Coffee consumption and skeletal muscle mass: A Cross-Sectional Study in NHANES 2011-2018

doi:10.21203/rs.3.rs-5239636/v1

Background

The impact of diet on people's health is indisputable. While animal and cell experiments may suggest a link between coffee intake and increased skeletal muscle mass, translating these findings to humans requires careful investigation. The aim of this research is to evaluate the correlation between adult American skeletal muscle mass and caffeine consumption.

Methods

This study was conducted among persons 20 years of age and above between 2011 and 2018, using information from the National Health and Nutrition Examination Survey (NHANES). We investigated the connection between skeletal muscle mass and caffeine intake using three multiple linear regression models. Afterwards, To look into variations in the correlation between caffeine consumption and skeletal muscle mass across several demographic attributes, such as gender, age, race, and body mass index (BMI) categories, subgroup analyses were conducted.

Result

A total of 8,125 participants met the inclusion criteria. All three multiple linear regression models indicated a positive correlation between caffeine intake and skeletal muscle mass. Age-stratified analysis showed significant positive correlations for participants aged 30 to 39 and 40 to 49 years old. BMI-stratified analysis revealed a significant positive correlation between caffeine intake and muscle mass among normal and overweight individuals

Conclusions

Our study results indicate a positive correlation between caffeine intake and muscle mass. Individuals aged 30–49 years and those with a normal or overweight BMI may potentially benefit more. Future cohort studies are necessary to confirm these conclusions and to explore the underlying mechanisms.

Caffeine

Skeletal muscle mass

NHANES

Cross-sectional study

Diet

Sarcopenia, a condition marked by the falling in both skeletal muscle function and mass, has become a growing health concern on a worldwide scale (1–3). Estimates indicate that in the upcoming four decades, the worldwide incidence of sarcopenia is anticipated to surge from 50 million people to exceeding 200 million (4). The increasing incidence of sarcopenia poses significant challenges to both societal health and economic burdens. Reduced skeletal muscle mass not only decreases an individual's motor function but also increases the risk of several adverse outcome diseases such as diabetes mellitus, cardiovascular disease, osteoporosis, and many other diseases, ultimately leading to a decreased quality of life (5, 6). Moreover, the impact of sarcopenia is gradually extending beyond the elderly population to younger age groups, possibly attributed to a lack of physical activity, poor dietary habits, and the increasing prevalence of chronic disorders in daily life (7–9).

The correlation between nutrition and well-being has garnered significant interest in recent years. Of all the beverages, coffee stands out for its widespread consumption across all age groups. According to National Coffee Association data, over 64% of American adults drink coffee daily, averaging 3.1 cups per day (10). This equates to a staggering consumption of approximately 517 million cups each day. Caffeine, the primary active substances in coffee, has attracted the attention of researchers for its impact on human health (11–13). It is a widely used central nervous system stimulant and is associated with various physiological and metabolic processes, including energy metabolism, muscle contraction, and cognitive function (14–16). Additionally, studies demonstrate that coffee may lessen the risk of obesity (17, 18). Skeletal muscle not only assists with the body's movements and functions but also releases compounds that can slow down the aging process (19). Nonetheless, the effects of caffeine on skeletal muscle are now receiving comparatively little research. In an experiment on animals, it was discovered that senior mice given coffee had more grip strength and muscular mass than the control group (20). Despite these findings, the impact of caffeine consumption on muscle mass in the general population has not been thoroughly studied. Thus, more research on the connection between caffeine use and muscle mass is required. This study utilized the National Health and Nutrition Examination Survey (NHANES) database to analyze a large sample in an attempt to reveal the potential relationship between coffee consumption and muscle mass and to explore possible physiological and metabolic mechanisms.

Data sources

NHANES data are sourced from a national cross-sectional study using a careful multi-stage probability sampling methodology to ensure accuracy. This study collected data for 4 cycles (2011–2018). The study received approval from the Ethics Review Committee of the National Center for Health Statistic (NCHS), and prior to data collection, all participants provided informed consent.

