Bone canonical Wnt signaling is downregulated in type 2 diabetes and associates with higher advanced glycation end-products (AGEs) content and reduced bone strength

Giulia Leanza; Francesca Cannata; Malak Faraj; Claudio Pedone; Viola Viola; Flavia Tramontana; Niccolò Pellegrini; Gianluca Vadalà; Alessandra Piccoli; Rocky Strollo; Francesca Zalfa; Alec T Beeve; Erica L Scheller; Simon Y Tang; Roberto Civitelli; Mauro Maccarrone; Rocco Papalia; Nicola Napoli

doi:10.7554/eLife.90437

. 2024 Apr 10;12:RP90437. doi: 10.7554/eLife.90437

Bone canonical Wnt signaling is downregulated in type 2 diabetes and associates with higher advanced glycation end-products (AGEs) content and reduced bone strength

Giulia Leanza ^1,², Francesca Cannata ¹, Malak Faraj ¹, Claudio Pedone ³, Viola Viola ¹, Flavia Tramontana ^1,², Niccolò Pellegrini ¹, Gianluca Vadalà ⁴, Alessandra Piccoli ¹, Rocky Strollo ⁵, Francesca Zalfa ^6,⁷, Alec T Beeve ⁸, Erica L Scheller ⁸, Simon Y Tang ⁹, Roberto Civitelli ⁸, Mauro Maccarrone ^10,¹¹, Rocco Papalia ^4,^†,^✉, Nicola Napoli ^1,^2,^8,^†,^✉

Editors: Se-Min Kim¹², Christopher L-H Huang¹³

PMCID: PMC11006415 PMID: 38598270

Abstract

Type 2 diabetes (T2D) is associated with higher fracture risk, despite normal or high bone mineral density. We reported that bone formation genes (SOST and RUNX2) and advanced glycation end-products (AGEs) were impaired in T2D. We investigated Wnt signaling regulation and its association with AGEs accumulation and bone strength in T2D from bone tissue of 15 T2D and 21 non-diabetic postmenopausal women undergoing hip arthroplasty. Bone histomorphometry revealed a trend of low mineralized volume in T2D (T2D 0.249% [0.156–0.366]) vs non-diabetic subjects 0.352% [0.269–0.454]; p=0.053, as well as reduced bone strength (T2D 21.60 MPa [13.46–30.10] vs non-diabetic subjects 76.24 MPa [26.81–132.9]; p=0.002). We also showed that gene expression of Wnt agonists LEF-1 (p=0.0136) and WNT10B (p=0.0302) were lower in T2D. Conversely, gene expression of WNT5A (p=0.0232), SOST (p<0.0001), and GSK3B (p=0.0456) were higher, while collagen (COL1A1) was lower in T2D (p=0.0482). AGEs content was associated with SOST and WNT5A (r=0.9231, p<0.0001; r=0.6751, p=0.0322), but inversely correlated with LEF-1 and COL1A1 (r=–0.7500, p=0.0255; r=–0.9762, p=0.0004). SOST was associated with glycemic control and disease duration (r=0.4846, p=0.0043; r=0.7107, p=0.00174), whereas WNT5A and GSK3B were only correlated with glycemic control (r=0.5589, p=0.0037; r=0.4901, p=0.0051). Finally, Young’s modulus was negatively correlated with SOST (r=−0.5675, p=0.0011), AXIN2 (r=−0.5523, p=0.0042), and SFRP5 (r=−0.4442, p=0.0437), while positively correlated with LEF-1 (r=0.4116, p=0.0295) and WNT10B (r=0.6697, p=0.0001). These findings suggest that Wnt signaling and AGEs could be the main determinants of bone fragility in T2D.

Research organism: Human

eLife digest

Type 2 diabetes is a long-term metabolic disease characterised by chronic high blood sugar levels. This in turn has a negative impact on the health of other tissues and organs, including bones. Type 2 diabetes patients have an increased risk of fracturing bones compared to non-diabetics. This is particularly true for fragility fractures, which are fractures caused by falls from a short height (i.e., standing height or less), often affecting hips or wrists. Usually, a lower bone density is associated with higher risk of fractures. However, patients with type 2 diabetes have increased bone fragility despite normal or higher bone density.

One reason for this could be the chronically high levels of blood sugar in type 2 diabetes, which alter the properties of proteins in the body. It has been shown that the excess sugar molecules effectively ‘react’ with many different proteins, producing harmful compounds in the process, called Advanced Glycation End-products, or AGEs. AGEs are – in turn –thought to affect the structure of collagen proteins, which help hold our tissues together and decrease bone strength. However, the signalling pathways underlying this process are still unclear.

To find out more, Leanza et al. studied a signalling molecule, called sclerostin, which inhibits a signalling pathway that regulates bone formation, known as Wnt signaling. The researchers compared bone samples from both diabetic and non-diabetic patients, who had undergone hip replacement surgery. Analyses of the samples, using a technique called real-time-PCR, revealed that gene expression of sclerostin was increased in samples of type 2 diabetes patients, which led to a downregulation of Wnt signaling related genes. Moreover, the downregulation of Wnt genes was correlated with lower bone strength (which was measured by compressing the bone tissue). Further biochemical analysis of the samples revealed that higher sclerostin activity was also associated with higher levels of AGEs.

These results provide a clearer understanding of the biological mechanisms behind compromised bone strength in diabetes. In the future, Leanza et al. hope that this knowledge will help us develop treatments to reduce the risk of bone complications for type 2 diabetes patients.

Introduction

Type 2 diabetes (T2D) is a metabolic disease, with an increasing worldwide prevalence, characterized by chronic hyperglycemia and adverse effects on multiple organ systems, including bones (Hofbauer et al., 2022). Patients with T2D have an increased fracture risk, particularly at the hip, compared to individuals without diabetes. A recent meta-analysis reported that individuals with T2D have 1.27 relative risk of hip fracture compared to non-diabetic controls (Wang et al., 2019). Fragility fractures in patients with T2D occur at normal or even higher bone mineral density compared to healthy subjects, implying compromised bone quality in diabetes. T2D is associated with a reduced bone turnover (Rubin and Patsch, 2016), as shown by lower serum levels of biochemical markers of bone formation, such as procollagen type 1 amino-terminal propeptide and osteocalcin, and bone resorption, C-terminal cross-linked telopeptide in diabetic patients compared to non-diabetic individuals (Napoli et al., 2018; Hygum et al., 2017; Starup-Linde and Vestergaard, 2016; Starup-Linde et al., 2016). Accordingly, dynamic bone histomorphometry of T2D postmenopausal women showed a lower bone formation rate, mineralizing surface, osteoid surface, and osteoblast surface (Manavalan et al., 2012). Our group recently demonstrated that T2D is also associated with increased SOST and decreased RUNX2 genes expression, compared to non-diabetic subjects (Piccoli et al., 2020). Moreover, we have proved in a diabetic model that a sclerostin-resistant Lrp5 mutation, associated with high bone mass, fully protected bone mass and strength even after prolonged hyperglycemia (Leanza et al., 2021). Sclerostin is a potent inhibitor of the canonical Wnt signaling pathway, a key pathway that regulates bone homeostasis (Maeda et al., 2019).

