Influence of glucose, insulin fluctuation, and glycosylated hemoglobin on the outcome of sarcopenia in patients with type 2 diabetes mellitus

Blood Profiles of Community-Dwelling People with Sarcopenia: Analysis Based on the China Health and Retirement Longitudinal Study

INTRODUCTION

Routine blood factors can be economical and easily accessible candidates for sarcopenia screening and monitoring. The associations between sarcopenia and routine blood factors remain unclear. This study aimed to examine sarcopenia and blood factor associations based on a nation-wide cohort in China.

METHODS

A total of 1,307 participants and 17 routine blood indices were selected from two waves (year 2011 and year 2015) of the China Health and Retirement Longitudinal Study (CHARLS). The diagnosis of sarcopenia was based on the criteria proposed by the Asian Working Group for Sarcopenia (AWGS 2019). Generalized mixed-effects models were performed for association analyses. A logistic regression (LR) model was conducted to examine the predictive power of identifying significant blood factors for sarcopenia.

RESULTS

A higher sarcopenia risk was cross-sectionally associated with elevated blood concentrations of high-sensitivity C-reactive protein (hsCRP) (OR = 1.030, 95% CI [1.007, 1.053]), glycated hemoglobin (HbA1c) (OR = 1.407, 95% CI [1.126, 1.758]) and blood urea nitrogen (BUN) (OR = 1.044, 95% CI [1.002, 1.089]), and a decreased level of glucose (OR = 0.988, 95% CI [0.979, 0.997]). A higher baseline hsCRP value (OR = 1.034, 95% CI [1.029, 1.039]) and a greater over time change in hsCRP within 4 years (OR = 1.034, 95% CI [1.029, 1.039]) were associated with a higher sarcopenia risk. A higher BUN baseline value was related to a decreased sarcopenia risk over time (OR = 0.981, 95% CI [0.976, 0.986]), while a greater over time changes in BUN (OR = 1.034, 95% CI [1.029, 1.040]) and a smaller over time change in glucose (OR = 0.992, 95% CI [0.984, 0.999]) within 4 years were also related to a higher sarcopenia risk. LR based on significant blood factors (i.e., hsCRP, HbA1c, BUN, and glucose), and sarcopenia status in year 2015 yielded an area under the curve of 0.859 (95% CI: 0.836-0.882).

CONCLUSION

Routine blood factors involved in inflammation, protein metabolism, and glucose metabolism are significantly associated with sarcopenia. In clinical practice, plasma hsCRP, BUN, blood sugar levels, sex, age, marital status, height, and weight might be helpful for sarcopenia evaluation and monitoring.

A review on associated factors and management measures for sarcopenia in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by hyperglycemia, insulin resistance, and insufficient insulin secretion. Sarcopenia, as a new complication of diabetes, is characterized by the loss of muscle mass and the progressive decline of muscle strength and function in T2DM patients, which has a serious impact on the physical and mental health of patients. Insulin resistance, mitochondrial dysfunction, and chronic inflammation are common mechanisms of diabetes and sarcopenia. Reasonable exercise training, nutrition supplement, and drug intervention may improve the quality of life of patients with diabetes combined with sarcopenia. This article reviews the relevant factors and management measures of sarcopenia in T2DM patients, in order to achieve early detection, diagnosis, and intervention.

Diabetes Mellitus Should Be Considered While Analysing Sarcopenia-Related Biomarkers

Sarcopenia is a chronic, progressive skeletal muscle disease characterised by low muscle strength and quantity or quality, leading to low physical performance. Patients with type 2 diabetes mellitus (T2DM) are more at risk of sarcopenia than euglycemic individuals. Because of several shared pathways between the two diseases, sarcopenia is also a risk factor for developing T2DM in older patients. Various biomarkers are under investigation as potentially valuable for sarcopenia diagnosis and treatment monitoring. Biomarkers related to sarcopenia can be divided into markers evaluating musculoskeletal status (biomarkers specific to muscle mass, markers of the neuromuscular junction, or myokines) and markers assuming causal factors (adipokines, hormones, and inflammatory markers). This paper reviews the current knowledge about how diabetes and T2DM complications affect potential sarcopenia biomarker concentrations. This review includes markers recently proposed by the expert group of the European Society for the Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) as those that may currently be useful in phase II and III clinical trials of sarcopenia: myostatin (MSTN); follistatin (FST); irisin; brain-derived neurotrophic factor (BDNF); procollagen type III N-terminal peptide (PIIINP; P3NP); sarcopenia index (serum creatinine to serum cystatin C ratio); adiponectin; leptin; insulin-like growth factor-1 (IGF-1); dehydroepiandrosterone sulphate (DHEAS); C-reactive protein (CRP); interleukin-6 (IL-6), and tumor necrosis factor α (TNF-α). A better understanding of factors influencing these biomarkers' levels, including diabetes and diabetic complications, may lead to designing future studies and implementing results in clinical practice.

