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Musacchio N, Zilich R, Masi D, Baccetti F, Nreu B, Bruno Giorda C, Guaita G, Morviducci L, Muselli M, Ozzello A, Pisani F, Ponzani P, Rossi A, Santin P, Verda D, Di Cianni G, Candido R. A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy. Int J Med Inform 2024; 190:105550. [PMID: 39059083 DOI: 10.1016/j.ijmedinf.2024.105550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 07/07/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024]
Abstract
AIMS This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy. METHODS Employing the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into "inertia-NO" (20,067 patients with prompt intensification) and "inertia-YES" (13,029 patients without timely intensification). RESULTS The LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations. CONCLUSIONS Our analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns.
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Affiliation(s)
- Nicoletta Musacchio
- AMD-AI National Group Coordinator, UOS Integrating Primary and Specialist Care, ASST Nord Milano, Via Filippo Carcano 17, 20149 Milan, Italy
| | - Rita Zilich
- Mix-x Partner, Via Circonvallazione 5, Ivrea (TO), Italy.
| | - Davide Masi
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy.
| | - Fabio Baccetti
- ASL Nordovest Toscana. ASL Nordovest, Massa Carrara (MS), Italy.
| | - Besmir Nreu
- Diabetology Unit, Careggi Hospital, Largo G.A. Brambilla, 3, 50134 Florence (FI), Italy.
| | | | - Giacomo Guaita
- Diabetes and Endocrinology UNIT ASL SULCIS, Carbonia-Iglesias, Italy.
| | - Lelio Morviducci
- UOC Diabetologia e Dietologia, Ospedale S. Spirito - ASL Roma 1, Borgo Santo Spirito, Roma (RM), Italy.
| | - Marco Muselli
- Rulex Innovation Labs, Rulex Inc, Via Felice Romani 9/2, 16122 Genoa (GE), Italy.
| | - Alessandro Ozzello
- AMD regional past President, Gruppo nazionale AI AMD, Bruino, Torino (TO), Italy
| | | | - Paola Ponzani
- Diabetes and Metabolic Disease Unit ASL 4 Liguria, Chiavari (GE), Italy.
| | - Antonio Rossi
- IRCCS Ospedale Galeazzi-Sant'Ambrogio, 20149 Milan, Italy; Department of Biomedical and Clinical Sciences, Università di Milano, Milan, Italy.
| | | | - Damiano Verda
- Rulex Innovation Labs, Rulex Inc, Via Felice Romani 9/2, 16122 Genoa (GE), Italy.
| | - Graziano Di Cianni
- AMD Past President, Diabetes and Metabolic Diseases Unit, Health Local Unit Nord-West Tuscany, Livorno Hospital, Pad. 4 Viale Alfieri 36, Livorno (LI), Italy.
| | - Riccardo Candido
- AMD New President, Azienda Sanitaria Universitaria Giuliano Isontina, 34128 Trieste, Italy.
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Tuccinardi D, Watanabe M, Masi D, Monte L, Bonifazi Meffe L, Cavallari I, Nusca A, Maddaloni E, Gnessi L, Napoli N, Manfrini S, Grigioni F. Rethinking weight loss treatments as cardiovascular medicine in obesity, a comprehensive review. Eur J Prev Cardiol 2024:zwae171. [PMID: 38833329 DOI: 10.1093/eurjpc/zwae171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/27/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
The global escalation of obesity has made it a worldwide health concern, notably as a leading risk factor for cardiovascular disease (CVD). Extensive evidence corroborates its association with a range of cardiac complications, including coronary artery disease, heart failure, and heightened vulnerability to sudden cardiac events. Additionally, obesity contributes to the emergence of other cardiovascular risk factors including dyslipidaemia, type 2 diabetes, hypertension, and sleep disorders, further amplifying the predisposition to CVD. To adequately address CVD in patients with obesity, it is crucial to first understand the pathophysiology underlying this link. We herein explore these intricate mechanisms, including adipose tissue dysfunction, chronic inflammation, immune system dysregulation, and alterations in the gut microbiome.Recent guidelines from the European Society of Cardiology underscore the pivotal role of diagnosing and treating obesity to prevent CVD. However, the intricate relationship between obesity and CVD poses significant challenges in clinical practice: the presence of obesity can impede accurate CVD diagnosis while optimizing the effectiveness of pharmacological treatments or cardiac procedures requires meticulous adjustment, and it is crucial that cardiologists acknowledge the implications of excessive weight while striving to enhance outcomes for the vulnerable population affected by obesity. We, therefore, sought to overcome controversial aspects in the clinical management of heart disease in patients with overweight/obesity and present evidence on cardiometabolic outcomes associated with currently available weight management interventions, with the objective of equipping clinicians with an evidence-based approach to recognize and address CVD risks associated with obesity.
