SUMMARY
Background & aims
Previous studies have suggested that cocoa products, which are rich sources of flavonoids, may lower blood pressure, serum cholesterol, fasting blood glucose and improve endothelial function. However, it is unclear whether consumption of cocoa products including chocolate influences the risk of metabolic syndrome (MetS).
In a cross-sectional design, we sought to examine the association between chocolate consumption and the prevalence of MetS.
Methods
We studied 4098 participants from the National Heart, Lung, and Blood Institute (NHLBI) Family Heart Study aged 25–93 years. Chocolate consumption was assessed using a semi-quantitative food-frequency questionnaire. MetS was defined using the NCEP III criteria. Generalized estimating equations were used to estimate prevalence odds ratios of MetS according to frequency of chocolate intake.
Results
Of the 4098 participants (mean age 51.7 y) included in the analyses, 2206 (53.8%) were female. The prevalence of metabolic syndrome in our population was 30.2%. Compared with those who did not consume any chocolate, multivariate adjusted odds ratios (95% CI) for MetS were 1.26 (0.94, 1.69), 1.15 (0.85, 1.55), and 0.99 (0.66, 1.51) among women who reported chocolate consumption of 1–3 times/ month, 1–4 times/week, and 5+ times/week, respectively. Corresponding values for men were: 1.13 (0.82, 1.57), 1.02 (0.74, 1.39), and 1.21 (0.79, 1.85).
Conclusion
These data do not support an association between chocolate intake and the prevalence of MetS in US adult men and women.
Keywords: Chocolate, Metabolic syndrome, Cardiovascular disease risk
1. Introduction
National survey data from the NHANES (2003–2006) suggest that metabolic syndrome is very common, affecting about 34% of US adults who are 20–70 years of age.1 The prevalence of the syndrome increases dramatically with age, from about 7% among people in their 20s to over 40% among people older than 60 years of age.2 People with the syndrome are about twice as likely to develop cardiovascular disease (CVD) and over 4 times as likely to develop type 2 diabetes compared with subjects who do not have metabolic syndrome.2,3 Beyond the increased risk of developing CVD, metabolic syndrome has also been associated with an increased risk of CVD mortality.4–7 The persistent increase in the prevalence of obesity in the United States8 suggests that the current prevalence of the metabolic syndrome is now likely higher than that estimated from the NHANES (2003–2006) data. Even if the prevalence remained unchanged, the total number of people with MetS would have increased because of the US population growth.9 While MetS may have a genetic component, environmental factors are important modifiable risk factors for MetS.
Flavonoids are polyphenolic compounds found in many foods. Dark chocolate is an important source of flavonoids and consumption of dark chocolate is highly prevalent.10 Previous randomized trials and epidemiological studies have demonstrated that flavanols found in dark chocolate may reduce blood pressure, improve insulin sensitivity and reduce insulin resistance, reduce serum LDL and total cholesterol, and possibly increase serum HDL cholesterol concentration.11–15
However, it is unclear whether consumption of chocolate is associated with MetS. Hence, this study aims to examine the association between self-reported chocolate consumption (both dark and white) and the prevalence of MetS in an adult US population of men and women.
2. Methods and procedures
2.1. Subjects
The NHLBI Family Heart Study is a multicenter, population-based study designed to identify and evaluate genetic and non-genetic determinants of coronary heart disease (CHD), preclinical atherosclerosis, and cardiovascular disease risk factors. Families in the study had been chosen either at random (referred to as random group) or based on a higher than-expected risk of CHD (referred to as high-risk group) from previously established population-based parent studies: The Framingham Heart Study in Framingham, MA; the Atherosclerosis Risk in Communities Study cohorts in North Carolina and Minnesota; and the Utah Health Family Tree Study in Salt Lake City, UT. The high-risk group was defined based on a family risk score, which compares the family’s age and sex-specific incidence of CHD to that expected in the general population.16 A detailed description of the methods and design of the study has been published.17 The evaluation included a detailed medical and lifestyle history, which was obtained through interview. All interviewers were trained centrally and required periodic certification. We obtained written consent from each participant, and the study protocol was reviewed and approved by the Institutional Review Board at each of the participating institutions.
