Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.Methods: This prospective study validated the performance of three PGs  in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG .Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis. Obstructive sleep apnea (OSA) influences the progression of atrial fibrillation (AF) with a 1.9-fold increased risk of AF recurrence in AF patients with OSA compared to those without –4.The prevalence of OSA in AF patients is variable, depending on diagnosing technique and type of AF, with estimates between 21 and 85%. –6 In cliAccording to the 2020 European Society of Cardiology (ESC) guidelines for the diagnosis and management of AF, testing for OSA is reasonable before initiation of rhythm control therapy in symptomatic AF patients . HoweverTherefore, this prospective study wanted to validate and examine the performance of three different PGs devices combined with automated analysis, as detection tools for OSA in AF patients, compared with the gold standard PSG.The CarpOSAF study (Belgian Registration number: B300201835708) was a prospective, multicentre validation study in which various tools such as questionnaires, scoring systems and different PGs were evaluated as screening options for OSA in AF patients. Patients were included at two Belgian tertiary care centres, the Antwerp University Hospital and the Jessa Hospital Hasselt. The research protocol was approved by the Ethics Committees of the participating centres and the study was conducted in compliance with the Declaration of Helsinki. All patients provided written informed consent.From May 2018 until November 2020, patients referred for PSG at the two sleep clinics were evaluated for participation in the study. Inclusion criteria were (1) planned PSG for diagnosis of sleep-disordered breathing, (2) history of AF or atrial flutter, with proven diagnosis on an electrocardiogram, and (3) capable of signing the informed consent. Exclusion criteria consisted of (1) not able to speak and read Dutch, (2) age <18 years, (3) physical/cognitive impairment , and (4) participation in other studies.2DS2-VASc, HAS-BLED) were derived from the patients' medical file.For all study patients, a standard workup at the sleep clinic, including clinical measurements such as weight, height, neck- and waist circumference, was conducted. Demographic variables and AF specific characteristics , during which three different (cardio)respiratory PG devices were tested for validation, namely the ApneaLink Air (ALA), the SOMNOtouch RESP (STR) and the SpiderSAS (SpS).Each patient tested one of the three PG randomly and simultaneously with their planned overnight PSG evaluation. The study personnel conducted proper attachment of the PG to avoid interference with the PSG channels. The following morning, a Comfort Questionnaire (CQ) was filled out by the patient to evaluate the PSG examination . On retuAs mentioned, all subjects underwent a PSG examination . Data was manually scored by the staff of the sleep clinics according to the American Academy of Sleep Medicine (AASM) 2012 criteria in which an apnea was defined as a decrease in airflow by 90% from baseline for at least 10 s and a hypopnea was defined as a decrease in airflow ≥ 30% from baseline for at least 10 s associated with a decrease of oxygen saturation of at least 3% or an arousal . The apnSpecifications of each PG and the manufacturers' software are described in After transferring the patients' PG recordings to the designated software, a manual review of the channels' signal quality was performed by one study investigator (M.D.) focusing on the parameters nasal flow, respiratory effort and oxygen saturation, as these are contributing to the AHI determination. If one of the three channels failed to record a good signal for a minimum of 4 h, the data were excluded for analysis. This limit was chosen based on the suggestion of the clinical practise guideline for diagnostic testing of OSA, made by the AASM . AdditioT-test/paired T-test (parametric) and the Mann-Whitney U test/Wilcoxon signed rank test (non-parametric). The chi-squared test was used for categorical variables. Differences between the scores on the different CQ components were analysed using the Friedman test (non-parametric). P-values < 0.05 were considered statistically significant.Data were analysed using IBM SPSS version 27.0. Variables were described as numbers and percentages or as mean ± standard deviation, as appropriate. Normal distribution was assessed using the Shapiro-Wilk test. For continuous variables, differences between two groups were compared using the independent Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for the different cut-off values predicting risk for OSA of the three PGs at various levels of severity of OSA. The Cohen's Kappa was also calculated between the PGs and the PSG result (for simultaneous and separate measurements) to evaluate the agreement between these measurements for the different AHI cohorts. Receiver operating characteristics (ROC)-curves and corresponding areas under the curve (AUC) were generated for the three PGs to assess their predictive value for OSA in AF patients. These graphs were generated for the PSG groups with AHI thresholds ≥5, ≥15 and >30 events/h. Additionally, a ROC-curve/AUC was calculated for predicting at least moderate OSA, including only non-permanent AF patients. Appropriate AHI cut-off values for each PG were derived from the coordinates of the ROC-curves and determined based on the best discriminating ability to rule in (=high specificity) or rule out (=high sensitivity) clinically relevant OSA , in combination with an optimal Youden's J Index .As this was part of a validation study, no specific sample size was calculated. The aim was to include 50 consecutive AF patients, referred for a diagnostic PSG, in each centre, i.e., for a total of 100 patients.A total of 149 sleep clinic patients with a history of AF were evaluated for inclusion in the study. Nineteen patients were excluded, and 30 AF patients refused to participate, or did not show up or their PSG was cancelled . A total2  in the AHI was seen for the ALA and STR (p <0.01). On the other hand, the SpS detected more hypopneas compared to PSG, resulting in a significant Hypopnea Index difference between the SpS at home and PSG.When comparing the AHI of the PGs vs. the PSG measurements (both simultaneous and during separate nights), a significant difference  categorical change (at AHI cut-off of 15 events/h) was seen for 13.0, 36.8 and 9.5% of patients for the ALA, STR and SpS, respectively in these small samples .For the detection of at least mild OSA, the ALA and STR had a good AUC of 0.854 and 0.834, respectively . At the Coordinates of the AHI cut-off values with their corresponding sensitivity, specificity and Youden's J Index for the ALA, STR and SpS can be found in p <0.05 for all PGs vs. PSG, ALA vs. STR and SpS vs. STR). Self-application of the PGs at home was found easier for the ALA than the SpS and the STR (p <0.05). In addition, the study patients indicated that their sleep was less disturbed by the ALA, SpS and STR compared with PSG .An overall comfort score of 8.25 ± 1.42, 7.97 ± 1.39, 6.63 ± 1.90 and 5.97 ± 2.32 (out of 10) was given for the ALA, SpS, STR and PSG, respectively respiratory PG devices, combined with an automated algorithm for AHI determination, as OSA detection tools in AF patients, compared with PSG as gold standard. Our results indicate that the tested PGs combined with their automated algorithm can be used for detecting clinically relevant OSA  in AF patients, especially when appropriate cut-off values are chosen for each device, and for the screening scenario at hand.As mentioned before, OSA is highly underdiagnosed in the AF population, despite a prevalence of up to 86% in AF patients recruited in a community cardiology clinic . Our stuApnea/hypopnea (AH) indices of PG cannot simply substitute for the same index by PSG. Firstly, a lower AHI was seen for the ALA and STR compared to PSG, driven by a lower hypopnea detection. This is due to the fact that PG cannot detect arousals which also contributes to the hypopnea definition according to the 2012 AASM guidelines . ConsequSecondly, analysis of the paired PG evaluations showed only minor absolute AHI differences for the 3 PGs, although the intraindividual NtNV categorical change (at AHI cut-off of 15 events/h) varied between 9.5 and 36.8%. These findings are in line with a recent meta-analysis, in which the absolute AHI NtNV for PGs was calculated to be on average −0.2 events/h (95% CI −1.19–0.79), but also a high intraindividual NtNV categorical change was identified . The latThirdly, for detecting clinically relevant OSA , the ALA and SpS performed well with an AUC > 0.80 (at home) of which the ALA had a higher measurement agreement than the SpS, reflected by the Cohen's Kappa. When comparing these results with literature, caution is warranted since other studies often used other methods or definitions for AHI scoring or other gold standards for OSA diagnosis. For level 3 sleep tests in general, a meta-analysis indicated that the AUC for detecting at least moderate OSA ranged between 0.87–0.99 and 0.79–0.97 compared with simultaneous or separate PSG evaluation, respectively, although PG recordings were manually reviewed in the studies included . RegardiOur study reports an average success rate of 82% for PG registrations at home. In general, for level 3 sleep testing devices, as evaluated in this study, a meta-analysis reported a  success rate of 89.75% compared with 99.56% in patients evaluated by PSG . ImportaAs CPAP treatment in Belgium (as in other countries) is only reimbursed for clinically relevant OSA AHI ≥ 15) , we dete , we detThe three PGs scored significantly better than PSG in terms of convenience, i.e., better sleep quality and less troublesomeness caused by the PGs' equipment. When comparing the tested PG with each other, the ALA was easier to attach than the SpS and STR. This can be explained by the simplified attachment of the ALA with fewer channels. One prior study also investigated the convenience of the ALA, in which 98.5% of patients were positive about its use, 67.6% assessed the comfort very well and 32.4% only mentioned minor comments regarding the nasal cannula or oximeter .Finally, the ease of administering these PGs and the use of their software for clinicians should also be mentioned. A good manual for the patient can be time-saving, and a software that can generate a report within 5 min so that results and possibly