Exploring the Factors Influencing Walking Speed: A Cross-Sectional Study of Elderly Physical Examinations

doi:10.21203/rs.3.rs-4860985/v1

Background

Walking speed, as a simple measurement indicator, is valuable in assessing the motor function of the elderly and predicting hospitalization and mortality rates. This study utilizes physical examination data from community health service centers for individuals aged 65 and above, focusing on walking speed to explore the factors influencing substandard walking speed.

Methods

From January 2022 to December 2022, elderly individuals who underwent physical examinations at Qingling Community Health Service Center were selected. Based on inclusion and exclusion criteria, a total of 4865 participants were eligible. According to walking speed results, participants were divided into two groups: the qualified group (≤ 8 seconds) with 3889 people (73.8%) and the unqualified group (> 8 seconds) with 1276 people (26.2%). Additional information on the participants was obtained through the electronic information system. A comparison of relevant information between the two groups was conducted to summarize the influencing factors of walking speed, with P < 0.05 indicating statistical significance.

Results

Age, marital status, caregiving situation, weekly exercise routine, drinking habits, height, weight, BMI, blood urea nitrogen, low-density lipoprotein, Chinese visceral adiposity index, cardiometabolic index, and systemic immune-inflammation index are all risk factors for decreased walking speed. ROC curve analysis showed that the systemic immune-inflammation index had the best predictive efficiency.

Conclusion

Marital status, caregiving situation, weekly exercise routine, and drinking habits are all correlated with decreased walking speed. Age, weight, waist circumference, and the systemic immune-inflammation index (SII) are positively correlated with decreased walking speed. In contrast, height, BMI, blood urea nitrogen, LDL-C, Chinese visceral adiposity index (CVAI), and cardiometabolic index (CMI) are negatively correlated with decreased walking speed. Among these factors, SII has the strongest predictive efficiency for decreased walking speed.

Health sciences/Medical research/Biomarkers

Health sciences/Medical research/Epidemiology

Walking speed

elderly physical examination

Chinese visceral adiposity index

systemic immune-inflammation index

Sarcopenia is a clinical syndrome characterized by a decline in skeletal muscle mass and function due to aging, resulting in somatic dysfunction^[1]. The prevalence of sarcopenia in China can reach 23.9% among those over the age of 60, increasing the risk of falls and mortality in the elderly^[2]. As a common geriatric syndrome, sarcopenia has an insidious onset and high prevalence, increasing the burden on family caregivers and the healthcare system^[3]. With China's elderly population continuing to grow, addressing sarcopenia and related debilitating issues is critical to public health.

Although gait speed is easy to measure, six physiological systems are involved in walking: the central nervous system, peripheral nervous system, perceptual system, muscles, bones/joints, and energy production/transmission^[4].Walking is crucial to the quality of life of older adults, and gait speed has been described as the'sixth vital sign', serving as a core indicator of health and function in aging and disease. It is an objective measure of physical functioning and is associated with impaired mobility and other adverse health outcomes^[5].Walking speed is a crucial aspect of screening for sarcopenia and a simple indicator of physical functioning, reflecting an individual's level of physical strength^{[3, 6]}.

This study utilizes physical examination data from community health service centers for individuals aged 65 and above, using walking speed as a focal point. It aims to identify the factors influencing substandard walking speed in patients and to further uncover the risk factors for elderly frailty, with sarcopenia as a central element.

Selection of Research Subjects

This study selected elderly individuals aged 65 and above who underwent physical examinations at Wuhan University of Science and Technology Hospital (Qingling Community Health Service Center) from January 2022 to December 2022. Inclusion criteria: participants provided informed consent. Exclusion criteria: (1) severe systemic diseases; (2) missing or extreme values; (3) hematologic or immune system diseases; (4) bone and joint diseases affecting lower limb movement. This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Geriatric Hospital affiliated with Wuhan University of Science and Technology (Ethics No: 20240205003). All participants provided informed consent. This study has been registered with the Chinese Clinical Trial Registry and is currently under review.

General Data Collection

The experimental methods consist of three parts:

Questionnaire survey: collecting general demographic information of elderly individuals;

Physical examination: measuring height, weight, waist circumference (WC), and other indicators;

Hematological examination: using the Mindray BC6100 blood cell analyzer for routine blood tests (including red blood cell count, white blood cell count, neutrophil count, monocyte count, lymphocyte count, platelet count), and using the Hitachi 7180 biochemical analyzer for blood biochemical tests [measuring alanine aminotransferase, aspartate aminotransferase, total bilirubin, urea, creatinine, fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG)].

