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Data source

The GBD 2021 employed the most up-to-date epidemiological data, complemented by refined and standardized methodologies, to systematically and comprehensively quantify health losses across 369 diseases and injuries, as well as 87 risk factors, stratified by age, sex, and geographical location, encompassing 204 countries and territories. The GBD team is committed to annual updates to ensure the accuracy and relevance of their estimates [12]. The intricacies of the methodologies applied within GBD 2021 have been thoroughly documented in prior publications [13].

To address data gaps and ensure smoothness across age, time, and location, the collected data underwent modeling via spatiotemporal Gaussian process regression. This approach facilitated interpolation in regions with incomplete datasets. Furthermore, to correct for biases stemming from diverse case definitions and study methodologies across regions, a meta-regression framework incorporating Bayesian priors, regularization, and trimming techniques was employed.

From GBD 2021, we extracted estimates and their corresponding 95% uncertainty intervals (UIs) for incidence, deaths, prevalence, and DALYs attributed to MND. All rates reported herein are standardized to per 10,000 population. Additionally, the sociodemographic index (SDI), a composite indicator reflecting income, education, and fertility levels, serving as a proxy for sociodemographic development, was utilized to categorize the 204 countries and territories into five distinct groups: high, high-middle, middle, low-middle, and low, as defined by their SDI values [14].

Descriptive analysis

To gain a holistic understanding of the burden of MND, we conducted descriptive analyses at the global, regional, and national levels. Specifically, we visually presented the global trends in the number of cases, crude rate, and age-standardized rate (ASR) for incidence, deaths, prevalence, and DALYs related to MND, disaggregated by sex (both sexes, males, and females) and spanning the period from 1990 to 2021. Furthermore, we compared the number of cases and ASR for the aforementioned indicators across global, regional (comprising 54 GBD geographic regions), and national (encompassing 204 countries and territories) levels, as well as within the five SDI groups.

Trend analysis

In the Trend Analysis section, we initially employed the Estimated Annual Percentage Change (EAPC) to quantify the overarching trend in the burden of MND. Given the importance of standardization when comparing diverse groups with varying age structures or a single group experiencing temporal changes in its age profile, the EAPC-measured trend of the ASR emerges as a more robust metric for monitoring shifts in disease patterns [15]. To derive this metric, we constructed a linear regression model where the natural logarithm of the ASR (ln(ASR)) served as the dependent variable (y), and the calendar year acted as the independent variable (x). Subsequently, the EAPC was calculated using the formula (exp(β)-1) * 100%, with its 95% confidence interval (CI) also being extracted from the model [16]. In interpreting the EAPC estimates, if both the EAPC value and the lower bound of its 95% CI are greater than 0, the ASR is deemed to be in an increasing trend. Conversely, if both the EAPC value and the upper bound of its 95% CI are less than 0, the ASR is considered to be in a decreasing trend. In all other cases, the ASR is classified as stable. This approach ensures a rigorous and standardized methodology for assessing temporal trends in the ASR of MND.

Furthermore, we used age-period-cohort (APC) model to explore the underlying trends in DALYs stratified by age, period, and birth cohort. Typically, the APC model fits a log-linear Poisson model on the Lexis diagram of observed rates and quantifies the additional effects of age, period, and birth cohort. The methodological details of APC model are described in previous literature [17]. The multicollinearity between age, period, and birth cohort inevitably leads to identification issues, making it difficult to estimate the unique effects of each age, period, and birth cohort. To address this issue, the intrinsic estimator (IE) algorithm was used to estimate the coefficients of the APC model. This study employed the IE to solve the APC model, where coefficients greater than 0 indicate increased risk, and those less than 0 indicate decreased risk. The effect coefficients were transformed into natural logarithms to calculate the relative risk (RR), enabling the observation of the effects of age, period, and cohort on MND DALYs trends. The DALYs for MND and population data of each country or region were served as data input for APC model. The data was re-coded into consecutive six 5-year periods (1990–1994, 1995–1999, … , 2015–2019), consecutive 5-year age groups (0–4, 5–9, … , 90–94, 95 plus), consecutive 5-year birth cohorts (1895–1899, 1900–1904, … , 2015–2019) to estimate the overall temporal trend in incidence, prevalence, deaths, and DALYs.

