Trends in Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the US: National Health and Nutrition Examination Survey, 1988–2012
IMPLEMENTATION EVALUATION — Volume 14 — March 30, 2017
Justin Xavier Moore, MPH1,2,3; Ninad Chaudhary, MB,BS1,3; Tomi Akinyemiju, PhD1,2 (View author affiliations)
Suggested citation for this article: Moore JX, Chaudhary N, Akinyemiju T. Trends in Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the US: National Health and Nutrition Examination Survey, 1988–2012. Prev Chronic Dis 2017;http://dx.doi.org/10.5888/pcdxxexternal icon.
PEER REVIEWED
Abstract
Introduction
Metabolic syndrome is a cluster of cardiometabolic risk factors associated with increased risk of multiple chronic diseases including cancer and cardiovascular disease. The objectives of this study were to estimate the prevalence of metabolic syndrome overall and by race-gender, and assess trends in prevalence over the past 3 decades.
Methods
We analyzed data in 2016 using data from the National Health and Nutrition Examination Survey (NHANES) for 1988 through 2012. We defined metabolic syndrome using the joint harmonized criteria, specifically the presence of at least three of the following five components: high blood pressure, elevated triglycerides, reduced high-density lipoprotein (HDL) cholesterol, elevated fasting blood glucose and elevated waist circumference.
Results
Among US adults aged 18 years or older, the prevalence of metabolic syndrome rose by more than 35% from 1988 through 1994 and from 2007 through 2012, increasing from 25.3% to 34.2% during this period. Non-Hispanic black males were 23% less likely to have metabolic syndrome when compared with non-Hispanic white males (odds ratio [OR], 0.77; 95% confidence interval [CI], 0.66 – 0.89), however, non-Hispanic black females were 20% more likely to have metabolic syndrome when compared with non-Hispanic white females (OR, 1.20; 95% CI, 1.02 – 1.40). Lower education (OR, 1.56; 95% CI, 1.32 – 1.84) and older age (OR, 1.73; 95% CI, 1.67 – 1.80) were independently associated with increased likelihood of metabolic syndrome.
Conclusions
There was a temporal increase in metabolic syndrome prevalence from 1988 through 2012 with over a third of all US adults meeting the joint harmonized criteria for metabolic syndrome in 2012, and increasing prevalence regardless of sociodemographic group.
Introduction
Metabolic syndrome is a cluster of biological factors characterized by abdominal obesity, dyslipidemia, hypertension, and type 2 diabetes (1). The link between metabolic syndrome and increased risk of multiple chronic diseases such as cardiovascular disease (CVD), arthritis, chronic kidney disease, schizophrenia, and several types of cancer, and increased risk of mortality have been reported for many decades (2–13). Complicating efforts toward better understanding of the public health burden of metabolic syndrome, and identification of effective prevention strategies is the lack of consistency in the clinical definition and categorical cut-points for component conditions. Using the definition of metabolic syndrome from the International Diabetes Federation (IDF) and the National Cholesterol Education Program (NCEP), the prevalence of metabolic syndrome has been estimated at more than 30% in U.S, however using the Adult Treatment Panel (ATP III) criteria, prevalence of metabolic syndrome was observed to be about 22% among US adults (14–16).
The prevalence of obesity among US adults has continued to increase steadily over the past few decades, and is now at epidemic proportions, with over two thirds of US adults either overweight or obese (17). Concurrently, the prevalence of type 2 diabetes and hypertension has also steadily increased, cumulating in significant increases in the proportion of adults who likely meet the criteria for metabolic syndrome, and are thus are at increased risk of more serious chronic conditions, morbidity and mortality. It is therefore becoming increasingly urgent to understand the trends in metabolic syndrome prevalence with the goal of identifying etiological factors that may be subject to specific public health prevention strategies. In recognition of this growing problem and in an attempt to reconcile the multiple definitions and categorical cut-points for metabolic syndrome, several organizations including the IDF, the National Heart, Lung, and Blood Institute (NHLBI), American Heart Association (AHA), World Heart Federation (WHF), International Atherosclerosis Society, and the International Association for the Study of Obesity provided a joint statement harmonizing the definition and criteria for metabolic syndrome (18).
Purpose and Objective
Given what appears to be a consensus on the definition and categorical outpoints for metabolic syndrome, in the present analysis, we examine a nationally representative sample of adults in the US, estimate the prevalence of metabolic syndrome overall and by race-gender, and assess trends in prevalence over the past three decades. In addition, we determined the independent effects of socioeconomic factors on the prevalence of metabolic syndrome.
Primary Study Outcome
We defined metabolic syndrome using the Joint Scientific Statement on Harmonizing the Metabolic Syndrome (18). The harmonized criteria defined metabolic syndrome as present when three of the following five components are present: 1) elevated waist circumference (≥88cm for women and ≥102 cm for men), 2) elevated triglycerides (≥150 mg/dL) or drug treatment of elevated triglycerides, 3) reduced HDL cholesterol (<40 mg/dL in men and <50 mg/dL in women) or drug treatment of reduced HDL cholesterol, 4) elevated blood pressure (systolic ≥ 130 and/or diastolic ≥ 85 mm Hg) or antihypertensive drug treatment in a patient with a history of hypertension), 5) elevated fasting glucose (≥100 mg/dL) or drug treatment of elevated glucose. We defined metabolic components using NHANES questionnaire responses and laboratory responses listed in Appendix Table 1. NHANES did not collect laboratory values for HDL-Cholesterol for survey years 1999 through 2004, thus we relied on self-report of drug treatment of reduced HDL cholesterol. In this analysis, we reported the estimated proportion of adults who meet each component criteria, and who meet the formal definition of metabolic syndrome across the study periods (including individuals with missing or unknown data as a separate response category).
Study Covariates
To assess socio-demographic differences in the prevalence and trends of metabolic syndrome, we included age, race/ethnicity, education, and poverty to income ratio (PIR). Age was assessed as a continuous variable for participants ages 0 – 84, and those 85 and older were classified as 85 years of age (NHANES codes individuals 85 and older as 85 years). Race/ethnicity was categorized as Non-Hispanic White, Non-Hispanic Black, and Mexican-American. NHANES identified education as a response to the question “What is the highest grade or level of school completed or the highest degree received?” The education variable was further categorized into: 1) less than high school education; 2) high school graduate/GED/ or equivalent; 3) some college; 4) college graduate or above; and 5) unknown/refused. Poverty income ratio (PIR) was calculated as the ratio of total family income to poverty threshold values (in dollars). Persons who reported having had no income were assigned a zero value for PIR. PIR values less than 1 are considered below the official poverty line, whereas PIR values greater than 1 are above the poverty level (19).
Intervention Approach
Study design and participants
Since 1959, The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC) has collected, analyzed, and disseminated data on the health status of US residents as part of the National Health and Nutrition Examination Survey (NHANES) (20). NHANES is a nationally representative sample of about 5,000 US adults each year, where Mexican Americans and Non-Hispanic blacks are oversampled, and weighted analysis used to generate generalizable estimates. NHANES data includes demographic, socioeconomic, dietary, and health-related questionnaires, and includes clinical measures of blood pressure, fasting blood glucose, triglycerides and HDL cholesterol in addition to self-reported medication use for health conditions. We conducted cross-sectional analysis of the NHANES data and examined trends in metabolic syndrome over time by establishing three time periods; 1998 through 1994 (first period), 1999 through 2006 (second period), and 2007 through 2012 (third period). These periods were chosen to account for the lack of continuous annual data over the entire 24-year period (no data for years 1996 through 1998) and variations in the NHANES sampling design over time. It should be noted that comparisons between periods are still appropriate as long as sampling weights and units were accounted for in statistical analyses.
