Musculoskeletal complications in patients with diabetes mellitus

Research progress of non-coding RNA regulating the role of PANoptosis in diabetes mellitus and its complications

Diabetes is a chronic metabolic disease that is endemic worldwide and is characterized by persistent hyperglycemia accompanied by multiple severe complications, including cardiovascular disease, kidney dysfunction, neuropathy, and retinopathy. The pathogenesis of diabetes mellitus and its complications is multifactorial, involving various molecular and cellular pathways. In recent years, research has indicated that mechanisms of cell death play a significant role in the advancement of diabetes and its complications. PANoptosis is a complex phenomenon caused by three cell death pathways: programmed apoptosis, necroptosis and pyroptosis. The contribution of PANoptosis to diabetes and its complications remains incompletely understood. Non-coding RNA, an important molecule in gene expression regulation, has shown significant regulatory functions in a variety of diseases. This paper reviews the underlying mechanisms of diverse types of non-coding RNAs (including lncRNA, miRNA and circRNA) in regulating PANoptosis and their specific contributions in diabetes, aiming to explore how non-coding RNAs influence PANoptosis and their effects in diabetes.

Types of Injuries and the Severity of Shoulder Dysfunction Associated with Diabetes Mellitus in Patients with Functional Impairment: A Case-Control Study

BACKGROUND/OBJECTIVES

Patients with diabetes have been reported to experience a higher prevalence of shoulder disorders compared to those without diabetes or with other medical conditions. However, the specific types of shoulder injuries and the extent of functional impairment associated with diabetes mellitus remain unclear. This study aimed to assess the association between diabetes and specific shoulder injuries, as well as the degree of functional impairment in affected patients.

METHODS

A case-control study was conducted involving 136 patients with shoulder functional impairment (UCLA Shoulder Scale ≤ 27). The study included 38 patients with diabetes and 98 non-diabetic controls. Shoulder injuries were diagnosed using ultrasonography, focusing on the supraspinatus tendon, long head of the biceps tendon, subscapularis tendon, and the presence of adhesive capsulitis or rotator cuff tears.

RESULTS

Diabetic patients had significantly higher rates of poor shoulder function compared to non-diabetic controls (89.47% vs. 63.26%, adjusted OR [adOR] 5.22, 95% CI 1.57-17.32, p = 0.007). While both groups had high rates of supraspinatus and long head of the biceps tendon injuries (~80%), no significant differences were found between them (p > 0.300). However, diabetic patients were more than three times as likely to have subscapularis tendon injuries (adOR 3.15, 95% CI 1.26-7.90, p = 0.014) and massive rotator cuff tears (adOR 3.76, 95% CI 1.16-12.15, p = 0.027). Additionally, diabetes was associated with a fourfold increased risk of adhesive capsulitis (adOR 4.16, 95% CI 1.20-14.47, p = 0.025).

CONCLUSIONS

Diabetes mellitus is linked to greater functional and structural deterioration of the shoulder, highlighting the importance of considering diabetes as a risk factor for specific shoulder injuries. Early diagnosis and treatment may improve outcomes for diabetic patients with shoulder disorders.

Musculoskeletal Multimorbidity Burden and Trajectory in Relation to Later-Life Holistic Well-Being Among Middle-Aged and Elderly Individuals: A Prospective Study

OBJECTIVES

Understanding the patterns and implications of coexisting musculoskeletal conditions is crucial for developing effective management strategies and improving care for older adults. This study aimed to examine the associations between musculoskeletal multimorbidity burden and trajectory and holistic well-being among middle-aged and older adults.

METHODS

This prospective study employed data from nine consecutive waves of the English Longitudinal Study of Aging (ELSA), spanning 2002-2018. We used latent class trajectory models (LCTM) to identify groups based on changes in musculoskeletal multimorbidity status. Subsequently, we employed linear mixed models to investigate the associations between musculoskeletal disease burden, trajectory groups, and seven dimensions of holistic well-being: Activities of Daily Living (ADLs), Instrumental Activities of Daily Living (IADLs), depression, memory, loneliness, social interactions, and life satisfaction.

