The Rank Lecture Update Royal College of , London, July 4th 2016

‘ Dependent – The scourge of the 21st Century’

Nick Finer

Senior Principal Clinical Scientist Novo Nordisk A/S Hon Clinical Professor, Institute for Cardiovascular Science, University College London, UK Hon Consultant Endocrinologist and Bariatric , Department for Weight Loss, Endocrine and Metabolic , UCLH, London Disclosures

Consultancy and speakers bureaux (to 2015): Positions with charitable organisations receiving Novo-Nordisk, Vivus, Arena, grants from industry: Janssen, Orexigen, Mundipharma World Obesity Forum (to Jan 2016) Weight Concern

Government etc. (unremunerated): Employment: CRG Severe Complex Obesity (to Mar 2016) University College Hospitals Trust (to Aug 2015) D of H Obesity Advisory Group (to 2015) Senior Principal Clinical Scientist with Novo RCP Obesity Working Party (to June 2016) Nordisk (since March 2016) RCS Tier 3 development Wiley Blackwell, Editor-in-Chief Clinical Obesity NICE Public Advisory Committee

Phentermine/Topiramate and Lorcaserin are not approved in Europe Outline

• Obesity Dependent Diabetes Mellitus – Some – A syndemics perspective – Reconsidering management …as little as 5% weight loss is sufficient to prevent most obese subjects with impaired glucose tolerance developing . Since type 2 diabetes is obesity dependent, and obesity is the main aetiogical cause of type 2 diabetes, we propose the term ‘diabesity’ should be adopted. % of obesity (BMI ≥ 30 kg/m2) with top 5 countries in each region. EuropeanMen Region Asia Oceania Region Women 1. Greece 27.9% 1. Tonga 70.3% 2. Cyprus 27.0% 2. Samoa 63.0% 3. Scotland 26.6% 3. Nauru 60.5% 4. Ireland 25.8% 4. Niue 48.0% 5. N Ireland 25.0% 5. French Polynesia 44.3%

America’s Region Eastern Mediterranean Region 1. Venezuela 34.2% 1. Saudi Arabia 50.4% 2. USA 33.3% 2. Kuwait 47.9% 3. Panama 27.9% 3. Qatar 45.3% 4. Canada 27.8% 4. Egypt 39.5% 5. Mexico 26.8% 5. Iraq 38.2% http://www.worldobesity.org/site_media/library/resource_images/Top_5_adults_by_gender_November_2014.pdf Prevalence of obesity by age across birth cohorts for men and women

Over 50 years the age at which 20% of the population are obese has decreased by 15 – 25 years

Ng et al. Lancet. 2014 Aug 30;384(9945):766-81. doi: 10.1016/S0140-6736(14)60460-8. Epub 2014 May 29. Estimated annual global direct economic impact and investment to mitigate selected global burdens, 20121

Obesity

1. Based on 2010 DALYs data from the Global Burden of and 2012 economic indicator from the World Bank; excluding associated revenue or taxes; including lost productivity due to disability and death, direct cost, eg for healthcare, and direct investment to mitigate; GDP data on purchasing power parity basis; 2. Based on historical development between 1990 and 2010 of total global DALYs lost (Global Burden of Disease); 3. Includes military budget; 4. Includes functional illiteracy; 5. Includes associated crime and imprisonment; 6. Includes sexually transmitted ; excludes unwanted pregnancies; 7. Excludes time lost to access clean water source. DALYs, disability-adjusted life-years; GDP, gross domestic product World Health Organization Global Burden of Disease. McKinsey Global Institute analysis Estimated number of people age 20-79 years with diabetes worldwide and per region in 2015 and

Europe

2015 59.8 million

North America and 2040 71.1 million Caribbean World 2015 44.3 million Middle East and North Africa 2040 60.5 million 2015 415 million 2015 35.4 million South East Asia 2040 72.1 million 2040 642 million2015 78.3 million 2040 140.2 million Africa 2015 14.2 million Western Pacific 2040 34.2 million 2015 153.2 million

