
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72747
10.1038/s41598-024-72747-8
Article
Association of serum calcium levels with diabetic kidney disease in normocalcemic type 2 diabetes patients: a cross-sectional study
Yu Qing 1
Xu Lili 1
Liang Cuicui 2
Deng Yujie 1
Wang Ping 1
Yang Nailong nailongy@163.com

1
1 https://ror.org/026e9yy16 grid.412521.1 0000 0004 1769 1119 Department of Endocrinology and Metabolism, The Affiliated Hospital of Qingdao University, Qingdao, China
2 Qingdao Municipal Health Commission Hospital Development Center, Qingdao, China
14 9 2024
14 9 2024
2024
14 215133 7 2024
10 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
To explore the association between serum calcium levels within normal ranges and Diabetic Kidney Disease (DKD) in type 2 diabetes patients. In this cross-sectional study, we analyzed clinical data from type 2 diabetes patients admitted to the Endocrinology Department of the Affiliated Hospital of Qingdao University from January 1, 2021, to December 1, 2022. We measured serum calcium levels, corrected for albumin, and screened for diabetes-related complications, including DKD. The association between corrected serum calcium levels and DKD was evaluated using logistic regression, with adjustments made for potential confounders and a generalized additive model (GAM) to explore non-linear relationships, supplemented by subgroup analyses. Among the 3016 patients (52.55% male, 47.45% female), the mean corrected serum calcium was 2.29 ± 0.08 mmol/L. DKD was present in 38.73% of patients. A 0.1 mmol/L increase in corrected serum calcium was associated with a 44% increased risk of DKD (OR = 1.44, 95% CI 1.28–1.61, p < 0.0001). The GAM indicated a linear relationship between corrected serum calcium and DKD risk, consistent across subgroups. Corrected serum calcium levels were linearly associated with DKD risk in type 2 diabetes patients, underlining its potential role in risk assessment. These findings emphasize the clinical importance of monitoring serum calcium levels. However, the need for further prospective studies to confirm these findings is underscored by the study’s cross-sectional design.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72747-8.

Keywords

Serum calcium
Diabetic kidney disease
Type 2 diabetes mellitus
Cross-sectional study
Subject terms

Diabetes complications
Type 2 diabetes
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pmcIntroduction

Diabetes Mellitus, particularly type 2 diabetes (T2D), poses a significant global health challenge due to its increasing prevalence1,2. Among its complications, Diabetic Kidney Disease (DKD) affects approximately 40% of patients, leading to chronic kidney disease and end-stage renal disease globally3,4. Early detection and intervention are crucial for improving outcomes and reducing healthcare burdens5,6.

Serum Calcium, a critical electrolyte, plays a vital role in various physiological processes7,8 and is associated with an increased risk of Type 2 Diabetes (T2D)9,10. Correcting serum calcium levels for albumin offers a more accurate measure of its ionized form, crucial in diabetic patients who often have variable albumin levels due to kidney impairment and other comorbidities11,12. However, the relationship between serum calcium and microvascular complications, such as Diabetic Kidney Disease (DKD), is less clear, highlighting the need to further investigate calcium homeostasis in DKD13,14. This study aims to address the gap in understanding the association between corrected serum calcium levels and DKD risk in T2D patients, potentially offering new insights into calcium metabolism’s role in diabetic kidney disease and its implications for clinical management.

