Poor diet quality is strongly associated with elevated risk of cardiovascular disease morbidity and mortality. This scientific statement emphasizes the importance of dietary patterns beyond ...individual foods or nutrients, underscores the critical role of nutrition early in life, presents elements of heart-healthy dietary patterns, and highlights structural challenges that impede adherence to heart-healthy dietary patterns. Evidence-based dietary pattern guidance to promote cardiometabolic health includes the following: (1) adjust energy intake and expenditure to achieve and maintain a healthy body weight; (2) eat plenty and a variety of fruits and vegetables; (3) choose whole grain foods and products; (4) choose healthy sources of protein (mostly plants; regular intake of fish and seafood; low-fat or fat-free dairy products; and if meat or poultry is desired, choose lean cuts and unprocessed forms); (5) use liquid plant oils rather than tropical oils and partially hydrogenated fats; (6) choose minimally processed foods instead of ultra-processed foods; (7) minimize the intake of beverages and foods with added sugars; (8) choose and prepare foods with little or no salt; (9) if you do not drink alcohol, do not start; if you choose to drink alcohol, limit intake; and (10) adhere to this guidance regardless of where food is prepared or consumed. Challenges that impede adherence to heart-healthy dietary patterns include targeted marketing of unhealthy foods, neighborhood segregation, food and nutrition insecurity, and structural racism. Creating an environment that facilitates, rather than impedes, adherence to heart-healthy dietary patterns among all individuals is a public health imperative.
Prevailing dietary guidelines have widely recommended diets relatively low in red and processed meats and high in minimally processed plant foods for the prevention of chronic diseases. However, an ...ad hoc research group called the Nutritional Recommendations (NutriRECS) consortium recently issued "new dietary guidelines" encouraging individuals to continue their current meat consumption habits due to "low certainty" of the evidence, difficulty of altering meat eaters' habits and preferences, and the lack of need to consider environmental impacts of red meat consumption. These recommendations are not justified, in large part because of the flawed methodologies used to review and grade nutritional evidence. The evidence evaluation was largely based on the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria, which are primarily designed to grade the strength of evidence for clinical interventions especially pharmacotherapy. However, the infeasibility for conducting large, long-term randomized clinical trials on most dietary, lifestyle, and environmental exposures makes the criteria inappropriate in these areas. A separate research group proposed a modified and validated system for rating the meta-evidence on nutritional studies (NutriGRADE) to address several limitations of the GRADE criteria. Applying NutriGRADE, the evidence on the positive association between red and processed meats and type 2 diabetes was rated to be of "high quality," while the evidence on the association between red and processed meats and mortality was rated to be of "moderate quality." Another important limitation is that inadequate attention was paid to what might be replacing red meat, be it plant-based proteins, refined carbohydrates, or other foods. In summary, the red/processed meat recommendations by NutriRECS suffer from important methodological limitations and involve misinterpretations of nutritional evidence. To improve human and planetary health, dietary guidelines should continue to emphasize dietary patterns low in red and processed meats and high in minimally processed plant foods such as fruits and vegetables, whole grains, nuts, and legumes.
To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 ...diabetes.
We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite.
We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 1.24-1.48; I(2) = 9.5%), 36% for leucine (1.36 1.17-1.58; I(2) = 37.4%), 35% for valine (1.35 1.19-1.53; I(2) = 45.8%), 36% for tyrosine (1.36 1.19-1.55; I(2) = 51.6%), and 26% for phenylalanine (1.26 1.10-1.44; I(2) = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 0.81-0.96 and 0.85 0.82-0.89, respectively; both I(2) = 0.0%).
In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
Current evidence on associations between intakes of linoleic acid (LA), the predominant n–6 (ω-6) fatty acid, and mortality is inconsistent and has not been summarized by a systematic review and ...meta-analysis.
The aim was to perform a systematic review and meta-analysis of prospective cohort studies to examine associations between LA intake and mortality.
We conducted a comprehensive search of MEDLINE and EMBASE databases through 31 July 2019 for prospective cohort studies reporting associations of LA (assessed by dietary surveys and/or LA concentrations in adipose tissue or blood compartments) with mortality from all causes, cardiovascular disease (CVD), and cancer. Multivariable-adjusted RRs were pooled using random-effects meta-analysis.
Thirty-eight studies reporting 44 prospective cohorts were identified; these included 811,069 participants with dietary intake assessment (170,076 all-cause, 50,786 CVD, and 59,684 cancer deaths) and 65,411 participants with biomarker measurements (9758 all-cause, 6492 CVD, and 1719 cancer deaths). Pooled RRs comparing extreme categories of dietary LA intake (high vs low) were 0.87 (95% CI: 0.81, 0.94; I2= 67.9%) for total mortality, 0.87 (95% CI: 0.82, 0.92; I2= 3.7%) for CVD mortality, and 0.89 (95% CI: 0.85, 0.93; I2= 0%) for cancer mortality. Pooled RRs for each SD increment in LA concentrations in adipose tissue/blood compartments were 0.91 (95% CI: 0.87, 0.95; I2= 64.1%) for total mortality, 0.89 (95% CI: 0.85, 0.94; I2= 28.9%) for CVD mortality, and 0.91 (95% CI: 0.84, 0.98; I2= 26.3%) for cancer mortality. Meta-regressions suggested baseline age and dietary assessment methods as potential sources of heterogeneity for the association between LA and total mortality.
