Food sources of fat may clarify the inconsistent role of dietary fat intake for incidence of type 2 diabetes 1 4

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1 Food sources of fat may clarify the inconsistent role of dietary fat intake for incidence of type 2 diabetes 1 4 Ulrika Ericson, Sophie Hellstrand, Louise Brunkwall, Christina-Alexandra Schulz, Emily Sonestedt, Peter Wallström, Bo Gullberg, Elisabet Wirfält, and Marju Orho-Melander ABSTRACT Background: Dietary fats could affect glucose metabolism and obesity development and, thereby, may have a crucial role in the cause of type 2 diabetes (T2D). Studies indicated that replacing saturated with unsaturated fats might be favorable, and plant foods might be a better choice than animal foods. Nevertheless, epidemiologic studies suggested that dairy foods are protective. Objective: We hypothesized that, by examining dietary fat and its food sources classified according to fat type and fat content, some clarification regarding the role of dietary fat in T2D incidence could be provided. Design: A total of 26,930 individuals (61% women), aged y, from the Malmö Diet and Cancer cohort were included in the study. Dietary data were collected by using a modified diet-history method. During 14 y of follow-up, 2860 incident T2D cases were identified. Results: Total intake of high-fat dairy products (regular-fat alternatives) was inversely associated with incident T2D (HR for highest compared with lowest quintiles: 0.77; 95% CI: 0.68, 0.87; P-trend, 0.001). Most robust inverse associations were seen for intakes of cream and high-fat fermented milk (P-trend, 0.01) and for cheese in women (P-trend = 0.02). High intake of low-fat dairy products was associated with increased risk, but this association disappeared when low- and high-fat dairy were mutually adjusted (P-trend = 0.18). Intakes of both high-fat meat (P-trend = 0.04) and low-fat meat (P-trend, 0.001) were associated with increased risk. Finally, we did not observe significant association between total dietary fat content and T2D (P-trend = 0.24), but intakes of saturated fatty acids with 4 10 carbons, lauric acid (12:0), and myristic acid (14:0) were associated with decreased risk (P-trend, 0.01). Conclusions: Decreased T2D risk at high intake of high- but not of low-fat dairy products suggests that dairy fat partly could have contributed to previously observed protective associations between dairy intake and T2D. Meat intake was associated with increased risk independently of the fat content. Am J Clin Nutr 2015;101: Keywords: cohort study, diet, dietary fats, food intake, type 2 diabetes INTRODUCTION The worldwide adaption of westernized energy-rich diets is considered an important contributor to the increasing prevalence of obesity and type 2 diabetes (T2D). 5 These diets tend to be high in animal foods and low in unrefined plant foods, which generally result in high intakes of fat, SFAs, linoleic acid (LA; 18:2n 6), and protein but lower in dietary fiber and several micronutrients. Because fat is energy dense, and fatty acids affect glucose metabolism, fat intake may have a crucial role in the development of T2D. Potential effects on gene expression, cell membrane function, lipid metabolism, and gut microbiota may also explain associations with T2D (1 4). Evidence from randomized lifestyle interventions indicated that reduced intakes of total and saturated fats, in combination with increased fiber intake and physical activity, prevent the development of T2D in individuals with impaired glucose tolerance (5, 6). However, associations between dietary fat and T2D from epidemiologic studies have been inconsistent (7), and the importance of dietary fat content and food sources of fat with regard to risk of T2D remains to be clarified. The replacement of dietary intakes of SFAs with PUFAs may, via various mechanisms, lead to improved insulin sensitivity (8), and epidemiologic studies have indicated that the replacement of foods high in SFAs with food sources of MUFAs and PUFAs could be favorable in the prevention of diabetes development (8). In addition, high blood concentrations of LA may counteract the development of hyperglycemia and T2D (9). In line with those findings, plant sources of fat were suggested to be a better choice than animal sources (10). Indeed, high intakes of red meat and meat products show positive associations with risk of T2D (11). Nevertheless, several epidemiologic studies indicated that high intake of dairy products may be protective (12). Effects of different dairy products or dairy components, including possible 1 From the Department of Clinical Sciences, Malmö, Diabetes and Cardiovascular Disease, Genetic Epidemiology (UE, SH, LB, C-AS, ES, and MO-M) and the Department of Clinical Sciences, Malmö, Nutritional Epidemiology, Lund University, Lund, Sweden (PW, BG, and EW). 2 Supported by the Swedish Research Council, the Region Skåne, the Skåne University Hospital, the Novo Nordic Foundation, and the Albert Påhlsson Research Foundation. 3 Supplemental Tables 1 and 2 are available from the Supplemental data link in the online posting of the article and from the same link in the online table of contents at 4 Address correspondence to U Ericson, Clinical Research Centre, Building 60, Floor 13, Skånes Universitetssjukhus in Malmö, Entrance 72, Jan Waldenströms gata 35, SE Malmö, Sweden. ulrika.ericson@ med.lu.se. 5 Abbreviations used: LA, linoleic acid; MDC, Malmö Diet and Cancer; T2D, type 2 diabetes. Received November 12, Accepted for publication March 6, First published online April 1, 2015; doi: /ajcn Am J Clin Nutr 2015;101: Printed in USA. Ó 2015 American Society for Nutrition 1065 Supplemental Material can be found at: DCSupplemental.html

