Energy Expenditure in People with Diabetes Mellitus: A Review

Resting energy expenditure differs among individuals with different levels of perceived thermal sensitivity: A cross-sectional study

Metabolic rate has been used in thermophysiological models for predicting the thermal response of humans. However, only a few studies have investigated the association between an individual's trait-like thermal sensitivity and resting energy expenditure (REE), which resulted in inconsistent results. This study aimed to explore the association between REE and perceived thermal sensitivity. The REE of healthy adults was measured using an indirect calorimeter, and perceived thermal intolerance and sensation in the body were evaluated using a self-administered questionnaire. In total, 1567 individuals were included in the analysis (women = 68.9%, age = 41.1 ± 13.2 years, body mass index = 23.3 ± 3.3 kg/m2, REE = 1532.1 ± 362.4 kcal/d). More women had high cold intolerance (31.8%) than men (12.7%), and more men had high heat intolerance (23.6%) than women (16.1%). In contrast, more women experienced both cold (53.8%) and heat (40.6%) sensations in the body than men (cold, 29.1%; heat, 27.9%). After adjusting for age, fat-free mass, and fat mass, lower cold intolerance, higher heat intolerance, and heat sensation were associated with increased REE only in men (cold intolerance, P for trend = .001; heat intolerance, P for trend = .037; heat sensation, P = .046), whereas cold sensation was associated with decreased REE only in women (P = .023). These findings suggest a link between the perceived thermal sensitivity and REE levels in healthy individuals.

24-h energy expenditure in people with type 1 diabetes: impact on equations for clinical estimation of energy expenditure

BACKGROUND/OBJECTIVES

Type 1 diabetes (T1D) is associated with an increase in resting metabolic rate (RMR), but the impact of T1D on other components of 24-h energy expenditure (24-h EE) is not known. Also, there is a lack of equations to estimate 24-h EE in patients with T1D. The aims of this analysis were to compare 24-h EE and its components in young adults with T1D and healthy controls across the spectrum of body mass index (BMI) and derive T1D-specific equations from clinical variables.

SUBJECTS/METHODS

Thirty-three young adults with T1D diagnosed ≥1 year prior and 33 healthy controls matched for sex, age and BMI were included in this analysis. We measured 24-h EE inside a whole room indirect calorimeter (WRIC) and body composition with dual x-ray absorptiometry.

RESULTS

Participants with T1D had significantly higher 24-h EE than healthy controls (T1D = 2047 ± 23 kcal/day vs control= 1908 ± 23 kcal/day; P < 0.01). We derived equations to estimate 24-h EE with both body composition (fat free mass + fat mass) and anthropometric (weight + height) models, which provided high coefficients of determination (R2 = 0.912 for both). A clinical model that did not incorporate spontaneous physical activity yielded high coefficients of determination as well (R2 = 0.897 and R2 = 0.880 for body composition and anthropometric models, respectively).

CONCLUSION

These results confirm that young adults with established T1D have increased 24-h EE relative to controls without T1D. The derived equations from clinically available variables can assist clinicians with energy prescriptions for weight management in patients with T1D.

Causal relationship between basal metabolic rate and kidney function: a bidirectional two-sample mendelian randomization study

Background

The relationship between basal metabolic rate (BMR) and Chronic kidney disease (CKD) remains unclear and controversial. In this study, we investigated the causal role of BMR in renal injury, and inversely, whether altered renal function causes changes in BMR.

Methods

In this two-sample mendelian randomization (MR) study, Genetic data were accessed from published genome-wide association studies (GWAS) for BMR ((n = 454,874) and indices of renal function, i.e. estimated glomerular filtration rate (eGFR) based on creatinine (n =1, 004, 040), CKD (n=480, 698), and blood urea nitrogen (BUN) (n =852, 678) in European. The inverse variance weighted (IVW) random-effects MR method serves as the main analysis, accompanied by several sensitivity MR analyses. We also performed a reverse MR to explore the causal effects of the above indices of renal function on the BMR.

Results

We found that genetically predicted BMR was negatively related to eGFR, (β= -0.032, P = 4.95*10-12). Similar results were obtained using the MR-Egger (β= -0.040, P = 0.002), weighted median (β= -0.04, P= 5.35×10-11) and weighted mode method (β= -0.05, P=9.92×10-7). Higher BMR had a causal effect on an increased risk of CKD (OR =1.36, 95% CI = 1.11-1.66, P =0.003). In reverse MR, lower eGFR was related to higher BMR (β= -0.64, P = 2.32×10-6, IVW analysis). Bidirectional MR supports no causal association was observed between BMR and BUN. Sensitivity analyses confirmed these findings, indicating the robustness of the results.

Conclusion

Genetically predicted high BMR is associated with impaired kidney function. Conversely, genetically predicted decreased eGFR is associated with higher BMR.

Poorly controlled glycemia and worse beta cell function associate with higher resting and total energy expenditure in adults with obesity and type 2 diabetes: A doubly labeled water study

BACKGROUND

Some studies comparing persons with and without type 2 diabetes (T2DM) show no difference in resting energy expenditure (REE). However, the degree of glycemic control may be a crucial factor in determining energy requirements. Few studies have employed the doubly labeled water (DLW) method in persons with T2DM to objectively measure daily energy expenditure.

