Abstract
Background
This study aimed to clarify between serum calcium and mortality in osteoarthritis (OA) patients using NHANES data (2003–2018).
Method
A total of 1,249 adults with OA were included. Mortality outcomes were ascertained through linkage to the National Death Index (NDI). Cox proportional hazards models were used to assess the independent association of serum calcium concentration with the risk of death. We used a two-stage Cox proportional hazards model to elucidate the nonlinear relationship between serum calcium concentration and the risk of death in patients with OA. Stratified analyses were performed to identify patients at higher risk.
Results
During follow-up (median: 9.7 years), 220 all-cause deaths occurred (56 CVD-related, 64 cancer-related). After adjusting for relevant variables, serum calcium concentration showed an inverse linear association with all-cause and CVD mortality. Although smoothed curve fitting visually suggested U-shaped and L-shaped patterns for all-cause and CVD mortality, respectively, formal tests for nonlinearity did not reach statistical significance (P for log-likelihood ratio > 0.05). Specifically, the risk was elevated at lower calcium levels and stabilized or showed a non-significant increase at higher levels. In the piecewise linear model, serum calcium below 9.50 mg/dL was inversely associated with all-cause mortality, where each 1 mg/dL increase was associated with a 48% lower risk (HR 0.52, 95% CI: 0.30–0.91). Above 9.50 mg/dL, the association was non-significant (HR 1.09, 95% CI: 0.51–2.35). A similar L-shaped pattern was observed for CVD mortality below the inflection point (HR 0.26, 95% CI: 0.09–0.74). However, formal likelihood ratio tests comparing the threshold model with a linear model did not reach statistical significance (P > 0.05), indicating that the data do not provide strong evidence against a linear association.
Conclusion
Our primary analysis supports an inverse linear association between serum calcium and mortality in OA patients. Although exploratory analyses revealed potential non-linear patterns visually, these were not statistically confirmed. Therefore, the overall relationship is best characterized as linear within the observed range, with lower serum calcium associated with higher mortality risk. Therefore, these findings highlight a potential optimal range for serum calcium but require verification in future studies designed to confirm nonlinearity and assess the clinical utility of calcium management.
Keywords: Osteoarthritis, Calcium, All-cause mortality, Cardiovascular mortality, Cancer mortality
Background
Osteoarthritis (OA) is a common chronic joint disease that is prevalent worldwide [1]. According to statistics, by 2020, OA affected 595 million people, accounting for 7.6% of the world’s total population [2]. Studies have demonstrated that patients with OA exhibit elevated risks of cardiovascular disease (CVD) and all-cause mortality compared to the general population [3–5]. Consequently, identifying modifiable risk factors for OA and mitigating associated complications are critical public health priorities. Calcium, an essential mineral element [6], plays pivotal roles in fundamental biological processes including gene expression regulation, cellular proliferation, and apoptosis [7]. However, the physiological implications of serum calcium levels demonstrate complexity, exhibiting both protective and detrimental associations across different health outcomes. Paradoxically, elevated serum calcium concentrations have been associated with reduced OA prevalence [8], yet this biochemical profile may concurrently elevate cardiovascular risk. The relationship between calcium supplementation and cardiovascular disease risk remains debated. While some studies, including a randomized controlled trial in a healthy community, have reported that calcium or calcium plus vitamin D supplements not only failed to prevent fractures but were also associated with an increased risk of kidney stones and cardiovascular events [9, 10], other large-scale meta-analyses and trials have found no significant increase in CVD risk [11, 12]. This ongoing debate highlights the complexity of calcium’s role in cardiovascular health. This study investigated the association between serum calcium concentrations and all-cause/CVD mortality in OA patients. Our objectives were to: (1) elucidate these relationships; (2) establish optimal serum calcium thresholds (in both mg/dL and mmol/L); and (3) generate hypotheses that could inform future research on calcium and vitamin D supplementation.
Methods
Study design and population
This retrospective cohort study utilized data from the National Health and Nutrition Examination Survey (NHANES) in the United States. NHANES is conducted by the National Center for Health Statistics (NCHS) under the Centers for Disease Control and Prevention (CDC) in the United States. It is a survey of the non-institutionalized civilian population in the United Statesand is nationally representative [13]. All NHANES protocols have been approved by the NCHS Research Ethics Review Committee [14]. Informed consent was obtained from all participants prior to their enrollment in this study. Research all use data can be found in the NHANES website (https://www.cdc.gov/nchs/nhanes/?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/index.htm). For this study, we selected the NHANES data set for eight cycles from 2003 to 2018 (2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018). Statistical analyses were performed using statistical package R (R 4.4.1) and SPSS 26.0. Participants with osteoarthritis (OA) were identified through affirmative responses to the question “Has a physician ever diagnosed you with arthritis?” followed by the selection of “osteoarthritis” in the subsequent question regarding arthritis type. In a sensitivity analysis to address potential misclassification of osteoarthritis, we defined a subgroup with higher diagnostic specificity. This subgroup was restricted to participants aged 50 years or older who, in addition to self-reporting a physician’s diagnosis of OA, also reported experiencing activity limitations due to their arthritis. All primary analyses were repeated within this subgroup to test the robustness of the findings. Abnormal energy intake (< 500 or > 5000 kcal per day) was excluded on the basis of the WHO guidelines for the daily energy requirements of adults. There were 80,312 respondents in the 2003–2018 NHANES database, of which 3,764 patients with OA aged between 20 and 85 years were selected for this study, after excluding those with missing serum calcium and mortality data and those with abnormal energy intake data (< 500 kcal/day or > 5000 kcal/day), 1249 patients with OA were finally enrolled in this study (Fig. 1).
