Obesity bad - leads to diabetes and other non-communicable diseases
Fried, New York University Department of Nutrition, Food Studies & Public Health AND Simon, University of California Hastings College of the Law Assistant Professor & Marin Institute Research and Policy Director, 7
(Ellen and Michele, 7/20/2007, Duke Law Journal, “THE COMPETITIVE FOOD CONUNDRUM: CAN GOVERNMENT REGULATIONS IMPROVE SCHOOL FOOD?” http://scholarship.law.duke.edu/cgi/viewcontent.cgi?article=1324&context=dlj, Volume 56: 1491, Accessed 7/1/17, GDI - JMo)
Children’s health measures continue to worsen. Although obesity was cited decades ago as a negative impact of competitive foods, the focus was primarily centered on the epidemic of dental caries.25 Diabetes has also become a significant health issue for children. A 2003 study found the prevalence of children overweight at the onset of Type 1 diabetes had tripled from the 1980s to 1990s.26 This may suggest that obesity is contributing to the rise of both Type 1 and Type 2 diabetes in children. The condition known as “double diabetes,” previously only studied in adults, has also been reported for the first time in children.27 In addition, an estimated 61 percent of overweight youth have at least one additional risk factor for heart disease, such as high cholesterol or high blood pressure.28
Obesity causes health and mental issues within students
Centers for Disease Control, 13
[Center for Disease Control, “Make a Difference at Your School”, http://digitalcommons.hsc.unt.edu/cgi/viewcontent.cgi?article=1030&context=disease, 6/28/17, KW]
Since 1980, the percentage of obese children aged 6 to 11 has doubled, and the percentage of obese adolescents aged 12 to 19 has tripled. Childhood obesity has both immediate and long-term serious health impacts.
• In some communities, almost half of pediatric diabetes cases are type 2 diabetes, which was once believed to affect only adults.
• In one large study, 61% of obese 5- to 10-year-olds already had risk factors for heart disease, and 26% had two or more risk factors for the disease.
• Obese children have a greater risk of social and psychological problems, such as discrimination and poor self-esteem.
• Obese children have a 70% chance of being overweight or obese as adults—facing higher risks for many diseases, such as heart disease, diabetes, stroke, and several types of cancers.
The costs of treating obesity-related diseases are staggering and rising rapidly. In 2004, direct and indirect health costs associated with obesity were $98 billion.
Good eating habits and regular physical activity are critical for maintaining a healthy weight. Unfortunately, less than 25% of adolescents eat enough fruits and vegetables each day. Sixty-four percent of high school students don't meet currently recommended levels of physical activity.
Impact – Obesity Causes Deaths
Poor diets led to 300,000 easily preventable deaths
Dr. Micha, Tufts Friedman School of Nutrition Science and Policy professor, et al., 17
[Renata, an epidemiologist whose research focuses on the effects of diet on cardiometabolic diseases, José Peñalvo, Tufts University, Nutrition Science and Policy, assistant professor, worked for 6 years on how lifestyle determines cardiovascular health, Fred Cudhea, PhD in Biostatistics from Harvard, Fumiaki Imamura, Cambridge, Epidemiology, Senior Investigator Scientist and Harvard, Epidemiology, Postdoctoral Research Fellow, Colin Rehm, Einstein University, Epidemiology & Population Health, Clinical Assistant Professor, Dariush Mozaffarian, Tufts University, Nutrition Science and Policy, Dean, 3/7/17, The Jama Network, “Association Between Dietary Factors and Mortality from Heart Disease, Stroke, and Type 2 Diabetes in the United States”, http://jamanetwork.com/journals/jama/article-abstract/2608221, Jama Network, pg. 913-21 accessed 6/28/17, JBC] * Research done from data in 2012 but published in 2017
Dietary habits influence many risk factors for cardiometabolic health, including heart disease, stroke, and type 2 diabetes, which collectively pose substantial health and economic burdens.1 In both global2,3 and national4 modeling studies, the associations of suboptimal diet with overall health have been estimated. Understanding the relations of individual dietary components with cardiometabolic disease at the population level is essential to identify priorities, guide public health planning, and inform strategies to alter these dietary habits and improve health. In addition, the differences in these estimated health burdens by underlying personal characteristics, such as age, sex, race/ethnicity, and education, are relevant to consider more targeted approaches to reducing disparities.
