Osteoporosis is associated with increased CVD mortality and all-cause mortality in alcohol-consuming individuals: A cohort study using data from NHANES

Xiaoqin Qu; Jingcheng Jiang

doi:10.1371/journal.pone.0327180

. 2025 Jun 26;20(6):e0327180. doi: 10.1371/journal.pone.0327180

Osteoporosis is associated with increased CVD mortality and all-cause mortality in alcohol-consuming individuals: A cohort study using data from NHANES

Xiaoqin Qu ^1,², Jingcheng Jiang ^3,^*

Editor: Qian Wu,⁴

PMCID: PMC12200671 PMID: 40570002

Abstract

Background

Osteoporosis, a skeletal disorder characterized by reduced bone density and increased fracture risk, imposes a significant global health burden, particularly in aging populations. Previous studies have highlighted the negative impact of alcohol consumption on bone health, but the interplay between osteoporosis and mortality risk in alcohol-consuming individuals remains underexplored. This study aimed to investigate the association between osteoporosis and cardiovascular disease (CVD) mortality and all-cause mortality in U.S. adults who consume alcohol.

Methods

This prospective cohort study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning five cycles (2005–2010, 2013–2014, and 2017–2018). A total of 12,178 alcohol-consuming participants aged 20 years and older were included after excluding those with missing data or non-drinking status. Bone density was measured using dual-energy X-ray absorptiometry (DXA), and osteoporosis was defined using World Health Organization (WHO) T-score criteria (T-score ≤ −2.5). Mortality data were obtained through linkage with the National Death Index (NDI). Multivariable Cox proportional hazards regression models were employed to assess the relationship between osteoporosis and mortality outcomes, adjusting for demographic, socioeconomic, and clinical covariates.

Results

Kaplan-Meier survival analysis revealed higher all-cause and CVD mortality rates in participants with osteoporosis compared to those without (Log-rank test P < 0.001 for both). After adjusting for potential confounders, osteoporosis was associated with a 1.60-fold increased risk of all-cause mortality (HR = 1.60, 95% CI = 1.26–2.03, P < 0.001) and a 1.55-fold increased risk of CVD mortality (HR = 1.55, 95% CI = 1.06–2.28, P = 0.025). Stratified analyses across age, sex, smoking status, and cardiovascular risk factors showed consistent results, with no significant interaction effects (P > 0.05). Sensitivity analyses confirmed the robustness of these findings.

Conclusion

Osteoporosis is positively associated with increased risks of all-cause and CVD mortality in alcohol-consuming individuals. These findings underscore the need for further research to elucidate the underlying mechanisms and inform preventive strategies targeting this high-risk population.

Introduction

Osteoporosis is a disease characterized by reduced bone density and impaired microarchitecture, leading to increased skeletal fragility and elevated fracture risk [1]. It imposes a significant societal and economic burden globally, particularly in elderly populations [2]. Studies indicate that as populations age, the number of osteoporosis-related fractures and associated healthcare costs are projected to increase. By 2025, direct medical expenditures attributed to osteoporosis are estimated to reach approximately $25.3 billion [3].

The relationship between alcohol consumption and osteoporosis has garnered significant attention. Research indicates that chronic excessive alcohol consumption may negatively impact bone health, leading to reduced bone density and increased fracture risk [4]. The mechanisms through which alcohol affects bone health are complex and may involve interference with bone formation and resorption, as well as effects on the endocrine system [5]. Moreover, alcohol consumption may exacerbate the risk of osteoporosis by impairing nutrient absorption and metabolism [6]. Beyond individual health, alcohol consumption poses a threat to societal well-being. Alcohol-related health issues, such as cardiovascular diseases, liver diseases, cancers, and mental and neurological disorders, contribute to healthcare costs and productivity losses, imposing a heavy socioeconomic burden [7,8]. Studies have highlighted that alcohol is the third leading risk factor for global disease burden, following smoking and hypertension [9].

Previous studies have explored the relationship between alcohol consumption and osteoporosis, but most have focused on the direct impact of alcohol on bone density, neglecting the complex interplay between alcohol intake and osteoporosis [10]. To clarify the exact relationship between these risk factors, further research must be conducted. This will enable the implementation of effective preventive measures to reduce disease risk. Therefore, a large – scale cohort study involving this specific demographic, along with a more in – depth review of the impact on CVD mortality and all – cause mortality risk, is needed. In this study, we used the National Health and Nutrition Examination Survey (NHANES) to assess the relationship between osteoporosis and CVD and all – cause mortality risk in alcohol – consuming individuals in the United States.

Method

Study population

Since 1999, the NHANES has collected and analyzed health and nutrition data of the U.S. population to meet diverse information needs [11]. NHANES includes comprehensive health examinations, laboratory tests, and dietary interviews, covering participants of all ages. These data provide essential evidence for the development and evaluation of public health policies [12]. Through these interviews and tests, NHANES offers valuable data to understand the complex relationship among diet, nutrition, and health, and provides a scientific basis for public health policy – making [13].

Identification of drinking population

NHANES covers alcohol consumption in individuals aged 20 and older over the past 12 months, with variables prefixed as “ALQ”. Alcohol consumption categories are based on frequency and quantity [14]. Personal interviews using NHANES CAPI software are conducted at Mobile Examination Center(MEC). Participants are asked if they consume at least 12 alcoholic drinks per year. If they answer affirmatively, further questions are asked about drinking days per week, month, or year, resulting in five alcohol consumption statuses: (1) lifetime consumption of fewer than 12 drinks, (2) ≥12 drinks in a year but no consumption in the past year, or no consumption in the past year but lifetime consumption of ≥12 drinks, (3) current light drinking: ≤ 1 drink/day for women, ≤ 2 drinks/day for men, (4) current moderate drinking: ≥ 2 drinks/day for women, ≥ 3 drinks/day for men, or ≥2 drinking days/month, (5) current heavy drinking: ≥ 3 drinks/day for women, ≥ 4 drinks/day for men, or ≥5 days/month of binge drinking [≥4 drinks on an occasion for women, ≥ 5 for men]. The latter three statuses are defined as alcohol – consuming individuals [15]. We categorised alcohol consumption into three groups: light drinkers (≤1 drink per day for women and ≤2 drinks per day for men), moderate drinkers (≥2 drinks per day for women and ≥3 drinks per day for men, or ≥2 days of heavy drinking per month), and heavy drinkers (≥3 drinks per day for women and ≥4 drinks per day for men, or heavy drinking for ≥5 days per month). Heavy drinking was defined as ≥4 drinks on a single occasion for women and ≥5 drinks on a single occasion for men.

