Differential Associations of Subcutaneous and Visceral Fat with Bone Turnover Markers: A Study on Bariatric Surgery Patients with Severe Obesity and Individuals Without Obesity

doi:10.21203/rs.3.rs-4767002/v1

Backgrounds

Obesity is associated with alterations in bone turnover markers (BTMs). However, the association between regional fat distribution and bone metabolism has received less attention. This study therefore aimed to identify which specific fat compartments (i.e., abdominal and femoral subcutaneous fat, intra- and extraperitoneal fat, and total visceral fat) exert the most significant influence on circulating BTMs.

Methods

The study comprised a cohort of individuals with severe obesity (n = 46), studied both before and after weigh loss induced by metabolic bariatric surgery, and individuals without obesity and surgery who served as controls (n = 25). The BTMs included Tartrate-Resistant Acid Phosphatase 5b, C-terminal Telopeptide of Type I Collagen (CTX), Procollagen Type I N-terminal Propeptide (PINP), and Total (TotalOC), Carboxylated (cOC), and Undercarboxylated (ucOC) osteocalcin.

Results

In the pooled data, significant negative associations were observed between intraperitoneal and total visceral fat and CTX, TotalOC, cOC, and ucOC (all with p < 0.05). Similarly, extraperitoneal fat showed a significant negative relationship with cOC. Conversely, no significant associations were observed between abdominal and femoral subcutaneous fat and any of the measured BTMs in the study participants (all p > 0.05).

Conclusions

Distinct deleterious implications for bone formation and resorption were associated with intraperitoneal, extraperitoneal, and total visceral fat. These findings emphasize the importance of fat distribution in the body to skeletal health.

ClinicalTrials.gov registration numbers: NCT00793143 and NCT01373892.

Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic bone disease

Health sciences/Health care/Weight management

Biological sciences/Physiology/Metabolism/Metabolic diseases/Obesity

Biological sciences/Physiology/Metabolism/Fat metabolism

Subcutaneous and Visceral fat

Bone turnover marker

People with severe obesity

Metabolic bariatric surgery

Obesity, defined as a body mass index (BMI) of 30 kg/m² or greater, is a significant global public health challenge characterized by the excessive accumulation of fat in the body (1). Traditionally, obesity has been associated with improved bone strength and increased bone mineral density (BMD) in skeletal regions such as the lumbar spine and femoral neck, likely due to the anabolic effect of load-bearing effects of increased body weight on bones (2). However, there is growing evidence that obesity, especially severe obesity (BMI of 40 kg/m² or greater), may be associated with increased risk of fractures (3). This potential risk is thought to be mediated by various mechanisms, including alterations in bone-regulating hormones, inflammation, and bone cell metabolism (3). The physiological and biochemical mechanisms underlying this obesity-bone paradox need to be further investigated.

It is increasingly recognised that it is not obesity per se, but rather the distribution of fat in localized regions that are closely linked to the decline in BMD and the increased risk for fragility fractures (4). Specifically, a cross-sectional study found a negative correlation between visceral fat (VF) volume and trabecular BMD, while no significant association was observed with abdominal subcutaneous fat (SF) (5,6). Another study reported a negative association between SF and BMD, as well as a U-shaped relationship between VF and BMD(7). Femoral SF is positively associated with a protective lipid and glycemic profile, which are established risk factors for cardiometabolic conditions(8). This association suggests that individuals with higher levels of femoral SF may also experience potentially beneficial effects on BMD. However, there is limited understanding of the impact of femoral fat accumulation on overall bone metabolism.

Imaging and cadaver studies have shown that VF is not a homogeneous fat depot (9). Instead, it can be divided into intraperitoneal and extraperitoneal fat compartments [4]. Intraperitoneal fat is primarily composed of mesenteric and omental fat, and it is thought to be most strongly associated with the metabolic health risks of obesity (10). This association is largely attributed to the "portal hypothesis" which proposes that intraperitoneal fat drains directly into the portal vein, exposing the liver to free fatty acids and pro-inflammatory factors (11). In contrast, extraperitoneal fat is located within the intrapelvic, preperitoneal, retroperitoneal regions (12). It drains into the inferior vena cava, entering the systemic circulation while bypassing the liver (13). So far, to our knowledge no studies have investigated the potential differential effects of intraperitoneal and extraperitoneal fat on bone metabolism.

Bone turnover markers (BTMs) are biochemical surrogate markers of the activities of osteoblasts and osteoclasts, providing an estimate of bone metabolism at a whole-body level (14). BTMs are useful tools in the assessment of response to bone-active medication and they may assist fracture risk prediction (15) and provide supplementary information to radiographic measures of bone mass (16). Individuals with type 2 diabetes mellitus (T2DM) exhibit lower BTM values compared to those without diabetes (17). This reduction is further exacerbated in those with T2DM who also have metabolic syndrome and increased VF (18). Our group has previously assessed the effects of severe obesity and metabolic bariatric surgery-induced weight loss on BTMs (19). Here, we elaborate further on those findings by investigating the associations of abdominal and femoral SF, intra- and extraperitoneal VF, and total VF with BTMs in individuals with severe obesity both before and after metabolic bariatric surgery and in the persons without obesity.

2.1. Human participants, study design and methods

A total of 46 participants (female/male, 42/4) were recruited from individuals undergoing metabolic bariatric surgery at the Hospital District of Southwest Finland (20). In brief, inclusion criteria included an age range of 18-60 years and a BMI of ≥ 40 kg/m² (or ≥ 35 kg/m² with an additional obesity-related comorbidity). Among the 46 participants with severe obesity, 18 had T2DM, and out of the 28 participants without T2DM, 11 had impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) (21). Twenty-five (female/male, 23/2) individuals without obesity served as controls. The protocols were approved by the Ethics Committee of the Wellbeing Services County of Southwest Finland and were conducted in compliance with the Code of Ethics of the World Medical Association (Helsinki Declaration).

