Relationship between dietary macronutrients intake and biological aging: a cross-sectional analysis of NHANES data.

PURPOSE: This study aimed to investigate the association between macronutrient intake and biological age. METHODS: Data were collected from 26,381 adults who participated in the United States National Health and Nutrition Examination Survey (NHANES). Two biological ages were estimated using the Klemera-Doubal method (KDM) and PhenoAge algorithms. Biological age acceleration (AA) was computed as the difference between biological age and chronological age. The associations between macronutrient intakes and AA were investigated. RESULTS: After fully adjusting for confounding factors, negative associations were observed between AA and fiber intake (KDM-AA: ß - 0.53, 95% CI - 0.62, - 0.43, P < 0.05; PhenoAge acceleration: ß - 0.30, 95% CI - 0.35, - 0.25, P < 0.05). High-quality carbohydrate intake was associated with decreased AA (KDM-AA: ß - 0.57, 95% CI - 0.67, - 0.47, P < 0.05; PhenoAge acceleration: ß - 0.32, 95% CI - 0.37, - 0.26, P < 0.05), while low-quality carbohydrate was associated with increased AA (KDM-AA: ß 0.30, 95% CI 0.21, 0.38, P < 0.05; PhenoAge acceleration: ß 0.16, 95% CI 0.11, 0.21, P < 0.05). Plant protein was associated with decreased AA (KDM-AA: ß - 0.39, 95% CI - 0.51, - 0.27, P < 0.05; PhenoAge acceleration: ß - 0.21, 95% CI - 0.26, - 0.15, P < 0.05). Long-chain SFA intake increased AA (KDM-AA: ß 0.16, 95% CI 0.08, 0.24, P < 0.05; PhenoAge acceleration: ß 0.11, 95% CI 0.07, 0.15, P < 0.05). ω-3 PUFA was associated with decreased KDM-AA (ß - 0.18, 95% CI - 0.27, - 0.08, P < 0.05) and PhenoAge acceleration (ß - 0.09, 95% CI - 0.13, - 0.04, P < 0.05). CONCLUSION: Our findings suggest that dietary fiber, high-quality carbohydrate, plant protein, and ω-3 PUFA intake may have a protective effect against AA, while low-quality carbohydrate and long-chain SFA intake may increase AA. Therefore, dietary interventions aimed at modifying macronutrient intakes may be useful in preventing or delaying age-related disease and improving overall health.

Prevalence of comorbid depression and associated factors among hospitalized patients with type 2 diabetes mellitus in Hunan, China.

BACKGROUND: Depression and diabetes are major health challenges, with heavy economic social burden, and comorbid depression in diabetes could lead to a wide range of poor health outcomes. Although many descriptive studies have highlighted the prevalence of comorbid depression and its associated factors, the situation in Hunan, China, remains unclear. Therefore, this study aimed to identify the prevalence of comorbid depression and associated factors among hospitalized type 2 diabetes mellitus (T2DM) patients in Hunan, China. METHODS: This cross-sectional study involved 496 patients with T2DM who were referred to the endocrinology inpatient department of Xiangya Hospital affiliated to Central South University, Hunan. Participants' data on socio-demographic status, lifestyle factors, T2DM-related characteristics, and social support were collected. Depression was evaluated using the Hospital Anxiety and Depression Scale-depression subscale. All statistical analyses were conducted using the R software version 4.2.1. RESULTS: The prevalence of comorbid depression among hospitalized T2DM patients in Hunan was 27.22% (95% Confidence Interval [CI]: 23.3-31.1%). Individuals with depression differed significantly from those without depression in age, educational level, per capita monthly household income, current work status, current smoking status, current drinking status, regular physical activity, duration of diabetes, hypertension, chronic kidney disease, stroke, fatty liver, diabetic nephropathy, diabetic retinopathy, insulin use, HbA1c, and social support. A multivariable logistic regression model showed that insulin users (adjusted OR = 1.86, 95% CI: 1.02-3.42) had a higher risk of depression, while those with regular physical activity (adjusted OR = 0.48, 95% CI: 0.30-0.77) or greater social support (adjusted OR = 0.20, 95% CI: 0.11-0.34) had a lower risk of depression. The area under the curve of the receiver operator characteristic based on this model was 0.741 with a sensitivity of 0.785 and specificity of 0.615. CONCLUSIONS: Depression was moderately prevalent among hospitalized T2DM patients in Hunan, China. Insulin treatment strategies, regular physical activity, and social support were significantly independently associated with depression, and the multivariable model based on these three factors demonstrated good predictivity, which could be applied in clinical practice.

Prevalence of Anxiety and Associated Factors Among Inpatients with Type 2 Diabetes Mellitus in China: A Cross-Sectional Study.

