ABSTRACT
Introduction:
The combined impact of type 2 diabetes mellitus (T2DM), sarcopenia, and hypertension on incident cardiovascular disease (CVD) and mortality remains inconclusive. This study aimed to evaluate the additive effect of these co-morbidities on CVD and all-cause mortality among Chinese adults aged 45 years and older.
Materials and Methods:
A total of 12,398 participants were enrolled in the China Health and Retirement Longitudinal Study. Participants were categorized based on their T2DM, sarcopenia, and hypertension status. The study outcomes included incident CVD (including heart disease and stroke) and all-cause mortality. Multivariable logistic regression models and population attributable fractions (PAFs) were employed to investigate the associations between the coexistence of T2DM, sarcopenia, and hypertension with CVD incidence and mortality.
Results:
Compared to those with none of T2DM, sarcopenia, or hypertension, participants with any one, any two, or all three of these conditions exhibited increased risks of incident CVD and higher odds of all-cause mortality. The ORs for CVD and all-cause mortality significantly increased in the groups with none, any one, any two, and all three co-morbidities. (P for trend < 0.001) These three co-morbidities collectively explained 19.1% (95% confidence interval [CI]: 15.7, 22.3) of PAF for CVD and 19.0% (95% CI: 13.5, 24.2) for mortality. The results remained generally consistent in the sensitivity analyses.
Conclusions:
Participants with the coexistence of T2DM, sarcopenia, and hypertension faced a more than two-fold increase in the risk of CVD events and mortality. The estimated PAFs indicated that preventing these three co-morbidities could be beneficial in reducing CVD incidence and all-cause mortality.
KEY WORDS: Cardiovascular disease, diabetes mellitus, hypertension, mortality, sarcopenia
Introduction
Type 2 diabetes mellitus (T2DM), sarcopenia, and hypertension commonly coexist among the elderly and are highly prevalent worldwide. According to recent epidemiological reports, the estimated global prevalence of T2DM, sarcopenia, and hypertension is 6%, 10–27%, and 33%, respectively.[1,2,3] Furthermore, T2DM, sarcopenia, and hypertension are well-recognized risk factors for cardiovascular diseases (CVDs) and increased mortality,[4,5] posing a major public health threat.
Previous studies have demonstrated that T2DM, sarcopenia, and hypertension are each related to adverse clinical outcomes, including coronary artery disease, stroke, heart failure, cognitive dysfunction, and mortality.[4,6,7] Moreover, individuals with concurrent T2DM/sarcopenia/hypertension tend to have a more prominent impact on human health.[8,9,10,11] Additionally, T2DM, sarcopenia, and hypertension are closely interconnected. DM-related factors, such as inflammation, insulin resistance, and vascular complications, can impair muscle health. Sarcopenia, in turn, could exacerbate the progression of diabetes.[12] Likewise, recent studies have also demonstrated a similar bidirectional relationship.[13,14]
Nevertheless, previous studies have been limited to ethnic groups other than the Chinese population, and no studies to date have examined the additive effect of T2DM, sarcopenia, and hypertension on CVD incidence and all-cause mortality. Given that these three co-morbidities commonly coexist among the elderly population and the high incidence of CVD events and high mortality in China,[15] it is crucial to better characterize the combined effect of T2DM, sarcopenia, and hypertension on CVD incidence and all-cause mortality, which is essential for formulating effective preventive strategies and has tremendous public health implications.
Therefore, using data from the China Health and Retirement Longitudinal Study (CHARLS), this study aims to evaluate the additive effect of T2DM, sarcopenia, and hypertension on the incidence of CVD events and all-cause mortality among Chinese adults aged 45 years and older. Additionally, we estimated the population attributable fractions (PAFs) of the three co-morbidities for the incidence of CVD and mortality.
Materials and Methods
Detailed information on the CHARLS design, sampling method, and participants has been described elsewhere.[16] Briefly, CHARLS was launched in 2011, involving participants from 150 counties and 450 urban communities across 28 provinces in China. Participants aged 45 years and older, along with their spouses, were eligible for recruitment. Ultimately, 17,708 participants from 10,257 households were enrolled. The CHARLS protocol was approved by the ethics review committee (IRB00001052-11015), and all participants provided written informed consent.
This study utilized the baseline data from the survey conducted in 2011 and the follow-up data from 2020. A total of 17,708 eligible subjects were recruited in 2011. Individuals without data on prevalent T2DM, sarcopenia, and hypertension (N = 3,934), those under 45 years of age (N = 265), and those without follow-up data on mortality and incident CVD (N = 1,111) were excluded. As a result, 12,398 participants were finally included at baseline. In addition, we excluded 480 subjects without follow-up data on mortality when analyzing the relationships between co-morbidities and mortality. When analyzing the relationships between co-morbidities and CVD, 990 individuals without follow-up data on CVD events and 1587 participants with prevalent CVD were excluded [Supplemental Figure 1 (67.5KB, tif) ].
