We wanted to explore if FTO genotype interacts with fat intake, or leisure-time physical activity, on fat mass, lean mass and mortality.
Subjects and methods:
Among 22 799 individuals (44–74 years) in the population-based Malmö diet and cancer cohort that were genotyped for rs9939609 in FTO and had information on dietary intake (from a modified diet history method) and no history of diabetes, cancer or cardiovascular disease, 2255 deaths (including 1100 cancer and 674 cardiovascular deaths) occurred during 12.0 years of follow-up. Leisure-time physical activity was determined from a list of 17 different physical activities in a questionnaire. Body composition was measured using bioelectric impedance method.
FTO genotype associated strongly with both fat mass and lean mass (Ptrend<1 × 10−16 for both) but we found only significant interactions with fat intake, or physical activity, on fat mass (Pinteraction=0.01 and 0.004). No significant interaction between FTO genotype and fat intake (Pinteraction=0.72), or leisure-time physical activity (Pinteraction=0.07), on total mortality were observed. However, we observed a significant interaction between leisure-time physical activity and FTO genotype on cardiovascular mortality (Pinteraction=0.03). The highest vs lowest quintile of physical activity was associated with 46% (95% confidence interval, 17–64%) reduced cardiovascular mortality among TT-carriers (Ptrend=0.004), and 11% reduced cardiovascular mortality among A-allele carriers (Ptrend=0.68).
Our results indicate that FTO genotype associates with both fat mass and lean mass, but the level of fat intake and physical activity only modify the association with fat mass. In addition, FTO genotype may modify the association between physical activity and cardiovascular mortality.
Genetic variation in the fat mass and obesity-associated gene (FTO) has been associated with obesity in most of the examined populations, and is the strongest reported genetic predictor of obesity known so far. FTO gene is highly expressed in hypothalamus, a region involved in appetite regulation, and the A-allele has been associated with increased energy intake, especially fat intake,1, 2, 3 and altered appetite4 in children. There is also evidence in adults implicating that the risk allele carriers consume more energy.5 Interestingly, fto−/− mice are leaner despite relative hyperphagia as they have increased energy expenditure associated with systemic sympathetic activation.6 However, no difference in measured energy expenditure according to FTO genotype has been observed in human studies.7, 8, 9
As genetic variation in FTO is associated with fat mass, the A-allele may be related to mortality due to the increase in fatness. However, in a Danish study including 1628 individuals (205 deaths), the A-allele was related to increased mortality that was independent of fatness.10 We recently proposed an important role for dietary composition in modifying the susceptibility for obesity by the common variant in FTO, as we observed that low-fat diet may reduce the impact of the FTO risk allele on the risk of obesity.11 Several studies have also observed an interaction between physical activity and FTO genotype on the risk of obesity.12, 13, 14, 15 There may in a similar manner be an interaction between fat intake, or physical activity, and FTO genotype on mortality. The aim of this study was to examine the association between the FTO genotype and cause-specific mortality, and to study the association between fat intake, or physical activity, and mortality depending on FTO genotype. We also investigated if FTO interacts with the level of fat intake and physical activity on body composition measured as fat mass and lean body mass.
Subjects and methods
Study participants and data collection
The population-based Malmö diet and cancer (MDC) cohort with baseline examinations from March 1991 to October 1996 consist of 30 447 individuals. All men born between 1923–1945 and all women born between 1923–1950 and living in Malmö were invited to participate. The subjects visited the study center twice. The first visit included detailed instructions about the dietary data collection procedure, distribution of the dietary questionnaire and menu book and an extensive standardized questionnaire (to collect information on lifestyle, demographic and socioeconomic factors). Nurses conducted anthropometric measurements, measured blood pressure (once in supine position after 10 min rest) and collected blood. At the second visit, approximately 10 days after the first, trained dietary interviewers conducted individual interviews to complete the diet history and to check the correctness of completed questionnaires. A random 50% subsample of those who entered between 1991–1994 (n=11 456) were invited to participate in a study of the epidemiology of carotid artery disease.16 Of the 6103 who accepted, 5540 were rescheduled for blood sampling. The majority of these subjects underwent additional measurements of cardiovascular risk factors including fasting lipid levels according to standard procedures. Homeostasis model assessment index was used as a measure of insulin resistance and were calculated among non-diabetic subjects with the following formula: fasting insulin x fasting blood glucose/22.5.16
Ethical approval for the study was obtained from the Ethical Committee at Lund University (LU 51–90). Information on FTO genotype was available in 27 948 subjects. Out of these, 22 799 also had information on dietary intake and had no history of cardiovascular disease, cancer or diabetes.