This study aimed to investigate the prevalence of anxiety and its associated factors among inpatients with type 2 diabetes mellitus (T2DM) in China. This study was a cross-sectional study. Inpatients with T2DM admitted to the Endocrinology Department of Xiangya Hospital, Central South University in Hunan Province of China from March 2021 to December 2021 were consecutively included in this study. Participants were interviewed to obtain the data on socio-demographic characteristics, lifestyle characteristics, T2DM-related information, and social support. Anxiety was measured using the Hospital Anxiety and Depression Scale-anxiety subscale by experienced physicians. Multivariable logistic regression analysis was used to estimate the independent contribution of each independent variable to anxiety. A total of 496 inpatients with T2DM were included in this study. The prevalence of anxiety was 21.8% (95% confidence interval [CI]: 18.1%-25.4%). The results of multivariable logistic regression analysis indicated that age of at least 60 (adjusted odd ratio [aOR] = 1.79, 95% CI: 1.04-3.08), and having diabetes specific complications (aOR = 4.78, 95% CI: 1.02-22.44) were risk factors for anxiety, and an educational level of high school or above (aOR = 0.55, 95% CI: 0.31-0.99), regular physical activity (aOR = 0.36, 95% CI: 0.22-0.58), and high social support (aOR = 0.30, 95% CI: 0.17-0.53) were protective factors for anxiety. A predictive model based on these five variables showed good performance (area under the curve = 0.80). Almost one in five inpatients with T2DM suffered from anxiety in China. Age, educational level, regular physical activity, diabetes specific complications, and social support were independently associated with anxiety.

Sleep quality and its associated factors among patients with type 2 diabetes mellitus in Hunan, China: a cross-sectional study.

OBJECTIVES: Type 2 diabetes mellitus (T2DM) is a serious public health issue. Compared with the general population, patients with T2DM have a higher risk of poor sleep quality, which could ultimately result in poor prognosis. Therefore, this study aimed to evaluate sleep quality and its associated factors among patients with T2DM in Hunan, China. DESIGN: This was a cross-sectional study. SETTING: A tertiary hospital in Hunan, China. PARTICIPANTS: Patients with T2DM hospitalised at the Endocrinology Department were consecutively enrolled between March 2021 and December 2022. Sociodemographic characteristics, lifestyle factors and T2DM-related information were collected retrospectively. PRIMARY AND SECONDARY OUTCOME MEASURES: Sleep quality was evaluated using the Pittsburgh Sleep Quality Index, with a cut-off value of >7 suggesting poor sleep quality. Multivariate logistic regression analysis was used to determine factors associated with poor sleep quality. RESULTS: Of the 1039 participants included, 1001 provided complete data. The mean age of the study sample was 60.24±10.09 years, and 40.5% (95% CI 37.5% to 43.5%) of patients had poor sleep quality. Multivariate logistic regression analysis showed that female sex (adjusted OR (aOR) 1.70, 95% CI 1.25 to 2.29), unmarried status (aOR 1.72, 95% CI 1.05 to 2.83), diabetic retinopathy (aOR 1.38, 95% CI 1.04 to 1.83), diabetic foot (aOR 1.80, 95% CI 1.11 to 2.93) and a per capita monthly household income of >5000 RMB (aOR 0.66, 95% CI 0.47 to 0.93) were associated with poor sleep quality. CONCLUSIONS: Nearly two-fifths of patients with T2DM reported poor sleep quality in Hunan, China. Sex, marital status, diabetic retinopathy, diabetic foot and household income were independently associated with sleep quality among patients with T2DM in Hunan, China.

Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis.

BACKGROUND: As the population ages, mild cognitive impairment (MCI) and type 2 diabetes mellitus (T2DM) become common conditions that often coexist. Evidence has shown that MCI could lead to reduced treatment compliance, medication management, and self-care ability in T2DM patients. Therefore, early identification of those with increased risk of MCI is crucial from a preventive perspective. Given the growing utilization of decision trees in prediction of health-related outcomes, this study aimed to identify MCI in T2DM patients using the decision tree approach. METHODS: This hospital-based case-control study was performed in the Endocrinology Department of Xiangya Hospital affiliated to Central South University between March 2021 and December 2022. MCI was defined based on the Petersen criteria. Demographic characteristics, lifestyle factors, and T2DM-related information were collected. The study sample was randomly divided into the training and validation sets in a 7:3 ratio. Univariate and multivariate analyses were performed, and a decision tree model was established using the chi-square automatic interaction detection (CHAID) algorithm to identify key predictor variables associated with MCI. The area under the curve (AUC) value was used to evaluate the performance of the established decision tree model, and the performance of multivariate regression model was also evaluated for comparison. RESULTS: A total of 1001 participants (705 in the training set and 296 in the validation set) were included in this study. The mean age of participants in the training and validation sets was 60.2  ±  10.3 and 60.4  ±  9.5 years, respectively. There were no significant differences in the characteristics between the training and validation sets (p > .05). The CHAID decision tree analysis identified six key predictor variables associated with MCI, including age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy. The established decision tree model had 15 nodes composed of 4 layers, and age is the most significant predictor variable. It performed well (AUC = .75 [95% confidence interval (CI): .71-.78] and .67 [95% CI: .61-.74] in the training and validation sets, respectively), was internally validated, and had comparable predictive value compared to the multivariate logistic regression model (AUC = .76 [95% CI: .72-.80] and .69 [95% CI: .62-.75] in the training and validation sets, respectively). CONCLUSION: The established decision tree model based on age, educational level, household income, regular physical activity, diabetic nephropathy, and diabetic retinopathy performed well with comparable predictive value compared to the multivariate logistic regression model and was internally validated. Due to its superior classification accuracy and simple presentation as well as interpretation of collected data, the decision tree model is more recommended for the prediction of MCI in T2DM patients in clinical practice.

The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram-based model to detect mild cognitive impairment (MCI) in T2DM patients. METHODS: Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well-qualified investigators conducted face-to-face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM-related information, and history of depression and anxiety. Cognitive function was assessed using the Mini-Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. RESULTS: A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%-38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815-0.883), and the threshold probability ranged from 35.0% to 85.0%. CONCLUSIONS: Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.