Oct. 14, 2010 -- Many U.S. homes have disease-causing germs, but Americans rank among the highest in practicing good hygiene, a new study shows.
The 2010 study called Hygiene Home Truths conducted by the Hygiene Council says that even though the U.S. ranks high in hygiene practices, there is plenty of room for improvement, especially in bathrooms and kitchens.
The council sent germ- and mold-spore hunters armed with swabs into homes in the U.S., the United Kingdom, Germany, Canada, South Africa, Saudi Arabia, Malaysia, Australia, and India, looking for microscopic evidence of germs.
Homes of people who agreed to participate were swabbed for bacteria and mold.
The study says that 89% of U.S. samples were satisfactory or spotless, but that we’re still not doing enough cleaning, leaving behind bacteria and mold that increase the risk of illness.
According to the study, the biggest hotspot for germs was found to be the bathtub seal between the tub and tile, 40% of which were rated unsatisfactory or heavily contaminated.
The council says this area is particularly worrisome, because kids, when being bathed, are often close enough to these areas to pick up bad bugs.
The kitchen towel was the second most heavily contaminated area swabbed by germ sleuths, with 15% ranking unsatisfactory or heavily contaminated.
The council says a growing concern in U.S. households is the presence of the bacteria staphylococcus aureus, commonly called staph, which was identified on 11% of computer keyboards or mouses.
This indicates that some homeowners aren’t washing their hands, or at least not properly.
Among other findings for U.S. homes:
The council was formed in 2006 as a disease-fighting initiative involving public health experts worldwide. The global swab-down was sponsored by Reckitt Benckiser, maker of Lysol brand products, with the goal of identifying dirty spots and offering recommendations to help people make household items cleaner.
The study results “show that certain areas in our homes are being neglected when it comes to hygiene,” says the Laura Jana, MD, of the Hygiene Council. “For example, cleaning with a dirty cloth or not thoroughly washing hands will simply spread bacteria rather than kill harmful organisms. And when someone has been sick, this can be detrimental to the entire household.”
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 (
age: 58 y) with type 2 diabetes (15). The primary recommendations in medical nutrition therapy for diabetes from the American Diabetes Association (ADA) (
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).
View this table:
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
1 From the US Department of Agriculture/Agricultural Research Service Children's Nutrition Research Center, Houston, TX (DMB); the Baylor College of Medicine, Houston, TX (SAA); the Centers for Disease Control and Prevention, Atlanta, GA (BAB); the University of Vermont, Burlington, VT (NKF); the Washington University School of Medicine, St Louis, MO (JDG); the US Department of Agriculture/Agricultural Research Service, Beltsville, MD (DMK); and Harvard University, Boston, MA (FMS)
2 Reprints not available. Address correspondence to DM Bier, USDA/ARS Children's Nutrition Research Center, 1100 Bates Street, Houston, TX 77030-2600. E-mail: dbier{at}bcm.tmc.edu.
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ACKNOWLEDGMENTS
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REFERENCES
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The American Dietetic Association Complete Food and Nutrition Guide, a comprehensive, well-indexed handbook, includes a wide range of nutrition and food topics. It is aimed at the interested public and is written in a quick reference style and is sprinkled with self-assessment tools for each topic. These assessment tools pull the reader into the topic, make the information applicable to everyday life, and help identify areas in which more information is necessary. Of the 676 pages, 630 are textual, which are followed by list of resources, 28 pages of appendixes (mostly tables), and a complete index.
Topics are presented in an interesting order and not in the traditional manner. The first section discusses fitness and body weight, fats, vitamins and minerals, carbohydrates, fiber, and water. The second section discusses foods, food choices, and food shopping and is followed by a section on lifestyle stages—birth through aging. The last section is on "special issues," and includes topics such as athletics, vegetarian eating, and allergies and a small section on managing common chronic diseases and supplement use.
Each chapter of the book focuses on the reader, labeled "you," and the reader is challenged to become smarter about the topic presented and resultant behavior patterns. Food selection, food shopping, and home food storage issues are well covered, but no recipes are included. The book contains 676 pages of information, which is unique for food and nutrition guides aimed at the public.
I have seen this book grow and improve throughout its 3 editions, all of which have been popular with interested readers. The author has included the most recent governmental food guide information and nutrient recommendations. The writing style is very readable. The author uses clever phrases, such as "Carbs: Simply Complex," "Sodium and Potassium: A Salty Subject," and "Kitchen Nutrition: Delicious Decisions."
I highly recommend this book as a ready reference for the person who is not a nutrition professional but who has a strong interest in following and knowing the latest on nutrition topics and recommendations for health.
1 From the Johns Hopkins Bloomberg School of Public Health (JNN, KOO, S-CC, and LEC), the Johns Hopkins Hospital (FRW, JM, and MSN), and the Johns Hopkins School of Medicine, Department of Obstetrics and Gynecology (FRW), Baltimore, MD
2 Reprints not available. Address correspondence to KO O'Brien, Cornell University, Division of Nutritional Sciences, MVR, Room 340, Ithaca, NY 14853-6301. E-mail: koo4{at}cornell.edu.
ABSTRACT
Background: Because pregnant African American women and teens are at risk of low birth weight, they are frequently counseled to strive for gestational weight gains at the upper limits of the Institute of Medicine's recommended ranges.
Objective: The objective was to examine whether such weight gains improve birth outcomes in a cohort of disadvantaged African American adolescents of low (<19.8), average (19.8 to 26.0), or high (>26) prepregnancy body mass index (BMI; in kg/m2).
Design: Data were extracted from the medical charts of 1120 African American adolescents who received prenatal care at an inner-city maternity clinic between 1990 and 2000 and analyzed by using analysis of covariance and multivariate regression methods.
Results: Data were available for 815 adolescents, 711 of whom delivered at term (37 wk). Fifty-eight percent (n = 409) of all term deliveries and 74% of the high-BMI adolescents (n = 126) had gains in the upper half of or above the recommended ranges. For all BMI groups, the most significant differences in birth outcomes were found in comparisons of teens who gained below the recommended ranges with those who gained in the lower half of the recommendation range. Further gains were not clearly beneficial, particularly for infants of high-BMI mothers.
Conclusions: African American adolescents entering pregnancy underweight or at average weight should be counseled to gain within the recommended ranges, whereas overweight adolescents need support to avoid excessive gestational weight gain. Such advice would be prudent in light of the known associations between obesity and the increased likelihood of chronic diseases.
Key Words: Obesity • body mass index • disadvantaged African American adolescents • pregnancy • birth outcomes
INTRODUCTION
Pregnant adolescents in the United States are at high risk of adverse birth outcomes, including preterm delivery, low birth weight, and increased perinatal mortality (1). These elevated risks are thought to be due in part to the greater prevalence of such biological risk factors as low prepregnancy weight, the heightened nutritional demands of growth, and the structural and hormonal consequences of physiologic immaturity (2). The risks are also due to socioeconomic factors: adolescent mothers are more often poor, are more often members of racial minorities, have lower educational levels, and receive suboptimal prenatal care (3, 4).
Appropriate nutrient intake and weight gain during pregnancy are considered 2 of the most important modifiable behaviors for improved maternal and infant outcomes (5). In its 1990 report, the Institute of Medicine (IOM) suggested that because of their higher risk of lower birth weight for a given weight gain, young adolescents and African American women should be advised to gain at the upper limits of the IOM's recommended weight gain ranges for women of low and average prepregnancy body mass index (BMI: weight in kg/height squared in m) (5). However, adolescents have also been shown to be at increased risk of excessive gestational weight gain (2, 6, 7), and African American women have been found to have an increased risk of postpartum weight retention (8, 9) and overweight and obesity (10, 11). For African American women in particular, there is increasing evidence that higher weight gains during pregnancy do not improve infant outcomes and instead may elevate the mothers' long-term risk of chronic disease (12, 13). In 1997 an expert panel proposed an update of the 1990 IOM recommendations, suggesting that adolescents and African American adults be counseled to stay within the IOM-recommended BMI-specific weight gain ranges (14). Further research on the relation between observed weight gain and birth outcomes in African American adolescents is needed to provide a more solid basis for recommendations about optimal weight gain. The purpose of this study was to evaluate associations between levels of gestational weight gain and birth outcomes in a large sample of African American adolescents who delivered singleton infants in Baltimore between 1990 and 2000.
SUBJECTS AND METHODS
Subject characteristics
The data set used for this analysis was compiled from a retrospective medical chart review of all adolescents aged 17 y at conception who received prenatal care at the Maternity Center East clinic. This inner-city clinic was affiliated with the Johns Hopkins Hospital and served a predominantly disadvantaged African American community. Methods used to extract the medical data have been described elsewhere (15). Combined prepregnancy, weight gain, and birth weight measures were available for 815 (73%) of the 1120 African American pregnancies in the database. Examination showed no significant differences in maternal age, height, parity, smoking, or incidence of preeclampsia between subjects with and without birth weight or weight gain data (data not shown). Data on the characteristics of this population and on relations between hematologic status and birth outcomes, diet, and fetal growth were previously reported (15–17). The study was approved by the Joint Committee of Clinical Investigation at the Johns Hopkins Hospital.
Weight gain determinations
Prepregnancy BMI was calculated by using measured height and self-reported prepregnancy weight. Although the tendency for overweight subjects to underreport weight and for underweight subjects to overreport weight may introduce bias, previous research suggests that self-reported weights correlate well with actual weight among adults (18) and among teens (19). In these populations the correlation between reported prepregnancy weight and the first measured weight was 0.93; however, a detailed inspection of our data set showed that 7% of adolescents had large differences between the recalled prepregnancy weight and a first measured weight in the first trimester (gains of 1 kg/wk). The data were therefore adjusted according to the following rules: 1) accept reported prepregnancy weight if the resulting rate of gain was <1 kg/wk during the first trimester; otherwise 2) extrapolate prepregnancy weight if 2 weight gain measures were recorded before 15 wk gestation, from which a first trimester rate of gain could be estimated; 3) replace reported prepregnancy weight with the first measured weight if a measure was recorded by 8 wk gestation but the second measure was recorded after 15 wk; 4) drop observation if the rate of gain was implausible (1 kg/wk) and the correction was not feasible.
