The Association of Visceral and Subcutaneous Fat Areas with Phenotypic Age in Non-elderly Adults, Mediated by HOMA-IR and HDL-C

doi:10.21203/rs.3.rs-5239051/v1

Backgroud: Aging results in diminished adaptability, as well as declines in physiological and psychological functions and resilience. An epigenetic clock known as "PhenoAge" captures the concept of "pre-clinical aging." When phenotypic age surpasses chronological age, this condition is termed phenotypic age acceleration (PhenoAgeAccel). Subcutaneous adipose tissue, visceral adipose tissue, HOMA-IR, and HDL-C have all been shown to correlate with aging; however, the connections between these factors and PhenoAge are still insufficiently investigated.

Methods: Data for this study were sourced from the National Health and Nutrition Examination Survey (2015-2018), including a total of 2580 participants. Complex survey designs were taken into consideration. Logistic regression was used to assess the association between body fat area and PhenoAgeAccel, while subgroup analysis helped identify variations in population characteristics. A dose-response relationship between body fat area and PhenoAgeAccel was observed using restricted cubic spline (RCS) analysis. Mediation and interaction analyses were further employed to investigate the roles of HOMA-IR and HDL-C in this association. Clinical predictive metrics such as Area Under the Curve (AUC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) were utilized to determine the diagnostic value.

Results: In non-elderly adults, the association between body fat area and PhenoAgeAccel differs from chronological age. For subcutaneous fat area (SFA), this relationship was non-linear in individuals aged 18-44 years and 45-59 years, with thresholds of 2.969 m² and 3.394 m², respectively. In contrast, visceral fat area (VFA) exhibited a non-linear relationship with PhenoAgeAccel in individuals aged 18-44 years, but a linear relationship in those aged 45-59 years, with thresholds of 0.769 m²and 1.220 m², respectively. Mediation effect analysis revealed that HOMA-IR had a more pronounced effect in individuals aged 18-44 years, with a mediation proportion of 13.4% in the relationship between VFA and PhenoAgeAccel, and 6.9% in the relationship between SFA and PhenoAgeAccel. Conversely, HDL-C shows greater significance in individuals aged 45-59 years, with a mediation proportion of 21.7% in the relationship between VFA and PhenoAgeAccel, and 11.6% in the relationship between SFA and PhenoAgeAccel. HOMA-IR ≥2.73 or VFA >0.925 m², as well as HOMA-IR ≥2.73 or SFA >3.137 m² accelerate PhenoAge; whereas HDL-C levels >1.60 and ≤3.90 mmol/L combined with SFA ≤3.137 m² or VFA ≤0.925 m² decelerate PhenoAge.

Conclusion: Our main findings are as follows: 1) both subcutaneous and visceral fat area significantly influence PhenoAge. 2) HOMA-IR and HDL-C serve as partial mediators in the relationship between body fat area and PhenoAge. 3) higher levels of body fat area or insulin resistance accelerate aging, whereas lower levels of body fat area combined with higher levels of HDL-C decelerate aging.

subcutaneous fat area

visceral fat area

phenotypic age

aging

Aging is a complex and interconnected network of progressive functional deviations in harmful phenotypes, based on biological aging, from the phenotype of young adults. This results in a decline in resilience, physiological and psychological function,and adaptability[1]. The important pathological processes associated with aging are replicative cellular senescence, low-grade systemicinflammation, immunosenescence, mitochondrial dysfunction, insulin resistance, changes in body composition and hormone[2–4]. These processes ultimately impact blood pressure, blood lipids, as well as heart and brain function, thereby elevating the potential for age-related diseases and mortality. While chronological aging progresses at a fixed rate and cannot be modified, biological aging is variable and is shaped by factors like genetics, environmental influences, and lifestyle choices[5]. Studies have found that genomic instability is a hallmark of aging, and methylation patterns at specific CpG sites in DNA are strongly linked to chronological age[6]. Levine et al. created an epigenetic clock called "DNAm PhenoAge," which relies on DNA methylation (DNAm) aging biomarkers[7]. This clock replaces chronological age with phenotypic age, reflecting "pre-clinical aging" and potentially offering a better distinction of aging in children, young adults, or exceptionally healthy individuals. When phenotypic age surpasses chronological age, it is termed phenotypic age acceleration (PhenoAgeAccel), which serves as an indicator of accelerated aging[7, 8].