2.2. Blood collection and assays
All participants were asked to fast for 12 h before arrival at the study center.
Serum insulin was measured by a radioimmunoassay method (Diagnostic Products Corporation, Los Angeles, CA). LDL cholesterol concentrations were measured by using the method of Friede-wald,18 except for participants with triacylglycerol concentrations >4.5 mmol/L, for whom concentrations were measured by ultracentrifugation.19 Triacylglycerol concentrations were measured by using a peroxidase-coupled method.20 Total cholesterol concentrations were measured by using a commercial cholesterol oxidase method on a Roche Cobas Fara centrifugal analyzer (Boehringer Mannheim Diagnostics, Indianapolis).21 HDL cholesterol was measured after precipitation of the other lipoprotein fractions by using dextran sulfate.22
2.3. Chocolate assessment
Dietary information was collected with the use of a semi-quantitative food-frequency questionnaire that was administered by the staff and developed by Dr. Willett and colleagues at the Harvard School of Public Health.23,24 The reproducibility and validity of the food-frequency questionnaire has been documented elsewhere.24 Each subject was asked the following question: “In the past year, how often on average did you consume chocolate bars or pieces, such as Hershey’s Plain, M & M, Snickers, Reeses’; 1 ounce?” (Item #39 in the questionnaire). Possible answers included > 6 per day, 4–6 per day, 2–3 per day, 1 per day, 5–6 per week, 2–4 per week, 1 per week, 1–3 per month, and almost never.
2.4. Metabolic syndrome definition
We used the National Cholesterol Education Program Adult Treatment Program III25 to define MetS. Specifically, a subject was considered to have MetS if s/he had at least three of the following criteria: abdominal waist circumference (WC) >102 cm for men or >88 cm for women, fasting serum TGs of at least 150 mg/dl, serum HDL cholesterol <40 mg/dl for men or <50 mg/dl for women, average systolic blood pressure of at least 130 mmHg or diastolic blood pressure of at least 85 mmHg or current use of antihyper-tensive medication, and fasting serum glucose of at least 110 mg/dl or current use of hypoglycemic agents.
2.5. Other variables
Information on cigarette smoking was obtained by interview during the clinic visit. Oral contraceptive use and hormone replacement therapy were assessed using a reproductive history questionnaire and medication inventory. Frequency and duration of strenuous, moderate, and light physical activity during the previous year were estimated from a physical exercise questionnaire. Anthropometric data were collected with subjects wearing scrub suits. A balance scale was used to measure body weight, and height was measured using a wall-mounted vertical ruler.
Cardiovascular disease was assessed from the medical history and a 12-lead electrocardiogram. Prevalent CHD was defined as a self-reported history of myocardial infarction, percutaneous transluminal coronary angioplasty, or coronary artery bypass graft, or the presence of Q-waves on the resting 12-lead electrocardiogram. Detailed information on these covariates has been published.17,26,27
2.6. Statistical analyses
Baseline characteristics were compared across categories of chocolate consumption. Within each sex, using the GENMOD procedure, we fitted generalized estimating equations to calculate odds ratios for MetS, comparing consecutive categories of chocolate consumption to the lowest category. We examined potential confounding by age, age-squared, current smoking status, educational status, prevalent coronary heart disease, physical activity, family risk score for coronary heart disease, total fat and calorie (kcal) consumption, and alcohol consumption (five categories). Secondary analyses were restricted to subjects that were randomly selected or those with prevalent CHD, diabetes, or hypertension. We used SAS for Windows version 9.3 for all analyses.
3. Results
Of the 4098 participants included in the detailed analyses, 53.8% were female, 96.4% were white and 3.6% were African-American. Of the whites, 51.6% were defined as being at high family risk for CHD and 48.4% were from the random group.