Subjects were then classified according to the adult references used by the IOM into 4 categories: underweight (BMI < 19.8), average weight (BMI = 19.8–26.0), overweight (BMI = 26.1–29.0), and obese (BMI > 29). Because few overweight and obese adolescents delivered small-for-gestational-age infants (n = 12 and n = 14, respectively), and because weight gain recommendations are similar for these 2 groups, the data for these 2 groups were combined for the analysis. In addition, a preliminary analysis indicated that differences in total weight gain and birth weight between these 2 groups were not statistically significant.
The subjects were weighed at each prenatal visit in street clothes and no shoes following standard clinical practice, and total weight gain was calculated as the difference between the final recorded weight within 4 wk of delivery and the prepregnancy weight; 86% of these final weight measures were made within 2 wk of delivery. The number of weeks between the final weight measure and delivery was included in all multivariate analyses. In addition to being examined as a continuous variable, total weight gain at term (37 wk) was examined in 4 categories: below the IOM recommendation for BMI, in the lower half of the recommended range (12.5–15.2 for low-, 11.5–13.8 for average-, and 7.0–9.2 kg for high-BMI adolescents), in the upper half of the recommended range (15.3–18 kg for low-, 13.9–16 kg for average-, and 9.3–11.5 kg for high-BMI adolescents), and above the recommendation for BMI. Date of last menstrual period was estimated on the basis of a best obstetrical estimate algorithm using all available data (self-reported last menstrual period, physical examination, and earliest available ultrasound), and length of gestation was calculated as the distance between birth and last menstrual period (15).
Infant birth weight classification and outcome variables
The outcome variables examined included average birth weight and small-for-gestational-age (SGA) size, which was defined as birth weight less than the 10th percentile for gestational age according to a national sex-specific reference for fetal growth based on 1991 natality data (20). SGA birth weight was selected as an outcome variable rather than low birth weight because it adjusts for duration of pregnancy. Large-for-gestational-age (LGA) size according to the same national reference was also examined, but the incidence was too low (2%) to allow for multivariate analysis. Birth weight at term was categorized as suboptimal (<3000 g), optimal (3000–4000 g), and above optimal (>4000 g) on the basis of previous research findings that adverse outcomes are minimized for infants of African American adult and adolescent mothers when birth weights range between 3000 and 4000 g (21).
Other variables in the data set were examined for their association with these outcomes and with gestational weight gain on the basis of findings previously reported in the literature. These variables included young maternal chronologic age (<15 y at infant birth), low gynecologic age (2 y since the self-reported date of the onset of menses), parity (0 versus 1), height, self-reported history of smoking or drug use (any versus none), medical diagnosis of preeclampsia (blood pressure > 140/90 mm Hg accompanied by abnormally high urinary protein and symptoms of edema), health insurance status (private, Medicare, or none), iron status (results of hemoglobin and hematocrit screens, typically conducted at entry into prenatal care and at 28 wk gestation), gynecologic infections diagnosed at any time during pregnancy (including chlamydia, gonorrhea, bacterial vaginosis, vaginal infections, and urinary tract infections), adequacy of prenatal care as measured by the Kotelchuck index (based on timing of entry, total number of prenatal visits, and gestational age at delivery) (22), and infant sex.
Statistical analyses
Descriptive analyses included visual examination of distributions, means, and SDs for continuous variables and frequency distributions for categorical variables. Chi-square analysis was applied to test relations between pairs of categorical variables and one-way analysis of variance to test for significant relations between categorical and continuous variables. Significance for bivariate relations was set at P < 0.05. Analysis of covariance (ANCOVA) was used to examine differences in mean birth weight by prenatal weight-gain category (below, in the lower half of, in the upper half of, and above recommended ranges) with adjustment for variables that proved significant in the bivariate analyses and remained significant in the ANCOVA equation. Logistic regression models were developed to examine the effect of weight gain (as a continuous and as indicator variables for the 4 weight-gain categories) on the risk of being born SGA and suboptimal birth weight (<3000 g) after control for covariates and potential confounding factors. Linear regression models were used to test these effects on birth weight. Associations with SGA and suboptimal and mean birth weights were examined for the group as a whole and stratified by prepregnancy BMI. Because each BMI stratum included a wide range of BMI values, BMI was also included in the multivariate analyses as a continuous variable. All variables for which there was a significant difference for any BMI group or which are known confounders of the relation between weight gain and birth weight were tested in multivariate models. Significance for retention in the multivariate models was defined as P 0.10. We found a significant interaction between BMI and weight gain for both birth weight and suboptimal birth weight outcomes, which provided further evidence of the importance of stratifying our analysis by BMI and weight-gain categories. This interaction effect was not significant for SGA birth weight, possibly because of its lower incidence; thus, only main effects of this outcome are presented. Statistical analyses were performed by using STATA release 8.2 (Stata Corp LP, College Station, TX).
RESULTS
The distribution of selected anthropometric and sociodemographic variables in the group as a whole, and stratified by prepregnancy BMI, is presented in Table 1. Underweight adolescents were more likely to be taller (P < 0.01) and of low gynecologic age (P < 0.001); overweight adolescents were more likely to be older (P = 0.02) and multiparous (P < 0.01). Overweight adolescents were less likely to undergain weight and about twice as likely to overgain weight (P < 0.001), similar to the patterns found in adults (23). However, unlike many adult African American populations (14), our teens were more likely to overgain than to undergain weight.
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TABLE 1. Selected characteristics of African American adolescents by prepregnancy BMI (in kg/m2) category1
Mean (±SD) birth weight in this population was 3062 ± 647 g, and 37% of the newborns had suboptimal birth weights (<3000 g) (Table 2). The incidence of SGA birth weight (16%) was elevated and of LGA size (2%) was low compared with the incidence in adult African Americans from the same community (12). Risk of SGA and suboptimal birth weight were significantly and negatively associated with prepregnancy BMI (P < 0.02 and P < 0.001, respectively), gestational weight gain (P < 0.001 for both), and maternal height (P = 0.03 for both). The rate of smoking was low in this cohort; nevertheless, it was significantly associated with suboptimal birth weight (P = 0.05). Preeclampsia more frequently coincided with SGA birth weight (P < 0.01) but not with suboptimal birth weight (P = 0.08).
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TABLE 2. Unadjusted birth weight outcomes for African American adolescents by prepregnancy BMI (in kg/m2)1
The actual mean birth weights of infants born at term (37 wk) to mothers gaining below, within the lower or upper half, or above the BMI-specific recommended weight gain ranges and birth weights adjusted for potentially confounding factors are shown in Table 3. Also presented are the proportions of infants in each weight-gain category that were of suboptimal birth weight. In general, the most biologically and statistically significant reductions in birth weight were found in comparisons of gains below the recommended ranges with those in the lower half of the recommended range. The significance of further improvements in birth weight from gains in the upper half of the recommended ranges was inconsistent; the data adjusted for confounders suggested biologically modest additional increases in birth weight, with the highest increases (88.4 g) seen in the average-BMI group. Further increases in birth weight were achieved from weight gains above the recommended ranges, but the mean gestational weight gain in this category for all 3 BMI groups was >20 kg.
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TABLE 3. Actual and adjusted birth weights (g) for term deliveries of African American adolescents (n = 705) by low, average, and high prepregnancy BMI (in kg/m2) and gestational weight gain category1
The incidence of SGA and suboptimal birth weight in mothers who gained less than the recommended range for their BMI compared with those who gained within or above their recommended ranges were significantly higher in the low-BMI (P < 0.0001) and average-BMI (P < 0.01) groups but not in the high-BMI group (P > 0.30), although it should be noted that only 16.5% of these high-BMI teens gained below the recommended range. Differences in the rates of these outcomes between those who gained in the lower half and those who gained in the upper half of the recommended ranges were also inconsistent; however, the number of subjects in some categories was relatively small. For example, in the high-BMI teens, the incidence of SGA birth weight did not differ significantly between those who gained in the lower half of the recommended range and those who gained in the upper half of the recommended range, whereas the incidence of suboptimal birth weight increased. In low-BMI teens, the incidence of SGA birth weight increased, whereas the incidence of suboptimal birth weight decreased. In the average-BMI teens, for whom the sample size was largest, the incidence of both outcomes decreased. For all BMI groups, LGA birth weight and above-optimal birth weights (>4000 g) occurred almost exclusively in those who gained above their recommended range (data not shown).
For the group as a whole, the risk of SGA birth weight after control for potentially confounding variables was found to be significant (Table 4), which indicated that the largest benefits were achieved with gains from below to within the lower half of the recommended ranges. Regression analyses of birth weight outcomes examined separately by BMI category suggested that the effect on birth weight of increasing gains from below to within the recommended range was most significant for low- and for high-BMI teens (birth weight increased by 260 g in both groups).
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TABLE 4. Effect of weight-gain category on risk of small-for-gestational-age (SGA) birth in African American adolescents who delivered at term1
DISCUSSION
In this cohort of pregnant African American adolescents, birth weight outcomes markedly improved in teens in all BMI groups when weight gain increased from below to within the lower half of the IOM recommended range. The additional benefits from gains in the upper half of the recommended range were modest or equivocal. Additional weight gain conferred the greatest benefit on infants of low- and average-BMI teens and had the least effect on overweight teens, most of whom had infants of healthy birth weight regardless of the level of weight gain. Furthermore, there appeared to be a tendency for higher weight gains to be associated with a higher proportion of adverse outcomes in this BMI group. The vast majority of the heavier teens (64%) gained above the recommended range, which likely increased their risk of obesity and its associated chronic diseases. Moreover, whereas 23% of the study sample entered pregnancy overweight (n = 191), an additional 18.5% (n = 151) entered pregnancy with low or average BMIs but gained >40 kg; 22.5% (n = 183) entered pregnancy with low or average BMIs and gained more than the IOM recommended range and therefore were at increased risk of overweight after pregnancy. These risks may be considerable in light of research indicating that African American women have elevated rates of postpartum weight retention (9, 24).