For the United States, prior analyses have estimated the associations of suboptimal dietary habits with cardiometabolic health overall4 or for a limited number of dietary factors (eg, sodium, sugar-sweetened beverages).5Theresults for other individual dietary components, as well as differences by age, sex, race/ethnicity, and socioeconomic status, are not well established. The current investigation used a comparative risk assessment modeling design2,6,7 to estimate the cardiometabolic mortality related to suboptimal intakes of 10 dietary factors, individually and jointly, among US adults in 2012; to assess diet-associated mortality by disease subtypes (heart disease and subtypes, stroke and subtypes, and type 2 diabetes) and population subgroups (age, sex, race, and education); and to evaluate trends between 2002 and 2012.
Methods Study Design A comparative risk assessment model was used to estimate the numbers and proportions of cardiometabolic deaths associated with suboptimal intakes of 10 dietary factors in the United States, both individually and in combination (eAppendix 1 in the Supplement). The model incorporated separately derived data and corresponding uncertainty on (1) population demographics and dietary habits by sex, age, race, and education from the National Health and Nutrition Examination Survey (NHANES); (2) the estimated relationships of 10 foods and nutrients with heart disease, stroke, or type 2 diabetes mortality, by age, from meta-analyses of prospective cohorts and randomized clinical trials, further evaluated by several validity analyses; (3) the optimal population intake distributions of these dietary factors based on observed intakes associated with lowest risk in observational studies; and (4) observed US disease- specific cardiometabolic deaths by sex, age, race, and education from the National Center for Health Statistics (NCHS). This modeling investigation was exempt from human subjects review because it was based on published data and nationally representative, deidentified data sets that included no personally identifiable information.
Identification of Relevant Dietary Factors The methods and results for review, identification, and assessment of evidence for etiologic diet-disease relationships have been described (eAppendix 2 in the Supplement).8,9 Using Bradford-Hill criteria and considering consistency with other criteria for assessing potential causality of diet-disease relationships,10-12 probable or convincing evidence was identified for associations of 17 dietary factors with coronary heart disease (CHD), stroke, type 2 diabetes, body mass index (BMI), or systolic blood pressure (SBP) (eTables 1-5 in the Supplement). Of these, 10 were included in the present analysis (Table 1), excluding others with major overlap for estimating joint effects (eg, dietary fiber overlaps with whole grains, fish overlaps with omega-3 fats). Several other dietary factors were evaluated and not included because of insufficient evidence for casual relationships, including monounsaturated fats, vitamin D, magnesium, calcium, antioxidant vitamins, dairy products, cocoa, coffee, and tea. Evidence for potential associations of diet with other conditions such as cancer, osteoporosis, gallstones, inflammatory diseases, depression, cognitive function, or micronutrient deficiency diseases was not evaluated.
National Distributions of Dietary Intake and Demographics Dietary intakes were estimated using nationally representative data from multiple NHANES cycles, accounting for complex survey design and sampling weights,16 to be representative of the US population aged 25 years or older (eTable 6 in the Supplement). As previously described,17 intakes were assessed from up to 2 standardized 24-hour dietary recalls per person, accounting for within-person variation (eTables 7-10 in the Supplement).18 Optimal metrics and units for each dietary factor were characterized to be consistent with studies providing evidence on etiologic diet-disease relationships (Table 1; eTable 3 in the Supplement).8 All dietary factors were adjusted for energy intake (using the residual method18 or, for polyunsaturated fats, as percentage energy) to reduce measurement error and account for potential differences in body size, lean mass, metabolic efficiency, and physical activity.
