XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Type 2 diabetes mellitus (T2DM) represents a major health burden for the elderly population, affecting approximately 25% of people over the age of 65 years. This percentage is expected to increase dramatically in the next decades in relation to the increased longevity of the population observed in recent years. Beyond microvascular and macrovascular complications, sarcopenia has been described as a new diabetes complication in the elderly population. Increasing attention has been paid by researchers and clinicians to this age-related condition—characterized by loss of skeletal muscle mass together with the loss of muscle power and function—in individuals with T2DM; this is due to the heavy impact that sarcopenia may have on physical and psychosocial health of diabetic patients, thus affecting their quality of life. The aim of this narrative review is to provide an update on: (1) the risk of sarcopenia in individuals with T2DM, and (2) its association with relevant features of patients with T2DM such as age, gender, body mass index, disease duration, glycemic control, presence of microvascular or macrovascular complications, nutritional status, and glucose-lowering drugs. From a clinical point of view, it is necessary to improve the ability of physicians and dietitians to recognize early sarcopenia and its risk factors in patients with T2DM in order to make appropriate therapeutic approaches able to prevent and treat this condition.

A Narrative Review on Sarcopenia in Type 2 Diabetes Mellitus: Prevalence and Associated Factors

Energy metabolism, indirect calorimetry, and nutrition

Background Sarcopenia was a frequent chronic complication in patients with type 2 diabetes mellitus (T2DM), and previous evidence showed conflicting results regarding the prevalence and risk factors of sarcopenia in T2DM. In the current study, we aimed at systematically exploring the prevalence and risk factors of sarcopenia in patients with T2DM. Methods PubMed, Embase, and Cochrane Central Register of Controlled Trials were systematically searched to identify observational studies which investigated the prevalence and risk factors of sarcopenia in patients with T2DM. The quality of individual included studies was evaluated using The Newcastle-Ottawa scale. Pooled effects regarding prevalence and associated factors were calculated using random-effects models. The potential publication bias was assessed via funnel plot and Egger test. Results Twenty-eight studies involving 16,800 patients were included in our meta-analysis. The pooled prevalence of sarcopenia in patients with T2DM was 18% (95% CI 0.15-0.22; I2 = 97.4%). The pooled results showed that elder age (OR 4.73; 95% CI 4.30-5.19; I2 = 85.6%), male gender, chronic hyperglycemia (higher HbA1c) (OR 1.16; 95% CI 1.05-2.47; I2 = 99.2%) and osteoporosis (OR 1.16; 95% CI 1.05-2.47; I2 = 99.2%) was predictors for sarcopenia, whereas patients with lower BMI (OR 1.16; 95% CI 1.05-2.47; I2 = 99.2%) and metformin administrations (OR 1.16; 95% CI 1.05-2.47; I2 = 99.2%) were not prone to get sarcopenia. The funnel plot and statistical tests showed no obvious publication bias. Conclusions Sarcopenia was frequent in T2DM patients. Elder age, male gender and chronic hyperglycemia, Osteoporosis were significant risk factors for Sarcopenia. Lower BMI and metformin administrations were associated with lower risk of sarcopenia.

/pdf/the-prevalence-and-risk-factors-of-sarcopenia-in-patients-2hxeh6f2x5.pdf

The prevalence and risk factors of sarcopenia in patients with type 2 diabetes mellitus: a systematic review and meta-analysis

The European Working group on Sarcopenia in Older People recently updated the diagnostic criteria for sarcopenia. It is yet unclear how these modified criteria influence the rate of diagnosis in high risk populations, such as liver cirrhosis. We therefore assessed if the new diagnostic criteria for sarcopenia impacts on sarcopenia prevalence in liver cirrhosis. Within two years 114 cirrhotic patients were prospectively enrolled in the study. Sarcopenia was determined by muscle strength (handgrip strength), muscle mass (lumbal muscle index) and muscle performance (gait speed). Using the 2019 definition, the rate of pre-sarcopenia was significantly lower (30.7% versus 3.5%) due to the different starting points (2010 muscle mass, 2019 muscle strength) and cut-off values (muscle strength). The change in diagnostic criteria for sarcopenia drastically influences the rate of pre-sarcopenia diagnosis in cirrhotics. To evaluate, which diagnostic criteria should be chosen to diagnose sarcopenia in liver cirrhosis patients, prospective studies are needed.

https://www.mdpi.com/2072-6643/12/2/547/pdf

Sarcopenia and Liver Cirrhosis-Comparison of the European Working Group on Sarcopenia Criteria 2010 and 2019.