2.2. Analysis of biochemical and inflammatory markers

Plasma glucose, glycated hemoglobin, serum insulin, and high-sensitivity C-reactive protein were measured as described previously (21). Glycemic indices were assessed using a standard 75 g oral glucose tolerance test (OGTT)(22). Furthermore, insulin resistance was evaluated using two methods: the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) using the formula [(fasting glucose × fasting insulin)/22.5] (23) and the Matsuda Index(24). Stored serum samples were analyzed for cytokine and adipokine levels using the Bio-Plex 200 system (Bio-Rad Laboratories, Inc., CA, USA) and Bio-Plex Manager Software version 4.1. Multiplex quantification of cytokines was performed using the MILLIPLEX MAP Kit Human Adipokine Magnetic Bead Panels 1 and 2 (Millipore Corporation, MA, USA).

2.3. Bone turnover markers

The analysis of the bone turnover markers has previously been described (19). Briefly, fasting morning serum samples were collected from the study participants and stored as aliquots at - 80 °C. Bone resorption was assessed using C-terminal crosslinked telopeptides of type I collagen [CTX, (CrossLaps®, IDS Ltd., UK)], osteoclast activity with osteoclast-specific tartrate-resistant acid phosphatase 5b [TRAcP5b (BoneTRAP® ELISA, IDS Ltd., UK)], and low-grade inflammation by measuring TRAcP5a (25). Bone formation was evaluated using N-terminal propeptide of type I collagen [PINP, (IDS Ltd., UK)]. Serum total osteocalcin (TotalOC) and carboxylated (cOC) were determined with a two-site immunoassay, while undercarboxylated osteocalcin (ucOC) was assessed with an in-house assay based on hydroxyapatite binding(19) .The 'Resorption index' (CTX/TRAcP5b) and 'Coupling index' (PINP/CTX) were calculated to assess bone remodeling balance.

2.4. Measurement of fat mass

Body fat content was measured using bioelectric impedance (Omron BF400). Volumes of different fat depots were measured using a 1.5 Tesla MRI (Tesla Intera system; Philips Medical Systems, Best, the Netherlands). Axial T1-weighted dual fast field echo images (echo time 2.3 and 4.6, repetition time 120 ms, slice thickness 10 mm without gap) were acquired for the abdominal and femoral regions. Femoral SF mass was calculated from the femoral head to the patella surface. The abdominal visceral region was separated into the intraperitoneal and extraperitoneal compartments using specific anatomical reference points (Figure 1) (26). The fat regions were analyzed using sliceOmatic (Tomovision, Montreal, Quebec, Canada). Fat volumes (cm³) were converted to mass (kg) by taking into account the tissue density (27).

2.5. Metabolic bariatric surgery

The participants with severe obesity followed a 4-week very-low-calorie (VLCD) diet (800 kcal/day) before undergoing bariatric surgery. The individual with severe obesity underwent either Roux-en-Y gastric bypass (RYGB, n = 21) or sleeve gastrectomy (SG, n = 25). Post-bariatric surgery studies were conducted 6 months after the surgical intervention (21,27,28). All subjects were instructed to take daily calcium (1000 mg) and vitamin D3 (20 μg) supplements and multivitamin tablet (including 10 μg vitamin D3) after the surgery (19).

2.6. Statistical analysis

The study presented continuous variables as means with standard deviations. Normality of distribution was assessed using the Shapiro-Wilk test. Variables, including all BTMs, that were not normally distributed underwent logarithmic transformation before analysis. Unpaired t-tests were used to compare means between individuals with severe obesity and controls individuals without obesity for metabolic, clinical, and bone turnover marker variables. Paired t-tests were used to assess changes in these variables before and after metabolic bariatric surgery.

We used mixed-effects models to evaluate the impact of abdominal SF, extra- and intraperitoneal VF, total VF, and femoral SF on various bone markers (TRACP5a, TRACP5b, CTX, PINP, TotalOC, cOC, ucOC). Besides the fat compartments, surgery type, age, and weight were used as fixed covariates to explain bone marker levels while sample donor was used as a random effect. Control samples, lacking defined surgery types, were excluded from the model fitting. A conventional false discovery rate (FDR) cutoff of 0.05 signified statistical significance. Correlations between BTMs, with the different fat depots, inflammatory and glycemic parameters were assessed using Spearman’s rank correlation coefficient. Analyses were performed using R packages lmerTest (version 3.1-3) and lme4 (version 1.1-34), and Statistical Package for the Social Sciences (SPSS, Version 29.0 IBM Corp., Armonk, NY) was used for visualization of the data.

The descriptive characteristics of the study participants are shown in Table 1. and previously published (19,21). As expected, all indices of adiposity including of abdominal and femoral SF, as well as intra- and extraperitoneal VF were found to be significantly higher in the individual with severe obesity compared to the control subjects (Figure 2, all p < 0.001) (27,28). Moreover, persons with severe obesity exhibited poorer glycemic and lipid profiles and were characterized by low-grade inflammation when compared to the controls, as presented in Table 1 and reported in previous studies (19,21).