The Centers for Disease Control and Prevention (CDC) estimates that 35% of US adults have prediabetes (1). Approximately 11% of people with prediabetes will develop overt diabetes each year without any intervention (2). CDC’s National Diabetes Prevention Program (National DPP) (3,4) lifestyle modification intervention is an evidence-based approach to reducing the risk of developing type 2 diabetes among individuals with prediabetes. The Diabetes Prevention Program trial showed that, relative to no intervention, the lifestyle intervention promoting a healthful diet and increased physical activity decreased risk of developing diabetes after 10 years of follow-up by 34% (5).
The YMCA has adapted the National DPP model and offers it among its wellness programs. The 2010 through 2012 results from the New York State YMCA’s Diabetes Prevention Program (YMCA’s DPP) from 14 sites showed average weight loss approaching the 5% weight loss reported in the DPP randomized trial; 40% reached 5% weight loss and average weight loss was 4.2% (6). Although participants in the YMCA’s DPP were successful at reaching weight-loss goals, participants were predominantly white and female, and only 6.7% received Medicaid compared with 27% in New York State overall. Further analysis of the YMCA’s DPP showed that black participants as well as those with less education or with lower income were less likely to complete the program. Among the recommendations of this demonstration project was a “need for targeted approaches to reach and retain a broader population, including men, minorities, and individuals who are low-income and uninsured or publicly insured” (6,7).
Ackermann et al reported that 24% of participants placed in YMCA’s DPP did not attend any sessions at all, and 15% were lost to follow-up by months 4 through 6 (8). Within the clinical setting, factors associated with the referral of patients to a DPP by the health care provider and factors associated with attrition among patients currently enrolled in a DPP are the focus of much of the engagement strategies regarding DPPs (9). It is unclear what happens between the point of referral from the health care provider to the YMCA’s DPP and subsequent progression of the patient through the program. Our study examined the demographic and primary care practice–related factors associated with the placement (patients scheduled to participate) and enrollment (patients completing ≥3 sessions) of patients referred to YMCA’s DPP. Furthermore, we examined the effect of these same factors on weight loss.
The present analysis included all non-Hispanic white, non-Hispanic black and Mexican Americans adults aged 18 and older represented in the NHANES data set over the study period. Adults of other race/ethnicities were excluded due to limited sample sizes and inconsistent categorizations across the survey years; pregnant women were also excluded to reduce bias associated with pregnancy-associated diabetes or weight gain. A total of 51,371 participants over the study period were included in this analysis; 18,552 participants for years 1988 – 1994, 18,445 participants for years 1999 through 2006, and 14,374 participants for years 2007 through 2012.
The Institutional Review Board considered this study IRB exempt because of the use of publicly available, de-identified data.
Evaluation Methods
We defined metabolic syndrome using the Joint Scientific Statement on Harmonizing the Metabolic Syndrome (18). The harmonized criteria defined metabolic syndrome as present when three of the following five components are present: 1) elevated waist circumference (≥88cm for women and ≥102 cm for men), 2) elevated triglycerides (≥150 mg/dL) or drug treatment of elevated triglycerides, 3) reduced HDL cholesterol (<40 mg/dL in men and <50 mg/dL in women) or drug treatment of reduced HDL cholesterol, 4) elevated blood pressure (systolic ≥ 130 and/or diastolic ≥ 85 mm Hg) or antihypertensive drug treatment in a patient with a history of hypertension), 5) elevated fasting glucose (≥100 mg/dL) or drug treatment of elevated glucose. We defined metabolic components using NHANES questionnaire responses and laboratory responses listed in Appendix Table 2. NHANES did not collect laboratory values for HDL-Cholesterol for survey years 1999 through 2004, thus we relied on self-report of drug treatment of reduced HDL cholesterol. In this analysis, we reported the estimated proportion of adults who meet each component criteria, and who meet the formal definition of metabolic syndrome across the study periods (including individuals with missing or unknown data as a separate response category).
Study Covariates
To assess socio-demographic differences in the prevalence and trends of metabolic syndrome, we included age, race/ethnicity, education, and poverty to income ratio (PIR). Age was assessed as a continuous variable for participants ages 0 – 84, and those 85 and older were classified as 85 years of age (NHANES codes individuals 85 and older as 85 years). Race/ethnicity was categorized as Non-Hispanic White, Non-Hispanic Black, and Mexican-American. NHANES identified education as a response to the question “What is the highest grade or level of school completed or the highest degree received?” The education variable was further categorized into: 1) less than high school education; 2) high school graduate/GED/ or equivalent; 3) some college; 4) college graduate or above; and 5) unknown/refused. Poverty income ratio (PIR) was calculated as the ratio of total family income to poverty threshold values (in dollars). Persons who reported having had no income were assigned a zero value for PIR. PIR values less than 1 are considered below the official poverty line, whereas PIR values greater than 1 are above the poverty level (19).
Statistical Analysis
All analyses were performed in the year 2016 using NHANES generated sampling statistical strata, clusters, and weights as designated and described in details in the NHANES methodology handbook (20). Thus, results may be generalizable to the United States population. Socio-demographic characteristics, prevalence of metabolic syndrome, and individual metabolic components were estimated while accounting for specific stratum, primary sampling units, and weights unique for each NHANES period using appropriate SAS (SAS version 9.4) PROC SURVEY procedures (ie, FREQ, REG, and MEANS).
We estimated the prevalence of metabolic syndrome and individual components over time (1988–1994, 1999–2006, and 2007–2012), stratified by race and gender using weighted means and proportions. To determine the odds of metabolic syndrome adjusting for potential confounders such as education and PIR, we performed several logistic regression models for each time period with metabolic syndrome as the outcome, and sociodemographic variables as exposures. We performed similar analyses examining each individual component of metabolic syndrome. As a sensitivity analysis, to assess whether the increase in prevalence of metabolic syndrome is driven solely by increasing rates of obesity among US adults, we determined the prevalence of metabolic syndrome across the study period excluding participants with BMI of >30 and thus obese. We presented results of statistical models as adjusted odds ratios (ORs) and 95% confidence intervals (CIs).
Participant flow and baseline data
Of the 1,438 patients identified as potentially eligible, 49% could not be contacted (n = 703), 15% declined participation (n = 221), and 5% had incomplete screening information (n = 69) (Figure 1). From the remaining 445, we excluded those who were eligible but declined to participate or for whom information was incomplete, and we excluded those who did not meet the eligibility criteria. Of the 287 randomized participants, 262 completed the 12 months follow up (91% completion rate).
Figure 1. Recruitment of patients for Peer Support for Achieving Independence in Diabetes (Peer-AID) trial using community health workers to provide self-management support among low-income adults with diabetes, Seattle, Washington, 2010–2014. [A text version of this figure is also available.]
