The prevention of type 2 diabetes mellitus (T2DM) is a target priority for the WHO and the United Nations and is a key priority in the 2018 Berlin Declaration, which is a global call for early actions related to T2DM. Health-care policies advocate that individuals at high risk of developing T2DM undertake lifestyle modification, irrespective of whether the prediabetes phenotype is defined by hyperglycaemia in the postprandial state (impaired glucose tolerance) and/or fasting state (impaired fasting glucose) or by intermediate HbA1c levels. However, current evidence indicates that diabetes prevention programmes based on lifestyle change have not been successful in preventing T2DM in individuals with isolated impaired fasting glucose. We propose that further research is needed to identify effective lifestyle interventions for individuals with isolated impaired fasting glucose. Furthermore, we call for the identification of innovative approaches that better identify people with impaired glucose tolerance, who benefit from the currently available lifestyle-based diabetes prevention programmes.

Benefit of lifestyle-based T2DM prevention is influenced by prediabetes phenotype.

Aims: To identify profiles of type 2 diabetes from continuous glucose monitoring (CGM) data using ambulatory glucose profile (AGP) indicators and examine the association with prevalent complication...

Profiles of Intraday Glucose in Type 2 Diabetes and Their Association with Complications: An Analysis of Continuous Glucose Monitoring Data.

Weight loss induced by decreased energy intake (diet) or exercise generally has favorable effects on insulin sensitivity and cardiometabolic risk. The variation in these responses to diet-induced weight loss with or without exercise, particularly in older obese adults, is less clear. The objectives of our study were to (1) examine the effect of weight loss with or without exercise on the variability of responses in insulin sensitivity and cardiometabolic risk factors and (2) to explore whether baseline phenotypic characteristics are associated with response. Sedentary older obese (BMI 36.3 ± 5.0 kg/m2) adults (68.6 ± 4.7 years) were randomized to one of 3 groups: health education control (HED); diet-induced weight loss (WL); or weight loss and exercise (WL + EX) for 6 months. Composite Z-scores were calculated for changes in insulin sensitivity (C_IS: rate of glucose disposal/insulin at steady state during hyperinsulinemic euglycemic clamp, HOMA-IR, and HbA1C) and cardiometabolic risk (C_CMR: waist circumference, triglycerides, and fasting glucose). Baseline measures included body composition (MRI), cardiorespiratory fitness, in vivo mitochondrial function (ATPmax; P-MRS), and muscle fiber type. WL + EX groups had a greater proportion of High Responders in both C_IS and C_CMR compared to HED and WL only (all p < 0.05). Pre-intervention measures of insulin (r = 0.60) and HOMA-IR (r = 0.56) were associated with change in insulin sensitivity (C_IS) in the WL group (p < 0.05). Pre-intervention measures of glucose (r = 0.55), triglycerides (r = 0.53), and VLDL (r = 0.53) were associated with change in cardiometabolic risk (C_CMR) in the WL group (p < 0.05), whereas triglycerides (r = 0.59) and VLDL (r = 0.59) were associated with C_CMR (all p < 0.05) in WL + EX. Thus, the addition of exercise to diet-induced weight loss increases the proportion of older obese adults who improve insulin sensitivity and cardiometabolic risk. Additionally, individuals with poorer metabolic status are more likely to experience greater improvements in cardiometabolic risk during weight loss with or without exercise.

/pdf/individual-response-variation-in-the-effects-of-weight-loss-2pirnp27t8.pdf

Individual Response Variation in the Effects of Weight Loss and Exercise on Insulin Sensitivity and Cardiometabolic Risk in Older Adults.

Physical exercise is a powerful measure to prevent cardiometabolic diseases. However, the individual response to lifestyle interventions is variable and cannot, to date, be predicted. N-Lactoylphenylalanine (Lac-Phe) produced during exercise has recently been shown to mediate weight loss in obese mice. Lac-Phe could also contribute to, and potentially explain differences in, the effectiveness of exercise interventions in humans. Sedentary overweight and obese subjects completed an 8-week supervised endurance exercise intervention (n = 22). Before and after the intervention, plasma levels of Lac-Phe were determined by UHPLC-MS in the resting state and immediately after an acute bout of endurance exercise. Adipose tissue volume was quantified using MRI. Acute exercise caused a pronounced increase in Lac-Phe, both before and after the intervention. Higher levels of Lac-Phe after acute exercise were associated with a greater reduction in abdominal subcutaneous and, to a lower degree, visceral adipose tissue during the intervention. Lac-Phe produced during physical activity could contribute to weight loss by acting as a signaling molecule that regulates food intake, as previously shown in mice. Quantification of Lac-Phe during an exercise test could be employed as a tool to predict and potentially improve the individual response to exercise-based lifestyle interventions in overweight humans and those with obesity.

