High adiposity linked to presarcopenia and decreased cardiorespiratory fitness in sedentary young Indians independent of their high-fat–salt–sugar (HFSS) food frequency

Abstract

Presarcopenia potentially affects about 10% of youth globally, which may seem an underestimate given the growing incidence of sedentarism and overnutrition. Age-independent muscle loss (“presarcopenia”) has recently been linked to increased cardiovascular disease (CVD) risk in middle-aged adults. The early onset of cardiovascular complications, as observed in young Indians, prompts an analysis of non-traditional risk factors like presarcopenia. Therefore, we examine the correlation between musculoskeletal health (MH), cardiorespiratory fitness (CRF), and High-saturated Fat–Salt–Sugar (HFSS) consumption in 147 healthy young Indians (mean age: 21 ± 2.16 years, 57% females). Data on body composition, CRF, physical activity, and HFSS food frequency were collected, with one-third of participants exhibiting presarcopenia and high Percent Body Fat (PBF). These traits were more common in females and individuals with normal-weight obesity (p < 0.001). No significant differences were observed in MH and CRF status across different HFSS frequency groups. CRF strongly correlated with obesity parameters—PBF (r = − 0.81) and visceral fat level (r = − 0.75)—while MH strongly associated with fat-free mass (r = 0.97) at p < 0.001. Since fat mass and fat-free mass are inversely linked, indirect associations between MH and CRF are inferred, suggesting obesity as a common risk factor for presarcopenia and cardiometabolic decline.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-19152-x.

Introduction

Aging has been implicated as the primary catalyst for muscle decline, with elevated muscle loss most common in older adults¹. Despite its strong association with old age, involuntary muscle loss (or presarcopenia) begins in early adulthood, with a decline of 3–5% every decade of adult life^2,3. Common risk factors include poor nutrition, a sedentary lifestyle, inadequate sleep, and Vitamin D deficiency⁴. Although the etiology is not fully understood, persistent sedentary behavior is assumed to promote structural and functional changes in skeletal muscle output by altering the signaling pathways involved in muscle synthesis⁵. Chronic inactivity, poor nutrition, and a High-saturated Fat–Salt–Sugar (HFSS) diet can lead to muscle breakdown driven by heightened inflammation and disrupted energy metabolism⁶.

The relative impact of sedentarism versus natural aging on muscle loss remains unclear; however, chronic physical inactivity is considered the leading cause of age-independent muscle loss (or secondary sarcopenia), eventually leading to locomotive syndrome and a threefold increase in elderly mortality rates^7–10. Sarcopenia often coexists with a high body fat percentage, leading to “Sarcopenic obesity” or obesity with low lean muscle mass (OLLMM)^11,12. Studies on South Asians have highlighted a higher prevalence of normal-weight obesity or lean obesity compared with other ethnic groups^13–15.

In addition, sarcopenia and cardiovascular disease (CVD) appear to have an important multifactorial correlation¹⁶. Although subcutaneous adipose tissue is the primary site of excess fat storage, higher tissue adiposity often induces ectopic fat accumulation around non-adipose tissues, such as skeletal muscles, the liver, and other vital organs¹⁷. These fatty deposits may infiltrate muscle fibers (inter- and intramuscular “myosteatosis”) and reduce their metabolic efficiency via their inflammatory secretome (inducing low-grade systemic inflammation). Muscle cells are also hypothesized to have endocrine properties with a potential regulatory effect on cardiovascular health. Individuals with a high rate of myosteatosis have been reported to suffer from reduced cardiopulmonary efficiency regardless of their skeletal mass^18–20.

Muscle loss thus triggers cardiorespiratory inefficiency (as observed in middle-aged adults), leading to a significantly higher CVD risk and CVD-related mortality compared to their non-sarcopenic counterparts^21–23.

The latest diagnostic criteria (2019) for presarcopenia, as defined by the Asian Working Group on Sarcopenia (AWGOS), is the loss of muscle mass estimated by a height-adjusted skeletal muscle mass (SMM) or “skeletal muscle index” (SMI) of less than 7.0 kg/m² and 5.7 kg/m² (bioelectrical impedance analysis), for adult males and females, respectively, without loss of muscle function or performance²⁴.

In community health assessments, since body weight or body mass index (BMI) is the popular barometer of fitness, early signs of declining physical health, such as presarcopenia, lean obesity, and poor cardiorespiratory efficiency, remain undiagnosed^25–27. As young adults appear less at-risk, they are often excluded from comprehensive screening efforts, which further impedes early intervention.

Within the discussion of cardiometabolic fitness, it is worth underscoring that India is the CVD capital of the world, accounting for one-fifth of all CVD-related deaths globally²⁸. Additionally, Indians have a stronger predisposition to premature CVD, which is only partly explained by traditional risk factors²⁹. The incidence of the first acute Myocardial Infarction (MI) is seen a decade earlier in Indians than in their Western counterparts³⁰. And one in five coronary artery disease (CAD) patients in India is under the age of 40^31,32.