Diabetes and chronic hyperglycemia are also characterized by increased advanced glycation end-products (AGEs) production and deposition (Tan et al., 2002). AGEs may interfere with osteoblast differentiation, attachment to the bone matrix, function, and survival (Kume et al., 2005; Sanguineti et al., 2008). AGEs also alter bone collagen structure and reduce the intrinsic toughness of bone, thereby affecting bone material properties (Piccoli et al., 2020; Yamamoto and Sugimoto, 2016; Furst et al., 2016). In this work,we hypothesized that T2D and AGEs accumulation downregulate Wnt canonical signaling and negatively affect bone strength. Results confirmed that T2D downregulates Wnt/beta-catenin signaling and reduces collagen mRNA levels and bone strength, in association with AGEs accumulation.

Results

Subject characteristics

Clinical characteristics of study subjects are presented in Table 1. T2D and non-diabetic subjects did not differ in age, BMI, and menopausal age. As expected, fasting glucose was significantly higher in T2D compared to non-diabetic subjects (112.00 mg/dl [104.0–130.0]) mg/dl, vs. 94.00 [87.2–106.3], respectively; [p=0.009]. Median hemoglobin A1c (HbA1c) was determined in all T2D subjects within 3 months before surgery (6.95% [6.37–7.37]). Median disease duration in T2D subjects was (14.50 years [7.25–19.25]). Diabetes medications included monotherapy with metformin (n=12) and combination therapy with metformin plus insulin and glinide (n=3). There were no differences in serum calcium, eGFR (CKD-EPI equation), and serum blood urea nitrogen.

Table 1. Clinical features of the study subjects.

Results were analyzed using unpaired t-test with Welch’s correction and are presented as median and percentiles (25th and 75th).

	T2D subjects(n=15)		Non-diabetic subjects(n=21)		p-Value
Age (years)	73.00 (67.00–80.00)		73.00 (68.50–79.00)		0.644
BMI (kg/m²)	30.81 (24.44–34.00)		25.00 (24.00–31.50)	0.117
Menopausal age (years)	50.00 (42.50–52.75)		52.00 (48.00–53.00)		0.344
Fasting glucose levels (mg/dl)	112.00 (104.00–130.0)		94.00 (87.25–106.3)		**0.009
Disease duration (years)		14.50 (7.25–19.25)	–		–
HbA1c (%)		6.95 (6.37–7.37)	–		–
Serum calcium (mg/dl)		9.05 (8.800–9.550)	9.15 (9.000–9.550)		0.535
eGFR (ml/min/1.73 m²)		78,30 (59.90–91.10)	75.60 (61.35–88.55)		0.356
Serum blood urea nitrogen (mg/dl)	42.00 (36.00–53.00)		37.00 (31.75–46.50)		0.235

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** p value ≤ 0.01.

Bone histomorphometry

Bone samples of nine T2D and nine non-diabetic subjects were used for histomorphometry analysis. We found no significant differences in BV/TV and osteoid volume, while mineralized volume/total volume (Md.V/TV) trended lower in T2D subjects relative to controls (0.249% [0.156–0.336] vs 0.352% [0.269–0.454]; p=0.053) (Table 2).

Table 2. Histomorphometric parameters of trabecular bone of the study subjects.

Results were analyzed using unpaired t-test with Welch’s correction and are presented as median and percentiles (25th and 75th).

	T2D subjects(n=9)	Non-diabetic subjects(n=9)	p-Value
BV/TV (%)	0.248 (0.157–0.407)	0.358 (0.271–0.456)	0.120
Md.V/BV (%)	0.994 (0.984–0.998)	0.995 (0.985–0.997)	0.998
Md.V/TV (%)	0.249 (0.156–0.366)	0.352 (0.269–0.454)	0.053
OV/BV (%)	0.009 (0.002–0.009)	0.004 (0.002–0.015)	0.704
OV/TV (%)	0.001 (0.0002–0.0058)	0.001 (0.0007–0.0056)	0.896
OS/BS (%)	0.026 (0.022–0.161)	0.035 (0.009–0.117)	0.525

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Bone compression tests

Young’s modulus was lower in T2D compared to non-diabetic subjects (21.6 MPa [13.46–30.10] vs. 76.24 MPa [26.81–132.9]; p=0.0025), while ultimate strength and yield strength were not different between the two groups (Table 3).

Table 3. Bone mechanical parameters of trabecular bone of the study subjects.

Results were analyzed using unpaired t-test with Welch’s correction and are presented as median and percentiles (25th and 75th).

	T2D subjects(n=11)	Non-diabetic subjects(n=21)	p-Value
Young’s modulus (MPa)	21.60 (13.46–30.10)	76.24 (26.81–132.9)	0.002
Ultimate strength (MPa)	3.015 (2.150–13.86)	7.240 (3.150–8.898)	0.914
Yield strength (MPa)	2.525 (1.943–6.393)	6.150 (3.115–7.423)	0.159

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Gene expression

SOST mRNA was significantly higher in T2D than in non-diabetic subjects (Figure 1A, p<0.0001), whereas there was no difference in DKK1 gene expression between the two groups (Figure 1B). Of note, SOST mRNA transcript was very low in the majority of non-diabetic subjects (Figure 1A). LEF-1 (Figure 1C, p=0.0136), WNT10B (Figure 1D, p=0.0302), and COL1A1 (Figure 1F, p=0.0482) mRNA transcripts were significantly lower in T2D compared to non-diabetic subjects. Conversely, WNT5A was higher in T2D relative to non-diabetics (Figure 1E, p=0.0232). Moreover, GSK3B was significantly increased in T2D compared to non-diabetic subjects (Figure 1G, p=0.0456), but we did not find any significant difference in gene expression of AXIN2, BETA-CATENIN, and SFRP5 (Figure 1H–J) between our groups.

Correlation analysis of Wnt target genes, AGEs, and glycemic control

As shown in Figure 2, AGEs were inversely correlated with LEF-1 (Figure 2A, p=0.0255) and COL1A1 mRNA abundance (Figure 2B, p=0.0004), whereas they were positively correlated with SOST (Figure 2C, p<0.0001) and WNT5A mRNA (Figure 2D, p=0.0322). There was no correlation between AGEs content and WNT10B (Figure 2E; p=0.1938) or DKK1 gene expression (Figure 2F; p=0.9349). Likewise, we did not find any significant correlation between LEF-1, WNT5A, WNT10B, DKK-1, COL1A1 expression in bone and glycemic control in T2D individuals (Figure 3—figure supplement 1A–D). However, there were positive correlations between SOST and fasting glucose levels (Figure 3A, p=0.0043), SOST and disease duration (Figure 3B, p=0.00174), WNT5A, GSK3B, and fasting glucose levels (Figure 3C, p=0.0037; Figure 3D, p=0.0051).