Fig. 1.
Flow chart of study participants
Measurement of serum calcium concentrations
Venous blood specimens were collected at mobile examination centers (MECs) following a standardized protocol after a 12-hour fast. Serum total calcium concentration (reported in mg/dL; to convert to mmol/L, multiply by 0.25) was determined using a colorimetric assay on clinical chemistry analyzers. Throughout the 2003–2018 survey cycles, the predominant analytical method was the o-cresolphthalein complexone technique, implemented on various automated platforms such as the Beckman Synchron LX20 in earlier cycles and the Roche Cobas 6000 in later cycles. Detailed procedures for specimen collection, processing, and analysis are documented in the NHANES Laboratory/Medical Technician Procedures Manual (LPM) for each survey cycle.
It is well established that serum total calcium concentrations are influenced by serum albumin levels. In the present study, all primary analyses were based on measured total calcium values—an approach consistent with common practice in large epidemiological studies using NHANES data. Potential implications of this methodological choice are further discussed in the Discussion section.
Determination of mortality outcomes
Mortality status was ascertained using the publicly available mortality data provided by NHANES, which are generated through a standardized, previously established NHANES protocol involving probabilistic linkage between NHANES participants and the National Death Index (NDI). According to NHANES documentation, this linkage achieves a sensitivity of 98.2% and a specificity of 99.4% (NCHS Protocol #2020-01). Vital status (deceased/alive) and causes of death in the NHANES mortality files have undergone rigorous, multi-tier adjudication by NCHS, including checks against NDI primary codes, death certificates, and the Social Security Administration Death Master File. Cause-specific mortality in the NHANES data is classified according to the ICD-10 guidelines [15]. We used these pre-adjudicated, quality-controlled mortality outcomes as provided in the NHANES datasets for our analysis.
Assessment of covariates
Age, gender, race, education level, smoking status, alcohol consumption, household income, and disease status (duration of OA, history of hypertension or diabetes) for which questionnaires were collected in the NHANES database were included in this study. The participants’ body mass index (BMI, kg/m2 ) and waist circumference were also measured at mobile examination centres (MECs).
Among these covariates included in this study, race was categorized as Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, or other race; education levels are categorised as below high school, high school or equivalent, university or above. A person who has consumed at least 12 alcoholic beverages per year is considered to have consumed alcohol; identify as smokers those who have smoked at least 100 cigarettes in their lifetime. We also obtained the results of serum specimens from laboratory tests in the NHANES database, with strict procedural controls throughout the blood collection and analysis process [16]. And in processing the baseline data, we collected and included plasma glycohemoglobin (%), glucose (mg/dL), cholesterol (mg/dL), direct high-density lipoprotein cholesterol (mg/dL), low-density lipoprotein cholesterol (mg/dL), triglycerides (mg/dL), glomerular filtration rate (mL/min/1.73 m2) and uric acid ((mg/dL). Among them, the glomerular filtration rate (eGFR) is estimated and calculated by the CKD-EPI equation of the Collaborative Study on the Epidemiology of Chronic Kidney Disease [17].
Statistical analyses
All statistical analyses accounted for the complex, multi-stage sampling design of NHANES to produce nationally representative estimates. Appropriate sample weights, stratification, and clustering of the design were incorporated as recommended by the NCHS. For this study, which pools eight survey cycles (2003–2004 to 2017–2018), a new combined mobile examination center (MEC) weight was created by dividing the individual cycle MEC examination weights by the number of cycles (8). A survey design object was specified in R using the survey package, incorporating the combined MEC weight, stratification variable (SDMVSTRA), and primary sampling units (SDMVPSU). Cox proportional hazards regression analyses were performed using the svycoxph function from the survey package to assess the association between serum calcium and mortality. The proportional hazards assumption was tested using weighted Schoenfeld residuals. All reported hazard ratios (HRs) and 95% confidence intervals (CIs) are derived from these survey-weighted models.
Appropriate sample weights were considered in this study. Serum calcium levels were statistically analysed by transforming them from continuous to categorical variables based on quartiles: <9.10 mg/dL (< 2.27 mmol/L); 9.10–9.30 mg/dL (2.27–2.32 mmol/L); 9.30–9.60 mg/dL (2.32–2.40 mmol/L); and > 9.60 mg/dL (> 2.40 mmol/L). Categorical variables were analysed using the χ2 test, with data presented as percentages, and continuous variables were analysed using linear regression models, with data presented as mean ± standard deviation. In this study, COX survival analysis, generalized additive modeling, threshold effects analysis, and subgroup analysis were used to examine the effect of serum calcium levels on all-cause mortality, cardiovascular mortality, and cancer mortality in an osteoarthritis population. Cox proportional hazards models were used to assess the independent association of serum calcium concentration with the risk of death. For cause-specific mortality (CVD and cancer), we primarily modeled cause-specific hazards, where deaths from other causes were treated as censoring events. This approach is suited for investigating the biological effect of an exposure on the rate of a specific event. In particular, three models were constructed for COX survival analysis: model 1 adjusted for covariates, model 2 adjusted for age and sex, and model 3 adjusted for age, sex, race, education level, smoking status, alcohol consumption, household income, BMI, waist circumference, hypertension, diabetes mellitus, plasma glycated haemoglobin, glucose, cholesterol, and direct high-density lipoprotein cholesterol, LDL cholesterol, triglycerides, glomerular filtration rate, and uric acid were adjusted. In this study in order to investigate the relationship between serum calcium levels and mortality in the OA population, we chose to develop Cox proportional risk regression models using generalised additive models and threshold effects analysis to determine the non-linear relationship that exists between them. The generalized additive models (GAM) were fitted using a penalized spline method (with a default of 3 basis functions for the smooth term) to visualize the potential non-linear associations. The proportional risk hypothesis was tested using the Schoenfeld residuals test, and no significant violations were found (P > 0.05); interactions were assessed by the likelihood ratio test (significance level P < 0.05). When nonlinearity was detected, a piecewise linear Cox proportional hazards model was fitted to characterize the dose-response relationship. The inflection point (knot) was treated as an unknown parameter and was determined by selecting the value that maximized the profile likelihood function over a fine grid of serum calcium concentrations. Subsequent stratified analyses were performed using this piecewise linear model to evaluate the association separately below and above the identified thresholds. In addition, we stratified analyses by sex, age (< 60 or ≥ 60 years), race (white or non-white), hypertension, diabetes mellitus, and BMI (< 25.00 or ≥ 25.00). All analyses were performed using SPSS version 26.0 and R 3.4.3.A two-sided P < 0.05 was considered statistically significant.
