The Effect of Dietary Carbohydrate Manipulation on All Major Aspects of Energy Balance: A 12-week Randomised Controlled Trial
Phase: NA
Conditions: Physical Activity, Diet Modification
Sponsor: University of Bath

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The effect of dietary carbohydrate manipulation on 
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physical 
activity 
and 
other 
energy 
balance 
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components: 
Study 
protocol 
for 
a 
12-week 
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randomised controlled trial 
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NCT03574987 
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Document v3 01/09/2023 
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Aaron Hengist1, Russell G. Davies1, Francoise Koumanov1, Dylan Thompson1, James 
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A. Betts1, Jean-Philippe Walhin1, Peter J. Rogers2, Jeff M. Brunstrom2, Luc J.C. van 
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Loon3, Javier T. Gonzalez1,* 
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1Department for Health, University of Bath, UK 
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2School of Experimental Psychology, University of Bristol, UK 
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3Department of Movement Sciences, Maastricht University, Netherlands  
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Running title: Carbohydrate, physical activity and energy balance: RCT protocol 
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Key words: Carbohydrate, Physical Activity, Energy Balance 
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Word count: (excluding refs and fig legends) 
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Number of references:  
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*Corresponding author 
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Dr Javier T Gonzalez 
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J.T.Gonzalez@bath.ac.uk 
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Department for Health, University of Bath, BA2 7AY 
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Abstract 
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Carbohydrates are a staple component of modern diets. When classified by structure, 
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the smallest chain carbohydrates are referred to as sugars. Free sugars comprise 
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approximately 10-20% of energy intake in European populations. Global health 
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guidelines advocate reducing free sugars intake below 5% of energy intake. It is 
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unclear what impact these guidelines will have on physical activity and energy 
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balance. Little is known about the effects of total (sugar and non-sugar) carbohydrate 
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intakes on physical activity and energy balance too, therefore this study aims to 
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measure the behavioural and metabolic responses to manipulating the type and 
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amount of dietary carbohydrate in humans. Sixty humans, age 18-65 years, with a 
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body mass index between 18.5-29.9 kg∙m-2 will be recruited for a randomised 
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controlled trial. Participants will be randomised to a control diet (MODSUG), a low-
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sugar diet in line with public health guidelines (LOWSUG), or a low-carbohydrate 
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ketogenic diet (LOWCHO) for 12-weeks. Self-reported dietary intake and objectively-
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measured physical activity will be monitored throughout the intervention.  Participants 
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will undergo laboratory testing at baseline, week 4, and week 12 of the diet. This will 
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comprise muscle and adipose biopsies, a submaximal incremental treadmill protocol, 
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measures of body composition, resting metabolic rate, and a mixed-meal tolerance 
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test. Faecal samples will be obtained throughout the intervention to measure changes 
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to the gut microbiome. This research will provide evidence to inform public health 
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policy on the consumption of free sugars in healthy adults and will provide insight into 
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the physiological effects of a low-carbohydrate ketogenic diet.   
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Introduction  
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Carbohydrates are a staple component of modern diets, which reportedly comprise 
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nearly 50% of energy intake in the United Kingdom (roughly 224 grams per day) 
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(Roberts et al., 2018). Dietary carbohydrates are commonly classified according to 
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their size and structure as mono- or disaccharides (1-2 monomers), oligosaccharides 
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(3-9 monomers), or polysaccharides (more than 9 monomers) (SACN, 2015). The 
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smallest chain carbohydrates (e.g. glucose, fructose, sucrose) are commonly referred 
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to as ‘sugars’, whereas polysaccharides are comprised of many molecules (e.g. 
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amylose, amylopectin). Some of the sugars in foods can be defined as ‘free sugars’, 
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which are ‘all monosaccharides and disaccharides added to foods by the 
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manufacturer, cook or consumer, plus sugars naturally present in honey, syrups and 
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unsweetened fruit juices’ (Swan, Powell, Knowles, Bush, & Levy, 2018). Free sugars 
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by this definition reportedly comprise ~11% of energy intake in the United Kingdom 
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(57 grams per day) (Roberts et al., 2018), and intake across developed nations has 
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been reported to vary between 8% and 22% of energy intake (Wittekind & Walton, 
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2014). Both carbohydrates and free sugars are commonly ingested by many humans 
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in developed countries, and therefore an understanding of the metabolic and energy 
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balance effects of dietary carbohydrates and sugars is relevant to a wide population. 
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When referring to the intake of free sugars, this can often be considered as the co-
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ingestion of glucose and fructose (as sucrose or table sugar). Common sources of 
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sucrose intake in Europe are soft drinks (sugar sweetened beverages), along with fruit 
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juices, fruits, cakes and dairy products (Sluik, Engelen, & Feskens, 2015), therefore 
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providing rationale for the specific targeting of sugar sweetened beverages with the 
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Soft Drinks Industry Levy in the UK (Barber, 2017). Public health guidelines across the 
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world advocate that energy intake from free sugars should make up no more than 5% 
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of an individual’s total energy intake (Erickson, Sadeghirad, Lytvyn, Slavin, & 
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Johnston, 2017; SACN, 2015; World Health Organisation, 2015). There are various 
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reasons these guidelines may be beneficial for public health. There is reasonably 
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strong evidence (despite a lack of randomised controlled trials) that dietary sugar 
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intake is associated with the prevalence of dental caries (Freeman, 2014; Moynihan & 
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Kelly, 2014). Data around the effects of free sugars on metabolic health and body 
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weight are less conclusive. Increasing dietary sugar intake causes an increase in 
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energy intake (SACN, 2015). However, increasing (or decreasing) dietary free sugars 
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results in modest increases (or decreases) in body weight and isoenergetic exchange 
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of free sugars with other sources of carbohydrates does not result in weight change 
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(Morenga, Mallard, & Mann, 2013). Current evidence suggests that the increased 
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weight observed with increasing free sugar intake is predominantly due to an energy 
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surplus, via increasing energy intake. One rationale for reducing sugar intake, 
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therefore, is that this would create an energy deficit which would lead to weight loss 
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over time. However, an increase in energy intake without a clear change in body mass 
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indicates that there must be interactions with other aspects of energy balance. Current 
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evidence does not consider the effect of any compensatory changes in energy 
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expenditure. On the balance of current evidence, advice to reduce total sugar intake 
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seems sensible but a more complete understanding of the role of sugar intake on the 
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complex and intricate components of energy balance is warranted. 
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In recent years, the notion that carbohydrates per se are detrimental to metabolic 
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health has become prominent. Whilst the rate of absorption is dependent on the type 
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and matrix of carbohydrate, from a physiological perspective, ingestion of glucose as 
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a monomer or a polymer can be considered physiologically similar stimuli because 
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hydrolysis of glucose polymers is not thought to be rate-limiting to intestinal absorption 
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((Gonzalez, Fuchs, Betts, & van Loon, 2017), suggesting the overall carbohydrate 
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dose is an important consideration for general health outcomes. Furthermore, it is well-
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established that carbohydrate availability dictates the capacity to perform physical 
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work (Krogh & Lindhard, 1920), but the role of carbohydrate in regulating physical 
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activity behaviours has only recently been considered. Participants randomised to 