AIMS

To determine relationships between glycemia, body composition, and energy expenditure in adults with obesity and T2DM. We hypothesized that worse hyperglycemia, insulin resistance, and beta cell function would associate with higher resting and total energy expenditure (TEE).

METHODS

Two cohorts age 31-50 years were included: 78 with obesity and T2DM, 19 with normal weight and no chronic disease. Baseline data from clinical biomarkers, intravenous glucose tolerance tests, DXA and MRI for body composition, and dietary intakes were used in multivariable regression models to predict REE and TEE. Additionally, comparisons were made by categorizing participants as having controlled or uncontrolled glycemia based on glucose levels ≥175 mg/dL.

RESULTS

REE was higher in participants with T2DM by 534.08 ± 74.35 kcal/d (p < 0.001). Higher fasting glucose and HbA1C levels associated with higher TEE. Abdominal SAT and VAT were also predictors in regression models accounting for 76 % of the variance in REE and 89 % of TEE. Participants with uncontrolled glycemia had 22 % higher adipose/lean ratio, two-fold higher VAT/SAT ratio, 21 % higher HOMA-IR score, and worse beta cell function (mean difference in HOMA2-%β of 74.09 ± 14.01, p < 0.001) than those with controlled glycemia. Both REE and TEE were significantly higher in uncontrolled glycemia, difference in REE of 154.17 ± 96.28 kcals/day (p = 0.04) and difference in TEE of 480.64 ± 215.45 kcals/day (p = 0.03).

CONCLUSIONS

Poor beta cell function and uncontrolled glycemia associate with higher REE and TEE in persons with obesity and T2DM. This study is registered with clinicaltrials.gov identifier: NCT01239550.

Validity of Actigraph for Measuring Energy Expenditure in Healthy Adults: A Systematic Review and Meta-Analysis

PURPOSE

The objective of this systematic review and meta-analysis was to assess the validity of the Actigraph triaxial accelerometer device in measuring physical activity energy expenditure (PAEE) in healthy adults, with indirect calorimetry (IC) serving as the validity criterion.

METHODS

A comprehensive search was conducted using the PubMed, Web of Science, and sportdiscuss databases, in addition to manual searches for supplementary sources. Search strategies were employed that involved conducting single keyword searches using the terms "gt3x" and "Actigraph gt3x". The literature search encompassed the timeframe spanning from 1 January 2010 to 1 March 2023. The methodological quality of the studies included in the analysis was evaluated using both the Downs and Black checklist and the Consensus-Based Criteria for Selection of Measurement Instruments (COSMIN) checklist. The meta-analysis was conducted using the Review Manager 5.4 software. The standardized mean difference (SMD) was calculated and expressed as a 95% confidence interval (CI). The significance level was set at α = 0.05. A systematic assessment of the Actigraph's performance was conducted through the descriptive analysis of computed effect sizes.

RESULTS

A total of 4738 articles were retrieved from the initial search. After eliminating duplicate articles and excluding those deemed irrelevant, a comprehensive analysis was conducted on a total of 20 studies, encompassing a combined sample size of 1247 participants. The scores on the Downs and Black checklist ranged from 10 to 14, with a mean score of 11.35. The scores on the COSMIN checklist varied from 50% to 100%, with an average score of 65.83%. The meta-analysis findings revealed a small effect size (SMD = 0.01, 95% CI = 0.50-0.52, p = 0.97), indicating no statistically significant difference (p > 0.05).

CONCLUSIONS

The meta-analysis revealed a small effect size when comparing the Actigraph and IC, suggesting that the Actigraph can be utilized for assessing total PAEE. Descriptive analyses have indicated that the Actigraph device has limited validity in accurately measuring energy expenditure during specific physical activities, such as high-intensity and low-intensity activities. Therefore, caution should be exercised when utilizing this device for such purposes. Furthermore, there was a significant correlation between the activity counts measured by the Actigraph and the PAEE, indicating that activity counts can be utilized as a predictive variable for PAEE.

Defining Usual Oral Temperature Ranges in Outpatients Using an Unsupervised Learning Algorithm

Importance

Although oral temperature is commonly assessed in medical examinations, the range of usual or "normal" temperature is poorly defined.

Objective

To determine normal oral temperature ranges by age, sex, height, weight, and time of day.

Design, Setting, and Participants

This cross-sectional study used clinical visit information from the divisions of Internal Medicine and Family Medicine in a single large medical care system. All adult outpatient encounters that included temperature measurements from April 28, 2008, through June 4, 2017, were eligible for inclusion. The LIMIT (Laboratory Information Mining for Individualized Thresholds) filtering algorithm was applied to iteratively remove encounters with primary diagnoses overrepresented in the tails of the temperature distribution, leaving only those diagnoses unrelated to temperature. Mixed-effects modeling was applied to the remaining temperature measurements to identify independent factors associated with normal oral temperature and to generate individualized normal temperature ranges. Data were analyzed from July 5, 2017, to June 23, 2023.

Exposures

Primary diagnoses and medications, age, sex, height, weight, time of day, and month, abstracted from each outpatient encounter.

