Evolution + economics = obesity crisis

A recent paper I wrote with Steve Simpson and David Raubenheimer explores how our evolved biological mechanisms that control how much we eat combine with modern economic circumstances to create the obesity crisis.

Some time in the last ten years, the number of people who are overweight exceeded, for the first time in human history, the number of people who are undernourished. From the rise in obesity flows a cascade of health and social problems, reducing life expectancies, burdening healthcare services and hobbling workforce productivity. Despite it’s tragic importance, we don’t fully understand the causes of the obesity crisis.

On the face of it, weight gain and obesity seem simple: people are eating too much and exercising too little. But why? What makes some people eat so much more energy than they spend? And what has changed since the mid-20th Century so that so many more people are eating too much and not exercising enough? Continue reading Evolution + economics = obesity crisis

Table 1 Supplement. The countries with the highest and lowest proportion of obese adults, according to data from the World Health Organisation.

 

Rank Country Male obesity Female obesity Average obesity
1 Nauru 78 79.3 78.7
2 Samoa 80.2 69.3 74.8
3 Tokelau 67.8 58.6 63.2
4 Kiribati 58.9 41.7 50.3
5 Marshall Islands 53 39 46.0
6 Federated States of Micronesia 57 31 44.0
7 French Polynesia 44.3 36.4 40.4
8 Saudi Arabia 43.8 28.3 36.1
9 Panama 36.05 31.68 33.9
10 United States 34.3 33.1 33.7
11 United Arab Emirates 40 25.6 32.8
12 Iraq 38.2 26.2 32.2
13 Mexico 34.5 24.2 29.4
14 Kuwait 30 28 29.0
15 Egypt 39.5 18.2 28.9
16 Bahrain 34 23 28.5
17 New Zealand 26 24.7 25.4
18 Macedoniae 36.3 14.3 25.3
19 Seychelles 35.2 15 25.1
20 Australia 24 25.6 24.8
21 United Kingdom 24 24 24.0
22 Fiji 32.7 15.1 23.9
23 Malta 21.3 25 23.2
24 Canada 23.2 22.9 23.1
25 Croatia 23 22 22.5
26 Israel 25 20 22.5
27 Greece 18.2 26 22.1
28 Chile 25 19 22.0
29 Bosnia-Herzegovina 25 16.5 20.8
30 Oman 23.8 16.7 20.3
31 Lithuania 19.2 20.6 19.9
32 Nicaragua 19.6 . 19.6
33 Argentina 19.4 . 19.4
34 Honduras 18.8 . 18.8
35 South Africa 27.8 8.8 18.3
36 Moldova 18.2 . 18.2
37 Uruguay 19 17 18.0
38 Poland 19.9 15.7 17.8
39 Hungary 18.2 17.1 17.7
40 Serbia 20 14.4 17.2
41 France 17.6 16.1 16.9
42 Mauritania 16.7 . 16.7
43 Lebanon 19 14 16.5
44 Malaysia 18.8 13.9 16.4
45 Germany 19 13.6 16.3
46 Lesotho 16.1 . 16.1
47 Turkey 16.6 15.6 16.1
48 Vanuatu 20 12.2 16.1
49 Russia 20 11.8 15.9
50 Spain 15.4 15.68 15.5
51 Armenia 15.5 . 15.5
52 Dominican Republic 18.3 12.7 15.5
53 Finland 16 14.9 15.5
54 Jordan 20.1 10.3 15.2
55 Latvia 18.1 12.3 15.2
56 Slovenia 13.8 16.5 15.2
57 Bolivia 15 . 15.0
58 Czech Republic 16 13.66 14.8
59 Tunisia 23 6.4 14.7
60 Estonia 15 13.7 14.4
61 Portugal 15 13.7 14.4
62 Slovakia 15 13.5 14.3
63 Iran 19.2 9.1 14.2
64 Mauritius 20 8 14.0
65 Swaziland 23.1 3.9 13.5
66 Ireland 12 14 13.0
67 Kazakhstan 12.7 . 12.7
68 Sweden 14 11 12.5
69 Bulgaria 13.5 11.3 12.4
70 Cyprus 11.8 12.9 12.4
71 Iceland 12.3 12.4 12.4
72 Guatemala 12.2 . 12.2
73 Peru 12.5 11.5 12.0
74 Namibia 11.7 . 11.7
75 Cuba 15.4 7.95 11.7
76 Belgium 13.4 9.9 11.7
77 Denmark 11 11.8 11.4
78 Azerbaijan 17.9 4.9 11.4
79 Ukraine 11.3 . 11.3
80 Austria 9 13 11.0
81 Brazil 13 8.9 11.0
82 Colombia 12.3 8.8 10.6
83 Turkmenistan 10 . 10.0
84 Norway 13.3 6.4 9.9
85 Mongolia 12.5 7.2 9.9
86 Morocco 11 8.2 9.6
87 Ghana 9.3 . 9.3
88 Italy 9.1 9.3 9.2
89 Romania 9.5 7.7 8.6
90 Netherlands 9.5 7.2 8.4
91 Cameroon 8.2 . 8.2
92 Gabon 8.2 . 8.2
93 Switzerland 8 8 8.0
94 Congo 7.5 . 7.5
95 Thailand 9.8 5.2 7.5
96 Senegal 7.2 . 7.2
97 Tajikistan 7.1 . 7.1
98 Kyrgyzstan 9 4.8 6.9
99 Singapore 7.3 6.4 6.9
100 Mozambique 9.9 3 6.5
101 Haiti 6.3 . 6.3
102 Kenya 6.3 . 6.3
103 Uzbekistan 7 5.4 6.2
104 Benin 5.8 . 5.8
105 Nigeria 5.8 . 5.8
106 Liberia 5.7 . 5.7
107 Zimbabwe 7.2 3.9 5.6
108 Martinique 5 6 5.5
109 Zambia 5.4 . 5.4
110 Mali 5.2 . 5.2
111 Tanzania 4.4 . 4.4
112 Comoros 4.2 . 4.2
113 Philippines 5.2 3 4.1
114 Uganda 4.1 . 4.1
115 Yemen 4 . 4.0
116 Pakistan 5.2 1.6 3.4
117 Eritrea 4.4 2.32 3.4
118 Niger 3.2 . 3.2
119 Japan 3.3 2.86 3.1
120 Guinea 3 . 3.0
121 China 3.4 2.4 2.9
122 Togo 2.5 . 2.5
123 Burkina Faso 2.4 . 2.4
124 Malawi 2.4 . 2.4
125 Indonesia 3.6 1.1 2.4
126 Korea, South 3 1.7 2.4
127 India 2.8 1.3 2.1
128 Bangladesh 1.7 . 1.7
129 Chad 1.5 . 1.5
130 Rwanda 1.3 . 1.3
131 Cambodia 1.2 . 1.2
132 Laos 1.6 0.7 1.2
133 Central African Republic 1.1 . 1.1
134 Madagascar 1 . 1.0
135 Nepal 1 . 1.0
136 Ethiopia 0.7 . 0.7
137 Vietnam 0.58 0.3 0.4