The means and standard deviations of intake of each dietary factor were estimated in population strata by age (25- 34, 35-44, 45-54, 55-64, 65-74, or ≥75 years), sex (male or female), race/ethnicity (non-Hispanic white, non-Hispanic black, Mexican American/other Hispanic, or other race/mixed race), and education (less than high school diploma, high school diploma/ equivalent or some college, or 4-year college degree or greater). These demographic characteristics were classified in NHANES based on self-report. Dietary factors were modeled based on the mean and standard deviations using gamma (rather than normal) distributions, allowing for and incorporating skewed distributions. To maximize power for subgroups, 2002 intakes were estimated by combining 1999- 2000 and 2001-2002 cycles (the earliest with nationally representative 24-hour recalls; n = 8104;48.2%men)and 2012 intakes by combining 2009-2010 and 2011-2012 cycles (n = 8516; 47.6% men).
Estimated Diet-Disease Relationships Methods for reviewing and synthesizing evidence to estimate effect sizes (relative risks) for associations between dietary factors and cardiometabolic end points have been described (eAppendix 2 in the Supplement).8,9 The present analysis incorporated evidence from published or de novo meta-analyses of prospective cohorts or randomized clinical trials evaluating direct associations of dietary factors with CHD, stroke, or type 2 diabetes by age (Table 1;eTable 2 in the Supplement). We included additional BMI-mediated associations of sugar-sweetened beverages (SSBs) by age and overweight/ obesity status on deaths due to CHD, hypertensive heart disease, stroke, and type 2 diabetes and SBP-mediated associations of dietary sodium by age, race, and hypertensive status on deaths due to heart disease, stroke, and type 2 diabetes (eTable 5 in the Supplement).
These estimated effects can be used to model associations with cardiometabolic diseases if bias from confounding (which might overestimate effects) or measurement error (which might underestimate effects) is limited. To reduce bias from confounding, all identified observational studies in these meta-analyses used multivariable adjustment for other risk factors. Measurement error was generally not addressed, although some studies used serial measures of diet. In addition, associations of individual dietary factors with health may be different from joint associations when consumed as diet patterns; eg, healthful dietary factors such as fruits, vegetables, and whole grains tend to positively correlate in diets while inversely correlating with unhealthful dietary factors such as SSBs or processed meats. To determine the extent to which the estimated multivariable-adjusted effect sizes might be biased because of these limitations, 3 separate validity analyses were performed comparing the estimated effect sizes for individual dietary components to (1) observed associations of overall dietary patterns with clinical end points in long-term observational studies; (2) effects of dietary patterns on cardiovascular risk factors (low-density lipoprotein cholesterol, SBP) in randomized clinical feeding trials; and (3) effects of dietary patterns on hard end points in a large randomized clinical trial (eAppendix 2 and eTable 4 in the Supplement).9,19,20 Each of these validity analyses demonstrated that estimated effect sizes for individual dietary components were very similar to what would be expected based on these other lines of evidence.
Characterization of Optimal Intakes Optimal consumption levels for each dietary factor were characterized (Table 1) based on observed levels associated with lowest disease risk in meta-analyses of clinical end points, while further considering feasibility (observed national consumption levels in at least 2 to 3 countries around the world) and consistency with major dietary guidelines (eTable 3 in the Supplement).8 The population distribution (ie, standard deviation) around each optimal population mean was estimated from the optimal distributions of diet related metabolic risk factors in the Global Burden of Diseases study (10% of the mean).2 For each dietary factor, the modeling assumed no additional health benefits beyond the optimal intake distribution within each sex, age, race, and education stratum.