Mammals possess two main types of adipose tissue: white adipose tissue (WAT) and brown adipose tissue (BAT). WAT primarily serves as an energy reservoir by storing free fatty acids and is further divided into visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) based on its distribution throughout the body[9]. Although they share the same origin, VAT and SAT differ in anatomy and physiological functions. Compared to SAT, VAT has a higher cell density, more blood vessels, greater nerve supply, and an increased presence of inflammatory and immune cells. Additionally, VAT has a lower capacity for preadipocyte differentiation and a higher proportion of large adipocytes[10, 11]. Additionally, VAT is more metabolically active in lipid metabolism, more prone to insulin resistance, and can serve as a predictor of lifespan and mortality[12, 13]. In contrast, SAT acts as the main reservoir for long-term fat storage. When its capacity is exceeded, surplus lipids are stored in visceral depots[10]. The distribution and accumulation of adipose tissue are also correlated with age-related alterations in blood lipid profiles[14]. Specifically, the subcutaneous and visceral fat area, and the ratio of VFA to SFA are negative correlations with HDL-C levels[15, 16].

The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) is a technique used to assess the interaction between glucose and insulin levels in the fasting state, serving as a common method for estimating insulin resistance[17]. Multiple studies have indicated that elevated HOMA-IR or insulin resistance may be linked to aging or malnutrition[18]. However, the cutoff value for diagnosing insulin resistance using HOMA-IR varies across different ethnic populations[19]. A study based on National Health and Nutrition Examination Survey (NHANES) (1999–2002) found that for Americans, a HOMA-IR threshold of 2.73 could be used as the cutoff value for diagnosing insulin resistance[20].

Numerous studies have illustrated the association between body fat distribution, HOMA-IR, and HDL-C with aging, along with their connection to disease morbidity and mortality rates. However, there were few investigations exploring the relationship between these factors and PhenoAge. This study employed the National Health and Nutrition Examination Survey (NHANES) database to explore the association between subcutaneous fat area, visceral fat area, and PhenoAge in individuals aged 18–59 years. Additionally, it explored the mediating effects of HOMA-IR and HDL-C on the correlation between body fat area and PhenoAge, as well as their interactive effects with body fat area on PhenoAge.

Data source

The data for this study were obtained from the NHANES database, which is designed to evaluate the health and nutritional status of both adults and children in the United States. This analysis included two cycles of NHANES, 2015–2016 and 2017–2018, with a total of 19,225 participants. As shown in Fig. 1, after excluding individuals aged under 18 years or over 60 years old as well as participants with incomplete data on PhenoAge, subcutaneous fat area, visceral fat area, insulin, glucose, and other covariates, the final sample comprised 2580 participants.

Measurement of PhenoAge

According to existing literature[8, 21], PhenoAge was calculated involving age, creatinine, albumin, glucose, alkaline phosphatase, mean cell volume, lymphocyte percentage, white blood cell count, red cell distribution width, C-reactive protein—biomarkers indicative of liver function, kidney health, metabolic status, inflammation levels, and immune response. Blood samples were obtained in accordance with standardized protocols at the Mobile Examination Center (MEC). The formula for calculating PhenoAge is displayed in Fig. 2. When PhenoAge exceeds chronological age, it is considered to be PhenoAge acceleration (PhenoAgeAccel). Conversely, when PhenoAge is less than or equal to chronological age, it is considered to be PhenoAge deceleration/stasis.

Assessment of covariates

Demographic information, including gender (male/female), age, race (Mexican American/Non-Hispanic/Other Hispanic/Other Race), income status (At or above poverty/ below poverty), education (College graduate or above/High school graduate or equivalent college/Less than 12th grade), and marital status (married/others), was collected through interviews. The category 'At or above poverty' is defined as having a Poverty Income Ratio (PIR) of 1 or greater.

Measurements such as body mass index (BMI), waist circumference, subcutaneous fat area, visceral fat area, lipid levels, insulin levels, and blood glucose were obtained at the MEC by trained health technicians. Body fat areas were assessed using Dual-energy X-ray absorptiometry (DXA), which measured visceral and subcutaneous fat at approximately the level of the L4 and L5 vertebrae. Smoking status (yes/no) was determined based on whether participants had smoked at least 100 cigarettes in life. Alcohol consumption (yes/no) was classified differently according to study cycles: for the 2015–2016 cycle, it was defined as having consumed at least 12 alcoholic drinks in the past year or over a lifetime; for the 2017–2018 cycle, it was defined as having had any kind of alcohol and drinking more than 12 times. A history of cardiovascular disease was based on reports of congestive heart failure, coronary artery disease, angina, myocardial infarction, or stroke (MCQ160B-F). Hyperlipidemia history was determined using BPQ080, BPQ090D, and BPQ100D, while hypertension history relied on responses from BPQ020 and BPQ050A. Diabetes diagnosis was derived from responses to DIQ010, DIQ050, and DIQ070. Regular exercise was defined based on PAQ605, PAQ620, PAQ650, and PAQ665. The calculation of HOMA-IR involved both blood glucose and insulin levels as illustrated in Fig. 2.

