regression analysis was performed to show the relationship between the difference in CGA parameters and the change in DBI score at the sixth month.

Results

A total of 423 patients were included in the study. Participants' mean age was 79.40 ± 7.50, and 68.6% were female. The DBI 0 score group has better MMSE and MNA-SF scores and a lower rate of falls, polypharmacy, malnutrition, and risk of malnutrition in the baseline. Having malnutrition or risk of malnutrition is 2.21 times higher for every one-unit increase in DBI score. Additionally, during the 6-month follow-up, increased DBI score was associated with decreased MNA-SF and MMSE score, albumin.

Conclusions

The harmful effects of anticholinergics may be prevented because anticholinergic activity is a potentially reversible factor. Therefore, reducing exposure to drugs with anticholinergic activity has particular importance in geriatric practice.

Trajectory Analysis of Orthostatic Hypotension in Parkinson's Disease: Results From Parkinson's Progression Markers Initiative Cohort

Background: Orthostatic hypotension (OH) in Parkinson’s disease (PD) can lead to falls, impair quality of life, and increase mortality. A trajectory analysis of OH could be useful to predict and prevent the hypotension incidence early.

Methods: The longitudinal data of 660 patients with PD with disease duration up to 12 years were extracted from an integrated PD database. We used latent class mixed modeling (LCMM) to identify patient subgroups, demonstrating trajectories of changes in orthostatic blood pressure (BP) over time. The optimal number of subgroups was selected by several criteria including the Bayesian Information Criterion. Baseline information comparison between groups and backward stepwise logistic regression were conducted to define the distinguishing characteristics of these subgroups and to investigate the predictors for BP trajectory.

Results: We identified three trajectories for each orthostatic change of systolic blood pressure (ΔSBP), namely, Class 1 (i.e., the increasing class) consisted of 18 participants with low ΔSBP that increased continuously during the follow-up; Class 2 (i.e., the low-stable class) consisted of 610 participants with low ΔSBP that remained low throughout the follow-up; and Class 3 (i.e., the high-stable class) consisted of 32 participants with high ΔSBP at baseline that was relatively stable throughout the follow-up. Several parameters differed among subgroups, but only male sex [odds ratio (OR) = 4.687, 95% confidence interval (CI) = 1.024–21.459], lower supine diastolic blood pressure (DBP) (OR = 0.934, 95% CI = 0.876–0.996), and lower level of total protein at baseline (OR = 0.812, 95% CI = 0.700–0.941) were significant predictors of an increasing ΔSBP trajectory.

Conclusion: This study provides new information on the longitudinal development of ΔSBP in patients with PD with distinct trajectories of rapidly increasing, low-stable, and high-stable class. The parameters such as male sex, lower supine DBP, and lower total proteins help to identify the rapidly increasing class.

A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.