Based on the above findings, scientific speculation that premature muscle loss could be associated with early-onset cardiovascular complications seems probable. Our cross-sectional study aims to explore the relationship between CRF and MH status in young Indian participants, while accounting for participants’ HFSS food frequency.

Methodology

Study design and protocol

A single-center fitness assessment camp was organized on September 30th, 2024, at the Department of Life Science, Food Science, and Nutrition, Gujarat University, India, as part of the National Nutrition Month program.

For the study purpose, data on body composition (bioelectrical impedance)³³, cardiorespiratory fitness (CRF) (YMCA three-minute step-up test)³⁴, Physical activity (International Physical Activity Questionnaire (IPAQ))³⁵, and High-Fat–Salt–Sugar (HFSS) foods (using a designed food frequency questionnaire) were extracted from the Health Camp (2024) database for healthy young volunteers (18–30-year-olds) who reported 6–8 hours of sitting (daily) and engaging in less than 150 min of moderate to vigorous physical activity per week^{36 (For sample demographics and standard/reference values for test parameters, refer to Tables 1 and 2)}.

All data were collected with written informed consent from participants and in line with the Declaration of Helsinki³⁷. The study is ethically approved by the Institutional Ethics Committee, Gujarat University, India (Ref: GU-IEC (NIV)/03/PhD/036) .

Fitness assessment tests

Despite the high accuracy of DEXA scans (the gold standard in musculoskeletal screening), constraints such as time, expense, and device expertise made the bioelectrical impedance system (InBody 270™) a preferred choice³⁴. Likewise, the three-minute step test (previously validated in the Asian population)³⁸ was preferred due to its reliability and ease of execution.

• (i)Using the multi-frequency bioelectrical impedance device (InBody 270™), body composition characteristics were assessed following standard protocols (participant should be in light clothing, no metal/jewelry worn, no fluid intake immediately before the assessment, etc.).
• (ii)Cardiorespiratory fitness was assessed using the Young Men’s Christian Association (YMCA) three-minute step-up test.³⁴ The brief protocol is as follows: Subjects were seated in a temperature and humidity-controlled area with a five-minute resting phase, after which their resting heart rate (RHR) was recorded using a pulse oximeter. The test involved stepping up and down a 30 cm staircase at a frequency of 24 steps per minute; the subject stopped upon completion (as informed by the invigilator) or upon exhaustion, whichever came first. At the end of the test, the subject was seated back, and a second pulse rate or the post-activity heart recovery rate (HRR_post) was recorded for one minute. Peak oxygen consumption during strenuous activity (VO₂ maximum or VO₂ max) was indirectly calculated using the YMCA regression equation (Refer to Table 3)³⁹.
• (iii)To evaluate the frequency of consumption of high-fat–salt–sugar (HFSS) foods, a frequency questionnaire was developed, tailored to the food habits of the target population. The FFQ consisted of a total of nineteen popular HFSS foods (refer to the supplementary material), and data were collected through a pen-and-paper mode, with the interviewer briefing the participant on the purpose of the FFQ before they registered their responses.

Table 3.

Regression equation for predicting VO₂max using the YMCA step test (Chung et al.³⁹).

Test	Regression equation
YMCA step test	= 60.400 + 9.138 (sex; male 1, female 0) − 0.229 (age) + 0.045 (height) − 0.182 (weight) − 0.120 (HRRpost)

YMCA Young Men’s Christian Association, HRR_post post-activity heart recovery rate.

Participant sub-grouping criteria (SO vs. Non-SO)

Based on the AWGOS 2019 SMI thresholds, we first categorized participants as having either “Low” or “Normal” skeletal muscle index. Since muscle function tests were not performed, this study cannot comment on the participants’ muscle function/ performance. Likewise, no participant is diagnosed with Sarcopenia.

Next, we use the PBF classification criteria (refer to Table 2) to categorize individuals as having either “High” or “Normal” body fat percentage.

Table 2.

Healthy reference values for BMI, WHR, SMI, PBF, HRR_post, VO₂ max, and surrogate obesity/ muscle loss indices (for Indian/ South Asian population aged 18–39 years)^{41,42,47–49,77}.