Results
Baseline characteristics of study participants
This study ultimately included 1249 eligible participants (mean age 61.53 ± 13.49, 38.40% male). Analysis of the data from the included OA patients revealed that 27.48% had serum calcium levels < 9.10 mg/dL; 23.67% had serum calcium levels between 9.10 and 9.30 mg/dL; 29.31% had serum calcium levels between 9.30 and 9.60 mg/dL; and 18.75% had serum calcium levels > 9.60 mg/dL. A table of baseline characteristics for all participants analysed is shown in Table 1 below. Patients with higher serum calcium levels tended to be older and predominantly non-Hispanic white, and were accompanied by higher levels of education, lower rates of smoking, higher rates of alcohol consumption, and lower likelihood of obesity (P < 0.01). In addition, when metabolic biomarkers were compared on the basis of serum calcium levels, higher serum calcium levels were significantly associated with higher levels of cholesterol, direct HDL cholesterol, LDL cholesterol, triglycerides, and uric acid (P < 0.01), and higher serum calcium levels were significantly associated with lower glomerular filtration rate levels (P < 0.01).
Table 1.
Baseline characteristics of included participants
| Variable | Serum calcium concentrations(mg/dL) | P for trend | ||||
|---|---|---|---|---|---|---|
| Total | Q1 | Q2 | Q3 | Q4 | ||
| N(%) | 1249 (100) | 346 (27.48) | 298 (23.67) | 369 (29.31) | 236 (18.75) | |
| Age(year) | 61.53 ± 13.49 | 59.39 ± 14.14 | 61.00 ± 13.57 | 62.30 ± 12.99 | 64.07 ± 12.70 | < 0.01 |
| Sex (%) | < 0.01 | |||||
| Male | 480 (38.40) | 133 (38.44) | 116 (38.93) | 153 (41.46) | 78 (33.05) | |
| Female | 770 (61.60) | 213 (61.56) | 182 (61.07) | 216 (58.54) | 158 (66.95) | |
| Race (%) | < 0.01 | |||||
| Mexican American | 135 (10.80) | 44 (12.72) | 29 (9.73) | 40 (10.84) | 21 (8.90) | |
| Other Hispanic | 91 (7.28) | 26 (7.51) | 25 (8.39) | 25 (6.78) | 15 (6.36) | |
| Non-Hispanic White | 712 (56.96) | 193 (55.78) | 164 (55.03) | 218 (59.08) | 137 (58.05) | |
| Non-Hispanic Black | 234 (18.72) | 55 (15.90) | 58 (19.46) | 70 (18.97) | 51 (21.61) | |
| Other race—including multiracial | 78 (6.24) | 28 (8.09) | 22 (7.38) | 16 (4.33) | 12 (5.08) | |
| Educational level (%) | < 0.01 | |||||
| Lower than high school | 121 (9.68) | 34 (9.83) | 33 (11.07) | 28 (7.59) | 26 (11.02) | |
| High school or equivalent | 459 (36.72) | 114 (32.95) | 116 (38.93) | 135 (36.59) | 93 (39.41) | |
| More than high school | 670 (53.60) | 198 (57.22) | 149 (50.00) | 206 (55.82) | 117 (49.57) | |
| Smoking status (%) | < 0.01 | |||||
| Every day | 243 (19.44) | 66 (19.08) | 54 (18.12) | 77 (20.86) | 46 (19.49) | |
| Some day | 449 (35.92) | 127 (36.71) | 109 (36.58) | 137 (37.13) | 76 (32.20) | |
| Not at all | 558 (44.64) | 153 (44.21) | 135 (45.30) | 155 (42.01) | 114 (48.31) | |
| Alcoholic ≥ 12 drinks/year (%) | < 0.01 | |||||
| Yes | 817 (65.36) | 215 (62.14) | 204 (68.46) | 248 (67.21) | 150 (63.56) | |
| No | 433 (34.64) | 131 (37.86) | 94 (31.54) | 121 (32.79) | 86 (36.44) | |
| Family poverty income ratio | 2.56 ± 1.60 | 2.54 ± 1.59 | 2.49 ± 1.59 | 2.69 ± 1.65 | 2.48 ± 1.53 | 0.06 |
| BMI (kg/m2) | 30.94 ± 7.39 | 31.77 ± 7.74 | 31.42 ± 7.47 | 30.81 ± 7.39 | 29.36 ± 6.45 | < 0.01 |
| Waist circumference (cm) | 104.81 ± 16.45 | 106.14 ± 17.18 | 105.97 ± 16.68 | 104.58 ± 16.11 | 101.85 ± 15.17 | 0.01 |
| Hypertension (%) | < 0.01 | |||||