The prevalence of the metabolic syndrome was 30.2% (n = 1238). Nine hundred and fifty seven (77.3%) of the cases of MetS were observed among participants who had coronary heart disease or were being treated for hypertension and/or diabetes. Characteristics of the study participants by frequency of chocolate consumption are shown in Table 1. Higher intake of chocolate was associated with younger age, lower prevalence of coronary heart disease; higher levels of calorie intake, saturated, and poly-unsaturated fat intake; and lower plasma glucose concentrations.
Table 1.
Characteristics of 4098 subjects in the NHLBI FHS according to chocolate intake.
| Characteristics | Frequency of chocolate consumption
|
|||
|---|---|---|---|---|
| 0 | 1 | 2 | 3 | |
| <1/wk. | 1–4 times/wk. | 5+ times/wk. | ||
| n = 899 | n = 970 | n = 1632 | n = 597 | |
| Age (y) | 55.8 ± 13.1 | 51.9 ± 13.8 | 50.5 ± 13.7 | 48.4 ± 13.4 |
| Females (%) | 54.3 | 55.7 | 51.7 | 56.0 |
| Current smokers (%) | 14.6 | 12.7 | 13.0 | 16.6 |
| Alcohol drinkers (%) | 53.1 | 57.9 | 54.4 | 54.8 |
| High-risk group | 49.5 | 49.7 | 49.3 | 51.4 |
| Coronary heart disease | 15.0 | 12.8 | 9.4 | 5.4 |
| Exercise (min/d) | 36.2 ± 36.5 | 34.1 ± 39.5 | 31.1 ± 36.2 | 34.8 ± 41.2 |
| Calories (kcal) | 1561 ± 557 | 1646 ± 585 | 1778 ± 587 | 2120 ± 634 |
| Southgate diet fiber (g/d) | 19.1 ± 9.9 | 17.6 ± 8.2 | 17.4 ± 7.7 | 18.9 ± 8.0 |
| Education (y) | 16.0 ± 4.0 | 16.2 ± 3.8 | 16.2 ± 3.7 | 16.3 ± 3.6 |
| Saturated fats (g/d) | 16.6 ± 8.6 | 19.5 ± 9.7 | 22.9 ± 9.7 | 30.9 ± 11.3 |
| Polyunsaturated fats g/d | 7.4 ± 4.0 | 8.0 ± 3.8 | 9.2 ± 4.0 | 10.8 ± 4.5 |
| LDL cholesterol (mg/dl) | 123.4 ± 37.2 | 123.3 ± 33.7 | 126.0 ± 34.1 | 123.9 ± 35.0 |
| HDL cholesterol (mg/dl) | 52.4 ± 16.6 | 50.7 ± 15.4 | 49.8 ± 14.6 | 49.9 ± 14.5 |
| Triglycerides (mg/dl) | 149.2 ± 119.3 | 153.3 ± 123.4 | 144.4 ± 89.7 | 148.9 ± 102.0 |
| Insulin (m/l) | 13.9 ± 39.9 | 11.8 ± 16.2 | 10.9 ± 9.2 | 11.1 ± 8.2 |
| Glucose (mg/dl) | 105.7 ± 40.2 | 100.7 ± 32.9 | 97.5 ± 23.9 | 97.1 ± 31.3 |
Chocolate consumption was not associated with MetS in this study (Table 2). From the lowest to the highest level of chocolate consumption, the prevalence odds ratios were 1.0 (ref), 1.26 (0.94, 1.69), 1.15 (0.85, 1.55), and 0.99 (0.66, 1.51) among women; and 1.0 (ref), 1.13 (0.82, 1.57), 1.02 (0.74, 1.39), and 1.21 (0.79, 1.85) among men in a model adjusting for age, age-squared, current smoking status, education, physical activity, dietary cholesterol, family risk score for CHD risk, fat and calorie consumption, and alcohol consumption (5 categories). When analyses were restricted to subjects from families that were randomly selected or people diagnosed with coronary heart disease, treated diabetes, or hypertension, similar results were observed (Table 3).
Table 2.
