Handgrip strength and the Index of pulmonary dysfunction: Practical screening tools for cough dysfunction in huntington's disease

Abstract

Background

As Huntington's disease (HD) progresses, it impairs airway protection, increasing the risk of aspiration pneumonia—the leading cause of death in HD. Although voluntary peak cough flow (vPCF) assesses cough effectiveness, its clinical use is limited by time and equipment constraints.

Objective

This study evaluates handgrip strength (HGS) and the Index of Pulmonary Dysfunction (IPD) as simpler screening tools and aims to define gender-specific thresholds for identifying cough dysfunction (dystussia) in HD patients.

Methods

In this prospective cross-sectional study, 71 patients with HD underwent assessments of HGS, IPD, vPCF, respiratory muscle strength, and were rated using the Unified Huntington's Disease Rating Scale. Logistic regression and ROC analyses were used to assess the predictive value of HGS and IPD for dystussia (vPCF <300 L/min).

Results

Among 32 females and 39 males, 30 exhibited dystussia and had more advanced disease. HGS showed AUCs of 0.82 (females) and 0.89 (males), IPD 0.86 overall. Combining HGS and IPD improved accuracy.

Conclusions

HGS and IPD appear to be effective tools for initial dystussia screening in HD.

Trial registration

NCT06585332

Plain language summary

People with Huntington's disease (HD) often have a weak cough, increasing their risk of pneumonia. This study tested whether handgrip strength (HGS) and the Index of Pulmonary Dysfunction (IPD) could identify cough weakness. Among 71 HD patients, those with weak cough had more advanced disease. Both HGS and IPD proved effective for detecting cough dysfunction. These simple tools may help doctors find breathing issues early and take steps to prevent serious complications like pneumonia.

Introduction

In patients with Huntington's disease (HD), as the condition progresses it leads to a decline in the mechanisms that are essential for maintaining effective airway protection.^1,2 These mechanisms include preventing the aspiration of foreign or endogenous material into the airway through proper swallowing and, if aspiration occurs, removing the material by coughing. ³ Effective coughing requires the respiratory muscles to generate sufficient expiratory pressure and flow, both of which are critical for clearing airway secretions. ⁴ As the effectiveness of these protective functions decreases, there is a corresponding increase in the risk of aspiration pneumonia, which is a leading cause of hospital admissions and the primary cause of mortality in HD patients.^5,6

In clinical practice, cough effectiveness is commonly evaluated by measuring voluntary peak cough flow (vPCF). A higher peak cough flow indicates a greater ability to expel material from the subglottic area and, in turn, a lower risk of ineffective airway clearance. ⁴ Normal vPCF is typically above 360 L/min, ⁷ while values below 300 L/min are associated with cough dysfunction (dystussia). ⁸ Although vPCF monitoring and therapy are generally introduced in the late stages of HD, ⁹ recent research has shown that vPCF is significantly reduced compared to healthy individuals even in mid-stage HD.^1,10 Consequently, incorporating regular vPCF monitoring from the mid-stages of HD could enable early detection of dystussia, as well as the timely introduction of non-pharmacological interventions^10,11 in order to enhance cough effectiveness.

The regular, objective monitoring of vPCF is often impractical in most clinical settings due to time constraints and limited access to specialized equipment (pneumotachograph). Handheld peak flow meters can be useful as a screening tool, ¹¹ but they are also not routinely available in many clinics, meaning that a simple and time-efficient screening test for vPCF is needed.

Handgrip strength (HGS) and the Index of Pulmonary Dysfunction (IPD)^12,13 may potentially provide the necessary simple and practical screening tools for dystussia in HD patients. These tools have already been tested in other patient populations: HGS has been shown to be associated with vPCF ¹⁴ and respiratory muscle weakness in healthy adults,^15,16 as well as in other neurological disorders. ¹⁷ Similarly, the IPD has been demonstrated to be associated with dystussia in multiple system atrophy (MSA) ¹⁸ and respiratory muscle weakness in multiple sclerosis. ¹² Against this backdrop, the aim of this study was to evaluate the association of HGS and IPD with the presence of dystussia in HD patients and to establish gender-specific cut-off points for dystussia screening in clinical practice. We hypothesized that HGS and IPD would be useful screening tools for identifying dystussia in patients with HD.