referral for PSG evaluation can be discussed instantly during a patients' out-patient clinic visit or during a hospitalisation are of added value. These features are present for both the ALA and STR with their corresponding software.As our cohort included 13 patients with permanent AF, i.e., patients not considered anymore for rhythm control therapy and hence OSA screening, the validity of the PGs was reassessed excluding these patients: the new AUCs for detecting clinically relevant OSA showed only minor changes . AlthougBased on our results, a proposed detection strategy could consist of applying a PG device to all AF patients in whom rhythm control is pursued, regardless of apnea symptoms. If the PG evaluation produces reliable tracings, the automated algorithm software program can analyse the data to determine the AHI. The treating cardiologist or AF-clinic coordinator can do this read-out and interpretation. As CPAP treatment of OSA in AF patients is currently only associated with better rhythm and symptom control but not (yet) with clinical outcomes , a PG AHI cut-off associated with high specificity would be preferable  3). In . In 3). The strength of our study is the comparison of the PGs with PSG as gold standard for the diagnosis of OSA in which the simultaneous and separately executed PG evaluations provide direct insights in the detection of sleep-disordered breathing events by the automated analysis of PG recordings and the influence of NtNV. Additionally, our included patients had a mean age of 64 years for which the success rate of PG execution better reflects ‘real-world' clinical AF practise than prior PG studies.The included AF patients, referred for a diagnostic PSG, may not be representative for the selected AF population that one would like to screen in daily clinical practise , although 73.6% of the non-permanent AF patients had a mEHRA score ≥ 2a . The scoring of the PSG was performed in two different centres, which may include interscorer variability. Lastly, this study design was rather intensive (4 nights of PG testing), which could influence the success rates for each PG at home.This validation study underscores that PG combined with an automated algorithm for AHI determination and appropriately selected AHI cut-offs can be used as a reliable OSA detection tool in an AF clinic. As implementation of the current AF guidelines, the clinician can easily screen AF patients using a PG device and refer for confirmatory PSG evaluation (and CPAP treatment) to ensure proper addressing of OSA as a modifiable risk factor.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by University of Antwerp/Antwerp University Hospital and Jessa Hospital Ethics Committees. The patients/participants provided their written informed consent to participate in this study.MD, JV, HH, LD, KW, and PD contributed to conception and design of the study. MD, LK, and LD collected the data and organised the database. MD performed the statistical analysis and wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.This study was supported by the Antwerp University Hospital Cardiology Research Fund, and is part of the Limburg Clinical Research Center, supported by the foundation Limburg Sterk Merk, province of Limburg, Flemish government, Hasselt University, Ziekenhuis Oost-Limburg and Jessa Hospital.HH and LD did not receive any personal honoraria; they received unconditional research support through Hasselt University or University of Antwerp from Bayer, Daiichi-Sankyo, Boehringer-Ingelheim, Bracco Imaging Europe, Medtronic, Boston-Scientific, Biotronik, and St. Jude Medical. JV reports grants from SomnoMed, AirLiquide, Vivisol, Mediq Tefa, Medidis, Heinen-Löwenstein, OSG, Bioprojet, Desitin, Philips and ResMed outside the submitted work. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60 Movement analysis uses technological devices to identify problems or increase physical performance . HoweverThe most common approach of combining IMU data is a fusion algorithm, specifically for measuring joint angles. For this, it is necessary to estimate the orientation of each IMU using sensor fusion filter algorithms . Then, tIMU has several applications for movement sciences, but its applied use must overcome some limitations. As suggested by , complexThis study aimed to (1) develop a low-cost, easy-to-build hardware to estimate angles, (2) present an open-source software to process IMU data with documentation, and (3) perform a system proof-of-concept by three experiments with available analysis and code.In this section, there is material description, components, concepts, and techniques to develop the proposed system. Our hardware is named Joint Angle Measurement and Acquisition device (JAMA) and is composed of an IMU (GY-80) and a microcontroller (ESP32). The software library, fully developed in Python, was named Python for Joint Angle Measurement and Acquisition (PyJama), which implements three fusion filters for data processing. The system is available on GitHub  and github.com/tuliofalmeida/pyjama (accessed on 1 July 2021)) and was developed to work independently to analyze data collected with another IMU-based system or to use JAMA to collect data and analyze it with another software.Notation 1.γ denotes a general variable. In this work, two JAMAs estimated knee joint angles. Each JAMA used one microcontroller ESP32-DevKitC (ESP32), one IMU GY-80 with 10 degrees of freedom, two batteries (3.7 V and 3800 mAh), one power button, and two input connectors to charge the batteries. The main characteristics of these components are low energy consumption, robustness, and low cost. Communication between ESP32 and GY-80 used an Inter-Integrated Circuit (IEach JAMA costs around 24 dollars see  and has ESP32 extracts IMU data using Ia), gyroscope (m). Once the device was connected to Wi-Fi, a server was created at port 4000. These data were transmitted asynchronously over Wi-Fi (without any kind of processing), using the asynchronous Transmission Control Protocol communication (async-TCP) to respond to client requests (more details in Extracted data were organized in a buffer with the following sequence: accelerometer (Data transmission based on a 2.4 GHz Wi-Fi network (without internet connection and with a range of 10 m) was designed to provide movement freedom out of the laboratory. This communication is an Asynchronous Transmission Control Protocol (async-TCP), used to prevent data from being lost during transmission, as it is performing in parallel with other microcontroller tasks. The async-TCP is a third-party library that enables a trouble-free, multi-connection network environment for microcontrollers. This library allows inertial data, collected by a microcontroller timer interrupt routine, to be written to a buffer and sent via TCP without interfering with data transmission. Moreover, if the data amount is large, the library manages the data transmission in such a way as to continue sending even after the data collection routine has finished.Following the concept of asynchronous communication, the developed architecture is client/server-based. The client ran on an Acer Nitro 5 notebook (8th generation i5 processor with 16 GB RAM) connecting to the server via a hotspot (inherent in the Windows 10 operating system). The hotspot windows function creates a Wi-Fi network with an Internet Protocol (IP) address, network SSID, and password. The client application using Python (version 3.83) and communication with the ESP32 (distributed servers) connected to its IP. With the communication established, the ESP32 can send data to the client and stores the data in a text file.Before starting the client API execution, it is necessary to edit the code with each JAMA IP address, the desired sample rate , the exBuild JAMA and check connections;Compile and upload firmware to each JAMA device ;Activate the hotspot and check if all variables were correctly determined;Turn on JAMA devices, check the Wi-Fi connection, and place it in the joint;Run Python script to check connections and data streaming and recording;Run Python script to perform the data acquisition.The async-TCP uses two programming languages, C++ (server) and Python (client). Note that it is possible to configure the server directly on the hardware of each JAMA using C++ language. In code, one can set the network SSID and password so the JAMA can connect to the hotspot. After that, one can use the IP determined to identify each JAMA. Six steps summarize the JAMA operation:PyJama is a Python library that processes data from JAMA or other devices .Data pre-processing checks data integrity and sensor calibration, removes artifacts, and filters signals. Data processing combines accelerometer, gyroscope and magnetometer data to estimate IMU orientation and, therefore, joint angle.https://cvssp.org/data/totalcapture (accessed on 14 April 2021))) dataset [ dataset .DataHandler class provides functions to read raw data generated by JAMA, Vicon Blade, and Xsens, and MTw wireless IMU organizes a dictionary or matrix , calibrates, and saves a csv file.Data manipulation reads an organized data file to allow data processing functions. For this, DataProcessing implements data processing and sensor orientation-related functions to manipulate Euler angles, quaternions, rotation matrix, and the conversion functions among them (Euler ⟷ quaternion ⟷ rotation matrix and Euler ⟷ rotation matrix). Additionally, this class implements the Gradient Descent and Gauss–Newton algorithms  aims to estimate linear dynamic system parameters (or states) that approximate measurements performed . Many apentation . The impSimilar to other presented filters, the Madgiwck filter uses gyroscope measurements as reference because of better behavior at high frequencies and interference isolation from forces during movement. Accelerometer and magnetometer measurements are used as precise instructions to compensate for gyroscope deviation in low frequencies due to the accumulation of numerical errors and influence of artifacts .Formulations in quaternions require Madgiwck filter changes and specific implementation using the Gradient Descendant, as performed in its original formulation . MadgwicAlgorithm 1 Complementary Filterdata_comparison function, which plots the polynomial fit curves of two measures for visual comparison of the data.In particular, the human body has some cyclical movements, such as gait, and these cycles are traditionally identified and extracted by kinematic analysis ,18,19. IDataVisualisation class implements automatized plots in single