H ete rogeneity in Associations Between M ac ... - Diabetes Care

Clinical Care/Education/Nutrition O R I G I N A L

A R T I C L E

Heterogeneity in Associations Between Macronutrient Intake and Lipoprotein Profile in Individuals With Type 2 Diabetes ELIZABETH J. MAYER-DAVIS, PHD SARAH LEVIN, PHD JULIE A. MARSHALL, PHD

OBJECTIVE — To evaluate associations between macronutrient intake and lipoprotein profile among individuals with type 2 diabetes who participated in the San Luis Valley Diabetes Study (SLVDS) or the Insulin Resistance Atherosclerosis Study (IRAS). RESEARCH DESIGN AND METHODS — Diet was assessed by 24-h recall in the SLVDS (n = 421) and by validated food frequency interview in the IRAS (n = 437). Analyses adjusted for kilocalories, age, sex, and other covariates were conducted separately for the two study groups. For the SLVDS, repeated observations were included in mixed model analyses (865 observations). For the IRAS, standard regression analyses were conducted. Recent weight history and time of diabetes diagnosis were evaluated as possible modifiers of associations between nutrient intake and lipoprotein profile. RESULTS — Higher reported intake of total dietary fat was related to significantly higher levels of LDL cholesterol (P , 0.05) in both studies and in all subgroups. Reported intake of total and saturated fat was associated positively with total cholesterol, although statistical significance was not reached for all subgroups. Higher reported carbohydrate intake was associated with increased triglyceride concentrations (P , 0.01) only among individuals with previously undiagnosed diabetes in the SLVDS (n = 69) and only among individuals who gained weight (.5 lb, n = 87) during the previous year in the IRAS. CONCLUSIONS — Toward the goal of optimizing the lipoprotein profile of individuals with diabetes, these results emphasize the potential importance of reducing fat intake while recognizing that individualized approaches to diet are important to minimize the risk of cardiovascular disease. Diabetes Care 22:1632–1639, 1999

he optimal diet to reduce the risk of cardiovascular disease (CVD) in individuals with type 2 diabetes has been debated recently (1). The marked increase

T

in risk of CVD among such individuals (2–4) makes diet a particularly important issue. Individuals with type 2 diabetes often have elevated triglyceride concentrations

From the Department of Epidemiology and Biostatistics (E.J.M.-D., S.L.), University of South Carolina School of Public Health, Columbia, South Carolina; and the Department of Preventive Medicine and Biometrics (J.A.M.), University of Colorado Health Sciences Center, Denver, Colorado. Address correspondence and reprint requests to Elizabeth J. Mayer-Davis, Department of Epidemiology and Biostatistics, University of South Carolina, School of Public Health, Columbia, SC 29208. E-mail: ejmayerd@ sph.sc.edu. Received for publication 18 December 1998 and accepted in revised form 27 May 1999. Abbreviations: ADA, American Diabetes Association; CHD, coronary heart disease; CVD, cardiovascular disease; FFI, food frequency interview; HHHQ, Health Habits and History Questionnaire; IRAS, Insulin Resistance Atherosclerosis Study; MNT, medical nutrition therapy; NCC, Nutrition Coordinating Center; NCI, National Cancer Institute; NHANES, National Health and Nutrition Examination Survey; OGTT, oral glucose tolerance test; SLVDS, San Luis Valley Diabetes Study; USDA, U.S. Department of Agriculture. A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

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and decreased HDL cholesterol levels that contribute to their increased risk of CVD (5). LDL cholesterol is generally not elevated in individuals with diabetes; however, it remains an important predictor of CVD risk in these patients (5,6). Therefore, a major goal of medical nutrition therapy (MNT) is to reduce the risk of CVD through an improved lipoprotein profile. The current American Diabetes Association (ADA) recommendations (7) emphasize the need for individualization of MNT by distinguishing between individuals with and without obesity, with dyslipidemias, and with various other clinical characteristics or nutritional goals. Much of the debate regarding optimal diet stems from clinical studies that have shown the potential of high carbohydrate intake to elevate triglyceride levels. Many of these studies substituted monounsaturated fats for carbohydrates as a means to limit intake of saturated fats while moderating intake of carbohydrates. Garg (8) reported a meta-analysis (10 studies, 133 subjects) of the metabolic effects of high–monounsaturated fat diets under isocaloric conditions for 2–6 weeks. These diets, compared with high-carbohydrate low–saturated fat diets, reduced fasting plasma triglycerides and VLDL cholesterol, modestly increased HDL cholesterol, and did not adversely affect LDL cholesterol. The limited duration and relatively small sample size of these studies may limit their generalizability to free-living individuals with diabetes. Other studies have shown that traditional hypocaloric low-fat high-carbohydrate diets facilitated weight loss and long-term maintenance of weight loss in individuals with type 2 diabetes (9). Wing et al. (10) showed that individuals who lost weight on such a diet experienced significant sustained improvement in various metabolic parameters, including reduced triglycerides after 1 year, and they observed a dose-response relationship between weight loss and reduction in triglyceride concentration. Because most adults with type 2 diabetes are overweight, advising

