The paper investigates the effects of different types of trends (linear, periodic, and power-law) on the Detrended Fluctuation Analysis (DFA) of noisy signals. The authors systematically study how these trends influence the scaling behavior of the signals and identify crossovers in the scaling exponents. They find that the presence of trends leads to a superposition of the scaling behavior of the noise and the apparent scaling of the trend. The characteristics of these crossovers depend on the slope of the linear trend, the amplitude and period of the periodic trend, the amplitude and power of the power-law trend, and the length and correlation properties of the noise. Surprisingly, the crossover scales follow power-law scaling relations, indicating that the position of the crossover is related to the parameters of the trends. The paper also discusses how to use DFA to minimize the effects of trends and to distinguish between transitions in the dynamical properties of the noise and crossovers due to trends. The results provide a deeper understanding of the interpretation of DFA results in the presence of trends and offer guidelines for appropriate data analysis.The paper investigates the effects of different types of trends (linear, periodic, and power-law) on the Detrended Fluctuation Analysis (DFA) of noisy signals. The authors systematically study how these trends influence the scaling behavior of the signals and identify crossovers in the scaling exponents. They find that the presence of trends leads to a superposition of the scaling behavior of the noise and the apparent scaling of the trend. The characteristics of these crossovers depend on the slope of the linear trend, the amplitude and period of the periodic trend, the amplitude and power of the power-law trend, and the length and correlation properties of the noise. Surprisingly, the crossover scales follow power-law scaling relations, indicating that the position of the crossover is related to the parameters of the trends. The paper also discusses how to use DFA to minimize the effects of trends and to distinguish between transitions in the dynamical properties of the noise and crossovers due to trends. The results provide a deeper understanding of the interpretation of DFA results in the presence of trends and offer guidelines for appropriate data analysis.