National Mortality, BMI, and SBP Distributions by Sex, Age, Race, and Education National disease-specific deaths in each stratum for 2002 and 2012 were obtained from the NCHS, which includes the entire US population (https://www.cdc.gov/nchs/data_access /vitalstatsonline.htm). Deaths were excluded for foreign residents (individuals dying in the United States but whose place of residence is outside the United States), ages 25 years or younger, missing age information (2012: 0.005%; 2002: 0.006%), or, in education-stratified analyses, missing education information (2012: 2.1%; 2002: 6.2%). Diet-related cardiometabolic diseases were defined using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, including heart disease (the sum of CHD, hypertensive heart disease, and other cardiovascular disease), stroke (the sum of ischemic, hemorrhagic, and other stroke), and type 2 diabetes (Table 2; eTables 11-12 in the Supplement). Events were characterized by age, sex, race /ethnicity, and education as described above to match dietary strata. For associations mediated by BMI(SSBs), including with CHD, hypertensive heart disease, stroke, and type 2 diabetes, and by blood pressure (sodium), including with CHD, hypertensive heart disease, other cardiovascular disease, and stroke, the stratum-specific distributions (means and standard deviations) of BMI (based on measured heights and weights) and SBP (from certified examiners, using the mean of 3 measurements or 4 if necessary) in 2002 and 2012 were estimated from the 1999-2002and2009-2012NHANEScycles, respectively. Hypertension was defined as systolic blood pressure of at least 140mmHg, diastolic blood pressure of at least 90mmHg, or use of antihypertensive drugs.23
Cardiometabolic Disease Burdens Attributable to Key Dietary Targets All data inputs were combined in a comparative risk assessment model to estimate the absolute number and percentage of overall cardiometabolic deaths associated with suboptimal intake of each dietary factor. This framework21 incorporated each stratum-specific input and its uncertainty (except for uncertainty in baseline number of deaths, not reported by the NCHS) to estimate associated mortality by age and sex; by age, sex, and race; and by age, sex, and education. Stratification by all 4 demographic factors was not performed because of low sample size and unstable estimates in some strata. The main outcomes were the estimated absolute number and percentage of cardiometabolic mortality related to suboptimal intakes of 10 dietary factors, individually and jointly, in 2012. We also evaluated disease-specific and demographic-specific (age, sex, race, and education) mortality and trends between 2002 and 2012.
For each stratum, the model calculated the percentage of disease-specific mortality associated with each dietary factor by comparing the present distribution of consumption with the optimal distribution using the continuous population attributable fraction (PAF) formula (eAppendix 1 in the Supplement).21 This PAF was multiplied by the actual number of disease-specific deaths in that stratum of the US population to estimate the absolute number of disease-specific deaths in that stratum related to the dietary factor. The joint associations of all 10 dietary factors was estimated by proportional multiplication of each stratum-specific PAF (eAppendix 1). For comparing trends between 2002 and 2012, the estimated absolute (2012-2002) and relative (2012-2002/ 2002×100) associated mortality rates in 2002 were age- and sex-standardized to 2012 age-sex distributions.
Uncertainty was quantified using multiway probabilistic Monte Carlo simulations, jointly incorporating stratum specific uncertainties in dietary exposure distributions, diet disease relative risk estimates, and, for sodium, prevalence of hypertension and proportion of non-Hispanic blacks. Corresponding 95% uncertainty intervals (UIs) were derived from the 2.5th and 97.5th percentiles of 1000 estimated models. Different outcomes were evaluated without adjustment for multiple comparisons, so the UI bounds for each finding should be interpreted in that context. These analyses represent the estimated total cardiometabolic mortality associated with each dietary factor, including any mediated relationships through major cardiovascular risk factors (eg, the estimated mortality from low fruit or vegetable consumption would include any association mediated by their effects on lowering of blood pressure and blood cholesterol). Except for SSBs, additional potential relationships of dietary habits with obesity were not considered, which could underestimate total diet-related cardiometabolic mortality. All analyses were performed using R statistical software, version 3.1.0.