Genotyping of rs9939609 was performed either by matrix-assisted laser desorption ionization-time of flight mass spectrometry on the Sequenom MassARRAY platform (Sequenom, San Diego, CA, USA), or by Taqman. Genotyping was successful in 27 948 subjects, of those 28 754 subjects that were genotyped (97.2%). The distribution of rs9939609 was in Hardy–Weinberg equilibrium in the studied population (P=0.99).
A modified dietary history method specifically designed for the MDC study was used17 combining the following: (1) a 7-day menu book that collected information on cooked lunches and dinner meals and cold beverages, and (2) a 168-item dietary questionnaire covering foods regularly consumed during the past year. The participants estimated frequencies of food intake, and usual portion sizes were assessed using a booklet of photographic aids. Thereafter, during a 1-h interview, the participants were asked questions about food choices, food preparation practices and portion sizes of the foods collected in the menu book (using a more extensive book of photos). The interviewer also checked the menu book and dietary questionnaire for overlapping information, as well as for very high reported intakes. A slight change in coding routines was introduced in September 1994 in order to shorten the interview time. This change did not reveal any major influence on the ranking of individuals.18 The average daily intake of foods was calculated based on the information available in the menu book (and interview) and the questionnaire. The average daily food intake was converted to nutrient intake data using a database, which was specifically developed for the MDC study and originated from the Swedish National Food Administration.
The dietary variables used in this study were as follows: total energy (kcal per day) (including energy from fat, carbohydrates, protein, alcohol and fiber) and percentage of energy (from non-alcohol and non-fiber energy intake) from fat. Fat intakes were energy adjusted (gender-specific) by regressing the variable on total energy intake, and the individuals were divided into quintiles based on their residual ranking. The relative validity of the dietary method has previously been examined among 105 women and 101 men; 18 days of weighed food records (3 days every second month) collected over 1 year was used as the reference method.19 Energy-adjusted Pearson correlations for fat intakes were 0.69 for women and 0.64 for men.
Leisure-time physical activity was obtained from a list of different physical activities in the questionnaire that were adapted from the Minnesota leisure time physical activity instrument.20 Participants were asked to estimate the number of minutes per week, and for each of the four seasons, they spent performing 17 different physical activities (for example, running, walking and swimming). Participants also had an opportunity to add an activity if they lacked any. The duration of each activity was multiplied by an intensity factor, creating a leisure-time physical activity score. The score was separated into gender-specific quintiles. The physical activity questionnaire has been compared with an accelerometer method (CSA model 7164, CSA Inc., Shalimar, FL, USA) among 369 subjects. Accelerometer was monitored for 4 consecutive days, except when sleeping or during water-based activities. Spearman's correlation coefficients between the methods were 0.35 in males and 0.24 in females.21
The season of data collection were divided into winter (December–February), spring (March–May), summer (June–August) and fall (September–November). Weight (kg) was measured by trained project staff members using a balance-beam scale with subjects wearing light clothing and no shoes, and height (cm) was measured with a fixed stadiometer. Body mass index (BMI) was defined as weight divided by height in square meters (kg m−2). Fat mass and lean body mass was measured with the bioelectric impedance method (single-frequency analyzer, BIA 103; JRL Systems, Detroit, MI, USA). Other lifestyle and socioeconomic variables were obtained through the standardized questionnaire. Smoking habits were categorized into current smokers (including irregular smoking), ex-smokers and non-smokers. Educational status was categorized based on the type of education attained: elementary, primary and secondary, upper secondary, further education without a degree and university degree. Alcohol consumption was divided into six categories. Individuals with no consumption of alcohol in the menu book and who indicated no consumption of alcohol during the previous year in the questionnaire were categorized as zero consumers. The other subjects were categorized into gender-specific quintiles according to their reported alcohol consumption in the 7-day menu book. Diabetes at baseline was identified through self-reported diabetes diagnosis, or use of antidiabetic therapy. History of coronary event or stroke was identified through local and national registries.22 Individuals with dietary change in the past are suspected to have unstable food habits.23, 24 Dietary change in the past (yes/no) was derived from the questionnaire item: ‘Have you substantially changed your eating habits because of illness or for some other reason?’ We identified individuals that potentially report non-adequate energy intake by comparing the individually estimated physical activity level (total energy expenditure divided by basal metabolic rate), with energy intake divided by basal metabolic rate. This procedure is described in detail elsewhere.25 Total energy expenditure was calculated from self-reported information on physical activity at work, leisure-time physical activity, household work, estimated sleeping hours, self case and passive time; basal metabolic rate was calculated from age, weight and height as recommended by World Health Organization (WHO).26 Non-adequate energy reporters were defined as those with a ratio of reported energy intake to basal metabolic rate outside the 95% confidence limits of the calculated physical activity level.27
Vital status and causes of death
The National Tax Board provided information on vital status until 31 December 2006. We also used information regarding the underlying cause of death due to tumors (ICD9:140-239, ICD10:C, D00-D48), cardiovascular disease (ICD9:390-459, ICD10:I), neurological disease (ICD9:320-389, ICD10:G, H), respiratory disease (ICD9:460-519, ICD10:J), and digestive disease (ICD9:520-579, ICD10:K).