figures or sub-figures, identifies the size of the input data, and organizes the entire figure layout. The standardization of the axes is automatic, and it is simple to determine captions, titles, and subtitles. Structurally, the class functions use the matplotlib. In addition, users can choose to use and visualize the data with the orientation in quaternions or Euler angles, the last is the most common way to visualize joint rotations [An essential part of data analysis is its visual representation with figures and graphs. The otations .DataAnalysis class is easy to use for users with little data analysis experience. This class has functions that perform data processing in a few steps. It has functions to apply all implemented filters, perform all metric calculations and pattern extraction, and estimate the angle of a joint. In other words, it has a complete analysis routine implemented in these functions.The Algorithm 2 Kalman FilterAlgorithm 3 Madgwick FilterIn this section, the methodology used to prove the concept of the developed system is presented in three experiments. JAMA was attached to rehabilitation robot (Lokomat) and analyzed the data using PyJama. Second, JAMA was attached to the knee joint of a walking person on a treadmill (again using JAMA with PyJama). Third, PyJama processed an open dataset with gold standard device data.This project was approved by the ethics committee of Federal University of Rio Grande do Norte under the protocol 79649717.0.0000.5292 approved in 15 December 2017.A controlled task allowed to assess JAMA measurements and joint angle estimation using PyJama. JAMA was attached to hip and knee joints of Lokomat , a validated robot for gait rehabilitation with sagittal plane movements . The recY-axis aligned with gravity), acquisition frequency of 75 Hz, and 120 s of data transmission.Algorithm 4 Pattern ExtractionLokomat is a market-leading rehabilitation robot capable of providing highly repetitive gait training with physiological movement patterns. It consists of a pair of robotic legs and a user’s weight support device. The operator can determine joint angles and simulate gait speed , a contrVideo recording used an iPhone 8  with a resolution of 1920 × 1080 at 30 frames per second. JAMA was attached to the Lokomat using double-sided tape . Kinovea software tracked two points frame by frame: one in the hip joint and the other in the distal part of the robotic leg. Kinovea exports a .csv file with the angles frame by frame. Experiment 2 evaluates JAMA in a human gait task on a Lokomat treadmill with a speed of 4 km/h, as proposed by Lebleu et al. [the shin ,2,3, witPerformance of the PyJama was evaluated with data from gold standard devices available in the Total Capture dataset . Data weData were collected indoors, using eight high-definition Vicon Blade cameras (with 60 frames per second) duly calibrated with 2.5-m supports and 13 Xsens sensors distributed in a capture suit. The dataset provides the quaternion orientation from the Vicon system; the accelerometer, gyroscope, and magnetometer provided raw data, and quaternion orientation (Ground Truth) was taken from Xsens sensors. A walking task with markers and sensors on the thigh were used to test PyJama.A comparison performed between orientations provided in quaternions by Vicon and Xsens showed a significant difference. Thus, Vicon’s data to compare with PyJama’s estimates were discarded. Probably, this difference was due to the positioning of Xsens sensors and Vicon’s body markers, which require some specific type of adjustment. The calibration done by the authors considered the orientation of the bone of a virtual avatar , which wThis section presents the results of experiments performed to proof the concept for both JAMA and PyJama. All results are interactively available on Google Collaboratory notebooks, hosted on PyJama GitHub. In addition, a step-by-step tutorial on how to build and use JAMA is available on GitHub.joint_measures from DataAnalysis class were presented.In In this experiment the minimum point was 0data_comparison function.JAMA and Kinovea data were compared using the gait pattern average cycles of Lokomat and extracted by PyJama. Due to its better performance, Madgwick Filter was used to find the cycles. To facilitate visual comparison, we calculated the cycles for Madgwick Filter and Kinovea, which are presented in As expected, the results obtained with the application of the fusion filters were consistent, and Experiment 2 evaluated the gait of an adult participant using Madgwick filter, and the results in Experiment 1 support its use to analyze gait data.Similar to Experiment 1, a threshold determined subsets of gait cycles, and a similar joint behavior (flexion and extension) was extracted from JAMA and Kinovea.pattern_extraction has no parameters to adjust the order of the phases, only the input threshold was presented in Algorithm 4. Other influencing factors might be gait speed (4 km/h), JAMA limb positioning, and a difference in acquisition frequency between JAMA and the video (Hz and fps).vicon2dict and xSens2dict, returned data in a dictionary form to ensure analysis.Experiment 3 evaluated the manipulations and transformations performed by PyJama on Total Capture  dataset.Comparing quaternion data from Vicon and Xsens  obtained an average RMSE of 0.69 ± 0.04 . This manipulation presented an average RMSE of 0.73 ± 0.05  and then updated with the input data. If this initial data are better adjusted, the results of all measurements tend to have a lower RMSE and MAPE.This paper presented a low-cost device capable of extracting data from an IMU. Unlike other works ,22,23, iThe biggest limitation in using JAMA is the non-validation of two measurements. Most of the works that develop hardware using IMU use gold standard devices as a reference during development and for measurement validation ,3,24. AlIn this paper, for proof of concept, an acquisition frequency of 75 Hz were used, which can extract enough data for the algorithms developed with PyJama . Some alJAMA may not present good results for evaluating rapid movements, such as running (>8 km/h), in its current version. On the other hand, these hardware configurations are compatible for gait assessments ,29, asseThe main objective for the development of PyJama is to provide open-source algorithms for processing data from inertial sensors in different stages . There are several systems available for measuring angles, whether commercial or free, but few of them are open-source  and specFusion filters are one of the options for estimating orientation of inertial sensors. PyJama has the following Fusion Filters: Complementary Filter, Kalman Filter, and Madgwick Filter, and all of them use accelerometer, gyroscope, and magnetometer measurements to estimate orientation. By combining these different measurements, fusion filters provide more stable results for simpler sensor noise removal . These fCommercial systems such as Xsens  and PhysIn the state-of-the-art time series analysis for pattern recognition, information extraction and data fusion have been made by artificial neural networks in different areas ,34,35. RA similarity between filters implemented in PyJama and most neural network architectures is the use of optimizers. These optimization techniques aim to decrease the cost function, in other words, to decrease the measurements error ,6. PyJamFor data drift removal, a sensor calibration based on static upright pose measurements was implemented for the gyroscope and accelerometer measurements. More complex and/or faster movements tend to encounter greater measurement errors; for these cases other techniques may be needed, such as functional calibration, as presented in .Experiment 1 aimed to evaluate the performance of JAMA and PyJama in a controlled situation of knee joint movement with a robotic leg (Lokomat). Overall, PyJama estimated the knee angle correctly, and all filters showed an average range of motion of 60k filter .However, notwithstanding the metrics found are correct, the graphical representation  of the g systems , which sThe algorithm for pattern extraction presented was developed based on the angle and not the temporal perspective, which is the most common ,3,37. ThKinovea has adequate precision for biomechanical analysis . TherefoExperiment 2 aimed to test JAMA and PyJama in a real-world scenario evaluating the knee joint of a healthy adult during gait. Data acquired using JAMA and analyzed using the PyJama Madgwick Filter resulted in an average ROM of 44.62 . It is not possible to have a fair performance comparison among filters, as they have not the best tuning . TherefoProbably, the non-optimization of parameters negatively influenced the evaluation of Kalman filters with greater intensity and was probably for two reasons: (1) the implementation used was proposed by Comotti , where hIn general, the functions implemented in the PyJama were effective for processing the IMU data and fusion filters in real-time through the update functions .This study focused on a proof of concept, testing the systems with a rehabilitation robot and a healthy adult individual evaluating the knee joint in the sagittal plane. Tests for other joints and in other populations are needed in future studies, mainly for system validation.We based the proposed pattern extraction on the angle (Algorithm 4). This approach proved to be inferior to other techniques proposed in the literature , specifically due to the patterns extracted based on time ,29. BecaAlthough the system was able to extract the behavior of the joint, and specifically PyJama achieved a high accuracy when processing Xsens data, we can improve our system by adding algorithms to locate the joint centers and improve the robustness of the segment orientation automatically ,44. AnotThis paper proposed a low-cost system for measuring joint angles in three experiments as a proof-of-concept of JAMA and PyJama. We used PyJAMA to process the data collected by JAMA in two experiments, which was able to find some pattern behavior of the knee joint. In the gold standard data processing tests, PyJama reconstructed the original signal (RMSE 0.00 and MAPE 0.00%) and estimated the sensor orientation with a low error (RMSE 0.04 and MAPE 2.95%). The proposed system needs some specific conditions for sensor calibration. In addition, it needs a long time for sending async-TCP buffer data. In future work, we plan to enhance the communication protocol and implement new functions for data calibration and fusion, using different techniques.