Prevalence odds ratio (95% CIs) for MetS according to chocolate intake.
| Categories of chocolate consumption (/week)
| ||||
|---|---|---|---|---|
| 0 | <1 | 1–4 | 5+ | |
| Model 1 | ||||
| 1.00 | 1.20 (0.97, 1.49) | 1.09 (0.88, 1.35) | 1.14 (0.85, 1.51) | |
| Model 2 | ||||
| Women | 1.00 | 1.31 (0.99, 1.71) | 1.13 (0.88, 1.45) | 1.09 (0.77, 1.54) |
| Men | 1.00 | 1.14 (0.84, 1.54) | 1.05 (0.81, 1.37) | 1.29 (0.90, 1.81) |
| Model 3 | ||||
| Women | 1.00 | 1.26 (0.94, 1.69) | 1.15 (0.85, 1.55) | 0.99 (0.66, 1.51) |
| Men | 1.00 | 1.13 (0.82, 1.57) | 1.02 (0.74, 1.39) | 1.21 (0.79, 1.85) |
Model 1 – Main results in general population: Model was adjusted for the following variables: age, age-squared, current smoking status, educational status, coronary heart disease, physical activity, dietary cholesterol, risk group, fat and calorie consumption, and alcohol consumption (5 categories).
Model 2 – Sex stratified, age and age-squared adjusted-logistic regression model.
Model 3 – Adjusted for variables in model 2 in addition to the following variables: current smoking status, educational status, coronary heart disease, physical activity, dietary cholesterol, risk group, fat and calorie consumption, and alcohol consumption (5 categories).
Table 3.
Subgroup analyses – prevalence odds ratio (95% CIs) for metabolic syndrome according to chocolate intake restricted to the random population group, AND subjects with diagnosed coronary heart disease or treated diabetes and/or hypertension.
| Frequency of chocolate consumption (per week)
| ||||
|---|---|---|---|---|
| 0 | <1 | 1–4 | 5+ | |
| Model 4 | ||||
| Women | 1.00 | 1.16 (0.74, 1.83) | 0.94 (0.59, 1.50) | 1.24 (0.68, 2.28) |
| Men | 1.00 | 1.25 (0.77, 2.01) | 0.99 (0.63, 1.58) | 1.18 (0.62, 2.23) |
| Model 5 | ||||
| Women | 1.00 | 1.28 (0.94, 1.76) | 1.16 (0.85, 1.59) | 1.06 (0.69, 1.64) |
| Men | 1.00 | 1.30 (0.88, 1.93) | 1.12 (0.76, 1.64) | 1.27 (0.77, 2.08) |
Model 4 – Similar to model 3 but restricted to the random population group.
Model 5 – Similar to model 3 but restricted to population with coronary heart disease or those being treated for hypertension and/or diabetes.
When MetS components were individually examined, we observed no significant association between chocolate consumption and high serum TG, low serum HDL, or high blood pressure, (Table 4). However, a statistically significant lower prevalence of diabetes and a higher prevalence of adiposity (measured as increased waist circumference above 102 cm for men and 88 cm for women) was observed with chocolate consumption: adjusted odds ratios (95% CI) from the lowest to the highest category of chocolate intake were 1.0 (ref), 0.65 (0.50, 0.85), 0.61 (0.47, 0.80), and 0.44 (0.30, 0.65), respectively, for diabetes and 1.0 (ref), 1.34 (1.09, 1.66), 1.27 (1.02, 1.58), and 1.74 (1.31, 2.31), respectively, for obesity.
Table 4.
