OBJECTIVE

We examined young adults with and young adults without diabetes by using demographic data and cardiometabolic risk profiles and compared the risk profiles of younger versus older (aged ≥45 years) adults with diabetes.

RESEARCH DESIGN AND METHODS

Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007–2016. Diabetes was defined by self-report of health care provider diagnosis or by A1C levels of 6.5% or higher among those without a self-reported diagnosis. The cardiometabolic risk profile included adiposity, blood pressure, serum lipids, healthy eating, physical activity (PA), and exposure to tobacco smoke. Adjusted difference in difference was calculated as the difference among younger adults with and younger adults without diabetes minus the difference among older adults with and older adults without diabetes.

RESULTS

Adults with diabetes in both age-groups had higher levels of adiposity, hypertension, and cholesterol and lower levels of healthy eating and leisure-time PA. However, the differences in high cholesterol and adiposity by diabetes status were greater among young adults compared with older adults after adjustment for demographics and health insurance status. Elevated lipids were 9.6 percentage points higher (95% CI 4.6, 14.5) and obesity was 37.3 percentage points higher (95% CI 31.8, 42.7) among young adults with diabetes compared with those without diabetes than among older adults with diabetes compared with those without diabetes.

CONCLUSIONS

Young adults with diabetes have high rates of cardiometabolic risk factors, which can lead to an increased disease prevalence and mortality rate among these individuals as they age.

Amid well-documented increases in diabetes prevalence in the U.S. (1), the relative increases among youth aged 10–20 years have been the largest and most alarming (2,3). As of 2015, 4.6 million young adults aged 18–44 years had diabetes (1.6 million of whom were undiagnosed) (4), reflecting an annual percentage increase in prevalence of 4.3% since 1988–1994 (5). This increase in diabetes prevalence has resulted in a new cohort of young adults with potentially higher rates of morbidity (and subsequent use of health care) in early adulthood than ever before in the U.S.

The large population of young adults with diabetes is of concern for several reasons. First, the presence of diabetes among youth and young adults increases their exposure to chronic hyperglycemia and accompanying cardiometabolic risk factors early in life, which increase the risk of cardiovascular disease (6), kidney failure (7), and lifetime diabetes complications (8) in middle age and can contribute to the reduced life expectancy of young adults with diabetes (9). Second, compared with older adults, young adults may be less likely to be aware of type 2 diabetes or cardiometabolic risk factors (10), which may lead to delayed (or nonexistent) risk factor management among this population. For example, declines in mortality rates among people with diabetes have been seen during the past two decades in every age-group except young adults aged 20–44 years (11), suggesting suboptimal medical care and lagging risk factor management among this group.

There is increasing evidence that the pathophysiology of diabetes, particularly type 2 diabetes, diagnosed during adolescence or young adulthood presents a more rapid onset of disease (12) as evidenced by more rapid β-cell decline among adolescents compared with older adults diagnosed with diabetes (12). Despite relatively short duration of diabetes, diabetes and cardiovascular disease complications were observed among adolescents and young adults with diabetes (2).

Despite the growing concern about diabetes among youth, the young adult population with diabetes has rarely been characterized using nationally representative U.S. data. By using data from the National Health and Nutrition Examination Survey (NHANES), we assessed the distribution of demographics and the clinical and behavioral cardiometabolic risk factors among young adults aged 18–44 years and older adults aged 45 years or older with and without diabetes. We also compared the absolute and relative differences in cardiometabolic risk factors among young and older adults with diabetes to understand whether, and the extent to which, the risk factors and disease prevalence may differ among the young adult population.

Data Sources

We used data from NHANES 2007–2016 (the latest data available at the time of analysis), a continuous (nonoverlapping) cross-sectional survey of the noninstitutionalized civilian U.S. population conducted in 2-year cycles. NHANES collects data from participants via physical examination, laboratory tests, and questionnaires on health-related topics. Response rates for participation in the interview and physical examination ranged from 75.4% in 2007–2008 to 58.7% in 2015–2016. NHANES methods and protocols (including participant consent) for the questionnaires, laboratory tests, and examination have been described extensively (13).

There were 30,724 participants aged 18 years or older in NHANES with physical and examination data from 2007 to 2016. We excluded women who were pregnant at the time of the exam, as well as participants missing information on cardiometabolic risk factors, dietary information, or income. This yielded a final analytic sample of 23,798 adults (including 10,898 younger adults aged 18–44 years and 12,900 older adults aged 45 years or older) available for the analysis, representing >216 million adults.