 

NOTES: Data are standardised obesity data from the World Health Organisation’s Global Database on Body Mass Index (WHO 2010) which includes the results of a large number of surveys and studies. We only used data from surveys post 1998, and for countries where there were multiple surveys post-1998 we used the most recent. Data represent percentage of adult (older than 15 years) men and women with Body Mass Index greater than or equal to 30.0, the standard WHO definition of adult obesity rate. Average obesity is the average of male and female obesity where both estimates exist.

Appearing in Adelaide at Valentine’s Day event

Is love blind? Or beauty in the eye of the beholder? Despite these liberating sentiments, we are bombarded daily with messages about what is and isn’t attractive. Some of these messages come from evolutionary research that shows why people from many societies show surprising agreement when asked to rate pictures of faces or bodies for attractiveness.

This Valentine’s day, Professor Bill von Hippel and I will be at The Science Exchange in Adelaide for an interactive evening entitled “Lust – Is Love Blind”, part of the RiAus program on the Seven Deadly Sins. With a mix of talks, interactive exercises and a Q&A session we will expose the science of attraction. We will talk about the basics of attraction; the things that make the hottest stars and celebutantes so alluring. But we will also dig deeper and find out what science says about the rest of us: folks with merely average looks, charm and status. Should we be looking for Mr or Ms. Right, or should we settle for Mr or Ms. Good Enough?

Some of the material I will speak about is featured in my forthcoming book, Sex, Genes and Rock ‘n’ Roll: How Evolution has Shaped the Modern World.

National income inequality predicts women’s preferences for masculinized faces better than health does.

BROOKS, R., SCOTT, I.M., MAKLAKOV, A.A., KASUMOVIC, M.M., CLARK, A.P., PENTON-VOAK, I.S. National income inequality predicts women’s preferences for masculinized faces better than health does (Comment on paper by de Bruine et al). Proceedings of the Royal Society of London B.

See also: Blog Post | Article in The Economist

The price of protein: combining evolutionary and economic analysis to understand excessive energy consumption.

BROOKS, RC; SIMPSON, SJ; RAUBENHEIMER, D. 2010. The price of protein: combining evolutionary and economic analysis to understand excessive energy consumption.  Obesity Reviews 11: 887-894.