Bone density measurement and definition of osteoporosis

Since 2005, NHANES has performed dual-energy X-ray absorptiometry (DXA) scans of the proximal femur for eligible survey participants aged 8 and older at the MEC [16]. Pregnant women were excluded from DXA examinations. DXA scans were performed using a Hologic QDR-4500A fan-beam densitometer (Hologic, Inc., Bedford, Massachusetts). The radiation exposure from femoral DXA scans is extremely low, less than 20 microsieverts.

According to the World Health Organization (WHO) criteria, T-scores are calculated by comparing an individual’s bone mineral density (BMD) with the average BMD of healthy young adults [17]. This method plays a significant role in the diagnosis of osteoporosis. In this study, T-scores ≤ −2.5 were defined as osteoporosis, while T-scores between −1.0 and −2.5 were classified as non-osteoporotic [17].

Determination of death data

NHANES mortality and follow-up data are obtained by matching with the National Death Index (NDI), a resource that helps researchers determine if study participants have died [18]. NDI also provides selected mortality information for participants’ deaths, including death dates and causes, as well as International Classification of Diseases (ICD-10) codes [19]. In this study, mortality status was updated through April 28, 2022.

We collected data from 28,470 participants aged 20 and older across five cycles of NHANES (2005–2010, 2013–2014, and 2017–2018). Other cycles were excluded due to unavailable data on total femur and femoral neck bone mineral density. We excluded 11,890 participants due to missing alcohol – related data or non – drinking status, 4,387 due to missing osteoporosis data, and 15 due to incomplete mortality follow – up. A total of 12,178 participants were included (see Fig 1). The NCHS Research Ethics Review Board ensured informed consent from all participants. For detailed statistics, refer to the National Health and Nutrition Examination Survey website (https://www.cdc.gov/nchs).

Covariant

We referenced a series of comprehensive factors from prior studies as covariates [20]. Demographic factors included age, sex, and BMI, while sociocultural factors were represented by race, education, family income, marital status, and smoking status. BMI was categorized into three groups: 18.5 to 25 kg/m², 25–30 kg/m², and ≥30 kg/m². Race/ethnicity was captured by dividing participants into three groups: non-Hispanic White, non-Hispanic Black, and other (multiracial). Educational attainment was categorized as less than high school, high school diploma or equivalent, and college graduate or higher. Marital status was defined as cohabiting (married or living with a partner) versus living alone (widowed, divorced, separated, or never married). Family income, based on the poverty-income ratio, was stratified into three categories: ≤ 1.30, 1.31–3.50, and >3.5. Smoking status was divided into two groups: non-smokers (<100 cigarettes in a lifetime) and former smokers (>100 cigarettes in a lifetime but currently abstinent) were classified as non-smokers, while current smokers (>100 cigarettes in a lifetime and still smoking) were classified as smokers. We also considered physician-diagnosed conditions, including coronary heart disease, stroke, congestive heart failure, hyperlipidemia, hypertension, and diabetes, to highlight the profound impact of chronic diseases on our study.

We collected various blood biochemical indicators, including fasting serum glucose, alanine aminotransferase, aspartate aminotransferase, total bilirubin, serum albumin, gamma-glutamyl transferase, serum creatinine, serum uric acid, blood urea nitrogen, serum sodium, serum phosphorus, serum calcium, serum potassium, serum iron, serum chloride, triglycerides, total cholesterol, high-density lipoprotein, and low-density lipoprotein. The NHANES website provides detailed descriptions of blood sample collection, storage, transportation, and laboratory procedures.

Data statistical methods

Continuous variables were presented as mean (standard error, SE), and categorical variables as weighted percentages for descriptive statistics. Group comparisons were performed using chi – square tests and analysis of variance. Missing data for covariates were addressed using multiple imputation.

Multivariable Cox proportional hazards regression models were used to evaluate all – cause and CVD mortality risks. Hazard ratios (HR) and corresponding 95% confidence intervals (CI) for the exposed group were calculated, with non – osteoporotic participants as the reference. Covariates were adjusted in three steps: Model 1 was unadjusted. Model 2 adjusted for age, sex, race, marital status, family income and education, smoking, BMI, coronary heart disease, stroke, congestive heart failure, hyperlipidemia, hypertension, and diabetes. Model 3 further adjusted for fasting serum glucose, alanine aminotransferase, aspartate aminotransferase, total bilirubin, serum albumin, gamma – glutamyl transferase, serum creatinine, serum uric acid, blood urea nitrogen, serum sodium, phosphorus, calcium, potassium, iron, chloride, triglycerides, total cholesterol, high – density lipoprotein, and low – density lipoprotein, based on Model 2.

Kaplan – Meier survival curves were plotted for survival analysis, with intergroup differences assessed by the log – rank test. Stratified analyses were performed on subgroups defined by age, sex, smoking status, hyperlipidaemia, hypertension, and diabetes, as well as on alcohol – consumption subgroups categorised into light, moderate, and heavy drinking, to evaluate potential differences among these subgroups.

