The co-existence of peripheral and vestibular neuropathy in diabetes: a cross-sectional study.

PURPOSE: Diabetic neuropathy can lead to decreased peripheral sensation and motor neuron dysfunction associated with impaired postural control and risk of falling. However, the relationship between decreased peripheral sensation and impaired vestibular function in diabetes mellitus is poorly investigated. Therefore, the aim of this study was to investigate the relationship between peripheral and autonomic measurements of diabetic neuropathy and measurements of vestibular function. METHODS: A total of 114 participants with type 1 diabetes (n = 52), type 2 diabetes (n = 51) and controls (n = 11) were included. Vestibular function was evaluated by video head impulse testing. Peripheral neuropathy was assessed by quantitative sensory testing and nerve conduction. Autonomic neuropathy using the COMPASS 31 questionnaire. Data were analyzed according to data type and distribution. RESULTS: Measurements of vestibular function did not differ between participants with type 1 diabetes, type 2 diabetes or controls (all p-values above 0.05). Subgrouping of participants according to the involvement of large-, small- or autonomic nerves did not change this outcome. Correlation analyses showed a significant difference between COMPASS 31 and right lateral gain value (ρ = 0.23, p = 0.02,), while no other significant correlations were found. CONCLUSION: Diabetic neuropathy does not appear to impair vestibular function in diabetes, by means of the VOR. CLINICAL TRIALS: NCT05389566, May 25th, 2022.

Effects of Incretin Therapy on Skeletal Health in Type 2 Diabetes-A Systematic Review.

Diabetes poses a significant risk to bone health, with Type 1 diabetes (T1D) having a more detrimental impact than Type 2 diabetes (T2D). The group of hormones known as incretins, which includes gastric inhibitory peptide (GIP) and glucagon-like peptide 1 (GLP-1), play a role in regulating bowel function and insulin secretion during feeding. GLP-1 receptor agonists (GLP-1 RAs) are emerging as the primary treatment choice in T2D, particularly when atherosclerotic cardiovascular disease is present. Dipeptidyl peptidase 4 inhibitors (DPP-4is), although less potent than GLP-1 RAs, can also be used. Additionally, GLP-1 RAs, either alone or in combination with GIP, may be employed to address overweight and obesity. Since feeding influences bone turnover, a relationship has been established between incretins and bone health. To explore this relationship, we conducted a systematic literature review following the PRISMA guidelines. While some studies on cells and animals have suggested positive effects of incretins on bone cells, turnover, and bone density, human studies have yielded either no or limited and conflicting results regarding their impact on bone mineral density (BMD) and fracture risk. The effect on fracture risk may vary depending on the choice of comparison drug and the duration of follow-up, which was often limited in several studies. Nevertheless, GLP-1 RAs may hold promise for people with T2D who have multiple fracture risk factors and poor metabolic control. Furthermore, a potential new area of interest is the use of GLP-1 RAs in fracture prevention among overweight and obese people. Based on this systematic review, existing evidence remains insufficient to support a positive or a superior effect on bone health to reduce fracture risk in people with T2D. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Bone parameters in T1D and T2D assessed by DXA and HR-pQCT - A cross-sectional study: The DIAFALL study.

INTRODUCTION/AIM: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) have an increased risk of fractures due to skeletal fragility. We aimed to compare areal bone mineral density (aBMD), volumetric BMD (vBMD), cortical and trabecular measures, and bone strength parameters in participants with diabetes vs. controls. METHODS: In a cross-sectional study, we included adult participants with T1D (n = 111, MA = 52.9 years), T2D (n = 106, MA = 62.1 years) and controls (n = 328, MA = 57.7 years). The study comprised of DXA scans and HR-pQCT scans, biochemistry, handgrip strength (HGS), Timed Up and GO (TUG), vibration perception threshold (VPT), questionnaires, medical histories, alcohol use, and previous fractures. Group comparisons were performed after adjustment for sex, age, BMI, diabetes duration, HbA1c, alcohol, smoking, previous fractures, postmenopausal, HGS, TUG, and VPT. RESULTS: We found decreased aBMD in participants with T1D at the femoral neck (p = 0.028), whereas T2D had significantly higher aBMD at peripheral sites (legs, arms, p < 0.01) vs. controls. In T1D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), cortical area (p = 0.002) and thickness (p < 0.001), lower cortical porosity(p = 0.008), higher stiffness (p = 0.002) and failure load (p = 0.003) at radius and higher vBMD (p = 0.003), cortical vBMD(p < 0.001), bone stiffness (p = 0.023) and failure load(p = 0.044) at the tibia than controls. In T2D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), trabecular vBMD (p < 0.001), cortical area (p < 0.001) and thickness (p < 0.001), trabecular number (p = 0.024), lower separation (p = 0.010), higher stiffness (p < 0.001) and failure load (p < 0.001) at the radius and higher total vBMD (p < 0.001), cortical vBMD (p < 0.011), trabecular vBMD (p = 0.001), cortical area (p = 0.002) and thickness (p = 0.021), lower trabecular separation (p = 0.039), higher stiffness (p < 0.001) and failure load (p = 0.034) at tibia compared with controls. CONCLUSION: aBMD measures were as expected lower in T1D and higher in T2D than controls. Favorable bone microarchitecture and strength parameters were seen at the tibia and radius for T1D and T2D.

Impaired postural control in diabetes-a predictor of falls?