| Yes | 739 (59.12) | 199 (57.51) | 177 (59.40) | 215 (58.27) | 147 (62.29) | |
| No | 511 (40.88) | 147 (42.49) | 121 (40.60) | 154 (41.73) | 89 (37.71) | |
| Diabetes (%) | < 0.01 | |||||
| Yes | 288 (23.04) | 80 (23.12) | 68 (22.82) | 86 (23.31) | 54 (22.88) | |
| No | 962 (76.96) | 266 (76.88) | 230 (77.18) | 283 (76.69) | 182 (77.12) | |
| Energy (kcal/day) | 1906.78 ± 748.67 | 1968.01 ± 756.85 | 1979.53 ± 781.26 | 1916.54 ± 709.36 | 1708.57 ± 725.58 | < 0.01 |
| Glycohemoglobin (%) | 5.94 ± 1.06 | 5.92 ± 1.13 | 6.01 ± 1.10 | 5.90 ± 0.93 | 5.96 ± 1.10 | 0.31 |
| Glucose, refrigerated serum (mg/dL) | 108.44 ± 35.64 | 107.42 ± 38.09 | 108.90 ± 33.79 | 108.44 ± 32.99 | 109.44 ± 38.32 | 0.07 |
| Cholesterol (mg/dL) | 193.05 ± 42.51 | 182.80 ± 42.07 | 193.18 ± 41.12 | 195.27 ± 39.89 | 204.50 ± 45.66 | < 0.01 |
| Direct HDL-cholesterol (mg/dL) | 55.74 ± 18.19 | 53.76 ± 16.78 | 55.94 ± 17.57 | 55.67 ± 16.77 | 58.42 ± 22.37 | < 0.01 |
| LDL-cholesterol (mg/dL) | 111.75 ± 37.20 | 106.22 ± 35.55 | 111.80 ± 37.14 | 113.66 ± 36.09 | 116.92 ± 40.43 | < 0.01 |
| Triglycerides (mg/dL) | 127.97 ± 67.52 | 113.94 ± 57.02 | 127.25 ± 68.03 | 129.67 ± 67.58 | 145.81 ± 76.42 | < 0.01 |
| eGFR (mL/min/1.73 m2) | 90.66 ± 29.09 | 93.74 ± 29.14 | 91.53 ± 29.65 | 89.80 ± 27.12 | 86.41 ± 30.80 | < 0.01 |
| Uric acid (mg/dL) | 5.56 ± 1.50 | 5.32 ± 1.49 | 5.60 ± 1.57 | 5.61 ± 1.37 | 5.78 ± 1.58 | < 0.01 |
Q1: serum calcium level < 9.10 mg/dL (< 2.27 mmol/L); Q2: serum calcium level between 9.10 and 9.30 mg/dL (2.27–2.32 mmol/L); Q3: serum calcium level between 9.30 and 9.60 mg/dL (2.32–2.40 mmol/L); Q4: serum calcium level > 9.60 mg/dL (> 2.40 mmol/L). BMI: body mass index; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein cholesterol; eGFR: estimated glomerular filtration rate. Continuous variables are expressed as ± SD, categorical variables are expressed as n (%)
Relationships of serum calcium concentration with mortality
A total of 220 deaths from any cause were identified among 1249 participants, including 56 deaths from CVD and 64 deaths from cancer (Table 2). To investigate the independent role of serum calcium in mortality, we developed three separate Cox regression models. After multivariable adjustment for age, sex, race, education level, smoking status, alcohol consumption, household income, body mass index (BMI), waist circumference, hypertension, diabetes, hemoglobin A1c (HbA1c), glucose, total cholesterol, direct high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, estimated glomerular filtration rate (eGFR), and uric acid (Model 3), the multivariable-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) for all-cause mortality across ascending serum calcium categories (< 9.10 [< 2.27 mmol/L], 9.10–9.30 [2.27–2.32 mmol/L], 9.30–9.60 [2.32–2.40 mmol/L], > 9.60 [> 2.40 mmol/L] mg/dL) were 1.00 (reference), 0.90 (0.75–1.07), 0.82 (0.70–0.98), and 0.78 (0.64–0.95), respectively (P for trend = 0.016). Corresponding values for cardiovascular disease (CVD) mortality were 1.00 (reference), 0.87 (0.74–1.03), 0.82 (0.70–0.95), and 0.78 (0.65–0.93) (P for trend = 0.008); and for cancer mortality 1.00 (reference), 0.85 (0.73–1.01), 0.77 (0.66–0.90), and 0.75 (0.62–0.93) (P for trend = 0.001).It was also found that OA patients with higher serum calcium levels (≥ 9.10 mg/dL) had lower all-cause, CVD, and cancer mortality than those with serum calcium levels < 9.10 mg/dL.
Table 2.