2 1066 ERICSON ET AL. beneficial effects of yogurt, cheese, and specific fatty acids, were proposed to lie behind these observations (13 15). In addition, intake of fatty fish (16) as well as intakes and blood concentrations of total n 3 PUFAs (17), a-linolenic acid (18), and longchain fish n 3 PUFA from foods (19) were, in some studies, inversely associated with T2D risk, whereas results from other studies did not indicate that fatty fish or n 3 PUFA have an important protective role in the cause of T2D (8, 18, 20). In this population-based prospective study of men and women from the MDC (Malmö Diet and Cancer) cohort, we examined if dietary fat intake and, in particular, different types of fatty acids and food sources of fat classified according to fat type and fat content were associated with incidence of T2D. METHODS Study population and data collection The MDC study is a population-based prospective cohort study in Malmö, which is a city in the south of Sweden. Baseline examinations were conducted between 1991 and All women born between 1923 and 1950 and all men born between 1923 and 1945 who were living in the city of Malmö were invited to participate (n = 74,138). Details of the cohort and the recruitment procedures are described elsewhere (21). The only exclusion criteria were mental incapacity and inadequate Swedish language skills (eligible persons: n = 68,905). Participants filled out questionnaires that covered socioeconomic, lifestyle, and dietary factors, recorded meals, and underwent a diet-history interview. Anthropometric measures were conducted by nurses. Weight was measured by using a balance-beam scale with subjects wearing light clothing and no shoes. Standing height was measured by using a fixed stadiometer calibrated in centimeters. Waist circumference was measured midway between the lowest rib margin and iliac crest. Body composition was estimated by using a bioelectrical impedance analyzer (BIA 103,single-frequency analyzer; RJL Systems). The percentage of body fat was calculated by using an algorithm provided by the manufacturer. During the screening period, 28,098 participants (40% of eligible persons) completed all baseline examinations. Of nonparticipants, 49% did not reply to the invitation letter, 39% answered that they were not willing to take part, 7% died or moved before they had received an invitation, and 5% failed to complete all baseline examinations (21). MDC participants have been compared with participants in a mailed health survey in Malmö with a higher participation rate (75%) with regard to subjective health, sociodemographic characteristics, and lifestyle (21). In the current study, we included 26,930 participants without diabetes at baseline. We excluded 1168 participants on the basis of self-reported diabetes diagnosis, self-reported diabetes medication, or information from medical data registries that indicated a date of diagnosis preceding the baseline examination date. The ethical committee at Lund University approved the study (LU 51 90), and participants gave their written informed consent. Dietary data Dietary data were collected once during the baseline period. The MDC study used an interview-based modified diet-history method that combined 1) a 7-d menu book for the recording of intakes from meals that varied from day to day (usually lunch and dinner meals), cold beverages, and nutrient supplements and 2) a 168-item questionnaire for the assessment of consumption frequencies and portion sizes of regularly eaten foods that were not covered by the menu book. Finally, 3) a 45-min interview completed the dietary assessment. The MDC method has been described in detail elsewhere (22, 23). Diet analyses were adjusted for a variable called the diet-method version because slightly altered coding routines of dietary data were introduced in September 1994 to shorten the interview time (from 60 to 45 min). This adjustment resulted in 2 slightly different method versions (before or after September 1994) without any major influence on the ranking of individuals (23). The relative validity of the MDC method was evaluated in the Malmö Food study by comparing the method with 18-d weighed-food records (24, 25). Pearson correlation coefficients, which were adjusted for total energy, between the reference method and MDC method were, in women and men, respectively, 0.69 and 0.64 for total fat, 0.68 and 0.56 for SFA, 0.66 and 0.59 for MUFA, 0.64 and 0.26 for PUFA, 0.68 and 0.23 for LA, 0.58 and 0.22 for a-linolenic acid (18:3n 3), 0.38 and 0.24 for EPA (20:5n 3), 0.40 and 0.37 for docosapentaenoic acid (22:5n 3), 0.27 and 0.20 for DHA (22:6n 3), 0.51 and 0.43 for low-fat meat, 0.80 and 0.40 for high-fat meat, 0.92 and 0.92 for low-fat milk, 0.75 and 0.76 for high-fat milk, and 0.59 and 0.47 for cheese (24, 25). The mean daily intake of foods was calculated on the basis of the frequency and portion-size estimates from the questionnaire and menu book. Food intake was converted to energy and nutrient intakes by using the MDC nutrient database whereby the majority of the nutrient information comes from PC-KOST2-93 from the National Food Agency in Uppsala, Sweden. Nutrient intakes from supplements were calculated on the basis of supplement consumption recorded in the menu book. Supplement consumption was converted into nutrient intakes by using the MDC supplement database (26). Dietary variables examined in this study are listed and described in Supplemental Table 1. Examined nutrient intakes were the sum from foods and supplements. Main food sources of fat were identified in the MDC cohort (27) and primarily grouped according to fat type and fat content. Some less-important fat sources were also examined to facilitate the interpretation of results regarding high-fat alternatives of the same types of foods. Total intake of high-fat dairy products was defined as the sum of portions of butter; regular-fat alternatives ($2.5% fat) of milk, yogurt, and sour milk; cream (.12% fat); and regular-fat cheese (.20% fat). Portions (instead of grams) were used to analyze the sum of dairy products with different water contents and usually consumed in different weights (e.g., cheese and milk). Standard portion sizes from the MDC study or National Food Agency in Sweden were used (28) as follows: milk and yogurt (200 g/portion), cheese (20 g/portion), cream (25 g/portion), ice cream (75 g/portion), and butter (7 g/portion). Energy-adjusted dietary intakes were obtained by regressing intakes on nonalcohol energy intake. Quintiles of nutrient and food residuals were used as exposure categories. If.20% of the individuals were zero consumers, they constituted the lowest intake category, and the higher categories were defined as quartiles in consumers. Diabetes case ascertainment We identified 2860 incident cases of T2D during 377,642 person-years of follow-up via at least one of 7 registries (90%) or

