Abstract
Background
Post-induction hypotension (PIH) is a frequent complication among older adults undergoing general anaesthesia, associated with organ hypoperfusion and increased perioperative risk. The role of body mass index (BMI), a routinely assessed and potentially modifiable risk factor, in the development of PIH remains unclear in geriatric populations.
Methods
We conducted a retrospective cohort study of patients aged ≥ 60 years who underwent elective non-cardiac surgery under general anaesthesia between 2013 and 2022 at a tertiary hospital. Preoperative BMI was analysed both as a continuous and categorical variable. The primary outcome was PIH, defined as a systolic blood pressure < 90 mmHg or > 30% reduction from baseline during the post-induction period. Secondary outcomes included ICU admission, hospital length of stay, and in-hospital mortality. Non-linear associations were explored using restricted cubic spline models and segmented linear regression.
Results
A total of 70,487 patients were included in the final analysis, and PIH occurred in 73.3% of patients. A U-shaped association was observed between BMI and PIH, with the lowest risk between 24.2 and 29.7 kg/m². Patients below this range had significantly higher odds of PIH (OR 1.13; 95% CI 1.09–1.17), ICU admission (OR 1.31; 95% CI 1.24–1.39), and longer hospitalisation (IRR 1.08; 95% CI 1.06–1.10). Subgroup analyses showed significant interaction effects by sex, age, and hypertension. Sensitivity analyses excluding PIH as a covariate yielded consistent findings for secondary outcomes.
Conclusions
BMI demonstrates a non-linear relationship with PIH and related outcomes. Both underweight and obese older adults are at increased risk. BMI should be integrated into preoperative risk stratification, and maintaining BMI within a moderate range may help reduce peri-induction haemodynamic instability.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06691-1.
Keywords: Body mass index, Post-induction hypotension, Older adults, Anaesthesia, Perioperative risk
Introduction
The global ageing population has led to a rising number of older adults undergoing elective non-cardiac surgery under general anaesthesia [1]. These individuals often present with age-related physiological changes and a higher burden of comorbidities, placing them at elevated risk for perioperative complications. Post-induction hypotension (PIH) - defined as a significant drop in blood pressure following the induction of general anaesthesia - is a common intraoperative complication in this population, [2, 3] with a reported incidence ranging from 20% to 80% and higher vulnerability due to age-related cardiovascular changes [4, 5]. PIH is independently associated with serious complications, including myocardial injury, acute kidney injury, and increased postoperative mortality [6–10].
Despite its high prevalence and clinical significance, the underlying risk factors for PIH remain incompletely understood, particularly in older surgical populations. While factors such as age, comorbidities, and fasting status have been well studied in relation to PIH, the role of body mass index (BMI) remains unclear [2].This is surprising given that BMI is routinely measured in preoperative assessments and reflects multiple physiological dimensions, including nutritional status, cardiovascular reserve, and fluid distribution.
High BMI is generally considered associated with increased perioperative risks [11]. Previous studies have also suggested an “obesity paradox,” where overweight or mildly obese patients experience better outcomes than those with normal or low BMI in various clinical settings, including surgery [12]. In older adults, this paradox may be even more pronounced due to sarcopenia, frailty, and altered fat-to-muscle ratios. It is plausible that BMI interacts with age-related changes in vascular compliance and autonomic regulation, influencing haemodynamic responses to anaesthetic agents [13].
The aim of this study was to investigate the association between preoperative BMI and the risk of PIH in a large cohort of older adults undergoing elective non-cardiac surgery. We hypothesised a non-linear relationship in which both underweight and obese individuals would be at increased risk of PIH compared to those with moderate BMI. This work may offer insights for preoperative risk stratification and support the development of targeted interventions to optimise perioperative care in the ageing population.
Method
Study design
This was a retrospective cohort study conducted at Peking Union Medical College Hospital, a tertiary academic medical centre. The study was approved by the Institutional Review Board, and the need for informed consent was waived due to the use of de-identified data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline [14].
Study participants
We reviewed records from older adults (aged 60 years and above) who underwent elective non-cardiac surgery under general anaesthesia between 1 January 2013 and 31 December 2022. During the initial data cleaning phase, patients with missing or implausible BMI values, incomplete key perioperative timing data, intraoperative monitoring data, or other critical covariates were excluded. Based on the study objectives, further exclusions were applied for the following: [1] underwent emergency or cardiac surgery; [2] received anaesthesia methods other than general anaesthesia; [3] had a preoperative systolic blood pressure (SBP) < 90 mmHg; or [4] had a preoperative diagnosis of hypotension.
Exposures and covariates
The exposure was preoperative BMI (kg/m²), calculated from the last recorded height and weight taken in the ward prior to surgery. We also collected comprehensive patient demographics and procedure-related factors, including age, sex, comorbidities coded by International Classification of Diseases (ICD) − 10/11 (hypertension, diabetes, myocardial ischemia, arrhythmia, heart failure, stroke, peripheral vascular disease, asthma/chronic obstructive pulmonary disease, liver dysfunction, renal dysfunction, anemia, infectious disease, psychiatric disorder), American Society of Anesthesiologists (ASA) classification, type of surgery (head and neck, thoracic, abdominal, orthopedic/spinal, vascular, other surgeries), anaesthesia method (general anaesthesia, general anaesthesia combined with neuraxial anaesthesia, general anaesthesia combined with regional anaesthesia), anaesthesia induction period (categorised as morning before 12:00, afternoon from 12:00 to 16:00, or evening after 16:00 based on the anaesthesia start time; patients fast from 22:00 the night before surgery until the operation, and those waiting in the ward before surgery receive intravenous glucose infusions), baseline SBP and its categorization (< 140, 140–159, 160–179, and ≥ 180 mmHg), type of induction agents (propofol only, propofol combined with etomidate, etomidate only) and combination of inhalational anaesthesia. All covariates were selected a priori based on their potential to influence haemodynamic responses to anaesthesia. All clinical data were extracted from the hospital’s Electronic Anaesthesia Medical Record System (EAMRS) and Hospital Information System (HIS).