The levels of BTMs have been previously reported in the same cohort of study participants (19). Briefly, before surgery, the person with severe obesity had lower levels of CTX, PINP, TotalOC and cOC, compared to controls, suggesting a slower bone turnover rate in severe obesity (Table 2). Notably, both markers of resorption (CTX) and formation (PINP, OC) were lower in the individual with severe obesity compared to controls, but less so in the latter, translating into higher PINP/CTX ratio in the participants with obesity.

Following metabolic bariatric surgery, concentration of serum phosphate levels increased (19). Weight loss induced by the metabolic bariatric surgery resulted in increase in the levels of all BTMs analyzed, suggesting accelerated bone remodelling in response to rapid weight loss (Table 2, and previously reported (19)). The observed increase in both bone resorption (TRACP5b, CTX) and bone formation (PINP, TotalOC) markers highlights a dynamic state of bone turnover in these individuals and coupling between bone formation and resorption (Table 2, and previously reported (19)).

The mixed effect model fitting revealed significant negative association between intraperitoneal fat and bone markers CTX, TotalOC, cOC, and ucOC (Table 3, Figure 3A-3D, Supplementary table 1). Similarly, extraperitoneal fat was significantly and negatively associated with cOC (Table 3, Figure 3E). The volume of total VF (containing both intra- and extraperitoneal fat mass) followed the trends of intraperitoneal fat as it was significantly associated with CTX, TotalOC, cOC, and ucOC (Figure 3F-3I). In contrast, there were no significant associations between abdominal and femoral SF with any of the studied bone markers; FDR > 0.05 in all analyses. Table 3 summarizes the FDR values and correlation coefficients of all evaluated fat compartment effects on different BTMs in the fitted mixed effect model.

Our investigation into the associations between circulating cytokines and adipokines and BTMs revealed several significant correlations. Specifically, IL-8 and TNF-α displayed a positive correlation with TRACP5a (Table 4). MCP-1 was negatively correlated with cOC (Table 4). Leptin levels exhibited negative correlations with CTX, TotalOC, and cOC (Table 4, all p < 0.05).

Supplementary figure 1 presents additional analyses performed to assess the association between BTMs and glycemic parameters among the study participants. Our findings revealed negative correlations between the 2-hour glucose level and CTX (Suppl. Figure 1A), TotalOC (Suppl. Figure 1B), PINP (Suppl. Figure 1C), and ucOC (Suppl. Figure 1D) (all FDR < 0.05).

It has previously been shown that while severe obesity suppressed bone remodeling, this remodeling process was reactivated following metabolic bariatric surgery(19). Here, we elaborated further on the existing literature by assessing the associations of different fat depots with BTMs. Our main findings were that there were differences in the associations between different anatomical fat depots and bone metabolism. Both intraperitoneal and total VF (intra- + extraperitoneal) showed negative association with bone turnover, as assessed by CTX, TotalOC, cOC, and ucOC. However, we did not find any significant associations between abdominal or femoral SF and any of the BTMs in the study participants.

In this study, we observed a negative association between CTX levels and both intraperitoneal and total VF mass, suggesting lower bone resorption rate in individuals with high visceral adiposity. This is in line with a previous research which has demonstrated that postmenopausal women with higher amounts of VF exhibit decreased serum CTX compared to postmenopausal women with lower levels of VF (29). In the study by Jia and colleagues (30), a negative correlation was found between perirenal fat thickness and CTX levels. In the same study, it was observed that perirenal fat showed a stronger correlation with CTX levels compared to paranephric fat (30), which might be attributed to close proximity of perirenal fat to the kidney and its blood supply, lymph fluid drainage, and innervation systems (30).

Our findings further demonstrate negative associations between both intraperitoneal and total VF with various molecular forms of circulating osteocalcin, namely TotalOC, cOC, and ucOC. Previous research has identified a negative correlation between serum osteocalcin levels and obesity indices such as BMI, percentage body fat, and waist circumference (31). Our results are supported by the findings of a cross-sectional study involving adolescents with obesity, in which an inverse correlation was observed between VF area and osteocalcin levels, and this association was attenuated by the addition of 25-hydroxycholecalciferol deficiency and parathyroid hormone to the model (32).

Our study did not find any associations between abdominal SF depot with any of the measured BTMs. Our findings are consistent with prior research demonstrating that abdominal SF may have no effect (33) or even positive effect on bone strength and stability i.e., femoral cross-sectional and cortical bone areas, and their resistance to rotational acceleration (5). Previous research suggests a beneficial role of abdominal SF on bone structure and density(34). The study found that higher amounts of abdominal SF were associated with greater BMD in both volume and area, as well as improved bone microarchitecture in the radius and tibia(34). The same study also observed a negative association between SF and CTX(34). However, when they included leptin in their analysis, the association between SF and CTX disappeared. This suggests that leptin may be the key player mediating the positive effect of SF on bone metabolism. In our study, leptin levels were negatively correlated not only with CTX, but also with TotalOC, and cOC further suggesting its diverse effects on bone metabolism.

Despite femoral SF being generally considered to be metabolically protective (8), in our study of individuals with severe obesity, we did not find statistically significant associations between femoral SF accumulation and the levels of BTMs. In contrast, Zhang et al. (35) found a positive correlation between thigh fat mass and BMD at both the femoral neck and total lumbar spine. Several factors might explain the observed higher BMD in individuals with increased thigh/femoral fat. Clinically, obesity is protective against hip fractures in women with overweight/obesity, but carries high risk in humeral and tibial regions (36,37). This may be attributed to the protective padding effect provided by fat surrounding the pelvis and femur, which reduces impact forces experienced during falls (38). Furthermore, femoral SF displays increased lipoprotein lipase activity, promoting lipid storage within adipocytes and stimulating new fat cell formation (39). This buffering effect protects non-adipose tissues from excessive fatty acid exposure (40).