In prospective cohort studies, higher LA intake, assessed by dietary surveys or biomarkers, was associated with a modestly lower risk of mortality from all causes, CVD, and cancer. These data support the potential long-term benefits of PUFA intake in lowering the risk of CVD and premature death.
Purpose of Review
To examine recent literature on dairy products, dairy fatty acids, and cardiometabolic disease. Primary questions of interest include what unique challenges researchers face when ...investigating dairy products/biomarkers, whether one should consume dairy to reduce disease risk, whether dairy fatty acids may be beneficial for health, and whether one should prefer low- or high-fat dairy products.
Recent Findings
Dairy composes about 10% of the calories in a typical American diet, about half of that coming from fluid milk, half coming from cheese, and small amounts from yogurt. Most meta-analyses report no or weak inverse association between dairy intake with cardiovascular disease and related intermediate outcomes. There is some suggestion that dairy consumption was inversely associated with stroke incidence and yogurt consumption was associated with lower risk of type 2 diabetes. Odd chain fatty acids (OCFAs) found primarily in dairy (15:0 and 17:0) appear to be inversely associated with cardiometabolic risk, but causation is uncertain. Substitution analyses based on prospective cohorts suggested that replacing dairy fat with vegetable fat or polyunsaturated fat was associated with significantly lower risk of cardiovascular disease.
Summary
Current evidence suggests null or weak inverse association between consumption of dairy products and risk of cardiovascular disease. However, replacing dairy fat with polyunsaturated fat, especially from plant-based foods, may confer health benefits. More research is needed to examine health effects of different types of dairy products in diverse populations.
Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, ...and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.
AbstractObjectiveTo investigate the association of predicted lean body mass, fat mass, and body mass index (BMI) with all cause and cause specific mortality in men.DesignProspective cohort ...study.SettingHealth professionals in the United StatesParticipants38 006 men (aged 40-75 years) from the Health Professionals Follow-up Study, followed up for death (1987-2012).Main outcome measuresAll cause and cause specific mortality.ResultsUsing validated anthropometric prediction equations previously developed from the National Health and Nutrition Examination Survey, lean body mass and fat mass were estimated for all participants. During a mean of 21.4 years of follow-up, 12 356 deaths were identified. A J shaped association was consistently observed between BMI and all cause mortality. Multivariable adjusted Cox models including predicted fat mass and lean body mass showed a strong positive monotonic association between predicted fat mass and all cause mortality. Compared with those in the lowest fifth of predicted fat mass, men in the highest fifth had a hazard ratio of 1.35 (95% confidence interval 1.26 to 1.46) for mortality from all causes. In contrast, a U shaped association was found between predicted lean body mass and all cause mortality. Compared with those in the lowest fifth of predicted lean body mass, men in the second to fourth fifths had 8-10% lower risk of mortality from all causes. In the restricted cubic spline models, the risk of all cause mortality was relatively flat until 21 kg of predicted fat mass and increased rapidly afterwards, with a hazard ratio of 1.22 (1.18 to 1.26) per standard deviation. For predicted lean body mass, a large reduction of the risk was seen within the lower range until 56 kg, with a hazard ratio of 0.87 (0.82 to 0.92) per standard deviation, which increased thereafter (P for non-linearity <0.001). For cause specific mortality, men in the highest fifth of predicted fat mass had hazard ratios of 1.67 (1.47 to 1.89) for cardiovascular disease, 1.24 (1.09 to 1.43) for cancer, and 1.26 (0.97 to 1.64) for respiratory disease. On the other hand, a U shaped association was found between predicted lean body mass and mortality from cardiovascular disease and cancer. However, a strong inverse association existed between predicted lean body mass and mortality from respiratory disease (P for trend <0.001).ConclusionsThe shape of the association between BMI and mortality was determined by the relation between two body components (lean body mass and fat mass) and mortality. This finding suggests that the “obesity paradox” controversy may be largely explained by low lean body mass, rather than low fat mass, in the lower range of BMI.
Americans have a shorter life expectancy compared with residents of almost all other high-income countries. We aim to estimate the impact of lifestyle factors on premature mortality and life ...expectancy in the US population.
Using data from the Nurses' Health Study (1980-2014; n=78 865) and the Health Professionals Follow-up Study (1986-2014, n=44 354), we defined 5 low-risk lifestyle factors as never smoking, body mass index of 18.5 to 24.9 kg/m
, ≥30 min/d of moderate to vigorous physical activity, moderate alcohol intake, and a high diet quality score (upper 40%), and estimated hazard ratios for the association of total lifestyle score (0-5 scale) with mortality. We used data from the NHANES (National Health and Nutrition Examination Surveys; 2013-2014) to estimate the distribution of the lifestyle score and the US Centers for Disease Control and Prevention WONDER database to derive the age-specific death rates of Americans. We applied the life table method to estimate life expectancy by levels of the lifestyle score.