Background and Objectives: The prevalence of pediatric non-alcoholic fatty liver disease is increasing. A lot of new data are published regularly. Materials and Methods: Original clinical studies, review articles, and guidelines in children were searched for and the most relevant included in this review. Results: A total of 138 retrieved papers were classified into pathogenesis, epidemiology, diagnosis, and treatment. Pathogenesis is currently explained with the “multi hit hypothesis”, with complex interactions of genetic and environmental factors which trigger inflammation in steatotic liver. The prevalence is rising. A diagnosis can be made with laboratory tests, imaging, and liver biopsy after the exclusion of other causes of liver steatosis. The mainstay of treatment is lifestyle modification consisting of dietary intervention and increased physical activity. The progression to liver cirrhosis can occur even in children. Conclusions: Non-alcoholic fatty liver disease in children is a part of a metabolic syndrome in the majority of patients. Due to its complex etiology and high prevalence, multidisciplinary teams, together with public health professionals, should be involved in its treatment. Pediatric non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, with its prevalence rising in parallel with the increased rates of overweight and obesity. NAFLD is a multisystem disease also affecting extrahepatic organs, and it has a long-term impact on health which extends into adulthood and causes significant morbidity and mortality ,2. The criterion for diagnosis is ≥5% of hepatocytes with macrovesicular steatosis with no evidence of viral, autoimmune, inherited metabolic, or drug-induced liver diseases in a person without excessive alcohol intake . NAFLD rAn international panel of hepatologists proposed a different terminology to avoid the term non-alcoholic, which is sometimes difficult to assess in adults and is inappropriate in children: metabolic dysfunction associated fatty liver disease (MAFLD), instead of NAFLD. New diagnostic criteria were defined: hepatic steatosis detected by imaging, blood biomarkers or liver histology in overweight/obese persons or those with type 2 diabetes mellitus, or lean/normal weight persons with additional metabolic risks, such as high triglycerides or low high density cholesterol, glucose intolerance, increased waist circumference, and increased blood pressure . HoweverNAFLD encompasses a wide spectrum of manifestations, from isolated hepatic steatosis without inflammation, to advanced form non-alcoholic steatohepatitis (NASH) with histologic features of inflammation and fibrosis, which may lead to cirrhosis and to end-stage liver disease . An impoA lot is already known regarding NAFLD in children and new data are reported daily. The implementation of this knowledge on diagnosis and treatment poses a great challenge, as has been shown in a survey of the majority of practicing pediatric hepatologists in university hospitals in Canada . The diaThe purpose of this review is to summarize up-to-date and concise information on NAFLD in children and to provide brief information to practicing clinicians on recent advances and approaches to the prevention, diagnosis, and treatment of pediatric patients with NAFLD.The search strategy was focused on original clinical studies, review articles, and guidelines. The online databases searched included PubMed, Embase, and Cochrane. The searches were performed between 25 February and 31 March 2021. Search terms were “non-alcoholic fatty liver disease”, “non-alcoholic steatohepatitis”, “fatty liver”, “metabolic dysfunction associated fatty liver disease”, and “pediatric fatty liver disease”. The search was limited to publications in English, children less than 19 years old, clinical trials, systematic reviews, and guidelines. During the first check, the titles, abstracts, and key words were searched and publications not meeting the above search criteria were eliminated.In the next check, all retrieved articles were classified according to the subcategory , type of article , and publication date. For each subcategory, the most relevant systematic review or guideline was selected for inclusion in this review. Relevant control trials, which were not included in the systematic review or guidelines or were published after the search of the systematic review or guidelines had finished, were also selected for inclusion. Several searches in accordance with the above criteria revealed 304 clinical trials. Those not fulfilling the search criteria were not considered for further analysis (197 hits) due to various reasons . The search for guidelines or systematic reviews with the above search criteria revealed 59 hits, of which 13 were excluded as they were for only for adult population, 12 were not on NAFLD but different causes of hepatic steatosis, mostly in connection to parenteral nutrition, one was design, and two were not in English. The diagram of the study search and selection process is shown on The pathogenesis of NAFLD is not yet completely explained, but the “multi hit hypothesis” is currently accepted. “First hit” may represent liver fat accumulation caused by obesity and insulin resistance, which is influenced and maintained by complex interactions of genetic and environmental factors, as well as crosstalk between different organs and tissues, adipose tissue, and the pancreas, gut, and liver .PNPLA3. The carriers of the allele I148M have two-fold higher fat content than non-carriers. Another allele, S453I, which is protective, is found in African Americans and explains why the prevalence of NAFLD in this community is lower than in, e.g., the Hispanic community [Genes involved in inflammation, lipid metabolism, and oxidation also play an important role and are associated with progressive liver disease, insulin resistance, type 2 diabetes mellitus, and a higher risk for the development of hepatocellular carcinoma . Among sommunity . Prenatal factors predisposing for NAFLD are maternal body mass index, metabolic syndrome, gestational diabetes, and low birth weight of the child  influencFree sugars, such as sucrose or fructose, are consumed in quantities two to three times the recommended intake, which is less than 10% of energy intake, and cause a fatty liver due to overweight and obesity. Especially high intake of fructose, which is metabolized mostly in the liver, affects hepatic energy metabolism with modulation of the liver gene expression involved in the regulation of different metabolic pathways which lead to hepatic steatosis, with fructose being an inducer of and a substrate for hepatic lipogenesis . A high p < 0.001), body mass index , and also in AST, ALT, triglycerides, and LDL. Decreased physical activity is one of the major factors for NAFLD development in overweight or obese children. In a study of 115 overweight/obese children, hepatic fat content measured with magnetic resonance imaging was lower in those with better cardiorespiratory and musculoskeletal fitness as well as speed-agility . The infOften neglected, but of much importance, is adequate sleep. Sleep shortages have been shown to be more prevalent in a group of 67 children and young adults with NAFLD in comparison to controls without NAFLD, probably due to the increased risk of obesity ,16. Another risk factor for NAFLD is socioeconomic deprivation. It was assessed in a study of two large cohorts of children performed in the United States of America. A group of 334 was diagnosed by magnetic resonance imaging and 245 by liver biopsy. Social deprivation was determined by the Community Deprivation Index which takes into account income, education, public assistance, housing, and insurance status. Socioeconomic deprivation was associated with earlier onset of NAFLD but not with its severity .The psychosocial aspect is also an important part of NAFLD pathogenesis and has to be considered in the management of individual patients and in the planning of health programs combating obesity in children. The psychosocial influence on hepatic steatosis in children is bidirectional. On one hand, children with NAFLD as a consequence of obesity report increased levels of psychological distress, and on the other hand, mental disorders may be associated with the increased risk of obesity and NAFLD . Oscillospira and a higher level of 2-butanone was found [Obesity is not a necessary condition for the development of NAFLD. It can be found in 8 to 16% of non-obese children. The causes are visceral obesity in otherwise non-obese children, and genetic and environmental factors . Adiposeas found . The aboas found . The prevalence of NAFLD in children from the general population is 7.6%. In selected populations of obese children, it is present in 34.2%, as was found in a systematic review and meta-analysis of 74 studies . The diaDetailed gender analysis revealed prevalence of NAFLD in 9% (6.5% to 12.5%) of males and in 6.3% (3.8% to 10.4%) of females. In a subgroup analysis according to the nutritional status, 2.3% (1.5% to 3.6%) of those with normal weight, 12.5% (9.2% to 16.7%) of overweight and 36.1% (24.6% to 49.4%) of obese children had NAFLD [A recently published population study of 867 adolescents from the United States of America included in the National Health and Nutrition Examination Survey 2017–2018 found even more alarming data assessing the prevalence of NAFLD with transient elastography: 24.2% of all participants had any degree of steatosis, and 4.4% of them had significant liver fibrosis not associated with either overweight/obesity nor ALT level, but the findings were not confirmed or further analyzed by liver biopsy .Overweight (body mass index between 85th and 95th percentile) or obesity (body mass index more than 95th percentile), or abdominal obesity (waist circumference of ≥90th percentile for age and gender) or;Fasting plasma glucose of >100 mg/dL (5.6 mmol/L) or known diabetes mellitus type 2 or;Elevated