This study initially retrieved data from 39,156 participants sourced from NHANES (2011–2018). Firstly, we excluded 16,539 participants under the age of 20. Secondly, 11,791 participants were excluded due to the lack of appendicular skeletal muscle mass index. Lastly, 2,701 participants lacking data on caffeine intake on two occasions were excluded, resulting in the inclusion of 8,125 participants (Fig. 1).

Independent variable

Caffeine intake was collected through two 24-hour dietary recall interviews. The Food and Nutrient Database for Dietary Studies, provided by the United States Department of Agriculture (USDA), was utilized to process and calculate nutrient intake, including both macronutrients and micronutrients (21–23). The initial dietary recall interview occurred onsite at a Mobile Examination Center (MEC), followed by a second interview conducted over the phone within a window of 3 to 10 days afterwards. At the initial interview, the interviewer provided participants with a range of measurement tools such as cups, bowls, measuring cups and spoons, rulers, and circles to help them report food intake. Participants were then given a food modeling booklet to record food intake (24).

Outcome variable

Skeletal muscle mass as an outcome indicator was assessed using the skeletal muscle index (SMI). SMI was calculated by dividing Appendicular skeletal muscle mass (ASM) by body mass index (BMI), calculated as weight (kg) divided by height (m) squared (25). Dual-energy X-ray absorptiometry (DXA) was utilized to assess participants' body composition. ASM (kg)was defined as limb lean soft tissue mass, excluding total bone mineral content.

Covariates

Additionally, to account for the effects of confounding factors, the following covariates were included. Demographic variables comprised age, sex, race/ethnicity, educational status, and household poverty income ratio (PIR). Laboratory testing data included glycated haemoglobin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), blood urea nitrogen, total calcium, phosphorus, uric acid, cholesterol, total protein, triglycerides, albumin, and creatinine/urine. Physical activity was defined as whether moderate recreational activities, such as brisk walking, cycling, or swimming, causing a slight increase in respiration and heart rate, lasting at least 10 minutes, were undertaken.

Statistical methods

Statistical analyses were performed using R version 2.0 (http://www.r-project.org) and EmpowerStats (http://www.empowerstats.com), with significance set at P < 0.05. Considering the complex sampling design of NHANES, the sample weights were weighted in this study according to the NCHS analysis guidelines. Categorical variables were expressed as percentages, and differences between groups were evaluated using weighted chi-square tests. Continuous variables were presented as mean ± standard deviation, and between-group differences were assessed using weighted Student's t-test. The correlation between caffeine intake and SMI was analyzed by three weighted multiple linear regression models. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race/ethnicity. Model 3 was adjusted for all covariates included in this study. Additionally, subgroup analyses based on age, sex, race, and BMI categories were conducted to further explore the relationship between caffeine intake and sarcopenia index.

Basic characteristics

The 8125 participants had a mean age of 39.85 ± 11.49 years, with 47.94% of them being men and 53.06% being women. Caffeine intake was categorized into four quartiles, with detailed characteristics shown in Table 1. Among the four groups, statistically significant differences (P < 0.05) were observed in age, sex, race, education level, PIR, AST, ALP, BUN, cholesterol, total protein, triglycerides, VD, and SMI. The data show that participants with higher caffeine intake tended to be older, male, Non-Hispanic White, college graduates or above, and have higher PIR, BUN, cholesterol, triglycerides, VD, and SMI. Conversely, they exhibited lower levels of AST, ALP, and total protein.

