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1 From the Center for American Indian Health Research, University of Oklahoma Health Sciences Center, Oklahoma City, OK (JX, MB, and ETL); the Medstar Research Institute, Hyattsville, MD (SEA, BVH, and CM); the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD (CML and RRF); and the Indian Health Service, Aberdeen Area Office, Aberdeen, SD (EMZ)
2 The opinions expressed in this article are those of the authors and do not necessarily reflect the views of the Indian Health Service. 3 Supported by cooperative agreement grants no. U01HL-41642, U01HL-41652, and U01HL-41654 from the National Heart, Lung, and Blood Institute. 4 Address reprint requests to J Xu, Center for American Indian Health Research, University of Oklahoma Health Sciences Center, College of Public Health, PO Box 26901, Room CHB100, Oklahoma City, OK 73190. E-mail: susan-xu{at}ouhsc.edu.
ABSTRACT
Background: Little research has explored the association of macronutrient intake and glycated hemoglobin (HbA1c) in adults with diabetes.
Objective: The objective of the study was to examine the cross-sectional association between macronutrient intake and HbA1c in diabetic American Indians.
Design: A total of 1284 participants aged 47–80 y who had diabetes for 1 y at the second examination (1993–1995) of the Strong Heart Study were included in this study. Dietary intake was assessed by using a 24-h recall. Logistic regression models were used to evaluate the odds of poor glycemic control (HbA1c 7%) among sex-specific quintiles of macronutrient intake, after adjustment for the possible confounders age, sex, study center, body mass index, duration of diabetes, diabetes treatment, smoking, alcohol drinking, total energy intake, and physical activity.
Results: Higher total fat (>25–30% of energy), saturated fatty acids (>13% of energy), and monounsaturated fatty acids (>10% of energy) and lower carbohydrate intake (<35–40% of energy) were associated with poor glycemic control. Lower fiber intake and higher protein intake were marginally associated with poor glycemic control (P for trend = 0.06 and 0.09, respectively). No significant association was found between polyunsaturated fatty acids or trans fatty acids and glycemic control in this population.
Conclusions: These data suggest that a higher consumption of total fat and saturated and monounsaturated fatty acids and a lower intake of carbohydrates are associated with poor glycemic control in diabetic American Indians. Clinical trials focusing on whether modifications of macronutrient composition improve glycemic control in persons with diabetes are needed.
Key Words: Macronutrient intake • glycated hemoglobin • HbA1c • cross-sectional association • diabetes
INTRODUCTION
Diabetes is a leading health problem among American Indians and a growing health problem for the rest of the US population. Reports on the prevalence and incidence of diabetes from the Strong Heart Study (SHS), a longitudinal study of 4549 American Indians in Arizona, Oklahoma, North Dakota, and South Dakota (1), showed age-adjusted diabetes prevalence rates in American Indians aged 45–74 y from the 3 centers ranging from 33% to 72% (2). Recent data also showed that diabetes incidence rates in this population were several times higher than those in other ethnic groups (3). The associated complications result in a significantly lower quality of life and cost billions in health care dollars. In the United States, the annual cost in medical expenditures and lost productivity due to diabetes increased from $98 billion in 1997 to $132 billion in 2002 (4).
Glycemic control is fundamental to the management of diabetes, and its importance in reducing or delaying long-term microvascular and neuropathic complications has been shown in several studies (5-7). Glycated hemoglobin (HbA1c) concentrations are an indicator of average blood glucose concentration over the previous 2–3 mo. The HbA1c concentration is an important determinant of diabetes outcome. In diabetic persons, high HbA1c is a strong predictor of retinopathy, nephropathy, and subsequent mortality (8-10). In type 2 diabetes, HbA1c is an independent risk factor for progression of renal disease (11). A number of factors, including diet, may affect glycemic control. Positive associations of energy-adjusted saturated fatty acid (SFA) intake with HbA1c have been reported in persons without diabetes (12, 13), in elderly persons with diabetes (14), and in African American women (
SUBJECTS AND METHODS
The Strong Heart Study
The study design, survey methods, and laboratory techniques of the SHS were reported previously (1). Briefly, the SHS was started in 1988 as the first large epidemiologic study of cardiovascular disease in American Indians aged 45–74 y who reside in central Arizona, Oklahoma, North Dakota, and South Dakota. At baseline examination, the SHS cohort consisted of 4549 American Indians at baseline examination (1989–1991). The examination included a personal interview with each participant, a physical examination, and blood measurements. The second examination of the SHS was performed between 1993 and 1995 and included 3638 participants who returned to the examination. All survey methods and procedures were similar to those used at the baseline examination.