Results In both 2002 and 2012, national intakes of each dietary factor were suboptimal (Table 1; eTables 7-10 in the Supplement). In 2012, a total of 702 308 cardiometabolic deaths occurred in US adults, including 506 100 due to heart disease (including 371 266 due to CHD, 35 019 due to hypertensive heart disease, and 99 815 due to other cardiovascular disease), 128 294 from stroke (16 125 ischemic, 32 591 hemorrhagic, and 79 578 other), and 67 914 from type 2 diabetes (eTable 11 in the Supplement).
Estimated Cardiometabolic Mortality Attributed to Diet When all 10 dietary factors were evaluated in combination, they were associated with 318 656 estimated cardiometabolic deaths, or nearly 1 in 2 (45.4%) of all US cardiometabolic deaths in 2012. Among individual factors, largest numbers of estimated diet-related cardiometabolic deaths were related to high sodium (66 508 estimated cardiometabolic deaths [9.5%of all cardiometabolic deaths]), low nuts/seeds (59 374 [8.5%]), high processed meats (57 766 [8.2%]), low seafood omega-3 fats (54 626 [7.8%]), low vegetables (53 410 [7.6%]),low fruits (52 547 [7.5%]), and high SSBs (51 694 [7.4%]) compared with optimal consumption levels (Table 2; eFigure 1 in the Supplement). Lowest estimated mortality burdens were associated with low polyunsaturated fats (16 025 [2.3%]) and high unprocessed red meats (2869 [0.4%]).
Among cardiometabolic diseases, the largest numbers of deaths due to CHD were associated with low nuts/seeds (54 591 [14.7% of CHD deaths]), low seafood omega-3 fats (54 626 [14.7%]), high processed meats (45 637 [12.3%]), high SSBs (39 937 [10.8%]), and high sodium (37 744[10.2%]);due to total stroke, to low vegetables (28039 [21.9%]), low fruits (28 741 [22.4%]), and high sodium (13 787 [10.7%]); due to hypertensive heart disease, to high sodium (7505 [21.4%]); and due to type 2 diabetes, to high processed meats (11 900 [17.5%]), low whole grains (11 639 [17.1%]), and high SSBs (10 043 [14.8%]) (Table 2).
Findings by Sex, Age, Race, and Education Estimated cardiometabolic mortality associated with each dietary factor was modestly higher in men than in women, primarily because of generally unhealthier dietary habits in men (Figure 1; eFigure 5 and eTable 14 in the Supplement). The largest sex differences were seen for processed meats (10.8% of all cardiometabolic deaths in men and 5.4%inwomen; difference, +5.4%; 95%UI, 2.3%-8.3%) and SSBs (9.3%vs 5.3%; difference, +3.9%; 95% UI, 2.3%-5.4%). In men, the top 5 estimated dietary factors associated with cardiometabolic deaths were excess processed meats (38 632 deaths [10.8%of all cardiometabolic deaths]), sodium (35 777 [10.0%]), SSBs (33 314 [9.3%]); and insufficient nuts/seeds (31 587 [8.8%]) and seafood omega-3 fats (31 545 [8.8%]). In women, these were excess sodium (30 281 [8.8%]) and insufficient nuts/seeds (27 721 [8.1%]), vegetables (25 592 [7.4%]), fruits (24 449 [7.1%]), and omega-3 fats (23032 [6.7%]). Jointly, suboptimal diet was relatedto48.6% of estimated cardiometabolic deaths in men and 41.8%inwomen in 2012 (absolute difference, +6.9%; 95%UI, 3.3%-10.1%) (Figure 2).
