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Fatty Acids and Disease in Type 2 Diabetes Type metaboljsm diabetes T2D aand a metabolic disease with impact Turbocharge immune function brain Hair growth for damaged hair through mechanisms that Hair growth for damaged hair glucose diabeets, vascular damage and blood—brain barrier BBB Nutrition for healthy blood pressure, mitochondrial dysfunction, oxidative stress, brain insulin resistance, synaptic failure, neuroinflammation, and gliosis. Rodent models have diabefes developed for investigating T2D, and have contributed to our understanding of metabbolism Energy metabolism and diabetes in T2D-induced brain dysfunction. Alpha-lipoic acid and liver detoxification we summarize main Energy metabolism and diabetes on metqbolism energy metabolism alterations underlying dysfunction of neuronal and glial cells promoted by diet-induced metabolic syndrome that progresses to a T2D phenotype. Diabetes mellitus is among the top 10 causes of death in the world. Insulin-resistant diabetes or T2D often progresses from obesity, a pandemic that is favored by a sedentary lifestyle and the widespread consumption of food products rich in saturated fat and refined carbohydrates Swinburn et al. Many factors of the metabolic syndrome impact brain function, such as chronic hyperglycemia, microvascular complications, insulin resistance, dyslipidemia, and hypertension Duarte, ; Gaspar et al. Brain insulin signaling deficits have been proposed to impact the brain through mechanisms that include the modulation of energy metabolism, synaptic plasticity, learning and memory, as well as interacting with Aβ and tau, the building blocks of amyloid plaques and neurofibrillary tangles Craft et al.Energy metabolism and diabetes -
These specificities of PA need to be taken into account in the development of adapted tools to assess activity energy expenditure and daily energy expenditure in people with DM.
Few estimation tools are tested in subjects with DM and this results in a lack of accuracy especially for their particular patterns of activity. Thus, future studies should examine sensors coupling different technologies or method that is specifically designed to accurately assess energy expenditure in patients with diabetes in daily life.
In recent decades, a decrease in leisure activity followed by a rise in sedentary behaviors and the degradation of eating habits have been observed. These changes have led to an increasing risk of developing metabolic diseases such as diabetes mellitus DM 1 — 3. In , Wild et al.
Only 7 years later, Whiting et al. Various solutions exist to combat this increasing prevalence 6. Physical activity PA has been shown to be the main factor in primary prevention.
When associated with a healthy diet, PA is also considered to be an imperative component in the treatment of subjects with DM Indeed, in the short term, appropriate exercise can decrease glycemia by burning the overflow of blood glucose to generate energy.
Long term, PA can improve insulin sensitivity, glycemic control, systolic blood pressure, and weight loss. All these improvements are considered sufficient to decrease the rate of diabetes complications 19 and the relative risk of all-cause mortality 20 , Nevertheless, due to the impaired glucose regulation, exercise duration and intensity must be considered attentively.
Actually, long duration exercise without sugar intake may increase the risk of hypoglycemia. On the contrary, brief intense exercise may induce hyperglycemia requiring insulin intake. Underestimated energy expenditure may also lead to an underestimation of necessary medication. In these cases, exercise as a therapeutic tool must be precisely programed into daily life in order to see benefits in patients with DM.
The aim of this article is twofold. First, it presents the differences between the energy expenditure between of people with DM and healthy people. Second, it reports on the various methods for evaluating EE and their validity in subjects with DM.
In the literature, a simple model defines the daily total energy expenditure TEE as the sum of the basal energy expenditure BEE , the thermic effect of food TEF , and the activity energy expenditure AEE. BEE is the major component of internal heat produced.
The TEF symbolizes the energy used by the body when it processes certain unrefined foods as lean meats, vegetables, and whole grains.
AEE represents the energy expended through PA and volitional exercise and sports. AEE is the main parameter that allows modulation of TEE since it depends on the type of PA, its duration, and its intensity.