Cross-country inequality analysis

To ensure evidence-based health planning, we conducted a comprehensive cross-country inequality analysis aimed at monitoring health disparities. Specifically, we employed the Slope Index of Inequality (SII) as a key metric, which was derived from regressing the country-level prevalence of the disease across all age groups against a relative position scale tied to sociodemographic development. To account for heteroscedasticity, a robust linear regression model was applied. This method utilizes iteratively reweighted least squares, giving smaller weights to observations with larger residuals, thus minimizing the influence of outliers and ensuring more stable and reliable trend estimates [18]. This approach allowed us to quantify inequalities in MND at global level and across 21 GBD regions.

Furthermore, we calculated the Health Inequality Concentration Index by numerically integrating the area beneath the Lorenz Concentration Curve. This curve was meticulously fitted using the cumulative relative distribution of populations, ordered by their SDI, and the corresponding incidence, prevalence, deaths, and DALYs attributable to the disease [19, 20]. This methodology provided a robust assessment of the concentration of health burden across nations, enabling us to identify disparities and inform targeted interventions. A negative SII/concentration index indicates that as SDI increases, ASDR decreases, and vice versa. The greater the absolute value of the SII/concentration index, the greater the degree of inequality. Their inequality value and implications are presented in Table 1.

Decomposition analysis

To gain a profound understanding of the explanatory factors underpinning the variations in MND incidence, prevalence, deaths, and DALYs from 1990 to 2021, we performed a comprehensive decomposition analysis. This analysis dissected the contributions of population size, age structure, and epidemiological changes to the observed trends [22, 23]. By disentangling these components, we aimed to quantify the specific impact of each factor on the evolution of MND burden over time.

The decomposition methodology enabled us to estimate the number of incidence cases, prevalent cases, deaths, and DALYs attributable to each factor at every location under consideration. The calculation of these metrics for each component was carried out as follows:

({X_{ay,py,ey}} = sumnolimits_{i = 1}^{20} {left( {{a_{i,y}} * {p_y} * {e_{i,y}}} right)} )(X = incidence, prevalence, deaths and DALYs)

Where the ({X_{ay,py,ey}}) represented X based on the factors of age structure, population, and specific year (y); ({a_{i,y}}) represented the proportion of population for the age category (i) of the 20 age categories in year (y); ({p_{y}}) represented the total population in year (y) and ({e_{i,y}}) represented X rate for the age category (i) in year (y).

The contribution of each factor to the change in incidence, prevalence, deaths and DALYs from 1990 to 2021 was defined by the effect of one factor changing while the other factors were held constant.

Predictive analysis

To inform the formulation of effective public health policies and the optimal allocation of healthcare resources, we conducted a predictive analysis of the MND burden in the coming decades. For this purpose, we employed the Bayesian age-period-cohort (BAPC) model, augmented with the integrated nested Laplace approximation (INLA) technique. This advanced approach, which has been shown to outperform the conventional annual percentage change model in terms of coverage and precision, was utilized to forecast the global MND burden until 2046.

The adoption of INLA within the BAPC framework offers several advantages. By approximating marginal posterior distributions, it mitigates the mixing and convergence issues that are often encountered with the Markov Chain Monte Carlo sampling techniques traditionally applied in Bayesian methods [24]. This enhancement ensures more reliable and accurate predictions of the future MND burden, thereby supporting evidence-based decision-making in public health planning.

