DOI: https://doi.org/10.21203/rs.3.rs-1994717/v1
Growing evidence indicates that handgrip strength (HGS) is a conspicuous marker for assessing some diseases affecting middle-aged and elderly individuals. However, research regarding HGS and heart failure (HF) is sparse and controversial. Hence, we aimed to investigate the association between HGS and HF among adults aged 45 years and older in the United States.
In this cross-sectional study, we included 4880 adults older than 45 years who were part of the National Health and Nutrition Examination Survey (2011–2014). A general linear model was used to estimate the association between HGS and HF. Age, gender, race, income level, education level, body mass index level, smoking status, drinking status, diabetes, hypertension and stroke covariates were adjusted using a multiple regression model. And further subgroup analysis was conducted.
We documented 206 cases of HF, including 112 men and 94 women. HGS was negatively associated with HF after adjusting for all the covariates (odds ratio = 0.97, 95% confidence interval = 0.96, 0.98; P < 0.001). Compared with the lowest quintile, the highest quintile was associated with an 83% lower incidence of HF (odds ratio = 0.17, 95% confidence interval = 0.07, 0.40; P < 0.001). Subgroup analysis showed that the results remained stable.
In US adults older than 45, HGS level was an independent negative correlation with the incidence of HF after adjusting for covariates. Based on our findings, HGS may be a marker for predicting HF in middle-aged and elderly individuals.
Along with the global social structure of population aging, the trend of aging brings about an increase in the overall incidence and prevalence of heart failure (HF). Currently, the incidence of HF in Europe is approximately 5/1000 person-years in adults. At the same time, the prevalence is approximately 1% for those aged < 55 years and > 10% for those aged 70 years or older 1 . In the United States, HF currently affects 6 million people, and the direct costs of HF treatment are expected to cost up to $30.7 billion and will double by 20302,3. Importantly, studies have shown that even patients with mild symptoms may still have a high risk of hospitalization and death 4 . A study also showed that the most clinically stable HF patients, who had never had a prior or remote HF hospitalization, still had high absolute rates of cardiovascular death and hospitalization for HR during the course of the trial 5 . There is a need to explore more prevention strategies for HF, which necessitates a better understanding of the association between the risk factors and HF.
Handgrip strength (HGS) is a quick and straightforward measure of muscle function and is closely related to overall muscle strength6. Age-related reductions in overall muscle strength are associated with all-cause mortality and other adverse clinical events in middle-aged or elderly people and can be characterized by HGS7–9. For example, studies have shown that reduced HGS increases the risk of all-cause mortality and cancer10. Overall, HGS is associated with cardiovascular mortality and the incidence of cardiovascular disease11,12, but data on the association between HGS and HF remains sparse and controversial. For instance, Segar et al. suggested that decreased HGS had a nonsignificant association with a higher incident risk of both reduced and preserved ejection fraction heart failure13. At the same time, Hauptman et al. found that changes in HGS were not associated with 30-day HF readmission14. In contrast, a cohort study showed that relative HGS (absolute values of HGS divided by weight in kilograms) was inversely associated with risk of heart failure15. A case–control study suggested that HGS is an ideal indicator of risk stratification in patients with HF16. In a meta-analysis, HGS was considered to be an independent predictor of admissions for HF17.
Therefore, in this study, we examined the associations of HGS with HF among US adults aged 45 years and older using samples from a database of a multiracial population and tried to explain the mechanism.
Use of the dataset from the National Health and Nutrition Examination Survey (NHANES) was approved by the National Center for Health Statistics (NCHS) Institutional Review Board in compliance with the revised Declaration of Helsinki. The informed consents were obtained from all participants before data collection. All the methods were carried out in accordance with the relevant guidelines and regulations of NCHS Institutional Review Board.
The NHANES is a nationally representative survey conducted by the NCHS. It adopted a stratified, multistage probability cluster sampling design to select representative sample from United States civilians and assess their health or nutritional status. The survey data and methodological details about the NHANES are available at www.cdc.gov/nchs/nhanes/.
