Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hideaki; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

doi:10.1371/journal.pone.0173540

Cited by 9 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study by Gutiérrez-Pliego LE et al [66] individuals in the highest tertiles of unhealthy dietary pattern revealed higher BMI compared with individuals in the highest tertile of the prudent dietary pattern. Similar findings were also reported in other studies [67][68][69]. The current study has several limitations.…”

Section: Plos Onesupporting

confidence: 86%

Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses

et al. 2020

View full text Add to dashboard Cite

Background Lifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status. Methods The current cross-sectional study consists of 525 adults aged 18-64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits. Results Mean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1 st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2 nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3 rd class was

show abstract

Section: Plos Onesupporting

confidence: 86%

Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In 2021, the International Diabetes Federation (IDF) predicted that the population of individuals with diabetes would increase to 597 million ( 3 ). Individuals with T2DM also have a significantly increased risk of adverse outcomes such as cardiovascular disease (CVD), and cardiometabolic syndrome is connected to an increased risk of diabetes ( 4 ). Additionally, diabetic dyslipidemia and obesity have emerged as key biomarkers for an increased CVD risk in patients with diabetes.…”

Section: Introductionmentioning

confidence: 99%

The relationship between sugar-sweetened beverages, sleep disorders, and diabesity

Zhang

Liu²,

Xu³

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundDiabetes and obesity in adults are global issues. Obesity and type 2 diabetes mellitus (T2DM) are increasingly categorized under the umbrella term “diabesity.” Health risk factors (HRFs), which include altering sleep habits and reducing sugar-sweetened beverages (SSBs) consumption, have emerged as relatively novel and crucial strategies for preventing and treating diabetes.ObjectiveWe aimed to explore: 1) whether SSBs could affect diabesity in China’s community; 2) whether HRFs could moderate this relationship; and 3) whether a three-way interaction exists between HRFs, SSBs, and diabesity.MethodsOn December 10, 2018, we investigated diabetes complications in four cities in Anhui Province and obtained basic and lifestyle information using a detailed questionnaire. The primary exposure was SSBs and outcomes were body mass index (BMI) and waist circumference (WC), while glycated hemoglobin (HbA1c) and sleep patterns (including duration and disorders) were considered moderators.ResultsOverall, 1920 participants were enrolled, and those who did not complete the questionnaire were excluded. Finally, this study included 1765 participants, with a response rate of 92.0%. The mean age was (57.10 ± 10.0) years. Patients with lower educational levels were more likely to have a lower prevalence of WC (χ2 = 2.73) and BMI (χ2 = 3.47), and some HRFs were positively correlated with WC and BMI. Additionally, SSBs were significantly associated with BMI (β = 1.29) and WC (β = 2.97), and there was also differences based on sex, some HRFs, such as HbA1c, FBG and TG, showed higher levels in male participants, whereas TC level was higher in female participants. In the moderation analysis, sleep patterns were also associated with total cholesterol, triglyceride, and BMI. Furthermore, there were three-way interaction effects among HbA1c, sleep patterns, and SSBs on total cholesterol, triglyceride, BMI, and WC. Moreover, sensitivity analysis demonstrated that our results were robust.ConclusionSSBs positively correlated with patterns dose-dependently. Moreover, SSBs could also be associated with sleep patterns, and blood glucose levels were correlated with diabesity. A three-way interaction effect was discovered between SSBs, sleep patterns, blood glucose levels, and patterns. Therefore, understanding the diabesity caused by SSBs and other HRFs can help prevent its occurrence.

show abstract

“…In 2021, the International Diabetes Federation stated that the number of people with type 2 diabetes (T2DM) had risen to 597 million (3). T2DM is associated with a range of adverse outcomes, such as hypertension, hyperlipidemia, metabolic abnormalities, and other negative outcomes; among them, T2DM patients also have a significantly increased risk of cardiovascular disease (CVD) (4), and it has been reported that appropriate treatment of T2DM patients can significantly reduce CVD (5). Moreover, CVD remains one of the leading causes of death and disability in patients with T2DM (6)(7)(8).…”

Section: The Prevalence Of Cardiovascular Diseases and Influencing Fa...mentioning

confidence: 99%

The management correlation between metabolic index, cardiovascular health, and diabetes combined with cardiovascular disease

Zhang

Liu

et al. 2023

Front. Endocrinol.

View full text Add to dashboard Cite

BackgroundCardiovascular disease (CVD) has become a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Although there is also evidence that multifactorial interventions to control blood glucose, blood pressure, and lipid profiles can reduce macrovascular complications and mortality in patients with T2DM, the link between these risk factors has not been established.MethodsOn 10 December 2018, 1,920 people in four cities in Anhui Province were included. Latent category analysis (LCA) was used to explore the clustering mode of HRBs (health risk behaviors). The primary exposure was HRBs and exercise and diet interventions, and the primary outcome was CVD and other variables, including zMS, triglyceride-glucose index (TyG), TyG-WC (waist circumference), TyG-BMI, TG/HDL, and cardiovascular health (CVH). A multivariable logistic regression model was used to establish the relationship between HRBs, exercise, diet interventions, and CVD. Moderate analysis and mediation moderation analysis were employed by the PROCESS method to explore the relationship between these variables. Sensitivity analysis explored the robustness of the model.ResultsThe mean age was 57.10 ± 10.0 years old. Overall, CVD affects approximately 19.9% of all persons with T2DM. Macrovascular complications of T2DM include coronary heart disease, myocardial infarction (MI), cardiac insufficiency, and cerebrovascular disease. Elderly age (χ2 = 22.70), no occupation (χ2 = 20.97), medium and high socioeconomic status (SES) (χ2 = 19.92), higher level of TyG-WC (χ2 = 6.60), and higher zMS (χ2 = 7.59) were correlated with high CVD. Many metabolic indices have shown a connection with T2DM combined with CVD, and there was a dose−response relationship between HRB co-occurrence and clustering of HRBs and zMS; there was a dose−response relationship between multifactorial intervention and CVH. In the mediation moderation analysis, there was an association between HRB, gender, TyG, TyG-BMI, and CVD. From an intervention management perspective, exercise and no diet intervention were more significant with CVD; moreover, there was an association between intervention management, gender, zMS, TyG-WC, TyG-BMI, TG/HDL, and CVD. Finally, there was an association between sex, CVH, and CVD. Sensitivity analysis demonstrated that our results were robust.ConclusionsCVD is one of the common complications in patients with type 2 diabetes, and its long-term outcome will have more or less impact on patients. Our findings suggest the potential benefits of scaling up multifactorial and multifaceted interventions to prevent CVD in patients with T2DM.

show abstract

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

Cited by 9 publications

References 41 publications

Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses

Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses

The relationship between sugar-sweetened beverages, sleep disorders, and diabesity

The management correlation between metabolic index, cardiovascular health, and diabetes combined with cardiovascular disease

Contact Info

Product

Resources

About