Statistical analysis

According to the NHANES survey methods and analytic guidelines, the NHANES sample was selected using a complex four-stage sampling design. Sample weights were calculated using WTSAF2YR/2 for this study. Continuous variables are reported as medians ± standard deviation, and group comparisons were performed using one-way ANOVA. Categorical variables are reported as percentages, and group comparisons were performed using the Chi-square test. Logistic regression was employed to examine the association between body fat area and PhenoAgeAccel. Model 1 was unadjusted, Model 2 was adjusted for gender, age, and race, Model 3 was based on Model 2 and adjusted for exercise, drink, smoke, marriage status, education level, income, HbA1c, hypercholesterolemia history, hypertension history, diabetes history, and cardiovascular diseases history. The Restricted Cubic Splines (RCS) method was utilized for curve fitting. Mediation analyses were conducted to investigate the relationship between HOMA-IR, HDL-C, body fat area, and PhenoAgeAccel. By analyzing the combined effects of HOMA-IR or HDL-C with body fat area on PhenoAgeAccel, further insights into their joint impact were obtained. Clinical predictive factors, including Area Under the Curve (AUC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI), were utilized to assess the diagnostic value of various models in predicting PhenoAgeAccel.

All data analysis was performed using R software (version 4.2.2), STATA software (version 18.0), and Empower Stats software (version 4.2). P < 0.05 (two-tailed) was considered statistically significant.

Baseline characteristics

Table 1 presents an analysis of a total of 2580 participants from the United States. Individuals exhibiting PhenoAgeAccel tended to be older, identify as Hispanic, and to have elevated levels of BMI, HbA1c, insulin, waist circumference, subcutaneous fat area, and visceral fat area. Furthermore, they demonstrated a higher prevalence of hypertension, cardiovascular diseases, and diabetes and were more likely to engage in smoking behavior. Additionally, these individuals had lower levels of HDL-C, education level, and income (P < 0.05).

Association between body fat area and PhenoAgeAccel

Table 2 illustrates the association between body fat area and PhenoAgeAccel. In this analysis, subcutaneous fat area (SFA) and visceral fat area (VFA) were categorized based on quartiles. VFA showed a marked positive association with PhenoAgeAccel after adjusting for variables such as age, gender, race,exercise, alcohol consumption, smoking status, marital status, education level, income, HbA1c, and histories of hypercholesterolemia, hypertension, diabetes, and cardiovascular disease. When compared to Q1 (the lowest quartile), the odds ratio for Q4 (the highest quartile) was 8.339 (95%CI: 5.559 to 12.510), with P for trend <0.001. For SFA, no statistically significant association was found at the Q2 level. However, significant associations were identified at both Q3 and Q4 levels. In Model 3, compared to Q1, the odds ratio for Q4 was 7.756 (95%CI: 5.250 to 11.458), with P for trend <0.001.

Table1 Baseline characteristics according to age and phenotypic age.

	PhenoAge Deceleration/Stasis (n=1257)	PhenoAge Acceleration（n=1323)	P value
Age,years	37.94±12.34	39.37±12.32	0.044
Gender,%			0.331
Male	627(49.9)	693(52.4)
Female	630(50.1)	630(47.6)
Race,%			0.035
Mexican American	114(9.1)	148(11.2)
Other Hispanic	87(6.9)	120(9.1)
Non-Hispanic	1004(79.9)	974(73.6)
Other Race	52(4.1)	81(6.1)
Income,%			<0.001
Below poverty	132(10.5)	241(18.2)
At or above poverty	1032(82.1)	991(74.9)
Education level,%			<0.001
College graduate or above	490(39.0)	275(20.8)
High school grade or equivalent college	611(48.6)	860(65.0)
Less than 12th grade	156(12.4)	188(14.2)
Marital status,%			0.088
Married	640(50.9)	606(45.8)
others	557(44.3)	668(50.5)
Smoke ,%	460(36.6)	614(46.4)	0.002
Drink ,%	553(44.0)	527(39.8)	0.139
Regular exercise,%	1024(81.5)	1062(80.3)	0.646
TC, mmol/L	4.84±0.98	4.86±0.98	0.829
HDL-C, mmol/L	1.49±0.42	1.31±0.38	<0.001
LDL-C, mmol/L	2.86±0.87	2.96±0.87	0.084
Insulin, μU/mL	8.85±6.21	15.40±16.38	<0.001
HbA1c,%	5.28±0.38	5.77±1.17	<0.001
BMI, kg/m²	26.39±5.24	31.71±7.54	<0.001
WC, cm	91.47±13.78	104.68±17.82	<0.001
VFA, m²	0.84±0.49	1.25±0.65	<0.001
SFA, m²	2.82±1.42	4.00±1.87	<0.001
Self-report hypertension ,%	205(16.3)	382(28.9)	<0.001
Self-report diabetes,%	19(1.5)	139(10.5)	<0.001
Self-report hypercholesterolemia,%	280(22.3)	325(24.6)	0.408
Self-report cardiovascular diseases ,%	25(2.0)	73(5.5)	0.007

Continuous variables are presented as means ± standard deviations, and categorical variables are expressed as percentages. All analyses accounted for the complex survey design.