By age, in 25- to 64-year-olds, excess SSBs and processed Meats were the top estimated diet factors associated with cardiometabolic mortality; in 65-year-olds and older, these were excess sodium and insufficient nuts/seeds and vegetables (eFigure 2, eFigure 5, and eTable 14 in the Supplement). Overall, suboptimal dietwasassociatedwith64.2%of all estimated cardiometabolic deaths in 25- to34-year-oldsand35.7%in 75-yearolds and older (absolute difference, −28.6%; 95%UI, −32.9%to −24.0%)(Figure 2). The highest estimated proportional deaths at youngest ages (<44 years) were associated with SSBs followed by processed meat, fruits, nuts/seeds, and vegetables; at middle age (45-54 years), with SSBs, processed meat, nuts/ seeds, and seafood omega-3 fats; and at oldest age (≥65 years), with sodium. For example, estimated proportions of SSB related deaths were much higher at age 25-34 years (26.8%)and 35-44years (28.9%)than at age ≥75years (3.5%). Estimated proportions of deaths related to processed meat and nuts/seeds were higher at age 45-54 years (16.8%and 15.7%, respectively) than at age ≥75 years (4.9%and 6.8%).
By race/ethnicity, estimated proportional diet-related mortality was higher among blacks or Hispanics for most dietary factors assessed (Figure 2; eFigure 3, eFigure 5, eFigure 6, and eTable 14 in the Supplement). For example, estimated cardiometabolic mortality associated with SSBs was nearly twice as high in blacks (12.6%; the leading factor) vs whites (6.4%), and from low nuts/seeds, higher in Hispanics (11.7%; the leading factor) vs whites (7.9%). One exception was omega-3 fat–associated proportional mortality, which was higher in whites (8.0%). Relative rankings of cardiometabolic mortality related to different dietary factors were otherwise generally similar by race/ethnicity. Overall, suboptimal diet was associated with 53.1% of total estimated cardiometabolic deaths among blacks, 50.0% among Hispanics, and 42.8% among whites (absolute differences, +10.5% [95% UI, 8.0%-12.7%] for blacks vs whites and +7.2% [95% UI, 4.8%-9.8%] for Hispanics vs whites).
Estimated proportional diet-related cardiometabolic mortality was generally higher among individuals with low or medium education compared with high education (Figure 2; eFigure 4, eFigure 5, eFigure 7, and eTable 14 in the Supplement). This was most notable for nuts/seeds (in low vs high education, 10.7%vs 6.2%of cardiometabolic deaths), SSBs (8.4%vs 4.5%), and fruits (8.5%vs 6.4%). Overall, suboptimal diet was associated with 46.8% of cardiometabolic deaths for lower-, 45.7%for medium-, and 39.1%for higher-educated adults (absolute differences, +7.7%[95%UI, 4.9%-10.4%]for low vs high and +6.7% [95% UI, 4.1%-9.0%] for medium vs high).
Trends Between 2002 and 2012 Between 2002 and 2012, the total number of population adjusted US cardiometabolic deaths per year decreased by 26.5%. Improvements were seen in national intakes of some factors, including polyunsaturated fats, nuts/seeds, SSBs, whole grains, and fruits (eFigure 8 in the Supplement). Thus, absolute numbers of diet-related cardiometabolic deaths decreased for all dietary factors (eTable 13 in the Supplement). As a percentage of annual cardiometabolic deaths, which accounts for underlying trends in absolute death rates, estimated diet-associated mortality declined for polyunsaturated fats (−20.8% smaller proportion of deaths; 95% UI, −18.5% to −22.8%), nuts/seeds (−18.0%; 95% UI, −14.6% to −21.0%), and SSBs (−14.5%; 95% UI, −12.0% to −16.9%); remained relatively stable for whole grains, fruits, vegetables, seafood omega-3 fats, and processed meats; and increased for sodium (+5.8%; 95% UI, 2.9%-8.8%) and unprocessed red meats (+14.4%; 95% UI, 9.1%-19.5%) (Figure 3). In 2002, excess SSB intake was the third leading risk factor for diet-associated cardiometabolic death among these 10 dietary factors, with an estimated 73 162 associated deaths, or 8.6% of all cardiometabolic deaths (see eTables 5, 8, and 12 for 2002 inputs and eFigures 9-16 in the Supplement for 2002 results overall and by population subgroups). In comparison, by 2012, SSBs had declined to the seventh cause of diet-associated deaths.