DIABETES CARE, VOLUME 22, NUMBER 10, OCTOBER 1999

Mayer-Davis, Levin, and Marshall

against low-fat diets, regardless of the fat type, is questionable. Although the studies cited have the advantage of including experimental control subjects, observational epidemiological studies have the advantage of including large numbers of community-dwellings who represent otherwise understudied populations. Wolever et al. (11) reported no association between habitual carbohydrate intake and triglycerides in 354 individuals with diabetes. We evaluated associations between macronutrient intake and lipoprotein profile in individuals with type 2 diabetes who participated in the San Luis Valley Diabetes Study (SLVDS, n = 421) and the Insulin Resistance Atherosclerosis Study (IRAS, n = 437). The SLVDS collected 24-h dietary recall data, and the IRAS estimated usual nutrient intake by a validated food frequency interview (FFI) (12). Analyses were conducted separately for the two studies and considered possible differences in associations between macronutrients and lipoprotein profile according to recent weight history and time of diabetes diagnosis (undiagnosed before study participation versus previously diagnosed). RESEARCH DESIGN AND METHODS — The SLVDS began as a natural history study of type 2 diabetes and its macro- and microvascular sequela among Hispanic and non-Hispanic men and women in rural southern Colorado. There were three phases of the study, and subjects were followed for ,8 years (13). The IRAS is a multicenter study (Oakland, CA; Los Angeles, CA; San Antonio, TX; San Luis Valley, CO) of the relationship of insulin and insulin resistance to CVD and its risk factors among Hispanic, non-Hispanic white, and African-American men and women with normal glucose tolerance, impaired glucose tolerance, or type 2 diabetes (14). Sample selection Individuals included in this study were adults with type 2 diabetes from the SLVDS (1984–1992) or the IRAS (1992–1994). Overall study design has been reported elsewhere (13,14). In both studies, a 75-g oral glucose tolerance test (OGTT) was performed, and World Health Organization criteria (15) were applied to assign diabetes status. Individuals who reported current use of medications for diabetes were classified as having diabetes regardless of OGTT results. Of the 430 individuals classified with type 2 diabetes in the SLVDS, 421 were

used in these analyses. Participants were excluded if they were taking lipid-lowering drugs (n = 2) or if they were missing one of the independent or dependent measures other than LDL cholesterol (n = 7). Subjects with missing LDL cholesterol levels (n = 37) due to the absence of measures for individuals with triglyceride concentrations .400 mg/dl were retained in the analyses of the remaining dependent variables. Of the 421 participants, 140 were seen once, 277 were seen twice, and 152 were seen three times. All measurements were obtained at each visit. The average time between visits was 2.3 years (range 1.0–6.5 years). Of the 537 individuals classified with type 2 diabetes in the IRAS, 437 were included in these analyses. None took insulin in accordance with study protocols. Subjects were excluded if they were taking lipid-lowering drugs (n = 53) or if they were missing one of the independent or dependent measures (n = 47). For both studies, all protocols were approved by the appropriate institutional review boards, and participants gave informed consent. Variable measurement For the SLVDS, nutrient intake was assessed via 24-h dietary recall administered by bilingual interviewers trained and certified by the Nutrition Coordinating Center (NCC) at the University of Minnesota (16,17). The 24-h period was defined as the period up to initiation of the fasting period the evening before the clinic examination. Portion size estimation was facilitated by the interviewer by using two-dimensional food models and three-dimensional aids such as measuring devices (measuring cups and spoons) and appropriately sized drinking glasses. Data collection forms were edited in clinic and then were sent to the NCC for coding and nutrient analysis. Nutrient intake was estimated based on the NCC nutrient database (version 14). Data sources for this comprehensive nutrient composition database include the U.S. Department of Agriculture (USDA) and other government, scientific, and industry sources. The percentage of completeness of the nutrient database was 100% for calories, total fat, and total carbohydrates; more than 99% for saturated fatty acids, oleic acid, and polyunsaturated fatty acids; 81% for glucose; and 72% for fructose (M. Stevens, personal communication). For the IRAS, usual nutrient intake was assessed with a 1-year semiquantitative