Gait Speed Calculation

Gait speed is calculated by measuring the time it takes for participants to complete a task over a distance measured in meters and time in seconds. Participants are instructed to sit in a chair, and the timer starts when they stand up, walk 2.5 meters forward, walk around a marker, walk 2.5 meters back to the chair, and sit down, at which point the timer stops. The recommended time threshold for frailty in the 5-meter walk test is 5.0-7.7 seconds^[7]. Considering measurement error, a time of more than 8 seconds (including participants unable to walk) is defined as reduced gait speed, while 8 seconds or less is defined as normal gait speed.

Calculation of Other Indicators

(1) Body Mass Index (BMI) = [body weight (kg) / height (m²)]; (2) Chinese Visceral Adiposity Index (CVAI): Male CVAI = -267.93 + 0.68 × age + 0.03 × BMI + 4.00 × waist circumference (WC) + 22.00 × Log10 triglycerides (TG) − 16.32 × high-density lipoprotein cholesterol (HDL-C); Female CVAI = -187.32 + 1.71 × age + 4.23 × BMI + 1.12 × waist circumference (WC) + 39.76 × Log10 triglycerides (TG) − 11.66 × high-density lipoprotein cholesterol (HDL-C); (3) Atherogenic Index of Plasma (AIP) = Log (TG/HDL-C); (4) Cardiometabolic Index (CMI) = TG/HDL-C × (WC/height); (5) Lipid Accumulation Product (LAP): Female LAP = (WC − 58) × TG, Male LAP = (WC − 65) × TG; (6) Visceral Adiposity Index (VAI): Male VAI = [WC / (39.68 + 1.88 × BMI)] × (TG/1.03) × (1.31/HDL-C); Female VAI = [WC / (36.58 + 1.89 × BMI)] × (TG/0.81) × (1.52/HDL-C); (7) Remnant Cholesterol (RC) = total cholesterol (TC) - high-density lipoprotein cholesterol (HDL-C) - low-density lipoprotein cholesterol (LDL-C); (8) Triglyceride Glucose (TyG) index = ln (TG × FPG) / 2; (9) Renal function calculation: estimated glomerular filtration rate (eGFR) is calculated using the CKD-EPI formula. eGFR [ml·min⁻¹·(1.73m²)⁻¹] = 141 × min(SCr/κ, 1)^α × max(SCr/κ, 1)^-1.209 × 0.993^age × 1.018 (female), κ: 0.7 (female), 0.9 (male), α: -0.329 (female), -0.411 (male), SCr unit is mg/dl, min refers to the smaller value of SCr/κ or 1, max refers to the larger value of SCr/κ or 1; (10) Systemic Immune-Inflammation Index (SII) = (platelet count × neutrophil count) / lymphocyte count; (11) Systemic Inflammatory Response Index (SIRI) = (neutrophil count × monocyte count) / lymphocyte count.

Statistical Methods

Data were summarized using Excel 2021 and analyzed with SPSS 26.0. Continuous variables were first tested for normality. Normally distributed data are expressed as mean ± standard deviation (x̅±s) and compared between two groups using the independent samples t-test. Non-normally distributed data are expressed as median (quartiles) [M(Q1, Q3)] and compared using the Mann-Whitney U test. Categorical data are presented as frequencies or percentages and compared using the χ² test. Variables with significant statistical differences were included in univariate and multivariate correlation analyses, with P < 0.05 indicating statistical significance. For indicators with statistical differences in multivariate analysis, such as low-density lipoprotein, Chinese Visceral Adiposity Index, Cardiometabolic Index, and Systemic Immune-Inflammation Index, the corresponding ROC curves were plotted.

General Information

This study included 4865 participants, of whom 2075 were men (42.7%) and 2790 were women (57.3%). The age range was 65 to 104 years, with an average age of 74.69 years (± 7.99). Walking test grouping: 3889 participants (73.8%) passed the test, while 1276 participants (26.2%) failed. Marital status: 3186 were married (65.5%), 1635 were widowed (33.6%), 24 were unmarried (0.5%), and 20 were divorced (0.4%). Education level: 2591 participants (53.3%) had primary education or below, 1329 (27.3%) had junior high education, 642 (13.2%) had secondary or high school education, and 303 (6.2%) had college education or above. Living arrangements: 3105 participants (63.8%) lived at home, while 1760 (36.2%) lived in nursing homes. Caregiving arrangements: 2560 participants (52.6%) were cared for by their spouses, 382 (7.9%) by their children, 204 (4.2%) lived alone, 458 (9.4%) were cared for by nannies, and 1261 (25.9%) had other arrangements. Weekly exercise: 1818 participants (37.4%) exercised regularly, 576 (11.8%) exercised occasionally, 196 (4.0%) exercised sometimes, and 1912 (39.3%) never exercised. Smoking status: 91 participants (1.9%) had quit smoking, 396 (8.1%) smoked regularly, and 4378 (90.0%) never smoked. Drinking status: 4340 participants (89.2%) never drank, 98 (2.0%) drank regularly, 236 (4.9%) drank occasionally, 140 (2.9%) drank daily, and 51 (1.0%) had quit drinking. Assessment of self-care ability: 3967 participants (81.5%) were self-sufficient, 306 (6.3%) were mildly dependent, 370 (7.6%) were moderately dependent, and 222 (4.6%) were unable to care for themselves.