In the propensity score matching (PSM) analysis, patients with osteoporosis were 1:1 matched to healthy controls using the nearest neighbour method. During the matching process, all covariates were adjusted for as confounding factors (S1 Fig). Subsequently, the matched data underwent repeat COX multivariate analysis to verify result stability. For covariates with missing values of less than 5%, multiple imputation by chained equations (assuming data were missing at random, MAR) was applied. Five imputed datasets were generated, including all analysis variables and outcome indicators.

All data were analyzed using R (version 4.2.2, The R Foundation) and the Free Statistics analysis platform (version 2.0, Beijing, China). A p-value of less than 0.05 was considered statistically significant.

Result

Baseline characteristic

Our analysis included 12,178 alcohol-consuming participants aged 20 and older, with a weighted total of 107,882,689. Of these, 596 (weighted at 5,108,640) had osteoporosis, and 12,178 (weighted at 102,774,049) did not. Baseline data for both groups are in Table 1. Osteoporosis individuals tended to be female, non-Hispanic White, and older. They often had lower BMI and were less likely to smoke. Compared with non-osteoporosis subjects, those with osteoporosis had higher rates of hyperlipidemia but lower rates of hypertension and diabetes.

Table 1. Baseline characteristics (weighted) grouped based on the presence of osteoporosis.

Characteristic	Overall, N = 107,882,689	OP-, N = 102,774,049	OP + , N = 5,108,640	p-value¹
Age(mean.std.error)	48.72 (0.30)	47.98 (0.29)	63.66 (0.80)	<0.001
Sex, n (%)				<0.001
Male	6,695 (52.28%)	6,530 (53.67%)	165 (24.23%)
Female	5,483 (47.72%)	5,052 (46.33%)	431 (75.77%)
Race, n (%)				<0.001
Non-Hispanic White	6,083 (73.80%)	5,710 (73.35%)	373 (83.05%)
Non-Hispanic Black	2,287 (9.29%)	2,241 (9.59%)	46 (3.31%)
Other Race	3,808 (16.91%)	3,631 (17.07%)	177 (13.64%)
Marry, n (%)				<0.001
Married	7,596 (66.23%)	7,286 (66.85%)	310 (53.71%)
Never married	4,582 (33.77%)	4,296 (33.15%)	286 (46.29%)
PIR, n (%)				0.001
≤ 1.30	3,069 (15.77%)	2,913 (15.70%)	156 (17.16%)
1.31-3.50	4,484 (33.19%)	4,229 (32.75%)	255 (41.89%)
> 3.5	4,625 (51.05%)	4,440 (51.55%)	185 (40.95%)
EDU, n (%)				0.60
Less than high school	2,613 (13.29%)	2,489 (13.34%)	124 (12.18%)
High school or equivalent	2,848 (23.23%)	2,697 (23.12%)	151 (25.47%)
Above high school	6,717 (63.48%)	6,396 (63.54%)	321 (62.35%)
smoke, n (%)				0.23
no	9,184 (77.12%)	8,722 (77.24%)	462 (74.53%)
yes	2,994 (22.88%)	2,860 (22.76%)	134 (25.47%)
BMI, n (%)				<0.001
18.5 ~ 24.99 kg/m2	3,625 (31.49%)	3,272 (29.97%)	353 (62.01%)
25.00 ~ 29.9 kg/m2	4,400 (34.98%)	4,234 (35.45%)	166 (25.63%)
≥ 30.00 kg/m2	4,153 (33.53%)	4,076 (34.58%)	77 (12.36%)
CHD, n (%)				0.005
no	11,691 (96.58%)	11,140 (96.72%)	551 (93.73%)
yes	487 (3.42%)	442 (3.28%)	45 (6.27%)
stroke, n (%)				<0.001
no	11,797 (97.57%)	11,242 (97.76%)	555 (93.92%)
yes	381 (2.43%)	340 (2.24%)	41 (6.08%)
CHF, n (%)				0.001
no	11,882 (98.29%)	11,316 (98.37%)	566 (96.63%)
yes	296 (1.71%)	266 (1.63%)	30 (3.37%)
Hyperlipidemia, n (%)				0.033
no	3,474 (28.72%)	3,351 (28.98%)	123 (23.35%)
yes	8,704 (71.28%)	8,231 (71.02%)	473 (76.65%)
Hypertension, n (%)				<0.001
no	7,109 (62.17%)	6,842 (62.70%)	267 (51.49%)
yes	5,069 (37.83%)	4,740 (37.30%)	329 (48.51%)
DM, n (%)				0.76
no	10,272 (88.03%)	9,765 (88.01%)	507 (88.57%)
yes	1,906 (11.97%)	1,817 (11.99%)	89 (11.43%)
GLU.mmol/L	5.85 (0.02)	5.85 (0.02)	5.75 (0.06)	0.80
Alt.U/L	25.93 (0.21)	26.12 (0.21)	22.04 (0.85)	<0.001
Ast.U/L	25.71 (0.18)	25.67 (0.17)	26.60 (1.43)	0.33
TBil.umol/L	12.44 (0.09)	12.51 (0.09)	11.22 (0.41)	<0.001
Alb.g/L	42.67 (0.06)	42.71 (0.06)	41.91 (0.17)	<0.001
GGT.U/L	29.78 (0.45)	29.80 (0.46)	29.41 (2.25)	0.002
CRE.umol/L	79.85 (0.29)	79.96 (0.30)	77.64 (1.33)	<0.001
UA.umol/L	325.87 (1.01)	327.52 (1.06)	292.60 (3.70)	<0.001
BUN.mmol/L	4.79 (0.03)	4.77 (0.03)	5.24 (0.12)	<0.001
Na. mmol/L	139.41 (0.08)	139.39 (0.08)	139.79 (0.14)	<0.001
P. mmol/L	1.21 (0.00)	1.21 (0.00)	1.24 (0.01)	0.019
Ca. mmol/L	2.36 (0.00)	2.36 (0.00)	2.36 (0.01)	0.40
K. mmol/L	4.01 (0.01)	4.00 (0.01)	4.08 (0.02)	0.006
Fe. μmol/L	16.20 (0.09)	16.20 (0.10)	16.17 (0.34)	0.85
Cl. mmol/L	103.58 (0.08)	103.61 (0.08)	102.81 (0.19)	<0.001
TG. mmol/L	2.58 (0.03)	2.58 (0.03)	2.56 (0.13)	0.96
TC. mmol/L	5.10 (0.01)	5.09 (0.01)	5.24 (0.06)	0.059
HDL. mmol/L	1.41 (0.01)	1.40 (0.01)	1.65 (0.02)	<0.001
LDL. mmol/L	2.54 (0.01)	2.54 (0.01)	2.45 (0.07)	0.10
Year, n (%)				<0.001
2005-2006	2,422 (23.47%)	2,330 (23.80%)	92 (16.69%)
2007-2008	3,043 (23.78%)	2,942 (24.35%)	101 (12.21%)
2009-2010	3,383 (24.79%)	3,250 (25.10%)	133 (18.52%)
2013-2014	1,951 (16.40%)	1,812 (16.00%)	139 (24.39%)
2017-2018	1,379 (11.57%)	1,248 (10.75%)	131 (28.20%)
all_cause_mort, n (%)	1,187 (6.95%)	1,027 (6.21%)	160 (21.89%)	<0.001
cvd_mort, n (%)	290 (1.60%)	251 (1.44%)	39 (4.83%)	<0.001