triglycerides ≥150 mg/dL (≥1.7 mmol/L);HDL-cholesterol <40 mg/dL (<1.03 mmol/L);Systolic blood pressure ≥130 or diastolic blood pressure ≥85 mmHg;Homeostatic model assessment for insulin resistance (HOMA-IR ≥ 2.5);High-sensitivity C-reactive protein >2 mg/L.In lean children, the presence of at least two metabolic risk disturbances:The diagnostic criteria for NAFLD/MAFLD in children, in whom liver steatosis has been diagnosed by biomarkers, imaging, or liver biopsy, are ,26:OverwAn additional condition is that other causes of fatty liver, such as inborn metabolic diseases, autoimmune and infectious causes are ruled out regardless of obesity or other risk factors for NAFLD ,8,26,27.There is neither an ideal method nor an international consent on whom and with which method to screen. In adult populations, ALT was not a good indicator of liver injury, as one-third of patients with normal ALT already had NASH and significant liver fibrosis, whereas 50% of patients with elevated ALT had a normal liver on patohistological examination . SimilarOn some occasions, NAFLD can be found accidentally when performing diagnostics for, e.g., recurrent abdominal pain. Regardless of symptoms which are not present in NAFLD until the late stages, if increased liver transaminases or hyperechogenic liver by abdominal ultrasound are found, complete diagnostics have to be performed .The single most evaluated and commonly used test is ALT, but it is not specific, so other liver diseases with similar presentation have to be ruled out. Normal values are age- and gender-specific. ALT increased to more than twice the upper level after the exclusion of other causes in overweight children older than 10 years strongly indicates for NAFLD (88% sensitivity and 26% specificity) [Recently, it was found that ALT and GGT correlate well with liver biopsy in children with NASH. The analyzed data were from another clinical trial in which the treatment with cysteamine bitartrate was studied. In a group of 146 children who had liver chemistry and liver biopsy performed at the beginning of the study and after 52 weeks, ALT and GGT significantly improved in a subgroup with liver histology improvement .Laboratory panels for the screening of NAFLD or to determine its progression to NASH are searched for, but for now no laboratory test combination is included in the latest published international guidelines . In a stA major part of laboratory workup serves to rule out other diseases with a similar presentation. The scope of investigations depends on the age, history, physical examination, and risk factors for NAFLD and comprises infections and autoimmune, genetic, and endocrine causes . Abdominal ultrasound is not sensitive enough for the detection of lower degrees of liver steatosis (involving less than 33% of hepatocytes), but is a useful and widely available diagnostic method to rule out other causes of liver disease. Imaging methods to measure liver fibrosis are transient elastography, share wave elastography, and magnetic resonance elastography . Transient elastography is a valuable and reproducible ultrasound-based method to measure liver stiffness  and controlled attenuation parameter . Intra- and inter-operator agreement was assessed in 34 children. Agreement was better in intra- then in inter-observer liver stiffness measurement  than in intra- and inter-observer liver steatosis  . Two newer sonographic methods for fibrosis detection, point and two-dimensional share wave elastography, have been shown to have low levels of technical failure and have been comparable . The samMagnetic resonance elastography, another noninvasive method to assess liver fibrosis showed a correlation with the following histopathologic features: fibrosis and ballooning, as shown in a group of 50 adolescents . In the 2), liver biopsy is more difficult to perform due to a thicker subcutaneous tissue layer [Liver biopsy is the current standard for the diagnosis of NAFLD and to assess the severity, i.e., the presence of hepatic steatosis, inflammation, and fibrosis, and to rule out other diagnoses if the sample is representative (its length should be at least 2 cm). In extremely obese children , lobular inflammation, hepatocellular ballooning, and fibrosis are assessed . Liver biopsy is not uniformly performed in all children with NAFLD due to its invasiveness . HoweverFor now, there is no single evidence-based treatment for NAFLD in children, apart from lifestyle modification which causes weight loss. Some improvements of different aspects of NAFLD were seen after specific interventions, e.g., antioxidants, polyunsaturated fatty acids or probiotic supplementation, but there is not enough evidence to support these treatments universally. One of the problems in published studies is heterogeneity of outcome measures. In a recently published systemic review, only 14% of pediatric studies used paired histology as an outcome measure, and 62% of included children did not have a liver biopsy to diagnose NAFLD, most likely due to the invasiveness . Lifestyle interventions are currently the only successful treatment for NAFLD in children, but it is difficult to maintain the reduced body weight in the long run, as was shown in a study of 79 obese post-pubertal adolescents with a BMI of more than the 95th percentile for their age. They were randomized into two groups, to the lifestyle modification program  which consisted of knowledge, attitude, and practice regarding diet and exercise based on motivational interview and behavioral modification, and to the control group, in which diet and exercise advice were provided during routine consultations every 4 months. Anthropometrics, laboratory results and intra-hepatic triglyceride content (assessed by magnetic resonance spectroscopy) were measured at baseline, week 16, and week 68. The improvement was significantly better in the intervention group after 16 weeks of intervention , but at 68 weeks children in the intervention group regained some weight and there was no difference any more between the intervention and control group . Lifestyle interventions and a psychoeducation program was not enough to reduce hepatic fat as measured by magnetic resonance, as was shown in a study of 102 overweight or obese children aged 10.6 ± 1.1 year. The liver fat content significantly decreased only in the intervention group which had, in addition to the above mentioned lifestyle intervention and psychoeducation, supervised intense aerobic workouts three times weekly for 90 min. Similar improvements were also seen in laboratory metabolic and liver tests . Specific interventions for the treatment of obesity and NAFLD have to be integrated with cognitive behavioral techniques and should include the whole family to be more successful. Intervention strategies have to be community based to adapt them to the possibilities of the patient’s environment . Physical activity in children with NAFLD as the only intervention is only recently in the focus of research. Beforehand, it has been studied only in connection with other lifestyle interventions ,41. A systematic review and meta-analysis of the studies with supervised exercise training identified 16 studies published before January 2017. Fat content was measured with magnetic resonance or sonographically, and the types, intensity, volume, frequency, and duration of exercise varied. The study showed that isolated aerobic or resistance exercise trainings of equal to or more than 60 min, and a frequency equal to or more than three times per week reduced liver fat content and improved cardiovascular fitness and muscular strength . In a randomized controlled trial (RCT) of 102 overweight or obese children, the intervention included three supervised exercise sessions per week, while both the intervention and control groups had 11 sessions of family-based lifestyle and psycho-educational programs. Hepatic fat assessed by magnetic resonance imaging reduced in significantly more patients in the intervention than in the control group (54% vs. 34%), as did weight, BMI, and GGT .2 were randomly assigned to a high-intensity or low-intensity training 12-week intervention and a control group. In contrast to the control group, high- and low-intensity training groups also received nutritional, psychological, and clinical counseling. Some NAFLD markers improved in both intervention groups , but there was no change in glucose, insulin, and insulin resistance. Hepatic fat has not been assessed [In another RCT, 107 adolescents with a BMI of 34.7 ± 4.1 kg/massessed .2) led to an increased liver fat content assessed by computer tomography scans at the beginning and after seven days, which was probably attributed to the weight loss (−2.5 ± 0.9 kg) and decrease in BMI (−1.2 ± 0.4 kg/m2). ALT did not change while AST significantly increased [In contrary, an intense short-term (1 week) physical activity intervention together with reduced caloric intake in 57 obese children  improved ALT, AST, cholesterol, low-density lipoprotein-C, and triglycerides levels, caused waist circumference decrease, and improved liver sonography results without influencing weight and BMI [In addition to the meta-analysis, there are only a few small studies on probiotics in pediatric NAFLD. On the other hand, in an RCT of 64 obese children aged 10 to 18 years, another mixture of probiotics , liver steatosis improved after 4 months of treatment assessed by ultrasonography [In an RCT of 22 children with biopsy-proven NAFLD treated with a mixture of probiotics  . Omega-3Cysteamine is a precursor amino acid for the synthesis of glutathione which is a major intracellular antioxidant in liver. In a multicenter RCT of twice-daily treatment with cysteamine bitartrate, delayed release was compared to a placebo in 169 children with histologically determined NAFLD with the activity score of 4 or higher. The intervention lasted for one year. In the primary outcome, the improvement of liver histology, there