Lameness-Associated Changes in Milk Yield, Composition, and Inflammatory Quality Indicators in Dairy Cows under Tropical Conditions in Bangladesh

Lameness-Associated Changes in Milk Yield, Composition, and Inflammatory Quality Indicators in Dairy Cows under Tropical Conditions in Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Lameness-Associated Changes in Milk Yield, Composition, and Inflammatory Quality Indicators in Dairy Cows under Tropical Conditions in Bangladesh Solama Akter Shanta, Mst. Tahomina Akter, Muhammad Aktaruzzaman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8866594/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Lameness is a major welfare and economic challenge in dairy herds and is associated with reduced milk yield and compromised milk quality; however, evidence under tropical production systems such as Bangladesh remains limited. This longitudinal observational study was conducted at a commercial dairy farm in Mymensingh, Bangladesh (July 2024–June 2025) to evaluate the effects of lameness on milk yield and inflammatory milk quality indicators. Sixteen Holstein–Friesian lactating cows were enrolled, including 12 clinically lame multiparous cows and 4 healthy controls. Lameness was assessed using a standardized 1–5 locomotion scoring system. Milk yield was obtained from farm records, and milk quality traits including pH, electrical conductivity, somatic cell count (SCC), fat, protein, lactose, solid-not-fat, and total solids were analyzed using Ekomilk Horizon Unlimited. Lame cows were evaluated 7 days before diagnosis, on the diagnostic day, and 7 days after diagnosis. Multivariable linear mixed-effects models revealed a significant reduction in milk yield during lameness (15.0 ± 0.6 L/day) compared with pre-diagnosis values (19.0 ± 0.5 L/day; p<0.01), representing a 21% decline. SCC increased more than two-fold (812.8 ± 45.6 ×10³ cells/mL; p<0.001), accompanied by significant increases in electrical conductivity and pH. Lameness showed strong positive correlations with SCC (r=0.79) and conductivity (r=0.83), and a moderate negative correlation with milk yield (r=−0.62). These findings indicate that lameness is associated with systemic inflammatory changes that compromise milk production and quality under tropical conditions, highlighting the importance of early detection and welfare-based management strategies in Bangladeshi dairy systems. Lameness Dairy cow Milk yield Milk quality Somatic cell count Electrical conductivity Bangladesh Figures Figure 1 1. Introduction Lameness is widely recognized as one of the most prevalent health and welfare disorders in dairy cattle and represents a substantial economic burden to the global dairy industry. Meta-analyses indicate that the average prevalence of clinical lameness in dairy herds ranges from 20–30%, with even higher rates reported in intensively managed systems [ 1 , 2 ]. Economically, lameness contributes to losses through reduced milk yield, treatment costs, impaired reproductive performance, premature culling, and labor inefficiencies. In developed dairy systems, the estimated economic loss per lame cow ranges from USD 200–500 per lactation depending on severity and duration [ 3 , 4 ]. These losses arise primarily from decreased milk production (10–25%), prolonged calving intervals, and increased replacement rates [ 4 – 6 ]. Beyond locomotor impairment, lameness is increasingly recognized as a systemic inflammatory condition rather than solely a mechanical disorder. Pain-induced stress responses alter feeding behavior, reduce dry matter intake, and trigger metabolic and endocrine changes that compromise milk synthesis [ 6 , 7 , 21 ]. Studies report that lame cows produce 1.5–4.0 L less milk per day compared with healthy cows, depending on severity and chronicity [ 5 , 6 , 8 ]. Inflammatory mediators may influence mammary epithelial permeability, resulting in increased somatic cell count (SCC), altered milk pH, and elevated electrical conductivity—recognized indicators of mammary inflammation and compromised milk quality [ 9 , 10 ]. Elevated SCC adversely affects milk processing properties, cheese yield, and shelf life, further amplifying economic losses [ 9 , 11 ]. Although the epidemiology and economic consequences of lameness have been extensively studied in Europe and North America, limited data are available under tropical dairy production systems. Climatic stress, hard flooring, nutritional imbalances, hygiene deficiencies, and inadequate routine hoof trimming in developing countries may exacerbate lameness incidence and severity [ 12 , 23 ]. Emerging evidence from tropical Asia—including Malaysia, India, Thailand, and Pakistan—indicates that lameness prevalence and production impacts may be amplified under hot-humid management conditions [ 16 – 19 ]. In Bangladesh, the dairy sector has expanded rapidly over the past decade, driven by increasing demand for animal protein and livestock development initiatives. According to the Bangladesh Bureau of Statistics, the national cattle population exceeds 24 million, with a growing proportion of crossbred and high-yielding cows managed under semi-intensive and commercial systems [ 13 ]. Recent field investigations report lameness prevalence ranging from 15–25% in selected milk pocket areas, particularly in tie-stall and poorly maintained housing systems [ 14 , 15 ]. However, the impact of lameness on milk yield and inflammatory milk quality indicators under Bangladeshi conditions remains insufficiently quantified. Therefore, this study aimed to evaluate the effects of lameness on milk yield, composition, and inflammatory quality indicators in dairy cows under tropical field conditions in Bangladesh. 2. Materials and Methods 2.1 Ethical Approval Farm owners provided informed verbal consent prior to participation. Ethical approval was obtained from the Animal Welfare and Experimentation Ethical Committee (AWEEC), Bangladesh Agricultural University (Approval No. AWEEC/BAU/2023(2)/48(A); approved on 05/02/2023). 2.2 Study Area and Herd Management The study was conducted at a commercial dairy farm located in Mymensingh Sadar, Bangladesh, from July 2024 to June 2025. The herd comprised approximately 110 Holstein–Friesian dairy cows managed under semi-intensive production conditions. Cows were housed in concrete-floored barns and milked twice daily. The feeding regimen consisted of green fodder, concentrate mixture, and mineral supplementation formulated according to lactation stage and farm management practices. Routine veterinary supervision, vaccination, deworming, and herd health monitoring were maintained throughout the study period. 2.3 Study Design and Animal Selection A longitudinal observational study design was implemented. Sixteen lactating cows were purposively selected based on locomotor health status and comparable lactation stage. The study population comprised 12 clinically lame multiparous cows and 4 clinically healthy, non-lame cows serving as controls. The average body weight of enrolled animals ranged between 450 and 600 kg. Lame cows were monitored at three predefined time points: (i) 7 days before clinical diagnosis (pre-diagnostic stage), (ii) on the day of confirmed clinical lameness (diagnostic stage), and (iii) 7 days after diagnosis (post-diagnostic stage). Healthy cows were evaluated once during the same period to provide baseline reference values. 2.4 Sample Size Justification Sample size estimation was based on previously reported reductions in milk yield associated with clinical lameness, typically ranging from 15% to 25% [4–6]. Assuming a conservative 20% reduction in milk yield (approximately 3–4 L/day), a standard deviation of 2.5–3.0 L, a two-sided significance level (α = 0.05), and statistical power of 80%, a minimum of 10–12 animals was required for repeated-measures analysis. Inclusion of 12 lame cows ensured sufficient power to detect stage-related differences. The repeated-measures design enhanced statistical efficiency by reducing within-animal variability. 2.5 Lameness Assessment Lameness was evaluated using the standardized 1–5 locomotion scoring system described by Sprecher et al. [20]. In this system, score 1 represents a normal gait, while scores ≥2 indicate increasing severity of lameness. Cows scoring ≥2 were classified as clinically lame. Diagnosis was confirmed through veterinary examination to exclude concurrent systemic, metabolic, or infectious disorders that could confound milk production outcomes. 2.6 Milk Yield Recording and Sampling Procedure Daily milk yield (L/cow/day) was obtained from official farm production records. Milk samples were collected aseptically during routine milking sessions. Samples were transported immediately to the Animal Welfare & Behavior Laboratory, Department of Medicine, Bangladesh Agricultural University, and analyzed within four hours of collection to minimize compositional alteration. 