Methods

Research setting and population

We conducted a single-center, cross-sectional study in the Endocrinology Department at the Affiliated Hospital of Qingdao University from January 1, 2021, to December 1, 2022. This study included inpatients aged 18 years or older diagnosed with Type 2 Diabetes Mellitus (T2DM). The diagnosis of T2DM was established based on the American Diabetes Association’s guidelines, requiring at least one of the following criteria: (1) glycated hemoglobin (HbA1c) ≥ 6.5% (48 mmol/mol); (2) fasting plasma glucose (FPG) ≥ 7.0 mmol/L (126 mg/dL); (3) 2-hour plasma glucose ≥ 11.1 mmol/L (200 mg/dL) during a 75-g oral glucose tolerance test (OGTT); (4) random plasma glucose ≥ 11.1 mmol/L (200 mg/dL) in patients with classic symptoms of hyperglycemia or hyperglycemic crisis; (5) current use of antidiabetic medications15. The antidiabetic medications included: a) Biguanides (e.g., metformin)b) Sulfonylureas (e.g., glipizide, glyburide)c) Thiazolidinediones (e.g., pioglitazone)d) DPP-4 inhibitors (e.g., sitagliptin)e) GLP-1 receptor agonists (e.g., liraglutide)f) SGLT2 inhibitors (e.g., empagliflozin)g) Insulins.

We excluded patients with missing serum calcium or albumin levels, individuals with type 1 diabetes or other specific forms of diabetes, patients with end-stage renal disease (eGFR < 15 mL/min/1.73 m²) or those on dialysis, and conditions significantly affecting calcium metabolism (e.g., primary hyperparathyroidism, significant liver disease). Additionally, we excluded patients with recent use of medications known to significantly impact serum calcium levels, including calcium supplements, vitamin D preparations, thiazide and loop diuretics (e.g., hydrochlorothiazide, furosemide), bisphosphonates (e.g., alendronate), glucocorticoids (e.g., prednisone), lithium, teriparatide, denosumab, calcimimetics (e.g., cinacalcet), and certain anticonvulsants (e.g., phenytoin)16–18. “Recent use” was defined as administration within 30 days prior to serum calcium measurement. Additionally, we excluded pregnant women, patients with active malignancies or other severe consumptive diseases, as well as those with incomplete data regarding serum calcium, albumin, diabetes status, or renal function.Participant selection is detailed in Fig. 1.

Fig. 1 Flowchart of study population.

Measurement of serum calcium and albumin levels

Serum calcium levels were accurately measured using the Arsenazo III method and analyzed with a BS800 + ISE biochemical analyzer. Blood samples were collected after fasting, minimizing variations due to external factors like alcohol or medication. Albumin levels were determined using the bromocresol green method. We corrected serum calcium levels for albumin concentration using the formula: albumin-corrected calcium (mmol/L) = measured total calcium (mmol/L) + 0.02 × [40 − albumin (g/L)]19.

DKD status assessment

Diabetic Kidney Disease (DKD) in this study was identified based on chronic kidney disease attributed to diabetes, characterized by a urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g and/or an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, persisting for more than 3 months20. It is important to note that while DKD includes patients with eGFR < 60 mL/min/1.73 m², our study specifically excluded individuals with advanced renal disease, defined as an eGFR < 15 mL/min/1.73 m² or those requiring dialysis. This exclusion criterion was implemented to avoid confounding effects from severe renal impairment on serum calcium metabolism. eGFR was calculated using the formula by Andrew S. Levey et al.21.

Covariates

The study collected various covariates, including demographic and clinical data such as gender, age, diabetes duration, and complications. Anthropometric measurements included height, weight, and body mass index (BMI), calculated as weight in kilograms divided by height in meters squared. Lifestyle factors assessed were smoking and alcohol consumption, with alcohol consumption defined as consuming at least 30 g of alcohol weekly for a year or more, and smoking defined as having smoked at least 100 cigarettes in one’s lifetime22. Biochemical analyses involved fasting blood samples for lipid profiles, creatinine (Cr), uric acid (UA), fasting plasma glucose (FPG), liver function tests, glycosylated hemoglobin (HbA1c), and urinary albumin excretion rate (UAER), with all tests, except for HbA1c, performed using the Hitachi 7600 analyzer. Fatty liver diagnosis was based on ultrasound findings23. Hypertension was defined as having a systolic blood pressure (BP) ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, or current use of antihypertensive medication24. Diabetic retinopathy (DR) assessment utilized a slit lamp microscope and optical coherence tomography, with diagnoses made by an ophthalmologist based on funduscopic examination findings, categorizing patients as having DR (including proliferative and non-proliferative changes) or not25. Diabetic peripheral neuropathy (DPN) diagnosis was based on clinical symptoms, neurological examinations, and nerve conduction studies26.