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consume a carbohydrate-rich breakfast display an increase in 24-hour physical activity 
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energy expenditure (PAEE) compared with those randomised to remain fasted until 
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midday (Betts et al., 2014). The magnitude of this difference is greatest prior to midday, 
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near to when carbohydrate had been ingested and when glucose uptake to peripheral 
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tissue is increased (Betts et al., 2014). This points towards a stimulatory role of 
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carbohydrate or sugar on PAEE when carbohydrate is readily available to peripheral 
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tissue. The amount of carbohydrate present in skeletal muscle is largely dictated by 
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the amount of carbohydrate in the diet (Bergstrom, Hermansen, Hultman, & Saltin, 
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1967). As physical activity is performed by skeletal muscle, dietary carbohydrate 
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intake may be an important regulator of physical activity behaviour.  
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Studies in which carbohydrate has been manipulated and physical activity has been 
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measured have not been sufficient in answering whether manipulating dietary 
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carbohydrate leads to a change in PAEE (Smith, Gonzalez, Thompson, & Betts, 2017). 
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Often self-report measures of physical activity are used, which are not sufficiently 
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sensitive to discern meaningful differences. Studies which have measured physical 
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activity objectively, i.e. using pedometers (Foraker et al., 2014) or accelerometers 
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(Layman et al., 2009; Tay et al., 2014), are confounded by a lack of information about 
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actual carbohydrate intake or concurrent prescription of exercise interventions. More 
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recent evidence using doubly-labelled water suggests that carbohydrate restriction 
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increases total energy expenditure during weight maintenance after weight loss 
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(Ebbeling et al., 2018), however this effect was influenced by the methods of analysis 
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(Hall & Guo, 2018). The authors originally planned to use pre-weight loss energy 
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expenditure as a baseline, but changed this to post-weight loss energy expenditure 
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(Hall & Guo, 2018). The use of doubly-labelled water is inappropriate to measure total 
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energy expenditure when either the body water pool changes by >3% or the average 
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respiratory exchange ratio is unequal to the food quotient for the duration of the 
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measurement (Internation Atomic Energy Agency, 2009). Ebbeling et al. (2018) used 
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an assumed respiratory quotient based on the food quotient (i.e. macronutrient 
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composition) of each participant (Black, Prentice, & Coward, 1986), but this did not 
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incorporate changes which occur during weight loss. One study which investigated the 
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effects of carbohydrate availability per se on energy expenditure using doubly labelled 
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water in 5 participants suggested that physical activity was reduced on a low 
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carbohydrate diet (7% carbohydrate, 83% fat, 10% protein) compared with a high 
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carbohydrate diet (83% carbohydrate, 7% fat, 10% protein) (Bandini, Schoeller, & 
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Dietz, 1994), which points towards a potentially stimulatory role of carbohydrates to 
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PAEE. To address these issues, studies using an objective method of measuring 
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PAEE, which allow for incorporation of changes in substrate oxidation, are required. 
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Government targets to reduce sugar intake below 5% of total energy intake are not 
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aimed at overall carbohydrate intake per se. In the breakfast study mentioned, sugar 
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intake was significantly greater amongst individuals who ate breakfast compared with 
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individuals who fasted until midday (Betts et al., 2014). As such, it is plausible that a 
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regulatory role of carbohydrate on PAEE may be due to the type of carbohydrate rather 
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than the absolute amount. Therefore, when assessing the potential role of 
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carbohydrates on physical activity, it is important to consider the potential role of both 
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carbohydrate type and amount. Therefore, we designed a study to investigate the role 
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of carbohydrate type and amount on energy balance and physical activity, which 
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addresses some of the limitations of previous research by objectively measuring free-
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living PAEE, incorporating changes in substrate oxidation which occur with altering 
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carbohydrate availability. The purpose of this study is to measure the behavioural and 
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metabolic responses to manipulating the type and/or amount of dietary carbohydrate 
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in humans over a 12-week period. We approach this question from an integrated 
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physiological perspective. We are interested in energy balance, metabolism, 
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endocrinology, appetite, the gut microbiome, and food preference. The primary aim is 
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to investigate the effects of dietary carbohydrate manipulation on PAEE, using 
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combined accelerometry and heart rate monitoring, calibrated to each participant at 
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each time of measurement, based on substrate oxidation and resting metabolic rate. 
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This is the first study to objectively measure free-living energy expenditure 
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incorporating changes in substrate oxidation in response to carbohydrate 
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manipulation. The intervention arms are relevant from a public health perspective, with 
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a direct investigation of the effects of implementing current public health guidelines on 
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sugar intake. This research will provide evidence to inform public health policy on the 
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consumption of free sugars in healthy adults, and will provide insight into the 
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physiological effects of a low-carbohydrate ketogenic diet.   
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Methods  
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Study overview  
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Sixty metabolically healthy men and women, age between 18-65 years, will be 
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recruited to participate in a randomised controlled trial with three arms. All data 
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collection will be conducted in human physiology laboratories at the University of Bath. 
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Following free-living assessment of habitual diet and physical activity level, 
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participants will be randomised to adhere to one of three diets for a 12-week period in 
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which the dietary carbohydrate content and/or type is manipulated. The macronutrient 
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composition the intervention diets will aim for is as follows: 
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MODSUG (CONTROL) – 50% CHO (20% SUG), 15% PRO, 35% FAT 
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LOWSUG – 50% CHO (<5% SUG), 15% PRO, 35% FAT 
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LOWCHO – <8% CHO* (<5% SUG), 15% PRO, ≥77% FAT 
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*or ≤50 g CHO per day 
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A schematic of progression through the study is presented in Figure 1. Participants 
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will undergo three laboratory testing periods in total: at baseline, at week 4, and at 
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week 12 of the diet. Diet and physical activity will be monitored for 7 days prior to 
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randomisation, then at week 4 and week 12 of the intervention. Each laboratory test 
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will comprise two laboratory visits on consecutive days for measures to be taken. On 
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the first laboratory visit, participants will undergo muscle and adipose biopsies, 
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followed by a submaximal, incremental treadmill protocol to measure substrate 
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oxidation and calibrate the activity monitor. The following morning, participants will 
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return to the laboratory to undergo measures of body composition, resting metabolic 
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rate, and a mixed-meal tolerance test. Participants will be provided with education on 
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macronutrients and will be provided with guidance and feedback on which types of 
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foods will help them achieve the macronutrient composition of the prescribed diets. 
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Participants will also receive partial reimbursement of food expenses to help them 
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achieve desired dietary changes. Whilst macronutrient composition will be 
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manipulated, energy intake will not be prescribed. 
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[INSERT FIGURE 1 HERE] 
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Outcome measures 
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The primary outcome measure is physical activity energy expenditure (expressed as 
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kJ per day).  
