Sex-specific associations of fat mass and muscle mass with cardiovascular disease risk factors in adults with type 2 diabetes living with overweight and obesity: secondary analysis of the Look AHEAD trial

Sex differences in associations between body composition and cardiometabolic indicators in Chinese children: a cross-sectional study

OBJECTIVES

Obesity is a growing global public health problem that increases the risk of cardiovascular disease. The aim of the present study was to assess the effects of body composition on cardiometabolic indicators in children.

DESIGN

Cross-sectional analysis.

SETTING

China, the Beijing Children and Adolescents Health Cohort Study between 2022 and 2023.

PARTICIPANTS

This cross-sectional study included 5555 children and adolescents aged 6 to 17 years from 11 kindergartens and schools.

OUTCOME MEASURES

We measured body composition using multifrequency bioelectrical impedance analysis and assessed the cardiometabolic indicators, including blood pressure, plasma glucose and lipids. Linear regression and binary logistic regression were performed to assess the associations between body composition and cardiometabolic abnormalities.

RESULTS

In boys, fat mass index (FMI) was positively correlated with total cholesterol (TC) (in normal fat-free mass (FFM) group, β=0.036, 95% CI 0.027 to 0.046; in high FFM group, β=0.034, 95% CI 0.016 to 0.051) and fasting plasma glucose (FPG) (in normal FFM group, β=0.019, 95% CI 0.012 to 0.026; in high FFM group, β=0.030, 95% CI 0.005 to 0.054). FFMI was negatively associated with TC only in the normal fat group (β=-0.047, 95% CI -0.069 to -0.034) in boys. However, in girls, FMI was not significantly associated with TC and was positively associated with FPG only in the normal FFM group (β=0.033, 95% CI 0.024 to 0.041), and FFMI was negatively correlated with TC (in normal fat group, β=-0.058, 95% CI -0.079 to -0.038; in high fat group, β=-0.049, 95% CI -0.084 to -0.015). Normal FFM-high fat (OR=2.065, 95% CI 1.379 to 3.091) and increased visceral fat region (OR=1.357, 95% CI 1.195 to 1.540) were risk factors for high TC in boys but not in girls.

CONCLUSIONS

Body composition was significantly associated with cardiometabolic risk factors, and fat stored in different regions has differential influences on cardiometabolic indicators. There were sex differences in the relationships between body composition and cardiometabolic indicators. The findings suggest that body composition is more strongly correlated with cardiometabolic indicators in boys than in girls. Prevention of obesity and cardiometabolic abnormalities may be more important in boys.

Association between more plant-based diets and 24-h urinary creatinine excretion in 98,813 Dutch females and males: a cross-sectional study

BACKGROUND

Despite the potential health benefits and environmental gains of more plant-based diets, concerns remain about the quantity and quality of plant-based protein. Sufficient dietary protein is essential to prevent muscle loss and maintain muscle mass. However, evidence regarding the relation between plant-based diets and objectively measured muscle mass is scarce.

OBJECTIVES

We investigated, cross-sectionally, the association between groups with different dietary identities and muscle mass, indicated by their 24-h urinary creatinine excretion rate (CER).

METHODS

From the baseline assessment of the Dutch Lifelines cohort 2007-2013, 59,719 females aged 42 ± 12 y and 39,094 males aged 43 ± 12 y were included in this study. Participants' CER was used to estimate total body muscle mass. Dietary identities were self-reported and categorized as vegetarian, flexitarian, other, and no dietary identity. Associations between dietary identities and CER in females and males, separately and adjusted for relevant covariates, were analyzed using linear regression modeling.

RESULTS

Individuals with dietary identities (vegetarian, flexitarian, or other diet) had a lower protein intake than those without. Vegetarians had the lowest protein intake: vegetarian females and males consumed 0.88 ± 0.27 g/kg/d and 0.94 ± 0.29 g/kg/d, whereas females and males without an explicit dietary identity consumed 1.00 ± 0.27 g/kg/d and 1.02 ± 0.29 g/kg/d. Compared with the group without an explicit dietary identity, groups with vegetarian or flexitarian dietary identities were associated with lower CER for both females {β [95% confidence interval (CI)]: -84.9 (-97.1, -72.7) for vegetarian; -32.5 (-41.7, -23.3) for flexitarian} and males [β (95% CI): -112.4 (-151.4, -73.4) for vegetarian; -26.7 (-50.5, -2.9) for flexitarian].