2040 214.8 million

IDF Atlas 7th Edition, 2015. http://www.diabetesatlas.org BMI and age at diagnosis among people with newly diagnosed type 2 diabetes

40 38 38.3 38.3

36 37.1

)

2 34 36.3 35.9 35.0 32 Obese 33.9 30 31.7 31.1 BMI BMI (kg/m 28 Overweight 28.8 26 24 Normal weight 22 20 ≤30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 >70 (n=29) (n=33) (n=96) (n=172) (n=295) (n=372) (n=342) (n=378) (n=286) (n=434) Age at diagnosis of type 2 diabetes (years) Hillier TA and Pedula KL. Dia Care. 2001;24:1522-1527 Age-adjusted Prevalence of Obesity and Diagnosed Obesity (BMI ≥30Diabetes kg/m2) Among US Adults 1994 2000 2014

No Data <14.0% 14.0%–17.9% 18.0%–21.9% 22.0%–25.9% > 26.0% Diabetes 1994 2000 2014

No Data <4.5% 4.5%–5.9% 6.0%–7.4% 7.5%–8.9% >9.0%

CDC’s Division of Diabetes Translation. United States Surveillance System available at http://www.cdc.gov/diabetes/data Obesity and diabetes are becoming more prevalent and developing at an ever earlier age, but are they causally linked? The Cuban economic crisis: impact on obesity and diabetes

In 1991-1995, following the dissolution of the former Soviet Union and the tightening of the US embargo severe shortages of food and gas resulted in a widespread decline in dietary energy intake and increase in energy expenditure (mainly through walking and cycling as alternatives to mechanised transportation) • GDP growth fell by 6.79% • Dietary energy consumption fell from12.66 (3025) to 10.27 (2455) MJ/day (kcals/day) Economic Economic • No of people physically active crisis recovery increased 3-fold

Franco et al. BMJ 2013;346:f1515 doi: 10.1136/bmj.f1515 Prevalent of obesity and diabetes prevalence and in Cuba,1980-2010

Economic Economic Economic Economic crisis recovery crisis recovery

Franco et al. BMJ 2013;346:f1515 doi: 10.1136/bmj.f1515 Outline

• Obesity Dependent Diabetes Mellitus – Some epidemiology – A syndemics perspective – Reconsidering management “You think you understand ‘two’ because you understand one and one.

But you must also understand ‘and’.”

also known as Mawlānā Jalāl-ad-Dīn ,(موالنا جالل الدین محمد رومی :Mawlānā Jalāl-ad-Dīn Muhammad Rūmī (Persian or Maulana Jalal al-Din Rumi, but known to the English-speaking world simply (محمد بلخى :Muhammad Balḫī (Persian as Rumi , was a 13th century Persian (Tādjīk) poet, Islamic jurist, and theologian, born in Balkh province, which is now the border region of Tajikistan and Afghanistan http://www.goodreads.com/author/show/875661.Rumi accessed July 2016 Spoer and Fullilove. Clinical Obesity Volume 6, Issue 3, pages 171–174, June 2016 /Syndemic

• The term epidemic, first used in 1603, signifies a kind of relationship wherein something is put upon the people • A syndemic is the aggregation of two or more diseases in a population in which there is some level of positive biological interaction that exacerbates the negative health effects of any or all of the diseases. New Word for a Familiar Phenomenon

We have introduced the term ‘syndemic’ to refer to the set of synergistic or intertwined and mutually enhancing health and social problems facing the urban poor. , substance abuse, and AIDS, in this sense, are not concurrent in that they are not completely separable phenomena.”