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Secondary outcome measures include time spent and energy expenditure at different 
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intensities, weight, steps, energy intake, macronutrient intake, body composition and 
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bone mineral density (using dual x-ray absorptiometry), waist:hip ratio, blood pressure, 
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substrate oxidation, resting metabolic rate, interstitial glucose concentrations, fasting 
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and postprandial metabolite and hormone concentrations, protein glycation, gene and 
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protein expression in muscle and adipose tissue, muscle glycogen concentrations, 
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urinary ketone concentrations, gut microbiome measures, urinary metabolomics, 
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subjective appetite, and preference for food types. These are outlined in Figure 2 and 
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will be assessed subject to funda available.  
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[INSERT FIGURE 2 HERE] 
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Participant characteristics and eligibility  
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The following are criteria for inclusion in the study:  
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• Body mass index 18.5-29.9 kg∙m-2  
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• Age 18-65 years 
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• Able and willing to provide informed consent and safely comply with study 
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procedures 
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• Females to maintain record of regular menstrual cycle phase/contraceptive use 
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• No anticipated changes in physical activity during the first 4 weeks of the study 
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(e.g. holidays or training programmes)  
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The following are criteria for exclusion from the study: 
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• Any reported condition or behaviour deemed either to pose undue personal risk 
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to the participant or introduce bias 
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• Any diagnosed metabolic disease (e.g. type 1 or type 2 diabetes) 
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• Any reported use of substances which may pose undue personal risk to the 
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participants or introduce bias into the experiment 
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• Lifestyle not conforming to standard sleep-wake cycle (e.g. shift worker) 
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• Any reported recent (<6 months) change in body mass (± 3%) (Stevens et al., 
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2006) 
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• Use of antibiotic medication in the last 3 months  
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• Use of prebiotic or probiotic products in the last month 
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Recruitment and enrolment  
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Participants will be recruited from the University of Bath campus and the local 
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community by word of mouth, by poster advertisement, and by social media. 
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Participation will be centred on the provision of obtaining informed consent prior the 
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study and throughout participation. Participants will be sent an information sheet after 
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expressing interest in the study and will be asked to read thoroughly. Where possible, 
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researchers will talk through this information sheet in person. Participants will be asked 
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to take time to consider and ask questions about the study over the following week. 
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Pre-enrolment, participants will undergo a consultation to affirm the information 
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provided in the information sheet and provide clarity on the procedures that the 
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participant will undergo as part of the study. Once this information has been provided 
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and consent has been clarified verbally, participants will be asked to initial and sign an 
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informed consent form. Following this, participants will undergo eligibility screening by 
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completing a health questionnaire. We will assess body mass index to make sure they 
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meet this criterion. They will be reminded that their consent is completely voluntary 
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and will be reminded of their right to withdraw from the study procedures at any point 
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without providing justification.  
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Preliminary testing  
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Once participants are formally enrolled on the study, they will visit the laboratory to 
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begin their preliminary measures. Participants will be asked to fast for ~4-5 hours prior 
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to visiting the laboratory to minimise interference with respiratory measures (Compher, 
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Frankenfield, Keim, Roth-Yousey, & Evidence Analysis Working, 2006). Height will be 
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measured using a stadiometer (Seca Ltd., Birmingham, UK) in the Frankfurt plane with 
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shoes removed. Body mass and bioelectrical impedance will be measured using digital 
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scales (Tanita, Amsterdam, The Netherlands) with participants barefoot and wearing 
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light clothing. Hip and waist circumference will be measured using a handheld tape 
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measure (Seca Ltd., Birmingham, UK).  
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Resting metabolic rate will be measured using the Douglas bag technique. Participants 
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will rest in bed in a recumbent supine position for ~10 minutes. When rested, the 
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participant will be given a mouthpiece and nose clip to wear, and three 5-minute 
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samples of expired air will be collected into Douglas bags. Researchers will enter the 
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room to change the Douglas bag at the end of each sample and note down ambient 
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conditions. The expired fraction of oxygen and carbon dioxide will be entered into 
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equations by Frayn and Jeukendrup & Wallis (Frayn, 1983; Jeukendrup & Wallis, 
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2005) to calculate resting metabolic rate (RMR) and substrate oxidation. The fractions 
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of inspired oxygen and carbon dioxide will be measured concurrently to correct for the 
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dynamic ambient conditions (Betts & Thompson, 2012). Detailed methods for indirect 
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calorimetry are outlined below in the ‘Indirect calorimetry’ section. Then participants 
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will be asked to complete a food preference task on a laptop. The details of which are 
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outlined in the ‘Food preference task’ section.  
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Participants will then complete a 20-minute treadmill walk to calibrate the physical 
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activity monitor to their substrate oxidation and energy expenditure, based on a 
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protocol adapted from Brage et al. (Brage et al., 2007). The walk will comprise four 5-
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minute stages at 5.2 km∙h-1 with progressive inclines of 0%, 3%, 6% to 9% with 
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participants wearing a heart rate monitor (Polar, Warwick, UK). During the last minute 
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of each stage, heart rate will be recorded and expired breath will be collected into a 
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Douglas bag to measure energy expenditure and substrate oxidation.  
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Following the treadmill walk, participants will receive an Actiheart monitor (CamNtech 
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Ltd., Cambridge, UK). This is a small chest-worn device which records heart rate, inter-
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beat-interval, and physical activity energy expenditure. Participants will be shown how 
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to wear apply the device. They will be asked to keep it on for a 7-day period. This will 
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provide a free-living measure of energy expenditure. Participants will be provided with 
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a food diary and a set of portable weighing scales. They will be asked to record all 
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food and drink they ingest for a 7-day period coinciding with the 7-days of wearing the 
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Actiheart monitor. This will be used to evaluate macronutrient composition and will 
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provide an estimate of energy intake. The 7 days of actiheart and food diary will be 
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repeated in the 7 days prior the 4-week and 12-week laboratory tests during the 
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intervention. Participants will also be provided with a pedometer (3DTriSport, Realalt, 
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UK) which will be worn throughout the week of habitual monitoring and the 12-week 
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intervention. 
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Once participants have completed their week of habitual lifestyle monitoring and 
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laboratory testing has been arranged, they will be provided with a faecal collection kit 
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and asked to collect a faecal sample within 24 hours of visiting the lab. The kit will 
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contain 1 pair of disposable gloves, 1 “faeces catcher” paper, 1 collection pot, 1 plastic 
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Ziploc bag, 1 icepack and 1 small opaque cool bag for discreet transportation, along 
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with a detailed instruction sheet advising them how to collect the sample. Participants 
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will be advised to place the collection pot inside the Ziploc bag, and place this inside 
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the opaque cool bag to store in their fridge at 4 degrees, and then transport the sample 
