Among youth with diabetes mellitus, elevated blood pressure represents one of the most common comorbidities. Hence, exploring dietary factors that may help prevent or control hypertension in this population is of paramount importance. We investigated whether adherence to the Dietary Approaches to Stop Hypertension (DASH) diet is associated with hypertension in youth with diabetes mellitus from the SEARCH for Diabetes in Youth Study. Between 2001 and 2005, 2830 youth aged 10 to 22 years (2440 with type 1 and 390 with type 2 diabetes mellitus) completed a study visit. For each of the 8 DASH food groups, a score of 10 was assigned when the DASH recommendation was met. Lower intakes were scored proportionately, and the 8 individual scores were summed. The association between the overall DASH score and hypertension was evaluated using multiple logistic regression. The crude prevalence of hypertension was 6.8% for youth with type 1 and 28.2% for youth with type 2 diabetes mellitus. In youth with type 1, a higher adherence to DASH was inversely related to hypertension, independent of demographic, clinical, and behavioral characteristics (tertile 2 versus 1: odds ratio: 0.7, 95% CI: 0.5 to 1.0; 3 versus 1: odds ratio: 0.6, 95% CI: 0.4 to 0.9; Ptrend=0.007). For type 2 diabetes mellitus, the DASH diet was not associated with hypertension (tertile 2 versus 1: odds ratio: 0.8, 95% CI: 0.5 to 1.4; 3 versus 1: odds ratio: 0.9, 95% CI: 0.5 to 1.5; Ptrend=0.6). Prospective observational studies or clinical trials are needed to investigate whether adherence to the DASH guidelines may help prevent hypertension in youth with type 1 diabetes mellitus. In type 2 diabetes mellitus, more research with a larger sample is necessary.
Hypertension represents one of the most common comorbidities in persons with diabetes mellitus (DM) and seriously affects morbidity and mortality.1,2 Recent evidence from the SEARCH for Diabetes in Youth Study suggests that 30% of adolescents aged 10 to 19 years with type 1 or type 2 DM (T1DM or T2DM) have elevated blood pressure (BP), with a particularly high proportion in those with T2DM.3
Dietary modification is a central part of any treatment strategy for (pre)hypertensive youth.4 The Dietary Approaches to Stop Hypertension (DASH) trials demonstrated that a dietary pattern rich in vegetables, fruits, and low-fat dairy products can effectively lower systolic and diastolic BP in normotensive and hypertensive adults.5 However, subsequent attempts to evaluate the importance of the DASH diet have yielded mixed results.6–8 It has also been suggested that higher intakes of fruit, vegetables, and dairy might be related to lower BP in children.9 Studies investigating the total DASH pattern in youth, in particular, in the critical population of youth with DM, are lacking.2,10,11
The aim of the present analysis was to examine the association between adherence to the DASH dietary pattern and hypertension in youth with DM, using cross-sectional data from the SEARCH for Diabetes in Youth Study.
Methods
Study Population
SEARCH for Diabetes in Youth is a multicenter, observational study of physician-diagnosed DM in children, adolescents, and young adults <20 years of age. A detailed description has been published previously.12 Population-based ascertainment included youth with DM prevalent in 2001 and incident from 2002 through the present. Diabetes cases are being identified in geographically defined populations (Ohio, Washington, South Carolina, and Colorado), among health plan enrollees (Hawaii and California), and among several American Indian populations.
The study was reviewed and approved by the local institutional review board(s) and complied with the privacy rules of the Health Insurance Portability and Accountability Act. DM cases were asked to complete an initial survey, including age at diagnosis and self-reported race/ethnicity, and were subsequently invited to a study visit. Written informed consent was obtained from participants ≥18 years of age or from a parent in participants <18 years. Minors provided written assent.
In this analysis, DM type as assigned by the health care provider was used for classification into T1DM (combining T1A, T1B, and T1) and T2DM based on information collected from the health care providers at the time of the case report or from medical records.