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¹ Wilcoxon rank-sum test for complex survey samples; chi-squared test with Rao & Scott’s second-order correction.

OP-: No osteoporosis,OP + : osteoporosis,PIR: Poverty income ratio, EDU: education, BMI: Body Mass Index, CHD: coronary heart disease, CHF: Congestive Heart Failure,DM: diabetes mellitus, GLU: Fasting glucose, ALT: alanine aminotransferase, AST: Aspartate transaminase,TBil: Total Bilirubin,Alb: serum albumin,GGT: glutamyl transpeptidase, CRE: Blood creatinine,UA: Blood uric acid, BUN: blood urea nitrogen,Na: Serum sodium,P: Blood phosphorus,Ca: serum calcium,K: Serum potassium,Fe: serum iron,Cl: Serum chlorine,TG: Triglyceride,TC: triglyceride,HDL: high-density lipoprotein,LDL: low-density lipoprotein.

Osteoporosis and mortality rate

Among the 12,178 participants with a median follow – up of 123 months and a mean follow – up of 111 ± 47 months.Kaplan-Meier curves showed higher all-cause mortality in osteoporosis participants than in non-osteoporosis participants (Log-rank test P < 0.001, Fig 2). In multivariable Cox proportional hazards models, after adjusting for potential confounders, osteoporosis was associated with a 1.60 – fold higher risk of all – cause mortality (HR = 1.60, 95%CI = 1.26–2.03, P < 0.001) compared with non – osteoporosis (Table 2, Model 3).

Table 2. Association of osteoporosis with all-cause mortality and CVD mortality in the alcohol consuming population (weighted).

Variable	Model 1		Model 2		Model 3
	HR(95%CI)	P_value	HR(95%CI)	P_value	HR(95%CI)	P_value
All-cause mortality
OP-	Reference		Reference		Reference
OP+	5.47(4.41-6.78)	<0.001	1.71(1.35-2.16)	<0.001	1.60(1.26-2.03)	<0.001
CVD mortality
OP-	Reference		Reference		Reference
OP+	5.19(3.70-7.27)	<0.001	1.61(1.11-2.35)	0.013	1.55(1.06-2.28)	0.025

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Model 1: Adjustment.

Model 2: adjusted for age (continuous), race and ethnicity (non Hispanic white, non Hispanic black, Mexican American, other Hispanics, other/multiracial), education level (lower than high school, high school graduation or equivalent, college graduation or above), smoking status (smokers, non-smokers), drinking status (drinkers, non-smokers) BMI (18.5–24.99 kg/m2, 25–29.99 kg/m2, ≥ 30 kg/m2), coronary heart disease, stroke, congestive heart failure, hyperlipidemia, hypertension, diabetes.

Model 3: Further adjust serum glucose, alanine aminotransferase, aspartate aminotransferase, total bilirubin, serum albumin, glutamine transpeptidase, serum creatinine, serum uric acid, blood urea nitrogen, serum sodium ions, serum phosphorus, serum calcium ions, serum potassium ions, serum iron ions, serum chloride ions, triglycerides, total cholesterol, high-density lipoprotein, and low-density lipoprotein based on Model 2.

Our results also showed a significantly higher risk of CVD mortality in participants with osteoporosis than in those without (Log-rank test P < 0.001, Fig 2). In the multivariable Cox proportional hazards model adjusted for potential confounders, participants with osteoporosis had a 1.55 – fold higher risk of CVD mortality compared to those without (HR = 1.55, 95%CI = 1.06–2.28, P = 0.025) (Table 2, Model 3) (Fig 3).

In the multivariable Cox proportional hazards model of the propensity – matched data, after adjusting for potential confounding factors, osteoporosis was associated with a 1.32 – fold increased risk of all – cause mortality compared to non – osteoporosis (HR = 1.32, 95% CI = 1.03–1.67, P = 0.026). However, osteoporosis was not significantly associated with CVD mortality compared to non – osteoporosis (S1 Table).

Stratification and sensitivity analysis

Stratified analyses of subgroups (age, sex, smoking status, hyperlipidaemia, hypertension, diabetes) showed no interactions (P for interaction>0.05, Figs 4 and 5). Similarly, analyses stratified by alcohol – consumption levels (light, moderate, heavy) indicated robust results (P for interaction>0.05, Fig 6). Sensitivity analyses revealed no significant differences between included and excluded participants. Even after excluding patients with missing values, the link between osteoporosis and all – cause/CVD mortality remained significant.

