Prevalence and Associated Factors of Metabolic Bone Disease in Men with Type 2 Diabetes Mellitus

doi:10.21203/rs.3.rs-2091109/v1

Download PDF

Research Article

Prevalence and Associated Factors of Metabolic Bone Disease in Men with Type 2 Diabetes Mellitus

https://doi.org/10.21203/rs.3.rs-2091109/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

INTRODUCTION Postmenopausal women and people over the age of 70 are more likely to develop osteoporosis. It is recognized as significant comorbidity among those with type 2 diabetes mellitus (T2DM), and male fractures are on the rise with higher rates of mortality than women (37.5% vs 28.2% respectively). However, there is a scarcity of data linking T2DM and metabolic bone disease in men.

OBJECTIVE Our study aimed to determine the prevalence of metabolic bone diseases, including osteoporosis and osteopenia, and the associated factors, bone turnover markers, and vitamin D in men with T2DM.

METHOD This is a cross-sectional, single-center study in men above 50 years of age with T2DM, conducted from December 2021 to June 2022. Demographic data, baseline comorbidities, and biochemical profiles including urine albumin-creatinine ratio (UACR) was obtained. Bone turnover markers including C-terminal telopeptide of type I collagen (CTX) and bone-specific alkaline phosphates (bsALP) were measured. Osteoporosis and osteopenia were determined by Dual Energy X-ray Absorptiometry (DEXA).

RESULT Total of 148 men with T2DM with a median age of 64 (IqR 11) years was recruited. The prevalence of metabolic bone disease in the study population was 20.3%. Multivariate analysis shows total bilirubin level [OR: 1.13 (95% CI: 1.050, 1.223) p<0.001], high intact parathyroid hormone (iPTH) level ≥6.9 pmol/L [OR 3.05 (95% CI: 1.141, 8.187), p=0.026] and use of dipeptidyl peptidase-4 inhibitor (DPP4i) [OR 0.274 (95% CI: 0.093, 0.809) p=0.01] are predictors of metabolic bone disease in the study population.

CONCLUSION Metabolic bone disease affects about 1 in 5 men with T2DM patients, and its risk factors include increased total bilirubin and iPTH level. The use of DPP4i for the treatment of T2DM was shown to be somewhat protective against metabolic bone disease. There is a need to identify metabolic bone disease in men with T2DM so early intervention could be initiated to prevent fractures.

bone mineral density

metabolic bone disease

osteoporosis

osteopenia

bone turnover marker

type 2 diabetes mellitus

Bone metabolism involves an ongoing cycle of bone resorption and growth. The term metabolic bone disease refers to a broad category of conditions that affect bone density usually caused by abnormalities of minerals, vitamin D, and bone mass or bone structure [1]. The most common metabolic bone diseases are osteoporosis and osteopenia [1]. Osteoporosis is defined by World Health Organization (WHO) as a skeletal disorder characterized by compromised bone strength hence predisposing an individual to increased risk of fracture [2]. WHO recommended measurements of bone mineral density (BMD) in diagnosing osteoporosis and osteopenia and the use of FRAX score for fracture risk assessments, to identify individuals at increased risk of osteoporotic fractures. Osteoporosis is a common disease in postmenopausal women and the elderly above the age of 70. Due to the rapid growth of the elderly population, fracture incidence has continued to rise [3]. Osteoporosis is a silent disease that goes unnoticed until a fracture occurs. It has a significant negative impact on individuals, including high morbidity and mortality [4–6]. However, osteoporosis in men remains underdiagnosed and undertreated. According to epidemiological studies in 2000, 39% of osteoporotic fractures occurred in men [6]. In their lifetime, one in five males over the age of 50 may experience an osteoporotic fracture [7]. Osteoporosis related to diabetes mellitus is recognised but suffers from a lack of attention. The effect of T2DM on BMD and the risk of osteoporosis is still not clear. Several studies have shown increased fracture risks among T2DM even at high or normal BMD [7]. Diabetes may affect bone tissues through various mechanisms, including hyperinsulinemia, deposition of advanced glycosylation end products (AGE) in collagen, reduced serum levels of Insulin-Like Growth Factor-1 (IGF-1), hypercalciuria, microangiopathy, and inflammation [8, 9]. All of these lead to poor bone quality, which may not be represented by BMD alone. It involves many aspects of bone metabolism including bone formation, bone resorption, bone mineralization, and calcium and phosphate metabolism. Complications such as diabetic nephropathy may further worsen bone mineral metabolism as proteinuria and low estimated glomerular filtration rate (eGFR) have been demonstrated as independent risk factors for osteoporosis [10, 11].

There have only been 2 previous studies that looked at the prevalence of osteoporosis in men with T2DM, which were done amongst the Chinese population [12] [13]. To the best of our knowledge, there has been no similar study conducted in Malaysia, a multiethnicity country with different fracture risks. Lee et al 2019 showed the prevalence of osteoporosis is highest in the Chinese population with 63%, followed by Malay (20%), Indian (13%), and others (4%) [14].

Hence, this study aimed to identify the prevalence and associated factors of metabolic bone disease, such as osteoporosis and osteopenia amongst men with T2DM. This will include bone turnover markers i.e. bone-specific alkaline phosphatase (bsALP) and C-terminal telopeptide of type I collagen (CTX) to further demonstrate the underlying abnormal bone metabolism.