Outcomes
The primary outcome was PIH, defined as an SBP < 90 mmHg or a reduction > 30% from baseline during the post-induction period. The post-induction period refers to the interval from anaesthesia induction to surgical incision, with the first recorded SBP after admission selected as baseline SBP. The EAMRS captured intraoperative vital signs monitoring data with a granularity of 20 s. If invasive arterial pressure monitoring was not performed, non-invasive SBP was used; if an arterial catheter was placed, invasive SBP was used to form the post-induction period SBP sequence for the calculation of PIH. Secondary outcomes included intensive care unit (ICU) admission, length of hospital stay, and in-hospital mortality.
Statistical analysis
Descriptive statistics were used to summarise baseline characteristics across World Health Organization (WHO) BMI categories for the Chinese population: underweight (< 18.5 kg/m2), normal weight (18.5–23.9 kg/m2), overweight (24.0–27.9 kg/m2), and obese (≥ 28.0 kg/m2)0 [15]. To assess potential non-linear associations between BMI and outcomes, restricted cubic spline (RCS) models with four knots were constructed, using the median BMI (24.2 kg/m²) as the reference point. Where non-linearity was observed, segmented linear regression was applied to estimate change points in risk across the BMI spectrum. Subgroup analyses were conducted by sex, age group, and history of hypertension or diabetes. Interaction terms between BMI and subgroup variables were tested to evaluate heterogeneity.
For the primary outcome PIH, adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using multivariable logistic regression, adjusting for confounders based on clinical expertise, including sex, age, comorbidities, type of surgery, anaesthesia method, anaesthesia induction period, type of induction agents, and combination of inhalational anaesthesia. For ICU admission and in-hospital mortality, multivariable logistic models were also used. Length of hospital stay, due to its right-skewed distribution and overdispersion, was analysed using a negative binomial regression model, reporting incidence rate ratios (IRRs) with 95% CIs [16, 17]. In models assessing secondary outcomes, PIH was also included as a covariate besides the confounders mentioned above.
Subsequently, a sensitivity analysis was conducted to rule out the possibility PIH mediated the relationship between BMI and secondary outcomes by excluding PIH from the models as a confounder.
All statistical analyses were conducted using R version 4.4.1 and SPSS version 25.0. A two-sided p-value < 0.05 was considered statistically significant.
Results
Participant characteristics
Following initial data cleaning and subsequent exclusions based on study criteria, 70,487 older adults were ultimately included in the final analysis (Fig. 1). The median age was 66 years (Interquartile Range, IQR: 63–71), and 55.1% were female. The median BMI was 24.2 kg/m² (IQR: 22.2–26.6). Baseline characteristics across BMI categories are summarised in Table 1, showing that patients with underweight status (< 18.5 kg/m²) had higher prevalence of comorbidities such as anaemia and malnutrition-related indicators.
Fig. 1.
Flow Diagram of Patient Selection for the Final Study Cohort
Flowchart depicting the selection process of patients aged ≥ 60 years from the Electronic Anaesthesia Medical Record System (EAMRS) database between January 2013 and December 2022. A total of 95,726 surgical records were initially screened. After exclusion of cases with missing or extreme data (n = 10,281), and those not meeting inclusion criteria (non-general anaesthesia, emergency or cardiac surgery, baseline hypotension; n = 14,958), 70,487 patients were included in the final analysis. Abbreviations: PUMCH Peking Union Medical College Hospital, EAMRS Electronic Anaesthesia Medical Record System, BMI Body mass index, SBP Systolic blood pressure, HIS Hospital information system, ASA American Society of Anesthesiologists
Table 1.