Table 1

Basic Characteristics of Participants' Caffeine Intake Across Four Subgroups
Characteristic	Quartile 1 N = 2028	Quartile 2 N = 2021	Quartile 3 N = 2035	Quartile 4 N = 2041	P- Value
Age (years)	36.83 ± 11.98	38.32 ± 11.59	39.71 ± 11.53	43.83 ± 10.67	< 0.0001
Gender (%)					< 0.0001
Male	44.84	45.39	46.96	55.45
Female	55.16	54.61	53.04	44.55
Race (%)					< 0.0001
Mexican american	12.49	14.61	9.63	4.88
other hispanic	7.91	10.17	7.83	4.07
Non-Hispanic White	48.81	50.93	65.35	81.41
Non-Hispanic Black	19.58	13.61	7.95	3.20
Other Race	11.21	10.68	9.24	6.44
Education level (%)					< 0.0001
Less than 9th grade	4.69	4.34	3.03	1.79
9-11th grade	9.50	8.95	7.40	8.38
High school graduate/GED or equivalent	20.46	21.91	23.00	20.05
Some college or AA degree	32.92	31.88	31.93	34.22
College graduate or above	32.43	32.92	34.64	35.55
Moderate activities (%)					0.0573
Yes	49.06	46.43	50.02	50.42
No	50.94	53.57	49.98	49.58
PIR	2.73 ± 1.69	2.84 ± 1.69	3.09 ± 1.64	3.30 ± 1.60	< 0.0001
Hemoglobin A1c (%)	5.50 ± 0.89	5.53 ± 0.95	5.48 ± 0.84	5.55 ± 0.88	0.0654
ALT	25.45 ± 20.69	26.64 ± 20.58	25.69 ± 19.77	25.36 ± 17.56	0.1772
AST	24.68 ± 19.26	26.37 ± 19.01	24.86 ± 20.20	24.30 ± 12.69	0.0019
ALP	68.31 ± 22.73	68.09 ± 24.48	66.60 ± 25.13	66.16 ± 21.25	0.0063
BUN	12.68 ± 4.36	12.45 ± 4.17	13.01 ± 4.16	12.99 ± 4.21	< 0.0001
Total calcium	9.37 ± 0.34	9.37 ± 0.40	9.37 ± 0.34	9.39 ± 0.33	0.3818
Total protein	3.70 ± 0.59	3.71 ± 0.56	3.72 ± 0.54	3.73 ± 0.54	0.4526
Uric acid	5.21 ± 1.34	5.29 ± 1.39	5.32 ± 1.34	5.33 ± 1.32	0.0508
Cholestero	186.00 ± 38.92	193.05 ± 39.57	192.33 ± 38.75	199.10 ± 42.54	< 0.0001
Total protein	7.19 ± 0.43	7.19 ± 0.45	7.12 ± 0.44	7.05 ± 0.41	< 0.0001
TG	140.99 ± 168.13	148.73 ± 125.34	147.03 ± 116.80	160.46 ± 167.93	0.0002
VD	62.05 ± 25.32	64.43 ± 26.54	68.40 ± 27.66	71.80 ± 24.32	< 0.0001
Albumin/creatinine ration	18.13 ± 102.17	25.47 ± 185.46	18.63 ± 130.99	19.85 ± 171.68	0.4614
BMI	28.85 ± 6.99	29.01 ± 7.03	28.74 ± 6.65	28.72 ± 6.07	0.5070
SMI	0.80 ± 0.20	0.79 ± 0.20	0.80 ± 0.19	0.83 ± 0.19	< 0.0001

PIR, ratio of family income to poverty; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; BUN, blood urea nitrogen; TG, triglycerides;VD,Vitamin D; BMI, body mass index; SMI, skeletal muscle mass index.

Correlation analysis

All three models demonstrated a positive correlation between daily caffeine intake and SMI (Table 2). After adjusting for all variables included in this study, it was found that for every 1 g/d increase in caffeine intake, SMI increased by 0.02 units (95% CI: 0.01, 0.03, P = 0.0023). Figure 2 displays the results of the smoothed curve fitting, describing the non-linear positive correlation between daily caffeine intake and SMI. Further, daily caffeine intake was categorized into four groups and subjected to weighted multiple linear regression analysis. It was found that Quartile 1 and Quartile 4 were positively correlated in all three weighted multiple linear regression models.

