Weight adds weight to declining quality of life in diabetes

Keywords::

• Obesity
• Quality of life
• Questionnaire
• Tools
• Type 2 diabetes
• Utilities

It is a cliché in today’s times to state that type 2 diabetes has assumed epidemic proportions. It is also well known that type 2 diabetes is largely a lifestyle disease resulting from undesirable dietary habits and sedentary behaviors. The parallel epidemic of obesity is the trigger that has set off the cascade of events culminating in the dysglycemic states. Current lifestyles that include consumption of calorie dense foods, sedentary habits and leisure time inactivity have all contributed to the obesity epidemic. Obese individuals who are insulin resistant and hyperinsulinemic may go on to develop Type 2 diabetes.

The combination of obesity and type 2 diabetes, often referred to as ‘diabesity’, increases the risk of cardiovascular mortality and morbidity. The association of hypertension, dyslipidemia, gallbladder disease, sleep apnea, respiratory problems, and chances of endometrial, breast and prostrate cancer are high in these individuals. Overweight people may also suffer from social stigmatization and discrimination. A combination of all the above often results in psychological problems leading to depression. This can seriously get in the way of treatment modalities that are currently available.

While weight loss is the primary outcome of interest for most obesity treatments, measuring quality of life is a vital component as individuals evaluate their treatment outcome. Thomas A. Wadden and Suzanne Phelan1 describe QOL as a ‘buzz’ word in medicine, psychology & society at large. The term is used to describe events that range from satisfaction with one’s work or leisure activities to the physical and economic burden imposed by specific illnesses. QOL questionnaire is developed to specifically assess the effects of obesity on health-related quality of life. Five important aspects that need assessment are physical function, self-esteem, sexual life, public distress, and work. This in general covers all aspects of physical, social and emotional functions. Importantly, a QOL questionnaire must be relevant to obesity, reliable, validated and accepted by researchers for widespread use and should be easy to administer.

Unfortunately, obesity begets obesity in type 2 diabetes. Key therapeutic agents such as insulin and insulin secretagogues cause weight gain in these patients. Agents that do not cause weight gain like GLP1 analogs and DPP4 inhibitors are within the reach of every patient with diabetes. Bariatric surgery, though an option, is not without problems and also expensive. Added to this, as age advances, sarcopenia and fat accumulation is enhanced, worsening an already undesirable situation. So does this ‘excess weight’ add to the woes of a chronic disease from the patient’s perspective? Does an already compromised quality of life get worse because of excess weight?

Diabetes being a chronic disease and longevity being the rule rather than the exception with improved therapies, quality of life issues often take center stage. It is known that diabetes particularly affects the quality of life. The SHIELD, a longitudinal prospective study, is indicative of this as it not only measures quality of life in individuals with type 2 diabetes, but also assesses this impact over a period of time2. In this study, the EQ-5D was assessed at baseline and at the end of 5 years. Not only did it show that responders with diabetes had a lower quality of life index than responders without diabetes, but also that this quality of life declined over the years. Those with diabetic complications had an even greater decline. In another study ‘TRIAD’, health utility scores were measured at baseline in the study population3 using EQ-5D as the instrument where in 1.0 indicated perfect health. Diabetes related complications and co-morbidities were the major determinant of low scores.

It is also well known that obesity by itself, even without diabetes, affects quality of life adversely. A study from Yorkshire typifies this fact4. The relationship between BMI and health-related quality of life (HRQL) was assessed in a cohort comprising of 19,460 individuals using the EQ-5D score. Overweight and obese individuals had poorer scores than normal weight individuals. Additionally, for every unit increase in BMI, there was a 6% increased chance of reporting a problem with one of the EQ-5D parameters. There are more cross-sectional data to indicate this fact. But this may be an underestimation as shown in a study that measured quality of life at baseline and then again after weight loss. The improvement in HRQL, as measured after weight loss was achieved, was much more than predicted5. However, another recent systematic review and meta-analysis found no significant improvement after weight loss at least for mental attributes, and only a modest improvement in physical parameters as assessed by HRQL measures6.

In this scenario, in a patient with diabetes, the additional burden of being overweight/obese surely worsens the situation. An paper that looks at these combined parameters is from the population based KORA studies7. This paper also estimated the impact of BMI on quality of life in type 2 diabetes. The results showed that BMI was significantly associated with health utilities even when confounders of complications were adjusted for. The study suggests that weight loss by lifestyle measures could improve the quality of life in such patients. Furthermore there is also a suggestion that the negative effect generated by weight gain caused by certain therapeutic options should be considered while choosing therapies. Previous studies have amply demonstrated how weight impacts quality of life in patients with type 2 diabetes with respect to social activities, emotions, depression, anxiety, work performance and low self esteem7,8.

Patients’ perceptions of the parameters that would encompass this quality of life aspect need to be addressed. Furthermore, we now have validated tools that would help us to quantify such perceptions. These tools that measure such perceptions, particularly in chronic diseases such as diabetes, are called utilities.