RESULTS

In total, 5272 participants (mean age: 71.9 years; SD: 8.9) were included in the final analysis. Four distinct trajectories were identified: a low-burden group (48.37%), an emerging group (14.76%), a moderate-burden group (26.00%), and a persistent burden group (10.87%). After adjustment, the findings demonstrate that the musculoskeletal disorder burden significantly impacts ADLs, depression, memory, social interactions, and life satisfaction in middle-aged and older adults, with minor effects on IADLs and loneliness. Moreover, with the escalation of the burden, its impact significantly intensifies (p for trend is < 0.001). Compared with the low-burden group, participants in both the moderate and persistent burden groups exhibited significantly lower capabilities in ADLs, poorer memory, increased social interactions, and lower life satisfaction. The emerging group displayed a similar trend, though without statistically significant results.

CONCLUSIONS

Our study suggests that the extent and persistence of musculoskeletal disease burden can significantly affect holistic well-being among middle-aged and older individuals.

Correlating COVID-19 severity with biomarker profiles and patient prognosis

COVID-19's long-lasting and complex impacts have become a global concern, with diverse clinical outcomes. This study evaluated 226 participants to understand the clinical spectrum of COVID-19/Long COVID (LC), exploring how disease severity correlates with sociodemographic factors and biomarkers. Determinants related to COVID-19 severity included age (P < 0.001), lower education (P < 0.001), ethnicity (P = 0.003), overweight (P < 0.001), MTHFR gene rs1801133 (P = 0.035), cardiovascular diseases (P = 0.002), diabetes mellitus (DM) (P = 0.006), Factor VIII (FVIII) (P = 0.046), von Willebrand factor (VWF) (P = 0.002), and dimer D (DD) (P < 0.001). Six months later, in a portion of the monitored participants, a significant reduction in FVIII (P < 0.001), VWF (P = 0.002), and DD (P < 0.001) levels was observed, with only DD returning to normal values. Different systemic sequelae were identified, with higher incidences of joint pain and myalgia in participants with a clinical history of DM, chronic lung disease (CLD) and sustained high interleukin 6 values in the convalescent phase. CLD, COVID-19 severity and high DD levels increased the risk of developing dyspnea and palpitations. Women were more likely to develop lower limb phlebitis long-term, while sustained elevated FVIII in the convalescent phase was associated with an increased risk of swelling. Regular physical activity had a protective effect against swelling. This study highlights factors contributing to COVID-19 severity/LC, emphasizing endothelial cell activation as a potential mechanism.

Ultrasonographic Achilles Tendon Measurements and Static and Dynamic Balance in Prediabetes

Background and Objectives: There is a lack of studies examining balance problems and Achilles tendon thickness in prediabetes despite their common occurrence in diabetes mellitus. The aim of this study was to evaluate Achilles tendon size and static and dynamic balance, as well as the role of the Achilles tendon in balance, in prediabetic patients. Materials and Methods: A total of 96 participants were divided into three groups: (1) the control group, consisting of participants without diabetes mellitus; (2) the prediabetes group; and (3) the diabetes mellitus group. Ultrasonographic measurements of Achilles tendon sizes (thickness, width and area) were performed. Dynamic balance was assessed using the Berg Balance Scale, and static balance (the Fall and Stability Indices) was assessed using a Tetrax device. The Self-Leeds Assessment of Neuropathic Symptoms and Signs was utilized to identify neuropathic pain. Results: In the prediabetes group, the median dynamic balance scores [54.0 (51.0-56.0)] were lower than those of the control group [55.0 (54.0-56.0)] but higher than those of the patients with diabetes mellitus [52.50 (49.0-54.25)]; however, this difference did not reach statistical significance. The ultrasonographic measurements of the Achilles tendon size were similar among the three groups. On the other hand, in the prediabetes group, a positive correlation was observed between the bilateral Achilles tendon anterior-posterior thickness and Fall Index score (p = 0.045), while a negative correlation was found between the left Achilles tendon anterior-posterior thickness and the Berg Balance Score (p = 0.045). Conclusions: In prediabetes, neither Achilles tendon size nor static or dynamic balance appears to be significantly affected. However, in prediabetic patients, increased Achilles tendon thickness appears to be associated with increased risk of falls and decreased balance.

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study

BACKGROUND

Frailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.

METHODS

We conducted a cross-sectional and longitudinal study using data from the "Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC)," which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).

RESULTS

At baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model's AUC was 0.943 (95%CI 0.913-0.974), indicating good discrimination, with Hosmer-Lemeshow (H-L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model's AUC was 0.722 (95%CI 0.656-0.788), indicating moderate discrimination, with H-L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.