Baseline characteristics of study participants stratified by WHO BMI classification for the Chinese population
| Characteristics | Underweight (<18.5 kg/m2) | Normal weight (18.5–23.9 kg/m2) | Overweight (24.0–27.9 kg/m2) | Obese (≥ 28.0 kg/m2) | Overall |
|---|---|---|---|---|---|
| n = 2363 | n = 29,895 | n = 28,364 | n = 9865 | n = 70,487 | |
| Gender, No. (%) | |||||
| Male | 1079 (45.7%) | 13,397 (44.8%) | 13,471 (47.5%) | 3678 (37.3%) | 31,625 (44.9%) |
| Female | 1284 (54.3%) | 16,498 (55.2%) | 14,893 (52.5%) | 6187 (62.7%) | 38,862 (55.1%) |
| Age, median (IQR), y | 68 (64–75) | 66 (63–72) | 66 (63–71) | 66 (63–70) | 66 (63–71) |
| Age classification, No. (%) | |||||
| 60–74 | 1730 (73.2%) | 24,925 (83.4%) | 24,413 (86.1%) | 8665 (87.8%) | 59,733 (84.7%) |
| 75–84 | 538 (22.8%) | 4542 (15.2%) | 3693 (13.0%) | 1144 (11.6%) | 9917 (14.1%) |
| ≥ 85 | 95 (4.0%) | 428 (1.4%) | 258 (0.9%) | 56 (0.6%) | 837 (1.2%) |
| Height, median (IQR), cm | 164.0 (158.0–170.0) | 164.0 (159.0–170.0) | 165.0 (159.0–170.0) | 162.0 (157.0–170.0) | 164.0 (158.0–170.0) |
| Weight, median (IQR), kg | 46.0 (43.0–50.0) | 60.0 (55.0–64.0) | 70.0 (65.0–75.0) | 80.0 (73.0–86.0) | 65.0 (59.0–73.0) |
| BMI, median (IQR), kg/m2 | 17.6 (16.8–18.1) | 22.1 (20.8–23.1) | 25.6 (24.8–26.7) | 29.4 (28.7–31.1) | 24.2 (22.2–26.6) |
| Comorbidities | |||||
| Hypertension, No. (%) | 569 (24.1%) | 11,231 (37.6%) | 14,328 (50.5%) | 6300 (63.9%) | 32,428 (46.0%) |
| Diabetes, No. (%) | 257 (10.9%) | 5627 (18.8%) | 6257 (22.1%) | 2501 (25.4%) | 14,642 (20.8%) |
| Myocardial ischemia, No. (%) | 201 (8.5%) | 3185 (10.7%) | 3654 (12.9%) | 1510 (15.3%) | 8550 (12.1%) |
| Arrhythmia, No. (%) | 163 (6.9%) | 1673 (5.6%) | 1724 (6.1%) | 711 (7.2%) | 4271 (6.1%) |
| Heart failure, No. (%) | 42 (1.8%) | 363 (1.2%) | 372 (1.3%) | 141 (1.4%) | 918 (1.3%) |
| Stroke, No. (%) | 199 (8.4%) | 2802 (9.4%) | 2966 (10.5%) | 1091 (11.1%) | 7058 (10.0%) |
| Peripheral vascular disease, No. (%) | 131 (5.5%) | 1687 (5.6%) | 1582 (5.6%) | 671 (6.8%) | 4071 (5.8%) |
| Asthma/COPD, No. (%) | 171 (7.2%) | 1232 (4.1%) | 879 (3.1%) | 310 (3.1%) | 2592 (3.7%) |
| Renal dysfunction, No. (%) | 87 (3.7%) | 787 (2.6%) | 761 (2.7%) | 295 (3.0%) | 1930 (2.7%) |
| Liver dysfunction, No. (%) | 141 (6.0%) | 1904 (6.4%) | 2152 (7.6%) | 911 (9.2%) | 5108 (7.2%) |
| Hyperlipidemia, No. (%) | 113 (4.8%) | 2414 (8.1%) | 2985 (10.5%) | 1174 (11.9%) | 6686 (9.5%) |
| Anemia, No. (%) | 145 (6.1%) | 939 (3.1%) | 522 (1.8%) | 175 (1.8%) | 1781 (2.5%) |
| Infectious disease, No. (%) | 286 (12.1%) | 2968 (9.9%) | 2184 (7.7%) | 656 (6.6%) | 6094 (8.6%) |
| Psychiatric disorder, No. (%) | 53 (2.2%) | 537 (1.8%) | 481 (1.7%) | 161 (1.6%) | 1232 (1.7%) |
| ASA classification, No. (%) | |||||
| I | 202 (8.5%) | 3054 (10.2%) | 2488 (8.8%) | 588 (6.0%) | 6332 (9.0%) |
| II | 1659 (70.2%) | 22,580 (75.5%) | 21,665 (76.4%) | 7523 (76.3%) | 53,427 (75.8%) |
| III | 493 (20.9%) | 4194 (14.0%) | 4172 (14.7%) | 1740 (17.6%) | 10,599 (15.0%) |
| IV | 9 (0.4%) | 64 (0.2%) | 38 (0.1%) | 14 (0.1%) | 125 (0.2%) |
| V | 0 (0.0%) | 3 (0.0%) | 1 (0.0%) | 0 (0.0%) | 4 (0.0%) |
| Type of surgery, No. (%)a | |||||
| Head and Neck | 319 (13.5%) | 4367 (14.6%) | 4120 (14.5%) | 1391 (14.1%) | 10,197 (14.5%) |
| Thoracic | 249 (10.5%) | 3519 (11.8%) | 3077 (10.8%) | 785 (8.0%) | 7630 (10.8%) |
| Abdominal | 1440 (60.9%) | 16,531 (55.3%) | 14,323 (50.5%) | 4346 (44.1%) | 36,640 (52.0%) |
| Orthopedic/spinal | 227 (9.6%) | 3440 (11.5%) | 4813 (17.0%) | 2671 (27.1%) | 11,151 (15.8%) |
| Vascular | 75 (3.2%) | 903 (3.0%) | 916 (3.2%) | 261 (2.6%) | 2155 (3.1%) |
| Other | 53 (2.2%) | 1135 (3.8%) | 1115 (3.9%) | 411 (4.2%) | 2714 (3.9%) |
| Anesthesia method, No. (%) | |||||
| General | 2355 (99.7%) | 29,795 (99.7%) | 28,261 (99.6%) | 9835 (99.7%) | 70,246 (99.7%) |
| General + Neuraxial | 4 (0.2%) | 53 (0.2%) | 43 (0.2%) | 11 (0.1%) | 111 (0.2%) |