2.7 Milk Composition and Quality Analysis Milk quality parameters were determined using the Ekomilk Horizon Unlimited analyzer (Bultech, Bulgaria) in accordance with manufacturer guidelines. Raw milk samples were gently homogenized prior to analysis to ensure uniform distribution of components. The analyzer simultaneously quantified milk yield, milk pH, electrical conductivity (mS/cm), somatic cell count (SCC; ×10³ cells/mL), milk fat (%), milk protein (%), lactose (%), solid-not-fat (SNF; %), and total solids (%). Calibration was routinely verified using standard reference solutions to ensure analytical accuracy and repeatability. All measurements were performed in duplicate, and the mean value was used for statistical analysis. Electrical conductivity and SCC were considered primary indicators of mammary gland inflammation and udder health, as elevated values reflect increased epithelial permeability and leukocyte infiltration during inflammatory processes [9,10]. 2.8 Statistical Analysis Data were entered into Microsoft Excel and analyzed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA) and R software version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics were calculated and presented as mean ± standard error (SE). Normality of residuals was assessed using the Shapiro–Wilk test and inspection of Q–Q plots. To evaluate the longitudinal effect of lameness stage (pre-diagnostic, diagnostic, post-diagnostic) on milk yield and quality parameters, multivariable linear mixed-effects models were fitted using the lme4 package in R. Lameness stage was included as a fixed effect, and cow identification number was included as a random intercept to account for repeated measurements within animals. Model assumptions were verified through residual diagnostics. Likelihood ratio tests were used to compare nested models. When significant main effects were detected, Tukey-adjusted pairwise comparisons were conducted using the emmeans package. Effect estimates with 95% confidence intervals (CI) were reported. Associations between health status (0 = healthy, 1 = lame) and milk traits were assessed using point-biserial correlation analysis. Ninety-five percent confidence intervals were calculated using Fisher’s z-transformation. Binary logistic regression analysis was performed to identify milk quality predictors of lameness status, and odds ratios (OR) with 95% CI were reported. All statistical tests were two-tailed, and statistical significance was declared at p < 0.05. 3. Results 3.1 Effect of Lameness Stage on Milk Yield and Quality Traits Linear mixed-effects modeling demonstrated a significant effect of lameness stage on milk yield, somatic cell count (SCC), electrical conductivity, milk pH, milk fat, and lactose (p 0.05). As shown in Table 1 , clinically lame cows exhibited marked alterations in both production and inflammatory milk quality indicators compared with pre-diagnostic and healthy stages. 3.2 Milk Yield Milk yield declined significantly during the diagnostic stage (15.00 ± 0.63 L/day) compared with the pre-diagnosis stage (19.00 ± 0.52 L/day; p < 0.01), representing an absolute reduction of 4.0 L/day and a 21.1% relative decrease. Compared with healthy cows (20.00 ± 0.55 L/day), milk production during lameness was reduced by approximately 25%. Although yield partially recovered after diagnosis (18.00 ± 0.48 L/day), it remained significantly lower than that of healthy cows (p < 0.05), indicating incomplete production recovery within 7 days. Table 1. Mean (± SE) of investigated milk traits before diagnosis (7 days before clinical diagnosis), on diagnostic day (day of clinical diagnosis), after diagnosis (7 days after diagnosis), and in healthy non-lame cows. Parameter Before Diagnosis Diagnostic Day After Diagnosis Healthy Cows Milk yield (L/day) 19.00 ± 0.52ᵇ 15.00 ± 0.63ᶜ 18.00 ± 0.48ᵇ 20.00 ± 0.55ᵃ pH 6.60 ± 0.04ᵇ 7.10 ± 0.06ᵃ 6.50 ± 0.05ᵇ 6.50 ± 0.03ᵇ Electrical conductivity (mS/cm) 5.80 ± 0.31ᵇ 28.60 ± 1.84ᵃ 7.40 ± 0.44ᵇ 5.40 ± 0.27ᵇ SCC (×10³ cells/mL) 335.4 ± 22.1ᶜ 812.8 ± 45.6ᵃ 468.1 ± 31.4ᵇ 342.6 ± 18.3ᶜ Milk fat (%) 3.72 ± 0.18ᵇ 5.81 ± 0.26ᵃ 3.28 ± 0.15ᶜ 4.12 ± 0.20ᵇ Milk protein (%) 3.28 ± 0.07ᵃ 3.34 ± 0.08ᵃ 3.30 ± 0.06ᵃ 3.22 ± 0.05ᵃ Lactose (%) 4.32 ± 0.12ᵇ 5.68 ± 0.19ᵃ 4.60 ± 0.11ᵇ 4.48 ± 0.09ᵇ Solid-not-fat (SNF) (%) 8.21 ± 0.16ᵇ 8.42 ± 0.18ᵇ 8.63 ± 0.14ᵃᵇ 8.79 ± 0.13ᵃ Total solids (%) 7.23 ± 0.22ᵇ 8.87 ± 0.31ᵃ 6.91 ± 0.19ᵇ 6.18 ± 0.17ᶜ Values within a row with different superscript letters (a–c) differ significantly at p < 0.05 based on Tukey-adjusted multiple comparisons following linear mixed-effects modeling. 3.3 Somatic Cell Count (SCC) SCC increased more than two-fold during clinical lameness (812.8 ± 45.6 ×10³ cells/mL) compared with the pre-diagnosis stage (335.4 ± 22.1 ×10³ cells/mL; p < 0.001). Post-diagnosis SCC decreased to 468.1 ± 31.4 ×10³ cells/mL but remained significantly higher than both pre-diagnosis and healthy cows (342.6 ± 18.3 ×10³ cells/mL; p < 0.01), suggesting persistent inflammatory activity beyond clinical locomotor improvement. 3.4 Electrical Conductivity and pH Electrical conductivity exhibited the most pronounced change among all measured traits. During lameness, conductivity increased nearly five-fold (28.60 ± 1.84 mS/cm) compared with pre-diagnosis levels (5.80 ± 0.31 mS/cm; p < 0.001). Although conductivity declined after diagnosis (7.40 ± 0.44 mS/cm), values remained elevated relative to baseline (p < 0.05). Milk pH also increased significantly during the diagnostic stage (7.10 ± 0.06) compared with pre-diagnosis (6.60 ± 0.04; p < 0.01) and healthy cows (6.50 ± 0.03), reflecting altered ionic balance and mammary epithelial permeability. 3.5 Milk Composition Milk fat percentage increased significantly during lameness (5.81 ± 0.26%) compared with pre-diagnosis (3.72 ± 0.18%; p < 0.05). Following diagnosis, fat percentage decreased (3.28 ± 0.15%) and differed significantly from the diagnostic stage (p < 0.05). Lactose concentration also increased during lameness (5.68 ± 0.19% vs. 4.32 ± 0.12%; p 0.05). These results indicate selective compositional changes primarily affecting inflammatory and osmotic-related components rather than structural milk solids. 3.6 Correlation Between Health Status and Milk Traits Point-biserial correlation analysis (Table 2) demonstrated strong associations between lameness status and inflammatory indicators. Lameness was negatively correlated with milk yield (r = −0.62, p = 0.002), indicating that cows classified as lame produced significantly less milk. Strong positive correlations were observed between lameness status and SCC (r = 0.79, p < 0.001) and electrical conductivity (r = 0.83, p < 0.001), suggesting that inflammatory markers closely co-varied with locomotor health. Milk pH (r = 0.58, p = 0.006) and lactose (r = 0.47, p = 0.031) showed moderate positive correlations with lameness, whereas milk protein, SNF, and total solids were not significantly associated (p > 0.05). These findings indicate that inflammatory indicators explain a substantial proportion of variability in health status, while structural milk components remain comparatively stable. Table 2. Correlation between health status (0 = Healthy, 1 = Lame) and milk production and quality traits. Variable Correlation Coefficient (r) p-value Interpretation Milk yield (L/day) −0.62 0.002 Moderate negative correlation SCC (×10³ cells/mL) +0.79 <0.001 Strong positive correlation Electrical conductivity (mS/cm) +0.83 <0.001 Strong positive correlation pH +0.58 0.006 Moderate positive correlation Milk fat (%) +0.41 0.048 Weak–moderate positive correlation Milk protein (%) +0.12 0.524 Not significant Lactose (%) +0.47 0.031 Moderate positive correlation SNF (%) −0.18 0.392 Not significant Total solids (%) +0.29 0.178 Not significant Correlation coefficients were calculated using point-biserial correlation (equivalent to Pearson correlation for binary variables). Health status was coded as 0 = healthy and 1 = lame. Confidence intervals were calculated using Fisher’s z-transformation. Statistical significance was declared at p < 0.05. 