Table 2

Correlation between caffeine intake (g) and SMI
Exposure	Model 1 β (95% CI), P-Value	Model 2 β (95% CI), P-Value	Model 3 β (95% CI), P-Value
Caffeine intake (continuous)	0.08 (0.06, 0.10) < 0.0001	0.02 (0.01, 0.03) 0.0023	0.03 (0.01, 0.04) < 0.0001
Caffeine intake (quartile)
Quartile 1	Reference	Reference	Reference
Quartile 2	-0.01 (-0.02, 0.01) 0.2316	0.00 (-0.01, 0.01) 0.9917	0.00 (-0.00, 0.01) 0.5639
Quartile 3	-0.00 (-0.01, 0.01) 0.9958	0.00 (-0.00, 0.01) 0.3681	0.00 (-0.01, 0.01) 0.7169
Quartile 4	0.03 (0.02, 0.04) < 0.0001	0.01 (0.01, 0.02) < 0.0001	0.01 (0.01, 0.02) 0.0005
P for trend	< 0.0001	< 0.0001	0.0001

Model 1: no covariates were adjusted.

Model 2: age, gender, and race were adjusted.

Model 3: age, sex, race, education, PIR, hemoglobin A1c, albumin/creatinine ration ALT, AST, ALP, BUN, total calcium, cholesterol, total phosphorus, total protein, uric acid, TG, Vitamin D, BMI, and Moderate activities were adjusted.

SMI, skeletal muscle mass index; PIR, ratio of family income to poverty; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; BUN, blood urea nitrogen; TG, triglycerides; BMI, body mass index.

Subgroup analysis

Table 3 shows the correlation between caffeine intake and SMI after stratification by age, sex, race, and BMI. Interaction tests found significant impacts of BMI and age on the correlation between SMI and caffeine intake. In the BMI-stratified analysis, caffeine intake was positively correlated with SMI in normal and overweight groups (P < 0.05). In the age-stratified study, the subgroups of 30–39 years old and 40–49 years old showed a significant positive connection between caffeine intake and SMI. In the subgroup aged 20–29 years, a negative correlation between daily caffeine intake and SMI was observed, but it did not reach statistical significance. Figure 3 depicts the results of smoothed curve fitting, illustrating the non-linear positive correlation between daily caffeine intake and SMI among different age and BMI subgroups.

Table 3

Subgroup analyses of caffeine intake (d/g) and SMI
	β (95% CI), P for trend	P for interaction
Stratified by gender		0.0572
Male	0.02 (0.00, 0.03) 0.0182
Female	0.04 (0.02, 0.06) < 0.0001
Stratified by age		0.0057
20–30 years old	-0.02 (-0.06, 0.01) 0.2192
30–39 years old	0.06 (0.03, 0.08) 0.0002
40–49 years old	0.03 (0.01, 0.05) 0.0018
50–59 years old	0.01 (-0.01, 0.03) 0.2108
Stratified by race		0.6656
Mexican american	0.03 (-0.03, 0.10) 0.3029
Other Hispanic	0.04 (-0.02, 0.11) 0.1877
Non-Hispanic white	0.02 (0.01, 0.04) 0.0013
Non-Hispanic black	-0.01 (-0.08, 0.06) 0.8393
Other race	0.04 (0.01, 0.07) 0.0045
Stratified by BMI		0.0019
Normal	0.05 (0.03, 0.07) < 0.0001
Overweight	0.04 (0.02, 0.05) < 0.0001
Obese	-0.00 (-0.02, 0.02) 0.8961

In subgroup analyses stratified by gender, age, and race, The model adjusted for covariates such as age, sex, race, education, PIR, hemoglobin A1c, ALT, AST, ALP, BUN, total calcium, cholesterol, total phosphorus, total protein, TG,uric acid, Vitamin D, Albumin/creatinine ration, BMI, and moderate activities, but the model did not adjust for the stratification variables themselves.