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to the lab in the cool bag together with frozen icepacks. During the week-long 
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monitoring periods before each laboratory visit.  
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Participants will be fitted with a research-grade continual glucose monitor (CGM) 
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(Freestyle Libre, Abbott, UK) which will collect data for ~14 days. A member of the 
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research team will fit the continual glucose monitor and this will remain attached to the 
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participant until being removed by a member of the research team. A new CGM will 
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be fitted at 10 weeks into the intervention to record data between week 10 and 12. In 
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total, interstitial glucose will be measured for 7 days of habitual lifestyle, the first 7 days 
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of the diet intervention, and the final 14 days of the diet intervention.  
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Randomisation  
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Participants will be randomised to one of three diet interventions by a member of the 
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research team who is not participant-facing. Allocation will be stratified on two levels: 
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by sex (male vs female), and mean physical activity level (PAL) across the habitual 
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monitoring period (<1.70 vs ≥1.70). PAL is simple way of assessing an individual’s 
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habitual physical activity, and is calculated by taking total energy expenditure and 
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dividing by basal metabolic rate. A PAL ≥1.70 is associated with lower risk of many 
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metabolic diseases including type 2 diabetes and cardiovascular disease 
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(FAO/WHO/USU, 2001). Due to the nature of the intervention, dropouts are expected. 
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As such, we will be conducting a rolling recruitment to achieve statistical power 
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whereby dropouts will be replaced by the next recruited individual. A dropout will be 
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defined as an individual who does not wish to continue with the diet intervention within 
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the first 4 weeks (i.e. initial 4 weeks will be a completer’s analysis). When the first 4 
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weeks have been completed, any deviations in adherence to diets will be considered 
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as part of the research question (i.e. intention to treat analysis).  
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Laboratory tests  
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There are three laboratory tests across the study, one at baseline, one at week 4, and 
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one at week 12. These require laboratory visits which will be split across two days. 
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Day one will take place in the afternoon ~16:30 and day two will take place the 
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following morning ~08:00. On day one, participants will be asked to arrive at the 
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laboratory following a minimum 5-hour fast and they will be asked not to perform 
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structured exercise in this period. They will undergo an abdominal adipose tissue 
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biopsy. The abdominal region will be sterilised with iodine and local anaesthetic 
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(lidocaine hydrochloride) will be administered. After 5 minutes, when the area is numb, 
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a 14 G needle attached to a 50 mL syringe will be inserted into the subcutaneous 
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abdominal adipose tissue. A vacuum will be created using the syringe and adipocytes 
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aspirated into the syringe via the needle. Pressure will be applied to stop any bleeding 
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then the wound will be dressed. Treatment and analysis of samples is outlined in the 
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section titled ‘Adipose biopsy samples’.  
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Participants will then be given time to rest before any other procedures are carried out. 
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When the participant is ready and willing, they will undergo a muscle biopsy from the 
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vastus lateralis. The participant’s quadriceps will be sterilised with iodine before local 
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anaesthetic (lidocaine hydrochloride) is administered subcutaneously and on the 
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muscle fascia. When the area is numb, a small incision will be made to the skin and 
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muscle fascia using a sterile scalpel blade. Then a Bergstrom needle will be inserted 
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into the muscle belly and ~2-3 snips will be made with suction applied, as described 
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by Tarnopolsky et al. (Tarnopolsky, Pearce, Smith, & Lach, 2011). The cutaneous 
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incision will then be stitched up and pressure will be applied to the site. Muscle 
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sampling handling and analyses is outlined in the section titled ‘Muscle biopsy 
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samples’.  
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Following the muscle biopsy, participants will be allowed time to recover and rest. They 
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will then complete a graded exercise protocol on the treadmill. This will mirror the 
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Actiheart calibration protocol outlined in the ‘Preliminary measures’ section, with four 
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5-minute stages where the speed of the treadmill will be fixed but the gradient will be 
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increased. Participants will be asked to walk at 5.2 km∙h-1 at 0%, 3%, 6%, and 9% 
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incline. During the last minute of each stage heart rate will be measured using a heart 
390 
rate monitor (Polar, Warwick, UK), substrate oxidation and energy expenditure will be 
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measured by collecting expired air in Douglas bags, and ratings of perceived exertion 
392 
(RPE) will be measured using Borg’s 6-20 scale (Borg, 1970). Participants will be 
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asked to have a relaxing evening at home ready for the continuation of the laboratory 
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testing the following morning.   
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On day two, participants will be asked to arrive to the laboratory at around 08:00, 
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following an overnight fast of 10-12 hours. They will be asked to drink a pint of water 
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between waking and attending the laboratory and will be asked to refrain from 
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performing physical activity during their commute (i.e. take the bus or car, instead of 
400 
walking or cycling). Height will be measured using a stadiometer (Seca Ltd., 
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Birmingham, UK), with participants barefoot in the Frankfurt plane. Body mass will be 
402 
measured using digital scales (Tanita, Amsterdam, The Netherlands) with participants 
403 
barefoot and wearing light clothing. Hip and waist circumference will be measured 
404 
using a handheld tape measure (Seca Ltd., Birmingham, UK). Following this, 
405 
participants will undergo a whole-body dual x-ray absorptiometry (DXA) scan to 
406 
assess body composition and bone mineral density.  
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At ~08:45, participants will be escorted to the resting laboratory where they will rest in 
409 
a recumbent supine position for 15 minutes. When rested, the participant will be given 
410 
a mouthpiece and nose clip to wear, and three 5-minute samples of expired air will be 
411 
collected into Douglas bags. Researchers will enter the room to change the Douglas 
412 
bag at the end of each sample and note down ambient conditions. The expired fraction 
413 
of oxygen and carbon dioxide will be entered into equations by Frayn and Jeukendrup 
414 
& Wallis (Frayn, 1983; Jeukendrup & Wallis, 2005) to calculate resting metabolic rate 
415 
(RMR) and substrate oxidation. The fractions of inspired oxygen and carbon dioxide 
416 
will be measured concurrently to correct for the dynamic ambient conditions (Betts & 
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Thompson, 2012). Detailed methods for indirect calorimetry are outlined in the 
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‘Indirect calorimetry’ section.  
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Following this, a measure of blood pressure will be taken using an automated 
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sphygmomanometer (Diagnostec EW3106, Panasonic, Japan). A cannula will then be 
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inserted into a dorsal hand or antecubital vein and the hand of the corresponding arm 
423 
will be placed in a box heated to 55°C to arterialise the blood (Edinburgh et al., 2017). 
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Two mL of blood will be drawn to make sure blood is flowing from the cannula and 
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then it will be flushed with sterile saline solution (B. Braun, Pennsylvania, USA) to 
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maintain patency. Whilst waiting for the vein to arterialise, the participant will complete 
427 
visual analogue scales (VAS) for appetite and mood; these are 100-mm scales which 
428 
are attached to a statement with opposing extremes (e.g. not at = 0, extremely = 100). 
429 
Following this, participants will complete a food preference task; details of which are 
430 
outlined in the section titled ‘Food preference task’. Once this is complete, a baseline 
431 
blood sample will be collected. For all blood samples, 2 mL blood will be drawn and 
432 
disposed of, 10 mL blood will be collected for analysis, and then the cannula will be 
433 
flushed with 5-10 mL of sterile saline solution (B. Braun, Pennsylvania, USA) to 
434 
maintain patency. All samples will be dispensed into 2 sterile collection tubes. One 
435 
containing ethylenediaminetetraacetic acid (EDTA) (Sarstedt, Nümbrecht, Germany) 
436 
which will be immediately centrifuged to extract blood plasma. The other containing 
437 
plastic beads (Sarstedt, Nümbrecht, Germany) which will be allowed to clot at room 
438 
temperature for 15 minutes before being centrifuged to extract blood serum. All 
439 
samples will be centrifuged at 4000 g for 10 minutes at 4°C. Blood serum and blood 
440 
plasma will be aliquoted into Eppendorf tubes (Eppendorf, Hamburg, Germany) and 
441 
stored on dry ice for the remainder of the visit, before being moved and stored at -
442 
80oC until analysis.  
443 
 
