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Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a compositional data analysis Approach

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SENSORS 2021年第11期21卷 3593-3593页

作者： Biagi, Lyvia Bertachi, Arthur Gimenez, Marga Conget, Ignacio Bondia, Jorge Martin-Fernandez, Josep Antoni Vehi, Josep Fed Univ Technol Parana UTFPR Campus Guarapuava BR-85053525 Guarapuava Brazil Hosp Clin Barcelona Endocrinol & Nutr Dept Diabet Unit Barcelona 08036 Spain Ctr Invest Biomed Red Diabet & Enfermedades Metab Madrid 28029 Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia 46022 Spain Univ Girona Dept Informat Matemat Aplicada & Estadist Girona 17003 Spain Univ Girona Inst Informat & Aplicac Girona 17003 Spain

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, compositional data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients' outcomes and then tailor their therapies.

关键词： type 1 diabetes compositional data analysis decision support system diabetes management probabilistic model of transition

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Analysing cause-specific mortality trends using compositional functional data analysis

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2022年第1期185卷 61-83页

作者： Stefanucci, Marco Mazzuco, Stefano Univ Padua Padua Italy

We study the dynamics of cause-specific mortality rates among countries by considering them as compositions of functions. We develop a novel framework for such data structure, with particular attention to functional PCA. The application of this method to a subset of the WHO mortality database reveals the main modes of variation of cause-specific rates over years for men and women and enables us to perform clustering in the projected subspace. The results give many insights of the ongoing trends, only partially explained by past literature, that the considered countries are undergoing. We are also able to show the different evolution of cause of death undergone by men and women: for example, we can see that while lung cancer incidence is stabilizing for men, it is still increasing for women.

关键词： causes of death compositional data analysis functional data analysis mortality

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Application of compositional data analysis in geochemical exploration for concealed deposits: A case study of Ashele copper-zinc deposit, Xinjiang, China

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APPLIED GEOCHEMISTRY 2021年 130卷 104997-104997页

作者： Zheng, Chaojie Liu, Panfeng Luo, Xianrong Wen, Meilan Huang, Wenbin Liu, Gang Wu, Xiaogui Chen, Zesu Albanese, Stefano Guilin Univ Technol Coll Earth Sci 319 Yanshan St Guilin 541006 Peoples R China Univ Naples Federico II Dept Earth Environm & Resources Sci Naples Italy China Natl Geol Explorat Ctr Bldg Mat Ind Guangxi Branch Guilin 541002 Peoples R China Inst Geol & Mineral Explorat Ningxia Hui Autonomo Yinchuan 750021 Ningxia Peoples R China Xinjiang Bur Geol & Mineral Explorat & Dev Urumqi 830000 Xinjiang Peoples R China Xinjiang Habahe Asher Copper Co Ltd Altay 836700 Xinjiang Peoples R China

One of the top priorities of ore body exploration geochemistry is to effectively identifying "anomalies" and separating them from "background" in geochemical data analyses, during which an accurate understanding of geochemical distribution patterns of elements and anomaly separation has been the focus of ore body hunting. However, geochemical data are compositional data with closure effect and require necessary logratio transformation to open closed data for the following statistical analysis. The invested dataset of 13 elements derived from 2264 surface rock samples from the northwestern Ashele mining area was processed and respectively analyzed as the raw data, logarithmically-transformed data, and isometric logratio (ilr) transformed data with exploratory data analysis (EDA) technology. Of which, the ilr transformed data yields an ideal data structure and scale distribution. The data then was analyzed with principal component analysis (PCA) and robust principal component analysis (RPCA), along with the compositional biplots and the first principal component score maps to identify the spatial distribution of element assemblages. After that, a comparative study of the geochemical distribution patterns based on the raw data of mineralized elements and the principal component scores based on ilr transformation data is conducted to explore the connection with mineralization. As a result, four prospecting prediction targets of two types are delineated in the study area.

关键词： compositional data analysis Peripheral orebody prediction Ashele area in Xinjiang

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Cross-sectional and prospective associations of sleep, sedentary and active behaviors with mental health in older people: a compositional data analysis from the Seniors-ENRICA-2 study

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INTERNATIONAL JOURNAL OF BEHAVIORAL NUTRITION AND PHYSICAL ACTIVITY 2021年第1期18卷 124-124页