Figure 3. — (A) SOST positively correlated with fasting glucose levels (r=0.4846; p=0.0043). (B) SOST positively correlated with disease duration (r=0.7107; p=0.0174). (C) WNT5A positively correlated with fasting glucose levels (r=0.5589; p=0.0037). (D) GSK3B positively correlated with fasting glucose levels (r=0.4901; p=0.0051). Data were analyzed using nonparametric Spearman correlation analysis and r represents the correlation coefficient.

Figure 3—source data 1. Data represented by each point in Figure 3A–D.

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Figure 3—figure supplement 1. — (A) SOST positively correlated with fasting glucose levels (r=0.4846; p=0.0043). (B) SOST positively correlated with disease duration (r=0.7107; p=0.0174). (C) WNT5A positively correlated with fasting glucose levels (r=0.5589; p=0.0037). (D) GSK3B positively correlated with fasting glucose levels (r=0.4901; p=0.0051). Data were analyzed using nonparametric Spearman correlation analysis and r represents the correlation coefficient.

Figure 3—source data 1. Data represented by each point in Figure 3A–D.

elife-90437-fig3-data1.xlsx^{(8.7KB, xlsx)}

Correlation analysis of Wnt target genes and bone mechanical parameters

As shown in Figure 4, Young’s modulus was negatively correlated with SOST (Figure 4A, p=0.0011), AXIN2 (Figure 4D, p=0.0042), and SFRP5 (Figure 4F, p=0.0437), while positively correlated with LEF-1 (Figure 4B, p=0.0295) and WNT10B (Figure 4C, p=0.0001). Ultimate strength was associated with WNT10B (Figure 4F, p=0.0054) and negatively correlated with AXIN2 (Figure 4G, p=0.0472). Finally, yield strength was associated with LEF-1 (Figure 4H, p=0.0495) and WNT10B (Figure 4I, p=0.0020) and negatively correlated with GSK3B (Figure 4J, p=0.0245), AXIN2 (Figure 4K, p=0.0319), and SFRP5 (Figure 4L, p=0.0422). Non-significant correlations are reported in Figure 4—figure supplement 1A–Q.

Figure 4—figure supplement 1. — (A) SOST negatively correlated with Young’s modulus (MPa); (r=−0.5675; p=0.0011). (B) LEF-1 positively correlated with Young’s modulus (MPa); (r=0.4116; p=0.0295). (C) WNT10B positively correlated with Young’s modulus (MPa); (r=0.6697; p=0.0001). (D) AXIN2 negatively correlated with Young’s modulus (MPa); (r=−0.5523; p=0.0042). (E) BETA-CATENIN negatively correlated with Young’s modulus (MPa); (r=−0.5244; p=0.0050). (F) SFRP5 negatively correlated with Young’s modulus (MPa); (r=−0.4442; p=0.0437). (G) WNT10B positively correlated with ultimate strength (MPa); (r=0.5392; p=0.0054). (H) AXIN2 negatively correlated with ultimate strength (MPa); (r=−0.4180; p=0.0472). (I) BETA-CATENIN negatively correlated with ultimate strength (MPa); (r=−0.5528; p=0.0034). (J) LEF-1 positively correlated with yield strength (MPa); (r=0.4338; p=0.0495). (K) WNT10B positively correlated with yield strength (MPa); (r=0.6632; p=0.0020). (L) GSK3B negatively correlated with yield strength (MPa); (r=−0.4674; p=0.0245). (M) AXIN2 negatively correlated with yield strength (MPa); (r=−0.5067; p=0.0319). (N) BETA-CATENIN negatively correlated with yield strength (MPa); (r=−0.5491; p=0.0149). (O) SFRP5 negatively correlated with yield strength (MPa); (r=−0.5357; p=0.0422). Data were analyzed using nonparametric Spearman correlation analysis and r represents the correlation coefficient.

Figure 4—source data 1. Data represented by each point in Figure 4A–L.

elife-90437-fig4-data1.xlsx^{(9.1KB, xlsx)}

Discussion

We show that key components of the Wnt/beta-catenin signaling are abnormally expressed in the bone of postmenopausal women with T2D and they are associated with AGEs and reduced bone strength (Figure 5). LEF-1, a transcription factor that mediates responses to Wnt signal and Wnt target genes itself, and WNT10B, an endogenous regulator of Wnt/beta-catenin signaling and skeletal progenitor cell fate, are both downregulated in bone of postmenopausal women with T2D. Consistently, in this group, the expression of the Wnt inhibitor, SOST is increased, suggesting suppression of Wnt/beta-catenin signaling. Interestingly, our data suggest that sclerostin expression is very low in healthy postmenopausal women not affected by osteoporosis. Moreover, we reported an increase in the expression level of bone GSK3B, in line with downregulated Wnt/beta-catenin signaling in T2D. Our data also show that the expression of WNT5A, a non-canonical ligand linked to inhibition of Wnt/beta-catenin signaling, was increased, whereas COL1A1 was decreased. These findings are consistent with reduced bone formation and suppression of Wnt signaling in T2D. We have previously reported upregulation of SOST and downregulation of RUNX2 mRNA in another cohort of postmenopausal women with T2D (Piccoli et al., 2020). Of note, the cohort of T2D subjects studied here had glycated hemoglobin within therapeutic targets, implying that the changes in gene transcription we identified persist in T2D bone despite good glycemic control.