444 
Following the baseline blood sample, participants will be provided with a mixed meal 
445 
tolerance test (MMTT). Participants will ingest a milk chocolate milk shake (Ensure 
446 
Plus, Abbott, Illinois, US) equating to 30% of resting metabolic rate as determined by 
447 
indirect calorimetry in the first laboratory test (e.g. 600 kcal for an individual with a 
448 
resting metabolic rate of 2000 kcal∙day-1). The composition of this drink is similar in 
449 
composition to the control diet and is reflective of typical food intake of European 
450 
populations; the contribution of macronutrients to total calories are 54% CHO (23% 
451 
SUG), 31% FAT, 15% PRO. A timer will be started when the participant ingests the 
452 
first sip of the drink and they will be asked to try and finish ingesting the MMTT within 
453 
5 minutes. Blood will be collected at 15, 30, 45, 60, 90, 120, 150, 180, 210, and 240 
454 
minutes following ingestion of the MMTT. VAS measures will be collected each hour 
455 
minutes following ingestion of the MMTT. A 5-minute expired breath samples of will 
456 
be collected at each hour following ingestion of the MMTT to measure dietary induced 
457 

Page 16 
 
thermogenesis. Blood pressure will also be measured each hour. Participants will be 
458 
asked to remain in a semi-recumbent supine position for the duration of the 240 
459 
minutes, unless they need to use the toilet, and will be allowed to perform sedentary 
460 
tasks like watching television, reading, or working on a laptop. Participants will be 
461 
allowed to take their hand out of the heated box until 10 minutes prior each blood 
462 
sample to ensure the vein remains adequately arterialised. Towards the end of the 
463 
postprandial period, participants will be asked to complete a second food preference 
464 
task. Then, the cannula will be removed and pressure will be applied to prevent 
465 
bruising. Urine produced during the laboratory visit will be collected into a beaker for 
466 
measurement of urea nitrogen excretion, relevant for indirect calorimetry measures. 
467 
 