Comparison of Clinical Data Between Groups

All continuous variables were tested for normality and Q-Q plots were drawn. The results showed that all continuous variables conformed to a normal distribution. Comparison of general clinical data between the two groups showed statistically significant differences in age, gender, marital status, education level, living arrangements, caregiving arrangements, weekly exercise, smoking, drinking, self-care ability, height, weight, waist circumference, and body mass index (BMI) (see Table 1).

Table 1

Comparison of Basic Information between Qualified and Unqualified Walking Groups
Variable	Qualified Group (3589 cases)	Unqualified Group (1276 cases)	t (λ²) Value	p Value
Gender [n (%)]			13.250^a	< 0.001
Male	1586 (44.2%)	489 (38.3%)
Female	2003 (55.8%)	787 (61.7%)
Age	72.54 ± 6.67	80.71 ± 8.30	-31.694 ^b	< 0.001
Marital Status [n (%)]			925.143 ^a	< 0.001
Married	2792 (77.8%)	394 (30.9%)
Widowed	773 (21.5%)	862 (67.6%)
Unmarried	17 (0.5%)	7 (0.5%)
Divorced	7 (0.2%)	13 (1.0%)
Educational Level [n (%)]			130.745 ^a	< 0.001
Elementary or below	1747 (48.7%)	844 (66.1%)
Junior High	1045 (29.1%)	284 (22.3%)
Vocational or High School	557 (15.5%)	85 (6.7%)
College or above	240 (6.7%)	63 (4.9%)
Type of Elderly Care [n (%)]			1702.967 ^a	< 0.001
Home Care	2899 (80.8%)	206 (16.1%)
Nursing Home	690 (19.2%)	1070 (83.9%)
Caregiver [n (%)]			1814.590 ^a	< 0.001
Spousal Mutual Care	2410 (67.1%)	150 (11.8%)
Children	340 (9.5%)	42 (3.3%)
Alone	188 (5.2%)	16 (1.3%)
Housekeeper	123 (3.4%)	335 (26.3%)
Others	528 (14.7%)	733 (57.4%)
Weekly Exercise [n (%)]			1384.530 ^a	< 0.001
Always	1726 (48.1%)	92 (7.2%)
Often	528 (14.7%)	48 (3.8%)
Occasionally	308 (8.6%)	55 (4.3%)
Sometimes	170 (4.7%)	26 (2.0%)
Never	857 (23.9%)	1055 (82.7%)
Smoking [n (%)]			117.514 ^a	< 0.001
Quit	84 (2.3%)	7 (0.5%)
Frequently	375 (10.4%)	21 (1.6%)
Never	3130 (87.2%)	1248 (97.8%)
Drinking [n (%)]			151.099 ^a	< 0.001
Never	3085 (86.0%)	1255 (98.4%)
Frequently	91 (2.5%)	7 (0.5%)
Occasionally	229 (6.4%)	7 (0.5%)
Daily	136 (3.8%)	4 (0.3%)
Quit	48 (1.3%)	3 (0.2%)
Self-Care Ability Assessment [n (%)]			2361.198 ^a	< 0.001
Independent	3499 (97.5%)	468 (36.7%)
Mild Dependence	74 (2.1%)	232 (18.2%)
Moderate Dependence	10 (0.3%)	360 (28.2%)
Cannot Self-Care	6 (0.2%)	216 (16.9%)
Height (cm)	160.97 ± 7.42	159.83 ± 7.17	4.769	< 0.001
Weight (Kg)	62.08 ± 10.49	58.34 ± 9.00	12.205 ^b	< 0.001
Waist Circumference (cm)	79.95 ± 8.77	78.60 ± 31.09	2.346 ^b	0.019
BMI (Kg/m2)	23.91 ± 3.38	22.78 ± 2.77	11.738 ^b	< 0.001
Note: a represents χ2 value, b represents t value.

Comparison of Auxiliary Examination Results Between Groups

There were statistically significant differences between the two groups in neutrophil count, lymphocyte count, hemoglobin, alanine aminotransferase, aspartate aminotransferase, total bilirubin, urea, fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, Chinese Visceral Adiposity Index (CVAI), Cardiometabolic Index (CMI), Lipid Accumulation Product (LAP), remnant cholesterol, TyG index, eGFR, Systemic Immune-Inflammation Index (SII), and Systemic Inflammatory Response Index (SIRI) (p < 0.05). There were no significant differences between the two groups in white blood cell count, monocyte count, platelet count, creatinine, Atherogenic Index of Plasma (AIP), and Visceral Adiposity Index (VAI) (p > 0.05) (see Table 2).