SPSS (version 18; SPSS Inc., Chicago, IL, USA) and Stata (release 10; StataCorp LP, College Station, TX, USA) were used for the analyses. The difference in participant characteristics between those still alive and those that had died until follow-up were tested using general linear model adjusted for age and sex (logarithmically transformed continuous variables), χ2-test (categorical variables) and Cox proportional hazard's regression with time to follow-up until death, emigration or end of follow-up as time axis adjusted for age and sex. The associations between FTO genotype and obesity related traits and biological risk markers (logarithmically transformed continuous variables) were tested using general linear model adjusted for age and sex.
Cox proportional hazard's regression was used to examine the association between the FTO genotype, and total and cause-specific mortality. Age and sex were included as covariates in the basic model. In additional analyses we also adjusted for BMI.
The associations between mortality (total, cancer and cardiovascular mortality) and quintiles of energy-adjusted fat intake or leisure-time physical activity were examined in strata of FTO genotype (TT vs AT/AA) after individuals with previous history of coronary event, stroke, cancer or diabetes were excluded. In the analyses with fat intake, age, sex, total energy intake, diet method version (to avoid any undue influence of the different coding strategies of dietary data) and season were included as covariates in the basic model. In the analyses with leisure-time physical activity, age and sex were included as covariates in the basic model. In the multivariate models, alcohol consumption, smoking habits, BMI, education and quintiles of leisure-time physical activity or quintiles of energy-adjusted fat intake were also included as covariates. These variables were selected a priori for being associated with both mortality and dietary habits/physical activity level. Missing values among these variables were recorded as separate categories to avoid exclusion of individuals. Interaction was assessed by introducing a multiplicative factor between FTO genotype (TT vs AT/AA) and fat intake or leisure-time physical activity using continuous variables. In sensitivity analyses, we also excluded individuals reporting dietary change in the past as these individuals are suspected to have unstable food habits.
By using general linear models, we evaluated associations of FTO genotype with fat mass, lean body mass and body fat percentage in strata of energy-adjusted fat intake or leisure-time physical activity, adjusted for age and sex. Analyses with fat intake were also adjusted for energy intake, season and diet method version. The interaction between fat intake or leisure-time physical activity and FTO genotype was assessed by introducing a multiplicative factor with continuous variables.
Out of the 27 948 subjects with information on FTO genotype, a total of 3452 died (including 1569 cancer deaths and 1157 cardiovascular deaths) and 221 emigrated during a mean 12 years of follow-up. Diabetes at baseline, history of coronary event or stroke, history of cancer, current smoking and to have elementary as the highest education were associated with increased mortality. Zero consumers of alcohol had a higher mortality compared with alcohol consumers, and underweight and obese individuals had higher mortality compared with normal weight individuals (Table 1). The 15.6% of the subjects that were homozygous for the risk allele (A) had a mean weight of 76.2 kg compared with 74.2 kg for TT-carriers (Table 2). The FTO genotype was associated with both fat mass (P=2 × 10−16) and lean body mass (P=5 × 10−19); the associations were attenuated but remained significant after adjusting for each other (P=2 × 10−4 and 6 × 10−7, for fat mass and lean body mass, respectively). We observed a marginal increase in systolic and diastolic blood pressure among AA-carriers, which were eliminated when adjusting for BMI (P=0.70 and 0.42). Homeostasis model assessment index, but not TG, high-density lipoprotein cholesterol nor low-density lipoprotein cholesterol was significantly associated with FTO genotype. The association between FTO genotype and homeostasis model assessment index was attenuated after adjusted for BMI (P=0.15).
FTO and mortality
The results indicate no association between FTO genotype and total mortality (hazard ratio, 0.98; 95% confidence interval (95% CI), 0.89–1.08 for AA-carriers compared with TT-carriers; P for trend=0.62) (Table 3). Adjustments for BMI did not influence the risk estimates (P for trend=0.62). Similar results were observed for men (hazard ratio, 0.95; 95% CI, 0.83–1.08) and women (hazard ratio, 1.02; 95% CI, 0.88–1.18). FTO genotype was not significantly associated with any of the investigated cause-specific mortalities (Table 3).