114-item FFI modified from the National Cancer Institute (NCI)-Health Habits and History Questionnaire (HHHQ) (18,19) to include regional and ethnic food choices across the four clinical centers. For food, nine response categories were provided that ranged from “never or less than once per month” to “two or more times per day.” For beverages, responses ranged from “never or less than once per month” to “six or more times per day.” Portion size was ascertained as “small, medium, or large, compared with other men/women about your age.” For nutrient intake calculations, the database option for age- and sex-specific portion sizes, based on the National Health and Nutrition Examination Study (NHANES) 24-h recall data, were used. The nutrient database HHHQ-DIETSYS Analyses Software (Version 3.0; NCI, Bethesda, MD) was constructed to reflect foods included within each of the 114 line items based on the more specific data obtained from NHANES as described by Block (20). The primary source of nutrient composition data was derived from the USDA, with additional information from other government and scientific sources and industry. For each line item on the FFI, the percentage of completeness for each nutrient evaluated in this study was 100%. This database was expanded to incorporate the additional foods (red and green chiles, tortillas, other dishes using local recipes) and additional nutrients (glucose, fructose, sucrose, starch) based on values obtained from the Minnesota Nutrition Data System Version 2.3 (21). Interviewers were centrally trained, and interview quality was monitored quarterly by audiotape (E.J.M.-D.). The IRAS FFI has recently been validated in all three ethnic groups (12). Fasting lipoprotein concentrations were assayed for the SLVDS at the University of Colorado Health Sciences Center General Clinical Research Center. HDL cholesterol was determined by dextran sulfate magnesium precipitation (22), triglycerides were determined via an enzymatic method (22), and LDL cholesterol was calculated by using the Friedewald formula only when triglyceride values were ,400 mg/dl. In the IRAS, lipoprotein measurements were analyzed at Penn Medical Laboratory, Medlantic Research Foundation, Washington, D.C. LDL and HDL cholesterol were isolated by isopyknic ultracentrifugation, and VLDL top and bottom fractions were measured for cholesterol and triglyceride con1633

Macronutrient intake and lipoproteins

centrations (23). The triglyceride value for each subject in the IRAS is based on the average of two measures taken within a 4-week period. Height and weight were measured according to standard protocols. Weight history was obtained via self-report in both studies. The questions were identical except that the SLVDS concerned weight history in the previous 6 months, and the IRAS concerned the past year. For our analyses, participants who reported gaining $5 lb comprised the weight-gain group, those who reported losing $5 lb comprised the weight-loss group, and all others were included in the stable weight group. Standardized interviewing procedures were used to determine age, education, duration of diabetes, and frequency and duration of participation in vigorous activity. Statistical analyses Because nutrient intake was assessed with different methods in the two studies, all analyses were performed separately but in a parallel manner. The nutrients considered to be independent variables were total fat, saturated fat, oleic acid, polyunsaturated fat, total carbohydrates, simple carbohydrates, and starch. For the SLVDS, simple carbohydrates were defined as the sum of glucose and fructose (galactose and lactose were not available). For the IRAS, simple carbohydrates were defined as the sum of glucose, fructose, galactose, and lactose. Complex carbohydrates were defined as dietary starch from the SLVDS and the IRAS nutrient databases, respectively (not the mathematical difference between total carbohydrate and simple sugars). The four dependent measures were total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides. Because of right skewness of the original triglyceride variable, the natural log transformation of triglyceride was used in analyses. For descriptive purposes, simple correlation coefficients were obtained between the percentage of total calories from each nutrient variable and each lipoprotein. Regression modeling was used for hypothesis testing to accomplish adjustment for potentially confounding variables, including total calories, alcohol intake, BMI, vigorous activity, diabetes medication, duration of diabetes, age, sex, ethnicity, and education. Adjustment for total calories was accomplished by inclusion of the variable “total calories” rather than by using the variable “percentage of calories” from the macronutrient of interest to avoid the potential resid1634