Frontier analysis

To assess the interplay between the burden of MND and sociodemographic development, we employed a frontier analysis approach. This methodology aimed to delineate the lowest potentially attainable ASR of incidence, prevalence, deaths, and DALYs for each country or territory, contingent upon its SDI. The frontier serves as a benchmark, indicating the minimal achievable level given a country’s or territory’s development status. The deviation from this frontier, termed the effective difference, highlights potential untapped opportunities for improvement or gains, commensurate with the country’s or territory’s position on the development spectrum.

To accommodate non-linear relationships, we conducted a data envelope analysis utilizing the free disposal hull method. This analysis generated an age-adjusted frontier by SDI, utilizing data spanning from 1990 to 2021 [25]. To account for uncertainty, we implemented a bootstrapping procedure, drawing 1,000 samples with replacement from the entire dataset encompassing all countries and territories across all years. From these bootstrapped samples, we computed the mean incidence, prevalence, deaths, and DALYs at each SDI value.

Subsequently, we employed LOESS (Locally Estimated Scatterplot Smoothing) regression with a local polynomial degree of 1 and a span of 0.2 to produce a smooth frontier line [25]. This approach ensured a robust and visually interpretable representation of the frontier, while mitigating the influence of outliers. To further refine the analysis, super-efficient countries, which may distort the frontier due to exceptional performance, were excluded from the frontier generation process.

The primary aim of this study was to investigate the relationship between IDC-P/ICC and HRR mutations in a real-world cohort of 347 patients with prostate cancer. We further assessed the association between IDC-P/ICC and other molecular alterations, including MMR status, MSI, and TMB. Our study revealed that the presence of IDC-P/ICC was not associated with HRR mutations or MMR status. Notably, mutations in HRR were linked to a younger age and a higher Grade Group, yet they showed no correlation with IDC-P/ICC status or metastatic stage.

IDC-P and ICC are histologically distinct entities but exhibit similarly aggressive clinical courses, both being associated with advanced disease stages, recurrence, and poor outcomes [24, 25]. Although the 2019 ISUP consensus recommends that IDC-P and ICC be reported separately due to their distinct diagnostic and prognostic implications, in real-world practice, this distinction can be challenging because of overlapping morphologic features [21, 26]. Accordingly, the NCCN guidelines recommend germline testing for patients with either IDC-P or cribriform morphology, reflecting their shared clinical significance and association with genetic mutations [11]. At our institution, consistent with contemporaneous diagnostic practice during the study period, IDC-P and ICC were evaluated morphologically without the use of routine basal cell immunohistochemistry and were reported together rather than separately. While this approach reflects actual clinical practice, it differs from current consensus recommendations and may have introduced variability in histologic classification, which should be considered a limitation of our study.

Additionally, our data revealed discrepancies between biopsy and prostatectomy findings. While most IDC-P/ICC cases were detected on biopsy, approximately 13% were identified only on prostatectomy specimens, and one case was positive on biopsy but negative on prostatectomy, suggesting sampling variation or intratumoral heterogeneity. These findings align with prior studies showing the limited sensitivity of biopsy. Ericson et al. [27] reported a sensitivity of 56.5% with no added benefit from MRI fusion, and Masoomian et al. [28] found that biopsy detected IDC-P/ICC in only 26.9% of cases compared with 51.8% at prostatectomy, corresponding to a sensitivity of 47.2%. More recently, Bernardino et al. demonstrated that false-negative biopsies were associated with higher pathological stage and increased risk of biochemical recurrence [29]. Taken together, these results highlight that prostate biopsy has limited sensitivity for IDC-P/ICC detection, as reflected by the 13% underdetection rate in our cohort. This reinforces that biopsy IDC-P/ICC status alone is insufficient to guide genetic testing and underscores the need to integrate additional clinical and pathological factors.