This was a cross-sectional study conducted from December 2021 to June 2022 amongst men, above 50 years old, with the diagnosis of T2DM, who attended the outpatient clinics at Hospital UiTM Puncak Alam and Hospital UiTM Sungai Buloh. Those with endocrine disorders such as thyrotoxicosis, hypogonadism, primary hyperparathyroidism, hyperprolactinemia, Cushing's syndrome, acromegaly, rheumatoid arthritis, systemic lupus erythematosus, liver cirrhosis and malabsorptive disease e.g. celiac disease, radiation enteritis, and inflammatory bowel disease, end-stage renal disease (ESRD) on regular dialysis or eGFR of < 30 mls/min were excluded. Those who were taking medications that could have interfered with bone and calcium metabolism e.g. recombinant human parathyroid hormone, androgenic hormone, calcitonin, bisphosphonates, vitamin D metabolite, thyroid hormone, glucocorticoids, thiazolidinedione, rifampicin, heparin, warfarin, vitamin K, thiazides and anticonvulsants, patient with active malignancy, history of fracture within 1 year or has been bedbound for more than 3 days were also excluded.

Medical history, history of previous fractures (trivial and non-trivial), family history of hip fractures, and lifestyle including cigarette smoking, alcohol use, level of physical activity, duration of diabetes, presence of diabetic microvascular and macrovascular complications, other comorbidities, and daily pharmacotherapy. A smoker was defined as an individual currently smoking at least 1 cigarette/day and those who had stopped smoking for > 6 months were considered a non-smoker. Alcohol user was defined as individual consuming > 3 units/day. One unit equals 10ml or 8g of alcohol. Physically active was defined as the exercise of more than 150 minutes per week.

Anthropometry measurements were obtained during the clinic visits, including weight in kilogram; measured with a calibrated balance beam scale, height in meters; measured with a stadiometer, both without shoes and body mass index (BMI) was calculated using the height and weight obtained. Blood pressure in mmHg; was measured using calibrated, standardised electronic sphygmomanometer.

Blood samples were obtained from all the participants following overnight fasting of at least 8 hours. Intact parathyroid hormone (iPTH), 25-hydroxyvitamin D (25(OH)-D), and total testosterone levels were tested using the Beckman Coulter Access 2 analyser. Bone-specific alkaline phosphatase (bsALP) as a bone formation marker was tested using enzyme-linked immunosorbent assay (ELISA), following the manufacturer’s instructions (AFG bioscience United States) with intra-assay: CV < 8% and inter-assay: CV < 10%. The test reagents were provided by Stemcell Company. C-terminal telopeptide of type I collagen (CTX) as a bone resorption marker was tested using Roche Cobas E411. Other blood tests such as renal profile with eGFR calculated using CKD Epidemiology Collaboration (CKD-EPI) equation, liver function test, bone profile [alkaline phosphatase (ALP), corrected calcium and phosphate], fasting lipid profile, glycosylated haemoglobin (HbA1c), uric acid level, and thyroid function test (TFT) were analysed using automated techniques with the Roche diagnostic immunoassay analyser. Spot early morning urine sample was taken to test for urine albumin-creatinine ratio (UACR). Based on Malaysian clinical practice guidelines for diabetic nephropathy 2004, the following classifications are used; normal albuminuria ≤ 2.5 mg/mmol, microalbuminuria 2.5–25 mg/mmol, and macroalbuminuria ≥ 25 mg/mmol. All participants underwent a Dual Energy X-ray Absorptiometry (DEXA) scan on a Hologic QDR 2000 to measure the BMD over the lumbar spine (L1-L4) and femoral region, which then were categorised into the following; T-score ≤ − 2.5 osteoporosis, T-score between − 2.5 to − 1.0 osteopenia, T-score value ≥ -1.0 normal bone mass [15]. DEXA scan was conducted by a trained radiographer and had regular calibrations using the manufacturer’s software. Fracture risks were estimated using the fracture risk assessment (FRAX) tool for Asians (Reference country Singapore), providing calculated 10-year risks of a major osteoporotic fracture and hip fracture.

Statistical analysis was performed using SPSS version 26 and the baseline characteristics of participants were presented as mean ± standard deviation or median (IqR, interquartile range) for continuous data and count and percentage (%) for categorical data. Normality testing was analysed using the Kolmogorov-Smirnov test. Continuous variables were analysed using independent sample T-tests, data that were not normally distributed were analysed using the Mann-Whitney-U test and categorical variables were analysed using the chi-squared test, p-value < 0.05 were taken as statistically significant. All variables were tested using simple logistic regression. Variables with a p-value of < 0.25 were further analysed using multiple logistic regressions to develop predictor models (risk factors). P-values < 0.05 were considered statistically significant.

A total of 148 subjects were recruited in the study with ages ranging from 50–84 years, median age of 64 (IqR 11) years old including Malay (n = 123), Chinese (n = 9), Indians (n = 15) and others (n = 1). The mean BMI was 29.2 ± 5.4 kg/m2. The median duration of diabetes was 10 (IqR 15) years, eGFR of 68.4 (IqR 34.4) ml/min/1.73m2, and HbA1c of 7.7 (IQ IqR R 2.4) %. Table 1 demonstrates the baseline demographic of the overall study population and a comparison between the groups with normal BMD and those with metabolic bone diseases in men T2DM. The mean weight of those with metabolic bone diseases was significantly lower at 75.5 ± 19.6 kg compared to T2DM men with normal BMD 80.8 ± 15.0 kg, p = 0.029. The prevalence of metabolic bone disease in men with T2DM was 20.3% (95% CI: 0.141, 0.277), which included osteoporosis and osteopenia of 4% (6 cases) and 16.2% (24 cases) respectively.