Table 1—Selected characteristics of the SLVDS and the IRAS participants with type 2 diabetes

n Age (years) Women (%) Ethnicity (%) Hispanic African-American Non-Hispanic white High school education (%) Diagnosis of diabetes (%) Previously diagnosed Previously undiagnosed Duration of diabetes among those previously diagnosed (years) BMI (%) Normal (,25.00 kg/m2) Overweight (25–29.99 kg/m2) Obese ($30.00 kg/m2) Weight change status (%)† Lost .5 lb Gained .5 lb Stable weight Lipids and lipoproteins (mg/dl)‡ Total cholesterol LDL cholesterol§ HDL cholesterol Triglycerides¶ Dietary intake Total dietary calories Total fat (% kcal) Saturated fat (% kcal) Polyunsaturated fat (% kcal) Oleic acid (% kcal) Total carbohydrate (% kcal) Starch (% kcal) Simple carbohydrate (% kcal)i Physical activity level (%) No vigorous activity ,3 20-min sessions/week $3 20-min sessions/week Rarely/never 1–3 times/month 1 time/week 2–4 times/week $5 times/week Alcohol category (per week) 0g ,3 drinks $3 drinks

SLVDS

IRAS

421 58.6 ± 10.5 56.8

437 57.0 ± 8.4 52.6

65.3* NA 34.7 49.6*

33.4 34.6 32.0 77.6

83.6* 16.4

61.6 38.4

8.7 ± 7.9

6.8 ± 6.4

18.8 44.7 36.6

9.4 36.6 54.0*

30.9 12.8 56.3

31.8 19.9* 48.3

221.2 ± 47.2 134.6 ± 40.1 43.7 ± 14.1 197.3 ± 1.7

211.6 ± 42.4 139.8 ± 36.4 40.7 ± 12.2 142.6 ± 1.8

1,656 ± 972 38.8 ± 9.91 14.3 ± 4.3 6.14 ± 2.8 13.7 ± 4.4 44.6 ± 11.8 23.1 ± 8.1 6.7 ± 5.8

1,816 ± 828 36.0 ± 7.0 12.6 ± 3.1 7.0 ± 1.9 13.3 ± 2.9 45.6 ± 7.7 21.1 ± 5.7 12.9 ± 5.5

47.7 27.1 25.2 — — — — —

— — — 42.6 22.0 10.1 19.0 6.4

63.6 26.5 9.9

59.7 22.9 17.4

Data are means ± SD or %. *P , 0.05 between SLVDS and IRAS; †weight change was assessed over the past 6 months for SLVDS and over the past 1 year for IRAS; ‡milligrams per deciliter can be converted to SI units (mmol) by multiplying by 0.02586; §the sample size for LDL in the SLVDS was 381; ¶milligrams per deciliter were back transformed from the natural logarithm; ifor SLVDS, simple sugars were defined as glucose and fructose (galactose and lactose were not available), and for IRAS, simple sugars were defined as the sum of glucose, fructose, galactose, and lactose.



Table 2—Unadjusted Pearson correlation coefficients between lipids and nutrients (percentage of calories) or lipoprotein and nutrients (percentage of calories) in adults with type 2 diabetes at baseline Total cholesterol LDL cholesterol SLVDS IRAS SLVDS IRAS

HDL cholesterol SLVDS IRAS

Total fat 0.01 0.03 0.09* 0.03 0.04 Saturated fat 20.03 0.00 0.02 20.02 0.12* Polyunsaturated fat 0.05 0.06 0.09 0.11* 20.04 Oleic acid 0.03 0.03 0.11* 0.02 0.00 Total carbohydrate 0.05 0.00 20.03 0.01 20.05 Starch 0.04 0.01 0.00 0.00 20.03 Simple carbohydrate§ 0.03 20.01 20.06 0.01 20.05

20.12† 20.16‡ 0.04 20.15† 0.06 20.02 0.11*

Triglycerides SLVDS IRAS 20.13† 0.04 20.15† 0.07 20.02 20.10* 20.10* 0.07 0.16‡ 0.00 0.13† 0.0 0.12* 20.08

Data are correlation coefficients. For SLVDS, n = 421, and for IRAS, n = 437. *P , 0.05; †P , 0.01; ‡P , 0.001. For SLVDS, simple sugars were defined as glucose and fructose (galactose and lactose were not available), and for IRAS, simple sugars were defined as the sum of glucose, fructose, galactose, and lactose.