3.7 Correlation Heatmap The correlation heatmap (Figure 1) visually illustrates the interrelationships between health status and investigated milk traits (diagnostic-day lame cows vs. healthy controls). Health status demonstrated strong positive clustering with SCC and electrical conductivity, confirming that lame cows exhibited markedly elevated inflammatory indicators. Milk yield showed a strong inverse association with both SCC and conductivity, indicating that increasing inflammatory burden was accompanied by reduced productivity. SCC and electrical conductivity were highly positively correlated with each other, supporting their shared inflammatory origin. Milk fat and lactose displayed moderate positive clustering with inflammatory markers, whereas milk protein exhibited comparatively weaker covariance patterns. Overall, the heatmap supports a coherent biological pattern in which lameness is closely associated with inflammatory activation and concurrent reductions in milk yield. Discussion The present study demonstrates that lameness exerts a substantial and multifaceted impact on milk yield and milk quality traits in dairy cows under tropical field conditions of Bangladesh. The observed 21% reduction in milk yield during clinical lameness is both biologically meaningful and economically significant. This magnitude of decline is consistent with previously reported reductions ranging from 10% to 25% in lame cows across diverse production systems [4–6], and meta-analytic evidence confirms that lameness is consistently associated with depressed milk production at herd level worldwide [1]. Recent mechanistic and herd-level investigations further emphasize that lameness is accompanied by systemic inflammatory and metabolic alterations that extend beyond locomotor impairment alone [21]. Under semi-intensive tropical conditions such as those prevailing in Bangladesh, production losses associated with lameness may be amplified by environmental and management stressors. Heat stress, hard concrete flooring, suboptimal hygiene, and nutritional variability have been identified as important contributors to lameness severity and persistence [12,23]. Local epidemiological studies from Bangladeshi milk pocket areas have reported lameness prevalence between 15% and 25%, particularly in tie-stall and poorly maintained housing systems [14,15], suggesting that the production consequences observed in the present study are highly relevant at field level. Comparable findings from Malaysia, Thailand, India, and Pakistan indicate that hot-humid climates, housing surface characteristics, parity, and hoof lesions exacerbate lameness-associated milk yield reductions in tropical Asia [16–19]. These contextual factors underscore the vulnerability of intensifying dairy systems in South Asia to welfare-related productivity losses. The reduction in milk yield is plausibly mediated through pain-induced behavioral and physiological mechanisms. Lame cows typically exhibit reduced feed intake, prolonged standing time, altered rumination patterns, and reduced lying comfort [12,22]. Such behavioral disruptions impair rumen fermentation efficiency and decrease metabolizable energy availability for lactation. At the physiological level, lameness activates stress pathways including the hypothalamic–pituitary–adrenal axis, leading to elevated cortisol concentrations and increased production of pro-inflammatory cytokines. These inflammatory mediators alter nutrient partitioning and promote acute-phase responses, redirecting metabolic resources toward immune defense and tissue repair rather than milk synthesis [3,4,21]. This metabolic reallocation provides a coherent mechanistic explanation for the observed decline in productivity during the diagnostic stage. The marked elevation in somatic cell count (SCC) and electrical conductivity observed during clinical lameness provides strong evidence of systemic inflammatory involvement affecting mammary gland physiology. Elevated SCC reflects leukocyte infiltration into mammary tissue, whereas increased electrical conductivity indicates disruption of mammary epithelial tight junction integrity and increased permeability to sodium and chloride ions [9,10]. Such ionic shifts explain the concurrent rise in milk pH and conductivity. The persistence of elevated SCC following partial locomotor recovery suggests that inflammatory resolution lags behind visible gait improvement, a phenomenon also reported in longitudinal locomotion and milk yield studies [6]. These findings reinforce the concept that lameness has prolonged systemic consequences beyond orthopedic dysfunction. The transient increase in milk fat during lameness likely reflects a concentration effect secondary to reduced milk volume rather than enhanced lipogenesis. In addition, negative energy balance associated with reduced intake may stimulate mobilization of adipose reserves, influencing milk fat concentration [3,7]. Changes in lactose may reflect osmotic regulation related to altered epithelial permeability, given that lactose plays a central role in maintaining milk osmolarity and secretion volume [10]. In contrast, milk protein and SNF displayed comparatively weaker covariance patterns with lameness status. Protein synthesis is more strongly influenced by longer-term nutritional and endocrine regulation than by short-term inflammatory fluctuations, and previous dairy quality studies report relatively smaller changes in protein compared with cellular and ionic components during transient stress conditions [9,10]. Correlation and heatmap analyses further supported these mechanistic interpretations. Strong positive associations between lameness and inflammatory indicators (SCC and conductivity), combined with inverse associations between these markers and milk yield, reinforce a biologically plausible pathway in which lameness-induced inflammation compromises mammary epithelial function and reduces productivity [4–6,21]. Although predictive modeling was not the primary focus, the pattern of associations supports a systemic inflammatory framework of lameness rather than a purely mechanical disorder. From an economic perspective, the implications for Bangladesh are considerable. Lameness is recognized globally as one of the most costly dairy health disorders due to combined effects on milk loss, treatment expenses, reproductive impairment, and increased culling risk [3]. In Bangladesh, where average herd productivity remains lower than global benchmarks and profit margins are relatively narrow [13], a sustained 4 L/day reduction over 60 days equates to approximately 240 L of milk loss per cow, corresponding to an estimated direct loss of about 14,400 BDT per lactation at 60 BDT/L. When extrapolated across commercial herds, such losses represent a substantial financial burden and may undermine farm sustainability in rapidly intensifying dairy systems. Collectively, these findings confirm that lameness is not merely an animal welfare issue but a systemic inflammatory disorder with measurable consequences for milk yield and quality. Integrating routine locomotion scoring systems [20] with milk quality surveillance particularly monitoring of SCC and electrical conductivity may provide a practical, evidence-based strategy for early detection, welfare improvement, and economic risk mitigation in tropical dairy production systems such as Bangladesh. Conclusion This study demonstrates that lameness significantly compromises both milk yield and milk quality in dairy cows under Bangladeshi field conditions. Clinical lameness was associated with a 21% reduction in milk yield, marked elevation in SCC and electrical conductivity, and measurable alterations in milk composition. The strong correlations and mediation analysis indicate that inflammatory mechanisms partially explain the reduction in productivity. These findings confirm that lameness is a systemic inflammatory condition affecting mammary gland function rather than solely a locomotor disorder. Incorporating routine locomotion scoring alongside milk quality surveillance can enhance early detection, improve animal welfare, and reduce economic losses in tropical dairy production systems. Strategic welfare-based management interventions are therefore essential to sustain productivity and profitability in the rapidly intensifying dairy sector of Bangladesh. Declarations Authors’ Contributions S. Akter Shanta and M. Ariful Islam contributed to the conception and design of the study. Data collection was conducted by S. Akter Shanta, Mst. Tahomina Akter, Md. Siam Ahmed, and M. Aktaruzzaman. Data analysis performed by A. K. M. Anisur Rahman and S. Akter Shanta. The first draft was prepared by S. Akter Shanta, M. Ariful Islam, and A.K.M. Anisur Rahman. Review and editing were performed by M. Ariful Islam, S. Akter Shanta, and A. K. M. Anisur Rahman. All authors read and approved the final manuscript. Acknowledgments The authors thank Adib Dairy Farm and the Animal Welfare & Behavior Lab, Bangladesh Agricultural University, Mymensingh. Competing Interests The authors declare that they have no competing interests. Financial Declaration Ministry of Education (MoE), Dhaka, Bangladesh (Project ID: 2021/18/MoE) and Bangladesh Agricultural University Research System, Mymensingh (Project ID: 2021/75/BAU) References Oehm AW, Knubben-Schweizer G, Rieger A, Stoll A, Hartnack S. A systematic review and meta-analyses of risk factors associated with lameness in dairy cows. BMC Vet Res 2019, 15:346. doi:10.1186/s12917-019-2095-2 Cook NB. Prevalence of lameness among dairy cattle in Wisconsin as a function of housing type and stall surface. J Am Vet Med Assoc 2003, 223:1324–1328. doi:10.2460/javma.2003.223.1324 Dolecheck KA, Bewley JM. Animal board invited review: Dairy cow lameness expenditures, losses and total cost. Animal 2018, 12:1462–1474. doi:10.1017/S1751731118000575 Randall LV, Green MJ, Chagunda MGG, Mason C, Archer SC, Green LE, Huxley JN. Low body condition predisposes cattle to lameness: An 8-year study of one dairy herd. J Dairy Sci 2015, 98:3766–3777. doi:10.3168/jds.2014-8863 Green LE, Hedges VJ, Schukken YH, Blowey RW, Packington AJ. The impact of clinical lameness on the milk yield of dairy cows. J Dairy Sci 2002, 85:2250–2256. doi:10.3168/jds.S0022-0302(02)74304-X Archer SC, Green MJ, Huxley JN. Association between milk yield and serial locomotion score assessments in UK dairy cows. J Dairy Sci 2010, 93:4045–4053. doi:10.3168/jds.2010-3062 Olechnowicz J, Jaśkowski JM. Incidence and prevalence of lameness and their relationship to milk yield in high-yielding cows. Medycyna Weterynaryjna 2013, 66:–. Ma Y, Ryan C, Barbano DM, Galton DM, Rudan MA, Boor KJ. Effects of somatic cell count on quality and shelf-life of pasteurized fluid milk. J Dairy Sci 2000, 83:264–274. doi:10.3168/jds.S0022-0302(00)74873-9 Walstra P, Wouters JTM, Geurts TJ. Dairy science and technology. 