These findings are consistent with a number of other recent studies that recommend a reconsideration of the IOM weight recommendations for adolescents and African Americans (12, 14, 25, 26). A study by Schieve et al (25), which examined a much larger population of African American adult women (n = 33 101), reported adjusted birth weights that were slightly higher than ours and a considerably lower incidence of low birth weight but very similar trends. With greater power than the current study, Schieve et al also found that increases in gestational gains had a somewhat contradictory effect on the birth weight outcomes examined (mean birth weight and risk of low birth weight) in African Americans and concluded that gains in the upper half of the recommended range were of questionable benefit. Hickey et al (26) came to similar conclusions after examining a cohort of 2219 African American adults. Another study, which examined adult gravidas in Baltimore (12), calculated that among black women of average and high BMI, the gains required to reduce their risk of SGA birth weight to levels observed among white women would be excessive (25 kg for women of average prepregnancy BMI) and thus ill-advised. All 3 studies, as well as an expert panel convened in 1996 by the Federal Maternal and Child Health Bureau (14), cited the lack of clear evidence of benefits and the increased risk of postpartum weight retention and obesity among black women as reason to be cautious about recommending higher gestational weight gains. Our study was the first to examine the effect of weight gain below, within, and above the IOM recommendations in a sample of adolescents.
The African American teens in our study showed less tendency to undergain than did African American women in other studies (14, 26), but these findings were consistent with studies of adolescents (2, 14). In our study population, 29% of the adolescents who delivered at term gained excessive amounts of weight (>18 kg, or gains above the highest upper limit recommended by the IOM), a similar proportion to that reported for other adolescent cohorts (2, 6). Despite their higher weight gains, the median birth weight of 3151 g for this cohort was below the 1991 national reference of 3495 g for infants of similar gestational age (20), below the reference birth weight for African Americans of 3330 g (27), but similar to the birth weight reported for other adolescents (28). Thus, as did other researchers who studied pregnancy during adolescence, we observed that higher maternal weight gains did not necessarily contribute to higher infant birth weights.
The 1990 IOM report noted that the limited data available suggest that young adolescents (<2 y postmenarche) may give birth to smaller infants for a given weight gain than do older women (5). We found this to be the case in our African American cohort. In the subjects who delivered at term for whom we had data on age at menarche (n = 607), those of low gynecologic age had infants with significantly lower mean birth weights than did those of higher gynecologic age (3079 ± 347 compared with 3237 ± 437 g; P = 0.02), despite similar mean weight gains (15.1 ± 7.4 compared with 14.8 ± 7.1 kg; P > 0.10). This reduction in birth weight remained significant after control for smoking, prepregnancy BMI, parity, preeclampsia, gestation duration, and weight gain. However, total gestational gain and birth weight of infants born to mothers of young chronologic age (<15 y at infant birth) who delivered at term were not significantly different from those of older mothers (16.1 ± 6.5 compared with 14.8 ± 7.1 kg; P > 0.20) and (3269 ± 464 compared with 3218 ± 427 g; P > 0.40), respectively. The incidence of SGA and suboptimal birth weight did not differ significantly by either gynecologic or chronologic age.
Our adolescents were recruited from a disadvantaged community. According to the 2000 census, the median income in their neighborhood was <$27 000; 30.8% of the households with children aged <18 y were below the poverty level, 96.4% of the population was African American, and violent crime rates were high (29). A growing body of literature shows that neighborhood poverty and violence, racial discrimination, and associated psychological stress are associated with the risk of poor birth outcomes, particularly among African Americans (30, 31). It is possible that such adversity contributed, in ways that we did not measure, to the elevated rates of growth restriction and suboptimal birth weight observed.
Some limitations to our study should be considered when interpreting these results. Prepregnancy weight was self-reported, and we do not know whether the adolescents were willing and able to report their prepregnancy weight accurately. We adjusted 6.6% of the reported weights, and it is possible that additional inaccurate weights were undetected, which could have led to misclassification by both prepregnancy BMI and weight-gain category. Our adjustments tended to increase estimates of prepregnancy weight and decrease estimates of gestational weight gain but did not affect our overall findings or conclusions. Missing data on prepregnancy weight, weight gain, or birth weight reduced our power to detect differences in these variables when subjects were stratified by BMI and weight-gain category.
Our data did not allow us to determine which adolescents may have gained fat, lean mass, and height as a necessary part of their own maturation in addition to the gain supporting fetal growth. Also, we could not identify those adolescents that possibly retained excess maternal fat postpartum. Longitudinal research suggests that most female teens continue to grow in stature to age 18 y and beyond (32) and to gain weight as part of this growth until age 18 y (33), although there is evidence that black girls may mature earlier and gain less in height after age 14 y (34). In our cohort we noted no biologically or statistically significant differences in gestational weight gain among those with early (<11 y) or late (>12.9 y) onset of menses, which are potential markers of different rates of growth. It is noteworthy that teens who had given birth previous were significantly more likely to be overweight than were primiparas. Moreover, preliminary evidence from our research suggests that dietary quality among these teens is low (16) and, thus, poor eating habits may contribute to inappropriate weight gain. In conclusion, our data suggest no clear benefit from gestational weight gains at the upper limits of the IOM recommendations, particularly in teens who enter pregnancy overweight. The long-term costs to these mothers of additional weight gain may overshadow the modest improvements in birth weight. Other risk factors—such as poverty, racial discrimination, or dietary quality—may play a more important role than do higher levels of weight gain in optimizing birth weight in this population.
ACKNOWLEDGMENTS
JNN was responsible for the statistical analyses and manuscript preparation with the guidance of KOO and LEC. S-CC compiled the original data set, conducted extensive analyses of the data, and reviewed these analyses for consistency. JM and MSN provided prenatal care and nutrition counseling, respectively, to the subjects included in the data set and were responsible for much of the data entered into the medical charts. FRW reviewed all medical aspects of the analyses, including calculations of adjusted last menstrual period, adjustments of prepregnancy weights, and interpreting birth weight differences, and assisted with manuscript preparation and interpretation.
REFERENCES
1 From the Departments of Internal Medicine and Nutrition, University of California, Davis, Davis, CA (CHH), and Millenium Pharmaceuticals, Inc, Cambridge, MA (RR)
2 The full content of this editorial is available on the Internet at http://www.ajcn.org. 3 Reprints not available. Address correspondence to CH Halsted, The American Journal of Clinical Nutrition, 3247 Meyer Hall, University of California, Davis, One Shields Avenue, Davis, CA 95616-8790. E-mail: chhalsted{at}ucdavis.edu.
INTRODUCTION
On 2 May 2005, the National Institutes of Health (NIH) issued a policy aimed at ensuring rapid public access to the results of NIH-funded research. The new NIH Public Access Policy requests that principal investigators (PIs) of NIH-funded research deposit an electronic form of their completed manuscripts on the NIH National Library of Medicine’s PubMed Central website within 12 mo of journal acceptance (1). However, this new policy may be both redundant and in conflict with the publishing procedures and policies of most independent scientific journals. For example, the electronic version of each American Journal of Clinical Nutrition (AJCN) issue is immediately available at no cost to institutions in developing countries designated as low income by the World Bank, and the public can obtain at no cost all editorials and review articles immediately and all other content 12 mo after publication. At the same time, and to ensure scientific integrity, AJCN policies include ownership of copyright to all accepted material after rigorous copyediting before publication. Our copyright policy provides legal protection to authors, the AJCN, and its sponsor—the American Society for Clinical Nutrition (ASCN)—against commercial advertisers and others who might otherwise profit by distorted use of our published material. Such protection is particularly important in the realm of diet and nutrition, which is the source of a vast commercial enterprise. Although PIs are likely to follow the requests of their primary funding agency, they risk running afoul of our copyright policy and potentially of posting misleading information if manuscripts are submitted to PubMed Central for public access before they have been properly copyedited. The purpose of this editorial is to provide a roadmap to not only help prospective AJCN authors comply with the NIH directive but also to minimize conflict with AJCN copyright and publishing procedures.
THE NIH PUBLIC ACCESS POLICY
According to its Public Access Policy, the NIH now requests that PIs submit, to the National Library of Medicine’s PubMed Central, manuscripts accepted for publication on or after 2 May 2005 that result from currently funded or previously supported NIH research projects. The NIH policy is targeted at recipients of all research grant and career development award mechanisms, cooperative agreements, contracts, institutional and individual National Research Service Awards, and NIH intramural research studies. The NIH policy applies to peer-reviewed original research publications that have been supported directly, in whole or in part, by NIH funds but does not apply to book chapters, editorials, reviews, or conference proceedings (1). NIH-funded PIs are requested to comply with the terms of the NIH manuscript submission system (Internet: http://www.nihms.nih.gov) at the National Library of Medicine’s PubMed Central by submitting an electronic version of the final manuscript at the time of acceptance for publication. The policy states that "the author’s final manuscript is defined as the final version accepted for journal publication, and includes all modifications from the publishing peer review process" (1). When a PI deposits an article in PubMed Central, it will be kept internally at the NIH until a time stipulated by the PI for its release to the public. The NIH encourages PIs to permit public release of accepted manuscripts as soon as possible within the 12-mo time frame after the official date of final publication.