PIR, ratio of family income to poverty; ALT, alanine transaminase; AST, aspartate transaminase; ALP, alkaline phosphatase; BUN, blood urea nitrogen; TG, triglycerides, BMI, body mass index.

Coffee, renowned as the world's most consumed beverage, harbors numerous chemical compounds known for their anti-inflammatory and antioxidant properties, among which caffeine stands out as a principal constituent. Fortunately, the majority of studies have found that coffee consumption has numerous health advantages and can reduce the risk of various chronic diseases, such as Parkinson's disease, type 2 diabetes, coronary artery disease, arrhythmias, stroke, and many cancers (26–28). However, there is currently limited research assessing the impact of caffeine intake on muscle mass in the general population. Guo and colleagues (20) investigated the impact of caffeinated coffee consumption on muscle mass, strength, regeneration ability, and the inflammatory environment within muscle tissue using an elderly mouse model. They found that the mice who drank caffeinated coffee had larger muscle weights and greater strength than the mice who drank water. Similarly, our study showed a significant positive correlation between caffeine intake and skeletal muscle mass. Several other studies, including a cross-sectional study involving 2,578 individuals from Korea, which found a positive relationship between coffee consumption and skeletal muscle mass, partially corroborated our findings (29). Another cross-sectional study comprising data from the Korean National Health and Nutrition Examination Survey, which included 6,905 participants, found that small amounts of coffee consumption prevented sarcopenia in men (30). Our study did not find such a gender difference, but it is noteworthy that women seem to benefit more. we found that obesity appears to mask the benefits of coffee consumption on skeletal muscle mass. For obese individuals, the body often accompanies chronic inflammation, which impairs muscle cell metabolism, affecting muscle regeneration and repair capabilities, leading to a decrease in muscle mass and strength (31, 32). Secondly, obesity is closely associated with insulin resistance, affecting muscle cells' uptake and utilization of glucose, leading to insufficient energy supply to muscle cells, affecting normal muscle function and metabolism (33). Additionally, obesity increases the mechanical load on muscles and bones, increasing muscle workload and accelerating muscle aging (34).

The pathological and physiological mechanisms by which coffee affects skeletal muscle mass remain unclear. Potential mechanisms may include the following characteristics, as discovered through animal and in vitro cell models. Firstly, a study has revealed that coffee can trigger autophagy in various human tissues, including skeletal muscle (35). For healthy mitochondrial turnover and renewal, the removal of inflammatory agents, and the preservation of energy metabolism, autophagy is essential (36–38). Skeletal muscle exhibits one of the highest baseline rates of autophagy among human tissues and responds to various cellular stresses that can activate it (39). According to an experimental findings, rats who are autophagy-deficient experience severe muscular atrophy and weakness (40).

Autophagy, a complex biological process, is orchestrated by a diverse array of autophagy proteins (ATGs) and trophic sensors such as AMP-activated protein kinase (AMPK) and mammalian target of rapamycin complex 1 (mTORC1). Upon activation, AMPK initiates various cellular processes, including autophagy, to enhance ATP levels, often in response to elevated AMP levels. AMPK triggers autophagy by phosphorylating and inhibiting mTORC1. In their study, Mathew and colleagues (41) demonstrated that caffeine enhances autophagy by facilitating calcium-dependent activation of AMPK. By boosting autophagic flow and encouraging CaMKKβ/AMPK-dependent decreases in protein synthesis, Hughes et al. (42) demonstrated that brief caffeine treatment dramatically decreased skeletal muscle tube diameter.