T2DM and hypertension were defined as self-reports of diagnosis by physicians or the use of anti-diabetic and anti-hypertensive therapy (T2DM: N =683 [5.5%]; hypertension: N =2939 [23.7%]). Additionally, individuals were diagnosed with T2DM if their fasting blood glucose (FBG) was ≥126 mg/dL (N = 1,466 [15.9%]) or hemoglobin A1c (HbA1c) was ≥6.5% (N = 646 [5.2%]).[17] Hypertension was diagnosed if the systolic blood pressure (SBP)/diastolic blood pressure (DBP) was ≥140/90 mm Hg (N = 3,799 [30.6%]).[18]
Sarcopenia was diagnosed based on the Asian Working Group for Sarcopenia (AWGS) 2019 algorithm.[19] In brief, sarcopenia is diagnosed when low muscle mass is combined with low muscle strength or physical performance. Low muscle mass was defined as the sex-specific lowest 20% of the height-adjusted appendicular skeletal muscle mass (ASM) of the study population. ASM was calculated using an anthropometric equation that has been previously validated in the Chinese population.[20] The muscle strength and physical performance (including gait speed and chair stand test) were measured using standard methods as outlined in the cohort profile.[16] The low muscle strength and low physical performance were defined following the AWGS criteria.[19]
A total of 1,968 (15.9%), 1,958 (15.8%), and 5,045 (40.7%) participants had T2DM, sarcopenia, and hypertension. Participants were categorized into four groups based on their T2DM, sarcopenia, and hypertension status. These groups included participants with none of the co-morbidities, any one, any two, or all three co-morbidities. To explore the additive effect of any two of these co-morbidities on prognosis, the participants were further categorized into the following groups based on the presence of T2DM, sarcopenia, and hypertension: T2DM(+/-)/sarcopenia(+/-), hypertension(+/-)/sarcopenia(+/-), and T2DM(+/-)/hypertension(+/-).
The study outcomes included incident CVD events and all-cause mortality. Incident CVD comprised heart diseases and stroke, which were documented during face-to-face follow-up interviews. The CHARLS participants were followed every 2–3 years (2013, 2015, 2018, and 2020) through computer-assisted personal interviews conducted by trained staff. All staff members who received rigorous training and passed the final assessment were designated as interviewers. The ascertainment of CVD relied on self-reported diagnoses of heart diseases and strokes by physicians or self-reported treatments for these conditions. The heart diseases recorded in CHARLS include coronary artery disease, angina, heart attack, heart failure, and cardiac issues. All-cause death events were assessed through interviews with the participants’ family relatives. In the last follow-up survey, individuals who survived but were lost to follow-up in the current survey, or older family members who could not be located in the household, were classified as lost to follow-up. All the events were documented up to the final follow-up survey in 2020.
Baseline socio-demographic and co-morbidities (self-reported) data were recorded using a structured questionnaire in 2011. As shown in Table 1, the demographic data included age, sex, marital status (married or single), residential area (urban or rural), education (high school and above or below), and smoking and drinking status. A physical examination was conducted on each participant to collect their BP, pulse rate, and anthropometric measurements following the cohort profile. Fasting venous blood samples were drawn and stored at −80°C. Hemoglobin, lipid profiles, creatinine, FBG, HbA1c, uric acid, and C-reactive protein (CRP) were tested at the central laboratory. The estimated glomerular filtration rate (eGFR) was calculated using the modified diet of renal disease formula.[21]
Table 1.