Prevalence odds ratio (95% CIs) for individual metabolic syndrome components according to frequency of chocolate intake. Model was adjusted for: age, age-squared, current smoking status, educational status, coronary heart disease, physical activity, dietary cholesterol, risk group, fat and calorie consumption, and alcohol consumption (5 categories).
| MetS individual components | Frequency of chocolate consumption
|
|||
|---|---|---|---|---|
| 0 | 1 | 2 | 3 | |
| <1/wk. | 1–4 times/wk. | 5+ times/wk. | ||
| n = 899 | n = 970 | n = 1632 | n = 597 | |
| Obesity WC >88 or 102 | 1.00 | 1.34 (1.09, 1.64) | 1.25 (1.01, 1.53) | 1.66 (1.26, 2.19) |
| Triglyceride ≥ 150 mg/dl | 1.00 | 1.15 (0.94, 1.40) | 1.03 (0.84, 1.26) | 1.04 (0.79, 1.36) |
| Hypertension | 1.00 | 0.94 (0.75, 1.17) | 0.88 (0.70, 1.11) | 0.84 (0.61, 1.15) |
| HDL<40 mg/dl in men or <50 mg/dl in women | 1.00 | 1.10 (0.90, 1.36) | 1.08 (0.88, 1.32) | 1.01 (0.78, 1.32) |
| Fasting plasma glucose >110 mg/dl | 1.00 | 0.65 (0.50, 0.85) | 0.61 (0.47, 0.80) | 0.44 (0.30, 0.65) |
4. Discussion
In the present study, we demonstrated that chocolate consumption was not associated with prevalence odds of MetS among men or women. We also report a significant increase in the prevalence of adiposity (increase in WC) and a significant decrease in prevalence of diabetes with habitual consumption of chocolate. The prevalence of the metabolic syndrome in this study population was 30.2% which is lower than that reported in the NHANES 2003–2006 population (34%).
To the best of our knowledge, only one previous study28 has attempted to examine the association between chocolate consumption and the prevalence odds of MetS. This study examined the association between candy consumption and body weight measures, CVD risk or metabolic syndrome, using the NHANES 1999–2004 data, and reported a 15% reduced risk of MetS in chocolate candy consumers when compared with non-consumers. Our study, though, reports a null association. Their study adjusted for sex, age and ethnicity alone in the final model, this study includes other covariates – current smoking status, educational status, coronary heart disease, physical activity, dietary cholesterol, risk group, fat and calorie consumption, and alcohol consumption – which were considered as potential confounders. Secondly, the present study quantified and categorized the frequency of chocolate consumption over three different levels (<1/wk., 1–4/wk. and 5+/wk.), their study analyzed chocolate candy consumption as a dichotomous variable (yes/no). Thirdly, both studies used different ways for ascertainment of chocolate consumption.
Previous clinical trials and prospective cohort studies have explored the relationship between chocolate consumption and some of the individual components of the metabolic syndrome.11–14,29 Chocolate consumption has been shown to reduce blood pressure and serum LDL cholesterol levels,11,12 increase HDL cholesterol levels,14 increase insulin sensitivity and reduce insulin resistance in both healthy and glucose intolerant individuals.11
The MetS NCEP definition used in our study includes a threshold for central obesity and serum triglyceride concentration, which are the other two components. Most studies report a null association between chocolate consumption and increased serum TG concentration. Limited published data, however, report the relationship between habitual chocolate consumption and obesity.30
From completed studies on the effect of chocolate consumption on the individual MetS components, we can speculate on the directionality of effect of chocolate consumption on MetS – a significant reduction in the prevalence odds of MetS with chocolate intake. Since this was not the case, it may be suggested that the obesity component is responsible for the non-association which we observed. This view is supported by the outcome we obtained after analysis of the association between chocolate consumption and the individual MetS components (Table 4). While this analysis showed no association between chocolate intake and high average blood pressure or low HDL, we observed a trend toward lower odds of ‘hypertension’ and ‘low HDL’ with increasing frequency of chocolate intake. Same can be said for the diabetes component (this association was found to be highly significant) and this finding is consistent with similar studies.11,12 We also observed, however, significant higher odds of obesity prevalence with chocolate consumption and this is possibly driving the overall results in the null direction.