作者： Cabanas-Sanchez, Veronica Esteban-Cornejo, Irene Garcia-Esquinas, Esther Ortola, Rosario Ara, Ignacio Rodriguez-Gomez, Irene Chastin, Sebastien F. M. Rodriguez-Artalejo, Fernando Martinez-Gomez, David CEI UAM CSIC IMDEA Food Inst Madrid Spain Univ Granada Fac Sport Sci Dept Phys & Sports Educ PROFITH PROmoting FITness & Hlth Phys Act Res Grp Granada Spain Univ Autonoma Madrid Dept Prevent Med & Publ Hlth Madrid Spain Idipaz Madrid Spain CIBER Epidemiol & Publ Hlth CIBERESP Madrid Spain Univ Castilla La Mancha GENUD Toledo Res Grp Toledo Spain Biomed Res Networking Ctr Frailty & Hlth Aging CI Madrid Spain Glasgow Caledonian Univ Inst Appl Hlth Res Sch Hlth & Life Sci Glasgow Lanark Scotland Univ Ghent Dept Movement & Sport Sci Ghent Belgium

Background Most studies on the effects of sleep, sedentary behavior (SB), and physical activity (PA) on mental health did not account for the intrinsically compositional nature of the time spent in several behaviors. Thus, we examined the cross-sectional and prospective associations of device-measured compositional time in sleep, SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) with depression symptoms, loneliness, happiness, and global mental health in older people (>= 65 years). Methods data were taken from the Seniors-ENRICA-2 study, with assessments in 2015-2017 (wave 0) and 2018-2019 (wave 1). Time spent in sleep, SB, LPA and MVPA was assessed by wrist-worn accelerometers. Depression symptoms, loneliness, happiness, and global mental health were self-reported using validated questionnaires. Analyses were performed using a compositional data analysis (CoDA) paradigm and adjusted for potential confounders. Results In cross-sectional analyses at wave 0 (n = 2489), time-use composition as a whole was associated with depression and happiness (all p < 0.01). The time spent in MVPA relative to other behaviors was beneficially associated with depression (gamma = -0.397, p < 0.001), loneliness (gamma = -0.124, p = 0.017) and happiness (gamma = 0.243, p < 0.001). Hypothetically, replacing 30-min of Sleep, SB or LPA with MVPA was beneficially cross-sectionally related with depression (effect size [ES] ranged -0.326 to -0.246), loneliness (ES ranged -0.118 to -0.073), and happiness (ES ranged 0.152 to 0.172). In prospective analyses (n = 1679), MVPA relative to other behaviors at baseline, was associated with favorable changes in global mental health (gamma = 0.892, p = 0.049). We observed a beneficial prospective effect on global mental health when 30-min of sleep (ES = 0.521), SB (ES = 0.479) or LPA (ES = 0.755) were theoretically replaced for MVPA. Conclusions MVPA was cross-sectionally related with reduced depression symptoms and loneliness and elevated level of h

关键词： Depression Loneliness Happiness Elderly compositional data analysis Prospective

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compositional data analysis of Glucose Profiles of Type 1 Diabetes Patients 12

Compositional Data Analysis of Glucose Profiles of Type 1 Di...

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12th International-Federation-of-Automatic-Control (IFAC) Symposium on Dynamics and Control of Process Systems including Biosystems (DYCOPS-CAB)

作者： Biagi, Lyvia Bertachi, Arthur Antoni Martin-Fernandez, Josep Vehi, Josep Univ Girona Girona Spain Fed Univ Technol Parana UTFPR Guarapuava Brazil Ctr Invest Biomed Red Diabet & Enfermedades Metab Barcelona Spain

Time spent in different glucose ranges indicate the occurrence of adverse events and measure the quality of glucose control in type one diabetes (T1D) patients. This work proposes a compositional data (CoDa) approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles limited to 6-h duration were analyzed at four different times of the day. These glucose profiles were distributed into time spent in five glucose ranges, which determine the composition. The log-ratio coordinates of the compositions were categorized through a clustering algorithm, which later made possible the obtainment of a linear model that should be used to predict the category of a 6-h period in different times of day. Leave-one-out cross-validation was performed, achieving an average above 90% of correct classification. A probabilistic model of transition between the category of the past 6-h of glucose to the category of the future 6-h period was obtained. Results show that the CoDa approach not only works as new analysis tool and is suitable for the categorization of glucose profiles, but also is a complementary tool for the prediction of different categories of glucose control. This prediction could assist patients to take correction measures in advance to adverse situations. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： compositional data analysis Type 1 Diabetes Biomedical Systems Continuous Glucose Monitoring Decision Support System

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Relating starter cultures to volatile profile and potential markers in green Spanish-style table olives by compositional data analysis

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FOOD MICROBIOLOGY 2021年 94卷 103659-103659页