This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As a retrospective cross-sectional study, no direct contact was performed with the participants, so the privacy risk was minimal. The deidentified data were extracted from the 2011–2014 NHANES cycles since the information on HGS measurement was only provided in these cycles. In order to strictly screen the included participants, the following exclusion criteria were used: participants with cognitive impairment, depression were excluded because these conditions may cause abnormal HGS data18,19; participants missing data for HF or HGS; the age of participants under 45 years old. Data were analyzed from May to July 2022.
The exposure variable was HGS, which was measured using a handgrip dynamometer (Model T.K.K.5401) and calculated as the sum of the largest reading from each hand and expressed in kilograms. Detailed descriptions of the HGS measurement protocol are provided in the NHANES “Muscle Strength Procedures Manual”20.
The outcome variable was HF. In the NHANES, HF data were provided by a self-reported personal interview. Participants were considered to have HF if they answered yes to the question “Has a doctor or other health professional ever told you that you had heart failure21?
Variables thought to be confounders based on existing literature and clinical judgment were included2,21,22. In this study, covariates included demographic data (age, gender, race, education level, income level, body mass index level) and a questionnaire on medical history (diabetes mellitus, hypertension, stroke), lifestyle characteristics (smoking status, drinking status).
In NHANES, information on self-reported race and ethnicity were derived from responses to survey questions on race. Educational level was divided into 3 levels (high school or less, some college, and college graduate or higher). BMI is calculated from a given height and weight of participants. As used by US government departments to report NHANES dietary and health data, we categorized family income into the following 3 levels based on the family poverty income ratio: low income (≤ 1.3), medium income (> 1.3 to 3.5), and high income (> 3.5)23. Diabetes mellitus, hypertension, stroke were defined based on self-reported questionnaire. Smoking status was categorized into the following 3 groups: never smoked (or smoked < 100 cigarettes), former smoker (smoked at least 100 cigarettes but has quit), and current smoker. Drinking status was determined by the survey question, “In any 1 year, have you had at least 12 drinks of any type of alcoholic beverage?” Participants who answered “yes” were defined as alcohol drinkers24. The data acquisition process for all the covariates can be found at www.cdc.gov/nchs/nhanes/.
Descriptive analysis was applied to all participants’ data. Categorical variables are expressed as proportions (%). Continuous variables are expressed as the mean and standard deviation (SD) or median and interquartile range (IQR), as appropriate. Student’s t test and the chi-square test were used for continuous variables and categorical variables, respectively, to assess differences in clinical characteristics.
Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for HGS with HF using multiple logistic regression models. Age, gender, race, income level, education level, BMI, smoking status, drinking status, hypertension, diabetes and stroke covariates were adjusted. A general linear model was used to study the association between HGS and HF. A P for trend was assessed to study trends of association between HGS and HF among different HGS levels. Age level were classified into ≥ 60 years old and < 60 years old. BMI level was divided into two groups by 25. Subgroup analysis of age level, gender, race, income level, education level, BMI level, smoking status, drinking status, hypertension, diabetes and stroke covariates were performed using stratified logistic regression models. Interaction across subgroups was tested using the likelihood ratio test. A P < 0.05 was considered statistically significant. The analyses were performed with the statistical software packages R (http://www.R-project.org, The R Foundation) and Free Statistics software version 1.4 (Beijing Free Kelin Medical Technology Co, Ltd).
The flowchart for participant enrollment is presented in Fig. 1. Participants with cognitive impairment (n = 74) and depression (n = 840) were excluded. A total of 4880 participants aged 45 years and older remained after the exclusion of 4372 subjects with missing handgrip strength data and 4922 subjects with missing HF data.
Among the 4880 participants from the study, 206 participants were diagnosed with HF. The baseline characteristics of all participants are shown in Table 1. The average age of the study participants was 61.9 years. Males accounted for 50.4% of the total study population. All the variables were significantly different among persons classified into the different quintiles of HGS. The lowest quintile of HGS was ≤ 48 kg; the 2nd quintile was ≥ 48.1 and < 57.74 kg; the 3rd quintile was ≥ 57.75 and < 69.7 kg; the 4th quintile was ≥ 69.71 and < 85.4 kg; and the highest quintile was ≥ 85.5 kg. Compared with participants in the HGS quintile 1 group, the other quintile groups were younger, more likely to be female, had higher income levels, education levels, and BMI levels and had lower rates of diabetes, hypertension, and stroke. However, smoking and drinking rates were higher.