| General + Regional | 4 (0.2%) | 47 (0.2%) | 60 (0.2%) | 19 (0.2%) | 130 (0.2%) |
| Anesthesia induction period, No. (%) | |||||
| Before 12:00 | 1285 (54.4%) | 16,165 (54.1%) | 15,680 (55.3%) | 5390 (54.6%) | 38,520 (54.6%) |
| 12:00–16:00 | 831 (35.2%) | 10,586 (35.4%) | 9666 (34.1%) | 3476 (35.2%) | 24,559 (34.8%) |
| After 16:00 | 247 (10.5%) | 3144 (10.5%) | 3018 (10.6%) | 999 (10.1%) | 7408 (10.5%) |
| Baseline SBP, mean (SD), mmHg | 136 (22) | 139 (22) | 140 (22) | 141 (22) | 140 (22) |
| Baseline SBP classification, No. (%) | |||||
| <140 | 1370 (58.0%) | 15,657 (52.4%) | 13,875 (48.9%) | 4603 (46.7%) | 35,505 (50.4%) |
| 140–159 | 629 (26.6%) | 9263 (31.0%) | 9186 (32.4%) | 3228 (32.7%) | 22,306 (31.6%) |
| 160–179 | 300 (12.7%) | 3916 (13.1%) | 4122 (14.5%) | 1588 (16.1%) | 9926 (14.1%) |
| ≥ 180 | 64 (2.7%) | 1059 (3.5%) | 1181 (4.2%) | 446 (4.5%) | 2750 (3.9%) |
| Induction medications | |||||
| Induction agents | |||||
| Propofol only, No. (%) | 2145 (90.8%) | 28,130 (94.1%) | 26,821 (94.6%) | 9286 (94.1%) | 66,382 (94.2%) |
| Propofol + Etomidate, No. (%) | 121 (5.1%) | 1140 (3.8%) | 1061 (3.7%) | 398 (4.0%) | 2720 (3.9%) |
| Etomidate only, No. (%) | 97 (4.1%) | 625 (2.1%) | 482 (1.7%) | 181 (1.8%) | 1385 (2.0%) |
| Midazolam, No. (%) | 1778 (75.2%) | 23,232 (77.7%) | 22,267 (78.5%) | 7840 (79.5%) | 55,117 (78.2%) |
| Dexmedetomidine, No. (%) | 81 (3.4%) | 868 (2.9%) | 753 (2.7%) | 226 (2.3%) | 1928 (2.7%) |
| Sevoflurane, No. (%) | 1757 (74.4%) | 21,810 (73.0%) | 20,774 (73.2%) | 7302 (74.0%) | 51,643 (73.3%) |
| Nitrous oxide, No. (%) | 1481 (62.7%) | 19,218 (64.3%) | 18,858 (66.5%) | 6729 (68.2%) | 46,286 (65.7%) |
| Fentanyl, No. (%) | 2065 (87.4%) | 26,234 (87.8%) | 25,056 (88.3%) | 8740 (88.6%) | 62,095 (88.1%) |
| Remifentanil, No. (%) | 1025 (43.4%) | 13,492 (45.1%) | 12,654 (44.6%) | 4543 (46.1%) | 31,714 (45.0%) |
| Sufentanil, No. (%) | 214 (9.1%) | 2574 (8.6%) | 2236 (7.9%) | 736 (7.5%) | 5760 (8.2%) |
| Oxycodone, No. (%) | 154 (6.5%) | 1841 (6.2%) | 1642 (5.8%) | 569 (5.8%) | 4206 (6.0%) |
| Combined inhalational anesthesia, No. (%) | 1925 (81.5%) | 24,272 (81.2%) | 23,343 (82.3%) | 8202 (83.1%) | 57,742 (81.9%) |
| Duration of anesthesia induction, median (IQR), min | 29.0 (20.0–40.0) | 28.3 (20.3–38.0) | 28.3 (20.0–38.0) | 29.0 (20.8–39.3) | 28.3 (20.3–38.3) |
| Patient disposition, No. (%) | |||||
| PACU | 1620 (68.6%) | 22,673 (75.8%) | 22,180 (78.2%) | 7722 (78.3%) | 54,195 (76.9%) |
| ICU | 436 (18.5%) | 3118 (10.4%) | 2334 (8.2%) | 888 (9.0%) | 6776 (9.6%) |
| Ward/other | 307 (13.0%) | 4104 (13.7%) | 3850 (13.6%) | 1255 (12.7%) | 9516 (13.5%) |
Abbreviations: WHO World Health Organization, BMI Body mass index, COPD Chronic obstructive pulmonary disease, ASA American Society of Anesthesiologists, SBP Systolic blood pressure, PACU Post-anesthesia care unit, ICU Intensive care unit, PIH Post-induction hypotension, AUC Area under the curve, SD Standard deviation, IQR Interquartile range
a Head and neck surgery included otolaryngology and head and neck surgery, oral and maxillofacial/plastic surgery, and neurosurgery; thoracic surgery encompassed procedures on the lungs, chest wall, mediastinum, and esophagus; abdominal surgery included gastrointestinal, colorectal, hepatobiliary and pancreatic, urological, and gynecological procedures; other surgical types involved ophthalmology, breast surgery, gastrointestinal endoscopy, esophagoscopy, and bronchoscopy
Primary and secondary outcomes
The overall incidence of PIH was 73.3%. As shown in Table 2, the incidence of PIH, ICU admission, length of hospital stay, and in-hospital mortality was highest among underweight patients. In contrast, patients in the overweight category (BMI 24.0–27.9 kg/m²) had the lowest incidence of these adverse outcomes. Obese individuals (≥ 28.0 kg/m²) showed a slight increase in PIH and ICU admission but no significant excess risk in length of hospital stay or in-hospital mortality.
Table 2.
