Resting metabolic rates in obesity susceptible people measure much lower than predicted.

Prediction equations overestimate the energy requirements of obesity susceptible individuals

We often hear overweight people say they put on weight easily even though they don’t eat that much. When they are given a diet to lose weight,  with the calorie amounts worked out based on their resting metabolic rate, they complain they don’t lose weight, despite eating the prescribed calories.

In this study resting metabolic rate (RMR) was measured and compared to the standard calculations, what it showed was that in people susceptible to obesity their RMR really does measure lower compared to lean, obesity resistant people.

This study by post graduate student Rebecca Cooke, was presented at the Nutrition Society Conference in New Zealand in December 2016.

A group of 26 obesity susceptible people, 14 females and 12 males had their RMR measured. Another group of obesity-resistant people also had their RMR measured. These people remain lean in today’s obesogenic environment without effort. This group comprised 31 people; 14 females and 17 males.  The 2 groups were compared.

Each person’s RMR was worked out using standard calculations. The actual measured RMR was compared to the calculations.

Relative RMR was significantly lower in the obesity susceptible group compared to the obesity resistant group.

The females showed the biggest difference – the  RMR of obesity susceptible and obesity resistant ORI females differed by 25.2 kJ.kg^-1.d^-1.

All three RMR predictions overestimated RMR to some extent in all groups. In obesity resistant, the measured RMR was lower by 748  J.d^-1

In the obesity sensitive group, the measured RMR was 1443 J.d^{-1 l} lower. That is a massive 344 Calories lower.

My opinion of how to manage a low RMR

I predict my RMR is likely low. My mother told me stories of how I ate small amounts of food as a child yet remained above average chubby. I have a younger brother by 1 year. As a toddler my mother gave us both a bottle of milk, I drank half of mine and put it down, meanwhile, my brother drank all of his, then polished off mine.

What I have learned about myself:

Eat plenty of protein, protein takes more energy to process than carbohydrates or fat. I aim for 1.5 to 2 grams of protein per kg body weight per day. I eat protein at every meal – 20 – 30 grams net.

Eat lots of fibre rich vegetables. Add little to no added fat at each meal, there is enough fat in your protein source.

Add little to no added fat at each meal, there is enough fat in your protein source.

Eat 3 meals a day – snack only if you really need to.

Do weight resistant and high intensity exercise to build muscle and keep your metabolic rate high.

Get your sleep, skimping on sleep messes up my ability to manage my weight and my appetite.

To find out how many calories I need per day to keep me at my ideal, I follow a really good diet, sleep well and do exercise every day. I then put my intake into cron-o-meter.com and see how much I eat on a daily basis where my weight in stable.

Despite being more muscular now than when I was younger I sadly have found I need less food that I used to.

Abstract

Prediction equations overestimate the energy requirements of obesity susceptible individuals

Cooke, R.¹, Taylor, R.², Skidmore, P.¹, Jones, L.³, Brown, R.¹

¹Department of Human Nutrition, University of Otago, Dunedin, New Zealand

²Edgar National Centre for Diabetes and Obesity Research and Department of Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand

³School of Physical Education, Sport and Exercise Sciences, University of Otago, Dunedin, New Zealand

Background An evaluation of energy requirements is a necessary part of dietary counseling, particularly when weight reduction is the goal.  In many instances rather than actually measuring resting metabolic rate (RMR), predictive equations are used to estimate the RMR of individuals in a clinical setting.  Our objective was to compare the measured and predicted RMR of individuals who remain lean despite living in an obesogenic environment (obesity resistant individuals) with those who struggle to maintain a healthy body weight and report having to consume smaller amounts of food (obesity susceptible individuals).

Method Obesity resistant individuals (ORI) and obesity susceptible individuals (OSI) were identified using a simple 6-item screening tool.  Measurement of RMR was undertaken in 31 ORI (14 females, 17 males) and 26 OSI (14 females, 12 males) 12 hours after an overnight fast, using indirect calorimetry and following standard procedures.  Predicted RMR was calculated using the FAO/WHO/UNU (Food and Agricultural Organisation/World Health Organisation/United Nations University), Oxford and Miflin-St Jeor equations and compared to measured RMR.

Results Absolute RMR was significantly lower in ORI versus OSI (748 kJ.d^-1, 95%CI: 52, 1443; P=0.036); however, relative RMR was significantly lower in OSI compared to ORI (-15 kJ.kgBM^-1.d^-1, 95%CI: -24, -6; P=0.001) and lower in female OSI compared to all other groups (all P≤0.001).  The RMR of OSI and ORI females differed by 25.2 kJ.kg^-1.d^-1.  Given the mean weight of OSI females was 85.5kg this equates to a difference of 2155 kJ.d^-1.  All three prediction equations over-estimated RMR to some extent in both ORI and OSI but this difference was significant for OSI females (1664,

1466 and 1422 kJ.d^-1, FAO/WHO/UNU, Oxford and Miflin-St Jeor equations respectively).

Conclusions The use of prediction equations may lead to an overestimation of RMR and subsequently energy requirements particularly in females who identify as being susceptible to obesity.