Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling
Abstract Background Diabetes is a chronic disease, and hyperglycemia can increase the risk of diabetic complications and the need for more inpatient services. Therefore, the prevention and control of diabetes are important. This study aimed to identify the trajectories of metabolic control and its c...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12889-024-19098-1 |
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| author | Zahra Khosravi Ali Taghipour Monavar Afzalaghaee Habibollah Esmaily Ahmad Khosravi |
| author_facet | Zahra Khosravi Ali Taghipour Monavar Afzalaghaee Habibollah Esmaily Ahmad Khosravi |
| author_sort | Zahra Khosravi |
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| description | Abstract Background Diabetes is a chronic disease, and hyperglycemia can increase the risk of diabetic complications and the need for more inpatient services. Therefore, the prevention and control of diabetes are important. This study aimed to identify the trajectories of metabolic control and its correlates in people with type 1 diabetes. Method This is a longitudinal study with 2020 type 1 diabetic individuals aged 18 to 59 years. The participants’ glycosylated hemoglobin (HbA1c) was measured three times with a six-month interval between each measurement. The data were analyzed using group-based trajectory modeling. Multinomial logistic regression was used to determine the factors related to these groups. Results The results showed four trajectories of safe controlled (46.2%), moderate stable risk (28.7%), moderate increasing risk (12.5%), and high decreasing risk trajectory (12.6%) (entropy = 0.70). The results of multinomial logistic regression showed dyslipidemia could increase the odds of being in the three risk trajectories. Education, physical inactivity, and poor psychological status could also increase the odds of being in the moderate stable and high decreasing trajectories. Moreover, sex, job, and BMI could increase the odds of being in the high decreasing risk group (p < 0.05). Conclusion Since there are different trajectories of metabolic control of diabetes, it is necessary for healthcare providers and health experts to plan behavioral interventions based on the location of individuals in different trajectories and the related significant risk factors. In this way, appropriate prevention, care, and treatment programs can be provided for the people in each group. |
| format | Article |
| id | doaj-art-6882f8724abf461cb0854711daf91926 |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | BMC Public Health |
| spelling | doaj-art-6882f8724abf461cb0854711daf919262025-02-09T12:57:47ZengBMCBMC Public Health1471-24582025-02-012511810.1186/s12889-024-19098-1Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modelingZahra Khosravi0Ali Taghipour1Monavar Afzalaghaee2Habibollah Esmaily3Ahmad Khosravi4Department of Epidemiology, School of Health, Mashhad University of Medical SciencesDepartment of Epidemiology, School of Health, Mashhad University of Medical SciencesDepartment of Epidemiology, School of Health, Mashhad University of Medical SciencesDepartment of Biostatistics, School of Health, Mashhad University of Medical SciencesDepartment of Epidemiology, School of Public Health, Shahroud University of Medical SciencesAbstract Background Diabetes is a chronic disease, and hyperglycemia can increase the risk of diabetic complications and the need for more inpatient services. Therefore, the prevention and control of diabetes are important. This study aimed to identify the trajectories of metabolic control and its correlates in people with type 1 diabetes. Method This is a longitudinal study with 2020 type 1 diabetic individuals aged 18 to 59 years. The participants’ glycosylated hemoglobin (HbA1c) was measured three times with a six-month interval between each measurement. The data were analyzed using group-based trajectory modeling. Multinomial logistic regression was used to determine the factors related to these groups. Results The results showed four trajectories of safe controlled (46.2%), moderate stable risk (28.7%), moderate increasing risk (12.5%), and high decreasing risk trajectory (12.6%) (entropy = 0.70). The results of multinomial logistic regression showed dyslipidemia could increase the odds of being in the three risk trajectories. Education, physical inactivity, and poor psychological status could also increase the odds of being in the moderate stable and high decreasing trajectories. Moreover, sex, job, and BMI could increase the odds of being in the high decreasing risk group (p < 0.05). Conclusion Since there are different trajectories of metabolic control of diabetes, it is necessary for healthcare providers and health experts to plan behavioral interventions based on the location of individuals in different trajectories and the related significant risk factors. In this way, appropriate prevention, care, and treatment programs can be provided for the people in each group.https://doi.org/10.1186/s12889-024-19098-1Type 1 diabetesGlycosylated hemoglobinLatent class growth modeling |
| spellingShingle | Zahra Khosravi Ali Taghipour Monavar Afzalaghaee Habibollah Esmaily Ahmad Khosravi Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling BMC Public Health Type 1 diabetes Glycosylated hemoglobin Latent class growth modeling |
| title | Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling |
| title_full | Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling |
| title_fullStr | Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling |
| title_full_unstemmed | Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling |
| title_short | Metabolic control and its associated factors in type 1 diabetic people: longitudinal trajectory modeling |
| title_sort | metabolic control and its associated factors in type 1 diabetic people longitudinal trajectory modeling |
| topic | Type 1 diabetes Glycosylated hemoglobin Latent class growth modeling |
| url | https://doi.org/10.1186/s12889-024-19098-1 |
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