Contrary to previous studies suggesting a link between IDC-P/ICC and HRR mutations [7,8,9], our multivariable analysis revealed that IDC-P/ICC is not a significant predictor of HRR mutation status. These findings are in line with those of recent studies [30, 31]. Mahlow et al. [31] concluded that pathologic patterns alone are insufficient to predict HRR mutations in advanced prostate cancer. While their study included only six IDC-P cases, our larger cohort of 254 patients with IDC-P/ICC provides robust support for their conclusions. Lozano et al. [30] found no significant differences in IDC-P/ICC between germline BRCA2 (gBRCA2) carriers and non-carriers. However, they discovered that bi-allelic BRCA2 loss in primary prostate tumors was independently associated with both IDC-P and cribriform morphologies. They proposed that tumors with a gBRCA2 mutation and intact second allele may preserve some BRCA2 function, potentially preventing these histologies. Our study extends these findings by examining a broader spectrum of HRR genes, showing that somatic HRR mutation status is not indicated by the presence of IDC-P/ICC. Since our cohort primarily consisted of metastatic cases, it is important to note that while some non-metastatic patients were included, further studies are required to fully understand the role of IDC-P/ICC in localized prostate cancer. With HRR gene mutation prevalence around 25% in metastatic disease [32] but less than 10% in localized disease [33], the consistent lack of association between IDC-P/ICC and HRR mutations across studies raises questions about the appropriateness of relying on cribriform pattern status as a trigger for genetic testing, particularly as outlined in current NCCN guidelines [11].

Another potential explanation for the discrepancy between IDC-P/ICC and HRR mutations lies in our limited understanding of IDC-P pathogenesis and its underlying molecular events. The pathogenesis of IDC-P can vary, with most cases resulting from retrograde spreading of adjacent aggressive prostate cancer into ducts, but de novo IDC-P can also occur [2]. These different origins might contribute to the observed variability in HRR gene alterations in IDC-P/ICC. The heterogeneity in IDC-P origins could explain why our study and others have found inconsistent associations between IDC-P/ICC and not only HRR but also other mutations. Contrary to previous studies’ results [9, 10], our data do not show a statistical difference in MMR mutations between IDC-P/ICC positive and negative groups (2.8% vs. 1.1%, P = 0.687). Additionally, another potential poor prognostic marker, MSI-high status, was also not significantly different between the two groups (1.1% vs. 1.2%, P > 0.999). These findings prompt us to reconsider the use of IDC-P/ICC as a sole indicator for genetic testing and suggest that a combination of clinical and pathological factors may provide better stratification for identifying patients who would benefit from comprehensive genomic profiling. Given the relatively low prevalence of IDC-P/ICC, future research would benefit from multicenter collaborations with centralized pathologic review of specimens for genetic testing. Such large-scale efforts could provide more definitive insights into the relationship between IDC-P/ICC and molecular alterations.

HRR mutations occur not only in metastatic disease but also in locally advanced or regional diseases. Our study found that HRR gene mutations are associated with high Grade Groups and younger age at diagnosis, regardless of metastatic status. While current guidelines focus on metastatic disease for recommending genetic testing, emerging evidence suggests HRR status may provide valuable prognostic information even in localized disease [22]. With PARP inhibitors now showing efficacy in metastatic prostate cancer [13,14,15,16], there is potential for their application in localized disease as well [34]. This evolving landscape underscores the need for a more stratified approach to genetic testing in prostate cancer. Based on our study, a combination of younger age and higher Grade Group may be good indicators for genetic testing, rather than relying solely on a single morphological factor.

The major limitations of this study are its retrospective, single-center design, which may limit the generalizability of our findings to broader populations. Additionally, the lack of a standardized NGS testing protocol introduces potential selection bias, as genomic testing was performed at the discretion of the treating physician and was most often applied to patients with aggressive clinicopathological features, rather than uniformly across all prostate cancer cases. Furthermore, as we collected somatic NGS test data, the results cannot be directly applied to germline findings. Another limitation is that IDC-P and ICC were assessed morphologically without routine basal cell immunohistochemistry and were not reported separately, which may have introduced variability in classification. Despite these limitations, our study provides real-world evidence of the correlation of IDC-P/ICC with HRR, MMR, MSI, and TMB, which are important predictors in the era of molecularly targeted systemic treatments.