Table 2

Comparison of Auxiliary Examination Results between Qualified and Unqualified Walking Groups
Variable	Qualified Group (3589 cases)	Unqualified Group (1276 cases)	t Value	p Value
WBC Count (10^9)	5.98 ± 1.82	5.99 ± 1.76	-0.175	0.861
Absolute Lymphocyte Count (10^9)	1.94 ± 1.09	1.67 ± 0.62	8.628	< 0.001
Absolute Monocyte Count (10^9)	0.37 ± 0.12	0.38 ± 0.15	-1.09	0.276
Absolute Neutrophil Count (10^9)	3.49 ± 1.20	3.77 ± 1.48	-6.043	< 0.001
Hemoglobin (g/L)	134.44 ± 15.06	123.08 ± 17.38	20.744	< 0.001
Platelet Count (10^9)	211.39 ± 58.88	212.20 ± 79.97	-0.331	0.74
ALT (U/L)	22.44 ± 24.45	17.75 ± 13.05	8.56	< 0.001
AST (U/L)	23.48 ± 13.69	20.81 ± 8.40	6.544	< 0.001
Total Bilirubin (umol/l)	15.36 ± 5.96	13.33 ± 7.63	9.67	< 0.001
Urea (mmol/l)	6.06 ± 1.92	6.46 ± 2.46	-5.193	< 0.001
Creatinine (umol/l)	78.05 ± 34.64	79.59 ± 31.28	-1.469	0.142
Glucose (mmol/l)	5.97 ± 2.25	6.47 ± 3.46	-4.795	< 0.001
Total Cholesterol (mmol/l)	4.84 ± 1.09	4.37 ± 0.99	14.072	< 0.001
Triglycerides (mmol/l)	1.55 ± 1.04	1.37 ± 0.75	6.322	< 0.001
HDL-C (mmol/l)	1.40 ± 0.32	1.31 ± 0.32	8.571	< 0.001
LDL-C (mmol/l)	2.66 ± 0.77	2.42 ± 0.70	10.584	< 0.001
Chinese Visceral Adiposity Index	102.82 ± 34.53	108.75 ± 46.87	-4.775	< 0.001
Plasma Atherogenic Index	-0.01 ± 0.27	-0.02 ± 0.25	1.154	0.249
Cardiac Metabolic Index	0.62 ± 1.28	0.56 ± 0.40	2.728	0.006
Lipid Accumulation Product	30.30 ± 29.13	24.42 ± 34.48	5.447	< 0.001
Visceral Fat Index	1.91 ± 3.35	1.81 ± 1.36	1.04	0.298
Residual Cholesterol (mmol/l)	0.77 ± 0.32	0.65 ± 0.24	15.033	< 0.001
TyG Index	1.34 ± 0.22	1.28 ± 0.67	2.67	0.008
eGFR (ml/min)	73.20 ± 13.20	66.13 ± 14.87	15.021	< 0.001
Systemic Immune-Inflammation Index	420.13 ± 248.46	569.61 ± 557.32	-9.26	< 0.001
Systemic Inflammatory Response Index	0.76 ± 0.54	1.02 ± 1.20	-7.466	< 0.001

Univariate and Multivariate Analysis of Factors Influencing Reduced Gait Speed

Reduced gait speed was used as the dependent variable and other relevant indicators were assigned values (see Table 3). Regression analysis showed that age, marital status, caregiving arrangements, weekly exercise, drinking status, height, weight, BMI, blood urea nitrogen, low-density lipoprotein, CVAI, CMI, and SII were all risk factors for reduced gait speed (see Tables 4 and 5).

Table 3

Binary Logistic Regression Analysis Assignment for Factors Affecting the Decrease in Walking Speed
Variable Name	Classification Value
Walking Speed Test Group	Normal 1, Decreased 2
Gender	Male 1, Female 2
Marital Status	Married 1, Widowed 2, Unmarried 3, Divorced 4
Educational Level	Elementary or below 1, Junior High 2, Vocational or High School 3, College or above 4
Type of Elderly Care	Home 1, Nursing Home 2
Caregiver	Spousal Mutual Care 1, Children 2, Alone 3, Housekeeper 4, Others 5
Weekly Exercise	Always 1, Often 2, Occasionally 3, Sometimes 4, Never 5
Smoking	Quit 1, Frequently 2, Never 3
Drinking	Never 1, Frequently 2, Occasionally 3, Daily 4, Quit 5
Self-Care Ability Assessment	Independent 1, Mild Dependence 2, Moderate Dependence 3, Cannot Self-Care 4
Absolute Lymphocyte Count	Measured Value
Absolute Neutrophil Count	Measured Value
Hemoglobin	Measured Value
ALT	Measured Value
AST	Measured Value
Total Bilirubin	Measured Value
Urea	Measured Value
Glucose	Measured Value
Total Cholesterol	Measured Value
Triglycerides	Measured Value
HDL-C	Measured Value
LDL-C	Measured Value
Chinese Visceral Adiposity Index	Measured Value