High circulating sclerostin has been reported in diabetes (García-Martín et al., 2012; Gennari et al., 2012), and increased sclerostin is associated with fragility fractures (Yamamoto et al., 2013). Aside from confirming higher SOST expression, we also show that other Wnt/beta-catenin osteogenic ligands are abnormally regulated in the bone of T2D postmenopausal women. WNT10B is a positive regulator of bone mass; transgenic overexpression in mice results in increased bone mass and strength (Longo et al., 2004), whereas genetic ablation of WNT10B is characterized by reduced bone mass (Bennett et al., 2005; Kubota et al., 2009), and decreased number and function of osteoblasts (Bennett et al., 2005). More to the point, WNT10B expression is reduced in the bone of diabetic mice (Zhang et al., 2015). Therefore, the reduced WNT10B in human bone we found in the present study further supports the hypothesis of reduced bone formation in T2D. Accordingly, LEF-1 gene expression was also downregulated confirming that Wnt/beta-catenin pathway is decreased in T2D. Importantly, the overexpression of LEF-1 induces the expression of osteoblast differentiation genes (osteocalcin and COL1A1) in differentiating osteoblasts (Hoeppner et al., 2009). In fact, in this study we also demonstrated that a downregulation of LEF-1 in T2D bone goes along with a downregulation of COL1A1, strengthen data of a reduced production of bone matrix most likely as the result of reduced osteoblasts synthetic activity in diabetes (Manavalan et al., 2012; Khan et al., 2015). Reduced RUNX2 in T2D postmenopausal women also confirms previous findings (Piccoli et al., 2020) and further supports the notion of reduced osteoblast differentiation or function in diabetes. On the other hand, the contribution of upregulated WNT5A in diabetic bone is more complex. WNT5A regulates Wnt/beta-catenin signaling depending on the receptor availability (Mikels and Nusse, 2006). Non-canonical WNT5A activates beta-catenin-independent signaling, including the Wnt/Ca⁺⁺ (Dejmek et al., 2006) and planar cell polarity pathways (Oishi et al., 2003). Heterozygous Wnt5a null mice have low bone mass with impaired osteoblast and osteoclast differentiation (Maeda et al., 2012). Wnt5a inhibits Wnt3a protein by downregulating beta-catenin-induced reporter gene expression (Mikels and Nusse, 2006). In line with these findings, we showed that there was an increased gene expression of WNT5A in bone of T2D postmenopausal women, confirming a downregulated Wnt/beta-catenin signaling and impaired osteoblasts function. Moreover, GSK3B is a widely expressed serine/threonine kinase involved in multiple pathways regulating immune cell activation and glucose metabolism. Preclinical studies reported that GSK3B is a negative regulator of Wnt/beta-catenin signaling and bone metabolism (McManus et al., 2005; Chen et al., 2021), and its increase is associated with T2D and alterations in insulin secretion and sensitivity (Nunez Lopez et al., 2022; Xia et al., 2022). Our data confirmed that GSK3B is increased in T2D postmenopausal women and it is associated with reduced yield strength. In fact, we also showed an impaired bone mechanical plasticity in T2D, in line with other studies showing a reduced bone strength (Furst et al., 2016; Farr et al., 2014; Hunt et al., 2019). In addition, this study reported significant correlations of bone mechanical parameters and Wnt target genes, which might reflect the biological effect of downregulated Wnt signaling and AGEs accumulation on bone mechanical properties in diabetes.

We have previously shown that AGEs content is higher in T2D bone compared to non-diabetic bone, even in patients with well-controlled T2D (Piccoli et al., 2020). Here, we show that AGEs accumulation is positively correlated with SOST, WNT5A, and GSK3B gene expression, and negatively correlated with LEF-1, WNT10B, and COL1A1 mRNA. These findings are consistent with the hypothesis that AGEs accumulation is associated with impaired Wnt signaling and low bone turnover in T2D. We did not find any abnormalities in histomorphometric parameters in our subjects with T2D, consistent with our previous report (Piccoli et al., 2020). Reduced osteoid thickness and osteoblast number were reported in premenopausal T2D women with poor glycemic control compared to non-diabetic subjects but not in the group with good glycemic control (Andrade et al., 2020). Therefore, good glycemic control appears to prevent or rescue any changes in static histologic parameters of bone turnover that might be caused by uncontrolled diabetes.

Our study has some limitations. One is the cross-sectional design; another one is the relatively small number of T2D subjects enrolled. Moreover, we measured the mRNA abundance of the genes of interest, and we cannot assume that the differences we found reflect differences in protein abundance. Although osteoarthritis may affect some of the genes we studied (Weivoda et al., 2017), all study subjects were affected by variable degree of osteoarthritis, and the effect of such potential confounder is not likely to be different between T2D and control subjects. Finally, we did not use the tetracycline double-labeled technique to investigate dynamic bone parameters.

The main strength of our study is that this study is the first to explore the association of AGEs on Wnt pathway in postmenopausal T2D women. Moreover, we measured the expression of several Wnt genes directly on bone samples of postmenopausal T2D women.

In conclusion, our data show that, despite good glycemic control, T2D decreases expression of COL1A1 and Wnt genes that regulate bone turnover, in association with increased AGEs content and reduced bone strength. These results may help understand the mechanisms underlying bone fragility in T2D.

Materials and methods

Study subjects

We enrolled a total of 36 postmenopausal women (15 with T2D and 21 non-diabetic controls) undergoing hip arthroplasty for osteoarthritis, consecutively screened to participate in this study between 2020 and 2022. Diabetes status was confirmed by the treating diabetes physician. Participants were diagnosed with diabetes when they had fasting plasma glucose ≥126 mg/dl or 2 hr plasma glucose≥200 mg/dl during a 75 g oral glucose tolerance test; or HbA1c≥6.5% in accordance with the American Diabetes Association diagnostic criteria. Eligible participants were ≥60 years of age. Exclusion criteria were any diseases affecting bone or malignancy. Additionally, individuals treated with medications affecting bone metabolism such as estrogen, raloxifene, tamoxifen, bisphosphonates, teriparatide, denosumab, thiazolidinediones, glucocorticoids, anabolic steroids, and phenytoin, and those with hypercalcemia or hypocalcemia, hepatic or renal disorder, hypercortisolism, current alcohol, or tobacco use were excluded. The study was approved by the Ethics Committee of Campus Bio-Medico University of Rome (Prot..42/14 PT_ComEt CBM) and all participants provided written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki.

Specimen preparation

Femoral head specimens were obtained during hip arthroplasty. As described previously (Piccoli et al., 2020), trabecular bone specimens were collected fresh and washed multiple times in sterile PBS until the supernatant was clear of blood. Bone samples were stored at –80°C until analysis.

Bone histomorphometry

Trabecular bone from femur heads was fixed in 10% neutral buffered formalin for 24 hr prior to storage in 70% ethanol. Tissues were embedded in methylmethacrylate and sectioned sagittally by the Washington University Musculoskeletal Histology and Morphometry Core. Sections were stained with Goldner’s trichrome. Then, a rectangular region of interest (ROI) containing trabecular bone was chosen below the cartilage-lined joint surface and primary spongiosa. This region had an average dimension of 45 mm². Tissue processing artifacts, such as folding and edges, were excluded from the ROI. A threshold was chosen using the Bioquant Osteo software to automatically select trabeculae and measure bone volume. Finally, Osteoid was highlighted in the software and quantified semi-automatically using a threshold and correcting with the brush tool. Unstained and TRAP-stained (Sigma) slides were imaged at ×20 high resolution using a NanoZoomer 2.0 with bright field and FITC/TRITC (Hamamatsu Photonics). Images were then analyzed via Bioquant Osteo software according to the manufacturer’s instructions and published standards (v18.2.6, Bioquant Image Analysis Corp., Nashville, TN, USA).

Bone compression tests

We used cylindrical bone specimens of trabecular core (with a diameter of 10 mm and a length of 20 mm) from 11 T2D and 21 non-diabetic subjects to measure bone mechanical parameters (Young’s modulus, ultimate strength, and yield strength), as previously described (Piccoli et al., 2020).