/pdf/exercise-induced-n-lactoylphenylalanine-predicts-adipose-2vx7fuzl.pdf

Exercise-Induced N-Lactoylphenylalanine Predicts Adipose Tissue Loss during Endurance Training in Overweight and Obese Humans

Current studies in most Western countries have largely focused on body mass index (BMI) as an important risk factor for impaired glucose tolerance (IGT) and impaired fasting glucose (IFG), which have different pathophysiological bases. In people with obesity, the prevalence of IGT is higher and the prevalence of IFG is lower. The prevalence of IGT in the Asian population is higher than that in the white population, and the obesity rate in China is still increasing. However, few cohort studies explore the relationship between BMI and the incidence of IGT and IFG in China. We aimed to explore the relationship between BMI and the risk of IGT and IFG in Chinese adults and analyze the differences between them.The baseline data were obtained from the 2010 China Chronic Disease and Risk Factor Surveillance, of which 20 surveillance sites were followed up from 2016 to 2017. Finally, in this study, a total of 5,578 studies were grouped into BMI categories of underweight (BMI < 18.5 kg/m2), normal weight (18.5-23.9 kg/m2), overweight (24.0-27.9 kg/m2), and obesity (≥ 28.0 kg/m2). We used the unconditional logistic regression model to analyze the relationship between BMI and the risk of IGT and IFG.During an average follow-up of 6.4 years, 562 developed IGT and 257 developed IFG. After age, gender, urban and rural areas, physical activity, family history of diabetes, hypertension, abdominal obesity, dyslipidemia, and other factors were adjusted, overweight increased the risk of IGT by 35% [odds ratio (OR) 1.35, 95% confidence interval (CI) 1.08-1.70], and obesity increased the risk of IGT by 77% (OR 1.77, 95% CI 1.27-1.47). After the factors consistent with the above were adjusted, only obesity increased the risk of IFG by 122% (OR 2.22, 95% CI 1.39-3.54).In China, obesity is an important risk factor for IGT and IFG, and the risk of IGT increases during the overweight stage. 

/pdf/relationship-between-bmi-and-risk-of-impaired-glucose-270cna7z.pdf

Relationship between BMI and risk of impaired glucose tolerance and impaired fasting glucose in Chinese adults: a prospective study

Comparison Between Skinfolds And Dexa For Determination Of Body Composition In Weight Category Athletes: 2787

Cycling ergometers are routinely used in a laboratory setting to evaluate physiological function and monitor changes in training status. One limitation of many cycle ergometers, in relation to the performance testing, is their inability to replicate the cyclist own specific cycling position thereby bringing the validity of the ergometer used into question. Purpose: The purpose of this study was to compare the aerobic and anaerobic energy demands of two commercially available cycle ergometers in trained cyclists. The first ergometer allowed full adjustment of cycling position and was electromagnetically braked (EB). The second ergometer allowed for saddle height adjustment only and was resistance braked (RB). Methods: Ten trained male cyclists were tested on 2 separate occasions within a 14 day period under the same conditions. Subjects performed a 30 second Wingate maximal sprint test followed 60 minutes later by a continuous maximal incremental step test on either the EB or RB cycle ergometer, in a random order. The Wingate test was performed at 9% of body mass and for 30 seconds with a 5 second speed up period. The incremental test started at 100W and increased in resistance by 50W every 3 minutes until volitional exhaustion. Heart rate, VO2, power output and blood lactate were measured during the maximal incremental test. Results: The results showed a significant difference (p<0.01) for the Wingate test between the RB and EB both in terms of peak power output (POmax) and mean power output (POmean) with subjects generating greater power outputs on the EB. During the maximal incremental test, significant differences (p<0.01) were found between EB and RB for submaximal power output, heart rate, and VO2 at both lactate threshold 1 (1mmol.l-1 rise above baseline, LT1) and onset of blood lactate accumulation (4mmol.l-1 blood lactate reference point, OBLA), as well as peak power output at VO2max (PVO2max). Conclusions: Overall it was shown that significant differences in physiological demands were present between the two ergometers under both anaerobic and aerobic conditions. This is may in part be explained by the different positions that the cyclists adopted on either ergometer. Further research is required to compare the findings of the current study with actual cycling performance.

