Dataset for "Calorie restriction-induced leptin reduction and T-lymphocyte activation in blood and adipose tissue in men with overweight and obesity"

Raw data composed of adipose tissue ex vivo secretion, adipose tissue macrophage abundances, adipose tissue T cell counts and proportions, T cell activation marker expression and leptin and insulin receptor expression in adipose tissue, peripheral blood T cell counts and proportions, T cell activation marker expression and leptin and insulin receptor expression in peripheral blood, peripheral blood monocyte proportions and expression of leptin and insulin receptors, adipose tissue metabolic gene expression, serum metabolic and inflammatory protein concentrations, basic participant anthropometrics, dietary energy intake and daily expenditure, DEXA-derived body composition, OGTT results, and energy intake prescriptions during a 3 day 50 % calorie restriction protocol in 12 overweight and obese males.

Animal science
Food science and nutrition

Cite this dataset as:
Travers, R., Trim, W., Motta, A., Betts, J., Thompson, D., Unilever, 2024. Dataset for "Calorie restriction-induced leptin reduction and T-lymphocyte activation in blood and adipose tissue in men with overweight and obesity". Bath: University of Bath Research Data Archive. Available from:


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Raw data, normalised data, transformed data, and statistical analysis (normality, descriptives, and t-test) results for: Travers et al. Calorie restriction-induced leptin reduction and T-lymphocyte activation in blood and adipose tissue in overweight and obese men.


Rebecca Travers
University of Bath

Will Trim
University of Bath

James Betts
University of Bath

Dylan Thompson
University of Bath



University of Bath
Rights Holder


Data collection method:

The methodology can be found in the associated paper, below is an overview of these same methods. MATERIALS AND METHODS Experimental design Twelve abdominally overweight/ obese men aged between 35-55 years were recruited from the local community following ethical approval from the South West, Frenchay NHS Research Ethics Committee (REC Reference: 12/SW/0324). Each participant gave written informed consent. Only overweight and obese participants with waist circumference >94 cm (Lean et al., 1995) were recruited since these individuals showed greatest activation of T-lymphocytes within adipose tissue. After a 1-week period of monitoring energy intake and expenditure to confirm ‘energy balance’, participants reduced their caloric intake to 50 % of their normal intake for 3 consecutive days. Participants attended the Physiology Laboratory at the University of Bath before and after this intervention for analysis of immune cell activation and markers of inflammation and metabolism in blood and adipose tissue. An oral glucose tolerance test was also performed to assess glycaemic control before and after the 3-day calorie restriction intervention. Individuals were excluded from participation if they smoked, had personal history of cardiovascular disease, metabolic disease or dyslipidaemia, or were taking medications that may influence lipid or carbohydrate metabolism or immune system function. It was also required that participants had been weight stable for more than 3 months (no change in weight +/-3 %) (Stevens et al., 2006). This study is registered at (Reference: NCT02473835). Sample size determination There are no data regarding changes in T-lymphocyte activation in human adipose tissue in response to calorie restriction. However, significant differences in T-lymphocyte activation have been observed between lean and obese individuals (Travers et al., 2015). A short period of calorie restriction can reduce serum leptin values by around 40 % (Mars et al., 2006), which is sufficient to reduce typical values for a person with obesity to those of a lean person. Thus, a similar reduction in T-lymphocyte activation in response to calorie restriction might reasonably be anticipated. Previous data indicate that the CD69 mean fluorescence intensity (MFI) for lean CD4+CD69+ cells is 288 (+/45 SD) and obese is 411 (+/31 SD) with an effect size of 3.08 (G-Power) (Travers et al., 2015). To account for potentially greater variability in other activation markers, we recruited 12 participants. Monitoring of energy balance Participants were weighed before and after a 1-week period of ‘energy balance monitoring’ to ensure weight stability using a digital balance (Tanita Corp.; Amsterdam, Netherlands). Participants were fitted with a combined heart rate and accelerometry monitor (Actiheart™; Cambridgeshire, UK) to determine habitual total energy expenditure (TEE) (Thompson et al., 2006). TEE was adjusted for measured resting metabolic rate which was measured by indirect calorimetry (Frayn, 1983). A weighed food and fluid intake record was used during this period to estimate participants’ energy intake with dietary analysis performed using COMP-EAT Pro software (v.5.8.0, Nutrition Systems; UK). Participants were asked not to make any conscious changes to their normal lifestyle habits/routines during this period and, to avoid influencing their habitual routines, were not told that the activity monitoring and food records would be used to directly influence the diet prescribed/given during the 3-day calorie reduction period. This analysis was then used to confirm that participants were in a state of energy balance and to write a diet prescription for the 3-day intervention. The aim was to ensure that participants received 50 % of their 'normal' calorie requirements (i.e. average of energy intake and energy expenditure) using foods they would normally consume. Since there are errors associated with estimations of total energy expenditure (Thompson et al., 2006) and recording dietary intake (in particular underreporting) (Poslusna et al., 2009), we stipulated a priori that total energy intake and energy expenditure values had to be within 25 % of each other during the energy balance assessment and, furthermore, that the prescribed calorie intake had to be within 40–60 % of both the total energy expenditure and dietary intake values. If either of these requirements were not met, participants were asked to repeat the monitoring phase. Calorie restriction protocol To determine the exact calorie intake required to achieve a calorie restriction of 50 % normal energy requirements for the 3-days, an average of energy expenditure and dietary intake from the monitoring period was taken (provided that the above criteria were met). This value was then divided in half to give the ‘target’ calorie value for each of the 3 days in the diet. Subsequently, three separate days from the participant’s one-week diet record were selected and the weight of each item adjusted to meet this daily target kcal value whilst maintaining the overall relative proportions so that participants’ typical diet composition remained unaltered. During the 3-day intervention period, participants were asked to record the timing of each food/fluid intake