Our study identified a negative association between PINP, TotalOC and ucOC and 2-hour glucose levels after OGTT in study participants. A previous study found a significant negative correlation between ucOC and fasting glucose levels particularly in subjects without T2DM diagnosis but not in group of individuals with T2DM (41). In this same study, a negative correlation was also observed for TotalOC and fasting glucose. Interestingly, PINP levels did not correlate with fasting glucose (41).

The current study has several limitations. The predominance of female participants may limit the generalizability of the findings, as associations between fat distribution and bone metabolism could differ between sexes. While BTMs offer a faster, and potentially more sensitive method to detect dynamic changes in osteoblast and osteoclast activity compared to imaging techniques, they lack the ability to measure changes in bone size, shape, and BMD and mostly reflect the metabolically active trabecular compartment (16). Additionally, the study's subjects comprised a heterogeneous group, including subjects with obesity with IFG, IGT, and T2DM, both before and after weight loss through metabolic bariatric surgery, as well as metabolically healthy individuals without obesity. A major challenge with BTM measurements is the circadian variation, and to minimize the impact of these variations, all samples were obtained during a consistent time window (between 08:00 and 10:00 AM) (42). Another limitation is that our analysis did not investigate the potential influence of serum 25-hydroxyvitamin D3 and intact parathyroid hormone levels on the observed associations between serum cytokines and BTMs.

In conclusion, our study investigated the relationship between fat distribution and bone turnover, assessed by circulating levels of BTMs, in individuals with severe obesity before and after weight loss surgery, as well as in healthy participants without obesity. We observed negative associations between intraperitoneal and total VF, but not with SF depots, and BTMs. These findings suggest a potential role of VF in regulating bone metabolism. Additionally, the study confirmed possible associations between MCP1 and cOC, and the potential interaction between glucose levels and BTMs. While these observations point towards potential links, it is crucial to acknowledge that the cross-sectional design of the study limits its ability to establish causality. Future research is warranted to understand the underlying mechanisms and causal relationships between fat distribution and bone metabolism across individuals with different metabolic phenotypes and weight categories.

BMI, body mass index; BTM, bone turnover marker; VF, visceral fat; SF, subcutaneous fat; TRACP5a/b, tartrate-Resistant Acid Phosphatase 5a/b; CTX, C-terminal Telopeptide of Type I Collagen; PINP, Procollagen Type I N-terminal Propeptide; TotalOC, total osteocalcin; cOC, carboxylated osteocalcin; ucOC, undercarboxylated osteocalcin; IL-6, Interleukin 6; TNF-α, Tumour necrosis factor α.

ACKNOWLEDGMENTS

P.D. received funding from the Finnish Medical Association, Finnish Diabetes Research Foundation, Finnish Cultural Foundation, Finnish-Norwegian Medical Foundation, Instrumentarium Science Foundation, Päivikki and Sakari Sohlberg Foundation, Jalmari and Rauha Ahokas Foundation, Aarne Koskelo Foundation. K.K.I received funding from Research Council of Finland (grant 325498). This work was conducted within the Finnish Centre of Excellence in Molecular Imaging in Cardiovascular and Metabolic Research and was supported by the Research Council of Finland (grant No. 307402 to P.N.), the University of Turku, University Hospital, Åbo Akademi University (Finland). The funding agencies did not participate in study design, collection, analysis, or interpretation of data, writing of the report; nor the decision to submit the article for publication.

AUTHOR CONTRIBUTIONS

Conceptualization: P.D., E.R., K.K.I.; Investigation: P.D., E.R., H.H., K.K.L.; Acquisition, analysis, or interpretation of data: M.K.J., R.K.; Roles/Writing - original draft: P.D.; Writing - review & editing: K.K.I., E.R., H.H., K.K.K., M.M.-P., R.O., R.K., P.N; P.D; Approved the version to be published: K.K.I., E.R., H.H., K.K.K., M.M.-P., R.O., R.K., P.N; P.D is the corresponding author and has agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Conflict of interest: The authors declare no competing financial interests or personal relationships that could have influenced the research presented in this paper.