Statistical methods

Participant baseline characteristics are presented as means ± standard deviations for normally distributed variables, median (Interquartile Range (IQR))for skewed variables, and percentages for categorical variables. The Chi-square (χ2) test, Student’s t-test, and Mann-Whitney U test were used to analyze differences in diabetic nephropathy status among categorical, normally distributed, and skewed variables, respectively. Univariate logistic regression models evaluated the association between corrected Serum Calcium levels (per 0.1 change) and Diabetic Kidney Disease (DKD).Our analysis comprised unadjusted, minimally adjusted (Model I adjusting for age, sex, BMI), and fully adjusted models (Model II incorporating DKD-related variables identified in univariate logistic regression).To assess potential non-linear relationships, we employed Generalized Additive Models (GAM) using the ‘mgcv’ package in R. The model is expressed as: g(E(Y)) = β₀ + s(calcium) + s(age) + s(BMI) + βX.Where Y represents DKD presence/absence, g() is the logit link function, s() denotes smooth functions using thin plate regression splines, and X represents other covariates. Smooth terms were constrained to a maximum of 10 degrees of freedom, with optimal smoothing determined by generalized cross-validation. The calcium-DKD relationship was visualized using partial effect plots with 95% confidence intervals. Stratified binary logistic regression models were used for subgroup analyses. All analyses were conducted using R (The R Foundation) and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA). Statistical significance was set at a two-sided P-value < 0.05.

Results

As shown in Table 1, our study of 3016 type 2 diabetes patients revealed significant differences between those with and without Diabetic Kidney Disease (DKD). DKD patients were older, had a higher BMI, and a longer duration of diabetes. They exhibited poorer glycemic control, with higher fasting blood glucose and hemoglobin A1c levels. Lipid profiles showed elevated triglycerides and total cholesterol levels, along with a reduced estimated glomerular filtration rate. Corrected serum calcium levels were also higher in the DKD group. Additionally, DKD patients had higher prevalence of hypertension, diabetic retinopathy, and diabetic peripheral neuropathy. Lifestyle factors such as smoking and alcohol consumption were similar between the two groups. Analysis of corrected serum calcium levels across different categories of albuminuria (based on UACR) and stages of CKD (based on eGFR) within the DKD group revealed significant trends. Patients with higher levels of albuminuria showed variations in serum calcium levels (p < 0.05, Supplementary Table 1), with the highest levels observed in the macroalbuminuria group (UACR > 300 mg/g). Additionally, we observed a clear trend of increasing serum calcium levels with declining eGFR (p < 0.001, Supplementary Table 2). Patients with more severe stages of CKD (lower eGFR) demonstrated higher serum calcium levels.

Table 1 Baseline characteristics of participants.