Primary and secondary outcomes of study participants stratified by WHO BMI classification for the Chinese population
| Primary and Secondary Outcomes | Underweight (<18.5 kg/m2) | Normal weight (18.5–23.9 kg/m2) | Overweight (24.0–27.9 kg/m2) | Obese (≥ 28.0 kg/m2) | Overall |
|---|---|---|---|---|---|
| Primary Outcome | |||||
| PIH (OR) a | 1842 (78.0%) | 22,178 (74.2%) | 20,433 (72.0%) | 7247 (73.5%) | 51,700 (73.3%) |
| Secondary Outcomes | |||||
| ICU admission (OR) a | 436 (18.5%) | 3118 (10.4%) | 2334 (8.2%) | 888 (9.0%) | 6776 (9.6%) |
| Length of hospital stay (IRR) b | 10 (7–15) | 9 (6–14) | 8 (6–13) | 8 (6–13) | 8 (6–13) |
| In-hospital mortality (OR) a | 6 (0.3%) | 31 (0.1%) | 26 (0.1%) | 8 (0.1%) | 71 (0.1%) |
Abbreviations: WHO World Health Organization, BMI Body mass index, PIH Post-induction hypotension, ICU Intensive care unit, OR Odds ratio, IRR Incidence rate ratio
a Logistic regression models were used for PIH, ICU admission, and in-hospital mortality
b Negative binomial regression model was used for length of hospital stay due to its pronounced right-skewed distribution and overdispersion
BMI and PIH
An RCS curve was plotted with BMI as a continuous variable, showing a U-shaped nonlinear association between BMI and the risk of PIH (Fig. 2A). This non-linear trend was supported by segmented linear regression, which identified a change point at BMI 26.2 kg/m² (95% CI, 26.1–26.4) (Table 3). In the U-shaped RCS curve of PIH risk, with median BMI of 24.2 kg/m2 as the reference value, the other BMI value where OR for PIH equaled 1 was 29.7 kg/m2 (Fig. 3). The range between these two BMI cutoffs (24.2–29.7 kg/m2) was considered the low-risk BMI range for PIH incidence. Because of the large sample size and diverse surgical specialties, the median BMI of 24.2 kg/m² is considered representative, making the low-risk BMI range (24.2–29.7 kg/m²) solid.
Fig. 2.
Association Between Body Mass Index and Primary and Secondary Outcomes
Restricted cubic spline (RCS) models illustrating the association between body mass index (BMI) and four perioperative outcomes: (A) Post-induction hypotension (PIH), (B) Intensive care unit (ICU) admission, (C) Length of hospital stay, and (D) In-hospital mortality. The reference point was set at the median BMI of 24.2 kg/m². The solid red lines indicate adjusted estimates, and shaded areas represent 95% confidence intervals (CIs). Vertical dashed lines indicate the WHO-recommended BMI thresholds for the Chinese population: 18.5, 24.0, and 28.0 kg/m². Logistic regression was used for PIH, ICU admission, and in-hospital mortality; a negative binomial regression model was used for hospital stay. All models were adjusted for age, sex, comorbidities (hypertension, diabetes, myocardial ischaemia, arrhythmia, heart failure, stroke, peripheral vascular disease, asthma/chronic obstructive pulmonary disease, liver dysfunction, renal dysfunction, and anaemia), type of surgery, anaesthesia method, anaesthesia induction period, type of induction agents, and use of inhalational anaesthesia. Abbreviations: BMI Body mass index, PIH Post-induction hypotension, ICU Intensive care unit, OR Odds ratio, IRR Incidence rate ratio, CI Confidence interval
Table 3.
Segmented linear regression models estimated change points in the association between BMI and outcomes
| Primary and Secondary Outcomes | BMI change point, kg/m2 (95%CI) | OR/IRR/b per kg/m2 BMI increase below change point (95%CI) | P value below change point | OR/IRR/b per kg/m2 BMI increase above change point (95%CI) | P value above change point |
|---|---|---|---|---|---|
| Primary Outcome | |||||
| PIH (OR) a | 26.2 (26.1–26.4) | 0.97 (0.96–0.97) | < 0.001 | 1.02 (1.00–1.03) | 0.026 |
| Secondary Outcomes | |||||
| ICU admission (OR) a | 25.3 (25.1–25.5) | 0.92 (0.90–0.93) | < 0.001 | 1.05 (1.03–1.07) | < 0.001 |
| Length of hospital stay (IRR) b | 24.3 (24.2–24.4) | 0.97 (0.96–0.98) | < 0.001 | 1.00 (0.99–1.00) | 0.226 |
| In-hospital mortality (OR) a | NAf | NAf | NAf | NAf | NAf |
Abbreviations: BMI Body mass index, PIH Post-induction hypotension, ICU Intensive care unit, SBP Systolic blood pressure, AUC Area under the curve, OR Odds ratio, IRR Incidence rate ratio, CI Confidence interval
a Logistic regression models were used for PIH, ICU admission, and in-hospital mortality
b Negative binomial regression model was used for length of hospital stay due to its pronounced right-skewed distribution and overdispersion
f NA, not applicable. Due to the absence of a non-linear relationship between BMI and in-hospital mortality, segmented linear regression cannot find meaningful change point
All models were adjusted for sex, age, comorbidities (hypertension, diabetes, myocardial ischemia, arrhythmia, heart failure, stroke, peripheral vascular disease, asthma/COPD, liver dysfunction, renal dysfunction, anemia), type of surgery, anesthesia method, anesthesia induction period, type of induction agents, and combination of inhalational anesthesia
Fig. 3.