2nd edition. CRC Press 2005. International Dairy Federation. The global standard for quality raw milk. Brussels, Belgium: IDF 2011. Cook NB, Nordlund KV. The influence of the environment on dairy cow behavior, claw health and herd lameness dynamics. Vet J 2009, 179:360–369. doi:10.1016/j.tvjl.2007.09.016 Sadiq MB, Ramanoon SZ, Shaik Mossadeq WM, Mansor R, Syed-Hussain SS. Cow- and herd-level factors associated with lameness in dairy farms in Peninsular Malaysia. Prev Vet Med 2020, 184:105163. doi:10.1016/j.prevetmed.2020.105163 Bangladesh Bureau of Statistics: Yearbook of agricultural statistics 2023. Dhaka, Bangladesh. BBS 2023. Hasan M, Miah MAH, Juyena NS, Hani SM. Prevalence of lameness in cattle in selected areas of Bangladesh. Bangladesh Veterinarian 2017, 34:–. doi:10.3329/bvet.v34i1.38707 Islam M, Shanta S, Lima R, Mazumdar S. Effect of floor on welfare of lactating cows in small farms of Sirajgonj district, Bangladesh. Res Agric Livest Fish 2020, 7:87–95. Patoliya P, Kataktalware MA, Raval K, Devi LGL, Sivaram M, Praveen S, Meena P, Jeyakumar S, Mech A, Ramesha KPR. Assessing lameness prevalence and associated risk factors in crossbred dairy cows across diverse management environments. BMC Vet Res 2024, 20:229. doi:10.1186/s12917-024-0229-0 Mukund A, RK, Senani S, Sivaram M, Devi LGL, Niketha, Ramesha KPR. Risk factors associated with lameness in crossbred dairy cattle maintained under field conditions. J Livest Sci 2021, 11:–. doi:10.30954/2277-940X.03.2021.24 Prasomsri P. Effect of lameness on daily milk yield in dairy cow. Thai J Vet Med 2022, 52:679–687. doi:10.56808/2985-1130.3263 Ali S, Avais M, Durrani AZ, Ashraf K, Jabeen S, Hameed S, Awais M, Khan JA, Ahmad I. Prevalence and associated risk factors of lameness in cows at commercial dairy herds in Punjab, Pakistan. Pak J Zool 2020, 53:–. doi:10.17582/journal.pjz/20200322100328 Sprecher DJ, Hostetler DE, Kaneene JB. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology 1997, 47:1179–1187. doi:10.1016/s0093-691x(97)00098-8 Bicalho RC, Machado VS, Caixeta LS. Lameness in dairy cattle: A debilitating disease or a disease of debilitated cattle? A cross-sectional study of lameness prevalence and thickness of the digital cushion. J Dairy Sci 2008, –. doi:10.3168/jds.2008-1827 Sedlbauer M. Lameness and pain in dairy cows. Cariboo Agricultural Research Alliance 2005. https://cariboo-agricultural-research.ca/documents Sae-tiao T, Laodim T, Koonawootrittriron S, Suwanasopee T, Elzo MA. Tropical climate change and its effect on milk production of dairy cattle in Thailand. Livest Res Rural Dev 2019, 31: Article 194. http://www.lrrd.org/lrrd31/12/agrsk31194.html Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 19 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 09 Mar, 2026 Editor invited by journal 20 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 12 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8866594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604707265,"identity":"47afdd86-bd76-4295-8446-dabf7578eb73","order_by":0,"name":"Solama Akter Shanta","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Solama","middleName":"Akter","lastName":"Shanta","suffix":""},{"id":604707266,"identity":"e44f49d1-e7f2-470d-9f20-cb71c8527479","order_by":1,"name":"Mst. Tahomina Akter","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mst.","middleName":"Tahomina","lastName":"Akter","suffix":""},{"id":604707268,"identity":"4e4b75a9-4f9d-4bb7-abda-dba996f44d77","order_by":2,"name":"Muhammad Aktaruzzaman","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Aktaruzzaman","suffix":""},{"id":604707270,"identity":"310156b3-343c-4d35-b53a-908f3e037fb3","order_by":3,"name":"Md. Siam Ahmed","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Siam","lastName":"Ahmed","suffix":""},{"id":604707271,"identity":"5899d2f9-7840-415b-8e98-faee2907be63","order_by":4,"name":"A K M Anisur Rahman","email":"","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"A","middleName":"K M Anisur","lastName":"Rahman","suffix":""},{"id":604707274,"identity":"144430c4-9576-48fa-8c57-dadaf428af98","order_by":5,"name":"M. Ariful Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYFACxgaGBwwM9fuP98C4PAwSBLUkgMgzZ4jWAgRgLTdyiNSi2364+UNCzTZmxplvD37mYbCR3XCA9+ANfFrMziS2SSQcu83GLJ2XLM3DkGa84QBfsgVeLQcS2xgS2G7zsEnnGAC1HE7ccIDHDK/DzM4/BDrs320JHskzxr95GP4ToeVGYoNEYtttAwkJHjOgLQeI0fKwTSKx73aCAU9emuUcg2TjmYcJ+eV8+uMPH74BtbCfPXzjTYWdbN/xXvwhhgYMgJiZBPWjYBSMglEwCrADAMYMT1GZZy6UAAAAAElFTkSuQmCC","orcid":"","institution":"Bangladesh Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"Ariful","lastName":"Islam","suffix":""}],"badges":[],"createdAt":"2026-02-13 02:24:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8866594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8866594/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104459890,"identity":"5f3ec448-fed6-42b4-8861-2520fab5421c","added_by":"auto","created_at":"2026-03-12 03:40:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99051,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap for Health status (0=Healthy, 1=Lame) and all investigated traits (diagnostic-day lame vs healthy controls).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8866594/v1/4a089060d459845701294bb0.png"},{"id":104459939,"identity":"eb38ecd5-8547-420b-ac3e-118ddfa8fa69","added_by":"auto","created_at":"2026-03-12 03:40:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8866594/v1/be46a653-00b3-4f4a-a162-7a1b2973b028.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lameness-Associated Changes in Milk Yield, Composition, and Inflammatory Quality Indicators in Dairy Cows under Tropical Conditions in Bangladesh","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLameness is widely recognized as one of the most prevalent health and welfare disorders in dairy cattle and represents a substantial economic burden to the global dairy industry. Meta-analyses indicate that the average prevalence of clinical lameness in dairy herds ranges from 20\u0026ndash;30%, with even higher rates reported in intensively managed systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Economically, lameness contributes to losses through reduced milk yield, treatment costs, impaired reproductive performance, premature culling, and labor inefficiencies. In developed dairy systems, the estimated economic loss per lame cow ranges from USD 200\u0026ndash;500 per lactation depending on severity and duration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These losses arise primarily from decreased milk production (10\u0026ndash;25%), prolonged calving intervals, and increased replacement rates [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond locomotor impairment, lameness is increasingly recognized as a systemic inflammatory condition rather than solely a mechanical disorder. Pain-induced stress responses alter feeding behavior, reduce dry matter intake, and trigger metabolic and endocrine changes that compromise milk synthesis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Studies report that lame cows produce 1.5\u0026ndash;4.0 L less milk per day compared with healthy cows, depending on severity and chronicity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Inflammatory mediators may influence mammary epithelial permeability, resulting in increased somatic cell count (SCC), altered milk pH, and elevated electrical conductivity\u0026mdash;recognized indicators of mammary inflammation and compromised milk quality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Elevated SCC adversely affects milk processing properties, cheese yield, and shelf life, further amplifying economic losses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough the epidemiology and economic consequences of lameness have been extensively studied in Europe and North America, limited data are available under tropical dairy production systems. Climatic stress, hard flooring, nutritional imbalances, hygiene deficiencies, and inadequate routine hoof trimming in developing countries may exacerbate lameness incidence and severity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Emerging evidence from tropical Asia\u0026mdash;including Malaysia, India, Thailand, and Pakistan\u0026mdash;indicates that lameness prevalence and production impacts may be amplified under hot-humid management conditions [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Bangladesh, the dairy sector has expanded rapidly over the past decade, driven by increasing demand for animal protein and livestock development initiatives. According to the Bangladesh Bureau of Statistics, the national cattle population exceeds 24\u0026nbsp;million, with a growing proportion of crossbred and high-yielding cows managed under semi-intensive and commercial systems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent field investigations report lameness prevalence ranging from 15\u0026ndash;25% in selected milk pocket areas, particularly in tie-stall and poorly maintained housing systems [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the impact of lameness on milk yield and inflammatory milk quality indicators under Bangladeshi conditions remains insufficiently quantified. Therefore, this study aimed to evaluate the effects of lameness on milk yield, composition, and inflammatory quality indicators in dairy cows under tropical field conditions in Bangladesh.