Main Outcomes and Measures

Normal temperature ranges by age, sex, height, weight, and time of day.

Results

Of 618 306 patient encounters, 35.92% were removed by LIMIT because they included diagnoses or medications that fell disproportionately in the tails of the temperature distribution. The encounters removed due to overrepresentation in the upper tail were primarily linked to infectious diseases (76.81% of all removed encounters); type 2 diabetes was the only diagnosis removed for overrepresentation in the lower tail (15.71% of all removed encounters). The 396 195 encounters included in the analysis set consisted of 126 705 patients (57.35% women; mean [SD] age, 52.7 [15.9] years). Prior to running LIMIT, the mean (SD) overall oral temperature was 36.71 °C (0.43 °C); following LIMIT, the mean (SD) temperature was 36.64 °C (0.35 °C). Using mixed-effects modeling, age, sex, height, weight, and time of day accounted for 6.86% (overall) and up to 25.52% (per patient) of the observed variability in temperature. Mean normal oral temperature did not reach 37 °C for any subgroup; the upper 99th percentile ranged from 36.81 °C (a tall man with underweight aged 80 years at 8:00 am) to 37.88 °C (a short woman with obesity aged 20 years at 2:00 pm).

Conclusions and Relevance

The findings of this cross-sectional study suggest that normal oral temperature varies in an expected manner based on sex, age, height, weight, and time of day, allowing individualized normal temperature ranges to be established. The clinical significance of a value outside of the usual range is an area for future study.

Hepatic follistatin increases basal metabolic rate and attenuates diet-induced obesity during hepatic insulin resistance

OBJECTIVE

Body weight change and obesity follow the variance of excess energy input balanced against tightly controlled EE (energy expenditure). Since insulin resistance can reduce energy storage, we investigated whether genetic disruption of hepatic insulin signaling reduced adipose mass with increased EE.

METHODS

Insulin signaling was disrupted by genetic inactivation of Irs1 (Insulin receptor substrate 1) and Irs2 in hepatocytes of LDKO mice (Irs1L/L·Irs2L/L·CreAlb), creating a state of complete hepatic insulin resistance. We inactivated FoxO1 or the FoxO1-regulated hepatokine Fst (Follistatin) in the liver of LDKO mice by intercrossing LDKO mice with FoxO1L/L or FstL/L mice. We used DEXA (dual-energy X-ray absorptiometry) to assess total lean mass, fat mass and fat percentage, and metabolic cages to measure EE (energy expenditure) and estimate basal metabolic rate (BMR). High-fat diet was used to induce obesity.

RESULTS

Hepatic disruption of Irs1 and Irs2 (LDKO mice) attenuated HFD (high-fat diet)-induced obesity and increased whole-body EE in a FoxO1-dependent manner. Hepatic disruption of the FoxO1-regulated hepatokine Fst normalized EE in LDKO mice and restored adipose mass during HFD consumption; moreover, hepatic Fst disruption alone increased fat mass accumulation, whereas hepatic overexpression of Fst reduced HFD-induced obesity. Excess circulating Fst in overexpressing mice neutralized Mstn (Myostatin), activating mTORC1-promoted pathways of nutrient uptake and EE in skeletal muscle. Similar to Fst overexpression, direct activation of muscle mTORC1 also reduced adipose mass.

CONCLUSIONS

Thus, complete hepatic insulin resistance in LDKO mice fed a HFD revealed Fst-mediated communication between the liver and muscle, which might go unnoticed during ordinary hepatic insulin resistance as a mechanism to increase muscle EE and constrain obesity.

Causal Relationship Between Basal Metabolic Rate and Alzheimer's Disease: A Bidirectional Two-sample Mendelian Randomization Study

INTRODUCTION

Objective observational studies have shown that basal metabolic rate (BMR) decreases in patients with Alzheimer's disease (AD), but the causal relationship between BMR and AD has not been established. We determined the causal relationship between BMR and AD by two-way Mendelian randomization (MR) and investigated the impact of factors associated with BMR on AD.

METHODS

We obtained BMR (n = 454,874) and AD from a large genome-wide association study (GWAS) database (21,982 patients with AD, 41,944 controls). The causal relationship between AD and BMR was investigated using two-way MR. Additionally, we identified the causal relationship between AD and factors related with BMR, hyperthyroidism (hy/thy) and type 2 diabetes (T2D), height and weight.

RESULTS

BMR had a causal relationship with AD [451 single nucleotide polymorphisms (SNPs), odds ratio (OR) 0.749, 95% confidence intervals (CIs) 0.663-0.858, P = 2.40E-03]. There was no causal relationship between hy/thy or T2D and AD (P > 0.05). The bidirectional MR showed that there was also a causal relationship between AD and BMR (OR 0.992, Cls 0.987-0.997, N_SNPs18, P = 1.50E-03). BMR, height and weight have a protective effect on AD. Based on MVMR analysis, we found that genetically determined height and weight may be adjusted by BMR to have a causal effect on AD, not height and weight themselves.