468 
Indirect calorimetry  
469 
Energy expenditure and substrate utilisation will be determined using equations from 
470 
Frayn and Jeukendrup & Wallis (Frayn, 1983; Jeukendrup & Wallis, 2005), with 
471 
adjustments for the contribution of glycogen during low-intensity exercise: 
472 
 
473 
Fat oxidation at rest and during exercise (g∙min-1) =  
474 
(1.695 x VO2) - (1.701 x VCO2) - (1.77 x urinary nitrogen excretion) 
475 
 
476 
Carbohydrate oxidation at rest (g∙min-1) = 
477 
 (4.55 x VCO2) - (3.21 x VO2) - (2.87 x urinary nitrogen excretion) 
478 
 
479 
Carbohydrate oxidation during exercise (g∙min-1) = 
480 
 (4.344 x VCO2) - (3.061 x VO2) - (0.40 x urinary nitrogen excretion) 
481 
 
482 
At rest, these equations assume that glucose provides all the carbohydrate for 
483 
metabolism, whereas during low-intensity exercise carbohydrate metabolism is 
484 
achieved by an equal contribution from glucose and glycogen. These equations will 
485 
also be used with the assumption that the energy contents of fat, glucose, glycogen, 
486 
and nitrogen are 9.75, 3.74, 4.15, and 4.09 respectively (Jeukendrup & Wallis, 2005).  
487 

Page 17 
 
 
488 
Food preference task 
489 
Participants will complete food preference tasks. These will comprise an ‘alternative 
490 
forced choice task’ and an ‘ideal portion size task’ (Wilkinson et al., 2012). The AFCT 
491 
consists of 18 plates of food which are individually photographed on a white plate or 
492 
transparent bowl. The participant will choose which food they would ‘choose to eat 
493 
right now’. Foods are distinguished into three categories: sweet high-carbohydrate 
494 
foods, non-sweet high-carbohydrate foods, non-sweet low-carbohydrate foods. The 
495 
outcomes of this test will indicate preference for different types of foods corresponding 
496 
to the categories above. The ideal portion size task consists of 3 test foods: chocolate 
497 
mousse, roasted potatoes, and vegetarian sausages (corresponding to the 3 
498 
categories above). These are photographed in portions ranging from 20-1000 kcal in 
499 
increments. Participants can increase or decrease the amount of food they desire at 
500 
by being instructed to imagine they are offered this food right now, using the left and 
501 
right arrow keys to adjust the portion to show how much of it they would eat.  
502 
 
503 
Adipose biopsy samples 
504 
Samples will be cleaned using sterile saline solution (B. Braun, Pennsylvania, USA). 
505 
Cleaned adipose tissue will be aliquoted, weighed, and snap frozen in liquid nitrogen 
506 
before being stored at -80°C until further analyses. With the exact nature of the 
507 
analyses being dependent on the size of the sample, these samples will be assessed 
508 
primarily for gene expression (via real-time polymerase chain reaction) and protein 
509 
expression (via Western blotting). The sample required to perform these analyses are 
510 
~50 and ~30 mg respectively. 
511 
 
512 
Muscle biopsy samples 
513 
Muscle tissue samples will be promptly snap-frozen in liquid nitrogen and stored on 
514 
dry ice until being transferred to -80°C awaiting analysis. Tissue will be prioritised for 
515 
glycogen analysis, which will be performed similarly to previous studies (Jansson, 
516 
1981; van Loon, Saris, Kruijshoop, & Wagenmakers, 2000). Muscle tissue will be 
517 

Page 18 
 
freeze-dried before removal of non-muscle fibre material. Dried muscle tissue will be 
518 
heated in hydrochloric acid to hydrolyse glycogen to glycosyl units, before being 
519 
neutralised, and glucose concentration will be analysed. Leftover tissue will be used 
520 
to analyse gene expression (via real-time polymerase chain reaction) and protein 
521 
expression (via Western blotting), with the precise nature of the analyses to depend 
522 
on tissue sample size. 
523 
 
524 
Faecal tissue handling and processing  
525 
Faecal samples provided by participants will be homogenised, aliquoted and stored 
526 
frozen at -80°C for later processing. Total DNA will be extracted using a commercial 
527 
kit (QIAGEN QIAamp® Fast DNA Stool Mini Kit) and faecal water will be extracted for 
528 
metabolic profiling. Extracted DNA will be analysed to determine taxonomic 
529 
composition and functional potential via 16S rRNA sequencing and shotgun 
530 
metagenomic sequencing, respectively. A range of bioinformatic and statistical tools 
531 
will be used to determine taxonomic and functional diversity of microbes present. No 
532 
analyses will be conducted on the human components of the sequenced DNA. Faecal 
533 
water will be prepared through centrifugation and both faecal water and faecal matter 
534 
will be later processed using ‘omic’ technologies (e.g. mass spectrometry and nuclear 
535 
magnetic resonance) to determine presence of metabolites. Analyses of faecal and 
536 
urine samples collected will be conducted in Teagasc laboratories (Moorepark, Cork, 
537 
Ireland). On some occasions the analyses may be done in collaboration with a third 
538 
party including commercial companies. All samples and extracted DNA will be stored 
539 
at -80°C for future analysis.  
540 
 
541 
Implementing the intervention and monitoring adherence 
542 
Adherence to the study diets is a key component of the study and is one of the major 
543 
shortcomings of previous research into carbohydrate manipulation and energy 
544 
balance (Smith et al., 2017). There will be a focus on developing and maintaining a 
545 
friendly but professional relationship between the research team and participants – 
546 
easing them into the study procedures and familiarising them with the laboratories. To 
547 
further facilitate adherence, participants will be reimbursed up to a predetermined 
548 

Page 19 
 
amount of £18 per week for participants in the MODSUG or LOWSUG groups, and 
549 
£26 per week for participants in the LOWCHO group. This is due to the relative 
550 
difficulty in adhering to low carbohydrate diets and the relative expense of typically 
551 
suitable menus compared with the other groups. Members of the research team will 
552 
meet with participants weekly and will keep in touch with them via email to check how 
553 
they are finding the study. Participants will be asked to send a weekly measure of body 
554 
weight using scales they have been provided (Etekcity Digital Scales, California, USA) 
555 
and urinary ketone body concentrations in the fasted state (using Ketostix, Ascensia, 
556 
Newbury, UK). Participants will be asked to record 3-day food diaries during each 
557 
week. These will be analysed and feedback returned to the participant as soon as 
558 
possible. Feedback about what they have done well in the previous week and where 
559 
they might be able to improve to meet their macronutrient targets will also be provided. 
560 
We will focus on specific food items and tailor any substitutions to habitual dietary 
561 
habits.   
562 
 