Mortality in OA participants with serum calcium concentration HR (95% CI)
| Serum calcium concentrations(mg/dL) | P for trend | |||||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | |||
| All-cause mortality | ||||||
| Number of deaths (%) | 62 (17.92) | 47 (15.77) | 59 (15.99) | 52 (22.03) | ||
| Model 1 | HR (95% CI) | 1.00 | 0.89 (0.75,1.05) | 0.84 (0.71,0.99) | 0.78 (0.64,0.93) | 0.016 |
| P-value | 0.171 | 0.032 | 0.007 | |||
| Model 2 | HR (95% CI) | 1.00 | 0.92 (0.77,1.09) | 0.84 (0.71,0.99) | 0.80 (0.66,0.96) | 0.019 |
| P-value | 0.312 | 0.038 | 0.018 | |||
| Model 3 | HR (95% CI) | 1.00 | 0.90 (0.75,1.07) | 0.82 (0.70,0.98) | 0.78 (0.64,0.95) | 0.016 |
| P-value | 0.229 | 0.024 | 0.014 | |||
| CVD mortality | ||||||
| Number of deaths (%) | 20 (5.78) | 13 (4.36) | 10 (2.71) | 13 (5.51) | ||
| Model 1 | HR (95% CI) | 1.00 | 0.88 (0.75,1.03) | 0.85 (0.73,0.99) | 0.82 (0.69,0.97) | 0.034 |
| P-value | 0.114 | 0.032 | 0.021 | |||
| Model 2 | HR (95% CI) | 1.00 | 0.90 (0.77,1.06) | 0.84 (0.72,0.98) | 0.81 (0.68,0.96) | 0.017 |
| P-value | 0.197 | 0.028 | 0.017 | |||
| Model 3 | HR (95% CI) | 1.00 | 0.87 (0.74,1.03) | 0.82 (0.70,0.95) | 0.78 (0.65,0.93) | 0.008 |
| P-value | 0.106 | 0.011 | 0.007 | |||
| Cancer mortality | ||||||
| Number of deaths (%) | 14 (4.05) | 15 (5.03) | 20 (5.42) | 15 (6.36) | ||
| Model 1 | HR (95% CI) | 1.00 | 0.86 (0.73,1.00) | 0.81 (0.70,0.94) | 0.80 (0.67,0.94) | 0.007 |
| P-value | 0.055 | 0.005 | 0.008 | |||
| Model 2 | HR (95% CI) | 1.00 | 0.88 (0.75,1.03) | 0.80 (0.69,0.93) | 0.78 (0.66,0.93) | 0.002 |
| P-value | 0.110 | 0.005 | 0.005 | |||
| Model 3 | HR (95% CI) | 1.00 | 0.85 (0.73,1.01) | 0.77 (0.66,0.90) | 0.75 (0.62,0.93) | 0.001 |
| P-value | 0.059 | 0.001 | 0.001 | |||
Model 1 = unadjusted
Model 2 = age/sex
Model 3 = fully adjusted
Detection of non-linear relationships
To explore the possibility of a non-linear relationship, we used generalized additive models and smooth-curve fitting. The resulting curves visually suggested a U-shaped pattern for all-cause mortality (Fig. 2A) and an L-shaped pattern for CVD mortality (Fig. 2B). However, a formal likelihood ratio test comparing the threshold (non-linear) model to a simple linear model did not reach statistical significance for all-cause (P = 0.204) or CVD mortality (P = 0.633). Therefore, we cannot reject the null hypothesis of a linear association, and the linear model remains the statistically supported one. In an exploratory analysis, we employed a piecewise linear Cox model to characterize the visual patterns in the spline curves. The model estimated an inflection point at 9.50 mg/dL for the association between serum calcium and mortality. Below this level, each 1 mg/dL increase in serum calcium was associated with a significantly lower risk of all-cause and CVD mortality. Above this level, the association was non-significant. Critically, these piecewise estimates are descriptive of the visual trend and must be interpreted with caution, as the overall test for nonlinearity was not significant. It is important to emphasize that these piecewise estimates are intended to characterize the observed visual patterns and should be interpreted with caution, as the overall test for nonlinearity was not statistically significant. This analysis ultimately revealed an inflection point of 9.50 mg/dL for both all-cause and CVD mortality (Table 3). Specifically, the inflection point was 9.50 mg/dL (approximately 2.37 mmol/L; 95% CI: 9.20-9.80 mg/dL [2.30-2.45 mmol/L]) for all-cause mortality and 9.50 mg/dL (approximately 2.37 mmol/L; 95% CI: 9.15-9.85 mg/dL [2.29-2.46 mmol/L]) for CVD mortality. When serum calcium concentration was below this threshold (9.50 mg/dL), the risk of all-cause and CVD mortality decreased with increasing calcium concentration (all-cause: HR 0.52, 95% CI: 0.30–0.91; CVD: HR 0.26, 95% CI: 0.09–0.74). For each 1 mg/dL increase below the inflection point, the risk of all-cause mortality was reduced by 48%. In contrast, serum calcium concentrations above 9.50 mg/dL were not significantly associated with all-cause or CVD mortality (all-cause: HR 1.09, 95% CI: 0.51–2.35; CVD: HR 0.47, 95% CI: 0.07–2.97). These detailed piecewise linear associations are summarized in Table 3. It is important to note that the inverse associations observed in the quartile analysis (Table 2) reflect the overall protective trend against the reference group (Q1: <9.10 mg/dL). However, further investigation using nonlinear models revealed that this relationship was not monotonic, but followed U-shaped and L-shaped patterns for all-cause and CVD mortality, respectively, as detailed below (Table 3).
Fig. 2.
Nonlinear associations between serum calcium concentrations and all-cause mortality (A) and CVD mortality (B) in patients with OA. Adjusted for age, sex, race, education level, smoking status, alcohol consumption, household income, BMI, waist circumference, hypertension, diabetes mellitus, plasma glycohemoglobin, glucose, cholesterol, direct high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glomerular filtration rate and uric acid. Solid and dashed lines indicate estimated values and their corresponding 95% confidence intervals, respectively
Table 3.