Fat intake and mortality depending on FTO genotype
After excluding individuals with a history of cancer, cardiovascular disease and diabetes (as it may influence diet intakes) and those without diet information, 22 799 individuals remained and 2255 deaths (1100 cancer and 674 cardiovascular deaths) occurred during the follow-up. Individuals in the lowest quintile of energy-adjusted fat intake (mean 31.1 E% fat) were younger, had a higher BMI, higher leisure-time physical activity and higher alcohol intake compared with those in the highest quintile of fat intake (mean 47.0 E% fat). In addition, higher frequency of smokers was observed among those reporting high fat intake (Table 4).
In the basic model, the highest vs lowest quintile of energy-adjusted fat intake was associated with 35% (95% CI, 19–54%; P for trend<0.001) higher mortality. The association was clearly attenuated after adjusting for potential confounders (hazard ratio, 1.12; 95% CI, 0.98–1.28 for highest vs lowest quintile; P for trend=0.11). Fat intake was neither significantly associated with mortality among FTO A-allele carriers (P for trend=0.44) or among those homozygous for the T-allele (P for trend=0.25), and we observed no tendency of interaction between fat intake and FTO genotype on mortality (P=0.72) (Table 5). The results did not change after excluding the 22% of the individuals with self-reported dietary change in the past (P for interaction=0.95). Furthermore, we observed no significant interaction between fat intake and FTO genotype on cancer mortality (P for interaction=0.88) or cardiovascular mortality (P for interaction=0.37).
Physical activity and mortality depending on FTO genotype
After excluding individuals with a history of cancer, cardiovascular disease and diabetes and those without information on physical activity level, 22 665 individuals remained and 2242 deaths (1092 cancer and 671 cardiovascular deaths) occurred during the follow-up. Individuals with high reported level of leisure-time physical activity were older, had a lower BMI, a lower fat intake and were less often smokers compared with those with low physical activity (Table 4). After adjusting for potential confounders, a high degree of leisure-time physical activity was associated with decreased mortality (P for trend=0.001). We observed a tendency of interaction between leisure-time physical activity and FTO on total mortality (P=0.07). The highest vs lowest quintile of physical activity was associated with 33% (95% CI, 17–46%) reduced cardiovascular mortality among TT-carriers (P for trend=0.001), and 11% reduced cardiovascular mortality among A-allele carriers (P for trend=0.08). The significant interaction indicates that FTO genotype modifies the association between physical activity and cardiovascular mortality (P for interaction=0.03) (Table 6). Among those homozygous for the T-allele, the highest vs lowest quintile of physical activity was associated with 46% (95% CI, 17–64%) reduced cardiovascular mortality (P for trend=0.004). Among A-allele carriers, the highest quintile was only associated with 11% reduced mortality (P for trend=0.68). No tendency of interaction was observed for cancer mortality (P for interaction=0.80).
FTO genotype and body composition depending on fat intake and physical activity
In order to further understand how environmental factors modify the effect on FTO on weight gain, we examined the interaction between fat intake, or physical activity, and FTO on fat mass vs lean body mass. We found significant interactions between fat intake and FTO on fat mass (P for interaction=0.01) and percentage body fat (P for interaction=0.02), but not with lean body mass (P for interaction=0.14). The significant interaction with fat mass remained after adjusting for lean body mass (P for interaction=0.04), but the interaction with lean body mass was clearly abolished when adjusted for fat mass (P for interaction=0.92). The lowest fat intake quintile (mean intake 31 E%) attenuated the association between FTO and percentage body fat (0.03 increase per A-allele, P=0.77). With higher fat intake we observed larger differences in percentage body fat depending on FTO genotype (quintile 2, β=0.28, P=0.005; quintile 3, β=0.15, P=0.15; quintile 4, β=0.36, P=0.001; quintile 5, β=0.37, P=4 × 10−4) (Figure 1). After excluding misreporters of energy, we observed somewhat stronger results for fat mass (P for interaction=0.005) and percentage body fat (P for interaction=0.01).
We found significant interactions between physical activity and FTO on fat mass (P-interaction=0.004) and percentage body fat (P-interaction=0.005), but not with lean body mass (P-interaction=0.27). The A-allele was significantly associated with higher percentage body fat whatever their level of physical activity except in the highest quintile wherein the effect of FTO genotype on body fat was eliminated (difference in BMI units for each additional A-allele (β): quintile 1, β=0.46, P=2 × 10−5; quintile 2, β=0.30, P=0.004; quintile 3, β=0.31, P=0.003; quintile 4, β=0.24, P=0.02; quintile 5, β=0.03, P=0.76) (Figure 1).