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Ethical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFarm owners provided informed verbal consent prior to participation. Ethical approval was obtained from the Animal Welfare and Experimentation Ethical Committee (AWEEC), Bangladesh Agricultural University (Approval No. AWEEC/BAU/2023(2)/48(A); approved on 05/02/2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Area and Herd Management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted at a commercial dairy farm located in Mymensingh Sadar, Bangladesh, from July 2024 to June 2025. The herd comprised approximately 110 Holstein\u0026ndash;Friesian dairy cows managed under semi-intensive production conditions. Cows were housed in concrete-floored barns and milked twice daily. The feeding regimen consisted of green fodder, concentrate mixture, and mineral supplementation formulated according to lactation stage and farm management practices. Routine veterinary supervision, vaccination, deworming, and herd health monitoring were maintained throughout the study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Study Design and Animal Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA longitudinal observational study design was implemented. Sixteen lactating cows were purposively selected based on locomotor health status and comparable lactation stage. The study population comprised 12 clinically lame multiparous cows and 4 clinically healthy, non-lame cows serving as controls. The average body weight of enrolled animals ranged between 450 and 600 kg. Lame cows were monitored at three predefined time points: (i) 7 days before clinical diagnosis (pre-diagnostic stage), (ii) on the day of confirmed clinical lameness (diagnostic stage), and (iii) 7 days after diagnosis (post-diagnostic stage). Healthy cows were evaluated once during the same period to provide baseline reference values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sample Size Justification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample size estimation was based on previously reported reductions in milk yield associated with clinical lameness, typically ranging from 15% to 25% [4\u0026ndash;6]. Assuming a conservative 20% reduction in milk yield (approximately 3\u0026ndash;4 L/day), a standard deviation of 2.5\u0026ndash;3.0 L, a two-sided significance level (\u0026alpha; = 0.05), and statistical power of 80%, a minimum of 10\u0026ndash;12 animals was required for repeated-measures analysis. Inclusion of 12 lame cows ensured sufficient power to detect stage-related differences. The repeated-measures design enhanced statistical efficiency by reducing within-animal variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Lameness Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLameness was evaluated using the standardized 1\u0026ndash;5 locomotion scoring system described by Sprecher et al. [20]. In this system, score 1 represents a normal gait, while scores \u0026ge;2 indicate increasing severity of lameness. Cows scoring \u0026ge;2 were classified as clinically lame. Diagnosis was confirmed through veterinary examination to exclude concurrent systemic, metabolic, or infectious disorders that could confound milk production outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Milk Yield Recording and Sampling Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDaily milk yield (L/cow/day) was obtained from official farm production records. Milk samples were collected aseptically during routine milking sessions. Samples were transported immediately to the Animal Welfare \u0026amp; Behavior Laboratory, Department of Medicine, Bangladesh Agricultural University, and analyzed within four hours of collection to minimize compositional alteration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Milk Composition and Quality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMilk quality parameters were determined using the Ekomilk Horizon Unlimited analyzer (Bultech, Bulgaria) in accordance with manufacturer guidelines. Raw milk samples were gently homogenized prior to analysis to ensure uniform distribution of components. The analyzer simultaneously quantified milk yield, milk pH, electrical conductivity (mS/cm), somatic cell count (SCC; \u0026times;10\u0026sup3; cells/mL), milk fat (%), milk protein (%), lactose (%), solid-not-fat (SNF; %), and total solids (%). Calibration was routinely verified using standard reference solutions to ensure analytical accuracy and repeatability. All measurements were performed in duplicate, and the mean value was used for statistical analysis.\u003c/p\u003e\n\u003cp\u003eElectrical conductivity and SCC were considered primary indicators of mammary gland inflammation and udder health, as elevated values reflect increased epithelial permeability and leukocyte infiltration during inflammatory processes [9,10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were entered into Microsoft Excel and analyzed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA) and R software version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). Descriptive statistics were calculated and presented as mean \u0026plusmn; standard error (SE). Normality of residuals was assessed using the Shapiro\u0026ndash;Wilk test and inspection of Q\u0026ndash;Q plots.\u003c/p\u003e\n\u003cp\u003eTo evaluate the longitudinal effect of lameness stage (pre-diagnostic, diagnostic, post-diagnostic) on milk yield and quality parameters, multivariable linear mixed-effects models were fitted using the\u0026nbsp;\u003cstrong\u003elme4\u003c/strong\u003e package in R. Lameness stage was included as a fixed effect, and cow identification number was included as a random intercept to account for repeated measurements within animals. Model assumptions were verified through residual diagnostics. Likelihood ratio tests were used to compare nested models. When significant main effects were detected, Tukey-adjusted pairwise comparisons were conducted using the\u0026nbsp;\u003cstrong\u003eemmeans\u003c/strong\u003e package. Effect estimates with 95% confidence intervals (CI) were reported.\u003c/p\u003e\n\u003cp\u003eAssociations between health status (0 = healthy, 1 = lame) and milk traits were assessed using point-biserial correlation analysis. Ninety-five percent confidence intervals were calculated using Fisher\u0026rsquo;s z-transformation. Binary logistic regression analysis was performed to identify milk quality predictors of lameness status, and odds ratios (OR) with 95% CI were reported. All statistical tests were two-tailed, and statistical significance was declared at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e\u003cstrong\u003e3.1 Effect of Lameness Stage on Milk Yield and Quality Traits\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eLinear mixed-effects modeling demonstrated a significant effect of lameness stage on milk yield, somatic cell count (SCC), electrical conductivity, milk pH, milk fat, and lactose (p \u0026lt; 0.05), whereas milk protein, solid-not-fat (SNF), and total solids were not significantly influenced by stage (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, clinically lame cows exhibited marked alterations in both production and inflammatory milk quality indicators compared with pre-diagnostic and healthy stages.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.2 Milk Yield\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMilk yield declined significantly during the diagnostic stage (15.00 \u0026plusmn; 0.63 L/day) compared with the pre-diagnosis stage (19.00 \u0026plusmn; 0.52 L/day; p \u0026lt; 0.01), representing an absolute reduction of 4.0 L/day and a 21.1% relative decrease. Compared with healthy cows (20.00 \u0026plusmn; 0.55 L/day), milk production during lameness was reduced by approximately 25%. Although yield partially recovered after diagnosis (18.00 \u0026plusmn; 0.48 L/day), it remained significantly lower than that of healthy cows (p \u0026lt; 0.05), indicating incomplete production recovery within 7 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Mean (\u0026plusmn; SE) of investigated milk traits before diagnosis (7 days before clinical diagnosis), on diagnostic day (day of clinical diagnosis), after diagnosis (7 days after diagnosis), and in healthy non-lame cows.