Table 4

Univariate Correlation Analysis of Factors Affecting the Decrease in Walking Speed
Variable	B	SE	Wald χ2 Value	OR Value (95% CI)	P Value
Gender (Male as reference)
Female	0.242	0.067	13.221	1.274 (1.118–1.452)	< 0.001
Age	0.013	0.005	795.825	1.142 (1.131–1.153)	< 0.001
Marital Status (Married as reference)
Widowed	2.067	0.073	798.705	7.902 (6.847–9.120)	< 0.001
Unmarried	1.071	0.452	5.605	2.918 (1.202–7.081)	0.018
Divorced	2.577	0.472	29.828	13.16 (5.219–33.184)	< 0.001
Educational Level (Elementary or below as reference)
Junior High	-0.575	0.079	53.079	0.563 (0.482–0.657)	< 0.001
Vocational or High School	-1.152	0.124	86.703	0.316 (0.248–0.403)	< 0.001
College or above	-0.61	0.148	17.071	0.543 (0.407–0.726)	< 0.001
Type of Elderly Care (Home care as reference)
Nursing Home	3.083	0.087	1253.392	21.823 (18.399–25.884)	< 0.001
Caregiver (Spousal mutual care as reference)
Children	0.685	0.184	13.888	1.985 (1.384–2.846)	< 0.001
Alone	0.313	0.274	1.307	1.367 (0.800–2.338)	0.253
Housekeeper	3.779	0.135	784.674	43.759 (33.592–57.002)	< 0.001
Others	3.105	0.102	932.296	22.305 (18.274–27.224)	< 0.001
Weekly Exercise (Always as reference)
Often	0.534	0.185	8.34	1.706 (1.187–2.450)	0.004
Occasionally	1.209	0.181	44.459	3.35 (2.348–4.780)	< 0.001
Sometimes	1.054	0.236	19.914	2.869 (1.806–4.559)	< 0.001
Never	3.14	0.116	726.742	23.095 (18.382–29.017)	< 0.001
Smoking (Quit as reference)
Frequently	-0.397	0.453	0.771	0.672 (0.277–1.632)	0.38
Never	1.565	0.395	15.72	4.785 (2.207–10.373)	< 0.001
Drinking (Never as reference)
Frequently	-1.666	0.394	17.901	0.189 (0.087–0.409)	< 0.001
Occasionally	-2.588	0.385	45.164	0.075 (0.035–0.160)	< 0.001
Daily	-2.627	0.508	26.698	0.072 (0.027–0.196)	< 0.001
Quit	-1.873	0.596	9.876	0.154 (0.048–0.494)	0.002
Height (cm)	-0.021	0.004	22.541	0.979 (0.970–0.988)	< 0.001
Weight (Kg)	-0.039	0.004	122.267	0.962 (0.955–0.969)	< 0.001
Waist Circumference (cm)	-0.013	0.004	10.927	0.987 (0.980–0.995)	0.001
BMI (kg/m2)	-0.114	0.011	108.92	0.892 (0.874–0.912)	< 0.001
Absolute Lymphocyte Count (10^9)	-0.714	0.058	149.68	0.49 (0.437–0.549)	< 0.001
Absolute Neutrophil Count (10^9)	0.159	0.025	41.927	1.172 (1.117–1.230)	< 0.001
Hemoglobin (g/L)	-0.045	0.002	390.178	0.956 (0.952–0.960)	< 0.001
ALT (U/L)	-0.036	0.004	95.027	0.965 (0.958–0.972)	< 0.001
AST (U/L)	-0.044	0.005	74.436	0.957 (0.948–0.967)	< 0.001
Total Bilirubin (umol/l)	-0.065	0.007	98.849	0.937 (0.925–0.949)	< 0.001
Urea (mmol/l)	0.085	0.015	32.045	1.089 (1.057–1.122)	< 0.001
Glucose (mmol/l)	0.065	0.011	31.745	1.067 (1.043–1.091)	< 0.001
Total Cholesterol (mmol/l)	-0.431	0.033	168.248	0.65 (0.609–0.693)	< 0.001
Triglycerides (mmol/l)	-0.23	0.042	29.291	0.795 (0.731–0.864)	< 0.001
HDL-C (mmol/l)	-0.916	0.109	71.215	0.4 (0.324–0.495)	< 0.001
LDL-C (mmol/l)	-0.458	0.046	98.557	0.633 (0.578–0.692)	< 0.001
Chinese Visceral Adiposity Index	0.004	0.001	19.822	1.004 (1.002–1.006)	< 0.001
Cardiac Metabolic Index	-0.208	0.077	7.337	0.812 (0.699–0.944)	0.007
Lipid Accumulation Product	-0.01	0.002	41.125	0.99 (0.986–0.993)	< 0.001
Residual Cholesterol (mmol/l)	-1.805	0.137	172.561	0.164 (0.126–0.215)	< 0.001
TyG Index	-0.144	0.052	7.664	0.866 (0.782–0.959)	0.006
eGFR (mL/min)	-0.034	0.002	220.404	0.967 (0.963–0.971)	< 0.001
Systemic Immune-Inflammation Index	0.001	0	134.389	1.001 (1.001–1.002)	< 0.001
Systemic Inflammatory Response Index	0.514	0.054	89.773	1.671 (1.503–1.859)	< 0.001