3 FOOD SOURCES OF FAT AND INCIDENT TYPE 2 DIABETES 1067 at new screenings or examinations during follow-up (10%). The mean 6 SD follow-up time was y. Subjects contributed person-time from date of enrollment until date of diabetes diagnosis, death, migration from Sweden, or end of follow-up (December 2009), whichever occurred first. During follow-up, 0.5% of subjects had migrated from Sweden. If available, we used information on the date of diagnosis from 2 registries prioritized in the following order: 1) the regional Diabetes 2000 registry of Scania (29) and 2) the Swedish National Diabetes Registry (30). These registries required a physician diagnosis according to established diagnosis criteria (fasting plasma glucose concentration $7.0 mmol/l or fasting whole blood concentration $6.1 mmol/l, measured at 2 different occasions). Individuals with at $2 glycated hemoglobin values.6.0% with the Swedish Mono-S standardization system (corresponding to 6.9% in the US National Glycohemoglobin Standardization Program and 52 mmol/mol with International Federation of Clinical Chemistry and Laboratory Medicine units) (31, 32) were categorized as diabetes cases in the Malmö HbA1c Registry. In addition, cases were identified via 4 registries from the National Board of Health and Welfare in Sweden as follows: the Swedish National Inpatient Registry, the Swedish Hospitalbased outpatient care, the Cause-of-death Registry, and the Swedish Prescribed Drug Registry. Other variables Information on age was obtained from the personal identification number. Age was divided into 5-y categories. BMI (in kg/m 2 ) was calculated from the direct measurement of weight and height. Leisure-time physical activity was assessed by asking participants to estimate the number of minutes per week they spent on 17 different activities. The duration was multiplied by an activity specific intensity coefficient, and an overall leisure-time physical activity score was created. The score was divided into sex-specific quintiles. The smoking status of participants was defined as current smokers (including irregular smokers), ex-smokers, and never smokers. The total consumption of alcohol was defined by a 4-category variable. Participants who reported zero consumption in the menu book and indicating no consumption of any type of alcohol during the previous year were categorized as zero reporters. Other category ranges were,15 g alcohol/d for women and,20 g/d for men (low), g/d for women and g/d for men (medium), and.30 g/d for women and.40 g/d for men (high). Participants were divided into 4 categories according to their highest level of education (#8, 9 10, or y or university degree). Season was defined as the season of dietdata collection (winter, spring, summer, and fall). Dietary change in the past (yes or no) was based on the question Have you substantially changed your eating habits because of illness or some other reasons? Statistical analysis The SPSS statistical computer package (version 20.0; IBM Corp.) was used for all statistical analyses. All food variables were log transformed (e-log) to normalize the distribution before analysis. To handle log transformation for zero intakes, we added a very small amount ( g). We examined baseline characteristics in cases and noncases of T2D and across intake quintiles of fat and its food sources by using the general linear model for continuous variables (adjusted for age and sex) and with the chi-square test for categorical variables. In a post hoc analysis, we used the general linear model to examine intakes of nondairy foods (meat, fish, potatoes, fruit, vegetables, cereal products, margarine, pastry, chocolate, and sugar-sweetened beverages) across intake quintiles of cream and high-fat fermented milk. We used Cox proportional hazards regression model to estimate HRs of diabetes incidence associated with quintiles of dietary intakes adjusted for energy intake by using the residual method. The first quintile was used as the reference. Years of follow-up was used as the underlying time variable. We used covariates obtained from baseline examinations. The basic model included adjustments for age (continuous), sex (when applicable), method version, season (categorical), and total energy intake (continuous). Our full multivariate model further included adjustments for the following categorical variables: leisure-time physical activity, smoking, alcohol intake, and education, and, finally, BMI as a continuous variable. Because associations between dietary fat and T2D may partly be mediated via BMI, we also performed analyses with an intermediate multivariate model without the inclusion of BMI. Covariates were identified from the literature and indicated potential confounding in the MDC cohort because of associations with incident T2D and dietary intakes. Missing values for variables were treated as separate categories. Analyses with additional adjustments for waist circumference or dietary change in the past were also performed. Finally, additional adjustments were made for possible dietary confounders, which previously showed associations with T2D are found, in the examined foods or central in the same dietary pattern (intake quintiles of protein, fiber, sucrose, calcium, vitamin D, magnesium, meat, fruit and vegetables, sugar-sweetened beverages, or high-fat dairy products). We also performed all analyses for men and women separately. Tests for interactions between sex and nutrient and food intakes with regard to diabetes incidence were performed [sex 3 quintile of nutrients and foods (treated as continuous variables)]. Tests for interactions between BMI (#25 or.25) and dietary variables were also performed. In a sensitivity analysis, we excluded individuals with a reported dietary change in the past (24% of the individuals). In a second sensitivity analysis, we excluded individuals with prevalent cardiovascular disease (coronary event or stroke) at baseline (3%). All statistical tests were 2 sided, and significance was assumed at P, RESULTS Baseline characteristics At baseline, several established risk factors for T2D, as well as potential confounders of dietary associations, differed between cases and noncases of incident T2D (Table 1). Cases were older and had higher BMI, a more sedentary lifestyle, lower alcohol intake, higher protein intake, and lower intakes of carbohydrates and dietary fiber. In addition, there were more individuals who reported a dietary change in the past, more ever smokers, and fewer individuals with a high level of education in cases. Baseline characteristics differed also between low and high consumers of several fat sources (Table 2). Subjects who reported a high total