A total of 1,438 patients were identified as potentially eligible. After excluding 993 because they could not be reached (n = 703), declined (n = 221), or for whom information was incomplete (n = 69), 445 patients were screened by telephone for eligibility. From the 445, we excluded 158: 79 who were eligible (25 declined and 54 were not enrolled or had incomplete information) and 79 who were not eligible (9 because of age, 19 because of health issues; 9 because of homelessness, 23 because income was too high, 6 were involved in other studies, 6 were moving, and 7 were unable to be reached). The remaining 287 who were eligible were randomized into control (n = 142) and intervention (n = 145) groups. In the intervention group, 144 (99%), completed visit 1, 140 (97%) completed visit 2, 138 (95%) completed visit 3, 132 (91%) completed visit 4, 80 (55%) completed visit 5 (which was optional), and 130 (89%) completed the 12-month exit visit. In the control group, 134 (94%) completed the 12-month exit visit; 29 (20%) declined the education visit, 97 (68%) participated in the exit education visit, and 8 (6%) were control closed (3 were unwilling to continue, 4 were lost to follow up, and 1 was withdrawn by study staff). In the intervention group, 15 (10%) were intervention closed (7 were unwilling to continue, 5 were lost to follow-up, 2 moved out of King County, and 1 was lost for other reason).
Table 3 reports the treatment effect for all primary and secondary outcomes. We found no change in HbA1c values in the intervention group (from unadjusted mean of 9.09% to 8.58%, change of −0.51 points in HbA1c) compared with the control group (from unadjusted mean of 9.04% to 8.71%, change of −0.33 points) (P = .54). However we found a significant interaction between the baseline HbA1c value and intervention group (P = .04), with an increasing treatment effect seen in people with higher HbA1c values. In the subgroup analyses of individuals with a baseline HbA1c value higher than 9%, the intervention group had a nonsignificant 0.60-point greater decrease in HbA1c compared with the control group (Figure 2). In the subgroup analysis of individuals with a baseline HbA1c value higher than 10%, the intervention group had a significant 1.23-point greater decrease in HbA1c (P = .046) compared with the control group (Figure 2).
Figure 2. Decreases in glycated hemoglobin A1c (HbA1c ) from baseline to 12 months by intervention arm, total study population, subgroup with HbA1c higher than 9%, and subgroup with HbA1c higher than 10%, Peer Support for Achieving Independence in Diabetes (Peer-AID) trial using community health workers to provide self-management support among low-income adults with diabetes, Seattle, Washington, 2010–2014. P = .046 for the adjusted difference in HbA1c value between the control and intervention groups for the subgroup with HbA1c higher than 10%. [A tabular version of this figure is also available.]
| Population | Decrease in HbA1c | ||
|---|---|---|---|
| Control | Intervention | Adjusted Difference | |
| Total (n = 287) | −0.33 | −0.51 | −0.14 |
| Subgroup HbA1c >9% (n = 133) | −0.98 | −1.52 | −0.60 |
| Subgroup HbA1c >10% (n = 72) | −1.12 | −1.93 | −1.23 |
Although some secondary outcomes (such as systolic and diastolic blood pressures, number of emergency department visits) improved more among intervention participants than among the control group, the differences were not significant (Table 4). We found a decrease in self-reported physician visits (15% lower in the intervention group, P < .001), no improvement in quality of life in the intervention group (increase in PCS scale of 0.25 in controls vs 2.4 in intervention group, P = .07), and a nonsignificant difference in the MCS scale. We found a decrease in reported social burden subscale of the Diabetes-39 instrument (P = .05) in the intervention group relative to the control group. We found no differences in other diabetes-specific quality-of-life scales.
Results
There were a total of 51,371 participants representing an estimated 548,105,710 US adults aged 18 and older from 1988 through 2012 (Table 5). Over the observation period, the average age of US adults increased gradually, with mean age increasing from about 44 years between 1988 and 1994 (Mean: 44.43; 95% CI: 43.38 – 45.49) to almost 47 years between 2007 and 2012 (Mean: 46.78; 95% CI: 45.99 – 47.56). The proportion of both Non-Hispanic Blacks and Mexican-Americans also increased over the observation periods by 9.8% and 71.6%, respectively, while the proportion of college graduates increased from 19.87% in 1988 – 1994 to 26.77% in the 2007 – 2012 observation period. Mean PIR decreased over time from 3.15 (95% CI: 2.94 – 3.35) in 1988 – 1994 to 2.99 (95% CI: 2.89 – 3.10) in 2007 – 2012. In addition, mean BMI increased significantly over the study periods, from an average of 26.48 kg m-2 (95% CI: 26.30 – 26.66) in 1988 – 1994, to 28.17 (95% CI: 27.97 – 28.36) in 1999 – 2006, and 28.73 (95% CI: 28.53 – 28.93) in 2007 – 2012. Program overview
Montefiore Health System (MHS) is a large integrated health system in the Bronx and Hudson Valley serving roughly 85% government payer (Medicaid and/or Medicare) patients. Beginning in 2010, MHS partnered with the YMCA of Greater New York to provide the 1-year YMCA’s DPP to eligible patients visiting Bronx-based primary-care clinics. Eligibility was based on criteria established by the YMCA and CDC, which were being aged 18 years or older, having no previous diabetes diagnosis (excluding gestational diabetes), being overweight or obese (body mass index [BMI] ≥25; ≥22 if Asian), and having a hemoglobin A1c between 5.7% and 6.4% (or fasting plasma glucose 100–125 mg/dL or 2-hour plasma glucose 140–199 mg/dL).
During an office visit, eligible patients were told by their physician of their risk of developing diabetes and were asked if they were interested in participating in the YMCA’s DPP. If the patient expressed interest, the physician referred the patient to the YMCA using the referral order in the electronic health record (EHR) system. The EHR system generated a referral form populated with the patient’s demographic and BMI information, which was faxed to the YMCA’s DPP after the referring physician obtained consent from the patient. The YMCA then entered patient, physician, and practice information into its internal database and attempted to contact the patient for placement. The schedule and location for starting new groups for the 16 core sessions of the program were based on the availability of lifestyle coaches, space to hold sessions, and patient demand. Attempts were made to place patients in the YMCA’s core groups for up to 1 year after the referral. There were 66 core groups in which MHS patients were placed over the study period. The program was made available to all patients at no charge because of in-kind donations from the YMCA of Greater New York and other grant-based funding. The YMCA’s DPP sessions were offered by trained lifestyle coaches in English or Spanish depending on patient preference. This study protocol was reviewed and approved by the institutional review board of MHS, Albert Einstein College of Medicine.
Patient data
A database maintained by the YMCA had demographic information, information about participants’ primary care site, and the characteristics of sessions. De-identified data were made available by the YMCA to MHS staff for analysis. Demographic factors included age group (18–44, 45–59, and ≥60 y), preferred language (English, Spanish, other/missing), and sex (female, male, and missing).
Data on primary care sites included the number of referrals to the YMCA’s DPP made by each provider (<5, 5–19, and ≥20); type of referring site (teaching site [physician residents and medical students provide care with supervision from attending physicians] vs nonteaching site [attending physicians provide patient care]); season of referral (spring, summer, fall, or winter); primary season in which sessions were held (eg, a session starting in mid-February would be coded as spring because most sessions occurred during the spring); whether the primary care site was a Federally Qualified Health Center (FQHC); time between the referral of the patient and the start of the sessions (<2, 2–3, and ≥4 months); and time of day of the session (ie, weekday during the day, weekday during the evening, or Saturday).
The YMCA’s DPP categorized patients as “placed” if scheduled to attend a session or “never placed” if never scheduled to attend a session. Subsequently, placed patients were further categorized based on their attendance in YMCA’s DPP sessions. Patients who attended 3 or more sessions were categorized as “enrolled.” Patients who never attended any sessions or dropped out of the program before attending 3 sessions were categorized as “never enrolled.”