Association Between Body Mass Index and Post-induction Hypotension Using Restricted Cubic Spline and BMI Distribution Histogram
Restricted cubic spline (RCS) analysis illustrating the non-linear relationship between body mass index (BMI) and the odds of post-induction hypotension (PIH). The reference point was set at a BMI of 24.2 kg/m² (median value). The y-axis represents the odds ratio (OR) for PIH at each BMI value compared to the reference. The two red dashed vertical lines mark the BMI values where the estimated OR for PIH equals 1. The blue density histogram shows the distribution of BMI in the study cohort. Logistic regression models were adjusted for age, sex, comorbidities (including hypertension, diabetes, myocardial ischaemia, arrhythmia, heart failure, stroke, peripheral vascular disease, asthma/chronic obstructive pulmonary disease, liver dysfunction, renal dysfunction, and anaemia), type of surgery, anaesthesia method, induction period, type of induction agents, and inhalational anaesthesia use. Abbreviations: BMI Body mass index, PIH Post-induction hypotension, OR Odds ratio, CI Confidence interval
Multivariable logistic regression analysis revealed that compared to patients within the low-risk BMI range, those with a BMI below this range had a significantly higher risk of PIH (OR, 1.13; 95% CI, 1.09–1.17; P < 0.001), while patients with a BMI above this range did not exhibit a notable increase in PIH risk (OR, 1.05; 95% CI, 0.98–1.13; P = 0.169) (Table 4). Female, preoperative peripheral vascular disease, liver dysfunction, anemia, orthopedic/spinal surgery, vascular surgery, and combination of inhalational anaesthesia during the post-induction period all displayed significant correlation with increased PIH risk. On the other hand, preoperative hypertension, myocardial ischemia, abdominal surgery, afternoon anaesthesia, and the use of etomidate during induction were linked to a decreased risk of PIH (Table 4).
Table 4.
Multivariate logistic regression model: evaluating the association between BMI as a three-category variable (according to BMI cutoffs of 24.2 and 29.7) and PIH
| Variates | OR | 95% CI | P value |
|---|---|---|---|
| Gender, No. (%) | |||
| Male | Reference | ||
| Female | 1.23 | 1.19–1.27 | < 0.001 |
| Age, y | 1.00 | 1.00–1.00 | 0.188 |
| BMI, kg/m2 | |||
| 24.2–29.7 | Reference | ||
| < 24.2 | 1.13 | 1.09–1.17 | < 0.001 |
| > 29.7 | 1.05 | 0.98–1.13 | 0.169 |
| Comorbidities | |||
| Hypertension, No. (%) | 0.92 | 0.89–0.95 | < 0.001 |
| Diabetes, No. (%) | 1.01 | 0.97–1.05 | 0.683 |
| Myocardial ischemia, No. (%) | 0.91 | 0.86–0.96 | < 0.001 |
| Arrhythmia, No. (%) | 1.00 | 0.93–1.07 | 0.993 |
| Heart failure, No. (%) | 1.12 | 0.96–1.31 | 0.138 |
| Stroke, No. (%) | 0.95 | 0.89–1.00 | 0.067 |
| Peripheral vascular disease, No. (%) | 1.13 | 1.05–1.22 | 0.002 |
| Asthma/COPD, No. (%) | 1.08 | 0.99–1.18 | 0.104 |
| Renal dysfunction, No. (%) | 0.86 | 0.77–0.95 | 0.003 |
| Liver dysfunction, No. (%) | 1.13 | 1.06–1.21 | < 0.001 |
| Anemia, No. (%) | 1.14 | 1.02–1.27 | 0.027 |
| Type of surgery, No. (%)a | |||
| Head and Neck | Reference | ||
| Thoracic | 0.95 | 0.89–1.02 | 0.123 |
| Abdominal | 0.87 | 0.83–0.92 | < 0.001 |
| Orthopedic/spinal | 1.17 | 1.10–1.25 | < 0.001 |
| Vascular | 1.36 | 1.21–1.53 | < 0.001 |
| Other | 2.55 | 2.24–2.89 | < 0.001 |
| Anesthesia method, No. (%) | |||
| General | Reference | ||
| General + Neuraxial | 1.35 | 0.86–2.13 | 0.197 |
| General + Regional | 0.91 | 0.58–1.42 | 0.673 |
| Anesthesia induction period, No. (%) | |||
| Before 12:00 | Reference | ||
| 12:00–16:00 | 0.63 | 0.61–0.66 | < 0.001 |
| After 16:00 | 0.56 | 0.53–0.59 | < 0.001 |
| Induction agents | |||
| Propofol only, No. (%) | Reference | ||
| Propofol + Etomidate, No. (%) | 0.73 | 0.67–0.80 | < 0.001 |
| Etomidate only, No. (%) | 0.58 | 0.51–0.65 | < 0.001 |
| Combined inhalational anesthesia, No. (%) | 1.12 | 1.07–1.17 | < 0.001 |
Abbreviations: BMI Body mass index, PIH Post-induction hypotension, OR Odds ratio, CI Confidence interval
a Head and neck surgery included otolaryngology and head and neck surgery, oral and maxillofacial/plastic surgery, and neurosurgery; thoracic surgery encompassed procedures on the lungs, chest wall, mediastinum, and esophagus; abdominal surgery included gastrointestinal, colorectal, hepatobiliary and pancreatic, urological, and gynecological procedures; other surgical types involved ophthalmology, breast surgery, gastrointestinal endoscopy, esophagoscopy, and bronchoscopy
BMI and secondary outcomes
A similar U-shaped relationship was also observed between BMI and ICU admission (Fig. 2B). For length of hospital stay, an L-shaped association was detected, with a significant increase in risk at low BMIs, but minimal elevation in risk at higher BMIs (Fig. 2C). There was no non-linear relationship between BMI and in-hospital mortality (Fig. 2D). Segmented linear regression analysis estimated change points of 25.3 kg/m2 (95% CI, 25.1–25.5) for ICU admission and 24.3 kg/m2 (95% CI, 24.2–24.4) for length of hospital stay, respectively (Table 3).