Diabetes Definition

Diabetes was defined by self-reported diagnosis by a health care provider (excluding diagnosis only during pregnancy) or by glycated hemoglobin (A1C) levels 6.5% (48 mmol/mol) or higher. Mean diabetes duration was reported for participants who self-reported diabetes diagnosis.

Demographic Variables

Demographics included age, sex, race or ethnicity, income, and health insurance. As described previously, we stratified participants into two age categories: 18–44 years old and ≥45 years old. We categorized self-reported race or ethnicity as non-Hispanic white, non-Hispanic black, Hispanic, and non-Hispanic other. Household poverty level was defined by using the established poverty-income ratio (PIR), calculated as family income divided by the federal poverty level; a PIR of 1 indicates a family income at 100% of the federal poverty level. PIR was categorized as <1.33, 1.33–3.50, and ≥3.50. A PIR of <1.33 was selected as the lower category to correspond with qualification for the federal Supplemental Nutrition Assistance Program (14). Participants were considered to have health insurance if they reported coverage by a health insurance plan at the time of interview. Participants who reported health insurance also reported insurance type as private (including Medigap), Medicare/Medicaid, other government or state sponsored (e.g., military, Indian Health Service, State Children's Health Insurance Program), or both government and private insurance. Health insurance was categorized as no insurance, private insurance alone, public insurance alone, or both. Participants also reported whether they had a place to visit for routine health care (yes/no).

Cardiometabolic Risk Factors

We included measures of adiposity, serum cholesterol, blood pressure (BP), blood glucose control, chronic kidney disease (CKD), history of cardiovascular disease, healthy eating, leisure-time physical activity (PA), and exposure to tobacco smoke. Overall, adiposity was defined by using BMI (calculated as body weight in kilograms divided by the square of height in meters), and central adiposity was defined by using waist circumference–to–height ratio (WHtR). Participants were categorized as having obesity if they had either a BMI of ≥30 kg/m2 or a WHtR of ≥0.5 (15).

Serum total and HDL cholesterol levels were used to calculate the ratio of total to HDL cholesterol. High cholesterol levels were defined as total-to-HDL cholesterol ratio of ≥5.9 (16). Among those without self-report of cholesterol-lowering medication, uncontrolled cholesterol was defined as total-to-HDL cholesterol ratio of ≥5.9. Hypertension was defined as self-reported use of antihypertensive medications or systolic BP of ≥140 mmHg or diastolic BP of ≥90 mmHg (17). Among those who reported no antihypertensive medication use, uncontrolled hypertension was defined as systolic BP of ≥140 mmHg or diastolic BP of ≥90 mmHg. We used A1C to categorize participants with diabetes according to blood glucose control; poor blood glucose control was defined as A1C of ≥9% (75 mmol/mol).

CKD was defined as having an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 or elevated urinary albumin-to-creatinine ratio (ACR ≥30 mg/g), based on a single measurement of serum creatinine or ACR during the physical examination. eGFR was estimated using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation (18).

Participants were considered to have a history of cardiovascular disease if they reported ever being told they had any of the following conditions by a health care provider: congestive heart failure, coronary heart disease, angina pectoris, heart attack, or stroke. Behavioral risk factors included not meeting healthy eating or leisure-time PA recommendations and exposure to tobacco smoke. Healthy eating was measured by the Healthy Eating Index 2010 (HEI-2010), a measure of diet quality used to assess how well a set of foods aligns with key recommendations of the Dietary Guidelines for Americans (19). HEI-2010 scores range from 0 to 100; in our analysis, not meeting healthy eating recommendations was defined as HEI-2010 <75th percentile (<59.7) (20). Leisure-time PA was measured as self-reported minutes of moderate- to vigorous-intensity leisure-time PA per week. Not meeting leisure-time PA goal was defined as <150 min of moderate- to vigorous-intensity leisure-time PA per week. Tobacco smoking was assessed by serum cotinine levels. Being a current heavy smoker was defined by cotinine levels ≥10 ng/mL.