Table 5

Multivariate Correlation Analysis of Factors Affecting the Decrease in Walking Speed
Variable	B	SE	Wald χ2 Value	OR Value (95% CI)	P Value
Gender (Male as reference)
Female	-0.314	0.227	1.917	0.731 (0.469–1.139)	0.166
Age (years)	0.04	0.009	18.733	1.041 (1.022–1.060)	< 0.001
Marital Status (Married as reference)
Widowed	0.615	0.113	29.793	1.85 (1.483–2.307)	< 0.001
Unmarried	-0.592	0.505	1.375	0.553 (0.206–1.488)	0.241
Divorced	1.567	0.595	6.945	4.794 (1.494–15.382)	0.008
Educational Level (Elementary or below as reference)
Junior High	-0.158	0.11	2.062	0.854 (0.689–1.059)	0.151
Vocational or High School	-0.014	0.162	0.007	0.986 (0.718–1.354)	0.932
College or above	0.104	0.208	0.248	1.109 (0.738–1.668)	0.618
Type of Elderly Care (Home care as reference)
Nursing Home	0.403	0.288	1.958	1.497 (0.851–2.632)	0.162
Caregiver (Spousal mutual care as reference)
Children	0.164	0.215	0.584	1.179 (0.773–1.797)	0.445
Alone	-0.288	0.306	0.886	0.750 (0.412–1.365)	0.347
Housekeeper	1.69	0.304	30.977	5.418 (2.988–9.822)	< 0.001
Others	1.079	0.285	14.383	2.943 (1.685–5.142)	< 0.001
Weekly Exercise (Always as reference)
Often	0.436	0.201	4.697	1.546 (1.043–2.292)	0.03
Occasionally	0.344	0.209	2.696	1.410 (0.936–2.126)	0.101
Sometimes	0.67	0.272	6.058	1.955 (1.146–3.334)	0.014
Never	1.695	0.139	148.929	5.447 (4.149–7.151)	< 0.001
Smoking (Quit as reference)
Frequently	-0.402	0.568	0.502	0.669 (0.220–2.034)	0.479
Never	-0.342	0.525	0.424	0.710 (0.254–1.988)	0.515
Drinking (Never as reference)
Frequently	0.113	0.465	0.059	1.120 (0.450–2.784)	0.808
Occasionally	-1.103	0.435	6.443	0.332 (0.142–0.778)	0.011
Daily	-0.789	0.554	2.025	0.454 (0.153–1.347)	0.155
Quit	-0.54	0.733	0.544	0.582 (0.138–2.450)	0.461
Height (cm)	-0.156	0.045	12.234	0.856 (0.784–0.934)	< 0.001
Weight (Kg)	0.185	0.058	10.083	1.203 (1.073–1.349)	0.001
Waist Circumference (cm)	0.009	0.011	0.652	1.009 (0.987–1.032)	0.419
BMI (Kg/m2)	-0.492	0.149	10.963	0.611 (0.457–0.818)	0.001
Absolute Lymphocyte Count (10^9)	-0.12	0.114	1.12	0.887 (0.710–1.108)	0.29
Absolute Neutrophil Count (10^9)	0.011	0.064	0.03	1.011 (0.892–1.146)	0.863
Hemoglobin (g/L)	0.002	0.004	0.182	1.002 (0.994–1.009)	0.67
ALT (U/L)	-0.001	0.001	0.318	0.999 (0.997–1.002)	0.573
AST (U/L)	-0.001	0.006	0.007	0.999 (0.988–1.011)	0.931
Total Bilirubin (umol/l)	-0.007	0.009	0.728	0.993 (0.976–1.010)	0.394
Urea (mmol/l)	-0.016	0.007	5.209	0.984 (0.970–0.998)	0.022
Glucose (mmol/l)	0.037	0.028	1.747	1.037 (0.982–1.095)	0.186
Total Cholesterol (mmol/l)	-0.006	0.003	3.39	0.994 (0.988–1.000)	0.066
Triglycerides (mmol/l)	-0.002	0.028	0.007	0.998 (0.945–1.053)	0.933
HDL-C (mmol/l)	0.675	0.393	2.957	1.965 (0.910–4.243)	0.086
LDL-C (mmol/l)	-0.448	0.14	10.215	0.639 (0.485–0.841)	0.001
Chinese Visceral Adiposity Index	-1.012	0.423	5.726	0.364 (0.159–0.833)	0.017
Cardiac Metabolic Index	-0.976	0.491	3.958	0.377 (0.144–0.986)	0.047
Lipid Accumulation Product	0.002	0.004	0.333	1.002 (0.994–1.011)	0.564
Residual Cholesterol	0.018	0.088	0.041	1.018 (0.856–1.211)	0.84
TyG Index	0.312	0.213	2.153	1.366 (0.901–2.072)	0.142
eGFR (mL/min)	-0.005	0.007	0.544	0.995 (0.982–1.008)	0.461
Systemic Immune-Inflammation Index	0.001	0	4.925	1.001 (1.000–1.001)	0.026
Systemic Inflammatory Response Index	-0.174	0.101	2.979	0.840 (0.689–1.024)	0.084