The prevalence of metabolic syndrome among US adults in 1988 – 1994 was 25.3%, declining to 24.9% in 1999 – 2006 and then increasing sharply to 34.2% in 2007 – 2012 (Table 2). Among males, the prevalence of metabolic syndrome increased from 25.6% during the first time period to 33.4% during the third period. Similarly, the prevalence of metabolic syndrome increased for females from 25.0% during the first period to 34.9% during the third period. The largest increase in the prevalence of metabolic syndrome was observed among non-Hispanic black males, non-Hispanic white females, and non-Hispanic black females, with increases of 55%, 44% and 41% respectively over the study period, while the smallest increase was observed among Mexican-American females, an increase of 2% over the study period. Non-Hispanic white males experienced an increase of 31%, and Hispanic males experienced a 12.5% increase over the study period. There was no race/ethnic group for which the prevalence of metabolic syndrome declined over the study time period (Figure 3), although Mexican American males experienced a temporary decrease in metabolic syndrome prevalence between the first period (24.7%, SE: 1.53) and second period (17.1%, SE: 1.13). When stratified by race/ethnicity and age group, prevalence of metabolic syndrome increased from about 10% among 18 – 29 year olds in all race/ethnic groups to almost 70% among 70+ year-old adult females in 2007 – 2012 (Figure 4). In Supplemental tables (Tables 2 – 3), we provide data showing the prevalence of each metabolic syndrome component stratified by race-gender. The metabolic syndrome component with the most significant increase over the study period was elevated waist circumference (males: 23.5%, females: 38.2% in period 1; males: 42.6%, females: 60.9% in period 3), followed by elevated HDL cholesterol (males: 29.5%, females: 35.3% in period 1; males: 41.7%, females 46.2% in period 3).
Figure 3. Prevalence of metabolic syndrome among US adults, National Health and Nutrition Examination Survey (NHANES), 1988–2012. Metabolic syndrome was defined by using the criteria agreed to jointly by the International Diabetes Federation; the National Heart, Lung, and Blood Institute in the United States; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity (18). Abbreviation: SE, standard error. [A tabular version of this figure is also available.]
| Sex and Race/Ethnicity | NHANES Time Period | ||
|---|---|---|---|
| 1988–1994, % (SE) | 1999–2006, % (SE) | 2007–2012, % (SE) | |
| Men | |||
| Non-Hispanic white | 26.8 (1.2) | 27.5 (0.8) | 35.1 (1.1) |
| Non-Hispanic black | 17.3 (1.0) | 17.2 (0.8) | 26.8 (1.2) |
| Mexican American | 24.7 (1.5) | 17.1 (1.1) | 27.8 (1.3) |
| Women | |||
| Non-Hispanic white | 24.7 (1.1) | 25.2 (0.9) | 35.5 (1.2) |
| Non-Hispanic black | 24.6 (1.1) | 23.2 (0.8) | 34.7 (1.4) |
| Mexican American | 29.8 (1.2) | 20.8 (1.4) | 30.4 (1.7) |
Figure 4. Prevalence of metabolic syndrome among US adults over time by race/ethnicity–sex and age group, National Health and Nutrition Examination Survey (NHANES), 1988–2012. Metabolic syndrome was defined by using the criteria agreed to jointly by the International Diabetes Federation; the US National Heart, Lung, and Blood Institute in the United States; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity (18). Abbreviation: SE, standard error. [A tabular version of this figure is also available.]
| Age, Sex, and Race/Ethnicity | NHANES Time Period | ||
|---|---|---|---|
| 1988–1994, % (SE) | 1999–2006, % (SE) | 2007–2012, % (SE) | |
| 18–29 y | |||
| Men | |||
| Non-Hispanic white | 9.3 (1.3) | 7.2 (0.9) | 9.0 (1.6) |
| Non-Hispanic black | 7.3 (1.3) | 2.3 (0.6) | 5.8 (1.2) |
| Mexican American | 11.6 (1.4) | 5.4 (1.0) | 12.4 (1.8) |
| Women | |||
| Non-Hispanic white | 6.8 (1.2) | 3.7 (0.7) | 7.8 (1.3) |
| Non-Hispanic black | 6.7 (1.1) | 5.4 (0.9) | 9.1 (1.4) |
| Mexican American | 11.9 (1.3) | 4.4 (1.2) | 10.9 (2.0) |
| 30–49 y | |||
| Men | |||
| Non-Hispanic white | 23.4 (1.6) | 20.1 (1.1) | 28.4 (1.5) |
| Non-Hispanic black | 17.0 (1.5) | 13.0 (1.4) | 21.6 (1.9) |
| Mexican American | 26.9 (2.0) | 17.7 (1.6) | 27.0 (1.9) |
| Women | |||
| Non-Hispanic white | 17.3 (1.5) | 14.2 (1.0) | 21.9 (1.6) |
| Non-Hispanic black | 23.4 (1.1) | 17.8 (1.2) | 27.0 (2.0) |
| Mexican American | 32.1 (2.0) | 19.2 (1.7) | 24.5 (2.1) |
| 50–69 y | |||
| Men | |||
| Non-Hispanic white | 41.4 (2.5) | 39.4 (1.8) | 44.0 (2.7) |
| Non-Hispanic black | 29.8 (4.2) | 31.0 (2.8) | 39.2 (2.6) |
| Mexican American | 55.9 (3.9) | 39.7 (3.6) | 50.5 (4.0) |
| Women | |||
| Non-Hispanic white | 34.5 (2.4) | 32.9 (1.8) | 42.5 (2.1) |
| Non-Hispanic black | 43.4 (4.0) | 36.5 (2.8) | 52.7 (2.8) |
| Mexican American | 53.4 (4.1) | 44.3 (5.1) | 56.9 (4.2) |
| ≥70 y | |||
| Men | |||
| Non-Hispanic white | 44.0 (1.4) | 49.8 (1.2) | 57.6 (1.7) |
| Non-Hispanic black | 30.5 (2.2) | 43.2 (1.8) | 58.9 (1.8) |
| Mexican American | 51.1 (3.0) | 45.3 (2.7) | 56.8 (3.8) |
| Women | |||
| Non-Hispanic white | 44.3 (1.5) | 50.2 (1.4) | 62.1 (1.6) |
| Non-Hispanic black | 43.3 (2.0) | 48.5 (2.1) | 63.7 (2.3) |
| Mexican American | 56.3 (2.3) | 52.6 (2.4) | 69.0 (2.7) |
Upon adjusting for education, PIR, and age, non-Hispanic-Black males were less likely to have metabolic syndrome during the first period (OR, 0.55; 95% CI, 0.46 – 0.67), second period (OR, 0.64; 95% CI, 0.53 – 0.76), and third period (OR, 0.77; 95% CI, 0.66 – 0.89) compared with non-Hispanic white males. Non-Hispanic black females were more likely to have metabolic syndrome compared with non-Hispanic white females but only during the third period (OR, 1.20; 95% CI, 1.02 – 1.40). Compared with those with a college education or higher, lower levels of education were associated with significantly increased odds of metabolic syndrome. In addition, for every ten-year increase in age, odds of metabolic syndrome increased by 50% (OR, 1.50; 95% CI, 1.46 – 1.54) to 73% (OR, 1.73; 95% CI, 1.67 – 1.80).