Statistical Analysis

We compared demographic characteristics, health care access and utilization, and cardiometabolic risk factors using two-sided t tests among young and older adults by diabetes status. We used logistic regression to calculate predicted marginal and the absolute differences in risk factors by diabetes status adjusting for age (continuous), sex, race or ethnicity, PIR, and health insurance status among young adults. We repeated the analysis among older adults. We used the difference in differences analysis to examine whether differences in risk factors among younger adults by diabetes status were similar to the differences among older adults (21). Within each age-group, we computed adjusted odds ratios (AORs) for each risk factor to compare those with diabetes with those without diabetes. Model fit was examined by the log-likelihood ratio.

Analysis was performed by using SAS 9.3 and SUDAAN 11 software (Research Triangle Institute, Research Triangle Park, NC) to account for complex sampling design. P values <0.05 were considered statistically significant. Unreliable estimates (i.e., those with relative SEs >30%) were not reported.

Characteristics of Young Adults With Diabetes

Young adults with diabetes differed from those without diabetes on a number of demographic and health care access characteristics (Table 1). Compared with those without diabetes, young adults with diabetes were older (mean age of 36.4 years compared with 30.9 years, respectively), fewer were non-Hispanic white, and more were non-Hispanic black. Those with diabetes were more likely to have low PIR and to have only public health insurance compared with young adults without diabetes. Similar patterns were observed among adults 45 years or older (Table 1). Among older adults, those with diabetes tended to be older, were less likely to be non-Hispanic white, and had lower PIR than those without diabetes. Younger adults with diagnosed diabetes had a shorter duration of diabetes (mean 7.7 years) than older adults with diagnosed diabetes (mean 11.2 years).

Cardiometabolic Risk Factors

Young adults with diabetes had a less favorable cardiometabolic risk profile compared with young adults without diabetes (Table 2). Compared with those without diabetes, young adults with diabetes were more likely to have a high total-to-HDL cholesterol ratio, elevated BP levels or hypertension, uncontrolled high cholesterol, CKD, and self-reported history of cardiovascular disease. The percentage of young adults with diabetes who were obese, measured by BMI or WHtR, was twice as high compared with young adults without diabetes. Young adults with diabetes were more likely to have lower HEI-2010 scores and to engage in less leisure-time PA than their counterparts without diabetes. There were no significant differences in cotinine levels between the groups; approximately one-third of young adults had cotinine levels indicating current smoking (with ≥10 ng/mL). Similar patterns in cardiometabolic risk factors were observed among older adults with diabetes compared with those without diabetes (Table 2), with the exception of HEI-2010 scores, which were not significantly different.

Young and older adults with diabetes differed by a number of treatment and control measures (Table 2). Young adults with diabetes were less likely than older adults with diabetes to report medication for high cholesterol (20.5% and 55.5%, respectively) and antihypertension medication (29.3% and 63.7%, respectively). Among those with diabetes, 25.5% of young adults and 12.6% of older adults had A1C levels >9% (75 mmol/mol).

After adjustment for age, sex, race or ethnicity, PIR, and health insurance status, the difference between young adults with diabetes and those without diabetes was significantly different from zero for every risk factor except cotinine levels (Table 3). Young adults with diabetes were 14.0 percentage points (95% CI 9.4, 18.5) more likely to have elevated cholesterol levels and 4.2 percentage points (95% CI 1.3, 7.0) more likely to have elevated BP compared with those without diabetes. Young adults with diabetes were also less likely to report consuming a healthy diet or engaging in leisure-time PA compared with those without diabetes (7.0 percentage points [95% CI 2.3, 11.4] and 12.1 percentage points [95% CI 7.0, 17.2], respectively).

Observed differences in cardiometabolic risk factors across diabetes status were similar among younger compared with older adults, and we found that the percentage differences for hypertension, leisure-time PA, HEI-2010, and cotinine levels were not significantly different from zero. Although we observed differences by diabetes status in both groups, the magnitude of these differences was too small to be statistically significant. However, differences in differences greater than zero were observed for high cholesterol (9.6 [95% CI 4.6, 14.5]) and measures of adiposity (BMI 6.5 [95% CI 0.4, 12.6] and WHtR 9.5 [95% CI 4.6, 14.4]).

Young adults with diabetes also had greater relative differences, as measured by AOR, for cardiometabolic risk factors compared with those without diabetes (Fig. 1), with larger relative differences for obesity measures and lipids. Among young adults with diabetes, the AOR for a BMI of ≥30 kg/m2 was 5.1 (95% CI 3.9, 6.6), for WHtR the AOR was 3.6 (95% CI 2.3, 5.7), and for high cholesterol the AOR was 3.4 (95% CI 2.5, 4.6) compared with those without diabetes. In addition, older adults with diabetes had greater relative differences, as measured by AOR, for cardiometabolic risk factors compared with those without diabetes.