--

Singer M, Snipes C. Generations of suffering: experiences of a treatment program for substance abuse during pregnancy. Journal of Health Care for the Poor and Underserved 1992;3(1):222-34. Singer M. 1994. AIDS and the health crisis of the US urban poor: The perspective of critical medical . Social Science and 39(7): 931-948. Singer M. 1996. A dose of drugs, a touch of violence, a case of AIDS: Conceptualizing the SAVA syndemic. Free Inquiry in Creative Sociology 24(2): 99-110. Singer M, Clair S. Syndemics and : reconceptualizing disease in bio-social context. Quarterly 2003;17(4):423-441.

Syn·demic

Events • The term syndemic, first used in 1992, Co-occurring … places the responsibility for affliction squarely within the public arena Confounding • It acknowledges relationships and Connecting* signals a commitment to studying

Synergism health as a a fragile, dynamic state requiring continual effort to maintain

Syndemic and one that is imperiled when social and physical forces operate in harmful Systems ways

* Includes several forms of connection or inter-connection such as synergy, intertwining, intersecting, and overlapping Syndemic Orientation – a Working Definition

A way of thinking about public health work that focuses on connections among health-related problems, considers those connections when developing health policies, and aligns with other avenues of social change to assure the conditions in which all people can be healthy

Complements single-issue prevention strategies, which can be effective for discrete problems but often are mismatched to the goal of assuring conditions for health in its widest sense

Incorporates 21Milsteinst century B. Syndemic systems. In: Mathison science S, editor. and political sensibilities Encyclopedia of Evaluation. Thousand Oaks, CA: Sage Publications; 2004. Obesity system map showing key enablers of, and barriers to change

Education Media Education Education

Macro- Technology economic Drivers Nature of Work Built Environment Food Recreation, Production Transport and Supply Early Life Experiences Healthcare and Foresight: Tackling Obesities: Future Choices – Project Report Treatment Options http://s3.amazonaws.com/foresight/17.pdf Clustering of hot food takeaways and secondary schools, Newham

Secondary School with 400m buffer

Highest A5 Food Outlet with 400m buffer Lowest

http://wearewhatwedo.org/portfolio/chicken-shop/ Putative Contributors to the Obesity Epidemic

• • Epigenetics • Maternal Age • Reproductive Fitness • Assortative Mating and Floor Effects • Sleep Debt • Endocrine Disrupters • Pharmaceutical • Ambient Temperature • Intrauterine and intergenerational effects

McAllister et al. Crit Rev Food Sci Nutr. 2009 November ; 49(10): 868–913. doi:10.1080/10408390903372599. High-fat-diet-induced insulin resistance and gut microbiota

LPS: lipopolysaccharide BCAA: branched chain amino acids SCFA: short chain fatty acids

Saad et al. PHYSIOLOGY 31: 283–293, 2016. Published June 1, 2016; doi:10.1152/physiol.00041.2015 Hypothalamic gliosis in response to a high fat diet or obesity

• Astroglial cells increase • projection number and size • coverage of POMC and NPY neurons • contact with local blood vessels • Activated astrocytes • secrete cytokines • activate inflammatory signaling in neurons

ApoE, apolipoprotein E; BBB, blood-brain barrier; EZ, endozepines; FA, fatty acids; GLAST, glutamate aspartate transporter; GLUT-1, glucose transporter 1; GLUT-2, glucose transporter 2; KB, ketone bodies; IL6, interleukin 6; IL1β, interleukin 1 β; MCT-1, monocarboxylate transporter 1; MCT-4, monocarboxylate transporter 4; NO, nitric oxide; ObR, leptin receptor; PPARγ, peroxisome proliferator-activated receptor gamma; TNFα, tumor necrosis factor α.