Wen X, Zhang B, Wu B, Xiao H, Li Z, Li R, et al. Signaling pathways in obesity: mechanisms and therapeutic interventions. Signal Transduct Target Ther. 2022 Aug 28;7(1):1–31.
Etherington J, Harris PA, Nandra D, Hart DJ, Wolman RL, Doyle DV, et al. The effect of weight-bearing exercise on bone mineral density: a study of female ex-elite athletes and the general population. J Bone Miner Res Off J Am Soc Bone Miner Res. 1996 Sep;11(9):1333–8.
Shapses SA, Pop LC, Wang Y. Obesity is a concern for bone health with aging. Nutr Res N Y N. 2017 Mar;39:1–13.
Compston JE, Flahive J, Hosmer DW, Watts NB, Siris ES, Silverman S, et al. Relationship of weight, height, and body mass index with fracture risk at different sites in postmenopausal women: the Global Longitudinal study of Osteoporosis in Women (GLOW). J Bone Miner Res Off J Am Soc Bone Miner Res. 2014 Feb;29(2):487–93.
Gilsanz V, Chalfant J, Mo AO, Lee DC, Dorey FJ, Mittelman SD. Reciprocal relations of subcutaneous and visceral fat to bone structure and strength. J Clin Endocrinol Metab. 2009 Sep;94(9):3387–93.
Jain RK, Vokes T. Visceral Adipose Tissue is Negatively Associated With Bone Mineral Density in NHANES 2011-2018. J Endocr Soc. 2023 Jan 23;7(4):bvad008.
Lin Y, Zhong X, Lu D, Yao W, Zhou J, Wu R, et al. Association of visceral and subcutaneous fat with bone mineral density in US adults: a cross-sectional study. Sci Rep. 2023 Jul 1;13(1):10682.
Manolopoulos KN, Karpe F, Frayn KN. Gluteofemoral body fat as a determinant of metabolic health. Int J Obes. 2010 Jun;34(6):949–59.
Yang YK, Chen M, Clements RH, Abrams GA, Aprahamian CJ, Harmon CM. Human mesenteric adipose tissue plays unique role versus subcutaneous and omental fat in obesity related diabetes. Cell Physiol Biochem Int J Exp Cell Physiol Biochem Pharmacol. 2008;22(5–6):531–8.
Gastaldelli A, Miyazaki Y, Pettiti M, Matsuda M, Mahankali S, Santini E, et al. Metabolic effects of visceral fat accumulation in type 2 diabetes. J Clin Endocrinol Metab. 2002 Nov;87(11):5098–103.
Rytka JM, Wueest S, Schoenle EJ, Konrad D. The Portal Theory Supported by Venous Drainage–Selective Fat Transplantation. Diabetes. 2011 Jan;60(1):56–63.
Misra A, Garg A, Abate N, Peshock RM, Stray-Gundersen J, Grundy SM. Relationship of anterior and posterior subcutaneous abdominal fat to insulin sensitivity in nondiabetic men. Obes Res. 1997 Mar;5(2):93–9.
Gantz M. Intraperitoneal Adipose Tissue: Associated Health Risks, Quantification by Advanced Imaging Methods and Future Directions in Children. Open Obes J. 2011 Mar 1;3(1):34–41.
Viljakainen H, Ivaska KK, Paldánius P, Lipsanen-Nyman M, Saukkonen T, Pietiläinen KH, et al. Suppressed bone turnover in obesity: a link to energy metabolism? A case-control study. J Clin Endocrinol Metab. 2014 Jun;99(6):2155–63.
Ivaska KK, Gerdhem P, Väänänen HK, Akesson K, Obrant KJ. Bone turnover markers and prediction of fracture: a prospective follow-up study of 1040 elderly women for a mean of 9 years. J Bone Miner Res Off J Am Soc Bone Miner Res. 2010 Feb;25(2):393–403.
Greenblatt MB, Tsai JN, Wein MN. Bone Turnover Markers in the Diagnosis and Monitoring of Metabolic Bone Disease. Clin Chem. 2017 Feb;63(2):464–74.
Starup-Linde J, Vestergaard P. Biochemical bone turnover markers in diabetes mellitus — A systematic review. Bone. 2016 Jan 1;82:69–78.
Yamaguchi T, Kanazawa I, Yamamoto M, Kurioka S, Yamauchi M, Yano S, et al. Associations between components of the metabolic syndrome versus bone mineral density and vertebral fractures in patients with type 2 diabetes. Bone. 2009 Aug;45(2):174–9.
Ivaska KK, Huovinen V, Soinio M, Hannukainen JC, Saunavaara V, Salminen P, et al. Changes in bone metabolism after bariatric surgery by gastric bypass or sleeve gastrectomy. Bone. 2017 Feb;95:47–54.
Salminen P, Helmiö M, Ovaska J, Juuti A, Leivonen M, Peromaa-Haavisto P, et al. Effect of Laparoscopic Sleeve Gastrectomy vs Laparoscopic Roux-en-Y Gastric Bypass on Weight Loss at 5 Years Among Patients With Morbid Obesity: The SLEEVEPASS Randomized Clinical Trial. JAMA. 2018 Jan 16;319(3):241–54.
Hannukainen JC, Lautamäki R, Pärkkä J, Strandberg M, Saunavaara V, Hurme S, et al. Reversibility of myocardial metabolism and remodelling in morbidly obese patients 6 months after bariatric surgery. Diabetes Obes Metab. 2018 Apr;20(4):963–73.
Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med J Br Diabet Assoc. 1998 Jul;15(7):539–53.
Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004 Jun;27(6):1487–95.
Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999 Sep 1;22(9):1462–70.
Jm H, H Y, Sl A, Aj J, Je H, H S, et al. Serum tartrate-resistant acid phosphatase 5b, but not 5a, correlates with other markers of bone turnover and bone mineral density. Calcif Tissue Int [Internet]. 2002 Jul [cited 2023 Dec 19];71(1). Available from: https://pubmed-ncbi-nlm-nih-gov.ezproxy.utu.fi/12073156/
Abate N, Garg A, Coleman R, Grundy SM, Peshock RM. Prediction of total subcutaneous abdominal, intraperitoneal, and retroperitoneal adipose tissue masses in men by a single axial magnetic resonance imaging slice. Am J Clin Nutr. 1997 Feb;65(2):403–8.
Dadson P, Landini L, Helmiö M, Hannukainen JC, Immonen H, Honka MJ, et al. Effect of Bariatric Surgery on Adipose Tissue Glucose Metabolism in Different Depots in Patients With or Without Type 2 Diabetes. Diabetes Care. 2016 Feb;39(2):292–9.
Dadson P, Ferrannini E, Landini L, Hannukainen JC, Kalliokoski KK, Vaittinen M, et al. Fatty acid uptake and blood flow in adipose tissue compartments of morbidly obese subjects with or without type 2 diabetes: effects of bariatric surgery. Am J Physiol Endocrinol Metab. 2017 Aug 1;313(2):E175–82.
Sharma DK, Anderson PH, Morris HA, Clifton PM. Acute C-Terminal Crosslinking Telopeptide of Type I Collagen (CTX-1) Suppression with Milk Calcium or Calcium Carbonate Is Independent of Visceral Fat in a Randomized Crossover Study in Lean and Overweight Postmenopausal Women. J Nutr. 2022 Apr 1;152(4):1006–14.
Jia X, An Y, Xu Y, Yang Y, Liu C, Zhao D, et al. Low serum levels of bone turnover markers are associated with perirenal fat thickness in patients with type 2 diabetes mellitus. Endocr Connect. 2021 Sep 17;10(10):1337–43.
Pittas AG, Harris SS, Eliades M, Stark P, Dawson-Hughes B. Association between serum osteocalcin and markers of metabolic phenotype. J Clin Endocrinol Metab. 2009 Mar;94(3):827–32.
Lenders CM, Lee PDK, Feldman HA, Wilson DM, Abrams SH, Gitelman SE, et al. A cross-sectional study of osteocalcin and body fat measures among obese adolescents. Obes Silver Spring Md. 2013 Apr;21(4):808–14.
Hind K, Pearce M, Birrell F. Total and Visceral Adiposity Are Associated With Prevalent Vertebral Fracture in Women but Not Men at Age 62 Years: The Newcastle Thousand Families Study. J Bone Miner Res Off J Am Soc Bone Miner Res. 2017 May;32(5):1109–15.
Vilaca T, Evans A, Gossiel F, Paggiosi M, Eastell R, Walsh JS. Fat, adipokines, bone structure and bone regulatory factors associations in obesity. Eur J Endocrinol. 2022 Dec 1;187(6):743–50.
Zhang Y, Jia X, Liu X, An W, Li J, Zhang W. Relationship between different body composition and bone mineral density in Qinhuangdao city. Rev Assoc Medica Bras 1992. 2022 Apr;68(4):445–9.
Prieto-Alhambra D, Premaor MO, Fina Avilés F, Hermosilla E, Martinez-Laguna D, Carbonell-Abella C, et al. The association between fracture and obesity is site-dependent: a population-based study in postmenopausal women. J Bone Miner Res Off J Am Soc Bone Miner Res. 2012 Feb;27(2):294–300.
Beck TJ, Petit MA, Wu G, LeBoff MS, Cauley JA, Chen Z. Does obesity really make the femur stronger? BMD, geometry, and fracture incidence in the women’s health initiative-observational study. J Bone Miner Res Off J Am Soc Bone Miner Res. 2009 Aug;24(8):1369–79.
Bouxsein ML, Szulc P, Munoz F, Thrall E, Sornay-Rendu E, Delmas PD. Contribution of trochanteric soft tissues to fall force estimates, the factor of risk, and prediction of hip fracture risk. J Bone Miner Res Off J Am Soc Bone Miner Res. 2007 Jun;22(6):825–31.
Rebuffé-Scrive M, Enk L, Crona N, Lönnroth P, Abrahamsson L, Smith U, et al. Fat cell metabolism in different regions in women. Effect of menstrual cycle, pregnancy, and lactation. J Clin Invest. 1985 Jun;75(6):1973–6.
Frayn KN. Adipose tissue as a buffer for daily lipid flux. Diabetologia. 2002 Sep;45(9):1201–10.
Arponen M, Brockmann EC, Kiviranta R, Lamminmäki U, Ivaska KK. Recombinant Antibodies with Unique Specificities Allow for Sensitive and Specific Detection of Uncarboxylated Osteocalcin in Human Circulation. Calcif Tissue Int. 2020 Dec;107(6):529–42.
Qvist P, Christgau S, Pedersen BJ, Schlemmer A, Christiansen C. Circadian variation in the serum concentration of C-terminal telopeptide of type I collagen (serum CTx): effects of gender, age, menopausal status, posture, daylight, serum cortisol, and fasting. Bone. 2002 Jul;31(1):57–61.