Variable |
All participants |
Handgrip Strength(Kg) |
P Vaule |
||||
---|---|---|---|---|---|---|---|
Q1 (≤ 48.00) |
Q2 (48.10-57.74) |
Q3 (57.75–69.70) |
Q4 (69.71–85.40) |
Q5 (≥ 85.50) |
|||
Participants (n) |
4880 |
973 |
979 |
972 |
976 |
980 |
|
Heart Failure n(%) |
< 0.001 |
||||||
No |
4674 (95.80) |
906 (93.10) |
931 (95.10) |
929 (95.60) |
938 (96.10) |
970 (99.00) |
|
Yes |
206 (4.20) |
67 (6.90) |
48 (4.90) |
43 (4.40) |
38 (3.90) |
10 (1.00) |
|
Age (years) |
61.90 ± 10.70 |
69.10 ± 10.10 |
62.50 ± 10.30 |
60.50 ± 10.50 |
60.90 ± 10.20 |
56.40 ± 8.30 |
< 0.001 |
Gender n(%) |
< 0.001 |
||||||
Female |
2422 (49.60) |
882 (90.60) |
801 (81.80) |
580 (59.70) |
150 (15.40) |
9 (0.90) |
|
Male |
2458 (50.40) |
91 (9.40) |
178 (18.20) |
392 (40.30) |
826 (84.60) |
971 (99.10) |
|
Race n(%) |
< 0.001 |
||||||
Mexican American |
470 ( 9.60) |
99 (10.20) |
103 (10.50) |
85 (8.70) |
90 (9.20) |
93 (9.50) |
|
Non-Hispanic White |
2137 (43.80) |
480 (49.30) |
437 (44.60) |
407 (41.90) |
381 (39.00) |
432 (44.10) |
|
Non-Hispanic Black |
1209 (24.80) |
149 (15.30) |
205 (20.90) |
270 (27.80) |
268 (27.50) |
317 (32.30) |
|
Other |
1064 (21.80) |
245 (25.20) |
234 (23.90) |
210 (21.60) |
237 (24.30) |
138 (14.10) |
|
BMI (kg/m2) |
29.10 ± 6.50 |
28.40 ± 6.60 |
29.00 ± 6.80 |
29.40 ± 6.80 |
29.10 ± 6.30 |
29.70 ± 5.70 |
< 0.001 |
Income n(%) |
< 0.001 |
||||||
PIR ≤ 1.3 |
1497 (28.60) |
356 (37.60) |
294 (27.50) |
301 (28.20) |
302 (28.10) |
244 (21.60) |
|
1.3 < PIR ≤ 3.5 |
1688 (35.60) |
356 (38.00) |
339 (35.90) |
342 (36.20) |
332 (34.80) |
319 (33.30) |
|
PIR > 3.5 |
1695 (35.80) |
236 (24.40) |
345 (36.60) |
336 (35.50) |
352 (37.10) |
426 (45.20) |
|
Education n(%) |
< 0.001 |
||||||
High school or less |
1136 (23.30) |
304 (31.20) |
200 (20.40) |
212 (21.80) |
233 (23.90) |
187 (19.10) |
|
Some college |
2476 (50.80) |
491 (50.50) |
515 (52.70) |
502 (51.60) |
462 (47.40) |
506 (51.60) |
|
College graduate or higher |
1268 (26.00) |
180 (18.30) |
263 (26.90) |
258 (26.50) |
280 (28.70) |
287 (29.30) |
|
Diabetes n(%) |
< 0.001 |
||||||
No |
4003 (82.00) |
756 (77.70) |
800 (81.70) |
814 (83.70) |
795 (81.50) |
838 (85.50) |
|
Yes |
877 (18.00) |
218 (22.30) |
179 (18.30) |
158 (16.30) |
180 (18.50) |
142 (14.50) |
|
Hypertension n(%) |
< 0.001 |
||||||
No |
2947 (60.40) |
498 (51.20) |
630 (64.40) |
614 (63.20) |
594 (60.90) |
611 (62.30) |
|
Yes |
1933 (39.60) |
475 (48.80) |
349 (35.60) |
358 (36.80) |
382 (39.10) |
369 (37.70) |
|
Stroke n(%) |
< 0.001 |
||||||
No |
4639 (95.20) |
874 (90.00) |
939 (95.90) |
932 (95.90) |
939 (96.30) |
955 (97.60) |
|
Yes |
241 ( 4.80) |
99 (10.00) |
42 (4.10) |
41 (4.10) |
36 (3.70) |
23 (2.40) |
|
Smoking status n(%) |
< 0.001 |
||||||
Never smoker |
2560 (52.50) |