was no statistical difference between groups. However, ALT, AST, and lobular inflammation decreased significantly in the treated group ,57. Choline, ursodeoxycholic acid, and other nutritional supplements or medications have been studied, but, for now, without a convincing influence on NAFLD . GeneticBariatric surgery is an effective treatment together with other measures in selected adults with morbid obesity . Due to 2) and seven superobese (BMI more than 50 kg/m2) adolescents 18 months after gastric banding, weight loss was similar: 30 ± 19 kg and 28 ± 12 kg, respectively. Investigations for NAFLD  were performed before the gastric band placement only so an influence of NAFLD parameters was not assessed. Resolution of insulin resistance and dyslipidemia was greater in the morbidly obese group [In a study of 11 morbidly obese (BMI between 40 and 50 kg/mse group .2 (40.1 to 44.1 kg/m2) to 37.2 kg/m2 (35.2 to 39.2 kg/m2). Liver steatosis improved in 68.4% and totally reversed in 52.6%. The ALT level dropped concomitantly [An endoscopically placed duodenojejunal bypass liner is a reversible bariatric procedure. In a study of 19 post-pubertal morbidly obese adolescents with at least one co-morbidity due to the obesity one year after the intervention, the decrease in weight loss was 11.4% (7.4 to 15.3%), and BMI decreased from 41.1 kg/mmitantly .The prevention of NAFLD in children is similar to the prevention of obesity and consists of a healthy lifestyle, including a healthy diet (a Mediterranean diet being one of the possibilities), avoiding high intake of free sugars, physical activity, adequate sleep, and psychosocial well-being ,40,61. The NAFLD may proceed to liver cirrhosis even in the child age . HepatocIn this review we summarized up-to-date information on NAFLD in children to provide information on recent advances and approaches to the prevention, diagnosis, and treatment of pediatric patients with NAFLD for a clinician who is not deeply involved in the field. In the discussion, special emphases are made on the practical application of the current knowledge. Despite broad knowledge of pathogenic mechanisms leading to overweight and obesity, which are major risk factors for NAFLD, the prevalence of this insidious disease is increasing in children and adolescents and has become the most common cause of liver disease in children ,25. The disease is asymptomatic until the progression to end-stage liver disease that happens after many years or even decades .The treatment is lifelong lifestyle modification with a stress on healthy diet and physical activity, which is difficult to maintain .Lifestyle which leads to obesity/overweight and NAFLD is usually not an isolated problem of specific patient, but is mostly not only medical, but also a psychosocial problem of the whole family, so treatment interventions have to be multidisciplinary, multifaceted, and long-lasting, which is difficult to achieve .Free sugars and fats are added to ready-made foods to make them more palatable and to increase consumption and sales. Human taste is prone to give preference to sweet and energy dense foods .Public health authorities can rarely oppose the beverage and food industry which maintain high sugar and other inexpensive ingredient content, which promote sales but are harmful to health .There are several causes for the increased prevalence of NAFLD in children:Prevention is the most important to reduce NAFLD in children, and consequently in adults. Programs must extend beyond addressing already obese children by pediatrician and nutritionist and must include families and community . They shNAFLD must be actively searched for in children with increased risk, i.e., in overweight or obese children, or in those with dyslipidemias, insulin resistance, or diabetes mellitus type 2. There is no ideal test for screening yet, so ALT determination is used as the first test and it should start at the age of 9 years . Diagnosis is made by proving hepatic steatosis and the exclusion of other diseases which also present with fatty liver. The extent of diagnostic testing depends on the presentation and age. In an infant or young child with NAFLD, it is broader than in an adolescent with typical risk factors for NAFLD . DiffereFibrosis can be assessed and followed up by different modalities of ultrasound elastography or magnetic resonance elastography, with ultrasound methods being usually more accessible . Liver biopsy should be performed in those with unclear diagnosis or in children who have increased risk or signs of advanced fibrosis, splenomegaly, two to three times higher ALT despite lifestyle modification and some weight loss, and an increased ratio of AST/ALT > 1 .Regarding follow-up, there are no unique guidelines. Usually, children are monitored every year with at least ALT and, if accessible, with some imaging techniques to estimate the level of fibrosis in the liver .The goal of treatment is the regression of liver steatosis, inflammation, and fibrosis. At present, the only effective treatment of NAFLD in children is lifestyle modification which consists of dietary intervention and increased physical activity . In the To achieve long-term success, an integrative approach, including cognitive-behavioral techniques, family- and community-based interventions, and tele-health should be implemented .NAFLD in children is a big medical and public health challenge due to high and increasing prevalence and is a great influence on a child health and prognosis when they become adults. NAFLD is not an isolated disease, but is a part of a metabolic syndrome in the majority of patients; therefore, it has to be addressed by a multidisciplinary team of experts to prevent complications and improve overall health of the population.


In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 ), Alzheimer’s disease , and mild cognitive impairment ). Conclusion: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients’ health outcomes, particularly in elderly patients.Background: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. Objective: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. Methods: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. Results: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25–1.55;  The prevalence of cognitive impairment including dementia is increasing worldwide. It is estimated that more than 75 million people will have dementia by 2030 and 131.5 million by 2050 . PreviouNumerous epidemiological studies have highlighted an association between anemia and the increased risk of developing overall cognitive impairment (OCI) ,7. AlthoPeters et al.  reportedThe PRISMA  guidelines, which are based on the Cochrane Handbook for Systematic Reviews of Interventions, were used to conduct this study  14,15].,15.14,15We conducted an updated systematic review and meta-analysis. We searched electronic databases including EMBASE, PubMed, and Web of Science for published observational studies and randomized control trials between 1 January 1990 and 1 June 2020, with no language restrictions. We used the search terms “anemia” OR “iron deficiency”, and “dementia”, OR “Alzheimer”, OR “cognitive impairment”. We also searched the bibliography of retrieved articles to obtain relevant articles.Studies that assessed the association between anemia and OCI risk and matched all of the following criteria were included: (a) the primary outcome was OCI, (b) were observational or randomized control trials, (c) included at least 50 participants aged over 18 years old, (d) identified OCI patients using the standard diagnostic criteria (Feighner criteria or Research Diagnostic Criteria/DSM-III/DSM-III-R/DSM-IV/DSM-5/ICD-10), and (e) the diagnosis of anemia patients was based on WHO criteria, i.e., men: Hb < 13 g/dL, women: Hb < 12 g/dL, measured in blood samples.Studies were excluded if they were (a) published as a case report or case series with a low number of participants ; (b) published as editorial, letters, commentary, review articles, or conference abstracts; (c) provided insufficient information on OCI patient selection, confounding factors, adjusted variables, and effect size; or (d) used the same database with overlapping study durations.Required data were retrieved from selected articles by two independent authors (M.M.I. and S.A.). They developed a standard data collection form, checked all of the variables, and collected the needed information to calculate the risk ratio (RR). Afterwards, they retrieved and compiled the following information: (a) study characteristics: the author’s first name, publication year, country, study duration, study design; (b) demographic characteristics: the number of participants, number of male and female patients, and age; (c) the reference standard of dementia diagnosis, and number of dementia patients; and (d) the adjusted effect sizes such as hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs). We considered the adjusted effect sizes because it helps to reduce potential bias.Study quality was assessed using the Newcastle Ottawa Scale for observational studies. The summary score was used to categorize the study quality into the following two groups: high (NOS score higher than 7) and low .2) were considered to examine whether there was significant heterogeneity in their effect sizes. Previous studies were followed to categorize the heterogeneity as low , medium , and high  [Comprehensive meta-analysis  software was used to analyze the data. An adjusted risk ratio ) with the corresponding 95% CI was calculated to show the magnitude of the association between anemia and OCI. However, the random effect model was used to calculate the heterogeneity between the studies. The Cochran Q statistic and inconsistency statistics (Ihan 50%) ,17. FinaThe articles search was conducted on electronic databases and garnered 987 articles. We reviewed the titles and abstracts of those articles and excluded 962 articles due to a lack of