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Affiliation(s)
- Dario Tuccinardi
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Endocrinology and Diabetology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Mikiko Watanabe
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Davide Masi
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lavinia Monte
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Endocrinology and Diabetology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Luigi Bonifazi Meffe
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Endocrinology and Diabetology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Ilaria Cavallari
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Cardiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Annunziata Nusca
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Cardiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Ernesto Maddaloni
- Department of Experimental Medicine, Sapienza University of Rome, Viale Regina Elena 324, 00161 Rome, Italy
| | - Lucio Gnessi
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Nicola Napoli
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Endocrinology and Diabetology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Silvia Manfrini
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Endocrinology and Diabetology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
| | - Francesco Grigioni
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200-00128 Roma, Italy
- Research Unit of Cardiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21-00128 Roma, Italy
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3
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Agius R, Pace NP, Fava S. Phenotyping obesity: A focus on metabolically healthy obesity and metabolically unhealthy normal weight. Diabetes Metab Res Rev 2024; 40:e3725. [PMID: 37792999 DOI: 10.1002/dmrr.3725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/23/2023] [Accepted: 08/11/2023] [Indexed: 10/06/2023]
Abstract
Over the past 4 decades, research has shown that having a normal body weight does not automatically imply preserved metabolic health and a considerable number of lean individuals harbour metabolic abnormalities typically associated with obesity. Conversely, excess adiposity does not always equate with an abnormal metabolic profile. In fact, evidence exists for the presence of a metabolically unhealthy normal weight (MUHNW) and a metabolically healthy obese (MHO) phenotype. It has become increasingly recognised that different fat depots exert different effects on the metabolic profile of each individual by virtue of their location, structure and function, giving rise to these different body composition phenotypes. Furthermore, other factors have been implicated in the aetiopathogenesis of the body composition phenotypes, including genetics, ethnicity, age and lifestyle/behavioural factors. Even though to date both MHO and MUHNW have been widely investigated and documented in the literature, studies report different outcomes on long-term cardiometabolic morbidity and mortality. Future large-scale, observational and population-based studies are required for better profiling of these phenotypes as well as to further elucidate the pathophysiological role of the adipocyte in the onset of metabolic disorders to allow for better risk stratification and a personalised treatment paradigm.