Proportional trends in cardiometabolic mortality associated with dietary factors were generally similar by sex and age (eFigures 5, 14, and 17 in the Supplement). Trends by race were also consistent with overall results, with some exceptions. For instance, the percentage of cardiometabolic deaths associated with insufficient nuts/seeds declined in whites (from 10.0% to 7.9%; −21.8% [95% UI, −35.8% to −3.4%]) but not in blacks or Hispanics, while the percentage of cardiometabolic deaths associated with insufficient whole grains declined in Hispanics (from 12.9% to 7.6%; −41.2% [95% UI, −49.8% to −28.8%]) but not in whites or blacks, yet Hispanics started at higher levels and declined to more similar associated burdens by 2012. Trends in diet-associated cardiometabolic deaths were also generally similar by education, except that the percentage of cardiometabolic deaths associated with low nuts/seeds declined in adults with high (8.7% to 6.2%; −29.7% [95% UI, −36.0% to −23.3%]) but not low (10.9% to 10.7%; −3.0% [95% UI, −8.4% to 6.3%]) education; and with SSBs declined more among adults with high (5.9% vs 4.5%; −23.9% [95% UI, −29.5% to −17.9%]) compared with low (9.2% vs 8.4%; −8.3% [95% UI, −12.6% to −4.0%]) education.
Discussion Based on a comparative risk assessment model and nationally representative data, an estimated 45.4% of all cardiometabolic deaths (n=318 656 due to heart disease, stroke, and type 2 diabetes) were associated with suboptimal intakes of 10 dietary factors in 2012. By sex, larger diet related proportional mortality was estimated in men than in women, consistent with generally unhealthier dietary habits in men. Suboptimal diet was also associated with larger proportional mortality at younger vs older ages, among blacks and Hispanics vs whites, and among individuals with low and medium education vs high education.
Among individual dietary components, the largest estimated mortality was associated with suboptimal sodium (9.5%) followed by nuts/seeds, processed meats, seafood omega-3 fats, vegetables, fruits, SSBs, and whole grains (each between 5.9%-8.5%), and, last, polyunsaturated fats (2.3%) and unprocessed red meats (0.4%). Estimated deaths related to processed meats and SSBs were higher among men than women. By age, SSBs were the leading estimated factor associated with cardiometabolic mortality between ages 25 and 64 years and sodium at age 65 years or older. Disparities were evident by race, especially for excess SSBs among blacks and insufficient nuts/seeds among Hispanics, and by education, especially for low nuts/seeds and fruits and excess SSBs among less-educated adults. Income-related disparities in current levels and trends over time of national consumption of nuts/seeds, fruits, and SSBs have been reported,17 which likely contribute to the disparities in diet associated mortality by race and education identified in the present investigation.
Health Care Costs
Obese people have higher medical bills
Haynes-Maslow, Union of Concerned Scientists Food and Environment Program PhD, MHA food systems and health analyst, and O’Hara, Union of Concerned Scientists PhD agricultural economist, 15
(Lindsey and Jeffrey K. February 2015, Union of Concerned Scientists, “Lessons from the Lunchroom.” http://www.ucsusa.org/sites/default/files/attach/2015/02/lessons-from-the-lunchroom-report-ucs-2015.pdf, p.2, Accessed 7/1/17, GDI - JMo)
Increased healthcare costs due to poor diets and obesity are a reality, even for young Americans just out of the school system. Our analysis of survey data from the U.S. Department of Health and Human Services shows the asso- ciations between diet, obesity, and individuals’ medical expenditures. We found that among 18- to 25-year-old respondents, those who were cautioned by a doctor to reduce their consumption of fatty foods (a proxy for having a diet too high in fat and cholesterol) were 20 percent more likely than their peers to be obese, and they had annual medical expenses nearly one-third higher. The situation only worsens with advancing age. Among respondents aged 18 to 85, the average annual medical expenditures among those who were cautioned about their diets were 90 percent higher than those who were not cautioned about their diets. 44>