Baseline characteristics comparison by number of co-morbidities
| Characteristics at baseline (2011) | Overall (N=12398) | Number of co-morbidity | P | |||
|---|---|---|---|---|---|---|
|
| ||||||
| None (N=5399) | Any one (N=5142) | Any two (N=1742) | All three (N=115) | |||
| Demographic | ||||||
| Age (years) | 59.4±9.5 | 55.6±7.8 | 61.0±9.3 | 65.6±9.8 | 71.7±7.6 | <0.001 |
| Male, n (%) | 5901 (47.6) | 2615 (48.4) | 2476 (48.2) | 757 (43.5) | 53 (46.1) | 0.003 |
| Married, n (%) | 10813 (87.2) | 4983 (92.3) | 4392 (85.4) | 1356 (77.8) | 82 (71.3) | <0.001 |
| Urban residence, n (%) | 2271 (18.3) | 1009 (18.7) | 945 (18.4) | 304 (17.5) | 13 (11.3) | 0.160 |
| High school and above, n (%) | 1280 (10.3) | 677 (12.5) | 476 (9.3) | 125 (7.2) | 2 (1.7) | <0.001 |
| Smoking status, n (%) | <0.001 | |||||
| Non-smoker | 7428 (59.9) | 3268 (60.5) | 3031 (59.0) | 1063 (61.0) | 66 (57.4) | |
| Former smoker | 1179 (9.5) | 440 (8.2) | 535 (10.4) | 189 (10.9) | 15 (13.0) | |
| Current smoker | 3791 (30.6) | 1691 (31.3) | 1576 (30.7) | 490 (28.1) | 34 (29.6) | |
| Drinking status, n (%) | <0.001 | |||||
| Non-drinker | 8324 (67.1) | 3494 (64.7) | 3490 (67.9) | 1256 (72.1) | 84 (73.0) | |
| <1 time/month | 967 (7.8) | 490 (9.1) | 370 (7.2) | 101 (5.8) | 6 (5.2) | |
| >1 time/month | 3107 (25.1) | 1415 (26.2) | 1282 (24.9) | 385 (22.1) | 25 (21.7) | |
| Physical examination | ||||||
| SBP (mm Hg) | 131.8±29.6 | 118.2±11.2 | 139.3±33.5 | 150.3±37.6 | 153.1±20.4 | <0.001 |
| DBP (mm Hg) | 75.9±12.1 | 71.1±8.8 | 79.2±13.2 | 80.9±12.2 | 77.6±11.8 | <0.001 |
| Pulse (beat per minute) | 72.4±10.4 | 71.7±9.8 | 72.6±10.8 | 73.8±11.0 | 72.5±12.0 | <0.001 |
| Body mass index (kg/m2) | 23.4±3.7 | 23.3±3.0 | 23.6±3.9 | 22.9±4.6 | 19.3±2.6 | <0.001 |
| Waist circumference (cm) | 84.2±12.6 | 83.2±11.8 | 85.2±13.0 | 85.2±13.9 | 78.9±9.9 | <0.001 |
| Laboratory | ||||||
| Hemoglobin (g/dL) | 14.3±2.2 | 14.2±2.2 | 14.3±2.2 | 14.2±2.3 | 13.8±2.4 | 0.029 |
| Triglyceride (mg/dL) | 134.5±106.9 | 120.2±75.3 | 137.0±103.6 | 168.7±164.2 | 145.4±191.1 | <0.001 |
| Total cholesterol (mg/dL) | 192.2±38.9 | 189.2±36.6 | 193.0±39.6 | 198.2±42.1 | 197.9±40.7 | <0.001 |
| LDL-C (mg/dL) | 114.2±34.8 | 113.7±32.8 | 114.9±35.5 | 113.9±38.8 | 113.4±31.6 | 0.421 |
| HDL-C (mg/dL) | 51.2±14.8 | 51.9±14.1 | 50.9±14.8 | 50.1±16.4 | 54.8±16.9 | <0.001 |
| eGFR (mL/min/1.72m2) | 97.9±24.7 | 100.9±23.0 | 96.2±23.9 | 94.4±30.2 | 89.3±28.0 | <0.001 |
| FBG (mg/dL) | 109.3±35.8 | 98.4±11.8 | 109.5±34.1 | 137.0±58.1 | 159.4±78.3 | <0.001 |
| HbA1c (%) | 5.4±0.9 | 5.2±0.4 | 5.4±0.9 | 5.9±1.4 | 6.1±1.7 | <0.001 |
| Uric acid (mg/dL) | 4.53±1.29 | 4.39±1.19 | 4.62±1.33 | 4.68±1.41 | 4.52±1.36 | <0.001 |
| C-reactive protein (mg/l)* | 1.1 (0.6, 2.2) | 0.9 (0.5, 1.9) | 1.2 (0.6, 2.4) | 1.4 (0.7, 3.0) | 1.4 (0.7, 4.8) | <0.001 |
| Self-reported co-morbidity | ||||||
| Dyslipidemia, n (%) | 1086 (8.8) | 245 (4.5) | 536 (10.4) | 295 (16.9) | 10 (8.7) | <0.001 |
| Heart disease, n (%) | 1376 (11.1) | 375 (7.0) | 674 (13.1) | 313 (18.0) | 14 (12.2) | <0.001 |
| Stroke, n (%) | 276 (2.2) | 49 (0.9) | 136 (2.6) | 86 (4.9) | 5 (4.4) | <0.001 |
| Kidney disease, n (%) | 835 (6.7) | 335 (6.2) | 363 (7.1) | 128 (7.4) | 9 (7.8) | 0.209 |
| Lung disease, n (%) | 1507 (12.2) | 537 (10.0) | 681 (13.2) | 266 (15.3) | 23 (20.0) | <0.001 |
| Liver disease, n (%) | 479 (3.9) | 204 (3.8) | 202 (3.9) | 69 (4.0) | 4 (3.5) | 0.980 |
| Stomach or other digestive disease, n (%) | 2960 (23.9) | 1354 (25.1) | 1189 (23.1) | 388 (22.3) | 29 (25.2) | 0.037 |
| Malignant tumor, n (%) | 112 (0.9) | 47 (0.9) | 45 (0.9) | 17 (1.0) | 3 (2.6) | 0.256 |
| Memory-related disease, n (%) | 206 (1.7) | 60 (1.1) | 91 (1.8) | 52 (3.0) | 3 (2.6) | <0.001 |
| Psychiatric disease, n (%) | 236 (1.9) | 102 (1.9) | 102 (2.0) | 30 (1.7) | 2 (1.7) | 0.925 |