Argente-Arizón et al. Front Endocrinol (Lausanne). 2015 Mar 26;6:42 Beyond Scapegoating

“When we attribute behavior to people rather than system structure the focus of management becomes scapegoating and blame rather than the design of organizations in which ordinary people can achieve extraordinary results.” -- John Sterman

Sterman J. System dynamics modeling: tools for learning in a complex world. California Management Review 2001;43(4):8-25. In the UK, more than one third of CCGs* are restricting access to routine procedures based on BMI • Hip and knee replacements banned or delayed due to specific BMI levels • Contravention of clinical guidance • HCPs should support and encourage patients to lose excessive weight • CCGs should encourage access to weight management • Patients should challenge CCGs

*CCGs: clinical commissioning groups

Smokers and overweight patients: Soft targets for NHS savings? RCS Policy Unit, April 2016 Outline

• Obesity Dependent Diabetes Mellitus – Some epidemiology – A syndemics perspective – Reconsidering management Percentage of people with Type 2 diabetes in England and Wales achieving their treatment targets by diabetes type and audit year

Type 2 and other

2009-10 2010-11 2011-12 2012-13 2013-14 2014-15

HbA1c < 58 mmol/mol 66.6 66.5 65.8 64.9 66.8 66.1

Blood Pressure < 140/80* 60.8 61.4 66.6 68.6 73.6 74.2 Cholesterol < 5mmol/L 78.2 78.0 77.4 76.7 77.8 77.5

Meeting all three treatment targets 35.0 35.1 37.4 37.3 41.4 41.0

* The blood pressure target does not exactly match NICE (<140/80) but was changed to align with the relevant QOF indicator (<140/80) . More information can be found here

29 Managing Type 2 Diabetes Over Time: Lessons From the UKPDS

30 Riddle M. Diabetes Spectrum Volume 13 Number 4, 2000, Page 194 UKPDS 7: fasting glucose and weight loss Dietary advice: follow British Diabetic Association recommendations (50% carbohydrate, 30% fat, 20% protein) Energy restriction: <100% IBW 1,672 kcal (7 MJ) 110-129% IBW 1,361 kcal (5.7 MJ) 130-149% IBW 1,217 kcal (5.1 MJ) >150% IBW 1,098 kcal(4.6 MJ

4 centres () specialised dietitians 9 average () NHS dietitian 3 poor (*) availability

31 UKPDS. Metabolism, Vol 39, No 9 6eptember). 1990: pp 905-9 12 How much weight loss do we need to achieve to improve health?

“…the curious power of modest weight loss …”

David Allison, PhD

Slide courtesy of Donna Ryan Design, Analysis, and Interpretation of Randomized Clinical Trials in Obesity Dec 4–5, 2006 Newark, New Jersey • Over 6–12 months interventions that caused any weight loss significantly reduced and maintained at two years: – systolic blood pressure (2.68 mmHg, 95% CI 3.37, 2.11) – diastolic blood pressure (1.34 mmHg, 95% CI 1.71, 0.97) – low-density lipoprotein cholesterol (0.20 mmol L1, 95% CI 0.29, 0.10) – triglycerides (0.13 mmol L1, 95% CI 0.22, 0.03) – fasting plasma glucose (0.32 mmol L1, 95% CI 0.43, 0.22) – haemoglobin A1c (0.40%, 95% CI 0.52, 0.28) The Diabetes Prevention Program experience: impact of weight loss on the risk of diabetes

20

years 15

- person

10

per 100 per 5 Diabetes incidencerate Diabetes

0 −10 −5 0 +5 Change in weight from baseline (kg)

Adapted from Hamman RF et al. Diabetes Care 2006;29:2102–7

Look AHEAD 1-year data: impact of weight loss on glycaemic measures

HbA1c* Fasting Plasma Glucose

0 0

−0.2 −10

(%)

c 1 −0.4 −20

−0.6 −30

Change in HbA in Change −0.8 −40 Change in FPG FPG in (mg/dL) Change

−1.0 −50 Weight ≥2 to ≥5 to ≥10 to ≥15% Weight ≥2 to ≥5 to ≥10 to ≥15% stable <5% <10% <15% stable <5% <10% <15% Weight loss category Weight loss category

Adjusted LS mean (95% CI). Stable weight defined as ±2% of baseline weight;*p<0.0001 for graded association by weight loss.