BP and Anthropometry
Physical examinations at the study visits included measurements of height, weight, and BP and were conducted according to standardized protocols by trained and certified staff members. Three BP measurements were obtained at the right arm with a mercury manometer after the patient had been sitting for ≥5 minutes, with a break of ≥30 seconds between readings. Five cuff sizes were available. The mean of the 3 measurements was calculated for this analysis.
In accordance with the Fourth Report on the Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents,4 hypertension was defined as having a diastolic or systolic BP (SBP or DBP) ≥95th percentile for age, height, and sex (in participants ≥18 years, 140 mm Hg and 90 mm Hg served as cutoffs for SBP and DBP) or taking antihypertensive medication.1,13
Standard deviation scores (SDSs) of anthropometric parameters were constructed according to the US Centers for Disease Control and Prevention (CDC) 2000 growth standards,14 with interpolations made for those who were >20 years of age at the time of the measurement. Obesity was defined as having a body mass index (BMI) ≥95th percentile for age and sex.15
Dietary Assessment
At study visits, dietary intake was assessed in participants ≥10 years of age with a food frequency questionnaire (FFQ), which represented a modified version of the Block Kid’s Food Questionnaire.16 Details of the dietary assessment methodology in SEARCH can be found elsewhere.17 The SEARCH FFQ incorporates 85 food lines, and for each food, the participants indicated whether it was consumed in the past week, on how many days, and the average portion. Portion size was queried either as a number (eg, number of slices) or in relation to pictures of food in bowls or plates. The nutrient and portion-size databases were based on the Nutrition Data System for Research (database 3, version 4.05/33, 2002, University of Minnesota) and industry sources.
Staff certified on the SEARCH protocol instructed study participants on the completion of the SEARCH FFQ, which was generally self-administered with interviewer assistance. However, interview administration was offered if necessary.
DASH Score
Adherence to the DASH eating plan was assessed with an index variable that was composed of the 8 DASH food groups (grains, vegetables, fruits, dairy, meat, nuts/seeds/legumes, fats/oils, and sweets).18,19 For each food group, a maximum score of 10 could be achieved when the intake met the recommendation,19 whereas lower intakes were scored proportionately. If lower intakes are favored by DASH, reverse scoring was applied, and a score of 0 was applied to intakes ≥200% the upper recommended level. The resulting 8 component scores were summed to create the overall DASH adherence score, which could range from 0 to 80.
Details of the index components and the score standards are summarized in Table 1. To more closely reflect the recommendations of DASH,18–20 the grain and dairy components consisted of 2 items and addressed a qualitative goal in addition to an absolute quantitative one.
Table 1. Components of the DASH Index
Score Component
Maximum Score
Standard for Maximum Score
Standard for Minimum Score of 0
Data are for 2000 kcal/d. Intakes between minimum and maximum levels were scored proportionally.
Grains
Total
5
≥6 servings per day
0 servings per day
High fiber
5
≥50% of daily servings
0% of daily servings
Vegetables
10
≥4 servings per day
0 servings per day
Fruit
10
≥4 servings per day
0 servings per day
Dairy
Total
5
≥2 servings per day
0 servings per day
Low-fat
5
≥75% of daily servings
0% of daily servings
Meat, poultry, fish, eggs
10
≤2 servings per day
≥4 servings per day
Nuts, seeds, legumes
10
≥4 servings per week
0 servings per week
Fats, oils
10
≤3 servings per day
≥6 servings per day
Sweets
10
≤5 servings per week
≥10 servings per week
Wherever possible, we adjusted the standard serving sizes in SEARCH to those of the DASH eating plan. Adjustments were not feasible for a variety of mixed (meat) dishes, but usually 1 (medium) piece or portion represented 1 serving. Because the DASH eating plan of the National Heart, Lung, and Blood Institute gives recommendations for 4 levels of daily energy intake (1600/2000/2300/3100 kcal/d), we assigned each individual the energy level that was closest to the estimated energy requirement based on age, sex, and physical activity level.21 Physical activity in SEARCH was assessed by questions derived from the Youth Risk Behavior Surveillance System.22 Youth who reported neither moderate nor vigorous physical activity ≥5 days per week were classified as sedentary, those who were either moderately or vigorously physically active ≥5 days per week as low active, and those who were both moderately and vigorously physically active ≥5 days per week as active.