Thus, in the sections below, we will present the differences between the three EE components in patients with DM and healthy people. Studies exploring BEE in patients with DM report no difference in absolute BEE 22 — Nevertheless, this preliminary result could be explained by the sample heterogeneity of these studies.
Several possible physiological mechanisms may induce changes in BEE. In subjects with DM, fasting blood glucose FBG may be another independent determinant of BEE 26 — These results are supported by the study of Ryan et al.
These results have been explained by two hypotheses. First, BEE may be increased by a rise in the loss of glucose in the urine glycosuria Finally, the second hypothesis indicated that people with impaired glucose regulation had an increase in fasting hepatic gluconeogenesis with the rise of glycemia Gluconeogenesis is an important energy-consuming process that transforms free fatty acid into glucose causing by the decrease of insulin plasma level.
Studies examining the effects of DM on the TEF estimated a lower TEF in patients with insulin resistance than in healthy people 23 , Thus, it appears that TEF decreases progressively with the development of DM. Indeed, TEF is negatively correlated with FBG and insulin concentrations both are predictors of insulin resistance.
The decrease in TEF may be induced by a reduced rate of glycogenesis in the skeletal muscle 36 and an impaired activation of the sympathetic nervous system As discussed later in this report, TEF will not be studied further because it does not represent an adjustable component of TEE. Taken together, the results of these studies suggest that people with DM present a total AEE less than that of healthy people.
These directives concern the general population as well as patients with DM. According to these guidelines, 31— In addition, studies indicated a lower rate of patients with DM following the guidelines than in the general population Mitsui et al.
To further analyze the practice of PA, activities can be qualified by their type leisure, recreational, domestic , by their intensity light, moderate, vigorous , or by their duration. By questioning the activity preferences of Mexican—American patients with DM, study reported that the most common activities were gardening By using a questionnaire, Kriska et al.
By contrast, Ford and Herman 63 showed that patients with DM were equally likely to have engaged in PA. By contrast, Fontvieille et al. The lower AEE observed in patients with DM may result from a smaller amount of PA accumulated throughout the day.
However, other parameters such as metabolic and mechanical efficiency can also reduce energy expenditure. With equal level of activity, different AEE may reflect the different use of energy fuel. Indeed, the energetic equivalent of 1 l of oxygen changes according to the type of foodstuffs metabolized in a simple model, 1 l of oxygen equals 5.
In a second hypothesis, mechanical efficiency may be another parameter explaining the difference in AEE between patient with DM and healthy people. For instance, walking is one of the most convenient daily PA recommended for increasing TEE.
Studies of patients with DM showed a decrease in comfortable walking speed, cadence, and stride length and an increase of plantar pressure at the heel, mid-foot, and first metatarsophalangeal joint as compared with the control group 67 , Moreover, it was also demonstrated that patients with DM have a less total concentric work in lower limb than healthy people 69 , but more cocontraction in the muscles at the ankle and knee joint during the stance period This altered gait pattern observed in patients with DM seems to reflect a stabilization strategy to compensate for the development of peripheral neuropathy diminished sensory information and maximum strength of the lower limbs.
At speeds of walking range from 0. However, Maiolo et al. In light of the previous results, the lower AEE in subjects with DM seems to be due to a low level of daily PA with more low intensity activities and an energetic inefficiency during walking.
Due to the complex nature of PA, AEE assessment requires precise and adapted tools. In the remainder of our review, we will describe potential tools validated for use in daily life that are suitable for research with patients with DM.
Measure of BEE is typically taken by IC. Although these methods are very accurate, they require significant human and financial resources. Thus, equations have been proposed to simply estimate the BEE with variables such as age, height, or weight. Many of these equations were constructed with data from the general population and then tested in people with DM Table 1.