CONCLUSION

Our study showed that higher BMR reduced the risk of AD, and patients with AD had a lower BMR. Because of a positive correlation with BMR, height and weight may have a protective effect on AD. The two metabolism-related diseases, hy/thy and T2D, had no causal relationship with AD.

Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis

PURPOSE

Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE.

METHODS

This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots.

RESULTS

Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 - 34.0) kg/m². The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI.

CONCLUSION

ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority.

Causal Associations Between Basal Metabolic Rate and COVID-19

Many coronavirus disease 2019 (COVID-19) risk factors, including obesity and diabetes, are associated with an abnormal basal metabolic rate (BMR). We aimed to evaluate whether BMR could impact the susceptibility to or severity of COVID-19. We performed genetic correlation and Mendelian randomization (MR) analyses to assess genetic correlations and potential causal associations between BMR (n = 448,348) and three COVID-19 outcomes: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, COVID-19 hospitalization, and critical COVID-19 (n = 1,086,211-2,597,856). A multivariable MR (MVMR) analysis was used to estimate the direct effect of BMR on COVID-19 independent of BMI and type 2 diabetes. BMR has positive genetic correlations with the COVID-19 outcomes (genetic correlations 0.213-0.266). The MR analyses indicated that genetic liability to BMR confers causal effects on SARS-CoV-2 infection (odds ratio 1.14, 95% CI 1.09-1.20, P = 1.65E-07), hospitalized COVID-19 (1.31, 1.18-1.46, P = 8.69E-07), and critical COVID-19 (1.04, 1.19-1.64, P = 4.89E-05). Sensitivity analysis of MR showed no evidence of directional pleiotropy or heterogeneity, indicating the robustness of its results. The MVMR analysis showed that the causal effects of BMR on hospitalized COVID-19 and critical COVID-19 were dependent on BMI and type 2 diabetes but that BMR may affect the SARS-CoV-2 infection risk independently of BMI and type 2 diabetes (odds ratio 1.09, 95% CI 1.03-1.15, P = 4.82E-03). Our study indicates that a higher BMR contributes to amplifying the susceptibility to and severity of COVID-19. The causal effect of BMR on the severity of COVID-19 may be mediated by BMI and type 2 diabetes.

Resting energy expenditure of a diverse group of South African men and women

BACKGROUND

In South Africa, overweight/obesity is a public health concern, disproportionally affecting Black females. A contributory role of a lower resting energy expenditure (REE) is suggested for African Americans. The present study assessed the REE of Black and White South African adults aiming to better understand the underlying predictors to overweight/obesity and transform this into locally appropriate recommendations.

METHODS

In 328 (63% female; 39% Black) healthy South African adults, REE was measured with indirect calorimetry and body composition with multifrequency bioelectrical impedance analysis. The REE was estimated with 30 sets of published equations. Black-White differences in REE, as measured and adjusted (analysis of covariance), were determined with quantile regression. Reliability/agreement of estimated (against measured) REE was determined with intra-class correlations (ICCs) and Bland-Altman analysis. A new equation was developed by median regression followed by preliminary validation.

RESULTS

Measured REE (adjusted for age along with fat-free mass [FFM], FFM index, FFM plus fat mass, FFM index plus fat mass index) in White subjects was significantly higher (p < 0.001) than in Black subjects for men and women alike, regardless of obesity class. None of the sets of estimation equations had good agreement with measured REE for Black, White, male and female subjects simultaneously. A new estimation equation, based on whole-body variables, had good reliability (ICC = 0.79) and agreement (mean difference: 27 kJ) and presents practical opportunities for groups at the local grass-roots level.

CONCLUSIONS

The REE in Black South African adults is lower than in White adults. Tailored REE equations may improve REE estimation of racially/ethnically diverse South African groups and contribute to improved obesity management.

Associations of muscle-strengthening exercise with overweight, obesity, and depressive symptoms in adolescents: Findings from 2019 Youth Risk Behavior Surveillance system

Background

Previous studies have focused on the opposite relation between muscle strength, obesity, and depression in adults. Moreover, the evidence has indicated that obesity and depression in adults might be significantly decreased with Muscle Strength Exercise (MSE) albeit it might be insufficient. Therefore, the current study aimed to investigate the association between MSE, adiposity, and depression among United States adolescents.

Materials and methods

This cross-sectional study used the Youth Risk Behavioral Survey (YRBS) data. In YRBS, a cluster sample was used, and the investigation was divided into three stages. The study surveyed 13,677 high school students and conducted self-reported questionnaires on sex, grade, race/ethnicity, MSE days, overweight, obesity, and depressive symptoms. The study got the nationally representative population of American students in Grade 9 to 12 (around 12–18 years).

Results

A total of 13,677 participants (female = 6,885, male = 6641) were included in the final analysis. The participants meeting the guidelines’ requirements seemed more likely to be obese than those not meeting (OR = 1.28, 95% CI = 1.06–1.55). There was no statistical significance in the relations between the MSE guidelines and overweight and depression (OR = 0.86, 95% CI = 0.73–1.01: OR = 0.94, 95% CI = 0.83–1.06). For all the participants, the prevalence of those conforming to MSE was 30.1%. One-fifth of the participants reported no MSE per week, 7.8% reported 3 days of MSE per week, and 7.7% reported 7 days.

