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Lithovius R, Mutter S, Parente EB, Mäkinen VP, Valo E, Harjutsalo V, Groop PH. Medication profiling in women with type 1 diabetes highlights the importance of adequate, guideline-based treatment in low-risk groups. Sci Rep 2023; 13:17893. [PMID: 37857707 PMCID: PMC10587128 DOI: 10.1038/s41598-023-44695-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 10/11/2023] [Indexed: 10/21/2023] Open
Abstract
Effective treatment may prevent kidney complications, but women might be underprescribed. Novel, data-driven insights into prescriptions and their relationship with kidney health in women with type 1 diabetes may help to optimize treatment. We identified six medication profiles in 1164 women from the FinnDiane Study with normal albumin excretion rate based on clusters of their baseline prescription data using a self-organizing map. Future rapid kidney function decline was defined as an annual estimated glomerular filtration rate (eGFR) loss > 3 ml/min/1.73 m2 after baseline. Two profiles were associated with future decline: Profile ARB with the highest proportion of angiotensin receptor blockers (odds ratio [OR] 2.75, P = 0.02) and highly medicated women in profile HighMed (OR 2.55, P = 0.03). Compared with profile LowMed (low purchases of all), profile HighMed had worse clinical characteristics, whereas in profile ARB only systolic blood pressure was elevated. Importantly, the younger women in profile ARB with fewer kidney protective treatments developed a rapid decline despite otherwise similar baseline characteristics to profile ACE & Lipids (the highest proportions of ACE inhibitors and lipid-modifying agents) without a future rapid decline. In conclusion, medication profiles identified different future eGFR trajectories in women with type 1 diabetes revealing potential treatment gaps for younger women.
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Affiliation(s)
- Raija Lithovius
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Stefan Mutter
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Erika B Parente
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Ville-Petteri Mäkinen
- Systems Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Erkka Valo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Valma Harjutsalo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
| | - Per-Henrik Groop
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland.
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC, Australia.
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Biomedicum Helsinki, Helsinki University, Haartmaninkatu 8 [C318b], PO Box 63, 00014, Helsinki, Finland.
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Zhang H, Wei X, Pan J, Chen X, Sun X. Anemia and frailty in the aging population: implications of dietary fiber intake (findings of the US NHANES from 2007-2018). BMC Geriatr 2023; 23:634. [PMID: 37805459 PMCID: PMC10560428 DOI: 10.1186/s12877-023-04352-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Frailty has long been seen as an indicator of reduced physical functions in the elderly, which may be caused by a variety of chronic illnesses or cancerous tumors. Dietary fiber was connected with anemia and frailty, whereas it was uncertain if dietary fiber consumption modifies the impact of anemia on frailty in elderly adults. METHODS We performed a secondary analysis using older adults aged 60 years and over from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 cycles. Dietary fiber intake was estimated using two 24-h dietary recalls. Participants were dichotomized as frail or non-frail based on a modified Fried physical frailty phenotype from previous NHANES studies. The weighted logistic regression was used to estimate the odds ratio (OR) and confidence interval (CI) for the associations between hemoglobin levels and frailty at high- and low-dietary fiber intake levels. RESULTS A total of 9644 older adults were included in this study, and the weighted sample was 56,403,031, of whom 3,569,186 (6.3%) were deemed to be frail, and the remainder were deemed to be non-frail. Among the low dietary fiber intake group, higher hemoglobin was significantly associated with a lower risk of frailty (OR = 0.79, 95% CI: 0.71-0.87), and anemia was associated with an almost threefold elevated risk of frailty (OR = 3.24, 95% CI:1.98-5.29) in the fully adjusted model. However, this phenomenon was not observed in groups with high dietary fiber intake. In addition, L-shaped dose response relationship was found in the high dietary fiber intake group (P overall association < 0.001; P non-linear association = 0.076). Whereas the dose response relationship was not significant in the high dietary fiber intake group (P overall association 0.752; P non-linear association = 0.734). CONCLUSIONS Frailty was positively associated with the severity of anemia in older adults with low, but not high, dietary fiber intake. Adequate fiber intake may be an innovative dietary strategy to reduce frailty in older adults.