Abbreviations: TC, total cholesterol; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HbA1c, Hemoglobin A1c; BMI, body mass index; WC, waist circumference; VFA, visceral fat area; SFA, subcutaneous fat area.

Table 2 Association of body fat area with phenoage acceleration.

		Model1 OR(95%CI),P	Model2 OR(95%CI),P	Model3 OR(95%CI),P
VFA	Q1	Ref.	Ref.	Ref.
	Q2	1.714(1.258, 2.334), 0.001	1.898(1.380,2.610), <0.001	1.990(1.436,2.757), <0.001
	Q3	3.377(2.475,4.067), <0.001	4.019(1.380,5.699), <0.001	4.053(2.864,5.736), <0.001
	Q4	6.316(4.564,8.740), <0.001	8.713(5.956,12.748), <0.001	8.339(5.559,12.510), <0.001
	P for trend	<0.001	<0.001	<0.001
SFA	Q1	Ref.	Ref.	Ref.
	Q2	1.098(0.804,1.597), 0.557	1.149(0.827,1.597), 0.407	1.308(0.930,1.840), 0.123
	Q3	2.338(1.717,3.182), <0.001	2.800(2.000,3.922), <0.001	3.020(2.133,4.276), <0.001
	Q4	5.746(4.132,7.992), <0.001	7.796(5.356,11.346), <0.001	7.756(5.250,11.458), <0.001
	P for trend	<0.001	<0.001	<0.001

Convert VFA and SFA into categorical data based on quartiles. Model1 did not include any covariable. Model2 was adjusted for age, race, gender. Model3 was based on Model 2 and adjusted for exercise, drink, smoke, marriage status, education level, income, HbA1c, hypercholesterolemia history, hypertension history, diabetes history, and cardiovascular diseases history.. All data accounted for complex survey designs.

Abbreviations: VFA, visceral fat area; SFA, subcutaneous fat area; OR, odds ratio; CI, confidence interval.

Subgroup analysis

Fig.3 displays that interaction tests on the data revealed a differential relationship between VFA and PhenoAgeAccel across subgroups defined by chronological age, gender, and diabetes status. In the chronological age subgroup (P-interaction = 0.002), this association was most pronounced in individuals aged 18-44 years. For the gender (P-interaction = 0.026) and self-report diabetes (P-interaction <0.001) subgroups, males and individuals with diabetes exhibited a stronger association between VFA and PhenoAgeAccel.

The relationship between SFA and PhenoAgeAccel varied across subgroups defined by chronological age, alcohol consumption, and diabetes status. In the chronological age subgroup (P-interaction = 0.035), this association was most pronounced in individuals aged 18-44 years. For the drink (P-interaction = 0.004) and self-report diabetes (P- interaction <0.001) subgroups, individuals who consume alcohol and those without diabetes exhibited a stronger association between SFA and PhenoAgeAccel.

RCS

Using chronological age as a subgroup, we conducted RCS tests to evaluate the dose-response relationship between body fat area and PhenoAgeAccel. Fig.4 demonstrates the results that in the population aged 18-59 years, both VFA and SFA exhibited non-linear associations with PhenoAgeAccel, with thresholds of 0.925m² and 3.137 m², respectively. When analyzed by age subgroup, the RCS revealed that for SFA and PhenoAgeAccel, non-linear relationships were observed in the 18-44 years and 45-59 years groups, with corresponding thresholds of 2.969 m² and 3.394 m². In contrast, for VFA and PhenoAgeAccel, a non-linear relationship was identified in individuals aged 18-44 years with threshold of 0.769 m², while a linear relationship was noted in those aged 45-59 years with threshold of 1.220 m².

Mediation effect analysis

As illustrated in Fig.5, HOMA-IR and HDL-C acted as partial mediators in the relationship between body fat area and PhenoAgeAccel. For HOMA-IR, as displayed in Table3, the mediating effect accounted for 10.2% of the relationship between VFA and PhenoAgeAccel in participants aged 18-59 years. When stratified by chronological age, the mediating effect of HOMA-IR was 13.4% in the 18-44 years and 6.2% in the 45-59 years. Additionally, HOMA-IR's mediating effect on the relationship between SFA and PhenoAgeAccel was measured at 4.7%. When stratified by chronological age, the mediating effect of HOMA-IR was 6.9% in the 18-44 years and 3.5% in the 45-59 years. Notably, the mediating effect of HOMA-IR is more pronounced in individuals aged 18-44 years compared to those aged 45-59 years.

