Lack of Seasonal Differences in Basal Metabolic Rate in Humans: A Cross-Sectional Study

Weight Loss Using an mHealth App Among Individuals With Obesity in Different Economic Regions of China: Cohort Study

Background

With the increasing prevalence of obesity, weight loss has become a critical issue in China. Self-managed weight loss through a mobile health (mHealth) app may be a prospective method. However, its practicability in different economic regions of China is unknown.

Objective

This study aims to evaluate the effectiveness of self-managed weight loss through an mHealth app among individuals with obesity in different economic regions of China and to demonstrate the feasibility of online self-management for weight loss.

Methods

A total of 165,635 Chinese adults who signed up for the mHealth app were included to analyze the body composition characteristics of individuals from different economic regions by χ2 analyses. Furthermore, 2 types of participants with obesity using mHealth monitoring, including 74,611 participants with a BMI ≥24.0 kg/m2 and 22,903 participants with a normal BMI but an excessive percentage of body fat (PBF), were followed for 6 months to explore the weight loss and fat loss effects in different economic regions of China and to find independent predictors associated with weight loss success by 2-tailed Student t test and multivariable logistic regression analysis.

Results

There were 32,129 users from low-income regions and 133,506 users from high-income regions. The proportion of users with obesity in low-income regions was higher than in high-income regions, both based on BMI (15,378/32,129, 47.9% vs 59,233/133,506, 44.4%; P<.001) and PBF classification (19,146/32,129, 59.6% vs 72,033/133,506, 54%; P<.001). Follow-up analyses showed that the weight loss effect among participants with overweight or obesity in low-income regions was greater than in high-income regions (mean -4.93, SD 6.41 vs mean -4.71, SD 6.14 kg; P<.001), while there was no significant difference in fat loss (mean -2.06%, SD 3.14% vs mean -2.04%, SD 3.19%; P=.54). In the population with normal-weight obesity, the weight loss (mean -2.42, SD 4.07 vs mean -2.23, SD 4.21 kg; P=.004) and fat loss effects (mean -1.43%, SD 2.73% vs mean -1.27%, SD 2.63%; P<.001) were stronger in high-income regions than in low-income regions. In addition, multivariable logistic regression analyses showed that age, baseline PBF, skeletal muscle rate, and measurement frequency were related to weight loss, whereas gender and baseline body metabolic rate only showed a correlation with weight loss in the population in high-income regions.

Conclusions

This study found a high proportion of mHealth app users with obesity in low-income regions. Individuals with overweight and obesity in different economic regions of China experienced significant weight loss and fat loss using an mHealth app. Moreover, individuals in high-income regions paid more attention to body fat and had better fat reduction effects. Therefore, promoting self-monitoring of weight and PBF through an mHealth app could be an important intervention that could be implemented across all regions of China.

Thermoregulation of Tuvan pastoralists and Western Europeans during cold exposure

OBJECTIVES

This study compared the metabolic and vascular responses, to whole-body and finger cold exposure, of a traditional population lifelong exposed to extreme cold winters with Western Europeans.

METHODS

Thirteen cold acclimatized Tuvan pastoralist adults (45 ± 9 years; 24.1 ± 3.2 kg/m2 ) and 13 matched Western European controls (43 ± 15 years; 22.6 ± 1.4 kg/m2 ) completed a whole-body cold (10°C) air exposure test and a cold-induced vasodilation (CIVD) test, which involved the immersion of the middle finger into ice-water for 30 min.

RESULTS

During the whole-body cold exposure, the durations until the onset of shivering for three monitored skeletal muscles were similar for both groups. Cold exposure increased the Tuvans' energy expenditure by (mean ± SD) 0.9 ± 0.7 kJ min-1 and the Europeans' by 1.3 ± 1.54 kJ min-1 ; these changes were not significantly different. The forearm-fingertip skin temperature gradient of the Tuvans was lower, indicating less vasoconstriction, than the Europeans during the cold exposure (0 ± 4.5°C vs. 8.8 ± 2.7°C). A CIVD response occurred in 92% of the Tuvans and 36% of the Europeans. In line, finger temperature during the CIVD test was higher in the Tuvans than the Europeans (13.4 ± 3.4°C vs. 3.9 ± 2.3°C).

CONCLUSION

Cold-induced thermogenesis and the onset of shivering were similar in both populations. However, vasoconstriction at the extremities was reduced in the Tuvans compared to the Europeans. The enhanced blood flow to the extremities could be beneficial for living in an extreme cold environment by improving dexterity, comfort, and reducing the risk of cold-injuries.

Dependence of Seasonal Dynamics in Healthy People's Circulating Lipids and Carbohydrates on Regional Climate: Meta-Analysis

We analyzed the seasonal dynamics of lipid profile, glucose, and insulin in healthy subjects from 29 studies conducted in 23 regions, located in different climate zones ranging from subarctic to tropical. Our meta-analysis showed that people have higher the level of TC (total cholesterol), LDL (low-density lipoprotein), HDL (high-density lipoprotein), FBG (fasting blood glucose) in winter than in summer regardless of gender. Regional climate had a significant impact on the seasonal dynamics of lipid profile and glucose. TC, HDL, FBG seasonal fluctuations were more prominent in a climate that had a marked increase in average monthly atmospheric pressure in winter compared with summer as opposed to a climate where atmospheric pressure did not vary significantly in winter and summer. In a climate with humid winters, TC seasonal changes were significantly greater than in the regions with humid summers, most likely due to LDL seasonal changes, since HDL seasonal dynamics with peaks in winter were more prominent in the regions with humid summers. The level of triglycerides had prominent seasonal dynamics with peak values in winter only in the regions with a large difference in winter and summer air temperatures. The results of our current and prior meta-analysis allow for the conclusion that the seasonal dynamics of circulating lipids and glucose are frequently linked to the seasonal dynamics of thyroid-stimulating hormone and hematocrit. Dependence of the seasonal changes in the biochemical parameters on annual fluctuations in air temperature, atmospheric pressure and relative humidity is more obvious than on photoperiod changes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12291-022-01064-6.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Estimating the agreement between the metabolic rate calculated from prediction equations and from a portable indirect calorimetry device: an effort to develop a new equation for predicting resting metabolic rate

BACKGROUND

Many studies have been performed over time in order to determine the reliability of metabolic rate prediction equations.

PURPOSE

To evaluate the agreement, in terms of bias, absolute bias and accuracy between metabolic rate prediction equations and measured metabolic rate using indirect calorimetry system (IC), investigating also the factors affecting this agreement.

METHODS

The anthropometric features of 383 Caucasian participants of all Body Mass Index (BMI) classes were recorded and Resting Metabolic Rate (RMR) was measured by using the IC Fitmate portable device. The resulting values were compared with the predictive values of Harris & Benedict, Schofield, Owen, FAO-WHO-UNU, Mifflin and Harrington equations.

RESULTS

A closer approximation in agreement was obtained using the Harrington equation (based on BMI, age and gender). The equations using variables, such as weight, height, age and gender demonstrated higher agreement than the equations using merely weight and gender. Higher educational level was associated with normal weight, while higher calorific ratio was found in the class of normal-weighted individuals. An inverse relationship between ΒΜΙ and RMR was also observed and a logarithmic equation for calculating RMR was created, which was differentiated in relation to BMI classes, using the weight and gender variables.

CONCLUSION

A better measurement agreement between RMR prediction equations and IC may be achieved due to BMI consideration. The present findings contributed to a better understanding of the measured parameters, confirming the inverse relationship between BMI and RMR. Age group and gender variables may also exert significant role on the bias response of some RMR equations.