CI, confidence interval; HbA1c, glycosylated haemoglobin; LS, least squares

Wing RR et al. Diabetes Care 2011;34:1481–6

Progressive weight loss has dose-dependent and tissue-dependent biologic effects Effects of moderate and subsequent progressive weight loss on metabolic function and adipose tissue biology in humans with obesity

5% Weight loss 11% Weight Loss 16% Weight Loss Intrahepatic triglyceride content Intra-abdominal adipose tissue volume Adipose tissue insulin sensitivity Liver insulin sensitivity Muscle insulin sensitivity Beta cell function Adipose tissue biology* Inflammatory markers *Upregulation of genes involved in cholesterol flux, downregulation of genes involved in lipid synthesis, ECM remodeling and oxidative ECM, extracellular matrix

Magkos F et al. Cell Metabolism 2016;23:1–11 Lifelong patterns of BMI and cardiovascular phenotype in the 1946 British birth cohort study

1273 (45%) of 2856 participants eligible in 2006–10 (at age 60–64 years) O/O: overweight or obese

37 Charakida et al. Lancet Diabetes Endocrinol. 2014 Aug;2(8):648-54. doi: 10.1016/S2213-8587(14)70103-2 SCOUT: Weight change and predicted 5 year outcomes depending on severity of CVD

Caterson et al. Diabetes Obes Metab. 2012 Jun;14(6):523-30 Selected CDC Projects Featuring System Dynamics Modeling (2001-2008)

• Syndemics • Grantmaking Scenarios Mutually reinforcing afflictions Timing and sequence of outside • Diabetes assistance In an era of rising obesity • Upstream-Downstream Effort • Obesity Balancing disease treatment with Lifecourse consequences of prevention/protection changes in caloric balance • Infant Health • Healthcare Reform Fetal and infant morbidity/mortality Relationships among cost, quality, equity, and health status • Heart Disease and Preventing and managing multiple • Chronic Illness Dynamics risks, in context Health and economic scenarios for downstream and upstream reforms

Milstein B, Homer J. Background on system dynamics simulation modeling, with a summary of major public health studies. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; February 1, 2005. . Modeling the Population Health Dynamics of

• Diabetes & Obesity

Bobby Milstein Syndemics Prevention Network Centers for Disease Control and Prevention Atlanta, Georgia [email protected] http://www.cdc.gov/syndemics

Texas Public Health Association Galveston, TX Syndemics February 26, 2007 Prevention Network Although we expect obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years

Obese Fraction and Diabetes per Thousand 130 0.7 Diabetes Prevalence

85 0.35

Obesity 40 Prevalence 0 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year)

Risk multiplier on diabetes Diabetesonset from prevalence obesity = 2.6 keeps growing after obesity stops Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future

Obese Fraction and Diabetes per Thousand Unhealthy Days per Thou and Frac Managed 130 500 0.65 Managed 0.7 fraction Diabetes Prevalence

85 375 0.325 0.35

Obesity Unhealthy Days 40 Prevalence 250 0 from Diabetes 0 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year)

Diabetes prevalence keeps If diseaseReduction management in unhealthy days gains per growing after obesity stops complicated case if conventionally end,managed: the burden 33%; grows if intensively managed: 67% A Huge Push for Prediabetes Management What if the fraction of people with prediabetes getting managed increases from 6% to 32% by 2015 without any increase in diabetes management?

People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thousand

150 500 Base 450 125 Base

PreD mgmt PreD mgmt 400 100

350 Diabetes onset rate reduced 12% relative to base scenario 75 Not nearly enough to offset the excess onset due to high300 levels of obesity By 2050, diabetes prevalence reduced only 9%. 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050

The improvement is relatively modest—the growth is not stopped Managing Prediabetes AND Reducing Obesity

A scenario in which obesity is reduced

People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500

Base 450 125 Base PreD mgmt

PreD mgmt 400 PreD & 100 Why is obesityPreD & reduction so powerful? Ob 25% Ob 25% Mainly because of its strong350 effect on onset rate among PreD & Ob 18% 75 people with prediabetes; but, also, because it reduces 300 PreD & Ob 18% prediabetes prevalence itself.