	Non-diabetic kidney disease (n = 1848)	Diabetic kidney disease (n = 1168)	P-value	
Age (years old), mean (sd)	57.56 (12.24)	60.86 (13.34)	< 0.001	
Sex, n (%)	0.088	
 Male	994 (53.79%)	591 (50.60%)		
 Female	854 (46.21%)	577 (49.40%)		
BMI (kg/m2), n (%)	0.001	
 ≤ 24	651 (35.42%)	339 (29.27%)		
 > 24, ≤ 28	795 (43.25%)	527 (45.51%)		
 > 28	392 (21.33%)	292 (25.22%)		
Smoking history, n (%)	0.871	
 Non-smokers	1354 (73.51%)	858 (73.77%)		
 Smokers	488 (26.49%)	305 (26.23%)		
Alcohol consumption history, n (%)	0.096	
 Non-drinkers	1374 (74.51%)	897 (77.19%)		
 Drinkers	470 (25.49%)	265 (22.81%)		
 Diabetic duration (years), median (IQR)	6.00 (2.00–12.00)	10.00 (3.00–16.00)	< 0.001	
 FBG (mmol/L), mean (sd)	7.34 (2.40)	7.58 (2.68)	0.004	
 HbA1c (%), mean (sd)	8.37 (2.00)	8.97 (2.00)	< 0.001	
 TG (mmol/L) median (IQR)	1.31 (0.92–1.99)	1.46 (0.98–2.36)	< 0.001	
 TC (mmol/L), mean (sd)	4.66 (1.23)	4.80 (1.33)	0.002	
 HDL-C (mmol/L), mean (sd)	1.19 (0.33)	1.21 (0.32)	0.062	
 LDL-C (mmol/L), mean (sd)	2.73 (0.91)	2.80 (1.04)	0.052	
 UA (umol/L), mean (sd)	316.75 (86.06)	348.93 (100.37)	< 0.001	
 ALB g/dL, mean (sd)	40.08 (3.42)	38.98 (4.22)	< 0.001	
 AST (U/L) median (IQR)	17.00 (14.00–21.00)	17.00 (14.00–21.00)	0.821	
 ALT (U/L) median (IQR)	18.00 (13.07–26.83)	17.45 (13.00-25.13)	0.077	
 eGFR (mL/min per 1.73 m2) median (IQR)	93.78 (74.25-127.69)	66.84 (53.68–95.63)	< 0.001	
 Serum phosphorus (mmol/L), mean (sd)	1.21 (0.22)	1.20 (0.23)	0.179	
 Serum magnesium (mmol/L), mean (sd)	0.84 (0.08)	0.84 (0.09)	0.089	
 Corrected serum calcium (mmol/L), mean (sd)	2.28 (0.08)	2.31 (0.08)	< 0.001	
Hypertension (%)	< 0.001	
 No	943 (51.03%)	415 (35.53%)		
 Yes	905 (48.97%)	753 (64.47%)		
Fatty liver disease, n (%)	0.024	
 No	938 (50.76%)	642 (54.97%)		
 Yes	910 (49.24%)	526 (45.03%)		
Diabetic retinopathy, n (%)	< 0.001	
 No	1222 (66.13%)	586 (50.17%)		
 Yes	626 (33.87%)	582 (49.83%)		
Diabetic peripheral neuropathy, n (%)	< 0.001	
 No	702 (37.99%)	318 (27.23%)		
 Yes	1146 (62.01%)	850 (72.77%)		
FBG fasting blood glucose, HbA1c hemoglobin A1c, eGFR estimated glomerular filtration rate, AST aspartate aminotransferase, ALT alanine aminotransferase, UA uric acid, TC total cholesterol, TG triglycerides, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol.

As presented in Table 2, The univariate analysis identified several factors associated with Diabetic Kidney Disease (DKD) risk in type 2 diabetes patients. Positively correlated factors included corrected serum calcium levels, age, BMI (especially in higher categories), diabetes duration, fasting blood glucose (FBG), HbA1c, triglycerides (TG), total cholesterol (TC), and uric acid (UA). Hypertension, fatty liver disease, diabetic retinopathy, and diabetic peripheral neuropathy also showed positive associations with DKD risk. Fatty liver disease exhibited a negative relationship with DKD risk. No significant associations were found with smoking, alcohol consumption, HDL-C, LDL-C, AST, ALT, serum phosphorus, and serum magnesium. Interestingly, female gender displayed a trend towards increased risk but was not statistically significant.

Table 2 Univariate logistic regression analysis of risk factors associated with diabetic kidney disease in patients with type 2 diabetes.