| Arthritis or rheumatism, n (%) | 4450 (35.9) | 1831 (33.9) | 1900 (37.0) | 671 (38.5) | 48 (41.7) | <0.001 |
| Asthma, n (%) | 496 (4.0) | 182 (3.4) | 214 (4.2) | 91 (5.2) | 9 (7.8) | <0.001 |
SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbA1c, hemoglobin A1c. *Present as median (interquartile range), and log transformed when being tested in the linear regression model
Differences in baseline characteristics were compared using the one-way ANOVA test for normal distribution variables, the Kruskal–Wallis H-test for skewed distribution variables, and the Chi-square test for the categorical variables, respectively. The associations between the numbers of the three co-morbidities with CVD incidence and mortality were assessed using multivariable logistic models. A directed acyclic graph (DAG) was drawn to establish a theoretical causality between exposure and covariates with the outcome and select minimally sufficient confounding based on the backdoor criteria.[22] According to the DAG [Supplemental Figure 2 (228.1KB, tif) ], the minimally sufficient confounders including age, sex, education, smoking status, drinking status, body mass index (BMI), waist circumference, pulse, hemoglobin, lipid profiles, uric acid, eGFR, CRP, heart disease, stroke, dyslipidemia, kidney disease, lung disease, and liver disease. We utilized multiple imputations (20 imputations) by the chained equations method[23] to fill in the missing values (proportion of the missing value: pulse: 0.2%; BMI: 0.7%; waist circumference: 0.4%; hemoglobin: 13.3%; low-density lipoprotein cholesterol: 12.8%; high-density lipoprotein cholesterol: 12.8%; triglyceride: 12.8%; total cholesterol: 12.8%; eGFR: 12.9%; uric acid: 12.8%; CRP: 12.8%). We calculated the PAFs and 95% confidence intervals (CIs) of individual and co-existing co-morbidities for CVD events and all-cause mortality in multivariable models. This estimation helps to determine the proportions of events that could potentially be prevented if these co-morbidities were eliminated.[24] The absolute numbers of CVD events and mortality attributable to the individual and co-existing co-morbidities were also provided.
Several sensitivity analyses were performed. First, we excluded participants with missing values of covariates at baseline and repeated the analyses. Second, a fully adjusted logistic regression model was conducted to assess the association between co-morbidities and outcomes. The model was adjusted for the critical characteristics presented in Table 1, based on the previous literature and clinical relevance, including age, sex, marriage, residential area, education, smoking status, drinking status, pulse, BMI, waist circumference, hemoglobin, lipid profiles, eGFR, uric acid, CRP, and self-reported co-morbidities. Third, the E-value was computed to assess the impact of unaccounted confounding on the associations between exposure and study outcomes.[25] All analyses were carried out using Stata 15.0 (StataCorp, College Station, TX, USA), and statistically significant was defined as two-sided P values < 0.05.
Results
Among the 12,398 participants, the mean age was 59.4 years, and 47.6% were male. Among all participants, 5399 (43.6%), 5142 (41.5%), 1742 (14.1%), and 115 (0.9%) individuals had none, any one, any two, and all three of these conditions. Compared to the other groups, participants with three co-morbidities were the oldest, had the lowest socioeconomic status, and had the lowest BMI and waist circumference levels. In addition, the comorbid burden markedly increased with the number of co-morbidities. The other characteristics of the study participants are presented in Table 1 and Supplemental Table 1.
Supplemental Table 1.