Conclusion

The main finding of this study indicated a positive relationship between the normative MSE required in guidelines and low-level obesity. Beyond that, the evidence was insufficient to confirm the positive links between MSE and depression among American adolescents. Our study could offer evidence for future MSE interventions in adolescents.

Energy Expenditure in Free-Living Japanese People with Obesity and Type 2 Diabetes, Measured Using the Doubly-Labeled Water Method

We determined the total energy expenditure (TEE) of healthy overweight or obese people, and those with impaired glucose tolerance and/or impaired fasting glycemia (IGT/IFG), or type 2 diabetes (T2DM) using the doubly-labeled water method. As a second purpose, we compared the measured TEE with the target energy intake recommended in the treatment guidelines for diabetes. The participants were normal glucose tolerance (NGT), and IGT/IFG (n=11) and T2DM (n=9) patients, who were 50-59 y and had a body mass index >25 kg/m². The median TEE/body mass (BM) values were 32.6, 33.3, and 34.4 kcal/kg BM and the TEE/target BM values (target BM: BM at a BMI of 22 kg/m²) were 43.7, 50.2, and 46.5 kcal/kg target BM for each group, respectively, and did not differ significantly among them. Obese Japanese participants with T2DM in this study had lower TEE/BM than previously studied in non-obese participants with T2DM. In IGT/IFG or T2DM patients, if 30 kcal/kg target BM was used as the energy coefficient, on the basis of the treatment guidelines, the difference between TEE and the target energy intake would be -1,174±552 kcal (-38±11%). When 35 kcal/kg target BM was used as the energy coefficient, the difference between TEE and the target energy intake would be -877±542 kcal (-27±13%). Thus, the energy coefficients used to estimate target energy intake during lifestyle modification in obese/overweight patients with T2DM are considered to be quite low during the first step of diet therapy.

Comparison of Equations Estimating Resting Metabolic Rate in Older Adults with Type 2 Diabetes

Measuring resting metabolic rate (RMR) is time-consuming and expensive, and thus various equations for estimating RMR have been developed. This study's objective was to compare five equations in elderly people with type 2 diabetes (T2DM). RMR was measured in 90 older adults (≥65 years) with T2DM (mean body mass index (BMI) of 31.5 kg/m2), using indirect calorimetry. Results were compared to four frequently used equations (those of Cunningham, Harris and Benedict, and Gougeon developed for young adults with T2DM, and that of Lührmann, which was developed for the elderly), in addition to a new equation developed recently at the Academic College at Wingate (Nachmani) for overweight individuals. Estimation accuracy was defined as the percentage of subjects with calculated RMR within ±10% of measured RMR. Measured RMR was significantly underestimated by all equations. The equations of Nachmani and Lührmann had the best estimation accuracy: 71.4% in males and 50.9% in females. Skeletal muscle mass, fat mass, hemoglobin A1c (HbA1c), and the use of insulin explained 70.6% of the variability in measured RMR. RMR in elderly participants with T2DM was higher than that calculated using existing equations. The most accurate equations for this specific population were those developed for obesity or the elderly. Unbalanced T2DM may increase caloric demands in the elderly. It is recommended to adjust the RMR equations used for the target population.

Short-term impact of COVID-19 lockdown on metabolic control of patients with well-controlled type 2 diabetes: a single-centre observational study

AIMS/HYPOTHESIS

The strict rules applied in Italy during the recent COVID-19 pandemic, with the prohibition to attend any regular outdoor activity, are likely to influence the degree of metabolic control in patients with type 2 diabetes. We explored such putative effect immediately after the resolution of lockdown rules, in the absence of any variation of pharmacologic treatment.

METHODS

One-hundred and fourteen patients with adequate metabolic control took part in this single-centre, prospective, observational study. The metabolic profile tested 1 week after the end of the lockdown was compared with the last value and the mean of the last three determinations performed before the pandemic emergency (from 6 months to 2 years before).

RESULTS

After 8 weeks of lockdown, an increase of HbA1c > 0.3% (mean +0.7%) was observed in 26% of the participants; these were also characterized by a persistent elevation in serum triglycerides able to predict the worsening of glucose control.

CONCLUSIONS

Lockdown determined a relevant short-term metabolic worsening in approximately one-fourth of previously well-controlled type 2 diabetic individuals; pre-lockdown triglycerides were the only parameter able to predict such derangement of glucose control.

New predictive equations to estimate resting energy expenditure of non-dialysis dependent chronic kidney disease patients

BACKGROUND

Determination of resting energy expenditure (REE) is an important step for the nutritional and medical care of patients with chronic kidney disease (CKD). Methods such as indirect calorimetry or traditional predictive equations are costly or inaccurate to estimate REE of CKD patients. We aimed to develop and validate predictive equations to estimate the REE of non-dialysis dependent-CKD patients.

METHODS

A database comprising REE measured by indirect calorimetry (mREE) of 170 non-dialysis dependent-CKD patients was used to develop (n = 119) and validate (n = 51) a new REE-predictive equation. Fat free mass (FFM) was assessed by anthropometry and by bioelectrical impedance (BIA).