602 (61.90) |
591 (60.40) |
516 (53.10) |
410 (42.10) |
441 (45.00) |
|
Former smoker |
1514 (31.10) |
274 (28.20) |
262 (26.80) |
279 (28.70) |
381 (39.10) |
318 (32.40) |
|
Current smoker |
806 (16.40) |
96 (9.90) |
127 (12.80) |
178 (18.10) |
184 (18.90) |
221 (22.60) |
|
Drinking status n(%) Never drinker Former drinker |
2483 (50.90) 1393 (28.60) |
593 (61.00) 269 (27.70) |
581 (59.40) 250 (25.60) |
502 (51.60) 259 (26.60) |
394 (40.40) 340 (34.90) |
413 (42.10) 275 (28.10) |
< 0.001 |
Current drinker |
1004 (20.50) |
113 (11.30) |
147 (15.00) |
211 (21.70) |
241 (24.70) |
292 (29.80) |
|
Notes: data presented are mean ± SD or n(%). Abbreviations: BMI, body mass index; PIR, poverty income ratio. |
Table 2 shows the ORs and 95% CIs for the risk of HF determined by HGS. When analyzed in continuous form, HGS was significantly associated with the incidence of HF. This association was found in the unadjusted model (OR = 0.97, 95% CI = 0.97, 0.98), adjusted Model I (OR = 0.97, 95% CI = 0.96, 0.98) and adjusted Model II (OR = 0.97, 95% CI = 0.96, 0.98).
When treated as a categorical variable, in the unadjusted model, there was a decreasing risk for developing HF as the quintile of HGS increased (P for trend < 0.001). Compared with those in the lowest quintile, participants who had a measurement of HGS in the highest quintile had an 86% decreased risk of the development of HF (OR = 0.14, 95% CI = 0.07, 0.27). After adjustment for age, race, gender, income level, education level, BMI level, smoking status, drinking status, diabetes, hypertension and stroke, the odds ratios were 1.03 (0.66, 1.61), 0.83 (0.49, 1.38), 0.52 (0.29, 0.94), and 0.17 (0.07, 0.40) for HGS quintiles 2–5, respectively (P for trend < 0.05).
Nonadjusted |
AdjustⅠ |
AdjustⅡ |
|
---|---|---|---|
HGS (kg) |
0.97 (0.97, 0.98) |
0.97 (0.96, 0.98) |
0.97 (0.96, 0.98) |
HGS quintiles |
|||
Q1 (≤ 48.00) |
1(Ref) |
1(Ref) |
1(Ref) |
Q2 (48.10-57.74) |
0.70 (0.48, 1.02) |
0.93 (0.61, 1.43) |
1.03 (0.66, 1.61) |
Q3 (57.75–69.70) |
0.63 (0.42, 0.93) |
0.71 (0.44, 1.15) |
0.83 (0.49, 1.38) |
Q4 (69.71–85.40) |
0.55 (0.36, 0.82) |
0.46 (0.26, 0.81) |
0.52 (0.29, 0.94) |
Q5 (≥ 85.50) |
0.14 (0.07, 0.27) |
0.15 (0.06, 0.34) |
0.17 (0.07, 0.40) |
P for trend |
< 0.001 |
< 0.001 |
< 0.000 |
Note: Data presented are ORs and 95% CIs. Adjust I model adjusts for age, race, gender, income, and education. Adjust II model adjusts for adjust I + BMI, smoking status, drinking status, diabetes, hypertension and stroke. |
To determine whether the association between HGS and HF was stable in different subgroups, we performed stratified analyses and interactive analyses (Table 3). No interactive role was found in the association between HGS and HF (P for interaction > 0.05).