within the prescribed food diary and to confirm that they had consumed the correct amount of food to help improve compliance with diet instructions. Participants were asked not to make any conscious changes to habitual physical activity during the calorie restriction period. Sample collection days Participants were asked not to perform any strenuous physical activity for 48 hr and to refrain from consuming caffeine/alcohol for 24 hr before both trial days (i.e., pre- and post-intervention). Trial days were scheduled so participants had been free from any self-reported illness for a minimum of 2 weeks in order to reduce immune system disturbance. On both main trial days, participants arrived at the Physiology Resting Laboratory in the morning following a 10 hr fast (approximately 8 am) and after consuming 1 pint of water upon waking. Participants arrived in the laboratory at the same time on both trial days. Measurements of height, waist circumferences and body mass (post- void using a digital balance; TANITA corp.) were determined on both trial days. Participants’ body composition were characterised at baseline using dual energy X-ray absorptiometry (DEXA; Discovery, Hologic; Bedford, UK) and estimates of total and central fat mass [L1-L4; (Glickman et al., 2004)] and FMI (Kelly et al., 2009) determined. Blood and adipose sampling A cannula was inserted into an antecubital forearm vein and blood sample(s) taken for isolation of peripheral blood mononuclear cells (PBMCs) by density gradient separation (Lympholyte; Cedarlane Laboratories Ltd.; Ontario, Canada) and analysis of plasma and serum metabolic/inflammatory markers (Travers et al., 2015). Subcutaneous adipose tissue samples (~1 g) were obtained under local anaesthetic (1 % lidocaine) approximately 5 cm lateral to the umbilicus using a ‘needle aspiration’ technique (Walhin et al., 2013). Approximately 100 mg whole adipose tissue was transferred to an RNase/DNase free sterile centrifuge tube, homogenised in Trizol reagent (Invitrogen; MA, USA) and frozen on dry ice for later RNA isolation. The remainder was used for adipose tissue culture [explants cultured for 3 hr at a concentration of 100 mg/ mL (Fain et al., 2004)], and preparation of the stromavascular fraction (SVF), both described previously (Travers et al., 2015, Trim et al., 2021). Due to the limited size of some adipose tissue samples, priority was given to preparing tissue for analysis of SVF to address the main aim of this study (n = 12). Lowest priority was given to gene expression analysis. Paired samples (pre- and post-calorie restriction) were available for 9 participants for explant secretion analysis and 7 participants for gene expression analysis. Oral glucose tolerance test Participants were asked to consume a glucose drink consisting of 75 g anhydrous glucose (maltodextrin) solution (Polycal, Nutricia; Wiltshire, UK) and cannula blood samples were taken every 15 mins for the following 2 hr for measurement of plasma glucose and serum insulin concentrations. Analysis of SVF and PBMCs by flow cytometry Flow cytometry (using the FACSverse, BD; NJ, USA) was used to identify CD4+/CD8+ T-lymphocytes (CD45+CD3+ cells) in SVF and PBMCs together with respective levels of activation. Due to the limited size of the SVF samples for analysis, cells were labelled using a single antibody cocktail comprising; CD4-FITC, CD8-PE-Cy7, CD69-APC, CD25-APC-Cy7, CD3-V450 and CD45-V500 (BD; USA). PBMCs were labelled using; CD45-V500, CD3-V450, CD295-FITC, CD220-PE, CD4-PerCP, CD8-PE-Cy7, CD69-APC and CD25-APC-Cy7 (BD; USA). RT-PCR Total RNA was extracted from whole adipose tissue, quantified and 1 μg reverse transcribed to cDNA as described previously (Travers et al., 2015). Real-time PCR was performed using a StepOneTM (Applied Biosystems; MA, USA) with pre-designed primers and probes obtained from Applied Biosystems for measurement of LEPTIN (Hs00174877_m1), ADIPONECTIN (Hs00605917_m1), GLUT4 (Hs00168966_m1), IRS2 (Hs00275843_s1), HSL (Hs00193510_m1), LPL (Hs01012567_m1), PPARγ (Hs01115513_m1), MCP1 (Hs00234140_m1), IL6 (Hs00985639_m1), IL8 (Hs99999034_m1), IL1Ra (Hs00893626_m1), and IL18 (Hs00155517_m1) expression. Peptidylpropyl isomerase A (PPIA) was used as an endogenous control (Neville et al., 2011). Results were analysed using the comparative Ct method and expression normalized to an internal calibrator specific to each gene using the formula 2- ∆∆CT; where ∆∆CT is [(CT gene of interest – CT PPIA) – lowest ∆CT for gene of interest] and statistical analysis performed on LN-transformed values (Livak and Schmittgen, 2001). Biochemical analysis Plasma glucose, serum total cholesterol, HDL cholesterol, triglycerides and CRP concentrations and ALT activity were measured using commercially available assay kits and analyser (Daytona Rx, Randox; Crumlin, UK). ELISA was used for the measurement of serum Insulin concentrations (Mercodia; Uppsala, Sweden), and both serum and adipose tissue Leptin and Adiponectin secretion (R&D systems; MN, USA). A fluorescent bead multiplex system (Luminex, BIO-RAD; CA, USA) was used for the measurement of serum and adipose tissue secretion of TNF, IL-10, IL-8, MCP-1, IL-1a, IL-1Ra, IL-18, IL-6, G-CSF, IP-10, MIP-1β, and M-CSF. Serum IL-6, G-CSF, M-CSF, IL-1a, and