Baseline characteristics of participants with type 2 diabetes mellitus, sarcopenia or hypertension
| Characteristics at baseline | T2DM (N=1968) | Sarcopenia (N=1958) | Hypertension (N=5045) |
|---|---|---|---|
| Demographic | |||
| Age (years) | 60.6±9.2 | 68.9±8.6 | 61.1±9.7 |
| Male, n (%) | 919 (46.7) | 929 (47.5) | 2301 (45.6) |
| Married, n (%) | 1726 (87.7) | 1440 (73.5) | 4184 (82.9) |
| Urban residence, n (%) | 422 (21.4) | 196 (10.0) | 974 (19.3) |
| High school and above, n (%) | 194 (9.9) | 68 (3.5) | 470 (9.3) |
| Smoking status, n (%) | |||
| Non-smoker | 1195 (60.7) | 1092 (55.8) | 3068 (60.8) |
| Former smoker | 223 (11.3) | 188 (9.6) | 547 (10.8) |
| Current smoker | 550 (28.0) | 678 (34.6) | 1430 (28.3) |
| Drinking status, n (%) | |||
| Non-drinker | 1371 (69.7) | 1378 (70.4) | 3505 (69.5) |
| <1 time/month | 134 (6.8) | 116 (5.9) | 340 (6.7) |
| >1 time/month | 463 (23.5) | 464 (23.7) | 1200 (23.8) |
| Physical examination | |||
| SBP (mm Hg) | 136.7±31.5 | 133.2±30.9 | 151.2±36.5 |
| DBP (mm Hg) | 77.5±11.7 | 72.4±12.1 | 83.6±11.9 |
| Pulse (beats per minute) | 74.3±10.9 | 72.4±10.8 | 72.9±10.9 |
| Body mass index (kg/m2) | 24.5±3.9 | 19.0±2.4 | 24.3±4.0 |
| Waist circumference (cm) | 88.0±13.1 | 75.4±9.6 | 87.4±12.9 |
| Laboratory | |||
| Hemoglobin (g/dL) | 14.3±2.2 | 13.7±2.1 | 14.4±2.3 |
| Triglyceride (mg/dL) | 191.0±184.3 | 105.7±75.6 | 149.0±122.8 |
| Total cholesterol (mg/dL) | 198.5±44.6 | 189.9±39.1 | 195.9±39.4 |
| LDL-C (mg/dL) | 112.5±39.5 | 111.7±34.3 | 116.3±26.2 |
| HDL-C (mg/dL) | 46.9±15.3 | 57.7±16.0 | 49.7±14.6 |
| eGFR (mL/min/1.72 m2) | 97.6±30.5 | 94.9±25.8 | 94.3±25.3 |
| FBG (mg/dL) | 157.3±62.4 | 106.0±33.1 | 113.7±41.0 |
| HbA1c (%) | 6.3±1.6 | 5.3±0.8 | 5.5±1.0 |
| Uric acid (mg/dL) | 4.66±1.38 | 4.41±1.32 | 4.71±1.36 |
| C-reactive protein (mg/L)* | 1.4 (0.7, 3.1) | 0.9 (0.5, 2.3) | 1.3 (0.7, 2.7) |
| Self-reported co-morbidity | |||
| T2DM, n (%) | – | 238 (12.2) | 1062 (21.1) |
| Sarcopenia, n (%) | 238 (12.1) | – | 787 (15.6) |
| Hypertension, n (%) | 740 (37.6) | 787 (40.2) | – |
| Dyslipidemia, n (%) | 357 (18.1) | 72 (3.7) | 727 (14.4) |
| Heart disease, n (%) | 314 (16.0) | 211 (10.8) | 817 (16.2) |
| Stroke, n (%) | 78 (4.0) | 43 (2.2) | 202 (4.0) |
| Kidney disease, n (%) | 152 (7.7) | 123 (6.3) | 371 (7.4) |
| Lung disease, n (%) | 266 (13.5) | 371 (19.0) | 645 (12.8) |
| Liver disease, n (%) | 88 (4.5) | 69 (3.5) | 195 (3.9) |
| Stomach or other digestive disease, n (%) | 440 (22.4) | 526 (26.9) | 1086 (21.5) |
| Malignant tumor, n (%) | 24 (1.2) | 14 (0.7) | 50 (1.0) |
| Memory-related disease, n (%) | 42 (2.1) | 50 (2.6) | 112 (2.2) |
| Psychiatric disease, n (%) | 37 (1.9) | 38 (1.9) | 93 (1.8) |
| Arthritis or rheumatism, n (%) | 741 (37.7) | 754 (38.5) | 1891 (37.5) |
| Asthma, n (%) | 89 (4.5) | 121 (6.2) | 213 (4.2) |
T2DM, type 2 diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; HbA1c, hemoglobin A1c. *Present as median (interquartile range), and log transformed when being tested in the linear regression model
A total of 2034 (among 9821 participants, 20.7%) participants had incident CVD, and 1605 (among 11918, 13.5%) participants died during a 9-year follow-up period. Compared to those with none of T2DM, sarcopenia, and hypertension, participants with any one, any two, or all three of the above condition exhibited 49% (95% CI: 1.33, 1.67), 91% (95% CI: 1.68, 2.21), and 97% (95% CI: 1.08, 3.36) increased risks of incident CVD and 21% (95% CI: 1.03, 1.42), 77% (95% CI: 1.46, 2.15), and 167% (1.70, 4.22) higher odds of all-cause mortality, respectively. The ORs for CVD events and all-cause mortality significantly increased in the none, any one, any two, and all three co-morbidities groups. (p for trend < 0.001) [Figure 1].
Figure 1.