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Affiliation(s)
- Rachel Agius
- University of Malta Medical School, Msida, Malta
- Mater Dei Hospital, Msida, Malta
| | | | - Stephen Fava
- University of Malta Medical School, Msida, Malta
- Mater Dei Hospital, Msida, Malta
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Su Z, Efremov L, Mikolajczyk R. Differences in the levels of inflammatory markers between metabolically healthy obese and other obesity phenotypes in adults: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis 2024; 34:251-269. [PMID: 37968171 DOI: 10.1016/j.numecd.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/28/2023] [Accepted: 09/04/2023] [Indexed: 11/17/2023]
Abstract
AIMS The aim of this study was to systematically review and analyze differences in the levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) comparing metabolically healthy but obese (MHO) with metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO) subjects. DATA SYNTHESIS We searched PubMed, Embase, Web of Science, and Scopus for studies that matched the relevant search terms. Differences in inflammatory marker levels between MHO and the other three phenotypes were pooled as standardized mean differences (SMD) or differences of medians (DM) using a random-effects model. We included 91 studies reporting data on 435,007 individuals. The CRP levels were higher in MHO than in MHNO subjects (SMD = 0.63, 95% CI: 0.49, 0.76; DM = 0.83 mg/L, 95% CI: 0.56, 1.11). The CRP levels were higher in MHO than in MUNO subjects (SMD = 0.16, 95% CI: 0.05, 0.28; DM = 0.39 mg/L, 95% CI: 0.09, 0.69). The CRP levels were lower in MHO than in MUO individuals (SMD = -0.43, 95% CI: -0.54, -0.31; DM = -0.82 mg/L, 95% CI: -1.16, -0.48). The IL-6 levels in MHO were higher than in MHNO while lower than in MUO subjects. The TNF-α levels in MHO were higher than in MHNO individuals. CONCLUSIONS This review provides evidence that CRP levels in MHO are higher than in MHNO and MUNO subjects but lower than in MUO individuals. Additionally, IL-6 levels in MHO are higher than in MHNO but lower than in MUO subjects, and TNF-α levels in MHO are higher than in MHNO individuals. SYSTEMATIC REVIEW REGISTRATION PROSPERO number: CRD42021234948.
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Affiliation(s)
- Zhouli Su
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, D-06112 Halle (Saale), Germany
| | - Ljupcho Efremov
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, D-06112 Halle (Saale), Germany; Department of Radiation Oncology, Martin-Luther-University Halle-Wittenberg, D-06120 Halle (Saale), Germany
| | - Rafael Mikolajczyk
- Institute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, D-06112 Halle (Saale), Germany.
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Masi D, Zilich R, Candido R, Giancaterini A, Guaita G, Muselli M, Ponzani P, Santin P, Verda D, Musacchio N. Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group. J Clin Med 2023; 12:4095. [PMID: 37373787 DOI: 10.3390/jcm12124095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/25/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing.
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Affiliation(s)
- Davide Masi
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | | | - Riccardo Candido
- Associazione Medici Diabetologi, Giuliano Isontina University Health Service, 34149 Trieste, Italy
| | - Annalisa Giancaterini
- UOSD Diabetology, Department of Exchange and Nutrition Diseases, Brianza Health Service, Pio XI Hospital, 20833 Desio, Italy
| | - Giacomo Guaita
- Diabetes and Endocrinology Unit, ASL SULCIS, 9016 Iglesias, Italy
| | - Marco Muselli
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
| | - Paola Ponzani
- Diabetes and Metabolic Disease Unit, ASL 4 Liguria, 16043 Chiavari, Italy
| | | | - Damiano Verda
- Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy
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Musacchio N, Zilich R, Ponzani P, Guaita G, Giorda C, Heidbreder R, Santin P, Di Cianni G. Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes. J Diabetes 2023; 15:224-236. [PMID: 36889912 PMCID: PMC10036260 DOI: 10.1111/1753-0407.13361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 12/08/2022] [Accepted: 01/09/2023] [Indexed: 03/10/2023] Open
Abstract
AIMS The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia. METHODS Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients seen at clinics within the Italian Association of Medical Diabetologists between 2005-2019 were analyzed using logic learning machine (LLM), a "clear box" ML technique. Data were subjected to a first stage of modeling to allow ML to automatically select the most relevant factors related to inertia, and then four further modeling steps individuated key variables that discriminated the presence or absence of inertia. RESULTS The LLM model revealed a key role for average glycated hemoglobin (HbA1c) threshold values correlated with the presence or absence of insulin therapeutic inertia with an accuracy of 0.79. The model indicated that a patient's dynamic rather than static glycemic profile has a greater effect on therapeutic inertia. Specifically, the difference in HbA1c between two consecutive visits, what we call the HbA1c gap, plays a crucial role. Namely, insulin therapeutic inertia is correlated with an HbA1c gap of <6.6 mmol/mol (0.6%), but not with an HbA1c gap of >11 mmol/mol (1.0%). CONCLUSIONS The results reveal, for the first time, the interrelationship between a patient's glycemic trend defined by sequential HbA1c measurements and timely or delayed initiation of insulin therapy. The results further demonstrate that LLM can provide insight in support of evidence-based medicine using real world data.