4 1068 ERICSON ET AL. TABLE 1 Baseline characteristics for cases and noncases of incident T2D in the MDC cohort after the exclusion of individuals with prevalent diabetes at baseline ( ) 1 Baseline variable n Cases (n = 2860) Noncases (n = 24,070) P 2 Sex, F, % 26, ,0.001 Age, y 26, (58.5, 59.0) (58.0, 58.2),0.001 BMI, kg/m 2 26, (28.2, 28.5) 25.4 (25.4, 25.4),0.001 Waist, cm 26, (92.6, 93.4) 84.6 (84.4, 84.7),0.001 Body fat, % 26, (28.1, 28.5) 25.3 (25.3, 25.4),0.001 Systolic blood pressure, mm Hg 26, (146, 147) 140 (140, 141),0.001 Diastolic blood pressure, mm Hg 26, (88.7, 89.4) 85.5 (85.4, 85.7),0.001 Hb A 1c, % (5.16, 5.23) 4.75 (4.74, 4.77),0.001 Fasting blood glucose, mmol/l (5.86, 5.96) 4.88 (4.86, 4.90),0.001 Triglycerides, mmol/l (1.68, 1.79) 1.31 (1.29, 1.33),0.001 HDL cholesterol, mmol/l (1.21, 1.26) 1.38 (1.37, 1.39),0.001 LDL cholesterol, mmol/l (4.22, 4.37) 4.14 (4.11, 4.17),0.001 Fasting plasma insulin, mu/l (12.3, 13.5) 7.1 (6.89, 7.35),0.001 HOMA-IR (2.31, 2.53) 1.54 (1.50, 1.57), Leisure-time physical activity score 5 26, (7270, 7760) 8220 (8130, 8310),0.001 Alcohol intake, 6 g/d 25, (11.4, 12.3) 12.4 (12.3, 12.6) 0.02 Smoking, previous and current, % 26, Education.10 y, % 26, ,0.001 Dietary change in the past, % 26, ,0.001 Energy, kcal/d 26, (2300, 2340) 2350 (2340, 2360) 0.01 Protein, E% 26, (15.8, 16.0) 15.4 (15.4, 15.5),0.001 Carbohydrates, E% 26, (45.7, 46.2) 46.3 (46.2, 46.4) 0.01 Fat, E% 26, (38.0, 38.5) 38.5 (38.4, 38.6) 0.36 Saturated fat, E% 26, (16.1, 16.4) 16.6 (16.5, 16.6),0.001 Fiber, g/1000 kcal 26, (8.9, 9.1) 9.1 (9.1, 9.2) 0.04 Calcium, mg/d 26, (1150, 1180) 1150 (1140, 1160) 0.10 Dairy products, portions/d 26, (6.2, 6.5) 6.6 (6.6, 6.7),0.001 Dairy products, low-fat, portions/d 26, (2.0, 2.1) 1.8 (1.8, 1.8),0.001 Dairy products, high-fat, portions/d 26, (4.0, 4.3) 4.6 (4.6, 4.7),0.001 Margarine, g/d 26, (30, 32) 31 (30, 31) 0.10 Eggs, g/d 26, (24, 26) 23 (23, 24),0.001 Meat and meat products, g/d 26, (119, 122) 114 (113, 114),0.001 Fish, high-fat, g/d 26, (15.9, 17.4) 16.1 (15.8, 16.4) 0.25 Pastry and biscuits, g/d 26, (36, 39) 39 (39, 40) Chocolate, g/d 26, (7.6, 8.5) 8.0 (7.8, 8.2) E%, percentage of energy; Hb A 1c, glycated hemoglobin; MDC, Malmö Diet and Cancer; T2D, type 2 diabetes. 2 A general linear model was used for continuous variables and adjusted for age and sex. The examination of diet was also adjusted for the diet-method version, season, and energy intake. The chi-square test was used for categorical variables. 3 Mean; 95% CI in parentheses (all such values). 4 P value for ln-transformed values. 5 A high score indicates a high level of leisure-time physical activity. 6 In subjects who reported that they consumed alcohol during the year before baseline examinations. dietary fat content were younger and had lower BMI, but apart from these variables, they were characterized by a rather unhealthy lifestyle pattern; they had a more-sedentary lifestyle and higher alcohol intake, and there were also more ever smokers and fewer individuals with a high level of education in subjects who reported a high dietary fat content. Finally, fewer of these individuals reported a dietary change in the past. Except for the observation regarding education, a similar pattern was seen for individuals with a diet rich in high-fat dairy products. Dietary content of total fat and fatty acids in relation to incidence of T2D We did not observe any significant associations between the dietary content of total fat and incidence of T2D (P-trend = 0.24) (Table 3). In the full multivariate analysis, we observed a significant inverse association between intake of SFA and T2D (P-trend = 0.01). However, the association disappeared after adjustment for intake of high-fat dairy products (P-trend = 0.61). Moreover, in analyses of SFAs with different chain lengths, we only observed significant decreased risk of T2D at high aggregated intakes of short- to medium-chain SFAs with 4 10 carbons (P-trend, 0.001) as well as at high intakes of lauric acid (12:0) (P-trend = 0.003) and myristic acid (14:0) (P-trend, 0.001). In contrast, high intakes of SFAs with a longer chain length, palmitic acid (16:0) (P-trend = 0.10) and stearic acid (18:0) (P-trend = 0.36), were not associated with T2D. Intakes of MUFAs and PUFAs were not significantly associated with T2D in the full multivariate analysis. Except for an interaction between n 3 PUFA intake and sex (P =0.046),wedid not detect any significant interactions between fat intakes and sex. Men in the highest intake quintile of n 3 PUFAs tended to be at decreased risk (HR: 0.87; 95% CI: 0.74, 1.02; P = 0.08), whereas

5 FOOD SOURCES OF FAT AND INCIDENT TYPE 2 DIABETES 1069 TABLE 2 Baseline characteristics in quintiles of energy-adjusted dietary intakes of fat and food sources of fat in individuals without prevalent diabetes from the MDC cohort ( ) 1 Dietary intake quintile Leisure-time physical (median intake/d) Age, y BMI, kg/m 2 activity score Alcohol intake, 2 g/d Sex, F, % Smoking, ex/current, % Education,.10 y, % Dietary change in the past, % n 26,930 26,894 26,754 25,286 26,930 26,920 26,865 26,893 Fat (E%) Quintile 1 (31) 58.5 (58.3, 58.7) (25.7, 25.9) 9000 (8800, 9200) 10 (10, 11) Quintile 5 (46) 58.1 (57.9, 58.3) 25.4 (25.3, 25.5) 7400 (7300, 7600) 15 (14, 15) P-trend,0.001,0.001,0.001, ,0.001,0.001,0.001 Dairy products, low-fat (portions) Quintile 1 (0.1) 57.7 (57.5, 57.9) 25.3 (25.2, 25.4) 7800 (7600, 8000) 14 (13, 14) Quintile 5 (4) 58.5 (58.3, 58.7) 26.3 (26.2, 26.4) 8600 (8400, 8700) 11 (10, 11) P-trend,0.001,0.001,0.001,0.001,0.001, ,0.001 Dairy products, high-fat (portions) Quintile 1 (0.9) 59.2 (59.0, 59.4) 26.1 (26.0, 26.2) 8200 (8100, 8400) 11 (10, 11) Quintile 5 (8.3) 57.1 (56.9, 57.3) 25.3 (25.2, 25.4) 8000 (7800, 8200) 14 (13, 14) P-trend,0.001, ,0.001,0.001,0.001,0.001,0.001 Margarine (g) Quintile 1 (5) 58.1 (57.9, 58.3) 25.4 (25.3, 25.5) 8200 (8000, 8500) 13 (13, 14) Quintile 5 (59) 58.9 (58.7, 59.1) 25.8 (25.7, 25.9) 7600 (7400, 7800) 11 (11, 11) P-trend,0.001,0.001,0.001,0.001, , Eggs (g) Quintile 1 (4) 57.5 (57.3, 57.7) 25.4 (25.3, 25.5) 8100 (7900, 8300) 11 (11, 12) Quintile 5 (45) 58.5 (58.3, 58.7) 26.2 (26.1, 26.3) 8100 (7900, 8300) 14 (14, 14) P-trend,0.001, ,0.001,0.001, Meat and meat products (g) Quintile 1 (55) 58.9 (58.7, 59.1) 25.9 (24.9, 25.0) 8900 (8700, 9100) 11 (11, 11) Quintile 5 (163) 56.7 (56.5, 56.9) 26.4 (26.2, 26.5) 7500 (7300, 7700) 15 (14, 15) P-trend,0.001,0.001,0.001,0.001,0.001,0.001,0.001,0.001 Fish, high-fat (g) 0 4 (0) 56.7 (56.6, 57.0) 25.8 (25.7, 25.8) 7800 (7600, 8000) 10 (10, 11) (46) 59.9 (59.7, 60.1) 25.9 (25.8, 25.9) 8200 (8000, 8400) 15 (14, 15) P-trend, ,0.001, , ,0.001 Pastry and biscuits (g) Quintile 1 (6) 56.0 (55.8, 56.2) 25.8 (25.7, 25.9) 8100 (7900, 8300) 15 (15,16) Quintile 5 (72) 61.4 (61.1, 61.6) 25.6 (25.5, 25.7) 8100 (7900, 8300) 10 (10, 11) P-trend, ,0.001,0.001,0.001,0.001,0.001 Chocolate (g) Quintile 1 (0) 58.4 (58.2, 58.6) 25.9 (25.8, 26.0) 8000 (7800, 8200) 13 (13, 13) Quintile 5 (16) 58.3 (58.0, 58.5) 25.6 (25.5, 25.8) 8000 (7800, 8100) 13 (12, 13) P-trend ,0.001, , A general linear model was used for continuous variables and adjusted for age, sex, diet-method version, and season. The chi-square test was used for categorical variables. Quintiles for dietary intakes were adjusted for energy by using the residual method. P-trend values were calculated across quintiles for continuous variables. In addition, P values were calculated for the comparison of percentages in highest and lowest quintiles for categorical variables. MDC, Malmö Diet and Cancer; E%, percentage of energy. 2 In subjects who reported that they consumed alcohol during the year before baseline examinations. 3 Mean; 95% CI in parentheses (all such values). 4 Zero consumers; higher categories are quartiles in consumers.