Test parameters	Males	Females
Body mass index (BMI) (kg/m2) ICMR 2010 cutoffs	< 18.5 (Underweight) 18.5–22.9 (Normal)  > 22.9 (Overweight/ Obese)	< 18.5 (Underweight) 18.5–22.9 (Normal)  > 22.9 (Overweight/ Obese)
Waist to hip ratio (WHR)	< 0.90	< 0.80
Skeletal muscle index (SMI) (kg/m2)	> 7.0 kg/m2	> 5.7 kg/m2
Percent body fat (PBF) (%)	10–20%	14–28%
Visceral fat level (VFL)	1–9	1–9
Fat mass to fat-free mass ratio	< 0.24	< 0.40
Fat-mass index (FMI) (kg/m2)	> 6.6	> 6.6
Fat-free mass index (FFMI) (kg/m2)	< 18	< 15
Resting heart rate (RHR) (beats/min)	60–90	60–90
Post-activity heart recovery rate (HRRpost) (beats/min)	< 100	< 100
Peak O2 consumption (VO2 max.) (ml/kg/min)	< 33.0 (Very poor) 33.0–36.4 (Poor) 36.5–42.4 (Fair) 42.5–46.4 (Good) 46.5–52.4 (Excellent)  > 52 (Superior)	< 23.6 (very poor) 23.6–28.9 (poor) 29.0–32.9 (Fair) 33.0–36.9 (Good) 37.0–41.0 (Excellent)  > 41.0 (Superior)

Finally, the two sub-groups used for statistical analyses (SO vs. Non-SO) are based on a combination of SMI & PBF classification criteria. Individuals with Low SMI plus High PBF are grouped under “characteristics of Sarcopenic Obesity” or “SO”. Those with either normal SMI or normal PBF are grouped under “Non-Sarcopenic Obese” or “Non-SO”.

Statistical analysis

Statistical Analysis and graphical presentations were performed using GraphPad Prism version 8.4.2, DATAtab (an online statistical tool), and MS Excel 2013 package add-ins (Analysis ToolPak and Xrealstatsx) .

Table 4.

Mean, SD, minimum, and maximum values for body composition components and Step-test parameters.

	VO2 max	RHR (beats/min)	HRRpost (beats/min)	Protein	Mineral	Weight	Body fat mass	BMI	PBF (%)	SMM	Waist-hip ratio	Fat free mass	SMI (kg/m2)	Visceral fat level
Mean	42.05	116.42	96.57	8	2.83	58.28	17.6	21.73	27.4	22.15	0.87	40.68	6.16	7.83
Std. Deviation	4.88	16.26	14.18	1.93	0.62	13.38	8.07	4.06	12.34	5.8	0.06	9.54	1.09	4.41
Minimum	31.54	81	65	5.2	1.92	35.4	4.2	13	− 0.81	13.5	0.75	26.6	4.1	1
Maximum	53.45	160	130	13.8	5.04	113.9	49.1	35.7	51.4	39.4	1.07	69.7	9.1	21

The normality of the data variables was assessed using the Shapiro–Wilk test and Q–Q (quantile–quantile) plots (see Table 5). Frequencies, percentages, means, and standard deviations (or standard errors) were calculated for the Participants’ Demographic Characteristics (Table 4). All variables were divided into two groups: dependent variables (e.g., HRR_post, VO₂ max, SMI, PBF) and independent variables (e.g., age, sex, all other body composition attributes, HFSS frequency) (see Table 1 for the complete list). Independent t-tests (with Welch correction for variables with unequal variance) or Mann–Whitney U tests were performed for all continuous variables, such as body composition variables, VO₂ max, resting heart rate (RHR), and post-activity heart recovery rate (HRR_post). Spearman correlation analyses were performed for VO₂ max, SMI, PBF, and other key variables.

Table 5.

Tests for normality distribution of SMI, PBF, and VO₂ max.

Normality Distribution tests	SMI		PBF		VO2 max
	Statistics	p	Statistics	p	Statistics	p
Kolmogorov–Smirnov	0.09	0.14	0.09	0.153	0.09	0.142
Kolmogorov–Smirnov (Lilliefors Corr.)	0.09	0.003	0.09	0.004	0.09	0.003
Shapiro–Wilk	0.97	0.004	0.97	0.002	0.98	0.009
Anderson–Darling	1.21	0.004	1.41	0.001	1.32	0.002

Table 1.

Sample Demographics, Body Composition Analysis, Cardiorespiratory Fitness attributes, and reported HFSS frequency for SO versus Non-SO.