Methodology

Patients

A prospective cross-sectional study was conducted involving HD patients from the Movement Disorders Centre, Department of Neurology, General University Hospital in Prague. The evaluation took place between April and October 2024. The research was approved by the Ethics Committee of the General University Hospital in Prague (93/21 S-IV). All patients gave their written informed consent before participating in the study, and the trial was pre-registered at clinicaltrials.gov (NCT06585332).

The inclusion criteria were: adult HD patients (age > 18 years) and genetically confirmed diagnoses of HD. The exclusion criteria were: 1) a diagnosis of other concomitant neurological diseases; 2) a history of cardiovascular or lung disease; 3) the presence of acute symptoms (e.g., fever, cough, phlegm, wheezing, or dyspnea) at the time of the assessment; 4) significant dominant hand deformities that could compromise the accuracy of the HGS; and 5) an inability to ensure that patients could reliably perform the assessments, for instance due to significant cognitive deficits. A determination was made by the researcher if there were doubts about the potential accuracy of HGS or IPD measurements.

Since no previous studies had evaluated HGS and IPD for dystussia screening in HD patients, we used pilot data to determine the target sample size. In the pilot study of 29 HD patients (15 females and 14 males), HGS exhibited an area under the ROC curve (AUC) of 0.89 (95% CI: 0.73–1.00), while IPD exhibited an AUC of 0.79 (95% CI: 0.63–0.95). Building on these findings, we performed a power analysis, setting the null hypothesis AUC at 0.50, with the aim of detecting an AUC ≥ 0.80 with 80% power (β = 0.20) at an α level of 0.05. This indicated that 26 male and 26 female HD patients would be required. Additionally, the analysis showed that at least 13 female and 13 male participants with dystussia were required to meet the diagnostic objectives. All sample-size calculations were conducted using MedCalc^® Statistical Software version 23.0.9 (MedCalc Software Ltd, Ostend, Belgium).

Measurements

Assessment visits were conducted by experienced respiratory physiotherapists with expertise in HD diagnoses using the measurements listed below.

Index of pulmonary dysfunction

The IPD consists of four items (Supplementary Table 1). ¹² The first two items are direct questions to the patient regarding difficulties handling mucus/secretions and diminished cough strength, while the third item assesses the patient's ability to generate a strong cough on demand. The final item evaluates the patient's ability to count following a single maximal inspiration. The range of possible total IPD scores is 4–11, with higher scores being associated with more severe pulmonary dysfunction. ¹²

Respiratory muscle strength

Respiratory muscle strength was assessed by measuring the maximal inspiratory pressure (MIP) and the maximal expiratory pressure (MEP) by means of a flanged rubber mouthpiece connected to a pressure manometer (Micro RPM, Vyaire Medical). Assessments were performed following the recommendations of the European Respiratory Society guidelines on respiratory muscle testing. ¹⁹ To ensure accuracy, at least three measurements with less than 10% variation between them were taken, with the highest value being recorded as the MIP or MEP. There was an interval of at least one minute between each MIP and MEP measurement to allow the participant to recover.

Voluntary peak cough flow

The vPCF was assessed by means of a pneumotachograph (BTL cardiopoint Spiro, BTL industries), which meets the recommendations of the American Thoracic Society and the European Respiratory Society for range and accuracy in forced expiratory maneuvers ²⁰ The assessments were performed in line with established guidelines, ¹⁹ with the subjects seated and wearing nose clips to prevent air leakage through the nose. Following a full inhalation, the participants were instructed to cough as hard as they could into the mouthpiece connected to a pneumotachograph. To ensure accuracy, at least three measurements were taken with less than 5% variation between them; the highest value was recorded as the vPCF. There was an interval of at least one minute between each vPCF measurement to allow the participant to recover. If this criterion was not fulfilled during the first three attempts, the assessment continued, up to a maximum of nine vPCF attempts. Dystussia was defined as vPCF <300 L/min. ⁸

Handgrip strength

HGS was measured using a digital hand dynamometer (DHD 1, Seahan Corporation, Supplementary Figure 1), as recommended by the American Society of Hand Therapists. ²¹ The participants were seated in a chair in an upright position, with their elbow at a 90° angle and the wrist in a neutral position. The participants were instructed to squeeze the dynamometer as hard as they could and received verbal encouragement as they attempted to do so. Three trials, each lasting three seconds, were conducted to assess the HGS of the dominant hand, with a one-minute interval between measurements. The highest value was recorded as the HGS.