Subjects and methods
HbA1c was measured in all participants at the second examination by HPLC (17). Diabetes at the second examinations was defined according to the ADA criteria (18): ie, a subject was taking insulin or oral antidiabetic medication or had a fasting glucose concentration 7 mmol/L (126 mg/dL). The SHS participants with diabetes primarily had type 2 diabetes (19). Diabetes treatments were determined by questionnaire and categorized as taking insulin alone, insulin with oral hypoglycemic agents, oral hypoglycemic agent alone, and no medication. Height and weight were measured while each subject wore light clothing and no shoes. Body mass index (BMI; in kg/m2) was calculated. Smoking status and alcohol intake were determined by questionnaire. Physical activity (including reported leisure and occupational activities) over the past year and in the past week was assessed by questionnaire only in the first examination of the SHS and expressed as hours per week (20).
All participants at the second examination underwent collection of dietary data at the SHS clinics via a single 24-h dietary recall. The response rate for the 24-h recall was 95% in participants with or without diabetes. The interviews were conducted by local field staffers who were centrally trained and certified according to standardized methods (21). Detailed information about staff training, project supervision, and quality assurance was reported previously (22). Dietary intake data were collected and analyzed by using the Minnesota Nutrition Data System [NDS software; version 2.1; Nutrition Coordinating Center (NCC), University of Minnesota, Minneapolis, MN], including the Food Database (version 4A) and the Nutrient Database (version 18) (23, 24). trans Fatty acids (TFA) were not available in NDS Version 2.1; therefore, to include them in the nutrient data, final intake data were computed with the NCC Nutrient Database (version 36; NDS-R 2005). This time-related database updates analytic data while retaining nutrient profiles that are true to the version used for data collection (25).
The analysis was based on data from participants who had diabetes and whose HbA1c measurements and dietary data were available at the second examination (n = 1624). Physical activity data were obtained from the first examination. Exclusion criteria were diabetes 1 y (n = 199), total energy consumption 600 kcal/d (n = 52), and any condition affecting energy intake, such as dialysis treatment, kidney transplant, or liver cirrhosis (n = 89). The final population for the analysis consisted of 1284 participants who attended the second examination of the SHS, who were 47–80 y old, and who had diabetes.
Written informed consent was obtained from all participants. The Indian Health Service and participating institutional review board and the participating tribes approved the study.
Statistical analysis
Baseline (at the second examination) characteristics were summarized for men and women and presented as means ± SDs for continuous variables, as medians and interquartile ranges if continuous variables were skewed, or as numbers and percentages for categorical variables. Tests for difference between HbA1c < 7% and HbA1c 7% among men and women as well as between men and women in the 2 HbA1c categories were based on a chi-square test for categorical variables and a t test for continuous variables (skewed variables were normalized before the test). Interactions between sex and each variable were examined in a logistic regression model. The adjusted mean macronutrient intake by quintiles of HbA1c was calculated by using the general linear models (GLM) procedure and LSMEANS statement in SAS software (version 9.0; SAS Inc, Cary, NC). Variables in the models included sex, age, study center (AZ, OK, ND, or SD), BMI, duration of diabetes, diabetes treatment (insulin alone or with oral hypoglycemic agents, oral hypoglycemic agents, or no medication), smoking and alcohol drinking (current, past, or never), total energy intake, and physical activity. Macronutrient intake was expressed as the percentage of energy, and fiber intake was expressed as g/1000 kcal. Fiber was log transformed, and sucrose as a percentage of energy was third root transformed before being entered into the model and was back transformed when the results for means and CIs among quintiles of HbA1c were presented. Tests for linear trend were conducted by using the CONTRAST statement in PROC GLM of SAS software (version 9.0; SAS Inc). Sex-specific quintiles of macronutrients were used because of sex differences in dietary intakes. Logistic regression models were used to investigate the association between sex-specific quintiles of macronutrients and poor (HbA1c 7%) or good (HbA1c < 7%) glycemic control, and the models were successively controlled for the covariates listed above. The sex x quintile of macronutrient intake and the sex x smoking status interactions and the sex x quintile of macronutrient intake x smoking status interaction for HbA1c status were examined in the multivariate-adjusted logistic regression models. Tests for linear trend were conducted by modeling the median of each quintile-defined category of macronutrient intake as a continuous variable in logistic regression models. All analyses were performed with SAS software (version 9.0; SAS Inc). All P values are 2-tailed, and significance was defined as P < 0.05 for all tests.