POTENTIAL PITFALLS OF THE NIH POLICY
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THE WASHINGTON DC PRINCIPLES COALITION
Last year, in response to governmental and other efforts to promote immediate public access to scientific material, the AJCN joined a coalition of 104 other not-for-profit publishers and developed the Washington DC Principles for Free Access to Science (DC Principles) (2). First among these principles is the following statement: "As not-for-profit publishers, our mission is to maintain and enhance the independence, rigor, trust, and visibility that have established scholarly journals as reliable filters of information emanating from clinical and laboratory research." These principles emphasize that we already support 1) the efficient online access to our journal content through HighWire Press, which includes extensive electronic reference linkages to hundreds of other journals; 2) the immediate availability of important articles of interest, such as editorials and review articles, at the time of publication; 3) the immediate availability of all articles to scientists in low-income countries; and 4) the equal opportunity for all scientists worldwide to publish in scientific journals, regardless of economic circumstance. Furthermore, the coalition questions the value of the immediate public distribution of scientific content (3). This stance supports the concepts offered in a previous AJCN editorial, ie, that the public is too often confused by conflicting data and that scientific "facts" are seldom established by a single experiment, require reproducibility, and should only be considered valid after confirmation by independent and unbiased follow-up studies (4).
THE AJCNS RESPONSE
In meeting the challenges posed by the new NIH Public Access Policy, the AJCN takes the position that the PI is ultimately responsible for any conflicts that may arise from compliance with the NIH policy, ie, from the premature submission of accepted manuscripts. According to the copyright agreement that all authors sign when submitting a manuscript to the Journal, the AJCN owns the copyright to all material destined for publication. To facilitate compliance with the NIH policy, the AJCN will grant the PI permission to deposit an accepted manuscript in PubMed Central with the stipulation that the PI accepts any liability that may arise from the release of the manuscript in its unpublished form. To avoid this potential liability, the AJCN recommends that the PI delay submission of the manuscript to PubMed Central until after the copyediting process is complete. At that point, the PI will be provided with a PDF of the final version of the article that is to be published in the AJCN. Provision of this final version of the article to PubMed Central will avoid any ambiguity that could otherwise result from the existence of one uncopyedited and another copyedited version of the same article.
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ACKNOWLEDGMENTS
Neither author had any financial or other personal conflict with the statements expressed in this editorial.
REFERENCES
1 From the Department of Family Medicine, University of Wisconsin–Madison, Madison, WI
2 Supported by grant no. U01-86098 from the National Cancer Institute. 3 Reprints not available. Address correspondence to AK Adams, Department of Family Medicine, University of Wisconsin–Madison, 777 South Mills Street, Madison, WI 53715. E-mail: alex.adams{at}fammed.wisc.edu.
ABSTRACT
Background: Prevalence rates of overweight are higher among American Indian children than among any other ethnic group, but little research has explored contributing influences.
Objective: The objective was to determine the prevalence and predictors of body mass index (BMI; in kg/m2) 85th percentile in American Indian children in Wisconsin.
Design: A retrospective analysis was conducted with linked pediatric and pregnancy nutrition surveillance systems and birth records from 1997 through 2001. Participants were American Indian mothers and children (aged 0–3 y) who were participating in the Special Supplemental Nutrition Program for Women, Infants, and Children in Wisconsin. Outcome measurements included indicators of BMI 85th percentile identified by using binary logistic regression.
Results: Of the 3-y-olds, 22.2% were overweight and 18.7% were at risk of overweight. Of their mothers, 42.5% had smoked during pregnancy. Smoking at the initial prenatal visit significantly predicted overweight and risk of overweight in children at age 3 y (odds ratio: 2.16; 95% CI: 1.05, 4.47). Despite being smaller at birth, the children of smoking mothers had a significantly (P < 0.05) greater increase in weight-for-length z score between birth and age 3 y than did children of nonsmokers. This greater increase was due to a significantly (P < 0.02) greater increase in weight in children of smokers than in those of nonsmokers and not to a relatively slower increase in height.
Conclusions: Our findings suggest the early influence of maternal smoking on the prevalence of overweight at age 3 y in a high-risk American Indian population and provide evidence that interventions to reduce smoking in pregnant women may be warranted.
Key Words: Childhood • overweight • smoking during pregnancy • American Indians
INTRODUCTION
The prevalence of childhood obesity in 2–5-y-olds has increased 34% in the past 10 y, and the highest rates appear in minority populations (1). The most recent data indicated that 14.3% of children aged 2–5 y were overweight, and an additional 15.4% were at risk of overweight. It is estimated that overweight children are 1.4–4 times as likely to become overweight adults as are normal-weight children (2-4), and this results in a public health issue of great significance. American Indian communities have the highest rates of childhood obesity of any ethnic group in the United States (1). American Indian adult mortality due to cardiovascular disease is highest and diabetes is second highest among American Indians living in the Bemidji Indian Health Service Area comprising the states of Minnesota, Wisconsin, and Michigan (5). However, little research has been conducted on the prevalence or predictors of obesity among American Indians in this area. Because of the inherent difficulty of treating overweight and obesity and because of the link of overweight and obesity to adult disease, it is imperative that preventive measures are employed (6, 7).
Previous research on contributors to childhood obesity focused primarily on older children and white children and identified genetic, neonatal, environmental, and lifestyle factors related to overweight. These included sex (8-11), race (11), maternal BMI (8-10, 12), paternal BMI (8, 12), gestational diabetes (13, 14), smoking during pregnancy (12, 15, 16), birth weight (8, 10, 12, 17), breastfeeding (16-19), television watching in h/d (11, 12), sleep in h/d (8, 12), rate of weight gain during the first 6 mo of life (10), and family socioeconomic status (11, 16).
To examine some of these factors in a younger and underrepresented American Indian population, this study used linked data from 5 y of Wisconsin Pediatric Nutrition Surveillance System (PedNSS), Pregnancy Nutrition Surveillance System (PNSS), and birth records to identify predictors of overweight in American Indian children at age 3 y. Maternal and child predictors included were birth weight, breastfeeding, maternal prepregnancy body mass index (BMI; in kg/m2), family income, maternal weight change during pregnancy, smoking, and education. This information will help in the design and evaluation of community-based obesity prevention programs in American Indian tribes in Wisconsin.
SUBJECTS AND METHODS
Subjects
The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) operated by the Food and Nutrition Service of the US Department of Agriculture collects information on maternal prenatal and postnatal characteristics and demographics and also performs child growth and nutrition measurements from birth to age 5 y. These data, along with Head Start and maternal and child health data, are reported to the Centers for Disease Control and Prevention (CDC) by the states and stored as 2 data sets, the PNSS and the PedNSS. In Wisconsin, only WIC data are reported to the CDC. These data sets offer an opportunity to look at familial and environmental determinants of overweight in children from lower socioeconomic environments. WIC serves 48% of American Indian infants and children and 65% of American Indian women (20).
The PedNSS and PNSS data sets were obtained from the CDC for all Wisconsin records for the years 1997 through 2001. In Wisconsin, PedNSS data are collected by local WIC clinics, amalgamated, and submitted monthly to the CDC. Information on child growth, nutrition, and general health is included. The PNSS data set contains information on maternal factors related to gestational and postnatal health. Permission for the use of Wisconsin PedNSS and PNSS data was obtained from the Bureau of Family and Community Health, Division of Public Health, Wisconsin Department of Health and Family Services. Birth records for all American Indian births from 1997 through 2001 were obtained from the Bureau of Health Information, Division of Health Care Financing, Wisconsin Department of Health and Family Services. These records included demographic and birth data for both the mother and the child. For the purposes of this study, mothers were identified as American Indian if they self-selected "American Indian" on any of the PedNSS, PNSS, or birth records. In addition, children were identified as American Indian if one or both parents self-selected the child as "American Indian" on the birth record.
Procedures followed were in accordance with the ethical standards of the institutional committee on human experimentation. Approval was obtained from the state WIC office and the University of Wisconsin Institutional Review Board.
An employee of the Wisconsin Department of Health and Family Services matched mothers' PNSS and children's PedNSS records to birth records. We obtained 1649 PNSS maternal records and 21 525 American Indian PedNSS records (representing unique child visits; there were multiple visits per child) for children between the ages of 0 and 60 mo. A total of 6769 American Indian birth records were obtained, of which 3439 (51%) were matched to PNSS and PedNSS records.
The 3439 mother-child pairs were further decreased to 252 unique pairs when children born at <36 wk gestation (n = 424) and children for whom maternal smoking (n = 267), birth (n = 321), or 36-mo weight and height (n = 2797) information was missing were excluded. PedNSS assessments occurring at 36 ± 3 mo were included as 36-mo data. Children with a clinical gestational age < 36 wk were excluded from the analyses, as were children with birth defects that affect growth and development (eg, cerebral palsy). The construction of the sample is shown in Figure 1.
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FIGURE 1.. Construction of final sample (n = 252) from Pregnancy Nutrition Surveillance System (PNSS), Pediatric Nutrition Surveillance System (PedNSS), and Wisconsin birth records. WI, Wisconsin; AI, American Indian; ID, identification.
Study design and predictor variables
This study is a retrospective analysis of available data. Outcome measures were risk of overweight and overweight at age 36 mo and weight-for-length (WFL) (kg/cm) z scores at birth and 36 mo. Persons at risk of overweight were defined as those with BMI 85th percentile and < 95th percentile, and those who were overweight were defined as those with BMI 95th percentile for age- and sex-specific CDC standards (21). Because CDC BMI values for children begin at age 2 y, WFL z scores were used as an outcome measure to allow for comparisons between size at birth and that at 36 mo and for the measure of the change between these 2 time points. WFL z scores were computed by using variables from the CDC that were based on national standards (22). According to WIC protocols, infants and children were weighed to the nearest half-ounce (14 g) and the nearest quarter-pound (110 g), respectively, while wearing underclothes or light clothing at routine visits. At the same time, height was measured to the nearest 1/8 inch (0.31 cm) while the subject was not wearing shoes (23). Large-for-gestational age (LGA) status was defined as >4000 g and small-for-gestational age status was defined as <2500 g at birth.