Table 2 refers to the biochemical profiles of the overall study population and the comparison between those with and without the metabolic bone disease. Those with metabolic bone diseases had significantly higher total bilirubin (14.21 ± 7.92 vs 9.81 ± 4.61 mmol/L), p = 0.001, and lower corrected calcium (2.25 ± 0.06 vs 2.28 ± 0.10 mmol/L), p = 0.024 compared to individuals with normal BMD. There was also an association between iPTH and CTX with metabolic bone disease. Those with metabolic bone disease have a significantly higher iPTH ≥ 6.9 pmol/L as compared to those with normal BMD (p = 0.017). Additionally, those with metabolic bone disease had a significantly higher proportion of subjects with a CTX value of > 0.6 ng/ml, 20% as compared to those with normal BMD, 5.1%, p = 0.016. With regards to pharmacotherapy, the use of DPP4i also shows statistically significant with p = 0.020. Those taking DPP4i do not develop the metabolic bone disease.

Table 1

Baseline characteristics of overall study population and comparison between groups with and without metabolic bone disease
Variables	All patient N = 148, % (n)	Metabolic bone disease		P-value
Variables	All patient N = 148, % (n)	No, n = 121 % (n)	Yes n = 30 % (n)	P-value
Age (years)	64 (IqR 11)	63.5 ± 7.1	65.3 ± 7.3	0.274
50–59	31.8 (47)	33.9 (40)	23.3 (7)	0.540`
60–69	47.3 (70)	45.8 (54)	53.3 (16)
≥ 70	20.9 (31)	20.3 (24)	23.3 (7)
Ethnicity Malay	83.1 (123)	83.9 (99)	80.0 (24)	0.611`
Non Malay	16.9 (25)	16.1 (19)	20.0 (6)
Weight (kg)	78.0 (IqR 20.0)	80.8 ± 15.0	75.5 ± 19.6	0.029
BMI (kg/m2) [16]	29.2 ± 5.4	29.4 ± 5.0	28.3 ± 6.5	0.326
<25	79.7 (118)	16.9 (20)	33.3 (10)	0.046`
Obese ≥ 25	20.3 (30)	83.1 (98)	66.7 (20)
SBP (mmHg)	142.6 ± 20.0	141.9 ± 19.3	145.1 ± 22.5	0.443
< 140	44.6 (66)	45.8 (54)	40.0 (12)	0.571`
≥ 140	55.4 (82)	54.2 (64)	60.0 (18)
DBP (mmHg)	77.2 ± 11.5	77.0 ± 11.3	77.7 ± 12.3	0.797
< 90	85.1 (126)	84.7 (100)	86.7 (26)	1.000^
≥ 90	14.9 (22)	15.3 (18)	13.3 (4)
Smoking Yes	20.3 (30)	21.2 (25)	16.7 (5)	0.582`
No (never/ stop ≥ 6 mth)	79.7 (118)	78.8 (93)	83.3 (25)
Exercise habit Active (150min/wk)	27.0 (40)	25.4 (30)	33.3 (10)	0.384`
Sedentary	73.0 (108)	74.6 (88)	66.7 (20)
Duration of diabetes mellitus (years)	10 (IqR 15)	12.5 ± 8.5	10.5 ± 9.0	0.160
< 10	39.9 (59)	37.3 (44)	50.0 (15)	0.204`
Long-standing ≥ 10	60.1 (89)	62.7 (74)	50.0 (15)
eGFR stage ≤3A	81.1 (120)	83.1 (98)	73.3 (22)	0.225`
3B	18.9 (28)	16.9 (20)	26.7 (8)
NephropathyYes (eGFR > 2 +/-proteinuria)	73.0 (108)	73.7 (87)	70.0 (21)	0.681`
No	27.0 (40)	26.3 (31)	30.0 (9)
Retinopathy Yes	20.3 (30)	19.5 (23)	23.3 (7)	0.640`
No	79.7 (118)	80.5 (95)	76.7 (23)
Neuropathy Yes	56.8 (84)	59.3 (70)	46.7 (14)	0.212`
No	43.2 (64)	40.7 (48)	53.3 (16)
IHD Yes	62.8 (93)	61.0 (72)	30.0 (21)	0.363`
No	37.2 (55)	39.0 (46)	70.0 (9)
CVA Yes	15.5 (23)	12.7 (15)	26.7 (8)	0.087^
No	84.5 (125)	87.3 (103)	73.3 (22)
Insulin Yes	54.7 (81)	56.8 (67)	46.7 (14)	0.320`
No	45.3 (67)	43.2 (51)	53.3 (16)
Metformin Yes	75.7 (112)	78.9 (93)	63.3 (19)	0.078`
No	24.3 (36)	21.2 (25)	36.7 (11)
Sulfonylurea Yes	24.3 (36)	25.4 (30)	20.0 (6)	0.536`
No	75.7 (112)	74.6 (88)	80.0 (24)
DPP4i Yes	38.5 (57)	43.2 (51)	20.0 (6)	0.020`
No	61.5 (91)	56.8 (67)	80.0 (24)
SGLT2i Yes	41.2 (61)	55.9 (52)	30.0 (9)	0.162`
No	58.8 (87)	44.1 (66)	70.0 (21)