Variables	SO	Non-SO	p-value*
A. Gender
Male (n)	9	53
% males	14.5%	85.5%
Female (n)	46	39
% females	54.1%	45.9%	p < 0.0001
B. Age (mean) (years)	21.52	21.93
C. Three minute- Step-up test (mean values ± SD)
Before activity (RHR) (beats per minute)	94.78 ± 12.76	97.64 ± 14.79
Post activity (HRRpost) (beats per minute)	114.75 ± 16.02	117.42 ± 16.23
VO2 max. (ml/kg/min)	40.97 ± 3.95	49.47 ± 5.63	p = 0.015
D. Body composition analysis (mean values ± SD)
Height (cm)	156.87 ± 6.78	167.44 ± 9.36	p < 0.001
Weight (kg)	51.06 ± 5.67	62.59 ± 14.68	p < 0.001
(i) Normal weight obesity (n)	45	27	p < 0.0001
(ii) Overweight and obese (n)	4	25
(iii) Underweight and obese (n)	6	0
Total body water (TBW) (kg)	24.61 ± 3.27	32.97 ± 6.73
Body protein (kg)	6.57 ± 0.89	8.86 ± 1.86	p < 0.001
Mineral (kg)	2.42 ± 0.30	3.08 ± 0.62	p < 0.001
Body fat mass (kg)	17.47 ± 4.25	17.68 ± 9.62
Body mass index (BMI) (kg/m2)	20.79 ± 2.34	22.29 ± 4.69
Skeletal muscle mass (SMM) (kg)	17.82 ± 2.71	24.74 ± 5.58	p < 0.001
Percent body fat (PBF) (%)	34.05 ± 6.23	27.01 ± 10.67	p < 0.001
(i) Higher PBF (n)	55	52
(ii) Normal PBF (n)	0	37
(iii) Under PBF (n)	0	3
Waist-hip ratio (WHR)	0.85 ± 0.03	0.88 ± 0.06	p < 0.001
Visceral fat level	8.13 ± 2.93	7.65 ± 5.06
Fat-free mass (kg)	33.59 ± 4.44	44.91 ± 9.2	p < 0.001
Fat mass to fat-free mass ratio	0.53 ± 0.15	0.40 ± 0.22	p < 0.0001
Skeletal muscle index (SMI) (kg/m2)	5.32 ± 0.58	6.67 ± 1.0	p < 0.001
Fat mass index (FMI) (kg/m2)	7.18 ± 2.04	6.42 ± 3.61
Fat-free mass index (FFMI) (kg/m2)	13.61 ± 1.12	15.88 ± 2.07
E. High-fat–salt–sugar (HFSS) food frequency
(i) High (Daily/Bi-weekly) (n)	35	59
(ii) Moderate (Weekly) (n)	15	28
(iii) Low (Monthly/Bi-monthly/Rarely) (n)	5	5

*Statistically significant p-value (p < 0.05) using Chi-square analysis/Fischer’s exact t-test/Mann Whitney U test (non-parametric) for categorical variables and independent t-test/ Kruskal Wallis test (non-parametric) for continuous variables are presented.

Based on SMI & PBF cutoffs for healthy persons (Table 2), participants were grouped into “characteristics of Sarcopenic Obesity (SO)” and “Not-Sarcopenic Obese (Non-SO)”. To find if there exists a significant difference in various sets of categorical variables (e.g., gender, HFSS frequency) among the two groups, a two-tailed Chi-square analysis/Fisher’s exact t-test was performed. A Kruskal–Wallis test was performed to determine whether a substantial variance exists in VO₂ max, PBF, and SMI among individuals with high versus low HFSS consumption frequency. Statistically significant covariates in the inter-group comparisons were further used to develop a regression model for VO₂ max. and SMI. Analysis of covariance (ANCOVA) was performed to investigate the effect of covariates, such as sex, on VO₂ max. Unless specified, Pearson’s Correlation values for p < 0.05 (CI = 95%) were considered significant. Using DATAtab, regression equations were derived for dependent variables: cardiometabolic fitness (VO₂ max.) and musculoskeletal status (SMI), using independent variables like ‘gender/ sex’, ‘body weight’, and ‘PBF’, in the sample cohort (n = 147)*.

*There are no studies on presarcopenia prevalence in the Indian youth; hence, based on recent Korean data for youth presarcopenia⁴⁰, the prevalence was assumed at 5.4%. Using DATAtab, the minimum sample size was calculated as n = 114, with a study power of 80% and a 6% margin of error at a 0.05 significance level.

Study limits

This cross-sectional study suffers from the following critical limitations:

The study does not categorize participants based on the number of sedentary hours they spend. Since most participants were college students, we did not observe substantial differences in their physical activity levels that would allow for appropriate segmentation. The HFSS FFQ is a basic version and is strongly susceptible to participant recall bias. The FFQ’s effectiveness is also limited because it cannot measure caloric intake, macronutrients, or identify dietary deficiencies. Therefore, any lack of correlation between HFSS frequency, CRF, and musculoskeletal health must be re-examined before drawing definitive conclusions. Since muscle function tests were not performed, the prevalence of sarcopenia in this cohort remains unknown. Sex as a biological variable remains deeply intertwined with the reported cardiometabolic outcomes. While females were observed to have a relatively high risk for SO, the small sample size and absence of serological analyses (for micronutrient deficiencies, etc.) indicate the need for larger datasets to confirm these findings.

Results

Participant demographics

Based on our inclusion and exclusion criteria, we extracted data for 147 healthy young college-going participants (57% females). For more details, refer to Table 1 and Fig. 1.