6 1070 ERICSON ET AL. TABLE 3 HRs (95% CIs) of incident T2D associated with intakes of total fat and different fatty acids in the MDC cohort 1 Nutrient quintile (median intake) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex Fat (E%) (31) 590/76, (35) 598/75, (0.92, 1.16) 1.02 (0.91, 1.14) 1.00 (0.88, 1.12) 3 (38) 565/75, (0.87, 1.10) 0.97(0.86, 1.09) 0.95 (0.85, 1.07) 4 (41) 550/75, (0.86, 1.09) 0.93 (0.83, 1.05) 0.93 (0.83, 1.05) 5 (46) 557/74, (0.88, 1.11) 0.93 (0.82, 1.04) 0.96 (0.85, 1.08) P-trend Saturated fat (E%) (12) 626/76, (14) 663/75, (1.00, 1.24) 1.11 (0.99, 1.24) 1.07 (0.96, 1.19) 3 (16) 542/76, (0.82, 1.04) 0.92 (0.82, 1.03) 0.91 (0.81, 1.02) 4 (18) 534/75, (0.83, 1.05) 0.91 (0.81, 1.03) 0.93 (0.82, 1.04) 5 (22) 495/74, (0.79, 1.00) 0.85 (0.75, 0.96) 0.91 (0.81, 1.02) P-trend 0.002, Fatty acids 4:0 10:0 (E%) (0.7) 731/75, (1.0) 628/76, (0.81, 1.00) 0.93 (0.83, 1.03) 0.92 (0.83, 1.03) 3 (1.3) 542/76, (0.73, 0.91) 0.86 (0.76, 0.96) 0.88 (0.78, 0.98) 4 (1.7) 493/75, (0.68, 0.85) 0.79 (0.71, 0.89) 0.84 (0.75, 0.95) 5 (2.6) 466/74, (0.64, 0.82) 0.74 (0.66, 0.83) 0.83 (0.74, 0.93) P-trend,0.001,0.001,0.001 Lauric acid (12:0) (E%) (0.6) 650/75, (0.8) 608/75, (0.86, 1.08) 0.95 (0.84, 1.06) 0.98 (0.87, 1.09) 3 (1.0) 557/75, (0.78, 0.98) 0.87 (0.78, 0.97) 0.92 (0.82, 1.03) 4 (1.1) 560/75, (0.80, 1.00) 0.88 (0.78, 0.98) 0.92 (0.82, 1.03) 5 (1.4) 485/75, (0.71, 0.90) 0.76 (0.67, 0.86) 0.84 (0.75, 0.95) P-trend,0.001, Myristic acid (14:0) (E%) (1.1) 701/75, (1.4) 644/75, (0.86, 1.07) 0.98 (0.88, 1.09) 0.98 (0.88, 1.08) 3 (1.7) 540/75, (0.75, 0.94) 0.86 (0.77, 0.96) 0.86 (0.76, 0.96) 4 (2.0) 501/75, (0.70, 0.89) 0.81 (0.72, 0.91) 0.87 (0.77, 0.98) 5 (2.7) 474/74, (0.68, 0.86) 0.76 (0.68, 0.86) 0.83 (0.74, 0.94) P-trend,0.001,0.001,0.001 Palmitic acid (16:0) (E%) (6) 614/76, (7) 619/75, (0.94, 1.18) 1.06 (0.95, 1.18) 1.03 (0.92, 1.15) 3 (8) 556/75, (0.86, 1.09) 0.96 (0.85, 1.08) 0.93 (0.82, 1.04) 4 (9) 559/75, (0.89, 1.12) 0.97 (0.87, 1.09) 0.97 (0.87, 1.09) 5 (10) 512/74, (0.82, 1.03) 0.87 (0.78, 0.98) 0.92 (0.81, 1.03) P-trend Stearic acid (18:0) (E%) (2.7) 593/76, (3.3) 556/76, (0.85, 1.07) 0.94 (0.84, 1.06) 0.93 (0.83, 1.05) 3 (3.6) 580/75, (0.90, 1.14) 0.99 (0.88, 1.11) 0.96 (0.86, 1.08) 4 (4.0) 564/75, (0.89, 1.12) 0.95 (0.85, 1.07) 0.94 (0.84, 1.06) 5 (4.5) 567/74, (0.90, 1.14) 0.92 (0.82, 1.04) 0.94 (0.83, 1.05) P-trend MUFAs (E%) (11) 545/76, (12) 561/76, (0.92, 1.17) 1.03 (0.91, 1.16) 0.99 (0.88, 1.12) 3 (13) 555/75, (0.91, 1.16) 1.01 (0.89, 1.13) 0.98 (0.87, 1.10) 4 (14) 595/75, (0.98, 1.23) 1.05 (0.94, 1.18) 1.03 (0.91, 1.16) 5 (16) 604/74, (0.98, 1.24) 1.02 (0.91, 1.15) 1.01 (0.89, 1.13) P-trend PUFAs (E%) (4) 496/74, (5) 570/74, (1.00, 1.28) 1.12 (0.99, 1.26) 1.08 (0.96, 1.22) 3 (6) 573/76, (0.97, 1.24) 1.09 (0.96, 1.23) 1.04 (0.92, 1.17) (Continued)

7 FOOD SOURCES OF FAT AND INCIDENT TYPE 2 DIABETES 1071 TABLE 3 (Continued) Nutrient quintile (median intake) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex 4 (7) 600/75, (1.01, 1.29) 1.13 (1.00, 1.28) 1.08 (0.96, 1.22) 5 (8) 621/75, (1.04, 1.32) 1.13 (1.00, 1.28) 1.07 (0.95, 1.20) P-trend Total n 3 PUFAs (E%) (0.7) 570/75, (0.8) 533/76, (0.81, 1.03) 0.92 (0.82, 1.03) 0.90 (0.80, 1.02) 3 (0.9) 550/76, (0.82, 1.04) 0.92 (0.81, 1.03) 0.91 (0.81, 1.02) 4 (1.1) 575/75, (0.85, 1.07) 0.95 (0.84, 1.07) 0.93 (0.83, 1.05) 5 (1.4) 632/74, (0.91, 1.15) 1.03 (0.92, 1.16) 1.00 (0.89, 1.12) P-trend ALA (E%) (0.5) 606/75, (0.6) 548/76, (0.79, 1.00) 0.88 (0.78, 0.99) 0.85 (0.76, 0.95) 3 (0.7) 581/75, (0.85, 1.07) 0.93 (0.83, 1.04) 0.94 (0.84, 1.05) 4 (0.8) 542/75, (0.79, 0.99) 0.86 (0.76, 0.96) 0.85 (0.76, 0.95) 5 (1.0) 583/74, (0.86, 1.08) 0.92 (0.82, 1.03) 0.94 (0.83, 1.05) P-trend Long-chain n 3 PUFAs (E%) (0.07) 519/76, (0.12) 565/75, (0.93, 1.18) 1.06 (0.94, 1.19) 1.01 (0.90, 1.14) 3 (0.19) 577/75, (0.93, 1.18) 1.07 (0.95, 1.20) 0.99 (0.88, 1.12) 4 (0.29) 550/75, (0.85, 1.09) 1.01 (0.90, 1.14) 0.92 (0.81, 1.04) 5 (0.52) 649/74, (0.99, 1.26) 1.18 (1.05, 1.33) 1.07 (0.94, 1.20) P-trend Total n 6 PUFAs (E%) (3.2) 488/74, (4.0) 582/74, (1.05, 1.33) 1.17 (1.04, 1.32) 1.13 (1.00, 1.28) 3 (4.7) 577/75, (1.01, 1.28) 1.13 (1.00, 1.28) 1.07 (0.95, 1.21) 4 (5.5) 600/76, (1.04, 1.32) 1.16 (1.03, 1.31) 1.11 (0.98, 1.25) 5 (6.8) 613/75, (1.05, 1.34) 1.15 (1.02, 1.29) 1.09 (0.97, 1.23) P-trend Ratio n 3:n (0.14) 595/76, (0.17) 565/76, (0.84, 1.06) 0.95 (0.85, 1.07) 0.90 (0.80, 1.01) 3 (0.19) 587/75, (0.89, 1.12) 1.02 (0.91, 1.14) 1.00 (0.90, 1.13) 4 (0.23) 569/75, (0.86, 1.08) 0.99 (0.88, 1.12) 0.98 (0.87, 1.10) 5 (0.30) 544/73, (0.81, 1.03) 0.94 (0.84, 1.06) 0.91 (0.81, 1.03) P-trend Ratio ALA:LA (0.11) 631/77, (0.14) 576/76, (0.83, 1.04) 0.93 (0.93, 1.04) 0.91 (0.81, 1.02) 3 (0.15) 577/75, (0.84, 1.06) 0.94 (0.84, 1.06) 0.93 (0.83, 1.04) 4 (0.17) 595/74, (0.91, 1.14) 1.03 (0.92, 1.16) 1.03 (0.93, 1.16) 5 (0.21) 481/74, (0.72, 0.92) 0.80 (0.71, 0.91) 0.86 (0.76, 0.97) P-trend HRs were calculated by using a Cox proportional hazards model. ALA, a-linolenic acid; E%, percentage of energy; LA, linoleic acid; MDC, Malmö Diet and Cancer; T2D, type 2 diabetes. 2 Adjusted for age (continuous), sex (when applicable), method version (categorical), season (categorical), and total energy intake (continuous). 3 Adjusted as for the basic model and for the following categorical variables: leisure-time physical activity, smoking, alcohol intake, and education. 4 Adjusted as for the basic model and for the following categorical variables: leisure-time physical activity, smoking, alcohol intake, education, and BMI (continuous). the findings for women with highest intakes were rather in the opposite direction (HR: 1.14; 95% CI: 0.97, 1.35; P = 0.12). However, no significant trends across quintiles were seen in men or women (P-trend $ 0.12). Food sources of fat and incidence of T2D We did not observe any significant association between total intake of dairy products (i.e., high fat and low fat) but a lower incidence of T2D with higher total intake of high-fat dairy products (P-trend, 0.001) (Table 4), and similar protective associations were seen for both fermented (P-trend = 0.01) and nonfermented (P-trend, 0.001) high-fat dairy products. Decreased risk of T2D was seen with higher intakes of cream (Ptrend = 0.001), butter (P-trend = 0.001), and high-fat fermented milk (P-trend = 0.007) as well as higher intake of cheese in women (P-trend = 0.02, P-interaction with sex = 0.01).