BMI: body mass index, SBP: systolic blood pressure, DBP: diastolic blood pressure, eGFR: estimated Glomerular Filtration Rate, IHD: ischemic heart disease, CVA: cerebrovascular accident, SU: Sulfonylurea, DPP4i: Dipeptidyl peptidase-4 inhibitor, SGLT2i: Sodium-glucose cotransporter-2 inhibitor

` p-value taken from Pearson chi-square. ^ p-value taken from Fisher's exact test

Table 2

Biochemical profiles of the overall study population and comparison between those with and without the metabolic bone disease
Biochemical profile
Variables	All patient N = 148, % (n)	Metabolic bone disease		P-value
Variables	All patient N = 148, % (n)	No, n = 121 % (n)	Yes n = 30 % (n)	P-value
HbA1c (%)	7.7 (IqR 2.4)	8.15 ± 1.96	8.41 ± 2.28	0.625
Control ≤8	55.4 (82)	56.8 (67 )	50.0 (15)	0.505`
Uncontrolled > 8	44.6 (66)	43.2 (51)	50.0 (15)
Creatinine (umol/L)	102.0 (IqR 45.5)	109.67 ± 33.89	113.03 ± 36.01	0.740
eGFR (ml/min/1.73m2)	68.4 (IqR 34.4)	66.78 ± 20.73	65.45 ± 22.64	0.769
Total bilirubin (mmol/L)	8.9 (IqR 6.0)	9.81 ± 4.61	14.21 ± 7.92	0.001
Albumin (g/L)	44.2 (IqR 4.3)	43.84 ± 3.57	43.48 ± 3.04	0.562
LDL (mmol/L)	2.1 (IqR 1.2)	2.26 ± 1.02	2.31 ± 0.81	0.640
HDL (mmol/L)	1.1 (IqR 0.3)	1.15 ± 0.30	1.16 ± 0.27	0.589
Total cholesterol (mmol/L)	3.9 (IqR 1.3)	4.11 ± 1.18	4.17 ± 0.92	0.633
TG (mmol/L)	1.5 (IqR 0.9)	1.68 ± 0.86	1.49 ± 0.67	0.458
Corrected calcium (mmol/L)	2.28 ± 0.10	2.28 ± 0.10	2.25 ± 0.06	0.024
Phosphate (mmol/L)	1.14 ± 0.19	1.14 ± 0.18	1.11 ± 0.24	0.414
ALP (u/L)	87.5 (IqR 43.0)	88.45 ± 30.98	104.57 ± 56.18	0.172
iPTH (pmol/L) [17]	4.0 (IqR 3.2)	4.56 ± 2.31	5.79 ± 2.98	0.043
Normal < 6.9	82.8 (121)	85.6 (101)	66.7 (20)	0.017`
High ≥ 6.9	18.2 (27)	14.4 (17)	33.3 (10)
25(OH)D (nmol/L) [18]	64.7 (IqR 25.4)	67.35 ± 20.34	67.94 ± 22.71	0.656
Normal ≥ 75	31.1 (46)	31.4 (37)	30 (9)	0.943`
Insufficient 50–75	50.7 (75)	50.0 (59)	53.3 (16)
Deficient ≤ 50	18.2 (27)	18.6 (22)	16.7 (5)
bsALP (u/L)	564.5 (IqR 263.4)	675.53 ± 415.84	575.96 ± 239.96	0.407
CTX (ng/ml)* [19]	0.2 (IqR 0.1)	0.31 ± 0.15	0.38 ± 0.25	0.357
Normal ≤ 0.6	92.1 (136)	94.9 (112)	80.0 (24)	0.016^
High resorption > 0.6	8.1 (12)	5.1 (6)	20.0 (6)
Total Testosterone (nmol/L)	13.2 (IqR 6.4)	13.85 ± 5.59	13.64 ± 5.54	0.771
Normal ≥ 8	88.5 (131)	89.8 (106)	83.3 (25)	0.340^
Low < 8	11.5 (17)	10.2 (12)	16.7 (5)
Uric acid (umol/L) [20]	370 (IqR 158)	388.76 ± 102.90	409.97 ± 122.22	0.537
Hyperuricemia > 350	59.5 (88)	60.2 (71)	56.7 (17)	0.727
Low ≤ 350	40.5 (60)	39.8 (47)	43.3 (13)
UACR (mg/mmol)	7.0 (IqR 39.8)	37.45 ± 65.22	27.73 ± 42.44	0.983
Normal ≤ 2.5	53 (35.8)	35.6 (42)	36.7 (11)	0.948`
Microalbuminuria 2.5–25	48 (32.4)	33.1 (39)	30.0 (9)
Macroalbuminuria ≥ 25	47 (31.8)	31.4 (37)	33.3 (10)

eGFR: estimated Glomerular Filtration Rate, HbA1c: Glycosylated haemoglobin, ALT: alanine transaminase, GGT: gamma-glutamyl transferase, LDL: low-density lipoprotein, HDL: high-density lipoprotein, TG: Triglyceride, ALP: alkaline phosphatase, iPTH: intact parathyroid hormone, 25(OH)D: 25 hydroxyvitamin D, bsALP: bone-specific alkaline phosphatase, CTX: C-terminal telopeptide of type I collagen, UACR: urine albumin-creatinine ratio