CONCLUSIONS

Individuals with identities favoring plant-based diets have a lower dietary protein intake and a lower CER, indicating lower total body muscle mass. When plant-based diets are being promoted, it is important to monitor and evaluate the potential public health impact on muscle mass.

Normalized Creatinine-to-Cystatin C Ratio and Risk of Cardiometabolic Multimorbidity in Middle-Aged and Older Adults: Insights from the China Health and Retirement Longitudinal Study

BACKGRUOUND

Normalized creatinine-to-cystatin C ratio (NCCR) was reported to approximate relative skeletal muscle mass and diabetes risk. However, the association between NCCR and cardiometabolic multimorbidity (CMM) remains elusive. This study aimed to explore their relationship in a large-scale prospective cohort.

METHODS

This study included 5,849 middle-age and older participants from the China Health and Retirement Longitudinal Study (CHARLS) enrolled between 2011 and 2012. The baseline NCCR was determined as creatinine (mg/dL)/cystatin C (mg/L)×10/body mass (kg). CMM was defined as the simultaneous occurrence of two or more of the following conditions: heart disease, stroke, and type 2 diabetes mellitus. Logistic regression analysis and Cox regression analysis were employed to estimate the relationship between NCCR and CMM. The joint effect of body mass index and NCCR on the risk of CMM were further analyzed.

RESULTS

During a median 4-year follow-up, 227 (3.9%) participants developed CMM. The risk of CMM was significantly decreased with per standard deviation increase of NCCR (odds ratio, 0.72; 95% confidence interval, 0.62 to 0.85) after adjustment for confounders (P<0.001). Further sex-specific analysis found significant negative associations between NCCR and CMM in female either without or with one CMM component at baseline, which was attenuated in males but remained statistically significant among those with one basal CMM component. Notably, non-obese individuals with high NCCR levels had the lowest CMM risk compared to obese counterparts with low NCCR levels in both genders.

CONCLUSION

High NCCR was independently associated with reduced risk of CMM in middle-aged and older adults in China, particularly females.

Lipoprotein profile as a predictor of type 2 diabetes with sarcopenia: a cross-sectional study

PURPOSE

This study investigated the relationship between lipoprotein profiles and sarcopenia in patients with type 2 diabetes mellitus (T2DM). The objective is to provide a solid theoretical foundation and treatment strategies for clinical prevention and management of diabetes, particularly in individuals with concurrent sarcopenia.

METHODS

In this study, we selected inpatients aged over 60 years diagnosed with T2DM who were admitted to the Department of Geriatrics at Qinghai University Affiliated Hospital from July 2023 to June 2024 as research subjects. We collected general patient data, including gender, age, ethnicity, height, weight, and calculated body mass index (BMI). Key indices measured included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoproteins A and B (ApoA and ApoB), phospholipids, lipoprotein(a) [Lp(a)], very low-density lipoprotein (VLDL), and free fatty acids (FFA). Additionally, we assessed limb skeletal muscle mass, grip strength, walking speed, and calculated the appendicular skeletal muscle mass index (ASMI). Based on Asian diagnostic criteria for sarcopenia, patients were categorized into a non-sarcopenic group or a group with T2DM combined with sarcopenia. Baseline laboratory data along with ASMI measurements, grip strength assessments, and walking speeds were statistically analyzed for both groups.

RESULTS

Compared with T2DM patients without sarcopenia, the levels of HbA1c, Lp(a), FFA, serum albumin, TC, TG, HDL-C, ApoA and VLDL in type 2 diabetic patients with sarcopenia were statistically significant (all P < 0.05). When multivariate adjustments were made for these clinical features, age (OR = 1.18, 95%CI: 1.11-1.25, P < 0.001), BMI (OR = 0.81, 95%CI: 0.72-0.92, P < 0.001), ApoA (OR = 0.03, 95%CI: 0.00-0.90, P = 0.043), Lp(a) > = 15.5 mg/dL (OR = 3.14, 95%CI: 1.51-6.54, P = 0.002) and FFA > = 0.48 g/L (OR = 4.11, 95%CI: 1.97-8.57, P < 0.001) were independent predictors of diabetes mellitus with sarcopenia. ROC curve analysis showed that free fatty acids (AUC = 0.721, 95%CI: 0.660-0.782, P < 0.001) in T2DM with sarcopenia has good predictive value judgment.