Maternal predictor variables analyzed (n = 252, unless otherwise noted) were age (in years), prepregnancy BMI (n = 226), weight change (kg gained or lost) during pregnancy (n = 239), smoking before pregnancy (no. of cigarettes/d), smoking at initial WIC visit (no. of cigarettes/d), smoking at first postpartum visit (no. of cigarettes/d), education (no. of years), and income (n = 242). At the initial WIC visit, maternal height was measured to the nearest 1/8 inch (0.31 cm) while the subject was not wearing shoes, and pregravid weight was recorded to the nearest pound (450 g) via self-report or referral data. Smoking before pregnancy was ascertained retrospectively at the initial WIC visit and was classified as any smoking during the 3 mo before the pregnancy (23). All smoking variables were reduced to a bivariate measure of whether (yes or no) the mother ever smoked at each of the 3 time points (ie, smoking before pregnancy, assessed retrospectively at the first WIC visit; smoking at the initial WIC visit, which was used as "smoking during pregnancy"; and smoking at first postpartum visit). The initial WIC visit was defined as the first visit to WIC for a single pregnancy. Most initial WIC visits occur during the first or second trimester (24).
Child predictor variables (n = 252, unless otherwise noted) were birth weight (g), birth length (cm), sex, clinical gestational age (no. of weeks),and breastfeeding (no. of days/wk) (n = 192). Breastfeeding was reduced to a bivariate measure of ever breastfed (yes or no) if the child was breastfed for 1 d. Other variables were the changes in weight and height from birth to 36 mo, expressed as percentages of increase.
Statistical analysis
The relations among maternal and child predictor variables and risk of overweight and overweight at 36 mo were examined by using zero-order Pearson's correlations. Predictor variables with significant correlations at P < 0.05 were combined in a binary logistic regression model of risk of overweight and overweight at 36 mo. Results are reported as odds ratios and 95% CIs. Change in WFL z scores (birth to 36 mo) comparing children of smokers and nonsmokers was examined by using a two-way repeated-measures analysis of variance (ANOVA) with time (birth or 36 mo) as a within-group factor and smoking or nonsmoking as a between-group factor. Differences in the percentage change in height and weight were tested with univariate ANOVAs. Analyses were performed with SPSS software (version 12; SPSS Inc, Chicago, IL).
RESULTS
Seventy-three percent of mothers who were matched to children (ie, enrolled in WIC) were single, and 40.6% smoked during pregnancy. The average length of education was 12.1 y. In a comparison of matched WIC mothers with non-WIC mothers who had birth records, chi-square analysis found that the WIC mothers were more likely than were the non-WIC mothers to be single (73.0% and 41.0%, respectively; P < 0.001), to have smoked during their pregnancy (40.6% and 29.9%, respectively; P < 0.001), and to have less education (12.1 and 12.6 y, respectively; P < 0.001). A comparison of the characteristics of our final sample, ie, mothers who were not enrolled in WIC, and those of the larger sample of matched WIC American Indian mother-child pairs, is shown in Table 1. There were no significant mean differences in predictor variables between our final sample of 252 mother-child pairs and other American Indian mother-child pairs enrolled in WIC (n = 3015).
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TABLE 1. . Characteristics of mothers and children in the final sample, all matched Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)-enrolled American Indian (AI) mother-child pairs of 36 wk gestation and AI non-WIC mothers1
Of the children from the 252 mother-child pairs analyzed, 22.2% of 3 y olds were overweight, and an additional 18.7% were at risk of overweight. Most children had a normal birth weight, but 18.7% of children were LGA. Of the mothers, 54.0% ever breastfed, and 42.5% smoked during pregnancy. Most (57.9%) mothers were either overweight or obese before pregnancy (Table 1).
Child and mother characteristics that showed significant intercorrelations were entered simultaneously into a binary logistic regression model predicting BMI 85th percentile at 36 mo. Only smoking at initial WIC visit [odds ratio (OR): 2.16; 95% CI: 1.05, 4.47) was a significant predictor of children at risk of overweight or overweight at age 3, although birthweight (OR: 1.82; 95% CI: 0.09, 3.71) and ever breastfed (OR: 0.53; 95% CI: 0.26, 1.06) tended toward significance (Table 2).
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TABLE 2. . Odds ratios (OR) and 95% CIs for BMI 85th percentile at 36 mo using binary logistic regression1
Children were divided into birth weight sextiles to examine the relative size of effects across birth weights. Children with higher birth weights had higher WFL z scores at 36 mo, and this was seen across all birth weight groups when 2-way repeated measures ANOVA was performed (P < 0.01). For all birth weight groups except small-for-gestational-age children, change in WFL z scores at 36 mo was positive. LGA children had WFL z scores 1 SD above those of other children of similar age and same sex at 36 mo.
Overall, the mean increase in WFL z score increase from birth to 36 mo was significantly (P = 0.009) more pronounced in children of mothers who smoked (1.33) than in children of mothers who did not smoke (0.88). These children of smokers were significantly smaller at birth, but, at 36 mo, they were significantly larger than were the children of nonsmoking mothers, independent of birth weight, as indicated by the significant difference in the increase in z score between the 2 groups of children (P = 0.009; Figure 2). This relation was also found at multiple time points between birth and 36 mo when a subset of children with 6 measurements was analyzed (n = 183; P = 0.035).
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FIGURE 2.. Mean (±SE) changes in weight-for-length z score of children of nonsmoking and smoking mothers between 2 time points, birth and 36 mo (n = 252). z Score change x time interaction was significant, P = 0.009 (repeated-measures ANOVA).
Changes in WFL can be based on either a relatively greater increase in weight or a relatively slower increase in length. Birth weights were significantly higher in the nonsmoking group (3460 and 3622 g, respectively; P < 0.05), but birth lengths did not differ significantly between the 2 groups of children (50.4 and 50.8 cm, respectively), which indicated that the lower WFL z scores at birth in children of smokers were due to lower relative weight and not to greater relative length. When changes in WFL z scores were considered relative to changes in weight and height by using univariate ANOVAs, only the mean percentage change in weight differed significantly between the children of smokers and those of nonsmokers (Figure 3 and Figure 4), which indicated that the larger increase in the WFL z score of children of smoking mothers was due to relatively greater increases in weight and not to slower increases in height. In addition, birth weight was negatively correlated with mean percentage change in both weight and height (Figures 3 and 4).
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FIGURE 3.. Mean percentage change in weight from birth to 36 mo by birth weight group and mother's smoking status at initial Women, Infants, and Children visit (n = 252). Birth weight group main effect, P < 0.001; maternal smoking effect, P = 0.024; group x smoking interaction, NS (ANOVA).
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FIGURE 4.. Mean percentage change in height from birth to 36 mo by birth weight group and mother's smoking status at initial Women, Infants, and Children visit (n = 252). Birth weight group main effect, P < 0.001; maternal smoking effect, P = 0.714; group x smoking interaction, NS (ANOVA).
We also examined the association of postnatal smoking (women who began smoking after delivery) on child weight at age 36 mo. Prenatal and postnatal smoking correlated highly (r = 0.80). However, when we compared child growth between children of postnatal smokers and children of nonsmokers, no significant differences were found.
DISCUSSION
This retrospective analysis of linked PedNSS, PNSS, and birth record data for Wisconsin American Indians documented high rates of overweight risk status and overweight at age 3 y. Maternal smoking was a significant predictor of overweight risk status and overweight. Children of mothers who smoked during pregnancy showed significantly greater rates of weight gain than did children of nonsmokers, which resulted in significantly greater increases in WFL z score between birth and age 3 y.
In our study population, 22.2% of children were at risk of overweight and 18.7% were overweight. These rates are higher than those of overweight reported nationally for 3-y-old American Indian WIC participants—14.4% (20). Moreover, 18.7% of the children in the sample in the current study were LGA, whereas national American Indian and Wisconsin all-race proportions are 11.3% and 8.7%, respectively (1). This high rate of LGA is especially troubling, given the correlation between birth weight and later BMI seen in this study and in others. Rates of breastfeeding were comparable to reported all-race national and state rates of 52.5% and 55.0%, respectively, but were slightly below national rates of 59% for American Indians participating in WIC (25).
In our population, children of mothers who smoked at the initial WIC visit were almost twice as likely as children of nonsmokers to have a BMI 85th percentile at age 36 mo. The increased overweight risk and incidence of overweight among children of smokers seen in our study was similar to, if not slightly higher than, that seen in other populations (15, 26-28). However, because of the nature of our data, we were not able to establish a dose-dependent relation for smoking as seen in studies by Power and Jefferis (26) and von Kries et al (15).
The prevalence of maternal smoking during pregnancy seen in the current study is higher than that reported in other studies (15, 26, 27, 29, 30). However, our data corresponded to those from a recent report indicating that 40% of Wisconsin American Indian mothers smoked during pregnancy (28). Mothers enrolled in WIC were significantly more likely to have smoked during pregnancy than were mothers not enrolled in WIC. This agrees with national trends for WIC or lower-income mothers (24, 25).
In the current study, we used smoking status at initial WIC visit to establish whether the mother smoked during pregnancy. Almost half (48.4%) of the mothers who visit WIC do so within the first trimester of their pregnancy, and 39.8% visit WIC during the second trimester (24). By using smoking at initial WIC visit, we captured both mothers who smoked throughout the pregnancy and mothers who smoked during the first part of their pregnancy and quit thereafter. Toschke et al (31) showed an equal effect of smoking in the first trimester only and of smoking throughout pregnancy on overweight at age 5–6 y.
Children of mothers who smoked during pregnancy were, on average, 160 g smaller at birth than were children of nonsmoking mothers, which is consistent with findings of other studies (26). In our sample, however, the children of mothers who smoked were not shorter at birth than were the children of nonsmokers, and this finding is at odds with the literature. Furthermore, the children of mothers who smoked during pregnancy did not show any significant differences from the children of nonsmoking others in height at 36 mo, whereas other studies found that the children of smokers were shorter than the children of nonsmokers at ages 2 and 7 y (31, 32). Nevertheless, these results agree with other studies that showed no significant difference in height at age 3 y after adjustment for maternal, environmental, and birth characteristics (33-35). The lower birth weights of infants of mothers who smoked is important because studies have shown both a greater risk of morbidity in obese persons who were small at birth than in those with a normal birth weight (36) and a greater number of risk factors for adult disease in children who displayed "catch-up" growth between ages 1 and 2 y (34). Paradoxically, other studies show that lower birth weights are correlated with lower BMIs, which suggests that the decreased birth weight of children of smokers may attenuate the magnitude of their later overweight (37).