` p-value taken from Pearson chi-square. ^ p-value taken from Fisher's exact test

Age, BMI, and duration of diabetes mellitus were not statistically significant as associated factors for metabolic bone diseases. However, the usage of DPP4i and lower bilirubin showed protection against metabolic bone disease OR: 0.328 (95% CI: 0.125, 0.863), p = 0.024 and OR: 1.129 (95% CI: 1.050, 1.214), P = 0.001 respectively. Meanwhile, the iPTH and CTX variables also showed significant associations in men with T2DM and metabolic bone disease. T2DM men with serum iPTH levels of ≥ 6.9 pmol/L had three times higher odds of developing the metabolic bone disease compared to if they have iPTH levels of < 6.9 pmol/L [2.97 (95% CI: 1.188, 7.429), p = 0.020] and T2DM men with serum CTX of > 0.6 ng/ml had 4.6 times higher odds of developing the metabolic bone disease compared to men with CTX of ≤ 0.6 ng/ml [4.667 (95% CI: 1.385, 15.719), p = 0.013]

From the simple logistic regression analysis, variables with a p-value of < 0.25 were further analysed using multiple logistic regression. A total of 17 independent variables were entered into multiple logistic regression (age, weight, BMI, duration of diabetes mellitus, eGFR stage, neuropathy, CVA, use of metformin, DPP4i, SGLT2i, serum total bilirubin, GGT, corrected calcium, ALP, iPTH, bsALP, CTX). Table 3. showed the final model after multiple logistic regression analyses. Higher total bilirubin, high serum iPTH, and the use of DPP4-inhibitors remained independent predictors for metabolic bone diseases within this study population. Men with T2DM have increased odds of developing the metabolic bone disease by 1.1 times with an increase of 1umol/L of total bilirubin P < 0.001 and those with high serum iPTH level of ≥ 6.9pmol/L have a 3 times increased odds of developing the metabolic bone disease compared to those with normal iPTH level. Patients who are taking DPP4i have a lower odds of developing the metabolic bone disease by 72.6% compared to those who are not taking the agent OR: 0.274 (95% CI: 0.093, 0.809), p = 0.019.

Table 3

Factor associated with metabolic bone disease in men T2DM using multiple logistic regression, backward LR (likelihood ratio) methods
Variable		Crude OR (95% CI)	Adj. OR (95% CI)^	Wald Statistic (df)^	P-value^
Total bilirubin		1.129 (1.050, 1.214)	1.138 (1.058, 1.223)	12.186 (1)	< 0.001
iPTH	Normal < 6.9	1	1
	High ≥ 6.9	2.971 (1.188, 7.429)	3.056 (1.141, 8.187)	4.936 (1)	0.026
DPP4i	No	1	1
	Yes	0.328 (0.125, 0.863)	0.274 (0.093, 0.809)	5.486 (1)	0.019
^result taken from multiple logistic regression

The model reasonably fits well. Model assumptions are met. There are no interaction and multicollinearity problems.

The prevalence of the metabolic bone disease among men with T2DM in this study of 20.3% is lower compared to previous studies done by Xia et al, which showed a prevalence of 65.7% [12] and Chen et al, with a figure of 59.1% [13]. This is most likely because while the current study had a predominantly Malay cohort, both previous studies were conducted within the Chinese population, which had been reported to have a higher risk of osteoporosis compared to other ethnicities [14]. Furthermore, the mean age of the two studies was higher compared to our study population, which was 78 and 68 years old, respectively, whilst our study population's median age was 64 years of age. Notably, we included those above the age of 50 years to emphasise the importance of early screening for metabolic bone disease among this high-risk population.

Previous research has shown that T2DM is an independent risk factor for fractures, which is associated with elevated fracture risk at a higher BMD level compared to the normal population [21]. Thus, fracture risks in T2DM patients cannot be explained by BMD results alone, and therefore necessitates the use of bone turnover markers as surrogate biomarkers to determine more accurate fracture risks in this population. Bone resorption markers include hydroxyproline; pyridinium crosslinks, CTX and tartrate-resistant acid phosphatase (TRAP) [22]. Some bone formation markers include procollagen type 1 N propeptides (P1NP), osteocalcin and ALP [22]. In this study, we used CTX as the bone resorptive marker and bsALP as the bone formation marker. This is about the International Osteoporosis Foundation, which recommended that the serum CTX be used as the reference marker for bone resorption due to its specificity to bone and performance in clinical studies [23]. BsALP is specifically looking at bone isoform and is a good bone formation marker in CKD patients as it is unaffected by renal function [24]. There is a statistically significant difference in CTX levels between T2DM men with metabolic bone disease and without the disease. Twenty per cent (20%) of participants with the metabolic bone disease had high CTX levels of > 0.6 ng/mL, compared to 5.1% of those without the condition with a p-value of 0.016. This would suggest that men with T2DM experience more bone resorption. However, in this study, we were unable to demonstrate a significant difference in bsALP between men with T2DM, with and without the metabolic bone disease. This is most likely due to the overall low prevalence of metabolic bone disease within the cohort.