ual confounding by total calories that may occur with the nutrient density approach (percentage of calories), which by definition retains calories as part of the variable value (24). The resulting b-coefficient from this model can be interpreted as the unit difference in the outcome variable predicted for the unit difference in the nutrient intake, independent of total calories and analogous to isocaloric conditions. For the SLVDS, the “mixed” procedure from SAS (25) was used to accommodate the repeated measures. Models accounted for the nonindependence between observations on the same subject by specifying a random subject effect that produced an error structure referred to as “compound symmetry.” Marshall et al. (26) have used

the SLVDS data to demonstrate that statistical power is increased by using this approach in evaluating associations between nutrients and serum lipoproteins. For the IRAS, linear regression models were used to evaluate the relationship between each of the nutrients (in grams per day) and each of the lipoprotein measures. Two multiplicative interaction terms were created and added to the models one at a time to investigate whether the estimated association between each independent (nutrient) variable and each dependent variable differed according to weight history (nutrient 3 weight history) or time of diabetes diagnosis (nutrient 3 previous diagnosis). Weight history was considered to be a potential effect modifier

based on literature that suggests differential effects of macronutrients on lipid outcomes depending on whether experimental diets are isocaloric (8) or hypocaloric (10). Time of diabetes diagnosis was considered because of the potential for bias if individuals with a prior diagnosis of diabetes changed their usual dietary habits accordingly. Because of the limited statistical power generally available for testing the significance of interaction terms (27), a was set at 0.10; results are given by subgroups accordingly. Specific results for associations between nutrients and lipid or lipoprotein variables are given when the P value for the association between the nutrient and the outcome was ,0.05. RESULTS — Selected baseline characteristics of the SLVDS and IRAS study participants are shown in Table 1. A greater proportion of the SLVDS participants were Hispanic, had less than a high school education, and were previously diagnosed with diabetes compared with the IRAS participants. A greater percentage of the IRAS participants were overweight or obese (combined) and had gained at least 5 lb in comparison to the SLVDS participants (P , 0.05). Baseline Pearson correlation coefficients between percentage of calories from macronutrients and the lipids or lipoproteins are shown in Table 2 for both studies. Correlation coefficients are weak, and few are statistically significant.

Table 3—Association of macronutrients with plasma total cholesterol concentrations (in milligrams per deciliter) using 24-h dietary recall data (SLVDS, n = 421) or estimated usual intake by FFI (IRAS, n = 437)

n Fat (b/10 g) Total Saturated Oleic acid Polyunsaturated Carbohydrate (b/20 g) Total Starch Simple

All

SLVDS Previously undiagnosed

Previously diagnosed

All

Weight gain

Weight loss

Weight stable

421

69

352

437

87

139

211

— — — —

3.49 ± 1.1† 5.73 ± 2.5* 8.04 ± 2.5† 11.57 ± 4.5†

NS NS NS NS

— — — NS

NS NS 8.66 ± 4.1* —

4.15 ± 1.9* NS NS —

NS NS NS

— NS —

NS — NS

21.29 ± 0.6* — 22.00 ± 0.9*

— — NS

NS NS —

NS NS —

22.88 ± 1.4* NS

IRAS

Data are b ± SEM. Results are adjusted for covariates and are given by subgroup only when the P value for the interaction term (subgroup*nutrient) was ,0.10. The b coefficients ± SEM represent the change in total cholesterol concentration for a 10-g increase in fat and a 20-g increase in carbohydrate. Analyses for subgroups (weight change status or diagnosis status) are only presented when a significant interaction existed. In these cases, the analyses for “All” is irrelevant and not presented. The NS results are nonsignificant results in the named population. Covariates in all models included total calories, alcohol intake, BMI, frequency of participation in vigorous activity, diabetes medication, duration of diabetes, age, sex, ethnicity, and education. Simple sugar is the sum of fructose and glucose for the SLVDS, and the sum of fructose, galactose, glucose, and lactose for the IRAS. *P , 0.05; †P , 0.01.