CONCLUSION

Age, BMI, ApoA, Lp(a), and FFA were independent predictors of T2DM with sarcopenia. Serum free fatty acids have a good predictive value in the judgment of T2DM complicated with sarcopenia.

The non-linear relationships between fat mass and lean body mass with arthritis

INTRODUCTION

Body composition has been associated with various health outcomes, but its specific relationship with arthritis risk remains unclear. The study aimed to examine the associations between lean body mass (LBM) and fat mass (FM) with arthritis risk in men and women and to identify their threshold values.

METHODS

The data were obtained from the CHARLS, a prospective cohort study from 2011 to 2018. Multivariate Cox regression models evaluated the associations between LBM and FM and arthritis risk. Smoothing curves and two-piece linear regression models were applied to identify the inflection points of LBM and FM associated with arthritis risk.

RESULTS

A total of 6,761 participants were included in this study. During a mean follow-up period of 6.66 years, 944 participants (13.96%) developed new-onset arthritis, with an incidence rate of 20.72 per 1,000 person-years. Multivariate Cox regression analysis demonstrated a significant linear association between FM and the risk of new-onset arthritis in men. Individuals in the highest FM quartile (Q4) had the highest risk of developing arthritis (HR = 1.25, 95% CI: 1.03-1.51). Two-piece linear regression models revealed nonlinear relationships between LBM, FM, and arthritis risk. Specifically, in men, LBM was negatively associated with arthritis risk when it was below 43.79 kg (HR = 0.97, 95% CI: 0.95-0.99), but this association was no longer significant above this threshold (HR = 1.01, 95% CI: 0.98-1.03). In women, arthritis risk significantly decreased when LBM exceeded 39.04 kg (HR = 0.92, 95% CI: 0.87-0.96). Additionally, in women, FM exhibited a U-shaped relationship with arthritis risk, with the lowest risk observed at an FM level of 17.16 kg.

CONCLUSIONS

Among Chinese adults aged 45 and older, maintaining appropriate levels of LBM and FM may help reduce arthritis risk. Based on the nonlinear findings, it is recommended to maintain LBM below 43.79 kg for men, above 39.04 kg for women, and to keep FM at approximately 17.16 kg for women, which may be appropriate.

Exploring the relationship between glycemic variability and muscle dysfunction in adults with diabetes: A systematic review

This review is to systematically explore the relationship between muscle dysfunction and diabetes in adults, and to examine the impact of glycemic variability on muscle health and the development of diabetes-related complications. The review was conducted using three databases: MEDLINE, Scopus, and EMBASE, targeting peer-reviewed journal articles written in English and published from January 2014 to September 2024. The methodological quality assessment of the eligible studies was conducted using Joanna Briggs Institute Critical Appraisal Checklists. A total of 17 studies were included. Most studies were undertaken in Asian countries (n = 11) and focused on adults with type 2 diabetes (n = 12). There were 8,392 adults with diabetes, and their mean age ranged from 52 to 75 years old. The measurements for muscle function and glycemic variability varied across studies. The research findings regarding the relationship between muscle dysfunction and glycemic variability metrics among adults with diabetes, both with and without complications were inconsistent. For adults with diabetes and sarcopenic obesity, poor glycemic control was identified as an independent risk factor for sarcopenic obesity. Additionally, all included studies were rated as moderate or high quality in relation to their methodology. In conclusion, this review underscores the complex and inconsistent relationship between glycemic variability and muscle dysfunction in older adults with diabetes. Poor glycemic management is a significant risk factor for sarcopenic obesity, highlighting the need for tailored interventions to improve glycemic control and muscle health in this population.

Low muscle mass index is associated with type 2 diabetes risk in a Latin-American population: a cross-sectional study

Objective

Diabetes mellitus is a growing disease with severe complications. Various scores predict the risk of developing this pathology. The amount of muscle mass is associated with insulin resistance, yet there is no established evidence linking muscle mass with diabetes risk. This work aims to study that relationship.