In one of the few published studies specifically exploring the topic of obesity and chocolate consumption, body weight and mesenteric white adipose tissue weight were significantly lower in rats fed with real cocoa diet than in those fed the mimetic cocoa diet,30 suggesting that ingested cocoa may actually prevent high-fat diet-induced obesity by modulating lipid metabolism, especially by decreasing fatty acid synthesis and transport systems30 – this, though, is at odds with our finding. This difference may be due to the wide variation in the way everyday chocolate products are manufactured and prepared before consumption – a variation that may influence the total calorie intake and ultimately, weight gain among people who consume chocolate, a thought also supported by the positive relationship between total calorie intake and chocolate consumption observed in Table 1. Unmeasured correlates of cocoa beverage ingestion (that increase calorie intake) may also exist that were not included in our analysis but could explain some of the effects we observed.
This study has some limitations. It is not possible to draw causal inference from the data due to the cross-sectional design. Because chocolate consumption was self-reported using a semi-quantitative food-frequency questionnaire, it is possible that some reporting bias may have biased the results toward the null. It might be possible that subjects already diagnosed with CHD, diabetes, or hypertension may selectively recall consuming higher quantities of chocolate. On the other hand, the inverse association observed between ‘elevated fasting plasma glucose’ and chocolate consumption as shown in Table 4 could be the result of reverse association, because individuals who are diagnosed diabetics should and often do make alterations in their diet,31 including the avoidance of processed sugars as would be found in chocolate. We were also unable to assess the association specifically between dark chocolate and MetS in our study due to lack of detailed data on type of chocolate consumed. If dark but not white chocolate is effective in influencing MetS prevalence, then our inability to separate these two types of chocolate might have obscured a true association. Residual confounding by unmeasured correlates of chocolate consumption is also possible. Nevertheless, the large sample size, the multicenter design of the study (within the US), and the availability of data on numerous covariates are strengths of this paper.
In summary, this study does not provide evidence for an association between self-reported chocolate consumption and the prevalence of MetS among men and women.
Acknowledgments
This research was supported by National Institute of Mental Health grant MH-52055 and by NHLBI cooperative agreement grants U01 HL-56563, U01 HL-56564, U01 HL-56565, U01 HL-56566, U01 HL-56567, U01 HL-56568, and U01 HL-56569. This article is presented on behalf of the investigators of the NHLBI Family Heart Study. We thank the subjects participating in this study.
Footnotes
Conflict of interest
The authors declare no conflict of interest.
Statement of authorship
LD and OT conceived the study, carried out the statistical analyses and drafted the paper. CE, DA and SH participated in the design and coordination of this multicenter study and also helped review the manuscript. KN, JP and AK provided significant advice and insight with the analysis of the data and they helped review the draft. All authors have read and approved the final manuscript.
Contributor Information
Oluwabunmi A. Tokede, Email: oluwabunmi_tokede@hms.harvard.edu.
Luc Djoussé, Email: ldjousse@partners.org.
References