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Macronutrient intake and lipoproteins Table 4—Association of macronutrients with plasma LDL cholesterol concentrations (in milligrams per deciliter) using 24-h dietary recall data (SLVDS, n = 381) or estimated usual intake by FFI (IRAS, n = 437)


All



All

Weight gain

381

62

319

437

87

139

211

— — — —

4.33 ± 1.0‡ 6.19 ± 2.2† 10.40 ± 2.2† 14.39 ± 3.9†

1.77 ± 0.7† NS 3.87 ± 1.5† NS

3.29 ± 1.4* — NS NS

— NS — —

— 8.69 ± 3.7* — —

— NS — —

21.25 ± 0.5* NS 21.48 ± 0.7*

— — —

— — —

— NS —

23.55 ± 1.5* — NS

NS — NS

22.59 ± 1.2* — NS

IRAS Weight loss

Weight stable

Data are b ± SEM. Results are adjusted for covariates and are given by subgroup only when the P value for the interaction term (subgroup*nutrient) was ,0.10. The b coefficients ± SEM represent the change in total cholesterol concentration for a 10-g increase in fat and a 20-g increase in carbohydrate. Analyses for subgroups (weight change status or diagnosis status) are only presented when a significant interaction existed. In these cases, the analyses for “All” is irrelevant and not presented. The NS results are nonsignificant results in the named population. Covariates in all models included total calories, alcohol intake, BMI, frequency of participation in vigorous activity, diabetes medication, duration of diabetes, age, sex, ethnicity, and education. Simple sugar is the sum of fructose and glucose for the SLVDS and the sum of fructose, galactose, glucose, and lactose for the IRAS. *P , 0.05; †P , 0.01; ‡P , 0.001.

The results from regression modeling are summarized in Tables 3–6. In the SLVDS, time of diabetes diagnosis, but not weight history, was a significant effect modifier (P , 0.10) in several instances. Conversely, in IRAS, effect modification by weight history, but not time of diabetes diagnosis, was detected. Tables 3–6 are structured to show results accordingly and show either data for all when the interaction term was P . 0.10 or for the subgroups of interest when the interaction term was P , 0.10.

Total cholesterol In the SLVDS, associations of macronutrients with total cholesterol varied by time of diabetes diagnosis for each macronutrient except starch (Table 3). For total fat, saturated fat, oleic acid, and polyunsaturated fat, there was a significant positive association between reported fat intake and plasma cholesterol among those previously undiagnosed with diabetes. Total and simple carbohydrate intake was significantly negatively associated with

serum cholesterol among those previously diagnosed. In the IRAS, increased reported intake of total fat (weight-loss group) and of oleic acid (weight-gain group) was associated with increased cholesterol concentrations. In the stable weight group, total carbohydrate was inversely associated with plasma cholesterol. LDL cholesterol In both the SLVDS and the IRAS, for all subgroups, increased reported intake of

Table 5—Association of macronutrients with plasma HDL cholesterol concentrations (in milligrams per deciliter) using 24-h dietary recall data (SLVDS, n = 421) or estimated usual intake by FFI (IRAS, n = 437)


All



All

Weight gain

Weight loss

Weight stable

421

69

352

437

87

139

211

— — NS

NS NS — 22.98 ± 1.3*

NS NS — NS

NS NS NS NS

— — — —

— — — —

— — — —

— — NS

20.62 ± 0.3* NS —

NS NS —

NS NS NS

— — —

— — —

— — —

IRAS

Data are b ± SEM. Results are adjusted for covariates and are given by subgroup only when the P value for the interaction term (subgroup*nutrient) was ,0.10. The b coefficients ± SEM represent the change in total cholesterol concentration for a 10-g increase in fat and a 20-g increase in carbohydrate. Analyses for subgroups (weight change status or diagnosis status) are only presented when a significant interaction existed. In these cases, the analyses for “All” is irrelevant and not presented. The NS results are nonsignificant results in the named population. Covariates in all models included total calories, alcohol intake, BMI, frequency of participation in vigorous activity, diabetes medication, duration of diabetes, age, sex, ethnicity, and education. Simple sugar is the sum of fructose and glucose for the SLVDS, and the sum of fructose, galactose, glucose, and lactose for the IRAS. *P , 0.05.