Regarding HDL-C, as displayed in Table4, the mediating effect accounted for 15.3% of the relationship between VFA and PhenoAgeAccel in non-elderly adults (individuals aged 18-59 years). When stratified by chronological age, the mediating effect of HDL-C was found to be 8.4% in the 18-44 years old and 21.7% in the 45-59 years old. In addition, HDL-C's mediating effect on the relationship between SFA and PhenoAgeAccel was measured at 7.2%. Further analysis revealed that HDL-C did not exhibit a significant mediating effect in the 18-44 years old but demonstrated an effect of 11.6% in those aged 45-59 years old. Notably, the mediating effect of HDL-C was more significant in individuals aged 45-59 years than in those aged 18-44 years.

Table 3 Association of body fat area with PhenoAgeAccel mediated by HOMA-IR.

		OR(95%CI)
Body Fat Area	Effect	Total	Age≥18, <45 years	Age≥45, <60 years
VFA	ACME	0.020(0.010,0.040)	0.037(0.023,0.060)	0.009(0.002,0.036)
	ADE	0.177(0.133,0.208)	0.238(0.183,0.288)	0.142(0.086,0.191)
	Prop. Mediated	10.2%	13.4%	6.2%
SFA	ACME	0.004(0.001,0.008)	0.007(0.003,0.012)	0.003(0.001,0.010)
	ADE	0.086 (0.077,0.096)	0.091 (0.079,0.123)	0.074(0.054,0.092)
	Prop. Mediated	4.7%	6.9%	3.5%

All models were adjusted for age, race, gender, exercise, drink, smoke, marriage status, education level, income, HbA1c, hypercholesterolemia history, hypertension history, diabetes history, and cardiovascular diseases history.

Abbreviations: ACME, stands for average causal mediation effects; ADE, stands for average direct effects; OR, odds ratio; CI, confidence interval.

Table 4 Association of body fat area with PhenoAgeAccel mediated by HDL-C.

		OR(95%CI)
Body Fat Area	Effect	Total	Age≥18, <45 years	Age≥45, <60 years
VFA	ACME	0.030(0.019,0.041)	0.023(0.002,0.044)	0.033(0.016,0.049)
	ADE	0.167(0.136,0.208)	0.251(0.199,0.307)	0.118(0.062,0.170)
	Prop. Mediated	15.3%	8.4%	21.7%
SFA	ACME	0.007(0.003,0.010)	0.003(-0.002,0.008)	0.009(0.004,0.014)
	ADE	0.084(0.073,0.095)	0.095(0.081,0.107)	0.067(0.049,0.084)
	Prop. Mediated	7.2%	3.2%	11.6%

All models were adjusted for age, race, gender, exercise, drink, smoke, marriage status, education level, income, HbA1c, hypercholesterolemia history, hypertension history, diabetes history, and cardiovascular diseases history.

Abbreviations: ACME, stands for average causal mediation effects; ADE, stands for average direct effects; OR, odds ratio; CI, confidence interval.

Joint associations of body fat area and HOMA-IR, HDL-C with PhenoAgeAccel

We established the HOMA-IR threshold for diagnosing insulin resistance at 2.73[20]. As illustrated in Fig.6, we investigated the relationship between body fat area combined with HOMA-IR and PhenoAgeAccel across chronological age subgroups. The results indicated that in the interaction between VFA and HOMA-IR, individuals aged 18-44 years with HOMA-IR ≥2.73 and VFA >0.925 m² had a higher risk of PhenoAgeAccel compared to those with HOMA-IR <2.73 and VFA ≤0.925 m² (OR: 4.541, 95% CI: 3.273 to 6.301). In the participants aged 45-59 years, a significant interaction promoting PhenoAgeAccel occurred only when both HOMA-IR≥2.73 and VFA>0.925m² (OR: 2.690, 95%CI: 1.739 to 4.161). Furthermore, in the analysis of SFA combined with HOMA-IR, the risk of PhenoAgeAccel was greatest among individuals aged 18-44 years exhibiting both HOMA-IR≥2.73 and SFA>3.137m² relative to those with HOMA-IR<2.73 and SFA≤3.137m² (OR: 5.705, 95%CI: 4.125 to 7.892). For participants aged 45-59 years, an interaction promoting PhenoAgeAccel was observed when both HOMA-IR>1.67 and SFA>0 .925m² were present; notably, this group exhibited the highest risk for PhenoAgeAccel when combining conditions of HOMA-IR≥2 .73 and SFA>3 .137m² compared to those with lower values (OR: 3.017 , 95%CI : 1.934 to 4.706 ).