RESULTS

The multiple regression analysis generated three equations: (1) REE (kcal/day) = 854 + 7.4*Weight + 179*Sex - 3.3*Age + 2.1 *eGFR + 26 (if DM) (R² = 0.424); (2) REE (kcal/day) = 678.3 + 14.07*FFM.ant + 54.8*Sex - 2*Age + 2.5*eGFR + 140.7* (if DM) (R² = 0.449); (3) REE (kcal/day) = 668 + 17.1*FFM.BIA - 2.7*Age - 92.7*Sex + 1.3*eGFR - 152.3 (if DM) (R² = 0.45). The estimated REE (eREE) was not different from the mREE (P = 0.181), a high ICC was found and the mean difference between mREE and eREE was not different from zero for the three equations in the validation group. eREE accuracy between 90 and 110% was observed in 55.3%, 62.5% and 61% of the patients for Eqs. (1), (2) and (3), respectively.

CONCLUSION

The equations showed acceptable accuracy for REE prediction making them a valuable tool to support practitioners to provide more reliable energy recommendations for this group of patients.

A validation study of the use of smartphones and wrist-worn ActiGraphs to measure physical activity at different levels of intensity and step rates in older people

BACKGROUND

Physical activity promotes healthy ageing in older people. Accurate measurement of physical activity in free-living environment is important in evaluating physical activity interventions.

RESEARCH QUESTION

What is the criterion validity of 1)an ActiGraph to identify physical activity at different intensity levels and 2)an ActiGraph and a smartphone to measure step rate?

METHODS

Community-dwelling older people aged≥60 were recruited. The index tests were using ActiGraph worn in different positions (i.e.,both wrists and hip) to measure physical activity intensity and step rate and using smartphone (i.e., Samsung J2 pro and Google Fit) worn in different positions (i.e.,trousers pocket and waist pouch) to measure the step rate. The reference standards were using indirect calorimetry (i.e.,CosMedK4b 2) to measure physical activity intensity and using direct observation for step rate. Subjects were exposed in different physical activity intensity levels (i.e.,sedentary:MET < 1.5,light: MET = 1.5-2.99, moderate:MET = 3.0-6.0, vigorous:MET>6) and step rates through walking on a treadmill at different speeds (i.e.,2-8 km) for approximately 30 min. Spearman's rho, ROC analysis, and percentage error were employed to report the criterion validity.

RESULTS

31 participants completed the tests. ActiGraphs worn in different body positions could significantly differentiate physical activity intensity at the levels of "light- or-above" (VM cut-off = 279.5-1959.1,AUC = 0.932-0.954), "moderate-or-above" (VM cut- off = 1051.0-4212.9,AUC = 0.918-0.932), and "vigorous" (VM cut-off = 3335.4-5093.0, AUC = 0.890-0.907) well with different cut-off points identified. The step rate measured by direct observation correlated significantly with ActiGraph and smartphone (rho = 0.415-0.791). Both ActiGraph and smartphone at different positions generally underestimated the step rate (%error= -20.5,-30.3).

SIGNIFICANCE

A wrist-worn ActiGraph can accurately identify different physical activity intensity levels in older people, but lower cut-off points in older people should be adopted. To measure step rate, a hip-mounted ActiGraph is preferable than a wrist- worn one. A smartphone employing Google Fit generally underestimates step rate but it gives a relatively more accurate estimation of step rate when the older people walk at a speed of 4-8 km/h.

Low basal metabolic rate as a risk factor for development of insulin resistance and type 2 diabetes

INTRODUCTION

Identification of physiological factors influencing susceptibility to insulin resistance and type 2 diabetes (T2D) remains an important challenge for biology and medicine. Numerous studies reported energy expenditures as one of those components directly linked to T2D, with noticeable increase of basal metabolic rate (BMR) associated with the progression of insulin resistance. Conversely, the putative link between genetic, rather than phenotypic, determination of BMR and predisposition to development of T2D remains little studied. In particular, low BMR may constitute a considerable risk factor predisposing to development of T2D.

RESEARCH DESIGN AND METHODS

We analyzed the development of insulin resistance and T2D in 20-week-old male laboratory mice originating from three independent genetic line types. Two of those lines were subjected to divergent, non-replicated selection towards high or low body mass-corrected BMR. The third line type was non-selected and consisted of randomly bred animals serving as an outgroup (reference) to the selected line types. To induce insulin resistance, mice were fed for 8 weeks with a high fat diet; the T2D was induced by injection with a single dose of streptozotocin and further promotion with high fat diet. As markers for insulin resistance and T2D advancement, we followed the changes in body mass, fasting blood glucose, insulin level, lipid profile and mTOR expression.

RESULTS

We found BMR-associated differentiation in standard diabetic indexes between studied metabolic lines. In particular, mice with low BMR were characterized by faster body mass gain, blood glucose gain and deterioration in lipid profile. In contrast, high BMR mice were characterized by markedly higher expression of the mTOR, which may be associated with much slower development of T2D.

CONCLUSIONS

Our study suggests that genetically determined low BMR makeup involves metabolism-specific pathways increasing the risk of development of insulin resistance and T2D.