8 1072 ERICSON ET AL. TABLE 4 HRs (95% CIs) of incident T2D associated with intake of food sources of fat in the MDC cohort 1 Nutrient quintile (median intake/d) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex Dairy products, total (portions) (3) 641/ (4) 601/ (0.90, 1.12) 1.04 (0.93, 1.16) 1.00 (0.89, 1.12) 3 (5) 596/ (0.94, 1.17) 1.10 (0.97, 1.23) 1.04 (0.93, 1.16) 4 (7) 539/ (0.87, 1.09) 1.03 (0.92, 1.16) 0.99 (0.88, 1.11) 5 (10) 483/ (0.77, 0.98) 0.90 (0.79, 1.01) 0.90 (0.80, 1.02) P-trend Dairy products, low-fat (portions) (0.1) 541/ (0.7) 536/ (0.89, 1.13) 1.04 (0.92, 1.17) 1.00 (0.89, 1.13) 3 (1.5) 494/ (0.92, 1.04) 0.97 (0.86, 1.10) 0.93 (0.82, 1.05) 4 (2.4) 606/ (1.02, 1.28) 1.19 (1.06, 1.34) 1.08 (0.96, 1.22) 5 (4.0) 683/ (1.15, 1.45) 1.34 (1.20, 1.51) 1.14 (1.01, 1.28) P-trend,0.001, Dairy products, low-fat, nonfermented 0.11 (portions) 1 (0.02) 557/ (0.3) 508/ (0.81, 1.03) 0.94 (0.84, 1.06) 0.93 (0.82, 1.05) 3 (0.9) 500/ (0.79, 1.01) 0.93 (0.82, 1.05) 0.92 (0.81, 1.04) 4 (1.6) 579/ (0.92, 1.17) 1.07 (0.95, 1.20) 0.98 (0.87, 1.10) 5 (3.0) 716/ (1.18, 1.48) 1.31 (1.17, 1.46) 1.12 (1.00, 1.25) P-trend,0.001, Dairy products, low-fat, fermented 0.78 (portions) 0 5 (0) 1249/10, (0.2) 398/ (0.96, 1.21) 1.13 (1.00, 1.26) 1.07 (0.96, 1.20) 2 (0.5) 403/ (0.94, 1.19) 1.13 (1.01, 1.27) 1.09 (0.98, 1.22) 3 (1.1) 385/ (0.87, 1.10) 1.05 (0.94, 1.18) 0.99 (0.88, 1.12) 4 (2.4) 425/ (0.94, 1.18) 1.15 (1.03, 1.29) 1.06 (0.95, 1.18) P-trend Dairy products, high-fat (portions) (0.9) 739/ (2.3) 611/ (0.77, 0.95) 0.88 (0.79, 0.98) 0.88 (0.79, 0.98) 3 (3.3) 552/ (0.74, 0.92) 0.86 (0.77, 0.96) 0.89 (0.79, 0.99) 4 (5.0) 514/ (0.70, 0.88) 0.82 (0.73, 0.92) 0.89 (0.79, 1.00) 5 (8.3) 444/ (0.61, 0.77) 0.69 (0.62, 0.78) 0.77 (0.68, 0.87) P-trend,0.001,0.001,0.001 Dairy products, high-fat, nonfermented 0.60 (portions) 1 (0.1) 716/ (0.4) 622/ (0.82, 1.02) 0.95 (0.85, 1.06) 0.99 (0.89, 1.10) 3 (0.9) 528/ (0.69, 0.87) 0.82 (0.73, 0.91) 0.88 (0.78, 0.98) 4 (2.4) 534/ (0.70, 0.88) 0.80 (0.72, 0.90) 0.88 (0.79, 0.99) 5 (5.8) 460/ (0.63, 0.79) 0.70 (0.62, 0.79) 0.80 (0.71, 0.90) P-trend,0.001,0.001,0.001 Dairy products, high-fat, fermented 0.01 (portions) 1 (0.3) 709/ (1.1) 634/ (0.82, 1.02) 0.94 (0.85, 1.05) 0.98 (0.88, 1.09) 3 (1.7) 535/ (0.70, 0.88) 0.82 (0.74, 0.92) 0.85 (0.76, 0.95) 4 (2.4) 516/ (0.70, 0.88) 0.85 (0.75, 0.95) 0.88 (0.79, 0.99) 5 (3.6) 466/ (0.68, 0.86) 0.83 (074, 0.94) 0.89 (0.79, 1.01) P-trend,0.001, Milk, total (g) (71) 570/ (221) 513/ (0.81, 1.02) 0.93 (0.83, 1.05) 0.92 (0.82, 1.04) 3 (331) 535/ (0.86, 1.08) 0.99 (0.88, 1.12) 0.95 (0.84, 1.07) 4 (450) 586/ (0.98, 1.24) 1.12 (1.00, 1.26) 1.07 (0.95, 1.20) 5 (633) 656/ (1.15, 1.44) 1.27 (1.14, 1.43) 1.09 (0.98, 1.23) P-trend,0.001, (Continued)

9 FOOD SOURCES OF FAT AND INCIDENT TYPE 2 DIABETES 1073 TABLE 4 (Continued) Nutrient quintile (median intake/d) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex Milk, low-fat (g) (0) 666/ (57) 479/ (0.86, 1.08) 1.01 (0.90, 1.14) 0.99 (0.88, 1.12) 2 (182) 463/ (0.84, 1.06) 1.00 (0.88, 1.12) 0.93 (0.83, 1.05) 3 (322) 570/ (1.01, 1.27) 1.20 (1.07, 1.34) 1.10 (0.98, 1.23) 4 (546) 682/ (1.20, 1.49) 1.37 (1.24, 1.54) 1.15 (1.04, 1.29) P-trend,0.001, Milk, low-fat, nonfermented (g) (0) 833/ (43) 440/ (0.87, 1.10) 1.03 (0.92, 1.16) 1.00 (0.90, 1.13) 2 (157) 448/ (0.92, 1.16) 1.08 (0.96, 1.22) 1.02 (0.91, 1.14) 3 (289) 497/ (1.01, 1.26) 1.17 (1.04, 1.30) 1.05 (0.94, 1.18) 4 (503) 642/ (1.33, 1.64) 1.46 (1.32, 1.62) 1.21 (1.09, 1.34) P-trend,0.001, Milk, low-fat, fermented (g) (0) 1938/16, (29) 224/ (0.90, 1.20) 1.08 (0.94, 1.25) 1.04 (0.90, 1.20) 2 (71) 239/ (0.93, 1.22) 1.12 (0.98, 1.28) 1.04 (0.91, 1.20) 3 (140) 216/ (0.81, 1.08) 1.00 (0.87, 1.15) 0.96 (0.83, 1.11) 4 (250) 243/ (0.82, 1.08) 1.02 (0.89, 1.17) 1.04 (0.91, 1.19) P-trend Milk, high-fat (g) (6) 647/ (29) 627/ (0.90, 1.12) 1.01 (0.90, 1.13) 1.00 (0.90, 1.12) 3 (68) 568/ (0.83, 1.04) 0.93 (0.83, 1.04) 0.93 (0.84, 1.05) 4 (161) 513/ (0.74, 0.94) 0.84 (0.75, 0.95) 0.89 (0.79, 1.00) 5 (330) 505/ (0.76, 0.96) 0.84 (0.75, 0.94) 0.91 (0.81, 1.03) P-trend,0.001, Milk, high-fat, nonfermented (g) (3) 601/ (16) 577/ (0.86, 1.08) 0.98 (0.87, 1.10) 0.95 (0.84, 1.06) 3 (33) 595/ (0.92, 1.16) 1.04 (0.93, 1.17) 1.04 (0.93, 1.17) 4 (63) 555/ (0.87, 1.10) 0.97 (0.86, 1.09) 0.98 (0.87, 1.10) 5 (271) 532/ (0.83, 1.06) 0.89 (0.79, 1.00) 0.94 (0.84, 1.06) P-trend Milk, high-fat, fermented (g) (0) 1812/13, (25) 287/ (0.83, 1.07) 0.97 (0.86, 1.10) 0.99 (0.87, 1.13) 2 (61) 283/ (0.81, 1.04) 0.95 (0.84, 1.08) 0.97 (0.86, 1.10) 3 (107) 270/ (0.79, 0.99) 0.91 (0.80, 1.03) 0.94 (0.83, 1.07) 4 (179) 208/ (0.59, 0.79) 0.73 (0.63, 0.84) 0.80 (0.69, 0.92) P-trend,0.001, Milk, nonfermented (g) (24) 509/ (119) 532/ (0.93, 1.19) 1.06 (0.94, 1.20) 1.05 (0.93, 1.19) 3 (244) 540/ (0.95, 1.21) 1.08 (0.95, 1.22) 1.05 (0.92, 1.18) 4 (357) 579/ (1.05, 1.34) 1.16 (1.03, 1.31) 1.07 (0.95, 1.21) 5 (515) 700/ (1.38, 1.74) 1.47 (1.31, 1.65) 1.24 (1.10, 1.39) P-trend,0.001, Milk, fermented (g) (0) 1132/ (36) 469/ (0.87, 1.08) 1.03 (0.92, 1.14) 1.04 (0.94, 1.16) 3 (75) 440/ (0.80, 1.00) 0.95 (0.85, 1.06) 0.93 (0.83, 1.04) 4 (143) 431/ (0.77, 0.96) 0.94 (0.84, 1.05) 0.97 (0.86, 1.08) 5 (250) 388/ (0.68, 0.86) 0.85 (0.76, 0.96) 0.91 (0.81, 1.02) P-trend, Cheese (g) (11) 666/ (27) 589/ (0.80, 0.99) 0.93 (0.83, 1.04) 0.94 (0.84, 1.05) 3 (40) 571/ (0.79, 0.99) 0.94 (0.84, 1.06) 0.92 (0.82, 1.03) (Continued)