Research methods and procedures

This cross-sectional study included 1,388 employees. The FINDRISC score was used to assess type 2 diabetes risk, and bioimpedance was used for body composition analysis. Appendicular skeletal muscle mass adjusted by body mass index (ASM/BMI) was analyzed. Sociodemographic, clinical and anthropometric measures were evaluated, logistic regression models with sex stratification were conducted and ROC curves were calculated to determine the ability of ASM/BMI index to predict T2D risk.

Results

It was observed that patients with higher ASM/BMI had a lower FINDRISC score in both men and women (p < 0.001). A logistic regression model showed and association between ASM/BMI and diabetes risk in women [OR: 0.000 (0.000-0.900), p = 0.048], but not in men [OR: 0.267 (0.038-1.878), p = 0.185]. However, when the body mass index variable was excluded from the model, an association was found between muscle mass adjusted to BMI and diabetes risk in both men [OR: 0.000 (0.000-0.016), p < 0.001], and women [OR:0.001 (0.000-0.034), p < 0.001]. Other risk factors were having a low level of physical activity, waist circumference, age and sedentary lifestyle. A ROC curve was built and the optimal ASM/BMI cut-of value for predicting T2D risk was 0.82 with a sensitivity of 53.71% and specificity of 69.3% [AUC of 0.665 (0.64-0.69; p < 0.0001)].

Conclusion

When quantifying the risk of type 2 diabetes in both women and men, assessing muscle mass can help detect adult individuals with a high risk of developing type 2 diabetes.

In people with type 2 diabetes, sarcopenia is associated with the incidence of cardiovascular disease: A prospective cohort study from the UK Biobank

AIM

To investigate the association of sarcopenia with cardiovascular disease (CVD) incidence in people with type 2 diabetes.

MATERIALS AND METHODS

A prospective cohort study with 11 974 White European UK Biobank participants with type 2 diabetes, aged 40-70 years, included. Sarcopenia was defined based on the European Working Group on Sarcopenia in Older People as either non-sarcopenic or sarcopenic. Outcomes included CVD, stroke, heart failure (HF) and myocardial infarction (MI). The association between sarcopenia and the incidence of outcomes was investigated using Cox proportional hazard models adjusted for sociodemographic and lifestyle factors. The rate advancement period was used to estimate the time period by which CVD is advanced because of sarcopenia.

RESULTS

Over a median follow-up of 10.7 years, 1957 participants developed CVDs: 373 had a stroke, 307 had an MI and 742 developed HF. Compared with non-sarcopenia, those with sarcopenia had higher risks of CVD (HR 1.89 [95% CI 1.61; 2.21]), HF (HR 2.59 [95% CI 2.12; 3.18]), stroke (HR 1.90 [95% CI 1.38; 2.63]), and MI (HR 1.56 [95% CI 1.04; 2.33]) after adjustment for all covariates. Those with sarcopenia had CVD incidence rates equivalent to those without sarcopenia who were 14.5 years older. Similar results were found for stroke, HF and MI.

CONCLUSIONS

In people with type 2 diabetes, sarcopenia increased the risk of developing CVD, which might occur earlier than in those without sarcopenia. Therefore, sarcopenia screening and prevention in patients with type 2 diabetes may be useful to prevent the complications of CVD.

Reduced thoracic skeletal muscle size is associated with adverse outcomes in diabetes patients with heart failure and reduced ejection fraction: quantitative analysis of sarcopenia by using cardiac MRI

BACKGROUND

Sarcopenia is frequently found in patients with heart failure with reduced ejection fraction (HFrEF) and is associated with reduced exercise capacity, poor quality of life and adverse outcomes. Recent evidence suggests that axial thoracic skeletal muscle size could be used as a surrogate to assess sarcopenia in HFrEF. Since diabetes mellitus (DM) is one of the most common comorbidities with HFrEF, we aimed to explore the potential association of axial thoracic skeletal muscle size with left ventricular (LV) remodeling and determine its prognostic significance in this condition.