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBefore Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Day\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAfter Diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Cows\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMilk yield (L/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e19.00 \u0026plusmn; 0.52ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e15.00 \u0026plusmn; 0.63ᶜ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e18.00 \u0026plusmn; 0.48ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e20.00 \u0026plusmn; 0.55ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e6.60 \u0026plusmn; 0.04ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e7.10 \u0026plusmn; 0.06ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.50 \u0026plusmn; 0.05ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6.50 \u0026plusmn; 0.03ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eElectrical conductivity (mS/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e5.80 \u0026plusmn; 0.31ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e28.60 \u0026plusmn; 1.84ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e7.40 \u0026plusmn; 0.44ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e5.40 \u0026plusmn; 0.27ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eSCC (\u0026times;10\u0026sup3; cells/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e335.4 \u0026plusmn; 22.1ᶜ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e812.8 \u0026plusmn; 45.6ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e468.1 \u0026plusmn; 31.4ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e342.6 \u0026plusmn; 18.3ᶜ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMilk fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.72 \u0026plusmn; 0.18ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5.81 \u0026plusmn; 0.26ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.28 \u0026plusmn; 0.15ᶜ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4.12 \u0026plusmn; 0.20ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eMilk protein (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e3.28 \u0026plusmn; 0.07ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e3.34 \u0026plusmn; 0.08ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e3.30 \u0026plusmn; 0.06ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e3.22 \u0026plusmn; 0.05ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eLactose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e4.32 \u0026plusmn; 0.12ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e5.68 \u0026plusmn; 0.19ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e4.60 \u0026plusmn; 0.11ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e4.48 \u0026plusmn; 0.09ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eSolid-not-fat (SNF) (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e8.21 \u0026plusmn; 0.16ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e8.42 \u0026plusmn; 0.18ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e8.63 \u0026plusmn; 0.14ᵃᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e8.79 \u0026plusmn; 0.13ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003eTotal solids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e7.23 \u0026plusmn; 0.22ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e8.87 \u0026plusmn; 0.31ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003e6.91 \u0026plusmn; 0.19ᵇ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e6.18 \u0026plusmn; 0.17ᶜ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues within a row with different superscript letters (a\u0026ndash;c) differ significantly at p \u0026lt; 0.05 based on Tukey-adjusted multiple comparisons following linear mixed-effects modeling.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.3 Somatic Cell Count (SCC)\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eSCC increased more than two-fold during clinical lameness (812.8 \u0026plusmn; 45.6 \u0026times;10\u0026sup3; cells/mL) compared with the pre-diagnosis stage (335.4 \u0026plusmn; 22.1 \u0026times;10\u0026sup3; cells/mL; p \u0026lt; 0.001). Post-diagnosis SCC decreased to 468.1 \u0026plusmn; 31.4 \u0026times;10\u0026sup3; cells/mL but remained significantly higher than both pre-diagnosis and healthy cows (342.6 \u0026plusmn; 18.3 \u0026times;10\u0026sup3; cells/mL; p \u0026lt; 0.01), suggesting persistent inflammatory activity beyond clinical locomotor improvement.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.4 Electrical Conductivity and pH\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eElectrical conductivity exhibited the most pronounced change among all measured traits. During lameness, conductivity increased nearly five-fold (28.60 \u0026plusmn; 1.84 mS/cm) compared with pre-diagnosis levels (5.80 \u0026plusmn; 0.31 mS/cm; p \u0026lt; 0.001). Although conductivity declined after diagnosis (7.40 \u0026plusmn; 0.44 mS/cm), values remained elevated relative to baseline (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eMilk pH also increased significantly during the diagnostic stage (7.10 \u0026plusmn; 0.06) compared with pre-diagnosis (6.60 \u0026plusmn; 0.04; p \u0026lt; 0.01) and healthy cows (6.50 \u0026plusmn; 0.03), reflecting altered ionic balance and mammary epithelial permeability.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.5 Milk Composition\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMilk fat percentage increased significantly during lameness (5.81 \u0026plusmn; 0.26%) compared with pre-diagnosis (3.72 \u0026plusmn; 0.18%; p \u0026lt; 0.05). Following diagnosis, fat percentage decreased (3.28 \u0026plusmn; 0.15%) and differed significantly from the diagnostic stage (p \u0026lt; 0.05). Lactose concentration also increased during lameness (5.68 \u0026plusmn; 0.19% vs. 4.32 \u0026plusmn; 0.12%; p \u0026lt; 0.05), whereas milk protein, SNF, and total solids did not differ significantly across stages (p \u0026gt; 0.05). These results indicate selective compositional changes primarily affecting inflammatory and osmotic-related components rather than structural milk solids.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.6 Correlation Between Health Status and Milk Traits\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePoint-biserial correlation analysis (Table 2) demonstrated strong associations between lameness status and inflammatory indicators. Lameness was negatively correlated with milk yield (r = \u0026minus;0.62, p = 0.002), indicating that cows classified as lame produced significantly less milk. Strong positive correlations were observed between lameness status and SCC (r = 0.79, p \u0026lt; 0.001) and electrical conductivity (r = 0.83, p \u0026lt; 0.001), suggesting that inflammatory markers closely co-varied with locomotor health.\u003c/p\u003e\n\u003cp\u003eMilk pH (r = 0.58, p = 0.006) and lactose (r = 0.47, p = 0.031) showed moderate positive correlations with lameness, whereas milk protein, SNF, and total solids were not significantly associated (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThese findings indicate that inflammatory indicators explain a substantial proportion of variability in health status, while structural milk components remain comparatively stable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Correlation between health status (0 = Healthy, 1 = Lame) and milk production and quality traits. \u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"660\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation Coefficient (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eMilk yield (L/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026minus;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eModerate negative correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eSCC (\u0026times;10\u0026sup3; cells/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eStrong positive correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eElectrical conductivity (mS/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eStrong positive correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eModerate positive correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eMilk fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eWeak\u0026ndash;moderate positive correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eMilk protein (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eLactose (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eModerate positive correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eSNF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026minus;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eTotal solids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e+0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 222px;\"\u003e\n \u003cp\u003eNot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCorrelation coefficients were calculated using point-biserial correlation (equivalent to Pearson correlation for binary variables). Health status was coded as 0 = healthy and 1 = lame. Confidence intervals were calculated using Fisher\u0026rsquo;s z-transformation. Statistical significance was declared at p \u0026lt; 0.05.