Statistical Analysis
We considered all of the youth whose DM was prevalent in 2001 or incident between 2002 and 2005. Of 8031 registered youth who met these criteria, 3707 attended a study visit and were aged ≥10 years at that time. Youth with other or unknown clinical DM type (n=39) were excluded. Among the remaining 3668 participants, 462 did not fill out the FFQ, and 159 were excluded because of serious quality problems with the dietary data. This number was further reduced after excluding those without measured BP (n=151). Other information relevant to this analysis, such as physical activity or parental education, was missing in 156 subjects. Thus, the final sample size was 2830 (2440 with T1DM and 390 with T2DM).
Proportionately more youth with T2DM than T1DM were excluded (n=248 versus n=590; 39% versus 19.5%), mainly because of lack or limited quality of the dietary data. Excluded youth with T1DM were more likely to be boys than those with T1DM in the final data set, whereas excluded youth with T2DM were older and characterized by a longer DM duration (P<0.05).
We investigated the relation between DASH adherence and hypertension by calculating crude and adjusted prevalence of hypertension in tertiles of DASH adherence. In addition, odds ratios (ORs) with 95% CIs were calculated, relative to the lowest tertile. Potential confounders were evaluated on an individual basis and in full models. BMI-SDS and energy intake (kilocalories per day) were considered as potential mediators, because they might lie on the pathway between the DASH diet pattern and hypertension. Linear trends across categories were performed by assigning each individual the median value of the category and modeling this variable continuously.
Because of the differences in both etiology and management, the analyses were undertaken separately for youth with T1DM and T2DM, further justified by an interaction between DASH adherence and DM type (P=0.046). In addition, potential effect modifiers were evaluated. All of the analyses were carried out using SAS (version 9.1, SAS Institute), and a P value < 0.05 was considered statistically significant.
Results
The overall prevalence of hypertension in the study sample was 9.8%; however, marked differences existed between youth with T1DM and T2DM (6.8% versus 28.2%; P<0.0001). Adherence to DASH was slightly higher in T1DM than in T2DM (39.9±9.1 versus 36.6±9.1; P<0.0001; data not shown).
General characteristics of the study sample according to DASH adherence are summarized in Table 2. In both T1DM and T2DM, a greater adherence to DASH was associated with younger age, a higher parental education, less experience with smoking, and higher physical activity. No clear relationship existed between the DASH score and obesity or BMI-SDS. As expected, however, more youth with T2DM than with T1DM were obese (76.2% versus 12.1%; P<0.0001; data not shown).
Table 2. General Characteristics According to DM Type and DASH Adherence
Characteristic
T1DM (n=2440), Tertiles of DASH Score
T2DM (n=390), Tertiles of DASH Score
1 (Lowest)
3 (Highest)
P*
1 (Lowest)
3 (Highest)
P*
Numbers are frequencies, medians (Q1;Q3), or means±SDs.
*Statistics: χ2 test for categorical, Kruskal-Wallis test, and ANOVA for continuous variables (including all 3 tertiles).
†Data are according to the 2000 Centers for Disease Control and Prevention growth charts.14
Consumption of all of the food groups in our DASH index differed significantly between tertiles (P<0.05; Table 3). In particular, youth in the highest tertiles consumed twice as many servings of fruits and low-fat dairy products than those in the lowest. Achieving a higher DASH score was associated with a lower intake of total and saturated fat but higher intakes of carbohydrates, fiber, calcium, magnesium, and potassium (P<0.05). A higher adherence to DASH was not related to total energy intake. Despite being in the highest tertile of the DASH score, the average single food scores were still remarkably low at 0.4 of 5 (whole grains), 3.6 of 5 (low-fat dairy), 5.1 of 10 (fruits), 5.3 of 10 (vegetables), and 8.4 of 10 for meat (data not shown).