- 1.Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States, 2003–2006. Natl Health Stat Rep. 2009 May 5;13:1–7. [PubMed] [Google Scholar]
- 2.Meigs JB. Epidemiology of the metabolic syndrome, 2002. Am J Manag Care. 2002 Sep;8(Suppl 11):S283–92. quiz S293–286. [PubMed] [Google Scholar]
- 3.Haffner SM, Ruilope L, Dahlof B, Abadie E, Kupfer S, Zannad F. Metabolic syndrome, new onset diabetes, and new end points in cardiovascular trials. J Cardiovasc Pharmacol. 2006 Mar;47(3):469–75. doi: 10.1097/01.fjc.0000211729.30927.4d. [DOI] [PubMed] [Google Scholar]
- 4.Alberti KG, Zimmet P, Shaw J. Metabolic syndrome–a new world-wide definition. A consensus statement from the International Diabetes Federation. Diabet Med. 2006 May;23(5):469–80. doi: 10.1111/j.1464-5491.2006.01858.x. [DOI] [PubMed] [Google Scholar]
- 5.Hu G, Qiao Q, Tuomilehto J, Balkau B, Borch-Johnsen K, Pyorala K. Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Arch Intern Med. 2004 May 24;164(10):1066–76. doi: 10.1001/archinte.164.10.1066. [DOI] [PubMed] [Google Scholar]
- 6.Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002 Dec 4;288(21):2709–16. doi: 10.1001/jama.288.21.2709. [DOI] [PubMed] [Google Scholar]
- 7.Hunt KJ, Resendez RG, Williams K, Haffner SM, Stern MP. National Cholesterol Education Program versus World Health Organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio Heart Study. Circulation. 2004 Sep 7;110(10):1251–7. doi: 10.1161/01.CIR.0000140762.04598.F9. [DOI] [PubMed] [Google Scholar]
- 8.Mokdad AH, Serdula MK, Dietz WH, Bowman BA, Marks JS, Koplan JP. The continuing epidemic of obesity in the United States. JAMA. 2000 Oct 4;284(13):1650–1. doi: 10.1001/jama.284.13.1650. [DOI] [PubMed] [Google Scholar]
- 9.Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA. 2002 Jan 16;287(3):356–9. doi: 10.1001/jama.287.3.356. [DOI] [PubMed] [Google Scholar]
- 10.Bruinsma K, Taren DL. Chocolate: food or drug? J Am Diet Assoc. 1999 Oct;99(10):1249–56. doi: 10.1016/S0002-8223(99)00307-7. [DOI] [PubMed] [Google Scholar]
- 11.Grassi D, Desideri G, Necozione S, Lippi C, Casale R, Properzi G, et al. Blood pressure is reduced and insulin sensitivity increased in glucose-intolerant, hypertensive subjects after 15 days of consuming high-polyphenol dark chocolate. J Nutr. 2008 Sep;138(9):1671–6. doi: 10.1093/jn/138.9.1671. [DOI] [PubMed] [Google Scholar]
- 12.Grassi D, Lippi C, Necozione S, Desideri G, Ferri C. Short-term administration of dark chocolate is followed by a significant increase in insulin sensitivity and a decrease in blood pressure in healthy persons. Am J Clin Nutr. 2005 Mar;81(3):611–4. doi: 10.1093/ajcn/81.3.611. [DOI] [PubMed] [Google Scholar]
- 13.Grassi D, Necozione S, Lippi C, Croce G, Valeri L, Pasqualetti P, et al. Cocoa reduces blood pressure and insulin resistance and improves endothelium-dependent vasodilation in hypertensives. Hypertension. 2005 Aug;46(2):398–405. doi: 10.1161/01.HYP.0000174990.46027.70. [DOI] [PubMed] [Google Scholar]
- 14.Mursu J, Voutilainen S, Nurmi T, Rissanen TH, Virtanen JK, Kaikkonen J, et al. Dark chocolate consumption increases HDL cholesterol concentration and chocolate fatty acids may inhibit lipid peroxidation in healthy humans. Free Radic Biol Med. 2004 Nov 1;37(9):1351–9. doi: 10.1016/j.freeradbiomed.2004.06.002. [DOI] [PubMed] [Google Scholar]
- 15.Tokede OA, Gaziano JM, Djousse L. Effects of cocoa products/dark chocolate on serum lipids: a meta-analysis. Eur J Clin Nutr. 2011 Aug;65(8):879–86. doi: 10.1038/ejcn.2011.64. [DOI] [PubMed] [Google Scholar]
- 16.Hunt SC, Williams RR, Barlow GK. A comparison of positive family history definitions for defining risk of future disease. J Chronic Dis. 1986;39(10):809–21. doi: 10.1016/0021-9681(86)90083-4. [DOI] [PubMed] [Google Scholar]