Among nonobese US adults, the prevalence of metabolic syndrome appeared to remain stable overall over the study period (Table 5; 1988–1994 prevalence: 16.0%, 1999 – 2006 prevalence: 16.78%, 2006–2012 prevalence: 16.05%). However, the prevalence of metabolic syndrome among the nonobese increased from 15.3% to 25.1% among non-Hispanic white females, from 14.5% to 20.1% among non-Hispanic black females, and from 9.5% to 16.9% among non-Hispanic black males.
Implications for Public Health
To date, this is one of the largest studies with data over almost three decades using the joint harmonized criteria of metabolic syndrome to characterize the prevalence, trends and socio-demographic distribution of this major condition among US adults. We observed that by 2012, over a third of all US adults meet the criteria for metabolic syndrome, with the highest burden among non-Hispanic black and lower socioeconomic status adults. We observed that this increase is not driven solely by the rising prevalence of obesity among US adults, as metabolic syndrome prevalence remained constant over time even among the nonobese (>16% for all time periods). We observed that prevalence of metabolic syndrome increases rapidly with age, suggesting that given the demographic trends in the US population of increasing age, further increases in metabolic syndrome prevalence are to be expected, with concomitant increases in related chronic diseases and conditions.
Other published studies have provided results that are in line with our findings, although prevalence estimates vary depending on which metabolic syndrome criteria are used. For instance, Beltran-Sanchez et al (2013) observed a prevalence of about 23% using the ATP criteria with NHANES data (21). Using the definitions by both the International Diabetes Federation (IDF) and the National Cholesterol Education Program (NCEP) Ford et al also observed similar trends of metabolic syndrome prevalence in the United States with 28% in 1988–1994 and 31.9% in 2000 (14,15). In a more recent study, Aguilar et al (2015) estimated the prevalence of metabolic syndrome from 2003 through 2012 to be 33%, similar to our 34.17% during the 2007 through 2012 period (22). Nevertheless, none of the aforementioned studies have examined the independent association of education, income, and age with prevalence of metabolic syndrome. In this study we found that regardless of the time period lower education and older age significantly increased the odds of metabolic syndrome. In addition, no other study to our knowledge has examined the prevalence of metabolic syndrome among the non-obese. Given the recent consensus on the clinical definition and categorical cut-points for metabolic syndrome, it will be important for research studies to focus next on identifying etiological factors, to inform prevention strategies for this condition.
Our observation that metabolic syndrome prevalence increases with age suggests that the efforts must begin to increase awareness of prevention strategies earlier, ideally once any one of the constituent components eg, obesity are present, before the development all three components required for the formal definition of metabolic syndrome. The observed increased prevalence of metabolic syndrome among older adults seen our study may be explained by increased sedentary lifestyle, functional disability, and decreased walking and physical activity among older adults (23–25). Additionally, our observation that lower socio-economic status, measured based on educational attainment and poverty-to-income ratio, is strongly associated with metabolic syndrome may also provide clues into avenues for prevention. Public health strategies that are well known to be important for chronic disease prevention in general can make significant impact in reducing the prevalence of metabolic syndrome. For instance, by improving access to fresh fruits and vegetables in low-income communities which are often food deserts and heavily targeted for fat- and calorie- dense but nutritionally poor foods; increased availability of safe, walk-friendly environments to encourage physical activity; and improved access to healthcare, such as through the Affordable Care Act Medicaid Expansion program, for timely management of metabolic syndrome components (26). Population-specific studies will be important in identifying sub-groups for which metabolic syndrome is an urgent health issue and for which disease management strategies are urgently needed, for instance, non-Hispanic-White and Black females who currently have a prevalence of about 35%. Simultaneously, research studies to identify specific biomarkers associated with metabolic syndrome that are linked with the development of specific chronic diseases, such as stroke or cancer, will significantly enhance the early detection of these diseases. This effort will be critically important for the 66 million US adults who currently meet the criteria for metabolic syndrome, and who remain at risk for serious chronic diseases and conditions as a result.
There are several strengths and limitations relevant to this study. First NHANES is a nationally representative, standardized survey on a multitude of health related issues. This ensures that the results are generalizable and have high validity. There is unlikely to be selection bias as NHANES is a continuous survey of randomly selected individuals across the US, who responds to the same survey administered by trained personnel. However, a study limitation is the well-known ethnic differences in the association between obesity or BMI and health (27). Although the joint harmonized criteria recommends that study-population specific cut-offs for obesity be used, it remains unclear what the ideal BMI cut-off is for non-Hispanic blacks and Hispanics. More work in this area will provide much needed clarity on the prevalence of metabolic syndrome using race/ethnic-specific BMI cut-off values. Another limitation is that it is likely that we may have underestimated the overall prevalence of metabolic syndrome (and the individual components) in the United State population due to lack of data on individual components from a proportion of individuals represented in NHANES. However, this is unlikely to result in systematic selection bias as we assume that data are missing at random.
There is a temporal increase in metabolic syndrome prevalence among US adults, particularly non-Hispanic white and non-Hispanic black females, and individuals of lower socioeconomic status. As the US population ages, these rates are likely to continue to increase, concurrent with age-related increases in other serious chronic diseases such as stroke, cardiovascular diseases and cancer. Future work will be needed to quantify the chronic disease burden associated with metabolic syndrome in US adults. Existing preventions strategies, if implemented in population subgroups at highest risk, may have significant impact in reducing these trends.
Acknowledgments
Mr. Moore received grant support from R25 CA47888 from the National Cancer Institute (NCI), and Dr. Akinyemiju was supported by grants K01TW010271 and U54 CA118948 from the National Institute of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Conflict of interest statement: Mr. Moore received grant support from R25 CA47888 from the National Cancer Institute, and Dr. Akinyemiju was supported by grants K01TW010271 and U54 CA118948 from the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Financial disclosure: All authors have no financial disclosures.
Author Information
Corresponding Author: Tomi Akinyemiju, PhD, Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL XXXXX. Telephone: . Email: tomiakin@uab.edu.
Author Affiliations: 1Department of Epidemiology, University of Alabama at Birmingham, Birmingham AL. 2Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL. 3Department of Emergency Medicine, University of Alabama School of Medicine Birmingham, Alabama..