Our results remained robust after we considered several additional variables, including size at birth and the mother's prepregnancy weight. The significance of a relation between maternal smoking and child overweight at age 3 y, independent of these factors, suggests mechanisms separate from growth restriction through which smoking affects early childhood growth. Mechanisms relating to alterations in the fetal environment that affect endocrine balance or metabolic functions or the mechanisms of the direct effect of nicotine on brain development have been put forth by others (15, 31, 38).
The correlation of smoking with weight gain from birth to 36 mo, independent of birth weight, could also be explained by lifestyle factors that correlate with smoking—eg, poor nutritional choices and reduced physical activity—and promote weight gain. However, we did not see an association between maternal postnatal smoking and child overweight at age 3 y. Thus, the association between maternal smoking and later growth may be due to the in utero effect of smoking and not to other variables that may be associated with smoking. Toschke et al (31) postulated that smoking in early pregnancy has a direct metabolic effect on the offspring, whereas later smoking may be a marker for other lifestyle factors.
Similar to studies in other populations (8, 10, 39), the current study did not find a significant effect of breastfeeding on overweight at age 3 y. A reason for this may be that the percentage of mothers who exclusively breastfed was not comparable between our study and other studies that showed an association (17, 18). Alternatively, the effects of breastfeeding may not become apparent until later childhood. For example, Bergmann et al (16) showed a protective effect of breastfeeding on overweight at age 6 y, but not at age 3 y.
Limitations of the current study included the large reduction in number of subjects because of nonmatching and missing data. The greatest loss of mother-child pairs was due to the requirement of WIC visits until age 36 mo. It is possible that mothers who remained in WIC long enough to be included differed in some way (eg, nutritional status) that would affect child growth, but none of the relevant measures we examined reflected such differences. An additional obstacle was the standards for collecting PedNSS and PNSS data, which are geared toward easy reporting by WIC personnel rather than toward scientific analysis. Also lacking were data on paternal smoking, which was correlated to child overweight in another study (40). Finally, key differences were noted between WIC and non-WIC participants. However, when differences were considered in more detail, income, education, maternal weight gain, and age did not correlate with the change in WFL z score. This suggests that the relation between smoking and change in WFL z score is independent of these factors and may hold true in a non-WIC population.
To our knowledge, this is the first study to show a relation between smoking in pregnancy and later overweight in American Indian children. Given the limitations and potential biases inherent in retrospective analysis, prospective cohort studies would be an ideal next step in evaluating the suggested relation between smoking and overweight. Our results have important implications for health care and point to the need for targeted interventions to reduce smoking in pregnant women and women of childbearing age. A similar message should be communicated at the initial WIC visit and at subsequent WIC visits throughout a pregnancy.
ACKNOWLEDGMENTS
We thank Richard Miller of the Wisconsin Bureau of Health Information for his invaluable support in linking the data. We also thank Connie Welch and Patti Herrick of the Wisconsin WIC Program for their assistance in obtaining these data. We thank Judith S Kaur, of the "Spirit of E.A.G.L.E.S." program at the Mayo Clinic, Rochester, MN, for assistance in obtaining project funding. Finally, we thank David Brown for his comments and suggestions throughout this process. None of the authors had personal or financial conflicts of interest.
AKA obtained study funding, established the study concept and design, acquired data, supervised the execution of the study, reviewed and revised the manuscript, and provided critical intellectual content. HEW contributed to the study concept and design, provided administrative support throughout the study, and wrote the manuscript draft. RJP provided statistical expertise and analyzed and interpreted data, revised the manuscript, and provided critical intellectual content.
REFERENCES
1 From the Laboratory of Membrane Biochemistry and Biophysics, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD (KDS, RJP, SB, MM, and NS); the Food Composition Laboratory, Beltsville Human Nutrition Research Center, US Department of Agriculture, Beltsville, MD (RJP and VPF); the Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (HR); and the Departments of Obstetrics & Gynecology, School of Medicine (JJ, MB-A, JEW, RJS, and JHH) and Psychology (JHH), Wayne State University, Detroit, MI
2 Supported by the National Institute on Alcohol Abuse and Alcoholism research grant N01-AA83019 to JHH. 3 Address reprint requests to N Salem Jr, LMBB, NIAAA, 5625 Fishers Lane, Room 3N-07, Rockville, MD 20852. E-mail: nsalem{at}niaaa.nih.gov.
ABSTRACT
Background:African American women and socioeconomically challenged women are at risk of compromised folate status and, thus, of folate-related birth defects. Data are limited on circulating folate concentrations in pregnant African American women after folic acid fortification of the food supply was implemented.
Objective:The objective was to determine the influence of smoking and alcohol consumption on plasma 5-methyltetrahydrofolic acid (5-MTHFA) concentrations in pregnant African American women.
Design:Alcohol consumption, smoking exposure, and other characteristics of pregnant African American women reporting to an inner-city antenatal clinic were assessed. At 24 wk of gestation, blood samples and food-frequency intake data were collected. Plasma 5-MTHFA concentrations were determined by liquid chromatography–mass spectrometry for 116 subjects and examined in a correlational study design.
Results:Dietary folate and markers of alcohol consumption were positively associated, whereas exposure to smoke was negatively associated with plasma 5-MTHFA. More than one-half of the participants in this population failed to meet the recommended dietary allowance for dietary folate equivalents of 600 µg/d during pregnancy.
Conclusions:Most inner-city African American women are not meeting the recommended dietary allowance for dietary folate during pregnancy, and smoking may further compromise their folate status. Programs to reduce smoking and raise awareness about the importance of folate and multivitamin supplementation during pregnancy need to target this population.
Key Words: 5-Methyltetrahydrofolic acid • folate • folic acid • fortification • African American women • pregnancy • nutrition • smoking • alcohol • electrospray mass spectrometry • human plasma • polyunsaturated fatty acids
INTRODUCTION
In 1996 the federal government mandated that grain-based foods manufactured in the United States be fortified with folic acid (140 µg/100 g). This fortification was considered the most effective method of increasing folate intake in women of childbearing age (1). Fortification of grain products with folic acid increases folate concentrations in middle-aged and older populations (2, 3) and in women of childbearing age (4, 5). The implementation of folic acid fortification has lowered the incidence of neural tube defects (NTDs) in the United States by 20% on the basis of birth certificate data (6) and by >50% when the incidence of NTDs in stillbirths and terminated pregnancies were included in the analysis (7). Congenital anomalies of the central nervous system, including spina bifida and anencephaly, have not decreased in Detroit African Americans after folic acid fortification (3.0 per 1000 live births in 1995 compared with 3.1 per 1000 live births in 2000) (8).
In women of childbearing age, African American women have lower folate concentrations in serum and erythrocytes than do white, Hispanic (4, 5), and Asian (4) women in the United States. These lower values coincide with lower intakes of dietary folate equivalent (DFE), after energy adjustment, in pregnant African American women than in white women (9). Socioeconomically disadvantaged women also have lower folate status than advantaged women (4). Low socioeconomic status has been associated with a cluster of health-detrimental behaviors, including poor diet, smoking, and lack of physical exercise (10). Other methods of increasing folate status such as multivitamin supplementation by inner-city African American women are difficult and require substantial effort because awareness about folate and NTDs is low (11).
Active and passive exposures to tobacco smoke are associated with lower serum and erythrocyte folate concentrations (12), and smoking during pregnancy decreases maternal plasma folate concentrations (13). Exposure to smoke is associated with both lower intake of dietary folate and lower blood folate status. The association of smoke exposure to folate status persists after adjusting for differences in intake, suggesting nondietary as well as dietary influences (12). Smoking and alcohol consumption have been positively correlated in African American women (14). Halsted et al (15) reviewed the negative effects of chronic alcohol intake on folate homeostasis in alcoholics and in animal models, but the effects may be complicated by the folate content of some alcoholic beverages, specifically beer (16). The effects of alcohol on folate metabolism during pregnancy in humans remain unclear (17).
The primary purpose of this study was to examine the influence of diet, smoking, and alcohol consumption in pregnant African American women at risk of alcohol-related birth defects on plasma concentrations of 5-methyltetrahydrofolic acid (5-MTHFA), the predominant circulating folate metabolite. We expected these results to provide insight into the effectiveness of folic acid fortification on the intakes of DFE and the concentrations of plasma 5-MTHFA in a population known to be at risk of both folate deficiency and of giving birth to infants with complex disorders of the nervous system. In addition, this study provides highly accurate and specific determinations of plasma 5-MTHFA by a stable-isotope liquid chromatography–mass spectrometry method, the advantages of which have been discussed previously (18).
SUBJECTS AND METHODS
Subjects and study design
Pregnant African American women (n = 116) presenting between February 1999 and January 2001 at the antenatal clinic at Wayne State University in Detroit were recruited on the basis of reported alcohol intake (described in "Alcohol and smoking exposure"). Women with high-risk pregnancies were excluded from the study. All procedures and protocols received prior approval by the Wayne State University Human Investigations Committee, and informed consent was obtained during the initial clinical visit.