We categorized HDL-C into four quartiles (Q1-Q4) based on its levels. As illustrated in Fig.7, we explored the association between body fat area and HDL-C in conjunction with PhenoAgeAccel across different chronological age subgroups. The findings indicated that in the interaction between VFA and HDL-C, for individuals aged 18-59 years, VFA≤0.925 m² and HDL-C>1.60, ≤3.90 mmol/L were identified as protective factors for PhenoAgeAccel (OR: 0.576, 95%CI: 0.393 to 0.845), whereas VFA>0.925 m² and HDL-C≥0.16, <1.09 mmol/L were recognized as risk factors. An elevate in HDL-C levels was associated with a lower risk of PhenoAgeAccel due to their interaction effects. For individuals aged 18-44 years, VFA≤0.925 m² and HDL-C>1.60, ≤3.90 mmol/L also served as protective factors for PhenoAgeAccel (OR: 0.583, 95%CI: 0.377 to 0.903). For individuals aged 45-59 years, the interaction between VFA and HDL-C did not demonstrate statistical significance regarding the occurrence of PhenoAgeAccel. In contrast, for individuals aged 18-59 years, the combination of SFA≤3.137 m² with HDL-C>1.60, ≤3.90 mmol/L was identified as a protective variable for PhenoAgeAccel (OR: 0.690, 95% CI: 0.482 to 0.988), whereas SFA>3.137 m² combined with HDL-C≥0.16, <1.09 mmol/L constituted a risk factor for PhenoAgeAccel. Notably, a rise in HDL-C levels was linked to a lower risk of developing PhenoAgeAccel, attributed to their interaction effects. For those aged 18-44 years, SFA>3.137 m² combined with HDL-C≥0.16, <1.09 mmol/L was also recognized as a risk factor for PhenoAgeAccel . For those aged 45-59 years , the combination of SFA≤3.137 m² and HDL-C>1.60,≤3.90 mmol/L served as a protective factor against PhenoAgeAccel (OR: 0.517, 95%CI: 0.277 to 0.963), while SFA>3.137 m² combined with HDL-C≥0.16,<1.09 mmol/L constituted a risk factor for PhenoAgeAccel.

Incremental predictive values of body fat area for PhenoAgeAccel

Table5 shows that in individuals aged 18-44 years, the basic model combined with VFA+HDL-C (AUC: 0.561), VFA+HOMA-IR (AUC: 0.671), SFA (AUC: 0.649), SFA+HDL-C (AUC: 0.636), and SFA+HOMA-IR (AUC: 0.698) demonstrated statistically significant differences compared to the basic model + VFA (AUC: 0.579) (P<0.001). The NRI and IDI tests further revealed that incorporating VFA+HOMA-IR, SFA, SFA+HDL-C, and SFA+HOMA-IR into the basic model enhanced its predictive capability for PhenoAgeAccel relative to the basic model + VFA (P<0.05). In individuals aged 45-59 years, the combination of the basic model with VFA+HDL-C (AUC: 0.578), VFA+HOMA-IR (AUC: 0.684), and SFA+HOMA-IR (AUC: 0.681) also exhibited statistically significant differences from the basic model + VFA (AUC: 0.607) (P<0.001). Additionally, the NRI and IDI tests indicated that introducing VFA+HOMA-IR and SFA+HOMA-IR into the basic model improved its ability to predict PhenoAgeAccel compared to the basic model + VFA (P<0.05).

Table 5 Incremental predictive values for PhenoAgeAccel.

Age≥18,<45years	AUC(95%CI)	P	IDI(95%CI)	P	NRI(95%CI)	P
Basic model
+VFA	0.579 (0.551,0.607)	Ref.	Ref.	Ref.	Ref.	Ref.
+VFA+HDL-C	0.561 (0.533,0.589)	<0.001	0.001 (-0.001,0.003)	0.288	-0.001 (-0.019,0.017)	0.876
+VFA+HOMA-IR	0.671 (0.644,0.697)	<0.001	0.021 (0.014,0.028)	<0.001	0.038 (0.001,0.070)	0.018
+SFA	0.649 (0.622,0.675)	<0.001	0.043 (0.033,0.053)	<0.001	0.132 (0.087,0.178)	<0.001
+SFA+HDL-C	0.636 (0.609,0.663)	<0.001	0.043 (0.033,0.053)	<0.001	0.125 (0.079,0.171)	<0.001
+SFA+HOMA-IR	0.698 (0.672,0.723)	<0.001	0.051 (0.040,0.062)	<0.001	0.130 (0.085,0.175)	<0.001
Age≥45,<60years	AUC(95%CI)	P	IDI(95%CI)	P	NRI(95%CI)	P
Basic model
+VFA	0.607 (0.572,0.643)	Ref.	Ref.	Ref.	Ref.	Ref.
+VFA+HDL-C	0.578 (0.542,0.614)	<0.001	0.011 (0.004,0.017)	0.001	0.019 (-0.019,0.058)	0.325
+VFA+HOMA-IR	0.684 (0.651,0.717)	<0.001	0.014 (0.007,0.021)	<0.001	0.038 (0.002,0.073)	0.039
+SFA	0.618 (0.583,0.653)	0.207	0.023 (0.012,0.033)	<0.001	0.088 (0.036,0.140)	0.001
+SFA+HDL-C	0.595 (0.559,0.630)	0.192	0.032 (0.021,0.044)	<0.001	0.097 (0.044,0.150)	<0.001
+SFA+ HOMA-IR	0.681 (0.648,0.714)	<0.001	0.031 (0.020,0.041)	<0.001	0.098 (0.046,0.150)	<0.001