10 1074 ERICSON ET AL. TABLE 4 (Continued) Nutrient quintile (median intake/d) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex 4 (53) 532/ (0.77, 0.96) 0.94 (0.83, 1.05) 0.93 (0.83, 1.05) 5 (82) 502/ (0.77, 0.98) 0.96 (0.85, 1.07) 0.92 (0.81, 1.04) P-trend Cream (g) (0.3) 671/ (5) 623/ (0.85, 1.06) 0.99 (0.88, 1.10) 1.01 (0.90, 1.13) 3 (11) 590/ (0.82, 1.02) 0.96 (0.86, 1.07) 1.00 (0.89, 1.12) 4 (18) 501/ (0.68, 0.86) 0.81 (0.72, 0.91) 0.88 (0.78, 0.99) 5 (32) 475/ (0.63, 0.80) 0.75 (0.67, 0.85) 0.85 (0.76, 0.96) P-trend,0.001, Ice cream (g) (0) 632/ (3) 539/ (0.76, 0.96) 0.89 (0.79, 1.00) 0.89 (0.79, 1.00) 3 (6) 575/ (0.84, 1.05) 0.97 (0.86, 1.08) 0.93 (0.83, 1.04) 4 (13) 530/ (0.77, 0.96) 0.91 (0.81, 1.02) 0.87 (0.77, 0.98) 5 (29) 584/ (0.84, 1.05) 1.00 (0.89, 1.12) 0.93 (0.83, 1.04) P-trend Butter/butter blends (g) 1 (0) 1781/13, (3) 281/ (0.73, 0.94) 0.88 (0.77, 1.00) 0.89 (0.78, 1.01) 3 (16) 290/ (0.77, 0.99) 0.90 (0.79, 1.02) 0.94 (0.83, 1.06) 4 (28) 255/ (0.67, 0.87) 0.77 (0.67, 0.88) 0.83 (0.73, 0.95) 5 (33) 253/ (0.72, 0.93) 0.79 (0.69, 0.91) 0.86 (0.75, 0.98) P-trend,0.001, Margarine total (g) 1 (5) 541/ (13) 499/ (0.84, 1.07) 0.97 (0.86, 1.10) 0.94 (0.83, 1.07) 3 (25) 578/ (0.95, 1.21) 1.10 (0.97, 1.23) 1.04 (0.93, 1.18) 4 (38) 601/ (0.96, 1.22) 1.08 (0.96, 1.21) 1.03 (0.91, 1.15) 5 (59) 641/ (0.97, 1.22) 1.04 (0.93, 1.17) 0.99 (0.88, 1.11) P-trend Margarine, low-fat (g) (0) 1102/ (8) 382/ (0.93, 1.18) 1.09 (0.96, 1.23) 1.01 (0.89, 1.14) 2 (19) 421/ (0.97, 1.22) 1.12 (1.00, 1.26) 1.08 (0.96, 1.21) 3 (30) 440/ (0.96, 1.20) 1.08 (0.96, 1.20) 1.02 (0.92, 1.14) 4 (52) 515/ (1.02, 1.26) 1.09 (0.98, 1.21) 1.02 (0.91, 1.13) P-trend Margarine, high-fat (g) (3) 617/ (6) 565/ (0.82, 1.03) 0.92 (0.82, 1.03) 0.91 (0.81, 1.02) 3 (8) 557/ (0.81, 1.02) 0.90 (0.80, 1.01) 0.88 (0.78, 0.98) 4 (12) 543/ (0.81, 1.02) 0.92 (0.82, 1.03) 0.94 (0.84, 1.06) 5 (26) 578/ (0.85, 1.06) 0.94 (0.84, 1.05) 0.97 (0.86, 1.08) P-trend Oils and dressing (g) (0) 566/ (1) 632/ (1.03, 1.30) 1.21 (1.08, 1.35) 1.16 (1.04, 1.31) 3 (4) 560/ (0.92, 1.17) 1.11 (0.98, 1.25) 1.06 (0.94, 1.20) 4 (7) 561/ (0.93, 1.18) 1.13 (1.00, 1.27) 1.09 (0.97, 1.22) 5 (14) 541/ (0.92, 1.17) 1.14 (1.01, 1.28) 1.09 (0.96, 1.23) P-trend Eggs (g) (4) 528/ (12) 565/ (0.96, 1.21) 1.08 (0.96, 1.21) 1.07 (0.95, 1.20) 3 (19) 538/ (0.90, 1.15) 1.02 (0.90, 1.15) 0.99 (0.88, 1.12) 4 (28) 592/ (1.03, 1.30) 1.16 (1.03, 1.31) 1.10 (0.98, 1.24) 5 (45) 637/ (1.13, 1.42) 1.27 (1.13, 1.42) 1.14 (1.02, 1.28) P-trend,0.001, (Continued)

11 FOOD SOURCES OF FAT AND INCIDENT TYPE 2 DIABETES 1075 TABLE 4 (Continued) Nutrient quintile (median intake/d) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex Meat and meat products, total (g) (55) 394/ (84) 522/ (1.12, 1.46) 1.24 (1.09, 1.41) 1.13 (1.00, 1.29) 3 (102) 581/ (1.24, 1.60) 1.36 (1.19, 1.54) 1.20 (1.06, 1.37) 4 (123) 499/ (1.27, 1.64) 1.36 (1.19, 1.55) 1.15 (1.01, 1.31) 5 (163) 764/ (1.61, 2.06) 1.68 (1.48, 1.91) 1.36 (1.20, 1.55) P-trend,0.001,0.001,0.001 Meat and meat products, low-fat (g) 1 (9) 504/ (24) 553/ (0.94, 1.19) 1.06 (0.94, 1.20) 1.05 (0.93, 1.19) 3 (35) 573/ (1.02, 1.29) 1.16 (1.03, 1.31) 1.14 (1.01, 1.29) 4 (49) 592/ (1.06, 1.35) 1.21 (1.08, 1.37) 1.17 (1.04, 1.32) 5 (75) 658/ (1.19, 1.50) 1.34 (1.19, 1.51) 1.25 (1.11, 1.41) P-trend,0.001,0.001,0.001 Meat, red, low-fat, nonprocessed (g) (1) 509/ (15) 545/ (0.96, 1.22) 1.09 (0.99, 1.22) 1.11 (0.99, 1.26) 3 (24) 542/ (0.96, 1.22) 1.08 (0.96, 1.22) 1.07 (0.95, 1.21) 4 (36) 602/ (1.05, 1.33) 1.19 (1.06, 1.34) 1.17 (1.04, 1.32) 5 (57) 662/ (1.16, 1.46) 1.28 (1.15, 1.46) 1.24 (1.10, 1.39) P-trend,0.001,0.001,0.001 Meat products, low-fat, processed (g) (0) 628/ (3) 582/ (1.04, 1.31) 1.20 (1.07, 1.34) 1.16 (1.04, 1.30) 2 (8) 520/ (0.94, 1.18) 1.07 (0.96, 1.21) 1.07 (0.95, 1.20) 3 (14) 542/ (0.98, 1.23) 1.12 (1.00, 1.26) 1.09 (0.97, 1.22) 4 (27) 588/ (1.08, 1.35) 1.23 (1.10, 1.37) 1.16 (1.04, 1.30) P-trend Meat and meat products, high-fat (g) 1 (16) 444/ (36) 531/ (1.01, 1.30) 1.10 (0.97, 1.25) 1.04 (0.92, 1.18) 3 (51) 566/ (1.07, 1.37) 1.13 (1.00, 1.28) 1.06 (0.94, 1.20) 4 (68) 655/ (1.25, 1.60) 1.30 (1.15, 1.47) 1.17 (1.04, 1.32) 5 (93) 664/ (1.28, 1.63) 1.27 (1.12, 1.44) 1.09 (0.97, 1.24) P-trend,0.001, Meat, red, high-fat, nonprocessed (g) (4) 504/ (16) 527/ (0.90, 1.15) 1.00 (0.88, 1.13) 0.96 (0.85, 1.08) 3 (25) 587/ (1.01, 1.28) 1.10 (0.97, 1.24) 1.04 (0.93, 1.18) 4 (36) 610/ (1.04, 1.32) 1.12 (0.99, 1.26) 1.04 (0.92, 1.17) 5 (55) 632/ (1.08, 1.37) 1.13 (1.00, 1.27) 1.01 (0.90, 1.14) P-trend, Meat products, high-fat, processed (g) (2) 433/ (16) 552/ (1.05, 1.36) 1.16 (1.02, 1.32) 1.12 (0.99, 1.28) 3 (29) 578/ (1.11, 1.42) 1.20 (1.16, 1.36) 1.18 (1.04, 1.33) 4 (38) 667/ (1.30, 1.66) 1.37 (1.21, 1.55) 1.29 (1.14, 1.46) 5 (50) 630/ (1.26, 1.62) 1.29 (1.14, 1.46) 1.15 (1.01, 1.30) P-trend,0.001, Poultry (g) (0) 114/ (1) 412/ (0.92, 1.16) 1.04 (0.93, 1.17) 1.02 (0.91, 1.14) 3 (13) 396/ (0.88, 1.11) 1.02 (0.91, 1.14) 1.00 (0.90, 1.13) 4 (22) 458/ (1.00, 1.24) 1.15 (1.03, 1.28) 1.10 (0.99, 1.23) 5 (36) 480/ (1.00, 1.24) 1.14 (1.03, 1.28) 1.06 (0.96, 1.18) P-trend Fish and shellfish, low-fat (g) (0) 620/ (9) 579/ (0.84, 1.05) 0.98 (0.87, 1.10) 0.95 (0.85, 1.07) 3 (21) 551/ (0.81, 1.02) 0.95 (0.84, 1.06) 0.95 (0.84, 1.06) (Continued)