High-dose thiamine supplementation may reduce resting energy expenditure in individuals with hyperglycemia: a randomized, double - blind cross-over trial

Background

Despite the crucial role of thiamine in glucose and energy metabolism pathways, there has been no published study examining the impact of thiamine on energy metabolism in humans.

Objective

To assess the effects of thiamine supplementation on resting energy expenditure (REE) in individuals with hyperglycemia.

Methods

Twelve hyperglycemic patients completed this double-blind, randomized trial, where all participants received both thiamine (300 mg/day) and matched placebo for 6 weeks in a cross-over manner. REE was assessed by indirect calorimetry. Anthropometric measurements, fasting and 2-h plasma glucose, and glucose-induced thermogenesis were also assessed at the beginning and on the completion of each six-week phase.

Results

Participants consuming thiamine supplements experienced a significant decrease in the REE assessed at week six compared to the baseline [mean (SE): 1478.93 (73.62) vs.1526.40 (73.46) kcal/d, p = 0.02], and the placebo arm (p = 0.002). These results did not change significantly after adjusting for the participants' body weight and physical activity as potential confounders. Six-week intervention had no significant effect on the participants' body weight or waist circumference, in either supplement or placebo arms (all p values>0.05). However, correlation analysis highlighted significant positive relationships between the changes in REE, and those in fasting (r_s = 0.497, p = 0.019) and 2-h plasma glucose (r_s = 0.498, p = 0.018) during the six-week intervention period.

Conclusion

Supplementation with high-dose thiamine may attenuate REE in patients with impaired glucose regulation. Our findings suggest that the impact of thiamine on REE may in part be explained by improved glycemic control.

Trial registration

Australian New Zealand Clinical Trials Registry ACTRN12611000051943. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12611000051943.

Diabetes and work: The need of a close collaboration between diabetologist and occupational physician

AIM

The Italian Society of Occupational Medicine (SIML), the Italian Diabetes Society (SID) and the Association of Diabetologists (AMD) joined a working group that produced a consensus paper aimed to assess the available evidence regarding the interplay between specific working conditions, including shift- and night-time work, working activities at high risk of accidents and work at heights, working tasks requiring high-energy expenditure, working activities at extreme temperatures and diabetes.

DATA SYNTHESIS

Diabetes is a group of metabolic disorders caused by defects in insulin secretion and/or action affecting millions of people worldwide, many of whom are or wish to be active members of the workforce. Although diabetes, generally, does not prevent a person from properly performing his/her working tasks, disease complications can significantly compromise a person's ability to work. Therefore, it appears evident the need to understand the relationship between occupational risk factors and diabetes. The working group included in the document some practical recommendations useful to ensure diabetic workers the possibility to safely and effectively undertake their jobs and to adequately manage and treat their disease, also in the workplace. In this perspective concerted action of all the workplace preventive figures, occupational physicians and diabetologists should be strongly encouraged.

CONCLUSIONS

Further studies are necessary to define workplace-based interventions, which should be minimally invasive towards the work organization, allowing diabetic workers to fully realize their work skills while improving their wellbeing at work.

Correlation between basal metabolic rate, visceral fat and insulin resistance among type 2 diabetes mellitus with peripheral neuropathy

BACKGROUND

Basal Metabolic Rate (BMR) means the amount of energy utilized by body in physical and psychological resting rate, after a night sleep, awake without any previous physical activity post meal (10 h after last meal) & neutral environment. In people with type 2 diabetes mellitus (T2DM) there is an increase in BMR which is said to be associated with the level of glycaemic control. So, the objective of the study was to find out the correlation between BMR, Insulin resistance and Visceral fat in T2DM with peripheral neuropathy.

MATERIALS & METHODS

A total of 50 participants with T2DM with peripheral neuropathy were included. Age group of 30-75 years were selected for the study. Participants with a known history of neurological disease, locomotor disability, and pregnancy were excluded from the study. Demographic details of the participants like duration of diabetes mellitus, age, Fasting Blood Glucose, Fasting Insulin, HOMA-IR, Glycated Haemoglobin (HBA1c), Neuropathy and Blood pressure values were noted. We measured Basal Metabolic Rate (BMR) by using Mifflin-St Jeor predictive equation in T2DM with peripheral neuropathy.

RESULTS

The mean age of the participants is 60.16 ± 10.62. The mean duration of T2DM 13.44 ± 11.92. In the present study we found a statistical significant correlation between BMR and HOMA IR (r = 0.913*; p = 0.000), BMR & Fasting blood sugar (FBS) (r = 0.281*; p = 0.048), BMR and Visceral fat (VF) (r = 0.332*; p = 0.018).

CONCLUSION

Basal metabolic rate is correlated to Homa-IR, visceral fat, fasting blood sugar and musculoskeletal mass among T2DM with peripheral neuropathy.