The basic model involving age, gender, race, exercise, drink, smoke, marriage status, education level, income, HbA1c, hypercholesterolemia history, hypertension history, diabetes history, and cardiovascular diseases history.

Abbreviations: AUC, area under the curve; NRI, net reclassification improvement; IDI, integrated discrimination improvement; OR, odds ratio; CI, confidence interval.

This research investigated the association between subcutaneous fat area, visceral fat area, and PhenoAge in individuals aged 18-59 years. Additionally, it examined the mediating roles of HOMA-IR and HDL-C in the association between fat area and PhenoAge, as well as their interactive effects with body fat area on PhenoAge. The findings of our research are presented as follows: 1) body fat area significantly influences PhenoAge. Visceral fat area exhibits a non-linear correlation with PhenoAgeAccel in individuals aged 18-59 years, while subcutaneous fat area shows a non-linear correlation with PhenoAgeAccel in those aged 18-44 years and a linear correlation in those aged 45-59 years. 2) HOMA-IR and HDL-C serve as partial mediators in the relationship between body fat area and PhenoAge. In individuals aged 18-44 years, HOMA-IR has a greater mediating effect; conversely, in individuals aged 45-59 years, HDL-C has a greater mediating effect. 3) higher levels of body fat area or insulin resistance accelerate PhenoAge, whereas lower levels of body fat area combined with higher levels of HDL-C decelerate PhenoAge.

Multiple studies have indicated that body fat distribution is closely linked to aging[22]. With age, fat is gradually redistributed to the abdomen and visceral organs, resulting in an increase in visceral fat and a reduction in subcutaneous fat. This redistribution leads to a higher VAT/SAT ratio[23,24]. This transition is linked to impaired adipogenesis, altered cytokine and hormone secretion, and dysfunction in lipid accumulation and metabolism[6,25,26]. VAT influences the aging process mainly through immune inflammation mediated by macrophages, dendritic cells, T cells, and B cells[27]. For instance, in aging mice, the phenotype of adipose tissue macrophages shifts toward a pro-inflammatory state, characterized by reduced M2 proportions and an absence of M1 or M2-like polarization, which leads to elevated levels of pro-inflammatory cytokines, including IL-6, MCP-1, and TNF-α, while simultaneously decreasing the expression of PPAR-γ—all driven by NLRP3[27,28]. Moreover, hypertrophy, hyperplasia, and fibrosis of visceral adipose tissue can also significantly influence the aging process[29,30]. Nevertheless, it is currently posited that subcutaneous adipose tissue serves a protective function in the aging process. Research has demonstrated that a reduction in subcutaneous adipose tissue thickness, and changes in adipocyte size are correlated with an acceleration of facial aging[31]. Our study demonstrated that both visceral and subcutaneous fat area have a significant impact on the aging process. The RCS analysis revealed a dose-response relationship between body fat area and PhenoAgeAccel, along with an identified effect threshold. For non-elderly adults, the threshold for VFA is 0.925 m², while that for SFA is 3.137 m²; additionally, different thresholds are observed for individuals aged 18-44 years and those aged 45-59 years. This indicates that when body fat area falls below these thresholds, the aging process is attenuated; when exceeding these thresholds, body fat area accelerates the aging process. Furthermore, the association between VFA and PhenoAgeAccel exhibits a non-linear pattern in individuals aged 18-59 years, whereas SFA demonstrates a non-linear relationship in those aged 18-44 years and a linear relationship in individuals aged 45-59 years.