RESULTS
There were 420 diabetic men and 864 diabetic women included in the analysis. Mean HbA1c was 8.6 ± 2.1% and 9.0 ± 2.3% in men and women, respectively. As reflected by the HbA1c value, women had poorer glycermic control than did men. Baseline characteristics of diabetic participants by HbA1c status and sex are shown in Table 1. Men and women with HbA1c 7% were significantly younger, had significantly lower BMI and longer duration of diabetes, and were significantly more likely to be taking insulin combined with oral medication or insulin alone than were their counterparts with HbA1c < 7% (P < 0.05 for all). Women with HbA1c 7% were significantly more likely to be current smokers and never or former drinkers than were those with HbA1c < 7% (P < 0.05 for both). Men with HbA1c 7% reported significantly higher intakes of total fat, SFA, MUFA, and TFA but significantly lower intakes of carbohydrate (as % of energy for all) and fiber (g/1000 kcal) than did those with HbA1c < 7% (P < 0.05 for all). Women with HbA1c 7% reported significantly lower intakes of carbohydrates and sucrose but a significantly higher intake of protein as a percentage of energy than did those with HbA1c < 7% (P < 0.05). Among participants with HbA1c 7%, men were significantly younger, had significantly lower BMI and higher level physical activity, were significantly less likely to be taking insulin combined with oral medication or insulin alone, and were significantly more likely to be former smokers and current drinkers than were women (P < 0.05 for all). Men also reported significantly higher intakes of energy, total fat, SFA, MUFA, and TFA but significantly lower intakes of carbohydrates (as % of energy for all) and fiber (as g/1000 kcal) than did women (P < 0.05 for all). Among participants with HbA1c < 7%, men were significantly more likely to be current smokers and drinkers, had a significantly higher level of physical activity, and reported significantly higher energy intakes than did women (P < 0.05). There were significant interactions between sex and smoking status or total fat, SFA, MUFA, and fiber intakes for HbA1c status (P < 0.05 for all).
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TABLE 1. Baseline characteristics of 420 diabetic men and 864 diabetic women by glycated hemoglobin (HbA1c) status1
The mean macronutrient intakes by quintiles of HbA1c after adjustment for sex, age, study center, BMI, duration of diabetes, diabetes treatment, smoking status, sex x smoking status, drinking status, energy intake, and physical activity are shown in Table 2. Higher total fat and MUFA and lower carbohydrate intakes were significantly associated with higher HbA1c concentrations (P < 0.05). Higher SFA, protein, and sucrose intakes were marginally associated with higher HbA1c concentrations.
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TABLE 2. Adjusted mean macronutrient intake by quintile (Q) of glycated hemoglobin (HbA1c)1
Odds ratios (and 95% CIs) for the cross-sectional association between the quintile of macronutrient intake and the odds of poor glycemic control (HbA1c 7%) are given in Table 3. The odds of poor glycemic control were significantly higher with increasing quintiles of total fat, SFA, MUFA, and protein intake and significantly lower with increasing quintiles of carbohydrates and sucrose, after adjustment for sex, age, and study center (P < 0.01 for all). The results for total fat, SFA, MUFA, and carbohydrate intake did not change after further adjustment for BMI, duration of diabetes, diabetes treatment, smoking status, sex x smoking status, alcohol drinking status, energy intake, and physical activity. Compared with the lowest quintile of total fat (<25–30% of energy), the odds of poor glycemic control were higher in all other quintiles of total fat intake (>25–30% of energy). The odds of poor glycemic control were higher in the third and fourth quintiles of SFA intake (>13% of energy) than in the lowest quintile (<8% of energy). Compared with the lowest quintile of MUFA intake (<10% of energy), the odds of poor glycemic control were higher in all other quintiles of MUFA intake (>10% of energy). Compared with the lowest quintile of carbohydrate (<35–40% of energy), the odds of poor glycemic control were lower in all higher quintiles of carbohydrates (>35–40% of energy). The result did not change when energy was omitted from the model (P for trend = 0.005). When we further investigated the separate effects of various sugars (ie, sucrose, fructose, lactose, glucose, and maltose), we found no association between the specific sugar and glycemic control (only sucrose data shown). The trend test showed that a lower fiber intake and a higher protein intake were marginally associated with higher odds of poor glycemic control. The results were unchanged after we either omitted physical activity from the models or restricted total energy intakes to 1500–4500 kcal/d for men and 1000–4000 kcal/d for women (data not shown).