During up to 34 years of follow-up, we documented 42 167 deaths. The multivariable-adjusted hazard ratios for mortality in adults with 5 compared with zero low-risk factors were 0.26 (95% confidence interval CI, 0.22-0.31) for all-cause mortality, 0.35 (95% CI, 0.27-0.45) for cancer mortality, and 0.18 (95% CI, 0.12-0.26) for cardiovascular disease mortality. The population-attributable risk of nonadherence to 5 low-risk factors was 60.7% (95% CI, 53.6-66.7) for all-cause mortality, 51.7% (95% CI, 37.1-62.9) for cancer mortality, and 71.7% (95% CI, 58.1-81.0) for cardiovascular disease mortality. We estimated that the life expectancy at age 50 years was 29.0 years (95% CI, 28.3-29.8) for women and 25.5 years (95% CI, 24.7-26.2) for men who adopted zero low-risk lifestyle factors. In contrast, for those who adopted all 5 low-risk factors, we projected a life expectancy at age 50 years of 43.1 years (95% CI, 41.3-44.9) for women and 37.6 years (95% CI, 35.8-39.4) for men. The projected life expectancy at age 50 years was on average 14.0 years (95% CI, 11.8-16.2) longer among female Americans with 5 low-risk factors compared with those with zero low-risk factors; for men, the difference was 12.2 years (95% CI, 10.1-14.2).
Adopting a healthy lifestyle could substantially reduce premature mortality and prolong life expectancy in US adults.
IMPORTANCE Epidemiologic studies have suggested that higher intake of added sugar is associated with cardiovascular disease (CVD) risk factors. Few prospective studies have examined the association ...of added sugar intake with CVD mortality. OBJECTIVE To examine time trends of added sugar consumption as percentage of daily calories in the United States and investigate the association of this consumption with CVD mortality. DESIGN, SETTING, AND PARTICIPANTS National Health and Nutrition Examination Survey (NHANES, 1988-1994 III, 1999-2004, and 2005-2010 n = 31,147) for the time trend analysis and NHANES III Linked Mortality cohort (1988-2006 n = 11 733), a prospective cohort of a nationally representative sample of US adults for the association study. MAIN OUTCOMES AND MEASURES Cardiovascular disease mortality. RESULTS Among US adults, the adjusted mean percentage of daily calories from added sugar increased from 15.7% (95% CI, 15.0%-16.4%) in 1988-1994 to 16.8% (16.0%-17.7%; P = .02) in 1999-2004 and decreased to 14.9% (14.2%-15.5%; P < .001) in 2005-2010. Most adults consumed 10% or more of calories from added sugar (71.4%) and approximately 10% consumed 25% or more in 2005-2010. During a median follow-up period of 14.6 years, we documented 831 CVD deaths during 163,039 person-years. Age-, sex-, and race/ethnicity-adjusted hazard ratios (HRs) of CVD mortality across quintiles of the percentage of daily calories consumed from added sugar were 1.00 (reference), 1.09 (95% CI, 1.05-1.13), 1.23 (1.12-1.34), 1.49 (1.24-1.78), and 2.43 (1.63-3.62; P < .001), respectively. After additional adjustment for sociodemographic, behavioral, and clinical characteristics, HRs were 1.00 (reference), 1.07 (1.02-1.12), 1.18 (1.06-1.31), 1.38 (1.11-1.70), and 2.03 (1.26-3.27; P = .004), respectively. Adjusted HRs were 1.30 (95% CI, 1.09-1.55) and 2.75 (1.40-5.42; P = .004), respectively, comparing participants who consumed 10.0% to 24.9% or 25.0% or more calories from added sugar with those who consumed less than 10.0% of calories from added sugar. These findings were largely consistent across age group, sex, race/ethnicity (except among non-Hispanic blacks), educational attainment, physical activity, health eating index, and body mass index. CONCLUSIONS AND RELEVANCE Most US adults consume more added sugar than is recommended for a healthy diet. We observed a significant relationship between added sugar consumption and increased risk for CVD mortality.
To review the contribution of the Nurses' Health Studies (NHS and NHS II) in addressing hypotheses regarding risk factors for and consequences of obesity.
Narrative review of the publications of the ...NHS and NHS II between 1976 and 2016.
Long-term NHS research has shown that weight gain and being overweight or obese are important risk factors for type 2 diabetes, cardiovascular diseases, certain types of cancers, and premature death. The cohorts have elucidated the role of dietary and lifestyle factors in obesity, especially sugar-sweetened beverages, poor diet quality, physical inactivity, prolonged screen time, short sleep duration or shift work, and built environment characteristics. Genome-wide association and gene-lifestyle interaction studies have shown that genetic factors predispose individuals to obesity but that such susceptibility can be attenuated by healthy lifestyle choices. This research has contributed to evolving clinical and public health guidelines on the importance of limiting weight gain through healthy dietary and lifestyle behaviors.
The NHS cohorts have contributed to our understanding of the risk factors for and consequences of obesity and made a lasting impact on clinical and public health guidelines on obesity prevention.
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