RNA extraction and gene expression by RT-PCR

Total RNA from trabecular bone samples was extracted using TRIzol (Invitrogen) following the manufacturer’s instructions. The concentration and purity of the extracted RNA were assessed spectrophotometrically (TECAN, InfiniteM200PRO), and only samples with 260/280 absorbance ratio between 1.8 and 2 were used for reverse transcription using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Carlsbad, CA, USA) according to the manufacturer’s recommendations. Transcription products were amplified using TaqMan real-time PCR (Applied Biosystems, Carlsbad, CA, USA) and a standard protocol (95°C for 10 min; 40 cycles of 95°C for 15 s and 60°C for 1 min; followed by 95°C for 15 s, 60°C for 15 s, and 95°C for 15 s). Beta-actin expression was used as an internal control (housekeeping gene). Relative expression levels of Sclerostin (SOST), Dickkopf-1 (DKK-1), Wnt ligands (WNT5A and WNT10B), T-cell factor/lymphoid enhancer factor 1 (LEF-1), collagen type I alpha 1 chain (COL1A1), glycogen synthase kinase 3 beta (GSK3B), axis inhibition protein 2 (AXIN2), beta-catenin (BETA-CATENIN), and secreted frizzled-related protein 5 (SFRP5) were calculated using the 2^-∆Ct method.

Statistical analysis

Data were analyzed using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA). Patients’ characteristics were described using means and standard deviations or medians and 25th–75th percentiles, as appropriate, and percentages. Group data are presented in boxplots with median and interquartile range; whiskers represent maximum and minimum values. We assessed data for normality and Mann-Whitney test was used to compare variables between groups. Data were analyzed using nonparametric Spearman correlation analysis and the correlation coefficients (r) were used to assess the relationship between variables. We used Grubbs’ test to assess and exclude outliers. For bone histomorphometry, we performed a priori sample size calculation using G*Power 3.1.9.7, based on the t-test, difference between two independent groups setting. Analysis demonstrated that given an effect size of 2.2776769 (Manavalan et al., 2012), we needed a total of 12 patients (6/group) to reach a power of 0.978.

Acknowledgements

This work was supported by an internal Grant of Campus Bio-Medico University of Rome.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Rocco Papalia, Email: r.papalia@policlinicocampus.it.

Nicola Napoli, Email: n.napoli@policlinicocampus.it.

Se-Min Kim, Icahn School of Medicine at Mount Sinai, United States.

Christopher L-H Huang, University of Cambridge, United Kingdom.

Funding Information

This paper was supported by the following grant:

Università Campus Bio-Medico di Roma Internal grant to Mauro Maccarrone, Rocco Papalia, Nicola Napoli.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Writing - original draft, Project administration, Writing – review and editing.

Resources, Data curation, Investigation.

Data curation, Investigation, Visualization, Methodology, Writing – review and editing.

Conceptualization, Data curation, Software, Formal analysis, Investigation, Writing – review and editing.

Data curation, Investigation, Methodology, Writing – review and editing.

Investigation, Methodology, Writing – review and editing.

Software, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing.

Resources, Supervision, Funding acquisition, Investigation, Methodology.

Data curation, Formal analysis, Investigation, Methodology.

Conceptualization, Supervision, Visualization, Writing – review and editing.

Writing – review and editing.

Investigation, Visualization, Methodology, Writing – review and editing.

Conceptualization, Visualization, Writing – review and editing.

Resources, Supervision, Visualization, Writing – review and editing.

Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Project administration.

Conceptualization, Data curation, Supervision, Funding acquisition, Investigation, Visualization, Project administration, Writing – review and editing.

Ethics

Human subjects: The study was approved by the Ethics Committee of the Campus Bio-Medico University of Rome (Prot..42/14 PT_ComEt CBM) and all participants provided written informed consent. All procedures were conducted in accordance with the Declaration of Helsinki.

Additional files

MDAR checklist

elife-90437-mdarchecklist1.docx^{(100.9KB, docx)}

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files; source data files have been provided for all tables and figures of the manuscript, including figure supplements.

References

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eLife. doi: 10.7554/eLife.90437.3.sa0

eLife assessment

Se-Min Kim ¹

This study provides valuable insights into understanding bone fragility in T2D patients through the use of human skeletal tissue, reinforcing previous pre-clinical studies or observational studies using serum samples that the Wnt signaling pathway may play a critical role in T2D-related bone impairment. The methods are solid, but a limited number of subjects and a small set of genes with lack of data in terms of cellular properties of skeletal tissue are viewed as weaknesses.

eLife. doi: 10.7554/eLife.90437.3.sa1

Reviewer #1 (Public review):

Anonymous

Summary: Leanza et al. investigated the regulation of Wnt signaling factors in the bone tissue obtained from individuals with or without type 2 diabetes. They showed that typical canonical Wnt ligands and downstream factors (Wnt10b, LEF1) are down-regulated, while Wnt5a and sclerostin mRNA is unregulated in diabetic bone tissue. Further, Wnt5a and sclerostin associated with the content of AGEs and SOST mRNA levels also correlated with glycemic control and disease duration.

Strengths:

- A strength of the study is the investigation of Wnt signaling in bone tissue from humans with type 2 diabetes. Most studies measure only serum levels of Wnt inhibitors, but this study takes it further and looks into bone specifically.

- The measurement of AGEs and its correlation to the Wnt signaling molecules is interesting and important. The correlation of sclerostin and Wnt5a with AGEs and disease duration suggests that inhibited Wnt signaling is paralleled by higher AGE levels and potentially weaker bone.

- The methodology in terms of obtaining the bone samples and the rigorous evaluation of RNA integrity is great and provides a solid basis for further analyses.

Weaknesses:

- A weakness may include the rather limited number of samples.

Overall, this study validates findings from the group that have reported similar findings in 2020. This validates their methodology and shows that alterations in Wnt signaling are reproducible in human bone tissue.

eLife. doi: 10.7554/eLife.90437.3.sa2

Reviewer #2 (Public review):

Anonymous

Summary:

This study reports the levels of expression of selected genes implicated in Wnt signaling in trabecular bone from femur heads obtained after surgery from post-menopausal women with (15 women) or without (21 women) type 2 diabetes. They find higher expression levels of SOST and WNT5A, and lower expression levels of LEF-1 and WNT10B in tissues from subjects with T2D, correlating with glycemia and advanced glycation products. No significant differences in bone density were observed. Overall, this is a cross-sectional, observational study measuring a limited set of genes found to vary with glycemia in postmenopausal women undergoing hip surgery.

Strengths:

The study demonstrates the feasibility of measuring gene expression in post-surgical trabecular bon samples and finds differences associated with glycemia despite a relatively small number of subjects. It can form the basis for further research on the causes and consequences of changes in elements of the WNT signaling pathway in bone biology and disease.