作者： Garrido-Fernandez, Antonio Benitez-Cabello, Antonio Rodriguez-Gomez, Francisco Jimenez-Diaz, Rufino Noe Arroyo-Lopez, Francisco Morales, M. Lourdes Campus Univ Pablo de Olavide Dept Biotecnol Alimentos CSIC Inst Grasa Bldg 46Ctra Sevilla UtreraKm 1 Seville 410013 Spain Univ Seville Fac Farm Dept Nutr & Bromatol Toxicol & Med Legal Area Nutr & Bromatol C-P Garcia Gonzalez 2 Seville 41012 Spain

This work relates native lactic acid bacteria (LAB) (Lactobacillus pentosus LPG1, L. pentosus Lp13, and Lactobacillus plantarum Lpl15) and yeast (Wickerhamomyces anomalus Y12) starters to the volatile components (VOCs) produced in green Spanish-style table olives. For this aim, the VOC profile was considered as compositional data (CoDa). The CoDa analysis generated new information on the relationship among inocula and VOCs through the tetrahedral plot, CoDa-biplot, variation array matrix, and CoDa dendrogram. The ilr (which includes pivot) coordinates (Euclidean space) from VOCs produced more reliable starters' clustering than the original data. The potential VOC markers, identified by a test based on the pairwise comparison of the logratio variation arrays from the whole data set and the individual groups, were (starters in the parenthesis): 2-phenylethyl acetate (LPG1, Y12, Y12 + LAB), methanol (Lpl15), cis-2-penten-1-ol (LPG1, Y12, Y12 + LAB), 2-methyl-3-hexanol (LPG1, Y12), U (non-identified) C (m/z 83-112-97) (Y12) and UF (m/z 95-154-110) (LPG1, Y12 + LAB). Besides, some VOCs were partial/totally inhibited by specific starters: 2-methyl-1-propanol (Lp13, Y12 + LAB), 2 phenyl ethanol (Lp13), furfuryl methyl ether (Y12 + LAB), purpurocatechol (Y12, Y12 + LAB), 4-ethyl guaiacol (Lp13, Lpl15), 4-ethyl phenol (Lpl15), 5-tert-butylpyrogallol (Lp13, Lpl15), and UE (m/z 111-198) (Lp13). A better understanding of the relationship between starters and their VOC may facilitate modelling the flavour and quality of Spanish-style green table olive fermentations.

关键词： Fermentation Inoculation Segregation Clustering Classification compositional data analysis

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A study on prospective associations between adiposity and 7-year changes in movement behaviors among older women based on compositional data analysis

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BMC GERIATRICS 2021年第1期21卷 203-203页

作者： Pelclova, Jana Stefelova, Nikola Olds, Timothy Dumuid, Dorothea Hron, Karel Chastin, Sebastien Pedisic, Zeljko Palacky Univ Olomouc Inst Act Lifestyle Fac Phys Culture Tr Miru 117 Olomouc 77111 Czech Republic Palacky Univ Olomouc Fac Sci Olomouc Czech Republic Univ South Australia Alliance Res Exercise Nutr & Act ARENA Adelaide SA Australia Glasgow Caledonian Univ Sch Hlth & Life Sci Glasgow Lanark Scotland Victoria Univ Inst Hlth & Sport Melbourne Vic Australia

IntroductionIt is unclear whether adiposity leads to changes in movement behaviors, and there is a lack of compositional analyses of longitudinal data which focus on these associations. Using a compositional approach, this study aimed to examine the associations between baseline adiposity and 7-year changes in physical activity (PA) and sedentary behavior (SB) among elderly women. We also explored the longitudinal associations between change in adiposity and change in movement-behavior *** longitudinal study included 176 older women (mean baseline age 62.8 (4.1) years) from Central Europe. Movement behavior was assessed by accelerometers and adiposity was measured by bioelectrical impedance analysis at baseline and follow-up. A set of multivariate least-squares regression analyses was used to examine the associations of baseline adiposity and longitudinal changes in adiposity as explanatory variables with longitudinal changes in a 3-part movement-behavior composition consisting of SB, light PA (LPA) and moderate-to-vigorous PA (MVPA) as outcome *** significant associations were found between baseline adiposity and longitudinal changes in the movement-behavior composition (p >0.05). We found significant associations of changes in body mass index (BMI) and fat mass percentage (FM%) with changes in the movement-behavior composition. An increase in BMI was associated with an increase of SB at the expense of LPA and MVPA (beta =0.042, p =0.009) and with a decrease of MVPA in favor of SB and LPA (beta =-0.059, p =0.037). An increase in FM% was significantly associated only with an increase of SB at the expense of LPA and MVPA (beta =0.019, p =0.031).ConclusionsThis study did not support the assumption that baseline adiposity is associated with longitudinal changes in movement behaviors among elderly women, but we found evidence for change-to-change associations, suggesting that a 7-year increase in adiposity is associated with a concurr