adherence to our pre-specified selection criteria. Afterwards, we screened the full text of the selected 25 articles and assessed the reference lists of relevant articles, retrieving no additional articles. We further excluded 6 articles that did not fulfil the inclusion criteria. Finally, 20 articles were considered in the meta-analysis ,34,35,36This updated meta-analysis comprised of 12 cohort studies and 8 case-control studies that are presented in The publication years ranged between 1997  and 2020We used the Newcastle Ottawa scale (NOS) to evaluate the methodological quality of the included studies, which is recommended by the Cochrane collaboration for nonrandomized studies. The NOS score included case-control and cohort studies ranging from 5 to 8, with a mean score of 7.3.n = 20, aRR, 1.39 . There was a moderate risk of heterogeneity among the studies   in patients with anemia. The risk of OCI was significantly higher in patients when compared to those who did not have any kind of anemia ( = 0.02) .p < 0.001)) .Among 15 studies, participants who had anemia were more likely to develop all-cause dementia compared to those who did not have any kind of anemia ) . There wp = 0.002)) .Moreover, six studies assessed the risk of Alzheimer’s disease (AD) in patients with or without anemia. The patients with anemia were significantly associated with an increased risk of AD ) . There wp = 0.02)). There was a moderate risk of heterogeneity among the studies   .We also evaluated the risk of overall cognitive impairment (OCI) in the patients with anemia by methodological quality, study design, and study locations. The subgroup analyses are presented in p < 0.001)). Nine studies with a low methodological quality assessed the risk of anemia and OCI risk. The overall risk of OCI was significantly higher in patients with anemia ).Eleven high methodological quality studies (based on NOS) examined the risk of OCI in patients with anemia in comparison to patients without anemia. The risk of OCI was higher in patients with anemia ). However, the risk of OCI in the patients with anemia was noticeably higher in a case-control study design ).In the cohort studies, patients with anemia were more likely to develop OCI when compared to those who did not have anemia ). In North America, four studies have evaluated the risk of OCI in patients with anemia. The pooled RR of OCI was 1.37  in patients with anemia. Furthermore, in Asia, nine studies have assessed the risk of OCI in patients with anemia. The pooled RR of OCI was 1.29  in the patients with anemia when compared to those who did not have any kind of anemia.Seven European studies demonstrated that patients with anemia were significantly associated with an increased risk of OCI . Our findings support previous evidence that anemia increased the risk of OCI -fold. Ou. The finSeveral biological mechanisms can be used to explain the association between anemia and OCI. First, the most convincing explanation would be related to the limited oxygenation of peripheral tissues due to a low hemoglobin concentration. Previous studies reported that anemia is linked to a decreased cerebral blood flow, leading to OCI ,38. A liConsidering the worldwide prevalence of anemia, and the significant association between anemia and OCI risk presented in our updated study, the follow-up of anemia patients with symptoms of MCI could have a considerable effect on public health. Although MCI is mainly asymptomatic, it provides a possibility to prevent the progression of AD or dementia and its complications. Previous studies reported that lifestyle changes could be more effective in preventing OCI, and early psychosocial intervention would be cost effective ,47. A raOur study has several strengths that need to be addressed. First, it is a comprehensive study that included 20 articles and showed an association between anemia and different types of cognitive impairment risk. Second, the heterogeneity of our study is moderate; therefore, the evidence provided here is less biased. Third, we have considered adjusted effect sizes while collecting our data from the retrieved articles; hence, there is less possibility of confounding factors because they adjusted the possible confounding factors in their studies.Our study also has several limitations. First, our study could not provide any findings based on the severity of anemia. Second, our study did not provide results based on the duration of anemia and the risk of OCI, although the included studies had at least a one-year follow-up period.The main purpose of this updated meta-analysis is to clarify the association between anemia and the risk of OCI. The findings of this study show that anemia is significantly associated with an increased risk of OCI. The early identification of a patient’s conditions and the proper management of anemia patients might contribute to the prevention of OCI risk. Future studies should focus on the biological mechanism of the association between anemia and OCI risk, as well as whether interventions designed to improve hemoglobin levels are effective in reducing OCI risk.


Compared with control, plasma tri-methylamine-N-oxide, homocysteine, neopterin, and tyrosine were increased and plasma acetic acid was lowered following the resistant protein diet . Compared with control, estimated glomerular filtration rate (p < 0.01) and liver function marker aspartate aminotransferase (p < 0.05) were also lower following the resistant protein diet. A resistant protein diet shifted the composition of the fecal microbiome. The microbial fermentation of resistant protein affected the levels of circulating metabolites and the biomarkers of health status toward a profile indicative of increased inflammation and the risk of chronic kidney disease.High-heat processed foods contain proteins that are partially resistant to enzymatic digestion and pass through to the colon. The fermentation of resistant proteins by gut microbes produces products that may contribute to chronic disease risk. This pilot study examined the effects of a resistant protein diet on growth, fecal microbiome, protein fermentation metabolites, and the biomarkers of health status in pigs as a model of human digestion and metabolism. Weanling pigs were fed with standard or resistant protein diets for 4 weeks. The resistant protein, approximately half as digestible as the standard protein, was designed to enter the colon for microbial fermentation. Fecal and blood samples were collected to assess the microbiome and circulating metabolites and biomarkers. The resistant protein diet group consumed less feed and grew to ~50% of the body mass of the standard diet group. The diets had unique effects on the fecal microbiome, as demonstrated by clustering in the principal coordinate analysis. There were 121 taxa that were significantly different between groups (adjusted- Protein is an essential macronutrient in the human diet needed for tissue growth and maintenance, the synthesis of enzymes and hormones that drive multiple integrated systems and functions, and as a source of energy . The recResistance to enzymatic digestion may result from certain conformations, natural ligands, and substituents that sterically protect otherwise digestible proteins . AlternaIt is normal for a small proportion of dietary protein to be fermented in the large intestine. On average, 6–18 g of protein reaches the large intestine per day, a mixture of dietary and endogenous proteins  that is via several mechanisms related to gut microbiota composition, the production of neuroactive metabolites, and the promotion of inflammation. This pilot study aimed to examine the effects of a standard vs. highly resistant protein diet, fed at a normal protein level (21% w/w) on growth, gut microbiome, metabolomic profiles, and the biomarkers of disease risk, to identify the outcomes of interest for further study. Here, pigs are used as an appropriate animal model for human digestion and metabolism.The metabolic pathways of protein fermentation yield multiple products capable of exerting independent effects on host tissues , 18, 19.ad libitum. Pigs were acclimatized on their respective diets for 7 days and monitored daily throughout this period. Following acclimatization, individual feeding was conducted two times daily in metabolic cages throughout the study, and pigs were retained in the cages on the morning of day 20 until a fecal sample was produced . This work was carried out in accordance with the Australian code for the care and use of animals for scientific purposes  and is rproduced . For thevia the mask inhalation of Sevoflurane and blood samples were collected from the anterior vena cava into plain tubes (10 ml) for biochemistry, fluoride oxide tubes for glucose evaluation, and ethylenediaminetetraacetic acid (EDTA) tubes for hematology (10 ml). Samples were stored at 4°C before transport and analysis for general hematology and biochemistry markers , such as plasma alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), urea, and creatinine. Kidney function marker, estimated glomerular filtration rate (eGFR), was calculated from body weight and the measures of plasma urea and creatinine, using a formula appropriate for swine  and stored at −80°C. Venous blood samples were collected as a terminal sample immediately prior to euthanasia (6–8 days after day 20 sample collection). Animals were anesthetised or swine .1) content . The heat treatment of the resistant protein diet was designed to simulate the high heat processing that many ultra-processed food products undergo, to create a (somewhat extreme) model for a diet high in ultra-processed foods and resistant proteins.The standard and resistant protein diets were matched for energy, macro-, and micro-nutrient composition . The stain vitro digestibility of proteins in the standard and resistant protein diets was measured by the method reported in Wu et al.  amplification and sequencing were performed