563 
The absolute cut-off of 50 g of CHO in the LOWCHO diet is according to 
564 
recommendations from ketogenic diet proponents (Volek & Phinney, 2012). This will 
565 
comprise 8% of energy intake if an individual’s daily energy intake is 2337.5 kcal, with 
566 
the assumption that 1 g of carbohydrate provides 3.74 kcal of energy. It is anticipated 
567 
that, in most individuals, 50 g of carbohydrate will equate to £ 8% of energy intake. 
568 
However, in individuals consuming fewer kilocalories per day, the recommended 
569 
grams per day of carbohydrate will be decreased to equate to 8% of energy intake 
570 
(e.g. an individual consuming 1600 kcal per day will be asked to consume ~34 g of 
571 
carbohydrate per day).  
572 
 
573 
We will attempt to check the accuracy of information from food diaries by rearranging 
574 
the energy balance equation, using measures obtained from DXA throughout the 
575 
study, as validated previously (Racette et al., 2012; Sanghvi, Redman, Martin, 
576 
Ravussin, & Hall, 2015). It is worth noting that the energy expenditure values will be 
577 
extrapolated from the 7 days of recording to reflect the time between measurements. 
578 
The energy content of the tissues may also be influenced by the change in water 
579 
content induced by the intervention, particularly in the ketogenic LOWCHO diet. 
580 

Page 20 
 
Hydration status has been shown to influence measures of non-fat mass by DXA with 
581 
little influence on fat mass measures (Toomey, McCormack, & Jakeman, 2017). For 
582 
this reason, we will interpret rearranged energy balance data in the context of these 
583 
limitations.   
584 
 
585 
Diet analysis  
586 
There is no consensus on the exact caloric value of each macronutrient because fibre 
587 
ingestion, water ingestion, energy balance, dietary macronutrient composition, dietary 
588 
food variety, chewing during meals, meal timing/distribution, preparation/cooking of 
589 
food, alcohol intake, physical activity level, sex, age, disease status, and stress are all 
590 
thought to influence the metabolisable energy of foods (Sanchez-Pena et al., 2017). 
591 
Further complicating individual variability in the energy harvested from macronutrients 
592 
is the gut microbiome, particularly through the production of short-chain fatty acids 
593 
(Canfora, Jocken, & Blaak, 2015). Despite these limitations, metabolisable caloric 
594 
values for each macronutrient have been estimated in the literature, suggesting that 
595 
the caloric values of carbohydrate, fat, protein, and alcohol are around 3.74, 9.75, 
596 
4.09, and 7.10 kcal∙g-1 (Jeukendrup & Wallis, 2005; Morgan & Levine, 1988). Whilst 
597 
these exact values may be slightly contended amongst academics who may prefer to 
598 
use values from older studies (Atwater, 1910; Frayn, 1983), they have been chosen 
599 
because the same values have been used for oxidation of macronutrients during 
600 
measures of energy expenditure. Diet analysis for preliminary measures, the 3-day 
601 
food diaries for week 1-3 and week 5-11, and the 7-day food diaries from week 4 and 
602 
week 12 will be analysed using diet analysis software (Nutritics, Dublin, Ireland). 
603 
Grams for each macronutrient will be exported and these will be multiplied by the 
604 
caloric factor mentioned. This will be used to provide energy in kilocalories and as a 
605 
percentage of total energy intake. Total energy intake will not be fed back to 
606 
participants, just macronutrient proportions as a percentage of total energy intake. One 
607 
major limitation with current sugar guidelines is that they target ‘free sugars’, but there 
608 
is no legal requirement for manufacturers to include this information, the requirement 
609 
is to provide information on total sugars. Therefore, as we are unable to definitively 
610 
determine free sugars in commercial products, we will use all sugars other than from 
611 
fruits and vegetables as a proxy for ‘free sugars’. We will report the following in our 
612 

Page 21 
 
results: carbohydrate, fat, protein, alcohol, total sugars, sugars from fruit and 
613 
vegetable sources, all other sugars. This means our intervention will target total sugars 
614 
minus fruit and vegetable sugars, as it is impossible to measure the intake of free 
615 
sugars per se.  
616 
 
617 
Laboratory test design 
618 
It is worth clarifying the rationale behind the test design. Obtaining muscle and adipose 
619 
biopsies in the fasted and rested state is preferable, but obtaining biopsies 
620 
immediately prior to the mixed meal tolerance test would risk these procedures 
621 
influencing metabolic measures. The treadmill walk should also be performed in the 
622 
fasted state, but there should be sufficient washout between the treadmill walk and the 
623 
mixed meal tolerance test. Furthermore, it is inappropriate to perform the treadmill 
624 
walk immediately before biopsies due to the potential effects this would have on gene 
625 
and protein expression. Therefore, it was decided to perform the treadmill walk 
626 
following the biopsy procedures, allowing for a rest period between the procedures 
627 
and the walk. This enables calibration of the Actiheart monitor to each participant’s 
628 
individual physiology at each visit, which allows for fluctuations in substrate oxidation 
629 
and resting metabolic rate across the course of the intervention. The treadmill walk 
630 
acts to standardise physical activity levels the evening before the meal test, which 
631 
reduces the chance of seeing metabolic effects which are caused solely by the 
632 
previous evening’s activity. Participants will attend the lab the morning after the biopsy 
633 
and treadmill walk in an overnight fasted state having eaten dinner a of their choice at 
634 
home. This allows assessment of body composition and the mixed meal tolerance test 
635 
to be performed prior to lunchtime of the second laboratory testing day.  
636 
 
637 
Statistical analyses and power calculation  
638 
A required sample size for the present study was estimated based on the Bath 
639 
Breakfast Project (Betts et al., 2014) using G*Power 3.1 software (Faul, Erdfelder, 
640 
Lang, & Buchner, 2007). The mean ± standard deviation PAEE for the fasting vs 
641 
breakfast groups during the morning (when differences in carbohydrate availability 
642 
between groups were present) were 311 ± 124 kcal vs 492 ± 227 kcal. Based on this 
643 