Parathyroid hormone (PTH) itself is not a bone turnover marker, but its release is regulated by low serum calcium concentration. PTH has both bone formation and bone resorption action. Net bone mass is depending on level and exposure to PTH. High and prolonged exposure to PTH will result in increased osteoclastic activity and subsequent bone resorption. Higher iPTH is, therefore, associated with higher bone turnover activities [25–27]. This is reflected in our study, which observed that those with a higher iPTH of ≥ 6.9 pmol/L had a 3-times higher odd ratio of developing the metabolic bone disease [OR 3.056 (1.141, 8.187)]. This finding is consistent with previous studies by Agrawal et al which showed high iPTH is associated with low BMD [28]. In the current study 27 subjects had high iPTH levels > 6.9 pmol/L, of these 44.4% had deficient vitamin D and 33.3% had insufficient vitamin D. It is widely known that there is an inverse relationship between vitamin D levels and iPTH levels [29] as vitamin D deficiency can result in secondary hyperparathyroidism. Although the prevalence of vitamin D deficiency/ insufficiency in this study was high (68.9%), there is no association between osteoporosis and vitamin D insufficiency in our study population, which was similarly reported by a previous study by Ahmed et al [30].

Interestingly, the use of DPP4i in men with T2DM was shown to be protective against metabolic bone disease. This seems to be consistent with a recent meta-analysis of 28 randomized clinical trials, which concluded that the use of DPP4i reduces the risk of fractures when compared with placebo [31]. However, this effect is still controversial in contrast to other meta-analyses by Fu et al [32] and Mamza et al [33] which showed no differences in fracture risks of patients taking DPP4i. Yang et al [34] suggested that DPP4is may promote bone formation and reduces bone resorption through DPP4 substrates and DPP4-related energy metabolism, which is mainly seen in sitagliptin and not with other DPP4-Is. This is consistent with our observation in our study as the majority (70.1%, n = 40) of our patients were prescribed sitagliptin, meanwhile, the rest were on vildagliptin (7%, n = 4) and linagliptin (22.8%, n = 13). Although the current study was not primarily aimed at looking at pharmacotherapy effects on the outcomes measured it was nonetheless interesting to postulate a potential benefit of this particular drug.

Osteoporosis in men is frequently caused by secondary factors such as hypogonadism and hyperthyroidism. In this study, we screened all patients with thyroid function tests and total testosterone levels to exclude secondary causes of osteoporosis, of which one patient was excluded from the study due to newly diagnosed thyrotoxicosis. Fink et al reported that bone loss is accelerated with low total testosterone levels [35]. In our study, 88.5% of the participant had normal testosterone levels and testosterone levels were not statically different between those with metabolic bone disease and those with normal BMD. This could positively indicate that our study subjects do not have evidence of hypogonadism and that those with the condition had been adequately identified and treated.

Osteoporosis is a known complication of advanced liver disease e.g. chronic cholestasis and primary biliary cholangitis [36]. It has been proposed that bilirubin diminished osteoblast differentiation and mineralization [37] leading to low bone formation and it also increased osteoclast genesis and increased osteoclast viability as bilirubin increased the expression of osteoclastogenic related mRNAs leading to an increase in bone resorption [38]. Both mechanisms contribute to the development of osteoporosis. However, the association between bilirubin levels and osteoporosis has not been well established, with conflicting results. Yan [32] and Wu et al [33] have examined the connection between bilirubin and metabolic bone disease in T2DM patients and have found that raised bilirubin levels lower the incidence of osteoporosis. In contrast, Zhao et al showed that bilirubin levels did not affect BMD or the incidence of fractures based on Mendelian randomization (MR) study [39]. However, to the best of our knowledge, there has been no previous report demonstrating a negative correlation between total bilirubin ad BMD, although there has been proposed pathophysiology that bilirubin might affect bone metabolism leading to an increased risk of metabolic bone disease [37, 38]. Our study observed that an increase of 1 mmol/L of total bilirubin showed an increase in the odds of developing metabolic bone disease by 1.1 times [OR: 1.138 (95% CI: 1.058, 1.223), P-value < 0.001]. This could be further studied in a bigger sample looking specifically into the population with higher bilirubin levels such as chronic liver disease.

Many previous studies had shown strong relationships between age, BMI and the duration of diabetes mellitus and the development of osteoporosis. However, we did not see a significant result for these variables, which was most likely due to the small sample size. Independent sample T-test identified weight, BMI, corrected calcium level and CTX as significant risk factors for metabolic bone disease. However, this was no longer demonstrated with the multivariate analyses. Future studies with a larger sample size and the addition of a control group may be beneficial.

In the current study, the prevalence of metabolic bone disease was higher when BMD was assessed at the neck of the femur compared total femur or total spine (20.3% vs 10.1% total hip and 6.1% total spine). The DEXA scan result, men with T2DM and metabolic bone disease notably showed the lowest value of femoral neck t-score (-1.82 ± 0.75) compared to the total hip t-score (-0.73 ± 0.84) and total spine t-score (-0.24 ± 0.03). Which is comparable to Chen's report [13]. According to Ann et al, T2DM individuals exhibit a higher loss of BMD at the femoral neck [40]. T2DM individuals have deteriorated cortical microarchitecture [41]. All this explain increased hip fracture risk in T2DM individual.