BMI below the low-risk range was associated with not only a higher risk of ICU admission (OR, 1.31; 95% CI, 1.24–1.39; P < 0.001) but also longer hospital stay (IRR, 1.08; 95% CI, 1.06–1.10; P < 0.001). Nevertheless, BMI above the low-risk range only elevate the risk of ICU admission (OR, 1.21; 95% CI, 1.08–1.36; P = 0.001) but not length of hospital stay (IRR, 0.99; 95% CI, 0.95–1.02; P = 0.415) (Supplementary Tables 1 and Supplementary Table 2). BMI showed a statistical relationship with in-hospital mortality in the high range, but with a wide CI (OR, 2.63; 95% CI, 1.02–6.83; P = 0.046) (Supplementary Table 3). These findings reinforce the robustness of the optimal BMI range (24.2–29.7 kg/m²) across multiple perioperative outcomes. Furthermore, PIH was significantly related to increased risk of ICU admission (OR, 1.39; 95% CI, 1.30–1.48; P < 0.001), length of hospital stay (OR, 1.13; 95% CI, 1.11–1.15; P < 0.001), and in-hospital mortality (OR, 3.14; 95% CI, 1.48–6.66; P = 0.003).
Subgroup analyses
Subgroup analyses revealed significant interactions between BMI and sex (P_interaction = 0.037), age group (P_interaction = 0.008), and hypertension status (P_interaction = 0.037). Among men, low BMI was more strongly associated with PIH, while in women, higher BMI conferred greater risk (Fig. 4A). The protective effect of moderate BMI was more evident in patients aged 60–74 years, with diminished effect in those ≥ 75 years (Fig. 4B). In patients with preoperative hypertension, high BMI was associated with increased PIH risk, whereas among non-hypertensive patients, low BMI posed a greater risk (Fig. 4C). No significant interaction was found with diabetes status (Fig. 4D).
Fig. 4.
Association Between Body Mass Index and Post-induction Hypotension Stratified by Sex, Age, Hypertension, and Diabetes
Restricted cubic spline (RCS) models showing the non-linear association between body mass index (BMI) and the risk of post-induction hypotension (PIH) in four subgroups: (A) Sex, (B) Age group, (C) Hypertension status, and (D) Diabetes status. P-values indicate interaction between BMI and the stratification variable. Logistic regression models were adjusted for age, sex, comorbidities (hypertension, diabetes, myocardial ischaemia, arrhythmia, heart failure, stroke, peripheral vascular disease, asthma/chronic obstructive pulmonary disease, liver dysfunction, renal dysfunction, anaemia), type of surgery, anaesthesia method, induction period, type of induction agents, and use of inhalational anaesthesia. Abbreviations: BMI Body mass index, PIH Post-induction hypotension, OR Odds ratio, CI Confidence interval
Sensitivity analysis
Sensitivity analysis excluding PIH as a covariate yielded results consistent with the primary models. The associations between BMI and ICU admission, as well as length of hospital stay, remained stable, suggesting that the observed effects were not mediated by PIH. For in-hospital mortality, the increased risk associated with high BMI (> 29.7 kg/m²) became statistically non-significant (OR 2.50; 95% CI: 0.97–6.48), likely due to the low event rate (Table 5).
Table 5.
Sensitivity analysis for excluding PIH as a confounder for secondary outcomes
| Variable | Confounding with PIH | Confounding without PIH | ||||
|---|---|---|---|---|---|---|
| OR/IRR | 95% CI | P value | OR/IRR | 95% CI | P value | |
| ICU admission (OR)a | ||||||
| BMI, kg/m2 | ||||||
| 24.2–29.7 | Reference | |||||
| < 24.2 | 1.31 | 1.24–1.39 | < 0.001 | 1.32 | 1.24–1.40 | < 0.001 |
| > 29.7 | 1.21 | 1.08–1.36 | 0.001 | 1.21 | 1.08–1.36 | 0.001 |
| Length of hospital stay (IRR)b | ||||||
| BMI, kg/m2 | ||||||
| 24.2–29.7 | Reference | |||||
| < 24.2 | 1.08 | 1.06–1.10 | < 0.001 | 1.08 | 1.07–1.10 | < 0.001 |
| > 29.7 | 0.99 | 0.95–1.02 | 0.415 | 0.99 | 0.96–1.02 | 0.430 |
| In-hospital mortality (OR)a | ||||||
| BMI, kg/m2 | ||||||
| 24.2–29.7 | Reference | |||||
| < 24.2 | 1.66 | 0.98–2.82 | 0.058 | 1.67 | 0.99–2.83 | 0.056 |
| > 29.7 | 2.63 | 1.02–6.83 | 0.046 | 2.50 | 0.97–6.48 | 0.058 |
Abbreviations: BMI Body mass index, PIH Post-induction hypotension, ICU Intensive care unit, OR Odds ratio, IRR Incidence rate ratio, CI Confidence interval
a Logistic regression models were used for ICU admission and in-hospital mortality
b Negative binomial regression model was used for length of hospital stay due to its pronounced right-skewed distribution and overdispersion
Discussion
In this large cohort of older adults undergoing elective non-cardiac surgery, we observed a non-linear, U-shaped association between preoperative BMI and the risk of PIH. Patients with BMI in the range of 24.2–29.7 kg/m²—encompassing overweight to mild obesity—had the lowest incidence of PIH, ICU admission, and length of hospital stay. These findings support the presence of an “obesity paradox” in perioperative medicine, particularly in the ageing surgical population.