The afore-mentioned perceptions are descriptive states that are defined for such diseases. Utilities are simply a way to measure these perceptions. They take into account physical, mental and social factors associated with that health state9. A desirable optimal health state gets a score of 1.0 and death is denoted by a score of 0.0. All other states have a value between these two states. Negative values are for those states worse than death. The higher the value of such a utility, the higher is the preference of the participant from a general population to be in that described stage/state of the disease. These utilities are then made use of to estimate life expectancy when adjusted for quality of life. This is especially important considering the fact that therapeutic weight loss seems to increase the value of the utilities10.

There are different ways of measuring utilities. The utility scores themselves are arrived at with one of three methods5.

1. Use of standard techniques such as SG (standard gamble) or TTO (time trade-off). In these, hypothetical health states are created that responders have to imagine being in. Therefore, the situation being hypothetical, responders could be drawn from an entirely normal population or could be patients suffering from the disease in question. In one study, it was concluded that it did not matter who the responders were, as most responders probably were never in the health states used in the measurement11.

2. Measures such as EQ-5D and SF-6D are questionnaires that have already been tested on the general population, and are then tested on patients to obtain utility scores.

3. Direct measurement of patients’ own health by again using SG or TTO.

The attributes of the different methods decide the use of one or the other in a particular situation. Quality-adjusted life years (QALY) are health outcome measures that are attained when utility scores are combined with survival times. Health utility scores may vary by region, being affected by such variables as prevalent cultural norms12. Weight loss being an important therapeutic intervention in type 2 diabetes, such utilities, and QALY derived from them, are important in terms of decisions regarding reimbursement policies and cost-effectiveness.

‘The impact on utilities of differences in body weight among Canadian patients with type 2 diabetes’ by Lane et al. published in this issue aims to investigate the effect of weight in type 2 diabetic individuals as a quality of life determinant by measuring the utilities13. The study itself is inspired by similar work done by another investigator, in the UK (Matza et al.14). The investigators assume that there could be differences in the way different cultural populations perceive obesity, which would perhaps bring about different values of such utilities, when measured. This study looks at such utilities for the first time in a Canadian population. Matza’s study provides the backdrop. The hypothetical health states were developed based on descriptions that included clinical, therapeutic, glycemic, dietary, functional status, weight, social and economic factors that could impact the state. Out of the six health states computed, one was the base case and two were states that had adverse events, which served to cast aside those with illogical responses. The base case was assumed to be a well controlled type 2 diabetic, on oral therapy, at current weight. The other three health states differed from base case only in change of weight (3%, 5%, and 7%). The development of these states was largely drawn from Matza’s study. Importantly, the proportion of weight change was maintained by using percentages of the change rather than absolute values of weight. The respondents in this study were type 2 diabetes patients, with duration of minimum 2 years, from Canada, but from two different regions. The sample size chosen was similar to that in the previous study, to allow for a 95% CI around a mean utility of +0.0392. One plus point of the study was that participants were stratified by current weight so as to mitigate the effect of weight increase or decrease on utilities, by participants’ own weight.

Since Canadian patients tend to have generally higher utilities based on a previous multi-national study, the TTO method was preferred over the SG15. The mean BMI of the participants was 32 kg/m². The results were not surprising in that they showed that health states with higher weights had a larger impact on utilities than those with lower weights. The age or region made no difference to the utility values. In general, women had lower values across all health states. The decrease in utility associated with a higher BMI score was more than the increases in utility with a lower BMI score (0.0472 vs 0.0171).

The results in this study are similar to those found by Matza et al. The study adds valuable data to the impact of weight on health care utilities. Although utilities vary amongst countries, this data can be used for comparison. It is also important that the study used diabetes patients as participants as healthy individuals probably cannot hypothesize too well the burden of a chronic disease. However, the inclusion criteria had participants that had diabetes for more than 2 years without an outer limit. The perceptions of a patient who has had diabetes for 2–3 years could be very different from those of a patient who has had it for 10 years, and could result in quite different utility scores.

It is important to have country specific utilities as the economic valuations differ based on these. Ultimately, life expectancy calculation will depend on this. Another important factor that emerges from such studies is that when increased weight has such an impact on quality of life, any therapy that increases weight even slightly may be abhorrent to the patient. Life style modifications and good control of diabetes to achieve ideal body weight and euglycemia are simple and easy ways of improving quality of life in an obese patient with diabetes, but this is unlikely to be achieved universally in the near future.

The bigger picture is clearly telling us to ‘move on’. Knowing that the QOL in patients with diabetes and obesity is not anywhere near to a normal individual, efforts to curb ‘childhood obesity’, research towards new drug discoveries for obesity, and health care planning have to take the ‘front seat’.

Transparency

Declaration of funding

This editorial was not funded.

Declaration of financial/other relationships

S.R.A. and N.D. have disclosed that they have no significant relationships with or financial interests in any commercial companies related to this study or article.

Acknowledgments

The authors thank Prof. K.M. Prasanna Kumar, former Prof. and HOD, Dept. of Endocrinology, M.S. Ramaiah Medical College, Bangalore, India, and Chief Consultant CDEC, BDH, Bangalore, India, for his editorial assistance in preparing this manuscript.