METHODS

A total of 243 diabetes patients with HFrEF were included in this study. Bilateral axial thoracic skeletal muscle size was obtained using cardiac MRI. Patients were stratified by the tertiles of axial thoracic skeletal muscle index (SMI). LV structural and functional indices, as well as amino-terminal pro-B-type natriuretic peptide (NT-proBNP), were measured. The determinants of elevated NT-proBNP were assessed using linear regression analysis. The associations between thoracic SMI and clinical outcomes were assessed using a multivariable Cox proportional hazards model.

RESULTS

Patients in the lowest tertile of thoracic SMI displayed a deterioration in LV systolic strain in three components, together with an increase in LV mass and a heavier burden of myocardial fibrosis (all P < 0.05). Moreover, thoracic SMI (β = -0.25; P < 0.001), rather than body mass index (β = -0.04; P = 0.55), was independently associated with the level of NT-proBNP. The median follow-up duration was 33.6 months (IQR, 20.4-52.8 months). Patients with adverse outcomes showed a lower thoracic SMI (40.1 [34.3, 47.9] cm2/m2 vs. 45.3 [37.3, 55.0] cm2/m2; P < 0.05) but a similar BMI (P = 0.76) compared with those without adverse outcomes. A higher thoracic SMI indicated a lower risk of adverse outcomes (hazard ratio: 0.96; 95% confidence interval: 0.92-0.99; P = 0.01).

CONCLUSIONS

With respect to diabetes patients with HFrEF, thoracic SMI is a novel alternative for evaluating muscle wasting in sarcopenia that can be obtained by a readily available routine cardiac MRI protocol. A reduction in thoracic skeletal muscle size predicts poor outcomes in the context of DM with HFrEF.

Effects of blood flow restriction training on muscle fitness and cardiovascular risk of obese college students

Purpose: The aim of this study was to investigate the effect of blood flow restriction (BFR) combined with low-intensity resistance training (RT) on cardiovascular risk factors in obese individuals. Methods: Twenty-six male obese college students were recruited and randomly assigned to a control group (CON, n = 8), a low-intensity RT group (RT, n = 9), and a combined BFR training and low-intensity RT group (BFRT, n = 9). Results: The subjects in BFRT group showed significant reductions in body fat percentage and waist-to-hip ratio and a significant increase in lean mass and muscle mass; the peak torque, peak power, and endurance ratio of knee extensors and elbow flexors were significantly upregulated; the root mean square (RMS) for the medial femoral muscle, lateral femoral muscle and biceps significantly increased; the diastolic blood pressure (DBP) showed a significant decrease. The BFRT group also showed significant up-regulations in RMS of the difference between the adjacent R-R intervals (RMSSD), high-frequency power (HF) of parasympathetic modulatory capacity, the standard deviation of R-R intervals (SDNN) of overall heart rate variability (HRV) changes and low-frequency power (LF) of predominantly sympathetic activity. In addition, glycated hemoglobin (HbA1C), insulin resistance index (HOMA-IR) and fasting blood glucose (FBG) were all significantly downregulated in BFRT group. In parallel, low-density lipoprotein (LDL-C) significantly reduced while high-density lipoprotein (HDL-C) significantly increased in BFRT group. Conclusion: BFR combined with low-intensity RT training effectively improved body composition index, increased muscle mass, improved neuromuscular activation, enhanced muscle strength and endurance, which in turn improved abnormal glucolipid metabolism and enhanced cardiac autonomic regulation.

Predicting risk of obesity in overweight adults using interpretable machine learning algorithms

Objective

To screen for predictive obesity factors in overweight populations using an optimal and interpretable machine learning algorithm.

Methods

This cross-sectional study was conducted between June 2011 and January 2012. The participants were randomly selected using a simple random sampling technique. Seven commonly used machine learning methods were employed to construct obesity risk prediction models. A total of 5,236 Chinese participants from Ningde City, Fujian Province, Southeast China, participated in this study. The best model was selected through appropriate verification and validation and suitably explained. Subsequently, a minimal set of significant predictors was identified. The Shapley additive explanation force plot was used to illustrate the model at the individual level.

Results

Machine learning models for predicting obesity have demonstrated strong performance, with CatBoost emerging as the most effective in both model validity and net clinical benefit. Specifically, the CatBoost algorithm yielded the highest scores, registering 0.91 in the training set and an impressive 0.83 in the test set. This was further corroborated by the area under the curve (AUC) metrics, where CatBoost achieved 0.95 for the training set and 0.87 for the test set. In a rigorous five-fold cross-validation, the AUC for the CatBoost model ranged between 0.84 and 0.91, with an average AUC of ROC at 0.87 ± 0.022. Key predictors identified within these models included waist circumference, hip circumference, female gender, and systolic blood pressure.