Discuss

In this large prospective cohort study of alcohol consumers, we observed a positive correlation between osteoporosis and risks of all – cause and CVD mortality, with HRs of 1.60 (95%CI = 1.26–2.03) for all – cause mortality and 1.55 (95%CI = 1.06–2.28) for CVD mortality. The significant associations between osteoporosis and event – based all – cause/CVD mortality were consistent across subgroups defined by age, smoking status, and CVD risk factors (diabetes, hypertension, hyperlipidemia). Sensitivity analyses also confirmed the robustness of the results.

Our findings are consistent with prior studies [21,22]. First, individuals with osteoporosis often have comorbidities such as malnutrition and chronic diseases, which may increase mortality risk [23]. Additionally, alcohol consumption itself may negatively affect bone density, worsening the severity of osteoporosis and further elevating mortality [22].

The mechanisms underlying the synergistic effects of osteoporosis and alcohol consumption on cardiovascular disease (CVD) and all – cause mortality risk are multidimensional and interactive [24]. At the biological level, acetaldehyde, a metabolite of alcohol, inhibits osteoblast activity and activates osteoclasts, reducing bone density. Simultaneously, the oxidative stress response induced by acetaldehyde impairs vascular endothelial function and promotes atherosclerotic plaque formation [25,26]. Notably, alcohol – induced disruption of vitamin D metabolism exacerbates bone calcium loss and increases cardiac afterload by up – regulating the renin – angiotensin system, creating a vicious cycle of the “bone - vascular calcification axis”[27].

From a sociological perspective, long – term alcohol consumers often exhibit imbalanced dietary patterns (e.g., low calcium and high sodium intake) and poor exercise adherence [28]. These factors, combined with osteoporosis, form a “social exposome” of metabolic syndrome. Chronic release of inflammatory cytokines accelerates arterial stiffness [29,30]. Clinically, elevated serum bone resorption markers in osteoporosis patients not only reflect bone metabolic disorders but also correlate positively with vascular calcification markers, suggesting that bone – derived factors may directly participate in vascular remodeling via paracrine pathways [31,32].

Furthermore, alcohol – induced activation of hepatic cytochrome P450 enzymes accelerates the metabolism of anti – osteoporosis drugs, leading to treatment resistance. Bone pain and other clinical symptoms may prompt patients to increase alcohol consumption, forming a behavioral feedback loop [33,34]. These multi – system interactions ultimately result in endothelial dysfunction, accelerated cardiac remodeling, and reduced immune surveillance, manifesting as elevated mortality risk.

This study, based on NHANES data of alcohol – consuming individuals, provides clear insights into the relationship between osteoporosis and the risks of all – cause and CVD mortality. Its major strengths lie in the large sample size, population – based design, and the ability to examine osteoporosis alongside all – cause and CVD mortality and subgroup – related risks. As a prospective cohort study with strict inclusion and exclusion criteria, it adjusted for various confounders, including demographic, chronic disease, and biochemical indicators, offering a unique perspective with great novelty and clinical significance. However, as an observational cohort study, the design cannot exclude the possibility of reverse causation or residual confounding factors. Participants with missing alcohol – consumption data, non – drinkers, and those with missing osteoporosis data were excluded, which introduced selection bias. Further in – depth studies are needed in view of these limitations. like integrating DXA scans into alcohol cessation programs for clearer guidance to clinicians and policymakers.

Osteoporosis is positively correlated with all – cause and CVD mortality risks in alcohol – consuming individuals. These findings warrant further attention and research to better understand the health impacts of osteoporosis.

Supporting information

S1 Fig. Standardized mean difference plot after propensity score matching.

(PDF)

pone.0327180.s001.pdf^{(17KB, pdf)}

S1 Table. Association between osteoporosis and CVD and all-cause mortality after propensity score matching.

(DOCX)

pone.0327180.s002.docx^{(14.9KB, docx)}

Acknowledgments

We thank all the participants who volunteered as part of the NHANES. We thank the Free Statistics team for providing technical assistance and valuable tools for data analysis and visualization. The authors acknowledge Jie Liu of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Huanxian Liu, Department of Neurology, Chinese PLA General Hospital, for his contribution to statistical support, study design consultations, and comments regarding the manuscript.

Abbreviations

CVD: Cardiovascular Disease
OP: Osteoporosis
NHANES: National Health and Nutrition Examination Survey
MEC: Mobile Examination Center
DXA: dual-energy X-ray absorptiometry
who: World Health Organization
BMD: bone mineral density
NDI: National Death Index
ICD-10: International Classification of Diseases (ICD-10)
BMI: body mass index
CI: confidence interval
PIR: Poverty income ratio
CHD: coronary heart disease
CHF: Congestive Heart Failure
DM: diabetes mellitus
GLU: Fasting glucose
ALT: alanine aminotransferase
AST: Aspartate transaminase
TBil: Total Bilirubin
Alb: serum albumin
GGT: glutamyl transpeptidase
CRE: Blood creatinine
UA: Blood uric acid
BUN: blood urea nitrogen
Na: Serum sodium
P: Blood phosphorus
Ca: serum calcium
K: Serum potassium
Fe: serum iron
Cl: Serum chlorine
TG: Triglyceride
TC: triglyceride
HDL: high-density lipoprotein
LDL: low-density lipoprotein
HR: Hazard ratios

Data Availability

The data underlying the results presented in the study are available from (https://www.cdc.gov/nchs/nhanes/index.htm).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. 2025 Jun 26;20(6):e0327180. doi: 10.1371/journal.pone.0327180.r001

Author response to Decision Letter 0

16 Apr 2025

PLoS One. doi: 10.1371/journal.pone.0327180.r002

Decision Letter 0

Qian Wu

Dear Dr. Jiang,

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Reviewer #1: This study provides valuable epidemiological evidence for the association between osteoporosis and mortality risk in alcohol drinkers, but it still needs rigorous statistical analysis and causal inference verification.