Young adults with diabetes have a worse cardiometabolic risk profile than young adults without diabetes, being almost twice as likely to have obesity, elevated lipids, or hypertension and less likely to report being physically active or consuming a healthy diet. We found that differences in severe obesity and lipid levels were much larger between young adults with and without diabetes compared with older adults with and without diabetes. We also found among participants with diabetes that young adults were more likely to have poor blood glucose control (A1C ≥9% [75 mmol/mol]) compared with older adults.

These findings are a public health concern because diabetes beginning in youth or young adulthood presents a future population health burden affecting morbidity and mortality rates, loss of quality of life, and a burden on the health care system (2,7,8,22,23). Among the population, it is estimated that an individual diagnosed with diabetes at age 40 years will lose 6–7 years of life (24) and 19 quality-adjusted life years compared with an individual without diabetes (25). Recently, the SEARCH for Diabetes in Youth study found that some individuals diagnosed with diabetes in childhood had already presented subclinical cardiovascular diseases within a few years of diagnosis (2). Diagnosis of diabetes by 10 years of age corresponds to a 1.2 times increased risk of all-cause mortality and a 1.6 times increased risk of cardiovascular disease mortality (8). Regardless of diabetes status, elevated risk factors, such as A1C levels and obesity, increase the risk of death before age 55 years (26).

Although young adults with diabetes had lower BP levels compared with older adults with diabetes, young adults had higher cholesterol levels. High cholesterol levels are a major risk factor for cardiovascular disease (27). Because total cholesterol levels tend to increase with age (28), it was unexpected to find higher elevated total cholesterol-to-HDL ratio among young adults versus older adults with diabetes. This finding may be more attributable to the difference in HDL cholesterol levels than total cholesterol levels because HDL cholesterol levels tend to be higher among older than younger adults (29). We also found that older adults were more than twice as likely to report use of cholesterol-lowering medication. Current clinical guidelines by the American Diabetes Association recommend treatment of elevated cholesterol levels regardless of age (17). Regardless, diabetes and high cholesterol are independent major risk factors for cardiovascular disease, and with the combined presence of both, cardiovascular risk is further increased (30). Furthermore, the risk of ischemic heart disease mortality can be lowered to a larger extent among young adults compared with older adults when cholesterol levels are similarly reduced (31).

We found that more than one-quarter of young adults with diabetes already have CKD, increasing their risk of kidney failure and cardiovascular disease (32). While this high prevalence is concerning, it is important to note that the difference in the prevalence of CKD by diabetes status among young adults is not significantly different than the difference observed among older adults. However, the prevalence of CKD increases with age (33) and whether this difference in CKD prevalence will widen as the young adult population with diabetes ages is unknown.

Eating a healthy diet and engaging in leisure-time PA are associated with lower morbidity and mortality rates (34) and are recommended for cardiovascular disease prevention (35). Although there were no significant differences in these healthy behaviors between younger and older adults, young adults with diabetes were significantly less likely to report consuming a healthy diet or participating in PA compared with their counterparts without diabetes. Promoting healthy eating and increased PA among young adults may reduce cardiometabolic risk in this group.

Although there was no significant difference in smoking status for young adults with and without diabetes, young adults have higher levels of smoking compared with the older adult population. This is a particular concern because smoking in combination with diabetes dramatically increases the risk of cardiovascular disease (36). Our findings of a high prevalence of smoking among the young adult population are supported by a recent article that found that young adults aged 18–25 years had the highest levels of smoking initiation (37).

Possible reasons and causes for the observed differences by diabetes status and age are likely complex. Health care access and utilization might contribute to these differences. While report of having a usual place to receive health care was similar across groups, we found that young adults were almost twice as likely to have no health insurance compared with older adults. The lack of health insurance may influence health care decisions and access by young adults to treatments for diabetes or elevated cholesterol.

