The Frequency of Malnutrition in Patients with Type 2 Diabetes

Prevalence and influencing factors of malnutrition in diabetic patients: A systematic review and meta-analysis

The prevalence of malnutrition in diabetic patients and its influencing factors remain poorly described. We aim to investigate the prevalence of malnutrition and the influencing factors in diabetic patients through meta-analysis. Utilizing search terms, such as diabetes, malnutrition, and prevalence, we systematically searched eight databases, including Embase, PubMed, Web of Science, The Cochrane Library, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and VIP Database, from inception to May 4, 2023. The search aimed to identify studies related to the prevalence of malnutrition and its influencing factors in adult patients with diabetes. Cohort studies, case-control studies, and cross-sectional studies that met the inclusion criteria were included in the analysis. Stata 16.0 software was used for meta-analysis. Quality of the evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). The study protocol is registered with Prospective Register of Systematic Reviews (PROSPERO), CRD42023443649. A total of 46 studies were included, involving 18 062 patients with ages ranging from 18 to 95 years. The overall malnutrition prevalence was 33% (95% confidence interval [CI]: 0.25-0.40), compared with an at-risk prevalence of 44% (95% CI: 0.34-0.54). Sixteen factors associated with malnutrition in diabetic patients were identified. This meta-analysis provides insights into the prevalence of malnutrition and its risk factors in diabetic patients. Regular nutritional screening for patients with risk factors is essential for early detection and intervention.

Association between Visual Impairment and Nutritional Risk among Older Adults with Diabetes: A Population-Based Cross-Sectional Study

PURPOSE

Despite the high prevalence of visual impairment caused by diabetic retinopathy and nutritional problems among older adults with diabetes, evidence regarding factors related to nutritional risk in this population is limited. Therefore, this study aimed to identify the correlates of nutritional risk among older adults with diabetes, focusing on visual impairment.

METHODS

This study was a secondary data analysis of the 2020 National Survey of Older Koreans aged 65 years and above. The sample comprised 2,376 older adults with diabetes, and complex sample ANOVA and Rao-Scott chi-square tests were used to compare the groups according to visual impairment. Complex-sample logistic regression analyses were conducted to verify the association between visual impairment and nutritional risk.

RESULTS

Older adults with diabetes, who also have severe visual impairment, are more likely to have nutritional risk status than those without impairment after controlling for covariates (odds ratio [OR] = 2.44, 95% confidence interval [CI] 1.16~5.13). Among the covariates, depression (OR = 3.58, 95% CI 2.60~4.94), dependent activities of daily living status (OR = 2.79, 95% CI 1.60~4.86), and experience of hospitalization during the past year (OR = 2.51, 95% CI 1.57~4.03) were strongly associated with nutritional risk.

CONCLUSION

Severe visual impairment increases the nutritional risk among older adults with diabetes. Therefore, it is essential to prevent visual impairment due to exacerbation of diabetes through appropriate management. Additionally, tailored nutritional interventions for visually impaired older adults with diabetes that consider visual characteristics are required.

The syndromic triad of COVID-19, type 2 diabetes, and malnutrition

The coronavirus disease 2019 (COVID-19) pandemic challenges our collective understanding of transmission, prevention, complications, and clinical management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Risk factors for severe infection, morbidity, and mortality are associated with age, environment, socioeconomic status, comorbidities, and interventional timing. Clinical investigations report an intriguing association of COVID-19 with diabetes mellitus and malnutrition but incompletely describe the triphasic relationship, its mechanistic pathways, and potential therapeutic approaches to address each malady and their underlying metabolic disorders. This narrative review highlights common chronic disease states that interact epidemiologically and mechanistically with the COVID-19 to create a syndromic phenotype-the COVID-Related Cardiometabolic Syndrome-linking cardiometabolic-based chronic disease drivers with pre-, acute, and chronic/post-COVID-19 disease stages. Since the association of nutritional disorders with COVID-19 and cardiometabolic risk factors is well established, a syndromic triad of COVID-19, type 2 diabetes, and malnutrition is hypothesized that can direct, inform, and optimize care. In this review, each of the three edges of this network is uniquely summarized, nutritional therapies discussed, and a structure for early preventive care proposed. Concerted efforts to identify malnutrition in patients with COVID-19 and elevated metabolic risks are needed and can be followed by improved dietary management while simultaneously addressing dysglycemia-based chronic disease and malnutrition-based chronic disease.