Table 1. The Harris—Benedict equation is most frequently used to estimate BEE in the general population, but studies in patients with DM have presented mixed results. Huang et al. Finally, Miyake et al. To improve the BEE estimate, other equations taking into account the physiological specificities of subjects with DM were developed Table 2 and compared with equations established for the general population, and with IC Table 3.
Besides, de Figueiredo Ferreira et al. Table 2. Table 3. References and results of studies comparing predictive basal energy expenditure with the gold standard in patients with type 2 diabetes. If BEE in subjects with DM cannot be accurately assessed using direct or IC, it can be adequately estimated using equations.
The results of the aforementioned studies suggest that BEE in subjects with DM is better determined by using specific and adapted equations. Thus, the best results were obtained with the Gougeon equation and this may be due to the inclusion of the FBG as a variable. For everyday use, many field evaluation tools for estimating AEE have been developed and tested in subjects with DM, such as diaries, questionnaires, or motion sensors.
These methods and tools are often classified into two categories: subjective and objective methods. Subjective methods include processes that usually require subjects to record their professional, home, and leisure activities.
Among them, activity recall, logs, or questionnaires are declarative methods that provide a detailed account nature, intensity, duration of all daily PA. The AEE and TEE are then determined using the factorial method 84 , where each activity intensity was weighted by its intensity expressed in Met, as it is referred to the compendium 85 , then multiplied by its duration.
At least, when added to the measured or predicted BEE, it is possible to predict the AEE and TEE with the sum of all activity estimates during the day.
Thus, the use of questionnaires is widely reported in the literature on the topic because they are easy to use for large epidemiologic surveys. Nevertheless, few studies examined their validity with reference measures in subjects with DM. The International Physical Activity Questionnaire is most frequently used in epidemiologic research.
It asks subjects about their PA during past week. Studies comparing the AEE estimated by this questionnaire with those estimated with accelerometer found a positive correlation in patients with DM with a correlation coefficient ranging from 0.
Moreover, the use of the factorial method to assess the AEE may be inaccurate when applied to individuals with different fat mass or FMM.
Indeed, the compendium was developed to identify different classes of PA and normalize MET intensities in healthy populations Objective methods include tools that are based on physiological data skin temperature or heart rate , mechanical data pedometer, accelerometer, or inertial sensor , or a combination of both such as the SenseWear Armband BodyMedia, Pittsburgh, PA, USA or the Actiheart Mini-Mitter Co.
The accurate measurement of TEE and AEE in subjects with DM is very challenging because, as demonstrated previously, patients with DM perform primarily low intensity PA, which may influence the assessment of AEE in several tools 93 , Therefore, these devices must be validated specifically for this population.
Few devices have been evaluated in people with DM. Mignault et al. Machac et al. As studies involving patients with DM are rare, the results of measurements for individuals with physical activities similar to those of patients with DM low walking speed, light intensity exercise could be enlightening.
Studies in overweight and obese people without DM compared the Ormon and the SenseWear Armband with IC showing a similar overestimation of AEE during an exercise test 96 , In older adults, Mackey et al.
Colbert et al. In another study, the Caltrac Hemokinetics, Inc. In their study of elderly men, Rafamantanantsoa et al. As observed, objective methods suffer from acknowledged limitations. Accelerometry-based tools demonstrated poor accuracy at slow walking speeds and a decrease in precision with increasing body mass index The device based on heart rate had a high variation in accuracy because the relation between heart rate and energy expenditure is not linear during rest and light intensity activity , Yet, this range of intensities represented a major part of daily life activities in type 2 diabetic person.
Consequently, limited methods tested in people with DM may be used to self-check the calories burned daily. Currently, new technologies are accessible and marketed for estimating TEE or AEE and need to be tested in diabetic population.
As previously discussed, different methods exist for the assessment of TEE or AEE, but few have been validated in people with DM. Future research should address the weaknesses of methods already tested or experiment with new devices, perhaps by combining several technologies.