Body composition assessment

Using multifrequency bioelectrical impedance, values for height, weight, body mass index (BMI), total body water (TBW), total body protein and minerals, fat mass, percent body fat (PBF), fat-free mass (FFM), waist-hip ratio (WHR), visceral fat level (VFL), skeletal muscle mass (SMM), and skeletal muscle index (SMI) were estimated. Mean values (± standard deviation) for these are detailed in Table 1.

The mean height, weight, and BMI characteristics for the cohort were 163.5 ± 10 cm, 58.3 ± 13.4 kg, and 21.7 ± 4.1 kg/m². According to the Indian Council of Medical Research (ICMR) 2010 BMI criteria^41,42, almost half the participants (n = 72, 49%) had an optimal BMI score. A higher BMI was observed in only 29.9% of them, while 21.1% were classified as underweight. However, when body fat percentage was analyzed (based on global cutoffs⁴³ for young adults), 75% of those within a healthy BMI range had excess body fat.

Even if the PBF is assessed at a higher cutoff (i.e., an upper limit of 33% instead of 28%, as some studies suggest for Asian women)^44,45, 67.1% (n = 57) of females and 61.9% (n = 91) of the total participants qualify as overweight or obese. An independent t-test analysis revealed a significantly higher PBF in females compared to males (85.9% vs. 54.8%, p < 0.0001). This may help explain the higher prevalence of sarcopenic obesity (SO) characteristics among female participants in our cohort (see Table 1).

To further evaluate adiposity levels, we analyzed their waist-hip ratio (WHR)⁴⁶. Disturbingly, 70.7% of participants exhibited unhealthy WHR scores, indicating central obesity. The majority had low total body water (66.7%), low body protein (63.9%), and low mineral content (57.1%), with a higher body fat mass (51.7%). Analyzing surrogate indices indicative of muscle loss, such as the fat-mass index (FMI)⁴⁷, Fat-free mass index (FFMI)⁴⁸, and fat-mass to fat-free mass ratio⁴⁹ revealed a higher fat-mass-to-fat-free mass ratio in 75.5% of individuals. Their FMI and FFMI measures further confirmed the higher prevalence of body fat (48.3%) and lower muscle mass (84.4%).

Regarding visceral fat, one in four participants (n = 40, 27.2%) had a concerning (> 10) visceral fat level (based on the InBODY 270™ system threshold), given its association with metabolic disorders⁵⁰. Guidelines recommend a healthy VFL at a 10–12% limit of total body fat⁵¹. High visceral fat tissue is a major cardiometabolic risk factor and has been consistently linked with the development of type 2 diabetes and cardiovascular mortality, regardless of PBF and subcutaneous fat^52,53. We found traditional parameters (like body weight and BMI) less reliable; instead, we categorized participants based on their PBF, as “normal-weight obese (NO)”, “overweight and obese (OO)”, and “low-weight obese (LO)” to assess relative differences in muscle loss.

Low skeletal muscle mass (SMM) was observed in 67.4% of the participants, and based on AWGOS (2019) criteria, loss of muscle mass or presarcopenia was diagnosed in 41.5% (n = 61) of participants. A chi-square analysis revealed a significantly higher incidence of presarcopenia in females (67% females vs. 43.5% males, p < 0.0001) and participants with normal-weight obesity (74% NO vs. 13.6% OO, Fisher’s test, p < 0.0001). Spearman correlation showed a negative and weak (but statistically significant) relationship between PBF (%) and SMI (r (145) = − 0.27, p = 0.001). SMI was strongly correlated with lean body mass attributes, such as total body water (r = 0.97, p < 0.001), fat-free mass index (r = 0.94, p < 0.001), and body weight (r = 0.82, p < 0.001). Since muscle performance was not assessed, “characteristics of sarcopenic obesity (SO)” were defined as having both presarcopenia and high PBF, as they are more likely to develop sarcopenia and face a higher risk of metabolic disease. Based on this, approximately one in three participants (n = 55, 37.4%) exhibited SO. Those without SO traits were categorized as “Not-Sarcopenic obese” (Non-SO), and the two groups were compared regarding CRF levels and HFSS frequency in the following analysis.

Cardiorespiratory fitness (CRF) assessment

Using the YMCA three-minute step-up protocol, we collected participants’ data for resting heart rate (RHR) (before activity) and post-activity Heart Recovery Rate (HRR_post). Peak oxygen consumption (VO₂ max.) (ml/kg/min) during the activity was indirectly estimated using the YMCA regression equation by Chung et al.³⁹ (Refer to Eq. (1)). All participants completed the test (Fig. 1).

Fig. 1.

(a): Mean ± SEM for test variables for male versus female participants. (b): Mean ± SEM for test variables for Not-sarcopenic obese (Non-SO) versus sarcopenic obese (SO) participants.