Threshold effect analysis of serum calcium concentrations on all-cause and CVD mortality in OA patients using a piecewise linear Cox model
| Adjusted HR (95% CI), P-value | |
|---|---|
| All-cause mortality | |
| Fitting by the standard linear model | 0.69(0.47,1.01),0.056 |
| Fitting by the piecewise linear model | |
| Inflection point (95% CI) |
9.50 (9.20, 9.80) mg/dL [2.37 (2.30, 2.45) mmol/L] |
|
Serum calcium concentrations < 9.50 mg/dL (< 2.37 mmol/L) |
0.52(0.30,0.91),0.023 |
|
Serum calcium concentrations ≥ 9.50 mg/dL (≥ 2.37 mmol/L) |
1.09(0.51,2.35),0.823 |
| P for Log-likelihood ratio | 0.204 |
| CVD mortality | |
| Fitting by the standard linear model | 0.31(0.14,0.67),0.003 |
| Fitting by the piecewise linear model | |
| Inflection point (95% CI) | 9.50 (9.15, 9.85) mg/dL |
| Serum calcium concentrations < 9.50 mg/dL | 0.26(0.09,0.74),0.012 |
| Serum calcium concentrations ≥ 9.50 mg/dL | 0.47(0.07,2.97),0.423 |
| P for Log-likelihood ratio | 0.633 |
Adjustments were made for age, sex, race, education level, smoking status, alcohol use, household income, BMI, waist circumference, hypertension, diabetes mellitus, plasma glycosylated hemoglobin, glucose, cholesterol, direct HDL cholesterol, LDL cholesterol, triglycerides, glomerular filtration rate and uric acid
Stratified analyses
The advantage of higher serum calcium concentrations (≥ 9.50 mg/dL) versus lower serum calcium concentrations (< 9.50 mg/dL) on survival in patients with OA was similar in a broad subgroup stratified by age, gender, race, BMI, history of hypertension, and history of diabetes (Fig. 3.). We found a significant interaction between serum calcium concentration and whether or not there was a history of hypertension (p = 0.003). At the same time, there was a significant association between lower serum calcium concentrations (< 9.50 mg/dL) and whites and hypertensive patients (HR 0.60; 95% CI : 0.38, 0.97, p = 0.036 and HR 0.42; 95% CI : 0.27, 0.67, p < 0.001), and higher serum calcium concentrations (≥ 9.50 mg/dL) were associated with diabetic patients was significantly associated (HR 0.58; 95% CI : 0.37, 0.91, p = 0.018).
Fig. 3.
Forest plot for stratified analysis of serum calcium concentration and all-cause mortality. In addition to the variables themselves, adjustments were made for age, sex, race, education level, smoking status, alcohol use, household income, BMI, waist circumference, hypertension, diabetes mellitus, plasma glycosylated haemoglobin, glucose, cholesterol, direct HDL cholesterol, LDL cholesterol, triglycerides, glomerular filtration rate and uric acid
Discussion
In this large, nationally representative cohort of OA patients, we found a significant inverse association between serum calcium concentration and the risk of all-cause, cardiovascular, and cancer mortality, indicating that lower serum calcium levels are associated with higher mortality. Although exploratory non-linear models visually indicated potential U-shaped and L-shaped patterns for all-cause and CVD mortality, respectively, these patterns were not statistically distinct from a linear association. Thus, the primary finding supports an inverse linear relationship. In this study, we chose to include data related to OA patients from 2003 to 2018 from the US NHANES database to analyse the association between serum calcium and mortality in the OA prevalent population. In this retrospective cohort study with a large sample size, strong correlations were found between survival and all-cause mortality, CVD mortality, and cancer mortality in patients with OA, and a U-shaped association between serum calcium levels and all-cause mortality and an L-shaped association with CVD mortality in studies with nonlinear relationships. This result suggests that lower serum calcium levels are significantly associated with higher all-cause mortality and CVD mortality. These findings may contribute to the understanding of risk factors for all-cause and CVD mortality in OA patients and could inform the design of future studies to determine if modifying serum calcium levels is beneficial.
Currently, OA is recognized as a common musculoskeletal disorder characterized by progressive cartilage degeneration and subchondral bone remodeling [18, 19]. Calcium is an important factor in the maintenance of bone strength, and serum calcium levels reflect the state of calcium metabolism in the body, which in turn indirectly affects the progression of osteoarthritis. It has been suggested that abnormal calcium metabolism may be involved in the progression of osteoarthritis by affecting subchondral bone remodelling [20]. Previously, a cross-sectional study of 2855 Chinese individuals showed that serum calcium concentration was negatively associated with the risk of radiologically diagnosed knee OA, suggesting that high levels of calcium may play a protective role in the control of radiologically determined knee OA [21]. Meanwhile, in another meta-analysis study, three separate meta-analysis studies were conducted on different target populations and similar causal relationships were found [22]. However, Hunter et al. concluded in their study that there was no causal relationship between serum calcium levels and OA in female white twins [23].Similarly Zoli et al. did not find a causal relationship between serum calcium levels and hand OA in the case-control study they conducted [24]. It suggests that the interrelationship between serum calcium levels and osteoarthritis remains controversial, and that the differences in findings may have been influenced by differences in study sites, populations, and covariates. Therefore, we chose to include the US population and screen for relevant variables to delve into the relationship between the role of serum calcium levels and osteoarthritis.