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Affiliation(s)
- HuanRui Zhang
- Department of Geriatric, The First Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China
| | - Xuejiao Wei
- Department of Health Services and Policy Research, Queen's University, Kingston, ON, Canada
| | - Jiani Pan
- Department of Geriatric, The First Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China
| | - XiTao Chen
- Department of Thoracic Surgery, The First Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China.
| | - XiaoDi Sun
- Department of Geriatric, The First Hospital of China Medical University, NO.155 Nanjing North Street, Heping Ward, Shenyang, 110001, China.
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Liu P, Wang Z, Liu N, Peres MA. A scoping review of the clinical application of machine learning in data-driven population segmentation analysis. J Am Med Inform Assoc 2023; 30:1573-1582. [PMID: 37369006 PMCID: PMC10436153 DOI: 10.1093/jamia/ocad111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
OBJECTIVE Data-driven population segmentation is commonly used in clinical settings to separate the heterogeneous population into multiple relatively homogenous groups with similar healthcare features. In recent years, machine learning (ML) based segmentation algorithms have garnered interest for their potential to speed up and improve algorithm development across many phenotypes and healthcare situations. This study evaluates ML-based segmentation with respect to (1) the populations applied, (2) the segmentation details, and (3) the outcome evaluations. MATERIALS AND METHODS MEDLINE, Embase, Web of Science, and Scopus were used following the PRISMA-ScR criteria. Peer-reviewed studies in the English language that used data-driven population segmentation analysis on structured data from January 2000 to October 2022 were included. RESULTS We identified 6077 articles and included 79 for the final analysis. Data-driven population segmentation analysis was employed in various clinical settings. K-means clustering is the most prevalent unsupervised ML paradigm. The most common settings were healthcare institutions. The most common targeted population was the general population. DISCUSSION Although all the studies did internal validation, only 11 papers (13.9%) did external validation, and 23 papers (29.1%) conducted methods comparison. The existing papers discussed little validating the robustness of ML modeling. CONCLUSION Existing ML applications on population segmentation need more evaluations regarding giving tailored, efficient integrated healthcare solutions compared to traditional segmentation analysis. Future ML applications in the field should emphasize methods' comparisons and external validation and investigate approaches to evaluate individual consistency using different methods.
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Affiliation(s)
- Pinyan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Ziwen Wang
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
| | - Marco Aurélio Peres
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore, Singapore
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Tanaka S, Kitamura H, Tsuruya K, Kitazono T, Nakano T. Prevalence, treatment status, and predictors of anemia and erythropoietin hyporesponsiveness in Japanese patients with non-dialysis-dependent chronic kidney disease: a cross-sectional study. Clin Exp Nephrol 2022; 26:867-879. [PMID: 35507237 DOI: 10.1007/s10157-022-02227-8] [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: 01/19/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Hyporesponsiveness to erythropoiesis-stimulating agents (ESAs) has been highlighted as a potential risk factor for cardiovascular disease in patients with chronic kidney disease (CKD). METHODS We assessed cross-sectionally the prevalence, associated factors, and treatment status of anemia and ESA hyporesponsiveness in 4460 non-dialysis-dependent CKD patients enrolled in a multicenter cohort in Japan. Anemia was defined as a hemoglobin (Hb) level of less than 11 g/dL or receiving ESA therapy. ESA hyporesponsiveness was defined by the erythropoietin-resistance index (ERI), which was the erythropoietin dose per week divided by body weight and Hb level (U/kg/week/g/dl). RESULTS Of the 4460 patients, 1050 (23.5%) had anemia. ESAs were administered to 626 patients, reaching a percentage of 57.5% of patients with stage G5 CKD. However, the ESA treatment rate was only 49.0% in patients with a hemoglobin level of < 11 g/dL. The proportion of patients receiving iron supplementation was lower than that of patients receiving ESAs regardless of CKD stage or hemoglobin level, and a significant proportion of patients did not receive iron supplementation, even those with iron deficiency. The ERI increased with CKD stage progression, and the multiple regression analysis showed that age, female sex, body mass index, cholesterol, glomerular filtration rate, and intact parathyroid hormone level were independent contributors. CONCLUSIONS Our findings demonstrate that many Japanese patients with non-dialysis-dependent CKD receiving ESAs fail to maintain adequate hemoglobin levels. These results suggest the need for interventions for ESA hyporesponsiveness factors in addition to iron supplementation.