Clinical assessments

The Unified Huntington's Disease Rating Scale Total Motor Score (UHDRS-TMS), Functional Assessment (FA), Independence Scale (IS) ²² and Total Functional Capacity Scale (TFC), ²³ were evaluated for each participant. All assessments were conducted by an experienced rater certified by the European Huntington's Disease Network (EHDN).

Statistical analysis

Differences between dystussic and non-dystussic groups of patients were compared using a non-parametric Mann-Whitney U-test, while correlations among MIP, MEP and HGS, IPD were assessed using a Spearman correlation coefficient. To evaluate the ability of HGS and IPD to predict dystussia, a logistic regression and the receiver operating characteristic (ROC) analysis were employed. Since handgrip strength may vary by gender,^24,25 we performed a gender-specific ROC subanalysis for HGS. However, in line with earlier studies,^12,13,18 we did not anticipate notable sex-based differences for IPD, and therefore analyzed all data together for this measure. The area under the ROC curve (AUC) and corresponding 95% confidence interval (CI) were calculated for each test, and optimal cut-off values were derived using the Youden Index. In determining cut-offs, AUC values above 0.80 were considered good, while values above 0.90 were deemed excellent. P-values of less than 0.05 were considered statistically significant. The analysis was performed using an R statistical package, version 4.3.3.

Results

A total of 87 HD patients were approached: 11 declined to participate due to their distance from the hospital or personal unwillingness, while five were excluded by the researchers due to doubts regarding the accuracy with which they could perform HGS or IPD measurements. Fourteen of the 32 female patients and 16 of the 39 male patients had dystussia. Patients with dystussia had significantly longer disease duration, higher UHDRS-TMS, and higher IPD, as well as lower UHDRS-IS, UHDRS-FA, UHDRS-TFC, MEP, MIP, and HGS. Detailed comparisons of demographic and clinical characteristics between dystussic and non-dystussic female and male groups are summarized in Table 1 and Table 2 respectively. HGS is positively correlated with MIP (r = 0.75; p < 0.0001) and MEP (r = 0.74; p < 0.0001), while IPD is negatively correlated with the MIP (r = −0.67; p < 0.0001) and MEP (r = −0.67; p < 0.0001).

Table 1.

Comparison of demographic and clinical characteristics between dystussic and non-dystussic female group.

Variable	Dystussic (n = 14)	Non-Dystussic (n = 18)	p value
Age (years)	50.73 ± 10.42	45.47 ± 11.02	0.2264
Weight (kg)	66.07 ± 18.63	71.41 ± 13.92	0.2883
Height (cm)	163.13 ± 5.77	163.88 ± 5.54	0.8583
BMI	24.60 ± 6.69	26.48 ± 4.14	0.2200
Disease duration (years)	8.00 ± 5.71	4.18 ± 2.63	0.0352*
UHDRS-TMS	42.67 ± 24.93	13.76 ± 11.61	0.0014*
UHDRS-IS	59.67 ± 32.43	90.59 ± 9.17	0.0054*
UHDRS-FA	13.60 ± 10.75	23.00 ± 3.16	0.0090*
UHDRS-TFC	6.13 ± 5.34	11.06 ± 2.51	0.0072*
IPD	7.00 ± 1.71	5.19 ± 1.05	0.0012*
MEP (cmH2O)	40.93 ± 34.40	90.00 ± 32.41	0.0005*
MIP (cmH2O)	27.73 ± 20.67	60.06 ± 22.11	0.0009*
vPCF (L/min)	191.00 ± 105.80	414.41 ± 61.31	<0.0001*
HGS (kg)	17.20 ± 10.84	29.29 ± 6.36	0.0022*

Values are mean ± standard deviation; * p < 0.05.