There are five main limitations to our analysis. First, we did not distinguish between type 1 and 2 diabetes in this analysis. Previously, the prevalence of type 1 diabetes in NHANES, which is based on use of insulin alone and age of diagnosis <30 or 40 years, was 0.34–0.42% among those aged 20–40 years (38). Recent studies of diabetes complications among adolescents and young adults found the prevalence higher among those with type 2 diabetes but common for both type 1 and type 2 diabetes (2). The disease pathophysiology of type 2 diabetes may differ by age of diagnosis; individuals diagnosed at younger ages are at increased risk of developing earlier complications caused by more rapid decline of β-cell function and development of complications (12). Whether there are differences in the disease process for type 1 diabetes depending on age of diagnosis is unknown because most studies of type 1 diabetes and its complications have focused on individuals diagnosed in childhood (39).

Second, because of the relatively small number of young adult NHANES participants with diabetes, we had to combine multiple survey cycles to characterize the population more fully. This prevented a full exploration of trends in the prevalence of risk factors among young adults with diabetes. The results presented are the time-averaged mean from 2007 to 2016; therefore, it is unknown whether there is a secular trend. Third, we defined diabetes by self-report or A1C levels because of the smaller number of participants with measured fasting plasma glucose. There is a possibility that participants with diabetes based on fasting plasma glucose levels were misclassified. Fourth, self-report of income level and health insurance is potentially subject to social desirability bias. Lastly, NHANES is a cross-sectional survey, which means we cannot determine causality. We also do not have follow-up information on outcomes, such as cardiovascular disease complications, for the participants.

Strengths of this study are as follows: the results are nationally representative of the noninstitutionalized civilian U.S. population; cardiometabolic risk factors are obtained through standardized measurement and laboratory procedures; and we examined and compared characteristics of the young adult and older adult diabetes populations.

The overall burden of diabetes among young adults is expected to more than double to >5 million in the U.S. by 2030 (40). Although lower mortality rates among adults with diabetes in the U.S. have been seen during the past two decades, the age-group that did not see improvements was the young adult population (11). In addition, even though this study did not examine associations between cardiometabolic risk factors among young adults and mortality, previous studies suggest that these risk factors may lead to early mortality among this group (26). A better understanding of the modifiable differences between the young and older populations with diabetes can illuminate how best to improve the cardiometabolic risk profile of this younger population, which can be an important contribution to reducing future diabetes-related morbidity and mortality rates.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

See accompanying article, p. 1845.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. S.H.S. drafted the manuscript and contributed to the analysis of data. S.H.S., K.R.S., G.I., C.M., and E.W.G. contributed to the conception and design of the work, the acquisition of data, the interpretation of data, and revision of the manuscript and provided final approval for publication. S.H.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in poster form at the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019.