12 1076 ERICSON ET AL. TABLE 4 (Continued) Nutrient quintile (median intake/d) n cases/ Multivariate model person-years Basic model 2 without BMI 3 Full multivariate model with BMI 4 P-interaction with sex 4 (33) 553/ (0.80, 1.00) 0.94 (0.84, 1.06) 0.95 (0.85, 1.06) 5 (55) 557/ (0.80, 1.00) 0.98 (0.87, 1.10) 0.97 (0.86, 1.09) P-trend Fish, high-fat (g) (0) 691/ (3) 549/ (0.90, 1.14) 1.05 (0.93, 1.17) 1.07 (0.95, 1.20) 2 (9) 534/ (0.87, 1.10) 1.03 (0.92, 1.15) 1.02 (0.91, 1.14) 3 (23) 507/ (0.80, 1.02) 0.96 (0.86, 1.08) 0.93 (0.82, 1.04) 4 (46) 579/ (0.93, 1.16) 1.11 (0.99, 1.25) 1.05 (0.94, 1.18) P-trend Pastry and biscuits (g) (6) 660/ (20) 573/ (0.77, 0.97) 0.90 (0.80, 1.00) 0.92 (0.82, 1.03) 3 (33) 542/ (0.72, 0.91) 0.84 (0.75, 0.94) 0.87 (0.78, 0.98) 4 (48) 554/ (0.74, 0.93) 0.86 (0.77, 0.97) 0.90 (0.80, 1.01) 5 (72) 531/ (0.71, 0.90) 0.82 (0.72, 0.92) 0.89 (0.79, 1.01) P-trend, Chocolate (g) (0) 656/ (2) 564/ (0.74, 0.93) 0.87 (0.78, 0.97) 0.88 (0.79, 0.99) 3 (4) 561/ (0.76, 0.96) 0.91 (0.81, 1.02) 0.94 (0.84, 1.06) 4 (8) 542/ (0.77, 0.97) 0.92 (0.82, 1.03) 0.93 (0.83, 1.05) 5 (16) 537/ (0.80, 1.01) 0.94 (0.84, 1.06) 1.01 (0.90, 1.14) P-trend HRs were calculated by using a Cox proportional hazards model. MDC, Malmö Diet and Cancer; T2D, type 2 diabetes. 2 Adjusted for age (continuous), sex (when applicable), method version (categorical), season (categorical), and total energy intake (continuous). 3 Adjusted as for the basic model and for the following categorical variables: leisure-time physical activity, smoking, alcohol intake, and education. 4 Adjusted as for the basic model and for the following categorical variables: leisure-time physical activity, smoking, alcohol intake, education, and BMI (continuous). 5 Zero consumers; higher categories are quartiles in consumers. Although high intake of total low-fat dairy products was associated with increased risk (P-trend = 0.01), this association was NS when intakes of low- and high-fat dairy products were mutually adjusted (P-trend = 0.18), whereas the protective association with high-fat dairy products remained significant (P-trend = 0.003). Furthermore, the association with low-fat dairy products also disappeared after adjustment for protein intake (P-trend = 0.37); similar observations were made for lowfat nonfermented milk. Results regarding high-fat dairy products remained unchanged. High intakes of meats, both low-fat (Ptrend, 0.001) and high-fat (P-trend = 0.04) meat and meat products, were associated with increased risk of T2D. Increased risk seemed mainly driven by intakes of low-fat nonprocessed red meat (P-trend, 0.001) and high-fat processed meat products (P-trend = 0.01). Finally and similarly to what has previously been reported after analyses with shorter follow-up time in the MDC cohort (33), high egg intake was associated with increased risk. All observed associations remained virtually unchanged after additional adjustments for dietary change in the past. A post hoc analysis indicated that intakes of several nondairy foods tended to differ significantly across intake quintiles of cream and high-fat fermented milk; decreased intakes of both sugar-sweetened beverages and fiber-rich bread and cereals were, for example, seen across quintiles (Supplemental Table 2). However, adjustment for dietary intakes (fiber, sucrose, calcium, vitamin D, magnesium, meat, fruit and vegetables, or sugar-sweetened beverages) did not substantially affect any of our observed associations. Except for the interaction between cheese intake and sex [also reflected in the interaction between intake of high-fat fermented dairy products and sex (P = 0.01)], we did not observe any significant interactions between any other examined food intakes and sex. Statistical models without BMI Overall, statistical models without BMI did not substantially change our observations. However, inverse associations between several of the high-fat dairy foods and T2D were somewhat stronger before adjustment for BMI (i.e., for cream, high-fat fermented milk, and butter). In addition, individuals in the highest quintile of high-fat nonfermented milk tended to be at decreased risk before adjustment for BMI (HR: 0.89; CI: 0.79, 1.00). Moreover, high-fat nonprocessed red meat was significantly associated with increased risk of T2D only before adjustment for BMI (P-trend = 0.01). The inclusion of waist circumference in our statistical models did not substantially affect any results. Sensitivity analysis In an analysis excluding individuals who reported less-stable food habits (24% of participants), we did not observe an inverse association between total intake of SFAs and T2D (P-trend = 0.69 in the full multivariate model including BMI). However,

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