1.This study utilized NHANES data spanning multiple survey cycles, with an initial sample size of 12,178 participants. However, the application of exclusion criteria (e.g., missing data or exclusion of non-drinkers) may introduce selection bias.

2.Notably, the final analytical cohort exhibited significant class imbalance, with only 596 participants classified as having osteoporosis compared to 11,582 non-osteoporosis participants. Such imbalance could compromise statistical validity by inflating type II error risks and biasing predictive models toward the majority class. To address these limitations, we recommend implementing resampling-based balancing strategies (e.g., oversampling minority cases using SMOTE or targeted subsampling of majority controls) from a data processing perspective. Additionally, algorithm-level adjustments such as class weight modifications or cost-sensitive loss functions should be prioritized to mitigate bias during model training.

3.Although the study appropriately adjusted for demographic factors, chronic diseases, and biochemical indicators in Model 3 - demonstrating a rigorous methodological approach - potential residual confounding may persist due to unmeasured covariates. The primary limitations include the lack of alcohol consumption characteristics (such as temporal variations in alcohol intake), details on pharmacological treatments for chronic conditions, and physical activity metrics. These unaddressed factors could introduce residual bias, necessitating future investigations to incorporate sensitivity analyses or stratified approaches to enhance causal inference validity.

4.The study's primary innovation lies in its specific focus on the association between osteoporosis and mortality within alcohol-consuming populations. However, the investigation did not examine potential non-linear relationships between alcohol consumption levels and bone mineral density, a methodological gap that constrains precise identification of high-risk subgroups.

5.The study demonstrated statistically significant associations between osteoporosis and both all-cause mortality (HR = 1.60, P < 0.05) and cardiovascular disease (CVD) mortality (HR = 1.55, P < 0.05). However, the broad confidence intervals (e.g., CVD mortality: 95% CI = 1.06–2.28), indicating limited stability of the results. Accurate subgroup analysis may better address this issue, and we look forward to the author's further research.

6.As an observational cohort study, this research design cannot eliminate the possibility of reverse causation (e.g., the potential that severe chronic diseases might reduce bone density) or residual confounding factors. The findings should be interpreted strictly as correlative associations and must not be overinterpreted as demonstrating causal relationships.

Reviewer #2: General

The manuscript addresses an important and timely topic, the association between alcohol consumption and osteoporosis, with potential implications for cardiovascular disease (CVD) and all-cause mortality. Below are a few comments;

Introduction

The introduction provides a clear rationale for the study, outlining the global burden of osteoporosis (e.g., projected $25.3 billion in costs by 2025) and the known effects of alcohol on bone health.

The introduction does not clearly define the scope of alcohol consumption (e.g., light, moderate, or heavy drinking) or discuss how varying levels might influence the osteoporosis-mortality relationship, which could set clearer expectations for the study.

The final paragraph’s call for a “large-scale cohort study” is redundant since the study itself fulfills this need, making the statement less impactful.

Method

The study design is robust, leveraging a large, nationally representative cohort (NHANES) with a clear description of the five cycles (2005–2010, 2013–2014, 2017–2018) and sample size (12,178 participants).

The exclusion criteria (e.g., 11,890 participants excluded for missing alcohol data or non-drinking status, 4,387 for missing osteoporosis data) are described, but the potential impact of these exclusions on selection bias is not discussed.

The definition of alcohol consumption (≥12 drinks/year) is broad and may include very light drinkers, potentially diluting the effect of heavier drinking on outcomes. The rationale for this threshold is not justified.

The follow-up duration (until April 2022) is mentioned, but the median or mean follow-up time is not reported, which is critical for interpreting survival analyses.

Results

The results do not explore the dose-response relationship between alcohol consumption levels (light, moderate, heavy) and mortality outcomes, which could add depth to the findings.

Discussion

The limitation section is brief and does not address key methodological concerns, such as potential selection bias from exclusions, residual confounding, or the broad definition of alcohol consumption.

Reviewer #3: Thank you for the opportunity to review this manuscript. Below are my comments and suggestions for strengthening the manuscript further.

- Line 73: Include specific studies that explain how alcohol affects bone breakdown and formation

- Lines 60-66: Elaborate on the link between osteoporosis and mortality in drinkers. Include key factors like inflammation and vitamin D deficiency etc.

- Line 156 specifies the software and weight variables used in the analysis.

- lines 133-135 The paper mentions using multiple imputation to address missing data, but it lacks details on the imputation model. Provide a clear description of the imputation methods

- on lines 269-271. The clinical recommendations are vague, with only "further research." To improve, propose specific actions like integrating DXA scans into alcohol cessation programs for clearer guidance to clinicians and policymakers.

Reviewer #4: this study while being restricted to alcohol consumers does not permiit us to answer the question : how does alcohol modify the relationship between osteoporosis and mortality. Adding this facet improve the study trememndouly

Reviewer #5: This study addresses an underexplored intersection between osteoporosis, alcohol consumption, and mortality, offering insights into a high-risk population. The focus on alcohol consumers adds specificity to existing literature on osteoporosis and CVD. The use of NHANES data ensures a large, nationally representative sample, enhancing generalizability. Meanwhile, the prospective cohort design and multivariable Cox regression models appropriately account for confounders (e.g., demographics, comorbidities, biochemical markers) and Sensitivity and stratified analyses strengthen the robustness of findings. However, there few suggestions for improvement before acceptance�

1. The discussion hypothesizes biological pathways (e.g., acetaldehyde toxicity, vitamin D disruption) but lacks direct evidence from the data.

Recommendation: Incorporate mediation analyses or biomarker correlations (e.g., inflammatory markers, vitamin D levels) to substantiate proposed mechanisms.