1.
Geiss
LS
,
Wang
J
,
Cheng
YJ
, et al
.
Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012
.
JAMA
2014
;
312
:
1218
1226
[PubMed]
2.
Dabelea
D
,
Stafford
JM
,
Mayer-Davis
EJ
, et al.;
SEARCH for Diabetes in Youth Research Group
.
Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood
.
JAMA
2017
;
317
:
825
835
[PubMed]
3.
Mayer-Davis
EJ
,
Lawrence
JM
,
Dabelea
D
, et al.;
SEARCH for Diabetes in Youth Study
.
Incidence trends of type 1 and type 2 diabetes among youths, 2002-2012
.
N Engl J Med
2017
;
376
:
1419
1429
[PubMed]
4.
Centers for Disease Control and Prevention
.
National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in the United States, 2017
.
Atlanta, GA
,
U.S. Department of Health and Human Services
,
2017
5.
Menke
A
,
Casagrande
S
,
Geiss
L
,
Cowie
CC
.
Prevalence of and trends in diabetes among adults in the United States, 1988-2012
.
JAMA
2015
;
314
:
1021
1029
[PubMed]
6.
Reis
JP
,
Allen
NB
,
Bancks
MP
, et al
.
Duration of diabetes and prediabetes during adulthood and subclinical atherosclerosis and cardiac dysfunction in middle age: the CARDIA study
.
Diabetes Care
2018
;
41
:
731
738
[PubMed]
7.
Pavkov
ME
,
Bennett
PH
,
Knowler
WC
,
Krakoff
J
,
Sievers
ML
,
Nelson
RG
.
Effect of youth-onset type 2 diabetes mellitus on incidence of end-stage renal disease and mortality in young and middle-aged Pima Indians
.
JAMA
2006
;
296
:
421
426
[PubMed]
8.
Huo
L
,
Magliano
DJ
,
Rancière
F
, et al
.
Impact of age at diagnosis and duration of type 2 diabetes on mortality in Australia 1997-2011
.
Diabetologia
2018
;
61
:
1055
1063
[PubMed]
9.
Rhodes
ET
,
Prosser
LA
,
Hoerger
TJ
,
Lieu
T
,
Ludwig
DS
,
Laffel
LM
.
Estimated morbidity and mortality in adolescents and young adults diagnosed with type 2 diabetes mellitus
.
Diabet Med
2012
;
29
:
453
463
[PubMed]
10.
Bucholz
EM
,
Gooding
HC
,
de Ferranti
SD
.
Awareness of cardiovascular risk factors in U.S. young adults aged 18-39 years
.
Am J Prev Med
2018
;
54
:
e67
e77
[PubMed]
11.
Gregg
EW
,
Cheng
YJ
,
Srinivasan
M
, et al
.
Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data
.
Lancet
2018
;
391
:
2430
2440
[PubMed]
12.
Lascar
N
,
Brown
J
,
Pattison
H
,
Barnett
AH
,
Bailey
CJ
,
Bellary
S
.
Type 2 diabetes in adolescents and young adults
.
Lancet Diabetes Endocrinol
2018
;
6
:
69
80
[PubMed]
13.
Centers for Disease Control and Prevention, National Center for Health Statistics. National Health and Nutrition Examination Survey Data for Continuous NHANES [Internet]. U.S. Department of Health and Human Services. Available from https://wwwn.cdc.gov/nchs/nhanes/default.aspx. Accessed 1 October 2018
14.
U.S. Department of Agriculture, Food and Nutrition Service. SNAP Eligibility [Internet], 2018. Available from https://www.fns.usda.gov/snap/eligibility#What%20are%20the%20SNAP%20income%20limits?. Accessed 10 October 2018
15.
Hsieh
SD
,
Muto
T
.
The superiority of waist-to-height ratio as an anthropometric index to evaluate clustering of coronary risk factors among non-obese men and women
.
Prev Med
2005
;
40
:
216
220
[PubMed]
16.
Christianson
TJ
,
Bryant
SC
,
Weymiller
AJ
,
Smith
SA
,
Montori
VM
.
A pen-and-paper coronary risk estimator for office use with patients with type 2 diabetes
.
Mayo Clin Proc
2006
;
81
:
632
636
[PubMed]
17.
American Diabetes Association
.
10. Cardiovascular disease and risk management: Standards of Medical Care in Diabetes—2019
.
Diabetes Care
2019
;
42
(
Suppl. 1
):
S103
S123
18.
Levey
AS
,
Stevens
LA
,
Schmid
CH
, et al.;
CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
.
A new equation to estimate glomerular filtration rate
[published correction appears in Ann Intern Med 2011;155:408].
Ann Intern Med
2009
;
150
:
604
612
[PubMed]
19.
U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. Healthy Eating Index (HEI) [Internet]. Available from https://www.fns.usda.gov/resource/healthy-eating-index-hei. Accessed 10 February 2019
20.
Onvani
S
,
Haghighatdoost
F
,
Surkan
PJ
,
Larijani
B
,
Azadbakht
L
.
Adherence to the Healthy Eating Index and Alternative Healthy Eating Index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies
.
J Hum Nutr Diet
2017
;
30
:
216
226
[PubMed]
21.
Dimick
JB
,
Ryan
AM
.
Methods for evaluating changes in health care policy: the difference-in-differences approach