Confounding factor |
HGS quintiles |
P for |
P for |
||||||
---|---|---|---|---|---|---|---|---|---|
category |
Q1 |
Q2 |
Q3 |
Q4 |
Q5 |
trend |
interaction |
||
(≤ 48.00) |
(48.10-57.74) |
(57.75–69.70) |
(69.71–85.40) |
(≥ 85.50) |
|||||
Age level (years) |
0.17 |
||||||||
45–60 |
1(Ref) |
0.82 (0.24, 2.74) |
0.26 (0.07 ~ 1.04) |
0.20 (0.04, 0.86) |
0.01 (0, 0.14) |
0.001 |
|||
>60 |
1(Ref) |
1.02 (0.62, 1.66) |
1.01 (0.57 ~ 1.78) |
0.61 (0.31, 1.18) |
0.33 (0.13, 0.83) |
0.02 |
|||
Gender n(%) |
0.63 |
||||||||
Female |
1(Ref) |
0.89 (0.50, 1.57) |
0.43 (0.17, 1.05) |
0.79 (0.21, 2.96) |
0 (0, Inf) |
0.133 |
|||
Male |
1(Ref) |
2.43 (0.90, 6.58) |
2.01 (0.77, 5.24) |
0.97 (0.36, 2.56) |
0.31 (0.09, 0.90) |
0.001 |
|||
Race n(%) |
0.12 |
||||||||
Mexican American |
1(Ref) |
1.34 (0.30, 5.99) |
0.89 (0.16, 5.07) |
0.13 (0.01, 1.64) |
0 (0, Inf) |
0.014 |
|||
Non-Hispanic White |
1(Ref) |
0.90 (0.48, 1.67) |
0.90 (0.45, 1.83) |
0.49 (0.21, 1.13) |
0.08 (0.01, 0.38) |
0.003 |
|||
Non-Hispanic Black |
1(Ref) |
0.61 (0.23, 1.58) |
0.44 (0.16, 1.21) |
0.61 (0.21, 1.80) |
0.40 (0.11, 1.51) |
0.191 |
|||
Other |
1(Ref) |
4.03 (0.71, 22.88) |
0.66 (0.07, 6.63) |
1.37 (0.14, 13.85) |
0 (0, Inf) |
0.216 |
|||
Income n(%) |
0.61 |
||||||||
PIR ≤ 1.3 |
1(Ref) |
1.12 (0.51, 2.46) |
1.48 (0.64, 3.42) |
1.07 (0.39, 2.93) |
0.58 (0.15, 2.30) |
0.754 |
|||
1.3 < PIR ≤ 3.5 |
1(Ref) |
0.91 (0.46, 1.79) |
0.59 (0.27, 1.29) |
0.26 (0.10, 0.70) |
0.09 (0.02, 0.37) |
0.001 |
|||
PIR > 3.5 |
1(Ref) |
1.05 (0.37, 2.99) |
0.58 (0.17, 1.99) |
0.44 (0.12, 1.62) |
0.10 (0.01, 0.69) |
0.016 |
|||
Education n(%) |
0.56 |
||||||||
High school or less |
1(Ref) |
0.79 (0.34, 1.81) |
0.71 (0.27, 1.85) |
0.33 (0.10, 1.07) |
0.35 (0.09, 1.46) |
0.066 |
|||
Some college |
1(Ref) |
1.24 (0.67, 2.28) |
0.88 (0.43, 1.80) |
0.68 (0.30, 1.56) |
0.09 (0.02, 0.38) |
0.003 |
|||
College graduate |
1(Ref) |
0.48 (0.13, 1.76) |
0.52 (0.14, 1.88) |
0.19 (0.04, 0.88) |
0.09 (0.01, 0.66) |
0.011 |
|||
or higher |
|||||||||
BMI level (kg/m2) |
0.74 |
||||||||
≤ 25 |
1(Ref) |
0.88 (0.36, 2.14) |
0.29 (0.09, 0.96) |
0.30 (0.08, 1.06) |
0.21 (0.04, 1.05) |
0.013 |
|||
>25 |
1(Ref) |
1.08 (0.64, 1.83) |
1.13 (0.63, 2.02) |
0.61 (0.31, 1.22) |
0.17 (0.06, 0.48) |
0.003 |
|||
Diabetes n(%) |
0.96 |
||||||||
No |
1(Ref) |
1.15 (0.64, 2.05) |
0.97 (0.51, 1.85) |
0.56 (0.26, 1.22) |
0.21 (0.07, 0.63) |
0.006 |
|||
Yes |
1(Ref) |
0.81 (0.39, 1.65) |
0.62 (0.26, 1.46) |
0.45 (0.18, 1.18) |
0.12 (0.03, 0.52) |
0.006 |
|||
Hypertension n(%) |
0.71 |
||||||||
No |
1(Ref) |
1.29 (0.67, 2.48) |
0.76 (0.35, 1.67) |
0.61 (0.26, 1.46) |
0.14 (0.03, 0.58) |
0.008 |
|||
Yes |
1(Ref) |
0.81 (0.43, 1.54) |
0.85 (0.43, 1.69) |
0.41 (0.18, 0.94) |
0.18 (0.06, 0.54) |
0.003 |
|||
Stroke n(%) |
0.67 |
||||||||
No |
1(Ref) |
0.98 (0.61, 1.59) |
0.67 (0.38, 1.18) |
0.49 (0.26, 0.92) |
0.15 (0.06, 0.37) |
0.001 |
|||
Yes |
1(Ref) |
1.37 (0.37, 5.08) |