Conclusion

CatBoost may be the best machine learning method for prediction. Combining Shapley's additive explanation and machine learning methods can be effective in identifying disease risk factors for prevention and control.

Association of predicted fat mass and lean body mass with diabetes: a longitudinal cohort study in an Asian population

Objective

The relationship between body composition fat mass (FM) and lean body mass (LBM) and diabetes risk is currently debated, and the purpose of this study was to examine the association of predicted FM and LBM with diabetes in both sexes.

Methods

The current study was a secondary analysis of data from the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) cohort study of 15,463 baseline normoglycemic participants. Predicted LBM and FM were calculated for each participant using anthropometric prediction equations developed and validated for different sexes based on the National Health and Nutrition Examination Survey (NHANES) database, and the outcome of interest was diabetes (types not distinguished) onset. Multivariate Cox regression analyses were applied to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of predicted FM and LBM with diabetes risk and further visualized their associations using a restricted cubic spline function.

Results

The incidence density of diabetes was 3.93/1000 person-years over a mean observation period of 6.13 years. In women, predicted LBM and FM were linearly associated with diabetes risk, with each kilogram increase in predicted LBM reducing the diabetes risk by 65% (HR 0.35, 95%CI 0.17, 0.71; P < 0.05), whereas each kilogram increase in predicted FM increased the diabetes risk by 84% (HR 1.84, 95%CI 1.26, 2.69; P < 0.05). In contrast, predicted LBM and FM were non-linearly associated with diabetes risk in men (all P for non-linearity < 0.05), with an L-shaped association between predicted LBM and diabetes risk and a saturation point that minimized the risk of diabetes was 45.4 kg, while predicted FM was associated with diabetes risk in a U-shape pattern and a threshold point with the lowest predicted FM-related diabetes risk was 13.76 kg.

Conclusion

In this Asian population cohort, we found that high LBM and low FM were associated with lower diabetes risk according to anthropometric equations. Based on the results of the non-linear analysis, we believed that it may be appropriate for Asian men to keep their LBM above 45.4 kg and their FM around 13.76 kg.

The role of predicted lean body mass and fat mass in non-alcoholic fatty liver disease in both sexes: Results from a secondary analysis of the NAGALA study

Objective

High body mass index (BMI) is an important risk factor for non-alcoholic fatty liver disease (NAFLD). However, the association of body composition such as fat mass (FM) and lean body mass (LBM) with NAFLD has not been adequately studied. The purpose of this study was to clarify the contribution of body composition FM and LBM to NAFLD.

Methods

We analyzed data from 7,411 men and 6,840 women in the NAGALA cohort study. LBM and FM were estimated for all subjects using validated anthropometric prediction equations previously developed from the National Health and Nutrition Examination Survey (NHANES). Using multiple logistic regression and restricted cubic spline (RCS) to analyze the association and the dose-response curve of predicted LBM and FM with NAFLD in both sexes.

Results

The prevalence of NAFLD in man and woman subjects was 27.37 and 6.99%, respectively. Predicted FM was positively and linearly associated with NAFLD in both sexes, with each 1 kg increase in predicted FM associated with a 27 and 40% increased risk of NAFLD in men and women, respectively. In contrast, predicted LBM was negatively associated with NAFLD in both sexes, with each 1 kg increase in predicted LBM reducing the risk of NAFLD by 4 and 19% in men and women, respectively. In addition, according to the RCS curve, the risk of NAFLD did not change in men when the predicted LBM was between 47 and 52 kg, and there seemed to be a saturation effect; further, the threshold value of the saturation effect was calculated to be about 52.08 kg by two-piecewise logistic regression, and the protective effect on NAFLD would be significantly enhanced when the man predicted LBM was greater than 52.08 kg.

Conclusion

The current findings suggested that body composition LBM and FM had opposite associations with NAFLD in both sexes, with higher LBM associated with a lower risk of NAFLD and higher FM increasing the risk of NAFLD, especially in women.