This was a cross-sectional study, thus limiting the causal relationship. Furthermore, the convenient sampling in a single-centre study made the data limited and could not be representative of the general population. The sample size of this study was relatively small, which was mainly attributed to the Covid-19 pandemic and the movement control restrictions. Future studies could include a control group to determine the effects of T2DM on BMD compared with a normal population without T2DM. Due to logistic reasons, the DEXA scan was performed by 2 different trained radiographers but could have led to some technical variations in the tests. Therefore, further prospective, long-term, multicentre observational data, particularly on trabecular bone mass, could perhaps provide more valuable insights into this increasingly important and debilitating disease in the future.

In conclusion, our study showed that about one in 5 men with T2DM have metabolic bone disease, with its associated factors including higher total bilirubin, iPTH and use of DPP4i. This study underscores the importance of screening for metabolic bone disease in this population to allow early detection, initiation of treatment and other preventive measures.

Acknowledgements

We would like to thank Madam Khatijah, radiographer, UiTM who has help to preform bone mineral density for most of our study participant. We would like to show our gratitude to Madam Salina and Madam Liza from clinical diagnostic laboratories, UiTM for their assistance in running testosterone, 25-hydroxyvitamin D and intact parathyroid hormone. We would also like to expand our gratitude to all those who have directly and indirectly help with this research and lastly we would like thank all the patient that participate in this study.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profifit sectors. The publication of this research funded by the Faculty of Medicine, Sungai Buloh Campus, Universiti Teknologi MARA, Selangor, Malaysia

Availability of data and materials

Data are kept at the Department of Internal Medicine, Universiti Teknologi MARA, Selangor, Malaysia. Raw data that support the finding of this study are available upon request from the corresponding author, (Wan Muhamad Hatta) and are subject to the applicable and relevant personal data protection laws and regulations.

Ethics approval and consent to participate

Ethical approval was obtained from the Universiti Teknologi Mara (UiTM) Research Ethics Committee following the International Committee on Harmonization of Good Clinical Practice Guidelines, Malaysian Good Clinical Practice Guidelines, and the Declaration of Helsinki. Written informed consent was obtained from each participant.

Competing interests

I hereby certify that they have no competing interests with respect to this research

Consent for publication

All authors consented for the publication in this journal

Authors’ contributions

Hanis Rabeah Kamaruzaman: conceived and designed the study, data collection, performing the analysis and writing the menuscript, Sharifah Faradila Wan Muhamad Hatta: conceptualization and design the methodology from the study, interpreting the result, Nur Hidayati Mohd Sharif: conceptualization and design the methodology from the study, Zaliha Ismail: performing analysis, Mohd Yusri Idorus: help with resources and running the test sample ,Thuhairah Hasrah Abdul Rahman: help with resources , Rafezah Razali: help with resources, Rohana Abdul Ghani: supervised the research and help in writing, reviewing and editing the manuscript critically. All authors have read and agreed to the published version of the manuscript. The authors take full responsibility for the content and conclusion stated in this manuscript

Author details

¹Department of Internal Medicine, Faculty of Medicine, Hospital Al –Sultan Abdullah, UiTM Puncak Alam, 47000, Sungai Buloh, Selangor, Malaysia