Correlation Between Lipid Metabolism Indices and Gait Speed

The predictive efficacy of urea nitrogen, low-density lipoprotein, CVAI, CMI, and SII for reduced gait speed varied. The predictive efficacy of CVAI (area under the ROC curve, AUC = 0.549 [95% CI: 0.531, 0.568]) was better than that of urea nitrogen (AUC = 0.540 [95% CI: 0.521, 0.560]), CMI (AUC = 0.484 [95% CI: 0.466, 0.502]), and low-density lipoprotein cholesterol (AUC = 0.540 [95% CI: 0.390, 0.426]). However, the predictive efficacy of SII (AUC = 0.602 [95% CI: 0.583, 0.621]) was the highest (see Fig. 1).

Gait speed has been widely accepted as an indicator of health risk in older adults and is regarded as the sixth vital sign^{[8, 9]}. It has good predictive value for mortality, cardiovascular diseases, and cancer. Several mechanisms explain the link between reduced gait speed and the risk of adverse outcomes: (1) Muscular factors, including reduced motor units, impaired muscle activation, replacement of type I fibers by type II fibers, leading to decreased contraction speed and strength; (2) Neurological factors, such as decreased skin sensitivity, reduced nerve conduction velocity and reaction time, reduced gray matter volume with functional brain damage, and white matter lesions; (3) Numerous inflammatory markers are also associated with sarcopenia and muscle strength loss and may independently predict decreased gait speed and progression to severe gait impairment^[10].

This study shows that age, height, and weight are associated with reduced gait speed. This is consistent with Jennifer A. Schrack's view that walking speed decreases with age, and considering that increases in age, weight, and BMI lead to higher energy costs, resulting in reduced gait speed^{[11, 12]}. The effect of height on gait speed is generally positive; as height and stride length increase, the walking speed is faster at the same walking frequency. However, some studies suggest that the advantage brought by height diminishes with age^[13]. Univariate analysis shows that education level is associated with reduced gait speed, but multivariate analysis did not find a significant correlation. Alexis Elbaz et al. believe that participants with higher education levels generally have better physical performance^[14]. Since this study did not consider participants' occupations, family income, and other factors, it cannot determine the correlation between education level and reduced gait speed.

This study found that marital status is associated with gait speed, with married participants having a lower risk of reduced gait speed compared to non-married participants (divorced, widowed, unmarried). This indicates that social support is related to gait speed decline, consistent with the findings of Chava Pollak et al.^{[15, 16]}. The study also found differences in caregiving arrangements, with those receiving care from a nanny or others having a higher risk of reduced gait speed compared to those cared for by their spouses. Considering the mutual influence between caregiving and reduced gait speed, participants with reduced gait speed are more likely to receive care from a nanny or others.

There was no significant correlation between living arrangements and reduced gait speed. According to a survey on elderly living preferences in Wuhan, about 63.4% of elderly individuals prefer to age at home, which is similar to the findings of this study^[17].The study found that participants who drank occasionally had the best gait speed performance. However, there is no consensus on whether alcohol consumption is beneficial^[18].

This study indicates that blood urea nitrogen (BUN), low-density lipoprotein (LDL), Chinese visceral obesity index (CVI), cardiometabolic index (CMI), and systemic immune-inflammation index (SII) are all associated with a decrease in walking speed.BUN levels are negatively correlated with the risk of reduced walking speed. Under stable conditions, BUN levels are primarily related to dietary protein intake and serve as a protective factor against reduced walking speed^[19].