References
- Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001;285(19):2486–97. CrossRefexternal icon PubMedexternal icon
- Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol 2010;56(14):1113–32. CrossRefexternal icon PubMedexternal icon
- Bjørge T, Lukanova A, Jonsson H, Tretli S, Ulmer H, Manjer J, et al. Metabolic syndrome and breast cancer in the me-can (metabolic syndrome and cancer) project. Cancer Epidemiol Biomarkers Prev 2010;19(7):1737–45. CrossRefexternal icon PubMedexternal icon
- Borena W, Strohmaier S, Lukanova A, Bjørge T, Lindkvist B, Hallmans G, et al. Metabolic risk factors and primary liver cancer in a prospective study of 578,700 adults. Int J Cancer 2012;131(1):193–200. CrossRefexternal icon PubMedexternal icon
- Borena W, Edlinger M, Bjørge T, Häggström C, Lindkvist B, Nagel G, et al. A prospective study on metabolic risk factors and gallbladder cancer in the metabolic syndrome and cancer (Me-Can) collaborative study. PLoS One 2014;9(2):e89368. CrossRefexternal icon PubMedexternal icon
- Lindkvist B, Johansen D, Stocks T, Concin H, Bjørge T, Almquist M, et al. Metabolic risk factors for esophageal squamous cell carcinoma and adenocarcinoma: a prospective study of 580,000 subjects within the Me-Can project. BMC Cancer 2014;14(1):103. CrossRefexternal icon PubMedexternal icon
- Stocks T, Bjørge T, Ulmer H, Manjer J, Häggström C, Nagel G, et al. Metabolic risk score and cancer risk: pooled analysis of seven cohorts. Int J Epidemiol 2015;44(4):1353–63. CrossRefexternal icon PubMedexternal icon
- Ulmer H, Bjørge T, Concin H, Lukanova A, Manjer J, Hallmans G, et al. Metabolic risk factors and cervical cancer in the metabolic syndrome and cancer project (Me-Can). Gynecol Oncol 2012;125(2):330–5. CrossRefexternal icon PubMedexternal icon
- Nagel G, Stocks T, Späth D, Hjartåker A, Lindkvist B, Hallmans G, et al. Metabolic factors and blood cancers among 578,000 adults in the metabolic syndrome and cancer project (Me-Can). Ann Hematol 2012;91(10):1519–31. CrossRefexternal icon PubMedexternal icon
- Almquist M, Johansen D, Björge T, Ulmer H, Lindkvist B, Stocks T, et al. Metabolic factors and risk of thyroid cancer in the Metabolic syndrome and Cancer project (Me-Can). Cancer Causes Control 2011;22(5):743–51. CrossRefexternal icon PubMedexternal icon
- Johansen D, Stocks T, Jonsson H, Lindkvist B, Björge T, Concin H, et al. Metabolic factors and the risk of pancreatic cancer: a prospective analysis of almost 580,000 men and women in the Metabolic Syndrome and Cancer Project. Cancer Epidemiol Biomarkers Prev 2010;19(9):2307–17. CrossRefexternal icon PubMedexternal icon
- Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care 2005;28(7):1769–78. CrossRefexternal icon PubMedexternal icon
- Wu SH, Liu Z, Ho SC. Metabolic syndrome and all-cause mortality: a meta-analysis of prospective cohort studies. Eur J Epidemiol 2010;25(6):375–84. CrossRefexternal icon PubMedexternal icon
- Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S. Diabetes Care 2005;28(11):2745–9. CrossRefexternal icon PubMedexternal icon
- 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;287(3):356–9. CrossRefexternal icon PubMedexternal icon
- Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med 2003;163(4):427–36. CrossRefexternal icon PubMedexternal icon
- Yang L, Colditz GA. Prevalence of Overweight and Obesity in the United States, 2007-2012. JAMA Intern Med 2015;175(8):1412–3. CrossRefexternal icon PubMedexternal icon
- Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. ; International Diabetes Federation Task Force on Epidemiology and Prevention; Hational Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; International Association for the Study of Obesity. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120(16):1640–5. CrossRefexternal icon PubMedexternal icon
- Shargorodsky J, Curhan SG, Curhan GC, Eavey R. Change in prevalence of hearing loss in US adolescents. JAMA 2010;304(7):772–8. CrossRefexternal icon PubMedexternal icon
- National Health and Nutrition Examination Survey Data. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention: Center for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). 2016.
- Beltrán-Sánchez H, Harhay MO, Harhay MM, McElligott S. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010. J Am Coll Cardiol 2013;62(8):697–703. CrossRefexternal icon PubMedexternal icon
- Aguilar M, Bhuket T, Torres S, Liu B, Wong RJ. Prevalence of the metabolic syndrome in the United States, 2003-2012. JAMA 2015;313(19):1973–4. CrossRefexternal icon PubMedexternal icon
- Strath S, Swartz A, Parker S, Miller N, Cieslik L. Walking and metabolic syndrome in older adults. J Phys Act Health 2007;4(4):397–410. CrossRefexternal icon PubMedexternal icon
- Mankowski RT, Aubertin-Leheudre M, Beavers DP, Botoseneanu A, Buford TW, Church T, et al. ; LIFE Research Group. Sedentary time is associated with the metabolic syndrome in older adults with mobility limitations—The LIFE Study. Exp Gerontol 2015;70:32–6. CrossRefexternal icon PubMedexternal icon
- Denys K, Cankurtaran M, Janssens W, Petrovic M. Metabolic syndrome in the elderly: an overview of the evidence. Acta Clin Belg 2009;64(1):23–34. CrossRefexternal icon PubMedexternal icon
- Akinyemiju T, Jha M, Moore JX, Pisu M. Disparities in the prevalence of comorbidities among US adults by state Medicaid expansion status. Prev Med 2016;88:196–202. CrossRefexternal icon PubMedexternal icon
- Ogden CL, Carroll MD, Fryar CD, Flegal KM. Prevalence of Obesity Among Adults and Youth: United States, 2011-2014. NCHS Data Brief 2015;(219):1–8. PubMedexternal icon
Tables
Table 1. Sociodemographic Characteristics of Study Participants, National Health and Nutrition Examination Survey (NHANES), 1988- 2012
| NHANES period | |||
|---|---|---|---|
| Characteristic | 1988 – 1994 | 1999 – 2006 | 2007 – 2012 |
| Participants (N) | 18,552 | 18,445 | 14,374 |
| Estimated Na | 167,331,669 | 184,010,197 | 196,763,844 |
| % (SE)b or Mean (95% CI) |
% (SE)b or Mean (95% CI) |
% (SE)b or Mean (95% CI) |
|
| Sex | |||
| Male | 48.42 (0.40) | 49.34 (0.37) | 48.88 (0.42) |
| Female | 51.58 (0.40) | 50.66 (0.37) | 51.12 (0.42) |
| Mean age, y | 44.43 (43.38 – 45.49) | 45.63 (45.02 – 46.24) | 46.78 (45.99 – 47.56) |
| Age group | |||
| 18 – 29 years | 23.79 (0.81) | 21.01 (0.59) | 20.62 (0.90) |
| 30 – 49 years | 41.14 (0.96) | 40.46 (0.83) | 35.74 (0.74) |
| 50 – 69 years | 12.17 (0.38) | 16.05 (0.45) | 18.46 (0.48) |
| 70+ years | 22.89 (1.03) | 22.48 (0.68) | 25.18 (0.67) |
| Race/Ethnicity | |||
| Non-Hispanic white | 82.53 (0.82) | 79.46 (1.23) | 77.40 (1.83) |
| Non-Hispanic black | 11.96 (0.68) | 12.41 (0.98) | 13.14 (1.26) |
| Mexican American | 5.51 (0.44) | 8.13 (0.77) | 9.46 (1.22) |
| Education | |||
| < High school diploma | 24.43 (0.94) | 18.26 (0.64) | 17.21 (0.90) |
| High school diploma/GED | 34.65 (0.74) | 25.68 (0.63) | 22.71 (0.75) |
| Some college | 20.28 (0.70) | 29.03 (0.56) | 29.84 (0.60) |
| College graduate | 19.87 (0.86) | 23.48 (1.05) | 26.77 (1.18) |
| Unknown/Refused | 0.77 (0.13) | 3.54 (0.17) | 3.46 (0.22) |
| Mean poverty-to-income ratio | 3.15 (2.94 – 3.35) | 3.03 (2.95 – 3.12) | 2.99 (2.89 – 3.10) |
| Mean BMI kg m-2 | 26.48 (26.30 – 26.66) | 28.17 (27.97 – 28.36) | 28.73 (28.53 – 28.93) |
aEstimated using sampling weights from National Health and Nutrition Examination Survey (NHANES).
bPresented as proportion and standard error.