Calcium is an important mineral for maintaining human health and is involved in many functions of the skeletal, muscular, nervous, and cardiovascular systems. Therefore, the relationship between serum calcium and mortality has been well studied. Studies have been conducted in multiple hospital settings and have found an association between low serum calcium and increased all-cause mortality. In patients with myocardial infarction [25], patients with chronic heart failure [26], patients with coronary heart disease [27], and elderly patients with sepsis [28], low serum calcium levels are common, and studies have found that low serum calcium has a potentially harmful effect on increasing all-cause mortality. A cohort analysis including 20,512 Danish participants also reported an association between calcium levels and all-cause mortality, and a further study found a U-shaped association between the two [29], a result that is consistent with ours in that a U-shaped association was found, but with differences in inflection points, and the differences may have arisen from abnormalities in calcium metabolism in the OA population. Again, not all studies produced valid results. A prospective study investigating the relationship between serum 25OHD, dietary vitamin D and calcium and mortality in a Scottish cohort ultimately concluded that there was no evidence of a causal relationship between dietary vitamin D and calcium levels and mortality [30]. We suspect that this problem is due to differences in the choice of ethnicity and the inclusion base, while the choice of dietary sources of calcium versus serum calcium may also have an impact on causality.
Currently, there are very limited studies exploring the relationship between serum calcium and CVD mortality. In a known study, Yarmohammadi, H. reported that lower serum calcium levels were independently associated with an increased risk of cardiac arrest, demonstrating that low serum calcium concentrations may be a potential risk factor for cardiovascular disease [31]. Van Hemelrijck, M. also reported a causal relationship between serum calcium levels and mortality in the Third National Health and Nutrition Examination Survey (NHANES III) and demonstrated that lower serum calcium levels are often associated with increased CVD mortality in the male population [32]. Low calcium may increase the risk of CVD by promoting vascular smooth muscle cell calcification or exacerbating OA-related inflammation. In our study, a causal relationship between serum calcium and CVD mortality was similarly concluded, but on the other hand, as we chose to include the OA population, a different conclusion emerged from the previous study in the subgroup analysis, as we did not find an effect of lower serum calcium concentrations on CVD mortality in the male population, and here we suggest that this result is influenced by the specificity of the disease OA impact. Meanwhile, in another study of serum calcium and the risk of all-cause and cause-specific mortality, a U-shaped association between albumin-adjusted calcium and cardiovascular mortality was noted in the literature [33]. This likewise informs our findings.
The relationship between cancer mortality in the OA population and serum calcium was similarly included and examined in our study. Jorde et al. reported a negative association between serum calcium and cancer mortality after adjusting for covariates such as age, sex, BMI and smoking, with subjects with higher serum calcium tending to have a lower risk of cancer [34]. Although this study did not choose to limit the OA population and did not adjust by albumin, and there is a possibility of bias in the results, there is consistency with our study in the final results. Similar studies in the Swedish Apolipoprotein Mortality Risk (AMORIS) study have also shown a negative association between total serum calcium and breast cancer [35]. Nevertheless, the literature examining the non-linear relationship between serum calcium and cancer mortality is limited, and in-depth studies for certain types of cancers are still needed to obtain definitive conclusions. In this study, we first performed an analysis of baseline data collated from the OA population and found that of the 1249 participants selected for inclusion, 27.48% had serum calcium levels < 9.10 mg/dL; 23.67% had serum calcium levels between 9.10 and 9.30 mg/dL; 29.31% had serum calcium levels between 9.30 and 9.60 mg/dL; 18.75% of patients had serum calcium levels > 9.60 mg/dL. And then survival models were developed and adjusted for relevant covariates to explore the correlation between serum calcium and all-cause mortality, CVD mortality, and cancer mortality in the OA population, and concluded that in a study of the association between serum calcium and all-cause mortality based on age, sex, race, education level, smoking status, alcohol consumption, household income, BMI, waist circumference, hypertension, diabetes, plasma glycated haemoglobin, glucose, cholesterol, direct high-densitylipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glomerular filtration rate, and uric acid variables after multivariate adjustment (Model 3), high serum calcium concentrations were significantly associated with reduced all-cause, CVD, and cancer mortality. And cancer mortality showed a strong linear association with serum calcium concentration. It is worth mentioning that the highest percentage of all-cause mortality (22.03%) but the lowest adjusted HR was observed for serum calcium concentration > 9.60 mg/dL without adjusting for covariates (model 1), suggesting that our high serum calcium may play a protective role through other variables, such as disease status. Following the identification of the serum calcium-mortality association, we subsequently conducted advanced nonlinear analyses to characterize the dose-response relationship. For this purpose we used a generalized additive model and smoothed curve fitting (penalised spline method) for our analyses, and ultimately concluded that serum calcium concentration had a U-shaped association with all-cause mortality and an L-shaped association with CVD mortality in the OA population, but no non-linear relationship was detected with cancer mortality. Finally, subgroup analyses were performed to test the effect of serum calcium concentration on mortality under different conditions, and it was concluded that the advantage of higher serum calcium concentration (≥ 9.50 mg/dL) versus lower serum calcium concentration (< 9.50 mg/dL) on survival of patients with OA was similar in broad subgroups stratified by age, sex, race, BMI, history of hypertension, and history of diabetes. At the same time, we found that the international normal range for serum calcium is 8.5–10.5 mg/dL (2.12–2.62 mmol/L), and that this inflection point is right within the normal range, with potential for clinical guidance [36]. We therefore conclude that there is a U-shaped association between serum calcium concentration and all-cause mortality and an L-shaped association with CVD mortality in the OA population.