	β (95% CI)/OR (95% CI)	P value	
Age (years old)	1.02 (1.02, 1.03)	< 0.0001	
Sex, n (%)	
 Male	Ref		
 Female	1.14 (0.98, 1.32)	0.0877	
BMI (kg/m2)	
 ≤ 24	Ref		
 > 24, ≤28	1.27 (1.07, 1.51)	0.0058	
 > 28	1.43 (1.17, 1.75)	0.0005	
Smoking history, n (%)	
 Non-smokers	Ref		
 Smokers	0.99 (0.83, 1.17)	0.8712	
Alcohol consumption history, n (%)	
 Non-drinkers	Ref		
 Drinkers	0.86 (0.73, 1.03)	0.0958	
 Diabetic duration (years)	1.04 (1.03, 1.05)	< 0.0001	
 FBG (mmol/L)	1.04 (1.01, 1.07)	0.0102	
 HbA1c (%)	1.16 (1.11, 1.20)	< 0.0001	
 TG (mmol/L)	1.06 (1.02, 1.10)	0.0014	
 TC (mmol/L)	1.10 (1.03, 1.16)	0.0025	
 HDL-C (mmol/L)	1.24 (0.99, 1.56)	0.0625	
 LDL-C (mmol/L)	1.08 (1.00, 1.17)	0.0519	
 UA (umol/L)	1.004 (1.003, 1.005)	< 0.0001	
 AST (U/L)	1.001 (0.996, 1.006)	0.7969	
 ALT (U/L)	0.999 (0.997, 1.001)	0.471	
 Serum phosphorus (mmol/L)	0.80 (0.57, 1.11)	0.1798	
 Serum magnesium (mmol/L)	0.45 (0.18, 1.13)	0.0890	
 Corrected serum calcium (mmol/L) (per 0.1 change)	1.51 (1.38,1.66)	< 0.0001	
Hypertension (%)	
 No	Ref		
 Yes	1.89 (1.63, 2.20)	< 0.0001	
Fatty liver disease	
 No	Ref		
 Yes	0.84 (0.73, 0.98)	0.0242	
Diabetic retinopathy	
 No	Ref		
 Yes	1.94 (1.67, 2.25)	< 0.0001	
Diabetic peripheral neuropathy	
 No	Ref		
 Yes	1.64 (1.40, 1.92)	< 0.0001	
CI confidence interval, Ref reference.

As shown in Table 3,Our study meticulously analyzed the link between serum calcium levels, adjusted for albumin, and the risk of Diabetic Kidney Disease (DKD) in type 2 diabetes patients. In the non-adjusted model, a 0.1 mmol/L increase in serum calcium was associated with a 51% higher risk of DKD (OR = 1.51, 95% CI: 1.38–1.66, p < 0.0001). After adjusting for age, sex, and BMI (Adjusted I Model), this association remained significant, with a 50% increased risk (OR = 1.50, 95% CI: 1.36–1.65, p < 0.0001). In the most comprehensive model (Adjusted II Model), which accounted for age, sex, BMI, diabetes duration, FBG, HbA1c, TC, TG, UA, hypertension, fatty liver disease, DR, and DPN, a 0.1 mmol/L increase in serum calcium still corresponded to a 44% higher risk of DKD (OR = 1.44, 95% CI: 1.28–1.61, p < 0.0001).

Table 3 Relationship between serum calcium and DKD in different models.

Exposure	Non-adjusted (OR, 95% CI, P)	Adjust I (OR, 95% CI, P)	Adjust II (OR, 95% CI, P)	
Corrected serum calcium (mmol/L) (per 0.1 change)	1.51 (1.38, 1.66), < 0.0001	1.50 (1.36, 1.65), < 0.0001	1.44 (1.28, 1.61), < 0.0001	
Adjust I Model: Adjusted for age, sex, and BMI.

Adjust II Model: Further adjusted for diabetes duration, FBG, HbA1c, TC, TG, UA, hypertension, fatty liver disease, DR, and DPN.

In Fig. 2, we utilized Generalized Additive Models (GAM) to examine the potential non-linear relationship between corrected Serum Calcium levels and Diabetic Kidney Disease (DKD). Our findings indicated a linear relationship between corrected Serum Calcium levels and DKD risk, even after adjusting for significant variables including age, sex, BMI, diabetes duration, FBG, HbA1c, TC, TG, UA, hypertension, fatty liver disease, diabetic retinopathy, and diabetic peripheral neuropathy.