Abbreviations: BMI, body mass index; FA, functional assessment; HD, Huntington's disease; HGS, handgrip strength; IPD, Index of Pulmonary Dysfunction; IS, independence scale; MEP, maximal expiratory pressure; MIP, maximal inspiratory pressure; TFC, total functional capacity; UHDRS-TMS, Unified Huntington's Disease Rating Scale Total Motor Score; vPCF, voluntary peak cough flow.

Table 2.

Comparison of demographic and clinical characteristics between dystussic and non-dystussic male groups.

Variable	Dystussic (n = 16)	Non-Dystussic (n = 23)	p value
Age (years)	49.94 ± 12.46	49.61 ± 8.79	0.9772
Weight (kg)	67.29 ± 15.87	81.04 ± 12.10	0.0080 *
Height (cm)	176.53 ± 6.31	178.87 ± 7.11	0.3616
BMI	21.57 ± 5.09	25.40 ± 3.98	0.0112 *
Disease duration (years)	10.93 ± 3.99	6.00 ± 3.44	0.0004 *
UHDRS-TMS	55.20 ± 16.63	21.52 ± 13.92	<0.0001*
UHDRS-IS	44.33 ± 19.90	85.00 ± 15.04	<0.0001*
UHDRS-FA	7.67 ± 5.98	21.48 ± 3.88	<0.0001*
UHDRS-TFC	3.12 ± 3.05	9.68 ± 2.44	<0.0001*
IPD	7.67 ± 1.35	5.50 ± 1.41	0.0001 *
MEP (cmH2O)	39.88 ± 28.05	111.00 ± 45.07	<0.0001*
MIP (cmH2O)	22.56 ± 15.49	76.22 ± 33.38	<0.0001*
vPCF (L/min)	176.19 ± 78.22	505.30 ± 120.66	<0.0001*
HGS (kg)	20.66 ± 10.72	38.97 ± 11.11	<0.0001*

Values are mean ± standard deviation; * p < 0.05.

Abbreviations: BMI, body mass index; FA, functional assessment; HD, Huntington's disease; HGS, hand grip strength; IPD, Index of Pulmonary Dysfunction; IS, independence scale; MEP, maximal expiratory pressure; MIP, maximal inspiratory pressure; TFC, total functional capacity; UHDRS-TMS, Unified Huntington's Disease Rating Scale Total Motor Score; vPCF, voluntary peak cough flow.

Predicting dystussia using HGS and IPD

Female and male HGS were significantly positively associated with the dystussia [OR 0.83 (95% CI 0.73–0.96; p = 0.0101) and OR 0.84 (95% CI 0.74–0.94; p = 0.0029) respectively]. Similarly, IPD was significantly negatively associated with dystussia in HD patients [OR 2.99 (95% CI 1.71–5.24 p = 0.0001). The area under the ROC curve for HGS to exclude dystussia was 0.82 (95% CI 0.67–0.97) in female and 0.89 (95% CI 0.79–0.99) in male HD patients. For IPD, the AUC was 0.86 (95% CI 0.77–0.94) across all HD patients. The combination of HGS and IPD yielded an AUC of 0.86 (95% CI 0.73–1.00) in females and 0.91 (95% CI 0.82–1.00) in males. Table 3 summarizes the sensitivity and specificity of the cut-off points for HGS, IPD, and a combination of the two in screening for dystussia. Figure 1 illustrates the ROC curves for HGS, IPD, and a combination of the two.

Table 3.

ROC curves for HGS and IPD in predicting dystussia.

Sex	Variable	AUC (95% CI)	Cut-off point	Sensitivity (95% CI)	Specificity (95% CI)	NPV (95% CI)	PPV (95% CI)
Female	HGS	0.82 (0.66–0.97)	≤24.4	0.87 (0.67–1.00)	0.71 (0.47–0.94)	0.86 (0.69–1.00)	0.72 (0.59–0.92)
	HGS +  IPD	0.87 (0.73–1.00)	≥ −0.2074 †	0.83 (0.58–1.00)	0.81 (0.62–1.00)	0.87 (0.72–1.00)	0.77 (0.60–1.00)
Male	HGS	0.89 (0.80–0.99)	≤33.1	0.94 (0.81–1.00)	0.74 (0.56–0.91)	0.94 (0.83–1.00)	0.71 (0.59–0.88)
	HGS +  IPD	0.91 (0.82–1.00)	≥ −0.3717 ††	0.87 (0.67–1.00)	0.82 (0.63–0.96)	0.90 (0.78–1.00)	0.77 (0.62–0.93)
Both	IPD	0.86 (0.77–0.94)	≥ 6.0	0.96 (0.89–1.00)	0.58 (0.42–0.74)	0.96 (0.87–1.00)	0.62 (0.54–0.72)