Weaknesses:

The small number of targeted genes does not provide a comprehensive view of the transcriptional landscape within which the effects are observed. The gene expression changes are not associated with cellular or physiological properties of the tissue, raising questions about the biological significance of the observations.

eLife. 2024 Apr 10;12:RP90437. doi: 10.7554/eLife.90437.3.sa3

Author response

Giulia Leanza ¹, Francesca Cannata ², Malak Faraj ³, Claudio Pedone ⁴, Viola Viola ⁵, Flavia Tramontana ⁶, Niccolò Pellegrini ⁷, Gianluca Vadalà ⁸, Alessandra Piccoli ⁹, Rocky Strollo ¹⁰, Francesca Zalfa ¹¹, Alec T Beeve ¹², Erica L Scheller ¹³, Simon Y Tang ¹⁴, Roberto Civitelli ¹⁵, Mauro Maccarrone ¹⁶, Rocco Papalia ¹⁷, Nicola Napoli ¹⁸

¹Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy; Operative Research Unit, Roma, Italy

²Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy; Operative Research Unit, Roma, Italy

³Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy; Operative Research Unit, Roma, Italy

⁴Operative Research Unit of Unit of Geriatrics, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128 Roma, Italy, Roma, Italy

⁵Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy, Roma, Italy

⁶Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy; Operative Research Unit of Osteometabolic and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128 Roma, Italy, Roma, Italy

⁷Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy, Roma, Italy

⁸Operative Research Unit of Orthopedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128 Roma, Italy, Roma, Italy

⁹Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy, Roma, Italy

¹⁰Department of Human Sciences and Promotion of the Quality of Life San Raffaele Roma Open University Via di Val Cannuta 247 00166 Rome, Italy, Roma, Italy

¹¹Predictive Molecular Diagnostic Unit, Pathology Department, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128, Roma, Italy, Roma, Italy

¹²Department of Medicine, Division of Bone and Mineral Diseases. Musculoskeletal Research Center, Washington University School of Medicine, St. Louis, MO USA, St Louis, United States

¹³Washington University, Saint Louis, United States

¹⁴Department of Orthopaedic Surgery, Washington University in St. Louis, St. Louis, Missouri, USA, St Louis, United States

¹⁵Washington University in St Louis, St. Louis, United States

¹⁶Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio snc, 67100 L'Aquila, Italy; European Center for Brain Research, Santa Lucia Foundation IRCCS, 00164 Rome, Italy, Roma, Italy

¹⁷Operative Research Unit of Orthopedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128 Roma, Italy, Roma, Italy

¹⁸Department of Medicine and Surgery, Research Unit of Endocrinology and Diabetes, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21 - 00128 Roma, Italy; Operative Research Unit of Osteometabolic and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200 - 00128 Roma, Italy, Roma, Italy

Roles

Giulia Leanza: Author

Francesca Cannata: Author

Malak Faraj: Author

Claudio Pedone: Author

Viola Viola: Author

Flavia Tramontana: Author

Niccolò Pellegrini: Author

Gianluca Vadalà: Author

Alessandra Piccoli: Author

Rocky Strollo: Author

Francesca Zalfa: Author

Alec T Beeve: Author

Erica L Scheller: Author

Simon Y Tang: Author

Roberto Civitelli: Author

Mauro Maccarrone: Author

Rocco Papalia: Author

Nicola Napoli: Author

The following is the authors’ response to the original reviews.

REVIEWER #1

Leanza et al. investigated the regulation of Wnt signaling factors in the bone tissue obtained from individuals with or without type 2 diabetes. They showed that typical canonical Wnt ligands and downstream factors (Wnt10b, LEF1) are down-regulated, while Wnt5a and sclerostin mRNA are unregulated in diabetic bone tissue. Further, Wnt5a and sclerostin associated with the content of AGEs and SOST mRNA levels also correlated with glycemic control and disease duration.

Strengths:

A strength of the study is the investigation of Wnt signaling in bone tissue from humans with type 2 diabetes. Most studies measure only serum levels of Wnt inhibitors, but this study takes it further and looks into bone specifically.

The measurement of AGEs and its correlation to the Wnt signaling molecules is interesting and important. The correlation of sclerostin and Wnt5a with AGEs and disease duration suggests that inhibited Wnt signaling is paralleled by higher AGE levels and potentially weaker bone.

The methodology in terms of obtaining the bone samples and the rigorous evaluation of RNA integrity is great and provides a solid basis for further analyses.

Weaknesses:

A weakness may include the rather limited number of samples. Especially for some sub-analyses (e.g. RNA analyses), only a subset of samples was used.

How was the sample size determined? It seems like more samples might have been necessary to obtain significant results for methods with a higher standard deviation (e.g. histomorphometry).

We apology for the oversight in the description of the statistical analysis and we thank the reviewer for the careful reading. For sample size calculation of bone histomorphometry we used the cohort of the only paper analyzing trabecular bone in T2D postmenopausal women by dynamic histomorphometry (Manavalan JS et al, JCEM 2012). We performed a priori sample size calculation using G*Power 3.1.9.7., based on the t-test, difference between two independent groups setting. Analysis demonstrated that given an effect size of 2.2776769, we needed a total of 12 patients (6/group) to reach a power of 0.978. Regarding gene expression analyses, it was performed not in a subset of patients, but in all recruited subjects for this study. Based on the results of gene expression analysis on our main outcome (Wnt signaling), we demonstrated that for SOST gene the effect size was 1.2733824, with a power of 0.9490065, confirming that sample size was sufficient to achieve adequate statistical power.

Why is the number of samples different for the mRNA measurements? In most cases, there were 9, but in some 8 and in some 10?

We sincerely thank the reviewer for the opportunity to clarify such important aspects. The number of samples used for mRNA quantification may differ between the different analyzed genes due to multiple reasons: First, we used for the real-time PCR only samples with high quality ratio (260/280) between 1.8-2.0 as stated in the method section of the manuscript (Page 8, lines 163-164). Moreover, we decided not to use the undetermined values, undetectable after the amplification cycles (40 cycles in total), as specified in the method section (Page 8, line 167).

Overall, this study validates findings from the group that reported similar findings in 2020. This validates their methodology and shows that alterations in Wnt signaling are reproducible in human bone tissue.

We thank the reviewer for the positive comment, we really value her/his opinion.

COMMENTS:

(1) The authors could provide more details on how much of the bone was analyzed for bone histomorphometry (what area?).

We truly thank the reviewer for allowing us to explain more in depth our methodology. First, a biopsy containing trabecular bone from the femoral head was fixed in 10% neutral buffered formalin for 24 h prior to storage in 70% ethanol. Tissues were embedded in methylmethacrylate and sectioned sagittally by the Washington University Musculoskeletal Histology and Morphometry Core. Sections were stained with Goldner’s trichrome. Then, a rectangular region of interest containing trabecular bone was chosen below the cartilage-lined joint surface and primary spongiosa. This region had an average dimension of 45 mm2. Tissue processing artifacts, such as folding and edges, were excluded from the ROI. A threshold was chosen using the BIOQUANT software to automatically select trabeculae and measure bone volume. Finally, Osteoid was highlighted in the software and quantified semi-automatically using a threshold and correcting with the brush tool (as shown in the image below).