Associations between the number of co-morbidities with cardiovascular disease and mortality, adjusting covariates based on the directed acyclic graph model. OR, odds ratio; CI, confidence interval. Co-morbidities include type 2 diabetes mellitus, sarcopenia, and hypertension. Directed acyclic graph model adjusted for age, sex, education, smoking status, drinking status, BMI, waist circumference, pulse, hemoglobin, lipid profiles, uric acid, eGFR, CRP, heart disease, stroke, dyslipidemia, kidney disease, lung disease, and liver disease
Figure 2 depicts the associations between any two of T2DM, sarcopenia, and hypertension with CVD incidence and all-cause mortality. Participants with T2DM(+)/sarcopenia(+), hypertension(+)/sarcopenia(+), and T2DM(+)/hypertension(+) had the highest risks of incident CVD, with the corresponding ORs of 1.45 (95% CI: 1.01, 2.15), 1.72 (95% CI: 1.36, 2.18), and 2.10 (95% CI: 1.74, 2.52), respectively. (All P for trend < 0.001) Similar trends were observed for all-cause mortality [Figure 2].
Figure 2.
Additive effects of type 2 diabetes mellitus, sarcopenia, and hypertension on cardiovascular disease and mortality, adjusting covariates based on the directed acyclic graph model. OR, odds ratio; CI, confidence interval; T2DM, type 2 diabetes mellitus. Panel A: Additive effects of type 2 diabetes mellitus and sarcopenia on cardiovascular disease and mortality. Panel B: Additive effects of hypertension and sarcopenia on cardiovascular disease and mortality. Panel C: Additive effects of type 2 diabetes mellitus and hypertension on cardiovascular disease and mortality. Directed acyclic graph model adjusted for age, sex, education, smoking status, drinking status, BMI, waist circumference, pulse, hemoglobin, lipid profiles, uric acid, eGFR, CRP, heart disease, stroke, dyslipidemia, kidney disease, lung disease, and liver disease
The PAF of T2DM, sarcopenia, and hypertension was 2.1% (95% CI: 0.5, 3.6), 1.0% (95% CI: −0.5, 2.5), and 16.3% (95% CI: 13.3, 19.2) for incident CVD. These three co-morbidities collectively explained 19.1% (95% CI: 15.7, 22.3) of PAF for CVD [Figure 3 Panel A], and such PAF corresponded to 440 (95% CI: 362, 514) CVD events in the current study [Supplemental Figure 3 (97.9KB, tif) Panel A]. Besides, T2DM, sarcopenia, and hypertension contributed 3.1% (95% CI: 1.0, 5.1), 5.2% (95% CI: 2.2, 8.1), and 11.3% (95% CI: 7.0, 15.4) of all-cause mortality. T2DM, sarcopenia combined with hypertension was responsible for 19.0% (95% CI: 13.5, 24.2) of mortality [Figure 3 Panel B], which corresponded to 305 (95% CI: 217, 388) deaths [Supplemental Figure 3 (97.9KB, tif) Panel B].
Figure 3.
Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease and mortality. Panel A: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease. Panel B: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for mortality. PAF, population attributable fraction; CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus
Results remained generally consistent when we excluded participants with missing covariates. [Supplemental Figures 4 (69.2KB, tif) -6 (94.1KB, tif) ] or fully adjusting for the critical baseline characteristics [Supplemental Figures 7 (68.6KB, tif) and 8 (182.4KB, tif) ]. Finally, the E-value analysis indicated that the required OR of an unmeasured confounder that can mask the observed effects for CVD events and all-cause mortality should be over 1.86 and 2.13 [Supplemental Figure 9 (189.7KB, tif) ], respectively, which suggested the robustness to the residual confounders.
Discussion
The present national-wide cohort study has the following novel findings. First, the prevalence of T2DM, sarcopenia, and hypertension was extremely high in our study cohort. Over 40% of the study participants had at least one of these conditions, while around 15% had two or more of these co-morbidities. Among these co-morbidities, hypertension was the strongest contributor to future CVD and premature mortality. Furthermore, individuals with co-existing conditions of T2DM, sarcopenia, and hypertension faced a more than two-fold increase in the risk of CVD incidence and mortality. The estimated PAFs indicated that preventing these three co-morbidities could effectively reduce CVD events and mortality.
The prevalence of T2DM, sarcopenia, and hypertension is increasing strikingly worldwide,[1,2,3] posing an alarming public health threat. Given that age is an unmodifiable risk factor for all of these co-morbidities,[5,18,26] such prevalence is subject to increase along with the aging society. In addition, numerous studies have demonstrated the bidirectional relationships between T2DM, sarcopenia, and hypertension,[6,12,14] which could further accelerate the development of each condition. Our present study unveiled that more than 40% of adults aged ≥45 years had at least one of T2DM, sarcopenia, or hypertension, and approximately 15% had two or more of these chronic conditions. This accounts for around 250 million and 90 million Chinese individuals aged 45 years and older having at least one or more than two of these co-morbidities, according to the 2010 China population census data.[27] The amount increased by approximately 40 million in 2020,[28] which has led to a major public health burden. Therefore, immediate efforts are necessary to improve the simultaneous care and prevention of T2DM, sarcopenia, and hypertension among the elderly population in China, thereby enhancing the primary prevention of CVD and reducing mortality.