The primary strengths of this study include the utilization of a large-scale nationally representative cohort, which provided robust statistical power and minimized selection bias through comprehensive population coverage. And the average follow-up was longer at 9.7 years. Second, the population included in this study is nationally representative, so our results are applicable to the U.S. adult population. In our exploratory analysis, the lowest risk appeared to be around 9.50 mg/dL, which falls within the normal clinical range. This suggests a potential optimal range that future studies could investigate, but our data do not provide strong evidence for a specific threshold effect. Moreover, in our study, survival analysis model closure was developed and non-linear statistical analyses were performed, and a wide range of covariates were considered, so our conclusions should be more robust and reasonable.
This study has several limitations. First, the reliance on total serum calcium, rather than the physiologically active ionized fraction or albumin-adjusted values, is a notable constraint. Total calcium concentrations are influenced by serum albumin levels, which may introduce measurement inaccuracy and limit the biochemical precision of our exposure assessment. Second, and most critically, the lack of data on key regulators of mineral metabolism—namely parathyroid hormone (PTH) and 25-hydroxyvitamin D—prevents us from ruling out residual confounding. Thus, the observed associations may partly reflect the influence of these unmeasured variables. Third, as a retrospective cohort analysis of a U.S. population, our findings may not be generalizable to other ethnicities or healthcare settings, and despite comprehensive adjustment for confounders, the observational design precludes the establishment of causality. Future research should incorporate direct measurements of ionized calcium, PTH, and vitamin D within longitudinal or interventional frameworks to confirm a causal relationship. Fourth, our definition of OA relied on self-reported physician diagnosis and type, which is subject to potential misclassification due to participant recall error or diagnostic inaccuracy in the community. Although this method is widely used in large epidemiological studies like NHANES and has been validated in previous research, it may lack the specificity of a clinical or radiographic confirmation. Fifth, our definition of osteoarthritis relied on self-reported physician diagnosis and type, which is subject to potential misclassification. This could arise from participant recall error or diagnostic inaccuracy in the community setting, potentially diluting the observed associations toward the null. Although we had planned to conduct a sensitivity analysis restricted to older adults with activity limitations to enhance diagnostic specificity, time constraints precluded us from performing this additional analysis. However, it is important to note that the self-report method used is a well-validated and widely employed approach in large-scale surveys like NHANES for surveillance purposes. Furthermore, the consistency of our primary findings across key subgroups (e.g., by age and sex) provides some reassurance regarding the robustness of the observed associations. Nevertheless, the potential for residual misclassification remains, and future studies using clinically or radiographically confirmed OA are warranted to confirm our findings.
Conclusions
After multivariate adjustment, lower serum calcium concentrations were significantly associated with higher risk of all-cause, CVD, and cancer deaths in US OA adults. Among these, there was a non-linear association between serum calcium concentration and all-cause and CVD mortality, and a linear association with cancer mortality. In conclusion, lower serum calcium concentrations were independently associated with a higher risk of all-cause and cause-specific mortality in US adults with OA. The overall dose-response relationship was linear, although visual inspection of smoothed curves hinted at potential non-linearity that was not statistically verified. The observed range of lowest risk around 9.50 mg/dL may serve as a hypothesis for future research to confirm if a specific target for calcium management is beneficial. The observed range around this threshold warrants further investigation in future studies to determine if maintaining serum calcium within this level is beneficial in reducing mortality risk. Our findings raise the possibility that monitoring and adjusting serum calcium concentrations could be of value; however, any potential benefit of adjustment requires verification through interventional studies.
Author contributions
QS conceived the idea for this study and wrote the manuscript. XL and LMD collected the data and performed the statistical analysis. WPX and NHL reviewed the data and revised the manuscript. All authors participated in data analysis, drafted and critically revised the manuscript, agreed to be responsible for all aspects of the work, read and approved the final manuscript.
Funding
This study was supported by the Youth Program of the National Natural Science Foundation of China (Grant No. 8257150290), the National Natural Science Foundation of China (Grant No. 82104897), the Program of the Medicine Health Technology of Shandong Province (Grant No. 202304071516),the Youth Program of the Natural Science Foundation of Shandong Province (Grant No. ZR2020QH312 and ZR2022QH208), and the Traditional Chinese Medicine science and technology projects of Shandong Province (Grant No. Q-2022063).
Data availability
This study analyzes the publicly available datasets. The data can be found in the NHANES database: https://www.cdc.gov/nchs/nhanes/index.html. Further queries can be directly to the corresponding author.
Declarations
Ethics approval and consent to participate
The NHANES study protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board. All participants provided written informed consent. For this secondary analysis of de-identified, publicly available data, the Institutional Review Board of the First Clinical Medical College of Shandong University of Traditional Chinese Medicine determined that additional ethical approval was not required.
Human ethics and consent to participate
All procedures involving human participants in the original NHANES study were performed in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all individual participants included in the original NHANES survey.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Qi Sun and Xin Li contributed equally to this manuscript.
Nianhu Li and Wenpeng Xie contributed equally to this manuscript.
Contributor Information
Nianhu Li, Email: tigerlee073@126.com.
Wenpeng Xie, Email: 71002083@sdutcm.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study analyzes the publicly available datasets. The data can be found in the NHANES database: https://www.cdc.gov/nchs/nhanes/index.html. Further queries can be directly to the corresponding author.