by the Australian Genome Research Facility . PCR amplicons were generated using the 341F and 806R primers to amplify the V3-V4 region of the 16S gene. Thermocycling was completed with an Applied Biosystem 384 Veriti and using Platinum SuperFi mastermix  for the primary PCR. The first stage PCR was cleaned using magnetic beads, and samples were visualized on 2% Sybr Egel (Thermo-Fisher). A secondary PCR to index the amplicons was performed with TaKaRa Taq DNA Polymerase (Clontech). The resulting amplicons were cleaned again using magnetic beads, quantified by fluorometry (Promega Quantifluor) and normalized. The eqimolar pool was cleaned a final time using magnetic beads to concentrate the pool and then measured using a High-Sensitivity D1000 Tape on an Agilent 2200 TapeStation. The pool was diluted to 5 nM and molarity was confirmed again using a High-Sensitivity D1000 Tape. This was followed by sequencing on an Illumina MiSeq  with a V3, 600 cycle kit (2 × 300 base pairs paired-end).de novo ASVs annotation, a greengenes classifier (version gg_13_8_train_set_97.fa.gz) with “assignTaxonomy” function was used. In some of the downstream analyses, ASVs were collapsed into corresponding phylum or genus bins. Additionally, a phylogenetic tree was generated using phangorn R package  test, implemented in “Adonis” function, from vegan R package , 32 on wFor differential taxa occurrence, we used DESeq2 R package . Raw taxThe quantification of metabolites was undertaken using an Agilent 1200 series high-performance liquid chromatography (HPLC) system (Agilent 255 Technologies) coupled to tandem mass spectrometry  with electrospray ionization following previously validated protocols –40. All t-test. Differences between the groups for feed intake, body mass, and thiamine intake on each day were assessed using the Mann–Whitney U-test for independent groups, with a significance threshold of p < 0.05. Data from targeted metabolomics analysis were mean-centered and divided by the standard deviation (SD) of each analyte. Significance was identified by a fold change threshold of 2 and a FDR (q) of 0.1, using Metaboanalyst (version 5.0).This was a pilot study, designed to discover the outcomes of interest in relation to high resistant protein diets, and therefore was not powered based on any one outcome measure. Data were tested for normality using Shapiro–Wilk test. Data for eGFR and liver function markers were analyzed using an independent in vitro treatment with gastric and intestinal enzymes, the digestibility of the resistant protein diet was found to be approximately 50% that of the standard diet , and their feed intakes and bodyweights were significantly different for the duration of the study (p < 0.05), which can account for their lower rate of growth (1) content was significantly reduced in the resistant protein diet  indicating nutrient loss associated with autoclaving. The feed intakes of the resistant protein diet group produced a nutritional deficiency in thiamine (meeting 45–85% of recommended daily intake).The mean bodyweights for the pigs in the resistant protein diet group  and standard diet group (8.3 ± 1.4 kg) were significantly different at baseline . Other hematology and biochemistry markers indicated similar functional and health status between the two groups (The biomarkers of liver function were similar between the two groups (ALP and GGT o groups .p > 0.05). However, the difference in the microbial composition between the groups was evident in the analyses of β-diversity (p < 0.05). In the unweighted UniFrac principal coordinate analysis plot, the first dimension explained 41% of the total variability in the data, suggesting that different bacteria were present according to diet  were only observed in the standard diet group  replicates analysed for each treatment.Compared with the standard diet group, metabolites analysis identified that tyrosine, homocysteine, tri-methylamine-N-oxide (TMAO), and neopterin were significantly increased, and acetic acid was significantly lowered in the resistant protein diet group . SeveralThis study aimed to determine the effects of substituting a standard form of dietary protein with a resistant form of protein, produced by the high-heat treatment of the feed. In both diets, the total protein was 21% of total solids, as required for the developmental stage of life of the pigs. As such, the study did not compare the effects of a high with standard protein diet, but modelled the acute response to colonic fermentation of indigestible, resistant protein , which iin vitro enzymatic digestibility  caused significant time and temperature-dependent reductions in the enzymatic digestibility of protein . Similartibility .The high-heat processing of protein-containing foods results in reduced protein digestibility (resistant proteins) and the chemical products of Maillard reactions between proteins and carbohydrates, known as Maillard reaction products (MRPs) , 46. In The concept of resistant protein that is incompletely digested in the small intestine and fermented in the colon, has been presented previously. While early reports suggested that protein fermentation was beneficial , the emeBacteroidetes phylum (Bacteroidetes was higher (though not significantly) in the resistant protein diet group compared with the standard diet group  and nitrogen utilisation efficiency has been observed , following the resistant protein diet compared with the standard diet , were not increased statistically  , was incvia antagonism of the advanced glycation end-product receptor  were increased in the resistant protein diet group . Chronicreceptor  and, morreceptor . In a sireceptor . In furtreceptor . CollectThis study demonstrated the novel effects of a diet high in resistant protein. While the design of the feeds and range of outcome markers measured were a strength of the study, we acknowledge several limitations. The sample size was small with 4 pigs per group and this was further reduced for certain analyses where only 3 pigs from each group were included due to the sample collection process or limitations in analysis capacity. The lower feed intake, due to an apparent and unforeseen dislike for the feed, in the resistant protein diet group was likely responsible for the reduced growth and weight gain, meaning this cannot be attributed to the poor bioavailability of the resistant protein. The lack of feed intake in this group may have indirectly affected other markers of metabolism and health status. Furthermore, the thiamine deficiency that resulted from the nutrient loss caused by the heating of resistant protein feed may also have negatively influenced the health status and feed intake of the pigs in the resistant protein diet group . FurtherThis study has demonstrated the effects of feeding a resistant protein diet, that modelled a highly processed and non-bioavailable form of protein, to pigs for 4 weeks on gut microbial composition, microbial metabolites, and markers of health status. The resistant protein was approximately half as digestible as the standard protein, with the lower bioavailability, reflected by the significantly lower gain of body mass. The consumption of resistant protein diet induced a shift in the composition of the gut microbiome and the elevation of protein-derived fermentation metabolites, confirming that the fermentation of resistant protein selects for a distinct microbial assemblage and promotes the production of nitrogenous metabolites. Plasma metabolomic analysis indicated that the levels of tyrosine, TMAO, homocysteine, and neopterin were increased, and the levels of acetic acid were reduced in the resistant protein diet group. In addition, responses to the resistant protein diet indicated reduced renal function and increased risk of CKD. In summary, the resistant protein diet appeared to invoke gut microbiome and host-mediated responses that contributed to risk factors for chronic diseases. Although the diet fed to pigs in the current study represented an extreme diet containing the high levels of resistant protein, there is some level of resistant protein present in most protein-containing processed foods and it is expected that habitual consumption over a longer period could drive the biological processes observed under these research conditions. The results justify the need to increase awareness and monitor the levels of resistant protein in processed foods and their potential relationship with adverse health outcomes.https://www.ncbi.nlm.nih.gov/bioproject/798448.The original contributions presented in the study are publicly available. This data can be found here: The animal study was reviewed and approved by Monash Animal Research Platform-1 Animal Ethics Committee Monash University, Clayton 3800, Australia.MM, SS-P, and LB: conceptualization. MM, AG, SS-P, GW, and LB: methodology. MM and VK: formal analysis. MM, AG, VK, MS, KT, TMW, TW, and LB: investigation. MC, FZM, and LB: resources. MM, MS, and KT: data curation. MM and LB: writing—original draft. MM, MC, AG, VK, FZM, SS-P, MS, KT, GW, TW, and LB: writing—review and editing. MM, MS, KT, TW, and LB: visualization. MC, FZM, GW, TMW, and LB: supervision. MM: project administration. MC, FZM, SS-P, TMW, and LB: funding acquisition. All authors have read and agreed to the published version of the manuscript.This project was funded by Monash University under the Fraunhofer-Gesellschaft's ICON program. FZM was supported by a grant of the National Health & Medical Research Council (NHMRC) (1159721), National Heart Foundation Future Leader Fellowship (101185) and Vanguard Grants, and by a Senior Medical Research Fellowship from the Sylvia and Charles Viertel Charitable Foundation Fellowship. MC is the recipient of a Career Development Award from JDRF Australia (4-CDA-2018-613-M-B), the recipient of the Australian Research Council Special Research Initiative in Type 1 Juvenile Diabetes.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.