Early onset age increases the risk of musculoskeletal damage in patients with type 2 diabetes

Introduction

It's not clear whether there are differences in musculoskeletal damage and body composition among different age groups of type 2 diabetes. Therefore, the purpose of this study is to analyze the difference between early-onset type 2 diabetes (EOT2D) and non-early-onset type 2 diabetes (NOT2D) in musculoskeletal damage.

Methods

A total of 964 patients with type 2 diabetes mellitus were selected by 1:1 propensity score matching, including 534 males and 430 females, with an average age of 52 ± 7 years and an average course of 10 ± 8.5 years. Bone mineral density and body composition were measured, and combined with biochemical tests, linear regression and binary logic regression were used to analyze the relationship between EOT2D, NOT2D and musculoskeletal damage. In addition, 414 patients with T2DM were selected according to whether they were hospitalized twice or not, and the median follow-up period was 44 months. COX survival analysis further elucidates the relationship between EOT2D, NOT2D and musculoskeletal damage.

Results

Compared with patients with non-early-onset type 2 diabetes, A/G was negatively correlated with the age of onset, and had statistical significance. EOT2D has a higher risk of sarcopenia, osteoporosis and even musculoskeletal damage. With the prolongation of the course of the disease, the risk of muscle mass and/or bone mineral density decrease in EOT2D increases.

Conclusion

EOT2D brings a greater risk of sarcopenia and/or osteoporosis, as well as a higher risk of reduced ASM and BMD. In addition, fat distribution may be more central.

Correlation of CT-derived pectoralis muscle status and COVID-19 induced lung injury in elderly patients

OBJECTIVES

To explore the association between CT-derived pectoralis muscle index (PMI) and COVID-19 induced lung injury.

METHODS

We enrolled 116 elderly COVID-19 patients linked to the COVID-19 outbreak in Nanjing Lukou international airport. We extracted three sessions of their CT data, including one upon admission (T1), one during the first 2 weeks when lung injury peaked (T2) and one on day 14 ± 2 (T3). Lung injury was assessed by CT severity score (CTSS) and pulmonary opacity score (POS). Pneumonia evolution was evaluated by changes of CT scores at T2 from T1(Δ).

RESULTS

The maximum CT scores in low PMI patients were higher than those of normal PMI patients, including CTSS1 (7, IQR 6-10 vs. 5, IQR 3-6, p < 0.001), CTSS2 (8, IQR 7-11 vs. 5, IQR 4-7, p < 0.001) and POS (2, IQR 1-2.5 vs. 1, IQR 1-2, p < 0.001). Comorbidity (OR = 6.15, p = 0.023) and the presence of low PMI (OR = 5.43, p = 0.001) were predictors of lung injury aggravation with ΔCTSS1 > 4. The presence of low PMI (OR = 5.98, p < 0.001) was the predictor of lung injury aggravation with ΔCTSS2 > 4. Meanwhile, presence of low PMI (OR = 2.82, p = 0.042) and incrementally increasing D-dimer (OR = 0.088, p = 0.024) were predictors of lung injury aggravation with ΔPOS = 2.

CONCLUSIONS

PMI can be easily assessed on chest CT images and can potentially be used as one of the markers to predict the severity of lung injury in elderly COVID-19 patients.

Relationship between eosinophils counts and muscle mass decline in older people with type 2 diabetes: A prospective study of the KAMOGAWA-DM cohort

Sarcopenia has become an important issue in older individuals with type 2 diabetes. However, no previous studies investigated the relationship between eosinophil count and muscle mass decline. In this prospective cohort study, we aimed to investigate this relationship in older people with type 2 diabetes. Impedance body composition was used to assess body composition and skeletal muscle mass index (SMI, kg/m²) was calculated as appendicular muscle mass (kg)/height squared (m²). The decrease in SMI (kg/m² per year) was calculated as (baseline SMI [kg/m²] - follow-up SMI [kg/m²]) divided by the follow-up period (years). The rate of SMI decrease (%) was calculated as follows: (decrease in SMI [kg/m² per year] ÷ baseline SMI [kg/m²]) × 100; muscle mass decline was defined as the rate of SMI decrease of ≥0.5%. Complete blood counts, including eosinophil counts, were also measured. Among 141 participants, 54.6% experienced muscle mass decline during mean (standard deviation)19.4 (7.3) months of follow-up. The eosinophil counts of participants with muscle mass decline were higher than those of participants without muscle mass decline (216.5 [147.8] vs. 158.6 [113.1] cells/mm³, p = 0.004). Eosinophil counts were negatively associated with the rate of SMI decrease according to Spearman's rank correlation coefficient (r = 0.182, p = 0.031). According to logistic regression analyses, there was the relationship between eosinophil counts and incident muscle mass decline after adjusting for covariates (odds ratio of Δ 1 incremental of logarithm (eosinophil counts) 2.04 (95% confidence interval 1.15-3.61, p = 0.011). This study showed that eosinophil counts are associated with incident muscle mass decline. If an individual with type 2 diabetes has high eosinophil counts in blood tests, then it is necessary to pay more attention to the possibility of progression of muscle atrophy.