Table 3. Median Energy, Food Group, and Nutrient Intakes According to DM Type and DASH Adherence
Diet Characteristic
T1DM (n=2440), Tertiles of DASH Score
T2DM (n=390), Tertiles of DASH Score
1 (Lowest)
2
3 (Highest)
P*
1 (Lowest)
2
3 (Highest)
P*
*Statistics: χ2 test for categorical and Kruskal-Wallis test for continuous variables.
†Data show the frequency of consumption (%).
Mean DASH score
29.9
39.9
50.0
…
26.7
36.5
46.6
…
Energy, kcal/d
1752.2
1753.1
1783.6
0.5
1709.7
1579.4
1634.2
0.4
Food groups, servings per 1000 kcal
Total grains
2.05
2.15
2.15
0.01
1.93
2.13
2.09
0.08
High-fiber grains†
22.4
24.3
32.4
<0.0001
22.3
29.2
33.9
0.1
Vegetables
0.94
0.96
1.06
<0.0001
1.11
1.14
1.33
0.03
Fruit
0.53
0.81
1.16
<0.0001
0.50
0.85
1.04
<0.0001
Total dairy
0.76
0.91
1.10
<0.0001
0.46
0.59
0.82
<0.0001
Low-fat dairy
0.20
0.42
0.70
<0.0001
0
0.13
0.44
<0.0001
Meat
1.57
1.26
1.04
<0.0001
1.70
1.49
1.33
0.003
Nuts and seeds
0
0.18
0.44
<0.0001
0
0.09
0.35
<0.0001
Fats and oils
2.25
1.79
1.39
<0.0001
2.08
1.93
1.60
0.005
Sweets
0.89
0.85
0.68
<0.0001
1.02
0.91
0.65
0.002
Nutrients, per 1000 kcal
Cholesterol, g
114.4
118.2
124.4
<0.0001
113.7
117.8
119.0
0.02
Fat, g
43.7
42.2
40.4
<0.0001
43.7
42.7
41.3
0.04
Saturated fat, g
16.1
15.3
14.4
<0.0001
15.9
15.0
14.1
<0.0001
Protein, g
40.2
39.6
39.4
0.004
40.0
38.0
39.2
0.5
Fiber, g
6.0
6.6
7.8
<0.0001
5.8
6.8
8.2
<0.0001
Calcium, mg
501.0
588.3
669.4
<0.0001
326.7
392.0
436.0
<0.0001
Magnesium, mg
121.0
135.4
152.6
<0.0001
105.8
119.8
138.6
<0.0001
Potassium, mg
1166.5
1286.1
1461.6
<0.0001
1048.2
1170.7
1348.6
<0.0001
In youth with T1DM, mean DBP decreased across tertiles of DASH adherence (P=0.04; Table 4). A similar tendency in SBP existed for both T1DM and T2DM (P=0.1) but was eliminated by adjusting for potential confounders (data not shown). Comparable results were obtained for the prevalence of hypertension: with increasing adherence to DASH, the crude prevalence declined markedly for youth with T1DM. Adjustment for confounders did not change this result (Figure). A decreasing tendency in the crude hypertension prevalence across tertiles of DASH adherence could also be observed in T2DM, but the differences did not reach statistical significance. Adjusting for confounders further attenuated this tendency.
Table 4. BP Characteristics Overall and According to Diabetes Type and DASH Adherence
Characteristic
Total (n=2830)
T1DM (n=2440), Tertiles of DASH Score
T2DM (n=390), Tertiles of DASH Score
1 (Lowest)
2
3 (Highest)
P*
1 (Lowest)
2
3 (Highest)
P*
Numbers are frequencies, or means±SDs.
*Statistics χ2 test for categorical and ANOVA for continuous variables.
Figure. Adjusted hypertension prevalence according to diabetes type and DASH adherence. Data were adjusted for sex, age, study center, race/ethnicity, diabetes mellitus duration, family history of high BP, and vigorous physical activity (<5 days per week, yes/no).