Fig. 2 Linear relationship between corrected serum calcium levels and diabetic kidney disease after comprehensive adjustments. The y-axis titled “RISK of CKD” represents the predicted risk of developing Chronic Kidney Disease (CKD) based on our Generalized Additive Model (GAM), with higher values indicating a greater risk of CKD occurrence. The red curve represents the fitted relationship from the GAM, while the blue curves indicate the 95% confidence interval.

As illustrated in Fig. 3, we conducted stratified analyses across subgroups defined by age (in tertiles), sex, BMI, diabetes duration, FBG, HbA1c (≥ 7%), TC (≥ 6.2mmol/L), TG (≥ 2.3 mmol/L), UA (≥ 420 µmol/L), diabetic retinopathy (DR), and diabetic peripheral neuropathy (DPN). The forest plot results consistently showed an association between corrected serum calcium levels and Diabetic Kidney Disease across these subgroups. Notably, we assessed this association per 0.1 unit increase in corrected calcium levels.

Fig. 3 The results of subgroup analyses.

Discussion

In our study, after adjusting for potential confounders, we found a significant linear relationship between serum calcium levels, corrected for albumin, and the risk of Diabetic Kidney Disease (DKD) in type 2 diabetes patients. This association was further validated through generalized additive model (GAM) analysis, with subgroup analyses reinforcing the robustness of our results.

Several pathophysiological mechanisms could explain this association. Elevated serum calcium may lead to glomerular calcification, impairing filtration and promoting inflammatory responses that contribute to glomerulosclerosis27–29. The elevated serum calcium–phosphorus product is linked to vascular calcification and increased arterial stiffness, exacerbating pressure in renal glomeruli and potentially accelerating kidney damage30,31. Additionally, disrupted calcium signaling can cause podocyte injury, a key aspect of DN progression32,33. Calcium’s role in oxidative stress is also notable, as it can directly damage renal tissues34,35. Conversely, several pathophysiological mechanisms in DKD may contribute to elevated serum calcium levels. Secondary hyperparathyroidism, induced by phosphate retention and impaired vitamin D activation, increases parathyroid hormone secretion, enhancing bone resorption and intestinal calcium absorption36. Mineral metabolism disturbances in DKD progression can disrupt calcium-phosphorus homeostasis37. Advanced DKD-associated metabolic acidosis promotes calcium efflux from bone38. These mechanisms underscore the complex bidirectional relationship between DKD progression and calcium homeostasis, highlighting the importance of calcium level monitoring in DKD management.

Our study, which identifies a link between elevated serum calcium levels and Diabetic Kidney Disease, aligns with findings on diabetes-related complications. For instance, regular calcium supplement intake is linked to increased cardiovascular risk in diabetic patients39, suggesting a specific sensitivity to calcium in diabetes. Elevated serum calcium is also associated with a greater risk of vision-threatening diabetic retinopathy40. Additionally, a cohort study highlights a relationship between higher blood calcium levels and type 2 diabetes risk41. However, the role of serum calcium differs by renal disease etiology; for example, in IgA nephropathy, serum phosphorus, not calcium, is identified as a key factor42, highlighting the need for etiology-specific research.

A key strength of our study is the application of Generalized Additive Models (GAM), which effectively delineated the linear relationship between corrected serum calcium levels and Diabetic Kidney Disease (DKD). Our rigorous statistical analysis, including adjustments and subgroup consistency, indicates that a 0.1 mmol/L increase in serum calcium correlates with a 44% heightened risk of DN. These findings pave the way for future research, especially in investigating the mechanisms behind these associations and conducting prospective studies for validation.