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Mayer-Davis, Levin, and Marshall Table 6—Association of macronutrients with plasma log triglycerides (in milligrams per deciliter) using 24-h dietary recall data (SLVDS, n = 421) or estimated usual intake by FFI (IRAS, n = 437)


All



All

Weight gain

Weight loss

Weight stable

421

69

352

437

87

139

211

NS NS NS NS

— — — —

— — — —

NS NS NS NS

— — — —

— — — —

— — — —

— — —

0.03 ± 0.01† NS 0.04 ± 0.01*

NS NS NS

NS NS

0.06 ± 0.02† — —

NS — —

NS — —

IRAS

Data are b ± SEM. Results are adjusted for covariates and are given by subgroup only when the P value for the interaction term (subgroup*nutrient) was ,0.10. The b coefficients ± SEM represent the change in total cholesterol concentration for a 10-g increase in fat and a 20-g increase in carbohydrate. Analyses for subgroups (weight change status or diagnosis status) are only presented when a significant interaction existed. In these cases, the analyses for “All” is irrelevant and not presented. The NS results are nonsignificant results in the named population. Covariates in all models included total calories, alcohol intake, BMI, frequency of participation in vigorous activity, diabetes medication, duration of diabetes, age, sex, ethnicity, and education. Simple sugar is the sum of fructose and glucose for the SLVDS, and the sum of fructose, galactose, glucose, and lactose for the IRAS. *P , 0.05; †P , 0.01; ‡P , 0.001.

total dietary fat was associated with significantly increased LDL cholesterol (P , 0.05) (Table 4). Conversely, increased reported intake of total carbohydrate was related to lower LDL cholesterol for all subgroups (P , 0.05), with the exception of IRAS weight-loss group. For the SLVDS, among those previously undiagnosed with diabetes, each subtype of dietary fat was positively associated with LDL cholesterol. For both those previously diagnosed and undiagnosed, oleic acid was positively associated with LDL cholesterol. HDL cholesterol Few statistically significant associations between macronutrients and HDL cholesterol were observed in either study (Table 5). For SLVDS participants who were previously undiagnosed with diabetes, reported intake of polyunsaturated fat and total carbohydrate were inversely associated with HDL cholesterol. No significant associations were observed in the IRAS cohort. Triglycerides Reported dietary fat intake was not associated with triglyceride concentration in either study (Table 6). In the SLVDS, total and simple carbohydrate intake was positively associated with triglycerides among those previously undiagnosed but not among those previously diagnosed. In the IRAS, total carbohydrate intake was positively associated with triglycerides in the weightgain group but not in the weight-loss or the stable weight groups.

CONCLUSIONS — From two epidemiological studies that included a total of 858 individuals with type 2 diabetes, our major finding was that higher reported intake of total dietary fat was related to significantly increased levels of LDL cholesterol in all subgroups studied. Results were similar, albeit somewhat less consistent, for total cholesterol. Higher reported intake of carbohydrate was related to increased triglyceride concentrations only for selected subgroups of the study populations. The finding of a consistent association of dietary fat intake with LDL cholesterol may have important clinical implications because of the evidence that increased levels of LDL cholesterol are a contributing cause of coronary heart disease (CHD), specifically in individuals with diabetes (5). Because of the imprecision of self-reported dietary intake data, the observed associations are likely underestimates of the true association between dietary fat and LDL cholesterol (28). Furthermore, Lehto et al. (5) noted that the reported magnitude of the risk of CHD associated with LDL cholesterol is likely to be underestimated because of the increased atherogenicity of the smaller and more dense LDL particles that occur more commonly in individuals with diabetes than in those without the disease. Thus, the findings from our study underscore the potential benefit of limiting total fat intake for all individuals with diabetes. Regarding subtypes of dietary fat in relation to LDL cholesterol, findings were less consistent across study groups. As


expected, higher reported intake of saturated fat was associated with significantly higher LDL cholesterol levels but only for those previously undiagnosed in the SLVDS and for those with recent weight loss in the IRAS. Effect estimates for saturated fat in all other subgroups were also positive but did not reach statistical significance (not shown), perhaps because of limited statistical power within subgroups and the heterogeneity of individual responsiveness to diet (29,30). Among SLVDS participants, the statistically significant positive associations of oleic acid and polyunsaturated fat (for previously undiagnosed subjects only) with LDL cholesterol were somewhat surprising given the published evidence of a neutral effect (or better) for oleic acid (8,31) and an LDL cholesterol–lowering effect for polyunsaturated fat (31). It has been noted, however, that individuals with diabetes may or may not respond to specific dietary fatty acids in a manner similar to those without diabetes (1). Wolever et al. (11) reported associations between habitual diet and lipids in individuals with diabetes in which higher intake of polyunsaturated fats was related to significantly higher total cholesterol levels among 98 individuals taking sulfonylurea medications but not among those using other treatment regimens. Thus, among free-living individuals with diabetes, lipid response to usual dietary patterns may be quite heterogeneous and may include the potential for adverse effects of both oleic acid and polyunsaturated fats. 1637