.
JAMA
2014
;
312
:
2401
2402
[PubMed]
22.
American Diabetes Association
.
Economic costs of diabetes in the U.S. in 2012
.
Diabetes Care
2013
;
36
:
1033
1046
[PubMed]
23.
Squires
E
,
Duber
H
,
Campbell
M
, et al
.
Health care spending on diabetes in the U.S., 1996-2013
.
Diabetes Care
2018
;
41
:
1423
1431
[PubMed]
24.
Gregg
EW
,
Zhuo
X
,
Cheng
YJ
,
Albright
AL
,
Narayan
KM
,
Thompson
TJ
.
Trends in lifetime risk and years of life lost due to diabetes in the USA, 1985-2011: a modelling study
.
Lancet Diabetes Endocrinol
2014
;
2
:
867
874
[PubMed]
25.
Narayan
KM
,
Boyle
JP
,
Thompson
TJ
,
Sorensen
SW
,
Williamson
DF
.
Lifetime risk for diabetes mellitus in the United States
.
JAMA
2003
;
290
:
1884
1890
[PubMed]
26.
Saydah
S
,
Bullard
KM
,
Imperatore
G
,
Geiss
L
,
Gregg
EW
.
Cardiometabolic risk factors among US adolescents and young adults and risk of early mortality
.
Pediatrics
2013
;
131
:
e679
e686
[PubMed]
27.
Stone
NJ
,
Robinson
JG
,
Lichtenstein
AH
, et al.;
American College of Cardiology/American Heart Association Task Force on Practice Guidelines
.
2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines
[published correction appears in Circulation 2014;129:S46–S48].
Circulation
2014
;
129
(
Suppl. 2
):
S1
S45
[PubMed]
28.
Hatmi
ZN
,
Mahdavi-Mazdeh
M
,
Hashemi-Nazari
SS
,
Hajighasemi
E
,
Nozari
B
,
Mahdavi
A
.
Trend of lipid ratios associated with well known risk factors of coronary artery disease in different age: a population based study of 31,999 healthy individuals
.
Int J Cardiol
2011
;
151
:
328
332
[PubMed]
29.
Sumner
AD
,
Sardi
GL
,
Reed
JF
 III
.
Components of the metabolic syndrome differ between young and old adults in the US population
.
J Clin Hypertens (Greenwich)
2012
;
14
:
502
506
[PubMed]
30.
Nichols
GA
,
Joshua-Gotlib
S
,
Parasuraman
S
.
Independent contribution of A1C, systolic blood pressure, and LDL cholesterol control to risk of cardiovascular disease hospitalizations in type 2 diabetes: an observational cohort study
.
J Gen Intern Med
2013
;
28
:
691
697
[PubMed]
31.
Lewington
S
,
Whitlock
G
,
Clarke
R
, et al.;
Prospective Studies Collaboration
.
Blood cholesterol and vascular mortality by age, sex, and blood pressure: a meta-analysis of individual data from 61 prospective studies with 55,000 vascular deaths
.
Lancet
2007
;
370
:
1829
1839
[PubMed]
32.
Gansevoort
RT
,
Correa-Rotter
R
,
Hemmelgarn
BR
, et al
.
Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention
.
Lancet
2013
;
382
:
339
352
[PubMed]
33.
Murphy
D
,
McCulloch
CE
,
Lin
F
, et al.;
Centers for Disease Control and Prevention Chronic Kidney Disease Surveillance Team
.
Trends in prevalence of chronic kidney disease in the United States
.
Ann Intern Med
2016
;
165
:
473
481
[PubMed]
34.
Abdelmawgoud
A
,
Brown
CJ
,
Sui
X
, et al
.
Relationship of physical activity and healthy eating with mortality and incident heart failure among community-dwelling older adults with normal body mass index
.
ESC Heart Fail
2015
;
2
:
20
24
[PubMed]
35.
LeFevre
ML
;
U.S. Preventive Services Task Force
.
Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults with cardiovascular risk factors: U.S. Preventive Services Task Force Recommendation Statement
.
Ann Intern Med
2014
;
161
:
587
593
[PubMed]
36.
Manson
JE
,
Colditz
GA
,
Stampfer
MJ
, et al
.
A prospective study of maturity-onset diabetes mellitus and risk of coronary heart disease and stroke in women
.
Arch Intern Med
1991
;
151
:
1141
1147
[PubMed]
37.
Perry
CL
,
Pérez
A
,
Bluestein
M
, et al
.
Youth or young adults: which group is at highest risk for tobacco use onset
?
J Adolesc Health
2018
;
63
:
413
420
[PubMed]
38.
Menke
A
,
Orchard
TJ
,
Imperatore
G
,
Bullard
KM
,
Mayer-Davis
E
,
Cowie
CC
.
The prevalence of type 1 diabetes in the United States
.
Epidemiology
2013
;
24
:
773
774
[PubMed]
39.
Diaz-Valencia
PA
,
Bougnères
P
,
Valleron
AJ
.
Global epidemiology of type 1 diabetes in young adults and adults: a systematic review
.
BMC Public Health
2015
;
15
:
255
[PubMed]
40.
Lin
J
,
Thompson
TJ
,
Cheng
YJ
, et al
.
Projection of the future diabetes burden in the United States through 2060
.
Popul Health Metr
2018
;
16
:
9
[PubMed]
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