† The form of the logistic regression model for female HGS + IPD: −3.7097 + (−0.1054)×HGS + 1.0458×IPD.

†† The form of the logistic regression model for male HGS + IPD: −1.4938 + (−0.1147)×HGS + 0.6905×IPD.

Abbreviations: AUC, area under the ROC curve; HGS, handgrip strength; IPD, Index of Pulmonary Dysfunction; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic.

Figure 1.

ROC curves for HGS, IPD, and a combination of HGS + IPD in screening for dystussia.

Note: The green shading represents the 95% confidence intervals. Abbreviations: AUC, area under the ROC curve; HGS, handgrip strength; IPD, Index of Pulmonary Dysfunction; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic.

Discussion

In most clinical settings, ongoing vPCF monitoring is challenging due to time constraints and limited access to specialized equipment, including pneumotachograph and handheld peak flow meters. The present study is the first to assess the accuracy of HGS and IPD when it comes to identifying HD patients with dystussia. Overall, both IPD and HGS were feasible in 71 of 76 patients (93%), including those with prominent dystonia or chorea. Our findings indicate that HGS, IPD, and a combination of the two demonstrate diagnostic significance vis-à-vis the occurrence of dystussia in HD patients. Established cut-off points revealed by the ROC analysis may serve as practical, readily available proxies for dystussia screening. Importantly, the sex-specific analysis of HGS took into account biological differences in HGS results.

In both the female and male groups, patients with dystussia showed more advanced disease characteristics compared to those without it, which suggests that dystussia in HD is associated with more pronounced disease severity and functional decline, in line with previous research. ¹ Interestingly, weight and BMI were significantly lower in dystussic men compared to their non-dystussic counterparts, whereas no comparable difference was seen in women. This sex-specific discrepancy may stem from differences in body composition, such as the fact that men generally have greater muscle mass. ²⁶ Moreover, previous work has suggested that men with HD experience more pronounced reductions in lean body mass and truncal fat, whereas women's body composition appears to be less affected. ²⁷

The ROC analysis shows that HGS and IPD both independently demonstrate good diagnostic accuracy in identifying dystussia among HD patients (with AUCs ranging from 0.82 to 0.89 for HGS in females and males, respectively, and 0.86 for IPD). It is noteworthy that IPD exhibited exceptionally high sensitivity (0.96) and negative predictive value (0.96), making it effective for ruling out dystussia—a performance that is consistent with findings in other neurological populations. ¹⁸ However, its relatively low specificity (0.58) and positive predictive value (0.62) increase the likelihood of false positives. An important finding is that, in combination, HGS and IPD achieved even higher AUCs (0.87 in females and 0.91 in males), thus enhancing both sensitivity and specificity and reducing false positives and negatives. As a result, applying them in tandem is the most robust diagnostic strategy.

From a clinical standpoint, it is necessary to strike a balance between simplicity and comprehensive accuracy. Since IPD can be administered rapidly (in about one minute, with no equipment needed) and displays high sensitivity, it is ideal for ensuring that patients with potential dystussia are not overlooked. By contrast, HGS testing involves the use of a dynamometer and following standardized protocols. As a result, it is slightly more time-consuming, but has strong predictive power, especially in males. Additionally, HGS testing is already recommended for routine malnutrition screening, ²⁴ which is important in HD patients,^25,26 effectively allowing clinicians to “kill two birds with one stone” by assessing dystussia and nutritional status at the same time. Critically, combining IPD with HGS provides the best overall accuracy, mitigating the limitations of each method taken on its own by lowering false-positive rates and enhancing the clinical utility of these tools for regular dystussia screening in HD.