Accelerometry showed higher error particularly during low intensity activities and cycling 99 , , In order to overcome this limitation, Bonomi et al. Kwapisz et al. Inertial sensors consisting of a three dimensions accelerometer and gyroscope and a magnetometer can be used to identify the type of activity performed like previous accelerometers These methods based on activity recognition could be a reliable solution for estimating more precisely the AEE in subjects with DM.
Finally, the Actiheart combining an accelerometer and a heart rate monitor demonstrated high accuracy in standardized and free-living conditions for the prediction of AEE in healthy adults, but need to be tested in people with DM , This review emphasizes that there are differences between energy expenditure in patients with DM and healthy people.
Patients with DM presented a higher BEE than healthy people. This difference seems to be due to an increase in FBG resulting in a higher glycosuria or gluconeogenesis. In addition, people with DM seem to have a lower AEE than healthy people.
This review highlights that this lower AEE in patients with DM could be linked to a lower amount of activity low compliance with PA recommendations and the prevalence of low intensity activities. However, more studies should be conducted to determine the influence of diabetic altered gait on energy expenditure during PA.
All these results demonstrate the need to develop adapted tools and methods to estimate free-living total energy expenditure in patients with DM. The results of this review indicate that there are valid equations for estimating BEE in patients with DM, but few of the methods tested give an accurate assessment of TEE and AEE in daily life.
Other methods, such as those based on activity recognition with wearable sensors should be considered in the future to improve the estimation of daily TEE in subjects with DM. However, these possibilities need to be tested under everyday life conditions with patients with DM. Drafting of manuscript: NC.
Critical revision: NC, NP, TC, CV, and GD. Final approval of the version to be published: NC, NP, TC, CV, and GD.
Agreement to be accountable for all aspects of the work: NC, NP, TC, CV, and GD. None of the authors of this manuscript have a direct financial relation with the commercial identities mentioned in this paper that might lead to a conflict of interests for any of the authors. The authors wish to express their gratitude to Karim Assaley for technical assistance and to Shannon de Viviés for revising it for English translation.
This work was supported by a Regional Research Grant grant D from the Réunion Région and from the European Regional Development Fund FEDER. AEE, activity energy expenditure; BEE, basal energy expenditure; DM, diabetes mellitus; IC, indirect calorimetry; PA, physical activity; V ˙ C O 2 , rate of carbon dioxide production; V ˙ O 2 , rate of oxygen consumption; TEE, total energy expenditure; TEF, thermic effect of food.
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Physical Activity and Health: A Report of the Surgeon Genera. Atlanta, GA: U. Access to Document Link to publication in Scopus. Link to the citations in Scopus. Fingerprint Dive into the research topics of 'Protein and energy metabolism in type 1 diabetes'. Together they form a unique fingerprint.
View full fingerprint. Cite this APA Standard Harvard Vancouver Author BIBTEX RIS Hebert, S. Clinical Nutrition , 29 1 , In: Clinical Nutrition , Vol. Hebert SL , Nair KS. Clinical Nutrition. doi: Hebert, Sadie L. In: Clinical Nutrition. TY - JOUR T1 - Protein and energy metabolism in type 1 diabetes AU - Hebert, Sadie L.
AU - Nair, K. Sreekumaran N1 - Funding Information: The studies are supported by grants from the National Institute of Health R01 DK and UL1 RR
Ane L. Hebert, K. Profound metabolic changes occur in idabetes with type 1 diabetes mellitus during metabokism deprivation. Energy metabolism and diabetes include an Blood tests for diabetes diagnosis Dextrose Muscle Glycogen Support basal energy expenditure and reduced mitochondrial function. In addition, protein metabolism is significantly affected during insulin deprivation. A greater increase in whole-body protein breakdown than protein synthesis occurs resulting in a net protein loss. During insulin deprivation the splanchnic bed has a net protein accretion which accounts for the total increase in whole-body protein synthesis while muscle is in a net catabolic state.
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