Through this study, we revealed that HOMA-IR and HDL-C served as partial mediators in the relationship between body fat area and PhenoAgeAccel. On the one hand, adipose tissue influences HOMA-IR and HDL-C levels. A study examining the differences in fat distribution phenotypes and their relationship with HOMA-IR revealed that in obese populations, hypertrophic visceral adipose tissue adipocytes secrete pro-inflammatory cytokines through inflammation mediated by pro-inflammatory M1 macrophages, which disrupt insulin signaling, diminish insulin sensitivity, and independently promote insulin resistance[12]. Changes in VAT fatty acid metabolism and adipogenic gene expression are associated with markers of insulin resistance, whereas hypertrophy of abdominal subcutaneous adipocytes may result in lipid efflux, ectopic fat accumulation, and VAT expansion, thereby aggravating systemic insulin resistance[32]. The efflux of cholesterol from adipose tissue is crucial for the development of mature and functional HDL-C particles[33]. Inflammation in adipose tissue enhances the release of inflammatory mediators such as TNF-α, IL-6, IL-1β, and CRP, while concurrently suppressing the expression and activity of cholesterol transport proteins like ABCA1, which hinders the maturation of HDL-C[34]. Moreover, inflammation in adipose tissue raises circulating concentrations of free fatty acids and VLDL-TG, promoting the formation of triglyceride-enriched HDL-C particles. This alteration accelerates hepatic clearance and ultimately lowers plasma HDL-C concentrations[35,36]. On the other hand, HOMA-IR and HDL-C play a role in the aging process and are linked to the onset of various diseases. Research indicates that in men, visceral fat accumulation can accelerate epigenetic aging, a phenomenon mediated by insulin resistance[18]. In non-diabetic elderly individuals, insulin resistance significantly elevates the risk of frailty and functional decline[37]. It is broadly recognized that HDL-C has the potential to attenuate aging and decrease the incidence of various diseases. HDL-C has the capacity to facilitate cellular lipid clearance and attenuate atherosclerosis. Furthermore, it may delay the aging process by directly modulating senescence signals or influencing the survival factor KLOTHO[38].

Another notable finding from our study was that the effect values of HOMA-IR and HDL-C exhibit variation across different chronological age subgroups. We hypothesized that this discrepancy was linked to the metabolic changes occurring throughout the aging process. Although elevated insulin levels or insulin resistance can accelerate aging, reduce lifespan, and increase the prevalence of age-related diseases, elderly individuals exhibit specific deficiencies in insulin secretion, resulting in lower insulin levels compared to the younger[39]. The influence of age on HDL-C levels is not statistically significant; however, older individuals exhibit higher HDL-C levels compared to the younger individuals[14]. This elucidates why, in our study, HOMA-IR exhibits a more pronounced mediating effect in individuals aged 18 to 44 years, whereas HDL-C does not function as a mediator. Conversely, in those aged 45 to 59 years, HDL-C demonstrates a more significant mediating effect; however, the interaction between HDL-C and body fat area may not exert an influence on PhenoAgeAccel.

Finally, our findings indicated that VFA, SFA, HDL-C, and HOMA-IR possess diagnostic value for PhenoAgeAccel, suggesting that body fat area, HOMA-IR, and HDL-C may serve as predictors of the rate of aging, the occurrence of age-associated diseases, and mortality in individuals.

Advantages and disadvantages of the research

Our study presents several advantages. Firstly, we employed weighted logistic regression along with restricted cubic splines to examine both linear and nonlinear associations between body fat area and PhenoAgeAccel. All data accounted for complex survey designs, enhancing the accuracy of our findings. Secondly, we utilized mediation analysis to incorporate two mediating factors, HOMA-IR and HDL-C, into the relationship between body fat area and PhenoAgeAccel, thereby elucidating the potential mechanisms through how body fat area influences PhenoAgeAccel. Furthermore, we performed subgroup analyses to examine the similarities and differences in how body fat area, HOMA-IR, and HDL-C impact PhenoAgeAccel across different age groups, thereby enhancing the applicability of our findings. Nonetheless, our study has several limitations. The data were sourced from the NHANES database, which contains some missing information. Additionally, since our research was cross-sectional, prospective studies are required to validate these findings.

In conclusion, our main findings are as follows: 1) body fat area significantly influences PhenoAge. 2) HOMA-IR and HDL-C serve as partial mediators in the relationship between body fat area and PhenoAge. 3) higher levels of body fat area or insulin resistance accelerate aging, whereas lower levels of body fat area combined with higher levels of HDL-C decelerate aging.

Author Contribution

concept design: Yuanhong Liu and Shumin Mu; data collection and data analysis: Min Xu and Liqing Wang; prepared tables and figures: Yuanhong Liu, Linyun Meng and Mengran Li; drafting of the article: Yuanhong Liu; study supervison: Shumin Mu. All the authors approved the final article.

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No competing interests reported.

The Association of Visceral and Subcutaneous Fat Areas with Phenotypic Age in Non-elderly Adults, Mediated by HOMA-IR and HDL-C

Status:

Version 1

Abstract

Figures

Background

Methods

Data source

Measurement of PhenoAge

Assessment of covariates

Statistical analysis

Results

Discussion

Conclusions

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1