CONCLUSION

Frailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

The association between diabetes mellitus and musculoskeletal disorders: a systematic review and meta-analysis

Background

Despite the fact that DM patients are living longer, research on the prevalence of MSDs and other related illnesses is still lacking compared to that of other comorbidities. This study systematically reviewed and meta-analyzed cohort studies to determine the association between diabetes mellitus (DM) and musculoskeletal disorders (MSDs).

Methods

A comprehensive search of international databases, including Medline (PubMed), Web of Science, Scopus, and Embase, was conducted up to June 2023 to identify relevant studies investigating the association between MSDs and DM.

Results

The meta-analysis included ten cohort studies with a total of 308,445 participants. The pooled risk ratio (RR) estimate for the association between MSDs and DM was 1.03 (95% CI 1.00-1.06). Based on subgroup analysis, the association between longer duration (more than 7), European, below the age of 70, and female patients was higher than the others.

Conclusion

In conclusion, the results of this meta-analysis suggest that there may be an association between MSDs and diabetes in people with diabetes. These findings add to the existing knowledge on this topic and highlight the importance of recognition and management of MSDs in people with DM. There is a need for further research to investigate the underlying mechanisms and to develop targeted interventions for the prevention and management of MSDs in this population.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=381787, identifier CRD42022381787.

Enhancing digital health services: A machine learning approach to personalized exercise goal setting

Background

The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users' dynamic behavior and the changing in their health conditions.

Objective

This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.

Methods

We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyze time series data and infer user's exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and running.

Results

In our study, we conducted the statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal-setting strategies. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning approach.

Conclusions

Our study demonstrates the adaptability of machine learning algorithm to users' exercise preferences and behaviors in exercise goal setting, emphasizing the substantial influence of goal design on service effectiveness.

Diabetic microvascular disease in non-classical beds: the hidden impact beyond the retina, the kidney, and the peripheral nerves

Diabetes microangiopathy, a hallmark complication of diabetes, is characterised by structural and functional abnormalities within the intricate network of microvessels beyond well-known and documented target organs, i.e., the retina, kidney, and peripheral nerves. Indeed, an intact microvascular bed is crucial for preserving each organ's specific functions and achieving physiological balance to meet their respective metabolic demands. Therefore, diabetes-related microvascular dysfunction leads to widespread multiorgan consequences in still-overlooked non-traditional target organs such as the brain, the lung, the bone tissue, the skin, the arterial wall, the heart, or the musculoskeletal system. All these organs are vulnerable to the physiopathological mechanisms that cause microvascular damage in diabetes (i.e., hyperglycaemia-induced oxidative stress, inflammation, and endothelial dysfunction) and collectively contribute to abnormalities in the microvessels' structure and function, compromising blood flow and tissue perfusion. However, the microcirculatory networks differ between organs due to variations in haemodynamic, vascular architecture, and affected cells, resulting in a spectrum of clinical presentations. The aim of this review is to focus on the multifaceted nature of microvascular impairment in diabetes through available evidence of specific consequences in often overlooked organs. A better understanding of diabetes microangiopathy in non-target organs provides a broader perspective on the systemic nature of the disease, underscoring the importance of recognising the comprehensive range of complications beyond the classic target sites.

Advances in the Management of Diabetes Mellitus: A Focus on Personalized Medicine

Diabetes mellitus poses a substantial global health challenge, necessitating innovative approaches to improve patient outcomes. Conventional one-size-fits-all treatment strategies have shown limitations in addressing the diverse nature of the disease. In recent years, personalized medicine has emerged as a transformative solution, tailoring treatment plans based on individual genetic makeup, lifestyle factors, and health characteristics. This review highlights the role of genetic screening in predicting diabetes susceptibility and response to treatment, as well as the potential of pharmacogenomics in optimizing medication choices. Moreover, it discusses the incorporation of lifestyle modifications and behavioral interventions to empower patients in their health journey. Telemedicine and remote patient monitoring are also examined for their role in enhancing accessibility and adherence. Ethical considerations and challenges in implementing personalized medicine are addressed. The review envisions a future where personalized medicine becomes a cornerstone in diabetes management, ensuring improved patient outcomes and fostering more effective and patient-centric care on a global scale.