T2DM, sarcopenia, and hypertension are common among the elderly population and are usually interconnected. The underlying mechanism may involve the sharing of detrimental factors, including inflammation, vascular complications, and oxidative stress.[12,29] What is more, prior studies have shown that the concurrent presence of any two of T2DM, sarcopenia, and hypertension can have joint negative effects on human health and prognosis.[9,10,11,30,31] In line with these studies, the present study indicated that the coexistence of two out of T2DM, sarcopenia, and hypertension additively increased the odds of future CVD events and premature mortality. Furthermore, our study extended previous findings by elucidating those individuals aged ≥45 years with co-existing T2DM, sarcopenia, and hypertension faced more than double the risk for incident CVD events and all-cause mortality compared to their healthy counterparts, which were significantly greater than for those with any one or two of these co-morbidities. Implementing primary prevention strategies for these co-morbidities could significantly decrease the occurrence of future CVD events and mortality, as indicated by the PAF estimations. Based on the prior epidemiological report,[32] it is estimated that approximately 1.5 cases of CVD and 1.3 deaths per 1000 person-years could be effectively prevented.
The present study has several public health implications. First, consistent with the latest Global Burden Study,[4] the current study observed that hypertension was the largest contributor to CVD incidence and mortality, re-emphasizing the significance of hypertension management among Chinese elderly people. Nevertheless, the awareness and control rate of hypertension remains alarmingly low in China. Therefore, promoting primary care and improving health resource allocation should be prioritized for hypertension management.[33] Second, in addition to controlling hypertension, our study demonstrated that T2DM and sarcopenia, when combined with hypertension, collectively exacerbated the hazardous impacts on prognosis. However, it is reported that only a small minority of Chinese CVD-free adults maintained ideal cardiovascular health, and the under-management of cardiovascular risk factors was surprisingly common in China.[34,35] Therefore, our study can inform public and health policy decisions and enhance primary care interventions at the population level. Third, epidemiological investigations are crucial for non-communicable diseases. Estimated PAFs of common co-morbidities, based on a nationally representative sample, could be valuable in efficiently and effectively guiding management.
Several limitations of the present study are noteworthy. First, this study was conducted on middle-aged and elderly adults in China. Caution should be taken when generalizing these conclusions to other regions and races with different lifestyles and genetics. Second, causality cannot be reliably established between co-morbidities and CVD events and all-cause mortality due to the observational nature of the study. In addition, residual unmeasured confounding may exist, even though we adjusted for multiple potential covariates and used a DAG to select the minimally sufficient confounding. Nonetheless, we performed the E-value analysis and showed that an unmeasured confounder capable of attenuating the observed effects for CVD events and mortality should have an OR exceeding 1.86 and 2.13 [Supplemental Figure 9 (189.7KB, tif) ], which is substantially higher than those associated with known risk factors for CVD incidence and mortality, underscoring the robustness of the analysis to residual confounding. Third, baseline co-morbidities and study outcomes were self-reported without independent adjudication, which may result in recall bias and an inaccurate estimation of the prevalence of co-morbidities and events. For example, individuals with chronic conditions tend to have a higher risk of mortality and are less likely to participate in surveys compared to those without such conditions. This leads to an underestimation of co-morbidities and related outcomes. However, previous studies have demonstrated a strong correlation between self-reported information and official records.[36] Last but not least, although our study included a large cohort, the sample size was relatively small in those with all three co-morbidities. Our statistical power thus may not be adequate, and the accurate OR estimations were unattainable due to the wide CIs. Future studies involving a larger sample regarding the longitudinal associations between the coexistence of multiple chronic conditions with CVD incidence and mortality are warranted.
In summary, this large national-wide cohort study demonstrates an extremely high prevalence of co-existing with T2DM, sarcopenia, and hypertension. Participants with the coexistence of T2DM, sarcopenia, and hypertension faced a more than two-fold increase in the risk of future CVD and premature mortality. The estimated PAFs suggested that preventing these three co-morbidities could significantly reduce future CVD events and mortality, underscoring the importance of implementing effective prevention strategies and enhancing the management of these co-morbidities to decrease CVD incidence and mortality rates in China.
Ethics approval and consent to participate
The CHARLS study protocol was approved by the ethics review committee at Peking University (IRB00001052-11015), Beijing, China. Written informed consent was obtained from all participants.
Availability of data and materials
The data analyzed in this study are available from the Institute of Social Science Survey, Peking University, Beijing, China. (http://charls.pku.edu.cn).
Conflicts of interest
There are no conflicts of interest.