IL1Ra were detectable in fewer than 3 individuals so results were not included in statistical analysis. TNF and IL-10 were not detectable in either serum or adipose culture supernatant. Statistical analysis Estimates of glucose control were calculated using homeostasis model assessment for insulin resistance [HOMA-IR; (Matthews et al., 1985)] and insulin sensitivity index [ISI comp/ Matsuda index; (Matsuda and DeFronzo, 1999)]. LDL cholesterol was calculated using the Friedewald equation (Friedewald et al., 1972) Comparisons were made between pre- and post- calorie restriction values using two-tailed, paired t-tests, or non-parametric equivalents where data were non-normally distributed (Shapiro-Wilks p>0.05). Correlation analysis was performed using Pearson’s r. Statistical analysis was performed using GraphPad Prism 9.1.2 (GraphPad Software, LLC.; FL, USA). Effect sizes were calculated using Cohen’s d. p≤0.05 was considered to be statistically significant. Citations: FAIN, J. N., MADAN, A. K., HILER, M. L., CHEEMA, P. & BAHOUTH, S. W. 2004. Comparison of the release of adipokines by adipose tissue, adipose tissue matrix, and adipocytes from visceral and subcutaneous abdominal adipose tissues of obese humans. Endocrinology, 145, 2273-82. FRAYN, K. N. 1983. Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol Respir Environ Exerc Physiol, 55, 628-34. FRIEDEWALD, W. T., LEVY, R. I. & FREDRICKSON, D. S. 1972. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem, 18, 499-502. GLICKMAN, S. G., MARN, C. S., SUPIANO, M. A. & DENGEL, D. R. 2004. Validity and reliability of dual-energy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl Physiol (1985), 97, 509-14. KELLY, T. L., WILSON, K. E. & HEYMSFIELD, S. B. 2009. Dual energy X-Ray absorptiometry body composition reference values from NHANES. PLoS One, 4, e7038. LEAN, M. E., HAN, T. S. & MORRISON, C. E. 1995. Waist circumference as a measure for indicating need for weight management. BMJ, 311, 158-61. LIVAK, K. J. & SCHMITTGEN, T. D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25, 402-8. MARS, M., DE GRAAF, C., DE GROOT, C. P., VAN ROSSUM, C. T. & KOK, F. J. 2006. Fasting leptin and appetite responses induced by a 4-day 65%-energy-restricted diet. Int J Obes (Lond), 30, 122-8. MATSUDA, M. & DEFRONZO, R. A. 1999. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care, 22, 1462-70. MATTHEWS, D. R., HOSKER, J. P., RUDENSKI, A. S., NAYLOR, B. A., TREACHER, D. F. & TURNER, R. C. 1985. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia, 28, 412-9. NEVILLE, M. J., COLLINS, J. M., GLOYN, A. L., MCCARTHY, M. I. & KARPE, F. 2011. Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization. Obesity (Silver Spring), 19, 888-92. POSLUSNA, K., RUPRICH, J., DE VRIES, J. H., JAKUBIKOVA, M. & VAN'T VEER, P. 2009. Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. Br J Nutr, 101 Suppl 2, S73-85. STEVENS, J., TRUESDALE, K. P., MCCLAIN, J. E. & CAI, J. 2006. The definition of weight maintenance. Int J Obes (Lond), 30, 391-9. THOMPSON, D., BATTERHAM, A. M., BOCK, S., ROBSON, C. & STOKES, K. 2006. Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling. J Nutr, 136, 1037-42. TRAVERS, R. L., MOTTA, A. C., BETTS, J. A., BOULOUMIE, A. & THOMPSON, D. 2015. The impact of adiposity on adipose tissue-resident lymphocyte activation in humans. Int J Obes (Lond), 39, 762-9. TRIM, W. V., WALHIN, J.-P., KOUMANOV, F., BOULOUMIÉ, A., LINDSAY, M. A., CHEN, Y.-C., TRAVERS, R. L., TURNER, J. E. & THOMPSON, D. 2021. Divergent immunometabolic changes in adipose tissue and skeletal muscle with ageing in healthy humans. The Journal of Physiology. WALHIN, J. P., RICHARDSON, J. D., BETTS, J. A. & THOMPSON, D. 2013. Exercise counteracts the effects of short-term overfeeding and reduced physical activity independent of energy imbalance in healthy young men. J Physiol, 591, 6231-43.

Data processing and preparation activities:


Technical details and requirements:

GraphPad Prism 9.1.2 (GraphPad Software, LLC.; FL, USA)

Additional information:

All data are laid out in columns, with participants in rows.

Legal and Ethical Documents

PIS effects … 27.11.2012.docx
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Participant information sheet for informed ethical consent to participate.

Trial 2 Consent … 27.11.2012.docx
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Participant ethical consent form template.


Biotechnology and Biological Sciences Research Council

BBSRC Industrial CASE Partnership Grant

Biotechnology and Biological Sciences Research Council

Targeting Bed Reset-Induced Adipose Tissue Dysfunction with Anti-Inflammatory and Antioxidant Nutrients

Publication details

Publication date: 27 March 2024
by: University of Bath

Version: 1


URL for this record:

Related papers and books

Travers, R. L., Trim, W. V., Motta, A. C., Betts, J. A., and Thompson, D., 2024. Calorie restriction-induced leptin reduction and T-lymphocyte activation in blood and adipose tissue in men with overweight and obesity. International Journal of Obesity. Available from:

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Will Trim


Faculty of Humanities & Social Sciences

Research Centres & Institutes
Centre for Nutrition, Exercise and Metabolism (CNEM)