Our results align with previous studies reporting better surgical outcomes among overweight and mildly obese individuals. However, this is the first study, to our knowledge, to demonstrate a U-shaped association between BMI and PIH specifically in older adults. Previous research has either focused on younger populations or used simplified BMI categories, potentially overlooking subtle non-linear trends [18, 19].
The “obesity paradox”, initially observed in cardiac surgeries [20, 21] has recently been confirmed in various non-cardiac surgeries, particularly in elderly patients [22–24]. Although our results lack direct theoretical support, previous studies offer plausible explanations for this “obesity paradox” in PIH. First, in elderly populations, low BMI often reflects malnutrition, whereas overweight and mildly obese individuals typically have better nutritional reserves, which might be protective during surgery and anaesthesia [25]. Second, obese elderly individuals often exhibit increased heart rates, circulatory blood volume, cardiac output, and end-diastolic volumes due to activation and adaptation of the Renin-Angiotensin-Aldosterone System (RAAS) and sympathetic systems, helping to compensate for vasodilation during anaesthesia induction [26]. Besides, the obese individuals who were known as “metabolically healthy obesity”, has better cardiovascular conditions and a stronger ability to regulate PIH [27]. Meanwhile, BMI does not reflect whole body composition, and muscle mass, rather than fat content, is the determinant for favorable clinical outcomes in obese patients [28].
Our study also revealed that lower BMI was associated with higher ICU admission and longer hospital stays, consistent with previous research findings [29, 30]. However, no significant relationship was found between BMI and in-hospital mortality, in contrast to some prior studies [29]. This discrepancy may be due to the small number of in-hospital deaths (n = 71) in our cohort, leading to insufficient statistical power. Furthermore, multivariable regression analyses indicated that PIH was an independent risk factor for these three secondary outcomes, consistent with previous studies [6].
We also found that sex, age and hypertension status modulated the relationship between BMI and PIH, suggesting that risk assessment and interventions for PIH should be individualized. Male patients with low BMI and female patients with high BMI should have their BMI managed within an optimal range to avoid high PIH risk. Compared with younger elderly patients, the protective effect of the low-risk BMI range appears to be diminished in the oldest-old. This may be due to age-related declines in organ reserve, impaired haemodynamic regulation, and a higher burden of comorbidities, which may play a more dominant role in determining the risk of PIH, thereby attenuating or overshadowing the influence of BMI [31]. In patients with preoperative hypertension, the relationship between BMI and PIH was stronger, particularly in higher BMI ranges, where PIH risk increased rapidly. This suggests that patients with preoperative hypertension should aim to maintain BMI within a stricter range to reduce PIH risk. One possible explanation is that, in elderly patients with preexisting hypertension, elevated BMI is often accompanied by sustained sympathetic activation, RAAS upregulation, chronic low-grade inflammation, and insulin resistance. These changes reduce vascular compliance, making patients more susceptible to hemodynamic fluctuations during anaesthesia induction and thus raising the risk of PIH [32, 33].
The clinical implications of our findings are twofold. First, BMI may serve as a simple, routinely available marker to guide preoperative optimisation in older adults. Second, underweight patients, who are often under-recognised, may benefit from targeted nutritional support or cardiovascular assessment before surgery.
This study has limitations. First, as a retrospective observational study, it is impossible to exclude all potential confounding factors affecting the outcomes. Second, detailed information on anaesthetic drug doses, intraoperative vasopressor use, fluid therapy, preoperative fasting time, and cuff size was not available. Third, interventions such as vasopressors administered during the post-induction period could prevent SBP from dropping, which may result in some patients being misclassified as non-PIH. Fourth, missing data, mainly due to technical issues unrelated to patient characteristics, were directly excluded, which may introduce bias. Although the remaining sample size was sufficient for statistical analysis, future studies should consider using imputation methods to assess the robustness of these findings. Finally, our findings are based on a single-centre cohort in China, and generalisability to other populations may require further validation.
In the future, multicentre prospective studies with larger sample sizes, or randomized controlled trials, are needed to validate our findings. Additionally, future research should consider integrating BMI with other indicators such as muscle mass, fat mass, waist circumference, waist-to-hip ratio, and waist-to-height ratio to define obesity comprehensively, and to explore the specific mechanisms of the “obesity paradox” in PIH.
Conclusion
In conclusion, our study highlights a U-shaped relationship between BMI and the risk of PIH in older surgical patients. Maintaining BMI within the 24.2–29.7 kg/m² range may reduce perioperative haemodynamic instability and adverse outcomes. These findings support the incorporation of BMI into preoperative assessment and suggest that even mild underweight status warrants clinical attention in geriatric surgical care.
Supplementary Information
Acknowledgements
We thank all colleagues in the Department of Anaesthesiology, Peking Union Medical College Hospital, for their support.
Authors’ contributions
YW contributed to the conceptualisation, data curation, statistical analysis, visualisation, and drafting of the manuscript.JW contributed to data collection and data management.YZ assisted with methodology and statistical modelling.XX participated in study conceptualisation and design.YT provided support in software coding and data processing.LL contributed to data investigation and clinical interpretation.LS oversaw overall study conceptualisation, formal analysis, funding acquisition, project administration, supervision, and critical revision of the manuscript.All authors read and approved the final manuscript.
Funding
This work was supported by National Major Science and Technology Projects of China (2023ZD0501801).
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Peking Union Medical College Hospital (approval number: K5072). The requirement for informed consent was waived due to the retrospective design and use of de-identified electronic medical record data. All procedures were conducted in accordance with relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Clinical trial number
Not applicable.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yanbingshi Wang, Jing Wang and Yuelun Zhang contributed equally to the work.
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Associated Data
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Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.