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Affiliation(s)
| | | | - Paola Ponzani
- Diabetes and Endocrinology UnitLocal Health Autlhority 4 ChiavariChiavariItaly
| | | | - Carlo Giorda
- Diabetes and Endocrinology UnitASL TO5ChieriItaly
| | | | | | - Graziano Di Cianni
- USL Tuscany Northwest Location Livorno, Diabetes and Metabolic DiseaseLivornoItaly
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Masi D, Gangitano E, Criniti A, Ballesio L, Anzuini A, Marino L, Gnessi L, Angeloni A, Gandini O, Lubrano C. Obesity-Associated Hepatic Steatosis, Somatotropic Axis Impairment, and Ferritin Levels Are Strong Predictors of COVID-19 Severity. Viruses 2023; 15:v15020488. [PMID: 36851702 PMCID: PMC9968194 DOI: 10.3390/v15020488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
The full spectrum of SARS-CoV-2-infected patients has not yet been defined. This study aimed to evaluate which parameters derived from CT, inflammatory, and hormonal markers could explain the clinical variability of COVID-19. We performed a retrospective study including SARS-CoV-2-infected patients hospitalized from March 2020 to May 2021 at the Umberto I Polyclinic of Rome. Patients were divided into four groups according to the degree of respiratory failure. Routine laboratory examinations, BMI, liver steatosis indices, liver CT attenuation, ferritin, and IGF-1 serum levels were assessed and correlated with severity. Analysis of variance between groups showed that patients with worse prognoses had higher BMI and ferritin levels, but lower liver density, albumin, GH, and IGF-1. ROC analysis confirmed the prognostic accuracy of IGF-1 in discriminating between patients who experienced death/severe respiratory failure and those who did not (AUC 0.688, CI: 0.587 to 0.789, p < 0.001). A multivariate analysis considering the degrees of severity of the disease as the dependent variable and ferritin, liver density, and the standard deviation score of IGF-1 as regressors showed that all three parameters were significant predictors. Ferritin, IGF-1, and liver steatosis account for the increased risk of poor prognosis in COVID-19 patients with obesity.
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Affiliation(s)
- Davide Masi
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | - Elena Gangitano
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | - Anna Criniti
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | - Laura Ballesio
- Department of Radiology, Anatomo–Pathology and Oncology, Sapienza University of Rome, 00185 Rome, Italy
| | - Antonella Anzuini
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | - Luca Marino
- Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00185 Rome, Italy
- Emergency Medicine Unit, Department of Emergency-Acceptance, Critical Areas and Trauma, Policlinico “Umberto I”, 00161 Rome, Italy
| | - Lucio Gnessi
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
| | - Antonio Angeloni
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
- Emergency Medicine Unit, Department of Emergency-Acceptance, Critical Areas and Trauma, Policlinico “Umberto I”, 00161 Rome, Italy
| | - Orietta Gandini
- Department of Molecular Medicine, Sapienza University of Rome, 00161 Rome, Italy
| | - Carla Lubrano
- Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
- Correspondence:
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Ernesti I, Baratta F, Watanabe M, Risi R, Camajani E, Persichetti A, Tuccinardi D, Mariani S, Lubrano C, Genco A, Spera G, Gnessi L, Basciani S. Predictors of weight loss in patients with obesity treated with a Very Low-Calorie Ketogenic Diet. Front Nutr 2023; 10:1058364. [PMID: 36761216 PMCID: PMC9905243 DOI: 10.3389/fnut.2023.1058364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The Very Low-Calorie Ketogenic Diet (VLCKD) has emerged as a safe and effective intervention for the management of metabolic disease. Studies examining weight loss predictors are scarce and none has investigated such factors upon VLCKD treatment. Among the molecules involved in energy