Table 2. Prevalence and Odds Ratios (ORs) for Metabolic Syndrome in US Adults Stratified by Race and Gender, National Health and Nutrition Examination Survey (NHANES), 1988 – 2012
| NHANES Period | |||
|---|---|---|---|
| 1988 – 1994 | 1999 – 2006 | 2007 – 2012 | |
| Characteristic | % (SE)a | % (SE)a | % (SE)a |
| Metabolic Syndrome | 25.29 (0.85) | 24.99 (0.55) | 34.17 (0.74) |
| Elevated Waist Circumferenced | 31.12 (0.60) | 47.98 (0.79) | 51.92 (0.91) |
| Elevated Triglyceridese | 26.52 (0.82) | 24.99 (0.55) | 28.77 (0.73) |
| Reduced HDL Cholesterolf | 32.53 (1.09) | 25.13 (0.65) | 44.03 (0.94) |
| Elevated Blood Pressureg | 33.92 (0.83) | 40.62 (0.65) | 42.72 (0.89) |
| Elevated Fasting Glucoseh | 28.49 (1.05) | 19.65 (0.63) | 26.07 (0.64) |
| Adjusted ORb (95% CI) |
Adjusted ORb (95% CI) |
Adjusted ORb (95% CI) |
|
| Race-Male Gender | |||
| Non-Hispanic white male (Referent) | – | – | – |
| Non-Hispanic black male | 0.55 (0.46 – 0.67) | 0.64 (0.53 – 0.76) | 0.77 (0.66 – 0.89) |
| Mexican American male | 1.10 (0.87 – 1.40) | 0.82 (0.68 – 0.99) | 1.04 (0.89 – 1.23) |
| Race-Female Gender | |||
| Non-Hispanic white female (Referent) | – | – | – |
| Non-Hispanic black female | 1.12 (0.96 – 1.31) | 1.18 (0.99 – 1.39) | 1.20 (1.02 – 1.40) |
| Mexican American Female | 1.65 (1.36 – 2.00) | 1.30 (1.05 – 1.60) | 1.20 (0.98 – 1.46) |
| Education | |||
| <High school graduate | 1.45 (1.18 – 1.78) | 1.44 (1.21 – 1.72) | 1.56 (1.32 – 1.84) |
| High school graduate or equivalent | 1.56 (1.30 – 1.87) | 1.51 (1.26 – 1.81) | 1.59 (1.35 – 1.88) |
| Some college | 1.38 (1.15 – 1.65) | 1.39 (1.22 – 1.60) | 1.48 (1.27 – 1.73) |
| Unknown/Refused | 1.27 (0.40 – 4.06) | 0.83 (0.58 – 1.19) | 0.60 (0.41 – 0.88) |
| ≥College graduate (Referent) | – | – | – |
| PIR | 0.96 (0.92 – 1.00) | 1.03 (0.99 – 1.06) | 0.98 (0.94 – 1.02) |
| Agec (per 10 year increase) | 1.50 (1.46 – 1.54) | 1.67 (1.63 – 1.72) | 1.73 (1.67 – 1.80) |
a Presented as proportion and standard error.
b Odds ratios adjusted for education, poverty-to-income ratio, and age.
c Odds ratios for 10 year increase in age.
d Waist circumference ≥88cm for females and ≥102 cm for males.
e Triglycerides ≥150 mg/dL or drug treatment of elevated triglycerides.
f HDL cholesterol <40 mg/dL for males and <50 mg/dL for females.
g Systolic blood pressure ≥130 and/or diastolic blood pressure ≥85 mm Hg or antihypertensive drug treatment.
h Fasting glucose ≥100 mg/dL or drug treatment of elevated glucose.
Table 3. Prevalence of and Odds Ratios for Metabolic Syndrome in US Adultsa, Stratified by Race and Sex, National Health and Nutrition Examination Survey, 1988–2012, by Period
| Characteristic | NHANES Period | ||
|---|---|---|---|
| 1988–1994 | 1999–2006 | 2007–2012 | |
| Metabolic syndrome, % (SE) | 16.04 (0.69) | 16.78 (0.58) | 16.05 (0.69) |
| Elevated waist circumferenceb | 16.94 (0.46) | 27.54 (0.75) | 30.53 (1.08) |
| Elevated triglyceridesc | 21.20 (0.90) | 21.08 (0.64) | 24.02 (0.88) |
| Reduced HDL cholesterold | 26.75 (0.96) | 20.14 (0.60) | 35.11 (1.06) |
| Elevated blood pressuree | 28.66 (0.80) | 34.56 (0.68) | 35.80 (1.03) |
| Elevated fasting glucosef | 24.37 (1.01) | 15.66 (0.63) | 20.39 (0.67) |
| Non-Hispanic white, % (SE) | |||
| Male | 17.90 (1.00) | 18.39 (0.73) | 24.24 (1.11) |
| Female | 15.28 (0.81) | 17.37 (0.86) | 25.08 (1.32) |
| Non-Hispanic black, % (SE) | |||
| Male | 9.61 (0.81) | 9.96 (0.64) | 16.89 (1.22) |
| Female | 14.48 (1.03) | 14.02 (0.92) | 20.89 (1.39) |
| Mexican American, % (SE) | |||
| Male | 15.33 (1.24) | 11.08 (1.19) | 15.21 (1.08) |
| Female | 17.23 (0.76) | 14.10 (1.14) | 18.04 (1.71) |
| All races, % (SE) | |||
| Male | 16.82 (0.85) | 16.75 (0.62) | 22.48 (0.98) |
| Female | 15.28 (0.72) | 16.80 (0.74) | 24.11 (1.13) |
| Race-male sex, adjusted OR (95% CI)g | |||
| Non-Hispanic white | 1 [Reference] | ||
| Non-Hispanic black | 0.48 (0.38–0.61) | 0.57 (0.46–0.70) | 0.74 (0.62–0.89) |
| Mexican American | 1.17 (0.88–1.56) | 0.93 (0.72–1.21) | 0.91 (0.70–1.19) |
| Race-female sex, adjusted OR (95% CI)g | |||
| Non-Hispanic white | 1 [Reference] | ||
| Non-Hispanic black | 1.18 (0.95–1.48) | 1.06 (0.85–1.32) | 1.06 (0.82–1.35) |
| Mexican American | 1.70 (1.37–2.11) | 1.50 (1.21–1.87) | 1.30 (0.97–1.75) |
| Education, adjusted OR (95% CI)g | |||
| <High school graduate | 1.59 (1.23–2.05) | 1.44 (1.14–1.82) | 1.62 (1.29–2.03) |
| High school graduate or equivalent | 1.64 (1.28–2.10) | 1.47 (1.17–1.85) | 1.68 (1.32–2.15) |
| Some college | 1.39 (1.06–1.82) | 1.35 (1.12–1.62) | 1.42 (1.14–1.78) |
| Unknown/Refused | 1.89 (0.47–7.64) | 0.57 (0.31–1.06) | 0.50 (0.27–0.95) |
| College graduate | 1 [Reference] | ||
| Poverty to income ratio, adjusted OR (95% CI)g | 1.00 (0.95–1.05) | 1.02 (0.99–1.06) | 1.02 (0.97–1.09) |
| Ageh, adjusted OR (95% CI)g | 1.61 (1.55–1.67) | 1.81 (1.76–1.86) | 1.93 (1.84–2.02) |
Abbreviations: CI, confidence interval; HDL, high density lipoprotein, OR, odds ratio; SE, standard error.
a Excludes obese (body mass index ≥30.0) NHANES participants.
b Waist circumference ≥88 cm for women and ≥102 cm for men.
c Triglycerides ≥150 mg/dL or drug treatment of elevated triglycerides.
d HDL cholesterol <40 mg/dL for males and <50 mg/dL for females.
e Systolic blood pressure ≥130 mm Hg, or diastolic blood pressure ≥85 mm Hg, or antihypertensive drug treatment.
f Fasting glucose ≥100 mg/dL or drug treatment of elevated glucose.
g Odds ratios adjusted for education, poverty-to-income ratio, and age.
h Odds ratios for 10-year increase in age.