A nomogram based on nutritional status and A2DS2 score for predicting stroke-associated pneumonia in acute ischemic stroke patients with type 2 diabetes mellitus: A retrospective study

Background

Stroke-associated pneumonia (SAP) commonly complicates acute ischemic stroke (AIS) and significantly worsens outcomes. Type 2 diabetes mellitus (T2DM) may contribute to malnutrition, impair innate immunity function, and increase the probability of SAP occurrence in AIS patients. We aimed to determine early predictors of SAP in AIS patients with T2DM and to construct a nomogram specifically for predicting SAP in this population by combining the A²DS² score with available nutrition-related parameters.

Methods

A total of 1,330 consecutive AIS patients with T2DM were retrospectively recruited. The patients were randomly allocated to the training (n = 887) and validation groups (n = 443). Univariate and multivariate binary logistic regression analyses were applied to determine the predictors of SAP in the training group. A nomogram was established according to the identified predictors. The areas under the receiver operating characteristic curve (AUROC) and calibration plots were performed to access the predictive values of the nomogram. The decision curve was applied to evaluate the net benefits of the nomogram.

Results

The incidence of SAP was 9% and 9.7% in the training and validation groups, respectively. The results revealed that the A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP. A novel nomogram, A²DS²-Nutrition, was constructed based on these five predictors. The AUROC for A²DS²-Nutrition (0.820, 95% CI: 0.794–0.845) was higher than the A²DS² score (0.691, 95% CI: 0.660–0.722) in the training group. Similarly, it showed a better predictive performance than the A²DS² score [AUROC = 0.864 (95% CI: 0.828–0.894) vs. AUROC = 0.763 (95% CI: 0.720–0.801)] in the validation group. These results were well calibrated in the two groups. Moreover, the decision curve revealed that the A²DS²-Nutrition provided an additional net benefit to the AIS patients with T2DM compared to the A²DS² score in both groups.

Conclusion

The A²DS² score, stroke classification, Geriatric Nutritional Risk Index, hemoglobin, and fast blood glucose were independent predictors for SAP in AIS patients with T2DM. Thus, the proposed A²DS²-Nutrition may be a simple and reliable prediction model for SAP occurrence in AIS patients with T2DM.

MiR-100 overexpression attenuates high fat diet induced weight gain, liver steatosis, hypertriglyceridemia and development of metabolic syndrome in mice

BACKGROUND

Diet-induced obesity can result in the development of a diverse spectrum of cardiovascular and metabolic diseases, including type 2 diabetes, dyslipidemia, non-alcoholic liver steatosis and atherosclerotic disease. MicroRNAs have been described to be important regulators of metabolism and disease development.

METHODS

In the current study, we investigated the effects of ubiquitous miR-100 overexpression on weight gain and the metabolic phenotype in a newly generated transgenic mouse strain under normal chow and high fat diet and used microarray expression analysis to identify new potential target genes of miR-100.

RESULTS

While transgenic overexpression of miR-100 did not significantly affect weight and metabolism under a normal diet, miR-100 overexpressing mice showed a reduced weight gain under a high fat diet compared to wildtype mice, despite an equal calorie intake. This was accompanied by less visceral and subcutaneous fat development and lover serum LDL cholesterol. In addition, transgenic miR-100 mice were more glucose tolerant and insulin sensitive and demonstrated increased energy expenditure under high fat diet feeding. A comprehensive gene expression profiling revealed the differential expression of several genes involved in lipid storage- and metabolism, among them CD36 and Cyp4A14. Our data showed a direct regulation of CD36 by miR-100, leading to a reduced fatty acid uptake in primary hepatocytes overexpressing miR-100 and the downregulation of several downstream mediators of lipid metabolism such as ACC1, FABP4, FAS and PPARγ in the liver.

CONCLUSIONS

Our findings demonstrate a protective role of miR-100 in high fat diet induced metabolic syndrome and liver steatosis, partially mediated by the direct repression of CD36 and attenuation of hepatic lipid storage, implicating miR-100 as a possible therapeutic target in liver steatosis.