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TABLE 3. Adjusted odds ratios (ORs) (and 95% CIs) of poor or good glycemic control between higher sex-specific quintiles and the lowest quintile (Q) of macronutrient intake1
Statistically significant interactions for glycemic control were detected between sex and smoking status (P < 0.01), sex and quintile of total fat (P = 0.05), sex and quintile of MUFA (P = 0.05), and sex and quintile of fiber (P < 0.01). The sex x smoking status x quintile interactions were not significant. Analyses stratified by sex are presented in Table 4. Higher total fat and MUFA intakes and lower fiber intakes were significantly associated with poor glycemic control in men but not in women (Table 4).
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TABLE 4. Adjusted odds ratios (ORs) (and 95% CIs) of poor or good glycemic control between higher sex-specific quintiles and the lowest quintile (Q) of macronutrient intake stratified by sex1
DISCUSSION
In middle-aged to elderly American Indians with diabetes, a higher consumption of total fat, SFA, and MUFA and a lower consumption of carbohydrates were associated cross-sectionally with poor glycemic control. A lower fiber intake and a higher protein intake were marginally associated with poor glycemic control. PUFA and TFA intakes were not associated with glycemic control.
The distributions of macronutrients recommended for persons with diabetes at various times in the last 80+ y are shown in Table 5. Our finding that total fat intake > 25–30% of energy was associated with poor glycemic control is consistent with the 1986 guidelines (26), which were in effect when the data collection for SHS began. At that time, there was no specific guidance on SFA and MUFA intakes. However, during the SHS data collection, the ADA recommended that persons with diabetes consume <10% of energy from SFA (27). We found that SFA intake <13% of energy and MUFA intake <10% of energy were associated with good glycemic control. Our finding that carbohydrate intake >35–40% of energy was associated with good glycemic control is in line with the 1986 ADA nutritional recommendations to increase carbohydrates up to 55–60% of energy, but it is not consistent with the recommendation to increase MUFA together with carbohydrates to provide 60–70% of energy intake. We assume that the ADA recommendations are based on MUFA from vegetable fat; the main MUFA sources of the SHS population were unknown, but the high correlation between MUFA and SFA (r = 0.79) suggests the MUFA may be obtained from animal sources.
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TABLE 5. Recommended distribution of macronutrients for diabetes1
The data on fat intake are consistent with data from several studies. Marshall et al (28) found that high-fat, low-carbohydrate diets were associated longitudinally with the onset of non-insulin-dependent diabetes mellitus [for an increase of 40 g/d in total fat, the adjusted OR (95% CI) was 1.62 (1.09, 2.41); for a decrease of 90 g/d in carbohydrate intake, the adjusted OR was 1.56 (1.12, 2.19)]. SFA intake has been positively associated with risk of incident type 2 diabetes in populations such as Japanese Americans (29), Pima Indians, and Mexican Americans (30). Total fat and SFA intakes were associated with a greater risk of incident type 2 diabetes in the Health Professionals Follow-up Study, but that association was not independent of BMI (31). In cross-sectional studies, HbA1c increased 0.98 ± 0.33% for every additional 10% of energy from total fat in men with insulin-dependent diabetes (32); SFA intake was positively associated with HbA1c in elderly people with diabetes (14) and in African American women with type 2 diabetes (15). In contrast, the Nurses Health Study found no association between incident type 2 diabetes and total fat, SFA, or MUFA intake but did find an association between incident type 2 diabetes and TFA and PUFA intakes (33). The sex difference shown in our analysis may explain some of the differences in the findings of the above studies. In our study population, total fat, MUFA, and fiber intakes were significantly associated with glycemic control in men but not in women.