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.7 Correlation Heatmap\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe correlation heatmap (Figure 1) visually illustrates the interrelationships between health status and investigated milk traits (diagnostic-day lame cows vs. healthy controls). Health status demonstrated strong positive clustering with SCC and electrical conductivity, confirming that lame cows exhibited markedly elevated inflammatory indicators. Milk yield showed a strong inverse association with both SCC and conductivity, indicating that increasing inflammatory burden was accompanied by reduced productivity.\u003c/p\u003e\n\u003cp\u003eSCC and electrical conductivity were highly positively correlated with each other, supporting their shared inflammatory origin. Milk fat and lactose displayed moderate positive clustering with inflammatory markers, whereas milk protein exhibited comparatively weaker covariance patterns. Overall, the heatmap supports a coherent biological pattern in which lameness is closely associated with inflammatory activation and concurrent reductions in milk yield.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that lameness exerts a substantial and multifaceted impact on milk yield and milk quality traits in dairy cows under tropical field conditions of Bangladesh. The observed 21% reduction in milk yield during clinical lameness is both biologically meaningful and economically significant. This magnitude of decline is consistent with previously reported reductions ranging from 10% to 25% in lame cows across diverse production systems [4–6], and meta-analytic evidence confirms that lameness is consistently associated with depressed milk production at herd level worldwide [1]. Recent mechanistic and herd-level investigations further emphasize that lameness is accompanied by systemic inflammatory and metabolic alterations that extend beyond locomotor impairment alone [21].\u003c/p\u003e\n\u003cp\u003eUnder semi-intensive tropical conditions such as those prevailing in Bangladesh, production losses associated with lameness may be amplified by environmental and management stressors. Heat stress, hard concrete flooring, suboptimal hygiene, and nutritional variability have been identified as important contributors to lameness severity and persistence [12,23]. Local epidemiological studies from Bangladeshi milk pocket areas have reported lameness prevalence between 15% and 25%, particularly in tie-stall and poorly maintained housing systems [14,15], suggesting that the production consequences observed in the present study are highly relevant at field level. Comparable findings from Malaysia, Thailand, India, and Pakistan indicate that hot-humid climates, housing surface characteristics, parity, and hoof lesions exacerbate lameness-associated milk yield reductions in tropical Asia [16–19]. These contextual factors underscore the vulnerability of intensifying dairy systems in South Asia to welfare-related productivity losses.\u003c/p\u003e\n\u003cp\u003eThe reduction in milk yield is plausibly mediated through pain-induced behavioral and physiological mechanisms. Lame cows typically exhibit reduced feed intake, prolonged standing time, altered rumination patterns, and reduced lying comfort [12,22]. Such behavioral disruptions impair rumen fermentation efficiency and decrease metabolizable energy availability for lactation. At the physiological level, lameness activates stress pathways including the hypothalamic–pituitary–adrenal axis, leading to elevated cortisol concentrations and increased production of pro-inflammatory cytokines. These inflammatory mediators alter nutrient partitioning and promote acute-phase responses, redirecting metabolic resources toward immune defense and tissue repair rather than milk synthesis [3,4,21]. This metabolic reallocation provides a coherent mechanistic explanation for the observed decline in productivity during the diagnostic stage.\u003c/p\u003e\n\u003cp\u003eThe marked elevation in somatic cell count (SCC) and electrical conductivity observed during clinical lameness provides strong evidence of systemic inflammatory involvement affecting mammary gland physiology. Elevated SCC reflects leukocyte infiltration into mammary tissue, whereas increased electrical conductivity indicates disruption of mammary epithelial tight junction integrity and increased permeability to sodium and chloride ions [9,10]. Such ionic shifts explain the concurrent rise in milk pH and conductivity. The persistence of elevated SCC following partial locomotor recovery suggests that inflammatory resolution lags behind visible gait improvement, a phenomenon also reported in longitudinal locomotion and milk yield studies [6]. These findings reinforce the concept that lameness has prolonged systemic consequences beyond orthopedic dysfunction.\u003c/p\u003e\n\u003cp\u003eThe transient increase in milk fat during lameness likely reflects a concentration effect secondary to reduced milk volume rather than enhanced lipogenesis. In addition, negative energy balance associated with reduced intake may stimulate mobilization of adipose reserves, influencing milk fat concentration [3,7]. Changes in lactose may reflect osmotic regulation related to altered epithelial permeability, given that lactose plays a central role in maintaining milk osmolarity and secretion volume [10]. In contrast, milk protein and SNF displayed comparatively weaker covariance patterns with lameness status. Protein synthesis is more strongly influenced by longer-term nutritional and endocrine regulation than by short-term inflammatory fluctuations, and previous dairy quality studies report relatively smaller changes in protein compared with cellular and ionic components during transient stress conditions [9,10].\u003c/p\u003e\n\u003cp\u003eCorrelation and heatmap analyses further supported these mechanistic interpretations. Strong positive associations between lameness and inflammatory indicators (SCC and conductivity), combined with inverse associations between these markers and milk yield, reinforce a biologically plausible pathway in which lameness-induced inflammation compromises mammary epithelial function and reduces productivity [4–6,21]. Although predictive modeling was not the primary focus, the pattern of associations supports a systemic inflammatory framework of lameness rather than a purely mechanical disorder.\u003c/p\u003e\n\u003cp\u003eFrom an economic perspective, the implications for Bangladesh are considerable. Lameness is recognized globally as one of the most costly dairy health disorders due to combined effects on milk loss, treatment expenses, reproductive impairment, and increased culling risk [3]. In Bangladesh, where average herd productivity remains lower than global benchmarks and profit margins are relatively narrow [13], a sustained 4 L/day reduction over 60 days equates to approximately 240 L of milk loss per cow, corresponding to an estimated direct loss of about 14,400 BDT per lactation at 60 BDT/L. When extrapolated across commercial herds, such losses represent a substantial financial burden and may undermine farm sustainability in rapidly intensifying dairy systems.\u003c/p\u003e\n\u003cp\u003eCollectively, these findings confirm that lameness is not merely an animal welfare issue but a systemic inflammatory disorder with measurable consequences for milk yield and quality. Integrating routine locomotion scoring systems [20] with milk quality surveillance particularly monitoring of SCC and electrical conductivity may provide a practical, evidence-based strategy for early detection, welfare improvement, and economic risk mitigation in tropical dairy production systems such as Bangladesh.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that lameness significantly compromises both milk yield and milk quality in dairy cows under Bangladeshi field conditions. Clinical lameness was associated with a 21% reduction in milk yield, marked elevation in SCC and electrical conductivity, and measurable alterations in milk composition. The strong correlations and mediation analysis indicate that inflammatory mechanisms partially explain the reduction in productivity. These findings confirm that lameness is a systemic inflammatory condition affecting mammary gland function rather than solely a locomotor disorder. Incorporating routine locomotion scoring alongside milk quality surveillance can enhance early detection, improve animal welfare, and reduce economic losses in tropical dairy production systems. Strategic welfare-based management interventions are therefore essential to sustain productivity and profitability in the rapidly intensifying dairy sector of Bangladesh.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.\u0026nbsp;Akter Shanta and M. Ariful Islam contributed to the conception and design of the study. Data collection was conducted by S.\u0026nbsp;Akter Shanta, Mst. Tahomina Akter, Md. Siam Ahmed, and\u0026nbsp;M.\u0026nbsp;Aktaruzzaman.\u0026nbsp;Data analysis performed by A. K. M. Anisur Rahman\u0026nbsp;and S. Akter Shanta. The first draft was prepared by S.\u0026nbsp;Akter Shanta, M. Ariful Islam, and\u0026nbsp;A.K.M.\u0026nbsp;Anisur Rahman. Review and editing were performed by M. Ariful Islam,\u0026nbsp;S.