Table 1. Anthropometric and metabolic characteristics of the study subjects

Variables	Controls (n=25)	Severely obese (n=46)		^#P-value
Variables	Controls (n=25)	Before surgery	After surgery
Age (years)	45.84 ± 10.23	44.93 ± 9.51	45.22 ± 9.34	0.888
Sex (Female/male)	23/2	42/4	42/4	-
Weight (kg)	64.79 ± 7.74	116.69 ± 13.79***	90.04 ± 13.47***	<0.001
BMI (kg/m²)	23.04 ± 2.5	42.11 ± 4.03***	32.51 ± 4.17***	0.939
Waist (cm)	77.38 ± 9.4	119.13 ± 10.3***	100.33 ± 10.93***	<0.001
Body fat (%)	31.13 ± 6.54	49.3 ± 5.81***	42.42 ± 5.41***	<0.001
Abdominal SF mass (kg)	4.08 ± 1.74	18.7 ± 5.81***	11.38 ± 4.08***	<0.001
Intraperitoneal VF mass (kg)	0.73 ± 0.59	2.89 ± 1.19***	1.57 ± 0.95***	<0.001
Extraperitoneal VF mass (kg)	0.66 ± 0.5	1.68 ± 0.93***	1.11 ± 0.85**	<0.001
Total VF mass (kg)	1.39 ± 1.08	4.57 ± 2.0***	2.68 ± 1.75***	<0.001
Femoral SF mass (kg)	6.38 ± 2.28	14.83 ± 4.23***	10.4 ± 3.88***	<0.001
Fasting glucose (mmol/L)	5.36 ± 0.55	6.32 ± 1.23***	5.39 ± 0.65	<0.001
2-hour glucose (mmol/L)	5.63 ± 1.18	8.87 ± 3.21***	5.81 ± 2.82	<0.001
Fasting insulin (mmol/L)	6.0 ± 3.55	15.34 ± 11.4***	7.53 ± 4.73	<0.001
Cholesterol (mmol/L)	4.68 ± 0.85	4.29 ± 0.81	4.19 ± 0.74*	0.549
Triglycerides (mmol/L)	0.71 ± 0.31	1.25 ± 0.45***	1.0 ± 0.42**	0.005
HDL-cholesterol (mmol/L)	1.86 ± 0.44	1.26 ± 0.24***	1.43 ± 0.27***	0.008
LDL-cholesterol (mmol/L)	2.5 ± 0.7	2.46 ± 0.71	2.3 ± 0.66	0.135
HbA1c (%)	5.64 ± 0.28	5.7 ± 0.62	5.52 ± 0.39	0.009
CRP (mg/L)	0.88 ± 0.90	4.28 ± 3.98***	1.58 ± 1.55*	<0.001
Matsuda Index	8.9 ± 5.68	3.19 ± 1.96***	5.09 ± 2.47***	0.007
HOMA-IR	1.42 ± 0.93	4.7 ± 5.41***	1.83 ± 1.24*	<0.001
IL-6 (pg/mL)	2.65 ± 2.87	3.16 ± 2.26*	2.03 ± 1.39	<0.001
IL-8 (pg/mL)	4.78 ± 2.04	6.14 ± 2.91*	6.98 ± 6.13	0.297
TNF-α (pg/mL)	4.14 ± 2.6	6.05 ± 3.42*	5.81 ± 3.31*	0.567
MCP-1 (pg/mL)	235.23 ± 117.67	309.6 ± 163.47*	293.39 ± 125.95*	0.789
Leptin (ng/mL)	9.09 ± 6.68	47.76 ± 20.45***	23.22 ± 15.95***	<0.001
Resistin (ng/mL)	14.02 ± 4.52	17.45 ± 5.57**	17.7 ± 5.67**	0.617
Adiponectin (μg/mL)	19.71 ± 11.8	21.36 ± 48.77	20.76 ± 12.66	<0.001

Data are presented as the mean ± SD. SF, subcutaneous fat; VF, visceral fat; 2-hour glucose, glucose concentration two hours after a standardized 75-gram oral glucose tolerance test; HDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; CRP, C-reactive protein; HOMA-IR, homeostatic model assessment of insulin resistance; interleukin-6/8 (IL-6/8); TNF-α, tumor necrosis factor-alpha; MCP-1, monocyte chemoattractant protein-1. *p < 0.05, **p < 0.01, ***p < 0.001 for independent samples t-tests comparing obese vs. controls; #p-value for paired samples t-test comparing obese individuals before vs. after surgery; p < 0.05 was considered statistically significant.

Table 2. Bone turnover markers and other bone variables in the study participants

Variables	Controls (n=25)	Severely obese (n=46)		^#P-value
Variables	Controls (n=25)	Before surgery	After surgery	^#P-value
TRACP5a (U/L)	2.17 ± 0.58	2.21 ± 0.5	2.51 ± 0.6*	0.007
TRACP5b (U/L)	2.58 ± 0.94	2.44 ± 0.64	3.63 ± 1.03***	<0.001
CTX (ng/mL)	0.52 ± 0.37	0.32 ± 0.18**	0.8 ± 0.37***	<0.001
PINP (ng/mL)	54.07 ± 39.72	38.27 ± 15.1*	73.12 ± 29.32***	<0.001
TotalOC (ng/mL)	8.39 ± 3.69	5.25 ± 1.92***	9.63 ± 3.86	<0.001
cOC (ng/mL)	7.79 ± 3.83	4.94 ± 2.41**	8.44 ± 3.97	<0.001
ucOC (ng/mL)	2.37 ± 1.91	1.71 ± 1.09	4.24 ± 2.7**	<0.001
TRACP5a/b (ratio)	0.94 ± 0.38	0.93 ± 0.19	0.72 ± 0.17*	<0.001
cOC/TotalOC (ratio)	0.93 ± 0.14	0.92 ± 0.28	0.87 ± 0.22	0.243
ucOC/TotalOC (ratio)	0.26 ± 0.16	0.3 ± 0.17	0.43 ± 0.17***	<0.001
PINP/CTX (ratio)	112.05 ± 32.69	142.57 ± 58.4*	96.58 ± 28.3*	<0.001
CTX/TRACP5b (ratio)	0.19 ± 0.08	0.13 ± 0.07***	0.22 ± 0.08	<0.001
Ca²⁺(mmol/L)	1.24 ± 0.03	1.23 ± 0.04	1.23 ± 0.05	0.253
Phosphate (mmol/L)	1.03 ± 0.13	0.98 ± 0.15	1.09 ± 0.16	<0.001

Data are presented as the Mean ± SD. TRACP5a: Tartrate-Resistant Acid Phosphatase Isoform 5a; TRACP5b: Tartrate-Resistant Acid Phosphatase Isoform 5b; CTX: C-Terminal Telopeptide of Type I Collagen; PINP: N-Terminal Propeptide of Type I Collagen; TotalOC: Total Osteocalcin; cOC: Carboxylated Osteocalcin; ucOC: Undercarboxylated Osteocalcin; TRACP5a/b: Ratio of Tartrate-Resistant Acid Phosphatase Isoforms 5a to 5b; cOC/TotalOC: Ratio of Carboxylated Osteocalcin to Total Osteocalcin; ucOC/TotalOC: Ratio of Undercarboxylated Osteocalcin to Total Osteocalcin; PINP/CTX: Ratio of N-Terminal Propeptide of Type I Collagen to C-Terminal Telopeptide of Type I Collagen; CTX/TRACP5b: Ratio of C-Terminal Telopeptide of Type I Collagen to Tartrate-Resistant Acid Phosphatase Isoform 5b; Ca²⁺: Concentration of ionized calcium in the blood serum. *p < 0.05, **p < 0.01, ***p < 0.001 for the Mann-Whitney U test comparing obese vs. controls; #p-value for the Wilcoxon Signed-Rank test comparing obese individuals before vs. after surgery. p < 0.05 was considered statistically significant.