/pdf/a-comparison-of-the-physiological-demands-of-two-bblmwxt74j.pdf

A comparison of the physiological demands of two commercially available cycle ergometers in trained cyclists

Introduction: In sport science it is well known that individual diet has an important role in affecting a person’s performance [1]. In this regard, in the last 10-20 years energy and sport drinks have become increasingly popular among elite and amateur athletes. The worldwide consumption of energy drinks increased in 2006 to 906 million gallons [2]. Just in Brazil, from 2008 to 2010, the production of energy drinks doubled [3]. A possible reason for their popularity is that they claim to help enhance physiological performance by replenishing the electrolytes lost during exercise and by boosting the body’s energy demands [4]. Although this claim is still under debate the effect of energy drinks may be quite different depending on the type of activity undertaken by athletes and it may be highly individual [5]. It is important to determine which changes in the sweat profiling are triggered by the consumption of different types of energy drinks and link those to sport performance. Our attention focuses on the role of caffeine and sodium since these two are common components in sport drinks. Although caffeine is widely part of daily diet, it is considered to be a drug and its level in urine has to be less than 15 µg mL-1 during sport competitions [6,7]. Electrolyte balance and adequate hydration levels have an important role in avoiding heat-related illness and can have an overall effect on enhancing or impairing human sports performances [8,9]. However, it is difficult to advise an optimum sodium intake suitable for all since sweat sodium concentrations and sweat rates vary greatly between individuals [10,11]. 
Aim: Sweat contains a range of clinically relevant metabolites and is easily accessible for analysis. This work investigated the effect of different sports drinks on sweat composition in order to explore the physiological effects of these drinks. This type of technology is essential to meet the demands for non-invasive analysis of biological media in the biomedical field to monitor health and in sport science to assess physiology and performance [12].
Methodology: Two different sports drinks were chosen for this study, Hi Five Extreme Energy Source, and Precision Hydration sports drink. Bottled water was used as a control. The two sports drinks were chosen as they have large differences in caffeine and sodium content. 
Objective: To evaluate the effect of these three drinks on sweat composition. High-performance liquid chromatography (HPLC) methods were employed to determine the concentration of the caffeine in sweat samples. Flame-photometry and in-house built sodium sensors have been employed to detect sodium levels. 
Results and Discussion: At the first stage some energy drinks were chosen, the caffeine and sodium content were compared with those on the label and it was confirmed that the values are similar. After, preliminary test with a subject, the HPLC chromatogram for caffeine showed overlapping peaks which did not permit the identification of caffeine peak. In view of all the issues, in order to establish a new method in the HPLC system three different mobile phases were tested and the best one was 30% methanol and 70% water where the peak can be fully resolved between 1,75 and 1,85 minutes.

Impact of sport drinks on sweat composition

Background: Lifestyle interventions have been shown to delay or prevent the onset of type 2 diabetes among high risk adults. A better understanding of the variability in physiological responses would support the matching of individuals with the best type of intervention in future prevention programmes, in order to optimize risk reduction. The purpose of this study was to determine if phenotypic characteristics at baseline or following a 12 weeks lifestyle intervention could explain the inter-individual variability in change in glucose tolerance in individuals with high risk for type 2 diabetes. Methods: In total, 285 subjects with normal glucose tolerance (NGT, FINDRISC score > 12), impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) were recruited for a 12 weeks lifestyle intervention. Glucose tolerance, insulin sensitivity, anthropometric characteristics and aerobic fitness were measured. Variability of responses was examined by grouping participants by baseline glycemic status, by cluster analysis based on the change in glucose tolerance and by Principal Component Analysis (PCA). Results: In agreement with other studies, the mean response to the 12 weeks intervention was positive for the majority of parameters. Overall, 89% improved BMI, 80% waist circumference, and 81% body fat while only 64% improved fasting plasma glucose and 60% 2 h glucose. The impact of the intervention by glycaemic group did not show any phenotypic differences in response between NGT, IFG, and IGT. A hierarchical cluster analysis of change in glucose tolerance identified four sub-groups of "responders" (high and moderate) and "non-responders" (no response or deteriorated) but there were few differences in baseline clincal and physiological parameters or in response to the intervention to explain the overall variance. A further PCA analysis of 19 clinical and physiological univariables could explain less than half (48%) of total variability. Conclusion: We found that phenotypic characteristics from standard clinical and physiological parameters were not sufficient to account for the inter-individual variability in glucose tolerance following a 12 weeks lifestyle intervention in inidivuals at high risk for type 2 diabetes. Further work is required to identify biomarkers that complement phenotypic traits and better predict the response to glucose tolerance.

/pdf/phenotypic-responses-to-a-lifestyle-intervention-do-not-1qobezar9e.pdf

Phenotypic Responses to a Lifestyle Intervention Do Not Account for Inter-Individual Variability in Glucose Tolerance for Individuals at High Risk of Type 2 Diabetes.

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Paula Fitzpatrick

Author Tools

Chat about Author

Papers

Comparison Between Skinfolds And Dexa For Determination Of Body Composition In Weight Category Athletes: 2787

A comparison of the physiological demands of two commercially available cycle ergometers in trained cyclists

Impact of sport drinks on sweat composition

Phenotypic Responses to a Lifestyle Intervention Do Not Account for Inter-Individual Variability in Glucose Tolerance for Individuals at High Risk of Type 2 Diabetes.