Based on Indian cutoff values for peak O₂ consumption⁵⁴, most of the participants (n = 137, 93.2%) qualified with a “Good” to “Excellent” CRF status. However, their post-activity heart recovery rate (HRR_post) was rated as either “very poor” or “poor to fair” (n = 115, 78.2%) based on the YMCA age-adjusted cutoff³⁴.

When correlated (using Spearman correlation) with various body composition variables, VO₂ max showed the strongest (negative) correlation with PBF (r = − 0.81, p < 0.001) and the fat mass to fat-free mass ratio (r = − 0.81, p < 0.001), followed by fat mass index (r = − 0.78, p < 0.001) and visceral fat level (r = − 0.75, p < 0.001). In contrast, VO2 max showed a weak correlation with other components such as SMI (r = 0.29, p < 0.001), body protein (r = 0.38, p < 0.001), and WHR (r = − 0.21, p = 0.009).

Furthermore, the Mann–Whitney U test analysis revealed significantly higher VO₂ max values and, consequently, greater CRF in the Non-SO group compared to the SO participants (p = 0.015) (Fig. 2a–d).

Fig. 2.

(a): Peak oxygen consumption in Normal (“Non-SO”) versus Possible sarcopenic obese (“SO”) participants. (b): Percent Body Fat in Normal (“Non-SO”) versus Possible sarcopenic obese (“SO”) participants. (c): Box-plot depicting sex-specific differences in SMI and VO₂ max. (d): Skeletal Muscle Mass (SMM) in Normal (“Non-SO”) versus Possible sarcopenic obese (“SO”) participants.

A multiple linear regression analysis was performed to examine the influence of the variables PBF (%) and gender on the variable VO₂ max. (To avoid collinearity-based errors, different measures of adiposity were not simultaneously used.) Using multiple linear regression (Adjusted R² = 0.73, standard error of estimate = 2.55), VO₂ max is expressed as:

The regression model showed that PBF (%) and gender explained 73.09% of the variance in VO₂ max. An ANOVA test demonstrated that the effect was significantly different from zero, and this model provided a better fit compared to a model without any predictors (F = 195.55, p < 0.001, R² = 0.73). Similarly, regression analysis for SMI showed that ‘weight’ and ‘PBF’ could explain 94.07% of the variance in SMI (F = 1143.15, p < 0.001, Adjusted R² = 0.94).

These assessments do not establish a direct relationship but reveal a clear bidirectional interaction between muscle mass, cardiorespiratory efficiency, and tissue adiposity. From Fig. 3a–g, we observe that SMM/SMI and VO₂ max do not show strong positive associations, but both exclusively and verifiably correlate with adiposity parameters. Recent studies with larger cohorts have successfully validated the link between elevated fat mass and the fat-mass-to-fat-free mass ratio, as well as higher prognosis for CVD, NAFLD, and overall mortality. Notably, the fat-mass-to-fat-free mass ratio performs better than other adiposity indicators, such as BMI^55–57.

Fig. 3.

The red Regression line represents (a)  Weak positive trends for VO₂ max and SMM for the sample cohort (n = 147, r = 0.38, p < 0.001). (b): Strong negative trends for VO₂ max and FMI (adiposity) for the sample cohort (n = 147, r = − 0.78, p < 0.001). (c): Moderately negative association for VO₂ max and PBF (adiposity) for the sample cohort (n = 147, r = − 0.4, p < 0.001). (d): Strong positive trends for SMM and FFMI (lean mass) for the sample cohort (n = 147, r = 0.88, p < 0.001). (e): Strong negative trends for VO₂ max. and fat to fat-free mass ratio for the sample cohort (n = 147, r = − 0.81, p < 0.001). (f): Moderate negative trends for SMM and fat-to-fat-free mass ratio for the sample cohort (n = 147, r = − 0.4, p < 0.001). (g): Weak positive trends for VO₂ max. & SMI for the sample cohort (n = 147, r = 0.29, p < 0.001).

HFSS food frequency assessment

Based on participants’ responses to the FFQ, we classified them into “Low-frequency consumers” (frequency ranging from bimonthly to rarely), “Moderate frequency consumers” (weekly), and “High-frequency consumers” (daily or biweekly). HFSS food frequency (non-normal distribution) was correlated with CRF status using the Spearman rank correlation, which showed its negligible effect on HRR_post (r (145) = − 0.03, p = 0.754) and participants’ VO₂ max levels (r (145) = 0.08, p = 0.308) for different HFSS frequencies. Likewise, the correlation effects were negligible between PBF (%) and reported HFSS frequency (r (145) = − 0.02, p = 0.777). Therefore, no impact was observed on the CRF status of participants based on their reported frequency of consumption of HFSS foods.