A structured interview at the first antenatal visit determined eligibility and assessed demographic characteristics, alcohol intake, and smoking exposure (19). Socioeconomic status was measured with the use of a modified Hollingshead index (20). At the regular obstetrical visit at 24 wk of gestation, a 15-mL fasting blood sample was collected by venipuncture. Specimens were collected into heparinized tubes, kept cold (4 °C) until centrifuged (2000 x g for 5 min at 4 °C) to separate plasma and erythrocytes, and frozen at –75 °C until analyzed. Nutritional status was assessed with the use of a validated food-frequency questionnaire (21) modified to quantify selected dietary fats. Quantification was based on the US Department of Agriculture National Nutrient Database for Standard Reference, release 14 (22). Intakes of individual nutrients were adjusted for total energy intake by the nutrient residual method (23) to reduce measurement error (24). The energy adjusted nutrient intakes were used for statistical analyses and are herein described as "adjusted" throughout. DFEs were calculated according to the method recommended by the Food and Nutrition Board (25). Briefly, for natural food sources of folate, 1 µg folate = 1 µg, DFE; for synthetic vitamin preparations, 1 µg folic acid = 2 µg DFEs; and, for mixed food and supplement products, µg DFE is calculated by food folate (in µg) x 1 plus folic acid (in µg) x 1.7. All participants were advised about nutrient supplementation during pregnancy and received a prescription for a prenatal vitamin.
Alcohol and smoking exposure
At-risk drinking was determined by several proven screening tests, including the Michigan Alcoholism Screening Test (MAST) and the Tolerance, Annoyed or Angry, Cut down or quit, Eye opener (T-ACE) questionnaire (26-28). Quantitative alcohol intakes were determined by 14-d recalls from the time of conception and at the first prenatal visit. Recall information generated estimates of alcohol intakes as grams of absolute alcohol per day around the time of conception (AAD0) and at the time of the first prenatal visit (AAD1), grams of absolute alcohol per drinking day (AADD0; AADD1), and proportion of drinking days over those 14 d (PROPDD0; PROPDD1). Cigarette smoking by both the mother and the father were determined by maternal recall of the number of cigarettes smoked per day around the time of conception and at the first prenatal visit. All women with AAD0 14.2 g (0.5 oz) were recruited into the study, plus a random 8% sample of the remaining patients were recruited. This selection strategy oversampled the high-risk drinking women (19, 29).
Sample analyses
Liquid chromatography–mass spectrometry was used to determine the plasma 5-MTHFA concentrations as described previously (18). Before extraction, 13C5-5-MTHFA (10 ng) was added to 0.5 mL plasma as an internal standard. The analyte was isolated with the use of solid-phase extraction (Strata phenyl column 100 mg/mL; Phenomenex, Torrance, CA), washed with 0.03 M K2HPO4, and eluted with 0.5 mL HPLC mobile phase (acetonitrile:methanol:water, 26:14:60). Extract (40 µL) was injected onto a C18 HPLC column (150 x 4.6 mm; Phenomenex) with the use of a binary pumped Agilent 1100 HPLC (Palo Alto, CA) interfaced to an ion trap mass spectrometer (Finnigan LCQ, San Jose, CA), and samples were analyzed by electrospray ionization in the positive ion mode.
Statistical analyses
Pearson's correlations (two-tailed) were used to determine the bivariate associations of plasma 5-MTHFA with energy-adjusted DFEs and selected maternal variables. As the accuracy of DFE estimates are questionable, bivariate correlations to adjusted DFEs were also determined according to rank by Spearman's correlation coefficients. Adjusted DFE, food folate, fortified folate, and plasma 5-MTHFA were grouped according to adjusted DFE quartiles for trend analyses and mean comparisons by one-way analysis of variance (ANOVA) with a priori comparisons between the lowest and highest quartiles. Associations with 5-MTHFA were also evaluated with the use of multiple linear regression analyses. A parsimonious model with all included independent variables having P values < 0.10 and a controlled model with potential confounders included as independent variables were generated. Variables were included on the basis of information from the literature, influence on the model R2 value, and degree of collinearity with other variables. Adjusted DFE was entered as both continuous data and after being assigned the appropriate rank. Associations with specific types of alcoholic beverages were also examined by linearregression. In addition, participants were grouped according to levels of periconceptional smoking (>0 or 0) and drinking (AAD0 > 0 or AAD0 = 0) to examine demographic characteristics. The subgroups were analyzed by a two-factor ANOVA with interaction for drinking and smoking with Holm's (30) post hoc comparisons of individual means. Data are presented as the mean ± SD with P value < 0.05 accepted as significant. Ad hoc bivariate correlations between maternal characteristics with father's smoking were also determined. The Mantel-Haenszel statistical procedure was used to compare percentages of drinking fathers. All statistical analyses were completed with SPSS for WINDOWS statistical software (release 11.5.1; SPSS Inc, Chicago).
RESULTS
Maternal characteristics
The analyses of plasma 5-MTHFA were completed for samples from 116 subjects with an age range of 16–38 y. Mean gestational age at the first prenatal visit was 16.4 ± 6.5 wk. The means ± SDs for selected demographic characteristics, alcohol intake variables, smoking exposure variables, and selected dietary intakes are shown in Table 1. Demographic characteristics were examined by two-factor ANOVA for categorized drinking and smoking status with interaction (data not shown). No significant effects of categorized drinking and smoking were observed on plasma 5-MTHFA. A significant interaction (P < 0.05) between smoking and drinking was detected for education with women who smoke and drink having the lowest acquired education (11.2 ± 1.3 grade acquired) and the nonsmoking drinkers having significantly higher acquired education (12.4 ± 2.0 grade acquired). Drinking women scored significantly higher (4.2 ± 1.0) than nondrinking women (3.6 ± 1.1) on the Hollingshead index, indicating lower socioeconomic status. The percentage of drinking fathers was higher for drinking women (77%) than for nondrinking women (27%), and the amount of father's smoking was almost 3 times greater for women who smoked (9.4 ± 10.4 cigarettes/d) than for nonsmoking women (3.2 ± 6.1 cigarettes/d). Smoking women also ate fewer carbohydrates (45.8 ± 8.0%) and more fat (38.9 ± 5.4% of energy) than nonsmoking women (carbohydrates: 48.9 ± 5.9% of energy; fat: 36.3 ± 4.4% of energy). Several significant associations of these various maternal characteristics and exposures with adjusted DFE intakes and plasma 5-MTHFA were determined by Pearson's correlations (along with Spearman's correlations for adjusted DFE intakes) and are presented in Table 1. Pre-pregnancy maternal smoking and maternal smoking during pregnancy were negatively associated with adjusted DFE as assessed by Spearman's correlations ( = –0.22, P = 0.017 for both). The association was slightly weaker when assessed by Pearson's correlations (r = –0.18, P = 0.049 for pre-pregnancy smoking; r = –0.18, P = 0.051 for smoking during pregnancy). Adjusted DFE was also negatively associated with dietary fat intake and positively associated with dietary vitamin B-12 and carbohydrate intake in both correlation analyses. Vitamin B-6 intake was also positively associated with adjusted DFE by the Spearman's correlational test only. Plasma 5-MTHFA was positively correlated to various screening test scores that indicate at-risk drinking and negatively correlated to the amount of father's smoking.
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TABLE 1. Characteristics and dietary intakes of pregnant African American women and correlations with adjusted dietary folate equivalents (DFE) and plasma 5-methyltetrahydrofolic acid (5-MTHFA)1
Associations with plasma 5-MTHFA
Plasma 5-MTHFA was positively associated with adjusted DFE (r = 0.29, R2 = 0.09, P = 0.001) by linear regression (Figure 1). This association was lower but remained significant when adjusted DFE was ranked and regressed against plasma 5-MTHFA (r = 0.25, R2 = 0.06, P = 0.007) or when adjusted DFE and plasma 5-MTHFA were examined by Spearman's correlation test ( = 0.19, P = 0.045). On the basis of the initial linear regression relation, the plasma 5-MTHFA concentration of 18.2 ng/mL (95% CI: 4.23, 32.28 ng/mL) would be predicted from a DFE intake of 600 µg/d, the current recommended dietary allowance (RDA) for DFE for pregnant women (25). The means, ranges, and CIs for adjusted DFE intakes, food folate, fortified folate, and plasma 5-MTHFA according to quartiles of adjusted DFE are shown in Table 2. About 57% of the participants reported adjusted DFE < 600 µg/d. The 57th percentile of plasma 5-MTHFA was 19.4 ng/mL. Mean differences between the lowest and highest quartile of adjusted DFE, food folate, and fortified folate were significant (P < 0.001) by a priori contrasts after significant one-way ANOVA result. Trend analyses confirmed that the adjusted folate intakes and plasma 5-MTHFA increased from the lowest to highest quartile (Table 2). Without fortification, almost the entire sample (98th percentile) would fail to consume the recommended 600 µg DFE/d, as indicated by adjusted food folate intake (Table 2). Fortification contributed an estimated 198 µg DFE/d as indicated by adjusted fortified folate intake. Contributions from daily multivitamin use are minimal because compliance by urban African American women educated about folic acid and NTDs has been previously reported to be only 9% (31).
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FIGURE 1.. Relation between adjusted dietary folate equivalent intakes and plasma 5-methyltetrahydrofolic acid in pregnant African American women. Dietary folate intakes were adjusted with the use of the nutrient residual model and were linearly correlated to plasma folate by linear regression: r = 0.29, R2 = 0.09, P = 0.001 (n = 116). RDA, recommended dietary allowance; EAR, estimated average requirement.
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TABLE 2. Various folate intake variables and plasma 5-methyltetrahydrofolic acid (5-MTHFA) concentrations of pregnant African American women by quartiles of adjusted dietary folate equivalents (DFE)1
Plasma 5-MTHFA was positively correlated with adjusted DFE, MAST score, and total energy intake and negatively correlated with father's smoking (Table 3). The multiple regression analyses were restricted to 77% of the sample (n = 89), largely because of missing data about father's smoking. These associations persisted after control for maternal education level, total pregnancies, and body mass index. Mother's age, socioeconomic status, maternal smoking, alcohol intakes, and other screening tests were also considered. The effect of alcohol consumption around the time of conception (AAD0) approached significance (standardized ß = 0.17, P = 0.08) when substituted for MAST score in the unadjusted model from Table 3 when a single outlier was excluded from the analysis (standardized ß = 0.11, P = 0.25 with outlier included). This substitution did not significantly change the associations of adjusted DFE, total energy, and father's smoking. No significant changes were observed in these results when the adjusted DFE intakes were ranked and substituted into either model.