We specify that in the methods section (Page 7, lines 146-152).

(2) Could the number of samples used for histomorphometry be increased? That may also lead to more significant results.

We sincerely appreciated this suggestion from the reviewer but unfortunately, all available samples for histomorphometry have been analyzed and we are not able to increase the number of recruited participants at this time. Recruitment of people with T2D undergoing hip replacement is extremely difficult giving the limited number of those approved for elective surgery and compliant with our inclusion criteria. Considering also the long time needed to process bone sample for gene expression and histology analysis would require several months to have a consistent increase in recruited subjects. However, we have previously calculated sample size for bone histomorphometry analysis using the only available data of trabecular bone in T2D postmenopausal women measured by dynamic histomorphometry (Manavalan JS et al, JCEM 2012). We performed a priori sample size calculation using G*Power 3.1.9.7., based on the t-test of two independent groups. Analysis demonstrated that given an effect size of 2.2776769, we needed a total of 12 patients (6/group) to reach a power of 0.978.

(3) It would have been interesting to assess the biomechanical behavior of the bone specimens. While it is known that BMD is often higher in patients with T2D, the resistance to fractures is lower. Ideally, bone strength measures could be correlated with Wnt molecule expression and AGEs.

We agree with the reviewer that the assessment of biomechanical parameters in our cohort would increase the importance of this study, giving more insights on the effect of downregulation of Wnt signaling on bone strength. Thus, we followed reviewer suggestion, and we performed bone compression tests on trabecular bone core. We found a significant decrease in bone plasticity of T2D compared to controls Young’s Modulus 21.6 (13.46-30.10 MPa) vs. 76.24 (26.81-132.9 MPa); (p=0.0025). We added results of bone compression test in a new paragraph (Page 8, lines 191-194). In order to assess the validity of our results, we performed a post-hoc power calculation using G*Power 3.1.9.7. We demonstrated that effect size was 1.4716626, with a power of 0.9730784, confirming that sample size was sufficient to achieve adequate statistical power. We added methods in the related section and biomechanical data in table 3; we modified the manuscript accordingly (modifications are shown in track changes). Moreover, we also performed correlation analysis between Wnt target genes, AGEs and biomechanical parameters showing significant correlations as reported in the added paragraph in the results section (Page 11, Lines 225-233).

REVIEWER #2

This study reports the levels of expression of selected genes implicated in Wnt signaling in trabecular bone from femur heads obtained after surgery from post-menopausal women with (15 women) or without (21 women) type 2 diabetes. They found higher expression levels of SOST and WNT5A, and lower expression levels of LEF-1 and WNT10B in tissues from subjects with T2D, correlating with glycemia and advanced glycation products. No significant differences in bone density were observed. Overall, this is a cross-sectional, observational study measuring a limited set of genes found to vary with glycemia in postmenopausal women undergoing hip surgery.

Strengths:

The study demonstrates the feasibility of measuring gene expression in post-surgical trabecular bone samples, and finds differences associated with glycemia despite a relatively small number of subjects. It can form the basis for further research on the causes and consequences of changes in elements of the WNT signaling pathway in bone biology and disease.

Weaknesses:

The small number of targeted genes does not provide a comprehensive view of the transcriptional landscape within which the effects are observed. The gene expression changes are not associated with cellular or physiological properties of the tissue, raising questions about the biological significance of the observations.

We thank the reviewer for the comment. Replying to his/her concerns we have increased the number of Wnt target genes including more interactors of Wnt/β-catenin pathway. We measured GSK3B, AXIN2, BETA-CATENIN and SFRP5 gene expression levels, showing a significant increase in GSK3B, in line with a downregulation of Wnt signaling in T2D. We modified the manuscript accordingly with this new analysis and updated the figure 1 panel (Page 10, lines 210-213). Unfortunately, in this paper we were not able to perform experiments on cellular or physiological properties. However, in order to analyze the biological effect of the analyzed genes on the phenotype, we measured bone strength by performing compression tests on trabecular bone cores (Page 10, lines 201-203 and table 3) and used biomechanical parameters for correlation analysis with targeted genes showing significant correlations of bone strength and Wnt genes. We modified adding a new paragraph in the result section and a new figure panel to the main manuscript (Page 11, lines 225-233 and figure 4).

COMMENTS:

(1) The small number of targeted genes does not provide a comprehensive view of the transcriptional landscape within which the effects are observed. Given the author's success in obtaining good-quality RNA from trabecular bone, a more comprehensive exploration would greatly improve the quality of the study.

We agree with the reviewer that increase the transcriptional landscape related to Wnt signaling would be of interest for this work and we really thank for this opportunity. We were able to increase the number of Wnt target genes including more interactors of Wnt/β-catenin pathway, using the same cohort of patients in which we performed the other analysis. We also measured GSK3B, AXIN2, BETA-CATENIN and SFRP5 gene expression levels, showing a significant increase in GSK3B, in line with a downregulation of Wnt signaling in T2D. We modified the manuscript accordingly with this new analysis and updated the figures panel (Page 10, lines 210-213 and Figure 1).

(2) The gene expression changes are not associated with cellular or physiological properties of the tissue, raising questions about the biological significance of the observations. Can the authors perform immunohistochemistry to associate the changes in gene expression with protein expression?

We sincerely acknowledge this comment for focusing the attention on a such important aspect. We have partially replied to this comment in the previous paragraph. Regarding immunohistochemistry analysis, it is not possible to further use the available samples. This is mainly due to the fact that non-decalcified bones were embedded in plastic to allow for separate analysis of newly formed osteoid and mineralized bone. This process leads to poor antigen preservation and unsuitable detection of most targets. Moreover, antibodies for Wnt are also unreliable due to the secreted nature of the protein. Overall, this approach is unlikely to work efficiently. Similarly, RNAscope is not possible due to the resin. Optimization and validation of these analyses will need to be saved for a future study with fresh specimens.

REVIEWER #3

The manuscript by Leanza and colleagues explores the regulation of Wnt signaling and its association with advanced glycation end products (AGEs) accumulation in postmenopausal women with type 2 diabetes (T2D). The paper provides valuable insights into the potential mechanisms underlying bone fragility in individuals with T2D. Overall, the manuscript is well-structured, and the methodology is sound. I would suggest some minor revisions to improve clarity.

Strengths:

The study addresses an important and clinically relevant question concerning the mechanisms underlying bone fragility in postmenopausal women with T2D.

The study's methodology appears sound, and the inclusion of postmenopausal women with and without T2D undergoing hip arthroplasty adds to the clinical relevance of the findings. Additionally, measuring gene expression and AGEs in bone samples provides direct insights into the study's objectives.

