Title: Who will benefit from bariatric surgery for diabetes? Using genetic markers and measurement of fat distribution to predict remission of diabetes
Funder: Department of Health and Social Care

Abstract: Background: An intimate relationship exists between type 2 diabetes (T2DM) and obesity, where increasing weight increases the risk of T2DM [1, 2]. The location in which this excess fat is deposited, rather than simply the amount, dictates the individual risk of developing diabetes. Visceral fat accumulation correlates with metabolic disease [3]. Genetic factors are partly responsible for how excess fat is stored [4]. Similarly, the risk of T2DM is influenced by monogenic mutations and multiple genetic factors that cumulatively influence cell function or insulin resistance [5-7]. Bariatric surgery powerfully improves diabetic control, and is increasingly being used for the primary purpose of treating T2DM, but this improvement is inconsistent between patients. Only 40% undergoing Roux-en-Y gastric bypass (RYGB) surgery achieved complete diabetes remission using American Diabetes Association criteria [8, 9]. Notably, 35% of diabetes patients after RYGB remained sub-optimally controlled with an HbA1c >7% after 3 years [10]. What factors are known to influence diabetes remission? Weight lost after surgery: this can only be retrospectively measured thus is not predictive. The type of surgical procedure: remission occurs more commonly after RYGB compared to gastric banding or sleeve gastrectomy [10, 11]. Timing of surgery: increased likelihood of remission in patients with a shorter duration of T2DM, those not on insulin and those with good pre-operative control [12]. Beyond these, however, little is known about other pre-operative factors that might influence the success of surgery in inducing diabetes remission. There is a pressing clinical need to predict which patients will respond best metabolically to surgery to: Improve supportive information for decision making by patients and clinicians; Allow clinicians to identify patients who will benefit most from surgery, even if they do not fulfil current criteria; Allow bariatric surgeons to adjust surgical procedures to achieve optimal metabolic and weight loss outcomes in patients identified as being less likely to benefit from standard surgery. Therefore, we plan to examine two promising assessment methods which could be applied pre-operatively to predict diabetes remission. &middot; Measuring fat distribution using Dual Energy X-ray Absorptiometry (DEXA) scanning [13]. DEXA is selected in preference to other methods as it is validated, quick, economical, and robust with a low ionising radiation exposure. Importantly, for rapid translation, most NHS Trusts have access to a DEXA scanner. &middot; Genetic profiling. Assess whether the identification of genetic variations associated with increased T2DM risk or changes in fat distribution can augment our ability to predict surgical response. With anticipated technological advances, this method will become more accessible and cost-effective, thus increasingly applicable to NHS patients. Question addressed: By defining fat distribution and genetic susceptibility markers prior to bariatric surgery, can we predict which patients will respond best in terms of diabetes remission? Plan of investigation: A prospective study will measure fat distribution prior to bariatric surgery in order to relate this to remission of diabetes after surgery. Patients will be profiled prior to and twelve months after bariatric surgery, including anthropometric measures, whole body DEXA to map body fat patterning [14], measure intra-abdominal fat [15] and estimate liver fat content [16]. The participants will be evaluated for metabolic function. Finally, they will have genetic profiling, interrogating loci associated with fat partitioning, -cell function and insulin resistance. This is a preliminary study to generate initial data and hypotheses to develop a predictive model(s) for diabetes remission after bariatric surgery. Simulated data will be generated to further demonstrate the performance of the predictive model under different scenarios. To validate and refine the model, a prospective validation study (beyond this project's scope) will be conducted in larger numbers.

Lay abstract: Background to the research Type 2 diabetes mellitus (T2DM) is an increasing health problem worldwide. The increase in T2DM is related to the increasing prevalence of obesity. We know that the heavier a person is, the higher their risk of diabetes. Obesity is not simply a matter of excess fat alone. The fat distribution, the locations in which the excess fat is stored, is important. If the excess fat is accumulated within the belly (visceral fat), this is known to sharply increase the risk of developing diabetes. We also know that genetics has an important role to play. Subtle changes in their genes affect how people store fat in their bodies, how their pancreas secretes the hormone insulin and the effectiveness of insulin in lowering sugar and regulating metabolism, i.e. the processes by which the body uses and stores food. The most effective treatment for obesity and diabetes is weight-loss surgery (bariatric surgery). This treatment also cuts down the risk of heart attacks and cancer. However, it is difficult to predict how much any individual patient will benefit from surgery. Only 4 in 10 patients who have diabetes before surgery see a full remission of their diabetes, i.e. their blood sugar levels remain at normal levels without any diabetes treatment. Nevertheless, bariatric surgery is now increasingly being undertaken in patients primarily to cure their diabetes, with weight loss being only a secondary goal. Currently, the body mass index (BMI, or weight in kg divided by the square of the height in meters) is used as the main criterion by which specialist centres decide who will have an operation or not. Diabetic patients with a BMI of >35 kg/m2 (or >30 in those with recent onset diabetes or Asian family origin) are eligible for surgery. However, the BMI criterion is crude, and we know that some patients with a BMI below the cut-off would benefit and vice-versa. Not only that, but studies show that the pre-operative BMI does not predict the chances of diabetes remission after surgery, suggesting that this criterion is not useful as a means of deciding which diabetic patients may benefit from surgery. Crucially, because bariatric surgery is expensive and can cause complications, we need better tools to predict who will benefit from surgery and who will not. In this way, patients that will benefit the most from surgery (even those that do not meet the BMI criteria) could have it. Surgeons could also use these tools to adjust the type and technique of operation (for example, the length of gut bypassed) so as to better achieve the goals of surgery. Aims of the research The aims of this study are to assess whether: the pattern of fat distribution in the body, and/or testing for genes that influence the risk of diabetes and the deposition of fat can be reliably used to help predict an individual's chances of remission from diabetes after bariatric surgery. Design and Methods used I will study diabetic patients who are about to undergo bariatric surgery. I will measure their fat distribution using a simple X-ray scan and take a DNA sample to look at their genes, particularly the ones that are known to alter fat distribution and the function of the pancreas. I will use the information gained to construct a computer tool' to predict who will benefit from surgery. Patient and public involvement This study has been designed in consultation with expert patients who have undergone bariatric surgery and with patients who are about to undergo such surgery to ensure that the study fits in with patient priorities, whilst yielding useful scientific information. Impact on the nation's health 5.4% of the UK population is eligible for bariatric surgery according to BMI criteria (over 2 million). As each procedure initially costs about 6000, at ~13 billion (1/7th of the current NHS budget) we clearly cannot afford surgery for all eligible candidates, and prioritisation of resources is essential. If our results show that we can develop a better test to predict the effects of bariatric surgery, this would have an immediate benefit for patients with obesity and diabetes, both in the NHS and worldwide. This would firstly allow us to determine who is likely to benefit from surgery, and increase the safety of patients. It would also allow bariatric surgeons to adjust the exact details and techniques of bariatric surgery so that patients obtain the optimal benefit from their surgery.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: METABOLIC AND ENDOCRINE