2.34 (0.61, 8.93) |
0.62 (0.09, 4.31) |
0.31 (0.02, 4.52) |
0.576 |
|||
Smoking status n(%) |
0.79 |
||||||||
Never smoker |
1(Ref) |
0.77 (0.41, 1.44) |
0.36 (0.16, 0.81) |
0.20 (0.08, 0.53) |
0.06 (0.01, 0.24) |
0.001 |
|||
Former smoker |
1(Ref) |
1.24 (0.56, 2.72) |
1.52 (0.67, 3.45) |
1.08 (0.43, 2.75) |
0.28 (0.07, 1.22) |
0.293 |
|||
Current smoker |
1(Ref) |
1.57 (0.39, 6.41) |
1.29 (0.30, 5.58) |
0.79 (0.14, 4.49) |
0.46 (0.06, 3.51) |
0.371 |
|||
Drinking status n(%) |
0.51 |
||||||||
Never drinker |
1(Ref) |
0.90 (0.43, 1.86) |
0.91 (0.38, 2.21) |
0.27 (0.06, 1.11) |
0.52 (0.11, 2.49) |
0.182 |
|||
Former drinker |
1(Ref) |
1.10 (0.58, 2.08) |
0.77 (0.38, 1.55) |
0.57 (0.27, 1.21) |
0.13 (0.04, 0.40) |
0.001 |
|||
Current drinker |
1(Ref) |
1.06 (0.22, 5.18) |
0.88 (0.14, 5.61) |
0.21 (0.01, 4.01) |
0 (0, Inf) |
0.100 |
|||
Notes: adjusted for age, gender, race, income, education, BMI, smoking status, drinking status, diabetes, hypertension and stroke. |
In this cross-sectional study, we revealed a linear relationship between HGS and the incidence of HF. The relationship was characterized as follows: a low HGS level was found to be associated with an elevated incidence of HF, independent of age, gender, race, income level, education level, BMI level, smoking status, drinking status, diabetes, hypertension and stroke. No interactive role was found in the association between HGS and HF, suggesting that the above conclusions remained stable in the different subgroups.
Given the sensitivity to physiological changes, HGS was used as a valid marker of muscle function25. Although HGS has been found to be associated with mortality and the incidence of some cardiovascular diseases, the relationship between HGS and HF remains unclear 26. Some studies have supported that HGS has no effect on the incidence of HF13,14. However, similar to our conclusion, a cohort study from England suggested that participants with a higher HGS had a lower incidence of HF. This conclusion was more obvious among participants aged > 65 years than among those aged ≤ 65 years in the subgroup analysis. However, the interaction between age and HGS for HF was not statistically significant 27. The results of other studies based on the UK Biobank and Swedish National Inpatient Registry also revealed that objective measurements of HGS are strongly and independently associated with a lower HF incidence28,29. These conflicting conclusions may be attributed to the heterogeneity among these studies, including differences in participant selection, study size, study designs, and controlled confounders. Based on previous literature, our study excluded groups with depression and cognitive impairment, fully considered confounding factors, and strictly limited the age of the included population, making the conclusion reliable and filling in the gaps of current research from different perspectives.