Exercise as 'precision medicine' for insulin resistance and its progression to type 2 diabetes: a research review

Type 2 diabetes and obesity epidemics are in effect in the United States and the two pathologies are linked. In accordance with the growing appreciation that ‘exercise is medicine,’ it is intuitive to suggest that exercise can play an important role in the prevention and/or treatment of these conditions. However, if exercise is to truly be considered as a viable alternative to conventional healthcare prevention/treatment strategies involving pharmaceuticals, it must be prescribed with similar scrutiny. Indeed, it seems reasonable to posit that the recent initiative calling for ‘precision medicine’ in the US standard healthcare system should also be applied in the exercise setting. In this narrative review, we consider a number of explanations that have been forwarded regarding the pathological progression to type 2 diabetes both with and without the concurrent influence of overweight/obesity. Our goal is to provide insight regarding exercise strategies that might be useful as ‘precision medicine’ to prevent/treat this disease. Although the etiology of type 2 diabetes is complex and cause/consequence characteristics of associated dysfunctions have been debated, it is well established that impaired insulin action plays a critical early role. Consequently, an exercise strategy to prevent/treat this disease should be geared toward improving insulin sensitivity both from an acute and chronic standpoint. However, research suggests that a chronic improvement in insulin sensitivity only manifests when weight loss accompanies an exercise intervention. This has resonance because ectopic fat accumulation appears to represent a central component of disease progression regardless of whether obesity is also part of the equation. The cause/consequence characteristics of the relationship between insulin resistance, pathological fat deposition and/or mobilsation, elevated and/or poorly-distributed lipid within myocytes and an impaired capacity to use lipid as fuel remains to be clarified as does the role of muscle mitochondria in the metabolic decline. Until these issues are resolved, a multidimensional exercise strategy (e.g., aerobic exercise at a range of intensities and resistance training for muscular hypertrophy) could provide the best alternative for prevention/treatment.

Functional Foods and Lifestyle Approaches for Diabetes Prevention and Management

Functional foods contain biologically active ingredients associated with physiological health benefits for preventing and managing chronic diseases, such as type 2 diabetes mellitus (T2DM). A regular consumption of functional foods may be associated with enhanced anti-oxidant, anti-inflammatory, insulin sensitivity, and anti-cholesterol functions, which are considered integral to prevent and manage T2DM. Components of the Mediterranean diet (MD)-such as fruits, vegetables, oily fish, olive oil, and tree nuts-serve as a model for functional foods based on their natural contents of nutraceuticals, including polyphenols, terpenoids, flavonoids, alkaloids, sterols, pigments, and unsaturated fatty acids. Polyphenols within MD and polyphenol-rich herbs-such as coffee, green tea, black tea, and yerba maté-have shown clinically-meaningful benefits on metabolic and microvascular activities, cholesterol and fasting glucose lowering, and anti-inflammation and anti-oxidation in high-risk and T2DM patients. However, combining exercise with functional food consumption can trigger and augment several metabolic and cardiovascular protective benefits, but it is under-investigated in people with T2DM and bariatric surgery patients. Detecting functional food benefits can now rely on an "omics" biological profiling of individuals' molecular, genetics, transcriptomics, proteomics, and metabolomics, but is under-investigated in multi-component interventions. A personalized approach for preventing and managing T2DM should consider biological and behavioral models, and embed nutrition education as part of lifestyle diabetes prevention studies. Functional foods may provide additional benefits in such an approach.

Resting Metabolic Rate Does Not Change in Response to Different Types of Training in Subjects with Type 2 Diabetes

BACKGROUND AND OBJECTIVES

Ambiguous results have been reported regarding the effects of training on resting metabolic rate (RMR), and the importance of training type and intensity is unclear. Moreover, studies in subjects with type 2 diabetes (T2D) are sparse. In this study, we evaluated the effects of interval and continuous training on RMR in subjects with T2D. Furthermore, we explored the determinants for training-induced alterations in RMR.

METHODS

Data from two studies, both including T2D subjects, were encompassed in this manuscript. Study 1 was a randomized, crossover study where subjects (n = 14) completed three, 2-week interventions [control, continuous walking training (CWT), interval-walking training (IWT)] separated by washout periods. Training included 10 supervised treadmill sessions, 60 min/session. CWT was performed at moderate walking speed [aiming for 73% of walking peak oxygen uptake (VO₂peak)], while IWT was performed as alternating 3-min repetitions at slow (54% VO₂peak) and fast (89% VO₂peak) walking speed. Study 2 was a single-arm training intervention study where subjects (n = 23) were prescribed 12 weeks of free-living IWT (at least 3 sessions/week, 30 min/session). Before and after interventions, RMR, physical fitness, body composition, and glycemic control parameters were assessed.

RESULTS

No overall intervention-induced changes in RMR were seen across the studies, but considerable inter-individual differences in RMR changes were seen in Study 2. At baseline, total body mass (TBM), fat-free mass (FFM), and fat mass were all associated with RMR. Changes in RMR were associated with changes in TBM and fat mass, and subjects who decreased body mass and fat mass also decreased their RMR. No associations were seen between changes in physical fitness, glycemic control, or FFM and changes in RMR.

CONCLUSION

Neither short-term continuous or interval-type training, nor longer term interval training affects RMR in subjects with T2D when no overall changes in body composition are seen. If training occurs concomitant with a reduction in fat mass, however, RMR is decreased.

CLINICAL TRIALS REGISTRATION WWWCLINICALTRIALSGOV

NCT02320526 and NCT02089477.