关键词： compositional data analysis Time-use epidemiology Fatness Obesity Sitting Exercise

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Comparative geochemical study between the tap waters and the bottled mineral waters in Calabria (Southern Italy) by compositional data analysis (CoDA) developments

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APPLIED GEOCHEMISTRY 2019年 107卷 19-33页

作者： Apollaro, Carmine Buccianti, Antonella Vespasiano, Giovanni Varde, Massimiliano Fuoco, Ilaria Barca, Donatella Bloise, Andrea Miriello, Domenico Cofone, Franco Servidio, Alessandro De Rosa, Rosanna Univ Calabria UniCAL Dept Biol Ecol & Earth Sci DiBEST Via P Bucci 4Cubo 15B I-87036 Arcavacata Di Rende CS Italy Univ Florence UniFI Dept Earth Sci Via G La Pira 4 I-50121 Florence Italy Inst Geosci & Earth Resources CNR IGG Via G La Pira 4 I-50121 Florence Italy Inst Dynam Environm Proc CNR IDPA Via Torino 155 I-30172 Venice Mestre Italy Univ Ferrara UniFEJ Dept Chem & Pharmaceut Sci DipSCF Via Luigi Borsari 46 I-44121 Ferrara Italy Inst Nanotechnol CNR NANOTEC Via P Bucci 4Cubo 31C I-87036 Arcavacata Di Rende CS Italy

This work focuses on comparing the inorganic content of tap and bottled mineral waters in the Calabria region (Southern Italy) performed by using robust compositional data analysis (CoDA) methods and related new developments associated with a classical graphical-numerical approach. Thirty-one samples of tap waters scattered throughout the Calabrian territory and twenty-one bottled natural mineral waters of various Calabrian brands were analyzed for major and trace inorganic components. In addition, a very sensitive method based on cold vapour atomic fluorescence spectrometry (CV-AFS) has been employed to determine total mercury (Hg-T) concentrations. compositional data analysis was used to identify the main interrelationships among physico-chemical parameters, highlighting the differences between the tap waters and the bottled mineral waters. Some elements, such as Al, Mn, Fe, Zn, and Pb resulted particularly enriched in the tap waters. This group of enriched elements was probably controlled by corrosion processes from metal pipes, asking for more monitoring on the state of the public distribution water network. Li, Ca, Cr, Ni, and Cd, on the other hand, appear to be mostly contributed by natural sources affecting the overall variability of the bottled mineral waters. In this context, Cr, Cu, Hg, and Pb are mainly enriched in the sparkling bottled mineral waters. Innovative compositional indices did not highlight great differences from the Maximum Allowed Concentrations (MAC) in the two types of water, so that for the health of the consumer it is indifferent to drink tap or bottled mineral water. The results represent a fundamental base to develop monitoring plans in time to check for the maintenance of quality standards.

关键词： Drinking waters Natural mineral waters Major ions Trace elements Mercury Geochemistry compositional data analysis Southern Italy

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Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis

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BIOSTATISTICS 2019年第4期20卷 698-713页

作者： Tang, Zheng-Zheng Chen, Guanhua Univ Wisconsin Dept Biostat & Med Informat Madison WI 53715 USA Wisconsin Inst Discovery Madison WI 53715 USA

There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes these limitations in modeling multivariate taxon counts. Based on this distribution, we propose a ZIGDM regression model to link microbial abundances to covariates (e.g. disease status) and develop a fast expectation-maximization algorithm to efficiently estimate parameters in the model. The derived tests enable us to reveal rich patterns of variation in microbial compositions including differential mean and dispersion. The advantages of the proposed methods are demonstrated through simulation studies and an analysis of a gut microbiome dataset.

关键词： compositional data analysis Differential abundance Hierarchical model Microbiome Score test Zero-inflated model

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Individual categorisation of glucose profiles using compositional data analysis

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STATISTICAL METHODS IN MEDICAL RESEARCH 2019年第12期28卷 3550-3567页

作者： Biagi, Lyvia Bertachi, Arthur Gimenez, Marga Conget, Ignacio Bondia, Jorge Antoni Martin-Fernandez, Josep Vehi, Josep Univ Girona Inst Informat & Aplicac Girona Spain Fed Univ Technol Parana UTFPR Guarapuava Brazil Hosp Clin Univ Endocrinol & Nutr Dept Diabet Unit IDIBAPS Inst Invest Biomed August Pi & Sunyer Barcelona Spain Ctr Invest Biomed Red Diabet & Enfermedades Metab Madrid Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Valencia Spain Univ Girona Dept Informat Matemat Aplicada & Estadist Girona Spain

The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information;therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control.

关键词： Continuous glucose monitoring type 1 diabetes compositional data analysis decision support system diabetes management

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