Cundy T, Reid IR, Grey A, CHAPTER 31 - Metabolic bone disease, in Clinical Biochemistry: Metabolic and Clinical Aspects (Third Edition), W.J. Marshall, et al., Editors. 2014, Churchill Livingstone. p. 604–635.
Xia J, et al. The relationship between insulin resistance and osteoporosis in elderly male type 2 diabetes mellitus and diabetic nephropathy. Ann Endocrinol (Paris). 2012;73(6):546–51.
Mithal A, et al. The Asia-Pacific Regional Audit-Epidemiology, Costs, and Burden of Osteoporosis in India 2013: A report of International Osteoporosis Foundation. Indian J Endocrinol Metab. 2014;18(4):449–54.
Hauger AV, et al. Osteoporosis and osteopenia in the distal forearm predict all-cause mortality independent of grip strength: 22-year follow-up in the population-based Tromso Study. Osteoporos Int. 2018;29(11):2447–56.
Abrahamsen B, et al. Excess mortality following hip fracture: a systematic epidemiological review. Osteoporos Int. 2009;20(10):1633–50.
Johnell O, et al. Mortality after osteoporotic fractures. Osteoporos Int. 2004;15(1):38–42.
Isaia GC, et al. Bone metabolism in type 2 diabetes mellitus. Acta Diabetol. 1999;36(1–2):35–8.
Montagnani A, et al. Osteoporosis and risk of fracture in patients with diabetes: an update. Aging Clin Exp Res. 2011;23(2):84–90.
Carnevale V, Romagnoli E, D'Erasmo E. Skeletal involvement in patients with diabetes mellitus. Diabetes Metab Res Rev. 2004;20(3):196–204.
Choi SW, et al. Association of bone mineral density with albuminuria and estimated glomerular filtration rate: the Dong-gu Study. Kidney Blood Press Res. 2013;37(2–3):132–41.
Tung CW, et al. Dipstick Proteinuria and Reduced Estimated Glomerular Filtration Rate as Independent Risk Factors for Osteoporosis. Am J Med Sci. 2018;355(5):434–41.
Xia J, et al. The relationship between insulin resistance and osteoporosis in elderly male type 2 diabetes mellitus and diabetic nephropathy. Ann Endocrinol. 2012;73(6):546–51.
Chen H-l, Deng L-l, Li J-f. Prevalence of Osteoporosis and Its Associated Factors among Older Men with Type 2 Diabetes. Int J Endocrinol. 2013;2013:285729.
Lee J-K, Khir ASM. The incidence of hip fracture in Malaysians above 50 years of age: variation in different ethnic groups. APLAR J Rheumatol. 2007;10(4):300–5.
Kanis JA. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. WHO Study Group. Osteoporos Int. 1994;4(6):368–81.
Deurenberg-Yap M, Deurenberg P. Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev. 2003;61(5 Pt 2):S80-7.
Aloia JF, Feuerman M, Yeh JK. Reference Range for Serum Parathyroid Hormone. Endocr Pract. 2006;12(2):137–44.
Holick MF. Vitamin D Status: Measurement, Interpretation, and Clinical Application. Ann Epidemiol. 2009;19(2):73–8.
Michelsen J, et al. Reference intervals for serum concentrations of three bone turnover markers for men and women. Bone. 2013;57(2):399–404.
Bardin T, Richette P. Definition of hyperuricemia and gouty conditions. 2014;26(2):186–91.
Schwartz AV, et al. Association of BMD and FRAX score with risk of fracture in older adults with type 2 diabetes. JAMA. 2011;305(21):2184–92.
Swaminathan R. Biochemical markers of bone turnover. Clin Chim Acta. 2001;313(1):95–105.
Vasikaran S, et al. International Osteoporosis Foundation and International Federation of Clinical Chemistry and Laboratory Medicine position on bone marker standards in osteoporosis. Clin Chem Lab Med. 2011;49(8):1271–4.
Chiang C. The use of bone turnover markers in chronic kidney disease-mineral and bone disorders. Nephrol (Carlton). 2017;22(Suppl 2):11–3.
Khan M, Jose A, Sharma S. Physiology, Parathyroid Hormone, in StatPearls. Treasure Island (FL); 2022.
Gaffney-Stomberg E, MacArthur MR, McClung JP. Parathyroid Hormone (PTH) and the Relationship Between PTH and Bone Health: Structure, Physiology, Actions, and Ethnicity, in Biomarkers in Bone Disease, V.B. Patel and V.R. Preedy, Editors. 2017, Springer Netherlands: Dordrecht. pp. 443–61.
Silva BC, Bilezikian JP. Parathyroid hormone: anabolic and catabolic actions on the skeleton. Curr Opin Pharmacol. 2015;22:41–50.
Agrawal NK, Sharma B. Prevalence of osteoporosis in otherwise healthy Indian males aged 50 years and above. Arch Osteoporos. 2013;8:116.
Chin K-Y, et al., Vitamin D Status in Malaysian Men and Its Associated Factors. 2014. 6(12): p. 5419–5433.
Ahmed AKMS, et al. Vitamin D and bone mineral density status among postmenopausal Bangladeshi women. IMC J Med Sci. 2019;12(2):44–9.
Montagnani A, Gonnelli S. Antidiabetic therapy effects on bone metabolism and fracture risk. Diabetes Obes Metab. 2013;15(9):784–91.
Fu J, et al. Dipeptidyl peptidase-4 inhibitors and fracture risk: an updated meta-analysis of randomized clinical trials. Sci Rep. 2016;6:29104.
Mamza J, et al. DPP-4 inhibitor therapy and bone fractures in people with Type 2 diabetes - A systematic review and meta-analysis. Diabetes Res Clin Pract. 2016;116:288–98.
Yang Y, et al. Effect of Dipeptidyl Peptidase-4 Inhibitors on Bone Metabolism and the Possible Underlying Mechanisms. Front Pharmacol. 2017;8:487–7.
Fink HA, et al. Association of Testosterone and Estradiol Deficiency with Osteoporosis and Rapid Bone Loss in Older Men. J Clin Endocrinol Metabolism. 2006;91(10):3908–15.
Menon KV, et al. Bone disease in primary biliary cirrhosis: independent indicators and rate of progression. J Hepatol. 2001;35(3):316–23.
Ruiz-Gaspà S, et al., Effects of bilirubin and sera from jaundiced patients on osteoblasts: Contribution to the development of osteoporosis in liver diseases. 2011. 54(6): p. 2104–2113.
Jurado S, et al. Bilirubin increases viability and decreases osteoclast apoptosis contributing to osteoporosis in advanced liver diseases. Bone. 2022;162:116483.
Zhao J, et al., Systematic Influence of Circulating Bilirubin Levels on Osteoporosis. 2021. 12.
Schwartz AV, et al., Diabetes and Bone Loss at the Hip in Older Black and White Adults. 2005. 20(4): p. 596–603.
Cirovic A, et al. Increased Cortical Porosity, Reduced Cortical Thickness, and Reduced Trabecular and Cortical Microhardness of the Superolateral Femoral Neck Confer the Increased Hip Fracture Risk in Individuals with Type 2 Diabetes. Calcified Tissue International; 2022.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Prevalence and Associated Factors of Metabolic Bone Disease in Men with Type 2 Diabetes Mellitus

Status:

Version 1

Abstract

Introduction

Methods And Material

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1