Our results indicate that FTO genotype is not associated with mortality, but that the decreased mortality, especially cardiovascular mortality, associated with physical activity may be modified by the FTO variant, as the decreased risk with high physical activity was more evident among those homozygous for the T-allele.
We were not able to replicate the results by the Danish study (205 deaths in a 13.5 year follow-up) of 42% lower mortality with the TT genotype compared with A-allele carriers.10 With a study sample of almost 3500 deaths we have power to detect even a relatively small association. In addition, with only 0.8% of the study population having emigrated abroad we have almost complete follow-up of the study population. Differences in food habits or other lifestyle factors between the Swedish and Danish populations may be one potential explanation of the different observations. However, no differences in fat intake were observed comparing intakes in MDC and the analogous Danish Diet, cancer and health cohort.28
The lack of significant association between high physical activity and lower cardiovascular mortality among A-allele carriers is not easily explained. A-allele carriers were characterized by several indicators of impaired cardiovascular health, including higher blood pressure and insulin resistance, which were mainly because of the higher BMI in this group. A previous study within the MDC study has observed that the increased relative risk for cardiovascular disease mortality with higher body fat % was reduced by leisure-time physical activity.22 Our results indicate that this group probably needs even higher degree of physical activity in order to decrease mortality. It is however important to note that the level of physical activity was self-reported and may therefore be uncertain. The correlation between physical activity level obtain by this method and an objective measure with accelerometer was quite low (0.35 in males and 0.24 in females).
Fat intake was not associated with increased mortality in either TT or A-allele carriers. Although the diet method used in MDC was especially designed to estimate fat intake in an elderly urban population, measurement errors in the dietary assessment may still introduce misclassification in dietary exposure, which may contribute to the absence of significant association between fat intake and mortality. However, the dietary data collected in MDC has high relative validity compared with many other studies, with correlation for energy-adjusted fat intake of 0.64 for men and 0.69 for women. In addition, the availability of information on past food habit change in this study is an advantage as we have the ability to identify individuals with suspected unstable food habits and consequently misclassification of dietary exposure. Another strength of the study is the extensive collection of lifestyle factors that can confound the relationship between diet and disease.
Our results that the A-allele is not only associated with higher fat mass but also with higher lean body mass independent on fat mass indicate that the FTO variant is related to an increased weight in general. This is in contrast to previous studies showing almost exclusively association with fat mass among children,1, 29 or that the association with lean body mass disappeared when adjusting for fat mass in adults.30
FTO is suggested to be associated with obesity mainly by influencing appetite and satiety regulation, and the A-allele has been associated with increased energy intake, especially fat intake.1, 2, 3, 5 We earlier reported an interaction between fat intake and FTO genotype on BMI, wherein FTO was associated with higher BMI only among individuals reporting a high fat intake.11 As BMI is a measure of overweight without taking the body composition into account, we now describe in a bigger sample size separate analyses of fat mass and lean body mass. We observed that fat intake modified significantly the effect size of FTO genotype on fat mass, but not on lean body mass.
The observation that low physical activity accentuates the susceptibility for obesity by the FTO variant has been shown in several studies.12, 13, 14, 15, 31 Our results indicating that the level of physical activity especially modifies the increase in fat mass, and not lean body mass, by FTO are important. Although individuals with higher physical activity level have lower percentage body fat, the association between FTO and percentage body fat was neutralized only among the 20% individuals with highest level of physical activity. Our results indicate that risk genotype carriers may benefit from a high level of physical activity. On the other hand we observe that physical activity is not associated with decreased mortality among A-allele carriers. As this is, to our knowledge, the first study examining the association between lifestyle factors and mortality depending on FTO genotype, replication of the results is needed in other cohort with data of high quality and statistical power.
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This study is supported by the Lund University Diabetes Center (LUDC), the Swedish Medical Research Council, equipment grant from the Knut and Alice Wallenberg Foundation, the Swedish Heart and Lung Foundation, the Region Skåne, the Malmö University Hospital, the Albert Påhlsson Research Foundation and the Crafoord foundation.
The authors declare no conflict of interest.
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Sonestedt, E., Gullberg, B., Ericson, U. et al. Association between fat intake, physical activity and mortality depending on genetic variation in FTO. Int J Obes 35, 1041–1049 (2011). https://doi.org/10.1038/ijo.2010.263
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