Currently, no conclusive statement can be made about how muscle function decline could lead to the incidence of HF. From the existing literature, we speculate that the possible mechanism is as follows.
First, inflammation and oxidative stress might be underlying mechanism for both muscle function decline and HF30. Inflammatory cytokines could alter blood vessel dynamics, which might result in alterations to muscle metabolism and muscle loss. For example, Wnt signaling pathway molecules were found play a critical role in tissue-specific stem cell aging and an increase in tissue fibrosis with age, involved in both calcification and loss of muscle mass, have been proposed as potential mediators31,32, In addition, the oxidative stress theory of aging suggested that age-associated functional losses was closely related to the accumulation of reactive oxygen and species (ROS)-induced damages26. The oxidative stress is involved in several age-related conditions including muscle function decline and HF33,34 The mechanism may be related to mitochondrial dysfunction leading to limited oxygen availability and subsequent reliance on anaerobic metabolism35,36.
Second, apoptosis may be another important underlying mechanism. Several apoptotic pathways have been linked with age-related muscle function37 and higher frequency of myonuclear apoptosis has also been found in the muscle of patients with HF relative to age-matched healthy controls38.
Thirdly, abnormal glucose metabolism may be a common risk factor for HF and muscle function decline. Considering that skeletal muscle is the main site for insulin-mediated glucose disposal and that insulin resistance is strongly associated with HF, it could be hypothesized that insulin resistance plays a main role in both HF and muscle function decline39. In addition, it has been reported that muscular strength was demonstrated to be associated with a reduced risk of long-term development of diabetes mellitus 40, which is known to be a major risk factor for the development of HF41,42.
Finally, muscle acted as a paracrine and exocrine organ, and the myokines may act in autocrine, paracrine, and endocrine manner. The release of myokines from skeletal muscle preserves or augments cardiovascular function, in the meanwhile, increased muscle strength may provide capabilities for more active lifestyles that are related to a lower HF risk29.
To the best of our knowledge, our study is the first to provide evidence that HGS is associated with the incidence of HF among American middle-aged and elderly adults. The data collection in the NHANES is carried out following standardized protocols, and the NHANES is designed to provide nationally representative estimates. Therefore, the current findings have ideal generalizability. It is helpful for clinicians to identify groups at high risk for HF.
There are also some limitations in our study. First, self-reported confounders might be susceptible to self-report bias. Second, limited by the cross-sectional study design, this study had less power regarding the determination of causal relationships between HGS and HF. Third, since the study was conducted in a population of middle-aged and elderly individuals, the findings are only generalizable to relatively healthy adults. Fourth, while we controlled for a broad range of lifestyle and health-related factors, correcting for possible confounders remains challenging. As we used questionnaires to estimate health status, residual confounding cannot be excluded.
Overall, this cross-sectional study indicated that higher HGS was closely associated with lower HF incidence, and this conclusion remained stable in participants aged ≥ 45 years, with different genderes, races, income levels, education levels, BMI levels, smoking status, drinking status, diabetes status, hypertension status and stroke status. Such a conclusion would warrant further prospective studies with intervention trials.
Data availability
Data described in the manuscript, code book, and analytic code are available from the corresponding author on request.
Author contributions
RML contributed the central idea, and HG, WLG, LLR, XMW and HWQ analyzed most of the data. RML wrote the initial draft of the paper, GHD guided the theory and design of the research and revised the article. All authors contributed to refining the ideas, carrying out additional analyses, and finalizing this paper. The author(s) read and approved the final manuscript.
Funding
National Key Research and Development Program of China (No. 2019YFC1710400; No. 2019YFC1710401)
The National Natural Science Foundation of China (No. 811774047; No. 82174172)
Competing interests
RML, GHD, HG, WLG, LLR, XMW and HWQ declare that they have no conflict of interest.