- 17.Higgins M, Province M, Heiss G, Eckfeldt J, Ellison RC, Folsom AR, et al. NHLBI Family Heart Study: objectives and design. Am J Epidemiol. 1996 Jun 15;143(12):1219–28. doi: 10.1093/oxfordjournals.aje.a008709. [DOI] [PubMed] [Google Scholar]
- 18.Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972 Jun;18(6):499–502. [PubMed] [Google Scholar]
- 19.David J, Paksi J, Naito N. Separation of lipoprotein (Lp) fraction by the Beckman TL-100 table-top ultracentrifuge (UC) Clin Chem. 1986;32:1094. [abstr] [Google Scholar]
- 20.McGowan MW, Artiss JD, Strandbergh DR, Zak B. A peroxidase-coupled method for the colorimetric determination of serum triglycerides. Clin Chem. 1983 Mar;29(3):538–42. [PubMed] [Google Scholar]
- 21.Allain CC, Poon LS, Chan CS, Richmond W, Fu PC. Enzymatic determination of total serum cholesterol. Clin Chem. 1974 Apr;20(4):470–5. [PubMed] [Google Scholar]
- 22.Warnick GR, Benderson J, Albers JJ. Dextran sulfate-Mg2+ precipitation procedure for quantitation of high-density-lipoprotein cholesterol. Clin Chem. 1982 Jun;28(6):1379–88. [PubMed] [Google Scholar]
- 23.Stein AD, Shea S, Basch CE, Contento IR, Zybert P. Consistency of the Willett semiquantitative food frequency questionnaire and 24-hour dietary recalls in estimating nutrient intakes of preschool children. Am J Epidemiol. 1992 Mar 15;135(6):667–77. doi: 10.1093/oxfordjournals.aje.a116346. [DOI] [PubMed] [Google Scholar]
- 24.Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semi-quantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992 May 15;135(10):1114–26. doi: 10.1093/oxfordjournals.aje.a116211. discussion 1127–1136. [DOI] [PubMed] [Google Scholar]
- 25.Services USDoHH. [[accessed 16.11.2011]];Third report of the expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (ATP III final report) 2011 http://www.nhlbi.nih.gov/guidelines/cholesterol/atp3_rpt.htm. 2001.
- 26.Djousse L, Pankow JS, Eckfeldt JH, Folsom AR, Hopkins PN, Province MA, et al. Relation between dietary linolenic acid and coronary artery disease in the National Heart, Lung, and Blood Institute Family Heart Study. Am J Clin Nutr. 2001 Nov;74(5):612–9. doi: 10.1093/ajcn/74.5.612. [DOI] [PubMed] [Google Scholar]
- 27.Ellison RC, Zhang Y, Qureshi MM, Knox S, Arnett DK, Province MA. Lifestyle determinants of high-density lipoprotein cholesterol: the National Heart, Lung, and Blood Institute Family Heart Study. Am Heart J. 2004 Mar;147(3):529–35. doi: 10.1016/j.ahj.2003.10.033. [DOI] [PubMed] [Google Scholar]
- 28.O’Neil CE, Fulgoni VL, 3rd, Nicklas TA. Candy consumption was not associated with body weight measures, risk factors for cardiovascular disease, or metabolic syndrome in US adults: NHANES 1999–2004. Nutr Res. 2011 Feb;31(2):122–30. doi: 10.1016/j.nutres.2011.01.007. [DOI] [PubMed] [Google Scholar]
- 29.Almoosawi S, Fyfe L, Ho C, Al-Dujaili E. The effect of polyphenol-rich dark chocolate on fasting capillary whole blood glucose, total cholesterol, blood pressure and glucocorticoids in healthy overweight and obese subjects. Br J Nutr. 2010 Mar;103(6):842–50. doi: 10.1017/S0007114509992431. [DOI] [PubMed] [Google Scholar]
- 30.Matsui N, Ito R, Nishimura E, Yoshikawa M, Kato M, Kamei M, et al. Ingested cocoa can prevent high-fat diet-induced obesity by regulating the expression of genes for fatty acid metabolism. Nutrition. 2005 May;21(5):594–601. doi: 10.1016/j.nut.2004.10.008. [DOI] [PubMed] [Google Scholar]
- 31.Franz MJ, Bantle JP, Beebe CA, Brunzell JD, Chiasson JL, Garg A, et al. Nutrition principles and recommendations in diabetes. Diabetes Care. 2004 Jan;27(Suppl 1):S36–46. doi: 10.2337/diacare.27.2007.s36. [DOI] [PubMed] [Google Scholar]