Our study has limitations to consider. The cross-sectional design limits our ability to infer causality between serum calcium levels and diabetic kidney disease (DKD). Future prospective cohort or case-control studies are necessary to clarify this relationship and establish the temporal sequence between serum calcium levels and DKD development. Our exclusion of patients with severe disturbances in calcium homeostasis, particularly those with end-stage renal disease on dialysis, whose metabolic profiles differ significantly from non-dialysis patients43,44, may introduce selection bias and limit the generalizability of our findings. While this decision was necessary to maintain a more homogeneous study population and to focus on the relationship between serum calcium and diabetic kidney disease (DKD) in patients without significant calcium metabolism alterations. Future studies should consider including a more diverse patient population, encompassing those with various degrees of calcium metabolism disorders and renal dysfunction. It’s also important to note that our findings are based on serum calcium levels within the normal range, so they may not extend to cases with abnormally high or low calcium levels. Further research is needed to explore the relationship between serum calcium levels beyond the normal range and the risk of DKD, which could provide a more comprehensive understanding of this association. Despite our efforts to adjust for known confounding factors, we acknowledge that unmeasured or residual confounding may still affect the observed association between serum calcium levels and Diabetic Kidney Disease (DKD). Factors such as dietary calcium intake, vitamin D levels, parathyroid hormone levels, and other diabetes-related complications not accounted for in our study could potentially influence this relationship. To quantify the potential impact of unmeasured confounding, we calculated the E-value for our main result. The E-value is a measure of the minimum strength of association that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain away the observed association. In our study, the E-value is 1.89 (95% CI: 1.66 - NA). This indicates that an unmeasured confounder would need to have a relative risk association of at least 1.89 with both serum calcium levels and DKD to completely explain away the observed association. While this analysis suggests that our findings are relatively robust to unmeasured confounding.

It is important to note that our primary analysis was unable to account for parathyroid hormone (PTH) and vitamin D levels, which are known to influence serum calcium levels, particularly in patients with Diabetic Kidney Disease (DKD). Due to the retrospective nature of this study, data on PTH and vitamin D levels were missing for more than 60% of the study population. However, we conducted a subgroup analysis including these variables on the available data (n = 587). This analysis supported our main findings, with an adjusted OR of 1.51 (95% CI: 1.14–2.03, p < 0.05) for the association between serum calcium levels and DKD. While this result reinforces our primary conclusion, the substantially reduced sample size limits the generalizability of these findings. Our study does not establish a specific cutoff value for serum calcium in DKD risk assessment, it underscores the importance of monitoring calcium levels in patients with type 2 diabetes. Clinicians should be aware that even within the normal range, higher serum calcium levels may indicate increased DKD risk. Future prospective studies and clinical trials are needed to determine whether interventions aimed at modulating serum calcium levels could affect DKD progression and to establish clinically relevant thresholds for heightened vigilance or intervention.

Conclusions

Our cross-sectional study indicates a significant positive correlation between serum calcium levels and Diabetic Kidney Disease in type 2 diabetes. These findings point to a potential role of calcium in DKD and suggest the need for further studies to understand the underlying mechanisms and broader implications.

Supplementary Information

Supplementary Tables.

Author contributions

Qing Yu and Lili Xu contributed equally to this work. Qing Yu and Lili Xu conceptualized and designed the study, analyzed data, and wrote the manuscript. Cuicui Liang provided technical support and assisted in data analysis and interpretation. Yujie Deng helped with data collection, and assisted in manuscript preparation. Ping Wang contributed to data analysis and interpretation, and critically reviewed the manuscript. Nailong Yang supervised the study, provided overall guidance, contributed to study design and data interpretation, and critically revised the manuscript. All authors read and approved the final manuscript.

Data availability

Data availability The data that support the findings of this study are not publicly available due to patient confidentiality concerns but are available from the corresponding authors upon reasonable request.

Competing interests

The authors declare no competing interests.

Ethical approval

This research was conducted in accordance with the principles set forth in the Declaration of Helsinki. Informed consent was obtained from all participants. This study received ethical approval from the Ethics Committee of the Affiliated Hospital of Qingdao University (QYFY WZLL 28303).

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Qing Yu and Lili Xu contributed equally to this work.
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