LDL levels are negatively correlated with decreased walking speed. LDL is responsible for transporting triglycerides to peripheral tissues, which can increase energy costs and slow down the decline in muscle strength. The current participants' LDL levels are 2.66 ± 0.77 and 2.42 ± 0.70 mmol/L, which overlap significantly with the recommended blood lipid levels^[20]. Considering the impact of blood lipids on cardiovascular diseases, further research is needed to balance these factors reasonably.

The Chinese Visceral Obesity Index (CVI) is derived from the visceral obesity index and has good consistency with visceral fat in Asians^[21]. CMI integrates characteristics of dyslipidemia and abdominal obesity and is considered a measure of visceral obesity^[22]. Both are negatively correlated with decreased walking speed, indicating that lipids primarily protect against muscle strength decline. However, this differs from Keita Kinoshita et al.'s view that abdominal fat increases with age, significantly increasing the risk of reduced walking speed^[22].

The Systemic Immune-Inflammation Index (SII) is an inflammatory marker, and muscle aging is related to chronic inflammation^[23]. For instance, interleukin-6 levels are associated with walking speed and can benefit from walking exercise^[23].The plotted ROC curve shows that BUN, CVI, and SII perform better than LDL and CMI.Based on this, we can make the following hypotheses: 1. Inflammation is the primary cause of reduced muscle strength in the elderly, followed by insufficient intake of protein and lipids, leading to further muscle atrophy; 2. Inflammation may mediate muscle atrophy caused by insufficient protein and lipid intake.

In summary, marital status is associated with gait speed, with married participants having a lower risk of reduced gait speed compared to non-married participants (divorced, widowed, unmarried). Caregiving arrangements show differences; those cared for by a nanny or others have a higher risk compared to those cared for by their spouses. Weekly exercise is related to gait speed, with other groups having a higher risk compared to the "always" exercise group. Occasionally drinking alcohol is negatively correlated with reduced gait speed. Age, weight, waist circumference, and the Systemic Immune-Inflammation Index (SII) are positively correlated with reduced gait speed. Height, BMI, blood urea nitrogen, low-density lipoprotein (LDL-C), Chinese Visceral Adiposity Index (CVAI), and Cardiometabolic Index (CMI) are negatively correlated with reduced gait speed. The Systemic Immune-Inflammation Index is more effective in predicting reduced gait speed.

Advantages and Limitations

This study comprehensively evaluated the impact of gender, age, marital status, education level, living arrangements, caregiving methods, weekly exercise, smoking and drinking habits, self-care ability, and physiological indicators on walking ability through multivariate logistic regression analysis, providing detailed data support and scientific evidence. With a large sample size covering various lifestyle and physiological factors, the study increased the reliability and external validity of the results. The findings offer specific guidance for improving the walking ability of the elderly, particularly in maintaining good nutrition, controlling chronic inflammation, and promoting regular exercise.

However, as a cross-sectional study, it cannot determine causality, and future research should consider longitudinal designs to validate the findings. Although the sample size is large, the geographic and demographic characteristics may affect generalizability, necessitating future studies in different regions and populations. Despite controlling for multiple variables, unmeasured confounding factors such as genetic background and environmental factors may still impact the results. Additionally, being conducted at a single research center, the findings may be influenced by specific medical and social environments, requiring multicenter studies to verify the generalizability.

Disclosure

The authors declare no competing financial interest.

Ethical Statement

This study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Geriatric Hospital affiliated with Wuhan University of Science and Technology (Ethics No.: 20240205003).

Author Contribution

Zhaolan Huang: Conceptualization, Methodology, Study Design, Project Implementation, Writing - Original Draft Preparation. Hui Gao: Data Analysis, Compilation of Tables and Figures. Wei Tan: Administrative, Technical, or Material Support, Critical Revision of the Article for Intellectual Content. Lailai Qu: Data Provision, Content Review.Xi Cheng: Content Review .

Acknowledgement

N/A

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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No competing interests reported.

Exploring the Factors Influencing Walking Speed: A Cross-Sectional Study of Elderly Physical Examinations

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Materials and Methods

Selection of Research Subjects

General Data Collection

Gait Speed Calculation

Calculation of Other Indicators

Statistical Methods

Results

General Information

Comparison of Clinical Data Between Groups

Comparison of Auxiliary Examination Results Between Groups

Univariate and Multivariate Analysis of Factors Influencing Reduced Gait Speed

Correlation Between Lipid Metabolism Indices and Gait Speed

Discussion

Conclusion

Advantages and Limitations

Declarations

Disclosure

Ethical Statement

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Status:

Version 1