New evidence points toward that impaired postural control judged by center of pressure measures during quiet stance is a predictor of falls in people with type 1 and type 2 diabetes-even in occurrence of well-known risk factors for falls. INTRODUCTION/AIM: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) are at risk of falling, but the association with impaired postural control is unclear. Therefore, the aim was to investigate postural control by measuring the center of pressure (CoP) during quiet standing and to estimate the prevalence ratio (PR) of falls and the fear of falling among people with diabetes compared to controls. METHODS: In a cross-sectional study, participants with T1D (n = 111) and T2D (n = 106) and controls without diabetes (n = 328) were included. Study procedures consisted of handgrip strength (HGS), vibration perception threshold (VPT), orthostatism, visual acuity, and postural control during quiet stance measured by CoPArea (degree of body sway) and CoPVelocity (speed of the body sway) with "eyes open," "eyes closed" in combination with executive function tasks. A history of previous falls and fear of falling was collected by a questionnaire. CoPArea and CoPVelocity measurements were analyzed by using a multiple linear regression model. The PR of falls and the fear of falling were estimated by a Poisson regression model. Age, sex, BMI, previous falls, alcohol use, drug, HGS, VPT, orthostatism, episodes of hypoglycemia, and visual acuity were covariates in multiple adjusted analyses. RESULTS: Significantly larger mean CoPArea measures were observed for participants with T1D (p = 0.022) and T2D (0.002), whereas mean CoPVelocity measures were only increased in participants with T2D (p = 0.027) vs. controls. Additionally, T1D and T2D participants had higher PRs for falls (p = 0.044, p = 0.014) and fear of falling (p = 0.006, p < 0.001) in the crude analyses, but the PRs reduced significantly when adjusted for mean CoPArea and mean CoPVelocity, respectively. Furthermore, multiple adjusted PRs were significantly higher than crude the analyses.    CONCLUSION: Impaired postural control during quiet stance was seen in T1D and T2D compared with controls even in the occurrence of well-known risk factors. and correlated well with a higher prevalence of falls.

Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models.

BACKGROUND: Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data. METHODS: Cross-sectional cohort study of 18 430 adults and children from the US population. Participants were examined with whole-body dual X-ray absorptiometry (DXA) scans, anthropometric assessment, and answered a demographic questionnaire. The primary outcomes were predicted total lean body mass (predLBM), total body fat mass (predFM), and trunk fat mass (predTFM) compared with reference values from DXA scans. RESULTS: Participants were randomly partitioned into 70% training (12 901) data and 30% validation (5529) data. The prediction model for predLBM compared with lean body mass measured by DXA (DXALBM) had a Pearson's correlation coefficient of R = 0.99 with a standard error of estimate (SEE) = 1.88 kg (P < .001). The prediction model for predFM compared with fat mass measured by DXA (DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.91 kg (P < .001). The prediction model for predTFM compared with DXA measured trunk fat mass (DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.13 kg (P < .001). CONCLUSIONS: In this study, neural network models based on anthropometric and demographic data could precisely predict body muscle and fat composition. Precise body estimations are relevant in a broad range of clinical diabetes applications, prevention, and epidemiological research.

Falls and Fractures in Diabetes-More than Bone Fragility.

PURPOSE OF REVIEW: Based on a systematic literature search, we performed a comprehensive review of risk factors for falls and fractures in patients with diabetes. RECENT FINDINGS: Patients with diabetes have an increased risk of fractures partly explained by increased bone fragility. Several risk factors as altered body composition including sarcopenia and obesity, impaired postural control, gait deficits, neuropathy, cardiovascular disease, and other co-morbidities are considered to increase the risk of falling. Diabetes and bone fragility is well studied, but new thresholds for fracture assessment should be considered. In general, the risk factors for falls in patients with diabetes are well documented in several studies. However, the fall mechanisms among diabetic patients have only been assessed in few studies. Thus, a gab of knowledge exits and may influence the current understanding and treatment, in order to reduce the risk of falling and thereby prevent fractures.

Validity of diagnostic coding for undernutrition in hospitals.

BACKGROUND & AIMS: We examined the accuracy of ICD-10 diagnostic coding for undernutrition in Danish Hospitals, including the use of Nutritional Risk Screening 2002 guidelines. METHODS: We investigated a random sample of hospitalized patients registered in the Danish National Registry of Patients with a discharge diagnosis of undernutrition between 2002 and 2011 in the North Denmark Region. Based on medical record review we estimated the positive predictive value (PPV) of the undernutrition diagnosis. Stratification was made by calendar period, hospital type (local vs. university), gender, age, speciality and type of diagnosis code. Subsequently, we evaluated the use of Nutritional Risk Screening 2002 as recommended by the European Society of Clinical Nutrition and Metabolism and the Danish National Board of Health. RESULTS: We could retrieve the medical records of 172/200 sampled patients with undernutrition (86%). Nineteen patients were classified as being definite (screening-confirmed) cases and another 103 patients as probable (clinically-confirmed) cases of undernutrition, yielding a PPV of 11.0% (95% confidence interval [CI]: 6.8-16.7) for definite undernutrition and 70.9% (95% CI: 63.5-77.6) for any confirmed undernutrition. Only 26.2% of patients coded with undernutrition had been screened according to the Nutritional Risk Screening 2002. CONCLUSIONS: This population-based study found modest agreement between ICD-10 codes for undernutrition compared to a standard method (Nutritional Risk Screening 2002) as documented in medical doctors' records in Danish hospitals. Diagnoses of undernutrition contained in hospital discharge registries should be used with caution.