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TABLE 3. Multiple linear regression results with plasma 5-methyltetrahydrofolic acid concentrations set as the dependent variable and dietary folate equivalents, smoking, drinking, and other maternal characteristics set as independent variables1
Maternal smoking was negatively, but not significantly, associated with changes in plasma 5-MTHFA (standardized ß = –0.12, P = 0.23) when entered in place of father's smoking. The stronger association between father's smoking and maternal plasma 5-MTHFA, compared with the association between mother's smoking and maternal plasma 5-MTHFA, was unexpected. Ad hoc Pearson's correlation coefficients between various maternal characteristics and father's smoking were determined. Father's smoking was positively correlated with mother's smoking (r = 0.27, P = 0.009) and frequency of maternal drinking (PROPDD0; r = 0.27, P = 0.009) and negatively correlated with maternal plasma 5-MTHFA (r = –0.21, P = 0.043).
Dietary sources of food folate and folic acid as determined by food-frequency questionnaire
The percentage contribution of individual foods to food folate intake, folic acid intake, and DFEs from food as determined by food-frequency questionnaires are presented in Table 4. Cold breakfast cereal contributed 26.9 ± 12.0% of the DFE intake from food, 45.7 ± 20.5% of the folic acid intake, and 4.2 ± 2.8% of the food folate intake. With the exception of orange juice, the foods providing the greatest contribution to folic acid intake were also the greatest contributors to the DFE intake. Orange juice provided the greatest percentage of food folate at 26.3 ± 18.0% that translated into 11.9 ± 9.6% of the DFE intake from food sources. After orange juice, the next 3 biggest contributors to food folate intake were milk, French fries, and breads, respectively.
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TABLE 4. Percentage contribution of individual food sources on the food-frequency questionnaire to food folate intake, folic acid intake, and dietary folate equivalents (DFE) from food1
Types of alcoholic beverages
After age, smoking, body mass index, total energy, and adjusted DFE were controlled for, alcohol consumption from alcoholic coolers at the first prenatal visit was negatively associated with plasma 5-MTHFA as a separate variable (ß = –1.90, P = 0.042) and with other alcohol beverages included as independent variables (ß = –1.86, P = 0.050). Beer was positively related to plasma 5-MTHFA in the various models but failed to reach statistical significance. Around the time of conception, the percentages of the total alcohol consumed for the beverage categories were 28% (beer), 13% (wine), 7% (coolers), and 51% (liquor). On average, women reported drinking 93% less alcohol at their first prenatal visit than they reported drinking around the time of conception. The percentages of distribution of each individual alcohol type consumed at the time of the first prenatal visit were 26% (beer), 37% (wine), 7% (coolers), and 30% (liquor).
DISCUSSION
In pregnant African American women at risk of alcohol-related birth defects, adjusted DFE intake was a positive and the most significant indicator of plasma folate status. The adjusted DFEs in this sample suggest that daily folate intake for at-risk pregnant women in this community is below the current RDA of 600 µg/d for more than 57% of the women and below the estimated average requirement (EAR) of 520 µg/d for 34% of the women. The estimated contribution of fortified folate in this population was 198 ± 98 µg/d (
± SD) that is similar to, but below, recent estimates of 215–240 µg/d of higher folate intake from fortified foods (32). Without folate fortification, the results suggest that 98% of the women would fail to reach the EAR for pregnant women, and 76% would fail to meet the EAR of 320 µg/d for women of childbearing age. With fortification, only 2% were below the EAR for women of childbearing age, and 3% had adjusted DFE intakes greater than the tolerable upper intake level of 1000 µg/d. Food fortified with folic acid contribute more than 50% of the total DFE intakes in this population (Table 4). However, these observations must be considered in light of the limitations in estimating dietary folate intakes (1,
The mean plasma concentration of 5-MTHFA in these subjects was 18.0 ± 7.1 ng/mL and is similar to serum folate concentrations in other socioeconomically disadvantaged women (4). The lowest plasma concentration of 5-MTHFA in this study was 6.6 ng/mL and is still higher than the mean serum folate concentrations (4.0 ng/mL) reported for non-Hispanic black women before fortification in the National Health and Nutrition Examination surveys (5). To our knowledge, this is the first study to assess the relations between dietary folate intake and blood folate concentrations in pregnant women and to estimate that a DFE intake of 600 µg/d results in plasma 5-MTHA concentrations of 18.2 ng/mL (95% CI: 4.23, 32.28 ng/mL). However, the applicability of this estimate is limited for several reasons. Because the present study determined plasma concentrations of 5-MTHFA by a highly accurate and specific liquid chromatography–mass spectrometry method, it is unlikely that plasma values are inaccurate. However, plasma folate concentrations decrease throughout pregnancy as a result of plasma volume expansion (34). Previous work by Ward et al (35) suggested that in middle-aged men a similar concentration of folate intake through the use of supplements rather than fortification results in a plasma concentration of = 17.4 ng/mL) (36). Also, it was shown that food-frequency questionnaires tend to overestimate folate intakes compared with folate-focused dietary recalls (37). Previous correlations between adjusted DFE and plasma folate reported in the literature of smaller studies in men with r = 0.26 (38) and nonpregnant women with r = 0.35 (
The effects of alcohol consumption and cigarette smoking on folate status are complex. It is difficult to isolate the effects of smoking and alcohol consumption because of their strong positive correlation. This strong association between smoking and drinking, and their apparently opposite effects on folate status, may be masking associations of maternal smoking and particular measures of alcohol consumption. The significant correlation between father's smoking and plasma 5-MTHFA is difficult to interpret. Father's smoking may be interpreted as an indicator of any of the following: passive smoke exposure, maternal smoking without the strong bias toward underreporting smoking during pregnancy, and combined tobacco smoke exposure. The latter interpretation may account for the stronger correlation between folate status and father's smoking than mother's smoking.
Lower blood folate concentrations in smokers is often attributed to lower dietary folate intakes (25), and in this study, too, a negative correlation was observed between pre-pregnancy maternal smoking and adjusted DFE. However, the negative association between father's smoking and maternal plasma 5-MTHFA persisted when DFE intake was included in the model, suggesting a nondietary mechanism, such as higher folate turnover, may be involved. A persistent negative association of smoke exposure after adjusting for dietary folate has been reported previously (12).
Alcohol consumption has been associated with lower blood folate status in chronic alcoholics (15), but it has been associated also with higher erythrocyte folate concentrations in drinking, pregnant women (16). In the present study, at-risk drinking, as indicated by screening tools, was associated with higher plasma 5-MTHFA both in bivariate models and in multivariable linear regression models with adjustments for potential confounds. Positive correlations between plasma folate concentrations and quantitative alcohol variables (ie, AAD0, AADD0, and PROPDD0) were not significant. The positive association between alcohol intake and blood folate concentrations may be a result of folate content in beer (16), although the consumption of spirits has also been positively associated with erythrocyte folate in women (39). Neither beer nor liquor consumption in the current study was significantly correlated with plasma concentrations of 5-MTHFA, although the coefficients were positive in all the regression models examined. The consumption of alcoholic coolers at the first prenatal visit was negatively correlated to plasma folate. The negative association of alcoholic coolers suggests a negative effect of ethanol on plasma folate status.
In African Americans, the prevalence of the C677T and A1298C mutation in methylene tetrahydrofolate reductase is low (4, 40). Therefore, folate status is largely determined by diet and environmental factors. In the current study, dietary folate, exposure to smoke, and indexes of at-risk drinking are significant predictors of plasma concentrations of 5-MTHFA in pregnant African American women. Dietary folate fortification contributed 200 µg DFE/d, but more than one-half of the pregnant women in this study failed to meet the RDA for folate of 600 µg DFE/d. Only 18% of inner-city African American women of childbearing age have heard of NTDs and only 9% know folate could prevent NTDs (11). For mothers in Michigan, folic acid awareness is lower among black women, women with unplanned pregnancies, and women with no high school education (41). Education programs can augment knowledge about NTDs and folate and can raise daily multivitamin use in pregnant African American women (31).
On the basis of the current results, fortification of food with folic acid alone may not be sufficient to provide the RDA for urban African American women during pregnancy; indeed, it does not appear that fortification has been capable of reducing the number of congenital central nervous system defects in African Americans living in the Detroit area (8). The results of this study suggest that targeted programs for education on folate use, as well as compliant, daily multivitamin supplementation during pregnancy, are required in this population and that the assessment of both supplementation compliance and concentrations of folate fortification of foods should continue (32). This study also highlights the need to control for lifestyle factors, including amount and type of alcohol consumption and active and passive tobacco exposure, when examining dietary folate and circulating folate concentrations in humans.
ACKNOWLEDGMENTS
We express our appreciation to Rudolf Moser of Merck Eprova AG for supplying a pure standard of 5-MTHFA. We are indebted to the Population and Provide Data Unit at the Vital Records and Health Data Development Section, Epidemiology Services Division, Bureau of Epidemiology, Michigan Department of Community Health for providing data on birth defects for African Americans in Detroit from 1993 to 2000. We thank the technical staff (E Russell, T Martin, L DiCerbo, et al) and the participants for their commitment to this study.
KDS was responsible for data management, interpretation, and statistical analysis and was the primary writer of the manuscript. RJP and VPF were responsible for the 5-MTHFA analysis. RJP made significant contributions to data interpretation and manuscript preparation. SB and MM contributed to data management and interpretation. MM was responsible for the study design. MB-A was responsible for study coordination and management and analyses of the data. JJ provided statistical advice and analyses. HR provided quantitative interpretation of the nutrition surveys. JEW made decisions for clinical exclusion and delivered the infants. RJS was the director of antenatal research and assisted in the study design, data interpretation, and manuscript preparation. JHH was a coprincipal investigator, secured partial funding, was responsible for the study design and management, and contributed to manuscript preparation. NS was a coprincipal investigator, provided funding, and contributed to the study design and management, data interpretation, and manuscript preparation. None of the authors had a conflict of interest.
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