homeostasis and, more specifically, in metabolic changes induced by ketogenic diets, Fibroblast Growth Factor 21 (FGF21) is a hepatokine with physiology that is still unclear. Methods We evaluated the impact of a VLCKD on weight loss and metabolic parameters and assessed weight loss predictors, including FGF21. VLCKD is a severely restricted diet (<800 Kcal/die), characterized by a very low carbohydrate intake (<50 g/day), 1.2-1.5 g protein/kg of ideal body weight and 15-30 g of fat/day. We treated 34 patients with obesity with a VLCKD for 45 days. Anthropometric parameters, body composition, and blood and urine chemistry were measured before and after treatment. Results We found a significant improvement in body weight and composition and most metabolic parameters. Circulating FGF21 decreased significantly after the VLCKD [194.0 (137.6-284.6) to 167.8 (90.9-281.5) p < 0.001] and greater weight loss was predicted by lower baseline FGF21 (Beta = -0.410; p = 0.012), male sex (Beta = 0.472; p = 0.011), and central obesity (Beta = 0.481; p = 0.005). Discussion VLCKD is a safe and effective treatment for obesity and obesity related metabolic derangements. Men with central obesity and lower circulating FGF21 may benefit more than others in terms of weight loss obtained following this diet. Further studies investigating whether this is specific to this diet or to any caloric restriction are warranted.
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Affiliation(s)
- Ilaria Ernesti
- Surgical Endoscopy Unit, Department of Surgical Sciences, Sapienza University of Rome, Rome, Italy,*Correspondence: Ilaria Ernesti,
| | - Francesco Baratta
- Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy
| | - Mikiko Watanabe
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Renata Risi
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Elisabetta Camajani
- Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Open University, Rome, Italy
| | - Agnese Persichetti
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Dario Tuccinardi
- Department of Endocrinology and Diabetes, University Campus Bio-Medico of Rome, Rome, Italy
| | - Stefania Mariani
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Carla Lubrano
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Alfredo Genco
- Surgical Endoscopy Unit, Department of Surgical Sciences, Sapienza University of Rome, Rome, Italy
| | - Giovanni Spera
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Lucio Gnessi
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Sabrina Basciani
- Section of Medical Pathophysiology, Food Science and Endocrinology, Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
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Martínez JA, Alonso-Bernáldez M, Martínez-Urbistondo D, Vargas-Nuñez JA, Ramírez de Molina A, Dávalos A, Ramos-Lopez O. Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases. World J Gastroenterol 2022; 28:6230-6248. [PMID: 36504554 PMCID: PMC9730439 DOI: 10.3748/wjg.v28.i44.6230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 10/07/2022] [Accepted: 11/17/2022] [Indexed: 11/25/2022] Open
Abstract
The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.
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Affiliation(s)
- J Alfredo Martínez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Marta Alonso-Bernáldez
- Precision Nutrition and Cardiometabolic Health, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | | | - Juan A Vargas-Nuñez
- Servicio de Medicina Interna, Hospital Universitario Puerta de Hierro Majadahonda, Madrid 28222, Majadahonda, Spain
| | - Ana Ramírez de Molina
- Molecular Oncology and Nutritional Genomics of Cancer, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Alberto Dávalos
- Laboratory of Epigenetics of Lipid Metabolism, Madrid Institute of Advanced Studies-Food Institute, Madrid 28049, Spain
| | - Omar Ramos-Lopez
- Medicine and Psychology School, Autonomous University of Baja California, Tijuana 22390, Baja California, Mexico
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