In addition, coffee has been suggested to enhance skeletal muscle insulin sensitivity and glucose uptake, although this conclusion is controversial. Skeletal muscle, as one of the primary target organs for insulin action, undergoes reduced synthesis and increased breakdown when insulin resistance occurs, leading to muscle mass loss in the end (43, 44). In the liver and skeletal muscle of KK-A(y) mice, Kobayashi et al. (45) showed that coffee consumption significantly boosted insulin-induced threonine phosphorylation of Akt, indicating that coffee consumption may help improve insulin resistance. In a study by Loopstra-Masters et al. (46) caffeine-containing coffee was found to be positively correlated with insulin sensitivity, while decaffeinated coffee was positively associated with β-cell function markers. Through animal studies, Jia et al. (47) discovered that coffee consumption reduced insulin resistance by raising the tyrosine phosphorylation of insulin receptor submater-1 (IRS-1), the p85/IRS-1 complex, and pAkt/PKB (protein kinase B). Additionally, their study indicated that coffee intake could alleviate inflammation in skeletal muscle by downregulating pro-inflammatory genes such as activating transcription factor 3, FBJ osteosarcoma oncogene, heat shock protein1A, heat shock protein1B, synaptosomal-associated proteinγ, and inflammation-related insulin signaling genes stearoyl-CoA desaturase 1 and protein phosphatase1. On the other hand, an investigation by Lee and colleagues (48) discovered a favorable correlation between insulin resistance and caffeine and its metabolites. Consequently, more investigation is required to define the underlying mechanisms and the link between caffeine and insulin sensitivity.

This study has the following limitations. Firstly, because the study was cross-sectional, there was no way to determine a causal association between caffeine consumption and muscle mass loss. Secondly, because this is an observational study, residual confounding may still occur despite careful evaluation of covariates encompassing a wide variety of potential confounding variables and their incorporation in the regression models. Thirdly, coffee contains various bioactive compounds and trace elements, different active ingredients, as well as trace elements, may all have an impact on muscle mass. Fourthly, due to data limitations, individuals over 60 years old were not included in this study, while a decrease in skeletal muscle mass is more prevalent in the elderly population. Lastly, according to Ye et al. (49) and Sartorelli et al. (50) the method and timing of coffee consumption can have different effects on health. Due to the lack of data, this study did not analyze the impact of coffee consumption method and timing on muscle mass.

However, our study also has certain advantages. Our study utilized NHANES data, which is nationally representative, and had a large sample size, enhancing the generalizability and external validity of our findings. Furthermore, we have increased the reliability of our results by adjusting for confounding variables. Best of all, this study provide new perspective for people with sarcopenia or fitness enthusiasts, as it reveals that coffee consumption has a beneficial effect on muscle mass. Due to the lack of data on individuals aged 60 and above in this study, we suggest that future research on this topic should consider factors related to the elderly population.

This study revealed a positive correlation between caffeine intake and muscle mass. Individuals aged 30–49 years and those with a normal or overweight BMI may potentially benefit more. Nevertheless, further cohort studies are still needed to validate our conclusions.

Funding

No fund

Conflict of interest

Li Zhang , Dongdong Cao , Xuemei Mao , Jinhong Su, Huan Lang , Zifan Xiao , Xiaolin Liao , Shuying Wang , Aiqiong Deng declare that they have no conflict of interest.

Ethics statement

The National Centre for Health Statistics gave its approval for the population this study was conducted on.

Acknowledgements

We would like to extend our gratitude to all participants of NHANES for their generous contribution to this study.

Data availability

This study analyzed publicly available datasets that can be accessed at https://www.cdc.gov/nchs/nhanes/index.htm. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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No competing interests reported.

Coffee consumption and skeletal muscle mass: A Cross-Sectional Study in NHANES 2011-2018

Status:

Version 1

Abstract

Background

Methods

Result

Conclusions

Figures

Introduction

Methods

Data sources

Independent variable

Outcome variable

Covariates

Statistical methods

Results

Basic characteristics

Correlation analysis

Subgroup analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1