50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 The more you reduce obesity, … Same with the burden of the sooner you stop the diabetes growth in diabetes—and the more you bring it down Intervening Effectively Upstream AND Downstream

With pure upstream intervention, burden still grows for many years before turning around. What would happen if improved diabetes management intervention is added on top of the Prediabetes + Obesity 25% reduction scenario?

People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500

Base 450 125 DownstreamBase improvement acts quickly against burden but cannot continue forever. PreD mgmt PreD mgmt 400 Significant upstream gains are thus essential 100 All 3 but will likely take 15+ years to achieve. 350 Pred & Ob 25% A flat-burdenPreD future & Ob is 25%possible but requires simultaneous 75 action on both fronts. All 3 -- 300 PreD & Ob 25% & Diab mgmt 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 With a combination of effective upstream and downstream interventions burden of diabetes remains nearly flat through 2050! ObesityObesity Treatment: Treatment: the present the present Low risk

Diets

VLED Pharma

Endo Low efficacy Barrier High efficacy Lap Balloons band Sleeve Roux- en-Y bypass

BPD- DS

High risk VLED: very low energy diet. BPD/DS: bilio-pancreatic diversion – duodenal switch Placebo-subtracted weight loss in patients with and without T2DM Naltrexone/ Orlistat1,2 Lorcaserin3,4 bupropion5,6 PHEN/TPM7,8 120 mg TID 10 mg BID 32/360 mg ER QD 7.5/46 mg ER QD 52 weeks 52 weeks 56 weeks 56 weeks

0

-3 -3.2 -3.2 -3.5 -3.6 -4.0 -4.9 -6 -5.2 -6.6

Percent weight loss at one year at loss weight Percent T2DM Non-T2DM -9 Values are placebo-subtracted and approximated from kg weight reductions where applicable; BID, twice daily; ER, extended release; QD, once daily; T2DM, type 2 diabetes mellitus; TID, three times a day 1. Torgerson JS et al. Diabetes Care 2004;27:155–61; 2. Berne C et al. Diabet Med 2005;22:612–8; 3. Apovian CM et al. Obesity (Silver Spring) 2013;21:935–43; 4. Hollander P et al. Diabetes Care 2013;36:4022–9; 5. Smith SR et al. N Engl J Med 2010;363:245–56; 6. O’Neil PM et al. Obesity 2012;20:1426–367; 7. Gadde KM et al. Lancet 2011;377:1341–52; 8. Garvey KC et al. Diabetes Care online September, 2014

Proportion (%) achieving 5% weight loss after 52 weeks

80 72.8 Placebo Medication 70 62

60

50 45.1 47.5 42 40 30 21 Percentage Percentage (%) 20.3 20 17 10 0 OrlistatOrlistat 120 mg1 TID LorcaserinLorcaserin 10 mg 2BID Naltrexone/NB 32/360 PHEN/TPMPHEN/TPM ER 15/92 mg4 120 mg TID 10 mg BID bupropion3 7.5/46 mg ER QD 32/360 mg QD BID, twice daily; ER, extended release; QD, once daily; T2DM, type 2 diabetes mellitus; TID, three times a day

1. Torgerson JS et al. Diabetes Care 2004;27:155–61; 2. Smith SR et al. N Engl J Med 2010;363:245–56; 3. Greenway FL et al. Lancet 2010; 595- 605; 4. Gadde KM et al. Lancet 2011;377:1341–52 Proportion (%) achieving 10% weight loss after 52 weeks