Study flowchart. T2DM, type 2 diabetes mellitus; CVD, cardiovascular disease
Directed acyclic graph for the association between the co-morbidities and cardiovascular disease and mortality. T2DM, type 2 diabetes mellitus; BMI, body mass index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; CRP, C-reactive protein
The absolute number of cardiovascular disease (a) and all-cause mortality (b) attributed to co-morbidities in overall participants. CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus
Associations between the number of co-morbidities with cardiovascular disease and mortality after excluding participants with missing covariates. OR, odds ratio; CI, confidence interval. Co-morbidities include type 2 diabetes mellitus, sarcopenia, and hypertension
Additive effects of type 2 diabetes mellitus, sarcopenia, and hypertension on cardiovascular disease and mortality after excluding participants with missing covariates. OR, odds ratio; CI, confidence interval; T2DM, type 2 diabetes mellitus. Panel A: Additive effects of type 2 diabetes mellitus and sarcopenia on cardiovascular disease and mortality. Panel B: Additive effects of hypertension and sarcopenia on cardiovascular disease and mortality. Panel C: Additive effects of type 2 diabetes mellitus and hypertension on cardiovascular disease and mortality
Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease and mortality after excluding participants with missing covariates. Panel A: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease. Panel B: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for mortality. PAF, population attributable fraction; CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus
Associations between the number of co-morbidities with cardiovascular disease and mortality based on the fully adjusted model. OR, odds ratio; CI, confidence interval. Co-morbidities include type 2 diabetes mellitus, sarcopenia, and hypertension
Additive effects of type 2 diabetes mellitus, sarcopenia, and hypertension on cardiovascular disease and mortality based on the fully adjusted model. OR, odds ratio; CI, confidence interval; T2DM, type 2 diabetes mellitus. Panel A: Additive effects of type 2 diabetes mellitus and sarcopenia on cardiovascular disease and mortality. Panel B: Additive effects of hypertension and sarcopenia on cardiovascular disease and mortality. Panel C: Additive effects of type 2 diabetes mellitus and hypertension on cardiovascular disease and mortality
E-values for the odds ratios of cardiovascular disease and mortality. OR, odds ratio; CVD, cardiovascular disease
Acknowledgment
We thank the China Health and Retirement Longitudinal Study for sharing their data and all the staff who participated in the CHARLS for their contributions to this work.
Funding Statement
Nil.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Study flowchart. T2DM, type 2 diabetes mellitus; CVD, cardiovascular disease
Directed acyclic graph for the association between the co-morbidities and cardiovascular disease and mortality. T2DM, type 2 diabetes mellitus; BMI, body mass index; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; CRP, C-reactive protein
The absolute number of cardiovascular disease (a) and all-cause mortality (b) attributed to co-morbidities in overall participants. CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus
Associations between the number of co-morbidities with cardiovascular disease and mortality after excluding participants with missing covariates. OR, odds ratio; CI, confidence interval. Co-morbidities include type 2 diabetes mellitus, sarcopenia, and hypertension
Additive effects of type 2 diabetes mellitus, sarcopenia, and hypertension on cardiovascular disease and mortality after excluding participants with missing covariates. OR, odds ratio; CI, confidence interval; T2DM, type 2 diabetes mellitus. Panel A: Additive effects of type 2 diabetes mellitus and sarcopenia on cardiovascular disease and mortality. Panel B: Additive effects of hypertension and sarcopenia on cardiovascular disease and mortality. Panel C: Additive effects of type 2 diabetes mellitus and hypertension on cardiovascular disease and mortality
Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease and mortality after excluding participants with missing covariates. Panel A: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for cardiovascular disease. Panel B: Population attributable fractions of type 2 diabetes mellitus, sarcopenia, and hypertension for mortality. PAF, population attributable fraction; CVD, cardiovascular disease; T2DM, type 2 diabetes mellitus
Associations between the number of co-morbidities with cardiovascular disease and mortality based on the fully adjusted model. OR, odds ratio; CI, confidence interval. Co-morbidities include type 2 diabetes mellitus, sarcopenia, and hypertension
Additive effects of type 2 diabetes mellitus, sarcopenia, and hypertension on cardiovascular disease and mortality based on the fully adjusted model. OR, odds ratio; CI, confidence interval; T2DM, type 2 diabetes mellitus. Panel A: Additive effects of type 2 diabetes mellitus and sarcopenia on cardiovascular disease and mortality. Panel B: Additive effects of hypertension and sarcopenia on cardiovascular disease and mortality. Panel C: Additive effects of type 2 diabetes mellitus and hypertension on cardiovascular disease and mortality
E-values for the odds ratios of cardiovascular disease and mortality. OR, odds ratio; CVD, cardiovascular disease
Data Availability Statement
The data analyzed in this study are available from the Institute of Social Science Survey, Peking University, Beijing, China. (http://charls.pku.edu.cn).