Page 22 
 
effect size, a between-subject design with 20 participants in each group would provide 
644 
an >85% chance (power) of detecting the expected effect with an α-level of 0.05. 
645 
Therefore, 60 participants will be recruited. As dropout with diet interventions is 
646 
expected to occur, a rolling recruitment model will be employed whereby dropouts are 
647 
replaced to achieve a complete dataset. The sample size estimations have been 
648 
based on the primary outcome of PAEE which, as the primary behavioural dependent 
649 
variable, also displays the most variance and thus the metabolic variables may require 
650 
fewer participants to achieve a similar power, given greater precision of measurement. 
651 
 
652 
Descriptive statistics will be calculated on Microsoft Excel (Microsoft, Washington, 
653 
USA). Two-way ANOVAs will be used to identify significant interactions between 
654 
conditions and time across the study. Post hoc adjustments will be employed to 
655 
determine the nature of these differences using GraphPad Prism (GraphPad Software 
656 
Inc., California, USA). Both p-value hypothesis testing and inference-based statistics 
657 
will be utilised. Where p-value hypothesis testing is used, significance will be accepted 
658 
at p = 0.05.  
659 
 
660 
UPDATED STATISTICAL ANALYSIS PLAN 
661 
For parallel group RCTs it is more efficient to use ANCOVA with baseline values as 
662 
the covariate to estimate the causal effects of an intervention (i.e., estimated 
663 
differences between interventions vs. control, see 
664 
https://doi.org/10.3945/ajcn.115.119768). Therefore, the statistical analysis plan was 
665 
adjusted accordingly in line with the following description.  SPSS v25 (IBM, USA) 
666 
were used for statistical analyses. Total (tAUC) and incremental area under the 
667 
curve (iAUC) were calculated using the Time Series Response Analyser. Figures 
668 
were drawn using Prism v9.5.0 (GraphPad Software Inc., USA). For all outcomes 
669 
with quantitative units at week 4 and week 12, ANCOVA was used to assess 
670 
differences between groups with baseline values as the covariate. Unadjusted 
671 
means are presented for baseline outcomes, but ANCOVA-adjusted means (and 
672 
mean differences vs MODSUG) are reported for week 4 and week 12 unless 
673 
otherwise stated. Since skeletal muscle protein and adipose mRNA levels were 
674 
expressed as the fold-change from baseline, one-way ANOVAs were used at week 4 
675 

Page 23 
 
and week 12 respectively to detect differences between groups. Post-hoc 
676 
comparisons were made according to the principle of closed testing to assess the 
677 
effect of sugar restriction (LOWSUG vs MODSUG) or ketogenic carbohydrate 
678 
restriction (LOWCHO vs MODSUG). Data from Visual Analogue Scales were 
679 
assessed by repeated-measures ANOVA of within-group comparisons due to their 
680 
subjective nature. Figure legends state whether means or ANCOVA-adjusted means 
681 
are presented for each outcome. Simple linear regression and Pearson correlation 
682 
coefficients were used to assess linear associations between outcomes where 
683 
appropriate. Significance was accepted at p ≤ 0.05. Data are presented as mean and 
684 
95% confidence intervals (CI) unless otherwise stated. 
685 
 
686 
 
687 
 
688 
 
689 
 
690 
 
691 
 
692 
 
693 
 
694 
 
695 
 
696 
 
697 
 
698 
 
699 
 
700 
 
701 
 
702 

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703 
 
704 
 
705 
 
706 
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Figures  
874 
Figure 1. Schematic of trial progression. Participants will complete preliminary 
875 
measures and then be randomised to one of three trial arms. Laboratory visits 
876 
will comprise three trials at baseline, week 4, and week 12 of the intervention. 
877 
They will be split across two days, day one in the afternoon and day two the 
878 
following morning. Physical activity and diet will be measured for 7 days during 
879 
week 4 and during week 12. DXA = dual x-ray absorptiometry.  
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881 
4 weeks
8 weeks
Preliminary 
measures
Trial 1
(Baseline, 
week 0)
Trial 2
(week 4)
Trial 3
(week 12)
45 minute lab visit to measure height, weight, waist/hip 
circumference, and perform a 20-minute treadmill walk
1 week of recording diet and wearing physical activity monitor
Day 1 – afternoon session – fat tissue biopsy, muscle tissue 
biopsy, 20-minute treadmill walk
Day 2 – morning session – DXA scan, meal test, 
appetite measures 
Day 1 – afternoon session – fat tissue biopsy, muscle tissue 
biopsy, 20-minute treadmill walk
Day 2 – morning session – DXA scan, meal test, 
appetite measures 
Day 1 – afternoon session – fat tissue biopsy, muscle tissue 
biopsy, 20-minute treadmill walk
Day 2 – morning session – DXA scan, meal test, 
appetite measures 
Week 3 – meeting to collect monitoring equipment before trial 1
Week 11 – meeting to collect monitoring equipment before trial 2

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Figure 2. Schematic of outcome measures in the study. PAEE = physical activity 
882 
energy expenditure, DXA = dual x-ray absorptiometry, RMR = resting metabolic 
883 
rate, TAG = triacylglyceride, NEFA = non-esterified fatty acids, FGF-21 = 
884 
fibroblast growth factor 21.  
885 
 
886 
Whole-body measures:
PAEE, steps, weight, 
energy intake, 
macronutrient composition, 
body composition
(DXA, waist:hip
circumference), blood 
pressure, interstitial glucose 
concentrations, RMR, 
substrate oxidation
Psychological measures:
Food preference, appetite and mood
Systemic measures:
Fasting/postprandial metabolites and 
hormones (including glucose, insulin, 
TAG, NEFA, lactate, β-hydroxybutyrate, 
leptin, FGF-21, cholesterol profile, gut 
hormones), protein glycation
Adipose measures:
Gene and protein expression
Muscle measures:
Muscle glycogen, gene and 
protein expression
Faecal/urinary measures:
Gut microbiome changes, faecal energy 
density, urinary β-hydroxybutyrate and nitrogen