45 41 Placebo Medication 40 37 35

30 22.6 25 21 21 20 15 Percentage Percentage (%) 10 7.7 7 7 5 0 Orlistat Orlistat120 mg 1TID LorcaserinLorcaserin 10 mg BID2 NB 32/360Naltrexone/ PHEN/TPMPHEN/TPM ER 15/924 mg 120 mg TID 10 mg BID bupropion3 7.5/46 mg ER QD 32/360 mg QD

BID, twice daily; ER, extended release; QD, once daily; T2DM, type 2 diabetes mellitus; TID, three times a day

1. Torgerson JS et al. Diabetes Care 2004;27:155–61; 2. Smith SR et al. N Engl J Med 2010;363:245–56; 3. Greenway FL et al. Lancet 2010; 595- 605; 4. Gadde KM et al. Lancet 2011;377:1341–52 SCALE trials of liraglutide 3.0 mg demonstrate weight loss

SCALE Obesity & Prediabetes1 SCALE Maintenance2 SCALE Diabetes3 SCALE Sleep Apnoea4 56 weeks; n=3731 12-week run-in, 56 weeks; n=413 56 weeks; n=846 32 weeks; n=359

0.2% 1.6% 2.0% 2.6%

5.7% 6.2% 5.9% 6.2%

8.0%

63.2 27.1 50.5 21.8 49.9 12.7 46.4 18.1

Liraglutide 3.0 mg Placebo Weight loss at end of trial % subjects achieving ≥5% weight loss

Data are observed means; last observation carried forward at end of trial

1. Pi-Sunyer X et al. N Engl J Med 2015:373;11–22; 2. Wadden TA et al. Int J Obes (Lond) 2013;37:1443–51; 3. Davies MJ et al. JAMA 2015;314:687–99; 4. Blackman A et al. Diabetologia 2014;57:Abstract 184–OR 5 year follow-up of an open-label, single-centre, RCT of Roux-en-Y gastric bypass vs medical : CV risk*

*

*A reduction of diabetes or cardiovascular drugs in addition to a reduction of 20% or more in HbA1c from baseline, LDL cholesterol less than 2.3 mmol/L, systolic blood pressure less than 135 mm Hg, or diastolic Mingrone et al. Lancet 2015; 386: 964–73 blood pressure less than 85 mm Hg STAMPEDE randomised trial in type 2 diabetes: 3 year outcomes – glycaemic control

At 5-years (study end) Bypass Sleeve Medical HbA1c ≤ 6.0%: 29% 23% 5% Glycaemic relapse: 40% 50% 80% % change in ACR: -16.7 -59.5 7.1 : 0 0 2%

Schauer et al. Published online March 31, 2014. DOI: 10.1056/NEJMoa1401329 Presented at ACC April 2016 Cumulative incidence of micro- and macro-vascular diabetes complications in the surgery and control groups of SOS

Sjostrom et al. JAMA. 2014;311(22):2297-2304. doi:10.1001/jama.2014.5988 Incidence Rates of Cardiovascular Events per 1000 Person-Years by Treatment Quintiles of Baseline Insulin and BMI in the Swedish Obese Subjects Study

Sjoström et al. JAMA. 2012;307(1):56-65 Cardiovascular outcome trials in type 2 diabetes: empagliflozin and liraglutide 1.8 mg.

Marso et al. N Engl J Med. 2016 Jun 13. [Epub ahead of print] Zinman et al. N Engl J Med. 2015 Nov 26;373(22):2117-28. doi: 10.1056/NEJMoa1504720. Epub 2015 Sep 17 Sims and Weed. Am J Clin Nutr.l987;46:726-33. Sims and Weed. Am J Clin Nutr.l987;46:726-33. 3 Add statin

2 Control BP 4 Add metformin (<140/80mmHg) (and aspirin if appropriate)

5 consider tight glucose control 1 Lifestyle (exercise,• Intensive diet, multidisciplinary stop smoking• interventions) • Weight loss pharmacotherapy Let’s make full Let’s give• Bariatricour patients Surgery Don’t turn the with diabetes a hand! handuse around of our thumb