Table 4. Descriptive Statistics for US Adultsa in the National Health and Nutrition Examination Survey, 1988–2012, by Period
| Characteristic | NHANES Period | ||
|---|---|---|---|
| 1988–1994 | 1999–2006 | 2007–2012 | |
| Participants, N | 14,379 | 13,979 | 9,491 |
| Estimated Nb | 134,052,945 | 126,920,459 | 127,964,209 |
| Sex, % (SE) | |||
| Male | 49.78 (0.47) | 50.90 (0.45) | 49.91 (0.54) |
| Female | 50.22 (0.47) | 49.10 (0.45) | 50.08 (0.54) |
| Age, mean (95% CI), y | 43.86 (42.91–44.80) | 45.02 (44.39–45.65) | 46.06 (45.08–47.05) |
| Age group, % (SE), y | |||
| 18–29 | 25.95 (0.94) | 23.55 (0.70) | 23.22 (1.22) |
| 30–49 | 40.64 (0.94) | 39.64 (0.92) | 35.13 (0.83) |
| 50–69 | 10.72 (0.36) | 14.54 (0.51) | 17.10 (0.56) |
| ≥70 | 22.68 (1.01) | 22.26 (0.70) | 24.56 (0.88) |
| Race, % (SE) | |||
| Non-Hispanic white | 83.81 (0.78) | 81.08 (1.12) | 80.16 (1.55) |
| Non-Hispanic black | 10.95 (0.63) | 10.82 (0.89) | 11.02 (1.05) |
| Mexican American | 5.24 (0.44) | 8.10 (0.72) | 8.83 (1.06) |
| Education, % (SE) | |||
| <High school graduate | 23.40 (0.95) | 17.65 (0.79) | 16.39 (0.99) |
| High school graduate or equivalent | 33.77 (0.77) | 24.58 (0.71) | 21.70 (0.82) |
| Some college | 20.69 (0.75) | 28.12 (0.65) | 27.96 (0.79) |
| College graduate | 21.26 (0.91) | 25.43 (1.24) | 29.58 (1.45) |
| Unknown/refused | 0.88 (0.16) | 4.23 (0.23) | 4.38 (0.32) |
| Poverty to income ratio, mean (95% CI) | 3.21 (3.09–3.33) | 3.08 (2.99–3.16) | 3.05 (2.94–3.16) |
| Body mass index,c mean (95% CI) | 24.10 (24.00–24.20) | 24.71 (24.63–24.80) | 24.86 (24.75–24.98) |
Abbreviation: CI, confidence interval; SE, standard error.
a Excludes obese (body mass index ≥30.0) NHANES participants.
b Estimated using sampling weights from NHANES.
c Calculated as weight in kilograms divided by height in meters squared.
Table 5. National Health and Nutrition Examination Survey Codes and Complementary Survey Questions or Laboratory Tests Administered for the Identification of Metabolic Components Used in Study Analysis
| Component | Period 1 (1988–1994) | Periods 2 and 3a (1999–2006 and 2007–2012) |
|---|---|---|
| Component 1: elevated waist circumferenceb | ||
| Waist circumference (≥88 cm for women and ≥102 cm for men) | Measurement from BMPWAIST: “Waist circumference (cm) (2 years and over)” | Measurement from BMXWAIST: “Waist circumference (cm) (2 years and over)” |
| Component 2: elevated triglycerides | ||
| Elevated triglycerides (≥150 mg/dL) | Measurement from TGP: “Serum triglycerides (mg/dL)” | Measurement from LBXTR: “Cholesterol — LDL and triglycerides” |
| Drug treatment of elevated triglycerides | Yes response to HAE8D: “Because of your high blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?” | Yes response to BPQ090D: “To lower (your/his/her) blood cholesterol, (have/has) (you/SP) ever been told by a doctor or other health professional] to take prescribed medicine?” |
| Component 3: reduced HDL cholesterolc | ||
| Reduced HDL cholesterol (<40 mg/dL in men and <50 mg/dL in women) | Measurement from HDP: “Serum HDL cholesterol (mg/dL)” | Measurement from LBDHDD: “Direct HDL-Cholesterol (mg/dL)” |
| Drug treatment of reduced HDL cholesterol | Yes response to HAE8D: “Because of your high blood cholesterol, have you ever been told by a doctor or other health professional to take prescribed medicine?” | Yes response to BPQ090D: “To lower (your/his/her) blood cholesterol, (have/has) (you/SP) ever been told by a doctor or other health professional] to take prescribed medicine?” |
| Component 4: elevated blood pressured | ||
| Elevated blood pressure (systolic ≥ 130 mm Hg and/or diastolic ≥ 85 mm Hg) | Systolic blood pressure from PEPMKN1R: “Overall average K1, systolic, blood pressure from household and examination center measurements (5 years and over)” OR diastolic blood pressure from PEPMNK5R: “Overall average K5, diastolic, blood pressure from household and examination center measurements” | Systolic blood pressure from BPXSY1: “Systolic: blood pressure (first reading) mm Hg” OR diastolic blood pressure from BPXDI1 “Diastolic: blood pressure (first reading) mm Hg” |
| Antihypertensive drug treatment in a patient with a history of hypertension | Yes response to HAE4A: “Because of your (high blood pressure/hypertension), have you ever been told by a doctor or other health professional to take prescribed medicine?” | Yes response to BPQ050A: “(Are you/Is SP) now taking prescribed medicine for hypertension?” OR to BPQ040A: “Because of {your/SP’s} (high blood pressure/hypertension), {have you/has s/he} ever been told to take prescribed medicine?” |
| Component 5: elevated fasting glucose | ||
| Elevated fasting glucose (≥100 mg/dL) | G1P: “Plasma glucose — first venipuncture (mg/dL)” | LBXGLU: “Plasma/Fasting Glucose (mg/dL)” |
| Drug treatment of elevated glucose | Yes response to HAD6: “Are you now taking insulin?” OR to HAD10: “Are you now taking diabetes pills to lower your blood sugar?” | Yes response to DID070 or DIQ070: “{Is SP/Are you} now taking diabetic pills to lower {{his/her}/your} blood sugar? These are sometimes called oral agents or oral hypoglycemic agents.” |
Abbreviation: HDL, high-density lipoprotein; NHANES, National Health and Nutrition Examination Survey; SP, survey proxy.
a Periods 2 and 3 used the same questionnaires and SAS (SAS Institute, Inc) coding.
b Body measurements were recorded for all examinees by a trained examiner in the mobile examination center (MEC).
c NHANES did not report laboratory values for direct HDL cholesterol for 1999 through 2004.
d Measurements taken in the MEC and during home examinations on all eligible individuals using a mercury sphygmomanometer.
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