Table 3. Mixed effects model of fat compartments predicting bone turnover markers

TRACP5a

TRACP5b

CTX

PINP

TotalOC

cOC

ucOC

Abdominal SF

0.907 (0.005)

0.766 (0.014)

0.739 (0.007)

0.202 (1.331)

0.766

(-0.052)

0.955

(-0.006)

0.907

(-0.014)

Extraperitoneal VF

0.759 (-0.057)

0.251

(-0.216)

0.129

(-0.102)

0.190

(-6.704)

0.065

(-1.247)

0.047

(-1.388)

0.077

(-0.680)

Intraperitoneal VF

0.895

(-0.024)

0.190

(-0.201)

0.047

(-0.125)

0.129

(-6.466)

0.048

(-1.109)

0.048

(-1.121)

0.047

(-0.662)

Total visceral fat

0.766

(-0.022)

0.190

(-0.123)

0.047

(-0.068)

0.129

(-3.813)

0.047

(-0.696)

0.047

(-0.754)

0.047

(-0.387)

Femoral SF

0.501

(-0.023)

0.501

(-0.037)

0.766 (0.008)

0.739 (0.732)

0.955

(-0.014)

0.907

(-0.028)

0.955 (0.005)

FDR values and the corresponding coefficients (in parentheses) of different fat compartments (rows) from the mixed-effect model explaining the bone marker levels (columns) using age, weight, surgery type, and the fat compartment as fixed effects and sample donor as a random effect. SF, subcutaneous fat; VF, visceral fat. TRACP5a, Tartrate-Resistant Acid Phosphatase Isoform 5a; TRACP5b, Tartrate-Resistant Acid Phosphatase Isoform 5b; CTX, C-Terminal Telopeptide of Type I Collagen; PINP, N-Terminal Propeptide of Type I Collagen; TotalOC, Total Osteocalcin; cOC, Carboxylated Osteocalcin; ucOC, Undercarboxylated Osteocalcin. The values are rounded to three decimals, and the statistically significant ones (p<0.05) are highlighted by bolding.

Table 4. Correlations of adipokines and cytokines and bone turnover markers in the study participants

	TRACP5a	TRACP5b	CTX	PINP	TotalOC	cOC	ucOC
IL-6	-0.0001	-0.08	-0.10	-0.02	-0.04	-0.04	-0.06
IL-8	0.31*	0.07	-0.04	-0.07	-0.09	-0.23	0.12
TNF-α	0.30**	0.04	-0.08	-0.09	-0.12	-0.23	0.01
MCP-1	0.13	0.04	-0.08	-0.07	-0.12	-0.29*	0.22
Leptin	0.04	-0.17	-0.31*	-0.22	-0.32*	-0.36**	-0.08
Resistin	-0.0003	0.14	0.11	0.11	0.08	0.14	0.02
Adiponectin	0.166	0.07	0.05	0.02	0.12	0.02	0.14

The data presented here are Pearson correlation coefficients of cytokines and bone turnover markers, including Tartrate-resistant acid phosphatase 5a/b (TRACP5a/b); C-terminal telopeptide of type I collagen (CTX); procollagen type I N-terminal propeptide (PINP); total (TotalOC), carboxylated (cOC), and undercarboxylated (ucOC) osteocalcin; interleukin-6/8 (IL-6/8); tumor necrosis factor-alpha (TNF-α); monocyte chemoattractant protein-1 (MCP-1). FDR-adjusted significance levels are indicated as *p < 0.05, **p < 0.01, and ***p < 0.001 for the study participants. Statistically significant results (p < 0.05) are highlighted in bold.

There is NO conflict of interest to disclose

Supplementalfigure1.tif
Supplemental Figure 1: An inverse correlation was depicted between plasma glucose levels following a 2-hour oral glucose tolerance test (2-hour glucose) and several markers of bone turnover, including C-terminal telopeptide of type 1 collagen (CTX) (r = -0.37, FDR = 0.008) [A], total osteocalcin (TotalOC) (r = -0.37, FDR = 0.004) [B], undercarboxylated osteocalcin (ucOC) (r = -0.32, FDR = 0.02) [C], and serum procollagen type I N-propeptide (PINP) (r = -0.33, FDR = 0.02) [D]. Statistically significant correlations were identified using a Spearman's rank correlation test with an FDR < 0.05. Points were colored to differentiate between patients with severe obesity before (BLUE), and after (RED) metabolic bariatric surgery, and control subjects without obesity (BLACK). Dashed lines indicate 95% confidence intervals for the regression lines.
Supplementaltable1.docx

Differential Associations of Subcutaneous and Visceral Fat with Bone Turnover Markers: A Study on Bariatric Surgery Patients with Severe Obesity and Individuals Without Obesity

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Abstract

Figures

1.0 INTRODUCTION

2.0. METHODS

3.0. RESULTS

4.0. DISCUSSION

Abbreviations

Declarations

References

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Supplementary Files

Status:

Version 1