Difference in sarcopenia prevalence and associated factors according to 2010 and 2018 European consensus (EWGSOP) in elderly patients with type 2 diabetes mellitus.

CONTEXT Malnutrition has a negative impact on patients with cancer. Identifying risk, nutritional status, and functional capacity can contribute to adequate and early nutritional therapy, which can reduce unfavorable clinical outcomes. OBJECTIVE To evaluate and summarize the main instruments of nutritional assessment and functional capacity and associate their results with clinical outcomes in hospitalized patients with cancer. DATA SOURCES A systematic search was performed in the PubMed/MEDLINE, Embase, SciELO, and LILACS databases. Studies in which researchers evaluated and compared screening, nutritional assessment, and functional capacity instruments and their associations with clinical outcomes were included. DATA EXTRACTION The data were extracted by 2 independent reviewers. RESULTS A total of 29 studies met the inclusion criteria (n = 20 441 individuals). The Nutritional Risk Screening-2002 (NRS-2002) and Patient-Generated Subjective Global Assessment (PG-SGA) were the most common tools used for nutritional assessment. High nutritional risk according to the NRS-2202 and worse nutritional status according to the PG-SGA and Subjective Global Assessment were positively associated with a longer hospital stay and mortality. Low functional capacity, according to handgrip strength, was associated with longer hospital stay and nutrition impact symptoms. CONCLUSIONS Tools such as the NRS-2002, PG-SGA, Subjective Global Assessment, and handgrip strength assessment are efficacious for assessing unfavorable clinical outcomes in hospitalized patients with cancer.

https://academic.oup.com/nutritionreviews/advance-article-pdf/doi/10.1093/nutrit/nuab090/41364525/nuab090.pdf

Methods of nutritional assessment and functional capacity in the identification of unfavorable clinical outcomes in hospitalized patients with cancer: a systematic review.

Evaluation of the resting energy expenditure (REE) is essential to ensure an appropriate dietary prescription for patients with type 2 diabetes. The aim of this record was to evaluate the accuracy of predictive equations for REE estimation in patients with type 2 diabetes, considering indirect calorimetry (IC) as the reference method. A cross-sectional study was performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland–Altman plots, paired t-tests, and Pearson’s correlation coefficients. Sixty-two patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1–36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (− 1.8% difference) and Oxford in men (− 1.3% difference). In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.

/pdf/predictive-equations-for-evaluation-for-resting-energy-3wmc1jrgp3.pdf

Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?

BACKGROUND In patients with type 2 diabetes mellitus (DM), an accurate assessment of food intake is essential for clinical nutritional management. Tools such as the food frequency questionnaire (FFQ) and 24-h food record (24HR) identify dietary habits in support of dietary planning. However, it is possible that these tools have reporting errors with respect to assessing food intake, particularly energy intake (EI). METHODS A cross-sectional study was conducted in patients with type 2 DM. EI was assessed by the FFQ and 24HR tools. Resting energy expenditure (REE) was measured by indirect calorimetry. Data were analysed using a kappa test, t-test and Spearman's correlation coefficients. Under-reporting was assessed using the EI/REE ratio. Patients with values <1.18 and <1.10 for FFQ and 24HR, respectively, were considered as under-reporting. RESULTS We evaluated 55 patients [mean (SD) 62.7 (5.3) years old, duration of diabetes 11.2 (7.3) years, 52.7% female]. The mean (SD) EI assessed by FFQ was 1797.7 (641.3) and as assessed by 24HR was 1624 (484.8) kcal day-1 . The mean (SD) REE was 1641.3 (322.3) kcal day-1 . The mean (SD) ratios FFQ/REE and 24HR/REE were 1.11 (0.38) and 1.01 (0.30), respectively. The tools showed a moderate agreement for under-reporting of EI (kappa = 0.404; P = 0.003). Moderate and positive correlations between REE were observed with FFQ (r = 0.321; P = 0.017) and 24HR (r = 0.364; P = 0.006). According to the tools, the under-reporting was observed in approximately 65% of patients. CONCLUSIONS The majority of patients with type 2 DM under-reported their calorie intake, as assessed by FFQ and 24HR. REE showed a positive correlation with both tools.

Under-reporting of the energy intake in patients with type 2 diabetes

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Difference in sarcopenia prevalence and associated factors according to 2010 and 2018 European consensus (EWGSOP) in elderly patients with type 2 diabetes mellitus.

Methods of nutritional assessment and functional capacity in the identification of unfavorable clinical outcomes in hospitalized patients with cancer: a systematic review.

Predictive equations for evaluation for resting energy expenditure in Brazilian patients with type 2 diabetes: what can we use?

Under-reporting of the energy intake in patients with type 2 diabetes