The manuscript presents data clearly, and the results are well-organized.

Weaknesses:

Title. The title could be more specific to better reflect the content of the study. Also, the abstract should concisely summarize the study's main findings, providing some figures.

We thank the reviewer for this suggestion, and we modified the title giving specific information on the main findings of this study. The new title is “Bone canonical Wnt signaling is downregulated in type 2 diabetes and associates with higher Advanced Glycation End-products (AGEs) content and reduced bone strength”. Moreover, we added as suggested a graphical abstract summarizing our study results.

Introduction: the introduction would benefit from the addition of a clearer, more focused statement of the research questions or hypotheses guiding this study.

We thank the reviewer for this opportunity and we reformulated the hypothesis of this study based on our data and new findings as follow:” we hypothesized that T2D and AGEs accumulation downregulate Wnt canonical signaling and negatively affect bone strength”. (page 6, lines 116-117).

Methods: more information is needed on the hystomorphometry analysis. Surgical samples from 8 T2D and 9 non-diabetic subjects were used for histomorphometry analysis. How did these subjects compare with the other subjects in the T2D and control groups? Were they representative? How were they selected?

We thank the reviewer for the opportunity to clarify this important point. The number of subjects included in the different analysis of the paper differ for multiple reasons.In particular, we used only bone specimen with enough trabecular bone material adequate to perform histomorphometry analysis. Therefore, the samples used in the histomorphometry analysis belong to the same subjects enrolled in the study and analyzed for the other experiments of this paper. However, we have previously calculated sample size for bone histomorphometry analysis using the only available data of trabecular bone in T2D postmenopausal women measured by dynamic histomorphometry (Manavalan JS et al, JCEM 2012). We performed a priori sample size calculation using G*Power 3.1.9.7., based on the t-test of two independent groups. Analysis demonstrated that given an effect size of 2.2776769, we needed a total of 12 patients (6/group) to reach a power of 0.978.

COMMENTS:

(1) In the Abstract, values and p-values for comparisons, and Spearman's rho and p-values for correlations should be provided. Most adverbs (thus, accordingly, importantly) could be omitted to improve conciseness and clarity.

We kindly thank the reviewers for this precise and careful comment. We changed the Abstract accordingly. According to the abstract style of the journal we initially reported only the main findings. We have now modified providing values and p values as requested. We defer to the wishes of the editor as to the format in which the abstract should be reported.

(2) Result presentation: 25th and 75th percentile should be provided rather than the interquartile range, to better reflect data distribution.

We thank the reviewer for the opportunity to better clarify this part of the results section. We changed the manuscript accordingly.

(3) Estimated glomerular filtration rate should be calculated and provided as a marker of renal function, rather than serum creatinine values.

We thank the reviewer for the comment, and we modify the manuscript accordingly, adding the eGFR values in table 1 and in the result section.

(4) The manuscript should include a statement confirming compliance with the Declaration of Helsinki, considering that human subjects were involved in the study.

We thank the reviewer for the comment. The study was conducted in accordance with the Declaration of Helsinki. Ethics Committee of Campus Bio-Medico University approved the present study. Informed consent was obtained from all subjects involved in the study. (Page 6, lines 134-137).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure 1—source data 1. Data represented by each point in Figure 1A–J.

elife-90437-fig1-data1.xlsx^{(11.7KB, xlsx)}

Figure 2—source data 1. Data represented by each point in Figure 2A–J.

elife-90437-fig2-data1.xlsx^{(8.5KB, xlsx)}

Figure 3—source data 1. Data represented by each point in Figure 3A–D.

elife-90437-fig3-data1.xlsx^{(8.7KB, xlsx)}

Figure 4—source data 1. Data represented by each point in Figure 4A–L.

elife-90437-fig4-data1.xlsx^{(9.1KB, xlsx)}

MDAR checklist

elife-90437-mdarchecklist1.docx^{(100.9KB, docx)}

Data Availability Statement

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PERMALINK

Bone canonical Wnt signaling is downregulated in type 2 diabetes and associates with higher advanced glycation end-products (AGEs) content and reduced bone strength

Giulia Leanza

Francesca Cannata

Malak Faraj

Claudio Pedone

Viola Viola

Flavia Tramontana

Niccolò Pellegrini

Gianluca Vadalà

Alessandra Piccoli

Rocky Strollo

Francesca Zalfa

Alec T Beeve

Erica L Scheller

Simon Y Tang

Roberto Civitelli

Mauro Maccarrone

Rocco Papalia

Nicola Napoli

Roles

Abstract

eLife digest

Introduction

Results

Subject characteristics

Table 1. Clinical features of the study subjects.

Bone histomorphometry

Table 2. Histomorphometric parameters of trabecular bone of the study subjects.

Bone compression tests

Table 3. Bone mechanical parameters of trabecular bone of the study subjects.

Gene expression

Figure 1. Gene expression analysis in trabecular bone samples.

Correlation analysis of Wnt target genes, AGEs, and glycemic control

Figure 2. Relationship between advanced glycation end-products (AGEs) (µg quinine/g collagen) bone content and mRNA level of the Wnt signaling key genes in type 2 diabetes (T2D) and non-diabetic subjects.

Figure 3. Relationship between fasting glucose levels (mg/dl) and disease duration with SOST and WNT5A mRNA levels.

Figure 3—figure supplement 1. Relationship between fasting glucose levels (mg/dl) and LEF 1, WNT5A, WNT10B, DKK-1, COL1A1 mRNA levels.

Correlation analysis of Wnt target genes and bone mechanical parameters

Figure 4. Relationship between Young’s modulus (MPa), ultimate strength (MPa), and yield strength (MPa) with mRNA levels of the Wnt signaling key genes in type 2 diabetes (T2D) and non-diabetic subjects.

Figure 4—figure supplement 1. Relationship between Young’s modulus (MPa), ultimate strength (MPa), and yield strength (MPa) with mRNA levels of the Wnt signaling genes in type 2 diabetes (T2D) and non-diabetic subjects.

Discussion

Figure 5. A graphical summary of the study.

Materials and methods

Study subjects

Specimen preparation

Bone histomorphometry

Bone compression tests

RNA extraction and gene expression by RT-PCR

Statistical analysis

Acknowledgements

Funding Statement

Contributor Information

Funding Information

Additional information

Competing interests

Author contributions

Ethics

Additional files

Data availability

References

eLife assessment

Se-Min Kim

Roles

Reviewer #1 (Public review):

Anonymous

Roles

Reviewer #2 (Public review):

Anonymous

Roles

Author response

Giulia Leanza

Francesca Cannata

Malak Faraj

Claudio Pedone

Viola Viola

Flavia Tramontana

Niccolò Pellegrini

Gianluca Vadalà

Alessandra Piccoli

Rocky Strollo