We next evaluated the association in multiple logistic regression models. After adjustment for demographic, clinical, and behavioral characteristics, the odds of having hypertension among youth in the highest tertile of DASH adherence was 40% lower than in the lowest tertile (model 1, 95% CI: 0.38 to 0.86; Table 5). In addition, a statistically significant trend toward decreasing odds existed across tertiles (P=0.007). Additional inclusion of energy intake (model 2) and BMI-SDS (model 3) did not affect these results. In youth with T2DM, the crude OR indicated a slightly decreasing trend across tertiles of DASH adherence too, but it did not reach statistical significance (P=0.4). Adjusting for demographic, clinical, and behavioral factors; energy intake; and BMI-SDS (models 1 to 3) further reduced these differences.
Table 5. ORs (95% CIs) for Hypertension According to DM Type and DASH Adherence
Model
T1DM (n=2440), Tertiles of DASH Score
T2DM (n=390), Tertiles of DASH Score
1 (Lowest)
2
3 (Highest)
Ptrend
1 (Lowest)
2
3 (Highest)
Ptrend
*Data are adjusted for sex, age, study center, race/ethnicity, DM duration, family history of high BP, and vigorous physical activity (<5 days/week, yes/no).
†Model 1+energy intake.
‡Model 2+BMI-SDS.
Crude
1.00
0.69 (0.48 to 1.00)
0.55 (0.37 to 0.82)
0.003
1.00
0.77 (0.45 to 1.32)
0.80 (0.47 to 1.37)
0.4
Model 1*
1.00
0.71 (0.49 to 1.04)
0.57 (0.38 to 0.85)
0.006
1.00
0.81 (0.46 to 1.42)
0.87 (0.49 to 1.57)
0.6
Model 2†
1.00
0.71 (0.49 to 1.04)
0.56 (0.37 to 0.84)
0.005
1.00
0.81 (0.46 to 1.42)
0.87 (0.49 to 1.57)
0.6
Model 3‡
1.00
0.70 (0.48 to 1.03)
0.57 (0.38 to 0.85)
0.006
1.00
0.97 (0.54 to 1.75)
0.98 (0.53 to 1.81)
0.9
Finally, adjustment for the micronutrients potassium, calcium, or magnesium did not attenuate the findings in T1DM, and the association remained statistically significant (data not shown). In addition, we evaluated potential effect modification by sex, age, race/ethnicity, and obesity by conducting stratified analyses and including interaction terms in models. No evidence for effect modification existed (P values for interaction terms >0.1).
Discussion
To the best of our knowledge, this is the first study to examine the association between the DASH diet pattern and hypertension in persons with DM, and the first to focus on youth. A greater adherence to DASH was associated with markedly decreased odds of hypertension in youth with T1DM. It is noteworthy that the effect size was large compared with observational studies that focused on single nutrients or food groups only when assessing diet-disease relationships, a potential advantage of dietary pattern analysis discussed previously.
We did not observe a significant association between DASH adherence and hypertension in youth with T2DM. The most likely reason for this is limited statistical power. Our sample size was sufficient to detect an OR of 0.41 between 2 tertiles with 80% power (α=0.05, 2-tailed). However, the absence of a relation might also stem from the differences in hypertension pathophysiology in T2DM compared with T1DM.1 In addition, most youth with T2DM were obese (76.8%). It is possible that, in the obese state, quantitative aspects of diet, in particular, energy balance, are of greater relevance than qualitative ones. Finally, the DASH adherence scores were statistically significantly lower in youth with T2DM than with T1DM.