\u0026nbsp;Akter Shanta, and A. K. M. Anisur Rahman. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Adib Dairy Farm and the Animal Welfare \u0026amp; Behavior Lab, Bangladesh Agricultural University, Mymensingh.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinistry of Education (MoE), Dhaka, Bangladesh (Project ID: 2021/18/MoE) and Bangladesh Agricultural University Research System, Mymensingh (Project ID: 2021/75/BAU)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eOehm AW, Knubben-Schweizer G, Rieger A, Stoll A, Hartnack S. A systematic review and meta-analyses of risk factors associated with lameness in dairy cows. BMC Vet Res 2019, 15:346. doi:10.1186/s12917-019-2095-2\u003c/li\u003e\n \u003cli\u003eCook NB. Prevalence of lameness among dairy cattle in Wisconsin as a function of housing type and stall surface. J Am Vet Med Assoc 2003, 223:1324\u0026ndash;1328. doi:10.2460/javma.2003.223.1324\u003c/li\u003e\n \u003cli\u003eDolecheck KA, Bewley JM. Animal board invited review: Dairy cow lameness expenditures, losses and total cost. Animal 2018, 12:1462\u0026ndash;1474. doi:10.1017/S1751731118000575\u003c/li\u003e\n \u003cli\u003eRandall LV, Green MJ, Chagunda MGG, Mason C, Archer SC, Green LE, Huxley JN. Low body condition predisposes cattle to lameness: An 8-year study of one dairy herd. J Dairy Sci 2015, 98:3766\u0026ndash;3777. doi:10.3168/jds.2014-8863\u003c/li\u003e\n \u003cli\u003eGreen LE, Hedges VJ, Schukken YH, Blowey RW, Packington AJ. The impact of clinical lameness on the milk yield of dairy cows. J Dairy Sci 2002, 85:2250\u0026ndash;2256. doi:10.3168/jds.S0022-0302(02)74304-X\u003c/li\u003e\n \u003cli\u003eArcher SC, Green MJ, Huxley JN. Association between milk yield and serial locomotion score assessments in UK dairy cows. J Dairy Sci 2010, 93:4045\u0026ndash;4053. doi:10.3168/jds.2010-3062\u003c/li\u003e\n \u003cli\u003eOlechnowicz J, Jaśkowski JM. Incidence and prevalence of lameness and their relationship to milk yield in high-yielding cows. Medycyna Weterynaryjna 2013, 66:\u0026ndash;.\u003c/li\u003e\n \u003cli\u003eMa Y, Ryan C, Barbano DM, Galton DM, Rudan MA, Boor KJ. Effects of somatic cell count on quality and shelf-life of pasteurized fluid milk. J Dairy Sci 2000, 83:264\u0026ndash;274. doi:10.3168/jds.S0022-0302(00)74873-9\u003c/li\u003e\n \u003cli\u003eWalstra P, Wouters JTM, Geurts TJ. Dairy science and technology. 2nd edition. CRC Press 2005.\u003c/li\u003e\n \u003cli\u003eInternational Dairy Federation. The global standard for quality raw milk. Brussels, Belgium: IDF 2011.\u003c/li\u003e\n \u003cli\u003eCook NB, Nordlund KV. The influence of the environment on dairy cow behavior, claw health and herd lameness dynamics. Vet J 2009, 179:360\u0026ndash;369. doi:10.1016/j.tvjl.2007.09.016\u003c/li\u003e\n \u003cli\u003eSadiq MB, Ramanoon SZ, Shaik Mossadeq WM, Mansor R, Syed-Hussain SS. Cow- and herd-level factors associated with lameness in dairy farms in Peninsular Malaysia. Prev Vet Med 2020, 184:105163. doi:10.1016/j.prevetmed.2020.105163\u003c/li\u003e\n \u003cli\u003eBangladesh Bureau of Statistics: Yearbook of agricultural statistics 2023. Dhaka, Bangladesh. BBS 2023.\u003c/li\u003e\n \u003cli\u003eHasan M, Miah MAH, Juyena NS, Hani SM. Prevalence of lameness in cattle in selected areas of Bangladesh. Bangladesh Veterinarian 2017, 34:\u0026ndash;. doi:10.3329/bvet.v34i1.38707\u003c/li\u003e\n \u003cli\u003eIslam M, Shanta S, Lima R, Mazumdar S. Effect of floor on welfare of lactating cows in small farms of Sirajgonj district, Bangladesh. Res Agric Livest Fish 2020, 7:87\u0026ndash;95.\u003c/li\u003e\n \u003cli\u003ePatoliya P, Kataktalware MA, Raval K, Devi LGL, Sivaram M, Praveen S, Meena P, Jeyakumar S, Mech A, Ramesha KPR. Assessing lameness prevalence and associated risk factors in crossbred dairy cows across diverse management environments. BMC Vet Res 2024, 20:229. doi:10.1186/s12917-024-0229-0\u003c/li\u003e\n \u003cli\u003eMukund A, RK, Senani S, Sivaram M, Devi LGL, Niketha, Ramesha KPR. Risk factors associated with lameness in crossbred dairy cattle maintained under field conditions. J Livest Sci 2021, 11:\u0026ndash;. doi:10.30954/2277-940X.03.2021.24\u003c/li\u003e\n \u003cli\u003ePrasomsri P. Effect of lameness on daily milk yield in dairy cow. Thai J Vet Med 2022, 52:679\u0026ndash;687. doi:10.56808/2985-1130.3263\u003c/li\u003e\n \u003cli\u003eAli S, Avais M, Durrani AZ, Ashraf K, Jabeen S, Hameed S, Awais M, Khan JA, Ahmad I. Prevalence and associated risk factors of lameness in cows at commercial dairy herds in Punjab, Pakistan. Pak J Zool 2020, 53:\u0026ndash;. doi:10.17582/journal.pjz/20200322100328\u003c/li\u003e\n \u003cli\u003eSprecher DJ, Hostetler DE, Kaneene JB. A lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology 1997, 47:1179\u0026ndash;1187. doi:10.1016/s0093-691x(97)00098-8\u003c/li\u003e\n \u003cli\u003eBicalho RC, Machado VS, Caixeta LS. Lameness in dairy cattle: A debilitating disease or a disease of debilitated cattle? A cross-sectional study of lameness prevalence and thickness of the digital cushion. J Dairy Sci 2008, \u0026ndash;. doi:10.3168/jds.2008-1827\u003c/li\u003e\n \u003cli\u003eSedlbauer M. Lameness and pain in dairy cows. Cariboo Agricultural Research Alliance 2005. https://cariboo-agricultural-research.ca/documents\u003c/li\u003e\n \u003cli\u003eSae-tiao T, Laodim T, Koonawootrittriron S, Suwanasopee T, Elzo MA. Tropical climate change and its effect on milk production of dairy cattle in Thailand. Livest Res Rural Dev 2019, 31: Article 194. http://www.lrrd.org/lrrd31/12/agrsk31194.html\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lameness, Dairy cow, Milk yield, Milk quality, Somatic cell count, Electrical conductivity, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-8866594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8866594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLameness is a major welfare and economic challenge in dairy herds and is associated with reduced milk yield and compromised milk quality; however, evidence under tropical production systems such as Bangladesh remains limited. This longitudinal observational study was conducted at a commercial dairy farm in Mymensingh, Bangladesh (July 2024–June 2025) to evaluate the effects of lameness on milk yield and inflammatory milk quality indicators. Sixteen Holstein–Friesian lactating cows were enrolled, including 12 clinically lame multiparous cows and 4 healthy controls. Lameness was assessed using a standardized 1–5 locomotion scoring system. Milk yield was obtained from farm records, and milk quality traits including pH, electrical conductivity, somatic cell count (SCC), fat, protein, lactose, solid-not-fat, and total solids were analyzed using Ekomilk Horizon Unlimited. Lame cows were evaluated 7 days before diagnosis, on the diagnostic day, and 7 days after diagnosis. Multivariable linear mixed-effects models revealed a significant reduction in milk yield during lameness (15.0 ± 0.6 L/day) compared with pre-diagnosis values (19.0 ± 0.5 L/day; p\u0026lt;0.01), representing a 21% decline. SCC increased more than two-fold (812.8 ± 45.6 ×10³ cells/mL; p\u0026lt;0.001), accompanied by significant increases in electrical conductivity and pH. Lameness showed strong positive correlations with SCC (r=0.79) and conductivity (r=0.83), and a moderate negative correlation with milk yield (r=−0.62). These findings indicate that lameness is associated with systemic inflammatory changes that compromise milk production and quality under tropical conditions, highlighting the importance of early detection and welfare-based management strategies in Bangladeshi dairy systems.\u003c/p\u003e","manuscriptTitle":"Lameness-Associated Changes in Milk Yield, Composition, and Inflammatory Quality Indicators in Dairy Cows under Tropical Conditions in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-12 03:40:20","doi":"10.21203/rs.3.rs-8866594/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T08:14:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T09:27:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T00:40:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159597733749038026416885395287919638119","date":"2026-03-12T09:58:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152958884781320467624438616241636532830","date":"2026-03-11T23:02:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156358067919428422122411091471801935079","date":"2026-03-11T22:05:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-09T04:22:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T11:04:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T02:19:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T02:19:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2026-02-13T02:17:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7ee9e87e-39c2-4cd6-89d1-7080e836a954","owner":[],"postedDate":"March 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T08:25:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-12 03:40:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8866594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8866594","identity":"rs-8866594","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}