2. Alcohol intake is categorized but not quantified in detail (e.g., grams/day, binge drinking patterns).

Recommendation: Analyze dose-response relationships or stratify by drinking severity (light/moderate/heavy) to explore thresholds for risk.

3. The handling of missing data (multiple imputation) is mentioned but not detailed. Recommendation: Describe imputation methods and compare complete-case results to assess bias.

4. Table 1’s p-values should be adjusted for multiple comparisons (e.g., Bonferroni correction).

This manuscript provides valuable epidemiological evidence linking osteoporosis to elevated mortality risks in alcohol consumers. While methodologically sound, it would benefit from deeper mechanistic exploration, refined alcohol categorization, clearer statistical clarification and adjusted writing and presentation. Addressing these limitations could elevate its impact.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Mohsin Raza

Reviewer #4: No

Reviewer #5: No

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PLoS One. 2025 Jun 26;20(6):e0327180. doi: 10.1371/journal.pone.0327180.r003

Author response to Decision Letter 1

15 May 2025

Reply to Editor and Reviewers

Manuscript Number: PONE-D-25-20480

Title: Osteoporosis is associated with increased CVD mortality and all-cause mortality in alcohol-consuming individuals: A cohort study using data from NHANES

Dear Editor and Reviewers,

We extend our sincere gratitude for the meticulous evaluation of our manuscript and the insightful comments provided. In response to all reviewer suggestions, we have systematically addressed each point through supplementary analyses, methodological refinements, and textual revisions. Revisions are highlighted in yellow within the text, and supplementary materials have been correspondingly updated. Below, we provide a point-by-point response to the reviewers' comments.�Represented in red font�

Reviewer #1

1�This study utilized NHANES data spanning multiple survey cycles, with an initial sample size of 12,178 participants. However, the application of exclusion criteria (e.g., missing data or exclusion of non-drinkers) may introduce selection bias.

Author Response: We thank the reviewer for raising this critical issue and fully concur with the concerns regarding selection bias. Potential selection bias may have occurred when excluding cases with missing data and non - drinkers. To mitigate this, we used multiple imputation for missing data. Also, a sensitivity analysis was done to check the robustness of the results.

2�Notably, the final analytical cohort exhibited significant class imbalance, with only 596 participants classified as having osteoporosis compared to 11,582 non-osteoporosis participants. Such imbalance could compromise statistical validity by inflating type II error risks and biasing predictive models toward the majority class. To address these limitations, we recommend implementing resampling-based balancing strategies (e.g., oversampling minority cases using SMOTE or targeted subsampling of majority controls) from a data processing perspective. Additionally, algorithm-level adjustments such as class weight modifications or cost-sensitive loss functions should be prioritized to mitigate bias during model training.

Author Response: We sincerely appreciate your insightful comments on the class imbalance problem. We have performed propensity score matching and obtained 830 osteoporosis patients and 830 non-osteoporosis participants. The revised analysis shows that the effect size remains stable (all-cause mortality HR = 1.32, 95% CI = 1.03–1.67), and stratified analyses have confirmed the robustness of the results. Line201-204, 236-239.

3�Although the study appropriately adjusted for demographic factors, chronic diseases, and biochemical indicators in Model 3 - demonstrating a rigorous methodological approach - potential residual confounding may persist due to unmeasured covariates. The primary limitations include the lack of alcohol consumption characteristics (such as temporal variations in alcohol intake), details on pharmacological treatments for chronic conditions, and physical activity metrics. These unaddressed factors could introduce residual bias, necessitating future investigations to incorporate sensitivity analyses or stratified approaches to enhance causal inference validity.

Author Response: We agree that unmeasured covariates (e.g., physical activity) may influence the results. Thus, we have redefined the levels of alcohol consumption as light, moderate, and heavy drinking. Results from the stratified analysis demonstrate the robustness of our findings. Line 109-116.

4�The study's primary innovation lies in its specific focus on the association between osteoporosis and mortality within alcohol-consuming populations. However, the investigation did not examine potential non-linear relationships between alcohol consumption levels and bone mineral density, a methodological gap that constrains precise identification of high-risk subgroups.

Author Response: We appreciate the reviewer's suggestion. We defined the alcohol exposure based on NHANES questionnaire results. The categories—light, moderate, and heavy drinking—are categorical variables, making restricted cubic spline analysis infeasible. However, we plan to collect continuous alcohol - intake data for future studies. 5�The study demonstrated statistically significant associations between osteoporosis and both all-cause mortality (HR = 1.60, P < 0.05) and cardiovascular disease (CVD) mortality (HR = 1.55, P < 0.05). However, the broad confidence intervals (e.g., CVD mortality: 95% CI = 1.06–2.28), indicating limited stability of the results. Accurate subgroup analysis may better address this issue, and we look forward to the author's further research.

Author Response: We have conducted stratified analyses to address the issue of wide confidence intervals raised by the reviewer. The corresponding content has been updated. Line 237-240.

6�As an observational cohort study, this research design cannot eliminate the possibility of reverse causation (e.g., the potential that severe chronic diseases might reduce bone density) or residual confounding factors. The findings should be interpreted strictly as correlative associations and must not be overinterpreted as demonstrating causal relationships.

Author Response: We have emphasized the observational nature of this study's associations in the discussion section. Line 308-314.

Reviewer #2

1�The introduction does not clearly define the scope of alcohol consumption (e.g., light, moderate, or heavy drinking) or discuss how varying levels might influence the osteoporosis-mortality relationship, which could set clearer expectations for the study.

Author Response: We've categorized alcohol consumption into three tiers: light, moderate and heavy, and performed stratified analyses. Line 109-116.

2�The final paragraph’s call for a “large-scale cohort study” is redundant since the study itself fulfills this need, making the statement less impactful.

Author Response: We have removed the inaccurate description of "large - scale cohort study". Line 304-308.

3�The exclusion criteria (e.g., 11,890 participants excluded for missing alcohol data or non-drinking status, 4,387 for missing osteoporosis data) are described, but the potential impact of these exclusions on selection bias is not discussed.