Since the publication of the DASH trial results in 1997,5 the DASH dietary pattern has been widely recommended to prevent and treat hypertension23 and is now part of the 2005 Dietary Guidelines for Americans.24 However, its practical application has been questioned recently.25 Although the DASH trials were designed as feeding studies and, thus, represented a best-case scenario with optimal adherence, intervention studies in free-living populations have been inconsistent.6,25
Only a limited number of studies have assessed adherence to DASH in observational settings. In the European Prospective Investigation into Cancer-Potsdam Study, women in the third, but not the highest, quartile of a DASH score had a lower risk for hypertension.26 A higher DASH score was associated with lower BP increases in the Supplémentation en Vitamines et Minéraux Antioxydants cohort too, but the effect was not stronger than that of fruit and vegetables alone.27 Although fat quality was evaluated in Supplémentation en Vitamines et Minéraux Antioxydants, the DASH index used in both studies only included standardized intakes of vegetables, fruits, and dairy. This might be an important shortcoming, because each DASH food group contributes critical nutrients.20 More comprehensive applications of the DASH diet to observational data were used in the Iowa Women’s Health Study7 and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.28 In the Iowa Women’s Health Study, no association between DASH adherence and incidence of (self-reported) hypertension and cardiovascular mortality existed. However, their DASH index was positively associated with energy and sodium intake, thus perhaps not capturing a truly favorable overall diet. By contrast, the cross-sectional analysis of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial focused on distal colorectal adenoma. A reduced risk with increasing DASH adherence existed in the group of male former smokers only.
Overall, the limited number of observational studies was almost exclusively based on adult study populations. One of the few studies focusing on children has demonstrated that an increased intake of fruits, vegetables, and dairy in the preschool years might be related to lower BP levels during childhood.9 In fact, it is generally assumed that a greater DASH adherence would equally benefit pediatric age groups. An increased intake of central food groups is accordingly recommended for (pre)hypertensive youth and prevention purposes.4 With regard to DM, the current American Dietetic Association guidelines on medical nutrition therapy in DM advocate a diet low in sodium and high in fruits, vegetables, and low-fat dairy products to lower BP in normotensive and hypertensive diabetic persons, with DASH being explicitly mentioned.29 Furthermore, a joint scientific statement by the American Heart Association and the American Dietetic Association, targeting primary prevention of cardiovascular disease in DM, recommends lifestyle therapy, including a diet pattern similar to DASH, for (pre)hypertensive patients.2 However, only studies in general populations served as references.
Until today, it was unclear through which mechanism(s) the DASH diet exerts its antihypertensive properties, although a natriuretic action has been proposed.30 Reduced sodium and increased potassium intake represent dietary factors that seem to lower BP, at least in adults.23 However, the original DASH trial held sodium intake constant, although a combination of DASH and sodium restriction was even more effective.31 Other dietary factors targeted by the DASH diet (fiber, saturated fat, and magnesium) differed significantly between tertiles of DASH adherence in our study, but the evidence for BP-lowering properties is inconclusive for them.23 Only a higher protein intake yielded promising results in observational and intervention studies, in particular, when substituting carbohydrates.32,33 However, differences in protein intake between tertiles of DASH adherence in our study were small, and intake of carbohydrates was positively associated with adherence to DASH. To summarize, it is most likely that DASH exerts a beneficial effect beyond that of single dietary factors, as has been hypothesized previously.34 Accordingly, adjusting for several nutrients that could function as mediators did not explain our results in T1DM.
Our study has several limitations. First, our results are based on cross-sectional data, and youth diagnosed with hypertension might have changed their dietary habits accordingly. However, taking into account awareness of hypertension did not change our results. Second, our BP data were based on 3 averaged BP measurements, taken at 1 visit instead of ≥3 different occasions.4 In addition, our definition of hypertension included taking antihypertensive medication, as is common practice in general,35 as well as in diabetic36–38 populations. It is possible that some of the SEARCH participants had been prescribed these medications for reasons other than to reduce BP levels, in particular, for renal protection. However, changing our definition of hypertension to having elevated SBP or DBP (≥95th percentile) or having a diagnosis of hypertension and/or elevated SBP or DBP (≥95th percentile) did not change our conclusions. Third, as already stated above, overall adherence to DASH was low, but most likely resulted in an underestimation of effects. Fourth, no consensus exists about how to best create a DASH index, ie, which items to include, whether to consider food groups and/or nutrients, or how to weight components. Fifth, we were not able to adjust for sodium intake, which might have mediated the association between DASH adherence and hypertension, but estimation of sodium from a FFQ is questionable.39 A final limitation is that applying an FFQ to estimate dietary intake is prone to measurement error. Because obese study participants tend to greater underreporting of dietary intakes,40 the lack of an association in T2DM could also stem from less valid dietary data. However, currently no biomarker capturing dietary patterns as a whole is available.