Macronutrient intake and lipoproteins

In general, when intake of dietary fat is reduced, total intake of carbohydrate is increased. The potential for high carbohydrate intake to increase the risk of CHD via raised triglyceride concentrations has been strongly suggested (8), although, to our knowledge, prospective data demonstrating the direct effect of carbohydrate intake on CHD risk among individuals with diabetes have not been published. In our study, a positive association between reported carbohydrate intake and triglycerides was not seen in most study participants. This is consistent with findings of Wolever et al. (11) in which neither total carbohydrate intake nor intake of simple sugars was associated with serum triglycerides (n = 354 individuals with diabetes). However, in our study, significant positive associations were observed in individuals with previously undiagnosed diabetes who participated in SLVDS and in IRAS participants who gained weight during the previous year. The results in the SLVDS participants who were previously undiagnosed are consistent with findings in subjects with normal glucose tolerance from this same study population (17). Several plausible explanations exist for the observed heterogeneity of associations between reported carbohydrate intake and triglyceride concentrations. The individuals with previously undiagnosed diabetes may differ from those with a previous diagnosis both in terms of knowledge and consequent health-related behaviors and in terms of metabolic status. Before diagnosis, these individuals had presumably not changed their dietary habits; therefore, biases related to temporal sequence that can occur in crosssectional analyses were minimized (32). Pertinent to associations of carbohydrate and triglycerides, the SLVDS participants who were previously undiagnosed had higher concentrations of circulating insulin than those with previously diagnosed diabetes (114.0 vs. 89.6 µU/ml, P = 0.01), which may be expected because insulin secretory capacity is likely compromised to a lesser extent in individuals with a shorter duration of diabetes. Higher levels of insulin have been related to increased production of VLDL triglycerides in the liver (33,34), although conflicting evidence exists from studies of isolated hepatocytes (35). Albeit speculative, the metabolic status of individuals with previously undiagnosed diabetes and higher concentrations of circulating insulin may be permissive regarding the potential effect of other factors (e.g., carbohydrates) to increase triglyceride concentration. 1638

Another possible contributor to the heterogeneity of results for carbohydrate and triglyceride associations is that dietary assessment methods differed between the two studies. The SLVDS used a 24-h dietary recall that reflected the period immediately preceding collection of a single fasting triglyceride concentration. Therefore, the SLVDS results may reflect primarily shortterm effects of carbohydrate levels on triglycerides. In contrast, the IRAS used an FFI that was designed to estimate usual intake of nutrients during a 1-year period, and triglyceride concentration was estimated from the average of two measures taken within 1 month. Thus, the IRAS results may reflect somewhat longer-term effects. Taken together, in the SLVDS, the positive association of total carbohydrate and starch with triglyceride concentration among those previously undiagnosed may reflect a relatively short-term effect in individuals whose metabolic status is permissive. In the IRAS, the results may be indicative of the potential for a longer-term adverse effect of carbohydrate intake among individuals who are in a positive energy balance, regardless of the time of diabetes diagnosis. It should be noted that, in both studies, the fasting triglyceride value was estimated from only one or two measurements. Triglyceride concentration has wide intraindividual variation, and a more stable estimate could have been derived from the mean of repeated measures; however, this was not possible for these analyses. Toward the goal of optimizing the lipoprotein profile for individuals with diabetes, data from these two large epidemiological studies emphasize the potential importance of reducing dietary fat intake to reduce concentrations of LDL cholesterol. This is consistent with recommendations for reduced fat intake toward the goal of weight loss, which is a key element of metabolic management of type 2 diabetes (1,36,37). The potential exists for a deleterious effect, at least in the short-term, of excess carbohydrate intake on triglyceride concentrations in selected subgroups of individuals with diabetes, namely those with recent onset of the disease or those who are gaining weight. For the latter group, reduced dietary fat intake may be beneficial in terms of weight management (9,38); therefore, concern about the potential deleterious effects of carbohydrates may only be relevant for individuals who are unable to avoid weight gain. Based on this

study, it may be appropriate for MNT for individuals with diabetes to reemphasize the importance of reducing total fat intake while recognizing that, like other aspects of clinical care, individualized approaches will sometimes be important to minimize the risk of CVD.

Acknowledgments — This study was supported by National Institutes of Health Grants R29-HL53798, U01-HL47889, U01-47890, U01-HL48902, DK-29867, DK-30747, and CRC-RR00051.

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