Author Response: We have added to the discussion section content on the potential impact of non - drinkers, missing alcohol - related data, and osteoporosis - related missing data on selection bias. Line 308 - 314.

4�The definition of alcohol consumption (≥12 drinks/year) is broad and may include very light drinkers, potentially diluting the effect of heavier drinking on outcomes. The rationale for this threshold is not justified.

Author Response: We've categorized alcohol consumption into three tiers: light, moderate and heavy, and performed stratified analyses. Line 109-116.

5�The follow-up duration (until April 2022) is mentioned, but the median or mean follow-up time is not reported, which is critical for interpreting survival analyses.

Author Response: We analyzed the follow-up duration, noting a median of 123 months and a mean of 111±47 months. This information has been added to the Results section. Line 222-224.

5�The results do not explore the dose-response relationship between alcohol consumption levels (light, moderate, heavy) and mortality outcomes, which could add depth to the findings.

Author Response: We have described the impact of alcohol consumption levels on mortality in the discussion section. Line 271-296.

6�The limitation section is brief and does not address key methodological concerns, such as potential selection bias from exclusions, residual confounding, or the broad definition of alcohol consumption.

Author Response: We have added a description of the study limitations in the discussion section. Line 308-314.

Reviewer #3

- Line 73: Include specific studies that explain how alcohol affects bone breakdown and formation

- Lines 60-66: Elaborate on the link between osteoporosis and mortality in drinkers. Include key factors like inflammation and vitamin D deficiency etc.

Author Response: We have added new content and included additional references. Line 76-77.

- Line 156 specifies the software and weight variables used in the analysis.

- lines 133-135 The paper mentions using multiple imputation to address missing data, but it lacks details on the imputation model. Provide a clear description of the imputation methods

Author Response: We have revised the description of the statistical software used in the Methods section and detailed the process of multiple imputation. Line 206-209.

Author Response: We have revised the discussion section as suggested. Line 309-315.

Reviewer #4

this study while being restricted to alcohol consumers does not permiit us to answer the question : how does alcohol modify the relationship between osteoporosis and mortality. Adding this facet improve the study trememndouly

Author Response: We have elucidated how alcohol consumption modifies the relationship between osteoporosis and mortality in the discussion section. Line 272-297.

Reviewer #5

1� The discussion hypothesizes biological pathways (e.g., acetaldehyde toxicity, vitamin D disruption) but lacks direct evidence from the data.

Author Response: We sincerely thank the reviewers for their valuable suggestions. In response, we plan to incorporate inflammatory markers and vitamin D levels into our subsequent research. We will conduct a mediation analysis to explore the underlying mechanisms.

2� Alcohol intake is categorized but not quantified in detail (e.g., grams/day, binge drinking patterns).

Author Response: We have redefined alcohol consumption as light, moderate, and heavy drinking. Results from stratified analyses show the robustness of our findings. Line 109-116.

3� The handling of missing data (multiple imputation) is mentioned but not detailed. Recommendation: Describe imputation methods and compare complete-case results to assess bias.

Author Response: Multiple imputation was carried out under the Missing at Random (MAR) assumption. Five datasets were imputed, including all analysis variables and outcomes. Continuous variables were imputed using predictive mean matching, and categorical variables via logistic regression, with relevant details added to the text. Line 206-209.

We sincerely thank the reviewers for their deep insight. Your feedback has greatly enhanced the rigor and clinical value of our study. Should there be further suggestions for modification, we will certainly cooperate to improve the manuscript.

Yours sincerely,

Jiang J Cheng

On behalf of all authors

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PLoS One. doi: 10.1371/journal.pone.0327180.r004

Decision Letter 1

Qian Wu

<p>Osteoporosis is associated with increased CVD mortality and all-cause mortality in alcohol-consuming individuals: A cohort study using data from NHANES

PONE-D-25-20480R1

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PLoS One. doi: 10.1371/journal.pone.0327180.r005

Acceptance letter

Qian Wu

PONE-D-25-20480R1

PLOS ONE

Dear Dr. Jiang,

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

Supplementary Materials

S1 Fig. Standardized mean difference plot after propensity score matching.

(PDF)

pone.0327180.s001.pdf^{(17KB, pdf)}

S1 Table. Association between osteoporosis and CVD and all-cause mortality after propensity score matching.

(DOCX)

pone.0327180.s002.docx^{(14.9KB, docx)}

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Submitted filename: Response to Reviewers.docx

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Data Availability Statement

The data underlying the results presented in the study are available from (https://www.cdc.gov/nchs/nhanes/index.htm).

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PERMALINK

Osteoporosis is associated with increased CVD mortality and all-cause mortality in alcohol-consuming individuals: A cohort study using data from NHANES

Xiaoqin Qu

Jingcheng Jiang

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Method

Study population

Identification of drinking population

Bone density measurement and definition of osteoporosis

Determination of death data

Fig 1. Flow chart of patient inclusion.

Covariant

Data statistical methods

Result

Baseline characteristic

Table 1. Baseline characteristics (weighted) grouped based on the presence of osteoporosis.

Osteoporosis and mortality rate

Fig 2. Osteoporosis and all-cause mortality survival curve (weighted).

Table 2. Association of osteoporosis with all-cause mortality and CVD mortality in the alcohol consuming population (weighted).

Fig 3. Osteoporosis and CVD mortality Survival Curve (Weighted).

Stratification and sensitivity analysis

Fig 4. Stratified analysis of osteoporosis and all-cause mortality (weighted).

Fig 5. Stratified analysis of osteoporosis and CVD mortality (weighted).

Fig 6. Stratified analysis of mortality rate based on alcohol consumption level.

Discuss

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Qian Wu

Roles

Author response to Decision Letter 1

Decision Letter 1

Qian Wu

Roles

Acceptance letter

Qian Wu

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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