These findings point to a step-by-step diagnostic algorithm. In a busy clinical setting, a neurologist may initially use no-equipment IPD as an efficient “first look” screening tool for dystussia. When time and resources allow, administering both tests simultaneously provides a more robust and reliable screening approach, thereby reducing false positives. If screening for dystussia is negative, routine follow-up monitoring is advisable. Once dystussia has been detected, HD patients should proceed to a second step of targeted examinations, such as voluntary peak cough flow, in order to objectively evaluate dystussia. ⁴ If the presence of dystussia is verified, we recommend: (i) targeted physiotherapy, particularly expiratory muscle strength training, which improves cough effectiveness in HD, ¹⁰ and, when indicated, mechanical insufflation-exsufflation for severe impairment (vPCF <160 L·min ^–1) ²⁸ ; (ii) because dystussia and dysphagia commonly co-occur, ²⁹ a speech-language pathology evaluation of swallowing. In settings with limited access to confirmatory testing (vPCF measurement), we recommend performing both tests whenever feasible. Nevertheless, the combined model still yields false positives (19% in women; 18% in men). From a practical standpoint, many individuals with HD will ultimately benefit from respiratory physiotherapy, therefore, in settings without confirmatory testing, it is reasonable to let clinical status guide decisions. For example, even if a patient reports no current difficulty with expectoration or swallowing, preventive EMST may help delay the onset of dystussia and dysphagia. Conversely, if the patient or caregivers report ineffective airway clearance and swallowing difficulty, consideration of mechanical insufflation–exsufflation is reasonable even without confirmatory testing.

The established sex-specific HGS cut-off points in this study (26.3 kg for females and 33.1 kg for males) for identifying dystussia were higher than the HGS cut-off points associated with other conditions, such as sarcopenia (16 kg for females and 27 kg for males) ²⁷ and mobility impairment (20 kg for females and 32 kg for males). ³⁰ This discrepancy likely stems from our definition of dystussia as vPCF <300 L/min, which represents a relatively mild cough deficit. More stringent cutoffs (e.g., < 160 L/min) ⁷ often reflect severe cough impairment and might yield lower HGS thresholds, similar to those seen in other contexts. However, our goal was to detect earlier stages of cough dysfunction rather than severe impairment. By identifying milder dysfunction, this approach facilitates the early identification of at-risk HD patients who may benefit from timely interventions before significant respiratory decline sets in. For example, sarcopenia, which is associated with as much as a fourfold increase in the risk of pneumonia, ³¹ reflects a more advanced stage of severity, thus illustrating the importance of intervening at an earlier point of decline to prevent serious complications. Furthermore, our study identified a cut-off score of 6 for the IPD, which aligns with findings in MSA patients, where an IPD score of ≥6 has similarly been shown to pick out individuals at risk of less effective coughing. ¹⁸

Our findings also revealed strong correlations between HGS and both MIP (r = 0.75) and MEP (r = 0.74), along with a robust association between HGS and dystussia (OR = 0.83 in women, 0.84 in men). These results support the existence of a pathophysiological link between HGS and the prediction of dystussia. As our study demonstrates, HGS is a well-established measure of general muscle weakness, ³⁰ including respiratory muscles. Respiratory muscle weakness contributes to lower inspiratory and expiratory pressures, ultimately reducing cough effectiveness.

Despite these promising findings, our study has several limitations. First, although we calculated the sample size from pilot data, the subgroup analyses were still relatively small and were conducted at a single center. Second, we did not conduct a longitudinal follow-up to determine how HGS and IPD might evolve as the disease progresses. Third, IPD and HGS are volitional measures and may be limited by cognitive impairment and, in the case of IPD, by speech impairments (e.g., dysarthria), which were not measured. In future studies researchers should consider undertaking larger, multi-center trials with a longitudinal design and include patients’ cognitive, swallowing and speech profiles.

Conclusion

This study demonstrates that both HGS and IPD may accurately identify dystussia in HD patients and therefore serve as tools for regular dystussia screening. Due to its minimal time and equipment requirements, IPD may serve as an ideal initial screening tool, after which HGS can be employed to reduce false positives.