Perspectives
In conclusion, a higher adherence to the DASH Eating plan was associated with decreased odds of hypertension in youth with T1DM. Prospective studies are needed to investigate whether advocating central aspects of DASH may help prevent and control elevated BP levels in this population. In T2DM, DASH adherence was not associated with hypertension, perhaps because of inadequate sample size.
Acknowledgments
The SEARCH for Diabetes in Youth Study is indebted to the many youth and their families, as well as their health care providers, whose participation made this study possible. Furthermore, we acknowledge the involvement of General Clinical Research Centers at the following institutions in the SEARCH for Diabetes in Youth Study: Medical University of South Carolina (grant M01 RR01070); Cincinnati Children’s Hospital (grant M01 RR08084); Children’s Hospital and Regional Medical Center and the University of Washington School of Medicine (grant M01RR00037 and M01RR001271); and Colorado Pediatric General Clinical Research Center (grant M01 RR00069). The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the National Institute of Diabetes and Digestive and Kidney Diseases.
Sources of Funding
A.L.B.G. was supported by the Heinrich Hertz-Foundation (Ministry of Innovation, Science, Research, and Technology, North Rhine-Westphalia, Germany). SEARCH for Diabetes in Youth is funded by the Centers for Disease Control and Prevention (PA No. 00097 and DP-05-069) and supported by the National Institute of Diabetes and Digestive and Kidney Diseases. Site contract numbers are as follows: California, U01 DP000246; Colorado, U01 DP000247; Hawaii, U01 DP000245; Ohio, U01 DP000248; South Carolina, U01 DP000254; Washington, U01 DP000244; and coordinating center, U01 DP000250.
Disclosures
None.
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On the cover: Validation of transgene expression was visualized by eGFP fluorescence (left). Middle panel shows immunostaining for NeuN, and the right panel shows an overlay of eGFP and NeuN; the LV-DNp85α—transduced neurons are indicated by white arrows. (See page 100.)
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
From the Department of Epidemiology and Biostatistics (A.L.B.G., A.D.L.) and Center for Research in Nutrition and Health Disparities (A.D.L., E.J.M.-D.), Arnold School of Public Health, University of South Carolina, Columbia; School of Medicine (R.A.B.), Wake Forest University Winston-Salem, NC; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver; Research and Evaluation (J.M.L.), Kaiser Permanente Southern California, Pasadena; Pacific Health Research Institute (B.L.R.), Honolulu, Hawaii; Children’s Hospital Medical Center (D.A.S.), Cincinnati, Ohio; and the University of North Carolina at Chapel Hill (E.J.M.-D.).
Correspondence to Angela D. Liese, Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, 800 Sumter St, Columbia, SC 29208. E-mail [email protected]
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Adherence to the DASH Diet and Risk of Hypertension: A Systematic Review and Meta-Analysis, Nutrients, 15, 14, (3261), (2023).https://doi.org/10.3390/nu15143261
The association between dietary quality scores with C-reactive protein and novel biomarkers of inflammation platelet-activating factor and lipoprotein-associated phospholipase A2: a cross-sectional study, Nutrition & Metabolism, 20, 1, (2023).https://doi.org/10.1186/s12986-023-00756-x
Adherence to healthy diet and risk of cardiovascular disease in adult survivors of childhood cancer in the St. Jude Lifetime Cohort: a cross-sectional study, BMC Medicine, 21, 1, (2023).https://doi.org/10.1186/s12916-023-02956-x
Association Between the Dietary Approaches to Hypertension Diet and Hypertension in Youth With Diabetes Mellitus
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eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.
Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.