Variance in body composition variables (statistically significant in SO vs. Non-SO comparisons), such as TBW, PBF, body weight, minerals, WHR, HRRpost, SMI, VO₂ max, and others across different HFSS consumption categories, was analyzed using the Kruskal–Wallis Test. A p-value of 0.639 and 0.608 (Kruskal–Wallis Test), respectively, for SMM and SMI (related to HFSS frequency), indicate no statistically significant differences in HFSS consumption (low vs. high) patterns concerning muscle mass.

Sex as a biological variable

In our analysis, females comprise a significant portion of the “low SMI”/“SO” group. To validate a sex-independent correlation between adiposity, muscle mass, and VO₂ max., we perform a gender-stratified Spearman correlation analysis. As shown in Table 6, the correlations were statistically significant within each group (Males and Females) at p < 0.05.

Table 6.

Sex-stratified Results for SMI, PBF, and VO₂ max.

Descriptive Statistics	Gender	SMI interpret	N	Mean	Std. Deviation	Correlation coefficient R (VO2 max., PBF)	Correlation coefficient, R (VO2 max., SMI)
VO2 max	F	LOW	57	39.86	2.15	− 0.59	− 0.55
		NORMAL	28	36.95	2.90
	M	LOW	27	48.91	2.17	− 0.67	− 0.65
		NORMAL	35	44.38	3.47

All R-values are statistically significant (p < 0.05).

Next, to understand the effect of SMI on cardiorespiratory fitness and adjust for covariates such as biological sex, we perform an analysis of covariance (ANCOVA). Refer to Table 7.

Table 7.

Analysis of Covariance (ANCOVA) for dependent variable ‘VO₂ max’.

Co-variates	Sum of squares	df	Mean square	F	P
Gender (M/F)	564.05	1	564.05	398.67	< 0.001
SMI interpret	133.07	1	133.07	94.05	< 0.001
GENDER (M/F)*SMI interpret	1.12	1	1.12	0.79	0.376
After (HRRpost) (beats/min)	519.84	1	519.84	367.43	< 0.001
PBF (%)	221.71	1	221.71	156.70	< 0.001
Error	199.49	141	1.41

For the factor SEX, the analysis returned an F-statistic, F(1, 141.00) = 398.67, p < 0.001. Because the p-value is lower than 0.05, we conclude that the male and female groups differ significantly in their impact on VO₂ max. Similarly, for the factor SMI, the analysis yielded F(1, 141.00) = 94.05, p < 0.001, indicating that the different SMI groups differ significantly in their impact on VO₂ max.

SEX*SMI interpretation: The interaction of SEX and SMI returned F(1, 141.00) = 0.79 with p = 0.376. Because the p-value is greater than 0.05, the interaction between these two covariates does not have a significant impact on VO₂ max.

For the covariate HRR_post (beats/min), the analysis yielded F(1, 141.00) = 367.43, p < 0.001. Hence, we conclude that the variable HRR_post has a significant effect on the dependent variable.

Similarly, for PBF, the F-statistic of 156.70 with p < 0.001 indicates that PBF has a significant effect on the dependent variable VO₂ max.

In summary, sex as a covariate remains intimately linked to the observed health outcomes in our study. Credible links appear between SMI, adiposity, and VO₂ max in both Male and female subgroups. Larger datasets are certainly needed to outline the impact of low SMI on CRF explicitly.

Discussion

This study highlights several negative changes in young participants leading sedentary lifestyles. Specifically, muscle loss (presarcopenia) and subsequent declines in cardiorespiratory efficiency despite having “good” VO₂ max values are observed in individuals with higher obesity levels. Our findings contrast with global estimates on youth presarcopenia prevalence (41.5% vs. 10% as per global estimates)²⁷. Despite “good” CRF status (n = 137, 93.2%), poor post-activity heart rate, possible sarcopenia, and central obesity were majorly reported. A statistically significant difference in peak O₂ levels between Non-SO and SO groups (Non-SO > SO, p = 0.015) and the co-occurrence of muscle loss with higher adiposity confirms a bidirectional relationship between body composition and CRF or aerobic fitness.

Our study found no correlation between HFSS food frequency and CRF/MH status; however, a growing body of research has causally linked HFSS food habits to high PBF, low-grade inflammation, and obesity risk⁵⁸. “High-consumers” of HFSS foods would, therefore, typically show a higher body fat percentage compared to “low-consumers” with similar activity levels. The lack of a statistically significant association could be due to the frequency questionnaire’s inefficiency in accurately estimating consumption levels. Participants may underestimate their intake of HFSS foods, and issues such as recall bias are common with methods like diet recall and FFQ^59–61. Perhaps a combination of monitoring techniques, like image capture of meals consumed, and other objective analytical tools, such as serum metabolite profiling, could have provided increased precision⁶².