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Affiliation(s)
- Shigeru Tanaka
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hiromasa Kitamura
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | | | - Takanari Kitazono
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Toshiaki Nakano
- Department of Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
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Oliveira Chaves L, Gomes Domingos AL, Louzada Fernandes D, Ribeiro Cerqueira F, Siqueira-Batista R, Bressan J. Applicability of machine learning techniques in food intake assessment: A systematic review. Crit Rev Food Sci Nutr 2021; 63:902-919. [PMID: 34323627 DOI: 10.1080/10408398.2021.1956425] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector. In this sense, Artificial Intelligence (AI) tools have been increasingly used, for example, the application of Machine Learning (ML) algorithms to extract useful information, find patterns, and predict diseases. This systematic review aimed to identify studies that used ML algorithms to assess food intake in different populations. A literature search was conducted using five electronic databases, and 36 studies met all criteria and were included. According to the results, there has been a growing interest in the use of ML algorithms in the area of nutrition in recent years. Also, supervised learning algorithms were the most used, and the most widely used method of nutritional assessment was the food frequency questionnaire. We observed a trend in using the data analysis programs, such as R and WEKA. The use of ML in nutrition is recent and challenging. Therefore, it is encouraged that more studies are carried out relating these themes for the development of food reeducation programs and public policies.
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Affiliation(s)
| | | | | | | | - Rodrigo Siqueira-Batista
- Department of Medicine and Nursing, Universidade Federal de Viçosa, Viçosa, Brazil.,School of Medicine of the Faculdade Dinâmica do Vale do Piranga, Ponte Nova, Brazil
| | - Josefina Bressan
- Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Brazil
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Alagoz S, Dincer MT, Eren N, Bakir A, Pekpak M, Trabulus S, Seyahi N. Prevalence of anemia in predialysis chronic kidney disease: Is the study center a significant factor? PLoS One 2020; 15:e0230980. [PMID: 32240223 PMCID: PMC7117725 DOI: 10.1371/journal.pone.0230980] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 03/12/2020] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVES Anemia is highly prevalent in chronic kidney disease patients; however, its identification and management have been reported to be suboptimal. In this study we aimed to describe the prevalence, severity, risk factors, and treatment of anemia in different nephrology centers, among chronic kidney disease patients who were not given renal replacement therapy. MATERIALS AND METHODS We performed a multicenter cross-sectional study in three different nephrology clinics. Adult (>18 years of age) chronic kidney disease patients with an estimated glomerular filtration rate (eGFR) below 60 ml/min, and who were not started dialysis were recruited. Demographic, clinical and laboratory data regarding anemia and its management were collected using a standard data form. Anemia was defined as a hemoglobin level below 12g/dL and severe anemia as a hemoglobin level below 10g/dl. RESULTS A total of 1066 patients were enrolled in the study. Anemia and severe anemia were present in 55.9% and 14.9% of the patients, respectively. The mean hemoglobin level for the whole cohort was 11.8±1.8 g/dL. Univariate analyses revealed that the mean hemoglobin level was significantly different among the centers. Moreover, the frequency of the presence of anemia stratified by severity was also significantly different among the centers. According to binary logistic regression analysis, gender, levels of eGFR and iron, ferritin ≥ 100 ng/mL, and the nephrology center were independent determinants of severe anemia. CONCLUSIONS We found a high prevalence of anemia among chronic kidney disease patients who were not on renal replacement therapy. Each center should determine the treatment strategy according to the patient's characteristics. According to our results, the center-specific management of anemia seems to be important.
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Affiliation(s)
- Selma Alagoz
- Department of Nephrology, Bagcilar Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Mevlut Tamer Dincer
- Department of Nephrology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Necmi Eren
- Department of Nephrology, Kocaeli University Medical Faculty, Kocaeli, Turkey
| | - Alev Bakir
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Halic University, Istanbul, Turkey
| | - Meltem Pekpak
- Department of Nephrology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Sinan Trabulus
- Department of Nephrology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Nurhan Seyahi
- Department of Nephrology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
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Gao S, Mutter S, Casey A, Mäkinen VP. Numero: a statistical framework to define multivariable subgroups in complex population-based datasets. Int J Epidemiol 2018; 48:369-374. [DOI: 10.1093/ije/dyy113] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Accepted: 05/28/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Song Gao
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Stefan Mutter
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Aaron Casey
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Ville-Petteri Mäkinen
- Heart Health Theme, South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
- Computational Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland
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