In the EURODIAB IDDM Complications Study (34), greater consumption of carbohydrates was associated cross-sectionally with a higher concentration of HbA1c in 2084 patients with type 1 diabetes mellitus; however, a greater intake of vegetable carbohydrate was inversely related to HbA1c. A detailed review and meta-analysis of the literature recommended that diabetic persons should consume 55% of energy from carbohydrates (35). An intervention study in 12 persons also found that HbA1c in those with type 2 diabetes decreased from 8.2% to 6.9% (P < 0.03) in the high-carbohydrate diet group (55%, 15%, and 30% of energy as carbohydrate, protein, and fat, respectively) after 8 wk (36). These results are consistent with our finding that a high carbohydrate intake was associated with good glycemic control. In contrast,, in another meta-analysis (37), low-carbohydrate diets that provided <45% of energy from carbohydrates were evaluated and found to result in good glycemic control within 6 mo when substituted for a conventional low-fat diet in patients with type 2 diabetes. However, they authors of the meta-analysis did not evaluate long-term risks or benefits.
A possible explanation of lower carbohydrates and poor glycemic control may be that a high-carbohydrate diet is a good marker of a compliant patient. In comparing persons with and without diabetes, the mean (±SEM) percentage of carbohydrate intake, after adjustment for sex and age, was 47.9 ± 0.3% and 49.8 ± 0.3% (P < 0.0001), respectively. This small difference could suggest that compliance does not change after diagnosis. Comparing carbohydrate intakes between types of treatment may further support this explanation, because participants with diabetes who are taking no medications consumed 50.2 ± 0.9% of energy from carbohydrates, whereas those taking insulin alone consumed 48.5 ± 0.6%, those who were treated by oral medication consumed 47.1 ± 0.5%, and those who were treated with insulin and oral medications consumed 46.3 ± 1.6% after adjustment for sex and age.
In the current analysis, no association was found between TFA intake and glycemic control. This lack of an association may have resulted from the small range of TFA intakes in this population. As far as we are aware, no study has investigated the association of TFA with glycemic control in any other populations of persons with diabetes. The lack of association in the present study merits further investigation, including the examination of the contributions of naturally occurring and hydrogenated vegetable oil sources.
There are several limitations of the current study. A single day's diet is a poor descriptor of a person's usual intake, because of intraindividual variability. However, our cohort of middle-aged and older American Indians is rather homogeneous. Their eating patterns are relatively simple, and they do not have access to a wide variety of foods where they live. Thus, we believe that the 24-h recall may be more informative in the present case than in other populations. Moreover, we estimated macronutrient intake for groups categorized by HbA1c concentration, which is an appropriate approach for 24-h recall data. We do not have data on food sources for the relevant nutrients from these recalls because they were performed in the mid-1990s; the NDS database at that time did not allow the extraction of food data. In the SHS, physical activity data were collected only at the first examination, but it was the second examination that served as the baseline for the present report. The report from the first examination of the SHS (20) showed that the level of physical activity was low in this population. In the current analysis, we assumed that there was no change in activity habits between the first and the second examinations. Furthermore, several confounders cannot be addressed in our analysis. Foods high in carbohydrates and fiber, such as whole grains and vegetables, contribute a wide range of micronutrients and phytochemicals that may confound the associations for carbohydrate and fiber. Finally, because the results were obtained from cross-sectional data, we are not able to draw conclusions about the temporal relation between macronutrient intake and glycemic control.
In conclusion, our data suggest that lower intakes of total fat, SFA, MUFA, and protein and a higher fiber intake are associated with good glycemic control in diabetic American Indians; some associations are stronger in men. In both sexes, there was a negative association between carbohydrate intake and poor glycemic control. These results support the recent ADA recommendations, which address a diet low in SFA and high in fiber. Clinical trials are needed to test whether improved glycemic control can be achieved by modifications to macronutrient composition such as those associated with good glycemic control in this study and whether diet influence on glycemic control is sex dependent.
ACKNOWLEDGMENTS
The authors acknowledge the assistance and cooperation of American Indian communities, without whose support this study would not have been possible. The authors also thank the Indian Health Service hospitals and clinics at each center, the directors of the Strong Heart Study clinics, and the clinic staffs.
The authors' responsibilities were as follows—JX and SEA: the study hypothesis, the analysis concept, data analysis and interpretation, and drafting of the manuscript; CL: assisted with analysis and interpretation of data; BVH, RRF, EMZ, and ETL: assisted with the study design and data collection; and all authors: critical revision of the manuscript. None of the authors had a personal or financial conflict of interest.
REFERENCES