Although muscle strength or performance was not assessed to meet the diagnostic criteria for sarcopenic obesity, the higher prevalence of its clinical markers (e.g., Low SMI, low body protein, and high PBF) suggests an impending sarcopenia prognosis. Furthermore, females have shown a higher SO risk, consistent with studies by Yang et al.⁶³ and Hwang et al.⁶⁴ but contradicts Altaf et al. (2024), who found that males have a higher risk for sarcopenia and sarcopenic obesity^61,62. This contradiction appears to be related to the sample size of the study. In larger samples, males are often more likely to be diagnosed with sarcopenia, as seen in geriatric populations⁶⁵. We don’t know if this applies to young adults. In our analysis of covariance, “sex” had a significant interference on the observed health outcomes, thereby limiting our ability to understand the true interaction between SMI and CRF. We also found higher SO prevalence in participants with normal-weight obesity. A statistically higher (p < 0.0001) fat-mass to fat-free mass ratio was observed in participants (60.87% Non-SO vs. 100% SO) with possible sarcopenia. A “high-fat mass with high/normal BMI” was subsequently identified as the most common at-risk phenotype in young adults. Although causality cannot be established using the present dataset, it is possible that the “high-fat mass with high/normal BMI” phenotype may be responsible for poor skeletal muscle metabolism and decline in CRF via mechanisms such as ectopic fat accumulation and myosteatosis.

Our current findings reveal a key limitation: it does not categorize SO changes in participants based on their level of sedentarism. The number of sedentary hours per day has a significant impact on fitness metrics, such as the rate of muscle mass change^66,67. The study also fails to account for hidden nutrient deficiencies that may speed up muscle decline (Refer to “Study Limits”). Vitamin D deficiency is surprisingly common in tropical countries, such as India^68,69. Vitamin D, protein, and calcium deficiencies have been associated with low muscle mass⁷⁰. Ironically, only a few studies have demonstrated positive Vitamin D supplementation effects on muscle mass and muscle remodeling⁷¹, with no clear consensus on its role in treating sarcopenia in the elderly^72,73.

It is also crucial to recognize that the cutoff points for BMI, PBF, and other indices used in this study are flexible and evolve as our understanding of the overall NCD burden improves. Additionally, inconsistencies exist in the scientific literature regarding the most appropriate cutoff values for Indians concerning SMI, FMI, FFMI, and other measures, which can significantly influence data interpretation.

Despite these shortcomings, our observations importantly highlight potential sarcopenia risks among youth and their effects on cardiorespiratory fitness. Additionally, the unusual susceptibility to possible sarcopenia in normal-weight obese participants calls for further investigation. The high exposure to major risk factors like HFSS foods during childhood and adolescence increases the growing NCD burden in India. The recently updated CVD guidelines in India renew the focus on premature CVD cases, especially in urban areas, by redefining dyslipidemia and establishing new cutoffs for “non-fasting” lipid measurements to improve screening and early detection^74,75.

Muscle loss and its cardiometabolic effects in young people are rarely addressed in major public health discussions. Heart health has become an increasing concern among Indian youth due to the early onset of issues and alarming reports of sudden, tragic cardiac deaths in young, seemingly healthy individuals^29,30,76. While the higher CVD risk among Indians is linked to their unique biological and social factors, early life exposures to risks like obesity and muscle decline could significantly hinder optimal CRF.

The limited sample size and exploratory nature of this study restrict our ability to comment on the causal relationship between presarcopenia and decline in CRF. However, the co-occurrence of low muscle mass and high adiposity among poor CRF individuals prompts further analysis. Our study presents a strong case for careful examination in larger epidemiological studies to establish the true prevalence of youth presarcopenia. Longitudinal studies and intervention trials could show whether diet and physical activity therapies can improve muscle mass and, consequently, CRF.

Conclusion

Presarcopenia is more prevalent in young adults than previously assumed. Our study infers an indirect association between skeletal muscle mass and cardiorespiratory fitness (both linked through adiposity). Body composition measures, such as total percent body fat and fat-free mass, predict cardiometabolic health and the decline of muscle mass. A high PBF is strongly linked to reduced cardiorespiratory efficiency. Since it has an indirect connection with skeletal muscle mass or overall lean fat-free mass, obesity may increase the risk of presarcopenia in sedentary young Indians. Our study also demonstrates that obesity assessment and the YMCA three-minute step test can be practical tools for predicting muscle loss and cardiometabolic efficiency during health camps. However, large-scale studies are needed to confirm these findings, especially the causal link between SMM loss and CRF decline.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

D.S.—Primary data collection and curation, preliminary Data Analysis and Paper writing (Original draft), R.S.—Conceptualization, Study design, Formal Analysis, and Review, R.R.—Data Interpretation, Critical review, and Approval of the submitted draft.

Funding

The study received no external funding.

Data availability

Data is provided as “related file” with the manuscript.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

All data were collected with written informed consent from participants for research and publication. The study adheres to the Declaration of Helsinki and has been ethically approved by the Institutional Ethics Committee of Gujarat University, India (Ref: GU-IEC (NIV)/03/PhD/036).